Term Review of the EU Biodiversity Strategy to 2020 in Relation to Target 3A

Home , Autumn ringlet, Brown hairstreak, Carcharodus lavatherae, Cinquefoil skipper, Favonius (butterfly), Gatekeeper (butterfly)

Service contract to support follow-up actions to the mid- term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture

Final Report

19th June 2017

Funded by

European Commission, DG Environment

In collaboration with

Disclaimer: The arguments expressed in this report are solely those of the authors, and do not reflect the opinion of any other party.

The report as a whole should be cited as follows: Siriwardena, G. and Tucker, G. (eds) (2017) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture. Report to the European Commission, Institute for European Environmental Policy, London.

The following individual chapters should be cited as follows: Chapter 2: Siriwardena, G and Pringle, H (2017) Development of a methodology for the assessment of potential agriculture-related drivers on the status of habitats and species. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture, pp 25-48. Report to the European Commission, Institute for European Environmental Policy, London. Chapter 3: Pringle, H, Koeble, R, Paracchini M L, Rega, C, Henderson, I, Noble, D, Gamero, A, Vorisek, P, Škorpilová, J, Schmucki, R, Siriwardena, G, Allen, B, and Tucker, G (2017) Review of data sources and preparation of a metadatabase. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture, pp 49-60. Report to the European Commission, Institute for European Environmental Policy, London. Chapter 4: Pringle, H, Siriwardena, G, Noble, D, Paracchini, M L, Koeble, R, Vorisek, P, Gamero, A, and Tucker, G, (2017) Integrated analysis of potential agricultural drivers of bird and butterfly population trends in the EU. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture, pp 61.87. Report to the European Commission, Institute for European Environmental Policy, London. Chapter 5: Siriwardena, G and Pringle, H, Tucker, G, Koeble, R, Paracchini, M L, and Rega, C (2017) Case study 1: Factors affecting the condition of agriculture-associated species and habitats within Natura 2000 sites. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture, pp 88-101. Report to the European Commission, Institute for European Environmental Policy, London. Chapter 6: Gamero, A, Skorpilova, J, Vorisek, P, Siriwardena, G and Tucker, G (2017) Case study 2: Effects of Natura 2000 site designation and management plans on farmland bird abundance trends. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture, pp 102-111. Report to the European Commission, Institute for European Environmental Policy, London. Chapter 7: Siriwardena, G, Pringle, H, Koeble, R and Paracchini, M L (2017) Case study 3: large-scale and long-term bird distributions in Britain and Ireland in relation to lowland agricultural land-use. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture, pp 112-126. Report to the European Commission, Institute for European Environmental Policy, London. Chapter 8: Pringle, H and Siriwardena, G (2017) Case study 4: UK agri-environment schemes and non-avian biodiversity. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to

target 3A – Agriculture, pp 127-146. Report to the European Commission, Institute for European Environmental Policy, London. Chapter 9: Klenke, R, Frey, B, and Zarzycka, A (2017) Case study 5: The effects of increased rape and maize cropping on agricultural biodiversity. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture, pp 147-183. Report to the European Commission, Institute for European Environmental Policy, London. Chapter 10: Gamero, A and Šálek, M (2017) Case study 6: Effects of habitat heterogeneity on biodiversity in intensive arable farms. In G Siriwardena & G Tucker (eds) Service contract to support follow-up actions to the mid-term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture, pp 184-197. Report to the European Commission, Institute for European Environmental Policy, London.

Acknowledgements We thank Rayka Hauser of DG Environment and the members of the Steering Committee from the European Commission, EEA and JRC for their helpful guidance throughout this study. We are also grateful to Maria Luisa Paracchini and Renate Koeble of the JRC for their considerable help with identification, collation and processing of data for this study. Much of the biodiversity data used this study is based on species atlases, surveys and monitoring studies that have been mostly carried out by volunteer naturalists across Europe in collaboration with conservation and scientific originations (e.g. Pan European Common Bird Monitoring Survey coordinators). We are therefore indebted to all of them for their dedication and work. The specific datasets used in the integrated assessment and each case study are referred to in their respective chapters.

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Table of Contents

Table of Contents ...... 5 Executive Summary ...... 9 1 Introduction ...... 20 1.1 The context of this study ...... 20 1.2 The aims, objectives and scope of this study ...... 22 1.3 The structure of this report ...... 23 2 Development of a methodology for the assessment of potential agriculture- related drivers on the status of habitats and species ...... 25 2.1 Overview of this chapter ...... 25 2.2 Forms of data ...... 25 2.3 Defining analytical questions ...... 27 2.4 Analytical concept ...... 28 2.5 Decision tree ...... 33 3 Review of data sources and preparation of a metadatabase...... 49 3.1 Introduction and approach ...... 49 3.2 Database structure...... 50 4 Integrated analysis of potential agricultural drivers of bird and butterfly population trends in the EU ...... 61 4.1 Introduction ...... 61 4.2 Methodology and data sources ...... 64 4.3 Results ...... 73 4.4 Discussion ...... 81 4.5 Conclusions ...... 87 5 Case study 1: Factors affecting the condition of agriculture-associated species and habitats within Natura 2000 sites ...... 88 5.1 Introduction ...... 88 5.2 The feasibility of analysing the relationship between agricultural land use and the status of habitats and species covered by the Nature Directives ...... 90 5.3 Conclusions on the feasibility of the analysis ...... 99 6 Case study 2: Effects of Natura 2000 site designation and management plans on farmland bird abundance trends ...... 102 6.1 Introduction ...... 102 6.2 Methods and data sources ...... 103 6.3 Results ...... 106

6.4 Discussion ...... 109 6.5 Conclusions ...... 110 6.6 Acknowledgements ...... 110 7 Case study 3: large-scale bird distributions in Britain and Ireland in relation to lowland agricultural land-use ...... 112 7.1 Introduction ...... 112 7.2 Methodology and data sources ...... 113 7.3 Results ...... 118 7.4 Discussion ...... 122 7.5 Conclusions ...... 126 7.6 Acknowledgements ...... 126 8 Case study 4: UK agri-environment schemes and non-avian biodiversity ...... 127 8.1 Introduction ...... 127 8.2 Methodology and data sources ...... 128 8.3 Results ...... 132 8.4 Discussion ...... 141 8.5 Conclusions ...... 144 8.6 Acknowledgements ...... 146 9 Case study 5: The effects of increased rape and maize cropping on agricultural biodiversity ...... 147 9.1 Introduction ...... 147 9.2 Methodology and data sources ...... 153 9.3 Results ...... 159 9.4 Discussion ...... 180 9.5 Conclusions ...... 182 9.6 Acknowledgements ...... 183 10 Case study 6: Effects of habitat heterogeneity on biodiversity in intensive arable farms ...... 184 10.1 Introduction ...... 184 10.2 Methodology and data sources ...... 185 10.3 Results ...... 189 10.4 Discussion ...... 195 10.5 Conclusions ...... 197 10.6 Acknowledgements ...... 197 11 Conclusions ...... 198 11.1 Introduction ...... 198

11.2 Analytical approaches ...... 198 11.2 The availability, quality and suitability of relevant data ...... 200 11.3 Integrated assessment of the relationships between agriculture and biodiversity in Europe ...... 204 11.4 Case studies ...... 206 11.5 General conclusions and recommendations ...... 210 12 Glossary ...... 212 13 References ...... 221 Annex 1 Metadatabase structure ...... 236 Annex 2 Integrated assessment...... 238 Annex 3 Case study 1 - Factors affecting the condition of agriculture-associated species and habitats within Natura 2000 sites ...... 245 Annex 4 Case study 2 - Effects of Natura 2000 site designation and management plans on farmland bird abundance trends ...... 247 Annex 5 Case study 3: Large-scale bird distributions in Britain and Ireland in relation to lowland agricultural land-use ...... 250 Annex 6 Case study 5 The effects of increased rape and maize cropping on agricultural biodiversity ...... 272 Annex 7 Decision Tree for Selection of Data and Analytical Methods for the Assessment of Environmental Effects on Biodiversity ...... 330

Acronyms

AES Agri-environment scheme BBS Breeding Bird Survey BTO British Trust for Ornithology CAP Common Agricultural Policy COS Czech Ornithological Society EEA European Environment Agency ES Environmental Stewardship ETC-BD European Topic Centre – biodiversity FSS Farm Structure Survey GLM Generalized Linear Model GLMM Generalized Linear Mixed Model JRC Joint Research Centre NUTS Nomenclature d'unités territoriales statistiques (NUTS 1 = Member State ; NUTS 2 = Country / State / Region ; NUTS 3 = sub-regions, e.g. county) PECBMS Pan-European Common Bird Monitoring Scheme RDP Rural Development Programme UTM Universal Transverse Mercator WCBS Wider Countryside Butterfly Survey

Executive Summary

SERVICE CONTRACT TO SUPPORT FOLLOW-UP ACTIONS TO THE MID-TERM REVIEW OF THE EU BIODIVERSITY STRATEGY TO 2020 IN RELATION TO TARGET 3A - AGRICULTURE

Agriculture is the predominant land-use in the EU and there is robust evidence that there are widespread declines in farmland biodiversity in agricultural areas. The drivers of these declines therefore need to be identified and addressed if the EU is to achieve its target of halting the loss of biodiversity by 2020. A variety of studies have indicated that farmland biodiversity declines are often the result of changes in agricultural systems and practices, including increased intensification and specialisation in many areas, or, in contrast, agricultural abandonment in others. However, further detailed evidence is required of the relationships between agriculture and biodiversity, across a wider range of agricultural habitats, species groups and Member States to reliably inform agricultural and other land-use policy responses.

To help to address this evidence gap, this study has been carried out with the objective of elaborating and applying an evidence-based methodology for analysing potential causal links between the state of biodiversity and certain agricultural management practices in the EU. This requires the development of an analytical framework that is sufficiently flexible to be applicable to multiple taxa or elements of biodiversity (such as indices describing communities of various groups of organisms, via empirical data or proxies such as habitat condition) and their responses to environmental variation in space and time.

In particular, it aimed to develop a methodology for statistically analysing potential agriculture-related drivers on the status and trends of selected flagship habitats and indicator species in the EU, and to use this to identify potential causality between changes in agricultural practices and the status and trends of biodiversity in the EU. The methodology was used to carry out an integrated EU-wide analysis of the relationship between agricultural land-use variables and biodiversity, and to guide six more detailed case studies of the causal links between agriculture and biodiversity.

In principle, the project aimed to cover all elements of biodiversity, in all farmland habitats, including semi-natural habitats that are impacted by farming (chiefly grazing). In practice it was limited to those groups for which high quality data are available at national and EU scales. This led to most analyses being conducted on data for birds, with butterfly data also contributing to the integrated analysis and two case studies, and mammal and spider data each contributing to one case study. One further case study involved a literature survey of a wider range of animal species and groups (predominantly carabid beetles, spiders, earthworms, hoverflies, solitary bees, bumblebees, butterflies, birds and mammals).

Development of a methodology for the assessment of potential agriculture-related drivers on the status of habitats and species

The first task involved the development of a robust methodology for analysing evidence of land-use and policy effects on biodiversity. Note that the aim was specifically to measure these effects, not to assess their importance relative to other potential drivers, such as those associated with climate change or urbanization. This study makes no judgement about the vast number of other potential influences. In future studies, however, the methodology developed here could be used to consider other potential drivers, such as climate variation, urbanization or human population density.

The process of selection of an appropriate method to answer a specific analytical question, while taking account of data availability and quality, is formalized as a decision tree. The decision tree has two parts: (i) data selection, assessment of data availability and data preparation; (ii) choice of analysis. This is presented graphically and annotated for use in a separate document (see Annex 7).

The recommended form of analysis, where possible, uses a generalized linear model (GLM) framework to examine the influences of environmental variables like management or land-use areas on changes in abundance between years (i.e. population growth rates). This requires time series of survey-effort-controlled abundance data at the level of the monitoring site, which could be a defined habitat area, a grid square, a point defined by its coordinates or a transect route, for example. Note that a “monitoring site” in this context refers specifically to an area selected for sampling, not necessarily to a protected or designated “site” or an administrative boundary. Other approaches are suggested for contexts in which lower quality data, such as presence-absence data, are all that are available. It is recognized that, while high quality should remain the goal of sampling protocols, methods are required to extract the maximum information from lower quality data that are currently sometimes the best available for a range of taxa and regions.

Review of data sources and preparation of a metadatabase

To test the developed methodology, and to support its future applications, the study identified and catalogued sources of relevant data on agricultural variables and biodiversity that could be used to investigate their interrelationships. A database was compiled in Microsoft Access (and supplied to the Commission with this report) that contains available metadata describing the data sources for potential agricultural driver variables relevant to biodiversity in the EU, as well as the biodiversity data sources themselves. The metadata should allow future users to identify where to find the data that are available at the time of the study, what those data allow analysts to do and how to access the information. In total, 623 biodiversity data sets and 404 agricultural data sets were identified, covering a wide range of Member States and ranges of years, but with more data, in general, from 2000 onwards.

The database includes descriptions of the data used in the analyses undertaken in this study (as described below), and the data themselves, where their use is not restricted by licence, which should facilitate their future analysis.

In addition, the results of some of the analyses of this study, as they provide explicit, quantitative links between agriculture and biodiversity, are included as additional tables in the database.

Integrated assessment of the effects of agricultural land-use and practices on trends in birds and butterflies in the EU

This part of the study involved an assessment of the relationships between agricultural land-use and practices and biodiversity that was integrated across Europe and across as wide a range of taxa as possible. The focus was on the influences of different aspects of agriculture relative to one another, not on the absolute importance of these factors, or of the importance of agriculture in general, compared to other land-use or environmental factors. “Integration” was taken to mean a consistent, quantitative analytical approach across taxa and Member States, combined with a qualitative collation of the results to produce an overall assessment for biodiversity and agriculture. Many previous studies have considered various factors in agriculture and their influences on aspects of biodiversity at national or regional scales, particularly for birds and butterflies, but analyses integrated at a continental scale have not previously been conducted.

As this component of the study aimed to use the most robust methodology, it was only possible to analyse raw site level data on birds from 27 Member States and butterflies from five Member States. All data were analysed for the maximum time series available, birds from 2002-2014 and 22,127 monitoring sites (with an average of 18 species per Member State), and butterflies from 2002-2015 and 1,587 monitoring sites (with an average of 85.5 species per Member State). Birds have an established classification with respect to habitat preference, so just farmland species were considered. Most butterflies are associated with grassland or other open habitats to some extent, so all butterfly species available were considered. Bird and butterfly data were analysed at the scale of the monitoring site (usually a 1km grid square or smaller spatial unit or habitat patch).

The effects of as many agricultural influences as possible were assessed (including crop types, boundary features, and indicators of management intensity (i.e. nitrogen and energy inputs) and landscape heterogeneity), using data at the finest common scale possible (the NUTS3 region), because this would maximize the variation in the data and, therefore, power. However, there were some significant limitations to the assessment, due to data availability. No data were available on the farming systems (such as rotations or input regimes, including organic farming) that were in use in particular areas, and the summarized agricultural land use data at the NUTS3 level do not allow cropping systems to be inferred for individual farms because the crops in a region could be distributed in too many diverse arrangements.

The intention was initially to carry out the analysis using pan-European data sets, but this resulted in data sets that were too large to analyse, even with the substantial computing resources available to the BTO. The datasets were therefore analysed at Member State level and summarized across Europe.

The results of the bird and butterfly analyses show both positive and negative associations between different species and different agricultural variables, probably reflecting both basic variation in habitat preferences (e.g. for different crop types) and the different effects of agricultural variation on different species. However, because agricultural data were only available at the NUTS3 scale, the results really reflect associations with landscapes or farming systems, rather than with the specific ecological conditions relevant to habitat quality for individual species that are determined by the details of in-field management or field contents. All results here with respect to associations with individual variables should be interpreted in this context, rather than as indicating direct effects of the land-use described.

Although there is no simple, general pattern across birds and butterfly species and agricultural variables to report, there were at least several significant relationships between the population growth rates of individual bird species and all individual predictor variables, across the range of individual Member States. Significant relationships were rarer for butterflies than for birds, probably mainly because data were available from fewer Member States, and therefore fewer NUTS3 regions. Whilst some variables showed positive associations more often than negative ones, the negative associations were also numerous for all of these variables. This is not surprising given that farmland is a highly heterogeneous habitat and that each species has different habitat and other ecological requirements. The effects of the agricultural variables on each species are therefore potentially important, but complex. As these relationships are too numerous to describe here, they are added to the database for potential future analysis.

There are important caveats to the interpretation of the results of this study, which must be taken into account. These include the limited taxonomic range that could be considered due to data availability, the lack of data (or data accessibility) for some Member States (especially for butterflies), lack of some key agricultural data (e.g. timing of sowing, pesticide use), the spatial scale of the data available (which is far larger than that at which biodiversity will actually respond), the coarseness of the available field boundary/linear feature data and the sheer size of the datasets that are available (which caused significant problems for analysis using the ideal model types). All of these mean that there is considerable qualitative uncertainty in the results obtained, especially at the EU level, as well as the statistical uncertainty in the model results.

It is also important to note that this study is fundamentally correlative, not experimental, so the relationships found may indicate either genuine, direct effects of the variable involved, or indirect effects of other variables that happen to be spatially associated with the focal variable.

Case Studies

The remainder of this project consisted of six case studies, each dealing with a specific issue within the broad area of agriculture-biodiversity relationships in Europe. Case studies were selected where particular, high-quality data were available to address specific issues in certain Member States or smaller regions, or were not suitable for inclusion in the integrated assessment analyses. Options for case studies were identified from the knowledge of datasets relevant to specific questions held by the project team; these options were then reviewed by the project steering group and the selection of studies was finalized.

Case study 1: Factors affecting the condition of agriculture-associated species and habitats within Natura 2000 sites

This case study explored the feasibility of analysing the impacts of agricultural factors and conservation management measures, such as agri-enviroment schemes (AES) on agricultural habitats and species targeted by the Habitat and Birds Directives, using Member State reporting data. It concluded that overall conservation status / trends assessment reporting data are at too large a scale (biogeographic regions / Member State) and therefore provide inadequate samples for this type of analysis. Whilst Natura 2000 site-level assessments of the degree of conservation of each habitat and species provide a very large data set, the assessments are not dated and cannot therefore provide a basis for meaningful analysis.

It is therefore suggested that, in order to enable future analyses of agriculture- related factors affecting species and habitats within Natura 2000 sites, the first priority should be to collate the monitoring data that are currently available and to record metadata on each dataset. In future, great improvements in the quality of evidence could then be obtained by collating time series of Natura 2000 monitoring data, rather than single, on-off conservation assessment records. Data from recording these changes would allow analyses of whether changes in condition are associated with agricultural changes or management interventions.

Much stronger analyses would also be possible with structured, randomized sampling to generate quantitative abundance or cover data for target taxa and habitats within Natura 2000 sites and, if possible, also for representative counterfactual areas. Standardization across Member States of the methods used in these surveys, and consistency in them over time, would be ideal here, but documentation of the methods, so that it is clear how data can be compared is essential for informed analyses, should be an absolute minimum.

Access to fine-scale land-use data (currently existing but not readily accessible in most Member States) would be highly advantageous, while bespoke records of habitat/land-use at the Natura 2000 site level, or validated predictive models based on existing, sampled data could be used to enhance the possible inference. The more

detailed such records are, the higher the certainty that the truly important influences on target species and habitats will be identified.

Case study 2: Effects of Natura 2000 site designation and management plans on farmland bird abundance trends

This case study aimed to assess the impacts of Natura 2000 designation, as well as the development of management plans for the sites, on farmland birds. This was investigated by comparing common farmland bird abundance trends over 1980 to 2013 from 23 EU countries from 10,578 farmland sites inside and outside the Natura 2000 network, with management plans or without, and with respect to variation in overlap with Natura 2000 protected areas. The 1,622 bird count sites (spatial units or habitat patches up to 1 km2) situated within the Natura 2000 network overlapped with 885 different Natura 2000 designated areas comprising 127,432 km2. The data selected within and outside Natura 2000 sites were matched as far as possible by the selection of farmland-dominated sampling locations, so there is no reason to suspect that the sampling was unrepresentative of Natura 2000 locations that are influenced by agriculture.

The results revealed that common farmland bird abundance was higher inside Natura 2000 areas, but that in general it decreased at similar rates as in sites outside the network, suggesting that the conservation sites were located in areas with good populations of birds, but that designation in itself was not sufficient to address causes of decline.

Larger coverage of Natura 2000 areas in bird survey sites was associated with more positive abundance trends of farmland birds. Additionally, farmland bird abundance trends were less negative in Natura 2000 sites with management plans compared to sites without them, when Natura 2000 coverage was large. However, count sites with management plans and large coverage of Natura 2000 areas represented only 10% of the Natura 2000 sites analysed in this study, and even there the abundance trends of farmland birds were not positive, indicating that more conservation actions are needed to improve farmland biodiversity in these protected areas.

This study thus suggests that the designation of Natura 2000 sites needs to be accompanied by conservation measures, as required under the Habitats Directive and implemented via management plans, in order to influence abundance trends of farmland birds positively. If further analyses are considered and were to be effective, they would require more information than is currently available on where management plans have been adopted and exactly what actual conservation actions have taken place on the ground as a result.

Case study 3: large-scale bird distributions in Britain and Ireland in relation to lowland agricultural land-use

This case study used bird atlas data sets, which provide uniquely high geographical coverage and opportunities to investigate the influences of landscape factors on bird distribution, to assess the influence of agricultural practices. Presence-absence data from Bird Atlas 2007-11 at the scale of 2×2 km squares (‘tetrads’) were analysed using a thorough process involving model-averaging to identify the most important crop type and livestock variables for each of a broad range of species found in lowland farmland, and the relative importance of each variable, both within and across species. A total of 34 species that regularly use farmland were considered, including both farmland specialist and more generalist species. The proportions of land classified as each of arable, pastoral, farmland, woodland and urban were included as control variables in all analyses. Fifteen crop or grassland variables were considered, together with the two agriculturally significant livestock variables (numbers of cattle and sheep/goats); all variables consisted of data per NUTS3 region. Across species, an average of 9,729 tetrads in 100 NUTS3 regions were used in Britain and 1,407 tetrads in eight regions were used in Ireland.

The results revealed that all of the variables considered had both positive and negative associations with the presence of several species, but that the patterns of these associations differed between Britain and Ireland. Particularly important in Britain were positive associations with areas of pasture and wheat, while rough grazing was predominantly negative. In Ireland, however, rough grazing tended to be a positive influence. Spring barley in Britain and beet in Ireland were noteworthy in being important for many species, but with an even combination of positive and negative patterns.

Overall, there were not many clear associations with land-uses that are often regarded as indicating more or less intensive agricultural management, which probably reflects the balance between farmland supporting farmland species, and yet also being a negative influence upon them if management is not sympathetic.

A range of important caveats need to be taken into account in interpreting the results, notably including the scales of both bird and agricultural data, which limit the sensitivity of the analyses and mean that land-use variables refer to landscape types associated with crops or livestock, rather than field contents specifically. Birds are mobile, but frequently use the landscape at a scale of c. 10-100 ha within a season, so field-level agricultural data are really required to describe the features of habitats to which birds respond. Using NUTS3-level data brings an implicit assumption that the distribution of land-uses in all the fields or other areas to which individual birds respond is the same as that across the entire region, which is highly unlikely to be true. Therefore, in future, it would greatly increase the quality of the analyses if agricultural data were available at a smaller spatial scale, especially if data on field boundary and other semi-natural habitats were also available.

Furthermore, whilst the 2×2 km tetrad is a good scale at which to assess local bird densities, it can encompass considerable variation in landscape composition, so relationships with specific habitat types, such as farmland, would be clearer from bird data at a finer spatial resolution.

Case study 4: UK agri-environment schemes and non-avian biodiversity

This study aimed to build on previous studies of the impacts of AES on bird populations by using analogous national‐scale survey data for butterflies and mammals. The available mammal data considered were for Brown Hare (Lepus europaeus, 15,074 squares), Rabbit (Oryctolagus cuniculus, 20,401 squares), Red Fox (Vulpes vulpes, 12,272 squares) and Hedgehog (Erinaceus europaeus), as collected from a random sample of 1 km survey squares under the BTO/JNCC/RSPB1 Breeding Bird Survey (BBS) in England, over the period 2002-2013. However, Hedgehog was subsequently omitted because it was recorded in too few squares to support analyses. The butterfly data used were obtained from the CEH/BC/JNCC/BTO2 Wider Countryside Butterfly Survey (WCBS) for a separate random sample of 1 km squares from 2006 to 2013, within which 18 species were recorded sufficiently frequently to support analyses (with an average of 1,291 squares being considered per butterfly species).

Spatial data on the uptake and area coverage of individual management option types relevant to the target taxa, such as those providing winter habitat for Brown Hare and floral resources for butterflies, were included as predictors of inter‐annual changes in species‐specific counts. These data were also used at the scale of the 1 km square.

The study found that there were significant, positive effects on Brown Hare population growth of AES options providing winter cover for the species (stubble management, winter cover crops and wild bird seed mixture). This result is an important addition to earlier findings of Brown Hare preferences for analogous habitats because spatial associations showing habitat selection do not necessarily imply knock-on positive effects on population change. Together with the results reported here, these previous results thus suggest that management potentially benefiting breeding hares may attract them and produce local peaks in density via redistribution, but not necessarily drive increases in total abundance. Rabbit population growth rates were positively associated with hedgerow management options, perhaps showing a benefit of cover of burrow entrances. No evidence for positive or negative effects of AES management on Red Fox was found, but it is important to note that the survey method on which the results are based is optimized for birds, and is not ideal for many mammals, particularly because of issues of detectability, which will have affected the data for Red Fox (and also Hedgehog).

British Trust for Ornithology/Joint Nature Conservation Committee/Royal Society for the Protection of Birds 2 Centre for Ecology and Hydrology/Butterfly Conservation/Joint Nature Conservation Committee/British Trust for Ornithology

Of the options specifically targeted at butterflies, those providing nectar resources in arable farmland were positively associated with two butterfly species and negatively with one. Options involving management of hedgerows (reduction of cutting frequency to increase woody vegetation density and height) have more general targets, but had largely negative associations with butterfly population growth rates. Overall, the support for positive effects of agri-environment management on butterfly populations is weak at best, while unintended negative effects may be in operation, particularly in hedgerows.

The causes of the negative associations between butterflies and hedgerow management, in particular, are not known, but could involve issues such as sub- optimal cutting regimes in management options or perhaps the suppression of food plants. It would be unwise to consider these results as being definitive, but they suggest that further investigation of operation of agri-environment option effects in practice, as opposed to in small-scale experimental trials, is required.

Overall, this study has found evidence adding to that already available for birds that national-scale AES management can produce positive effects on the population growth rates of widespread species. Whilst it has also found evidence that some management could have had unintended negative effects, this requires confirmation. Most importantly, the occurrence of positive effects as well shows that the problems lie with the specifics of management, not with the structure of the agri-environment schemes considered as a whole.

Case study 5: The effects of increased rape and maize cropping on agricultural biodiversity

This case study provides a meta-analysis of the impacts of increased oil-seed-rape and maize cropping on farmland biodiversity, considering all wildlife groups for which studies could be found. This focussed on Germany as the area used for rape and maize cultivation has increased substantially over the last 25 years, largely as a result of policies that have encouraged bioenergy production.

The meta-analysis was based on the results of 267 studies published in scientific journals, reports, and presentations over the last 25 years on the effects of maize and rape crops on biodiversity. The case study also took into account the results of studies that measured the impact of increased rape and maize cultivation on population trends of farmland bird species. In addition, it included a more detailed analysis of the impacts of maize and rape on the population of Common Crane (Grus grus) in Europe.

The results indicate that the impacts of maize and rape crops on biodiversity are mainly caused by their replacement of more biodiverse crops (such as grasslands, fallow land and semi-natural land). With the exception of two taxa showing no effects or small positive effects (carabid beetles, spiders), maize had the most detrimental impacts on biodiversity, negatively affecting earthworms, spiders,

hoverflies, solitary bees, bumblebees, butterflies, birds and mammals. More species/taxa benefit from, or at least are not negatively affected by, rape cultivation (spiders, carabid beetles, hoverflies, solitary bees, bumblebees, mammals). A similar pattern of results is apparent in relation to the magnitude of the impacts.

The study found that there is substantial evidence that the increased cultivation of maize and rape is leading to a loss of agricultural habitats with higher conservation value, which is one of the main drivers for the population decline in farmland birds. However, certain bird species such as Common Crane may benefit from a higher supply of feeding habitat in their resting and stopover sites especially during autumn migration and partly also in winter.

Case study 6: Effects of habitat heterogeneity on biodiversity in intensive arable farmland

In recent decades, EU farmland has become increasingly homogeneous and more intensively cultivated, while farmland biodiversity has declined strongly. To investigate whether these trends may be linked, this case study measured and compared the abundance and species richness of birds, spiders and butterflies, and fine-scale habitat land-use, at two spatial scales in intensive arable regions in Austria and the Czech Republic with highly contrasting habitat heterogeneity. Sampling was conducted in each of 2014 and 2015 in 25 25 ha (500 x 500m) squares in each of the two Member States.

Biodiversity values of the taxonomic groups measured in each plot were positively correlated with each other. In particular, butterfly species richness was correlated with bird and spider abundance and bird species richness, suggesting that it may be a good indicator of general farmland biodiversity in this intensive arable landscape.

The study found strong and consistent evidence that biodiversity as measured in relation to species richness increased in relation to habitat heterogeneity, for all three taxa groups examined. The farmland heterogeneity measure that had most positive effects on biodiversity was the larger proportion of non-cropped elements (e.g. margins, hedges and grassland patches), followed by smaller patch size. Furthermore, species of conservation concern in the EU that were examined (Annex I bird species and grassland butterflies) were more common in areas with smaller patch sizes. This indicates the biodiversity value of conservation measures that maintain or promote agricultural landscapes with non-cropped elements and small fields.

Overall conclusions

This project has revealed a range of significant relationships between agricultural variables and measures of biodiversity or change in biodiversity, including both positive and negative associations. This is not surprising because the analyses consider species that are associated with farmland, so they would be expected to benefit from certain agricultural land-uses. Conversely, since farmland is a highly

heterogeneous habitat, it is also not surprising that aspects of agricultural land-use have negative influences on one or more species. It is important to note, however, that this study is fundamentally correlative, not experimental, so the relationships found may indicate either genuine, direct effects of the variable involved, or indirect effects of other variables that happen to be spatially associated with the focal variable.

A significant constraint identified in this study has been that many data of potential interest are accessible only at the scale of large regions, generally NUTS3 or NUTS2 level. The value of making available (for example) high-resolution agricultural data, to match the biodiversity data that are already available (freely or under licence), should be considered to facilitate future studies. Further important caveats arise from the limited taxonomic and Member State ranges in the available biodiversity data and the lack of available data on key agricultural practices of potential influence, such as timing of sowing, pesticide use and farming systems (including organic farming). The available raw data for field boundaries and linear features are also far coarser than would be ideal for identifying biodiversity responses. All of these mean that there is considerable qualitative uncertainty in the results obtained, especially at the EU level, as well as the statistical uncertainty in the model results. Finally, analysing such large European-scale data sets is challenging for computer resources, which limits the complexity of analyses that can be performed in practice, although this constraint is likely to be reduced in the future, as available computing power increases.

In conclusion, this project has confirmed that the associations between agriculture and biodiversity are diverse and complex; many species have become adapted to and even depend on agricultural habitats (especially extensively farmed systems), but changes in agriculture in recent decades have led to negative impacts upon many of these species. CAP policy measures, such as agri-environment schemes have aimed, at least partly, to mitigate such detrimental impacts. However, farmland is a highly heterogeneous habitat and associations with different species and taxa are complex. Therefore, it is unrealistic to expect to obtain simple answers to questions relating to effects of agriculture on biodiversity and this study has not found such simple answers. Average responses across groups can be determined and specific questions relating to particular species or groups, such as those relating to landscape heterogeneity or Natura 2000 site protection, can be answered, although the results are still likely to be complex.

Further developments of methods to analyse informal biological records are likely to increase the range of taxa that can be considered and continuing improvements in remote-sensing are likely to make better analyses possible. However, these changes will only partly address the principal constraint of data availability. The development of monitoring should focus on providing accessible data on the key factors for biodiversity and for agriculture that are needed to support future assessments aimed at delivering policy-relevant evidence.

1 Introduction

1.1 The context of this study Agriculture is the predominant land-use in the EU and therefore it is particularly important to ensure that agricultural systems and practices are compatible with the conservation of biodiversity in order to achieve the EU target of halting the loss of biodiversity and ecosystem services by 2020. However, there is robust evidence of widespread declines in farmland biodiversity in Europe over the last 30-40 years, primarily as a result of widespread increases in agricultural intensification and specialisation, and, in some areas, agricultural abandonment (e.g. Donald et al, 2001; Donald et al, 2006; Poláková et al, 2011; Underwood et al, 2013; Siriwardena et al. 1998). These are now being exacerbated by a range of new pressures, including the increased use of land for bioenergy (including crops for liquid-biofuels, such as from oil-seed-rape and biomass crops such as maize).

These biodiversity declines are of particular concern with respect to the remaining traditional low-intensity farming systems that maintain diverse semi-natural habitats, as these are of particularly high biodiversity importance. Such farming systems are often referred to as High Nature Value farming systems (HNV) (Baldock et al, 1993; Baldock, 1999; EEA, 2004; Veen et al, 2009), and they still make up around a third of the EU agricultural area3 (Paracchini et al, 2008). Many of these areas include semi-natural habitats, and their associated species are of European conservation importance and therefore the subject of conservation measures under the EU Habitats Directive and Birds Directive. However, despite the protection of 10% of farmed land within the Natura 2000 network4, 86% of grassland types that are the focus of the Habitats Directive5 have an unfavourable conservation status (EEA, 2015).

Declines in species associated with semi-natural agricultural habitats are also evident. For example, grassland butterfly populations have declined by almost 50% since 1990 across Europe due to the intensification or abandonment of grasslands (EEA, 2013; van Swaay et al, 2010; van Swaay et al, 2006), and wild bees and their forage plants are disappearing (Bommarco et al, 2011; Carvell et al, 2006; Goulson et al, 2008; Kosior et al, 2007). Farmland mammals such as Brown Hare (Lepus europaeus), European Hamster (Cricetus cricetus), and bats have declined significantly (La Haye et al, 2012; Robinson and Sutherland, 2002; Temple and Terry, 2007; Ziomek and Banaszek, 2007). Overall, 64% of species that are the focus of the Habitats Directive6 and are associated with grasslands have an unfavourable conservation status (EEA, 2015).

3 75 million hectares (Paracchini et al, 2008) 4 The Natura 2000 network comprises Special Protection Areas (SPAs) designated under Article 4 of the Birds Directive (for birds listed in Annex I of the Directive and for migratory species) and Special Areas of Conservation (SACs) designated under Article 4 of the Habitats Directive (for habitats and species of Community interest). 5 i.e. Habitats of Community Interest listed in Annex I of the Habitats Directive 6 i.e. Species listed in Annex II of the Habitats Directive (which do not include birds)

Furthermore, biodiversity monitoring over recent decades has demonstrated that the impacts of past and continuing agricultural improvements, intensification and specialisation have led to further, more widespread declines across intensive agricultural habitats. The best available evidence of this comes from farmland bird populations, as they have been relatively well monitored. Their trends have been summarized as a Common Farmland Bird Indicator, one of the indicators used by the EEA to monitor the status of biodiversity in the EU.7 The index of common farmland bird population changes in the EU shows an average 31% decline in their populations between 1990 and 2013 (based on 35 species, most of which have declined, but some of which have remained stable or have increased)8.

The EU has legislative and policy instruments that aim to conserve biodiversity, including on farmland, through in particular habitat protection and conservation management obligations under the Birds and Habitats Directives. Furthermore, the EU Common Agricultural Policy (CAP) has increasingly incorporated environmental objectives over recent decades and has accordingly included measures that aim to maintain and restore important habitats and habitat features for species (Mileu, IEEP and ICF, 2016). Of particular importance, and especially for semi-natural habitats, have been agri-environments schemes and some other measures included in Rural Development Programmes (RDPs), supported by Pillar 2 of the CAP. Cross- compliance measures applied to direct payments under Pillar 1 of the CAP also aim to incentivise farmers to protect the environment, as do the more recent Pillar 1 greening measures (i.e. Ecological Focus Areas, crop diversification and grassland measures) introduced under the 2014-2020 CAP. Consequently, one of the targets of the EU Biodiversity Strategy9 (3a) is ‘By 2020, maximise areas under agriculture across grasslands, arable land and permanent crops that are covered by biodiversity- related measures under the CAP so as to ensure the conservation of biodiversity and to bring about a measurable improvement in the conservation status of species and habitats that depend on or are affected by agriculture and in the provision of ecosystem services as compared to the EU2010 Baseline, thus contributing to enhance sustainable agricultural management.’

In order to support Target 3a, and better target, design and implement farmland conservation measures (such as agri-environment schemes) more detailed and representative evidence of the extent to which agricultural management is a cause of biodiversity declines and of the effectiveness of conservation measures is needed. For example, evidence of the specific causes of impacts of agricultural factors on farmland biodiversity to date has been biased towards a few taxa and states, such as birds in the UK and France, although some such evidence is available for many countries and regions. It is therefore particularly important to assess the causes of biodiversity declines across a wider range of Member States. More specifically, evidence is needed of the impacts of changes in the types and diversity of crops

7 http://biodiversity-chm.eea.europa.eu/information/indicator/F1090245995 8 http://www.ebcc.info/index.php?ID=588 9 Communication on our life insurance, our natural capital: an EU biodiversity strategy to 2020, COM(2011) 244 final. Hereafter referred to as the “Biodiversity Strategy”

grown in the landscape (such as increases in bioenergy crops), particular cropping or livestock management practices, and the effects of agri-environment schemes and farmland conservation measures. The importance of semi-natural habitat, such as hedgerows, woodlands and grass strips, within the wider farmed landscape (its presence and its spatial arrangement) is also worthy of investigation, because such habitats are of high biodiversity importance and may be particularly vulnerable to intensification. All of these issues can affect both relatively intensive and extensive farming systems, the former being particularly important in respect of the health of the broader environment and the latter, which are especially vulnerable to abandonment, commonly forming the HNV farming systems in Natura 2000 sites that have particularly high biodiversity value.

1.2 The aims, objectives and scope of this study This European Commission study was carried out to further help identify and tackle agriculture related causes of declines in farmland biodiversity, to improve the targeting and implementation of conservation measures and to inform the future development of environmental aspects of agricultural policy. Its general objective has been to elaborate and to apply an evidence-based methodology for analysing potential causal links between the state of biodiversity and certain agricultural management practices in the EU.

In particular it had the following specific objectives:

 Develop and test a methodology for evidence-based assessment of potential agriculture-related drivers on the status and trends of selected flagship habitats and indicator species in the EU.

 Highlight and analyse patterns which indicate potential causality between (changes in) agricultural practices and the status and trends of biodiversity in the EU, while taking into account other possible intervening factors.

 Identify and provide a detailed analysis of evidence-based causal links between biodiversity status and agricultural practices in selected case study areas.

This required the development of an analytical framework that is sufficiently flexible to be applicable to all terrestrial agricultural habitats (including semi-natural habitats that are the focus of the Habitats Directive, and other HNV farmland, as well as intensively managed farmland), multiple taxa and biodiversity indicators (such as indices describing communities of various groups of organisms, via empirical data or proxies such as habitat condition) and their responses to environmental variation in space and time. The analyses proposed also need to be applicable to a wide range of environmental datasets and biodiversity response variables, as are relevant to the range of policy and evidence priorities likely to arise in the foreseable future.

In addition to identifying statistical approaches that will deliver sound evidence for the impacts or otherwise of land-use, policy and other potential influences on

wildlife in the agricultural environment, the study also set out to identify where data quality or quantity is insufficient to allow conclusions to be reached (implying that further data collection is required). Data availability at smaller spatial scales increases the power and sensitivity of analyses, so is always desirable in studies like this one.

The central analytical element of the project consisted of an integrated assessment of the relationships between agriculture and biodiversity in Europe, making use of all of the data that were available to address this very broad set of issues at the EU scale, which could involve cropping and livestock patterns, farming systems, specific practices, chemical inputs, organic farming, agri-environment measures and conservation interventions in protected sites that are subject to agricultural management, each then investigated with respect to all aspects of biodiversity for which data were available. In practice, only bird and butterfly responses to cropping and livestock variation could be investigated at large scales, across multiple Member States.

The remainder of the study consisted of six case studies, each dealing with a specific issue within the broad area of agriculture-biodiversity relationships in Europe. One of the reasons for carrying out the case studies was to try to overcome some data gaps in various parts of Europe and for various taxonomic groups, which limit the potential for integrated assessments of agricultural impacts on biodiversity at the continental scale. The selection of the case studies was therefore primarily based on the availability of extensive surveys or longer runs of historical data to establish the effects of changes in farming practices and/or the effectiveness of conservation measures. As a set, the case studies also aimed to represent a range of scales of analysis and regions of Europe. However, it is important to acknowledge that there will be limits to the expected geographical generality of each case study.

1.3 The structure of this report This study comprised the following three principal tasks:

 Task 1: The development of a methodology for evidence-based assessment of agriculture-related drivers on biodiversity status and trends in the EU, including: a) Providing an overview of existing information sources and relevant data sets to be considered for use in an integrated assessment. b) Criteria for the selection of the data sets to be used in the assessment.

 Task 2: Dataset preparation and integrated assessment, including: a) Preparation of all selected datasets in a suitable format and scale for performing an integrated assessment. b) Analysis of selected datasets to provide an integrated assessment of agriculture-related biodiversity status and trends and identify patterns indicating potential links with (changes in) land use/cover and farming practices.

 Task 3: The preparation of six case studies analysing well-evidenced causal links between agricultural practices and the status and trends of related flagship habitats and species.

The results of Task 1 are set out in Chapter 2, which includes a decision tree framework and explanatory notes in section Decision tree2.5 that can be reproduced as a stand-alone guidance for future analysis. A glossary to help the reader to understand the more technical terms used in the text is included as Chapter 12 of this report. The development of the database under Task 2a (which has been supplied to the European Commission with this study) is described in Chapter 3. Chapter 4 provides a full account of the integrated assessment carried out under task 2a. The six case studies are included individually in Chapters 5 - 10.

2 Development of a methodology for the assessment of potential agriculture-related drivers on the status of habitats and species

Authors: Gavin Siriwardena and Henrietta Pringle (BTO)

2.1 Overview of this chapter This chapter reviews possible analytical approaches to relate biodiversity variation in space and time to agriculture and sets out a decision tree process for selecting an appropriate method given the data that are available. This framework provides guidance for decision-makers and researchers from formulation of the question of interest to the choice of analytical technique. The decision tree leads to the strongest methodological approach for the form of data available, beginning with proven methods for high-quality data, leading to modifications for contexts in which the data available are poorer in respect of biodiversity detail, temporal repetition or spatial scale of availability. We focus on the use of analyses that have an established pedigree in the ecological literature and the elements of them that are suitable for evaluating variable effects; the approaches can also be used or extended to identify the best single statistical models or for prediction into the future or in different locations, but these functions are beyond the scope of this project. It is inevitable that a chapter describing statistical methods includes a considerable amount of statistical terminology or jargon. To assist readers with interpreting the text, a glossary has been provided as Chapter 12 in this report.

2.2 Forms of data The data to be considered in assessments of biodiversity relationships with agriculture take the form of spatially and, ideally, temporally specific measures of taxon or community presence, abundance or health, coupled with similarly specific measures of agricultural land-use, practices, management and policy implementation. Both biodiversity and land-use data will vary in form and the spatial scale of data availability, so a framework for analysis needs to be flexible.

Policy decisions or overall assessments of biodiversity responses to agricultural variation are likely to be based on aggregated or biological-community-level measures, such as the Farmland Bird Index (Gregory et al. 2005). Such measures are summaries of species-level, or even population-level, patterns that may reflect highly variable relationships with environmental influences, such as land-use. Therefore, it is appropriate to analyse species-level responses, reflecting the level at which relationships with environmental variables are likely to vary ecologically, whatever the desired output measures are. Outputs in respect of community measures can then be constructed by combining species-level results. For example, the implications of a hypothetical change in farming systems in a region for the Farmland Bird Index would be investigated by analysing the effects on the component species, which might include, say, three negative and seven positive effects across ten species. Combining the analytical outputs might then indicate a net positive effect on

the overall index. Similarly, patterns of species richness should, nominally, be constructed via analyses of presence and absence of individual species of interest.

In order to inform about effects of agriculture or management on biodiversity, the data for each of these need to have sufficiently fine spatial resolution to provide replication of the variation in each data set, such that there is sufficient power to detect the effects of interest. Nominally, all data sets need to have standardized data collection in space and time, and/or records of potential confounding factors, allowing analyses to control for variation in the latter. For example, spatial environmental data need to come from the same or comparable locations and biodiversity data from less formalized schemes need to be accompanied by effort recording (or a suitable proxy thereof). In general, structured survey data, with fixed protocols for recording zero counts (or absences) and allowing the separation of real variations in counts from variations in sampling effort, are the most valuable, while unstructured, casual records are much less useful, although their utility can be enhanced with the addition of data on the environmental context. Repeated sampling over time, typically annually, is clearly required to investigate effects on population change. Such sampling is particularly valuable if the same sample units are used in multiple years, because this allows spatial and temporal variation to be separated effectively. For all data sets, more analytical and interpretative power is provided by more replication, i.e. larger sample sizes in each category to be compared. The more variable data are within a given category, the larger the sample required to characterize the dominant patterns.

Sampling structure forms one of several dimensions of data quality. Others relate to the biological information being recorded and what it represents, relative to the target parameter, which is, ideally and typically, real local population size or density. Thus, single time point habitat selection information (presence/absence or abundance/coverage) can be sensitive but the possible inference is likely to depend on starting conditions as well as any contemporary management influences. It also fails to identify possible ecological trap effects (Battin 2004), wherein animals are attracted to a habitat (and are therefore recorded in higher numbers there) but this attraction is actually maladaptive (as has been found for Corncrakes (Crex crex) in silage grass, for example: Green et al. 1997). Temporal repeat observations provide more sensitive measures of effects of management on change in abundance or presence. Then, abundance or percentage cover (for plants) provides better indicators of health/condition/status and changes therein than simple presence- absence, because it is more sensitive to environmental variation. For example, the abundance of a species in a given location can decline a great deal in response to environmental change before local extinctions occur and such patterns have occurred in the decline of farmland birds in the UK: percentage declines in abundance far exceeded those in occupancy of grid squares (Siriwardena et al. 1998, Chamberlain et al. 2000).

Finally, regardless of the methods used for data collection, it is common for environmental and biodiversity data to be published at large spatial scales, such as in the form of national trends or regional (administrative area) summaries (e.g.

European and national bird indicators10, national agricultural statistics from Member States11). Such summary data need to be published with relevant information about variability (e.g. coefficients of variation in biodiversity indicator values and environmental variables that are above a given threshold that provides reasonable analytical power) to allow formal analyses to be conducted. Even so, the raw data or summaries at smaller spatial scales would be more tractable for analysis and thus be more valuable than any summary data. However, if summary data are all that are available for a region or subject, secondary analyses of this information may be the best analysis possible and the only way to include a region or an issue concerned in very large-scale assessments.

All the influences on data quality described above are considered specifically in the metadatabase described in Chapter Error! Reference source not found..

2.3 Defining analytical questions The first step in the analytical process, and therefore in the decision tree, is to define the question being asked and the types of data required to answer it. Questions will need to be as specific as possible for the analytical direction to be clear and tractable. For example, “do agri-environment schemes (AES) benefit birds?” would be too general, because “benefit” is vague and the time-frame and geographical extent are not specified, meaning that a wide range of forms of AES could potentially be considered. A better form of question would be “have AESs since 2005 led to increases in bird abundance on farmland in Europe?”. This specifies a time-frame and the region of interest, and that time series data on abundance, as opposed to distributional data, are ideally required.

In many cases, it may be that the data required to answer particular questions do not exist. For example, only some of the taxa of interest may have survey data available, or there may be data only on spatial variation in abundance, not on temporal changes. In such cases, the question needs to be re-cast such that (a) it can be answered and (b) the answer is not misleading. Thus, for example, a first question might be “how do pollinators respond to cropping changes in arable farmland in western Europe?”, but data are available only for spatial variation in bees and butterflies. These groups are potentially relevant to the pollination question, but other groups would also have to be surveyed to gain a comprehensive picture of pollination. Similarly, an assumption can be made that space-for-time substitution of biological effects is valid, such that variation between low and high areas of a given crop in space (between areas) is equivalent to a change from low to high areas over time, but this does not necessarily hold, so the assumption should be explicit in the formulation of the question and reflect the data that are available accurately. Hence, the question in this example might be revised to “how does bee and butterfly abundance vary with respect to cropping in arable farmland in western Europe?”.

10 http://ebcc.info/index.php?ID=510 11 http://ec.europa.eu/agriculture/statistics/agricultural/index_en.htm

2.4 Analytical concept

2.4.1 General concept Once a question is defined and potential datasets have been identified, it is necessary to identify an appropriate form of analysis for the quality of data available. Biodiversity data are challenging for analysis for various reasons, including imperfect detection (even in a standardized survey, it is likely that some individuals are present but not detected), random chance (e.g. surveys occurring just after or just before a resident animal dies) and inter-correlation (e.g. land-use and climate both vary geographically, so their effects can be difficult to separate). Hence, there has been considerable development of analytical methods and biological data provide popular challenges among statisticians. Here, a methodological approach is proposed that begins with proven methods for high-quality data and is then modified for contexts in which the data available are poorer in respect of biodiversity detail, temporal repetition or spatial scale of availability. The choice of methods is presented within a decision tree to support the choice of an approach for different analytical problems.

The recommended analytical concept is based on spatio-temporal models that have been used successfully to measure low-magnitude agri-environment impacts on birds (Baker et al. 2012). These models are sensitive and flexible to multiple analytical contexts (Freeman & Newson 2008), so they form the gold standard for assessment using time series of abundance data. Fundamentally, the data requirements are repeat measurements of biodiversity measures from a representative set of single locations for which relevant cropping/management/land- use data are also available. This concept is built on a considerable amount of modelling and analytical work that has investigated the relationships between wildlife abundance or distribution and farmland habitat at large spatial scales. Much of this work has considered bird populations, chiefly because this is the group for which the best large-scale data are available.

2.4.2 Generalized Linear Models The analytical framework is based around Generalized Linear (Mixed) Models (GL[M]Ms, McCullagh & Nelder 1989, McCulloch & Neuhaus 2001) with appropriate error distributions and link functions. More complex, newer methods incorporating Bayesian approaches to include prior information from other sources (McCarthy & Masters 2005, McVittie et al. 2014, Morris et al. 2015), such as other survey data sets, may also be useful, especially for rarer species and unstructured, but we do not describe them in detail because they are less accessible to non-specialist statisticians and because the methods are still in development (e.g. Isaac et al. 2014, Isaac & Pocock 2015). It is likely that bespoke analytical developments would be required for individual, specific data sources and analyses.

GLMs provide a flexible analytical framework with which to consider multiple forms of predictor and response data. Thus, the approach allows biodiversity measures to be counts, presence-absence data, percentage covers, trend slopes or numbers of species, for example. The structure of a GLM can be varied according to the nature of the response variable, via the choice of link function, which enables output to be

constrained to the same scale of measurement as the input response variable, so counts are constrained to be zero or greater (with a log link function) and probabilities of presence are constrained to be between zero and one (using a logit link with binary input data); proportions (e.g. percentage cover for plants) can be included in their raw form. Predictor variables (i.e. the influences on biodiversity responses that are to be evaluated) can be continuous variables (e.g. areas under different land-uses) or categorical ones (e.g. different levels of protection); predictors can also be included to form controls for influences that might confound those that are of primary interest (e.g. weather variation in a test of effects of cropping change over time). One important form of categorical control variable in the context of spatio-temporal analyses is a “site effect”, allowing biodiversity measures to take different baseline values at different survey locations, such that changes over time can be separated from pre-existing influences that vary in space. Confidence intervals around output parameter estimates and the statistical significance of predictor variables are estimated using error distributions appropriate to the form of data, such as the Poisson distribution for counts and the binomial distribution for probabilities or proportions. With counts, it is common for data to be overdispersed, i.e. for them not be truly Poisson-distributed because records of individual animals are not independent (such as birds found in pairs or in flocks). This can be corrected for in model fitting, or a negative binomial distribution can be assumed instead.

A refinement of the treatment of categorical variables in GLMs is to fit them as random effects, producing a Generalized Linear Mixed Model (GLMM). This is useful because the random variable is estimated as a distribution with a mean and variance, as opposed to a set of discrete levels of the variable. This may be more efficient and realistic, especially in models used for prediction (see below), but estimates of specific parameter values cannot be obtained and the approach assumes that the levels of the estimated random variable are distributed normally. Therefore, random effects are only suitable for use with control variables.

Biodiversity data are frequently subject to autocorrelation in space or in time, that is records from adjacent time points or locations are more likely to be similar than would be expected by chance. This may occur, for example, if the scale of one data set is smaller than that of the other, such that multiple sample points for biodiversity are found within an area for which just a single environmental data point is available. If it is due to factors that are not part of the test being conducted, such as regional location or altitude in a study of cropping patterns, this lack of independence means that the effective sample size of independent count units is somewhat smaller than the actual number of data points. In turn, this means that the uncertainty with which parameter values are known will be under-estimated. Within GLMs, this can be considered by fitting the model allowing for a particular correlation structure by using repeated measures (formally, a method known as generalized estimating equations, GEE), or by converting to a GLMM and fitting a random effect to the residuals (the variation in the original data that is not explained by the model) (see, e.g. Fuller et al. 2014). One recent, sophisticated application of a method to account for autocorrelation effects has developed this further to include spatial smoothing

terms to improve the fit of static abundance models to spatially auto-correlated data (Massimino et al. 2015, in prep.).

2.4.3 Testing environmental influences Testing the strength and statistical significance of any environmental influence is an important component of the evaluation of its effect. Parameter confidence intervals, as described above, inform about whether a variable, or a level of a categorical variable, can be considered to have an influence that is significantly different from zero, in the presence of the other variables in the model. The size or strength of the effect of individual parameters can be assessed from the magnitude of the associated parameter estimates, if they are standardized or measured on the same scale as the other variables to which they are compared. In addition, formal estimates of effect sizes from, for example, parameter estimates divided by their standard errors, provide dimensionless indices that are comparable between predictor variables measured on different scales (e.g. crop areas and field boundary lengths). However, to extract a probability value that a categorical variable as a whole is important, specific statistical tests are required. Depending on the structure of the analysis as a GLM, GEE model or GLMM, different tests can be applied to this problem and are standard in most analytical software: likelihood-ratio tests, F tests or score tests. These approaches are appropriate for hypothesis-testing with respect to the importance of individual predictor variables, but are less suitable for comparing multiple variables, for example, if an analysis aims to compare the influences of a range of different crop types, land-uses or agri-environment management options.

Given multiple predictor variables that need to be evaluated simultaneously, first, it is important to check for co-linearity. Many of the potential explanatory variables could be correlated because they reflect common responses to economic conditions, for example, which would require either dropping of one of any pair of inter- correlated variables or reducing the number of input predictors to a smaller set of uncorrelated variables using principal component analysis (PCA). Then, we recommend a model selection process in which the most parsimonious subset of the potential predictors of biodiversity (such as simultaneously varying crop area variables) is identified formally. The best approach to the latter is multi-model inference based on Akaike Information Criterion (AIC) values, or their equivalents as appropriate for the modelling approach being used (Burnham & Anderson 2002). Other, more traditionally used approaches involving stepwise addition or deletion of variables have been discredited in recent years because their results are unstable (Burnham & Anderson 2002).

Information criteria and the application of the broader approach known as “multi- model inference” are widely used for identifying the most parsimonious model combining subsets of a range of predictor variables. With respect to the specific purpose of evaluating the relative effects of different predictors, AIC, or similar criteria, are valuable in allowing the explanatory power of multiple sets of predictors to be compared simultaneously. A convention is that models within two AIC units of one another provide essentially equally good descriptions of the data and cannot be

separated. In such cases, there are multiple important influences on the response variable and uncertainty about which are most important. Conclusions should reflect this, as opposed to assuming that a single, definite set of important predictors exists.

2.4.4 Model forms and example applications The form of GLM that we recommend for use with the best spatio-temporal data on biodiversity, i.e. temporally replicated count data collected using standard methods, was introduced by Freeman & Newson (2008). This sophisticated abundance change approach maximizes the information value to be gleaned from time series data by a particular re-parameterization of ratio changes between adjacent time points; the outputs consist of relative effects of the predictor variables on population growth rates. Used with count data, this method uses a log link function and Poisson or negative binomial errors and, as with all GLMs, it allows the testing of a range of different forms of predictor variable. It has been applied, notably, by Baker et al. (2012) and Siriwardena et al. (2014) to investigate the effects of agri-environment management on farmland bird population growth rates in England.

Where repeat count data are not available, but standardized counts have been made for a single time point across a large spatial scale, analyses can aim to evaluate the influences of environmental variables on static distributions and we recommend GLMs with log link functions and Poisson/negative binomial errors for this purpose. The data will commonly come from Atlas projects, with counts modelled with respect to land-use variables and controls for background influences such as geographical location. Examples of the application of this approach can be found in Sanderson et al. (2009), Robinson et al. (2001), Pickett & Siriwardena (2011), Siriwardena et al. (2012) and Newson et al. (2008), collectively showing effects of gross land-use (arable/pastoral), field boundary habitats, land abandonment, crop areas and land-use heterogeneity, all of which were shown to be significant influences on relative abundance.

Presence-absence data are a common output from Atlas projects and perhaps the most common form of standardized data for many taxa. These are best analysed using logistic GLMs, in which the probability of presence in a survey unit is modelled with respect to environmental variables and controls. As well as presence-absence in single survey units, data may consist of numbers of recorded presences in sets of adjacent units (e.g. traps in a field or grid cells in a larger grid cell). These data can also be modelled using a logistic structure, considering the number of presences as the numerator and the number of adjacent sample units as the binomial denominator in a model form termed “events/trials”. Logistic models have been applied to land-use questions by Chamberlain et al. (2000) and Siriwardena et al. (2000).

Given repeat Atlas projects with standardized sampling, it becomes possible to consider change in species presence or, viewed at a large scale, in distribution, with respect to land-use variables. However, this is made complex by the range of situations that are likely to be found in a given sample unit: presence to presence, absence to absence, presence to absence and absence to presence. These cannot

readily be considered together in a single analysis without assumptions such as, for example, that a local gain is equivalent to local maintenance of presence. Nevertheless, analyses can still be conducted, separately, considering “local extinction” (sample units recording presence to presence versus those recording presence to absence) and “local colonization” (absence to absence versus absence to presence), with the conditions modelled as binomial variables in logistic GLMs (e.g. Chamberlain et al. 2000). Alternatively, sample units can be classified as present, absent, losing or gaining a species and environmental variables treated as responses in respect of their average values in each of these categories.

Analyses of raw data are always likely to be more powerful and informative than those using derived indices. However, it may be that the latter are the only accessible form of the data due to licensing restrictions. Temporal trends in abundance, in particular, for geographical units can be used as response variables in the form of species-specific slopes of linear trends or inter-annual changes in population index values. This has been done in a GLM format in order to disentangle the relative effects of land-use and weather on population trends (Eglington & Pearce-Higgins 2012). Data specific to sampling units can also be amalgamated into community/assemblage indices prior to analysis. This is inferior to analysing the variation in abundance of individual species and constructing patterns of change at the community level from the outputs, but is considerably faster because only a single analysis is involved. This has been done for a wide range of competing land- use influences on UK bird diversity (Bateman et al. 2013).

2.4.5 Caveat: evaluation and prediction It is important to note that analytical models aiming to evaluate biodiversity responses are not necessarily also suitable for predicting them. This is because identifying that one or more influences on a response variable are important does not mean that all such influences have been identified. Prediction is clearly potentially a valuable output from models, but to be used in prediction, models need first to be assessed for predictive power. This means that they must provide a good fit to the data and they must predict reliably, for example with models being built on one division of the original data and tested on another. This is not always done, however, with some modelling approaches claiming to allow prediction on the basis of the significance of analyses for evaluation (e.g. Butler & Norris 2013), which does not represent a sufficient level of support for output predictions to be considered to be reliable (Siriwardena et al. 2014). It is important to remember, therefore, that the “models” considered here are statistical analyses for the purpose of evaluating the effects of environmental influences, only. It may, ultimately, also be possible to develop these models further in order to generate predictions of the effects of future changes in the important predictor variables, but this would require additional research.

2.4.6 Analysis options when data do not support standard GLMs The discussion above has focused on structured survey data from organized schemes, which represent by far the most valuable form of biodiversity information. However, the collation of ad hoc or casual records of different species is becoming

increasingly widespread, through the use of online portals. These approaches produce unstructured data, basically presence-only records, with no formal data collection protocol. However, building in a level of recording of sampling effort allows the data to be used in GLM analyses of temporal change (e.g. Baillie et al. 2006, Newson et al. 2016). Further, sophisticated new analytical approaches are now in development to allow for extreme spatial variation in effort in these data sets and to maximize the information value of ad hoc records (Isaac et al. 2014, Isaac & Pocock 2015).

It is likely that no quantitative data will be available for some taxa of interest and that analyses will have to make use of qualitative information, such as records of status (e.g. presence of species on red or amber lists of conservation concern) or habitat condition (e.g. Natura 2000 sites in favourable or unfavourable condition). For such information, statistical assessment must, at best, consist of a semi- quantitative cross-tabulation of numbers of sites at different status levels, or of species with different statuses, with respect to agricultural variables. Such an analysis can be configured as a logistic model, in which the probability of records showing good or poor condition (for example) is modelled formally; all other issues around GLMs described above also apply here. In addition, status levels can be considered as an ordinal variable, with levels from “average”, to “good”, to “excellent”, for example (as used in Natura 2000 site condition reporting; see Case Study 3). In such cases, there is a known, qualitative hierarchy in the levels, but the sizes of the differences between the levels are uncertain and unquantified. In principle, it is possible to model both transitions in a single, “ordinal logistic” GLM, but software to construct such models incorporating repeated measures and random effects is less well-developed than that for abundance and presence- absence (probability) modelling.

2.5 Decision tree The recommendations above are reproduced in the form of a decision tree in Figures 2-1 and 2-2. The first part of the tree provides guidance on the choice of suitable data sets and formats, aiming to maximize data value (Figure 2-1). The second part of the tree then guides the choice of an appropriate analytical approach. The aim of the tree is primarily to illustrate the philosophy behind the choices of recommended methods and the reasoning for the valuation of different forms of data. Therefore, the tree provides important support as to the robustness of the conclusions from the analyses following under Tasks 2 and 3. It is expected that, in practice, analyses will be conducted by experienced analysts, so we do not aim to provide a comprehensive step-by-step guide to the analytical approach for a non-specialist.

Detailed guidance notes are provided below for each part of the tree (Section 2.5.1). These notes are intended potentially to form a stand-alone document, such that the tree and the associated notes can be extracted from the report and used in isolation, so there is some repetition of text and concepts used above. To facilitate use outside the report, the decision tree text below is reproduced as Annex 7, which can be read as a stand-alone document.

2.5.1 Decision tree guidance notes The aim of the decision tree (and glossary in Chapter 12) is to act as a guide to inform the selection of appropriate data and a suitable method of analysis in order to answer a question of interest. In Part 1 (Figure 2-1), the structure guides choices of data sets for the biodiversity “response” and the agricultural or land-use “predictor” that define the question, which is assumed to concern the relationship between the response and the predictor, together with any other data that may be required. Throughout, failures of the data chosen to meet the needs implied by the question (i.e. where the data for the predictor or the response would be insufficient to produce a reliable answer) indicate that it is necessary to reconsider the data selection, or to revise the question being asked. This aims to avoid expectations of the evidence provided by the analysis exceeding what is actually feasible.

Part 2 (Figure 2-4, below) provides guidance on two parallel processes: (i) screening the predictor variables chosen in Part 1 and preparing them for use in analyses; (ii) selecting an appropriate statistical method for the data that have been chosen. The most common forms of data are considered explicitly and potential analyses are described that are based on established procedures. Approaches to data analyses, especially considering more complex and unstructured data sources, form an active field of research, so new or enhanced methods are always in development; no attempt has been made to include new or developing approaches here.

In general, it is expected that the processes outlined in Part 2 would be followed, in practice, by expert analysts who would not need to use this specific tree as a guide, but it should inform others with an interest in the data or the results as to the logic behind analyses that are conducted. Part 1, however, should both be more accessible to the lay reader and useful to identify the forms of information that analysts will require in order to conduct projects of interest to commissioning bodies.

In the notes below, guidance is provided as to how to use the decision tree and on the meaning of the specific branches and nodes within it.

Figure 2-1 Decision tree, part 1: selection and format of datasets

Biodiversity and Agriculture Start QUESTION

Evidence Assessment Decision 1

Tree – Part 1: Data Preparation Identify predictor and response data sets needed to answer the

question

How many What areas or Spatial Scale Temporal Scale years’ data are needed to locations should answer be considered to 2 3 question? answer question?

Does the temporal coverage of Expand data Do the spatial coverages the respons e and predictor N N selection or of the response and datasets overlap appropriately, return to search predictor datasets allowing for any time-lags that for appropriate overlap? (see Figure 3-2) may exist before effects can be datasets or seen? reframe

question Y Tests are limited to Y effects on static distributions and 4

N Do the predictor Are data available from assumptions of Do the data N and response the same locations over space-for-time N N include enough datasets have the several years? These data substitution. meaningful same spatial are preferred, to show Question definition variation for may be affected. Is resolution? effects over time. useful analyses? this acceptable? Y

Y Y Y

N N Is the spatial scale large Is the temporal scale enough to ensure effects

large enough to avoid are not due to local confounding annual dispersal/changes? variation? Y

Y N

Was effort recorded or standardized (no. of 5 records per cell, time spent surveying, etc.), or is a suitable proxy available? Legend N Y Progression through decision tree Is sample size likely to be large enough to towards end point detect changes in response variable?

Y Cycle back towards start of decision tree (usually

following negative N answer) Are there suitable controls in the dataset to allow comparison of effects by accounting for confounding Return to reframe factors or have data been selected to avoid the problem? question if datasets cannot

answer that currently Y being considered.

End point of Go to analysis tree dataset selection tree

2.5.2 Decision Tree Part 1: Data preparation This part of the decision tree guides the process of data selection and aims to ensure the data chosen can be used to answer the question at the correct spatial and temporal scales, whether this means using different data or altering the question to get maximal value from the data available. This is important because scale issues can be critical in determining whether analyses will actually deliver the evidence that they are intended to deliver. For example, population change measures need to be collected over a sufficient period of time that changes can be expected to be observed. Then, spatially, biodiversity data need to be available at a spatial scale that is appropriate for the taxon concerned. For example, if the scale of data collection is too small, whether a species is recorded may be subject to an unacceptable level of random chance, or detection may reflect factors such as choice of foraging location rather than the presence of a breeding individual. In addition, the data on biodiversity and potential drivers (such as agricultural land‐use or policy measures) need to cover overlapping, mutually representative locations and time periods. From another perspective, it is the periods and locations of overlap that determine what an analysis combining the datasets will inform about.

Data can come from a range of sources and there is no pre-judgement as to data quality with respect to source in the decision process. Principally, biodiversity data may come from (i) surveys by professional surveyors, such as field assistants on dedicated projects or nature reserve staff, (ii) volunteer observers within organized surveys or (iii) unstructured records collected by the public outside any formal sampling scheme and subsequently collated by professionals. Note that (i) and (ii) are likely to produce higher quality data, while (ii) and (iii) both fall within the range of activities often referred to as “citizen science”. However, the purpose of the decision tree is to provide guidance on the selection of appropriate data sets with respect to the purpose of the analysis and data quality; the specific source of the data is irrelevant.

The numbered sections below refer to numbered areas on the decision tree.

1. Question

The first step is to define the question being asked. This must be: a) Specific, i.e. defining time period, area, species, response variable and landscape, as required. b) Answerable, i.e. data exist that can answer the question in a meaningful way, without giving potentially misleading results.

If the question posed initially fails to meet these criteria, the decision tree indicates that it should be rephrased. An example of a poor question would be “is agriculture linked to bird population change?”. This is too vague to lead to meaningful analyses and conclusions. A better question would be “how were temporal bird population trends in Germany between 1990 and 2010 associated with variation in cropping

patterns between survey areas?”. From the question definition, the decision tree provides guidance on identifying datasets that may be used to answer the question: do they cover the appropriate species, area, and predictors, etc., that are required by the question?

2. Temporal scale

Once the preliminary identification of datasets has occurred, the temporal scale of these data should be examined. Progression through the tree is via a series of questions, namely regarding appropriate matching of predictor and response datasets, and matching of datasets to the question. These are based around the following principles:

a) The data selected should encompass the time period defined in the question.

b) The response and predictor datasets should overlap in temporal coverage. The aim of the analysis is to assess the effects of a particular land-use (change) on a particular aspect of biodiversity. In order to show these effects, the response and predictor data selected must correspond to the same time period. Note that this may not be as straightforward as selecting data from the same year, as often there will be a time-lag between the predictor and response that is predictable from basic biology; e.g. a change in management practice is unlikely to elicit an immediate response in bird populations because it acts via either breeding success in the current year or survival between years and, hence, would be expected to effect a change in abundance only in the subsequent year.

c) The data should ideally be repeat observations over time, to reveal trends. This could include data collected on either an annual or a periodic basis. There will, however, be some cases where only static distribution data are available (for either management or biodiversity data or both). In these cases, the data can still be used, but for questions regarding spatial rather than temporal variation, e.g. “have changes in cattle densities since 2000 affected bird abundances?” could change to “does variation in stocking density affect bird abundances throughout Europe?”. (Such questions have underlain some previous studies, such as where a comparison of the effects of different management practices across Europe has been used to suggest implications of intensification over time (e.g. Donald et al 2001).

d) The temporal scale of the data should be large enough to avoid confounding annual variation. Populations are subject to stochastic variation, due to weather, demographic effects, etc., so it is important that the data selected cover several years to ensure that results are not biased or misleading because they reflect only unusual years or short-term fluctuations that obscure more important, long-term impacts. Similarly, data on land management practices should also encompass enough time to average out local spatial variation, e.g. in crop rotations, so that the results refer to the

real effects of the cropping regime associated with the existence of specific crops, unless short-term, field-level effects are of interest.

Answering ‘no’ to any of the questions in box 2 indicates that it is necessary to go back to the data selection stage, where a number of options are available to progress (see 4 below).

In parallel to considering questions relating to temporal scale issues around spatial scale need to be considered (see 3 below).

3. Spatial scale

a) The data selected should encompass or be representative of the areas and landscape defined in the question. Spatial coverage is more likely to be representative of the whole area of interest if the data derive from a random sample. If sampling was not random, spatial representation should be checked.

b) The response and predictor datasets should overlap in spatial coverage. In determining the effects of land management, as with temporal scale, it is vital that the response and predictor data correspond to the same locations. This may take a variety of forms, such that the precise locations of survey points are not exactly the same, but are in the same locality. Figure 2-1 shows simple examples of the co-location of datasets but, in reality, the situation is likely to be more complex, involving locations coinciding in a variety of ways (Figure 2-2).

c) The predictor and response datasets should ideally be recorded at the same spatial resolution, with data available in the configuration shown in Figure 2-1a, but are also likely to occur in the other configurations. The latter entail assumptions about the mutual representativeness of sampling locations, such as that the broader areas from which all are drawn are homogeneous, or consideration of the pattern of sampling in the modelling framework. The latter notably includes treating multiple response observations with single match predictor values being treated as repeated measures of the predictor’s effect, and requires assumptions to be made about the representativeness of the regional data for the local scale.

d) The spatial scale should be large enough to average out local sampling effects that represent “noise” in the data. This will depend on the dispersal ability of the species being assessed; highly mobile species will use large areas so records from a small area may not meaningfully reflect presence or local abundance. Conversely, large‐scale data may effectively average over the

important variation in more sessile organisms, and suitable data would need to be available a fine spatial resolution. Spatial scale is also important for predictor data, particularly if static data are being used. In the case of cropping data, the scale might often need to be large enough that a whole rotation is represented, in order to explain local patterns of biodiversity. Conversely, very fine‐scale data are required to inform about the locations of different field boundary habitats, for example, which are typically only up to a few metres wide, so land‐cover information needs to be sufficiently high in resolution to detect them.

Answering ‘no’ to any of the questions in box 3 indicates that it is necessary to return to the data selection stage, where a number of options are available to progress (see 4 below).

Figure 2-2 Examples of patterns of co‐location of predictor (P) and response (R) datasets within a broader region (oval area)

In (a), predictors and responses are collected for the same survey locations and are equally representative of the region. In (b), each comes from a different location, but they are matched in the sense that each can be considered to represent the region. In (c), predictor data are drawn from a larger spatial scale than the response, but the response location is assumed to be representative of the wider region. Hybrid combinations of these patterns are also suitable, but the suitability of situations like (b) and (c) depend on the survey locations being representative of one another, or on the homogeneity of the region, and this should be verified. a) b)

P R

Figure 2-3 Examples of the ways in which predictor (P, in small rectangles) and response (R, in ovals) variables may coincide within statistical regions in practice

Regions are presented as a hypothetical nest of NUTS3 areas within a NUTS2 region. Where predictor and response sampling does not overlap perfectly, assumptions about the mutual representativeness of the areas concerned are required, the accuracy of which should be verified. Where predictor and response areas overlap by differing amounts, or differ in size, or multiple areas of one type overlap with one of the other, corrections of data values by area, such as taking averages weighted by the extent of an overlap, or modelling solutions, such as allowing for repeated measures, are likely to be necessary.

R P

4. Data selection

Answering ‘no’ to questions in boxes 2 and 3 indicates that the data chosen are not suitable for the question under consideration, or that the question needs to be re‐ cast to ensure that the evidence that the analyses can produce matches the specific sense that was intended. This is important because reliable inference depends on the exact nature of the evidence that is available being clearly understood by users. Thus, negative answers often lead the user back towards the start of the tree, whereupon the options are:

a) Expand the data selection to ensure appropriate temporal or spatial coverage.

b) Search for other datasets that may be more appropriate to the question.

c) If no other datasets can sufficiently answer the question, redefine the question itself.

It is then necessary to progress through the tree again, with the new data or revised question.

5. Final stages of data selection tree

After progressing through the temporal scale and spatial scale questions, the next three questions are about the quality of the data, and hence the likelihood that analyses will provide meaningful or useful answers. These concern:

a) Effort recording. Ideally, biodiversity data would come from formal monitoring schemes with robust survey protocols. These schemes ensure that data are collected in a standardised way, allowing comparison between years, areas and observers. There are, however, many instances where such monitoring schemes do not exist, but instead data are collected in a less formal way. For these data still to provide interpretable measures of variation, they must be accompanied by some record of sampling effort, to allow correction or accounting for it and interpretation of residual variation in response data as real variation in abundance or presence. For example, more records of a species in recent years than previously does not necessarily imply a population increase, but could instead be due to more people opting to submit records or to a change in technique increasing the detection probability. At this point, it is important also to note that effort is critical in presence‐absence data: whether a failure to detect a species can truly be considered to represent absence depends on the effort expended in searching for it. (Note that new analytical methods are in development for unstructured “citizen science” data that allow more inference to be drawn in the absence of the formal effort data that are usually available for structured “citizen science” data and sampling that is conducted by professionals, so this step could be skipped in some circumstances; see point 10 below. However, data with recorded effort are always likely to be more valuable. Note also

that recording effort does not mean that sampling is sufficient for a given application: in practice, whether a given survey effort is sufficient to detect changes or effects will depend on the type of data collected, the size of the change and how variable it has been from location to location, all of which are impossible to predict in general terms.)

b) Sample size. As with the issues of temporal and spatial scale being large enough to show effects of management, the sample sizes involved must be sufficiently large to detect variation. The higher the replication of independent measures of predictors and responses, the stronger the analysis and the more robust the conclusions will be. If sample sizes are too small, there is both an increased risk that the sample is biased and likely to lead to misleading results and, more likely, one that power will be too low to detect any genuine relationships that exist.

c) Controls. With biodiversity and environmental data collected outside any experimental framework, it is always likely that other potential influences vary in such a way that they could obscure the effects of the predictor that is of real interest. The datasets available must allow for these confounding factors to be included in the analyses if reliable inference is to be derived from the results. The necessary background data will vary with each specific question, but might include areas of gross land‐use (e.g. woodland, arable, etc.) in an analysis of dependence on cropping patterns, or climatic data in an analysis of effects of change in land‐use during a period where climate is known to have changed.

2.5.3 Decision Tree Part 2: Choice of analytical approach

The data selected in Part 1 of the decision tree should consist of one or more predictor or explanatory variables, which describe the factors whose influences are of interest (e.g. agricultural land-use, climate, region or time variables), and one or more response variables describing the biodiversity of interest (note that the analyses described consider one response variable at a time). Part 2 (Figure 2-2) of the decision tree provides guidance on two parallel processes to process the chosen explanatory variables and to select an appropriate form of analysis.

The numbered sections below refer to numbered areas on the decision tree.

Figure 2-4 Decision tree, part 2: choice of statistical analyses

Datasets for analysis Biodiversity and Agriculture Evidence Assessment

Decision Tree – Part 2: Choice of Analysis

Response variables Explanatory variables

Unstructured Structured Derived data (likely to be 11 limited in use, and perhaps Continuous Categorical 10 9 7 more qualitative) 6 Likely to be ad hoc Crop type occurrence and Yield Latitude/ Area species richness data Distribution longitude (e.g. from citizen Condition Red List Site (presence/ status Land science recording level data Stocking absence) Length Time AES designation Counts Trends density cover type schemes). These Option datasets can be useful category for taxa that have no Percentage Species formal monitoring cover richness schemes in place. Check the distribution of the data and transform if highly skewed Poisson or Calculation of red list indices Check that levels of different negative binomial (change in red list status No sampling Binomial categorical variables are not errors with log link over time, or numbers of effort data errors with confounded. Proportions Normal errors red-listed species associated logit link Test for correlations between explanatory with binomial with identity link with a given habitat) for variables and drop correlated variables or With sampling errors different habitat types (e.g. use PCA to select unrelated variables effort data Juslen et al. 2016, Young et al. 2014). Consider interactions 13 Consider non-linear between predictor predictor functions variables 8 14 12

GLM or GLMM with spatio-temporal covariates, incorporating Chi-square tests to compare Bayesian models of occupancy to random effects where appropriate. categories or species – simple Both categorical and continuous variables can be estimate species occurrence (Van comparisons of numbers incorporated into any of the preferred analyses. Strien et al. 2010, Isaac et al. 2014). between categories. EXAMPLE MODEL FORMS

Legend Freeman & Newson (2007) Model for long-term trends of Comparison of pre-existing Spatial model, with repeated measures model with site effects for invertebrates with strong intra-seasonal trend estimates for species or random effect blocks where sampling changes in abundance with Progression Options/ Relevant End point: variation: smooth functions modelling the associated with different units show spatial autocorrelation, for respect to policy through decision examples for examples of recommended latter added to a GLM for the long-term habitats: simple averages abundance or presence-absence with implementation tree towards end a category model forms analysis trends in a Generalized Additive Model across species respect to cropping variables point framework (Dennis et al. 2013, 2016).

6. Continuous explanatory variables

These are quantitative variables on a continuous scale, such as the area of a crop, the length of hedgerow, time in years, yield, stocking density of livestock, or latitude/longitude. For all factors of interest that can be expressed as a continuous variable, it provides the most analytical power to do so (as opposed to dividing the range of variation into discrete categories).

It is important to consider the distribution of the explanatory data: if this is highly skewed (i.e. most of the values of the variable are at one end of the distribution, rather than being clustered in its centre), a transformation such as taking a logarithm, reciprocal or square root would be advisable, such that the spread of values is then more even and modelling processes are more likely to identify a clear relationship. Transformations may best be identified by trial and error, examining the distribution of the data before and after until a suitable one is found.

Where multiple explanatory variables are to be considered, it is important to check whether they are correlated. High correlation coefficients for pairs of variables (note that the significance of the correlation is not important) show that they are confounded in a data set; this means that their effects cannot be separated. For example, if data are available at region level and regions with high areas of crop A are the same regions that have high areas of crop B, the results of analyses with respect to crops A and B will be similar and their effects cannot be teased apart. In such cases, either one of the pair of variables can be dropped and the results interpreted with respect to possible effects of either or both variables, or all of the variables of interest can be processed using an ordination method such as principal components analysis (PCA) prior to the modelling of biodiversity data. The latter technique identifies a set of entirely uncorrelated variables, but at the cost that they may not be easily interpretable, because each variable is effectively a composite of multiple input variables, to varying degrees.

Finally, continuous variables may not have simple linear effects on biodiversity responses. For example, a biodiversity variable, such as species abundance or distribution might respond strongly positively to small areas of a habitat, but less strongly to larger areas; this might occur if a species needs a few trees for nesting, but adding further trees to the landscape has no benefit. Similarly, species may prefer conditions defined by intermediate levels of a variable and be less common where the variable takes high or low values; this might occur if there is a preference for a combination of cropped land and pasture, such that the species is rarer where either one dominates the landscape, but more common where the two are mixed. Such patterns are illustrated in Figure 2-5. Simple graphs of response against predictor data may be used to identify where relationships like this might be found, or they may be suggested by background biological knowledge. If they are thought to exist, non-linear functions can be included in analyses instead of simple linear ones. A common example is a quadratic function, which is a combination of the simple continuous variable and a second variable formed from the square of the variable. Analysing with such a function and testing the significance of the squared term provides a measure of whether the relationship involved is genuinely non-linear.

Figure 2-5 Example possible shapes of biodiversity response to a predictor variable

The solid line shows a simple linear response, the short-dashed line a monotonic, but non-linear, increasing response and the long-dashed line a peaking response.

Biodiversity response variable

Predictor variable

7. Categorical explanatory variables

Also referred to as factors, these are variables that describe qualitative variation, such as between crop types, levels of site designation, agri-environment scheme option categories or land cover types. They can also be categories derived from a quantitative scale, such as “low, medium and high”, but analyses are more powerful if quantitative data are analysed as continuous variables.

As with continuous variables, analytical power is compromised if the variation in these variables is confounded with one another. For example, if almost all sites designated as “protected” are “grassland” and almost all “grasslands” in the data set are “protected”, analyses will probably be unable to discriminate between the effects of grassland habitat and site designation. Cross-tabulation of the explanatory variables being considered is advisable to identify such issues if they are likely to occur.

8. Combining explanatory variables

All influences on biodiversity variables act together, in reality. Hence it is often important to consider multiple predictor variables and how they interact. Specifically, it may be of interest whether the effects of one predictor are larger or smaller in the presence of another. This means adding interaction terms to the analysis, for example to identify whether hedgerow management becomes more effective at providing a nesting resource where a certain crop type that provides a feeding resource is more common. These interactions can involve two continuous variables, two categorical variables or one of each. (Interactions involving more than two variables can also be considered, but the results can be difficult to interpret, so a very clear hypothesis is required.)

Once a set of one or more, uncorrelated, explanatory variables that clearly represent the predictor side of the analytical question considered in Part 1 of the decision tree has been identified, the variable(s) can be combined with the biodiversity responses in statistical analyses.

9. Derived response data

There are multiple forms of biodiversity response data. First, the only available data may be data derived from surveys and other analyses, such as regional summaries, trends or levels of conservation status. Where possible, it is always preferable to analyse raw data or data that are processed only as far as 45 providing the best information for the spatial unit of interest. For example, multiple survey visits in a single year might best be combined in an appropriate way for the taxon involved before analysis, rather than analyses of environmental relationships working with the raw data. However, if such data are not available, it may be necessary to use derived summary data, such as regional trends, which can be analysed with the modelling frameworks described below (see 13). Options are more limited for highly derived data such as qualitative status records (see 14), although it may be appropriate to recast “good” and “poor” condition or “secure” and “threatened” status, for example, as a binary variable (levels of 1 and 0, respectively), when more sophisticated analyses can be conducted (see 13).

10. Unstructured response data

A second form of biodiversity data is that of unstructured records. These are data commonly collected by certain “citizen science” schemes that are either focused on public engagement rather than measuring biodiversity as a primary aim, or consist of the collation of records made by amateur observers for their own interest. These data may be the only available source for some taxonomic groups in some countries, but they present considerable difficulties for analyses because there is usually little control of sampling effort or recording methods, and little information available on where, when and for how long recording was conducted. This means that it is difficult to separate real variation in biodiversity from artefacts due to variation in effort.

11. Structured response data

The third form of data is that in which raw information is available that has a degree of structure providing confidence that the variation in the data is meaningful in terms of real variation in biodiversity. This covers a broad range of data types, including records of species richness (or other community indices), percentage cover of plant species, presence/absence data and counts, provided that each is collected in a structured framework. These data are then best analysed using generalized linear models (GLMs) or generalized linear mixed models (GLMMs), which are flexible frameworks in which multiple continuous and categorical predictor variables can be fitted together if required and response data of multiple different forms, including those listed here, can be modelled appropriately (see 13).

12. Chi-square tests

Given the simplest form of response data, for example numbers of different response levels or statuses in different regions, Member States or landscape types, a simple form of analysis involves asking whether the distribution of levels between the categories of interest differs from what would be expected by chance. If it does, the result suggests that there could be a causal relationship. For example, protected sites might be more likely to be in better condition than unprotected ones than would be the case if condition levels were randomly distributed between these categories. A chi-square test assesses this difference in distribution formally. This test is simple and quick to conduct, but only allows a simple comparison of frequencies of occurrence, so analyses incorporating control parameters, for example, are not possible. It is also increasingly difficult to interpret when there are more categories amongst which data are distributed.

13. Generalized Linear (Mixed) Models

GLMs and GLMMs encompass the analytical procedures that are used most commonly with biodiversity data. Any combination of continuous and categorical variables, and interactions between them, can be incorporated, including variables included only as controls for variation that is not of interest (such as weather conditions, when the influences of land-use are the focus of the study). Controls can be treated in the same way as other predictor variables (GLMs) or considered as random effects (GLMMs), which can

46 provide more confidence that the results are widely applicable, as opposed to being limited to the sampled locations.

A critical feature of these model types is that the response data are modelled with a structure that reflects their form and distribution. Therefore, as well as data that have no intrinsic constraints to their values and that are normally distributed, such as trend slopes or many biometric trait data, appropriate transformations and distributions for other forms of data can also be specified. Count data can only be integers greater than zero, so they are modelled with a logarithmic link function (basically a transformation) and a Poisson or negative binomial error distribution. Presence-absence data are modelled using a logistic structure, i.e. outputting a probability of presence, which is limited to values between zero and one, and a binomial error distribution. Data that are already in percentages are also modelled with binomial errors.

Refinements to this framework include explicit allowance for repeated measures within a sampling unit to prevent the over-estimation of precision due to pseudoreplication, modelling of zero-inflation in Poisson data (where zero counts are more common than the Poisson distribution permits, which is common when surveys cover areas inside and outside a species’ distribution) and overdispersion (when the assumption of the Poisson distribution that counts of individuals are independent is violated, as is common when species are often encountered in groups). Specific model frameworks have also been developed for application to biodiversity trend analyses and measuring the influences of habitat or management variation (e.g. Freeman & Newson 2008).

Within GLM and GLMM frameworks, there are several commonly used metrics for assessing the significance of individual predictor variables and model parameter estimates (i.e. values for levels of predictor variables). These metrics can be used in testing, but comparing and integrating the effects of multiple predictor variables is complex and some traditional methods can give rise to misleading results. The approach known as multi-model inference or model-averaging (introduced by Burnham & Anderson 2002) is preferred where computing resources permit (note that this method can be prohibitively computer-intensive).

14. Bayesian occupancy models

Recent developments in biodiversity modelling are allowing greater inference than has been possible before to be gleaned from unstructured data, by making use of Bayesian modelling approaches (van Strien et al. 2010, Isaac et al. 2014). These are currently still in development and have been developed with a view to revealing temporal trends, but may be applicable to questions relating to environmental variation in due course.

2.5.4 Key Links in the Decision Tree The decision tree as a whole is intended to describe the process of identifying suitable data and analytical processes to answer defined questions, but there are key links or sensitive points in the tree that require particular attention because they reflect where problems with analyses are commonly found. These points are listed below:

 Temporal scale (point 2). Important policy questions commonly need reliable information on biodiversity to environmental or management change. It is inevitable that this requires multiple years of data (more years for more long-lived and more naturally variable species), but policy pressure may be exerted to look for answers before sufficient time has elapsed. Note that the time required will vary with the target taxon, and with the size and homogeneity of the effect that is occurring, so there is no simple answer to the question “how long is enough?”.

 Temporal scale and space-for-time substitution (point 2). Both because of the timescale issue above and because of the cost of establishing new, long-term trials, it is common to consider variation in space as a proxy for variation in time, e.g. to compare areas where a land-use is rare to those where it is common as a proxy for tests of the effects of increasing that land-use. This may or may not be supportable (the areas may differ in other fundamental factors, such as climate, or habitat age may be a critical influence, for example), but users need to be clear about the assumptions behind the analyses conducted.

 Spatial scale of predictor and response data (point 3). Issues of confidentiality around data supply and other constraints mean that agricultural and land-use data are often available only at large spatial scales, such as of that of the NUTS3 region. This means that the only data available to match with site- level biodiversity data, for example, are regional agricultural data. This means that all sampling or monitoring sites within a region are assigned the same agricultural data, so that (i) there is no variation at this scale from which to estimate effects and (ii) the data are likely to be inaccurate as measures of the real values of the variables involved at the site scale.

 Spatial scale and local movements (point 3). Target biodiversity frequently uses the environment at a different scale to that at which people record or manage the land. For example, many bird species, even non-migratory ones, will range more widely than the boundaries of individual farms or agricultural holdings, or use a larger area in winter than in summer, so relating their numbers or presence may mean that larger areas need to be considered to derive appropriate variables to describe the important environmental information.

 Effort recording (point 5 and point 10). This is a critical limitation of the value of unstructured data, such as biological records and other data collected by “citizen science” schemes, so needs to be considered. However, it does not apply to all citizen science, because most formally organized monitoring programmes for birds and butterflies, in particular, that use volunteer observers incorporate recording of effort.

 Confounded data (points 6 and 7). It is common with data that are only available at large scales, such as regional cropping information, for different variables to be confounded. For example, wheat is often the dominant crop in all arable areas. This means that the effects of the variables cannot be separated; for example, the effect of wheat as a crop in its own right cannot be separated from the general effect of arable management. This is a fundamental limitation of studies that do not use a formal experimental design and must be borne in mind when interpreting results.

 Derived data (point 9). Data ownership and access constraints may mean that raw or site-level data on biodiversity, in particular, are not available. Some analyses can generally be done with derived or summary data, but analyses lose power when variability declines and more factors tend to be confounded at larger scales (e.g. land-use variables and geographical location), as described above. Data at smaller scales are always preferable, although they do require higher capacity computer resources, simply because data sets are larger.

3 Review of data sources and preparation of a metadatabase

Authors: Henrietta Pringle1, Renate Koeble2, Maria Luisa Paracchini2, Carlo Rega2, Ian Henderson1, David Noble1, Anna Gamero3, Petr Vorisek3, Jana Škorpilová3, Reto Schmucki4, Gavin Siriwardena1, Ben Allen5 and Graham Tucker5.

Organisations: 1 BTO, 2 JRC, 3 CSO, 4 CEH, 5 IEEP.

3.1 Introduction and approach Assessments of relationships between agricultural variables and biodiversity require data on both elements. This part of the project aimed to catalogue these data sources, both to provide a resource for future work and to support the analyses conducted in the integrated assessment (Chapter 4) and six case studies (Chapters 5-10), including the collation of key metadata on each data set. Data sources were identified by JRC and the project partners, assisted by the steering group, using their knowledge and key contacts, as well as from internet searches and consultation of the relevant scientific literature. Online information was sought using standardized searches using terms taxon, country and region names, along with “monitoring”, “survey”, “recording”, “scheme” and “database”, including translations into other languages. The scientific literature was searched using Web of Science and Google Scholar to seek reports of studies that have collected large-scale data on relevant taxa in agricultural habitats. Search terms therefore included taxon names (scientific and vernacular, in different languages), “farmland”, “cropping”, “pasture”, “grazing” and/or “agriculture”, “landscape”, “regional” or “national”.

Metadata on each data set identified were collected, where possible from the primary source of the data. The basic metadata include all the requirements specified under INSPIRE12 (Section 1.1 et seq.). For biodiversity data, notable fields included the spatial coverage, temporal coverage, form of sampling, total sample size of records and sample size for each survey time point and elements (components) of biodiversity sampled, as well as the contact details for possible data supply, habitat condition proxies or land-use variables recorded and units used, sampling regime (complete coverage, random sample or biased sample) and scale of sampling units. Rules for data access and use, contact information for access and the degree of INSPIRE-compliance in each data set were also recorded.

Ideally, the data themselves would also have been collated and made available centrally, but to have done so would have required resolving numerous, problematic, data access, confidentiality and licensing issues, especially with respect to data at higher spatial resolution (which are more useful for analyses; see Chapter 2). Instead, the provided metadata should allow future users to identify where to find the data that are available, what those data allow analysts to do and how to access the information. However, it should be noted even the metadata associated with some datasets that would be useful for assessing relationships with biodiversity were not available, so there are gaps in such information in the metadatabase. Data in this category notably include farm-level cropping and agri-environment records, to which access is restricted for reasons of data protection and commercial sensitivity. If a way could be found to make these data available for research purposes, perhaps via a process of anonymization, it would greatly enhance the potential of future analyses to further examine the relationships of interest.

Collating the available information was a huge task, even where data were freely available; this was led here by JRC. The data sources were disparate, for example often involving multiple individual electronic files per dataset and Member State, and with variations in format and structure between Member States

12 http://inspire.ec.europa.eu/documents/Metadata/MD_IR_and_ISO_20131029.pdf 49 and over time. Some data formats were convenient, such as Microsoft Excel or text files, but others were PDFs (portable data format), which required considerable processing to produce files suitable for use by analytical software. In addition, data collation required the translation of names between languages, amalgamation/consolidation to common categories across Member States and years, and some spatial data processing in a Geographical Information System (GIS) package to consolidate across regions. Some of the data sets were also very large, making even automated processing slow and unwieldy.

The final database includes metadata as described above, as well as the data that were already in the public domain but that were collated and cleaned for the project. The critical results from the analyses conducted for this project, such as parameter estimates for the effects of given predictor variables on the taxa considered, are also included, as specific tables.

3.2 Database structure The metadatabase was developed in Microsoft Access and it has been populated with the information gathered about each data source. The metadatabase format was designed around three component spreadsheets: (i) a list of environmental drivers, (ii) a compilation of land-use data sources available for each Member State, and (iii) a compilation of ‘biodiversity’ data sources available for each Member State. In addition, sheets are included listing the key results from the project, notably the integrated assessment (Chapter 4), because these are key sources of evidence linking the driver and response variables. The database links drivers to data sources, with the aim that the spreadsheets are integrated according to the levels of enquiry required for any given test of relationships with biodiversity. The links in the database show where land-use/policy/management data and biodiversity data sets co-occur and therefore facilitate investigations of effects on biodiversity in space and/or time. For this report, the formats of each of the tables in the database are illustrated in Tables 3-1 to 3-3 below. A complete list of tables in the metadatabase is provided in Annex 1.1.

The database itself has been given to the European Commission as a Microsoft Access file (EU_Agric_biodiversity_mon.mdb).

3.2.1 Categories of potential agricultural driver of biodiversity variation Table 3-1 lists potential drivers of change in biodiversity variables (the latter including abundance, presence-absence, plant cover, species richness, etc.). The list of drivers includes agricultural characteristics such as cropping patterns and livestock rates, with a column identifying which land-use data sources may be relevant to each driver, in order to guide the selection of important types of data source and hence how potential analyses may proceed.

3.2.2 Agricultural data The format of the land-use, policy and management table is shown in Table 3-2. All major data sources have been identified by members of the consortium and included in the database; any gaps in the metadata reflect a lack of data availability, particularly with respect to data scope (i.e. the scale of resolution available, in practice, to future analyses). While many of the land-use data sets are repeated and transferable between Member States, this is not always the case, so representativeness across Member States is described; deviations or differences are critical in determining the analytical potential of the data, due to restrictions in geographical scope and data quality (e.g. whether the data comprise complete coverage or representative sampling at the Member State level; see Chapter 0). The database therefore contains datasets at both EU-level and Member-State-level to identify these differences in coverage, resolution and accessibility to datasets. Including Member-State-specific entries in the database also aids sorting, filtering and linking of data sets, because different data sets will have different coverages and availabilities by Member State.

Table 3-1 Metadatabase structure: potential agricultural drivers of change in biodiversity

This table shows the structure used in this part of the database and some example drivers

ID Variable/driver Sub-divisions Dataset/source Taxa

Numerical ID of Names of taxa that may driver sub-division. Name of variable (e.g gross land-use) Divisions of each variable (separate row for each Name of datasets with hyperlink to be affected by driver, with Linked to land-data division, e.g. permanent crops) open land use table hyperlink to open sources via land- biodiversity table data types

Gross land-use (using LUCAS sub Corine, LUCAS, EUROSTAT Birds, mammals, 1 Permanent Crops divisions) agricultural statistics invertebrates

Gross land-use (using LUCAS sub Birds, mammals, 5 Pasture LUCAS, FSS divisions) invertebrates

Table 3-2 Metadatabase structure: land use, policy and management datasets for consideration for analyses with respect to changes in biodiversity

This table shows the structure used in this part of the database and two example datasets Country Additional Countrycode Land ID Dataset Type of data Response Unit Spatial coverage ID description Numerical code of dataset type (land Drop down list of E.g. land cover, cover, agricultural response variables, Unit of data Numerical code of agricultural Any additional survey etc) unique multiple responses collection (e.g. area, Regional, national, country, unique to 2-letter ISO code Name of dataset statistics etc. Drop details to describe to each dataset can be selected. crop type, status, supranational each country. down list to aid the dataset. type. Each data Response variables yield) filtering. type linked to agri- linked to drivers. driver Remote-sensed land-use EU-28 + additional classification - GIS countries, and Excel databases Switzerland, Remote-sensed Land cover/Land with spatial and 29 EU28 1 Corine Land Cover Area Norway, Turkey etc. land cover/land-use Use quantitative data The 1990 data set on area and covers only EU27 distribution of (Croatia missing) Corine land cover types Vegetation data, spatial data on Broad and Priority Land cover/Land habitats (km and Use, Soil quality, UK Countryside ha), aquatic 26 UK 3 Countryside Survey Water quality, National Survey macrophyte survey, Ecosystem river habitats condition survey, pond survey data, soil cores

(Land-use datasets continued)

Scale available for Spatial resolution Smallest scale Years Update frequency Accessibility URL Data coordinator Accessibility level analysis 0=Unknown, How accessible the Website address for 1=Negotiation Drop down list to Person (or Finest spatial scale How often the dataset is to the information about required, Resolution at which aid filtering, NUTS3 Years in which data organisation) available for dataset is updated project, what dataset, or where 2=Available to buy data collected or smaller being collected responsible for the analysis with new data constraints are in the dataset can be or after finest resolution dataset place accessed registration, 3=Freely available Freely accessible online. EEA standard re-use policy: re-use of Polygon data set. content on the EEA Minimum Mapping website for First data set Unit (MMU) of 25 commercial or non- http://land.coperni Ove Caspersen reference year hectares (ha) for 1990-2000, 2000- commercial cus.eu/pan- European 25ha pixels NUTS3 or smaller 1990-2000. Since 3 areal phenomena 2006, 2006-2012 purposes is european/corine- Environment 2000 repetition and a minimum permitted free of land-cover/view Agency every 6 years. width of 100 m for charge, provided linear phenomena. that the source is acknowledged (http://www.eea.eu ropa.eu/legal/copyr ight) 1km squares, Registration generalized up to required, many http://www.countr Periodic, last survey contiguous land-use NUTS3 or smaller 1978-2007 detailed datasets ysidesurvey.org.uk/ CEH 2 2007 parcels through available after data-access modelling. registration

(Land-use datasets continued)

Level of INSPIRE- Contact details Primary or derived data Relevance Used in MAES Commentary Metadata progress compliance 0=dataset not investigated, 1=Dataset Is the data raw data (e.g. Tick-box to identify not known to exist after Relevance of the dataset Additional comments Low, medium high, based e-mail address of the data CORINE) or is it derived datasets used in MAES searches, 2=More for further analysis in the about data quality, its on quality of metadata coordinator from various sources (e.g. assessments of ecosystem metadata required, project potential uses etc. and accessibility derived condition indices) condition 3=Metadata complete, or as complete as possible without accessing dataset Low for agricultural questions because land- use definitions are coarse (arable habitat is a single category) and resolution is If you are interested in [email protected] too low to show fields, but CLCC between two a.eu Primary high as a measure of TRUE 3 High neighbour surveys, always control variables for large use the CLCC layer areas of semi-natural habitat (it is the only comprehensive land cover dataset available at EU level Low – low repetition of sampling and low spatial coverage in raw data. May [email protected] Primary FALSE 2 Medium be higher if consistent data exist for more Member States.

3.2.3 Biodiversity data The ‘biodiversity’ table structure is shown in Table 3-3. Bird data, as expected, have been found generally to be the most complete and most accessible form of large-scale biodiversity data, with a large proportion being identifiable centrally through data requests to the European Bird Census Council (EBCC) and the coordinators of the Pan-European Common Bird Monitoring Scheme (PECBMS)13, which collate and manage access to original content from national or regional structured atlases and/or monitoring programmes. An analogous system for butterflies has also recently been established via the European Butterfly Monitoring Scheme (eBMS)14, although the data request made for this study (Chapter 4) was the first received by this system and identified significant organizational issues.

The data collation process still required important checks of the key characteristics of all the biodiversity data sources, especially the minimum spatial resolution at which the data sets are both collected and ultimately available for analysis, which is variable between Member States. For each data source, routes to data accessibility or potential hurdles to accessibility have been identified.

13 http://www.ebcc.info/pecbm.html 14 http://www.butterfly-monitoring.net/ebms 55

Table 3-3 Metadatabase structure: biodiversity datasets for consideration for analyses with respect to influences of land-use, policy and management

This table shows the structure used in this part of the database and two examples Country Type of Additional Spatial Spatial Countrycode Biodiversity ID Dataset Taxa Response Unit ID scheme description coverage resolution Numerical code Drop down list Numerical E.g. atlas, Any of dataset type of response Unit of data code of monitoring additional Regional, Resolution at 2-letter ISO (atlas, monitoring Name of Taxonomic variables, collection country, scheme, trend details to national, which data code scheme etc), dataset group multiple (abundance, unique to data. Drop- describe the supranational collected unique to each responses can presence etc.) each country down list dataset. dataset type be selected German Species Common specific 10 DE 11 Birds Bird monitoring Breeding Abundance National Site Breeding Bird relative Survey abundance Site data Annual collected Butterfly abundances or Invertebrate with average 25 SE 19 Conservation Butterflies Abundance indices for National monitoring transect Europe data collated length of species 0.65km

Scale Update Contact available Smallest scale Sampling regime Year start Year end Accessibility URL Data coordinator frequency details for analysis Drop down list Details of Last year of Website address for Finest How often the How accessible the Person (or e-mail to aid filtering, sampling First year data collection. information about spatial scale dataset is dataset is to the organisation) address of the 10km x 10km methods, of data NA if data dataset, or where available for updated with project, what responsible for data being finest including choice collection collection the dataset can be analysis new data constraints are in place the dataset coordinator resolution of sites ongoing accessed Stratified random, Site 10km x 10km 2005 NA Under request line transects Free choice, sites Reports available visited 3-7 times online (counts, 10km x per season, using distribution maps and 10km x 10km 2010 NA 10km point counts and frequency histograms), fixed transect access to raw data walks unknown

(Biodiversity datasets continued)

Additional habitat condition Sample size per Level of INSPIRE Metadata Relevance Total sample size Used in MAES Commentary Access progress variables time period compliance progress recorded? 0=dataset not investigated, 1=Dataset not 0=Unknown, known to exist Tick-box to identify 1=Negotiation Relevance of the Additional Low, medium high, after searches, Details of any Total number of datasets used in required, dataset for further E.g. number of comments about based on quality of 2=More metadata additional habitat samples over MAES assessments 2=Available to buy analysis in the samples per year data quality, its metadata and required, variables recorded length of scheme of ecosystem or after project potential uses etc. accessibility 3=Metadata condition registration, complete, or as 3=Freely available complete as possible without accessing dataset Stratified random, FALSE Medium line transects Free choice, sites visited 3-7 times per season, using Summary=3, Raw= FALSE Low 3 point counts and 1 fixed transect walks

Apart from birds and butterflies, searches revealed a range of data sources, which were entered into the database. The process identified data sources for mammals, some insect groups, soil biodiversity, amphibians/reptiles and habitat assessments through, for example, EuMon (EU-wide monitoring methods and systems of surveillance for species and habitats of Community interest). This process is likely to have identified most significant national-scale or large-scale data sources, but is unlikely to have revealed localised or personal data sources maintained by individuals or institutions (i.e. individual scientist or amateur data collectors). Non- bird data sources tend to be dispersed and variable in their existence or content between Member States. They represent a mixture of structured schemes (e.g. for soil biodiversity), collections of casual records, derived summaries (reported or summarised analytical results) and contextual data or habitat assessments with some uncertainty about how the original field data were collected (structured, unstructured, objective, subjective, sampled or ‘assessed’ by expert opinion and so on). The scope of these data sets and their suitability for future analysis is likely to be variable, but the database includes the metadata variables needed to make decisions about how far these data can be taken. In practice, it is likely that usable, available data sets will be limited to a few taxonomic groups (e.g. butterflies) and variable numbers of Member States.

The content of the metadatabase for taxa apart from birds is summarized below, by broad taxonomic group. Birds are monitored via structured schemes in most Member States, although sampling methods and the extent to which historical data exist vary significantly. Methodologies for most schemes (other taxa as well as birds) provide information on presence or relative abundance, the latter requiring calibration for integration across boundaries.

Mammals: Many European countries have no formal schemes for monitoring mammals generally (and bats are still less well covered), although they exist for specific species in ten or more Member States, where population and range estimates are generated periodically (e.g. states contributing to EuMon). Structured national mammal population monitoring schemes have been initiated in some EU Member States, for example in the United Kingdom, under the Breeding Bird Survey (Noble et al. 2012) or periodic national surveys commissioned by the statutory government agencies, such as Dormouse (Muscardinus avellanariusI) and Otter (Lutra lutra) surveys. EuMon includes about 13 national bat monitoring schemes but it is unknown to what degree these may suffer from a lack of reliability in the identification of individual species. The latter will be more of an issue with older- design bat detectors, but most ongoing monitoring schemes for bats are based on either nest boxes, nursery roosts or wintering sites, so identification data are very reliable. New remote methods of monitoring trends in bats, using new generation bat detectors (removing the requirement for observer identification skills), have been trialled with some success and are now established in parts of the UK (Newson et al. 2015).

Butterflies: Butterfly monitoring schemes are run by around 13 Member States, including the Netherlands, Germany and the UK (UKBMS), and some of these schemes are recent in inception (Sweden & Luxembourg began in 2010, whilst the Romanian scheme is currently being established). This group represents one of the better monitored taxa in Europe. In addition to the datasets produced by these monitoring schemes, the LepiDiv project is currently updating distribution maps that formed the basis of a European Atlas published in 2011. Although these have all been added to the database, metadata gaps remain regarding spatial resolution, because data access is problematic for some of these datasets. Following protracted but ultimately unsuccessful negotiations with eBMS (see above), longer-term butterfly data suitable for analysis in the integrated assessment were made available directly from structured schemes in four Member States, plus the region of Catalonia in Spain; this list might be expanded in the future as other national schemes become more established and data access issues are resolved.

Pollinators: The STEP project (Status and Trends of European Pollinators: FP7) recommended a future pollinator monitoring programme as one of the ambitions of that project. No such programme could be found (for example via EuMon), although new programmes specifically to monitor pollinators are starting to emerge, for example in the UK where one is under commission. Such programmes are likely to integrate information from existing taxa monitoring and novel citizen science initiatives with new recording protocols aimed specifically at pollinators.

Amphibians and reptiles: EuMon identifies 35 schemes, mixed between species- specific and multi-species surveys, particularly in the Netherlands, Germany, the UK, Romania and Estonia. The NA2RE project compiles updated data about the distribution of European amphibians and reptiles. The present data are based on the last compilation performed by the Mapping Committee of the Societas Europaea Herpetologica (SEH), published as an open access article in 2014 in Amphibia- Reptilia, the leading European multi-disciplinary journal of the SHE. The article (Sillero et al. 201415) provides freely all NA2RE data in shapefile format through the Supplementary Materials 3 and 4. NA2RE is an evolution of the Atlas of European Amphibians and Reptiles, published by the Societas Europaea Herpetologica in 1997 and reedited in 2004 (with a new chapter about taxonomic changes). Note that all of the data contained in this database are unstructured records that have no associated survey effort control or recording.

Plants: EuMon records rare plant monitoring in several Member State or in specific national parks, e.g. in Poland. Monitoring of common species and communities, especially outside protected areas, is rare or non-existent. A new structured scheme, the National Plant Monitoring Scheme16, has recently been established in the UK, but data are not yet available for analysis.

15 Sillero, N., Oliveira, M. A., Sousa, P., Sousa, F., & Gonçalves-Seco, L. (2014). Distributed database system of the new atlas of amphibians and reptiles in Europe: the NA2RE project. Amphibia- Reptilia, 35(1), 33-39. 16 http://www.npms.org.uk/

3.2.4 Evidence: links between agriculture and biodiversity Following the work done under this project, the database has been updated to include the results of the data collation processes and analyses conducted in the integrated assessment (Chapter 4) and case study 3 (Chapter 6). The collated data represent the results of a significant data processing task, which should not need to be repeated (except for updates of the data considered here) in the future. The results are important because they inform about relationships between different variables from a range of data sources listed in the metadatabase. These data are presented in spreadsheets linked to those describing the source biodiversity and agricultural data in the database, so that users can follow the links explicitly.

4 Integrated analysis of potential agricultural drivers of bird and butterfly population trends in the EU

Authors: Henrietta Pringle1, Gavin Siriwardena1, David Noble1, Maria Luisa Paracchini2, Renate Koeble2, Petr Vorisek3, Anna Gamero3 and Graham Tucker4. With butterfly data provided by Reto Schmucki5, Constanti Stefanescu6, Chris van Swaay7, Mikko Kuussaari8 and Ian Middlebrook9.

Organisations: 1 BTO; 2 JRC; 3 CSO; 4 IEEP; 5 Centre for Ecology and Hydrology, UK; 6 Granollers Museum of Natural Sciences, Spain; 7 Dutch Butterfly Conservation; 8 Finnish Environment Institute; 9 Butterfly Conservation, UK.

4.1 Introduction

4.1.1 Aims, scope and overall approach of this integrated analysis The aim of this part of the study is to conduct an assessment of the relationships between agricultural land-use and practices and biodiversity that is integrated across Europe and across as wide a range of taxa as possible. “Integration” is taken to mean a consistent, quantitative analytical approach across taxa and Member States, combined with a qualitative collation of the results revealed to produce an overall assessment for biodiversity and agriculture. Despite the need to integrate biodiversity patterns and responses into simple, high-level messages for policy purposes, it is also important, however, to recognize that individual species all respond differently to the environment. Hence, considering biodiversity in respect of community measures such as a diversity index or species richness, a priori, is a gross simplification of true responses to environmental variation. Here, therefore, the assessment begins at the species level and collation or integration to consider community- or ecosystem-level implications occurs at the end of the process. The assessment analyses proceeded by applying the methodology developed described in Chapter 2 to the most suitable data on biodiversity and potential agricultural influences upon it that are available for the Member States of the EU.

Across the range of wildlife communities, species within each taxonomic group, habitats, proxy habitat condition measures, policy mechanisms, farming systems and land-use contexts found throughout the EU, there are a huge number of possible relationships between agriculture and biodiversity. In addition, as multiple different analytical approaches are available, there are even more possible analyses of these relationships. A comprehensive assessment of all spatial and temporal patterns as potentially influential factors for all elements of farmland biodiversity across all of Europe is, therefore, not practically possible. However, a tractable approach to produce the highest quality outputs possible is to make use of the strongest data sources with the greatest geographical coverage. Analyses providing the greatest inferential power are those with long-term, temporal data and high spatial coverage. Temporal data allow effects on population change to be measured (with purely spatial comparisons, associations with agricultural variables cannot be separated

from those of background habitat conditions or other environmental factors such as climate or altitude), while a large spatial extent is likely to include a wide range of levels of the target variables and, hence, offer greater analytical power. Greater spatial resolution in both biodiversity and agricultural data is also beneficial, because real relationships between drivers of change and the species affected occur at the scale of a species’ home range or that of the biological community; typically, this might be a few hectares to a few square kilometres. However, a cost to higher resolution data and larger data sets is that analyses become more demanding of computing power.

To conduct meaningful analyses of land-use effects with reasonable statistical power, it is essential to have multiple observations of biodiversity variables for different levels or values of the predictor variables. Both biodiversity and land-use need to be available at this scale, so they can then be linked by geographical unit. In practice, the limits on the quality of biodiversity data are those of temporal extent (long-term, historical data sets are rare across taxa) and survey structure (unstructured record data are reasonably common across taxa and Member States, but structured surveys in which biases in data collection are controlled or known, are rare). Conversely, the limits on agricultural data are generally spatial, i.e. data are typically available for large regions, such as the NUTS3 classification or above (Eurostat 2011), but not for individual survey areas, such as 1km squares. Finer scale data are available from down-scaling models in some instances, but such data should be used with caution because all models have associated prediction error, which can be difficult to incorporate in analyses, and the non-independence of predictions within a given region can introduce false precision in analytical results due to pseudo-replication.

4.1.2 Selection of data for analysis Following the data collation process described in Chapter 3, the strongest data potentially available for analysis were identified as abundance time series data for multiple Member States for birds and butterflies. Most agricultural data were widely available at the NUTS3 level, but not below. Therefore, the integrated assessment was planned to consider bird and butterfly responses to all available potential agricultural drivers. The limited range of taxa for which data are available is not ideal, but there is reason to believe that birds and butterflies could provide proxies for the responses of other groups, or for ecosystem health in a general sense. Birds are widely considered to be indicators of the health of the agricultural environment, reflecting their position near the top of the food chain, and because they sample the environment at rather large scales, so are little affected by small or ephemeral changes in conditions, but can also respond quickly to environmental change (Browder et al. 2002, Gottschalk et al. 2010, Gregory et al. 2003, 2005, 2009, Robledano et al. 2010, Venier & Pearce 2004). Thus, while we do not recommend that availability of bird data means that information on other taxa is unnecessary, indices derived from birds may provide an inherent level of integration across the rest of the ecosystem being considered. Butterflies have limited value as keystone species in ecosystems, or as providers of a pollination ecosystem service, but they are likely to respond to similar ecological drivers to some other important insect

groups, such as other Lepidoptera and pollinating Hymenoptera (notably bees: Apidae) and Diptera (notably hoverflies: Syrphidae).

Specifically, this integrated assessment asks which agricultural practices impact bird and butterfly trends negatively or positively, i.e. which practices or land-uses affect trends in abundance. The analysis focuses on the effects on population trends of the following sets of variables, selected on the basis of their possible importance and data availability:  cropping patterns (variables describing areas under different crops);  livestock type and density (with respect to grassland area);  nitrogen use in the form of mineral fertilizer and organic manure (as an indicator of overall farming intensity);  energy input;  linear feature type (from LUCAS data);  landscape heterogeneity, from the JRC Farm Heterogeneity Index.

Agricultural data were sourced from the metadatabase (Chapter 3) for these potential influences, with the best data available (i.e. those available at NUTS3 scale across Europe) being selected. No suitable data could be found for the other priorities identified by the Commission (see discussion in section 4.4 for further comment), but the data used were either in the public domain or provided by JRC. The biodiversity data were only available subject to licence permission from scheme co-ordinators. This proved to be a difficult process, involving liaison at the pan- European level, but then with European coordinators having themselves to liaise with national or regional scheme organizers before data access could be obtained. However, this process was smooth and timely for birds and the Pan-European Common Bird Monitoring Scheme, albeit with permission to use data not being provided for four Member States. Conversely, a response to a data request with respect to butterflies took three months and was associated with questions and conditions relating to analytical methods and commenting on outputs. Permission was formally refused by the European Butterfly Monitoring Scheme, but was granted for four individual national or regional schemes in late November 2016, allowing less time than would have been ideal to run and to interpret analyses.

In addition to testing the effects of the potential influences described above, control variables describing gross landscape character sourced from the CORINE land cover map were also included, in order to isolate the effects of agricultural variables specifically from those of broader geographical location and background landscape. The ideal analysis of these data would combine data from across Europe for each species, accounting for possible inter-correlation among data from sites in the same geographical area with a repeated measures structure (see Chapter 2). All possible combinations of the variables of interest would be tested and the relative importance of each variable ascertained from the results. However, initial analyses showed that this was not possible due to the size of the data set and the consequent analytical demands: computing resources, even using the high performance computing cluster at the BTO, were insufficient. The analyses were, therefore, conducted using a two-step process, with species-specific analyses being conducted

separately for each Member State for which suitable data could be sourced and then summarized post hoc, using meta-analysis techniques. Repeated measures approaches also had to be abandoned because tests for individual variables, species and Member States took more than 24 hours to run. This also meant that fitting multiple models with all possible combinations of the variables was not feasible. The possible consequences of these departures from the ideal modelling framework are considered in the Discussion.

4.2 Methodology and data sources With reference to the data sources and data quality parameters identified as indicated in Chapter 2 and collated in the metadatabase (in consultation with JRC), the best available, suitable data sets for all taxa from all countries in Europe were selected to enable an integrated assessment to be conducted. This did not include all of the data sources identified in Chapter 3 because access to a number of data sources was restricted, because some data sources were not available at an appropriate scale and because it was not tractable to conduct a comprehensive set of analyses for all taxa and agricultural drivers in one project (see above).

4.2.1 Bird data The majority of EU Member States participate in PECBMS (Pan-European Common Bird Monitoring Scheme), the exceptions being Malta and Croatia. Data for France and Hungary were submitted late and therefore could not be included in the analysis and Luxemburg data were only available at the national scale, so were also omitted. Of the remaining 23 Member States, the majority of schemes were in place by 2000, while seven schemes started between 2004 and 2007 (Table 4-1). Count data per site were spatially referenced with respect to NUTS3 region for matching with predictor data. The data were provided as site- and year-specific counts calculated using the algorithms recommended by each scheme organizer for their own data. Thus, counts per site were annual means, sums or maxima across survey visits, according to the protocol for data collation that is deemed most suitable for the form of data and the context of the surveys conducted in individual Member States. Data are processed in the same way for all years within each monitoring scheme.

For analysis, data were selected to produce a broadly consistent set of years across species. As a compromise between avoiding too much variation between Member States and maximizing the time period covered, data were included from 2002, or the earliest year available for the scheme, onwards (see Annex 2.4 for details by Member State). Summary data on sample size and numbers of species for which analyses could be conducted are presented in Annex 2.4.

Table 4-1 Bird monitoring schemes considered for use in integrated assessment

Total number of sites across all Spatial MS Coverage Years Site selection habitats and resolution years AT National 1998- 435 free choice Site BE Brussels 1992- 114 other Site BE Wallonia 1990- 4199 unknown Site stratified BG National 2005- 166 Site random CY National 2006- 158 unknown Site CZ National 1982- 331 free choice Site DK National 1976- 1758 free choice Site EE National 1983- 181 free choice Site FI National 1975- 1436 free choice Site FR * National 2001- 2541 unknown Site FR * National 1989-2001 Unknown unknown Site DE * National 1989-2010 Unknown Free choice National stratified DE National 2005- 1436 Site random stratified GR National 2006- 110 Site random stratified HU * National 1999- 441 National random stratified IE National 1998- 401 Site random IT National 2000- 958 random Site LV * National 1995-2006 Unknown random National LV National 2005- 97 random Site stratified semi- LI National 1994- 127 Site random stratified LU * National 2009- Unknown National random NL National 1984- 7402 free choice Site stratified PL National 2000- 1005 Site random stratified PT National 2004- 1029 Site random RO National 2007- 203 semi-random Site SK National 1994- 124 free choice Site stratified non- SI National 2007- 119 Site random stratified ES National 1998- 1239 Site random ES Catalonia 2002- 543 stratified Site

Total number of sites across all Spatial MS Coverage Years Site selection habitats and resolution years random SE National 1975- 1169 free choice Site SE National 1998- 716 systematic Site stratified UK National 1966- 5327 Site random *These datasets were omitted from analysis owing to the timing of data availability or the spatial resolution available

4.2.2 Butterfly data Butterfly monitoring is less well-developed than bird monitoring across Europe and the schemes that are in place tend to be newer. A request was made through the new European Butterfly Monitoring Scheme (eBMS)17 system, which aims to coordinate and to integrate the uses of butterfly data across Europe, for the use of monitoring data in this study. However, our request for the use of data were the first received by this system, the process of reaching a decision about access to data took several months and eBMS ultimately did not grant access because some contributors wanted more time than was available to contribute to the work (to discuss plans and outputs, and to comment on drafts). However, via contacts made independently and at short notice, access was secured to four of the five schemes that have run since before 2000 (the fifth involves the Flanders region of Belgium). These data from longer-term schemes were obtained at the year and site level from the scheme coordinators and concerned the UK, the Netherlands, Finland and Catalonia (Spain), for 2002-2015. Further metadata on the butterfly data sets used by species and Member State are presented in Annex 2.5. As with birds, site-level data were matched spatially with NUTS3 regions. The data were provided as weekly counts through the season (April to September), although missing values due to bad weather or surveyor unavailability were common. To generate an annual, site-level abundance measure given the variability of butterfly flight periods between years and the occurrence of unpredictable pulses of abundance, missing values were replaced with a value calculated by linear extrapolation between the previous and subsequent weeks. Site-year combinations with two or more consecutive missing values during the core activity period of June-August (or during the whole April- September season in Catalonia, where butterflies are active over a longer season) were omitted because imputed data could otherwise be overly dominant. The resultant annual abundance profiles were converted into measures of abundance per site by calculating the area under the profile (i.e. the total number of butterfly- weeks). These data were then standardized by dividing by the length of the sampling transect at the site, which provides the best measure of sampling effort per site.

17 http://www.butterfly-monitoring.net/ebms

4.2.3 Agricultural data All data sources identified for the metadatabase (see Chapter 2 and 3) were considered, but some had to be omitted due to poor spatial resolution (e.g. they were available only at NUTS2 level). Data relevant to several key factors identified by the Commission could then not be included either because they could not be found (e.g. areas of organic farming) or because the resolution of the data was too coarse (e.g. agri-environment investment, although see Case Study 3, Chapter 7). Nevertheless, in most cases, the data were not readily available in an analysable form and needed considerable processing effort because downloading was often necessary by individual Member State and year, after which variable names needed to be translated and the variables themselves re-formatted and rationalized to create a common data set to apply all over Europe (Chapter 3). This was extremely time-consuming and limited the time subsequently available for analysis.

Areas of crops and numbers of livestock were mainly sourced from the EUROSTAT database of Farm Structure Surveys from the years 2000, 2003, 2005, 2007 and 2010 (Table 4-2). Additional data were collated from agricultural statistics databases from individual Member States where available. Data were available at NUTS3 scale. The full list of variables used is shown in Table 4-3: crop categories were amalgamated on the basis of similarity of likely ecological effect, as constrained by the availability of data. For example, cereal crops were combined because the different crop species are managed similarly and have similar associated ecological relationships, but data were not available to split them into spring- and autumn-sowing, which is a management shift with major effects on bird ecology (e.g. Siriwardena et al. 1998, Chamberlain et al. 2000, Wilson et al. 1997, Gillings et al. 2005). Where data on a given variable were missing, data from the nearest available year were used. Some variables were only available in 2010, these being the most detailed FSS data available at NUTS3 scale. Where this was the case, the 2010 data were used as a proxy for the areas in other years. Where a missing variable was a sub-class of a variable, proportions were used to infer missing values (see Table 4-3 for details).

Table 4-2 Land-use datasets used in the integrated assessment

All variables were aggregated to NUTS3 level

Variables used in Dataset Member States Years Source integrated assessment All (AT data only 2000, 2003, Farm structure Crop areas, numbers available for 2000 2005, 2007, EUROSTAT survey of livestock and 2010) 2010 Farm structure Crop areas, numbers MS statistics CY, GR, LT, UK 2013 survey of livestock website Annual farm Crop areas, numbers MS statistics BE, CZ, DK 2010-15 statistics of livestock website Annual farm Crop areas, numbers MS statistics EE 2004-16 statistics of livestock website Annual farm Crop areas, numbers MS statistics ES 2012 statistics of livestock website Annual farm Crop areas, numbers MS statistics FI 2013-15 statistics of livestock website Annual farm Crop areas, numbers MS statistics IE 2008-15 statistics of livestock website Annual farm Crop areas, numbers MS statistics LV 2000-15 statistics of livestock website Annual farm Crop areas, numbers MS statistics NL 2001-15 statistics of livestock website Number of linear LUCAS All 2015 EUROSTAT features Farmland Heterogeneity of Heterogeneity All 2006 JRC farmed landscape Index Total area of each land Corine All except EL 2006 EEA use (controls) Mineral fertiliser, CAPRI database All 2004, 2008 JRC manure

CAPRI database Energy inputs All 2004 JRC

Table 4-3 Agricultural variables used in integrated assessment, aggregated to NUTS 3 level

All FSS and LUCAS variables were standardised by the area of NUTS3 region.

Variable name Description/units Comments FSS Crop and Ha or number of heads livestock data Cereals Pulses Root veg Includes sugar beet and potatoes Correlated with Cereals Fodder roots Fodder roots and brassicas Oilseed rape made up the majority of this Industrial crops, includes rape, Ind crops class, thus ‘industrial crops’ ≈ ‘oilseed hemp, flax, oilseeds. rape’. Plants harvested green, includes Green plants maize silage Where not listed separately to Green plants, the proportion of temporary grass Temp grass Temporary grass in other years was used to estimate areas in missing years. Fallow Correlated with Cereals and Root veg Perm grass Permanent grass Perm crops Permanent crops Horses Cattle Correlated with Perm grass and Shgoat Correlated with Fodder roots, Perm grass Shgoat Sheep and goats and Cattle Pigs Correlated with Green plants LUCAS linear Number of features features L 1 Grass margins < 3 m L 2 Heath/Shrub, tall herb fringes < 3m L 10 Single bushes, single tree L 11 Avenue trees L 12 Conifer hedges < 3 m Bush/tree hedges/coppices, visibly L 13 managed (e.g. pollarded) < 3 m Bush/tree hedges, not managed, L 14 with single trees, or shrubland deriving from abandonment < 3 m Grove/Woodland margins (if no L 15 hedgerow) < 3 m L 21 Dry stone walls Artificial constructions (other than L 22 dry stone walls) L 23 Fences L 31 Ditches, channels < 3 m L 32 Rivers, streams < 3 m L 41 Ponds, wetlands < 3 m Rock outcrops with some natural L 51 vegetation ‘roads’ Tracks, roads, railways CAPRI data Minfer Mineral fertiliser (KgN/ha)

Variable name Description/units Comments Mantot Manure (KgN/ha) Inp_tot Energy inputs (MJ/ha) Farmland

Heterogeneity Mean Farmland Heterogeneity FHI Indicator value

Data from the 2015 Land Use/Cover Area Frame Survey (LUCAS)18 were used to provide information about linear features. Surveyors record the number of linear features of different types (listed in Table 4-3) on a 250m transect running eastwards from the point being surveyed. Points are selected from a standard 2km grid. Within each survey year, a stratified sample of points is visited; between March and October 2015, a total of 273,401 points were surveyed across all Member States. Data on linear features from 2015 were used for every year of bird and butterfly survey data, because these data are higher in quality than those from the earlier years and the quality difference is likely to be more significant than changes over time in landscape features that tend not to vary over a decadal timespan.

The Farmland Heterogeneity Indicator (FHI) provides a measure of patchiness of the farmed landscape at the scale of 250m pixels (Weissteiner et al. 2016). This is based on areas of homogeneous reflectance in satellite images of land-use and the boundaries between these areas, with non-agricultural habitats having been masked out. The index is then the density of boundaries, which is inversely correlated to field size, but not a direct proxy because not all boundaries are detected. The index is derived from CORINE 2006 land cover data and shows the probability of encountering small patches on the ground, based on segmentation, edge density and image texture (Weissteiner et al. 2016). Thus the probability of finding small patches is lowest in class 1 (or percentile class 0+), and highest in class 5 (percentile class 80+). The FHI data were supplied by JRC per 1km square and mean values were calculated per NUTS3 region.

Crop-specific nitrogen input data were derived from models down-scaling from NUTS2 regional crop and input data with respect to soil characteristics to the scale of HSMUs (Homogeneous Spatial Mapping Units) in the CAPRI (Common Agricultural Policy Regionalised Impact19) modelling system for 2004 and 2008 (Britz & Witzke 2014, Pérez-Soba et al. 2015, Britz & Leip 2009, Leip et al. 2008, 2011; see Leip et al. 2008, p75-78, in particular for a detailed description of the modelling process). HSMUs are areas of similar environmental conditions, with a minimum size of 1km2 thus these data were aggregated to NUTS 3 level. Data from the nearest year were used to fill in gaps in the data so that all years had an amount of mineral fertiliser and manure per NUTS3 region (in kg N per ha). The total human energy input from fertilisers, labour, machinery and irrigation per NUTS3 region was also available for 2004 alone; these figures were therefore used for all years.

18 http://ec.europa.eu/eurostat/web/lucas/overview 19 http://www.capri-model.org/dokuwiki/doku.php?id=start

NUTS3 designations have changed between years, so in order to ensure correspondence between datasets, all regions were assigned to ‘universal’ NUTS 3 regions (Eurostat 2011). In cases where regions had been split or merged, data corresponding to regions were summed across the constituent regions of the larger region. Similarly, where boundaries changed to create completely new regions, regions were aggregated into one new region that encompassed all constituents. For example, if the boundaries of regions A and B changed to create regions C and D, all regions were assigned to a new region, E. All explanatory variables were summed per ‘universal’ NUTS3 region and standardised by the total area of the relevant NUTS3 area (or averaged in the case of CAPRI inputs and FHI data), to account for variation in region size. Examination of the correlation matrix was used to choose uncorrelated variables for analysis (R<0.8).

To account for gross landscape variation that may affect bird and butterfly abundances and therefore confound the results, land cover data from CORINE were used in the analyses as controls. The areas of arable, pasture, woodland and urban land cover per ‘universal’ NUTS3 region were therefore included in the models, so the results refer to the effects of variation within the farmed area, and not gross habitat availability.

All data were assigned to NUTS3 regions using the ArcMap 10 GIS system. Agricultural data were assigned to bird and butterfly survey years assuming an effect lag time of one year, i.e. that land-use in year N would affect numbers in year N+1. This is because conditions (including crops grown locally) will affect breeding success in year N and over-winter survival to the following year, and therefore population abundance in year N+1.

4.2.4 Analysis Analyses were conducted using two methods, as described and recommended in Chapter 2:

I. The approach developed by Freeman and Newson (2008), in which generalized linear models are used to model counts such that the output parameter effects for environmental predictor variables reflect the influences of the variables on inter-annual population growth rates. Using this approach, a positive result indicates a positive effect of the variable on growth rate. Each variable was tested separately with controls for arable, pasture, woodland and urban cover (per NUTS3 region), and a categorical site effect, included in the model. This is the approach recommended as the best option in Chapter 2; it considers the effects of the predictor variables on inter- annual population change, so assumes no particular shape for long-term changes in abundance.

II. A more traditional approach, again using generalized linear models (GLM), considering long-term, linear trends in abundance. Each model tested count as a function of a single land-use variable, with an effect of year included as a second continuous variables and the interaction between the variable and

year being included to consider the effects of the variable on the trend in abundance. Controls for the effects of the areas of arable, pasture, woodland and urban cover per NUTS3 region were again included, via interactions between each and year, together with a categorical site effect. This approach fits only a single linear pattern of population change over time, with the predictors considered as modifiers of the slope of the trend. The model is, therefore, rather restrictive with respect to patterns of change, especially with longer time series, and is likely to be less informative as to real influences on population change than the Freeman & Newson (2008) approach.

Models were fitted assuming a Poisson distribution of counts, accounting for overdispersion using Pearson’s χ2 goodness-of- fit statistic, using the GENMOD procedure in SAS 9.420. The significance of associations between land-use variables and abundance or growth-rates was tested using F-tests of the hypothesis that the effect size was zero. The species considered are listed in Annex 2.1, and Member States in Annex 2.2. Sample sizes by species, Member State, NUTS3 region and variable are shown in Annex 2.3 for variables, Annex 2.4 for birds and Annex 2.5 for butterflies.

Models were run for each Member State and species so that each species had several parameter estimates per variable, depending on the number of Member States in which it occurred. These estimates were collated using standard approaches used in meta-analyses (e.g. Arnqvist & Wooster 1995) to show the number and direction of significant effects per species and variable across the EU. Thus, to determine an overall sense of the influence of each variable on the abundance or growth rate of each species, estimates were averaged across Member states, weighting by the inverse of the standard error of each estimate. An accompanying combined standard error was calculated by summing the inverse of the variance across national estimates, and taking the square root of the inverse of this figure.

It is important to note that the size of the datasets used in these analyses, especially for birds in larger Member States with higher numbers of survey sites, was very large. This created significant problems for analysis, and the fitting of complex models in particular. Specifically, (i) pan-European models incorporating data from all Member States simultaneously and allowing testing of differences between them were not tractable, so results were combined in post-hoc summary analyses to inform about patterns at the continental scale, as described above; (ii) models formally allowing for repeated measures of effects within individual NUTS3 regions were not tractable, so the parameter estimates produced are likely to be unrealistically precise if there is high correlation in the data on individual species among sites within the same region (NB this is often not the case because sites often differ considerably in respect of local habitat structure, outweighing associations due to spatial proximity); (iii) multi-model inference analyses (Burnham & Anderson

20 www.sas.com

2002, as used in Chapter 7) were not possible because too many models would have been needed to be fitted, with each individual model taking a long time, so there was no formal analysis examining the independent effects of each variable in the presence of the others considered.

4.3 Results

4.3.1 Birds

Population growth rate tests

Summaries of the results of the population growth rate models fitted are presented in Annex 2.6, and EU-wide average parameter estimates are presented in Annex 2.7 and summarized in Table 4-4, where summaries of the statistical test results are also shown. Table 4-5 summarizes the same results by broad type of variable, i.e. the general form of variation in agriculture. More detailed, species-/Member-State- specific results are available in the metadatabase.

There were at least several significant relationships between individual species and all individual predictor variables, across the range of individual Member States. While there were patterns for some variables more often to show positive than negative associations, notably heath fringes (L2), woodland margins (L15), green plants, fodder root crops, root vegetables, cattle and cereals, or the opposite, such as energy inputs, mineral fertiliser inputs, grass margins (L1), managed hedges (L13), artificial boundaries (L22), fences (L23), rivers (L32) and roads, there were also numerous converse relationships for all of these variables, so there is no simple, general pattern to report (Table 4-4). Considering the parameter estimates averaged across Member States, there were broadly similar distributions of significant relationships, although the balance of positive and negative associations differed in some instances when non-significant associations were considered as well as significant ones, for example showing a preponderance of negative associations (22 versus 15 positive) for mineral fertilizer (Table 4-4). Such patterns should not be considered as having statistical support, but could reflect ones that would appear if more analytical power were available.

Standardized effect sizes in Table 4-4 were calculated by dividing species-specific averaged parameter estimates by their standard errors and then averaging across species, thus producing values on the same numerical scale that can be compared across variables. Therefore, the stronger positive influences on bird population growth rates were cereals, fodder root crops, green plants, shrub fringes (L2), avenue trees (L11) and woodland margins (L15). The strongest negative associations were found for managed hedges (L13), fences (L23), roads (L32), grassy margins (L1), ponds (L41), mean FHI, permanent crops, pulses, conifer hedges (L12), artificial boundaries (L22) and energy inputs (Table 4-4). It should be noted that several variables were highly correlated (Table 4-3), so the patterns for cereals, root vegetables and fallow, for example, are similar and it is impossible to be certain which of them has been more important for any given species. Note also that the relative magnitudes of the effects of Mean FHI, conifer hedges (L12), artificial

boundaries (L22) and energy inputs were somewhat smaller when all parameter estimates were included in the averages, as opposed to the ones that differed significantly from zero, alone (Table 4-5).

Table 4-4 Numbers of significance tests (totals across Member States and bird species, p<0.05) and of EU-average effects (across bird species, n=37, estimate 95% confidence intervals do not overlap zero) for effects of agricultural variables on population growth rates

Standardized average effects across species allow comparison between variables. See Annexes 2.6-7 for full results. Predictor No. of Member-State- No. of averaged parameters: Standardized effects variable specific tests significant (NS) Positive Negative Positive Negative Significant All

relationships relationships relationships relationships only results CEREALS 43 31 9 (10) 5 (13) 3.994 1.428 FALLOW 49 46 6 (13) 10 (8) -0.944 -0.326 PULSES 32 37 6 (8) 15 (8) -1.434 -0.841 ROOT VEG 48 32 11 (9) 4 (13) 2.358 0.917 FODDER ROOTS 42 35 15 (12) 5 (5) 2.181 1.374 IND. CROPS 37 31 7 (9) 8 (13) 0.028 -0.046 GREEN PLANTS 44 31 15 (8) 6 (8) 1.553 0.926 TEMP. GRASS 38 32 13 (5) 9 (10) 0.831 0.347 PERM. GRASS 38 37 13 (8) 8 (8) 0.974 0.535 PERM. CROPS 36 37 6 (10) 12 (9) -1.204 -0.588 HORSES 50 52 9 (10) 9 (9) 0.597 0.364 CATTLE 54 33 8 (17) 1 (11) 2.581 0.74 SHEEP/ GOATS 38 49 5 (9) 7 (16) -1.146 -0.557 PIGS 34 30 5 (18) 3 (11) 1.308 0.453 ENERGY INPUTS 29 43 6 (10) 8 (13) -1.059 -0.574 Fertiliser 46 36 7 (8) 5 (17) 0.403 -0.002 Manure 30 39 9 (9) 11 (8) -0.713 -0.355 Mean FHI 21 48 5 (11) 13 (8) -2.003 -0.796 L 1 34 44 3 (7) 15 (12) -2.385 -1.331 L 2 46 24 14 (13) 1 (9) 3.55 1.43 L 10 43 29 9 (12) 8 (8) 0.724 0.393 L 11 45 28 13 (12) 7 (5) 1.405 0.94 L 12 36 32 6 (8) 10 (13) -1.297 -0.691 L 13 33 50 4 (4) 18 (11) -3.596 -2.325 L 14 41 32 5 (11) 8 (13) -0.039 -0.107 L 15 47 26 15 (8) 5 (9) 1.489 0.86 L 21 30 36 9 (9) 9 (10) 0.849 0.328 L 22 30 43 8 (8) 12 (9) -1.146 -0.591

L 23 35 46 4 (10) 11 (12) -2.825 -1.175 L 31 39 47 9 (8) 10 (10) -0.448 -0.283 L 32 34 52 4 (12) 15 (6) -2.601 -1.227 L 41 44 41 5 (8) 17 (7) -2.323 -1.428 L 51 23 33 7 (11) 6 (13) -0.216 -0.145 roads 24 45 5 (5) 17 (10) -2.747 -1.664

There were no clear patterns for broad classes of variable, i.e. all crop types combined, all linear features combined, all nitrogen inputs, energy inputs and farmland heterogeneity to be especially more important than the others in driving population growth rates, with 40-50% of parameter estimates for each being significant (Table 4-5). There was also no tendency for any of these to be strongly more positive or negative, reflecting the results for individual variables in Table 4-4.

Table 4-5 Numbers of tests for individual bird species and variables for which EU- average population growth rates were significantly associated with broad classes of variable, summarized from Table 4 4

Positive Negative Type of variable NS relationships relationships Crop types 128 (25%) 102 (20%) 288 Farmland 5 (14%) 13 (35%) 19 heterogeneity Energy inputs 6 (16%) 8 (22%) 23 Nitrogen inputs 16 (22%) 16 (22%) 42 Linear features 120 (20%) 169 (29%) 303

Linear trend interaction tests

Annex 2.8 presents summaries of the linear interaction models and detailed results can be found in the metadatabase. Annex 2.9 shows EU-wide average parameter estimates, which are summarized in Table 4-6, along with summaries of the formal statistical tests that were conducted. Table 4-7 shows summaries of the same results by broad variable type.

These results showed a similar balance of positive and negative patterns as were found for population growth rates, i.e. no clear tendency for either to be strongly predominant across species and Member States (Table 4-6). The strongest positive effects were found for unmanaged hedges (L14), single bushes (L10), mineral fertiliser, ponds (L41), avenue trees (L11), shrub fringes (L2), permanent crops and cereals (Table 4-6), so higher values of these indices were associated with more positive trends. The strongest negative relationshisps involved roads, drystone walls (L21), pulses, conifer hedges (L12), temporary grass, grassy margins (L1), mean FHI, steams (L32), ditches (L31) and managed hedges (L13) (Table 4-6; note that some of these variables were also highly correlated with others (see Table 4-3) that therefore have similar parameter estimates and are not listed here). Differences in effect size when all, as opposed to only significant, parameter estimates were included were

larger than with the population growth rate tests, but still only involved small numbers of variables. The associations with fences (L23), unmanaged hedges (L14) and ponds (L41) were considerably more negative and those with mean FHI, ditches (L31)and temporary grass were more positive (Table 4-6). There were also no clear, new patterns revealed by combining these results by broad variable type (Table 4-7).

Table 4-6 Numbers of significant results (totals across Member States and bird species, p<0.05) and of EU-average effects (across bird species, n=37, estimate 95% confidence intervals do not overlap zero) for effects of agricultural variables on linear trends over time.

Standardized effects allow comparison between variables. See Annexes 2.8-2.9 for full results.

Predictor No. of averaged parameters: No. of tests Standardized effects variable significant (NS) Positive Negative Positive Negative Significant All

relationships relationships relationships relationships only results CEREALS 21 24 7 (5) 8 (6) 1.556 0.763 FALLOW 35 39 11 (7) 12 (6) -0.66 -0.389 PULSES 18 31 5 (7) 13 (1) -2.523 -1.513 ROOT VEG 51 29 14 (7) 8 (7) 2.119 1.36 FODDER ROOTS 22 21 8 (5) 5 (8) 2.057 0.81 IND. CROPS 29 28 5 (3) 10 (8) -0.062 -0.222 GREEN PLANTS 28 22 5 (9) 6 (6) -0.628 -0.164 TEMP. GRASS 22 15 6 (4) 4 (6) -1.85 -0.857 PERM. GRASS 38 33 9 (5) 6 (5) 0.4 0.203 PERM. CROPS 33 27 7 (3) 9 (6) 2.41 1.592 HORSES 27 42 6 (10) 13 (7) -2.545 -1.205 CATTLE 35 38 6 (12) 9 (9) -0.638 -0.146 SHEEP/ GOATS 28 37 6 (10) 10 (10) -1.744 -0.774 PIGS 43 33 14 (7) 7 (8) 0.259 0.041 ENERGY INPUTS 21 27 7 (4) 7 (8) -0.34 -0.256 Fertiliser 31 34 6 (8) 5 (7) 1.485 0.632 Manure 29 30 6 (6) 6 (8) -0.322 -0.143 Mean FHI 14 39 4 (9) 6 (7) -1.899 -0.578 L 1 18 32 3 (3) 10 (10) -2.373 -1.397 L 2 28 19 10 (10) 3 (3) 2.516 1.423 L 10 28 16 9 (6) 4 (7) 1.505 0.762 L 11 32 18 10 (9) 3 (4) 2.183 1.231 L 12 20 25 4 (7) 8 (7) -2.606 -1.22 L 13 27 38 5 (2) 10 (9) -2.893 -1.878 L 14 23 31 2 (9) 5 (10) -1.46 -0.474 L 15 30 14 10 (6) 4 (6) 1.337 0.856 L 21 19 28 6 (6) 9 (5) 0.12 -0.013 L 22 20 26 7 (4) 9 (6) -0.738 -0.507 L 23 28 30 4 (4) 10 (8) -3.02 -1.756 L 31 30 27 7 (7) 6 (5) 0.342 0.127

L 32 24 38 4 (7) 9 (5) -1.868 -0.907 L 41 26 25 5 (5) 10 (5) -1.486 -0.952 L 51 15 26 6 (6) 6 (8) -0.184 -0.031 roads 17 41 2 (5) 10 (9) -3.08 -1.616

Table 4-7 Number of tests for individual bird species and variables for which significant (p<0.05) interactions were observed between year and variable type on species abundance across the EU summarized from Table 4-6

Positive Negative Type of variable NS relationships relationships Crop type 109 (26%) 120 (29%) 187 Farmland 4 (15%) 6 (16%) 15 heterogeneity Energy inputs 7 (27%) 7 (27%) 12 Nitrogen inputs 11 (21%) 12 (23%) 29 Linear features 94 (23%) 116 (28%) 203

4.3.2 Butterflies

Population growth rate tests

Summaries of the results of the population growth rate models fitted to butterfly data are presented in Annex 2.10 and more detailed, species-/Member-State-specific results are available in the metadatabase. EU-wide average parameter estimates are presented in Annex 2.11 and summarized in Table 4-8, where summaries of the statistical test results are also shown. Table 4-9 summarizes the same results by broad type of variable, i.e. the general form of variation in agriculture.

As was found for birds, there were both positive and negative associations among the range of species considered for all of the predictor variables that were analysed. Across the three approaches for summarizing the effects identified (numbers of significant relationships across species and Member States, numbers of significant model-averaged parameters and standardized, integrated effect sizes, notably positive associations with population growth rates across species included cereals, fallow land, root vegetables, pigs, heath fringes (L2), conifer hedges (L12), woodland margins (L15) and ditches (L31) (Table 4-8). Conversely, negative associations predominated for crops harvested green, horse, cattle, farmland heterogeneity, single bushes/trees (L10), dry stone walls (L21) and roads (Table 4-8).

Table 4-8 Numbers of significance tests (totals across Member States and butterfly species, p<0.05) and of EU-average effects (across butterfly species, n=118, estimate 95% confidence intervals do not overlap zero) for effects of agricultural variables on population growth rates

Standardized average effects across species allow comparison between variables. See Annexes 2.10- 2.11 for full results.

Predictor No. of Member-State- No. of averaged parameters: Standardized effects variable specific tests significant (NS) Positive Negative Positive Negative Significan All

relationships relationships relationships relationships t only results CEREALS 29 19 17 (43) 7 (51) 2.322 0.385 FALLOW 37 12 21 (40) 7 (50) 1.931 0.353 PULSES 22 24 14 (32) 16 (57) -0.178 -0.221 ROOT VEG 41 21 29 (39) 13 (37) 2.187 0.802 FODDER 28 19 14 (47) 14 (44) 0.043 0.025 ROOTS IND. CROPS 26 12 15 (53) 8 (44) 1.273 0.338 GREEN 16 38 8 (35) 31 (42) -2.767 -0.937 PLANTS TEMP. GRASS 19 24 11 (46) 18 (46) 0.905 0.186 PERM. GRASS 19 20 11 (51) 14 (41) -0.618 -0.095 PERM. CROPS 21 15 14 (50) 9 (46) 0.744 0.198 HORSES 17 31 5 (38) 16 (60) -1.962 -0.51 CATTLE 15 22 8 (42) 14 (55) -1.027 -0.259 SHEEP/ GOATS 24 20 13 (42) 16 (47) -0.287 -0.124 PIGS 32 14 21 (44) 6 (48) 2.245 0.45 ENERGY 9 15 6 (17) 9 (16) -0.69 -0.152 INPUTS Fertiliser 28 26 20 (38) 16 (40) 0.061 0.02 Manure 29 18 22 (39) 11 (42) 0.959 0.292 Mean FHI 7 21 5 (13) 17 (14) -1.706 -0.866 L 1 11 7 8 (14) 5 (21) 1.302 0.199 L 2 23 6 20 (20) 2 (6) 3.479 1.848 L 10 13 15 6 (19) 11 (12) -1.039 -0.345 L 11 16 17 10 (14) 9 (16) 0.373 0.113 L 12 18 6 14 (19) 5 (11) 1.582 0.847 L 13 9 9 6 (17) 5 (21) -0.295 -0.127 L 14 21 7 15 (22) 5 (7) 2.558 1.358 L 15 23 8 17 (20) 4 (7) 2.17 1.285 L 21 11 18 5 (20) 9 (14) -0.561 -0.043 L 22 9 6 7 (18) 6 (17) 0.436 0.141 L 23 9 12 4 (21) 6 (17) -0.671 -0.178 L 31 25 6 17 (21) 1 (9) 3.167 1.541 L 32 14 10 13 (17) 5 (13) 1.202 0.553 L 41 23 6 15 (15) 5 (8) 1.869 1.079

L 51 11 9 8 (12) 7 (20) 0.666 0.088 roads 5 19 4 (17) 12 (20) -1.738 -0.654

Grouping variables by broad type, there was no clear-cut pattern for particular classes of variation to be very strongly more influential than the others, or for new patterns of clear positive or negative influence to be identified (Table 4-9). However, crop type was a significant influence considerably less commonly (23% of tests) than were linear features (35% of tests; Table 4-9).

Table 4-9 Numbers of tests for individual butterfly species and variables for which EU-average population growth rates were significantly associated with broad classes of variable, summarized from Table 4-8.

Positive Negative Type of variable NS relationships relationships Crop type 201 (12%) 189 (11%) 1270 Farmland 5 (10%) 17 (35%) 27 heterogeneity

Energy inputs 6 (13%) 9 (19%) 33

Nitrogen inputs 42 (17%) 27 (11%) 173

Linear features 169 (22%) 97 (13%) 505

Linear trend interaction tests

Annex 2.12 presents summaries of the linear interaction models and detailed results can be found in the metadatabase. Annex 2.13 shows EU-wide average parameter estimates, which are summarized in Table 4-10, along with summaries of the formal statistical tests that were conducted. Table 4.11 shows summaries of the same results by broad variable type.

The gross pattern for a mixture of positive and negative associations between trends and agricultural variables was similar to that found for population growth rates, i.e. there were no variables with clear, consistent influences on a large majority of the species tested (Table 4.10). However, across individual significance tests, model- averaged parameter significance and standardized effects, fallow land, root vegetables, industrial crops, permanent grass, permanent crops, heath fringes (L2), unmanaged hedges (L14) and ditches (L31) showed predominantly positive associations with population trends (Table 4-10). Conversely, pulses, plants harvested green, temporary grass, managed hedges (L13), fences (L23) and roads tended to show negative associations Table 4-10).

Table 4-10 Numbers of significant results (totals across Member States and butterfly species, p<0.05) and of EU-average effects (across butterfly species, n=118, estimate 95% confidence intervals do not overlap zero) for effects of agricultural variables on linear trends over time.

Standardized effects allow comparison between variables. See Annexes 2.12-2.13 for full results.

Predictor No. of averaged parameters: No. of tests Standardized effects variable significant (NS) Positive Negative Positive Negative Significant All results relationships relationships relationships relationships only CEREALS 28 18 22 (48) 15 (48) -0.23 -0.052 FALLOW 31 6 25 (10) 3 (15) 3.816 1.967 PULSES 11 26 8 (19) 19 (12) -1.803 -0.796 ROOT VEG 30 10 26 (13) 5 (11) 4.864 2.782 FODDER 12 8 10 (14) 6 (21) 1.457 0.248 ROOTS IND. CROPS 24 4 17 (21) 1 (17) 3.997 1.403 GREEN PLANTS 18 34 10 (46) 27 (39) -2.216 -0.624 TEMP. GRASS 18 35 7 (30) 29 (49) -1.98 -0.775 PERM. GRASS 26 17 21 (43) 11 (30) 2.661 0.844 PERM. CROPS 26 18 19 (44) 14 (43) 2.319 0.672 HORSES 10 12 3 (18) 5 (18) -1.129 -0.251 CATTLE 12 10 7 (19) 6 (17) -0.621 -0.265 SHEEP/ GOATS 6 16 4 (18) 9 (18) -0.59 -0.265 PIGS 31 18 19 (22) 14 (22) 0.517 0.269 ENERGY 11 11 7 (15) 7 (12) 0.523 0.239 INPUTS Fertiliser 49 29 30 (27) 19 (47) 0.818 0.247 Manure 36 34 28 (30) 21 (44) 0.354 0.008 Mean FHI 1 4 1 (7) 4 (4) -0.998 -0.097 L 1 8 9 6 (11) 9 (15) -0.223 -0.068 L 2 22 4 16 (16) 1 (8) 3.625 1.8 L 10 10 10 7 (16) 8 (12) -0.023 -0.059 L 11 11 12 6 (11) 8 (17) -0.546 -0.307 L 12 9 7 7 (20) 6 (11) 0.075 0.116 L 13 5 12 1 (13) 10 (16) -2.54 -0.691 L 14 17 8 14 (18) 8 (5) 1.542 1.019 L 15 15 6 10 (21) 4 (8) 0.888 0.517 L 21 11 13 8 (11) 8 (13) 0.198 0.013 L 22 8 8 5 (19) 7 (13) -0.407 -0.034 L 23 7 18 3 (11) 8 (20) -1.27 -0.539 L 31 25 7 16 (19) 2 (7) 3.014 1.472 L 32 11 15 10 (9) 11 (12) -0.079 -0.113 L 41 13 7 11 (13) 4 (13) 1.146 0.365 L 51 8 9 7 (11) 6 (14) 0.213 -0.069 roads 10 17 7 (9) 10 (18) -0.883 -0.554

Summarizing the results by types of variable suggested that each type had a similar general level of importance across species, with each type being significant in 31- 39% of cases tested, and no clear preponderance of positive or negative relationships for any type of variable (Table 4-11). Note that there was a large percentage difference for farmland heterogeneity, but involving only a small number of species (Table 4-11).

Table 4-11 Number of individual butterfly species and variables for which significant (p<0.05) interactions were observed between year and variable type on species abundance across the EU, summarized from Table 4-10.

Positive Negative Type of variable NS relationships relationships Crop type 198 (18%) 164 (15%) 725 Farmland 1 (6%) 4 (25%) 11 heterogeneity

Energy inputs 7 (17%) 7 (17%) 27

Nitrogen inputs 58 (23%) 40 (16%) 152

Linear features 134 (20%) 110 (16%) 430

4.4 Discussion

4.4.1 Major biodiversity responses The integrated assessment indicates that the broad land-use patterns associated with all the agricultural variables considered are significantly associated with temporal changes in bird and butterfly populations, with a broad range of both positive and negative relationships. It is important to note that this study was not experimental, so the associations with specific variables should be considered to only be indicative of the wide range of factors in landscapes that are likely to be associated with them. Such factors will notably include the key features of landscapes that tend to be associated with the specific crops, boundary types, etc., and the farming systems in which they are found, such as soil type, altitude, dominant agricultural practices and other crops found in a rotation with a dominant crop. Thus, results for spring barley, for example, are likely to reflect the effects of many variables associated with the whole farming systems in which spring barley is found. This does not mean that the specific variables, such as crop type, do not have direct effects, but in practice, given the (NUTS3) scale of the data, it is not possible to separate their effects from those of the landscapes or farming systems that include them.

The Freeman & Newson (2008) approach is the preferred one to use (Chapter 2), but the more common, more traditional linear interaction method was included here for comparison. The former is expected to be more sensitive, but the broad patterns found with each approach were similar, i.e. neither identified clearly different patterns for positive or negative effects of different variables or types of variable. This may be because many of the survey datasets considered currently include only short runs of data (no more than a decade or so), which can be more appropriately modelled in terms of linear changes over time than longer time series with proportionately more complex trends. However, it is recommended to use the Freeman & Newson population growth rate results (Table 4-4, Table 4-5, Table 4-8 and Table 4-9; Annexes 2.6-2.7 and 2.10-2.11) as a primary basis for interpretation.

Considering these results, there are very many tests for individual Member States, agricultural variables and species, which can be investigated in the database; it would not be feasible to investigate them all here. Instead, however, summaries of the patterns found at high levels can be inspected. Positive associations between bird population growth and cereals, fodder root crops and green plants (see Table 4-3 for definitions) probably reflect the fact that the analyses considered species known to select agricultural habitats: broadly, with more of these habitats in a region, these species fare better (Table 4-6). Similarly, negative associations with conifer hedges, heterogeneous landscapes, permanent crops and artificial boundaries are likely to reflect the same broad habitat preference. More surprising are positive associations with avenue trees and woodland margins, and negative ones with managed hedges, grassy margins and pulses (Table 4-6). The former might be expected to have negative influences on farmland birds, which are generally (evolutionarily) species of open landscapes. However, many also require woody habitats for nesting and use trees as song posts, which can both be important habitat components and those where they are most detectable during surveys. Hedges, grassy field boundaries and pulse crops are all land-use components often positively associated with birds as nesting or foraging habitat, pulses commonly being important crops because they are often sown in spring in landscapes dominated by winter cropping and hence provide valuable habitat heterogeneity. It may be that these results, together with the negative association with energy inputs, reflect the negative influences of more intensive management of farmland, via associations with broader regional factors, such as farming systems as a whole. For example, in the data available, NUTS3 regions with more hedges and pulse crops may feature higher intensity agriculture than regions where these features are rarer, and hence they have negative associations with a range of species despite the likelihood that, within regions, they might have positive influences. This shows one way in which the analyses consider entire farming systems.

Many more butterfly species than bird species were considered, but there were proportionally fewer significant relationships, probably because data were available from fewer Member States, and therefore fewer individual NUTS3 regions, leading to smaller ranges in the values of each predictor variable. The individual species are also often more restricted in range than are the birds considered, leading to still less variation in the predictors and so less analytical power. In addition, butterflies were

not selected for dependence on agricultural habitats, because there is no widely used classification for habitat preference for this group (unlike birds). This means that many of the species considered may have not been strongly associated with specifically farmland habitats, as opposed to semi-natural habitat elements found within landscapes dominated by agriculture. Hence, influences of the productive parts of the landscape, such as crop type and fertilizer inputs, may be small, and this is reflected in the broad pattern reported in Table 4-9. It is more difficult to interpret the butterfly results ecologically than it is for birds, because the species are less closely associated with in-field agricultural habitats. However, it is interesting that there were rather more, or stronger, positive than negative associations among the variables considered, when the approach to species selection was inclusive and many monitoring sites may not have been situated in productive farmland (Table 4- 10). This suggests that a range of aspects of agricultural management reflected in the variables involved are positive influences on many butterfly species, even if they are more associated with semi-natural habitats. It is probably significant that the positive influences included the less managed elements of farmland boundaries, such as ditches and unmanaged hedgerows, while fences, dry stone walls and managed hedgerows, which will provide habitat less like the semi-natural habitats that butterfly species are likely to prefer, tended to show more negative associations.

4.4.2 Important caveats to the results An obvious caveat to the assessment of “biodiversity” relationships in this study is that most taxonomic groups could not be considered because of a lack of survey data. All species are different and respond differently to the environment, and some species will always benefit and some suffer from any change in land-use or management. Therefore, the patterns found for the taxa studied here cannot be assumed to form an accurate representation of the responses of other groups, such as plants, beetles, spiders, amphibians or mammals. However, an objective definition of a healthy environment would be one in which biodiversity (in the sense of population sizes of a range of taxa) is stable, or perhaps increasing if in recovery from previous declines. At large scales, it is likely that many taxa will respond in the same way to pressures and management measures that produce systemic effects, such as changes in land-use across whole fields, although the speed and size of the response may well vary. Hence, while the details of relationships between land-use and birds or butterflies should be interpreted with caution as indicators of effects on other groups, they are likely to be reasonable measures at a gross scale, such as in suggesting that influences on biodiversity as a whole tend to be positive or negative. Moreover, birds are high in the food chain, so will tend to integrate the effects of environmental variation on the organisms on which they depend, and butterflies share habitats and sensitivities to the environment with various other invertebrate groups, notably including those important in the ecological function and service of pollination.

Data availability represents a major constraint on these analyses. Data from some Member States were unavailable at the time of analysis for birds and were missing from many of them for butterflies. This reflects a lack of survey activity in some cases, especially historically and for butterflies, but also refusal of data access in

other cases. In practice, the latter problem can only be solved by additional funding support, to pay for these data or to support analyses by the data owners themselves. Monitoring of butterflies and of other groups is improving constantly in many Member States, so future repeats of the analyses here are likely to have a considerably larger possible geographical scope. Nevertheless, for birds, data were available from most of the European Union and will therefore have covered a wide range of landscapes and farming systems. No major farming systems in Europe will have been absent from the range covered by the data used, so significant biases of omission should not have occurred.

A comprehensive, integrated assessment of the influences of agriculture on biodiversity requires data on all possible agricultural factors that might influence all wild species, together with data on all of the wild species. Clearly, the latter are not available, with data supporting analyses only available for two groups, birds and butterflies. In respect of agriculture, there are numerous factors that cannot be tested because data were not available at a suitable resolution, or because the relevant data are not collected or access to them is restricted. These include crop interspersion (FSS and HSMU cropping data are at too low a resolution to show interspersion at the field scale), timing of sowing of cereals, areas of organic farming, pesticide use, differences in trends between Natura 2000 and undesignated areas, traditional agricultural practices or the other nominal priorities identified by the Commission. Collation of data across Member States also revealed issues in the standardization of the agricultural data, with different classifications or different variables being used in different places in different years. Future projects might be able to address issues of spatial scale or resolution with enhanced data accessibility, but standardization and data gaps could be solved only with new data collection efforts at Member State or EU level, together with greater coordination of data collection, especially with respect to finer divisions of land-use type. If changes to classifications are required due to new policy priorities, it would be advisable to ensure backwards compatibility, such as by only sub-dividing existing categories, as opposed to combining them.

The results of this assessment may under-estimate the true influence of field boundary characteristics. This is because the boundary data from LUCAS are rather coarse (across the whole EU, there is an average of 0.06 survey points per square kilometre, or one point per 16km2) so any local variation in boundary character causes considerable noise in the data and limits sensitivity to these factors. Other evidence indicates that in intensively managed farmland, linear features between the fields may form the major local source of semi-natural habitat, which can support the entire life cycles of some species or provide important resources to allow species that can also utilise in-field habitats to persist in an area (e.g. Fahrig et al. 2011). They may also be more important drivers of local abundance than crop or in-field habitats, despite the latter covering far larger areas in the landscape (Siriwardena et al. 2012).

Another key limitation to the analyses is that the spatial scale of agricultural data means that local conditions in individual survey areas may be quite different to the

regional average. In practice, this could mean that the dominant local crops are poorly represented in the data, for example. However, the survey schemes considered here either feature repeat surveys of sets of sites that are often selected randomly, so should either be unbiased with respect to geographical variation in cropping or have consistent biases over time, so any lack of representation in the data will tend to cause noise in the data, rather than bias the results. Nevertheless, it is possible that the noise was sufficiently significant to obscure some biologically important patterns. A more fundamental problem with having data available only at large scales is that the variation available for analysis was only as large as occurred between NUTS3 regions; for smaller Member States or species that were found in only a few such regions, this meant that there was only a small number of different values of the variables for each test, regardless of the number of bird or butterfly survey locations available. This will have limited the power of the tests conducted and made some tests impossible, if land-use variables had the same values in all regions in which the species concerned was found.

The sheer size of the datasets involved precluded the ideal conceivable analyses due to the consequent demands on computing resources. This had three important effects: (i) tests integrating data from individual Member States across Europe could not be conducted, (ii) models explicitly allowing for correlations between survey locations within individual NUTS3 regions using a repeated measures structure were not feasible and (iii) multi-model inference analyses (as used in Case Study 3, for example, see Chapter 7) in which all possible combinations of the candidate predictor variables are modelled in order to produce unbiased, average parameter estimates, were not done. The implications of (i) have been minimized by the use of a post-hoc, “meta-analysis approach” to collate the results, but this does not allow a formal statistical test of the differences between Member States and is not the most efficient use of the data. The potential effect of (ii) is that significant inter-correlation between nearby survey locations means that there is pseudo-replication in the data and the apparent sample size used in calculating standard errors and conducting significance tests is artificially inflated, hence leading to misleadingly high levels of significance. This would mean that the results appear artificially clear, although it is important to note that a similarity in patterns of change from sites in, say, the same NUTS3 region could arise from their spatial proximity (i.e. because all sample the same, freely mixing population, or from genuinely independent, but parallel responses to the same patterns of land-use). Hence, assuming that all such correlation is a statistical artefact could be overly conservative. It is also common for abundance and population trends to be influenced by multiple factors (e.g. Siriwardena et al. 2012), all of which are unlikely to be similar among sets of monitoring locations, even where there is a strong effect of one particular variable. This means that inter-correlations between counts for adjacent sites are often not high, and that problems with pseudo-replication are small. However, this cannot be guaranteed here and a caveat that significance levels may be artificially high (and confidence intervals artificially tight) needs to remain in place.

Failing to conduct multivariate analyses as described under (iii) above means that there was no formal consideration of the relative importance of the different

agricultural variables and no formal identification of a single “best model” across all variables for any species or Member State. The significance values and parameter confidence intervals associated with each individual variable indicate which are likely to be more important, but without considering the covariances between them, so combining all the significant variables for a given Member State and species would not necessarily produce the best model for that species. Correlations between the predictor variables (see Annex 2.14) provide some insight here, because pairs of highly correlated variables would be unlikely both to be found in the best model, but not in the sense of contributing to the estimation of quantitative effects. The correlations can be used, however, to show where two variables that have been identified as significant may actually both refer to the same biological effect.

4.4.3 Additional elements to the analysis An issue highlighted in the terms of reference for this study to consider is the potential for response time lags between changes in agricultural practices and measurable impacts on biodiversity. In principle, given a defined list of target questions, it is straightforward to examine the effect of time lags for questions if suitable data are available. However, considering any individual time lag requires a further analytical process and it was not possible to consider time lags in the time available. In addition, the detection of lag effects requires detailed time series data on agricultural variables, which were often not available, too intermittent or highly variable in coverage between Member States. A further problem with the detection of time-lags, as with other differences of interest is that there need to be large ranges of variation in the predictor variables to provide statistical power. Hence, they need differences in variable values over time to be large, preferably following a pattern of step changes, such that there are demarcations between periods with low or zero values of the parameter of interest and periods with high values. Lags before an effect of the change is detectable can then be investigated. Conversely, if change is gradual or small, the predictor data from any given time point are likely to be highly correlated to those from adjacent time points for the same locations, and there is little power to detect effects. This is the pattern that is seen in the agricultural variables here, particularly at large spatial scales, such as NUTS3 regions.

A second additional area of interest a priori was to consider effects at multiple spatial scales, considering the implications of using data collected at different scales, such as the 1km square up to the NUTS3 region. This would inform the scale at which data need to be collected in the future to provide reliable evidence. Two issues limited the potential to conduct such analyses: the time required, as discussed above, and the availability of data. The data collation process (Chapter 2) showed that multiple data sources at different spatial scales were not available for the same taxa in the same regions: such common data would be required to assess the relative sensitivity of different monitoring approaches. As discussed above, finer-scale agricultural data are likely to support stronger analyses of agricultural effects and the NUTS3 scale is far larger than would be ideal for the taxa studied here, so there is little point in investigating the effects of considering data from larger spatial scales than NUTS3. Some datasets were potentially available at a smaller scale than NUTS3, but the process of data collation was so time-consuming that it was not possible also

to consider additional spatial scales. Analyses comparing different scales of habitat data were, therefore, not conducted.

4.5 Conclusions The results of this analysis reveal a range of significant relationships between agricultural variables and measures of biodiversity change, including both positive and negative associations. This is not surprising because the analyses consider species that are associated with farmland, so they would be expected to benefit from agricultural land-use. Conversely, since farmland is a highly heterogeneous habitat, it is also not surprising that aspects of agricultural land-use are negative influences on one or more species. In addition, research and monitoring over the last 20 years or so has demonstrated a range of ways in which changes in agricultural practices have led to declines in many species traditionally associated with farmland, while other species have increased because the newer practices improve habitat quality for them (e.g. Siriwardena et al. 1998, Chamberlain et al. 2000, Donald et al. 2006). It is important to note, however, that this study is fundamentally correlative, not experimental, so the relationships found may indicate either genuine, direct effects of the variable involved, or indirect effects of other variables that happen to be spatially associated with the focal variable.

The specific results of this integrated analysis may be of particular interest with respect to individual species and Member States, but there are too many individual results to discuss in detail here. However, the results have been added to the database described in Chapter 3, which have been supplied separately to the European Commission. Further exploration of the patterns of results by species, species group, Member State, or combination of these, can be conducted by reference to the database.

5 Case study 1: Factors affecting the condition of agriculture-associated species and habitats within Natura 2000 sites

Authors: Gavin Siriwardena1, Henrietta Pringle1, Graham Tucker2, Renate Koeble3, Maria Luisa Paracchini3 and Carlo Rega3.

Organisations: BTO 1, IEEP 2, JRC 3.

5.1 Introduction The most important legislative instruments focusing on biodiversity conservation in EU are the Birds Directive (Directive 2009/147/EC) and the Habitats Directive (Directive 92/43/EEC), hereafter referred to as the nature directives. The Habitats Directive requires habitats and species of Community interest21 to be maintained in, or restored to, Favourable Conservation Status (FCS) Table 5-1. In simple terms, this can be described as “a situation where a habitat type or species is prospering (in both quality and extent/population) and with good prospects to do so in the future as well” (ETC/BD, 2011). The objectives of the Birds Directive are not so clearly defined, but jurisprudence has resulted in them being treated as being analogous to the attainment of FCS (Milieu, IEEP and ICF, 2016).

Table 5-1 Definition of Favourable Conservation Status for habitats and species of Community interest under the Habitats Directive

Under Article 1(e), the conservation status of a natural habitat will be taken as ‘favourable’ when  its natural range and areas it covers within that range are stable or increasing, and  the specific structure and functions which are necessary for its long-term maintenance exist and are likely to continue to exist for the foreseeable future, and  the conservation status of its typical species is favourable as defined in (i).

Under Article 1(i), the conservation status of a species will be taken as ‘favourable’ when  population dynamics data on the species concerned indicate that it is maintaining itself on a long-term basis as a viable component of its natural habitats, and  the natural range of the species is neither being reduced nor is likely to be reduced in the foreseeable future, and  there is and will probably continue to be, a sufficiently large habitat to maintain its population on a long-term basis.

Source: Council Directive 92/43/1992 (Emphasis added).

Both directives include two main types of measures for achieving their objectives. Firstly, general protection measures for species wherever they occur (e.g. relating to hunting) and, secondly, the identification, designation, protection and conservation

21 i.e. habitats listed in Annex I and species listed in Annexes II and/or IV and V.

management of sites that are particularly important for habitats and species of Community importance and selected bird species22, as Natura 2000 sites23.

The nature directives also require Member States to monitor and report on their implementation. Under Article 11 of the Habitats Directive, Member States are required to undertake surveillance of the conservation status of natural habitats and species of Community interest (separately for each biogeographical regional within their country), and Article 17 requires them to report on this every six years. Under the Birds Directive, until 2007, reporting by Member States primarily reflected the strict legal interpretation of Article 12 of the Directive and focussed on their implementation activities. To align reporting with the Habitats Directive, Member States have since agreed to report on the actual state and trends of bird populations and to prepare six-yearly reports synchronised with those required under the Habitats Directives (European Commission, 2011).

The latest assessment of the conservation status of habitats and species of Community interest covered 2007-2012, whilst bird trends were reported for the period 2008-2012. A summary of the assessments were published in the State of Nature in the EU (EEA, 2015), and the data for each national / biographical assessment of each habitat and species are also publically available and downloadable from the ETC-BD website24.

According to a Council Implementing Decision, Member States are required to provide key information on each Natura 2000 site using a standard data form25. This includes an obligatory assessment of the degree of conservation (i.e. site-level status) of each habitat and species that is present on the site that is listed in Annexes I or II of the Habitats Directive respectively, listed in Annex I of the Birds Directive or is a migratory bird species.26 Furthermore, according to a European Council decision, they are supposed to update the forms regularly. As the forms have been entered onto the Natura 2000 database27 this provides a very large and site-specific dataset.

In theory, these assessments of conservation status / trend and site-level degree of conservation provide data that can be used to identify and examine factors that affect the effectiveness of the nature directives. This case study therefore explored the feasibility of analysing the relationship between agricultural factors and the overall conservation status / trends, or their Natura 2000 site-level degree of

22 i.e. species listed in Annex I of the Birds Directive and migratory species 23 i.e. Special Protection Areas (SPAs) designated under the Birds Directive, and Special Areas of Conservation (SACs) designated under the Habitats Directive 24 http://art17.eionet.europa.eu/article17/reports2012/ 25 http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32011D0484; Official Journal of the European Union OJ L 198 30.7.2011 http://data.europa.eu/eli/dec_impl/2011/484/oj. 26 The original SDF used the term ‘conservation status’ for describing the condition of each habitat type and species present at an individual site (rather than at the scale of a whole country or biogeographical region as is the case for Article 17 reporting). In the revised SDF, the term ‘conservation status’ is replaced with ‘degree of conservation’ to avoid any confusion between the terms. 27 http://www.eea.europa.eu/data-and-maps/data/natura-7

conservation, of habitats and species that are potentially influenced by agriculture. Such species could include those Birds Directive Annex I birds that are considered by the EEA to have crops and/or grasslands as one of their preferred ecosystems when breeding, and the Habitats Directive Annex II non-bird species that are considered by the EEA to have crops and/or grasslands as one of their preferred ecosystems28. Habitats considered could be those Habitats Directive Annex I habitats that are considered to be grasslands according to the MAES habitat typology (there are no Annex I crop lands).

5.2 The feasibility of analysing the relationship between agricultural land use and the status of habitats and species covered by the Nature Directives

5.2.1 Use of conservation status assessment data Although the results of the assessment of conservation status of habitats and species have been published in the State of Nature Report (EEA, 2015), no analysis has been published on the impact of agricultural land use factors on their status. Some information on threats and pressure, as indicated further in this section, is available and has been used in the State of Nature Report. This study therefore firstly considered whether such an analysis of habitat and species conservation status data would be feasible and worthwhile.

For reporting under Article 17 of the Habitats Directive, Member States have adopted three classes of Conservation Status: Favourable (FV), Unfavourable- Inadequate (U1) and Unfavourable-Bad (U2)(ETC/BD, 2011). In this respect, Favourable effectively represents a situation where the habitat or species can be expected to prosper without any further changes to existing management or policies. Unfavourable-Inadequate describes situations where a change in management or policy is required to return the habitat type or species to favourable status but there is no risk of extinction in the foreseeable future, while Unfavourable-Bad is for species in serious danger of becoming extinct (at least regionally) or habitat types that are degraded and losing their characteristic features.

It is important to note that the conservation status of each habitat and species should be assessed by Member States in relation to each biogeographical or marine region that occurs within their territory. Some Member States (e.g. Austria, Germany, United Kingdom) have developed methods for the evaluation of features (habitat types or species) at a local (site) scale, often using an indicator-based assessment (ETC/BD, 2011). When the majority of occurrences of a habitat or species are covered by such methods, an aggregation of the results can directly give assessments of “area” and “structure and function” for habitat types and “population” and “habitat for the species” for species of the conservation status assessment at a biogeographical level. However, such defined methods appear to be the exception. Little information is available centrally on the conservation status assessment methodology that is adopted in most countries (documentation at the EU level consists only of protocols for collating information at the Member State

28 http://www.eea.europa.eu/data-and-maps/data/linkages-of-species-and-habitat

level29) and it appears likely that it will vary considerably amongst them. For future analysis, it would be beneficial to look more intensively into the national systems, including into whether assessments are based on the entire distribution of the habitat type or species or habitat with each region (rather than only within Natura 2000).

Although reporting on the status of birds has been brought more into line with that under the Habitats Directives, a considerable difference is that trends reported under Art. 17 do not ask for a status assessment by the Member States. According to the guidelines agreed in 2011 (European Commission, 2011), Member States report on each species’ short-term population and range trend (i.e. over 12 years, with 2001-2012 being the most recent reporting period) and longer-term population and range trends from approximately 1980 (depending on when Member states carried out surveys at that time). A further difference is that bird trends are reported on a national basis rather than in relation to biogeographical areas.

Whilst these Article 17 and 12 datasets provide information that may be used to investigate the factors affecting the status of habitats and species, there are several substantial constraints on their utility for such purposes. Firstly, and most importantly, the statistical power that they would provide for any analysis is very low, as a result of the large-scale reporting, which is at biogeographical region scales for Article 17 reports and national scales for birds. Hence there are few potential data points for comparison across Europe, each of which represents a high-level summary of potentially rather variable information. As most Member States only have one or two terrestrial biogeographical regions within them, the potential maximum number of independent assessments for each terrestrial habitat and species of Community interest is little higher than the number of Member States that include that habitat type. Furthermore, as the habitats and species of Community interest in question are mostly rare, in practice, conservation assessments will mostly only be available from far fewer countries / regions. These low sample sizes will be exacerbated by variation in the assessment methods that have been used, leading to high variability in what the underlying data really mean. Whilst population trend data are available for all birds, including more widespread species that will therefore provide larger datasets, the limit of using national reporting restricts the sample size to a maximum of 27 per species (as Croatia was not required to provide assessments for the last reporting period).

Most EU-27 Member States provided Article 17 reports for the previous reporting period (i.e. 2001-2006), apart from Greece, which did not provide data, and Romania and Bulgaria, which were not required to provide assessments as new accession states. This therefore provides some time-series data that could be used in a more sophisticated and sensitive analysis of the effects of changes in farming practices. However, the sample sizes are likely to be even lower, as there were many more gaps in coverage of habitats and species in the previous reporting period. The lack of

29 http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32011D0484; Official Journal of the European Union OJ L 198 30.7.2011 http://data.europa.eu/eli/dec_impl/2011/484/oj.

a defined assessment methodology is also likely to have led to many apparent changes in status being due to changes in methods, or to better information becoming available within Member States. This problem has been recognised and therefore the Article 17 reporting guidelines request that Member States indicate whether changes in status are likely to be genuine or the result of changes in methods or knowledge. However, these fields in the database are not always filled in and there appear to be considerable discrepancies in the interpretation of this question, with some Member States indicating few or any genuine changes. Therefore, there appears to be little advantage to using currently available status change data to investigate the factors affecting the status of habitus and species.

5.2.2 Use of Natura 2000 site records on the degree of conservation The information that Member States are required to provide on each Natura 2000 site, using the standard form, includes an obligatory assessment of the degree of conservation of each targeted habitat and species that is present on the site. The degree of conservation of each habitat and species within the site is assessed according to three criteria (i.e. degree of conservation of structure and functions, and restoration potential) and summarised in three classes as outlined in Table 5-2 and Table 5-3 (see Annex 3.1 for the detailed guidance given to Member States). Although the same criteria must be used for all assessments, no further guidance is provided on the methods that should be used. Whilst the assessments may be expected to be based upon the best information available in each case, a variety of different methods are likely to have been used amongst the Member States and for different taxa. In some cases, assessments will be based on detailed, standardized surveys of local taxon abundance, whereas in other cases, it is possible that assessments are made simply using rapid site visits and subjective judgement. The criteria are sufficiently non-specific that the choice of a protocol to use is open to a wide range of interpretations, but the methods used for each site are not recorded in the database.

In each column in Table 5-2, the levels A-C represent a three-level ordinal variable, while D represents “no data”, either because no assessment was made or because the species concerned is absent from the site. There are also blanks in the database, but it is unclear how these may differ from entries of level D. Of the two columns describing elements of conservation status, the one that management due to Natura 2000 designation has the potential to change is the first, “Degree of conservation of the features of the habitat important for the species”. Entries within this category for individual designated sites could, therefore, be used in analyses of potential influences on site condition. On average, levels should move from C to A as conservation management succeeds.

Data are also available on the degree of isolation of the site (Table 5-2), although this is not a factor that is straightforward to manipulate directly. As this can have an important influence on the viability of species’ populations, and therefore the degree of conservation of a population at a site, this variable could also be included in analyses, as a predictor variable (see below).

Table 5-2 Assessment of species condition in individual Natura 2000 sites

Source: online Natura 2000 documentation30 Site assessment for species Degree of conservation of the features of the Isolation (degree of isolation of the Assessment habitat important for the species. Based on the population on site in relation to the level code conservation of features and restoration natural range of the species) possibilities A Excellent – elements in excellent condition Population (almost) isolated Good – elements well conserved OR Population not isolated, but on B Elements in average or partially degraded margins of area of distribution condition and easy to restore Average or reduced – All other combinations of Population not isolated within C conservation of features and restoration extended distribution range possibilities D N/A N/A

Habitat condition assessment records for individual Natura 2000 sites are also available for the habitats listed in Annex I of the Habitats Directive, with assessment categories as indicated in Table 5-3. As with species, the assessment category relevant for evaluating land-use and policy impacts would be the “degree of conservation of the habitat type”. Assessment levels can be treated in the same way as described for species above.

Table 5-3 Assessment of conservation status of habitats in Natura 2000 sites (per site)

Source: online Natura 2000 documentation31 Site assessment for habitats Degree of conservation of the habitat type, based on the degree of conservation of Assessment structure, degree of conservation of functions (measured as prospects of habitat to level code maintain structure for future) and restoration possibilities Excellent – Excellent structure OR A Structure well conserved and excellent prospects Good – structure well conserved and good prospects OR Structure well conserved and average/maybe unfavourable prospects and restoration easy or possible with average effort OR B Average structure/partially degraded, excellent prospects and restoration easy or possible with average effort OR Average structure/partially degraded, good prospects and easy restoration Average or reduced conservation – All other combinations of conservation of C structure, prospects and restoration possibilities D NA

Information on site management plans is available only from 2012 onwards32, coded as “yes” (Y), “no” (N) or “in preparation” (P)3: “yes” indicating that a plan is in place and the other categories (and blanks) showing the absence of a plan for the site.

30 http://www.eea.europa.eu/data-and-maps/data/natura-7 31 http://www.eea.europa.eu/data-and-maps/data/natura-7 32 http://www.eea.europa.eu/data-and-maps/data/natura2000

According to the Council Decision, “the content of the Natura 2000 Standard Data Form should be updated regularly based on the best available information for each site of the network...”. The dataset is also updated annually and published on the EA website33. However, in practice it is uncertain how often Member States do update their degree of conservation status assessments. Some but not all records are updated after the completion of each six-yearly reporting cycleand therefore some records could go back to when the site was first designated under Natura 2000. As the date of each degree of conservation assessment is not recorded, it is not possible to establish when the data refer to and thereby filter out outdated assessments. Similarly, the date that the management plan status relates is not provided, so it is therefore not known when existing plans were completed, or whether sites with no indication of an existing plan now have a plan in place.

5.2.3 Agricultural data According to this study’s metadatabase, potentially suitable land-use / agricultural data on candidate drivers of the variation in overall conservation status or site-level degree of conservation of habitats and species that are affected by farming practices are available on cropping, inputs and livestock type and density, and the level of spending on regional Rural Development Programmes (RDP). These data can be reasonably easily combined to match the biogeographic / national reporting on the conservation status of habitats and species and national bird trends. However, for the analysis of site-level degree of conservation such data need to be at the smallest scale possible, i.e. that of the Natura 2000 site itself, or the smallest regional level available, for all Member States. Such site-level data either do not exist or access to them is restricted for reasons of data protection and confidentiality. The data that are feely available are typically at larger scales, such as NUTS2 or NUTS3 regions (Eurostat 2011), which often describe large areas with highly variable land-use and land cover. Further, Natura 2000 sites are likely to be atypical of the regions in which they are found because they are exceptional locations that are deemed worthy of protection, making the broader region still poorer as a predictor of local conditions.

An alternative data source is that of agricultural management data drawn from maps of cropping, mineral fertilizer inputs, manure inputs and total energy inputs at the level of Homogeneous Soil Mapping Units (HSMUs, minimum area resolution 1km2). Such maps have been prepared by JRC, derived from models down-scaling from NUTS2 regional crop and input data with respect to soil/slope characteristics and other landscape parameters (Britz & Witzke 2014, Pérez-Soba et al. 2015, Britz & Leip 2009, Leip et al. 2008, 2011; see Leip et al. 2008 in particular for a detailed description of the methods used to down-scale the regional data). Data on each variable (areas of arable crops, grass and fallow/set-aside, numbers of cattle, sheep and goats, manure input [kgN/ha], mineral fertilizer input [kgN/ha] and energy input [MJ/ha]) can then be summed for all HSMUs that overlap with a given focal Natura 2000 area, weighting by the area of overlap. Data on these variables are available for 2004 and 2008, although data for some variables or regions are only available for one of these years. In such cases, (e.g. NUTS2 region FR01), data from the other year

33 http://www.eea.europa.eu/data-and-maps/data/natura-7

could be substituted. It is unlikely that individual cropping variables would be especially informative about effects on Natura 2000 sites, because the general influences of factors such as agricultural intensity would be best reflected in the sum of the local area of related land-uses, such as areas of cropped land and stocking densities. Hence, the HSMU-level data might best aggregate cropping/cover variables into variables such as (i) total arable area, (ii) total area of fallow/set-aside and (iii) area of grassland (details of category definitions are in Britz & Witzke 2014). The totals would then be standardized per unit area of Natura 2000 site.

In using the downscaled data, there is a trade-off between the potential inaccuracy from the down-scaling process and the gain of geographical specificity. Down-scaling relies on data being available that allow accurate prediction of how regional total areas of crops are distributed within regions and, while some local cropping patterns can be predicted from factors such as soil type, climate, altitude, topography and geographical location, these data are not always available and factors such as market pressure, farmer personal choice and variation in the other factors at scales smaller than those for which data are available could be critical. Hence, the predictions are likely to be unreliable at small scales. However, the alternative data are at very large scales, as described above, so are very unlikely to be representative of Natura 2000 sites. Therefore it would seem to be most appropriate to use the HSMU data on agricultural variables wherever available, summarized by Natura 2000 site, with appropriate caveats.

The only complete European data set available on livestock is derived from NUTS3 level Farm Structure Survey data34. To estimate regional grazing intensity, the total regional numbers of cattle, sheep and goats, both separately and combined, would therefore need to be divided by the area of pasture (taken from CORINE land cover data35) in the same NUTS3 regions. Although the best available data, this represents a coarse measure with respect to individual Natura 2000 sites, so the results involving it would need to be interpreted with caution.

CAP RDP spending figures on relevant policy measures can be obtained from the European Commission Directorate-General for Agriculture and Rural Development and used as further indices of investment in direct management. These data refer to realised expenditure over the CAP programming period of 2009-2015, with price corrections using the price level index to allow comparison of expenditure across Member States on the same scale. These data are available only at large spatial scales, typically NUTS 0 (Member State level) for most Member States, NUTS 1 for some (e.g. Austria, Belgium, Germany and UK) and NUTS2 regions for some (e.g. Italy and Spain). Although this scale is far larger than would be ideal, a large proportion of the total RDP spend may go to protected areas and potential divisions of RDP spending information for analysis are as follow: (i) natural handicap payments to farmers in mountain areas (measure 211) plus payments to farmers in areas with handicaps, other than mountain areas (measure 212), standardized by the area of

34 http://ec.europa.eu/eurostat/statistics-explained/index.php/Farm_structure_statistics 35 http://www.eea.europa.eu/publications/COR0-landcover

designated Less Favoured Area (data from 2005, last modified in 2012)36; (ii) Natura 2000 payments and payments linked to Directive 2000/60/EC (213) (measure 214), divided by the regional area of Natura 2000 sites; (iii) agri-environment payments (measure 214) divided by the area of agricultural land.

5.2.4 Potential analyses If it is considered that the data are sufficiently reliable, the conservation status levels (i.e. FV, U1 and U2) or the degree of conservation (i.e. A, B and C) data for species and habitats could be modelled as a function of land-use, administrative and policy predictor variables using generalized linear mixed models (GLMMs). As qualitative, three-level variables, the conservation status or degree of conservation data can be converted into quantitative, binary response in two ways:

(A) considering level A/B versus C; or FV/U1 versus U2 (B) considering level A versus B/C; or FV versus U1/U2.

Given that these assessments must be somewhat subjective in practice, there can be no absolutely correct answer as to whether one of the two changes in status should be considered to be more important. Therefore, both sets of analyses should be conducted.

The analyses could also be conducted for the A, B and C, or FV, U1 and U2 levels, simultaneously, either in an ordinal logistic framework or casting the levels as quantitative points on a scale from one to three. Note that the latter analyses involve an implicit assumption that the change between “average” and “good” and that between “good” and “excellent“, for example, are equivalent.

Table 5-4 and Table 5-5 list precise analytical questions and the variables that could be used as predictors and controls in analyses of species and habitat condition respectively, if the up-to-date data of the form currently available were provided. Models of the binary response variables (A and B) could be fitted using a logistic structure (assuming a binomial error distribution), modelling the probability that the response was at the higher degree of conservation with respect to the fixed and random effect predictors listed for each analytical question. Controls would be required (probably best fitted as random effects), in order to account for the variation between the precise levels of the potential confounding factors considered and also other, unmeasured factors, such as other taxonomic groups or habitat types. Such controls would therefore account for differences between Member States and taxonomic groups in tests of the influences of land-use on condition, for example. Given that the assessments of status or degree of conservation feature only three levels, reflecting unknown real, underlying variation in species densities or habitat quality, the recommended initial approach would be to keep analyses simple and to formulate them on the basis of individual questions relating to the influences of individual factors of interest, as opposed to trying to compare the relative influence of many different factors in single models.

36http://www.eea.europa.eu/data-and-maps/figures/less-favoured-areas

Table 5-4 Model definitions potentially suitable for considering environmental and policy influences on species’ conservation status or degree of conservation in Natura 2000 sites

Question Fixed effect predictor Random effect control(s) a. How do Natura 2000 designation effects Member State identity Taxonomic group differ between Member States? b. How do Natura 2000 designation effects Taxonomic group differ between taxa? c. How have management plans affected Natura Taxonomic group, Member Management plan (yes/no) 2000 designation State identity effects? Relevant regional Pillar II expenditure: (i) M211+212 Taxonomic group (Member d. How has CAP Pillar II payments per unit Less State identity not included investment affected Favoured Area, (ii) M213 because expenditure data were Natura 2000 Natura 2000 payments per unit only available at the national designation effects? area of Natura 2000, (iii) M214 level for many Member States) Agri-Environment payments per unit agricultural area. Area of (i) arable, (ii) fallow/set- aside and (iii) grassland per Natura 2000 site. Inputs of (iv) e. How has agricultural manure, (v) mineral fertilizer land-use affected and (vi) energy inputs per Taxonomic group, Member Natura 2000 Natura 2000 site. Densities of State identity designation effects? (vii) cattle, (viii) sheep, (ix) goat and (x) total livestock (per unit area of pasture) per NUTS3 region. f. How does Natura 2000 site isolation affect Degree of isolation variable: a Taxonomic group, Member species and habitat three-level factor (Table 5-2) State identity condition?

Table 5-5 Model definitions potentially suitable for considering environmental and policy influences on conservation status of habitats or degree of conservation in Natura 2000 sites

Question Fixed effect predictor Random effect control(s) a. How do Natura 2000 designation effects Member State identity None differ between Member States? b. How do Natura 2000 designation effects Habitat code None differ between habitat types? c. How have management plans affected Natura Habitat code, Member State Management plan (yes/no) 2000 designation identity effects? Relevant regional Pillar II expenditure: (i) M211+212 payments per unit Less Favoured Area, (ii) M213 Habitat code (Member State d. How has CAP Pillar II Natura 2000 payments per unit identity not included because investment affected area of Natura 2000, (iii) M214 expenditure data were only Natura 2000 Agri-Environment payments per available at the national level designation effects? unit agricultural area, (iv) the for many, smaller, Member total of (i)-(iii), because States) Member States may use these payment systems as alternative measures. Area of (i) arable, (ii) fallow/set- aside and (iii) grassland per Natura 2000 site. Inputs of (iv) manure, (v) mineral fertilizer e. How has land-use and (vi) energy inputs, per Habitat code, Member State affected Natura 2000 Natura 2000 site. Densities of identity designation effects? (vii) cattle, (viii) sheep, (ix) goat and (x) total livestock (per unit area of pasture) per NUTS3 region.

5.3 Conclusions on the feasibility of the analysis

5.3.1 Existing constraints The purpose of this case study is to consider if it is possible to use existing biodiversity and agriculture data to investigate the impacts of agriculture and conservation management interventions on agricultural habitats and species targeted by the Nature Directives, both in terms of their national / biogeographical conservation status and Natura 2000 site level degree of conservation. In principle, data suitable to conduct some analyses of this type exist and are available, but the scoping work and consultations carried out for this case study identified important problems with the use of these data for this type of analysis. Most obviously, the large scale of the Member State / biogeographical region conservation status assessments results in EU-level datasets with a low number of comparative data points for each habitat and species, which therefore have inadequate statistical power for the type of analysis envisaged here.

In contrast the Natura 2000 site-level assessments of degree of conservation appear to provide a very large dataset with a high potential for identifying statistically significant relationships with agriculture and conservation management measures. However, as described above, much of the available data on Natura 2000 site-level degree of conservation are of unknown age and some may never have been updated since sites were originally designated. Clearly, this makes the data unsuitable for analysis. In addition, no dated historical site-level degree of conservation data are available (such as condition levels on designation to compare to current levels), so impacts on changes in condition cannot be assessed.

Furthermore, as discussed elsewhere in this report, in order to analyse potential influences on site-level, data need to be available at a scale as close to that of the site itself as possible, to avoid the influence of confounding factors and to ensure that the data are as relevant as possible. Data that are only available at NUTS3 or NUTS2 level cannot give rise to results that we can be confident are informative at the site level. For example, at present, all data on management investment (i.e. RDP spending) are readily accessible only at very large spatial scales: mostly at NUTS2 or national level. This level of data cannot be accurate at the site level, unless all sites in a region have equivalent investment. Land-use and inputs data are available at smaller scales, but only following down-scaling from data for larger regions (Britz & Leip 2009, Leip et al. 2008, 2011), so they are potentially not accurately representative of smaller areas within them, such as individual Natura 2000 sites.

The available data on livestock are based on very large areas (NUTS3 regions), with no downscaling at all. Given that Natura 2000 sites are likely to have different characteristics from the average in NUTS 3 regions, and specifically usually less intensive agriculture, these data are likely to show higher grazing densities than would be truly representative. Nevertheless, there is no reason why this should be a source of bias either, because there is no reason why local livestock numbers in conservation areas should vary systematically in a different way to those in the wider region, although such an effect cannot be ruled out. It is more likely that grazing

impacts in Natura 2000 sites vary independently of regional livestock densities, such that the regional statistics are irrelevant and hence poor predictors of site condition.

5.3.2 Recommendations Currently, data are not available to allow the kinds of analyses that are proposed here, either because they are not collected by Member States (e.g. dates of the last, or previous assessment of the degree of conservation of habitats and species), or because they are not collated in a central database (e.g. previous degree of conservation assessments). It is suggested that the first priority should be to collate the monitoring data that are currently available and to record metadata on each dataset. Then, the kinds of analyses that are described above could be conducted to provide initial evidence on influences of various agricultural and conservation management influences upon it.

Subsequently, great improvements in the power of potential analyses and resulting scope and reliability of evidence could be obtained by obtaining time series of the site-level degree of conservation data (e.g. from six-yearly assessments), rather than the single most recent assessments. Patterns may change over time and repeat analyses with future data, using changes in the degree of conservation as the focus of the assessment, are likely to be informative. This would enable the effects of targeting of measures to be separated from the effectiveness of measures, thereby allowing a more sensitive and meaningful assessment of whether changes are associated with factors such as management plans or RDP spending.

The recommendation above for updated degree of condition or conservation status data represents a minimum requirement for future analyses of variation in degree of conservation. However, the principal limitations for such work will remain in data quality, both for the target measures and for the potential impacts upon them. Future work should therefore include enhancing these data. For the target taxa and habitats, structured, randomized sampling to generate quantitative abundance or cover data within Natura 2000 sites and, if possible, also in representative counterfactual areas, would be ideal. If this is not possible, for example due to cost, another possibility may be to utilize existing data further, that is using existing monitoring to conduct tests comparing abundance or trends inside and outside Natura 2000 sites, or measuring changes relative to a baseline in a target site. This has been done for birds in Case Study 2 (Chapter 6) in this study and may be possible more broadly as monitoring schemes for other taxa develop (e.g. for butterflies: Dennis et al. 2016, van Swaay et al. 2008), or making use of existing, unstructured biological records data in novel analytical designs (Isaac et al. 2014). Note that the latter may provide valuable new inference where targeted surveys are not available, but are unlikely to provide results of similar quality because sampling biases are inevitable and impossible to account for with certainty.

If new habitat or biodiversity sampling is conducted, or if standardization is to be applied to the national monitoring activity that is currently ongoing, several factors should be considered. First, the period between repeat survey measurements would most cost-effectively be tailored to the likely speed of the biodiversity changes that

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are of interest. For example, annual data may be too frequent because populations and habitats will take much more than a year to respond to management, and data on many taxa will be subject to stochastic variation due to weather in a given year. Repeat surveys every five or six years, each involving sampling that is sufficiently intensive to avoid major effects of conditions on individual visits (e.g. at least one visit at the right time of year and in good weather conditions would be important to record the abundance of a target butterfly species), might be a sensible compromise. Standardization across Member States of the methods used in these surveys, and consistency in them over time, would be ideal here, but documentation of the methods, so that it is clear how data can be compared is essential for informed analyses, should be an absolute minimum.

Inferring effects of management activities in a habitat area or other location is greatly facilitated by comparisons with counterfactual areas (i.e. similar locations where the management is not undertaken, or where there is no designation). It may not be possible to find such locations for rare habitats or species with very restricted populations, because all possible locations for monitoring are under management or are protected. Wherever possible, however, monitoring of counterfactual locations in parallel to that of Natura 2000 sites, using the same methods, is strongly recommended. Such monitoring provides quasi-experimental data and thus allows changes due to site management to be separated from those that occur due to background conditions. This might mean evidence that increases at the site level are independent of changes due to background environmental conditions, or evidence that management has actually succeeded even though a local population has not increased, because the same species is in decline elsewhere.

Higher quality habitat or species monitoring data are only part of what is required to develop effective assessments of the influences of site management and agricultural land-use. Data on the agricultural land-use side are, of course, also essential. A significant constraint identified in this study has been that many data of potential interest are accessible only at the scale of large regions, generally NUTS3 or NUTS2 level. However, these data are frequently composites of data from smaller-scale sampling conducted within Member States that, if made accessible, could support stronger research.

Given government or Commission interest in identifying the occurrence of, and reasons for, policy successes and failures, the value of collecting and making available high-quality information (for example high-resolution land use and other agricultural data) should be considered and traded off against factors that restrict the availability of more detailed information, notably the cost of data collection and policies to maintain data confidentiality at all spatial scales below NUTS3. As well as fine-scale land-use and management data from monitoring of agriculture by Member States, bespoke measurements at the Natura 2000 site level, or validated predictive models based on sampled data could be used to improve the inference that is possible from the environmental recording that has been conducted. The more detailed such records are, the lower the uncertainty in what the truly important influences on target species and habitats will be.

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6 Case study 2: Effects of Natura 2000 site designation and management plans on farmland bird abundance trends

Authors: Anna Gamero, Jana Skorpilova and Petr Vorisek (Czech Society for Ornithology), Gavin Siriwardena (BTO) and Graham Tucker (IEEP)

6.1 Introduction The Natura 2000 network of protected areas covers about 18% of EU terrestrial surface and consists of more than 27,300 sites, creating the largest network of protected areas in the world (Natura 2000 Barometer 2016; EU Commission 2014). Member States are required to designate Natura 2000 sites on the most suitable areas for habitats and species covered by the Birds and Habitats Directives. Member States are required to ensure that appropriate conservation measures are in place in Natura 2000 sites, and they are encouraged to facilitate this through the preparation of management plans, or similar instruments. However, evidence suggests that many sites lack management plans, and/or conservation measures are yet to be adequately implemented (Milieu, IEEP and ICF, 2016). Nevertheless, greater coverage by the Natura 2000 network has been related to more positive bird populations trends at national level, particularly for Annex I species (Donald et al. 2007; Gamero et al. 2016). Additionally, comparisons between sites within the Natura 2000 network and outside have shown higher abundances (Pellissier et al. 2013) and more positive trends for some bird species (Pellissier et al. 2014) within the protected areas. However, it is still unclear whether these positive associations with the Natura 2000 network for European birds are a consequence of the designation of sites in high biodiversity areas, or of the conservation and management actions conducted in some of those sites.

Of particular concern in the EU are ongoing declines in farmland biodiversity (Firbank et al. 2008), as for example demonstrated in declines in the European common farmland bird indicator37, which has decreased by more than 50% over the last 30 years (Gregory et al. 2005).

This case study aims to disentangle the effects of designation of farmland Natura 2000 areas and the application of management plans for them on farmland bird populations. We compared abundance trends of farmland bird species within and outside agricultural Natura 2000 areas. Additionally, we investigated whether farmland bird abundance trends in bird count sites that overlap with the Natura 2000 network were affected by the existence of management plans and the extent of the overlap with Natura 2000 areas.

37 www.ebcc.info/indicators2015.html

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6.2 Methods and data sources

6.2.1 Bird monitoring data Data on common breeding bird populations are collected annually by volunteer ornithologists throughout Europe as part of country-based breeding bird monitoring schemes. These use a variety of standardized data collection methods (i.e. point counts, territory mapping and line transects) that enable the results to be combined, as coordinated at the European level by the Pan-European Common Bird Monitoring Scheme (PECBMS)38. For this study, we collated estimated bird counts from 31,319 geo-referenced bird monitoring sites from 23 EU countries, from 1980 to 2013 (Annex 4.1; Figure 6-1). We used the centroid of transects, UTM squares or of the group of count points from a monitoring site as the geographical location of each of the bird count sites. We used data from farmland specialist species only (39 species, Annex 4.2), based on the European Bird Census Council’s (EBCC) species habitat classification for the EU39, from the years when the countries were EU members, and hence when the Natura 2000 network was in place.

Figure 6-1 Bird monitoring sites in farmland habitat used in the study

38 http://www.ebcc.info/pecbm.html 39 http://www.ebcc.info/pecbm.html

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6.2.2 Selection of Natura 2000 sites and farmland sites We categorized sites as farmland sites when more than 50% of the area delimited by a 1,000 metre radius around the count site centroid overlapped with farmland habitats (arable land, permanent crops, pastures, heterogeneous agricultural areas and natural grasslands), based on data from the Corine Land Cover from 2000 (European Environment Agency). We used this version of the Corine Land Cover because it was around the average year of the bird monitoring years with counts. Additionally, we calculated the percentage of overlap with Natura 2000 sites of each of these farmland sites (European Environment Agency, Figure 6-2). We discarded sites that had been counted for less than 7 years, and we removed all the observations of species infrequently recorded in a site (not recorded in more than 60% of the years when surveys were conducted), as these observations may lead to unreliable species abundance trends for a site. For the analyses, we used 10,578 farmland sites (Figure 6-1), from which 1,948 (18% of farmland sites, Annex 4.3) had an overlap with Natura 2000 areas (885 Natura 2000 areas covering a total of 127,432 km2). Spatial analyses were conducted with ArcGIS 10.4 (ESRI 2016).

Figure 6-2 Areas covered by Natura 2000 sites in 2015

6.2.3 Abundance trends of farmland birds in Natura 2000 and outside We compared species abundances over time between farmland monitoring sites that overlap significantly (>50%) with Natura 2000 network areas and sites that did not overlap at all with these protected areas, using a linear mixed model (LMM)(see

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section 2.5.3). We used the log-transformed estimated abundance for each species in a monitoring site and year as the response variable, and the interaction between monitoring year and being within the Natura 2000 network or not, as fixed effects. We controlled the model by species identity and monitoring site nested within monitoring scheme to control for repeated measures of all of these factors.

6.2.4 Effects of site designation, percentage of overlap with Natura 2000 network and management plans on farmland bird abundance trends. Information on the year of Natura 2000 site designation and existence of a management plan (yes/no) was available from the European Environment Agency for 1,108 farmland bird count sites, from which 418 had management plans (Annex 4.3). However, there was no available information on the starting year of management plans and, therefore, we could not include this data in the models. When count sites overlapped with more than one Natura 2000 site, we considered them to have a management plan if at least one of the Natura 2000 sites involved had a management plan. In such cases, we considered as the designation year the earliest first year from all of the overlapping Natura 2000 sites. Based on the designation year of the Natura 2000 sites, we calculated for each bird monitoring site, the number of years for which they had overlap with a designated Natura 2000 site (termed “Natura 2000 year”). We discarded all count sites that overlapped with Natura 2000 sites without information on these two variables (32% of the sites, Annex 4.3), and all observations from a site corresponding to years previous to the Natura 2000 site designation year.

To analyse the effects of the degree of overlap with Natura 2000 sites, and the existence of management plans on farmland bird abundance trends, we used a linear mixed model (LMM). We used the log-transformed estimated counts of farmland birds recorded at each site and year as the response variable with, Natura 2000 year, existence of management plans, percentage of Natura 2000 overlap, and the interactions of Natura 2000 year with the percentage of overlap with Natura 2000 and the existence of management plans as fixed effect predictors. Species and site nested within scheme were included as random intercepts. We included scheme to control for methodological differences in bird counts and species and site to control for repeated measures from the same site and species over time. We ran the model considering all farmland sites with some overlap with Natura 2000 network (1-100% overlap) and a subset of sites with large Natura 2000 overlap (>50% overlap).

All statistical analyses were conducted with R 3.1.0 (R Development Core Team 2014) and packages “lme4” (Bates et al. 2014), “lmerTest” (Kuznetsova et al. 2014) and “ggplot2” (Wickham 2009).

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6.3 Results We found that farmland bird abundance declined over time, and that these declines did not differ between sites with large overlap with Natura 2000 (>50%) and sites that did not overlap at all with Natura 2000 (Table 6-1, Figure 6-3). However, farmland bird abundance was significantly higher in the sites with large Natura 2000 overlap.

Table 6-1 Estimates, standard errors and P values of the fixed effect of the linear mixed models comparing abundance trends of 39 farmland bird species within Natura 2000 sites (>50% overlap) and outside (0% overlap)

Reference level for “within Natura 2000” is “no”. Estimate SE F P Within Natura 2000 0.310 0.029 110.59 <0.001 Year -0.103 0.001 395.74 <0.001 Year*Within Natura 2000 0.000 0.001 0.01 0.910 N=510912; sites=10578; monitoring schemes=24

Figure 6-3 Abundance trends for 39 farmland bird species in sites with large Natura 2000 overlap (>50%) in dark grey, and without Natura 2000 overlap in light grey

Lines represent predictions and bands represent 95% confidence intervals based on the predictions of the model in Table 6-1.

When considering only sites with some overlap with designated Natura 2000 sites and information on management plans and designation date, we found that farmland bird abundance declined over time, as shown by the negative correlation with Natura 2000 year (Table 6-2a). However, these declines were less steep, the

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larger the overlap with Natura 2000 was, as revealed by the significant interaction between Natura 2000 year and percentage of overlap with Natura 2000 (Table 2a).

Bird count sites that have some overlap with Natura 2000 sites and that have management plans represented 38 % of the analysed sites (Annex 4.3), and they did not have more positive abundance trends than sites without management plans (Table 6-2a). However, when including only sites with large Natura 2000 overlap (>50%), we found less negative abundance trends for farmland birds in sites with existing management plans (Table 6-2b; Figure 6-4).

Table 6-2 Results of the linear mixed models on the effects of management plans and degree of overlap with Natura 2000 sites on 39 farmland bird species abundance trends

Reference level for “Management plan” is “no”. Estimate SE F P (a) Natura 2000 overlap

Management plan 0.033 0.027 1.515 0.219 % of Natura 2000 overlap -0.058 0.036 2.651 0.104 Natura 2000 year -0.015 0.001 176.249 <0.001 Natura 2000 year * Management plan -0.001 0.002 0.349 0.555 Natura 2000 year * % of Natura 2000 overlap 0.010 0.002 17.152 <0.001 N=49,059; sites=1,108; monitoring schemes=21

(b) > 50% Natura 2000 overlap

Management plan 0.020 0.053 0.001 0.970 % of Natura 2000 overlap 0.148 0.129 0.797 0.372 Natura 2000 year -0.013 0.008 1.656 0.198 Natura 2000 year * Management plan 0.007 0.003 4.866 0.027 Natura 2000 year * % of Natura 2000 overlap 0.003 0.009 0.082 0.774 N=14,240; sites=308; monitoring schemes=16

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Figure 6-4 Farmland bird abundance in sites with some overlap with Natura 2000 network, with respect to the years and percentage of overlap with Natura 2000

The surface plot represents the interaction of the years as Natura 2000 sites and the percentage of spatial overlap with the Natura 2000 network on farmland bird abundance, based on the predictions of model (a) of Table 6-2.

Figure 6-5 Abundance trends for 39 farmland bird species in sites with 50% or more overlap with Natura 2000 sites

Lines represent predictions and bands represent 95% confidence intervals based on the model (b) from Table 6-2 for sites with management plans (dark grey) and without management plans (light grey).

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6.4 Discussion This study found that local abundance of common farmland birds declined over time in the 23 studied EU countries, which is in accordance with the general negative population trend of common farmland birds in Europe that has been reported elsewhere (Gregory et al. 200540). Designation of Natura 2000 sites seems to have captured areas with good farmland bird populations because their local abundance inside the network was higher than outside. Higher bird abundances in Natura 2000 sites as compared to outside have been found previously by other studies that included Natura 2000 sites in all habitats (Pellissier et al. 2013; Pellissier et al. 2014). However, farmland bird abundance declined inside and outside the Natura 2000 network, and these trends were similar, indicating that the designation of the network has not in general reduced the causes of these declines.

Information on the existence of management plans was missing from many sites, which constrained the second level of analysis (as it allowed the inclusion of only 68% of the bird monitoring sites). Nevertheless, we found that the negative abundance trends of farmland birds improved with increasing overlap of Natura 2000 network, whether or not management plans existed for the sites. This may be associated with lower disturbance and more stable ecological conditions in farmland sites with large overlap with the Natura 2000 network. We also found that management plans improved farmland abundance trends when sites had large overlap with the Natura 2000 network. However, farmland bird abundance trends were still not positive, suggesting that the conservation measures taken so far have not been sufficient to reverse the negative trends of farmland birds, even in Natura 2000 sites with management plans. This relates to the importance not only of designating protected areas, but also of applying proper conservation measures, and suggests that the management plans currently in place, or their implementation, have not been sufficient to achieve conservation goals at least with respect to farmland birds. This suggests that to reverse the trends, improved plans or additional management outside the Natura 2000 network would still be required. It should be borne in mind that some Natura 2000 sites in this study have been primarily designated for non-farmed habitats and species, and therefore their management plans would not be expected to address priorities for farmland birds. In addition, it is important to note that some of the target farmland species are migratory, so it is possible that their abundance is driven by conditions in their wintering areas, such that management in Natura 2000 sites used for breeding can have little effect.

Where significant farmland exists in a Natura 2000 site, management is recommended to include low-intensity farming and support for traditional agricultural practices that benefit biodiversity (Ostermann 1998; Muller 2002; Olmeda et al. 2014), but more than half of the sites analysed in this study had still no management plans. Additionally, the sites with management plans and large coverage of farmland Natura 2000 represented only 10% of all sites with some Natura 2000 overlap and available information, which probably partly explains the

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absence of a measurable beneficial impact from the designation of the network as a whole on overall farmland bird population trends.

This study used the currently most complete biodiversity monitoring dataset in the EU, and the available information on all Natura 2000 sites. Still, the statistical analyses were limited by data availability. Although the proportion of farmland bird monitoring sites within Natura 2000 sites (15%) was similar to the Natura 2000 network coverage across the terrestrial EU (18%), for a large number of sites there were no management plans in place (43%) or no information about them (32%). This reduced the number of available sites with management plans that could be used for comparisons. Additionally, information on the conservation focus of each of the Natura 2000 areas (i.e. particular species or habitats) was not included in the analysis. However, information on the actual conservation measures that have been triggered on the ground through Natura 2000 designation and through the adoption of management plans was not available. Further analysis could be improved by obtaining the missing information on the Natura 2000 areas from member states, and by incorporating additional variables that may also affect how bird species react to conservation actions, such as fine-scale land-use changes or Member State investment in the Natura 2000 network.

6.5 Conclusions  Natura 2000 sites in farmland habitats are located in areas with higher farmland bird abundance, but the designation of Natura 2000 sites per se does not slow declines in local abundance of farmland birds.  Farmland bird abundance trends are less negative for Natura 2000 sites that have management plans when Natura 2000 coverage of monitoring areas is large, indicating the importance not only of protecting sites, but also of applying conservation measures.  Although Natura 2000 protection with management plans seemed successful in reducing rates of decline, improved plans, actual implementation of conservation measures on the ground and additional management outside the Natura 2000 network are required to reverse the trends.  Information on whether management plans exist was missing for a large proportion of Natura 2000 sites used in the study (32%), which reduced the power of the analysis. Information is also missing on the actual implementation of conservation measures.  Similar trends of farmland birds inside and outside Natura 2000 sites may be a consequence of the large proportion of Natura 2000 sites that have no management plan (43%).

6.6 Acknowledgements We thank the volunteers that conducted the bird surveys in the field and the country coordinators that provided data for the analysis: Ainars Aunins, Tomasz Chodkiewicz, Przemek Chylarecki, Olivia Crowe, David Noble, Virginia Escandell, Lorenzo Fornasari, Sara Harris, Martin Hellicar, Sergi Herrando, Iordan Hristov, Frederic Jiguet, Primož Kmecl, Domingos Leitao, Ake Lindstrom, Juan Carlos del Moral, Renno Nellis, Jean-

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Yves Paquet, Danae Portolou, Päivi Sirkiä, Tibor Szep, Norbert Teufelbauer, Sven Trautmann, Chris van Turnhout, Zdeněk Vermouzek, Thomas Vikstrom.

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7 Case study 3: large-scale bird distributions in Britain and Ireland in relation to lowland agricultural land-use

Authors: Gavin Siriwardena and Henrietta Pringle (British Trust for Ornithology) and Renate Koeble and Maria-Luisa Paracchini (Joint Research Centre, European Commission)

7.1 Introduction Farmland forms the dominant general land-use in lowland Europe, so the majority of biodiversity is found in this landscape, especially in more widely developed regions. Cropping is the single most labile aspect of large-scale land-use in farmland, potentially changing annually in response to market pressures and potentially due to policy drivers, as well as in the course of regular rotations. Livestock numbers can also change in similar ways. Such variation in agricultural land-use has been shown to have important effects on wildlife populations (e.g. Siriwardena et al. 2000, 2012), so it is important that the key relationships are understood, both to facilitate prediction of possible conservation problems arising from land-use change and to identify where changes might have positive effects. Biodiversity impacts of cropping patterns and changes in them are, therefore, critical in any comprehensive assessment of the implications of policy and market change in this area. For example, the crop diversification rule implemented within Pillar One of the revised Common Agricultural Policy in 2015 aims specifically to promote and to maintain patterns of cropping that are expected to have positive effects, so evidence for relevant relationships with cropping should be of value in policy decision-making at both Member State and European Union level.

Bird Atlases represent the most comprehensive, standardized biodiversity datasets available for application to the problem of assessing land-use impacts on biodiversity at large spatial scales, providing information on distributions and sometimes abundances across entire Member States. The 2007-11 Bird Atlas of Britain and Ireland (Balmer et al. 2013) presents an opportunity to assess the associations between patterns of land-use and bird distributions in two Member States. By assuming space-for time substitution, these associations can inform about the likely effects of changes in land-use patterns across time. Additional atlas projects that are currently in progress in other member states will also allow analogous studies to this one to be conducted in the near future, for which the approach applied here can form a template. This will facilitate the assembly of integrated, continent-wide results in due course, although such analyses are not currently possible because the projects concerned are not yet complete.

Here, specifically, standardized presence-absence data for birds in Britain and Ireland at the scale of the 2×2km “tetrad” are related to the highest-resolution data available on agricultural land-use in the two Member States. The aim of the study is to identify the relative importance of different agricultural land-use drivers for determining the distribution of birds in farmland, Bird data were available for at least

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eight of the 25 tetrads in every 10×10km grid square in Britain and in more than half of the 10×10km grid squares in Ireland, providing very high spatial coverage. A multi- model inference procedure (Burnham & Anderson 2002) is used, in which all possible combinations of the candidate predictor variables are considered in the analyses, in order to produce an unbiased assessment of the relative importance of each agricultural variable considered.

7.2 Methodology and data sources

7.2.1 Atlas data Data for both the UK and Ireland came from the timed tetrad visit dataset collected for Bird Atlas 2007-11 (Balmer et al. 2013). Tetrads were visited at least twice per breeding season and, for each species, the number of individuals and evidence of breeding were recorded during one- or two-hour timed visits, within which observers were encouraged to visit all identifiable, significant habitat types, with the aim of approaching a complete species list for the tetrad with standardized survey effort. A minimum of eight tetrads were surveyed in every 10×10km square in Britain and in at least 50% of the 10×10km squares in Ireland (selected in a chequerboard pattern in areas of Ireland with a lower density of observers).

Agricultural data (see below) were available at the of European NUTS3 regions, so the Atlas tetrad data were spatially matched with these regions in ESRI ArcMap 10.3.141, based on 2010 classifications42. While agriculture influences very many habitat types in Europe, this study was concerned with the effects of the active management of productive land, whose primary purpose is the production of crops or livestock, rather than semi-natural habitats that have some agricultural input. This is important because the gross features of semi-natural or low-intensity systems, such as tree density or natural vegetation communities, will dominate relationships with wildlife communities (e.g. Siriwardena et al. 2012), whereas production areas are more labile and offer greater potential for adapting practices to benefit conservation priorities (e.g. Fahrig et al. 2011). Moreover, changes in land-use are more likely and more likely to be rapid in production landscapes than in those dominated by semi-natural habitats, because the economics of farming can lead to large-scale changes in cropping, for example, between one year and the next. Atlas data were, therefore, extracted for tetrads with 50% or more cover of lowland farmland, identified as the total of arable land and improved grassland, as identified by the Centre for Ecology and Hydrology Land Cover Map 2000 (Morton et al. 2011) which identifies land cover to a spatial resolution of 25m2, for Britain, and CORINE land cover43, which have a spatial resolution of 25ha, for Ireland. This will ensure that the analyses consider associations with agricultural land-use, rather than with gross variation in habitat, such as between upland and lowland, or woodland and farmland.

41 http://www.esri.com/ 42 http://ec.europa.eu/eurostat/web/nuts/history 43 http://www.eea.europa.eu/publications/COR0-landcover

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Only tetrads visited at least twice over the atlas period were selected, to avoid biases in regional survey effort. Data for all species that regularly use farmland were selected; these are listed in Table 7-1. Birds were considered to be present in the tetrad if observed during the first hour of any breeding season visit, excluding those seen flying overhead. Data from the second hour of timed visits were omitted because the spatial distribution of two-hour records was variable.

Table 7-1 Species included in analysis

No. of No. of No. of No. of Speci occupied occupied occupied occupied English name es Latin name UK Ireland UK Ireland code NUTS3 NUTS3 tetrads tetrads regions regions Buzzard BZ Buteo buteo 102 11660 8 248 Kestrel K. Falco tinnunculus 103 5714 8 466 Grey Partridge P.* Perdix perdix 93 1255 - - Lapwing L. Vanellus vanellus 103 4343 8 83 Curlew CU Numenius arquata 103 2568 8 150 Stock Dove SD Columba oenas 100 7170 8 250 Turtle Dove TD* Streptopelia turtur 62 544 - - Skylark S. Alauda arvensis 103 11802 8 604 Meadow Pipit MP Anthus pratensis 103 3753 8 1277 Yellow Wagtail YW* Motacilla flava 90 1205 - - Pied/White 103 10052 8 1865 PW Motacilla alba Wagtail Troglodytes 103 17654 8 2424 Wren WR troglodytes Dunnock D. Prunella modularis 102 15817 8 2194 Robin R. Erithacus rubecula 103 17670 8 2415 Blackbird B. Turdus merula 103 18584 8 2433 Song Thrush ST Turdus philomelos 103 14702 8 2182 Mistle Thrush M. Turdus viscivorus 102 6982 8 1274 Whitethroat WH Sylvia communis 102 10930 8 747 Long-tailed Tit LT Aegithalos caudatus 101 7569 8 539 Coal Tit CT Periparus ater 102 6973 8 1794 Blue Tit BT Cyanistes caeruleus 102 17626 8 2301 Great Tit GT Parus major 102 16928 8 2179 Rook RO Corvus frugilegus 103 12160 8 2288 Starling SG Sturnus vulgaris 103 11750 8 2149 House Sparrow HS Passer domesticus 103 14411 8 1971 Tree Sparrow TS Passer montanus 99 2667 8 138 Chaffinch CH Fringilla coelebs 103 18460 8 2387 Greenfinch GR Chloris chloris 103 14005 8 1660 Goldfinch GO Carduelis carduelis 102 15295 8 1910 Linnet LI Carduelis cannabina 103 9044 8 1365 Bullfinch BF Pyrrhula pyrrhula 101 5643 8 1488

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No. of No. of No. of No. of Speci occupied occupied occupied occupied English name es Latin name UK Ireland UK Ireland code NUTS3 NUTS3 tetrads tetrads regions regions Yellowhammer Y. Emberiza citrinella 101 10572 8 595 Reed Bunting RB Emberiza schoeniclus 103 4370 8 848 Corn Bunting CB* Emberiza calandra 86 896 - - *These species could not be analysed for Ireland owing to data sparsity

7.2.2 Background habitat data Variation in cropping and other agricultural land-use is likely to be a strong influence on the distribution of many bird species, but many other habitat features are also likely to be critical, even for species that prefer farmland, within farmland- dominated tetrads and if only as negative influences upon them. These land-uses are largely fixed in the landscape, i.e. vary on much slower timescales than cropping and livestock variables, so are best considered as static controls, independently of which land-use details vary. Hence, the proportion of land classified as arable, pastoral, farmland, woodland and urban was calculated per tetrad from the best available remote-sensed data available for each Member State. Including these measures in the analyses meant that the results refer to variation in land-use within agricultural land, and not to variation in the distribution of these areas. For the UK and Ireland, data were again taken from Land Cover Map 2000 (Morton et al. 2011) and CORINE44, respectively.

7.2.3 Agricultural Land-Use Data Information regarding agricultural land-use was obtained from the 2010 Farm Structure Survey data provided by Eurostat at the scale of NUTS3 administrative regions45. For each NUTS3 region, the area of land under different agricultural usage (e.g. crop type, fallow, grassland) and the number of livestock was summarised. The full set of variables considered is listed in Table 7-2. Given that NUTS3 regions are highly variable in size, areas of each arable crop type and type of grassland were standardised by dividing by the area of the NUTS3 region. The number of livestock was calculated per area of grassland (temporary grass, pasture, and rough grazing), giving an estimate of density, which is likely to represent the way in which variation in livestock numbers has a mechanistic effect on habitat quality for birds. To avoid problems of multicollinearity during the analyses, the correlation matrix of all the agricultural variables for each Member State was inspected and agricultural variables that were highly correlated (R≥0.8) were not included in models together. Correlated variables are listed in Table 7-2; the effects of these would not be separable in fitted models, so only the dominant one in terms of area was included in the analyses (see below). This means that effects of the omitted variables potentially drive any effects of the correlated variable that is tested. In other words, an effect of the latter should

44 http://www.eea.europa.eu/publications/COR0-landcover 45 http://ec.europa.eu/eurostat/web/agriculture/farm-structure

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be interpreted as being one of the farming system in which that land-use is found, together with the other variables involved.

Table 7-2 Agricultural variables used in the analysis, and their inter-correlations (R≥0.8) that might compromise model fitting for the British and Irish datasets

All correlations were positive, except where marked with (-). Complete list of Correlated Correlated variables for cropping variables Definition (per NUTS3 region) variables for Ireland considered the UK Pulses, Root crops, Rape, Pulses, potatoes, rape, Open Wheat Area of wheat Triticum spp. Fallow vegetables, fallow, Permanent crops Area of barley Hordeum spp. Spring barley Oats Sown in spring Area of barley Hordeum spp. Winter barley* Sown in winter Oats Area of oats Avena spp. Spring barley Area of carrots Daucus carota, turnips Brassica rapa, onions Rape, pulses, fallow, Root crops Allium cepa, parsnips Pastinaca Potatoes, Beet wheat sativa and other crops harvested for roots Wheat, pulses, Root Area of potatoes Solanum Beet, root crops, Open vegetables, Potatoes tuberosum crops fallow, Permanent crops Potatoes, root Beet Area of beet Beta vulgaris crops Potatoes, wheat, Area of horticultural vegetable Open vegetables pulses, Root crops, crops and flowers not grown and flowers fallow, Permanent under glass crops Pulses, Root crops, Area of (oilseed) rape Brassica Wheat, Pulses, potatoes, wheat, Open Rape napus oleifera Fallow vegetables, fallow, Permanent crops Rape, pulses, Root crops, potatoes, wheat, Pulses Area of legume crops (Fabaceae) Rape, Wheat Open vegetables, fallow, Permanent crops Area of crops grown to feed to Fodder crops Pasture livestock Wheat, pulses, Root Area of land left uncropped crops, potatoes, Open Fallow Rape, Wheat during the growing season vegetables, Permanent crops Area of permanent (long-term) Pasture Fodder crops pasture Area of rotational (ley) and short- Temporary grass Cattle, Sheep and goats term (<5 year) pasture Rough grazing Area of rough grassland Cattle Number of cattle per unit area of Temporary grass,

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Complete list of Correlated Correlated variables for cropping variables Definition (per NUTS3 region) variables for Ireland considered the UK grassland (pasture, temporary Sheep and goats grass and rough grazing) Number of sheep and goats per unit area of grassland (pasture, Sheep and goats Temporary grass, cattle temporary grass and rough grazing) * Barley areas for Ireland were not differentiated into winter and spring, so all were assumed to be spring crops, based on prior knowledge of the predominant farming system.

In addition to the crop, grass and livestock variables available in the FSS data (Table 7-1), a further important factor for birds in variation in cropping is the timing of sowing of cereals, because spring-sown cereals (almost all barley in the UK) are preceded by an over-wintered stubble or bare fallow and provide less dense crop vegetation during the bird breeding season than winter crops, hence providing better access to bare ground within the crop for foraging and nesting (e.g. Wilson et al. 1997), as well as greater winter resource availability for locally resident birds (e.g. Gillings et al. 2005, Butler et al. 2005). For England and Wales, data were available for areas of spring and winter barley, separately, from the UK government46, which were used in place of the FSS figures for barley. All barley in Scotland was assumed to be spring-sown.

7.2.4 Analysis The presence of each species per tetrad was analysed using logistic generalized linear models in the GENMOD procedure in SAS 9.247, assuming a binomial error distribution. Presence was modelled as binary response (1/0) per tetrad, weighted by the area of farmland per tetrad, in order that tetrads with greater agricultural land cover contributed more to the analyses (bird records could not be separated by habitat). The agricultural variables were included as predictor variables and their relative influences on bird presence/absence were compared (see below). Controls for the proportion of arable, pastoral urban and woodland cover were included in each model to account for these gross landscape influences, which might otherwise cause spurious associations with crop or livestock variables that vary regionally. To account for the possible non-independence (spatial autocorrelation) caused by samples of multiple tetrads within NUTS3 regions, a repeated measures model structure and generalized estimating equations were used in the modelling procedure.

As a first stage in the model selection procedure, the influence of each variable on each species was tested using univariate F-tests (controlling for the landscape variables described above) to identify the more important variables for consideration in multivariate models and hence to make the multi-model inference procedure with

46 https://www.gov.uk/government/statistical-data-sets/structure-of-the-agricultural-industry-in- england-and-the-uk-at-june

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models containing every possible combination of variables more tractable. While the effects of individual variables may be informative, inter-correlation between them, together with the reality that the influences of all environmental variation on biodiversity act together, mean that multivariate models are really needed to compare the relative importance of different potential predictor variables. Traditionally, model selection analyses of this kind were conducted using step-wise procedures, in which variables are added and/or deleted from models on the basis of their significance, as measured using standard statistical tests. However, this type of approach has been shown to be unreliable and prone to identifying misleading sets of variables, so a technique known as multi-model inference or model-averaging, which considers all possible models given a defined set of candidate predictors and generates average parameter estimates and measures of the relative importance for the predictors, is now recommended (Burnham & Anderson 2002). This approach was, therefore, applied here.

For each species, models were then run for every combination of the variables selected by the single variable tests (considering an inclusive significance threshold of p<0.1 so that variables that might be identified as important after controlling for another are less likely to be excluded a priori). Only uncorrelated variables were chosen for multivariate models, so if several variables were candidates based on their significance, the ‘dominant’ variable was chosen, as described above. Model fits were assessed using the quasi-information criterion (QIC) for models fitted with generalized estimating equations (Pan 2001). Models were assigned weights based on the difference between the QIC value of the model in question and the lowest QIC value of any model (Burnham and Anderson 2002). For each variable, weights of every model containing that variable were summed to obtain the variable-specific summed model weight. These “importance values” can then be used to rank the relative importance of that variable for explaining the variation in the response, with the parameter estimate and its standard error then describing the size of the effect and the precision with which it is known. We used 0.5 as the threshold value for an “important” variable. Model-averaged parameter estimates were calculated using the importance value for the variable in each model.

7.3 Results Correlations between the candidate predictor variables are shown in Annexes 5.1 and 5.2, for Britain and Ireland, respectively. For Britain, three other arable- associated land covers (areas of rape, pulses and fallow) were strongly correlated with wheat, so they were dropped; similarly, potatoes and beet were highly correlated with root crops, so only the latter was retained (Table 7-2). There was much more inter-correlation among the variables for Ireland (Annex 5.3) and this resulted in a variable set reduced to the areas of wheat, spring barley, rough grazing, pasture and beet, together with cattle density for further consideration.

The results of the tests of the influence of single variables are shown in Annexes 5.3 and 5.4, and are summarized below in Table 7-3 and Table 7-4. There was more evidence for associations, positive and negative, among the agricultural variables for the UK than for Ireland, which probably reflects the larger sample sizes and, hence,

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statistical power that were available for the UK. In the UK, notably commonly negative associations were found with cattle density and the areas of oats and wheat, whereas rough grazing was commonly positive and there were mixed associations, but with (near-) significant relationships for half or more of the species considered (Table 7-3). In Ireland, among the smaller number of variables considered, a smaller proportion gave rise to significant results and there was a rather even split between positive and negative patterns, with only rough grazing showing a clear tendency towards positive effects and no variables having predominantly negative associations (Table 7-4).

Table 7-3 Summary of (near-)significant effects of agricultural predictors from single-variable tests for birds in Britain

Variables significant at P=0.1 were taken forward into multivariate analyses, so this higher-than- standard threshold P-values was used Predictor Significant Negative Significant Positive Non- variable (P<0.1) (P<0.1) significant cattle 15 5 14 fodder crops 6 2 26 oats 15 4 15 open 5 2 27 vegetables pasture 12 9 13 permanent 10 7 17 crops root crops 5 1 28 rough grazing 7 19 8 sheep & goats 8 4 22 spring barley 8 12 14 temporary 4 9 21 grass wheat 20 6 8 winter barley 12 2 20 Grand Total 127 82 233

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Table 7-4 Summary of (near-)significant effects of agricultural predictors from single-variable tests for birds in Ireland

Variables significant at P=0.1 were taken forward into multivariate analyses, so this higher-than- standard threshold P-values was used Predictor Significant Negative Significant Positive Non- variable (P<0.1) (P<0.1) significant barley 4 7 17 beet 5 3 6 cattle 6 5 18 pasture 5 5 19 rough grazing 2 6 21 wheat 6 4 19 Grand Total 48 41 127

The multivariate models reveal the relative importance of the variables that were identified as significant individually and identify the parameterization of a “best model” for each species’ presence/absence in each of Britain and Ireland. One species for Britain (Corn Bunting Emberiza calandra) was identified and six for Ireland (Stock Dove Columba oenas, Skylark Alauda arvensis, Long-tailed Tit Aegithalos caudatus, Blue Tit Cyanistes caeruleus, Chaffinch Fringilla coelebs and Goldfinch Carduelis carduelis) were identified as having only a single significant individual predictor variable; one for Britain (Linnet Acanthis cannabina) and five for Ireland had no significant effects at all (Robin Erithacus rubecula Yellowhammer Emberiza citrinella Coal Tit Parus ater Great Tit Parus major, Linnet Acanthis cannabina) (Annexes 5.3 and 5.4), so not multivariate models were fitted for those species. Then, data sparsity or a lack of variation in presence among the tetrads considered meant that multivariate models could not be fitted successfully for Yellowhammer Emberiza citrinella in Britain and for Whitethroat Sylvia communis in Ireland. Details of the results of the model-averaging process are presented in Appendices 5 and 6: the parameter estimates shown in bold make up the most important set of predictor variables for each species.

The patterns of importance and the signs of the relationships across the species in Appendices 5 and 6 reveal which of the variables considered are critical across the farmland bird community, accounting for the inter-correlation and potentially competing effects of the different variables. As summarised in (Table 7-5), for Britain, notably positive agricultural variables were areas of pasture and wheat, while areas of rough grazing and, to a lesser extent, temporary grass, tended to be negatively associated with species’ presences. The area of oats also tended to have positive effects, but was not often identified as important. All of the other variables, except for vegetables grown in the open, were important for at least small numbers of species, but with relatively balanced numbers of species between positive and negative relationships. Spring barley, however, was noteworthy for being important for having been identified as significant in single variable tests for a large number of

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species and then also being revealed as important in the model-averaging process, but with an equal split between positive and negative associations.

Table 7-5 Summary table of important variables across species, as identified by model-averaging of multivariate models: species in Britain

Corn Bunting Emberiza calandra had only one significant predictor in the single-variable tests (Annex 5.3) and the effect of adding this to the general pattern is shown by the figures in parentheses. Total Total No. No. No. No. models models Predictor positive negative importan importan includin where variable estimate estimate t and t and g variable s s positive negative variable important Cattle 20 8 12 6 3 3 Fodder crops 7 4 3 4 3 1 Oats 18 14 4 6 4 2 Open 7 6 1 0 0 0 vegetables Pasture 20 12 8 16 11 5 Permanent 17 9 8 9 4 5 crops Rough 25 (26) 7 18 (19) 19 (20) 6 13 (14) grazing Root crops 6 6 0 2 2 0 Sheep & 12 8 4 2 1 1 goats Spring barley 19 9 10 15 7 8 Temporary 13 4 9 4 0 4 grass Winter 13 6 7 6 4 2 barley Wheat 25 16 9 9 8 1

For Ireland, the variables with relatively positive associations across species were the areas of rough grazing and wheat (Table 7-6). No variables had predominantly negative influences across species; the other variables were commonly identified as influences, but with a balance of positive and negative associations. As with spring barley in Britain, areas of beet were very commonly identified as important, but with an even spread of positive and negative associations.

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Table 7-6 Summary table of important variables across species, as identified by model-averaging of multivariate models: species in Ireland

Stock Dove Columba oenas, Skylark Alauda arvensis, Long-tailed Tit Aegithalos caudatus, Blue Tit Cyanistes caeruleus, Chaffinch Fringilla coelebs and Goldfinch Carduelis carduelis each had only one significant predictor in the single-variable tests (Annex 5.4) and the effect of adding these to the general pattern is shown by the figures in parentheses. Total Total No. No. No. No. models Predictor models positive negative importan importan where variable including estimate estimate t and t and variable variable s s positive negative important Barley 10 (12) 8 (10) 2 5 (7) 1 (3) 4 (spring) Beet 11 6 5 9 4 5 Cattle 10 (11) 5 5 (6) 4 3 1 (2) Rough 6 (7) 2 (3) 4 5 (6) 4 (5) 1 grazing Pasture 8 5 4 5 3 2 Wheat 8 (9) 4 (5) 5 6 (7) 4 (5) 2

7.4 Discussion

7.4.1 Influences of agriculture on bird distributions The relationships identified for Britain and Ireland were rather different, which is not surprising even though the majority of species and land-uses were common to the two islands. This is because the landscapes differ and the ranges of values of the different variables will have been quite different. For example, arable farming in general is much less common in Ireland and also restricted to limited areas, which explains the high levels of inter-correlation between agricultural predictor variables in Ireland (Annex 5.2). This difference, in itself, reflects an important general conclusion, which is that the effects of individual variables depend on the landscape context and are not necessarily transferable, for example between regions or countries. For example, it has been demonstrated that the proportions of each of arable and pastoral land-uses in the farmed landscape have different effects on bird abundance in areas that are dominated by each landscape type (Robinson et al. 2001, Siriwardena & Robinson 2002).

There was a wide range of patterns of association with agricultural variables across bird species that are found in farmland. This is because all the species differ in their ecologies, so they would not be expected to respond in exactly the same way to variations in environmental conditions, such as in agricultural land-use. Nevertheless, some variables had predominantly positive or negative association with the species considered in Britain or in Ireland. Associations with pasture and wheat (Table 7-5) commonly involved different species or differed in sign between the two land-uses, but the predominance of positive associations probably reflects the focus of the study on species associated with farmland, in which these two

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variables reflect the dominant land-uses in livestock- and arable-dominated systems, respectively. Species that responded positively to both variables tended to be generalists, such as Chaffinch Fringilla coelebs, Goldfinch Carduelis carduelis and Wren Troglodytes troglodytes (Annex 5.5), which are likely not to be closely tied to in-field habitats. Wheat was also generally positive in Ireland (Table 7-6), which is likely to reflect the rarity of arable habitats in Ireland, together with the preference of many “farmland” birds for arable habitats. The latter arises because arable field offer greater food resources for many birds, both from access to bare soil for invertebrates and from the availability of crop and weed seeds in many crops (e.g. Robinson et al. 2001).

Areas of rough grazing had contrasting associations with the bird community in Britain and Ireland, being predominantly negative in the former but positive in the latter. This probably largely reflects the differences in landscape context and bird communities. In Britain, rough grazing is more common in more upland, northern and western locations, which are associated with lower densities of lowland farmland birds, especially arable-associated ones (Balmer et al. 2013). In Ireland, however, more arable-associated species are often rare or absent, but rough grazing may identify less intensively managed areas, where habitat quality is consequently higher. Rough grazing land is usually unimproved by fertiliser and used only for light, extensive grazing (European Commission 2009), which can increase habitat heterogeneity at small scales (e.g. more tussocks providing cover from predators), but the taller grass and denser vegetation will also make food resources on and under the soil surface less accessible to many birds (Siriwardena et al 2000).

The strongly mixed patterns of association with spring barley in Britain (Table 7-5) and beet in Ireland (Table 7-6) are particularly interesting. Spring cereals are widely considered to represent a preferred land-use type for many birds in farmland, because they provide relatively sparse vegetation and access to the soil surface throughout the breeding season, relative to winter-sown crops (e.g. Wilson et al. 1997), and because they are often associated with the retention of over-wintered stubbles, which provide critical seed food resources for many species that are not available elsewhere (e.g. Gillings et al. 2005). However, they are now more common in northern and western Britain, where arable land-use in general is rarer, as are birds that are associated with it. Similarly, beet (another spring-sown crop) will be associated with more intensive arable farming in Ireland, leading to a positive association with species that benefit from such habitats, but also, simultaneously, being negatively associated with species that prefer the pastoral habitats that are more common in that country.

It is noteworthy that there were not many clear associations with land-uses that are often regarded as indicating more or less intensive agricultural management. Temporary grassland in Britain may be an exception, as it was generally negatively associated with presence (Table 7-5) and ploughing/reseeding/fertilizing of grass fields will reduce many invertebrate populations and decrease the accessibility of the soil surface as the sward becomes denser. Otherwise, the balances of negative and positive effects (Table 7-5 and Table 7-6) may reflect the balance between farmland

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supporting farmland species, and yet also being a negative influence upon them if management is not sympathetic. For example, pasture is a preferred feeding habitat for Lapwing Vanellus vanellus, but more intensive management of grassland causes a loss of sward heterogeneity (Milsom et al. 2000), reduced plant species diversity and, in turn, lower invertebrate diversity (O’Connor and Shrubb 1990). As ground-nesting species, they are also vulnerable to disturbance and trampling by cattle at high stocking densities (O’Connor and Shrub 1990).

It is important to note that small numbers of significant individual variable tests and a lack of selections as important in the model-averaging analyses do not mean that the land-uses involved are unimportant for the species concerned, especially if they are known to make significant use of in-field habitats. This is because the scales of both the bird and agricultural data are large, which could have obscured relationships between them; this is discussed further below. Similarly, analytical power will have been low for rarer and more ubiquitous species, because presence at the tetrad scale is relatively invariable.

Among the species tested, there is considerable variation in dependence on cropped/production areas or specialization on the farmed landscape. Some species, e.g. tits Paridae and thrushes Turdidae, are predominantly found in farmland but actively mostly use non-farmed, semi-natural habitat within it, such as hedgerows and copses. These species are included because the tests here really refer to landscapes typified by the crop types named in the variables considered, rather than the cropped areas themselves (discussed further below). However, this means that relationships with these species should clearly not be interpreted as informing about the direct influence of the crops or stocking densities themselves, or of their management, on these species.

7.4.2 Notes and caveats on the interpretation of the results The atlas data sets analysed here provide large sample sizes and hence considerable statistical power to detect effects of environmental variation. Analyses using a repeated measures framework will prevent pseudoreplication artificially inflating apparent sample sizes and power, while a multi-model inference framework avoids the problems with multiple statistical tests. However, all the tests conducted here are still fundamentally correlative, so statistical effects of a given agricultural variable may well show the effects of factors that happen to be correlated with that variable, as opposed to the land-use or livestock variable involved. In particular, a range of critical factors for bird presence and local abundance, such as habitat structure and the availability or quality of hedgerows and other semi-natural habitats (e.g. Siriwardena et al. 2012), may be associated with particular cropping patterns. This means that habitat association results should be interpreted with care and not taken literally.

Presence/absence at the tetrad level in the Atlas data set forms quite a coarse measure of distribution, but its benefit is the potential for high geographical coverage, which makes concerns over sampling design and representativeness moot. In contrast, the strongest abundance surveys, such as the BTO/JNCC/RSPB Breeding

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Bird Survey (BBS) in the UK feature randomized sample plot selection and count data nominally allowing small variations in abundance to be detected (including short- term temporal changes, which atlas data cannot identify given the long periodicity of surveys), but inevitably require quantitative or qualitative extrapolation of the results to cover the majority of the landscape, which cannot be surveyed directly. However, a particular issue for effects of land-use with presence/absence data is that abundance can fall quite a long way before it is reflected in a recorded absence. The converse may well be true for recorded presences, and in both cases surveyors may seek out locally rarer species/habitats in timed visits, so the threshold abundance that is recorded as “presence” may actually be rather low. Conversely, where abundance is low, stochasticity in detection during any given timed visit increases, so false absences may be recorded rather commonly.

The way in which the bird data are collected and analysed, i.e. observations within the first hour of surveys of a 2x2km square, is likely to lead to a large degree of variation in species abundances. By using these data only to identify presence/absence, this stochasticity will be overcome but, conversely, any subtle differences in relative densities will not be detected. For species that are ubiquitous or rare, where presence per tetrad is almost always 1 or 0 respectively, the analyses fail due to lack of variation in the response, or the power to detect significant effects is lost. In general, it is important to bear in mind that the effects reported here are on presence rather than abundance; thus any effects on local densities leading to the presence of, say, one or ten birds may not be differentiated.

It is important to understand the nature of the agricultural data used here, which were areas under different land-uses and livestock densities. This means that a range of potentially critical factors in agricultural management were not considered, because the necessary data do not exist. These include the timing of sowing (e.g. Wilson et al. 1997, Donald 2010), which was only available for barley, pesticide use and organic management (e.g. Hallman et al. 2014), and livestock varieties (grazing behaviour varies with breeds, affecting vegetation structure: rare breed grazing). As well as these fine scale issues, it is also possible that broader scale variation in bird distributions, perhaps driven by soil type, climate and natural vegetation or interactions between these factors, is confounded with regional patterns of cropping. The controls for gross landscape variation included here accounted for such patterns as far as was possible, but could not eliminate the possibility completely.

Another caveat to interpreting the results is the scale of the agricultural data. Land- use and livestock data were available at the NUTS3 scale, meaning that the models examine the relationship between the presence of a species in a tetrad and the land- use in the NUTS3 region. This therefore assumes that the broad scale land use and livestock data is reflective of land use in tetrads, but this may not be the case in some more heterogeneous landscapes.

A further consequence of the scale of the data, both for birds and for agricultural variables, is that the analysis does not really consider the effects of each individual

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crop or land-use on the target species. Instead, the results should be interpreted as informing about relationships with entire farming systems or landscapes associated with higher or lower values of those variables, because there are no data available on the specific association between bird presence and the land-use in particular fields or land areas. At the landscape level, an association with a crop, for example, might wholly or partly reflect the influence of the field boundary structure or crop rotation with which the crop is associated. Therefore, say, a species that selects vegetation found along waterways, such as Reed Bunting Emberiza schoeniclus, might be identified as preferring crops that tend to be grown in low-lying areas in which most field boundaries are formed by water-filled ditches. This issue of attribution to specific land-uses is particularly important in respect of the variables that were found to be highly correlated with others (Annex 5.1), because their influences will have been entirely confounded with one another at the scale of the agricultural landscape considered. Effects of changing the quantities of these variables, such as areas of wheat, for example, cannot reliably be predicted (even qualitatively) from the results of this study, because the patterns identified may actually relate to influences of other, correlated land-uses, as well as to fine-scale landscape features such as hedgerow structure (e.g. Siriwardena et al. 2012).

7.5 Conclusions Examining species presence with respect to large-scale agricultural land-use can reveal broad patterns in response and this study has identified a broad range of these patterns across species, reflecting the variation in ecology between species. In particular, it is noteworthy that land-use practices associated with both more and less intensive agriculture each have both positive and negative associations with the suite of farmland bird species considered here. However, the data used have various important limitations that need to be accounted for in interpretation and it would be unwise to base predictions of the effects of habitat or cropping change on the models fitted here.

Using agricultural data at a smaller scale, i.e. closer to that of the bird data would help to elucidate the associations found, to establish more clearly whether variations in bird distributions can be explained by cropping, especially if data on field boundary and other semi-natural habitats can also be incorporated. Further, data from two Member States were analysed here; in the future, as atlas data from other countries become available, there will be opportunities to investigate species responses across Europe. Patterns detected over large areas can then help to inform hypotheses regarding species responses to changes in land-use over time, particularly if efforts are made to address the caveats discussed above.

7.6 Acknowledgements We are grateful to Dawn Balmer and Simon Gillings for advice regarding the use of Bird Atlas 2007-11 data. Finally, we are indebted to the thousands of volunteer surveyors who contributed data to the Bird Atlas project, without whom the study would not have been possible.

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8 Case study 4: UK agri-environment schemes and non- avian biodiversity

Authors: Henrietta Pringle and Gavin Siriwardena (British Trust for Ornithology)

8.1 Introduction Agri-environment schemes (AES) are the principal policy tool supporting the management of productive farmland in Europe for biodiversity benefits (among other priorities). They attract significant funding from taxpayers via the Common Agricultural Policy (CAP) and national governments, so their cost-effectiveness is of broad public interest and importance. Measuring their biodiversity impacts is, therefore, critical, and also allows feedback from scheme performance to improve the design of management prescriptions.

Effects of agri-environment schemes on birds in England and Wales have been investigated using national survey data, revealing which management options have had positive effects on their target species at the national scale (Baker et al 2012, Siriwardena et al 2014, Dadam & Siriwardena 2014). Analogous survey data are available for butterflies and larger mammals and the same analytical approaches can be applied. This will add important new evidence to the existing knowledge of the efficacy of agri-environment management measures and specific farming practices, and thus inform future policy decisions. Agri-environment schemes aim to benefit biodiversity at landscape and national scales, covering all farming systems found across such scales. National biodiversity monitoring programmes therefore provide data at the optimum scale for measuring agri-environment effects.

The UK Butterfly Monitoring Scheme (UKBMS), run by Butterfly Conservation (BC), the Centre for Ecology and Hydrology (CEH) and the British Trust for Ornithology (BTO), in partnership with the Joint Nature Conservation Committee (JNCC), provides data on the population status of butterflies across the UK. Historical, long-running sites monitored under the UKBMS tend to comprise high quality, semi-natural habitat, mostly protected sites managed as nature reserves, sometimes supported by agri-environment schemes. These sites are therefore likely to be rich in butterflies and provide valuable data for monitoring changes in populations. These data have been used previously to examine associations between butterfly temporal trends and AES management. No evidence was found that AES options had any impact on population growth rates of butterflies tested (Oliver 2014), but a further comparison of trends within and outside AES management suggested more positive trends on AES sites than those without management (Brereton 2002), probably due to scrub clearance and grazing enhancing nectar and habitat availability for habitat specialists and species of conservation importance.

In 2009, the Wider Countryside Butterfly Survey (WCBS) was established to complement the previous UKBMS, by providing information from a randomized, more standardized survey across the whole countryside. Using WCBS data,

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associations between AES management and butterfly abundances have been shown (Oliver 2014), but associations between management and temporal trends of species in the wider countryside have yet to be examined. Here, these data are analysed to inform about effects of AES management on wider countryside butterfly species (as opposed to those associated primarily with large expanses of semi-natural habitat) for the first time.

National mammal data are available from the BTO/JNCC/RSPB UK Breeding Bird Survey (BBS), wherein mammals are recorded in parallel to the bird counts that are the primary target of the scheme. No other long‐term, national‐scale data on mammals are available and previous assessments of AES effects on mammals have been limited to site-level or regional comparisons, so the analyses here represent the first national-scale assessments for this group as well.

8.2 Methodology and data sources

8.2.1 Biodiversity data Mammal data have been collected as part of the BBS since 1995. Randomly selected (with stratification by observer density) 1km squares are visited at least twice a year, the first visit taking place between April and mid-May and the second at least four weeks later, between mid-May and the end of June. During each visit, two 1km transects are walked, with a primary aim of recording bird numbers, but any sightings or signs of mammals are also recorded as an optional addition. While records of a wide range of species are sought, some are not relevant to agri- environment evaluation because they are not conservation targets, are not likely to respond or are recorded too unreliably (e.g. deer, rodents and mustelids). Four species were selected for this study as potentially responding to AES management and potentially being sufficiently commonly recorded to support analyses: Brown Hare (Lepus europaeus), Rabbit (Oryctolagus cuniculus), Red Fox (Vulpes vulpes) and Hedgehog (Erinaceus europaeus). However, Hedgehog was subsequently omitted because it was recorded in too few squares to support analyses and it is highly likely that this apparent occurrence underestimates the real occurrence of the species. The maximum count of each species over two visits was used to estimate abundance per square per year. Additional mammal sign data were also available for some of these species, but these data are only likely to be good indicators of presence, which is likely to be much less sensitive to management effects than abundance. Influences on sign detection, i.e. the occurrence of false absences, in particular, are also likely to be variable between species and to have complicated effects. Sign data were, therefore, not considered in these analyses.

Methods derived from the BBS are also used to monitor butterflies in the WCBS. Since 2009, butterflies have been counted using ‘Pollard Walks’ (Pollard and Yates, 1993) along two 1km transects in randomly selected 1km squares, including an observer-selected subset of BBS squares and a random sample of WCBS-specific squares. Squares are visited 2-4 times a year by volunteer observers, with a minimum of two visits in July/August. All species named on the WCBS recording form were analysed in the present study, except for Orange Tip (Anthocharis cardamines)

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and Purple Hairstreak (Neozephyrus quercus), which are either too rare or too poorly recorded by the survey (Oliver 2014). Species counts were summed across visits to provide abundances per square per year (Oliver 2014). Note that sums of counts are appropriate for butterflies, as opposed to for mammals or birds, because it is likely that different individuals will be counted on subsequent visits spaced by more than two weeks. More sophisticated methods for the analysis of butterfly survey data, in which seasonal patterns of abundance reflecting the flight periods of different species and the successive generations of multivoltine species, have been developed by Dennis et al. (2015). This approach allows additional inference as to variation in flight periods and effectively controls for the influence of survey date on counts, when intra‐seasonal variation is high because flight periods vary. However, the method is considerably more computation‐intensive than that used here and is better suited to data sets with more survey counts per season, as in the UKBMS. Considering simple, summed counts has the potential to increase more noise into the analysis, so is conservative, and is also more tractable with sparse data sets for rarer species.

8.2.2 Environmental Stewardship data Data from the English AES were used: Environmental Stewardship (ES, Natural England 2014), which began in 2005 and closed to new applications at the end of 2014, and the earlier Countryside Stewardship Scheme (CSS, Natural England 2004), which began in 2002 and closed to new entrants in 2005. Agreements between governments and farmers under these schemes ran for five years (Entry-Level Stewardship [ELS] in ES) or ten years (CS and Higher-Level Stewardship [HLS] in ES). Uptake of these schemes, especially of ELS, was high, covering up to 70% of the usable agricultural area in lowland farmland in England (Davey et al. 2010). Therefore, while survey squares typically varied in AES coverage, it was rare that they featured zero management, and it is more accurate to characterize squares as lying on a gradient of management quantity rather than as being in or out of AES. All agreements were constructed from lists of management options that were chosen by farmers, with or without direct advice from government agency (Natural England) advisers, to suit local environmental targets and farming systems (Table 8-1). Options were selected for analysis that were likely to benefit individual species on the basis of targets specified in the scheme literature and ecological knowledge (Table 8-1 and Table 8-2), and options that provided similar resources were grouped together (‘resource group’ in Table 8-2), to increase sample sizes. Where possible, further analysis examined finer option groupings, divided by management type (Table 8-2), as opposed to resource provision. The amount of each option per BBS/WCBS square was calculated from the data in the agreement contracts made between farmers and government following the method used by Davey et al. (2010) and Baker et al. (2012). In summary, option data were available in terms of areas (in hectares) or lengths of habitat (in metres) per farm, which were summed across farms in a given survey square. Where farm boundaries crossed square boundaries, option quantities were downscaled by the proportion of the total area of each farm that overlapped the square before they were summed.

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Table 8-1 Management types considered as potential influences on mammal (Brown Hare, Rabbit, Red Fox and Hedgehog) and butterfly populations.

Specific options making up each type are listed in Table 8-2. Note that low sample sizes precluded any analyses for Hedgehog in practice.

Management Species Definition type considered Retention of crop stubbles until January/February with no Stubble Brown Hare chemical inputs to provide winter food for birds Sacrificial seed crops grown as strips of up to c. 20m along field Wild Bird Seed boundaries and left until the end of January primarily to provide Brown Hare Mix (WBS) seed food for farmland birds Quick-growing crops grown on land that would normally be left Winter cover bare over winter, primarily to protect soil and to prevent nitrate Brown Hare crops leaching In-field grass Permanent grass areas in fields sown in targeted areas primarily Brown Hare areas to reduce soil erosion and run-off (a) Unfertilized, unsprayed strips within cereal crops aiming to support rare arable weeds, invertebrates and birds in the Headlands, banks summer; (b) sown, unsprayed grassy strips in field centres Brown Hare aiming to support invertebrate predators of crop pests, breeding birds and insects Options for leaving land fallow through the breeding season for Uncropped areas Brown Hare birds: unsprayed areas of naturally regenerating vegetation A temporary grass/legume mix sown under a spring cereal crop Undersown and left for at least one year after cereal harvest, primarily to Brown Hare spring cereals reduce inputs Grass buffer strips of 2-6m sown around arable fields to protect Brown Hare, Arable Margins field boundary vegetation and to provide cover for wildlife Hedgehog, Fox Grass buffer strips of 2-6m sown or fenced off around grass Brown Hare, Grassland fields to protect field boundary vegetation and to provide cover Hedgehog, Margins for wildlife Fox, Rabbit Restricted cutting (maximum once every two years) and Hedgehog, Hedgerow spraying of hedgerows to promote vegetation density, Rabbit, flowering and berry production Butterflies Hedgerow management plus restricted cutting and spraying of Hedgehog, Hedge+ditch ditch vegetation where hedges have associated wet ditches, so Rabbit, as also to enhance bankside and aquatic vegetation Butterflies Nectar flower mix crops and wildflower strips comprising sown Wildflowers Butterflies plant mixes designed to provide nectar resources for pollinators Protection and maintenance of more extensive grass fields that Species-rich or are rich in plant species or have zero inputs of nitrogen (apart low input from up to 12.5 tonnes/ha per annum of farmyard manure in Butterflies grassland one option type) to maintain plant diversity and to support invertebrates Options to reduce inputs to grazing land (to varied degrees, Grassland, mixed with a maximum of 50kgN/ha in the most lenient option) and to Brown Hare, stocking diversify grazing activity, so as to enhance vegetation structure Rabbit and diversity for wildlife As stubble management, but with fields left fallow, unsprayed Extended stubble Brown Hare and unploughed until July

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Table 8-2 Option categories used in the analysis, the resources they provide and the butterfly and mammal taxa (Brown Hare, Rabbit and Red Fox) predicted potentially to benefit

Options were analysed in their ‘management type’ and ‘resource group’ groupings. Resource Resource Management Control Option codes† Species group type Type EF6, EG4, EF15, HG5, HF6, Stubble Brown Hare HF15, OS1, OS2, OS3, EF22 EF2, HF12, HF2, WM1, EF3, Winter WBS Brown Hare Winter Arable EG2 habitat hare Winter cover EJ13 Brown Hare crops* In-field grass EJ5 Brown Hare areas Headlands, EF9, EF7, HF14, EF10, CH1, Brown Hare banks CH2, R8 Summer Summer Uncropped Arable EF13, HF13, HF20, EF22, HF15 Brown Hare hare cover areas

Undersown spring EG1, HG1, HG6-7 Brown Hare cereals* Arable Marginal Arable EE1-3, HE10, EF11, HF20, Brown Hare, Red Arable margins cover Margins HE1-3, HF11, R3, R7 Fox EE4-6, HE4-6, HE11, R5, R6 Grass Marginal Grassland Brown Hare, Red Grassland EE7, HE7, EC25, EE10, HK1, margins cover Margins Fox, Rabbit HL1, EK1, EL1 Summer EB1-3, HB11, HB12, HB14, DR, Rabbit, habitat Hedgerow HR, HRC, HRM, HRL, HL, HRP, Hedge and Butterflies (food, Independent HP ditch breeding, Rabbit, Hedge+ditch EB8-10 cover) Butterflies Arable Nectar Arable Wildflowers EE12, EF4, EF1, WM2 Butterflies nectar Species-rich or Grass EK21, HK6, HK7, HK8, HE10, Nectar Grassland low input Butterflies nectar EK3 grassland* All year Grassland, EK2, EK3, EL2, EL3, EK5, Brown Hare, Grazing habitat Grassland mixed HK6,HK7, HK8, HK15-17 Rabbit (pastoral) stocking All year All year Extended habitat Arable EF22, HF15 Brown Hare Hare stubble (arable)

*These options occurred in insufficient WCBS and BBS squares so were omitted from the analysis. †Full definitions of options can be found in Natural England (2013ab).

Only squares comprising over 50% farmland cover were included for analysis, and squares were grouped into three landscape categories: arable (ratio of arable to pastoral areas ≥ 2), pastoral (ratio of pastoral to arable ≥2) or mixed (all other squares), based on the CEH Land Cover Map 2000. Arable and pastoral cover in this dataset are defined from satellite imagery identifying cropped land and improved grassland (as distinct from types of semi-natural grass). This was important because

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the effects of habitat creation and enhancement are expected to have different impacts where background habitats differ (e.g. Robinson et al. 2001).

Because the analysis was focused on the effects of ES on population growth and not simply on aggregative responses to option presence, option quantities were matched with square-specific mammal and butterfly counts after time lags sufficient for influences on breeding success or over-winter survival (for mammals) to have affected breeding abundance. Thus, with the exception of stubbles, management had to have been in place before 1 March of the preceding year for it to have potentially affected breeding abundance in the current year. Stubble options needed to have been in place before harvest in the preceding year, so the cut-off date was 31 July.

8.2.3 Data analysis Analysis of the effects of quantities of management options on population growth rates followed Baker et al. (2012), for mammal BBS data from 2002 to 2013 and WCBS data from 2006 (when pilot surveys were carried out) to 2013. This analytical approach uses generalized linear models parameterized to model counts such that the outputs reflect growth rates and influences upon them, making maximum use of the spatial and temporal replication present in the data (Freeman & Newson 2007). The effects of option quantities on the population growth rate of each species were modelled separately in the three different landscape types, and appropriate controls for the percentage area of arable (for arable options) and pastoral (for pastoral options) were applied. This is important because larger areas of options associated with a given farming system will occur by chance (on average) where that system is more prevalent, so apparent effects of management could otherwise be confounded with those of broader landscape context. See Table 8-2 for details of the controls used for each management variable.

Models were fitted assuming a Poisson distribution for observed mammal and butterfly counts using the GENMOD procedure in SAS 9.2 (SAS Institute Inc. 2008), accounting for overdispersion using Pearson’s χ2 goodness-of-fit statistic. The significance of AES effects on population growth rates was assessed using similarly adjusted likelihood-ratio test statistics of the hypothesis that the effect size was zero. Output parameter estimates show the association between quantities of management and population growth rate, so higher, more positive values reflect stronger effects (although note that values are not necessarily comparable between resource or management types because the units of management recording may have differed).

8.3 Results The effects of resource groups and management types on the population growth rates of butterfly and mammal species in different landscapes (arable, mixed and pastoral) are reported below. Only significant associations are reported in the text; full results are detailed in Tables 2-6. There were no significant relationships with grassland nectar resources.

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8.3.1 Butterflies

Arable nectar resource group

The population trends of two butterfly species were positively associated with arable nectar resources (Table 8-3). The Meadow Brown (Maniola jurtina) population trend was significantly more positive in the presence of arable nectar in arable landscapes, and the same was true of the Large White (Pieris brassicae) in mixed landscapes. The population change of the Peacock butterfly (Inachis io) was negatively associated with the presence of arable nectar resources in arable landscapes (Table 8-3). The arable nectar resource category included only one management type (wildflowers: Table 8-2).

Table 8-3 Effect of arable nectar resources on butterfly population trends

Land- Option Standard Sig Species N Estimate Chi2 P scape squares Error nf Brimstone Gonepteryx rhamni A 265 24 -21.164 24.446 0.761 0.383 Comma Polygonia c-album A 513 53 -8.735 11.520 0.583 0.445 Comma Polygonia c-album M 546 28 21.037 59.393 0.124 0.725 Common Blue A 459 42 -16.805 15.705 1.088 0.297 Polyommatus icarus Common Blue M 486 30 53.808 72.502 0.520 0.471 Polyommatus icarus Gatekeeper Pyronia tithonus A 600 60 -3.767 4.860 0.606 0.436 Gatekeeper Pyronia tithonus M 615 35 -66.063 43.449 2.689 0.101 Green Veined White Pieris napi A 544 55 -5.335 5.043 1.145 0.285 Green Veined White Pieris napi M 555 28 -26.998 61.517 0.196 0.658 Holly Blue Celastrina argiolus A 349 31 15.803 25.270 0.430 0.512 Large Skipper Ochlodes venata A 312 34 -13.434 16.820 0.717 0.397 Large White Pieris brassicae A 633 64 -7.622 8.348 0.830 0.362 Large White Pieris brassicae M 629 37 100.703 52.567 3.845 0.050 + Meadow Brown Maniola jurtina A 626 63 10.867 5.155 4.668 0.031 + Meadow Brown Maniola jurtina M 635 39 45.642 30.419 2.192 0.139 Painted Lady Vanessa cardui A 436 37 -16.330 18.750 0.695 0.404 Painted Lady Vanessa cardui M 470 29 -17.023 159.180 0.011 0.916 Peacock Inachis io A 610 61 -35.812 8.453 22.94 0.000 --- Peacock Inachis io M 597 35 17.173 138.507 0.015 0.902 Ringlet Aphantopus hyperantus A 513 55 11.384 6.985 2.805 0.094 Ringlet Aphantopus hyperantus M 512 33 -2.931 40.023 0.005 0.941 Small Copper Lycaena phlaeas A 241 22 -6.072 20.354 0.090 0.764 Small Heath Coenonympha A 225 22 57.951 70.702 0.795 0.372 pamphilus Small Skipper A 380 37 -9.765 10.615 0.874 0.350 Thymelicus sylvestris

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Land- Option Standard Sig Species N Estimate Chi2 P scape squares Error nf Small Skipper M 370 24 63.849 43.043 2.084 0.149 Thymelicus sylvestris Small White Pieris rapae A 636 64 1.732 5.482 0.100 0.752 Small White Pieris rapae M 630 38 82.667 48.833 2.867 0.090 Speckled Wood Pararge aegeria A 566 52 -0.325 8.629 0.001 0.970 Speckled Wood Pararge aegeria M 582 34 70.970 56.639 1.559 0.212

Hedge and ditch resource group

Only the population growth rate of Large Whites in mixed landscapes was positively associated with hedge and ditch resources (i.e. all hedge management options, with or without accompanying ditch management) (Table 8-4). Six species were negatively associated with hedge and ditch resources: Comma (Polygonia c-album), Gatekeeper (Pyronia tithonus) and Small Copper (Lycaena phlaeas) in arable landscapes, Brimstone (Gonepteryx rhamni) in mixed landscapes, and Common Blue (Polyommatus icarus) in pastoral landscapes. The growth rate of Marbled White (Melanargia galathea) was negatively associated with hedge and ditch resources in both arable and mixed landscapes.

Table 8-4 Effect of all hedge management resources on butterfly population trends

Land- Option Standard Species N Estimate Chi2 P Signf scape squares Error Brimstone Gonepteryx rhamni A 265 24 -0.026 0.085 0.092 0.761 Brimstone Gonepteryx rhamni M 289 28 -0.648 0.264 9.028 0.003 - Comma Polygonia c-album A 513 57 -0.109 0.054 4.733 0.030 - Comma Polygonia c-album M 546 54 -0.014 0.050 0.083 0.773 Comma Polygonia c-album P 358 35 -0.056 0.104 0.307 0.580 Common Blue Polyommatus icarus A 459 47 0.015 0.069 0.043 0.835 Common Blue Polyommatus icarus M 486 45 -0.045 0.075 0.421 0.516 Common Blue Polyommatus icarus P 347 30 -1.478 0.872 7.885 0.005 -- Gatekeeper Pyronia tithonus A 600 66 -0.067 0.028 6.400 0.011 -- Gatekeeper Pyronia tithonus M 615 59 -0.030 0.040 0.590 0.442 Gatekeeper Pyronia tithonus P 422 38 0.088 0.054 2.438 0.118 Green Veined White Pieris napi A 544 62 0.028 0.059 0.220 0.639 Green Veined White Pieris napi M 435 52 0.061 0.028 0.469 0.493 Green Veined White Pieris napi P 555 39 0.019 0.088 0.469 0.493 Holly Blue Celastrina argiolus A 349 34 -0.210 0.146 2.750 0.097 Holly Blue Celastrina argiolus M 356 34 0.003 0.160 0.000 0.985 Holly Blue Celastrina argiolus P 206 20 0.318 0.185 2.686 0.101 Large Skipper Ochlodes venata A 312 38 0.041 0.058 0.477 0.490

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Land- Option Standard Species N Estimate Chi2 P Signf scape squares Error Large Skipper Ochlodes venata M 302 32 0.003 0.282 0.000 0.991 Large Skipper Ochlodes venata P 284 24 0.048 0.238 0.038 0.845 Large White Pieris brassicae A 633 70 0.018 0.043 0.174 0.676 Large White Pieris brassicae M 629 62 0.077 0.030 6.347 0.012 + Large White Pieris brassicae P 491 42 0.104 0.061 2.812 0.094 Marbled White Melanargia galathea A 193 21 -0.196 0.094 5.454 0.020 - Marbled White Melanargia galathea M 291 31 -0.490 0.180 10.349 0.001 -- Meadow Brown Maniola jurtina A 626 71 -0.051 0.027 3.799 0.051 Meadow Brown Maniola jurtina M 635 61 0.016 0.036 0.194 0.659 Meadow Brown Maniola jurtina P 512 44 0.052 0.052 0.998 0.318 Painted Lady Vanessa cardui A 436 37 0.035 0.086 0.153 0.696 Painted Lady Vanessa cardui M 470 47 -0.111 0.229 0.414 0.520 Painted Lady Vanessa cardui P 352 28 -0.487 0.486 2.075 0.150 Peacock Inachis io A 610 65 -0.046 0.072 0.423 0.515 Peacock Inachis io M 597 57 -0.048 0.064 0.628 0.428 Peacock Inachis io P 465 42 -0.079 0.108 0.556 0.456 Ringlet Aphantopus hyperantus A 513 63 0.044 0.031 1.953 0.162 Ringlet Aphantopus hyperantus M 512 52 0.090 0.064 1.910 0.167 Ringlet Aphantopus hyperantus P 361 34 0.210 0.128 2.750 0.097 Small Copper Lycaena phlaeas A 241 24 -1.030 0.445 9.714 0.002 -- Small Copper Lycaena phlaeas M 311 29 -0.041 0.207 0.042 0.837 Small Copper Lycaena phlaeas P 294 30 -0.361 0.279 2.135 0.144 Small Heath Coenonympha pamphilus A 225 25 -0.182 0.279 0.453 0.501 Small Skipper Thymelicus sylvestris A 380 40 0.043 0.093 0.210 0.647 Small Skipper Thymelicus sylvestris M 370 34 -0.046 0.098 0.247 0.619 Small Skipper Thymelicus sylvestris P 298 25 -0.178 0.279 0.465 0.495 Small White Pieris rapae A 636 70 0.012 0.033 0.141 0.707 Small White Pieris rapae M 630 61 0.036 0.022 2.657 0.103 Small White Pieris rapae P 504 42 -0.060 0.075 0.667 0.414 Speckled Wood Pararge aegeria A 566 60 -0.027 0.054 0.265 0.607 Speckled Wood Pararge aegeria M 582 57 -0.010 0.030 0.117 0.732 Speckled Wood Pararge aegeria P 427 38 0.085 0.061 1.826 0.177 Peacock Inachis io A 610 65 -0.046 0.072 0.423 0.515 Peacock Inachis io M 597 57 -0.048 0.064 0.628 0.428

The hedge and ditch resource group was divided into management types; hedge management without accompanying ditch management, and hedge management with accompanying ditch management (Table 8-2). Three species were significantly positively associated with the presence of hedge management options (Table 8-5). Hedge management had a positive association with the trend of Large Whites in both mixed and pastoral landscapes, and with Meadow Brown and Speckled Wood (Pararge aegeria) in pastoral landscapes. Six species showed negative associations with hedge management on population growth rates (Table 8-5). In arable landscapes, these were Comma, Gatekeeper, Meadow Brown, Marbled White and

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Small Copper. The other species, Common Blue, was negatively associated with hedge management in both mixed and pastoral landscapes.

Table 8-5 Effect of hedge management options alone on butterfly population trends

Land- Option Standard Species N Estimate Chi2 P Signf scape squares Error Brimstone Gonepteryx rhamni A 265 24 -0.037 0.085 0.194 0.660 Brimstone Gonepteryx rhamni M 289 26 -0.094 0.060 2.946 0.086 Comma Polygonia c-album A 513 56 -0.120 0.053 6.048 0.014 - Comma Polygonia c-album M 546 52 -0.001 0.031 0.000 0.983 Comma Polygonia c-album P 358 34 -0.024 0.071 0.115 0.735 Common Blue Polyommatus icarus A 459 47 -0.017 0.069 0.059 0.808 Common Blue Polyommatus icarus M 486 44 -0.058 0.026 5.067 0.024 - Common Blue Polyommatus icarus P 347 29 -1.351 0.804 9.050 0.003 -- Gatekeeper Pyronia tithonus A 600 65 -0.079 0.027 9.408 0.002 -- Gatekeeper Pyronia tithonus M 615 57 -0.003 0.017 0.024 0.877 Gatekeeper Pyronia tithonus P 422 36 0.064 0.048 1.675 0.196 Green Veined White Pieris napi A 544 61 -0.004 0.056 0.005 0.946 Green Veined White Pieris napi M 555 50 0.022 0.020 1.268 0.260 Green Veined White Pieris napi P 435 37 0.088 0.070 1.576 0.209 Holly Blue Celastrina argiolus A 349 33 -0.171 0.128 2.208 0.137 Holly Blue Celastrina argiolus M 356 32 0.025 0.141 0.030 0.863 Holly Blue Celastrina argiolus P 206 20 0.125 0.154 0.634 0.426 Large Skipper Ochlodes venata A 312 38 0.061 0.056 1.130 0.288 Large Skipper Ochlodes venata M 302 30 0.017 0.047 0.135 0.714 Large Skipper Ochlodes venata P 284 23 0.208 0.154 1.552 0.213 Large White Pieris brassicae A 633 69 -0.033 0.037 0.817 0.366 Large White Pieris brassicae M 629 60 0.051 0.021 6.181 0.013 + Large White Pieris brassicae P 491 40 0.199 0.047 18.068 0.000 +++ Marbled White A 193 21 -0.211 0.094 6.389 0.011 - Melanargia galathea Marbled White M 291 29 -0.100 0.087 1.507 0.220 Melanargia galathea Meadow Brown Maniola jurtina A 626 70 -0.054 0.025 4.888 0.027 - Meadow Brown Maniola jurtina M 635 59 0.017 0.011 2.530 0.112 Meadow Brown Maniola jurtina P 512 42 0.097 0.043 4.902 0.027 + Painted Lady Vanessa cardui A 436 37 0.015 0.084 0.032 0.858 Painted Lady Vanessa cardui M 470 45 -0.048 0.137 0.147 0.702 Painted Lady Vanessa cardui P 352 27 -0.608 0.468 3.638 0.056 Peacock Inachis io A 610 64 -0.012 0.062 0.035 0.851 Peacock Inachis io M 597 55 -0.018 0.030 0.356 0.551

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Land- Option Standard Species N Estimate Chi2 P Signf scape squares Error Peacock Inachis io P 465 40 -0.094 0.094 1.052 0.305 Ringlet Aphantopus hyperantus A 513 62 0.032 0.031 1.063 0.302 Ringlet Aphantopus hyperantus M 512 50 -0.014 0.032 0.190 0.663 Ringlet Aphantopus hyperantus P 361 33 0.191 0.116 2.732 0.098 Small Copper Lycaena phlaeas A 241 24 -0.857 0.337 10.292 0.001 -- Small Copper Lycaena phlaeas M 311 29 -0.042 0.045 0.862 0.353 Small Copper Lycaena phlaeas P 294 29 -0.194 0.158 1.693 0.193 Small Heath Coenonympha A 225 25 -0.040 0.209 0.037 0.847 pamphilus Small Skipper Thymelicus sylvestris A 380 40 0.023 0.094 0.060 0.806 Small Skipper Thymelicus sylvestris M 370 34 0.008 0.035 0.047 0.827 Small Skipper Thymelicus sylvestris P 298 24 -0.122 0.216 0.351 0.554 Small White Pieris rapae A 636 69 0.008 0.031 0.075 0.784 Small White Pieris rapae M 630 59 0.007 0.015 0.253 0.615 Small White Pieris rapae P 504 40 -0.017 0.060 0.080 0.777 Speckled Wood Pararge aegeria A 566 59 -0.025 0.051 0.252 0.615 Speckled Wood Pararge aegeria M 582 55 -0.030 0.022 2.016 0.156 Speckled Wood Pararge aegeria P 427 36 0.097 0.041 5.437 0.020 +

Hedge management with accompanying ditch management was positively associated with growth rates of Comma and Meadow Brown in mixed landscapes, but negatively associated with the growth rate of Common Blue in arable landscapes (Table 8-6).

Table 8-6 Effect of hedge management with accompanying ditch management alone on butterfly population trends

Land- Option Standard Species N Estimate Chi2 P Signf scape squares Error Comma Polygonia c-album A 513 27 0.129 0.315 0.164 0.686 Comma Polygonia c-album M 546 29 0.896 0.420 4.509 0.034 + Common Blue Polyommatus icarus A 459 23 -2.551 1.688 6.047 0.014 - Common Blue Polyommatus icarus M 486 21 -0.829 0.485 3.190 0.074 Gatekeeper Pyronia tithonus A 600 33 -0.212 0.160 1.952 0.162 Gatekeeper Pyronia tithonus M 615 27 -0.047 0.238 0.039 0.843 Green Veined White Pieris napi A 544 28 -0.288 0.255 1.500 0.221 Green Veined White Pieris napi M 555 26 0.248 0.339 0.532 0.466 Large Skipper Ochlodes venata A 312 22 0.355 0.282 1.524 0.217 Large White Pieris brassicae A 633 32 -0.070 0.152 0.210 0.647 Large White Pieris brassicae M 629 30 -0.013 0.257 0.002 0.961 Meadow Brown Maniola jurtina A 626 33 0.085 0.123 0.480 0.488 Meadow Brown Maniola jurtina M 635 30 0.377 0.149 6.437 0.011 +

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Land- Option Standard Species N Estimate Chi2 P Signf scape squares Error Painted Lady Vanessa cardui M 470 25 -0.141 1.754 0.007 0.934 Peacock Inachis io A 610 31 -0.426 0.359 1.809 0.179 Peacock Inachis io M 597 28 0.417 0.368 1.268 0.260 Ringlet Aphantopus hyperantus A 513 29 0.145 0.186 0.602 0.438 Ringlet Aphantopus hyperantus M 512 27 0.121 0.335 0.129 0.720 Small Skipper Thymelicus sylvestris A 380 23 -3.795 3.667 3.714 0.054 Small Skipper Thymelicus sylvestris M 370 22 -0.063 0.376 0.028 0.866 Small White Pieris rapae A 636 32 -0.172 0.130 1.840 0.175 Small White Pieris rapae M 630 30 0.050 0.217 0.052 0.819 Speckled Wood Pararge aegeria A 566 25 -0.005 0.245 0.000 0.982 Speckled Wood Pararge aegeria M 582 29 -0.715 0.470 2.412 0.120

8.3.2 Mammals

Margin management options

There was no effect of grass margins (in grass or arable fields) on the population trends of any mammal species tested (Table 8-7).

Table 8-7 Effect of AES management options grouped by resource types on mammal population trends (Brown Hare, Rabbit and Red Fox)

Significant associations between management option(s) and population growth rate are highlighted in bold, and the size and direction of the effect is indicated (-/+ p<0.05, --/++ p<0.01, ---/+++ p<0.001). Option squares are the number of squares where the species occurred at least once and the management option was present, results are only shown for effects where option squares >20. A=Arable, M=Mixed, P=Pastoral. Estimate and standard error are multiplied by 10000 for clarity. Land- Option Standard Species N Parameter Estimate Chi2 P Signf scape squares Error Brown Hare A 6604 539 Arable margin -0.001 0.000 3.040 0.081 Brown Hare M 4647 328 Arable margin 0.000 0.000 0.083 0.773 Brown Hare P 3823 135 Arable margin 0.012 0.011 1.151 0.283 Rabbit A 7220 572 Arable margin 0.000 0.000 0.032 0.858 Rabbit M 6663 442 Arable margin -0.001 0.001 0.898 0.343 Rabbit P 6518 241 Arable margin 0.001 0.012 0.008 0.927 Red Fox A 3991 282 Arable margin 0.001 0.001 0.518 0.472 Red Fox M 4262 273 Arable margin -0.004 0.005 0.523 0.470 Red Fox P 4019 150 Arable margin -0.032 0.035 0.833 0.361 Brown Hare A 6604 120 Grass margin -0.051 0.096 0.279 0.597 Brown Hare P 3823 72 Grass margin 0.006 0.042 0.022 0.882 Rabbit A 7220 131 Grass margin -0.147 0.099 2.328 0.127 Rabbit P 6518 130 Grass margin -0.045 0.032 2.356 0.125 Red Fox A 3991 72 Grass margin -0.218 0.239 0.955 0.329 Red Fox P 4019 83 Grass margin -0.051 0.063 0.845 0.358 Brown Hare A 6604 172 All year hare -0.994 1.358 0.537 0.464 Brown Hare M 4647 117 All year hare 0.772 2.932 0.069 0.792 Brown Hare P 3823 38 All year hare 11.800 3.552 12.517 0.000 +++

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Land- Option Standard Species N Parameter Estimate Chi2 P Signf scape squares Error Brown Hare A 6604 314 Summer hare -0.051 0.029 3.343 0.067 Brown Hare M 4647 195 Summer hare -0.003 0.192 0.000 0.986 Brown Hare P 3823 66 Summer hare 0.079 0.348 0.051 0.822 Brown Hare A 1561 414 Winter hare -2.022 1.359 2.197 0.138 Brown Hare M 1027 255 Winter hare 8.556 3.682 5.521 0.019 + Brown Hare P 381 95 Winter hare 10.852 4.596 5.776 0.016 + Brown Hare A 6604 343 Grazing -57.361 82.166 0.493 0.483 Brown Hare M 4647 268 Grazing -41.209 40.879 1.045 0.307 Brown Hare P 3823 163 Grazing 59.690 50.701 1.337 0.248 Rabbit A 7220 377 Grazing 42.744 83.440 0.260 0.610 Rabbit P 6518 292 Grazing -61.168 30.380 4.352 0.037 -- Hedge and Rabbit A 7220 449 0.139 0.171 0.657 0.418 ditch Hedge and Rabbit M 6663 368 0.523 0.153 11.222 0.001 +++ ditch Hedge and Rabbit P 6518 256 0.113 0.413 0.074 0.785 ditch

Options providing cover for Brown Hare

The group of options providing summer cover (headlands, banks, uncropped areas and undersown spring cereals: Table 8-2) did not have any effect on Brown Hares, collectively (Table 8-7) or when analysed as different management types (Table 8-8). However, there was a positive association between the group providing winter cover (stubbles, WBS and winter cover crops) and population growth of Brown Hares in mixed and pastoral landscapes. As distinct management types, both WBS and stubbles had positive associations with the growth rate of Brown Hares in pastoral landscapes, with WBS management also positively associated with growth in mixed landscapes. The population growth of hares was positively associated with the area of extended stubble options (all year habitat in arable fields) in pastoral-dominated landscapes (Table 8-7).

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Table 8-8 Effect of individual management types on mammal population trends (mammals considered: Brown Hare, Rabbit and Red Fox)

Land- Option Standard Species N Parameter Estimate Chi2 P Signf scape squares Error Brown Hare A 6604 209 Headland -0.005 0.003 3.632 0.057 Brown Hare M 4647 128 Headland -0.002 0.020 0.010 0.919 Brown Hare P 3823 35 Headland -0.006 0.035 0.026 0.871 Brown Hare A 1333 392 Stubble -0.297 0.143 4.278 0.039 - Brown Hare M 912 249 Stubble 0.710 0.418 2.911 0.088 Brown Hare P 350 101 Stubble 1.254 0.434 8.616 0.003 ++ Uncropped Brown Hare A 6604 108 55.529 40.724 1.807 0.179 areas Uncropped Brown Hare M 4647 71 -28.043 28.485 1.016 0.313 areas Brown Hare A 1344 389 WBSM 4.187 2.912 2.135 0.144 Brown Hare M 886 235 WBSM 10.019 4.215 6.350 0.012 + Brown Hare P 311 73 WBSM 37.851 18.907 4.100 0.043 + Rabbit A 7220 448 Hedge 0.013 0.014 0.869 0.351 Rabbit M 6663 356 Hedge 0.039 0.011 11.77 0.001 +++ Rabbit P 6518 245 Hedge -0.020 0.032 0.392 0.531 Hedge and Rabbit A 7220 230 -0.010 0.057 0.033 0.856 ditch Hedge and Rabbit M 6663 172 0.110 0.070 2.347 0.126 ditch Hedge and Rabbit P 6518 96 -0.064 0.240 0.072 0.789 ditch

Grazing management (all-year habitat in pastoral fields)

The presence of grazing management options in pastoral landscapes was negatively associated with the growth rate of Rabbit populations (Table 8-7).

Hedge and ditch resource group

Examining the complete hedge and ditch resource group, a positive association was observed with the population growth rate of Rabbits in mixed landscapes (Table 8-7). This association was also observed with hedge management alone, but no associations were found with hedge and ditch options alone (Table 8-8).

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8.4 Discussion

8.4.1 Butterflies In two out of 29 instances, butterfly population trends were positively associated with arable nectar resources, while a negative association was observed once. Hedge management options occurred with greater frequency than the arable nectar options, resulting in 50 instances with sufficient sample sizes. Of these 50, only eight significant associations were observed; one positive and seven negative. It is important to note that 5% of tests would be expected to give rise to significant results by chance, so some of these patterns are likely to be spurious. However, many of the results identified here are statistically significant at less than p=0.05 and consistent patterns across multiple species are unlikely to arise by chance, so can be given more weight. It is specific, individual results that should be treated with caution and regarded as indicative, rather than definitive. Thus, overall, the evidence from this study for positive effects of AES management on butterfly populations is weak at best, while unintended negative effects may be in operation.

The options tested are those recommended by Natural England to enhance butterfly presence and abundance in the farmed landscape, primarily through the provision and enhancement of nectar resources (Natural England 2013ab, Farming for Wildlife leaflet48). Previous work using WCBS data by Oliver (2014) has shown models using this subset of options to be better predictors of butterfly densities than models using all ES options (in terms of the goodness of fit of the models assessed using AIC values). It was, however, the total count of these options rather than total area that provided the best goodness of fit, with three out of 21 species positively associated with the total count of the subset of ES options, and seven species associated with the number of CSS options. This could suggest that the diversity of options in the landscape is important to enhance butterfly densities, or that numbers of individuals are not closely linked to the absolute area of habitat. The latter might occur if different option types or specific habitat patches, even within a farm, vary in nectar resource or larval food plant density, because the distribution of different plant species is patchy. More generally, if butterfly densities are limited by the availability of larval food plants (the mostly wild species on which adults lay eggs and caterpillars then feed), rather than nectar resources, measures to enhance the latter would not be expected to have significant effects. In addition, this scenario would mean that adult butterflies might well not be recorded near the locations of the limiting resource, so weakening habitat associations at small spatial scales, even if an AES option did provide the resource. The solution would be to test the effects of larval food plant availability at a larger spatial scale (as previously done for the effects of over-winter stubble management on birds: Baker et al. 2012), but there are no relevant AES options to use in this way, and no data on other sources of the resource in or around WCBS squares are available.

Hedge management options were largely associated with negative effects on population change, with growth rates of Comma, Gatekeeper, Small Copper,

48 http://publications.naturalengland.org.uk/publication/35037?category=45001

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brimstone, Common Blue and Marbled White all becoming more negative with the addition of these options. In an earlier analysis of population trends on additional, non-UKBMS farmland transects, trends of six out of eight species that included Brimstone, Common Blue, Green-veined White (Pieris napi), Orange-tip, Large Skipper (Ochlodes venata), Small Copper, Marbled White and Meadow Brown were more negative in sites under agri-environment management (Brereton 2005). Although the effects of specific management options were not investigated, the main management at these sites was believed to focus on field margin and hedgerow management, indicating results consistent with those of our study. Hedgerow management is important in providing nectar resources and enhancing habitat availability for butterflies, but there is evidence to suggest the benefits will depend on the particular, specific management chosen. Options that promote hedge cutting every three years, and cutting in winter rather than autumn, are likely to have the most positive impacts on Lepidoptera abundance, but the most frequently applied option, cutting in autumn every two years, is of no benefit to butterflies compared to non-AES management (Staley et al 2016). The authors of the latter study suggest that management is varied in response according to the life- cycle of the species present, with a switch to cutting every third year to protect those that occur as eggs, larvae or pupae on woody hedgerow plants in September, which are therefore more vulnerable to cutting in autumn. However, Staley et al.’s results would suggest neutral, rather than negative, effects of management with cutting every two years, so this does not explain the negative associations shown here or by Brereton (2005). The quality of hedge, the plant community, the shape and structure of the hedge and hedge-bottom, and the quality of adjacent land are all known to affect the abundance and diversity of butterflies in hedgerows positively (Barr et al 2005), while taller, denser hedges have been shown to benefit butterfly diversity (Sparks et al 1994; Dover et al 1997). However, perhaps the effects of biannual cutting are more severe in AES options nationally than was found by Staley et al. (2016), or there are other, unforeseen negative effects such as larger hedgerows shading out important ground vegetation, or acting as barriers to dispersal, as suggested by Fry and Robson (1994, cited in Barr et al 2005). Consideration of butterfly abundance may also reveal different relationships to studies of species presence or community indices. Another possibility for AES management in general, coupled with monitoring at the 1km square scale, is that option-scale management has an attractive “honeypot” effect on butterflies in the wider landscape, reducing counts outside those patches regardless of the effect on overall abundance, although this is likely to be a short-term effect rather than one driving long-term population growth rates. Nevertheless, an alternative explanation could be that the length of hedgerow management is correlated with another component of land-use or habitat that has a negative influence on population trends, although it is unknown what that influence might be.

8.4.2 Mammals Natural England guidelines suggest that the main method of managing habitat for Brown Hare is by providing undisturbed cover (Natural England 2013a). In our study, the only options to show any associations with changes in population growth were those that provided this cover in winter (stubble, winter cover crops and wild bird

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seed mixture). The provision of cover during the summer, in the form of arable and grass margins, was not associated with any change in population growth rates, but extended stubbles, which are left to naturally regenerate through the summer thus providing cover all year round, were positively associated with growth rates in pastoral landscapes.

The importance of cover for Brown Hares has been suggested previously via the higher densities of hares reported in areas managed for game, which included beetle banks and game cover crops (Reynolds et al 2010), and the greater use of non- cropped areas, such as AES margins (Petrovan et al 2012). In terms of all-year-round cover, hares are regularly observed at higher densities in naturally regenerating vegetation in fallow land (e.g. set-aside; Vaughan et al 2003, Smith et al 2004), which may be seen as providing resources equivalent to those from the extended stubble option, which we report here as being positively associated with population growth rates. Our results present an important addition to these earlier findings, because spatial associations showing habitat selection do not necessarily imply knock-on positive effects on population change. In particular, the results here suggest that management potentially benefiting breeding hares may attract them and produce local peaks in density via redistribution, but not drive increases in total abundance, whereas management for winter benefits may act on more critical, limiting factors. Such a pattern has been found in the responses of resident bird species to AES management (Baker et al. 2012). Other studies have examined the effects of overall AES management on Brown Hare abundances at smaller spatial scales, with mixed results. Areas managed under the Arable Stewardship Scheme, a pilot scheme for ES in England, were shown to support higher densities and had larger increases in abundance than control sites (Browne and Aebischer 2003), in contrast to an earlier study of the same sites that showed no effect of management (Tapper 2001, cited in Browne and Aebischer 2003). No effect of management associated with Environmentally Sensitive Area (ESA) designation was observed on the spatial abundance of the Irish Hare (Lepus timidus hibernicus), a subspecies of the Mountain Hare that is ecologically similar to the Brown Hare (Reid et al 2007).

It has been suggested that AES management is of greater benefit to common species than rare species or those of conservation priority (Kleijn et al 2006, Reid et al 2007), and this has been shown for Rabbits in ESAs (Reid et al 2007). In our study, Rabbit population growth rates were positively associated with hedgerow management options, perhaps reflecting enhanced cover or reduced disturbance of warren entrances. Rabbits may impact on hare populations directly through competition for resources (Homolka 1987; Chapius 1990) and indirectly via their effect on predator abundance. For example, if hedgerow management is positively associated with Rabbit population growth, this may cause an increase in Red Fox numbers, which may in turn lead to more negative growth rates of Brown Hares in the presence of hedgerow management (Vaughan et al 2003). Because of this and their status as agricultural pests (Dendy et al 2003, Corbet and Harris 1996), positive effects of AES for Rabbits and the Red Fox may be undesirable and thus point to a need for scheme revisions. We therefore additionally tested for any associations between hedge

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management and growth rates of hares and foxes, but no evidence of any such effects was found (unpublished data).

No evidence for positive or negative effects of AES management on Red Fox was found here, which could indicate a genuine lack of effect, but this species is likely to be under-recorded in BBS because it is difficult to observe and normally not very active at the time of day at which BBS sampling is conducted. Thus, Red Fox is included here for completeness: poor detectability was expected to limit the power of the AES effect evaluation, but not to cause bias. Nevertheless, this means that it would be unwise to reach definitive conclusions about a lack of AES effects from non-significant results for this species.

Hedgehog is a species that has been in decline and that AES options are expected to benefit (Hof & Bright 2010ab, 2012), so it is unfortunate that the data available did not allow reliable tests to be carried out. The same detectability issues that affect foxes apply to Hedgehogs in BBS data, so even if monitoring sample sizes were increased considerably, the data collected might not be representative. However, both species are also recorded on BBS using sign data (faeces and, for foxes, scent): future analyses may be able to use these data to extract further information, but it should be noted that the analyses would have to consider presence-absence, rather than abundance, and that the activity, as opposed to local abundance, of individual animals could have a major, unknown impact on the data collected.

The issue of detectability of mammals in the BBS raises a specific caveat for evaluating the results in that the survey protocol was designed for monitoring birds, and hence has aspects that are not ideal for monitoring many mammals. The timing during the year is late for hares, because leverets can be large and potentially confused with adults by late spring (Toms et al. 1999), while the other species are not always active at the time of day at which BBS sampling is conducted, so detectability may be low and counts possibly only poorly reflect real abundance, or be confounded with annual productivity. There is no reason why these issues should cause bias and, hence, spurious significant results, but they may well reduce the sensitivity of the analyses. Some real AES effects may therefore not have been detected here and it would be valuable to repeat the analyses once more data are available in the future. A larger sample size and/or more bespoke mammal monitoring methods would also increase power, but such changes would require great deal more investment in national monitoring than is currently in place.

8.5 Conclusions This study has found mixed associations between agri-environment management and the population growth of butterflies and mammals. Of the specifically targeted options, those providing arable nectar resources were positively associated with two butterfly species and negatively with one, while options providing cover were generally positively associated with hare population growth rates. Options involving management of hedgerows are more general options to enhance the landscape and to provide food resources for birds and invertebrates, and these had largely negative

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associations with butterfly population growth rates, but positive associations with Rabbit growth rates.

Given that landscape-scale experiments are not possible, examining changes in national population growth rates provides the strongest available evaluation of AES impacts, where data are available. Previous studies have shown positive effects of management in terms of habitat selection and static abundances of species, as discussed above (Vaughan et al 2003, Smith et al 2004, Oliver 2014), but these may not necessarily translate into increases in growth rate. For this to occur, the resource being provided must be the limiting factor on a significant portion of the population, and changes must not be masked by dispersal and migration movements. Any effects of management on population growth rates are not likely to be detected immediately in the population, but instead there will be a lag between implementation of management and changes in population growth, due both to biological constraints on the speed of demographic responses and to the limitations of sampling programmes for detecting effects in the presence of competing influences and stochastic variation. This has been used to explain the difference in effects of Arable Stewardship on hares reported in two studies of the same sites: effects of management were only seen five (rather than three) years after the start of the scheme (Browne and Aebischer 2003). For butterflies, it has been suggested that management may need to be in place for seven years before any effects on population growth rates are seen (Brereton 2002). This figure is based on UKBMS data, but given that the WCBS has only been monitoring butterflies since 2006, this could have implications for the level of change it is possible to detect at this stage, especially since the survey protocol for WCBS is less intensive and, therefore, more likely to be affected by stochastic variation in counts.

In conclusion, ES management in England has had a positive effect on the population of a key target mammal, Brown Hare, and perhaps on two common butterflies, that can be considered to be conservation successes and that have not previously been reported. However, many more butterflies appear to have been affected negatively, which is a clear cause for concern. The results are not definitive, because the tests conducted were fundamentally correlative, rather than experimental, but they suggest that further research into the effects of AES management, and hedgerow options in particular, in wider countryside butterflies would be valuable. Specifically, the effects of the options as they are applied in practice on farms, rather than in trial plots run by experts on the target taxa, should be considered. It may be that critical aspects of nominal management prescriptions are frequently mis-applied by the average farmer (see, e.g., Chamberlain et al. 2009). Alternatively, seemingly superficial changes, such as involving the specific timing of management actions, may have very significant effects, but ones that could be addressed via simple revisions of management guidelines (e.g. Siriwardena et al. 2008, Staley et al. 2016). Further analyses using the novel statistical approaches developed by Dennis et al. (2015) might provide new insights if variation in individual butterfly species’ flight periods has caused bias in some of the results, although without systematic variation in weather conditions, it is difficult to see how this could become a confounding factor. Further research into the consequences of the positive effects on Rabbit

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abundance, which may have negative influences on other, higher priority, taxa, may also be warranted; these might then be followed by scheme revisions to reduce this effect, if appropriate. Such changes could be introduced, in England, within the new Countryside Stewardship scheme (which began in 2016 and features more flexibility and more direct adviser input than previous schemes), and could be incorporated within whatever AES-like solutions are brought in after the UK’s exit from the EU. Nevertheless, but it is important to note that the occurrence of positive effects as well shows that the problems lie with the specifics of the management that has been used for the problem species, not with the structure of the AES schemes considered as a whole. This scheme structure might well benefit from revision, but this is a separate consideration to that of enhancing management guidelines.

8.5.1 Key points  There were both positive and negative associations between agri-environment management and the population growth of butterflies and mammals.  Specifically targeted options providing arable nectar resources were positively associated with two butterfly species and negatively with one.  Options providing cover were generally positively associated with Brown Hare population growth rates.  Options involving management of hedgerows had largely negative associations with butterfly population growth rates, but positive associations with Rabbit growth rates.  AES management in England has had a positive effect on Brown Hare and perhaps on two common butterflies. However, many more butterflies appear to have been affected negatively, which is a clear cause for concern.  Future research should focus on the causes of the butterfly results, including consideration of the role of factors such as larval food plant density, and on possible negative consequences for other taxa of the positive effects on rabbits.

8.6 Acknowledgements We are indebted to the volunteer observers who have conducted BBS and WCBS fieldwork and have submitted data, as well as to the survey organizers within BTO and CEH. The schemes are funded by partnerships of BTO, JNCC and RSPB (BBS) and CEH, BC, BTO and JNCC (WCBS). We also thank Maria Luisa Paracchini and Renate Koeble (JRC), Kate Risely and David Noble (BTO) and Marc Botham (CEH) for help with access to data sources.

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9 Case study 5: The effects of increased rape and maize cropping on agricultural biodiversity

Authors: Reinhard Klenke (lead), Barbara Frey, Aleksandra Zarzycka (UFZ).

9.1 Introduction Oilseed Rape (Brassica napus), hereafter referred to as rape, and maize (Zea mays) cropping has increased greatly in the last 25 years in Germany (FNR 2016, Statistisches Bundesamt 2010a,b). Although this is a general trend which has been observable for over 70 years driven by various factors (including availability of new varieties adapted to European climates combined with increasing yields, increased milk & meat production leading to increased demands for feed), most of the increase in the last two decades is linked to regional and EU-wide bioenergy support policies (Bentsen et al. 2002, Berndes & Hansson 2007, Bentsen and Felby 2012, Schlegel & Kaphengst 2007, Thrän et al. 2015).

The growing of rape and maize is typically accompanied by a higher input of fertiliser and pesticides, loss of soil organic matter and an increase in soil erosion compared to alternative crop types (e.g. Brandt & Glemnitz 2013, Felten & Emmerling 2011, Gutzler et al. 2015, Roßberg et al. 2002, Table 9-1). During the past 25 years agricultural practices have changed from intensive use of full tillage in the 1990s towards a mainly ploughless minimum tillage combined with an increased application of glyphosate herbicide. Some of the maize can be genetically modified (GMO), although the area used for GMOs in Germany is very small49. Biodiversity impacts may result from habitat loss (e.g. where higher biodiversity value habitats such as semi-natural grasslands or fallow land are converted to maize or rape) and habitat degradation (e.g. as a result of more frequent or damaging cultivation techniques and increases in the use of fertiliser and pesticides). These changes may in turn lead to landscape scale effects, e.g. through reduced habitat diversity and increased fragmentation.

This study presents a quantitative review and meta-analysis of the evidence for impacts on biodiversity of increases in energy cropping. The study also examined relationships between these crops and species of conservation interest. While most crops for biofuel and biogas are grown on intensive farmland, some may be found on Natura 2000 land or adjacent to areas protected under national law; and this review specifically sought evidence for impacts on these protected areas. In addition, this review gave particular attention to direct and indirect negative impacts on species protected by the Habitats and Birds Directives both within and outside Natura 2000 sites, as well as impacts on other declining farmland species.

Germany was one of the earliest countries in the EU to switch its energy policies towards renewable sources. This has led not only to an increase of wind energy but

49 http://biologischevielfalt.bfn.de/ind_gentechnik.html

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also to a substantial increase in energy crops during the last 25 years. Renewable resources of all types of biogenic origin (wood, short rotation coppice, biofuel, biogas) now play an important role in the energy mix used in Germany. This development has avoided further greenhouse gas emissions but has also led to a substantial change in the areas of arable land used for different crops. The areas of maize and rape have increased significantly during the past 26 years, and at an extremely fast rate in some areas (Figure 9-1).

Figure 9-1 Arable land used for maize and rape during the past 26 years

Data source: Statistisches Bundesamt (www.destatis.de)

Despite this increase in maize and rape, there was a constant loss of total utilized agricultural area (UAA) in Germany in the last two decades (from 53.5 % in 2000 to 51.7 % in 2014). The consequence of this trend, mainly caused by soil sealing due to road construction and urbanization, is that the increase in maize and rape has led directly to the decrease of other types of agricultural land (UBA 2016-10-02a). Extensive grasslands, meadows and fallow land – types of land use of particular importance for nature conservation and biodiversity – have suffered the most from this development. After a short increase in these land-uses in the 1990s, there was a constant decrease in the area of grasslands, meadows and set-aside fields. This is at least partially driven by the pressure to grow more profitable crop types in relation to subsidies or feed-in tariffs, and the market influence driven by national and EU wide agriculture and energy policies50. Also, the area of fallow land decreased considerably after the decision by the EU Commission in autumn 2007 to abolish mandatory set-aside under the CAP. Figures showing statistics supporting these statements can be found in Annex 6.1. Agricultural activities, are ranked as the most important drivers putting pressure on biodiversity and causing loss of species in Germany, followed by forestry and hydrological modifications (Figure 9-2).

50 Such as the German Renewable Energy Sources Act EEG, https://en.wikipedia.org/wiki/German_Renewable_Energy_Sources_Act

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We expect that policies to decarbonise energy production will lead to further loss of biodiversity in Germany in the future. If EU policy is not wisely controlled and adapted, similar impacts are likely to occur in other EU Member States. An analysis of observed effects of energy cropping on biodiversity could therefore be helpful to evaluate existing policies and highlight unintended consequences of policy changes. Some of these problems are caused by conflicting goals of policies related to biodiversity conservation, mitigation of climate change effects and agriculture. This study could provide a methodological basis and results for studying the resilience of biodiversity to impacts caused by maize and rape cropping, in support of policy analysis related to the switching from fossil resource use to renewable energies.

The most important factors that impact species using arable fields are the influence of crop type on microclimate and plant community structure, the intensity of mechanical management, and the application of fertilisers and pesticide treatments. While the increase in maize has led to the largest changes in crop habitat structure, it has low levels of pesticide use. Rape cultivation leads to both substantial changes in crop vegetation structure and one of the highest agro-chemical loads of any crop.

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Figure 9-2 Relative importance of driving forces threatening animal populations and leading to their decline in Germany

Adapted from Günther et al. (2005) and Daten zur Natur 2016. Data from 2005. Abbreviations used for driving forces : AGR = Agriculture, FOR = Forestry, WAS = Water construction/Shipping, CON = Construction/Mining, SPR = Sports/Recreation, EMI = Emissions, TEN = Traffic/Energy, HNT = Hunting, ILP = Infrastructure/Landscape planning, FIS = Fishery, NAT = Nature conservation, REM = Removal, COA = Coastal protection, MIL = Military training grounds, NEB = Neobiota, SPA = Species and area related specifics, Biological risk factors, NPR = Natural processes, UNK = Unknown. Taxa considered: Mammals, Breeding birds, Reptiles, Amphibians, Carabids and Cicindelinae, Water beetles, Butterflies and Hesperidae, Grasshoppers, larger Branchiopoda, Dragonflies. Sum over synoptic frequency classes of species and drivers of decline explains how often this driver was mentioned among 3,178 single forms sent back from a mail questionnaire survey containing 38,871 causes of threat. Number of species under concern is the number of species mentioned as threatened by a specific driving force.

In particular, rape has the highest insecticide application rate because of its attractiveness for insect pests if grown continually on the same land (Table 9-1). Unfortunately, rape is also very attractive for pollinators, which might be increasing pressure on this already threatened group.

The substantial increase in the maize and rape area and the corresponding decrease of other crops with different treatments and particularly high value for nature conservation such as grasslands and fallow land will probably lead to measurable effects on farmland biodiversity. In some regions of Germany, such as Mecklenburg- Western Pomerania and Thuringia, the area of maize and/or rape has more than doubled (Figure 9-3), mainly driven by renewable energy and biofuel production subisidies (e.g. Flade & Schwarz 2013, Gevers et al. 2013, Sauerbrei et al. 2013). However, it is difficult to differentiate between the biodiversity impacts of maize and rape produced for biofuel production and the same crops grown for food or feed. Therefore, we have focussed on the impacts caused by the increase in maize and rape overall.

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Table 9-1 Standardized indices of treatment/application (with fungicides, herbicides, insecticides, growth regulators) for Germany (Roßberg et al. 2002)

Orange cells, lowest partial index for insecticides for maize vs. highest partial index for insecticides in rape51.

Partial Application Index

Number Application Growth Applicati Crop of farms Fungicides Herbicides Insecticides frequency regulators on Index surveyed Oats 131 1.63 0.07 0.98 0.33 0.26 1.64 Potatoes 130 8.56 6,08 1.55 0.94 0.00 8.57 Maize 489 1.24 0.00 1.22 0.03 0.00 1.25 Rape 644 3.41 0.68 1.18 1.44 0.12 3.42 Summer 320 2.13 0.72 1.21 0.15 0.05 2.13 Barley Triticale 319 2.26 0.46 0.96 0.09 0.74 2.25 Winter 724 2.76 1.10 1.07 0.10 0.49 2.76 Barley Winter 332 2.61 0.90 0.85 0.14 0.72 2.61 Rye Winter 790 3.74 1.39 1.37 0.36 0.62 3.74 Wheat Sugar beet 382 2.93 0.15 2.59 0.19 0.00 2.93

While maize can be grown on the same field for several years without substantial loss of yield, rape crops have to be rotated with an interval of three to four years as continued cultivation of rape over several years results in substantial yield loss because of the high disease infection rate and its attractiveness to insect pests. Both monoculture and crop rotation can lead to serious impacts on soil biodiversity and insect communities in these crops.

51 The Application Index is the number of applied plant protection substances in relation to their allowed application rate and the crop area. Each application of a pesticide is handled separately in the calculation independently of whether it is used as a single application or in a mixture with others in the tank. The area coefficient for each pesticide application is the relation between the area under treatment and the overall crop area of a company. A special application rate coefficient is calculated as the quotient of the actual application rate and the officially recommended crop specific application rate. Because of this relation to the officially recommended application rate the term normalised application rate or application index is used. The product of the area coefficient and the normalised application rate is the partial index related to the crop and the class of pesticides (fungicides, herbicides, insecticides, growth regulators). Maize has the lowest normalised treatment index overall while rape has rank four in the ranking among all crops mentioned in Table 1. For insecticides, probably the most relevant group of pesticides in relation to animal biodiversity, Maize has the lowest partial insecticide index, while rape has the highest. Around 60% of all insecticide applications in rape are based on only one substance (alpha- Cypermethrin resp. Cypermethrin, https://en.wikipedia.org/wiki/Cypermethrin).

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Figure 9-3 Regional cultivated area and temporal change for rape in the federal states of Germany in 2009. Changed after ZMP 2009/4. Source: Statistical Office

The type and timing of agricultural practices (e.g. using or avoiding ploughing) and the timing of sowing, especially in maize, can decide whether the crops have positive or negative biodiversity impacts, particularly for birds which choose such fields as breeding habitat because of their suitable appearance and availability of wet areas in early spring (e.g. Northern Lapwing, Vanellus vanellus), or as resting habitat after harvest (e.g. Common Crane, Grus grus, Greylag Goose, Anser anser, and Nordic geese, Anser fabalis, Anser albifrons, Anser erythropus, Anser brachyrhynchus). Crop structure in late crops can differ substantially from that in spring, causing ecological traps (i.e. where the habitat becomes unsuitable but the birds are committed to nesting). Sowing in April causes traps, sowing after the second decade in May can have positive effects for Northern Lapwings attempting to breed in secondary habitats because of a lack of grassland (e.g. Roodbergen et al. 2012) Additionally, crop structure of maize causes a rather dry microclimate and fertiliser and pesticide applications can decrease invertebrate populations compared to fields of other crop types (e.g. Roßberg et al 2002, Felten et al. 2011).

In this case study, we carried out a literature review of the impacts of energy cropping on biodiversity (especially maize and rape cultivation) and used statistical methods to analyse regarding the quality, direction, and dimension of the effects. We also carried out a statistical analysis of the impacts of increased maize and rape cultivation on the Common Crane (Grus grus) population. The geographical focus of all analyses is clearly on Germany but important or directly comparable results from other comparable countries were also considered.

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9.2 Methodology and data sources

9.2.1 Review and Meta-Analysis

Literature search

Publications were identified about the impacts of energy crop cultivation, and in particular maize and rape, on biodiversity in Europe, and in Germany in particular. This literature was reviewed to assess what type of information has been investigated and to characterize the available knowledge and the gaps that should be filled.

Papers were searched for in English using Google Scholar with combinations of the search strings: Germany, biodiversity, biofuel, biogas, biodiesel, Rape and corn/Maize. The focus was on rape and maize, but due to the scarcity of publications on these crops, papers focusing on other bioenergy crops were also gathered. Equivalent searches were then carried out in French, Spanish, Italian, German, Polish and Hungarian to include the grey literature of several European countries. The same search strings were used in each language but also wider ones (for instance «sustainability» or «environment» instead of «biodiversity») to broaden the results and to get more matches.

A particular keyword (‘relevant’) was used to identify the papers that contained biodiversity data. The other papers were excluded and analysed separately for information that was useful context but could not be used for the meta-analysis. The papers with relevant data were sorted into those that contained quantitative data useful for statistical analysis. Further criteria were developed by the researchers, which allowed us to combine views on the possibilities of thematic analysis and to see how the data could be diversely interpreted, in order to limit the bias of a single person’s interpretation. The results and arguments of decisions were discussed between both persons and led to a final decision about each paper. A detailed account of the literature search, classification and documentation is provided in Annex 6.2.

Overview of literature search results

Overall, 267 papers, reports and theses were downloaded from the internet or obtained from other sources. All of these papers were read and assessed regarding their suitability for the aim of our analysis; the results of this assessment were compiled in a table for further analyses. This table can be found in Annex 6.3. Publications selected for further analyses included papers giving only an overview, papers providing results based on ecological modelling, papers providing information based on analyses of survey data in form of tables, maps or figures with trends, papers based on combinations of statistical analyses and ecological modelling to make sound predictions, as well as papers from small- or medium-scale experiments with analyses of direct effects of energy crops on biodiversity. Some of the observed or predicted effects are based on changes in crop area, or changes in the area proportions between crops (landscape composition); others examined the effects of

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changes in landscape configuration, with some able to provide in-depth insights on crop and landscape changes and their impact on the population development and diversity of different taxa. If the published results were described well in either a qualitative or quantitative way, we tried to extract the information about the size of the overall study area, area and proportion of arable land, area and proportion used for the cropping of maize, rape, grassland, set-aside/fallow land and other crops (summarized information about all other crops the authors have reported). If there were information gaps (e.g. only area in ha or only proportion in %) we tried to deduce the missing information if we could find paragraphs mentioning, e.g., the size of the study area. The same approach was used for the information provided about the effects. Sometimes only a number of individuals was mentioned, other times a number of breeding pairs or an index of suitability for a certain habitat, combined with the area covered by this habitat (Area-weighted Habitat Suitability, e.g. in Gutzler et al. 2015).

We identified a total of 267 papers reporting bioenergy crop impacts on biodiversity from Finland, France, Germany, Hungary, Poland, Ireland, the United Kingdom, Malaysia, Indonesia, the USA or globally. The Spanish and French search terms led mainly to references reporting from outside of Europe or giving a general overview, and such literature was excluded from further analyses or only used to report about general trends.

We selected 38 studies for a more detailed examination of their relevance for further qualitative or quantitative analyses or for an overview of issues related to bioenergy or energy cropping based on the quality of information provided (figures, numbers, tables with counts, diversity). Three studies reported only about other crops or bioenergy sources, e.g. short rotation coppice or palm oil, ten studies reported either only impacts of maize on biodiversity or compared maize with other crops but not rape, nine studies reported about effects of rape and other crops, but not maize, whilst ten studies focused mainly on maize and rape as well as other crops. The seven studies from countries outside of Europe were excluded from further qualitative or statistical analyses. The studies that focussed only on energy plants not relevant for the aim of this study, e.g., short rotation coppice or Miscanthus were also excluded. Therefore, all further analyses are based on 24 papers. The number of studies is slightly smaller because some of the papers describe results from on the same study area. The following table (Table 9-2) gives an overview of the number of studies dealing with either only maize, both maize and rape or only rape that were checked in more detail.

As shown in this table, most of the studies concerned effects of energy cropping on birds. Several studies (7) made detailed analyses of distributions and differences with sophisticated statistical approaches (e.g. Felten & Emmerling 2011, Riedinger et al. 2015), some made simple analyses of trends (e.g. Flade 2014, Flade & Schwarz 2013), while others used data about species occurrence, agricultural land-use and landscape structure, as well as common knowledge about habitat and feeding requirements of bird species, to model habitat suitability and to predict either only area effects (Sauerbrei et al. 2014) or also special effects related to the land-use

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patterns (configuration) that can change as a consequence of energy cropping (e.g. Brandt & Glemnitz 2014, Gevers et al. 2011, Gutzler et al. 2015). This was done for several birds, one spider, one carabid, and two mammal species and included detailed analysis of management practices. Although they do not contain direct data, we have included results from such approaches if they were based on sound knowledge of both target species requirements and agricultural management techniques related to the crops in question. Purely conceptual studies in artificial landscapes, with a rather reduced knowledge of biological requirements, e.g. Everaars et al. (2014), were excluded from further analyses of their results.

Table 9-2 Number of studies focusing on effects of maize and rape on the different taxa.

Class Taxa Maize Maize & Rape Rape Total Result

Oligochaeta Earthworms 4 2 6 Arachnida Spiders 2 2 Insecta Carabid Beetles 3 1 4 Hoverflies 1 1 2 Bumblebees 1 3 4 Solitary Bees 1 2 3 Butterflies 1 1 Aves Birds 4 8 3 15 Mammalia Mammals 1 1 Total Result 17 8 13 38

For all this information we either extracted or back-calculated information about the treatment and the observed effects in absolute numbers, proportions and relative changes. More detailed information about the studies considered can be found in Table A6.4 in Annex 6.1. The size of the study plots and UAA ranged from only 1 ha up to 16,702,000 ha, respectively. The studies mentioned in this table only focused on Germany.

9.2.2 Statistical Analyses

General overview and Directional Index of Frequencies for the Meta-Analysis

To give an overview about the general trend and the directions of effects found among the different taxa and species we counted the reactions in both positive and negative directions and those that were inconclusive as well. Based on these frequencies a Directional Index of Frequencies DIF (1) was calculated. The index is the difference of the number of species with positive responses N+ and the number of species with negative responses N- on maize or rape cultures divided by the total number of responses including the inconclusive ones found (N+ + N- + N0).

(푁+−푁−) Directional Index of Frequencies: 퐷퐼 = (1) 퐹 (푁++푁−+푁0)

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This Index gives information on whether there are more positive (significantly above zero) or more negative responses (significantly below zero). The value is not simply the relationship between both because the number of species with unknown or reportedly inconclusive effects can weaken the strength of the effect.

For the interpretation and discussion of the results, it has to be considered that the number of scientific papers and the number of species for which we retrieved information are not entirely objectively derived, but influenced by various factors that are difficult to quantify. Therefore, this index is only a rough indicator of the general situation and should be interpreted carefully. The reliability of this index depends on the availability of studies available on the internet. Therefore, this index gives information about what is published and available and the effort that has been taken to retrieve information. Studies not published or documented otherwise in the internet cannot be included. However, this is a general problem with meta-analyses (e.g. Egger & Smith 1998) and we have made every effort to collate as many relevant studies as possible, so are are confident that we have missed very few relevant, published studies.

Analysis of effect direction and strengths found in literature

Analyses of evidence and strengths of effects caused by a special treatment from experimental results reported in scientific publications can be made with special statistical methods and software for meta-analysis (e.g. Viechtbauer 2010). For such aims, information has to be available about the effect caused, and the statistical characteristics of the results described by standard deviation or confidence intervals and the sample size. In our literature search, we could often only find part of the relevant information and also the experimental or observational designs differed. Therefore, we used a different approach to make results comparable and give an overview below.

Based on the information about the type and strength of the impact (e.g., area extent, crop cultivation, etc.), either directly available or back-calculated from other information provided by the authors, we have standardized the reported impacts and effects to 100 % and calculated a Relative Effect in comparison to the control, a previous observation or a baseline scenario. For this we used the following formula (2) where the Relative Effect Er is the quotient of the difference between the value of a given measurement category Et observed during treatment and the one of the control Ec observed before or compared to the treatment divided by Ec:

(퐸푡−퐸푐) Relative Effect: 퐸푟 = (2) (퐸푐)

Although it was in most cases possible to obtain some information either about sample size or the area of treatment, we were not able to standardise this for comparison, because the measurement categories and scales of the results were too different. Therefore, we did not include this information, although it would have been helpful to weight the results. Because of the lack of a common level of

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comparison and a higher number of controls in each of the studies it was also not possible to calculate standardized effect strengths.

The Relative Effect Er was related to the respective group of organisms either directly to a species or, if not possible, to a group.

Some publications gave information about differences in the diversity of the investigated organism groups; from some of the published studies it was possible to obtain data to calculate it because complete assemblage counts were provided. Here we used the formula from Shannon-Weaver to calculate Hz (Shannon 1948, https://en.wikipedia.org/wiki/Diversity_index).

The values for Er related to a species or a group were plotted in box-whiskers to show their range, and how the species or groups relate to each other. We used the same scale for results from maize and rape to make them comparable.

The extension of the boxes and whiskers in the plots indicates their 25 % and 75 % percentiles and, respectively, their minima and maxima. Outliers are separated and defined as a data point that is located outside the fences (“whiskers”) of the boxplot (e.g: outside 1.5 times the interquartile range above the upper quartile and below the lower quartile52).

Data and Statistical Analyses of Spatial Distributions of the Common Crane

A further aim of the study was to analyse the spatial distribution of selected bird species in Germany with respect to bioenergy cropping practices. We compiled data for species distributions and landscape patterns at a large scale for Germany.

We included available published information about trends in farmland birds, which were based on detailed data that were not available for this study. We also analysed the available data for the Common Crane Grus grus, as published by Mewes & Nowald (2012), the Statistical Office (Statistisches Bundesamt 2010a) and the Atlas of Agricultural Statistics (http://www.atlas-agrarstatistik.nrw.de/) from the agricultural survey of 2010.

The data provided in different projections and raster grids were matched and joined, and a linear regression was made to test for for a relationship acting at population level.

9.2.3 Information about selected farm bird habitat requirements and crop management and stand characteristics of maize and rape The following tables provide information about the habitat requirements of the selected farm bird species and crop management and crop stand characteristics typical for Germany. It can be seen that, except for Corn Bunting, there is very little match between crop stand density and height of maize and rape (Table 9-4) and the species requirements described in Table 9-3. Moreover, Corn Buntings rarely use

52 https://www.r-bloggers.com/whisker-of-boxplot/

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maize and rape crops, being more commonly associated with cereals, so even this apparent match may not lead to a biological response.

Table 9-3 Preferred crop stand properties for the breeding and feeding activities of farmland bird species

Adapted from Glemnitz et al. (2015). Species selection refers to the farmland bird index from the German biodiversity strategy. Data source: Fuchs and Matthews (2008, unpublished). Densities are measured as vegetation coverage.

Farmland Breeding Period Feeding Period species Skylark Density (35–80 %); height (20–50 cm) Density (35–80 %); height (20–50 cm) Density (70–100 %); height (50–120 Density (50–100 %); height (30–120 Corn Bunting cm) cm) Whinchat Density (30–60 %); height (0–30 cm) Density (30–60 %); height (0–30 cm) Red-backed Does not breed in fields Density (0–50 %); height (0–70 cm) shrike Yellowhammer Does not breed in fields Density (50–90 %); height (0–70 cm) Woodlark Does not breed in fields Density (25–50 %); height (5–50 cm)

Table 9-4 Summary of how maize and rape are typically grown in Germany

Compilation based on Anbautelegramm Winterraps and Anbautelegramm Mais, both from Landwirtschaftskammer Schleswig-Holstein, Landesbetrieb Landwirtschaft Hessen53.

Crop Description Maize Maize has high demands for a warm local climate and grows best on light to medium soils not too rich in organic matter. Maize has no specific demands regarding the crop sequence. In areas with higher proportion of maize, the fields should have a green cover crop during winter to avoid nutrient leaching and soil erosion. Another measure is no tillage management postponing the preparation for sowing from autumn to early spring often combined with use of herbicides. Recommended sowing time is mid- April in northern Germany, which can be postponed to mid-May to support species protection measures (depending on crop purpose, e.g. silage or grain). Sowing times in southern Germany can be earlier. Suggested density is 7-10 plants/m2.. The ideal combination is a warm local climate with soil temperatures above 8°C followed by warm weather periods with average air temperatures above 10°C while the soil surface should be dry enough at sowing. Growth begins from 14°C upwards, best temperature is 25-30°C. Plants develop up to 30 leaves first before growing up to a height of 150- 250cm. The crop stand therefore becomes more and more dense before it starts to grow fast in summer. Maize needs up to 180 - 200kg/ha nitrogen (dependent on soil conditions and including organic fertilizer) and normally around 40-50 (20-70) kg/ha P2O2 added directly in combination with the sowing. More phosphate can be added later depending on soil conditions. Maize cultivation can be often found in areas with high concentrations of cattle and pig farming where manure is used for soil fertilization too. Both high concentrations of farm animals producing manure and use of chemical fertilizers lead to a higher washing out of especially nitrogen and phosphate to surface water bodies and ground water. Maize cannot compete with weeds very well and needs herbicides and/or mechanical weeding especially in early growth stages. Glyphosate herbicide is used after harvest, before and after sowing, and one application is allowed also during the vegetation period. The slow growth of maize in the early stages combined with rather big interspaces between plants is followed by rapid growth and dense vegetation cover in later stages. The microclimate within the crop is rather dry leading to lower densities of arthropods as well as soil dwelling organisms such as earthworms. Maize is not native to Europe and, like other grasses, pollinated by wind. This is one reason why it is not a host for many plant diseases, or pests, and is not feeding habitat for insect pollinators or foraging insects in Europe. For

53 https://www.llh.hessen.de/pflanzenproduktion/ackerbau/mais/488-produktionstechnische-hinweise- mais.html, https://www.llh.hessen.de/pflanzenproduktion/ackerbau/raps.html

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birds, maize is a secondary breeding habitat with low suitability and a less productive and accessible feeding habitat than other crops.

Rape Rape grows best in medium to heavy soils or light soils with good supply of water. It has no special demands regarding the previous crop in the rotation but should not be grown more frequently than every four years. Rape has a high demand for fertilizers (N=204 kg/ha, P2Po2=70 kg/ha, K2O=160 kg/ha, MgO=45 kg/ha, S= 30-40 kg/ha, B=0.35 kg/ha). Rape can also be fertilized organically with manure. Winter Rape (the most commonly grown variety) is typically sown in August (mainly second half) or (in rather rare cases) in September. Sowing density is 40-60 seeds/m2. The plants stay small over winter but cover the soil fast. In spring, Winter Rape starts to grow fast and can reach almost its final height of 30- 150 cm (depends of variety) after flowering already in late May or early June. Rape needs intensive pesticide use against weeds, fungi and bacteria, and also pest insects of different types. Rape covers soil very well and can grow extremely dense in later stages making fields unsuitable as breeding habitat for farmland birds and foraging area for raptors in early summer. However, during winter and in spring, it can be a good feeding ground. For some species, e.g. the Common Crane it can also provide some shelter in later stages of growth at the borders to other habitats important for this species. The high amount and the diversity of agrochemicals applied during the whole vegetation period may harm especially arthropods living in rape fields due to direct or indirect effects. Rape has a mass flowering period where it is very attractive to insect pollinators.

9.3 Results

9.3.1 Review and Meta-Analysis The focus of our study was on Germany, but we also included relevant information from other countries in Europe, especially in Eastern Europe, where energy cropping is becoming increasingly important. We also searched for appropriate sources in Hungary and Poland in order to avoid publication bias. Unfortunately, most of the literature reporting about energy crops found from other countries was either too general (e.g. those from France) or not informative enough regarding the quality of data to enable statistical analyses of effect size and strength. We also tried to avoid the type of publication bias caused by focusing only on pure scientific literature. Therefore, we used GOOGLE Scholar, then scientific search engines (e.g. Web of Science), and followed information from agricultural internet portals or project web pages to get access to unpublished reports. Our results are partially based on such difficult-to-find resources.

Qualitative effects

The identified studies contained information about the impacts of energy cropping (maize and rape; other crops, grassland, fallow land for comparison) on detritivorous soil fauna (earthworms), soil-dwelling Collembola, spiders, scavenger beetles (Dermestidae), fungus beetles (Erotylidae), Carabid beetles (Carabidae), ladybird beetles (Coccinellidae), hoverflies (Syrphidae), solitary bees, honeybees, bumblebees and butterflies, as well as birds and mammals. However, some of these studies were made outside of Europe and so were excluded from further consideration in the meta-analysis. This has reduced the number of taxa for which we could compile data. The following table (Table 9-5) shows which impacts of the cultivation of maize and rape are relevant for the different taxa. Cells are coloured if evidence of impacts were found for maize (green) or rape (yellow).

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Table 9-5 Effects related to energy cropping and their relevance for different taxa.

Biodiversity/producti Addition or expansion of Biodiversity vity compared to Attractiveness and Impact cultivation of energy crops over compared to other Aggregation of other types of crops Habitat quality effects on bee measured loss in other crops, grasslands, types of energy fields and/or grassland, productivity set-aside or semi-natural land crops fallow

Crop type M* R* M R M R M R M R M R

Earthworms Spiders Carabid

Beetles Bumblebees Solitary Bees Hoverflies Butterflies Birds Mammals * M=Maize, R= Rape

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Few studies analysed biodiversity in fields cultivating only maize and other crops used for bioenergy production. The table shows that effects were found only for earthworms and carabid beetles. Because we did not find a study comparing rape and other types of crops the respective column for rape does not contain any cell marked yellow.

Evidence of impacts of maize

The results show clearly that increased maize cultivation is causing strong negative effects in most of the species groups (earthworms, hoverflies, solitary bees, bumblebees, birds). Only spiders show a slight positive effect, although negative effects are also reported. For carabid beetles, the number of positive impacts equals the number of negative impacts. From the only two mammal species for which we have data, the European Brown Hare (Lepus europaeus) shows a clear negative response, while the Common Field Vole (Microtus agrestis) shows no clear response. Among birds, some species seem to benefit, at least partly, from increased maize cultivation in Germany and adjacent countries.

The addition or expansion of maize cultivation and the loss in other crops, grasslands, set-aside or semi-natural land mainly affect spiders, carabid beetles, birds and mammals, while the expansion of rape cultivation seems to be more relevant for the pollinators and birds. The effects found in pollinators are probably partly related to rape’s attractiveness during the mass flowering phase.

These effects are acting at a landscape scale and integrate all the other effects found at smaller scales (e.g. at the level of experimental plots or fields). Because of a lack of large-scale data of earthworms, spiders, and most of the insects, it is probably unrealistic to expect more detailed and qualitative, but also quantitative, information at this scale for these groups. However, the literature provides some information from comparisons of maize cultivation with other types of energy crops, e.g. Miscanthus. In most cases, these other types of energy crops or crops such as wheat show better results with regard to biodiversity and productivity.

Such results were reported especially for comparisons of species diversities and also productivities (abundances, biomass) of spiders and insects found in maize fields with those found in other types of annual crops (Glemnitz et al. 2008). In most cases, maize fields had not only the lowest biodiversity but also the lowest productivity in studied taxa. The latter effect was more pronounced. However, it is difficult to compare the species communities because the different crops and especially also perennial habitats provide different microclimatic conditions, leading to special adaptations of their species communities. Maize, for instance, contains more species with a preference for warm and dry conditions than other crops. The insect communities found in fields with different crops showed no or only a small overlap in one study (Glemnitz et al. 2008).

In a comparison of maize with other crops and perennial habitats based on earthworm diversity and productivity maize performed worst. The more natural

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habitats show substantially higher numbers of species and abundances (Felten & Emmerling 2011), enhanced diversity of earthworm communities and a more balanced species composition in extensively managed soils under grassland, fallow and Miscanthus, compared to maize or rape fields.

Negative effects of maize and rape cropping on bird habitat quality were also found in Germany (e.g., Glemnitz et al. 2015). These authors integrated knowledge about composition and specific demands of crops, their dependency on the regional environmental and geographical conditions (regional climate, soil quality, ground water level etc.) as well as other factors related to bird habitats, and estimated the effects of subsidies for energy cropping at the regional level (districts of State Brandenburg). The results showed a high variation ranging from slightly positive or no obvious effects on mean habitat suitability at the regional level, to substantial loss of this value in larger areas mainly caused by the natural geographical differentiation of the above-mentioned factors. Instead of administrative units, the regionalisation should rather be based on natural units derived from analyses of landscape structures and functional relations.

It can be observed that fields with energy crops are increasingly aggregated around biogas plants in the rural landscape in Germany with a corresponding reduction in crop diversity in the landscape. The effects of such crop aggregation on biodiversity are difficult to assess in the field with monitoring data collected by volunteers. However, ecological modelling shows that birds like Skylark (Alauda arvensis) will suffer from such processes while Linyphiid spiders like Erigone atra may benefit from it due to a reduction of dispersal distances and increase of habitat availability (Gevers et al. 2011).

Evidence of impacts of rape

The evidence of the impacts of rape is less clear than for maize. Only earthworms and butterflies show a strong negative response while some bird species seem to benefit from increased rape fields. Positive impacts were identified on carabid beetles and solitary wild bee species, but only a small impact on bumblebees. Impacts on bee abundance might only be temporary because rape flowers only for a very short time and has a high insecticide application rate. The studies analysed (e.g. Riedinger et al. 2015) gave little information about long-term population effects or the role of source habitats such as semi-natural vegetation.

A high rape cover in one year can enhance densities of some solitary bees in the following year due to carry-over effects but such results could not be found for bumblebees (Riedinger et al. 2015). These authors also showed a strong attractiveness of rape for bees during mass flowering in the current year, leading to dilution (i.e., decreasing bee densities on landscape scale, due to concentration of foraging bees away from wild plants to rape fields). This modifies the effect of the previous year’s rape cover on wild bees, but again this was not the case for bumblebees. It seems that, as long as other factors such as nesting sites or natural enemies do not limit bee reproduction, the findings from Riedinger et al. (2015)

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suggest that long-term positive effects of mass-flowering crops on bee populations could occur, at least for non-Bombus generalists, especially during the time of its blooming. Westphal et al. (2009) also showed that early mass-flowering rape has a beneficial effect on colony growth of bumblebees (Bombus terrestris), which, however, did not translate into a greater likelihood of colonies producing sexual offspring. These authors explain this with food plant scarcity later in the colony cycle. Such special effects could not be found for maize in the literature.

The effects reported by Glemnitz et al. (2015) for maize also apply to rape but to a lesser extent because the area and the relative increase of area are smaller than for maize (Figure 9-1).

Lankoski and Ollikainen (2011) provided results for butterflies in Finland. They combined farmers’ decisions with biophysical conditions to model crop production in Finland using data about habitat specific biodiversity weights or ‘habitat quality indices’ derived from Finnish field surveys of vascular plants. They found that only reed canary grass for biodiesel is unambiguously desirable, whereas biodiesel from rape and bioethanol from wheat and barley cause negative net impacts on the environment in most cases.

Combined effects and direction of effects

Most of the effects found at small scales, e.g. by analyses of different crop types at the field level, can probably be scaled up with increasing expansion of energy crop cultivation, particularly if the expansion leads to a substantial loss of set-aside land or semi-natural habitats, as observed in Germany during recent years. Landscape- scale impacts will depend on the spatial distribution of maize and rape and their specific demands and will therefore differ regionally.

An analysis of the general direction (negative or positive) of the observed effects (e.g. available habitat, number of reproductive animals, abundances, diversity of taxa) reported in the scientific papers can be found in Table 9-6.

Generally, as indicated by the arithmetic mean of the index DIF calculated across all taxa groups, it seems that the increased cultivation of maize is causing a strong negative effect on the biodiversity in the countryside; while for rape this analysis shows no clear overall directional effect. A more detailed analysis of positive and negative effects at species level can be found in Annex 6.3 (Table A6.3).

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Table 9-6 Overview of the general direction (positive/negative) of effects found in the literature retrieved and selected for further analyses

Key: (red = negative, green = positive, yellow = 0, light coloured = below -0.5, medium coloured = 0.5, dark coloured above 0.5)

Effects caused by Maize Effects caused by Rape

Taxa - + +/- - + +/- Earthworms 27 0 0 14 0 6

Index DIF -1.00 -0.70 Spiders 1 2 0 0 0 0

Index DIF 0.33 - Carabid Beetles 1 1 10 0 3 0

Index DIF 0.00 1.00 Hoverflies 1 0 0 0 0 1

Index DIF -1.00 0.00 Wild Bees 1 0 0 0 5 0

Index DIF -1.00 1.00 Bumblebees 1 0 0 0 1 2

Index DIF -1.00 0.33 Butterflies 0 0 0 1 0 0

Index DIF - -1.00 Birds 42 8 0 16 9 0

Index DIF -0.68 -0.28 Mammals 1 0 1 0 0 0

Index DIF -0.50 -

Arithmetic Mean -0.61 0.06 of Index DIF

Size of effects

The following figures provide more insight into the strength of the energy-crop biodiversity impacts in the scientific literature. Figure 9-4 and Figure 9-5 show the effects on different taxa (species or taxonomical or ecological group, dependent on the accuracy of information provided), while Figure 9-8 and Figure 9-9 show the effects on diversity at the community level. Diversity is calculated for the complete community or only a restricted set of species depending on the study type. Relative impacts are compared by the relative difference or the level of change between the “control” or baseline data (scenario) and the “treatment”. Overall we found seven studies providing useful data for effects of maize cultivation on biodiversity (Brand & Glemnitz 2014, Felten & Emmerling 2011, Gevers et al. 2011, Glemnitz et al. 2008, Glemnitz et al. 2015, Gutzler et al. 2015, Sauerbrei et al. 2014) and eight studies providing data for effects of rape cultivation with an overlap of six (the same studies,

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plus Bourke et al. 2013 and Riedinger et al. 2015, but minus Glemnitz et al. 2008). For those studies where observations or predictions were made in the same landscape for both maize and rape, data and effects are the same in Figure 9-4 and Figure 9-5, unless another study contributed information about impacts on the species. This is the case, for instance, for the Grey Partridge (Perdix perdix) and the area-weighted Habitat Suitability Index (HIS). The results for the single studies and species can be found in Annex 6.3 (Figures A6.3 and A6.4, Table A6.4).

Plants (vascular plants in general and common weeds and rare arable herbs) show in most cases a negative reaction in studies which compared maize and rape fields or an increased proportion of these energy crops in the region to other crop fields or typical crop compositions at the landscape scale.

Earthworms, independently of whether they belong to the epigaeic54, endogaeic55 or anecic56 groups, showed stronger negative effects in maize that were somewhat smaller in rape, but still ranged on the negative side.

Carabid beetles showed varying impacts ranging from slightly negative to stronger positive effects in maize while the effects of rape cultivation range from slightly positive to very positive effects of more than 100% increase. Results for Bembidion lampros using an agent-based model show only a very small positive effect for maize and rape. Although maize caused the biggest change it is difficult to differentiate between the effects caused by maize or rape because of the landscape scale of the study.

Ground-living spiders are less abundant in maize fields, while the Linyphiid spider Erigone atra, which uses crop plants to support webs, may benefit from both maize and rape crop structure, assuming that prey densities are sufficiently high. This may arise because the spiders feed largely on aerial insects in both crops, and not on those directly associated with the crop or soil surface, which are relatively specioes- poor in maize.

54 epigaeic = on soil surface. Epigeic earthworms live on the surface of the soil in leaf litter. These species tend not to make burrows but live in and feed on the leaf litter (http://www.earthwormsoc.org.uk/earthworm-information/earthworm-information-page-2). After Domínguez & Edwards (2004) they are difficult to find “… When the environmental conditions within heterotrophic decomposition systems are unsuitable or food is limited, … , despite their great potential for rapid reproduction.” 55 endogaeic = below soil surface. „Endogaeic earthworm species live deeper in the soil profile and feed primarily on both soil and associated organic matter. [They] can burrow deep and ... they are k- selected species ... that require a long time to achieve their maximum weight and appear to be more tolerant of starvation than epigaeic species ... These species ... are important in ... soil formation processes, including root decomposition, soil mixing, and aeration.“ cited after Domínguez & Edwards 2004 56 anaecic = connecting (strata) vertically. „Anecic earthworm species live in more or less permanent vertical burrow systems which may extend several meters into the soil profile. ... They cast at the soil surface and emerge at night to feed primarily on surface litter, manure and other partially decomposed organic matter which they pull down into their burrows. ... Anecic earthworms ... are very important agents in organic matter decomposition, nutrient cycling, and soil formation, accelerating the pedological processes in soils worldwide.“ cited after Domínguez & Edwards (2004)

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For bumblebees, we found only results from studies on effects of rape. Maize is not insect-pollinated and is not a native plant in Europe, which is one reason for the smaller associated invertebrate fauna, as well as the strong microclimatic effect and the exclusion of weeds by herbicides especially in the earlier stages of growth. Therefore, effects of maize on landscape scale probably lead to an exclusion or dilution of insect populations as opposed to the attraction of rape for some insect groups.

The effects of rape on solitary bees reach a 400% increase or almost three times the dimension of highest effects among the other groups, and so are shown in Figure 9-5 with an extended scale where the plots for wild and solitary bees can be found (Figure 9-6). Positive effects, although not as strong, were also found for hoverflies on rape. Other studies referring only to pollinators in general have shown rather negative effects of both maize and rape on this group including also bees and hoverflies (e.g. Glemnitz et al. 2008).

Strong negative effects of maize cultivation were found for birds especially for Grey Partridge, followed by Corn Bunting (Miliaria calandra), Skylark, Red-backed Shrike (Lanius collurio), Red Kite (Milvus milvus), Woodlark (Lullula arborea) and Whinchat (Saxicola rubetra). Impacts are mainly caused by the substantial loss of rotational set-aside and fallow land. However, there was still some variability between species. Some results showing that under certain circumstances, Grey Partridge and Red- backed Shrike may not suffer if mitigation measures such as an increase in ecotones (field margins, conservation strips, hedges) are applied. Positive effects of maize were found for Northern Lapwing and Little Owl (Athene noctua). However, the studies assumed that the management of maize was done in compliance with recommendations for management suiting e.g. the Northern Lapwing, otherwise the effects would be probably be very detrimental because of the ecological trap effect mentioned already above.

The impact of cultivation of rape is similar because the study designs all considered both maize and rape, and the effects could not be divided easily. Some variation in responses is also caused by variation in the landscape structure, e.g. aggregation of maize fields around biogas plants compared to the rather scattered distribution of maize crops used to produce silage for cattle and corn for food.

In mammals, we did not find very pronounced effects; the European Brown Hare responds negatively to both maize and rape. The main effect was caused by the substantial loss of rotational set-aside. However, mitigation measures on field boundaries combined with a certain amount of rotational set-aside or fallow land helped to neutralize the negative impact, and in some cases turned it into a small positive effect. The Common Field Vole has not been found to respond significantly (e.g. Gevers et al. 2011).

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The results regarding effects on diversity correspond to the results at the taxon level, although the negative effects are slightly smaller while the positive effects, especially in rape, are more pronounced (Figure 9-8, Figure 9-9).

An overall analysis combining the values for Er over all taxa shows slightly negative effects for both maize and rape cultivation, while there is no clear tendency for the changes in diversity. The effects of maize are more homogeneous and pronounced in their tendencies although mainly caused by the loss of other crops and habitats such as grasslands, rotational set-asides and fallow land.

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Figure 9-4: Relative Effect Er of maize cultivation on different taxa Figure 9-5 Relative Effect Er of rape cultivation on different taxa found in published or unpublished studies. found in published or unpublished studies.

For birds, one result is included using the area weighted habitat suitability (HIS, see For birds, one result is included using the area weighted habitat suitability (HSI, see section 9.2.1, Gutzler et al. 2015) as response variable for the effect. section 9.2.1, Gutzler et al. 2015) as a response variable for the effect. For wild and solitary bees please see Figure 9-6.

Maize Oilseed rape

Spiders: Spiders Wild bees: Wild bees Wild bees: Solitary bees Spiders: Erigone atra Spiders: Erigone atra Pollinators: Pollinators Pollinators: Pollinators Plants: Cmn. weeds & rare arable herbs Plants: Vascular plants Mammals: Field vole Mammals: Field vole Mammals: Brown hare Mammals: Brown hare Earthworms: Lumbricus spec. Hoverflies: Hoverflies Earthworms: Lumbricidae, epigaeic Earthworms: Lumbricus spec. Earthworms: Lumbricidae, endogaeic Earthworms: Lumbricidae, epigaeic Earthworms: Lumbricidae, endogaeic Earthworms: Lumbricidae, anecic Earthworms: Lumbricidae, anecic Earthworms: Lumbricidae, all groups Earthworms: Lumbricidae, all groups Carabid beetles: Carabid beetles Carabid beetles: Carabid beetles Carabid beetles: Bembidion lampros Carabid beetles: Bembidion lampros Birds: Yellowhammer Bumblebees: Bumblebees Birds: Woodlark Birds: Yellowhammer Birds: Whinchat Birds: Woodlark Birds: Skylark Birds: Whinchat Birds: Skylark Birds: Red-backed shrike Birds: Red-backed shrike Birds: Red kite Birds: Red kite Birds: Northern lapwing Birds: Northern lapwing Birds: Little owl Birds: Little owl Birds: Grey partridge Birds: Grey partridge Birds: Corn bunting Birds: Corn bunting Birds: Area weighted HSI Birds: Area weighted HSI

-1,5 -1,0 -0,5 0,0 0,5 1,0 1,5 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5

rel. Effect size rel. Effect size

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Figure 9-6 Relative Effect Er of rape cultivation on different taxa Figure 9-7 Relative Effects Er found for maize and rape cultivation found in published or unpublished studies. compared over all taxa and diversity values

For birds, one result is included using the area weighted habitat suitability (HIS) as a response variable for the effect. The X-axis has an extended scale to show the strong effect for wild and solitary bees.

Oilseed rape Taxa combined

Wild bees: Wild bees Wild bees: Solitary bees Spiders: Erigone atra Oilseed rape Pollinators: Pollinators Plants: Vascular plants Mammals: Field vole Mammals: Brown hare Maize Hoverflies: Hoverflies Earthworms: Lumbricus spec. Earthworms: Lumbricidae, epigaeic Earthworms: Lumbricidae, endogaeic -1 0 1 2 3 4 Earthworms: Lumbricidae, anecic Earthworms: Lumbricidae, all groups rel. Effect size Carabid beetles: Carabid beetles Carabid beetles: Bembidion lampros Bumblebees: Bumblebees Birds: Yellowhammer Diversity combined Birds: Woodlark Birds: Whinchat Birds: Skylark Birds: Red-backed shrike Birds: Red kite Oilseed rape Birds: Northern lapwing Birds: Little owl Birds: Grey partridge Birds: Corn bunting Maize Birds: Area weighted HSI

-1 0 1 2 3 4 -1 0 1 2 3 4

rel. Effect size rel. Effect size

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Figure 9-8 Relative Effect Er of maize cultivation on diversity of Figure 9-9 Relative Effect Er of rape cultivation on diversity of communities of different taxa found in published or unpublished communities of different taxa found in published or unpublished studies. studies

Maize Oilseed rape

Solitary bees: Chao diversity

Earthworms: Shannon diversity

Carabid beetle diversity: Chao diversity

Birds: Shannon diversity

Bumblebees: Chao diversity

Birds: Shannon diversity

Birds: Relative diversity

-0,5 0,0 0,5 1,0 1,5 2,0 2,5 3,0 -0,5 0,0 0,5 1,0 1,5 2,0 2,5 3,0

rel. Effect size rel. Effect size

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9.3.2 Effects of maize and rape cropping on population trends Well-designed experiments or observations such as Riedinger et al. (2015) or Felten & Emmerling (2011) show the direct evidence of a certain crop type on biodiversity over a short time period, but it is rare to find such studies combined with long-term monitoring at a large scale. Long-term trends are currently only available from monitoring schemes of butterflies (van Swaay et al. 2008) and birds (van Strien et al. 2001, Vorisek et al. 2008), but these data allow analysis with respect to the temporal and spatial variation in land-use (see also Chapter 4). At this time, we are still lacking high quality large-scale datasets on the spatio-temporal development of cropping systems in Germany. The most sophisticated spatial analysis relevant to this study to date was done for birds by Sauerbrei et al. (2014), but it is based on data from 2010 only (Statistisches Bundesamt 2010, Statistisches Bundesamt 2010a).

Three further analyses are important in this regard but could be compared in the sophistication of their methodological approach and statistical power. They used trend calculations and their relations with the area of crop, fallow land, or grassland, but without a direct spatial relation and without an analysis of other important factors such as regional climate. They did not make a clear comparison with a control. The study of trends in farmland birds from Flade & Schwarz (2013) and Flade (2014) directly relates the trends of farmland birds to the recent development of energy cropping in Germany. Mewes et al. (2010) report on the current status of the Common Crane (Grus grus) in Germany. Analyses (e.g. Jarosz et al. 2013a, Jarosz et al. 2013b) in Poland give some insight on the effects of agricultural change from a system still different from western countries towards a system more comparable with regard to crop composition and landscape structure to western Europe.

Farmland birds in Germany and the role of maize

The populations of farmland birds in Germany have shown a continuous decline during the past 25 years (Figure 9-10). During these years, they have clearly remained below their historical reference values from 1970 and 1975 and far from the political goal of an index of 100% in the National Biodiversity Strategy of the Federal Government in Germany. The indicator shown in Figure 9-10 forms part of the composite Sustainability Indicator for Species Diversity (Dröschmeister & Sukopp et al. 2008), which is based on bird data. The sub-indicator for farmland is based on 10 bird species mainly living in landscapes with a high proportion of area used for agriculture, namely Whinchat, Skylark, Yellowhammer (Emberiza citrinella), Corn Bunting, Woodlark, Northern Lapwing, Red-backed Shrike, Red Kite, Little Owl, and Black-tailed Godwit (Limosa limosa).

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Figure 9-10 Temporal trend of the Indicator “Species diversity and landscape quality in agricultural land”

NB: The historical reference values for the years 1970 and 1975 were reconstructed and the trends are calculated based on a new and modified approach. The goals for the single species are population sizes that can be realistically expected in future. They are based on expert opinions and normed to 100 %. Adapted from „Daten zur Natur 2016“, BfN. Source: DDA 2015; Status of data: 10.2015.

As shown by a linear regression performed for the quotient of the areas of maize and fallow (obtained from the data underlying Figure 9-1 andError! Reference source not found.), and the Indicator “Species diversity and landscape quality in agricultural land” (as shown in Figure 9-10), the biodiversity indicator declines substantially as the area of maize becomes larger relative to that of fallow (Figure 9-11). Furthermore, 71.57% of the variance is explained by this relationship.

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Figure 9-11 Linear regression between the quotient of area of Maize/area fallow and the indicator “Species diversity and landscape quality in agricultural land” in Germany

Intercept = 73.8422, p < 2e-16 ***; beta for Maize.Fallow = -1.1549, p=2.24e-07 ***; Residual SE = 2.602 on 21 df; Multiple R2 = 0.7286, Adjusted R2 = 0.7157).

2 4 6 8 10 12 14

Maizzee.SFaetllAoswid e

Flade (2014) and Flade & Schwarz (2014) have conducted more detailed analyses using data from breeding bird surveys in Germany. The available data based on over 600 point-count surveys (up to 327 per year), combined with data from 648 of 1000 survey sites of the sample for the whole country and 813 out of 1544 survey sites of the additional programmes of the federal states, makes it possible to calculate trends for 112 breeding birds between 1991 and 2010 in Germany. Out of the 112 most common species, 30 species are considered as typical farmland birds in this study, the majority of these farmland birds showed declining trends.

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Figure 9-12 Development of the German agricultural landscape 1991-2010 (adapted and changed based on FLADE, 2012 cited in Flade & Schwarz 2014)

Area proportion between set-asides (fallows) and organic farming on one hand and Maize crops on the other in Germany in total (left), and in the federal state of Brandenburg alone (right) as example for an East-German state. The value 1 (bold line in the graph) would indicate that fallows/organic farming and Maize have the same area share. Red boxes are framing periods with a high proportion of fallow (due in part to set-aside requirements at the time) and organic farming.

Ratio of Set-aside land and organic Ratio of Set-aside land and organic farming farming to maize cultivation in Germany to maize cultivation in Brandenburg

Set-aside (without renewable resources) to maize Set-aside + organic farming to maize

Figure 9-13 Population index trends of 10 farmland bird species with increasing trends in the early 1990s and declines since then (Average Index = geometric mean).

The black columns indicate the area proportion of set-asides against Maize x 100 (compare Figure 9-12). Adapted and changed based on Flade & Schwarz (2013).

Set-aside-Grassland-Maize-Index

Red-backed Shrike

Common Cuckoo

European Greenfinch

European Goldfinch

Marsh Warbler

European Turtle Dove

Eurasian Skylark

Barred Warbler

Yellowhammer

Eurasian Tree Sparrow

Avarage index (10 species)

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The authors found that a distinct recovery of some the species after the German reunification in the early 1990s, in East Germany in particular, was followed by a decline of most species since 1996. In the latest period (after 2007), the declines seem to accelerate and population decreases are stronger in West Germany compared to East Germany. The results of their analyses revealed that periods with a high proportion of fallow (due in part to set-aside requirements at the time) and organic farming (1993-1995, 2002-2005) were favourable for the population development of many farmland bird species, whilst a high proportion of Maize crops had a negative effect. In particular, the strong increase of Maize crops for biofuel production after 2005 (caused by the new German Renewable Energies Act) and the abolition of the obligatory EU set-aside requirements since 2007 have not only led to declines in the majority of farmland species, but also in those species that benefited from the set-asides over a long period previously.

Both the trend in the Maize/set-aside proportion and the trend of the farmland birds are not directly correlated but show a time lag between one and three years. For the Yellowhammer, and the proportion of Maize/fallow + grassland for instance, the strongest correlation, of 0.82 (p<0.01), was found for a time lag of three years, suggesting the effect of population processes such as increased or decreased breeding success or survival.

Flade & Schwarz (2014) estimate that, according to the summarised data, a 10 % area cover of fallow on agricultural land would be necessary to halt the decline of farmland birds (they currently cover <1 %), or that twice as large fallow and organic farming area is needed in relation to Maize crops (the current ratio set-aside to Maize is, in some regions, as low as 1/20). They also found that a share of 33 % of organic farming plus 15 % extensive grassland use in one area of 1,300 km² (the Schorfheide-Chorin Biosphere Reserve) is sufficient to improve farmland bird trends substantially. In a small region with more than 95 % of organic farming within the Schorfheide-Chorin Biosphere Reserve, more farmland birds were increasing than declining.

An analysis of the threats and pressures affecting the 101 breeding bird species in Germany (Wahl et al. 2014) that trigger the selection of European Special Protected Areas (SPAs) shows that the most important causes of decline are (agriculture-derived) changes in land- use and management intensity. It is therefore of particular importance that this current study has found that such threats may be exacerbated by increased growing of maize and rape.

Farmland birds in Poland and maize and rape The literature search with the use of Polish keywords returned numerous results, with over 700 outcomes for initial searches. However, the majority of articles tackled the applied aspects of bioenergy crop cultivation and biofuel production. The reason for that is the fact that this sector of agriculture and fuel production is still in development phase in Poland. Many of the articles focused on economic aspects of biofuel production on Polish market, the legislative situation and agrotechnical issues, such as pest control. A major emphasis was placed on the role of biofuels in the local and global strategies for sustainable development.

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On the other hand, articles directly or indirectly discussing the impact of biofuel production on the environment and biodiversity were scarce. It was only possible to find nine articles with some level of relevance to this topic. Most of those articles have a form of a review and describe the environmental impacts in a general manner, without examples or case studies. Only two of the articles include data collected in the field. Both of them compare the difference in abundance and diversity of chosen animal species between diversified and simplified agricultural landscapes, including maize and rape monocultures (Karg and Bałazy 2009, Wuczyński 2016). The increase of the area covered with energy plants monocultures and gradual loss of the mosaic agricultural habitats were also named among the most serious threats to Red Kite population in Western Poland (Maciorowski and Urbańska 2013). For the period between 2000 and 2012 the overall results of the monitoring of common farmland birds show no evidence for a negative effect of energy crop acreage expansion on bird biodiversity in Poland. However, for two species of common farmland birds, Common Linnet (Linaria cannabina) and Yellowhammer slight declines in population numbers have been observed. These have been suggested to be mostly related to changes in breeding season food availability, i.e. of common weed seeds for the Common Linnet and of insects for the Yellowhammer. Therefore, these species were seen especially as indicators integrating information about the biodiversity and abundance of weeds and insects respectively (Jarosz et al. 2013a). Changes in land-use and an increase in the cultivation of maize and rape are among the explanations for these species’ declines (Jarosz et al. 2013a), but changes in landscape structure or diversity, for which no big differences could be found among the different regions in Poland, are not implicated (Jarosz et al. 2013b).

9.3.3 Effects of maize and rape cultivation at the population level - The Common Crane in Germany and France In Germany the Common Crane mainly occurs on the eastern side of the River Elbe. Most of the breeding pairs (80%) can be found in the federal states of Brandenburg and Mecklenburg-Western Pommerania. The breeding population has increased from 700 pairs in the 1970s to 7,000 pairs in recent years, with a marked growth in the middle of the 1990s followed by strong expansion, not only to the north and south, but especially also up to 240 kilometres to the west (Mewes et al. 2010, Figure 9-15, Figure 9-16). Reasons for the present development have been postulated to include a high standard of nature conservation, a high reproductive rate, the crane’s adaptability to adapt to variable environmental conditions in their breeding habitats, and an earlier return each year as a result of climate change, the latter following a shortening of migration distances and overwintering further north (Mewes et al 2010). Other important factors are thought to include the increased area of maize and rape and changed management of these crops (especially maize). Management of maize changed from rapid ploughing after the harvest in autumn in the early 1990ies to mostly no-tillage management today, leading to a large increase in undisturbed areas available for feeding and resting in autumn when tens of thousands of cranes gather together at the Baltic coast and North-eastern Germany before migration. An increase of breeding pair numbers can be observed in all parts of the breeding area in Germany. The strongest increase has occurred in Lower-Saxony, where wetland restorations, in combination with a sharp increase in maize cultivation, were probably the most important drivers (Mewes & Nowald 2012).

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Figure 9-14 Map of the distribution and breeding pair density of the Common Crane (Grus grus) in Germany (Mewes & Nowald 2012).

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Figure 9-15 Population development of breeding cranes in Germany (adapted from Mewes & Nowald 2012)

This development can be seen as a positive effect of the efforts of nature conservation since the middle of the last century, especially in Eastern Germany, accompanied by the effects of agricultural land-use change and climate change. However, the detailed mechanisms are not easy to detect, because several factors influence the conditions in the breeding area, and the conditions during resting and migration in several regions in Europe. The areas of maize and rape, for instance, have not only increased in Germany, although this effect is very pronounced, but also in Denmark, Poland and France. Especially for France, we found sources describing the increase of maize and rape cultivation as an important factor for the attraction and provision of food for the cranes, at least during the migration period (e.g. Merle 2010, Salvi 2010, Salvi 2012).

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Figure 9-16 Increasing breeding range of cranes in Germany (adapted from Mewes et al. 2010)

The multiple linear regression based on the data described in the Methods section (9.2.2) revealed no statistical relationship between proportions of maize, rape and grassland in relation to the available arable land in 5km grid cells and the density of breeding pairs of the Common Crane in Germany in year 2008. Although the variables Proportion of Maize (β = - 0.049084, p = 0.000162) and Proportion of Rape (β = 0.123572, p = 1.3e-14) were highly important for the explanation of the number of breeding pairs in the raster grid cells, the explained variance is very low (Multiple R2 = 0.04692, Adjusted R2 = 0.04526; Intercept = 3.337028, p = 2e-16). The Proportion of grassland showed a near-significant, positive effect (β = 0.016415, p = 0.067502). For the Proportion of Maize, the sign of β is negative indicating a slightly negative relationship. A second multiple linear regression was performed with change in numbers of breeding pairs between 1993 and 2008 as the dependent variable. In this case, only the Proportion of Rape was highly significant (β = 0.112262, p = 4.53e-15), followed by a less significant effect of the Proportion of Maize (β = -0.027327, p = 0.0182). Similarly to the first multiple linear regression, the explanatory power of this model was very low (Multiple R2 = 0.04447, Adjusted R2 = 0.04274; Intercept = 2.161814, p < 2.17e-10).

These results show a small, negative effect of maize on the breeding density of Common Crane, which is, superficially, in opposition to the hypothesis that the increase of maize has a substantial positive influence on the population, while, conversely, there was a positive effect of rape. One reason why we expected a stronger relationship between maize and Common Crane breeding pair density is the observable spatial coincidence and the increasing number of overwintering birds foraging preferably on maize in North-east Germany. Those birds may have an advantage in spring when they occupy their breeding territories. However, both energy crops are related to different aspects in the life cycle of the Common Crane. Maize is especially important during the phase of the pre-migration gathering after the breeding period in the northeast of Germany, both for the German breeding population, and for the resting cranes from Scandinavia and the Baltic Sea 179

countries. The availability of open and disturbance-free space in maize fields for resting in combination with energy rich food to prepare and fuel up energy reserves for long-distance flight has an indirect influence on the number of breeding pairs. Combined with the increasingly mild temperatures during winter in recent years (probably triggered by climate change), increased large-scale food availability can reduce mortality, improve body condition in early spring and increase the chances of finding a good breeding location, although this effect probably acts mainly on sub-adult individuals. Conversely, rape can improve feeding conditions directly during the early breeding period and maybe also provide shelter against disturbance in late spring and early summer.

However, we are sure that we would gain much better results and a detailed explanation of the effects of maize and rape with a more complete set of agri-environmental, landscape and climate related variables, in combination with detailed data about the phenology of migration, and a more sophisticated model (e.g. Schulz et al 2003) which considers also the indirect effects of increased survival, spatial traditions (leading to aggregations) and long term effects on population level. Unfortunately, this was not achievable within the time available for this study, and it remains a priority for further work.

9.4 Discussion This meta-analysis has revealed the complexity of effects caused by maize and rape cultivation. A difficult issue is the comparability of results coming from studies with completely different designs, spatial extent and type of analysis. This may have, at least partly, caused a certain bias. However, this bias is likely to affect the size of the effects found, rather than their directions. Comparing the studies, it is clear that different groups of animals and plants beside or inside maize and rape fields show different directions and sizes of effects. Sometimes, effects found as an impact of maize or rape cultivation can be both positive and negative on the same taxon group, or even only one species, but in most cases the results give a clear picture of the direction of impact. Our findings are supported by other analyses of trends on a larger scale, especially for birds (Flade & Schwarz 2014, Vorisek et al. 2008, Pe’er et al. 2014).

Positive effects of an increased proportion of crops used for bio-fuel and energy production compared to a rather traditional composition of crops could be found for some wild bees, bumblebees and hoverflies, benefiting especially from mass-flowering rape (Bourke et al. 2013, Riedinger et al 2015). However, it still remains unclear whether this is mainly an effect of short-term attraction or a long-term effect leading to higher productivity and sustainable population growth (Riedinger et al 2015). The strong positive signal might also be an effect of the method, because the results used for the calculation of Er were parameters of the regression function and therefore different from all other information used in the statistical analyses. It is also not clear whether there are thresholds in the proportion of arable land used for this type of cultivation at which positive effects would turn into negative ones. This is suggested by the large differences between the regions in Germany, as shown in Figure 9-3. From the results from Glemnitz et al. (2008) for pollinators, we would expect a rather negative effect, but more comparisons between different regions are necessary to reveal such effects. Potts et al. (2010) identify land-use change with the consequent loss and fragmentation of habitats as the most important drivers leading to a loss of pollinator diversity and productivity worldwide, followed by increasing pesticide application and

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environmental pollution, and decreased resource diversity. Other drivers following in this list are alien species, the spread of parasites and pathogens, and climate change. However, due to interaction of effects such as sub-lethal and chronic exposure of colonies to pesticides resulting in a compromised immune system with previous weakening of the colonies by disease, the causes and mechanisms behind declines might be much more complicated then it can be shown by a simple listing of factors (Potts et al. 2010).

The results show a clear picture of impacts on soil-dwelling earthworms. All of the earthworm ecological guilds are clearly negatively affected by both maize and rape, compared to other forms of agricultural use such as grassland, fallow land, or Miscanthus (e.g. Felten & Emmerling 2011).

Ground-dwelling insects, such as carabid beetles, may suffer from maize and rape cultivation, but there are also strong indications that they may benefit, especially if the landscape structure (e.g. in terms of configuration and composition) is improved. It should be kept in mind that is it difficult to compare and to evaluate the composition of the communities found in such fields because of the different edaphic conditions caused by the different soils where the crops are grown, management practices and vegetation structure (e.g. Glemnitz et al. 2008). A simple comparison of species number or indices of diversity might fail in this regard, and more detailed information, such as species lists or species- specific abundances may provide a deeper insight into these practice and land-use effects.

Spiders preferring the same habitat as carabid beetles, e.g. wolf spiders (Lycosidae), will be negatively affected while Linyphiid spiders like Erigone atra may benefit from the dense structure of maize vegetation and, to some extent, also rape fields. However, the latter effect would be dependent on structure, rather than prey availability, limiting the abundance of these spiders. Here, the number of reported results is very small and we need still more information to describe a clear picture.

As more clearly shown from the results, most farmland birds have suffered from an extension of the areas of both maize and rape cultivation, and agricultural intensification. The data on long-term population trends strongly support such an interpretation. For some species, the situation could be improved if mitigation measures aiming to adapt landscape composition and configuration were applied. This is especially true for Grey Partridge, Skylark and Red Kite. It has been shown that, in Germany, especially in the eastern part, farmland birds suffer not only from the expansion of maize and rape cultivation but also from the reduction of habitat diversity and structural elements (e.g. Flade 2013, Flade & Schwarz 2014, Sauerbrei et al. 2014).

Similar declining trends, although not of the same strength, were found in Poland for farmland bird species, especially Common Linnet and Yellowhammer (Jarosz et al. 2013a). However, it seems that the structural changes in the landscape have not reached such a level as can be observed in Germany (Jarosz et al. 2013b, Flade & Schwarz 2013), so analogous effects have yet to be seen there.

Mainly positive associations between Red-backed Shrike, Northern Lapwing and Little Owl and maize fields were found in Germany. However, especially for the Northern Lapwing,

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crop management and timing decides whether maize can be used as a secondary breeding habitat of lower quality. Moreover, the overall trend for this species is moving in the opposite direction as a consequence of the reduced reproduction caused by a substantial loss of grasslands and meadows, which are the primary habitats (Roodbergen et al. 2012).

This rather negative picture also shows in the effects found for the two mammals examined in the analysed literature, although for the European Brown Hare a certain mitigation of such effects seems to be possible.

One important effect acting mainly at the landscape scale, and difficult to find by a simple comparison, is the effect of loss of habitat on population viability and stability. The extension of maize and rape cultivation leads not only to a general reduction of crop diversity and habitat suitability for most of the species considered here but also to further fragmentation and isolation of the remaining habitats. This leads not only to increasing costs for movement between different types of habitat (e.g. habitats for feeding, breeding, resting) but also to higher costs of movement between meta-populations. The costs occur in terms of higher energy demands (food), or reduced offspring survival because parents cannot feed frequently enough or cannot observe and defend the nest continuously, or they can occur as an increased adult mortality caused by predators or traffic. These effects are related in a non-linear way to the loss of habitat and habitat suitability and can speed up the process of decline with each hectare of loss.

9.5 Conclusions Overall, the effects on biodiversity of maize cultivation compared to fallow land, permanent grassland but also other energy crops appear to be more strongly negative than those found for rape cultivation, where a few, stronger, positive effects occur in opposition to the numerous but less pronounced negative ones found in the literature (although different species are involved in each case).

Earthworms, ground-living insects and spiders, but also insect pollinators and most of the farmland birds, were clearly negatively affected by maize cultivation. On the one hand, it is a simple area effect because a loss of general suitable habitat must lead to a loss of individuals. If maize fields displace suitable fallow land or permanent grassland, all species are affected that have strong preferences for fallow land, grasslands or other displaced crops. On the other hand, we often found combined or selective effects. A reduction in habitat suitability for insects or spiders, for instance, leads to a reduction of available food for birds and their offspring. Fields that look suitable at the beginning of the breeding period but are not suitable when the crop develops are causing ecological traps (e.g. Schlaepfer et al 2002) as reported for Northern Lapwing and Skylark.

Again, we must stress that the picture for rape looks similar for most of the vertebrate species because the designs of the studies at the landscape level could not exclude confounding effects of other crops. In most cases, only the proportions of crop area were different between studies, but in a few cases the allocation of crop types or aggregation of crop areas also varied. Because the overall rape area and also the proportion in the study areas of the analysed papers are is smaller than that of maize, its displacement effects are also smaller. It is important to note that we were unable to conduct a complete assessment

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of the relative impacts of all crop types, so the apparent effects of rape could, in reality, reflect the influence of other, correlated land-uses instead.

Unlike maize, rape can provide at least some ecological functionality. Several species or species groups can feed on it in different stages on growth and on its seeds. During the mass flowering period rape is very attractive and provides abundant food for all kinds of insect pollinators. Unfortunately, this period is very short and followed by a long period where rape fields provide no food for flower visitors and the soil is inaccessible for many species. Ground living insects may benefit from the exclusion of predators; birds feeding on them or on soil- and ground-dwelling animals like earthworms and rodent species may suffer from this loss of foraging area.

Some of the results have also shown that structural changes at the landscape level, aiming for an optimization of field size, land-use composition, allocation of crops, grassland and fallow or broadened field strips and hedges, with an ecological focus, may provide effective mitigation of negative effects (e.g. Flade 2014, Gevers et al. 2011, Gutzler et al. 2015 or Everars et al. 2014).

A special case related to the extension of arable land used for the production of energy crops is the Common Crane. This species is likely to benefit the most from recent developments in this regard, combined with positive effects of increased winter temperatures in the breeding and stopover areas due to climate change.

9.6 Acknowledgements We would like to thank Rebecca Ingenhoff, Fanny Henrietta Juhász, Jamie Kalla, and Benedikt Mann for their assistance with retrieval of information, extraction and preparation of data, translation and redrawing of figures, and language checks.

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10 Case study 6: Effects of habitat heterogeneity on biodiversity in intensive arable farms

Authors: Anna Gamero (Pan-European Common Bird Monitoring Scheme, Czech Society of Ornithology) Martin Šálek (Institute of Vertebrate Biology, Academy of Sciences of the Czech Republic)

10.1 Introduction Changes in farming practices and management due to higher production demands and increased mechanization have led to the transformation of formerly structurally diverse and complex European farmlands into more homogeneous and intensively-used environments in recent decades (Benton et al. 2003). This increase in agricultural intensification and consequent decrease of hedgerows, field margins, and non-cropped areas (Benton et al. 2003; Stoate et al. 2009) has reduced the suitability and availability of breeding and foraging habitats for farmland biodiversity, contributing to a large loss in farmland biodiversity (Fuller et al. 1997; Benton et al. 2003; Wilson et al. 2005; Firbank et al. 2008).

Habitat heterogeneity has been generally found to increase the abundance and diversity of vascular plants (Poggio et al. 2010), arthropods (Weibull et al. 2000; Kerr et al. 2001; Weibull et al. 2003; Vanbergen et al. 2005) and mammals (Tapper and Barnes 1986; Smith et al. 2005). The effect of habitat heterogeneity depends on the trophic level and dispersal abilities of each taxonomic group (Benton et al. 2003), since different species operate at different spatial scales (Tscharntke et al. 2005; Oliver et al. 2010). Therefore, it is expected that more mobile taxa are influenced by composition and configuration of the habitat structure at larger spatial scales than more sedentary species with lower dispersal abilities (Tscharntke et al. 2005). Most studies investigating the effects of farmland heterogeneity on biodiversity have focused on particular species or taxonomic groups (Weibull et al. 2000; Smith et al. 2005), but studies investigating multiple taxonomic groups (Weibull et al. 2003; Billeter et al. 2008) can give better insights of these associations and be more useful for management and conservation.

In this study, we measured and compared the abundance and species richness of spiders, butterflies and birds, and fine-scale habitat land-use at two spatial scales, to investigate the effects of habitat heterogeneity and composition on biodiversity in two adjacent, intensively farmed arable landscapes in Austria and the Czech Republic. These countries were chosen to take advantage of the contrasting landscape patterns that persist from their different socioeconomic and political histories (Sklenicka et al. 2014). In contrast to Austria, where farming followed the market system with small-scale land ownership, in the Czech Republic, farming was heavily influenced by the socialist planning system with large-scale state farms (Sklenicka et al. 2014). This process resulted in heterogeneous landscape pattern with small fields in the Austrian part and large homogeneous crop fields in the Czech part of the study area. These habitat differences seem to result in strong differences in population trends of some taxa (Voříšek et al. 2010).

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Additionally, we checked the correlations between species richness and abundance among taxonomic groups to investigate whether any of the biodiversity measures provided a good indicator of overall farmland biodiversity in the region.

10.2 Methodology and data sources

10.2.1 Study area Data were collected from two cross-border study regions located in the Southern Moravia, Czech Republic (GPS: 48.78°N, 16.16°E) and Lower Austria, Austria (GPS: 48.69°N, 16.07°E), Central Europe (Fig. 1 A) in 2014 and 2015. The climate is continental with relatively dry (mean annual rainfall: 550 mm) and mild warm weather (mean annual temperature: 8.3 °C). The relief is predominantly flat with altitudes varying from 150 to 265 m a.s.l. Both study areas are characterized by an arable-dominated landscape (86.7% vs. 86.8% of arable land in the Czech and Austrian study sites, respectively), but they differ in habitat composition and heterogeneity (Table 10-1, Figure 10-1). Arable fields in both study areas are mainly used for intensive cultivation of cereals, maize, oil rape and sunflower. Grasslands mainly consist of intensive hayfields, grassland strips and, in smaller proportions, semi-natural grassland vegetation. A smaller part of the studied area is also composed of vineyards, productive orchards and non-agricultural habitats, the latter mainly consisting of corridors and hedges, which vary in width (range 3 - 42 m). Forested habitats are also present, and they are characterized by small uniform woodlots and evenly-aged secondary coniferous or mixed stands.

Table 10-1 Mean and standard deviation of habitat characteristics of the Austrian and Czech sampling plots.

Plot sizes refer to the sampling approaches for different taxa (see sections 10.2.2-6).

Austria Czech Republic

500 x 500 m plots

Mean patch size (m2) 10,595.36±6,182.37 42,272.42±53,929.33 Habitat diversity index 1.33±0.31 0.82±0.49 % non-cropped elements 6.92±8.41 7.29±8.46 20 x 20 m plots

Mean patch size (m2) 80.50±30.99 182.29±110.76 Habitat diversity index 1.11±0.34 0.48±0.47 % non-cropped elements 49.63±32.30 36.12±38.88

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Figure 10-1 Maps of the study areas the Czech Republic and in Austria

Key: (A) location in the Czech Republic and in Austria, and (B) the distribution of monitoring plots for birds, spiders and butterflies (grey squares) and the habitat types based on Corine land cover (2001).

10.2.2 Sampling design Data for this study was collected in connection to the LISA (Landscape Infrastructure and Sustainable Agriculture) project, namely adopting the study design and methodology for habitat composition, where the main objective was to identify changes in landscape structure and agricultural intensification before and after CAP greening measures57.

We recorded abundance and species richness of spiders, butterflies and birds, as well as habitat heterogeneity and composition within individual study plots in 2014 and 2015. Spiders, butterflies and birds were recorded in 25 squares in Austria and 25 squares in Czech Republic (Fig. 1 B). The average distance between two neighbouring squares was 4,655 m. The squares were 25 ha in size (500 x 500 m), were regularly distributed (regular sampling method, Fig. 1 B) and all had a high proportion of agricultural habitats (> 50 %). The count points (N=50) were located at the closest distance to the square centre within a linear structure (e.g. crossing of field roads, ditches or hedges) and spiders, butterflies and birds were recorded from the same location in the site.

57 http://www.eeb.org/index.cfm?LinkServID=0E2EEC07-5056-B741-DBA777455AA46334 186

10.2.3 Spider surveys Spiders were sampled by sweeping the vegetation in a 20 m radius circle around each count point. We made 100 sweeps per sampling point, splitting the sweeps according to the proportion of surrounding habitats/crops (Košulič et al. 2014). This method is mainly effective to record web-building spiders (Nyffeler and Sunderland 2003). All spider specimens were collected by tweezers from the sweep net and stored in 70% alcohol; adult spiders were identified to the species level in the laboratory. Juvenile specimens were identified to the family level. All localities were visited three times (May, June and July) in 2015. Surveys were conducted between 10:00 and 17:00 hours in suitable weather conditions (> 20°C, sunny, no wind, no wet vegetation). We used the species classification and their habitat associations (arable or grassland) from the World Spider Catalogue (2015) and Kasal & Kaláb (2015).

10.2.4 Butterfly surveys Butterflies were sampled in a 20 m radius circle around each census point using point counts over five (2014) or ten (2015) minute periods. They were either identified by sight or captured with a hand-net for closer examination. Individuals were identified to species level. The individuals that were captured for confirmation of species identification were released immediately afterwards in the same locality. All localities were visited two times in 2014 (May-July) and four times in 2015 (May-August). Surveys were conducted between 10:00 and 17:00 hours in suitable weather conditions (> 20°C, sunny, no wind). Nomenclature and habitat associations (arable or grassland) of butterfly species were taken from Beneš et al. (2002).

10.2.5 Bird surveys Birds were surveyed using a point count methodology (Bibby et al. 2000; Gregory et al. 2004). All birds heard or seen within the 25 ha square around the point count during a five- minute period were recorded. The surveys were conducted during the period of highest bird vocal activity (05:00-10:30 hours), under favourable weather (no rain, mist or strong wind). All localities were visited once in the peak of the breeding season (May) in 2014 and three times (May-June) in 2015. All birds were identified to species level and any signs of the breeding behaviour of individuals (e.g. male territorial songs, alarm calls, birds carrying nesting material, male and female birds together) were interpreted as presence of a breeding pair at the locality and individuals not showing breeding behaviour were interpreted as a half of breeding pair (Reif et al. 2008). Bird species were classified into four categories (farmland, forest, urban and wetland) based on the habitat associations of each bird species (Reif et al. 2006; Reif et al. 2008). Three bird species recorded in the study area and associated with farmland habitat were listed in the Annex I of the Birds Directive (Great bustard, Otis tarda; Red-backed shrike, Lanius collurio and Barred warbler, Sylvia nisoria). Wetland species and birds that were observed flying over the site and that did not land during the visit were excluded from analyses, because they were unlikely to be directly associated with the habitat at the survey point.

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10.2.6 Environmental characteristics Environmental characteristics were measured through field mapping of the agricultural land-use. In particular, we mapped environmental characteristics in squares of 500m x 500m and 20m x 20m around each point count where spiders, birds and butterflies were recorded to investigate the effect of fine-scale habitat characteristics on biodiversity at two spatial scales. We recorded the proportion of (a) arable fields (cereals, clovers and lucerne, maize, oilseed rape, sunflower, set-aside and other arable habitats), (b) non-cropped elements (grassland strips, grasslands, woody hedges and corridors) and (c) other habitats (vineyards, gardens and orchards, forests, water and urban areas).

Habitat heterogeneity was measured for each point count (at the two spatial scales: 500m and 20m side squares) as (a) the mean patch size (configurational heterogeneity), (b) habitat diversity (compositional heterogeneity) using the Shannon Diversity Index including all types of habitats, and (c) the percentage cover of non-cropped elements. Mean patch size was calculated following the methodology described in Šálek et al. (2015).The selected indices of heterogeneity represent widely used compositional and configurational characteristics of landscape structure (Fahrig et al. 2015). Representation of individual habitat characteristics and indices of habitat heterogeneity were calculated in the GIS environment (ESRI 2011).

10.2.7 Statistical analysis We calculated the abundance of birds, butterflies and spiders as the maximum number of individuals recorded during all visits in 2014 and 2015 for each taxonomic group. Species richness of birds, butterflies and spiders was calculated for each taxonomic group as the total number of species recorded in a sampling plot, including also all the visits from both sampling years. These abundances and species richness were used as response variables in the statistical analyses.

We checked whether the abundances and species richnesses of all taxonomic groups were correlated with each other using Spearman correlation of all the biodiversity measures recorded at each sampling point.

To investigate the effects of habitat heterogeneity and the percentage of non-cropped elements on each measure of farmland biodiversity we ran generalized linear mixed models (GLMM) for each taxonomic group’s (birds, butterflies and spiders) species richness and abundance. All models included the habitat diversity index (Shannon Diversity Index), mean patch size and percentage of non-cropped elements as fixed factors, and country (Austria or Czech Republic) as random intercept to control for potential differences in management practices and intensification between the two countries. We included all three measures of habitat heterogeneity simultaneously in the models after standardizing them (xi- mean(x))/SD(x)) to obtain parameter estimates at the same scale for each measure, while also taking the other two into account. Each model was run with three different error structures (Poisson (P), Negative binomial with variance=µ(1+µ/k) (NB), and Negative binomial with variance=Øµ (NB1)) as there were indications of over-dispersion (Box 10-1). We then compared the Akaike Information Criterion values (AIC) of the three models in order to select the one with better fit (i.e. lower AIC value) (Sakamoto et al. 1986). When the model selected was the one with a Poisson error structure, we also checked that there was no over-dispersion. The analyses were conducted using habitat characteristics at the

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larger (500m x 500m) and at the smaller (20m x 20m) spatial scales for total abundance and species richness of birds, spiders and butterflies and for the abundance and species richness of a subset of species of each taxonomic group according to their habitat specialization: farmland birds, grassland spiders, arable spiders, grassland butterflies and arable butterflies.

Additionally, we analysed the presence or absence of Annex I bird species in the studied plots using the same model structure as for the rest of groups, but with a binomial error structure, because the large number of zeros in the data set made an analysis on the abundance of these species unsuitable.

We used R 3.1.0 (R Development Core Team 2014), package glmmADMB (Fournier et al. 2012) to fit the models and package Hmisc (Harrell and Dupont 2015) to run the correlation matrix.

Box 10-1 model description

Model error structure Type of data Poisson Count data Negative binomial Count data with over-dispersion Binomial Presence/absence data

10.3 Results We found that several of the farmland biodiversity measures were significantly positively correlated with each other (Table 10-2), particularly butterfly species richness, which was correlated positively with all the rest of measures, except for spider species richness.

At the larger scale, we found a negative effect of mean patch size on butterfly species richness, bird and butterfly abundance (Table 10-3, Figure 10-2), and on the presence of Annex I bird species (Table 10-3, Figure 10-3). The percentage of non-cropped elements was positively correlated with abundance of spiders and butterflies and species richness of birds and butterflies (Table 10-3, Figure 10-4). These same groups were positively affected by the percentage of non-cropped elements at a smaller spatial scale (Table 10-4, Figure 10-5), but only butterfly abundance was negatively affected by mean patch size at this scale (Table 10-4, Figure 10-2).

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Table 10-2 Correlations (r) between abundance and species richness of birds, butterflies and spiders recorded at each sampling point

Numbers in bold indicate significant correlations at P<0.05 (*) or at P<0.001 (**).

Bird sp. Spider Spider sp. Butterfly Butterfly sp. richness abundance richness abundance richness Bird abundance 0.71** 0.02 -0.06 0.40* 0.40* Bird sp. richness -0.07 -0.19 0.25 0.29*

Spider abundance 0.44* 0.47** 0.54**

Spider sp. richness 0.16 0.23

Butterfly abundance 0.86**

Species richness of farmland birds and grassland butterflies, abundance of grassland spiders, grassland butterflies and arable butterflies were positively correlated with higher percentage of non-cropped elements at the larger scale (Table 10-3). At the smaller spatial scale, grassland butterfly species richness and abundance, and arable butterfly abundance, were all positively correlated with the percentage of non-cropped elements and negatively correlated with mean patch size (Table 10-4). Habitat diversity did not explain the variation in abundance or species richness of any of the taxonomic groups studied at any spatial scale (Table 10-3 and Table 10-4).

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Figure 10-2 Effects of large-spatial-scale mean patch size on (a) bird abundance, (b) butterfly abundance, (c) butterfly species richness and (d) effect of small-spatial-scale mean patch size on butterfly abundance

Dots represent the raw data, lines represent the model predictions based on the corresponding estimates of main effects of the GLMMs presented in Table 3 and 4, and bands represent the 95% confidence intervals.

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Table 10-3 Estimates and standard errors for the effects of the large-scale (500 x 500 m squares) habitat diversity index, mean patch size and % of non-cropped elements in each taxonomic group studied, error structures used to fit the models, and number of sites.

Numbers in bold indicate significant correlations at P<0.05 (*) or at P<0.001 (**). Habitat Mean Percentage of Sites Error structure Response variable diversity patch non-cropped

index size elements Total bird 0.052±0.063 -0.200±0.067* 0.060±0.036 NB1 50 abundance Total bird sp. 0.025±0.089 -0.103±0.091 0.117±0.053* NB 50 richness Total spider 0.136±0.094 -0.068±0.121 0.251±0.062** NB 49 abundance Total spider sp. 0.083±0.108 0.030±0.106 0.048±0.060 P 49 richness Total butterfly 0.125±0.180 -0.353±0.177* 0.386±0.105** NB 49 abundance Total butterfly sp. 0.120±0.101 -0.210±0.103* 0.165±0.055* NB 49 richness

Farmland bird -0.075±0.063 -0.100±0.071 0.046±0.040 NB 50 abundance Farmland bird sp. -0.069±0.099 -0.152±0.101 0.114±0.056* P 50 richness Annex I bird -0.650±0.571 -1.807±0.750* 0.524±0.367 B 50 presence Grassland spider 0.293±0.161 0.077±0.161 0.158±0.071* NB1 49 abundance Grassland spider 0.172±0.127 0.037±0.126 0.066±0.068 P 49 sp. richness Arable spider -0.206±0.213 0.004±0.202 0.018±0.128 NB 49 abundance Arable spider sp. -0.152±0.204 -0.005±0.195 -0.020±0.131 P 49 richness Grassland butterfly 0.292±0.224 -0.210±0.226 0.302±0.093* NB1 49 abundance Grassland butterfly 0.139±0.163 -0.450±0.293 0.188±0.076* NB1 49 sp. richness Arable butterfly -0.069±0.197 -0.327±0.192 0.335±0.118* NB 49 abundance Arable butterfly sp. 0.074±0.123 -0.066±0.122 0.050±0.067 P 49 richness

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Figure 10-3 Probability of Annex I bird species presence with respect to mean patch size at the larger spatial scale

The line represents the model prediction based on the corresponding estimates of main effects of the GLMM presented in Table 3, and bands represent the 95% confidence interval.

Figure 10-4 Effects of large-spatial-scale percentage of non-cropped elements on (a) spider abundance, (b) butterfly abundance, (c) bird species richness and (d) butterfly species richness

Dots represent the raw data, lines represent the model predictions based on the corresponding estimates of main effects of the GLMMs presented in Table 3, and bands represent the 95% confidence intervals.

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Table 10-4 Estimates and standard errors for the effects of small-scale (20 x 20 m squares) habitat diversity index, mean patch size and % of non-cropped elements in each taxonomic group studied, error structures used to fit the models, and number of sites

Numbers in bold indicate significant correlations at P<0.05 (*) or at P<0.001 (**). Mean Error Sites Habitat % non-cropped Response variable patch structure diversity index elements size Total bird abundance -0.069±0.076 -0.028±0.074 0.062±0.049 NB 49 Total bird sp. richness -0.111±0.099 -0.028±0.102 0.212±0.060** NB1 49 Total spider abundance 0.064±0.129 0.024±0.128 0.206±0.083* NB 48 Total spider sp. richness 0.047±0.106 0.021±0.115 0.017±0.074 P 48 Total butterfly abundance -0.216±0.154 -0.560±0.183* 0.292±0.115* NB 48 Total butterfly sp. richness -0.028±0.112 -0.278±0.150 0.217±0.071* NB1 48

Farmland bird abundance 0.019±0.075 0.024±0.076 0.029±0.048 NB 49 Farmland bird sp. richness -0.049±0.107 -0.129±0.126 0.078±0.072 P 49 Annex I bird presence 0.228±0.931 -1.600±1.820 0.889±0.558 B 50 Grassland spider abundance 0.118±0.173 0.143±0.179 0.194±0.122 NB 48 Grassland spider sp. -0.007±0.124 -0.047±0.139 0.026±0.085 P 48 richness Arable spider abundance 0.280±0.197 0.299±0.191 -0.008±0.143 NB1 48 Arable spider sp. richness 0.187±0.203 0.181±0.206 -0.013±0.144 P 48 Grassland butterfly -0.260±0.264 -0.899±0.415* 0.367±0.151* NB1 48 abundance Grassland butterfly sp. -0.120±0.180 -0.715±0.292* 0.360±0.108** NB1 48 richness Arable butterfly abundance -0.294±0.157 -0.439±0.190* 0.301±0.122* NB 48 Arable butterfly sp. richness -0.026±0.123 -0.124±0.144 0.047±0.083 P 48

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Figure 10-5 Effect of percentage of non-cropped farmland at the smaller spatial scale on (a) spider abundance, (b) butterfly abundance, (c) bird species richness and (d) butterfly species richness

Dots represent the raw data, lines represent the model predictions based on the corresponding estimates of main effects of the GLMMs presented in Table 4, and bands represent the 95% confidence intervals.

10.4 Discussion In the plots considered in this study, we found only positive significant correlations between the abundance and species richness of the different taxonomic groups, indicating that environmental characteristics of the plots often affect birds, butterflies and spiders similarly. However, bird and spider biodiversity were the least correlated groups in the plots, which could be a result of the very different spatial scales at which these taxa operate. The biodiversity measure that seemed to best indicate a general high biodiversity of a plot was butterfly species richness, as it was positively correlated with all but one of the rest of the biodiversity measures. Therefore, in this intensive arable system, butterfly diversity could be considered as the most appropriate indicator of farmland biodiversity in general, although the correlation coefficient was moderate. Other studies have found, however, much lower correlations between taxonomic groups of farmland biodiversity recorded in plots (Fahrig et al. 2015), indicating that our results suggesting butterfly biodiversity as a good indicator of farmland biodiversity may not be applicable in other agricultural systems or for other scales of measurement. 195

Farmland biodiversity was affected by habitat heterogeneity. We found that larger areas of non-cropped elements were important for the biodiversity of all taxonomic groups analysed here. Non-cropped land areas such as grassland strips and margins are subject to less disturbance (e.g. cutting) and lower levels of agrochemicals than their adjacent cultivated fields, and appear to support a higher diversity and abundance of butterflies (Dover et al. 2000), birds (Hinsley and Bellamy 2000; Henderson et al. 2012) and some spiders (Baines et al. 1998). Additionally, small patch sizes were found to be particularly important for butterflies and bird abundance. These results are probably linked, since in regions with small fields, a larger proportion of the in-field area is in closer proximity to non-cropped elements than in regions where fields are bigger.

When considering only species that are strongly associated with farmland habitats, we found a positive relationship between non-cropped elements and farmland birds, grassland and arable butterflies and grassland spiders. This indicates that these effects were not driven by generalist species benefiting from non-cultivated areas, but by farmland specialists as well. Further, we found a negative effect of mean patch size on grassland and arable butterfly abundance, grassland butterfly species richness and to the probability of occurrence of Annex I bird species. Our results show that the bird species of conservation concern in the EU and grassland butterfly species whose populations have declined in the EU by 30% in the last 20 years (European Environment Agency 2013) that were recorded in this study could benefit from farmland habitats with smaller field sizes. Still, because only three Annex I species were present in the study area (Great Bustard, Red-backed Shrike and Barred Warbler), our result only considers these bird species and not all other farmland Annex I species. Moreover, the results refer to community-level measures and not to individual species; the responses of any individual species cannot be inferred from the community results.

Unlike Oliver et al. (2010), we did not find that abundance and species richness of taxonomic groups with generally larger home ranges (birds, butterflies) were more affected by farmland heterogeneity measured at a larger scale than the taxonomic group with smaller home ranges (spiders). These different results may be because the large scale used in our study (500m x 500m) was smaller than the small scale (1,000m x 1,000m) used in Oliver et al. (2010).

Habitat diversity did not explain the biodiversity measures of any of the groups studied. This may be a result of the low variation and level of habitat diversity in the study area, which was strongly dominated by cereal crops. We found that the farmland heterogeneity measure that had most positive effects on biodiversity was the larger proportion of non- cropped elements, followed by smaller patch size, although in other similar studies it was found that the strongest predictor was patch size (Fahrig et al. 2015). In any case, both smaller field sizes and large non-cropped areas seem to be important for several farmland taxonomic groups and incentives to promote agricultural landscapes with these characteristics in other Member States with homogeneous intensive arable habitats could improve their farmland biodiversity.

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10.5 Conclusions  Butterfly species richness was the best indicator of overall biodiversity in this intensive arable system.  Smaller patch size and non-cropped areas were positively correlated with farmland biodiversity, indicating the potential benefit for biodiversity to increase habitat heterogeneity in EU agricultural landscape.  Species of conservation concern in the EU (Annex I bird species and grassland butterflies) were more common in areas with smaller patch sizes.

10.6 Acknowledgements We would like to thank Vladimír Hula, Marina Kipson, Renata Daňková, Jana Niedobová and Martin Střelec for their help in the field and Stanislav Grill for assistance with geographic information system and preparation of maps.

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11 Conclusions

11.1 Introduction This project aimed to develop a methodology for robust and repeatable analyses of evidence of links between agriculture and biodiversity across the EU, and to test this by identifying and collating the most relevant available data sources, and conducting integrated and specific analyses to quantify these links. In the process of doing this, restrictions, limitations and key gaps were identified and taken into account in the drawing of general conclusions.

The majority of the terrestrial area in most EU Member States is under some form of agriculture and the range of biodiversity (including genes, species and ecosystems) that is found in this land is immense, as is the variety of farming systems and practices. Links between specific agricultural land use and practices, on the one hand, and different components of biodiversity, on the other, are inevitably complex and influenced by multiple factors. Pressures and impacts may differ significantly for different species or groups analysed. As with any variation in the environment, it is inevitable that some species will benefit and others will suffer from any change in agricultural land-use or management. Some interesting and important issues relate to which species benefit and which suffer from particular land management practices, what these changes reveal about overall agroecosystem health or sustainabilityand, ultimately, how a better understanding can influence the setting of conservation priorities and the choice of policy measures at national level or across a broader range of habitats.

11.2 Analytical approaches

11.1.1 Key factors to consider Options for analyses were summarized and formulated into a decision tree, which is presented, with accompanying notes, in a form that is intended to be usable independently of the rest of this report. Part 1 of the tree is aimed at all users, in order to allow selection of data sets for analysis, including consideration of spatial scale, temporal resolution and necessary control variables to allow the influences of key agricultural variables to be separated from those of other factors, and to facilitate conclusions about the potential inference that the available data support. For completeness and to be explicit about the philosophy employed here, Part 2 describes the processes that an analyst would go through in deciding how to take analyses forward, including the choice of analytical method for the question of interest and the data that are available, but it is expected that experts undertaking such work would not need to follow the tree to know a suitable approach to take. Hence, Part 2 is not designed to allow a non-specialist to be able to move from data collation to a completed analysis. Section 2.5.1 in the report should be referred to for further information, as it is written to be used as a stand-alone document (and is reproduced as such as Annex 7).

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11.1.2 Methods for different data types The recommended analytical method for use with the best spatio-temporal data on biodiversity (i.e. temporally replicated count data collected using standard methods), is the generalized linear model (GLM) approach introduced by Freeman & Newson (2008). This method allows the testing of a range of different forms of predictor variable and has been applied to investigate the effects of agri-environment management on farmland birds, for example. Where repeat count data are not available, but standardized counts have been made for a single time point across a large spatial scale, analyses can aim to evaluate the influences of environmental variables on static distributions and another GLM formulation is recommended for this purpose. If only presence-absence data are available, they are best analysed using logistic GLMs, in which the probability of presence in a survey unit is modelled with respect to environmental variables and controls. Given repeat sampling of presence-absence, change in distribution can be investigated, considering “local extinction” and “local colonization”, again using logistic GLMs.

Analyses of raw data are always likely to be more powerful and informative than those using derived indices. However, it may be that the latter are the only accessible form of data. Assessments using the variation in derived regional estimates of population sizes or trends are then possible, but provide low statistical power and high chances that agricultural variation of interest will be correlated with that in other features of the region.

The recommendations focus on structured survey data from organized schemes, which represent by far the most valuable form of biodiversity information. However, the collation of ad hoc or casual records of different species is becoming increasingly widespread, through the use of online portals. These approaches produce unstructured data, basically presence-only records, with no formal data collection protocol. Sophisticated new analytical approaches are improving the information value of such data, but data from structured schemes will always be superior.

Where no quantitative data are available, analyses must make use of qualitative information, such as records of the conservation status of habitats and species of Community interest, or Natura 2000 site-level assessments of the degree of their conservation. In theory such information can be analysed with respect to agricultural variables, as considered in Chapter 5, but the resulting inference is likely to be weak at best, and subject to a range of assumptions.

11.1.3 Restrictions/limitations Individual species, as well as ecosystems, are different, so they potentially have different relationships with different types of land-use and thus require separate analyses. This means that a great many analyses need to be conducted to assess relationships across agroecosystems, so broad assessment processes will always be highly time-consuming and overall patterns are unlikely to be simple.

Methods for the analysis of biodiversity data for purposes such as measuring changes over time and the effects of environmental variation have been developing for more than 20 years and are now very sophisticated, so identifying suitable approaches for this study was not a problem. There are also numerous forms of data and available datasets on biodiversity

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and land‐use. Then, at the continental scale, there are huge quantities of data simply because of the areas involved. This creates significant challenges for analyses because each one can require considerable computer power and take many minutes, or even several hours, to complete. With more sophisticated, newer methods, individual analyses with large data sets could even take days to complete. In this way, the methods that have been developed in principle are some years ahead of developments in the computer resources that are available even in most high‐performance systems. This provides a significant limit to the extent to which ideal analytical possibilities can be applied to real‐world problems, so compromises can be necessary.

It is important to note that analytical models aiming to evaluate biodiversity responses are not necessarily also suitable for predicting them. This is because identifying that one or more influences on a response variable are important does not mean that all such influences have been identified. Prediction is clearly potentially a valuable output from models but, to be used in prediction, models need first to be assessed for predictive power. This means that they must provide a good fit to the data and they must predict reliably, for example with models being built on one division of the original data and tested on another. The “models” considered here are statistical analyses for the purpose of evaluating the effects of environmental influences, only. It may, ultimately, also be possible to develop these models further in order to generate predictions of the effects of future changes in the important predictor variables, but this would require additional research and there is no guarantee that reliable predictive models can be constructed from any given set of predictors.

11.2 The availability, quality and suitability of relevant data

11.2.1 Biodiversity data Biodiversity covers a huge range of taxonomic groups and individual species, for all of which we would ideally have high-quality data. All have different environmental requirements and many require different survey methods, even within birds or mammals, for example, so the structure of data sets can be highly variable. In terms of the data that are available in practice, there is a strong bias towards popular groups that are easy to survey visually, often because data collection relies on volunteer surveyors. Moreover, data quality is higher for the more popular groups, because the numbers of interested observers are sufficiently high to identify a subset who will undertake formal, structured sampling. The best monitoring schemes incorporate randomized selection of sampling locations and standardized protocols, so that sampling effort is controlled. Such data are available from multiple Member States and at large scales only for birds and butterflies. In the future, other taxa, notably bats, are likely to have more widely available, high-quality survey data, but current data for groups of animals and plants other than birds and butterflies consist of unstructured records. New methods to extract the maximum information from such data are in development, but are currently likely only to provide information on the presence or absence of species from particular areas. The presence of a species could mean that there are few individuals or a thousand, and records are possibly more likely to be made when finding a species is unusual. Hence, these data (and presence-absence data in general) provide less information and are less sensitive to the effects of environmental change than count data, which therefore provide greater possible value for inference.

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Biodiversity data are typically available at sampling site or record level, with spatially referenced locations allowing easy matching to environmental datasets. This was the case for all of the bird and butterfly data used in the integrated assessment analyses. However, there remain restrictions on data access, reflecting their ownership. While data may be collected using public funding in Member States, with sampling, by volunteers, there is also often considerable intellectual property invested in datasets by the organizations that have designed the surveys and that then manage data collation and analysis. This means that data holders can require financial support to maintain the collection of data and management of survey programmes, without which data will not be supplied. Alternatively, they may require analysts to obtain a licence to use the data and to consent to active involvement in the study of the data holder. This was avoided for birds in this project because the coordinators from PECBMS for the data holders from across Europe were on the project team, but was an issue for butterflies. More generally, these constraints on the sharing of data and on collation via a third party need to be taken into account for future studies and planned work programmes. They may lead to additional demands for financial resources to support research, or to revisions to the schedule on which work can be conducted in practice.

Finally, the basis for future integrated analyses is expected to be boosted by the ongoing initiative on the Mapping and Assessment of Ecosystems and their Services (MAES), within which work is advancing on a framework to define, to assess and to map the condition of ecosystems across the EU (including pressures and drivers) and to link it to ecosystem services.

11.2.2 Agricultural and management data Data are available on many broad cropping and land-use splits, but not all that are of interest for biodiversity impacts. For example, whether crops are sown in spring or autumn and whether spring-sown crops are preceded by over-wintered fallows or stubbles can have very large impacts on birds, but this information is rarely available or accessible. Information is also usually not available on crop varieties, pesticide or fertilizer inputs, patches of semi- natural habitat in farmland, field boundary types, agri-environment scheme management or farming systems.

In general, where relevant data on the landscape and agriculture are available, it is as large- scale summaries, rather than at local scales. An exception is provided by the data for some potential linear features from LUCAS, for which the sampling point data are available; but the sampling scheme for this data set is designed to inform about very large scales and has limited value for informing about landscape features at the scale of biodiversity sampling locations. Especially at larger scales, land-use or other spatially variable data reflect all features of the environment that are associated with that land-use, such as hedgerows with livestock fields, crop type with geographical location or soil type, dominant agricultural practices and other crops found in a rotation and irrigation with water-sensitive crops. This almost always means that apparent effects of one variable will be confounded with the possible effects of others, which therefore cannot be separated from those of the focal variable. However, it also means that the area of a given crop, say sugar beet, actually refers to an approximation of the area of the farming system that includes sugar beet (i.e. higher

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or lower areas of other crops that feature in the rotation with sugar beet). All land-use variables also potentially describe variation in the practices that are associated with those land-uses, such as harvesting dates and maintenance of crop stubbles. For example, larger areas of winter-sown crops will often mean earlier harvesting and a more common incidence of autumn cultivation. Thus, analyses indicating an “effect” of a variable or an “association” with it should really be interpreted as showing an association with one or more of the factors that are correlated with the variable. Note that that separating such influences may well be possible in principle, but requires both independent data on the factors involved (for example, crop type, field boundary structure and sowing time) and sufficient instances where different combinations of these factors occur to provide contrasts for testing. This is always more likely to occur by chance with data with finer spatial resolution, or otherwise requires an experiment to be set up.

The constraints described above arise because data were made available for the study only for large regions, but for cropping and livestock data, this is often an artificial situation: raw or site‐level data at small scales generally exist but are not accessible for reasons of data protection and commercial sensitivity. Hence, the existence of data that are potentially valuable for analyses does not translate into enhancements of the analyses that are possible in practice. Data sharing at EU level, with regard to making data of this kind accessible for analysis would greatly improve the quality of inference that is possible. The confidentiality and commercial issues remain important, but could be addressed, for example through anonymization of data by a trusted agency or by strict licensing applied to all data releases.

A further constraint of the data restrictions is that the co-occurrence of different crops and associated habitats described above is the only way in which farming systems have been taken into account. No data were available on farming systems per se, and the lack of data on factors such as sowing or ploughing time, chemical inputs, farm boundaries and crop rotations mean that systems could not readily be defined from the data. Hence, the influence of specific farming systems, such as organic farming, could not be investigated.

Data on cropping and other land-use factors are available from a range of sources, including Member State government records, EU-level RDP statistics and bespoke environmental survey programmes, such as LUCAS. However, a growing source of information, both in terms of raw data and modelled products processed by agencies such as JRC, is remote- sensed data, mostly from satellite photography. These data provide unparalleled coverage, free from the limitations to data access that arise from collection from farmers for commercial or regulatory purposes, for example. They can also provide high-resolution information, at scales as small as a few metres. However, the information that can be extracted is limited by factors such as cloud cover (meaning that few images are available for some regions), and the ability currently to distinguish different types of feature (e.g. semi-natural versus improved grassland). In addition, distinguishing many key agricultural parameters, such as crop type and timing of tillage, relies upon images being available from multiple points in the growing cycle; the necessary images for which may well not be available. This means that these data, while extremely valuable, do not necessarily provide all the information required for assessing relationships with biodiversity. The processing of remote-sensed data is becoming more sophisticated all the time, so these issues may

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become less significant in the future, particularly with regard to distinguishing crop and grassland types (Lausch et al. 2016).

11.2.3 Implications of scale of data As discussed above, many agricultural data sets are only available at large spatial resolutions, such as the NUTS3 region. Similarly, many biodiversity data are frequently published or summarized at regional or national scales, and as temporal trends as opposed to raw data. To reach conclusions from analyses linking environmental and biodiversity data, regardless of the methods used, it is critical to maximize the contrasts in the data, i.e. to have biodiversity response measures for a wide range of values of the potential agricultural predictors. In addition, to be able to separate the possible effects of different predictors, it is critical to maximize the availability of sample data from locations with contrasting relative values of these predictors. All of this means that analyses are stronger if data are available from smaller geographical units, rather than as summaries across large regions; in general, data availability at smaller spatial scales increases the power and sensitivity of analyses.

A further problem with only having data available at large spatial scales is that the information is unlikely to be representative of any given small area within the region for which data are available. This is especially likely when the smaller area is special in some way, for example a Natura 2000 site, but it applies to all situations where there is a mis- match between the scales of predictor and response data, as is the case with the biodiversity data (at site level, such as a 1km square) and agricultural data (at the NUTS3 region level) in the integrated assessment analyses. It is possible to down-scale data from regions to smaller areas, as JRC have done with various agricultural cover and input data (Leip et al. 2008), but this relies on a range of assumptions that may not hold, so we have been advised not to use these down-scaled data to inform about variation in land-use at small spatial scales (JRC, personal communication).

11.2.4 Database This project produced a database in Microsoft Access (supplied with this report) that provides metadata on all biodiversity, agriculture and land-use data sources found, and also includes the results from the integrated assessment analyses conducted here (Chapter 4). The collated data represent the results of a significant data processing task, which should not need to be repeated (except for updates of the data considered here) in the future. The results are important because they inform about relationships between different variables from a range of data sources listed in the metadatabase. These data are presented in spreadsheets linked to those describing the source biodiversity and agricultural data in the database, so that users can follow the links explicitly. This collation of information should make future analyses much more tractable, while the species-specific results from this project provide a resource for potential new meta-analyses using different combinations of agricultural predictor variables, Member States and bird or butterfly species.

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11.3 Integrated assessment of the relationships between agriculture and biodiversity in Europe

11.3.1 Elements of biodiversity and agriculture covered Analyses were only possible for birds and butterflies. Bird data analyses were conducted for 2002-2014 and butterfly analyses for 2002-2015. These data were obtained and analysed at the site level. “Sites” differed between Member States, but were approximately consistent in size (typically a 1km square or smaller) within Member States and all data were standardized for comparability between Member States. Agricultural data in terms of crop areas and livestock numbers from the EUROSTAT database of Farm Structure Survey (FSS) were used, in conjunction with data on fertilizer inputs from the Common Agricultural Policy Regionalized Impact (CAPRI) modelling system, data on linear features from the 2015 Land- use/cove Area Frame Survey (LUCAS) and an index of the heterogeneity of farmland derived by JRC. All analyses were limited by the availability of data on key potential influences and the scale of the data that were available, as discussed above.

Birds are high in the food chain so widely representative; butterflies, like bees and a range of other insects, depend on floral resources and the availability of the plants on which their larvae feed. Both groups, as a whole, can therefore be considered to be representative of broader ecosystem health. For birds, especially, farmland indicator species were chosen, whose populations are dependent on farmed habitats, so negative trends in abundance and negative effects on those trends from agricultural variables can be considered to be more broadly indicative of problems in the farmland environment. However, formally, this study was unable to investigate effects of agriculture on all other wildlife groups, including important ones from the point-of-view of agricultural systems such as pollinating insects, soil biota, rare arable plants and economically important mammals.

As discussed in Section 11.3.2, with non-experimental data and only a large spatial resolution for the data tested, it is inevitable that individual environmental predictor variables will, to some extent, actually act as proxies for other properties of the systems or landscape contexts in which they are found. Thus, results for spring barley, for example, are likely to reflect whole farming systems in which spring barley is found. However, it was equally not possible to define land-use, especially at the NUTS3 scale, in terms of farming systems. It is possible that, for example, a negative association with a crop variable actually masks a positive association with that crop that might be found if analyses could be conducted with field- or farm-scale data, or that has been shown by previous studies. Future work might usefully investigate such patterns further, probably using smaller scale field studies or case studies using national or regional data if analyses with finer spatial resolution are possible.

The analyses conducted here, as per the specification for the project, considered the relationships between agriculture and biodiversity, not agriculture relative to other possible influences or a complete analysis of all possible influences on wildlife populations. Such analyses with a broader scope would be far more complicated and introduce still more uncertainty, because there are many different ways in which to characterize variation in factors such as weather or climate, for example, each of which could produce different results with respect to agricultural variables if they are used as controls. A general point,

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however, is that the analyses here tested the relative effects of agricultural variables on biodiversity responses over time, comparing regions with different values of the variables. While other factors are certain to have been influential as well as the agricultural variables, and quite possibly more influential in some cases (e.g. weather variations, such as cold winters or wet springs, or urbanisation in some regions), these are likely to affect species similarly across a range of regions with different values for the agricultural variables. There is therefore no reason to suspect that any such factors that were not considered here will have varied systematically to confound effects of agricultural variation significantly.

11.3.2 Key conclusions As there are very many tests for individual Member States, agricultural variables and bird and butterfly species; it is not feasible to describe or interpret them all here. While some variables more often showed positive than negative associations, there were also numerous converse relationships for all of these variables, so there is no simple, general pattern to report. However, the patterns found across species can be summarized as average relationships across Member States or numbers of positive and negative relationships across species. The individual results have been added to the database described in Chapter 3, which has been supplied separately to the European Commission. Further exploration of the patterns of results by species, species group, Member State, or combination of these, can be conducted by reference to the database.

There were net positive associations between bird population growth and cereals, fodder root crops and green plants (see Table 4-3 for definitions of the predictor variables), probably reflecting the fact that species were considered that select agricultural habitats. Similarly, negative associations with conifer hedges, heterogeneous landscapes, permanent crops and artificial boundaries are likely to reflect the same broad habitat preference. Perhaps more surprising are positive associations with avenue trees and woodland margins, and negative ones with managed hedges, grassy margins and pulses (Table 4-6). The former might be expected to have negative influences on farmland birds, which are generally (evolutionarily) species of open landscapes. However, many also require woody habitats for nesting and use trees as song posts, which can both be important habitat components and those where they are most detectable during surveys. Hedges, grassy field boundaries and pulse crops are all land-use components often positively associated with birds as nesting or foraging habitat; pulses potentially being important crops because they are often sown in spring and hence provide habitat heterogeneity in landscapes dominated by winter cropping. It may be that these results, together with the negative association with energy inputs, reflect the negative influences of more intensive management of farmland, via associations with broader regional factors, such as farming systems as a whole.

For butterflies, there is no accepted set of farmland species of the kind that exists for birds, so the species selection was inclusive, and many monitoring sites will not have been situated in productive farmland. Therefore, it is interesting that there were more, or stronger, positive associations between butterfly population growth and agriculture variables than there were negative ones. This suggests that many aspects of agricultural management have positive influences on many butterfly species. However, it is probably significant that the positive influences included the less managed elements of farmland boundaries, such as ditches and unmanaged hedgerows, while fences, dry stone walls and

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managed hedgerows, which will provide habitat less like the semi-natural habitats that butterfly species are likely to prefer, tended to show more negative associations.

11.4 Case studies

11.4.1 Case study 1 – Factors affecting the condition of agriculture-associated species and habitats within Natura 2000 sites In theory, conservation status / trend and site-level degree of conservation data provide relevant information that can be used to identify and examine agricultural and conservation management factors that affect the effectiveness of the nature directives. This case study therefore explored the feasibility of analysing the relationship between agricultural factors and the overall conservation status / trends, or their Natura 2000 site-level ‘degree of conservation’, of habitats and species that are potentially influenced by agriculture. The study was forced to conclude that, currently, the available data do not allow such analyses, either because they provide insufficient samples (in the case of conservation status assessments) or because key data are missing on the site-level conservation assessments (i.e. on the date of the assessment). It is suggested that the first priority should be to collate the monitoring data that are currently available and to record metadata on each dataset. Great improvements in the quality of evidence could then be obtained by considering time series of monitoring data (e.g. from six-yearly assessments), rather than single, on-off condition records. Data recording these changes would allow analyses to inform an assessment of whether changes in condition are associated with changes in agricultural systems and practices, and conservation management interventions, such as management plans and agri-environment measures.

Much stronger analyses would be possible with structured, randomized sampling to generate quantitative abundance or cover data for target taxa and habitats within Natura 2000 sites and, if possible, also for representative counterfactual areas, would be ideal. Standardization across Member States of the methods used in these surveys, and consistency in them over time, would be ideal here, but documentation of the methods, so that it is clear how data can be compared is essential for informed analyses, should be an absolute minimum. An alternative to new sampling activity would need to use existing monitoring to conduct tests comparing abundance or trends inside and outside protected areas, or measuring changes relative to a baseline in a target site (see, for example, the case study in Chapter 6).

Data on land-use are, of course, essential, as well as biodiversity data. Access to currently confidential, fine-scale land-use data would be highly advantageous, while bespoke records of habitat/land-use at the Natura 2000 site level, or validated predictive models based on existing, sampled data could be used to enhance the possible inference. The more detailed such records are, the reliably we can identify the truly important influences on target species and habitats.

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11.4.2 Case study 2: Effects of Natura 2000 site designation and management plans on farmland bird abundance trends This case study aimed to disentangle the effects of designation of farmland Natura 2000 areas and the application of management plans for them on farmland bird populations, by comparing abundance trends of farmland bird species within and outside 885 agricultural Natura 2000 areas and testing the effects of management plans on trends within Natura 2000 areas.

The results showed that Natura 2000 sites in farmland habitats are located in areas with higher farmland bird abundance, but the designation of Natura 2000 sites per se has not slowed declines in local abundance of farmland birds. However, farmland bird abundance trends were less negative for Natura 2000 sites that have management plans, where Natura 2000 coverage of the area monitored was large (so data from monitoring are more representative of Natura 2000 coverage than where such overlap is small), indicating the importance not only of protecting sites, but also of applying conservation measures. In order to analyse the latter, however, data are needed on which concrete conservation measures have been triggered on the ground by Natura 2000 designation or the adoption of a management plan.

Although Natura 2000 protection with management plans seem to have been successful in reducing rates of decline, the results indicate that improved plans or additional management outside the Natura 2000 network may be required fully to reverse the trends and to generate increases in numbers. The similar trends for farmland bird populations inside and outside Natura 2000 sites may be a consequence of the large proportion of Natura 2000 sites (43%) that have no management plan. Information on whether management plans existed was missing for 32% of the sites considered, and there was insufficient information on the actual implementation of conservation measures, which reduced the power of the analysis.

11.4.3 Case study 3: large-scale and long-term bird distributions in Britain and Ireland in relation to lowland agricultural land-use Standardized presence-absence data for birds in Britain and Ireland at the scale of the 2×2km “tetrad” were related to the highest-resolution data available on agricultural land- use, in order to identify the relative importance of different agricultural land-use drivers for determining the distribution of birds in farmland. The very high spatial coverage allowed an unbiased assessment of the relative importance of each agricultural variable considered.

A broad range of patterns of agricultural land-use associations were found across species, reflecting the variation in ecology between species. It is noteworthy that land-use practices associated with both more and less intensive agriculture each have both positive and negative associations with the suite of farmland bird species considered here. However, the data used have various important limitations that need to be accounted for in interpretation and it would be unwise to base predictions of the effects of habitat or cropping change on the models fitted here.

Using agricultural data at a smaller scale (i.e. closer to that of the bird data and not just regional data), would help to elucidate the associations found, to establish more clearly

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whether variations in bird distributions can be explained by cropping, especially if data on field boundary and other semi-natural habitats could also be incorporated. Such guidelines could also be usefully applied to design analogous analyses for other Member States.

11.4.4 Case study 4: UK agri-environment schemes and non-avian biodiversity Data from the Wider Countryside Butterfly Survey (WCBS) for England were used to test associations between management and temporal trends of butterflies in the wider countryside (as opposed to those associated primarily with large expanses of semi-natural habitat). Similarly, mammal data from the BTO/JNCC/RSPB UK Breeding Bird Survey (BBS) were used to conduct long‐term, national‐scale assessments of AES effects on mammals. These analyses used methods previously used successfully to assess similar effects on birds. Eighteen species of butterfly and three species of mammal were considered, over the periods 2006-2013 and 2002-2013, respectively.

There were both positive and negative associations between agri-environment management and the population growth of butterflies and mammals, but key patterns included the result that specifically targeted options providing arable nectar resources were positively associated with two butterfly species and negatively with one. Among mammals, AES options providing cover were generally positively associated with Brown Hare population growth rates, while those involving management of hedgerows had positive associations with Rabbit growth rates. Hedgerow options had largely negative associations with butterfly population growth rates.

Overall, AES management in England has had a positive effect on Brown Hare and perhaps on two common butterflies. However, many more butterflies appear to have been affected negatively, which is a clear cause for concern. Future research should focus on the causes of the butterfly results and on possible negative consequences for other taxa of the positive effects on rabbits.

11.4.5 Case study 5: The effects of increased rape and maize cropping on agricultural biodiversity This case study presents a quantitative review and meta-analysis of the evidence for impacts on biodiversity of increases in maize and oil-seed-rape in Germany, which was been driven by policies encouraging energy crop production. The study also examined relationships between these crops and species of conservation interest. While most crops for biofuel and biogas are grown on intensive farmland, some may be found on Natura 2000 land or adjacent to areas protected under national law; and this review specifically sought evidence for impacts on these protected areas. In addition, particular attention was given to direct and indirect negative impacts on declining species, including those protected by the Habitats and Birds Directives.

The effects on biodiversity of maize cultivation, relative to those of fallow land, permanent grassland or other crops appear were generally negative, and more negative than those found for rape cultivation, where some strong, positive effects occur as well as numerous but less pronounced negative ones. Earthworms, ground-living insects and spiders, but also insect pollinators and most of the farmland birds, were clearly negatively affected by maize cultivation. These patterns relate to simple loss of more suitable crops (replaced by maize),

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but probably also knock-on effects on such losses at lower trophic levels (e.g. invertebrates) on higher trophic levels (e.g. birds). Crop fields may also provide ecological traps if they look suitable for a species at the beginning of the breeding period but are not suitable when the crop develops.

Unlike maize, rape can provide at least some ecological functionality and, because its area is smaller than that of maize, its displacement effects are also smaller. Several species or species groups can feed on it, or associated invertebrates, over its different stages of growth. During the mass flowering period rape provides abundant food for all kinds of insect pollinators. Unfortunately, this period is very short and followed by a long period where rape fields provide no food for flower visitors and the vegetation is too dense to allow access for many species, such as some birds. Ground living invertebrates may benefit from the exclusion of such predators; but the birds may suffer from the loss of foraging area. However, it is important to note that a complete assessment of the relative impacts of all crop types could not be conducted, so the apparent effects of rape could, in reality, reflect the influence of other, correlated land-uses instead.

Structural changes at the landscape level, aiming for an optimization of field size, land-use composition, allocation of crops, grassland and fallow or broadened field strips and hedges, with an ecological focus, may provide effective mitigation of some of the negative effects of large-scale and concentrated maize or rape cropping. These possibilities should be investigated in further studies.

A special case related to the extension of arable land used for the production of energy crops is the Common Crane. This species is likely to have benefited the most from recent developments in this regard, combined with positive effects of increased winter temperatures in the breeding and stopover areas due to climate change.

11.4.6 Case study 6: Effects of habitat heterogeneity on biodiversity in intensive arable farms In this case study, the abundance and species richness of spiders, butterflies and birds, and fine-scale habitat land-use at two spatial scales, were compared between two adjacent, intensively farmed arable landscapes in Austria and the Czech Republic to investigate the effects of habitat heterogeneity and composition on biodiversity. The different socioeconomic and political histories in these countries have led to contrasting habitat heterogeneity and composition patterns (a heterogeneous landscape with small fields in Austria and large homogeneous crop fields in the Czech area), but similar climate and crop types.

The study found strong and consistent evidence that biodiversity as measured in relation to species richness increased in relation to habitat heterogeneity, for all three taxa groups examined: birds, butterflies and spiders. The farmland heterogeneity measure that had most positive effects on biodiversity was the larger proportion of non-cropped elements, followed by smaller patch size. Furthermore, species of conservation concern in the EU that were examined (Annex I bird species and grassland butterflies) were more common in areas with smaller patch sizes. This indicates the biodiversity value of conservation measures that maintain or promote agricultural landscapes with non-cropped elements and small fields.

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11.5 General conclusions and recommendations The key conclusions from this project can be summarized as follows:

 Where data are available for analysis, there are well-developed statistical approaches available to provide appropriate inference from those data with respect to effects of different agricultural factors on biodiversity, but analyses need to be designed with care to work effectively with very large datasets.

 There are many significant relationships between agricultural variables and measures of (change in) biodiversity, including both positive and negative associations. These were revealed by the application of the best statistical methods available (Chapter 2), adapted to cope with large volumes of data (Chapter 3). The results reflect the preference of many species for farmland and the range of ways in which changes in agricultural practices have led to declines in many species traditionally associated with farmland, while other species have increased because newer practices improve habitat quality for them (e.g. Siriwardena et al. 1998, Chamberlain et al. 2000, Donald et al. 2006). More specific studies of the influence of basic agricultural variables, such as particular crop types, can show cause and effect more clearly, as demonstrated by the wide-ranging and strong evidence that increases in maize production in Germany have been negative for most taxa and have probably contributed to observed biodiversity declines (Chapter 9).

 The integrated assessment analyses (Chapter 4) and Case Studies 2, 3, 4 and 6 (Chapters 6, 7, 8 and 10) are all fundamentally correlative, rather than experimental, so the relationships found may indicate either genuine, direct effects of the variable involved, or indirect effects of other variables that happen to be spatially associated with the focal variable. It is very likely that such confounding factors particularly affected the results of tests using only very large-scale data on agricultural variation (Chapters 4 and 7).

 Improved accessibility to local-scale agricultural data would greatly improve analytical quality, help to avoid confounding factors and assist researchers in reaching clear, general conclusions. Given government or Commission interest in identifying policy successes and failures, the value of collecting and making available high-quality land- use (for example) information like high-resolution agricultural data should be considered and traded off against competing factors, notably the cost of data collection and pressure to maintain data confidentiality.

 An issue highlighted in the terms of reference for this study was the potential for response time lags between changes in agricultural practices and measurable impacts on biodiversity. In principle, this is straightforward, but requires further analytical processes and so was not possible in the time available. In addition, this requires detailed time series data on agricultural variables, which were often not available, too intermittent or highly variable in coverage between Member States.

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 A second additional area of interest a priori was to consider the implications of using data collected at different scales, such as the 1km square up to the NUTS3 region. This would inform the scale at which data need to be collected in the future to provide reliable evidence. However, finer resolution agricultural data to support such analysis were not available.

 The Case Studies conducted within this project covered disparate topics, so do not lend themselves to integration to generate general, overarching results. Three Case Studies considered refinements of specific issues related to those at the EU scale in the integrated assessment (Chapter 4): cropping effects on bird distributions (Chapter 7), effects on multiple taxa of two specific crops (Chapter 9) and effects of land-use heterogeneity on three taxonomic groups (Chapter 10). However, even here, the cropping and/or biodiversity data were at different scales or measured different aspects of biodiversity, so are not comparable with the integrated assessment results. The conclusions of the individual Case Studies are summarized above.

In conclusion, this project has confirmed that the associations between agriculture and biodiversity are diverse and complex. This is because there is a long history of adaptation of taxa to agriculture in Europe, with some agricultural processes (e.g. grazing) and resulting habitats being similar to former natural habitats so that many species now depend upon them. Conversely, however, rapid changes in agriculture in recent decades have reduced the suitability of agricultural habitats for many of these species. At the same time, some CAP measures (e.g. Pillar 1 greening, and Pillar 2 agri-environment schemes) are designed (at least partly) to mitigate such negative impacts in the areas that they apply. Therefore, it is unrealistic to expect to obtain simple answers to questions relating to effects of agriculture on biodiversity and this project has not found such simple answers. Whilst average responses across groups can be determined, and specific questions relating to particular species or groups, such as those relating to landscape heterogeneity or Natura 2000 site protection, can be answered, the results are still likely to be complex. Relationships between specific species and variables in the results of this study (as included in the database) can also be examined to address particular issues of interest.

To develop the analyses conducted here in the future, suitable analytical methods are available and are improving constantly as new approaches are developed and tested. The principal constraint is data availability, both for biodiversity (especially non-bird taxa) and for agriculture and related land-use influences. Some aspects of data availability are also improving, notably via the products of remote-sensing, but investment in data collection and mobilization would be highly beneficial for future work. In practice, large-scale, high- quality biodiversity data are likely to depend upon schemes organized around sampling by volunteers, which will always be biased towards more popular taxa, such as birds and butterflies, although more use of the available data for other taxa is likely to become possible as analytical methods develop. For agriculture and other land-use factors, continuing improvements in remote-sensing and the interpretation of the resulting data will provide improved information at small spatial scales, but there is a clear opportunity for a step-change in analytical quality if raw data or local-scale summaries from the monitoring of agriculture by Member States could be made available for analysis.

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12 Glossary

This is a list of selected relevant terms in statistics and ecology which appear in the main text. Terms that are underlined are defined elsewhere in this Glossary. More comprehensive online definitions can be found at sources including:

 Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/2/glossary/index.html). Project Leader: David M. Lane, Rice University.  SticiGui Glossary of Statistical Terms. Philip B. Stark, University of California (https://www.stat.berkeley.edu/~stark/SticiGui/Text/gloss.htm).

Akaike Information Criterion AIC is a numerical value by which competing models are ranked in terms of information loss from the data. The model with the lowest AIC value is considered to offer the most parsimonious fit to the data, i.e. the simplest model providing an adequate description of the variation in the data.

Association Two variables are associated if some of the variability of one can be accounted for by the other. Association does not imply causation, unlike a statistical effect. Correlation is a specific type of association.

Bayesian model Based on Bayes’ Theorem, where the prior probability of an event and the diagnostic value of a test are used to determine the posterior probability of the event, i.e. background information, such as previous experimental data, can be incorporated into the prediction.

Bias The extent to which a sample is unrepresentative of the target population.

Binomial distribution A probability distribution for independent events for which there are only two possible outcomes, such as a coin flip.

Categorical variable A variable which has two or more qualitative categories; these have no intrinsic ordering. It can take on one of these categories. This differs from a continuous variable.

Chao index A measure of species richness, i.e. the number of species present. This is not based on a particular predicted form of the species abundance distribution. Instead, it uses information on the frequency of rare species in a sample to estimate the number of undetected species.

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Chi-squared test A test based on the chi-squared distribution to compare the distribution of observations relative to the expectation by chance or another predetermined distribution. Formally, it tests the goodness of fit of theoretical and observed frequency distributions or to compare nominal data derived from unmatched groups of subjects. In the latter case it is used to determine whether the variables are independent. In general, short for Pearson’s chi- squared test, also written as χ2 test.

Confidence interval A confidence interval is an interval which has a known and selected probability (generally 95% or 99%) of containing the true value.

Continuous variable Variables that can take on any value in a certain range. Variables that are not continuous are known as discrete or categorical variables. No measured variable is truly continuous; however, discrete variables measured with enough precision can often be considered continuous for practical purposes.

Control variable A variable whose effect is not of interest as a predictor of the response variable in a model, but which may be an influence upon it and may confound or obscure effects of interest. Hence, the variable is included in the model, but the results referring to it may not be reported.

Correlation The extent of linear association between the ordering of two variables. Correlation is positive or direct when two variables move in the same direction and negative or inverse when they move in opposite directions. A positive correlation (coefficient) between variables indicates that a higher level of one variable will be associated with a higher level of another, while negative correlation indicates that a higher level of one will be associated with a lower level of another. Correlation is a scaled version of covariance. Also known as inter-correlation. See Spearman’s coefficient.

Covariance A measure of the association between two random variables. A higher value indicates a stronger association, but this must be scaled by the standard deviation to compare the associations across different datasets; see Correlation.

Dependent variable See Response variable.

Distribution (or error distribution) Often short for probability distribution: a mathematical function that provides the probability of occurrence of the possible values of a variable. For example, probabilities follow a binomial distribution, between zero and one, while counts often follow a Poisson distribution, which allows only integers and numbers of zero or greater.

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Driver In ecology, any factor that directly or indirectly causes a change in an ecosystem or a parameter describing the status of a species.

Effect Used in two contexts: (i) Ecologically, describes the relationship between a predictor and a response, with the implication of a causal relationship, e.g. “the effect of x on y”, as opposed to an association, which does not imply causation. (ii) Formally, in statistics, an influence on a response variable, often used as an element of a statistical model. May appear in the context of fixed or random effects. Fixed effects are the general form, and model the role of predictor variables; as such the predictor variables are treated as if they are non-random. Random effects are used in GLMMs, to account for the role of control variables.

F-test A type of statistical test used in models to indicate whether parameter estimates are significantly different from zero. More formally, a test used to assess whether a group of variables are jointly significant, or if the variances or standard deviations of two normally distributed populations are equal. Based on the F distribution, which is a ratio of two chi- squared distributions. If the calculated value exceeds the corresponding reference value from an F table at a specific confidence value, the variances are not equal.

Fixed and random effect predictors See Generalised linear mixed model.

Free choice sampling Sampling sites are chosen by surveyors as they see fit. In some cases this may distort results, e.g. if sites are chosen for high populations of the surveyed species.

Generalised estimating equations An alternative approach to fitting GLMs and can be considered an extension of them to longitudinal/repeated-measures data, as it is suitable for analysing non-independent observations. The key difference is that the models include an explicit account of the inter- correlation between data points, so that the precision of results is not over-estimated due to pseudoreplication and an artificially high apparent sample size.

Generalized linear model (GLM) A generalization of the linear regression model such that non-linear, as well as linear, effects can be tested for categorical and continuous predictor variables. GLMs allow non-normal error distributions of the response variable, using a link function of the exponential family of distributions, which defines the shape of the non-linear function that is modelled and constrains outputs to appropriate ranges (e.g. greater than zero for counts, or zero-to-one for proportions). For example, a GLM with a Poisson error distribution and log link function may be known as a Poisson regression, and is suitable for count data.

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Generalized linear mixed model (GLMM) An extension of GLMs in which the linear predictor contains random effects in addition to fixed effects. Also an extension of linear mixed models to non-normal data.

Independent Two variables or events are independent if they have no influence on each other (i.e. are not correlated).

Independent variable See Predictor variable.

Likelihood ratio test Compares the fit of two models based on the ratios of their log-likelihood functions. The test statistic approximates a chi-squared random variable. This is test is commonly used with GLMs to compare models with and without a given parameter, to test the significance of that parameter. See Chi-squared test.

Linear mixed model See Generalized linear mixed model.

Linear regression Method for predicting a response variable from one or more predictor variables, fitting these to the form of the best-fitting straight line. The fit is generally based on minimizing the sum of the squared errors of the prediction. Simple linear regression uses one predictor variable to predict the response variable. Multiple regression uses two or more predictor variables. The coefficient of determination is used to quantify the explanatory power of the model, and is represented by r2 for a simple linear model and R2 for a multiple linear model. The adjusted R2 value penalises the addition of more predictor variables.

Link function The link function in GLMs specifies a non-linear transformation of the predicted values so that the distribution of predicted values is one of several special members of the exponential family of distributions (e.g., gamma, Poisson, binomial, etc.). The link function is therefore used to model responses when a dependent variable is assumed to be non- linearly related to the predictors.

Meta-analysis Quantitative study of published results relating to a particular problem. Conclusions about heterogeneity and overall significance are usually based on combinations of published values of test estimates, such as average parameters weighted by their precision or the sample sizes from which they are derived.

Metadata Metadata are data that define and describe other data.

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Model A statistical model represents the relationships between two or more variables in the form of a mathematical equation. The model approximates the values of the dependent variable from the predictor variables, producing fitted values with a certain goodness-of-fit to the measured values. This goodness-of-fit reflects the explanatory power of the model. A statistical model contains assumptions describing a set of probability distributions. The response variable is modelled as the expected value of a distribution, and is based on predictor variables and an error term which is drawn from a distribution.

Multicollinearity The condition occurring when two or more of the predictor variables in a regression equation are correlated.

Multi-model inference/ Model averaging These are two elements of a broad modelling philosophy that aims to identify the best- supported model for a given data set from a set of predictors and to produce unbiased estimates of the parameters in the model. Broadly, this considers all possible models given a defined set of candidate predictor variables and generates average parameter estimates and measures of the relative importance for the predictors. Multi-model inference uses AIC values (or other information criteria) to compare models without the use of significance tests, and to rank their relative explanatory power per number of parameters included. Model averaging uses the AIC values to calculate average parameter estimates across the full model set, weighted by the support given to each mode by the data.

Normal distribution A common probability distribution whose values are symmetric about the mean. Natural variation in biological parameters such as heights and weights is commonly normally distributed. It is often used to represent random variables whose real distributions are unknown. Also known as a Gaussian distribution.

Ordinal variable An ordinal scale is a set of ordered values with no set distance between scale values. As such, it is not quantitative, but the values are ranked, unlike those considered as a categorical variable.

Overdispersion A condition in which data contains greater variability than would be expected from the distribution used for a statistical model. This is common with count data, where individuals’ locations are not independent, because they are found in pairs, groups, herds or flocks.

P value See significance.

Pearson’s χ2 goodness-of- fit See Chi-squared test.

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Poisson distribution A distribution that represents the number of events occurring randomly in a fixed time at a certain average rate. It is found where data values are integers and greater than zero. In ecology, it is often used for modelling count data.

Population In ecology, a group of organisms that can produce offspring. Generally, a population is spatially and temporally co-localised, consists of organisms of the same species, and is nominally functionally independent of other such populations. In statistics, a population is a large set of similar items or events which is of interest as a whole; many statistical tests are designed to draw conclusions about a certain population.

Predictor variable Predictors (also called independent, explanatory or input variables) are variables used to predict or to explain the value(s) of one or more dependent variables.

Principal components analysis (PCA) A multivariate technique that analyses a data table in which observations are described by several inter-correlated, quantitative, dependent variables. Its goal is to extract the important information that discriminates between subjects, represent it as a set of new uncorrelated predictor variables called principal components, and display the pattern of similarity of the observations and of the variables as points on graphs, the axes of which are the components. PCA is commonly used to reduce a set of candidate predictor variables down to a smaller set of uncorrelated variables, prior to modelling analyses.

Pseudoreplication The precision of test or model results and therefore the strength of inference provided depends on sample size: the more data, the better. However, if data points are not independent, e.g. the same individuals have been counted in two locations or populations are tightly linked across sampling areas, the apparent sample size is inflated and the data are said to be pseudoreplicated. More formally, this is defined as the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent. Pseudoreplication can be accounted for using modelling approaches such as generalized estimating equations.

Quasi-information criterion Also known as the quasi-likelihood model criterion or ‘quasi-likelihood under the independence model information criterion’. It is a measure of the relative quality of a statistical model for a dataset, and is used for model selection from a set of candidate models. It is the quasi-likelihood counterpart to the AIC, which is based on maximum likelihood, and is used in the same way with repeated measures models fitted using generalized estimating equations. Quasi-likelihood provides a method of dealing with overdispersion.

Random intercept Feature of a model in which the intercept is a random variable. See Linear mixed model.

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Random sampling The process of selecting a subset of a population for the purposes of statistical inference. Random sampling means that every member of the population is equally likely to be chosen. When this rule is violated, the sample is said to be biased. See also: stratified random sampling.

Random variable A variable whose values depend on the outcome of a random process. It can be expressed as a function which maps probability to a resulting value. A random variable has an associated probability distribution of the possible resulting values. Random variables may take discrete (one of a limited number of fixed values) or continuous values (one of an infinite number of possible values, in which case the variable is defined over an interval of values).

Repeated measures A condition or experimental design in which data includes several observations made on the same subject (likely to be a site over multiple years or multiple sites in a region in the context of this project), which are thus non-independent. Generalised estimating equations may be used to model data accounting for repeated measures or other non-independent data, and the inter-correlation and pseudoreplication that result from them.

Replication An aspect of experimental or sampling design in which the same method or test is applied to different subjects or samples, such that multiple measures of an effect are measured; with a sufficiently large sample, results can be considered to be statistically significant if the effect occurs more consistently than would be expected by chance. If all other influences are constant, statistical significance is higher with more replication. See pseudoreplication.

Response variable The variable that describes a parameter whose variation is being analysed or modelled with respect to one or more predictor variables. In most experiments or tests, the effects of the independent variable(s) on the response variable are observed. Also known as dependent or outcome variable.

Score test An equivalent to a likelihood ratio test for comparing models accounting for repeated measures that are fitted using generalized estimating equations.

Semi-random sampling Sampling sites are selected randomly from within a restricted area.

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accepting the result as valid. Conventionally, only a p-value of less than 0.05 is considered to show significance, i.e. to indicate that the relationship has not arisen by chance.

Shannon diversity index A measure of the number of taxa relative to the number of individuals per taxon for a given community, i.e. species diversity; as such, a measure of biodiversity. The value increases with both the richness (the number of species present) and evenness (the consistency of relative abundances of the species) of the community. Also known as Shannon's diversity index, the Shannon–Wiener index, the Shannon–Weaver index and the Shannon entropy.

Site In this report, a ‘site’ is simply a location for monitoring. Typically, these are defined spatial units, such as grid squares or cells delimited by latitude and longitude, or patches of habitat delimited by habitat boundaries, ownership or surveyor choice. Sites may be protected areas, such as Natura 2000 designated sites, or may be areas within these areas or overlapping them, but a ‘site’ for monitoring is not synonymous with a ‘site’ that is protected.

Space-for-time substitution This is a method of inferring a temporal trend from spatial patterns. Variation of x units in an environmental factor between location A and location B is assumed to be indicative of variation in x units between time point 1 and time point 2 at location A or location B (or, indeed, at location C). Thus, inference about likely or historical temporal change is gleaned from contemporary spatial variation.

Spatial autocorrelation A property of random variables taking values at pairs of locations a certain distance apart, that are more similar (positive autocorrelation) or less similar (negative autocorrelation) than would be expected for randomly associated pairs of observations.

Spearman correlation Short for Spearman's rank correlation coefficient. It is suitable for testing whether two ranked variables or one ranked and one measurement variable covary. It is the non- parametric version of the Pearson product-moment correlation, so is appropriate for use with data that are not normally distributed.

Standard deviation The standard deviation is a widely used measure of the variability of a sample. It is computed by taking the square root of the variance.

Standard error (SE) The standard error of a statistic is the standard deviation of the sampling distribution of that statistic. The standard error of the mean of a sample is the standard deviation divided by the square root of the sample size. 95% confidence intervals for a parameter estimate or a mean value are calculated as  1.96 × SE if a normal distribution is assumed.

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Stochastic An effect arising due to random chance.

Stratified random sampling In stratified random sampling, the population is divided into a number of subgroups (or strata). Random samples are then taken from each subgroup. This allows targeting of sampling effort in a controlled manner and subsequent analyses can take account of the stratification such that the results are still representative of the whole landscape, without bias.

Transect In ecology, a fixed path along which a species is sampled or observed, often in the context of recording numbers of the species.

Variance The variance is a widely used measure of variability in a sample. It is defined as the mean squared deviation of scores from the mean. It is also the square of the standard deviation.

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13 References

Arnqvist, G and Wooster, D (1995) Meta-analysis: synthesizing research findings in ecology and evolution. Trends in Ecology & Evolution, 10 (6): 236-240. Baillie, S R, Balmer, D E, Downie, I S and Wright, K H M (2006) Migration Watch: an internet survey to monitor spring migration in Britain and Ireland. Journal of Ornithology 147(2): 254- 259. Baines M, Hambler C, Johnson PJ, Macdonald DW, and Smith H (1998) The effects of arable field margin management on the abundance and species richness of Araneae (spiders). Ecography 21: 74-86 Baker, D J, Freeman, S N, Grice, P V and Siriwardena, G M (2012) Landscape scale responses of birds to agri-environment management: a test of the English Environmental Stewardship scheme. Journal of Applied Ecology 49: 871-882. Baldock, D (1999) Indicators for High Nature Value Farming Systems in Europe. Environmental Indicators and Agricultural Policy, CABI Publishing, Wallingford, UK. Baldock, D, Beaufoy, G, Bennett, G and Clark, J (1993) Nature Conservation and New Directions in the EC Common Agricultural Policy: The Potential Role of EC Policies in Maintaining Farming and Management Systems of High Nature Value in the Community. Institute for European Environmental Policy, London. Balmer, D E, Gillings, S, Caffrey, B, Swann, R L, Downie, I S, and Fuller, R J (2013) Bird Atlas 2007-11: the breeding and wintering birds of Britain and Ireland. Thetford: BTO. Barr C J, Britt C P, Sparks T H, and Churchward J M (2005) Hedgerow Management and Wildlife: A Review of Research on the Effects of Hedgerow Management and Adjacent Land Use on Biodiversity. Defra, London. Bateman, I J, Harwood, A R, Mace, G M, Watson, R T, Abson, D J, Andrews, B, Binner, A, Crowe, A, Day, B H, Dugdale, S, Fezzi, C, Foden, J, Hadley, D, Haines-Young, R, Hulme, M, Kontoleon, A, Lovett, A A, Munday, P, Pascual, U, Paterson, J, Perino, G, Sen, A, Siriwardena, G, van Soest, D and Termansen, M (2013) Bringing ecosystem services into economic decision making: Land use in the UK. Science 341(6141): 45-50. Bates, D, Maechler, M, Bolker, B and Walker S (2014) lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7. http://CRAN.R-project.org/package=lme4. Battin, J (2004) When good animals love bad habitats: ecological traps and the conservation of animal populations. Conservation Biology, 18(6): 1482-1491. Becker N, Haupt H, Hofbauer N, Ludwig G, Nehring S (Eds.) (2013): Rote Liste gefährdeter Tiere, Pflanzen und Pilze Deutschlands. Band 2: Meeresorganismen. Bundesamt für Naturschutz. Naturschutz und Biologische Vielfalt 70, 2. Münster. Beneš J, Konvička M, Dvořák J, Fric Z, Havelda Z, Pavlíčko A, Vrabec V, Weidenhoffer Z (2002) Butterflies of the Czech Republic: Distribution and conservation I, II. Společnost pro Ochranu Motýlů, Praha.

221

Bentsen N S and Felby C (2012) Biomass for energy in the European Union - a review of bioenergy resource assessments. Biotechnology for Biofuels 5: 25. DOI: 10.1186/1754-6834- 5-25 Berndes G and Hansson J (2007) Bioenergy expansion in the EU: Cost-effective climate change mitigation, employment creation and reduced dependency on imported fuels. Energy Policy 35: 5965 – 5979. DOI: 10.1016/j.enpol.2007.08.003 Bibby C J, Burgess N D, Hill D A (2000) Bird Census Techniques (2nd edition). Academic Press, London. Billeter R, Liira J, Bailey D, Bugter R, Arens P, Augenstein I, Aviron S, Baudry J, Bukacek R, Burel F, Cerny M, De Blust G, De Cock R, Diekötter T, Dietz H, Dirksen J, Dormann C, Durka W, Frenzel M, Hamersky R, Hendrickx F, Herzog F, Klotz S, Koolstra B, Lausch A, Le Coeur D, Maelfait JP, Opdam P, Roubalova M, Schermann A, Schermann N, Schmidt T, Schweiger O, Smulders MJM, Speelmans M, Simova P, Verboom J, Van Wingerden WKRE, Zobel M, Edwards PJ (2008) Indicators for biodiversity in agricultural landscapes: a pan-European study. Journal of Applied Ecology 45:141-150. Binot-Hafke M, Balzer S, Becker N, Gruttke H, Haupt H, Hofbauer N, Ludwig G, Matzke-Hajek G, Strauch M (Eds.) (2011): Rote Liste gefährdeter Tiere, Pflanzen und Pilze Deutschlands: Band 3: Wirbellose Tiere (Teil 1). Bundesamt für Naturschutz. Naturschutz und Biologische Vielfalt 70, 3. Münster. BMEL (2016): Federal Ministry for Food and Agriculture, Statistical yearbook. http://www.bmelv-statistik.de/de/statistisches-jahrbuch/ Bommarco, R, Lundin, O, Smith, H G and Rundlöf, M (2011) Drastic historic shifts in bumblebee community composition in Sweden. Proceedings of the Royal Society of London Series B No 279 (1727), pp309-315. Brandt K, Glemnitz M (2013): Assessing the regional impacts of increased energy Maize cultivation on farmland birds. Environmental Monitoring and Assessment 186: 679-697. Brereton, T. (2002). Developing a system for assessing the effect of agri-environment schemes on butterfly populations. Defra, UK. Brereton, T. (2005). Agri-environment schemes and butterflies: re-assessing the impacts and improving delivery of BAP targets. Defra, UK. Britz, W. and Leip, A., 2009. Development of marginal emission factors for N losses from agricultural soils with the DNDC-CAPRI meta-model. Agriculture, Ecosystems and Environment, 133(3-4): 267–279. Britz, W and Witzke, P (eds) (2014) Capri model documentation 2014. http://www.capri- model.org/docs/CAPRI_documentation.pdf Browder, S F, Johnson, D H, and Ball, I J (2002). Assemblages of breeding birds as indicators of grassland condition. Ecological indicators, 2 (3): 257-270. Browne, S J and N J Aebischer (2003). Arable Stewardship: impact of the pilot scheme on the brown hare and grey partridge after five years. Fordingbridge, Hampshire, The Game Conservancy Trust.

222

Burnham, K P and Anderson, D R (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretical Approach. 2d ed. New York: Springer-Verlag Butler, S J, Vickery, J A and Norris, K (2007) Farmland Biodiversity and the Footprint of Agriculture. Science, 315: 381-384. Butler, S J, and Norris, K (2013) Functional space and the population dynamics of birds in agro-ecosystems. Agriculture, ecosystems & environment, 164: 200-208. Butler, S J, Bradbury, R B, and Whittingham, M J (2005) Stubble height affects the use of stubble fields by farmland birds. Journal of Applied Ecology, 42(3): 469-476. Carvell, C, Roy, D B, Smart, S M, Pywell, R F, Preston, C D and Goulson, D (2006) Declines in forage availability for bumblebees at a national scale. Biological Conservation 132 (4): 481- 489. Chamberlain, D E, Fuller, R J, Bunce, R G H, Duckworth, J C, and Shrubb, M (2000) Changes in the abundance of farmland birds in relation to the timing of agricultural intensification in England and Wales. Journal of applied ecology, 37: 771-788. Chamberlain, D, Gough, S, Anderson, G, Macdonald, M, Grice, P, and Vickery, J (2009) Bird use of cultivated fallow ‘Lapwing plots’ within English agri‐environment schemes. Bird Study, 56(3): 289-297. Chapius, J L (1990) Comparison of the diet of two sympatric lagomorphs, Lepus europaeus (Pallas) and Oryctolagus cuniculus (L.), in an agroecosystem of the Ile-de-France. Zeitschrift für Säugetierkunde, 55: 176–185. Corbet, G B and Harris, S (1996) The Handbook of British Mammals, 3rd edn. Butler & Tanner Ltd, London, UK. Dadam D and Siriwardena, G (2015) Measuring the impact of Tir Cynnal and Tir Gofal on bird populations in Wales. BTO Report, Thetford. Dendy, J, McKillop, I G, Fox, S M and Western, G J (2003) Quantifying the costs of crop damage by rabbits. In: Conservation and Conﬂict: Mammals and Farming in Britain (Ed. by F H Tattsersall and W J Manley), pp. 21–219. Linnean Society Occasional Publications, Westbury Publishing, West Yorkshire, UK. Dennis, E B, Morgan, B J, Freeman, S N, Brereton, T M, and Roy, D B (2016) A generalized abundance index for seasonal invertebrates. Biometrics 72(4), 1305-1314. Domínguez, J and Edwards, C A (2004): Vermicomposting organic wastes: A review. In Shakir Hanna, S H and Mikhaïl, W Z A (Eds.): Soil Zoology for Sustainable Development in the 21st Century: A Festschrift in Honour of Prof. Samir I. Ghabbour on the Occasion of His 70th Birthday. Cairo: 369-395. http://jdguez.webs.uvigo.es/wp- content/uploads/2011/10/cairo1.pdf Donald, P F, Green, R E and Heath, M F (2001) Agricultural intensification and the collapse of Europe's farmland bird populations. Proceedings of the Royal Society of London Series B 268 (1462): 25-29. Donald, P F, Sanderson, F J, Burfield, I J and van Bommel, F P J (2006) Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990- 2000. Agriculture, Ecosystems & Environment 116 (3-4): 189-196.

223

Donald, P F, Sanderson, F J, Burfield, I J, Bierman, S M, Gregory, R D and Waliczky, Z (2007) International Conservation Policy Delivers Benefits for Birds in Europe. Science, 317: 810- 813. Donald, P (2010) The Skylark. Bloomsbury Publishing. Donald, P F, Green, R E, and Heath, M F (2001) Agricultural intensification and the collapse of Europe's farmland bird populations. Proceedings of the Royal Society of London B: Biological Sciences, 268(1462): 25-29. Dover J, Sparks T, Clarke S, Gobbett K, Glossop S (2000) Linear features and butterflies: the importance of green lanes. Agriculture, Ecosystems & Environment 80:227-242 Dover, J W, Sparks, T H and Greatorex-Davies, J N (1997). The importance of shelter for butterflies in open landscapes. Journal of Insect Conservation 1: 89-97. Dröschmeister R and Sukopp U (2009) Indicators and conservation policy: the German Sustainability Indicator for Species Diversity as an example. Avocetta 33: 149-156. EEA (2004) High Nature Value farmland: Characteristics, trends and policy challenges. EEA Report No 1/2004. European Environment Agency, Copenhagen. EEA (2013) The European Grassland Butterfly Indicator: 1990-2011. Technical report No 11/2013. European Environment Agency, Copenhagen. EEA (2015) State of nature in the EU: Results from reporting under the nature directives 2007-2012. Technical report No 2/2015. European Environment Agency, Copenhagen. Egger M and Smith G D (1998) Bias in location and selection of studies. BMJ : British Medical Journal 316 (7124): 61–66. Eglington, S M and Pearce-Higgins, J W (2012) Disentangling the relative importance of changes in climate and land-use intensity in driving recent bird population trends. PLoS ONE 7(3): e30407. ESRI 2011. ArcGIS Desktop, Release 10. Environmental Systems Research Institute, Redlands, CA ESRI 2016. ArcGIS Desktop 10.4. Environmental Systems Research Institute, Redlands, CA ETC/BD (2011) Assessment, monitoring and reporting under Article 17 of the Habitats Directive: Explanatory Notes and Guidelines for the period 2007-2012. Final version. European Topic Centre on Biological Diversity, Paris. European Commission (2014) Natura 2000. Nature and Biodiversity newsletter, 36. European Environment Agency (2013) The European Grassland Butterfly Indicator: 1990– 2011. Technical report No 11/2013 Eurostat 2011. Regions in the European Union: Nomenclature of territorial units for statistics NUTS 2010/EU-27. http://ec.europa.eu/eurostat/documents/3859598/5916917/KS-RA-11-011-EN.PDF Everaars J, Frank K, Huth A (2014): Species ecology and the impacts of bioenergy crops: an assessment approach with four example farmland bird species. GCB Bioenergy 6: 252-264.

224

Fahrig L, Girard J, Duro D, Pasher J, Smith A, Javorek S, King D, Lindsay KF, Mitchell S, Tischendorf L (2015) Farmlands with smaller crop fields have higher within-field biodiversity. Agriculture, Ecosystems & Environment 200:219-234 Fahrig, L, Baudry, J, Brotons, L, Burel, F G, Crist, T O, Fuller, R J, Sirami, C, Siriwardena, G M and Martin, J L (2011) Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecology letters, 14(2): 101-112. Felten D, Emmerling C (2011) Effects of bioenergy crop cultivation on earthworm communities — A comparative study of perennial (Miscanthus) and annual crops with consideration of graded land-use intensity. Applied Soil Ecology 49: 167-177. Firbank LG, Petit S, Smart S, Blain A, Fuller RJ (2008) Assessing the impacts of agricultural intensification on biodiversity: a British perspective. Philosophical Transactions of the Royal Society B: Biological Sciences 363: 777-787 Flade M and Schwarz J (2013): Bestandsentwicklung von Vogelarten der Agrarlandschaft in Deutschland 1991-2010 und Schlüsselfaktoren. Proceedings: Workshop „Farmland birds – ecological basis for the evaluation of biodiversity targets in agricultural lands”, 01.-02. March 2013, Kleinmachnow. 442: 8-17 Flade M (2014): Von der Energiewende zum Biodiversitäts-Desaster – zur Lage des Vogelschutzes in Deutschland. Vogelwelt 133, 149 – 158. Fournier DA, Skaug HJ, Ancheta J, Ianelli J, Magnusson A, Maunder M, Nielsen A, Sibert J (2012) AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim Methods Softw 27: 233-249. Freeman, S N and Newson, S E (2008) On a log-linear approach to detecting ecological interactions in monitored populations. Ibis 150: 250–258. Fry, G L A and Robson, W J (1994) The effect of field margins on butterfly movement. In N Boatman (Ed.), Field Margins: Integrating Agriculture and Conservation. BCPC Monograph No. 58: 111-116. Farnham: British Crop Protection Council. Fuller RJ, Trevelyan RJ, Hudson RW (1997) Landscape composition models for breeding bird populations in lowland English farmland over a 20 year period. Ecography 20:295-307 Fuller, R J, Bellamy, P E, Broome, A, Calladine, J, Eichhorn, M P, Gill R M and Siriwardena, G M (2014) Effects of woodland structure on woodland bird populations with particular reference to woodland management and deer browsing. Report to Defra (UK Government) from project WC0793. Gamero, A, Brotons, L, Brunner, A, Foppen, R, Fornasari, L, Gregory, RD, Herrando, S, Hořák, D, Jiguet, F, Kmecl, P, Lehikoinen, A, Lindström, Å, Paquet, J-Y, Reif, J, Sirkiä, PM, Škorpilová, J, van Strien, A, Szép, T, Telenský, T, Teufelbauer, N, Trautmann, S, van Turnhout, CAM, Vermouzek, Z, Vikstrøm, T and Voříšek, P (2016) Tracking Progress Towards EU Biodiversity Strategy Targets: EU Policy Effects in Preserving its Common Farmland Birds. Conservation Letters: DOI: 10.1111/conl.12292 Gevers J, Høye T, Topping C, Glemnitz M, Schröder B (2011): Biodiversity and the mitigation of climate change through bioenergy: impacts of increased maize cultivation on farmland wildlife. GCB Bioenergy 3: 472-482.

225

Gillings, S, Newson, S E, Noble, D G and Vickery, J A (2005) Winter availability of cereal stubbles attracts declining farmland birds and positively influences breeding population trends. Proceedings of the Royal Society- Series B, 272, 733-739. Glemnitz M, Zander P, Stachow U (2015) Regionalizing land use impacts on farmland birds. Environmental Monitoring and Assessment 187: 1-21. Gottschalk, T K, Dittrich, R, Diekötter, T, Sheridan, P, Wolters, V, and Ekschmitt, K (2010) Modelling land-use sustainability using farmland birds as indicators. Ecological indicators, 10(1): 15-23. Goulson, D, Lye, G C and Darvill, B (2008) The decline and conservation of bumblebees. Annual Review of Entomology, 53: 191-208. Gregory RD, Gibbons DW, Donald PF (2004) Bird census and survey techniques. In: Sutherland WJ, Newton I, Green RE (eds) Bird Ecology and Conservation: a Handbook of Techniques. Cambridge University Press, Cambridge, pp 17-55 Gregory RD, van Strien A, Vorisek P, Gmelig Meyling AW, Noble DG, Foppen RPB, Gibbons DW (2005) Developing indicators for European birds. Philosophical Transactions of the Royal Society of London B: Biological Sciences 360: 269-288 Gregory, R D, Noble, D, Field, R, Marchant, J, Raven, M, and Gibbons, D W (2003) Using birds as indicators of biodiversity. Ornis hungarica, 12(13), 11-24. Gregory, R D, Van Strien, A, Vorisek, P, Meyling, A W G, Noble, D G, Foppen, R P, and Gibbons, D W (2005) Developing indicators for European birds. Philosophical Transactions of the Royal Society B: Biological Sciences,360(1454), 269-288. Gregory, R D, Willis, S G, Jiguet, F, Vorísek, P, Klvanˇova, A, van Strien, A, Brian Huntley, B, Collingham, Y C , D Couvet, D and Green, R E (2009) An indicator of the impact of climatic change on European bird populations. PloS one, 4, e4678. Günther A, Nigmann, U Achtziger R, Gruttke H (2005) Analyse der Gefährdungsursachen planungsrelevanter Tiergruppen in Deutschland. Naturschutz und Biologische Vielfalt 21. Münster. Gutzler C, Helming K, Balla D, Dannowski R, Deumlich D, Glemitz M, Knierim A, Mirschel W, Nendel C, Paul C, Sieber S, Stachow U, Starick A, Wieland R W A, Yander P (2015) Agricultural land use changes – a scenario-based sustainability impact assessment for Brandenburg, Germany. Ecological Indicators 48: 505-517. Hallmann, C A, Foppen, R P, van Turnhout, C A, de Kroon, H, and Jongejans, E (2014) Declines in insectivorous birds are associated with high neonicotinoid concentrations. Nature, 511: 341-343. Harrell FE, Dupont C (2015) Hmisc: Harrell Miscellaneous. R package version 3.17-0. http://CRAN.R-project.org/package=Hmisc. Haupt, H, Ludwig G, Gruttke H, Binot-Hafke M, Otto C, Pauly A (Eds.) (2009): Rote Liste gefährdeter Tiere, Pflanzen und Pilze Deutschlands. Band 1: Wirbeltiere. Bundesamt für Naturschutz. Naturschutz und Biologische Vielfalt 70, 1. Münster.

226

Henderson IG, Holland JM, Storkey J, Lutman P, Orson J, Simper J (2012) Effects of the proportion and spatial arrangement of un-cropped land on breeding bird abundance in arable rotations. Journal of Applied Ecology 49: 883-891 Hinsley SA, Bellamy PE (2000) The influence of hedge structure, management and landscape context on the value of hedgerows to birds: A review. Journal of Environmental Management 60: 33-49 Hof, A R, and Bright, P W (2010a) The value of agri‐environment schemes for macro‐ invertebrate feeders: hedgehogs on arable farms in Britain. Animal Conservation, 13(5): 467- 473. Hof, A R, and Bright, P W (2010b) The impact of grassy field margins on macro- invertebrate abundance in adjacent arable fields. Agriculture, ecosystems & environment, 139(1): 280-283. Hof, A R, and Bright, P W (2012) Factors affecting hedgehog presence on farmland as assessed by a questionnaire survey. Acta Theriologica, 57(1): 79-88. Homolka, M (1987) A comparison of the trophic niches of Lepus europaeus and Oryctolagus cuniculus. Folia Zoologica, 36: 307–317. Isaac, N J, and Pocock, M J (2015) Bias and information in biological records. Biological Journal of the Linnean Society, 115(3): 522-531. Isaac, N J, Strien, A J, August, T A, Zeeuw, M P, and Roy, D B (2014) Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution, 5(10): 1052-1060 JabRef Development Team (2016): JabRef open source bibliography reference manager http://www jabref org Accessed 2016-10-02 Jarosz Z, Faber A and Syp A (2013a) Wpływ uprawy rolin bioenergetycznych na Srodowisko Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu 15: 131-135. Jarosz Z, Faber A and Syp A (2013b) Wpływ uprawy surowcow przeznaczonych na produkcje biopaliw na strukture zasiewow Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu 2013: 1-6 Juslén, A, Pykälä, J, Kuusela, S, Kaila, J, Mattila, J, Muona, J, Saari, S and Cardoso, P (2016) Application of the Red List Index as an indicator of habitat change. Biodiversity and Conservation 25 (3): 569-585. Karg J, Balazy S (2009): Wpływ struktury krajobrazu na występowanie agrofagów i ich antagonistów w uprawach rolniczych. Progress in Plant Protection/Postępy w Ochronie Roślin 49 (3): 1015-1034. Kerr JT, Southwood TRE, Cihlar J (2001) Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada. Proceedings of the National Academy of Sciences 98: 11365-11370. Kleijn, D, Baquero, R A, Clough, Y, Diaz, M, De Esteban, J, Fernández, F, Gabriel, D, Herzog, F, Holzschuch, A, Jöhl, R, Knop, E, Kruess, A, Marshall, E J P, SteffanDewenter, I, Tscharntke, T, Verhulst, J, West, T M and Yela, J L (2006) Mixed biodiversity beneﬁts of agri-environment schemes in ﬁve European countries Ecology Letters, 9: 243–254.

227

Klenke R A, Ring I, Kranz A, Jepsen N, Rauschmayer F, Henle K (Eds, 2013): Human - Wildlife Conflicts in Europe. Fisheries and Fish-eating Vertebrates as a Model Case Series: Environmental Science and Engineering, Subseries: Environmental Science. Springer Berlin Heidelberg. 347 p. Kosior, A, Celary, W, Olejniczak, P, Fijal, J, Król, W, Solarz, W and Plonka, P (2007) The decline of the bumble bees and cuckoo bees (Hymenoptera: Apidae: Bombini) of Western and Central Europe. Oryx No 41 (01): pp79-88. Košulič O, Michalko R, Hula V (2014) Recent artificial vineyard terraces as a refuge for rare and endangered spiders in a modern agricultural landscape. Ecological Engineering 68: 133- 142. Kuznetsova, A, Brockhoff, P and Christensen R (2014) lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). http://CRAN.R- project.org/package=lmerTest. La Haye, M J J, Neumann, K and Koelewijn, H P (2012) Strong decline of gene diversity in local populations of the highly endangered Common Hamster (Cricetus cricetus) in the western part of its European range. Conservation Genetics No 13 (2), pp311-322. Lausch, A, Bannehr, L, Beckmann, M, Boehm, C, Feilhauer, H, Hacker, J M, Heurich, M, Jung, A, Klenke, R, Neumann, C, Pause, M, Rocchini, D, Schaepman, M E, Schmidtlein, S, Schulz, K, Selsam, P, Settele, J, Skidmore, A K, Cord, A F. (2016) Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecological Indicators, 70: 317. Leip, A, Britz, W, Weiss, F, and de Vries, W (2011) Farm, land, and soil nitrogen budgets for agriculture in Europe calculated with CAPRI. Environmental Pollution 159 : 3243–3253. Leip, A, Marchi, G, Koeble, R, Kempen, M, Britz, W, and Li, C (2008) Linking an economic model for European agriculture with a mechanistic model to estimate nitrogen and carbon losses from arable soils in Europe. Biogeosciences, 5: 73–94. Ludwig G, Matzke-Hajek G (Eds.) (2011) Rote Liste gefährdeter Tiere, Pflanzen und Pilze Deutschlands: Band 6: Pilze (Teil 2) – Flechten und Myxomyzeten. Bundesamt für Naturschutz. Naturschutz und Biologische Vielfalt 70: 6. Macdonald, D W, Tattersall, F H, Service, K M, Firbank, L G and Feber, R E (2007) Mammals, agri‐environment schemes and set‐aside–what are the putative benefits? Mammal Review, 37(4): 259-277. Maciorowski G and Urbańska M (2013) Występowanie, zagrożenia i ochrona kani rudej Milvus milvus w zachodniej Wielkopolsce. Studia i Materiały Centrum Edukacji Przyrodniczo- Lenej 15: 239-246 Massimino, D, Johnston, A, Noble, D G, and Pearce-Higgins, J W (2015) Multi-species spatially-explicit indicators reveal spatially structured trends in bird communities. Ecological Indicators, 58: 277-285. McCarthy, M and Masters, P (2005) Profiting from prior information in Bayesian analyses of ecological data. Journal of Applied Ecology 42: 1012-1019. McCullagh, P, and Nelder, J A (1989) Generalized linear models (Vol. 37). CRC press.

228

McCulloch, C E, and Neuhaus, J M (2001) Generalized linear mixed models. John Wiley & Sons, Ltd. McVittie, A, Norton, L, Martin-Ortega, J, Siameti, I, Glenk, K and Aalders, I (2014) Operationalizing an ecosystem services-based approach using Bayesian Belief Networks: an application to riparian buffer strips. Ecological Economics, 110: 15-27. Merle S (2010): Development of the Wintering Crane Population in Central France and Agricultural Changes In Nowald, G, Weber, A, Fanke, J, Weinhardt, E and Donner, N (Eds) PROCEEDINGS of the VII th European Crane Conference - Breeding, resting, migration and biology. pp 42-45. Mewes W, Nowald G (2012): Verbreitung des Kranichs und Populationsdichte in Deutschland. In: Nationalatlas aktuell 6 (03.2012) 3 [27.03.2012]. Leipzig: Leibniz‐Institut für Länderkunde (IfL). URL: http://aktuell.nationalatlas.de/Kraniche.3_03‐2012.0.html. Accessed 2016-10-02. Milieu, IEEP and ICF (2016) Evaluation Study to support the Fitness Check of the Birds and Habitats Directives. Milieu Ltd, Institute for European Environmental Policy and the ICF International, Brussels. Milsom, T P, Langton, S D, Parkin, W K, Peel, S, Bishop, J D, Hart, J D, and Moore, N P (2000) Habitat models of bird species' distribution: an aid to the management of coastal grazing marshes. Journal of Applied Ecology, 37(5): 706-727. Morris, W K, Vesk, P A, McCarthy, M A, Bunyavejchewin, S and Baker, P J (2015) The neglected tool in the Bayesian ecologist’s shed: a case study testing informative priors’ effect on model accuracy. Ecology and Evolution 5: 102–108. Morton, D, Rowland, C, Wood, C, Meek, L, Marston, C, Smith, G, Wadsworth, R and Simpson, I (2011) Final Report for LCM2007-the new UK land cover map. Countryside Survey Technical Report No 11/07. Muller, S (2002) Appropriate agricultural management practices required to ensure conservation and biodiversity of environmentally sensitive grassland sites designated under Natura 2000. Agriculture, Ecosystems & Environment, No 89: 261-266. Natura 2000 Barometer (2016) European Commission. http://ec.europa.eu/environment/nature/natura2000/barometer/index_en.htm Natural England (2013a). Entry Level Stewardship – Environmental Stewardship Handbook, 4th Ed. Natural England, Peterborough. Natural England (2013b) Higher Level Stewardship – Environmental Stewardship Handbook, 4th Ed. Natural England, Peterborough. Newson, S E, Evans, K L, Noble, D G, Greenwood, J J, and Gaston, K J (2008) Use of distance sampling to improve estimates of national population sizes for common and widespread breeding birds in the UK. Journal of Applied Ecology, 45(5): 1330-1338. Newson, S E, Moran, N J, Musgrove, A J, Pearce-Higgins, J W, Gillings, S, Atkinson, P W, Miller, R, Grantham, M J and Baillie, S J (2016) Long-term change in spring and autumn migration phenology of common migrant breeding birds in Britain: results from large-scale citizen science bird recording schemes. Ibis. doi: 1111/ibi.12367.

229

Nyffeler M, Sundrland KD (2003) Composition, abundance and pest control potential of spider communities in agroecosystems: a comparison of European and US studies. Agriculture, Ecosystems & Environment 9: 579-613. O’Connor, R J and Shrubb, M (1990) Farming and birds. CUP Archive. Oliver T, Roy DB, Hill JK, Brereton T, Thomas CD (2010) Heterogeneous landscapes promote population stability. Ecology Letters 13: 473-484. Oliver, T H (2014) Assessing the importance of spatial location of agri environment options within the landscape to butterflies: Correlative analysis of datasets to assess the degree of success in the delivery of Environmental Stewardship objectives. In: Natural England Commissioned Reports. NERC Centre for Ecology and Hydrology. Ostermann, O P (1998) The need for management of nature conservation sites designated under Natura 2000. Journal of Applied Ecology, No 35: 968-973. Pan, W (2001) Akaike's information criterion in generalized estimating equations. Biometrics, 57(1): 120-125. Paracchini, M L, Petersen, J-E, Hoogeveen, Y, Bamps, C, Burfield, I and van Swaay, C (2008) High Nature Value Farmland in Europe - An Estimate of the Distribution Patterns on the Basis of Land Cover and Biodiversity Data. JCR Scientific and Technical Reports EUR 23480 EN. Office for Official Publications of the European Union, Luxembourg. Pe'er G, Dicks L V, Visconti P, Arlettaz R, Báldi A, Benton T G, Collins S, Dieterich M, Gregory R D, Hartig F, Henle K, Hobson P R, Kleijn D, Neumann R K, Robijns T, Schmidt J, Shwartz A, Sutherland W J, Turbé A, Wulf F and Scott A V EU agricultural reform fails on biodiversity. Science, American Association for the Advancement of Science, 344: 1090-1092. Pellissier, V, Schmucki, R, Jiguet, F, Julliard, R, Touroult, J, Richard, D and Evans, D (2014) The impact of Natura 2000 on non-target species, assessment using volunteer-based biodiversity monitoring. ETC/BD report for the EEA. Pellissier, V, Touroult, J, Julliard, R, Siblet, JP and Jiguet, F (2013) Assessing the Natura 2000 network with a common breeding birds survey. Animal Conservation, 16: 566-574. Pérez-Soba, M, Elbersen, B, Kempen, M, Braat, L, Staristky, I, van der Wijngaart, R Kaphengst, T et al (2015) Agricultural biomass as provisioning ecosystem service: quantification of energy flows. EUR27538 EN. doi:10.2788/679096 Petrovan S O, Ward A I, Wheeler P M (2013) Habitat selection guiding agri-environment schemes for a farmland specialist, the brown hare. Animal Conservation 16(3): 344-352. Pickett, S R A and Siriwardena, G M (2011) The relationship between multi-scale habitat heterogeneity and farmland bird abundance. Ecography 34: 955-969. Poggio SL, Chaneton EJ, Ghersa CM (2010) Landscape complexity differentially affects alpha, beta, and gamma diversities of plants occurring in fencerows and crop fields. Biological Conservation 143: 2477-2486. Poláková, J, Tucker, G M, Hart, K, Dwyer, J and Rayment, M (2011) Addressing biodiversity and habitat preservation through Measures applied under the Common Agricultural Policy. Report prepared for DG Agriculture and Rural Development, Contract No. 30-CE- 0388497/00-44. Institute for European Environmental Policy, London.

230

Pollard E, Yates T J, (1993) Monitoring Butterflies for Ecology and Conservation. Chapman & Hall, London. Potts S G, Biesmeijer J C, Kremen C, Neumann P, Schweiger O, Kunin W E (2010): Global pollinator declines: trends, impacts and drivers. Trends in Ecology & Evolution 25(6): 345-53. R Development Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, http://www.R-project.org/. Accessed 24 Apr 2014. Reid N, Mcdonald R A, Montgomery W (2007) Mammals and agri‐environment schemes: hare haven or pest paradise? Journal of Applied Ecology, 44(6): 1200-8. Reif J, Storch D, Voříšek P, Šťastný K, Bejček V (2008) Bird-habitat associations predict population trends in central European forest and farmland birds. Biodiversity and Conservation 17: 3307-3319. Reif J, Voříšek P, Šťastný K, Bejček V (2006) Trendy početnosti ptáků v České republice v letech 1982-2005. Sylvia 42: 22-37. Reynolds J C, Stoate C, Brockless M H, Aebischer N J, Tapper S C (2009) The consequences of predator control for brown hares (Lepus europaeus) on UK farmland. European Journal of Wildlife Research 56(4): 541-549. Riedinger V, Mitesser O, Hovestadt T, Steffan-Dewenter I, Holzschuh A (2015) Annual dynamics of wild bee densities: attractiveness and productivity effects of oilseed rape. Ecology 96: 1351-1360. Robinson, R A and Sutherland, W J (2002) Post-war changes in arable farming and biodiversity in Great Britain. Journal of Applied Ecology, 39 (1): 157-176. Robinson, R A, Wilson, J D and Sutherland, W J (2001) The importance of arable habitat for farmland birds in grassland landscapes. Journal of Applied Ecology 38 (5): 1059-1069 Robinson, R A, Wilson, J. D, and Crick, H Q (2001) The importance of arable habitat for farmland birds in grassland landscapes. Journal of Applied Ecology, 38(5): 1059-1069. Robledano, F, Esteve, M A, Farinós, P, Carreño, M F, and Martínez-Fernández, J (2010) Terrestrial birds as indicators of agricultural-induced changes and associated loss in conservation value of Mediterranean wetlands. Ecological indicators, 10: 274-286. Siriwardena, G M, Baillie, S R, Buckland, S. T, Fewster, R M, Marchant, J H, and Wilson, J D (1998) Trends in the abundance of farmland birds: a quantitative comparison of smoothed Common Birds Census indices. Journal of Applied Ecology, 37: 24-43. Roodbergen M, van der Werf B, Hötker H. Revealing the contributions of reproduction and survival to the Europe-wide decline in meadow birds: review and meta-analysis. Journal of Ornithology, 153(1): 53-74. Roßberg, D, Gutsche, V, Enzian, S and Wick, M (2002) Neptun 2000-Survey into application of chemical pesticides in agricultural practice in Germany. Berichte aus der Biologischen Bundesanstalt für Land und Forstwirtschaft, Saphir-Verlag, 2002, 98, 80 Sakamoto, Y, Ishiguro, M, and Kitagawa, G (1986) Akaike Information Criterion Statistics. D. Reidel Publishing Company.

231

Šálek M, Kučera T, Zimmermann K, Bartůšková I, Plátek M, Grill S, Konvička M (2015) Edges within farmland: Management implications of taxonspecific species richness correlates, Basic and Applied Ecology, 16: 714-725. Salvi A (2010) Eurasian Crane Grus grus Study in France over the last 30 Years: a Review. In Nowald, G, Weber, A, Fanke, J, Weinhardt, E and Donner, N (Eds) PROCEEDINGS of the VII th European Crane Conference - Breeding, resting, migration and biology. pp 30-37. Salvi A (2012) Cranes, Agriculture and Climate Change. In Harris, J. (Ed): Proceedings of the International Workshop, “Cranes, Agriculture, and Climate Change”. International Crane Foundation Baraboo, Wisconsin, USA, pp. 65-70. Sanderson, F J, Kloch, A, Sachanowicz, K, and Donald, P F (2009) Predicting the effects of agricultural change on farmland bird populations in Poland. Agriculture, ecosystems & environment, 129(1): 37-42. Sauerbrei R, Ekschmitt K, Wolters V, Gottschalk T (2013) Increased energy maize production reduces farmland bird diversity. GCB Bioenergy 6: 265-274. Schlaepfer M A, Runge M C, Sherman P W (2002) Ecological and evolutionary traps, Trends in Ecology & Evolution 17 (10): 474-480. http://dx.doi.org/10.1016/S0169-5347(02)02580-6. Schlegel S, Kaphengst T (2007) European Union Policy on Bioenergy and the Role of Sustainability Criteria and Certification Systems. Journal of Agricultural & Food Industrial Organization 5: 1-17. Schultz A, Klenke R, Lutze G, Voss M, Wieland R, Wilkening B (2003) Habitat Models as Tools for Situation Evaluation and Planning Support in Agricultural Landscapes. - In: Bissonette J, Storch I [Eds.]: Landscape theory and resource management: Linking theory with practice. - Island Press. pp. 261-282. Shannon C E (1948) A mathematical theory of communication. The Bell System Technical Journal 27, 379–423 and 623–656. Siriwardena, G M, and Robinson, R A (2002) Farmland birds: demography and abundance. Aspects of Applied Biology, 67: 179-188. Siriwardena, G M, Baillie, S R, Buckland, S T, Fewster, R M, Marchant, J H, and Wilson, J D (1998) Trends in the abundance of farmland birds: a quantitative comparison of smoothed Common Birds Census indices. Journal of Applied Ecology, 35 (1): 24-43. Siriwardena, G M, Baillie, S R, Fuller, R J, and Robinson, R A (2014) How can functional space for farmland birds best be studied? A comment on Butler and Norris (2013). Agriculture, Ecosystems & Environment, 192: 8-11. Siriwardena, G M, Cooke, I, and Sutherland, W J (2012) Landscape, cropping and field boundary influences on bird abundance. Ecography, 35 (2): 162-173. Siriwardena, G M, Crick, H Q, Baillie, S R, and Wilson, J D (2000) Agricultural land‐use and the spatial distribution of granivorous lowland farmland birds. Ecography, 23 (6): 702-719. Siriwardena, G, Dadam, D and Grice, P (2014) Responses of farmland birds to eight years of Environmental Stewardship. BTO Report, Thetford. Siriwardena, G M, Baillie, S R, Crick, H Q P and Wilson, J D, (2000) Agricultural land-use and the spatial distribution of granivorous lowland farmland birds. Ecography 23: 702-719.

232

Siriwardena, G M, Calbrade, N A and Vickery, J A (2008) Farmland birds and late winter food: does seed supply fail to meet demand? Ibis 150: 585–595. Siriwardena, G M, Cooke, I R and Sutherland, W J 2012 Landscape, cropping and field boundary influences on bird abundance. Ecography 35: 162–173. Sklenicka P, Šímová P, Hrdinová K, Salek M (2014) Changing rural landscapes along the border of Austria and the Czech Republic between 1952 and 2009: Roles of political, socioeconomic and environmental factors. Applied Geography 47: 89-98 Smith R K, Vaughan Jennings N, Harris S A (2005) A quantitative analysis of the abundance and demography of European hares Lepus europaeus in relation to habitat type, intensity of agriculture and climate. Mammal Review 35(1): 1-24. Sparks, T H, Mountford, J O and Parish, T (1994) Opportunities for conservation in the field boundaries of arable crops. Aspects of Applied Biology 4. Staley, J T, Botham, M S, Chapman, R E, Amy, S R, Heard, M S, Hulmes, L, Savage, J and Pywell, R F (2016) Little and late: How reduced hedgerow cutting can benefit Lepidoptera. Agriculture, Ecosystems & Environment, 224: 22-28. Statistisches Bundesamt (2010a) Ausgewählte Zahlen der Landwirtschaftszählung/Agrarstrukturerhebung 2010. Fachserie 3 Land- und Forstwirtschaft, Fischerei. Reihe 1, 1-115. Statistisches Bundesamt (2010b) Methodische Grundlagen der Landwirtschaftszählung 2010. Fachserie 3 Land- und Forstwirtschaft, Fischerei. Reihe 2. S. 6, 1-343. Statistisches Bundesamt (2015): Struktur der Bodennutzung) - Agrarstrukturerhebung - Fachserie 3 Reihe 2.1.2. https://www.destatis.de/DE/Publikationen/Thematisch/LandForstwirtschaft/Bodennutzung /Bodennutzung2030212157005.xls?__blob=publicationFile Stoate C, Báldi A, Beja P, Boatman N D, Herzon I, van Doorn A, de Snoo G R, Rakosy L, Ramwell C (2009) Ecological impacts of early 21st century agricultural change in Europe – A review. Journal of Environmental Management 91:22-46 Sudfeldt C, Dröschmeister R, Frederking W, Gedeon K, Gerlach B, Grüneberg C, Karthäuser J, Langgemach T, Schuster B, Trautmann S, Wahl J (2013): Vögel in Deutschland – 2013. DDA, BfN, LAG VSW, Münster. URL: http://www.dda- web.de/downloads/vid/vid_2013/files/assets/basic-html/index.html. Accessed 2016-10-02. Tapper SC, Barnes RFW (1986) Influence of Farming Practice on the Ecology of the Brown Hare (Lepus europaeus). Journal of Applied Ecology 23: 39-52. Temple, H J and Terry, A (2007) The Status and Distribution of European Mammals. Office of Official Publications of the European Communities, Luxembourg. Thrän D, Hennig C, Rensberg N, Denysenko V, Fritsche U R, Eppler U (2015) IEA Bioenergy Task 40: Country Report Germany 2014. DBFZ (German Biomass Research Centre), UFZ (Helmholtz Centre for Environmental Research), IINAS (International Institute for Sustainability Analysis and Strategy), DBFZ (German Biomass Research Centre), UFZ (Helmholtz Centre for Environmental Research), IINAS (International Institute for Sustainability Analysis and Strategy).

233

https://www.dbfz.de/fileadmin/user_upload/Referenzen/Studien/IEA_T40_Country_Report 2014.pdf Toms, M P, Siriwardena, G M and Greenwood, J J D (1999) Developing a mammal monitoring programme for the UK. British Trust for Ornithology, Thetford. Tscharntke T, Klein AM, Kruess A, Steffan-Dewenter I, Thies C (2005) Landscape perspectives on agricultural intensification and biodiversity – ecosystem service management. Ecology Letters 8: 857-874 UBA (2016-10-02a) URL: https://www.umweltbundesamt.de/daten/flaechennutzung/ struktur-der-flaechennutzung. Accessed 2016-10-02. Underwood, E, Baldock, D, Aiking, H, Buckwell, A, Dooley, E, Frelih-Larsen, A, Naumann, S, O'Connor, C, Poláková, J and Tucker, G M (2013) Options for sustainable food and agriculture in the EU. Technology options for feeding 10 billion people synthesis report. Report for the Science and Technology Options Assessment (STOA) panel of the European Parliament. Institute for European Environmental Policy with BIO Intelligence Service, Ecologic Institute and IVM - VU University, van Strien A, Pannekoek J, Gibbons D (2001) Indexing European bird population trends using results of national monitoring schemes: a trial of a new method. Bird Study 48: 200-213. Van Strien, A J, Termaat, T, Groenendijk, D, Mensing, V and Kéry M (2010) Site-occupancy models offer new opportunities for dragonfly monitoring based on daily species lists. Basic and Applied Ecology, 11: 495-503. van Swaay, C A M, van Strien, A J, Harpke, A, Fontaine, B, Stefanescu, C, Roy, D, Maes, D, Kühn, I, Õunap, E, Regan, E, Švitra, G, Heliölä, J, Settele, J, Warren, M S, Plattner, M, Kuussaari, M, Cornish, M, Garcia Pereira, P, Leopold, P, Feldmann, R, Jullard, R, Verovnik, R, Popov, S, Brereton, T, Gmelig Meyling, A and Collins, S (2010) The European Butterfly Indicator for Grassland Species 1990-2009. Report VS2010.010. De Vlinderstichting, Wageningen. van Swaay, C, Warren, M and Lois, G (2006) Biotope use and trends of European butterflies. Journal of Insect Conservation, 10 (2): 189-209. van Swaay, C A, Nowicki, P, Settele, J, and van Strien, A J (2008) Butterfly monitoring in Europe: methods, applications and perspectives. Biodiversity and Conservation, 17(14): 3455-3469. Vanbergen AJ, Woodcock BA, Watt AD, Niemelä J (2005) Effect of land-use heterogeneity on carabid communities at the landscape scale. Ecography 28: 3-16 Vaughan N, Lucas E A, Harris S, White P C (2003). Habitat associations of European hares Lepus europaeus in England and Wales: implications for farmland management. Journal of Applied Ecology 40(1): 163-175. Veen, P, Jefferson, R, de Smidt, J and ven der Straaten (eds) (2009) Grasslands in Europe of High Nature Value. KNNV Publishing, Zeist, The Netherlands. Venier, L A, and Pearce, J L (2004) Birds as indicators of sustainable forest management. The Forestry Chronicle, 80(1): 61-66.

234

Viechtbauer W (2010) Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36(3): 1 - 48. Vorisek P, Gregory R D, Van Strien A J, Meyling A G (2008) Population trends of 48 common terrestrial bird species in Europe: results from the Pan-European Common Bird Monitoring Scheme. Revista Catalana d’Ornitologia 24, 4-14. Voříšek P, Jiguet F, van Strien A, Škorpilová J, Klvaňová A, Gregory RD (2010) Trends in Abundance and Biomass of Widespread European Farmland Birds: How Much Have We Lost? BOU Proceedings of Lowland Farmland Birds III. www.bouorg.uk Wahl J, Dröschmeister R, Gerlach B, Grüneberg C, Langgemach T, Trautmann S, Sudfeldt C (2015): Vögel in Deutschland – 2014. DDA, BfN, LAG VSW, Münster. http://www.dda- web.de/downloads/vid/vid_2014/files/assets/basic-html/page1.html. Accessed 2016-10-02. Weibull A-C, Bengtsson J, Nohlgren E (2000) Diversity of butterflies in the agricultural landscape: the role of farming system and landscape heterogeneity. Ecography 23: 743-750. Weibull A-C, Östman Ö, Granqvist Å (2003) Species richness in agroecosystems: the effect of landscape, habitat and farm management. Biodiversity & Conservation 12: 1335-1355.

Weissteiner, C J, García-Feced, C, and Paracchini, M L (2016) A new view on EU agricultural landscapes: Quantifying patchiness to assess farmland heterogeneity. Ecological Indicators, 61: 317-327. Wickham, H (2009) ggplot2: elegant graphics for data analysis. Springer, New York.

Wilson JD, Whittingham MJ, Bradbury RB (2005) The management of crop structure: a general approach to reversing the impacts of agricultural intensification on birds? Ibis 147: 453-463.

Wilson, J D, Evans, J, Browne, S J, and King, J R (1997) Territory distribution and breeding success of skylarks Alauda arvensis on organic and intensive farmland in southern England. Journal of Applied Ecology, 1462-1478. World Spider Catalog (2015) Natural History Museum Bern, http://wsc.nmbe.ch, version 16.5 Wuczyński A (2016): Farmland bird diversity in contrasting agricultural landscapes of south- western Poland. Landscape and Urban Planning 148: 108-119 Ziomek, J and Banaszek, A (2007) The common hamster, Cricetus cricetus in Poland: status and current range. Folia Zoologica, 56 (3): 235-242.

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Annex 1 Metadatabase structure

Annex 1.1. List of tables in the metadatabase

Table Description All Land use data Table of identified land use datasets with appropriate metadata, including temporal and spatial resolution, contact information and level of INSPIRE compliance. Datasets can be EU-wide or at MS or regional level. Atlases Metadata for National Bird Atlases Biodiversity data (EU overview) Table of biodiversity data at EU level CommonBirdMonitoringSchemes Metadata for National Common (breeding) Bird Monitoring Schemes Countries List of countries and their 2-digit codes. All other tables are linked via this table through their country codes Land data types List of broad land data types and their ID number. All datasets in 'All Land use data' are categorised into these types and linked via this table in the sub- datasheet. Land use data (EU overivew) Table of biodiversity data at EU-level Agri drivers List of potential drivers of change in biodiversity. These are also summarised in the "Broad Drivers" table All Biodiversity data Table of identified biodiversity datasets with appropriate metadata, including temporal and spatial resolution, contact information and level of INSPIRE compliance. Datasets can be EU-wide or at MS or regional level. All Land use data Table of identified land use datasets with appropriate metadata, including temporal and spatial resolution, contact information and level of INSPIRE compliance. Datasets can be EU-wide or at MS or regional level. Biodiversity data types List of biodiversity data types and their ID number. All datasets in "All Biodiversity data" are categorised into these types and linked to this table in the sub- datasheet. Broad Driver2Land Response Table linking the response variables of the land use datasets to the broad drivers, to enable queries and filtering. Each driver is linked to several response variables, and each response variable is linked to several drivers. Driver2Land data type Table linking the land use data types to the Agri

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Table Description drivers table (fine scale). Each driver is linked to several data types, and each data type is linked to several drivers. Land Response Variables Table listing all the response variables for the land use datasets. This enables queries and filtering by response variable. MAES datasets Table listing datasets used in the MAES project that may be relevant. Results across EU: Linear predictors, Parameter estimates and standard errors from linear birds predictors of the interaction between land-use and year on species abundance Results across EU: Freeman & Newson, Parameter estimates and standard errors from F&N birds model, combined across EU, estimating associations between land-use and population growth rates Results by MS: F&N birds Direction and significance of associations between land-use and population growth rate, per MS Results by MS: Linear predictors, birds Direction and significance of associations between land-use and population growth rate, per MS (measured as interactions between year and land-use on species abundance). Results across EU: Linear predictors, Parameter estimates and standard errors from linear butterflies predictors, combined across EU, of the interaction between land-use and year on species abundance Results across EU: Freeman & Newson, Parameter estimates and standard errors from F&N butterflies model, estimating associations between land-use and population growth rates Results by MS: F&N butterflies Direction and significance of associations between land-use and population growth rate, per MS Results by MS: Linear predictors, birds Direction and significance of associations between land-use and population growth rate, per MS (measured as interactions between year and land-use on species abundance). Processed agricultural statistics FSS and other agricultural surveys matched to NUTS3 regions and standardised by area of region (source: Eurostat and MS statistics). Processed FHI data Farmland Heterogeneity indices per NUTS3 region, standardised by area (source: JRC). Processed input data Energy inputs, outputs, manure and fertilser statistics per NUTS3 region, standardised by area (source: JRC). Processed CORINE data CORINE land cover data per NUTS3 region, standardised by area of region (source EEA).

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Annex 2 Integrated assessment

Table A2.1. Lists of (a) farmland indicator bird species and (b) butterflies included in the analysis (a) Birds

Species Euring Latin name English name code code OR 1340 Ciconia ciconia White Stork K. 3040 Falco tinnunculus Kestrel RL 3580 Alectoris rufa Red-legged Partridge P. 3670 Perdix perdix Grey partridge TN 4590 Burhinus oedicnemus Stone curlew L. 4930 Vanellus vanellus Lapwing RK. 5320 Limosa limosa Black-tailed Godwit TD 6870 Streptopelia turtur Turtle Dove HP 8460 Upupa epops Hoopoe MC 9610 Melanocorypha calandra Calandra lark VL 9680 Calandrella brachydactyla Short-toed lark DL 9720 Galerida cristata Crested lark KL 9730 Galerida theklae Thekla lark S. 9760 Alauda arvensis Skylark SL 9920 Hirundo rustica Swallow TP 10050 Anthus campestris Tawny pipit MP 10110 Anthus pratensis Meadow pipit YW 10170 Motacilla flava Yellow wagtail WC 11370 Saxicola rubetra Whinchat SC 11390 Saxicola torquata Stonechat OH 11480 Oenanthe hispanica Black-eared wheatear WH 12750 Sylvia communis Whitethroat ED 15150 Lanius collurio Red-backed shrike ER 15190 Lanius minor Lesser grey shrike OO 15230 Lanius senator Woodchat shrike RO 15630 Corvus frugilegus Rook SG 15820 Sturnus vulgaris Starling UN 15830 Sturnus unicolor Spotless starling TS 15980 Passer montanus Tree sparrow OW 16040 Petronia petronia Rock sparrow NS 16400 Serinus serinus Serin LI 16600 Carduelis cannabina Linnet Y. 18570 Emberiza citrinella Yellowhammer CL 18580 Emberiza cirlus Cirl bunting OB 18660 Emberiza hortulana Ortolan bunting KH 18810 Emberiza melanocephala Black-headed bunting CB 18820 Miliaria calandra Corn bunting

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(b) Butterflies

Species English Latin name code Agl_io Aglais io Peacock Butterfly Agl_urt Aglais urticae Small Tortoiseshell Ant_car Anthocharis cardamines Orange Tip Ant_eup Anthocharis euphenoides Provencal Orange Tip Apa_ili Apatura ilia Lesser Purple Emperor Apa_iri Apatura iris Purple Emperor Aph_hyp Aphantopus hyperantus Ringlet Apo_cra Aporia crataegi Black-veined White Ara_lev Araschnia levana Map Butterfly Are_are Arethusana arethusa False Grayling Arg_adi Argynnis adippe High Brown Fritillary Arg_agl Argynnis aglaja Dark Green Fritillary Arg_lao Argynnis laodice Pallas’s Fritillary Arg_nio Argynnis niobe Niobe Fritillary Arg_pan Argynnis pandora Cardinal Arg_pap Argynnis paphia Silver-washed Fritillary Ari_age Aricia agestis Brown Argus Ari_art Aricia artaxerxes Northern Brown Argus Ari_cra Aricia cramera Southern Brown Argus Ari_eum Aricia eumedon Geranium argus Ari_mor Aricia morronensis Spanish Argus Ari_nic Aricia nicias silvery argus Bol_aqu Boloria aquilonaris Cranberry Fritillary Bol_dia Boloria dia Weaver’s Fritillary Bol_eun Boloria eunomia Bog Fritillary Bol_eup Boloria euphrosyne Pearl-bordered Fritillary Bol_pal Boloria pales Shepherd’s Fritillary Bol_sel Boloria selene Small Pearl-bordered Fritillary Bol_tho Boloria thore Thor’s Fritillary Bol_tit Boloria titania Purple Bog Fritillary Bre_dap Brenthis daphne Marbled Fritillary Bre_hec Brenthis hecate Twin Spot Fritillary Bre_ino Brenthis ino Lesser Marbled Fritillary Bri_cir Brintesia circe great banded grayling Cac_mar Cacyreus marshalli Geranium Bronze Cal_avi Callophrys avis Chapman’s Green Hairstreak Cal_rub Callophrys rubi Green Hairstreak Car_alc Carcharodus alceae Mallow Skipper Car_bae Carcharodus baeticus Southern Marbled Skipper Car_flo Carcharodus flocciferus Tufted Marbled Skipper Car_lav Carcharodus lavatherae Marbled Skipper

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Car_pal Carterocephalus palaemon Chequered Skipper Car_sil Carterocephalus silvicolus Northern Chequered Skipper Cel_arg Celastrina argiolus Holly Blue Cha_jas Charaxes jasius Two Tailed Pasha Cha_bri Chazara briseis The Hermit Coe_arc Coenonympha arcania Pearly Heath Coe_dor Coenonympha dorus Dusky Heath Coe_gly Coenonympha glycerion Chestnut Heath Coe_pam Coenonympha pamphilus Small Heath Coe_tul Coenonympha tullia Large Heath Col_alf Colias alfacariensis Berger’s Clouded Yellow Col_cro Colias crocea Clouded Yellow Col_hya Colias hyale Pale Clouded Yellow Col_pal Colias palaeno Moorland Clouded Yellow Col_phi Colias phicomone Mountain Clouded Yellow Col_eva Colotis evagore Desert Orange Tip Cup_alc Cupido alcetas Provençal short-tailed blue Cup_arg Cupido argiades short-tailed blue Cup_min Cupido minimus Small Blue Cup_osi Cupido osiris Osiris Blue Cya_sem Cyaniris semiargus Mazarine Blue Dan_chr Danaus chrysippus Plain Tiger Dan_ple Danaus plexippus Monarch Ere_aet Erebia aethiops Scotch Argus Ere_cas Erebia cassioides Common Brassy Ringlet Ere_emb Erebia embla Arctic Ringlet Ere_epi Erebia epiphron Mountain Ringlet Ere_eur Erebia euryale Large Ringlet Ere_gor Erebia gorgone Gavarnie Ringlet Ere_his Erebia hispania Spanish Brassy Ringlet Ere_lef Erebia lefebvrei Lefebvrei’s Ringlet Ere_lig Erebia ligea Arran Brown Ere_meo Erebia meolans Piedmont Ringlet Ere_neo Erebia neoridas Autumn Ringlet Ere_oem Erebia oeme Bright Eyed Ringlet Ere_tri Erebia triaria de Prunner’s Ringlet Ery_tag Erynnis tages Dingy Skipper Euc_bel Euchloe belemia Green Striped White Euc_cra Euchloe crameri Western Dappled White Euc_sim Euchloe simplonia Mountain Dappled White Euc_tag Euchloe tagis Portuguese Dappled White Eup_aur Euphydryas aurinia Marsh Fritillary Eup_des Euphydryas desfontainii Spanish Fritillary Eup_mat Euphydryas maturna Scarce Fritillary

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Fav_que Favonius quercus Purple Hairstreak Geg_nos Gegenes nostrodamus Mediterranean Skipper Gla_ale Glaucopsyche alexis Green Underside Blue Gla_mel Glaucopsyche melanops Black Eyed Blue Gon_cle Gonepteryx cleopatra Cleopatra Gon_rha Gonepteryx rhamni Brimstone Ham_luc Hamearis lucina Duke of Burgundy Hes_com Hesperia comma Silver-spotted Skipper Het_mor Heteropterus morpheus Large Chequered Skipper Hip_fag Hipparchia fagi Woodland Grayling Hip_fid Hipparchia fidia Striped Grayling Hip_her Hipparchia hermione Rock Grayling Hip_sem Hipparchia semele Grayling Hip_sta Hipparchia statilinus Tree Grayling Hyp_lyc Hyponephele lycaon Dusky Meadow Brown Iol_iol Iolana iolas Iolas Blue Iph_fei Iphiclides feisthamelii Iberian Scarce Swallowtail Iss_lat Issoria lathonia Queen of Spain Fritillary Lae_rob Laeosopis roboris Spanish Purple Hairstreak Lam_boe Lampides boeticus Long-tailed Blue Las_mae Lasiommata maera Large Wall Las_meg Lasiommata megera Wall Brown Las_pet Lasiommata petropolitana Northern Wall Brown Lep_rea Leptidea reali Real’s Wood White Lep_sin Leptidea sinapis Wood White Lep_pir Leptotes pirithous Lang’s Short Tailed Blue Lib_cel Libythea celtis Nettle Tree Butterfly Lim_cam Limenitis camilla White Admiral Lim_pop Limenitis populi Poplar Admiral Lim_red Limenitis reducta Southern White Admiral Lop_ach Lopinga achine Woodland Brown Lyc_alc Lycaena alciphron Purple Shot Copper Lyc_dis Lycaena dispar Large Copper British Race Lyc_hel Lycaena helle Violet Copper Lyc_hip Lycaena hippothoe Purple-edged Copper Lyc_phl Lycaena phlaeas Small Copper Lyc_tit Lycaena tityrus Sooty Copper Lyc_vir Lycaena virgaureae Scarce Copper Man_jur Maniola jurtina Meadow Brown Mel_gal Melanargia galathea Marbled White Mel_ine Melanargia ines Spanish Marbled White Mel_lac Melanargia lachesis Iberian Marbled White Mel_occ Melanargia occitanica Western Marbled White Mel_rus Melanargia russiae Esper’s Marbled White

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Mel_ath Melitaea athalia Heath Fritillary Mel_cin Melitaea cinxia Glanville Fritillary Mel_dei Melitaea deione Provençal fritillary Mel_dia Melitaea diamina False Heath Fritillary Mel_did Melitaea didyma Spotted Fritillary Mel_par Melitaea parthenoides Meadow Fritillary Mel_pho Melitaea phoebe Knapweed Fritillary Mel_tri Melitaea trivia Lesser Spotted Fritillary Mus_pro Muschampia proto Sage Skipper Nym_ant Nymphalis antiopa Camberwell Beauty Nym_pol Nymphalis polychloros Large Tortoiseshell Nym_xan Nymphalis xanthomelas Yellow Legged Tortoiseshell Och_syl Ochlodes sylvanus Large Skipper Pap_mac Papilio machaon Swallowtail Par_aeg Pararge aegeria Speckled Wood Par_apo Parnassius apollo Apollo Par_mne Parnassius mnemosyne Clouded Apollo Phe_alc Phengaris alcon Alcon large blue Phe_ari Phengaris arion large blue Phe_nau Phengaris nausithous dusky large blue Phe_tel Phengaris teleius scarce large blue Pie_bra Pieris brassicae Large White Pie_erg Pieris ergane mountain small white Pie_man Pieris mannii Southern Small White Pie_nap Pieris napi Green-veined White Pie_rap Pieris rapae Small White Ple_arg Plebejus argus Silver-studded Blue Ple_ida Plebejus idas Idas Blue Ple_opt Plebejus optilete cranberry blue Pol_c-a Polygonia c-album Comma Pol_ama Polyommatus amandus Amanda’s blue Pol_bel Polyommatus bellargus Adonis Blue Pol_cor Polyommatus coridon Chalkhill Blue Pol_dam Polyommatus damon Damon blue Pol_dap Polyommatus daphnis Meleager’s blue Pol_dor Polyommatus dorylas turquoise blue Pol_ero Polyommatus eros Eros Blue Pol_esc Polyommatus escheri Escher’s blue Pol_ful Polyommatus fulgens Forster’s Furry blue Pol_his Polyommatus hispanus Provencal Chalk Hill Blue Pol_ica Polyommatus icarus Common Blue Pol_niv Polyommatus nivescens mother-of-pearl blue Pol_rip Polyommatus ripartii Ripart’s anomalous blue Pol_the Polyommatus thersites Chapman’s blue

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Pon_cal Pontia callidice Peak White Pon_dap Pontia daplidice Bath White Pse_bat Pseudophilotes baton Baton Blue Pse_pan Pseudophilotes panoptes Panoptes Blue Pyr_alv Pyrgus alveus Large Grizzled Skipper Pyr_arm Pyrgus armoricanus Oberthur’s Grizzled Skipper Pyr_car Pyrgus carthami Safflower Skipper Pyr_cen Pyrgus centaureae Northern Grizzled Skipper Pyr_cir Pyrgus cirsii Cinquefoil Skipper Pyr_mal Pyrgus malvae Grizzled Skipper Pyr_mal Pyrgus malvoides southern grizzled skipper Pyr_ono Pyrgus onopordi rosy grizzled skipper Pyr_ser Pyrgus serratulae Olive Skipper Pyr_bat Pyronia bathseba Spanish Gatekeeper Pyr_cec Pyronia cecilia Southern Gatekeeper Pyr_tit Pyronia tithonus Gatekeeper Sat_aca Satyrium acaciae Sloe Hairstreak Sat_esc Satyrium esculi False Ilex Hairstreak Sat_ili Satyrium ilicis Ilex Hairstreak Sat_pru Satyrium pruni Black Hairstreak Sat_spi Satyrium spini Blue Spot Hairstreak Sat_w-a Satyrium w-album White-letter Hairstreak Sat_act Satyrus actaea Black Satyr Sat_fer Satyrus ferula Great Sooty Satyr Sco_ori Scolitantides orion Chequered Blue Spi_ser Spialia sertorius Red Underwing Skipper The_bet Thecla betulae Brown Hairstreak Thy_act Thymelicus acteon Lulworth Skipper Thy_lin Thymelicus lineola Essex Skipper Thy_syl Thymelicus sylvestris Small Skipper Tom_bal Tomares ballus Provencal Hairstreak Van_ata Vanessa atalanta Red Admiral Van_car Vanessa cardui Painted Lady Zeg_eup Zegris eupheme Sooty Orange Tip Zer_rum Zerynthia rumina Spanish Festoon

Table A2.2 List of Member States and corresponding 2-letter ISO codes. Species code Euring code AT AUSTRIA BE BELGIUM BG BULGARIA CZ CZECH REPUBLIC DE GERMANY DK DENMARK EE ESTONIA

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ES SPAIN FI FINLAND EL GREECE IE IRELAND IT ITALY LT LITHUANIA NL NETHERLANDS PL POLAND PT PORTUGAL RO ROMANIA SE SWEDEN SI SLOVENIA SK SLOVAKIA UK UNITED KINGDOM

Annexes 2.3 – 2.14 are provided in a separate document

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Annex 3 Case study 1 - Factors affecting the condition of agriculture-associated species and habitats within Natura 2000 sites

Annex 3.1. Natura 2000 Standard Data Form instructions and criteria on assessing the degree of conservation of Habitats and Species of Community interest58

DEGREE OF CONSERVATION: = A(c) of Annex III: Degree of conservation of the structure and functions of the natural habitat type, concerned and restoration possibilities.

This criterion comprises three sub-criteria: (i) degree of conservation of the structure, (ii) degree of conservation of the functions, (iii) restoration possibility.

Although the above sub-criteria could be evaluated separately, they should nonetheless be combined for the requirements of selection of sites proposed on the national list as they have a complex and interdependent influence on the process.

(i) Degree of conservation of structure This sub-criterion should be linked to the interpretation manual on Annex I habitats since this manual provides a definition, a list of characteristic species and other relevant elements.

Comparing the structure of a given habitat type present in the site with the data of the interpretation manual (and other relevant scientific information), and even with the same habitat type in other sites, it should be possible to establish a ranking system as follows, using the ‘best expert judgment’: I : excellent structure, II : structure well conserved, III : average or partially degraded structure.

In cases where the sub-class ‘excellent structure’ is given, the criterion A(c) should in its totality be classed as ‘A: excellent conservation’, independently of the grading of the other two sub-criteria.

In cases where the habitat type concerned on the site in question does not possess an excellent structure, it is still necessary to evaluate the other two sub-criteria.

(ii) Degree of conservation of functions

It can be difficult to define and measure the functions of a particular habitat type on the defined site and their conservation, and to do this independently of other habitat types. For this reason it is useful to paraphrase ‘the conservation of functions’ by the prospects (capacity and probability) of the habitat type concerned on the site in question to maintain its structure for the future, given on the one hand the possible unfavourable influences and on the other hand all the reasonable conservation effort which is possible.

58 http://ec.europa.eu/environment/nature/legislation/habitatsdirective/docs/standarddataforms/notes_en.pdf 245

I : excellent prospects, II : good prospects, III : average or unfavourable prospects.

In cases where the sub-class ‘I: excellent prospects’ or ‘II: good prospects’ are combined with the grading ‘II: structure well conserved’ of the first sub-criterion, the criterion A(c) should in its totality by classed ‘A: excellent conservation’ or ‘B: good conservation’ respectively, independently of the grading of the third sub-criterion which should not further be considered.

In cases where the sub-class ‘III: average or unfavourable prospects’ is combined with the grading ‘III: average or partially degraded structure’ of the first sub-criterion, the criterion A(c) in its entirety should be classed as ‘C: average or reduced conservation’ independently of the grading of the third sub-criterion which should not further be considered.

(iii) Restoration possibilities

This sub-criterion is used to evaluate to what extent the restoration of a habitat type concerned on the site in question could be possible.

The first thing to evaluate is its feasibility from a scientific point of view: does the current state of knowledge provide an answer to the ‘what to do and how to do it’ questions? This implies a full knowledge of the structure and functions of the habitat type and of the concrete management plans and prescriptions needed to restore it, that’s to say, to stabilise or increase the percentage of area covered by that habitat type, to re-establish the specific structure and functions which are necessary for its long-term maintenance and to maintain or restore a favourable conservation status for its typical species.

The second question that may be asked is the whether it is cost-effective from a nature conservation point of view? This assessment must take into consideration the degree of threat and rarity of the habitat type.

The ranking system should be the following, using ‘best expert judgement’: I : restoration easy, II : restoration possible with an average effort, III : restoration difficult or impossible.

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Annex 4 Case study 2 - Effects of Natura 2000 site designation and management plans on farmland bird abundance trends

Annex 4.1 Information on monitoring scheme methodology, years data were used, number of farmland sites and species used for each country in the analysiss

Country Scheme Method Start year num. sites num. species Austria Austrian Point 1998 147 16 Belgium Wallonian Point 1990 978 17 Bulgaria Bulgarian transect 2007 68 19 Cyprus Cypriot transect 2006 21 7 Czech Republic Czech Point 2004 81 21 Germany German transect 2005 500 24 Denmark Danish Point 1980 566 16 Estonia Estonian Point 2004 47 12 Finland Finish transect 1995 50 11 France French Point 1989 1107 30 Greece Greek Line 2007 45 21 Hungary Hungarian Point 2004 174 23 Ireland Irish Line 1998 237 10 Italy Italian Point 2000 427 29 Netherlands Dutch mapping 1990 2486 21 Poland Polish transect 2004 561 25 Portugal Portuguese Point 2004 47 24 Romania Romanian Point 2007 106 11 Sweden Swedish transect 1998 44 16 Slovenia Slovenian transect 2007 53 19 Slovakia Slovakian Count 2005 20 19 Spain Catalan transect 2002 105 29 Spain Spanish Point 1998 465 32 United Kingdom British transect 1994 3652 17

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Annex 4.2 Information on farmland bird species used in the analysis

Species name Family Eurasian Skylark (Alauda arvensis) Alaudidae Red-legged Partridge (Alectoris rufa) Phasianidae Tawny Pipit (Anthus campestris) Motacillidae Meadow Pipit (Anthus pratensis) Motacillidae Cattle Egret (Bubulcus ibis) Ardeidae Eurasian Thick-knee (Burhinus oedicnemus) Burhinidae Greater Short-toed Lark (Calandrella brachydactyla) Alaudidae Eurasian Linnet (Carduelis cannabina) Fringillidae White Stork (Ciconia ciconia) Ciconiidae Rook (Corvus frugilegus) Corvidae Cirl Bunting (Emberiza cirlus) Emberizidae Yellowhammer (Emberiza citrinella) Emberizidae Ortolan Bunting (Emberiza hortulana) Emberizidae Black-headed Bunting (Emberiza melanocephala) Emberizidae Common Kestrel (Falco tinnunculus) Falconidae Crested Lark (Galerida cristata) Alaudidae Thekla Lark (Galerida theklae) Alaudidae Barn Swallow (Hirundo rustica) Hirundinidae Red-backed Shrike (Lanius collurio) Laniidae Lesser Grey Shrike (Lanius minor) Laniidae Woodchat Shrike (Lanius senator) Laniidae Black-tailed Godwit (Limosa limosa) Scolopacidae Calandra Lark (Melanocorypha calandra) Alaudidae Corn Bunting (Miliaria calandra) Emberizidae Yellow Wagtail (Motacilla flava) Motacillidae Black-eared Wheatear (Oenanthe hispanica) Turdidae Eurasian Tree Sparrow (Passer montanus) Passeridae Grey Partridge (Perdix perdix) Phasianidae Rock Sparrow (Petronia petronia) Passeridae Whinchat (Saxicola rubetra) Turdidae Common Stonechat (Saxicola torquatus) Turdidae European Serin (Serinus serinus) Fringillidae European Turtle-dove (Streptopelia turtur) Columbidae Spotless Starling (Sturnus unicolor) Sturnidae

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Common Starling (Sturnus vulgaris) Sturnidae Common Whitethroat (Sylvia communis) Sylviidae Little Bustard (Tetrax tetrax) Otididae Common Hoopoe (Upupa epops) Upupidae Northern Lapwing (Vanellus vanellus) Charadriidae

Annex 4.3 Number of bird monitoring farmland sites used in the analysis with respect to their overlap with Natura 2000 areas and information on management plans Management No No Total plan management information plan Farmland Natura 2000 sites 418 690 514 1,622 (1-100% overlap) Farmland Natura 2000 sites 113 195 230 538 (>50% overlap) Farmland sites - - - 10,578 (with and without Natura

2000 overlap)

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Annex 5 Case study 3: Large-scale bird distributions in Britain and Ireland in relation to lowland agricultural land-use

Annex 5.1 Correlations between predictor variables for the UK Correlations greater than 0.8 are shown in red. Spring Winter Root Fodder Open Temporary Rough Permanent Sheep/ Variable Wheat Oats Pulses Potatoes Beet Rape Fallow Pasture Cattle barley barley crops crops vegetables grass grazing crops goats Wheat 1.000 0.048 0.639 0.421 0.905 0.648 0.490 0.636 0.086 0.954 0.545 -0.248 0.876 -0.400 -0.493 0.162 0.153 -0.152 Spring barley 0.048 1.000 -0.057 0.278 -0.045 0.277 0.433 -0.008 0.434 -0.001 0.250 0.544 0.128 -0.175 0.170 -0.066 -0.029 -0.154 Winter 0.639 -0.057 1.000 0.374 0.406 0.459 0.411 0.371 0.184 0.615 0.349 -0.178 0.600 -0.271 -0.436 0.015 0.438 -0.131 barley Oats 0.421 0.278 0.374 1.000 0.390 0.199 0.333 -0.015 0.387 0.375 0.160 0.202 0.435 -0.022 -0.309 0.324 0.229 0.107 Pulses 0.905 -0.045 0.406 0.390 1.000 0.486 0.307 0.542 -0.034 0.871 0.390 -0.259 0.796 -0.328 -0.441 0.182 0.016 -0.143 Root crops 0.648 0.277 0.459 0.199 0.486 1.000 0.862 0.854 0.403 0.549 0.674 -0.065 0.508 -0.309 -0.193 0.170 0.118 -0.167 Potatoes 0.490 0.433 0.411 0.333 0.307 0.862 1.000 0.478 0.540 0.382 0.605 0.077 0.406 -0.263 -0.127 0.218 0.195 -0.145 Beet 0.636 -0.008 0.371 -0.015 0.542 0.854 0.478 1.000 0.076 0.573 0.548 -0.238 0.474 -0.299 -0.212 0.055 -0.003 -0.175 Fodder crops 0.086 0.434 0.184 0.387 -0.034 0.403 0.540 0.076 1.000 0.046 0.346 0.497 0.066 0.201 0.070 0.317 0.189 0.258 Rape 0.954 -0.001 0.615 0.375 0.871 0.549 0.382 0.573 0.046 1.000 0.475 -0.276 0.862 -0.397 -0.460 0.105 0.143 -0.140 Open 0.545 0.250 0.349 0.160 0.390 0.674 0.605 0.548 0.346 0.475 1.000 -0.095 0.530 -0.321 -0.189 0.227 0.068 -0.117 vegetables Temporary -0.248 0.544 -0.178 0.202 -0.259 -0.065 0.077 -0.238 0.497 -0.276 -0.095 1.000 -0.187 0.457 0.166 0.036 0.266 0.075 grass Fallow 0.876 0.128 0.600 0.435 0.796 0.508 0.406 0.474 0.066 0.862 0.530 -0.187 1.000 -0.433 -0.463 0.160 0.100 -0.206 Pasture -0.400 -0.175 -0.271 -0.022 -0.328 -0.309 -0.263 -0.299 0.201 -0.397 -0.321 0.457 -0.433 1.000 -0.037 0.091 0.220 0.579 Rough -0.493 0.170 -0.436 -0.309 -0.441 -0.193 -0.127 -0.212 0.070 -0.460 -0.189 0.166 -0.463 -0.037 1.000 -0.178 -0.541 0.017 grazing Permanent 0.162 -0.066 0.015 0.324 0.182 0.170 0.218 0.055 0.317 0.105 0.227 0.036 0.160 0.091 -0.178 1.000 0.053 0.183 crops Cattle 0.153 -0.029 0.438 0.229 0.016 0.118 0.195 -0.003 0.189 0.143 0.068 0.266 0.100 0.220 -0.541 0.053 1.000 -0.086 Sheep/goats -0.152 -0.154 -0.131 0.107 -0.143 -0.167 -0.145 -0.175 0.258 -0.140 -0.117 0.075 -0.206 0.579 0.017 0.183 -0.086 1.000

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Annex 5.2. Correlations between predictor variables for Ireland Correlations greater than 0.8 are shown in red. Spring Root Fodder Open Temporary Rough Permanent Sheep/ Variable wheat oats pulses potatoes beet rape fallow pasture cattle barley crops crops vegetables grass grazing crops goats Wheat 1.000 0.648 0.522 0.907 0.922 0.900 -0.068 -0.881 0.951 0.801 -0.634 0.912 -0.766 -0.477 0.848 -0.175 -0.282 Spring barley 0.648 1.000 0.906 0.698 0.625 0.455 0.438 -0.432 0.796 0.348 0.049 0.510 -0.253 -0.659 0.517 0.491 0.436 Oats 0.522 0.906 1.000 0.472 0.388 0.224 0.527 -0.167 0.728 0.062 0.181 0.278 -0.062 -0.428 0.248 0.449 0.411 Pulses 0.907 0.698 0.472 1.000 0.992 0.947 0.040 -0.922 0.826 0.907 -0.579 0.965 -0.836 -0.477 0.968 -0.132 -0.216 Root crops 0.922 0.625 0.388 0.992 1.000 0.976 -0.034 -0.955 0.817 0.940 -0.649 0.985 -0.875 -0.449 0.978 -0.205 -0.295 Potatoes 0.900 0.455 0.224 0.947 0.976 1.000 -0.207 -0.980 0.752 0.977 -0.775 0.996 -0.919 -0.360 0.979 -0.374 -0.459 Beet -0.068 0.438 0.527 0.040 -0.034 -0.207 1.000 0.293 0.031 -0.254 0.634 -0.176 0.159 0.071 -0.148 0.498 0.463 Fodder crops -0.881 -0.432 -0.167 -0.922 -0.955 -0.980 0.293 1.000 -0.733 -0.975 0.809 -0.980 0.907 0.411 -0.968 0.359 0.441 Rape 0.951 0.796 0.728 0.826 0.817 0.752 0.031 -0.733 1.000 0.614 -0.451 0.777 -0.571 -0.565 0.708 0.032 -0.060 Open vegetables 0.801 0.348 0.062 0.907 0.940 0.977 -0.254 -0.975 0.614 1.000 -0.777 0.971 -0.922 -0.335 0.979 -0.395 -0.472 Temporary grass -0.634 0.049 0.181 -0.579 -0.649 -0.775 0.634 0.809 -0.451 -0.777 1.000 -0.743 0.843 -0.046 -0.700 0.794 0.829 Fallow 0.912 0.510 0.278 0.965 0.985 0.996 -0.176 -0.980 0.777 0.971 -0.743 1.000 -0.901 -0.412 0.987 -0.316 -0.402 Pasture -0.766 -0.253 -0.062 -0.836 -0.875 -0.919 0.159 0.907 -0.571 -0.922 0.843 -0.901 1.000 0.010 -0.890 0.615 0.685 Rough grazing -0.477 -0.659 -0.428 -0.477 -0.449 -0.360 0.071 0.411 -0.565 -0.335 -0.046 -0.412 0.010 1.000 -0.420 -0.629 -0.569 Permanent crops 0.848 0.517 0.248 0.968 0.978 0.979 -0.148 -0.968 0.708 0.979 -0.700 0.987 -0.890 -0.420 1.000 -0.278 -0.352 Cattle -0.175 0.491 0.449 -0.132 -0.205 -0.374 0.498 0.359 0.032 -0.395 0.794 -0.316 0.615 -0.629 -0.278 1.000 0.988 Sheep/goats -0.282 0.436 0.411 -0.216 -0.295 -0.459 0.463 0.441 -0.060 -0.472 0.829 -0.402 0.685 -0.569 -0.352 0.988 1.000

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Annex 5.3 Individual variable test results for Great Britain Predictor variable names are shown in bold where significant at P<0.1 and hence included in multivariate analyses. DNC denotes where models did not converge. Species Predictor Estimate SE Chi-square P cattle 0.164 0.238 0.47 0.493 fodder crops 161.911 118.204 1.24 0.265 oats 74.426 19.921 11.63 0.001 open vegetables -27.328 9.386 4.14 0.042 pasture 3.133 0.786 11.55 0.001 permanent crops 34.510 28.023 0.82 0.366 Buzzard rough grazing 0.887 0.816 1.09 0.296 Buteo buteo root crops -15.460 7.727 3.7 0.054 sheep & goats 0.076 0.068 1.29 0.255 spring barley 3.371 2.632 1.11 0.292 temporary grass 13.133 6.292 7.82 0.005 winter barley -26.403 6.476 8.92 0.003 wheat -3.107 1.036 7.32 0.007 cattle 0.330 0.141 4.84 0.028 fodder crops -163.476 55.074 6.2 0.013 oats 35.579 14.121 7.38 0.007 open vegetables 16.883 7.103 2.78 0.095 pasture -1.435 0.539 5.26 0.022 permanent crops 14.975 18.147 2.59 0.108 Kestrel rough grazing -4.016 0.647 20.89 0.000 Falco tinnunculus root crops 7.427 4.744 1.64 0.201 sheep & goats -0.145 0.049 6.52 0.011 spring barley -7.159 2.054 4.25 0.039 temporary grass -7.243 2.039 6.37 0.012 winter barley 12.657 5.022 11.67 0.001 wheat 4.288 0.641 21.68 0.000 cattle -0.291 0.244 1.33 0.250 fodder crops 0.126 86.079 0 0.999 oats 9.299 23.426 0.15 0.701 open vegetables 2.184 7.965 0.06 0.799 pasture -1.348 1.164 1.5 0.221 permanent crops -187.810 100.294 3.83 0.050 Grey Partridge rough grazing 0.722 0.699 1 0.317 Perdix perdix root crops -4.335 6.374 0.38 0.536 sheep & goats 0.100 0.080 1.34 0.248 spring barley 2.037 3.286 0.32 0.569 temporary grass -7.052 4.287 2.93 0.087 winter barley 10.919 5.549 2.56 0.110 wheat -0.653 1.158 0.3 0.584 Lapwing cattle -0.145 0.219 0.46 0.497 Vanellus vanellus fodder crops -20.889 105.311 0.05 0.829

252

Species Predictor Estimate SE Chi-square P oats -20.263 19.879 0.94 0.332 open vegetables 7.698 8.003 0.63 0.429 pasture -2.831 0.859 8.91 0.003 permanent crops -102.016 51.685 3.85 0.050 rough grazing 0.904 0.722 1.4 0.237 root crops 3.482 7.212 0.28 0.600 sheep & goats -0.108 0.073 2.47 0.116 spring barley 2.040 1.831 1.1 0.295 temporary grass -1.911 4.967 0.2 0.656 winter barley 16.182 6.244 6.27 0.012 wheat 0.707 1.077 0.44 0.505 cattle -1.099 0.344 8.52 0.004 fodder crops 159.689 207.271 0.51 0.476 oats -54.036 33.500 2.87 0.090 open vegetables 6.249 14.386 0.19 0.660 pasture -4.874 1.179 9.92 0.002 permanent crops -515.618 231.173 4.01 0.045 Curlew rough grazing 4.719 1.020 13.18 0.000 Numenius arquata root crops 6.886 10.092 0.51 0.474 sheep & goats 0.129 0.116 0.95 0.331 spring barley 9.906 3.285 3.65 0.056 temporary grass -9.240 8.247 1.12 0.289 winter barley 21.426 13.439 1.54 0.215 wheat -1.437 2.098 0.49 0.484 cattle 0.491 0.194 5.09 0.024 fodder crops 6.410 84.228 0.01 0.943 oats 35.259 15.698 3.75 0.053 open vegetables 26.481 8.714 4.92 0.027 pasture -0.598 0.777 0.56 0.454 permanent crops 97.293 34.514 5.37 0.021 Stock Dove rough grazing -6.265 0.788 19.39 0.000 Columba oenas root crops 18.057 4.165 6.42 0.011 sheep & goats 0.000 0.054 0 1.000 spring barley -11.070 3.452 4.11 0.043 temporary grass -8.682 3.717 3.79 0.052 winter barley 20.490 4.765 17.01 0.000 wheat 5.244 0.770 18.2 0.000 cattle -1.536 0.771 2.16 0.142 fodder crops -195.279 217.213 0.65 0.420 Turtle Dove oats -110.598 63.368 1.46 0.227 Streptopelia turtur open vegetables 35.202 11.038 2.75 0.098 pasture -9.249 1.998 5.89 0.015 permanent crops -1.071 68.396 0 0.988

253

Species Predictor Estimate SE Chi-square P rough grazing -26.432 11.578 4.68 0.031 root crops 30.505 5.932 1.36 0.244 sheep & goats -0.335 0.199 1.03 0.310 spring barley 8.998 27.042 0.1 0.750 temporary grass -54.358 11.891 5.1 0.024 winter barley 20.712 15.262 1.3 0.254 wheat 9.223 1.890 7.26 0.007 cattle -0.615 0.155 10.92 0.001 fodder crops -51.205 63.107 0.59 0.441 oats 28.877 14.649 2.76 0.097 open vegetables 24.972 6.465 10.67 0.001 pasture -3.321 0.511 19.68 0.000 permanent crops 15.948 10.130 813.08 0.000 Skylark rough grazing 0.638 0.522 1.13 0.287 Alauda arvensis root crops 6.088 4.174 1.69 0.194 sheep & goats -0.087 0.047 2.77 0.096 spring barley 6.005 2.253 2.71 0.100 temporary grass -3.738 3.674 1.15 0.283 winter barley 2.992 5.776 0.32 0.571 wheat 2.690 0.814 7.94 0.005 cattle -1.195 0.246 15.53 0.000 fodder crops -187.941 147.705 91470000 0.000 oats -90.850 36.886 5.6 0.018 open vegetables 15.364 8.661 1 0.317 pasture -3.035 0.796 2.26 0.133 permanent crops -664.458 543.943 2.86 0.091 Meadow Pipit rough grazing 4.340 0.817 18.08 0.000 Anthus pratensis root crops -0.831 7.273 0.01 0.920 sheep & goats -0.107 0.088 1.73 0.188 spring barley 10.564 2.794 3.69 0.055 temporary grass -0.457 5.474 0 0.944 winter barley -9.115 8.711 1.3 0.253 wheat -2.520 1.374 3.66 0.056 cattle 0.592 0.310 2.6 0.107 fodder crops -105.813 152.472 0.58 0.447 oats -26.208 28.141 0.82 0.364 open vegetables 24.378 7.772 2.56 0.109 Yellow Wagtail pasture -1.636 1.378 1.21 0.272 Motacilla flava permanent crops 13.785 18.945 0.48 0.489 rough grazing -9.672 4.221 8.15 0.004 root crops 12.478 8.263 1.81 0.178 sheep & goats -0.059 0.115 0.27 0.602 spring barley -34.995 16.231 4.93 0.026

254

Species Predictor Estimate SE Chi-square P temporary grass -25.751 7.220 6.34 0.012 winter barley 16.719 7.082 2.62 0.106 wheat 5.980 1.361 8.77 0.003 cattle -0.265 0.105 4.66 0.031 fodder crops 116.229 44.122 6.19 0.013 oats -17.835 8.328 4.7 0.030 open vegetables 4.563 4.330 0.77 0.381 pasture -0.708 0.342 3.69 0.055 Pied/White permanent crops -22.638 16.266 6.8 0.009 Wagtail rough grazing 2.551 0.345 12.34 0.000 Motacilla alba root crops 4.341 2.435 2.78 0.095 sheep & goats 0.007 0.027 0.06 0.800 spring barley 5.680 1.743 3.5 0.061 temporary grass 1.913 1.797 0.9 0.343 winter barley -2.288 2.901 0.75 0.385 wheat -1.207 0.520 6.04 0.014 cattle 1.016 0.161 17.37 0.000 fodder crops -18.765 101.249 0.03 0.857 oats 52.856 21.818 5.39 0.020 open vegetables 8.756 9.093 0.94 0.331 pasture 2.199 0.700 6.66 0.010 Wren permanent crops 64.219 54.539 4.36 0.037 Troglodytes rough grazing -4.158 0.654 14.93 0.000 troglodytes root crops 7.288 5.182 2.04 0.153 sheep & goats 0.065 0.063 1.08 0.299 spring barley -8.088 3.215 1.79 0.181 temporary grass -4.653 3.995 0.79 0.376 winter barley 24.097 8.669 7.56 0.006 wheat 4.489 1.219 9.54 0.002 cattle 0.447 0.127 8.37 0.004 fodder crops 56.191 60.331 0.81 0.368 oats 37.507 13.582 6.28 0.012 open vegetables 3.626 5.854 0.36 0.546 pasture 1.798 0.391 9.98 0.002 permanent crops 63.581 28.258 2.95 0.086 Dunnock rough grazing -2.662 0.453 11.72 0.001 Prunella modularis root crops 0.942 3.376 0.08 0.783 sheep & goats 0.107 0.043 5.27 0.022 spring barley -5.594 1.884 3.92 0.048 temporary grass -5.065 4.083 0.85 0.357 winter barley 5.980 5.006 1.57 0.211 wheat 1.876 0.889 4.26 0.039 Robin cattle 1.305 0.211 16.87 0.000

255

Species Predictor Estimate SE Chi-square P Erithacus rubecula fodder crops 17.850 133.328 0.02 0.898 oats 99.472 41.906 6.16 0.013 open vegetables 1.539 10.992 0.02 0.881 pasture 4.062 0.834 12.73 0.000 permanent crops 425.355 240.970 3.33 0.068 rough grazing -5.411 0.856 15.11 0.000 root crops 8.525 7.692 1.45 0.228 sheep & goats 0.211 0.110 3.13 0.077 spring barley -11.953 2.147 3.2 0.074 temporary grass -11.484 6.463 1.11 0.292 winter barley 35.621 15.493 6.54 0.011 wheat 5.440 2.208 6.9 0.009 cattle 0.789 0.202 9.87 0.002 fodder crops 61.746 102.551 0.37 0.543 oats 56.405 28.812 4.48 0.034 open vegetables -14.141 9.393 1.23 0.268 pasture 2.805 0.729 10.68 0.001 permanent crops 96.682 76.405 1.31 0.252 Blackbird rough grazing -3.019 0.618 11.9 0.001 Turdus merula root crops -2.901 6.467 0.17 0.677 sheep & goats 0.186 0.078 6.54 0.011 spring barley -5.851 2.693 2.83 0.093 temporary grass -1.609 2.684 0.29 0.593 winter barley 18.923 9.782 3.86 0.049 wheat 3.008 1.844 3.68 0.055 cattle 0.207 0.102 3.73 0.053 fodder crops 69.692 46.527 1.64 0.200 oats 30.291 10.055 6.59 0.010 open vegetables -8.424 4.455 1.7 0.193 pasture 1.609 0.321 17.56 0.000 permanent crops 32.257 13.128 1.89 0.170 Song Thrush rough grazing -1.110 0.417 4.86 0.028 Turdus philomelos root crops -3.140 2.530 1.12 0.290 sheep & goats 0.105 0.029 9.88 0.002 spring barley -1.770 2.202 0.48 0.491 temporary grass -1.308 3.019 0.15 0.701 winter barley 1.553 2.906 0.27 0.605 wheat -0.007 0.585 0 0.991 cattle 0.237 0.119 3.39 0.066 fodder crops -31.162 49.306 0.38 0.539 Mistle Thrush oats -4.647 9.703 0.23 0.633 Turdus viscivorus open vegetables 19.105 4.313 4.88 0.027 pasture 0.145 0.466 0.1 0.756

256

Species Predictor Estimate SE Chi-square P permanent crops 36.245 10.667 2.9 0.089 rough grazing -2.341 0.406 13.05 0.000 root crops 9.189 2.640 6.43 0.011 sheep & goats -0.001 0.029 0 0.979 spring barley -4.064 1.679 3.32 0.069 temporary grass -6.300 2.001 6.54 0.011 winter barley 7.635 2.860 5.99 0.014 wheat 1.860 0.564 6.55 0.011 cattle -0.140 0.162 0.74 0.390 fodder crops -79.583 49.022 1.74 0.188 oats 24.918 13.860 2.34 0.126 open vegetables 14.545 7.061 2.47 0.116 pasture -0.444 0.640 0.49 0.485 permanent crops 4727.932 . . . Whitethroat rough grazing -1.858 0.480 6.42 0.011 Sylvia communis root crops 1.812 3.640 0.19 0.662 sheep & goats -0.007 0.041 0.03 0.869 spring barley -4.871 1.486 3.85 0.050 temporary grass -6.695 2.200 4.54 0.033 winter barley -0.481 5.153 0.01 0.922 wheat 1.994 0.835 4.27 0.039 cattle 0.447 0.137 8.41 0.004 fodder crops -52.965 58.958 0.73 0.392 oats 33.497 16.006 4.45 0.035 open vegetables -0.888 6.944 0.02 0.887 pasture 0.191 0.598 0.09 0.760 Long-tailed Tit permanent crops 47.874 23.286 9.06 0.003 Aegithalos rough grazing -5.187 0.704 22.02 0.000 caudatus root crops 0.941 4.565 0.04 0.834 sheep & goats -0.039 0.041 0.78 0.376 spring barley -10.253 2.825 5.3 0.021 temporary grass -4.645 2.809 2.11 0.146 winter barley 12.825 5.129 10.31 0.001 wheat 3.039 0.858 12.1 0.001 cattle 0.152 0.174 0.65 0.420 fodder crops 297.336 57.381 6.88 0.009 oats -2.602 14.527 0.02 0.900 open vegetables -5.699 7.430 1.38 0.240 Coal Tit pasture 0.838 0.627 1.67 0.196 Periparus ater permanent crops -44.963 17.525 3.88 0.049 rough grazing 2.484 0.609 13.35 0.000 root crops -2.655 3.936 0.73 0.393 sheep & goats 0.010 0.039 0.04 0.838

257

Species Predictor Estimate SE Chi-square P spring barley 9.717 2.035 7.17 0.007 temporary grass 9.237 2.465 12.46 0.000 winter barley -5.465 4.442 2.04 0.153 wheat -2.788 0.708 15.13 0.000 cattle 0.540 0.258 3.31 0.069 fodder crops -57.924 87.236 0.4 0.526 oats 60.198 27.471 5.37 0.021 open vegetables -17.531 6.967 1.68 0.195 pasture 2.876 0.816 7.02 0.008 Blue Tit permanent crops 165.407 101.001 2.87 0.090 Cyanistes rough grazing -3.240 0.704 5.8 0.016 caeruleus root crops -6.042 5.663 0.94 0.331 sheep & goats 0.171 0.070 4.61 0.032 spring barley -6.774 2.319 2.4 0.121 temporary grass -4.062 5.184 0.41 0.522 winter barley 4.395 5.880 0.61 0.435 wheat 1.175 1.445 0.88 0.348 cattle 0.810 0.192 8.62 0.003 fodder crops 3.570 86.871 0 0.973 oats 45.677 28.633 3.38 0.066 open vegetables -4.353 7.036 0.32 0.570 pasture 2.309 0.981 4.83 0.028 permanent crops 427.517 401.226 2.8 0.094 Great Tit rough grazing -3.800 0.473 11.23 0.001 Parus major root crops 3.873 5.434 0.52 0.471 sheep & goats 0.084 0.084 0.86 0.353 spring barley -7.633 1.490 3.7 0.054 temporary grass -2.263 4.366 0.21 0.649 winter barley 7.601 6.847 1.91 0.167 wheat 2.688 1.168 5.54 0.019 cattle 0.093 0.136 0.47 0.495 fodder crops 76.923 52.112 2.08 0.150 oats 24.920 12.519 4.29 0.038 open vegetables 1.242 4.975 0.05 0.818 pasture -0.807 0.500 2.3 0.129 permanent crops -11.793 11.526 0.35 0.552 Rook rough grazing 0.786 0.548 1.57 0.210 Corvus frugilegus root crops 3.289 2.910 0.99 0.321 sheep & goats -0.089 0.037 4.68 0.030 spring barley 8.281 2.440 7.79 0.005 temporary grass 3.440 3.631 1.04 0.308 winter barley 1.916 3.366 0.32 0.571 wheat 1.203 0.624 3.33 0.068

258

Species Predictor Estimate SE Chi-square P cattle 0.037 0.213 0.02 0.899 fodder crops 80.438 90.670 4333994 0.000 oats -17.968 15.960 1.1 0.294 open vegetables 6.106 6.906 0.88 0.349 pasture -2.311 0.713 4.75 0.029 permanent crops -13.269 19.893 0.77 0.379 Starling rough grazing 2.538 0.853 5.58 0.018 Sturnus vulgaris root crops 6.750 5.463 3.28 0.070 sheep & goats -0.104 0.058 2.34 0.126 spring barley 7.451 3.174 2.11 0.146 temporary grass 2.719 3.740 0.37 0.541 winter barley -2.817 4.723 0.38 0.539 wheat -0.274 0.888 0.11 0.738 cattle -0.007 0.112 0 0.951 fodder crops 186.275 50.281 7.74 0.005 oats 0.438 13.814 0 0.976 open vegetables -9.831 5.087 4.17 0.041 pasture 1.634 0.412 12.71 0.000 permanent crops 37.182 20.975 0.67 0.412 House Sparrow rough grazing 1.525 0.355 8.92 0.003 Passer domesticus root crops -4.319 3.269 1.18 0.277 sheep & goats 0.119 0.036 6.25 0.012 spring barley -0.321 1.333 0.07 0.788 temporary grass 4.953 1.515 7.7 0.006 winter barley -8.981 3.067 8.54 0.004 wheat -2.566 0.479 20.91 0.000 cattle -0.242 0.365 0.42 0.518 fodder crops 514.382 172.225 1654000000 0.000 oats -27.115 34.374 0.66 0.418 open vegetables -22.558 19.987 0.7 0.401 pasture 1.708 1.590 0.89 0.346 permanent crops -591.742 198.217 6.31 0.012 Tree Sparrow rough grazing 5.856 1.280 12.63 0.000 Passer montanus root crops -10.395 10.571 0.48 0.489 sheep & goats 0.260 0.132 3.24 0.072 spring barley 12.969 3.799 4.05 0.044 temporary grass 12.023 5.596 3.04 0.081 winter barley 4.328 14.072 0.04 0.841 wheat -13.044 4.931 8.78 0.003 cattle 1.073 0.375 7.4 0.007 Chaffinch fodder crops 140.143 114.516 1.24 0.265 Fringilla coelebs oats 107.105 59.026 2.43 0.119 open vegetables 17.149 18.704 1.13 0.288

259

Species Predictor Estimate SE Chi-square P pasture 3.111 1.033 3.92 0.048 permanent crops 55.562 94.423 0.71 0.400 rough grazing -3.893 1.136 3.7 0.054 root crops 21.822 13.890 2.9 0.088 sheep & goats 0.292 0.172 1.61 0.205 spring barley -7.677 4.973 0.94 0.331 temporary grass -20.426 9.706 0.69 0.405 winter barley 36.348 18.177 3.74 0.053 wheat 6.967 3.119 4.18 0.041 cattle 0.187 0.106 2.92 0.088 fodder crops 3.671 47.221 0.01 0.940 oats 39.805 13.469 10.02 0.002 open vegetables 5.128 4.471 1.15 0.283 pasture -0.184 0.333 0.31 0.576 permanent crops 46.380 21.636 4.44 0.035 Greenfinch rough grazing -1.905 0.516 20.88 0.000 Chloris chloris root crops 3.380 2.669 1.3 0.254 sheep & goats -0.031 0.033 0.85 0.356 spring barley -3.842 1.139 5.03 0.025 temporary grass -4.088 1.846 2.74 0.098 winter barley 6.338 4.021 3.19 0.074 wheat 2.495 0.550 15.43 0.000 cattle 0.296 0.101 6.21 0.013 fodder crops 16.909 42.956 0.15 0.694 oats 12.628 12.569 1.04 0.308 open vegetables 7.985 5.214 2.1 0.147 pasture 0.869 0.360 4.46 0.035 permanent crops 13.718 9.675 0.88 0.348 Goldfinch rough grazing -1.991 0.496 7.26 0.007 Carduelis carduelis root crops 3.979 2.626 2.24 0.135 sheep & goats 0.095 0.035 5.59 0.018 spring barley -4.349 1.620 2.78 0.096 temporary grass -7.164 4.275 1.38 0.240 winter barley 13.399 3.651 7.91 0.005 wheat 1.853 0.705 5.69 0.017 cattle -0.074 0.114 0.39 0.531 fodder crops -22.954 49.505 0.17 0.679 oats -16.354 11.411 1.59 0.208 Linnet open vegetables 11.621 5.130 2.67 0.102 Carduelis cannabina pasture -0.632 0.472 1.83 0.176 permanent crops DNC

rough grazing 0.125 0.498 0.06 0.809 root crops 1.127 3.371 0.11 0.739

260

Species Predictor Estimate SE Chi-square P sheep & goats -0.027 0.036 0.5 0.479 spring barley 2.370 1.239 2.07 0.150 temporary grass -0.961 2.082 0.22 0.637 winter barley 4.567 3.944 1.12 0.290 wheat 0.424 0.662 0.39 0.533 cattle 0.320 0.121 5.91 0.015 fodder crops -63.006 45.560 1.73 0.189 oats 12.055 12.079 1.02 0.312 open vegetables -8.303 4.603 2.13 0.145 pasture 1.170 0.446 5.51 0.019 permanent crops 3.827 10.133 0.17 0.677 Bullfinch rough grazing -2.366 0.379 11.12 0.001 Pyrrhula pyrrhula root crops -4.018 3.643 1.34 0.247 sheep & goats 0.037 0.034 1.08 0.298 spring barley -4.976 2.736 2.4 0.121 temporary grass -1.885 2.408 0.53 0.466 winter barley 4.894 3.701 2.05 0.152 wheat 1.289 0.713 3.34 0.068 cattle -0.381 0.185 2.59 0.107 fodder crops 176.450 99.347 4.92 0.027 oats 69.085 16.793 8.97 0.003 open vegetables DNC

pasture -3.124 0.683 10.45 0.001 permanent crops DNC Yellowhammer rough grazing 1.325 0.721 2.33 0.127 Emberiza citrinella root crops DNC

sheep & goats 0.013 0.061 0.05 0.830 spring barley 13.125 2.302 7.07 0.008 temporary grass -4.584 3.541 0.86 0.355 winter barley 10.748 7.072 4 0.046 wheat 10.032 5.112 5.7 0.017 cattle -0.367 0.182 3.67 0.055 fodder crops -149.733 87.215 2.62 0.106 oats -34.420 17.316 3.2 0.074 open vegetables 24.844 9.390 2.66 0.103 pasture -2.357 0.660 8.5 0.004 Reed Bunting permanent crops -13.462 14.373 0.67 0.413 Emberiza schoeniclus rough grazing 0.331 0.619 0.25 0.615 root crops 14.652 8.508 2.54 0.111 sheep & goats -0.099 0.059 2.77 0.096 spring barley 0.454 3.127 0.02 0.901 temporary grass -7.775 3.448 4.85 0.028 winter barley -0.050 4.716 0 0.991

261

Species Predictor Estimate SE Chi-square P wheat 2.601 1.090 4 0.046 cattle 0.005 0.383 0 0.989 fodder crops -126.660 164.629 0.76 0.385 oats 26.849 28.716 0.81 0.367 open vegetables -14.660 10.759 1.38 0.239 pasture 0.192 1.459 0.02 0.898 permanent crops -47.934 50.374 0.61 0.434 Corn Bunting rough grazing -4.471 2.177 4.32 0.038 Emberiza calandra root crops -7.807 8.836 0.77 0.379 sheep & goats -0.197 0.160 1.95 0.163 spring barley -3.395 4.006 0.81 0.369 temporary grass -2.439 4.197 0.38 0.540 winter barley 5.636 7.247 0.55 0.459 wheat 0.645 1.454 0.19 0.666

Annex 5.4 Individual variable test results for Ireland.

Predictor variable names are shown in bold where significant at P<0.1 and hence included in multivariate analyses. DNC denotes where models did not converge.

Species Predictor Estimate SE Z P

barley 1.666 19.94 0.08 0.933 beet -4412.7 1741.7 -2.53 0.011 Buzzard cattle -1.494 0.820 -1.82 0.069 Buteo buteo pasture -3.997 1.610 -2.48 0.013 rough grazing 1.578 8.149 0.19 0.847 wheat 25.52 16.67 1.53 0.126 barley -2.975 3.644 -0.82 0.414 beet 1717.1 733.5 2.34 0.019 Kestrel cattle 0.319 0.167 1.91 0.056 Falco tinnunculus pasture 0.858 1.043 0.82 0.411 rough grazing 0.984 2.230 0.44 0.659 wheat -9.024 4.358 -2.07 0.038 barley -21.31 10.60 -2.01 0.045 beet -7132.7 636.1 -11.21 0.000 Lapwing cattle -0.868 0.739 -1.17 0.240 Vanellus vanellus pasture 2.996 1.813 1.65 0.098 rough grazing 1.611 5.597 0.29 0.774 wheat -8.621 11.99 -0.72 0.472 barley -5.436 4.962 -1.1 0.273 Curlew beet 1504.4 820.6 1.83 0.067 Numenius arquata cattle -0.142 0.198 -0.72 0.473 pasture -2.223 0.569 -3.91 0.000

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Species Predictor Estimate SE Z P

rough grazing 2.721 2.044 1.33 0.183 wheat -10.13 14.09 -0.72 0.472 barley 12.33 6.939 1.78 0.076 beet -473.2 1473.1 -0.32 0.748 Stock Dove cattle -0.682 0.630 -1.08 0.279 Columba oenas pasture -0.756 1.059 -0.71 0.475 rough grazing 2.194 3.439 0.64 0.524 wheat 7.113 7.813 0.91 0.363 barley -5.263 3.906 -1.35 0.178 beet -603.7 977.5 -0.62 0.537 Skylark cattle -0.402 0.218 -1.84 0.065 Alauda arvensis pasture -1.723 1.463 -1.18 0.239 rough grazing 1.494 1.079 1.39 0.166 wheat 5.890 3.643 1.62 0.106 barley -8.028 1.664 -4.82 0.000 beet -1101.0 655.7 -1.68 0.093 Meadow Pipit cattle -0.065 0.286 -0.23 0.820 Anthus pratensis pasture 2.536 0.774 3.28 0.001 rough grazing 0.695 1.728 0.4 0.688 wheat -11.267 2.448 -4.6 0.000 barley -5.289 5.720 -0.92 0.355 beet 381.5 442.8 0.86 0.389 Pied/White cattle 0.192 0.180 1.07 0.285 Wagtail Motacilla alba pasture 2.453 0.754 3.26 0.001 rough grazing 0.944 1.274 0.74 0.458 wheat -15.486 2.976 -5.2 0.000 barley 12.45 5.049 2.47 0.014 beet 3561.7 1188.1 3 0.003 Wren cattle 0.004 0.501 0.01 0.994 Troglodytes troglodytes pasture -0.591 3.696 -0.16 0.873 rough grazing 4.604 2.662 1.73 0.084 wheat 6.919 10.93 0.63 0.527 barley 8.832 6.278 1.41 0.160 beet 1128.3 1337.0 0.84 0.399 Dunnock cattle 0.853 0.383 2.22 0.026 Prunella modularis pasture 5.628 0.918 6.13 0.000 rough grazing -2.234 2.648 -0.84 0.399 wheat -12.692 11.49 -1.1 0.270 barley -7.320 7.982 -0.92 0.359 Robin beet -1572.5 1328.8 -1.18 0.237 Erithacus rubecula cattle -0.638 0.609 -1.05 0.295 pasture 1.500 4.447 0.34 0.736

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Species Predictor Estimate SE Z P

rough grazing 2.901 3.586 0.81 0.419 wheat 1.762 20.98 0.08 0.933 barley 0.885 12.09 0.07 0.942 beet 1265.3 1526.0 0.83 0.407 Blackbird cattle -1.049 0.507 -2.07 0.039 Turdus merula pasture -16.077 1.933 -8.32 0.000 rough grazing 8.789 2.452 3.58 0.000 wheat 7.305 13.62 0.54 0.592 barley 15.42 6.092 2.53 0.011 beet 2655.1 1147.0 2.31 0.021 Song Thrush cattle 0.582 0.471 1.23 0.217 Turdus philomelos pasture 1.780 1.979 0.9 0.368 rough grazing 0.487 3.357 0.15 0.885 wheat -0.644 11.21 -0.06 0.954 barley 5.557 5.111 1.09 0.277 beet -1396.9 807.0 -1.73 0.083 Mistle Thrush cattle -0.088 0.325 -0.27 0.786 Turdus viscivorus pasture -0.592 0.607 -0.98 0.329 rough grazing -1.855 2.481 -0.75 0.455 wheat 7.979 4.500 1.77 0.076 barley DNC

beet -2409.4 1071.9 -2.25 0.025 Whitethroat cattle -0.582 0.520 -1.12 0.263 Sylvia communis pasture 2.543 1.525 1.67 0.095 rough grazing 5.829 3.370 1.73 0.084 wheat -19.981 4.585 -4.36 0.000 barley 3.019 3.898 0.77 0.439 beet 1265.1 554.1 2.28 0.022 Long-tailed Tit cattle 0.392 0.275 1.43 0.154 Aegithalos caudatus pasture 1.118 1.385 0.81 0.420 rough grazing -0.072 2.081 -0.03 0.972 wheat -1.277 5.052 -0.25 0.800 barley 3.205 6.015 0.53 0.594 beet -695.1 1164.2 -0.6 0.551 Coal Tit cattle -0.091 0.440 -0.21 0.836 Periparus ater pasture 1.457 1.361 1.07 0.285 rough grazing 1.127 2.806 0.4 0.688 wheat -2.620 7.377 -0.36 0.723 barley 8.095 5.699 1.42 0.156 Blue Tit beet 824.5 901.7 0.91 0.361 Cyanistes cattle -0.039 0.533 -0.07 0.942 caeruleus pasture -1.609 1.833 -0.88 0.380 rough grazing 0.524 2.955 0.18 0.859

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Species Predictor Estimate SE Z P

wheat 14.27 5.742 2.49 0.013 barley 3.356 4.912 0.68 0.495 beet 90.73 748.6 0.12 0.904 Great Tit cattle -0.100 0.403 -0.25 0.804 Parus major pasture 0.771 1.352 0.57 0.569 rough grazing 1.005 2.365 0.42 0.671 wheat 5.862 9.292 0.63 0.528 barley 21.64 8.365 2.59 0.010 beet 3860.2 982.4 3.93 0.000 Rook cattle 0.782 0.468 1.67 0.095 Corvus frugilegus pasture -0.305 3.138 -0.1 0.923 rough grazing -2.114 3.464 -0.61 0.542 wheat 19.64 15.91 1.23 0.217 barley -13.591 5.729 -2.37 0.018 beet -1285.1 692.4 -1.86 0.064 Starling cattle -0.642 0.295 -2.18 0.030 Sturnus vulgaris pasture 0.116 1.268 0.09 0.927 rough grazing 3.666 2.026 1.81 0.070 wheat -17.477 4.725 -3.7 0.000 barley 7.148 1.395 5.12 0.000 beet 266.1 754.2 0.35 0.724 House Sparrow cattle 0.504 0.159 3.17 0.002 Passer domesticus pasture 2.305 1.468 1.57 0.116 rough grazing -2.764 1.159 -2.38 0.017 wheat 0.111 3.747 0.03 0.976 barley -14.815 14.05 -1.05 0.292 beet -6338.3 1177.1 -5.38 0.000 Tree Sparrow cattle -1.568 0.375 -4.19 0.000 Passer montanus pasture -3.898 1.343 -2.9 0.004 rough grazing 3.109 6.843 0.45 0.650 wheat 14.27 13.16 1.08 0.278 barley 19.88 11.29 1.76 0.078 beet -969.6 2340.0 -0.41 0.679 Chaffinch cattle 0.451 0.846 0.53 0.594 Fringilla coelebs pasture 3.580 3.036 1.18 0.238 rough grazing -3.270 5.089 -0.64 0.521 wheat 1.976 13.33 0.15 0.882 barley 3.655 2.176 1.68 0.093 beet -218.1 331.7 -0.66 0.511 Greenfinch cattle -0.083 0.104 -0.8 0.422 Chloris chloris pasture -1.378 0.799 -1.73 0.085 rough grazing -0.278 0.889 -0.31 0.755

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Species Predictor Estimate SE Z P

wheat 7.454 2.687 2.77 0.006 barley -1.048 5.246 -0.2 0.842 beet -240.6 698.4 -0.34 0.731 Goldfinch cattle -0.393 0.328 -1.2 0.231 Carduelis carduelis pasture 0.108 1.828 0.06 0.953 rough grazing 3.099 1.725 1.8 0.073 wheat -2.789 6.155 -0.45 0.651 barley 0.829 3.487 0.24 0.812 beet 474.3 416.4 1.14 0.255 Linnet cattle 0.064 0.223 0.29 0.775 Carduelis cannabina pasture 1.034 0.875 1.18 0.237 rough grazing 0.542 1.080 0.5 0.616 wheat -3.869 3.326 -1.16 0.245 barley 4.741 3.214 1.48 0.140 beet -96.62 418.7 -0.23 0.818 Bullfinch cattle 0.409 0.143 2.87 0.004 Pyrrhula pyrrhula pasture 1.047 1.048 1 0.318 rough grazing -2.993 0.542 -5.52 0.000 wheat 7.276 2.683 2.71 0.007 barley DNC beet DNC Yellowhammer cattle DNC Emberiza citrinella pasture 0.904 3.239 0.28 0.780 rough grazing DNC wheat DNC barley -9.541 3.344 -2.85 0.004 beet -785.2 962.8 -0.82 0.415 Reed Bunting cattle -0.590 0.204 -2.9 0.004 Emberiza schoeniclus pasture -0.891 1.331 -0.67 0.503 rough grazing 3.861 1.503 2.57 0.010 wheat -9.461 3.796 -2.49 0.013

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Annex 5.5 Table of model details for multivariate model fits: species in Britain.

Note that only one variable was identified as significant for each of Corn Bunting and Linnet (Annex 5.3), and that models failed for Yellowhammer. EST denotes the model-averaged parameter estimate, SE its standard error and WT the variable-specific summed model weight, or importance value, for the predictor concerned. Importance values greater than 0.5 are considered to denote important variables; parameter estimates for these variables are shown in red, bold text.

Perm Roug Sprin Tem Wint Fodd Shee cattl pastu anen h Root g porar er whea Species er oats open p & e re t grazi crops barle y barle t crops goats crops ng y grass y 100.7 - - Buzzard EST 0.000 1.869 0.000 0.003 58 30.34 0.009 16.83 SE 20.55 0.633 7.753 7.117 5.756 1.655 9 WT 1.000 0.000 1.000 0.000 0.001 1.000 0.007

- 21.79 - - - Kestrel EST 0.161 132.1 0.010 0.000 0.001 0.000 2.097 7 1.001 1.127 3.644 65 SE 0.195 35.02 6.048 4.934 0.457 1.146 0.138 1.063 3.706 2.924 0.709

WT 0.548 1.000 1.000 0.003 0.998 0.496 0.001 1.000 0.000 0.000 0.989

Grey - - EST Partridge 206.6 6.736 SE 106.5 3.601

WT 1.000 1.000

- - 11.90 Lapwing EST 2.493 100.0 3 SE 0.778 52.92 5.657

WT 1.000 1.000 1.000

Curlew EST 0.000 0.000 0.000 0.000 4.456 6.346

SE 0.541 43.98 2.408 369.5 1.009 3.148

WT 0.000 0.000 0.000 0.000 1.000 1.000

43.13 - - Stock Dove EST 0.000 0.029 0.000 0.000 0.000 2.557 2.740 1 3.899 1.336 SE 0.175 12.02 5.599 7.756 0.450 4.257 2.380 3.875 3.893 0.624

WT 0.000 0.005 0.000 1.000 1.000 0.000 0.411 0.000 0.615 1.000

- Turtle - - EST 7.945 40.57 1.173 Dove 1.172 5.096 2 SE 9.961 2.128 3.876 6.968 1.551

WT 0.473 0.374 0.564 1.000 0.424

- 31.38 - - Skylark EST 0.133 0.000 0.177 0.027 0.381 6 2.688 7.196 SE 0.115 9.946 9.332 0.374 9.578 0.047 1.543 0.899

WT 1.000 1.000 0.018 1.000 0.809 0.009 1.000 0.039

- Meadow EST 0.000 0.000 75.88 0.000 3.701 9.704 2.363 Pipit 6 782.6 SE 0.682 307.1 33.19 0.856 3.604 1.102 62 WT 0.000 0.000 0.980 0.000 1.000 1.000 1.000

Yellow - - - EST 4.260 Wagtail 2.174 26.74 0.002 10.09 SE 2.239 5.800 0.984 6 WT 0.809 1.000 0.001 1.000

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Perm Roug Sprin Tem Wint Fodd Shee cattl pastu anen h Root g porar er whea Species er oats open p & e re t grazi crops barle y barle t crops goats crops ng y grass y Pied/White 41.53 - - - - EST 0.001 2.518 6.068 1.639 Wagtail 3 1.222 0.058 1.169 0.005 SE 0.045 13.34 4.213 0.171 7.982 0.146 0.717 0.709 0.245

WT 0.049 0.965 0.270 0.317 0.097 1.000 0.987 1.000 0.096

- - Wren EST 0.415 0.134 2.688 1.434 5.031 0.265 0.210 SE 0.088 8.104 0.345 8.562 0.752 3.397 0.554

WT 0.958 0.020 1.000 0.292 0.169 0.278 1.000

- 14.50 - - Dunnock EST 1.101 19.43 0.000 0.026 0.002 8 1.855 2.326 SE 0.155 9.773 0.256 9.338 0.271 0.029 1.129 1.021

WT 0.012 0.737 1.000 0.856 1.000 0.006 1.000 0.022

- - - Robin EST 0.000 0.005 1.489 0.000 0.000 0.000 4.461 5.897 0.059 SE 0.411 56.12 0.794 160.6 0.711 0.072 2.063 6.382 1.121

WT 0.000 0.000 0.945 0.000 1.000 0.000 1.000 0.019 0.000

17.38 - - Blackbird EST 0.163 1.245 0.031 1.168 1.136 9 1.243 1.760 SE 0.069 6.448 0.271 0.254 0.021 0.569 1.843 0.574

WT 0.374 0.475 0.549 0.653 0.264 0.577 0.165 0.367

Song - - EST 6.367 1.417 0.015 Thrush 0.044 0.795 SE 0.114 14.12 0.187 0.249 0.045

WT 0.434 0.233 1.000 0.982 0.267

Mistle - - - - EST 0.313 3.608 7.956 3.910 0.559 Thrush 0.525 7.616 0.082 0.003 SE 0.119 4.543 6.858 0.552 2.252 1.242 1.522 2.197 0.589

WT 0.791 0.296 0.395 0.305 0.750 0.973 1.000 0.027 0.006

Whitethro - - EST 0.481 0.000 at 1.817 6.695 SE 0.515 2.219 2.745 1.085

WT 1.000 1.000 1.000 0.004

Long-tailed - - - EST 0.001 0.001 1.021 0.240 Tit 0.135 4.782 5.675 SE 0.076 9.872 7.510 0.429 1.509 4.426 0.766

WT 0.940 0.000 0.000 1.000 1.000 0.290 0.328

- - CoalTit EST 0.000 1.635 0.000 0.000 26.60 1.745 SE 142.4 11.27 0.736 8.184 5.531 0.876

WT 0.000 1.000 1.000 0.000 0.000 1.000

- - Blue Tit EST 6.379 2.292 74.00 0.001 0.485 3.467 SE 0.177 28.35 0.496 30.70 0.653 0.045

WT 1.000 0.210 1.000 0.990 1.000 0.079

- - - Great Tit EST 0.000 0.010 0.000 0.001 3.524 3.875 0.002 SE 0.245 26.39 1.063 97.92 0.484 1.982 1.132

WT 0.002 0.001 0.000 0.000 1.000 1.000 0.004

- Rook EST 0.000 7.824 1.249 0.057 SE 19.15 0.037 2.427 0.635

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Perm Roug Sprin Tem Wint Fodd Shee cattl pastu anen h Root g porar er whea Species er oats open p & e re t grazi crops barle y barle t crops goats crops ng y grass y WT 0.000 0.999 1.000 1.000

- Starling EST 0.000 2.336 0.000 2.289 SE 80.03 0.669 0.741 4.588

WT 0.000 1.000 1.000 0.000

House - - - - EST 173.3 1.026 0.000 0.041 Sparrow 0.007 0.008 8.409 0.008 SE 37.19 3.369 0.356 0.684 0.032 1.933 2.509 0.831

WT 1.000 0.006 1.000 0.000 0.853 0.007 1.000 0.007

Tree - - EST 417.0 1.473 0.000 0.005 0.000 Sparrow 486.7 0.005 SE 108.7 188.8 0.882 0.084 6.474 7.841 3.151

WT 1.000 1.000 0.995 0.000 0.001 0.001 0.000

- Chaffinch EST 0.000 3.550 0.052 16.70 4.613 0.048 11.69 SE 0.335 0.543 1.009 6.940 2.2 0 WT 0.000 0.992 0.057 0.024 0.794 0.665

- - - - - Greenfinch EST 2.084 28.38 0.525 0.023 1.441 1.990 0.004 0.125 SE 0.183 18.24 6.608 0.335 0.741 2.192 2.409 0.742

WT 0.121 0.090 0.998 1.000 0.971 0.001 0.041 0.401

- - - Goldfinch EST 1.657 0.001 8.596 2.003 0.001 0.012 0.026 SE 0.114 0.322 0.833 0.054 1.362 2.429 0.597

WT 0.018 1.000 0.016 0.017 0.060 0.998 1.000

- - Bullfinch EST 1.002 0.082 0.001 2.218 SE 0.154 0.417 0.386 2.048

WT 0.008 1.000 1.000 0.043

- Reed - EST 0.000 26.99 0.000 0.000 0.000 Bunting 2.297 0 SE 0.200 14.96 0.619 0.066 3.457 1.297

WT 0.000 0.996 1.000 0.000 0.000 0.000

Total models 20 8 19 8 21 18 25 7 12 20 13 14 26 including variable No. positive 8 4 14 7 12 9 7 7 8 9 4 6 17 estimates No. negative 12 4 5 1 9 9 18 0 4 11 9 8 9 estimates Total models where 6 4 7 1 16 10 19 3 2 16 4 7 10 variable important No. important and 3 3 4 1 11 4 6 3 1 7 0 4 9 positive No. important and 3 1 3 0 5 6 13 0 1 9 4 3 1 negative

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Annex 5.6 Table of model details for multivariate model fits: species in Ireland.

Note that fodder crops, oats, permanent crops, root crops and sheep/goat density were not selected as important for any species, that only one variable was identified as significant for each of Stock Dove Columba oenas, Skylark Alauda arvensis, Long-tailed Tit Aegithalos caudatus, Blue Tit Cyanistes caeruleus, Chaffinch Fringilla coelebs and Goldfinch Carduelis carduelis (Annex 5.4), and that models failed for whitethroat. EST denotes the model-averaged parameter estimate, SE its standard error and WT the variable-specific summed model weight, or importance value, for the predictor concerned. Importance values greater than 0.5 are considered to denote important variables; parameter estimates for these variables are shown in red, bold text.

Rough Species Barley Beet Cattle grazing Pasture Wheat Buzzard EST -4017.3 -0.016 -2.229

SE 1583.5 1.259 2.107

WT 1.000 0.017 0.800

Kestrel EST 1761.8 0.010 -11.16

SE 479.5 0.257 3.865

WT 1.000 0.131 0.999

Lapwing EST 1.56 -7506.4 3.034 SE 7.087 699.8 1.210 WT 0.386 1.000 0.878 Curlew EST 1326.1 -1.383

SE 550.002 1.193

WT 0.862 0.581

Meadow Pipit EST -5.904 -316.6 0.430 -2.311

SE 1.354 385.5 1.007 3.638

WT 0.839 0.432 0.260 0.328

Pied/White Wagtail EST 0.174 -15.294

SE 0.775 2.229

WT 0.395 1.000

Wren EST 24.129 8.618

SE 6.199 2.317

WT 0.892 0.863

Dunnock EST 0.059 5.447

SE 0.513 0.829

WT 0.176 0.997

Blackbird EST 0.079 -14.820 0.529

SE 0.450 1.853 2.962

WT 0.450 0.892 0.482

Rook EST 0.460 3779.6 0.044

SE 10.34 758.1 0.402

WT 0.048 0.998 0.216

Starling EST 1.160 2661.4 -2.945 -14.40 -34.87

SE 2.534 2328.4 1.486 8.937 7.700

WT 0.465 0.708 0.897 0.810 1.000

House Sparrow EST 2.231 0.269 -0.620

SE 2.310 0.159 1.047

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Rough Species Barley Beet Cattle grazing Pasture Wheat WT 0.510 0.632 0.402

Tree Sparrow EST -5335.7 -0.115 -1.887

SE 1024.9 0.442 1.109

WT 0.996 0.318 0.942

Greenfinch EST 1.673 -0.521 4.516

SE 1.095 0.562 1.698

WT 0.636 0.506 0.730

Bullfinch EST -0.078 -3.205 1.062

SE 0.121 0.516 2.441

WT 0.514 0.988 0.361

Reed Bunting EST -0.153 -0.495 0.380 -6.800

SE 4.046 0.166 1.503 3.185

WT 0.084 0.939 0.170 0.844

Total models including 10 11 10 6 8 8 variable No. positive estimates 8 6 5 2 5 4 No. negative estimates 2 5 5 4 4 5 Total models where variable 5 9 4 5 5 6 important No. important and positive 1 4 3 4 3 4 No. important and negative 4 5 1 1 2 2

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Annex 6 Case study 5 The effects of increased rape and maize cropping on agricultural biodiversity

Annex 6.1 Figures and maps supporting statements in the introduction of case study 5.

Figure A6.1 Trend in the area of permanent grasslands in Germany

Data sources: BMEL 2016; Statistical Office 2015; permanent grasslands are all types of grasslands that are not ploughed or used in a rotation system with other crops, and include meadows, pastures, dry grasslands, peat lands, coastal grasslands etc.

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Figure A6.2 Fallow land and set-aside on arable land in Germany between 1991 and 2016 (hectares; ha)

Data sources: Statistisches Bundesamt (1991–2009) und Eurostat (Data from 2010 on).

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Figure A6.3 Map of the proportion of maize cultivation per arable land in raster grid cells of 5x5 km. Adapted from Statistical Office (Source: Statistical Office 2010, http://www.atlas-agrarstatistik.nrw.de/).

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Figure A6.4 Map of the proportion of rape cultivation per arable land in raster grid cells of 5x5 km. Adapted from Statistical Office (Source: Statistical Office 2010, http://www.atlas- agrarstatistik.nrw.de/).

Propor on of Area of Rape cul vated in 2010 in Rela on to the Area of Arable Land in a Raster of 5×5 km

Propor on of Maize cul va on related to arable land from ... up to ... % No value Federal states or secret Districts 0 - 5 Municipali es 5 - 15 Natural units 15 - 30 Rivers 30 and above Planning units Watersheds

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Annex 6.2 Literature research

In each language, the search was led starting with the search strings ‘biodiversity’ and ‘biofuel’ combined with one crop’s name. Then, the search was specified according to the terms that appeared to be currently used in the publications. When no more results could be found matching the term ‘biodiversity’, the search was enhanced with broader search strings to get papers that could still contain information about biodiversity without naming it as a key element of their results. These complementary search strings were done at the discretion of each researcher, and so differ from one language to another. Indeed, each language has its specificities and use different terms to refer to the same concept, so translating literally each keyword would not always have made sense.

For instance, in English, using the keywords ‘environment’, ‘environmental impact’ or ‘sustainability’ led to interesting papers. Nevertheless, regarding the French language, the literal translation of ‘sustainability’ (durabilité) is not the expression mostly used, so it was adapted to ‘sustainable development’ (développement durable) for the French search, as this one is the expression most broadly used. Other adaptations like this one were done for the search in other languages, mainly in Polish and Hungarian.

Classification with JabRef

Before starting to read the first papers, it was decided to assign three keywords to each paper to classify them regarding their geographical position, the crop studied and the energy output. At that time, it was not yet decided how to identify the papers dealing with biodiversity issues. After reading a few papers, it was clear that the impacts on the environment studied were very varied, so it was decided to use the following rules for the classification:

 If the paper assesses an impact of any kind on the environment, use the keyword: #environmental-impact.

 If the nature of the impacts studied was detailed, use accordingly the keywords: #air, #water, #soil and/or #biodiversity to characterize which part of the ecosystem is studied.

This first level of classification was implemented and more papers were read. By reading more papers, it became clear that another level of classification was needed, that is to say that the classification could be more specific. Thus, a new rule was implemented:

 When it is present in the paper, use sub-categories for the categories #air and #biodiversity, respectively: the keyword #GHG for papers dealing with greenhouse gases pollution, and keywords with the name of taxa studied as follows: #birds, #arthropods, #invertebrates, #mammals, #fish, #reptiles, #amphibians.

The keywords and their meaning are described in Table A6.1 below.

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Table A6.1 Description of the keywords used and their meaning.

Meaning Specific key-words

#location of the study (country + region) Location of the study. Compulsory if mentioned #crops names Crops names. Compulsory if mentioned #type of energy produced (biodiesel, Type of energy produced bioethanol) #Class of the fauna studied (birds, mammals, Class of fauna studied reptiles, arthropods) Useful information about the fauna’s biodiversity (even if §relevant other crop than M or R, worldwide) #review Paper reviewing literature #biodiversity Broad effect on the biodiversity #environmental impact, #soil, #water, #air Effect on a part of the ecosystem that is not the fauna #GHG Evaluation of the greenhouse gas emissions Extent of the study. #climate change, #food security, #guideline The guideline can be addressed to farmers, decision makers practice or scientists. To specify that at least some of the insects studied provide #pollinators this service All the ground fauna which is not included in the Arthropods’ #invertebrate taxon #land use Land use change direct/indirect, deforestation, habitat loss Questions the cultivation of crops, cultural rotations, use of #agricultural methods fertilizers etc.

Documentation of the information retrieval from the Internet and search strategies for the relevant scientific literature

We have performed three different rounds of information retrieval and documented the steps in three protocols following these lines.

Protocol 1st Research round 01/06/16 - 09/06/16

Search engines: Google Scholar (http://scholar.google.com), Science Direct (http://www.sciencedirect.com)

Search strings: (1) Germany AND biodiversity AND biofuel AND rape (2) Germany AND biodiversity AND biofuel AND (rape OR Rape) Germany AND biodiversity AND biofuel AND (rape OR seed oil) Germany AND biodiversity AND biofuel AND (rape OR rapeseed) Germany AND biodiversity AND biofuel AND (rape OR canola) (3) Germany AND biodiversity AND biogas AND (corn OR maize) (4) Germany AND sustainability AND biogas AND (corn OR maize) Germany AND sustainability AND biofuel AND (rapeseed OR oilseed)

Steps followed:  We started with the search string (1) in Google scholar and downloaded the first papers.

277

 According to these papers, we noticed that other key-words were used to nominate the crop “rape” so we then enhanced the research with the strings (2).  When we could find no more papers that seemed relevant, we used the search string (3) to switch to the other crop. The results overlapped and lots of the papers had already been checked.  We noticed that the keyword “sustainability” was used broadly, so we launched a last search with the search strings (4), i.e. using “sustainability” instead of “biodiversity”.

Other processes used to find publications: When a search lead to a paper available on “science-direct”, we paid attention to the papers related to it that the website suggested. It led us to a few other interesting papers that did not appear in the results of the initial Google scholar search.

Analysing the papers, when an effect on the biodiversity was mentioned with a reference, we checked the reference on the paper’s bibliography. Sometimes it led to a paper already downloaded and sometimes to a new relevant paper that we could download. Several times a source seemed relevant but was not accessible. In those cases we tried to get the papers from the authors directly. However, there is still a small number of papers that could be not accessed. It is difficult to say how important the missing information from such papers is because sometimes articles provide very detailed information although the title is rather general while others although having specific titles don’t provide very useful data for our aims because of a lack of numbers or figures fitting in our approach and set up. We will try to continue this approach and set up a database from which more information including the missing one (in terms of inaccessible papers or alternate/changed search strings) can be obtained in future.

The key-word “§relevant” identifies effects on biodiversity with precise names of taxa. If mentioned, the taxa’s names are also noted as key-words.

Protocol 2nd Research round 15/06/16 – 28/06/16

Search engines: Google scholar

Search strings for papers in the German language: Deutschland AND Biodiversität AND (Agrotreibstoff OR Agrokraftstoff OR Biokraftstoff OR Bio-kraftstoff OR Biotreibstoff OR Bio-Treibstoff OR Agrartreibstoff OR Agrar-Treibstoff OR Agrarenergie OR Biogas) (has led to 819 hits)

Deutschland AND biologische Vielfalt AND (Bioenergie OR Biomassenutzung OR Energiepflanze OR Biokraftstoff OR Biomasseanbau) (has led to 1660 hits)

(Biodiversität OR Naturschutz) AND (Biodiesel OR Bio-Diesel OR Agrardiesel) AND Raps (has led to 566 hits)

278

(Biodiversität OR Naturschutz) AND (Bioethanol OR Bio-Ethanol OR Agrar-Ethanol OR Biogas) AND Maize (has led to 116 hits)

Other possible search strings that were not used: Umweltschutz, Ökobilanz, Nachhaltigkeit, biologische Vielfalt, Bioenergie, Umweltwirkungen, Biomassenanbau

Search strings for papers in the French language: France AND biodiversité AND (biocarburant OR agrocarburant) AND colza (has led to 645 hits)

Allemagne AND biodiversité AND (biocarburant OR agrocarburant) (has led to 1390 hits) biodiversité AND (biocarburant OR agrocarburant) AND mais (has led to 2740 hits)

We also tried the term “nécrocarburant” used by the ecologist in France but it did not lead to useful results. When we could find no more papers, we tried with the follower broader search strings:

Environnement AND (agrocarburant OR biocarburant) Environnement AND (biodiesel OR bioéthanol) Développement durable AND (biodiesel OR bioéthanol) AND (agrocarburant OR biocarburant)

Search strings for papers in the Spanish language: biodiversidad AND (biocarburante OR biocombustible OR agrocarburante OR agrocombustible) AND colza (has led to 839 hits) biodiversidad AND (biocarburante OR biocombustible OR agrocarburante OR agrocombustible) AND (maíz OR choclo) (has led to 3500 hits)

When we could find no more papers we tried with broader search strings: (bioenergía OR agroenergía) AND medioambiente AND (sostenibilidad OR sustentabilidad) Biomasa AND impacte medioambiental AND (bioetanol OR agroetanol OR biodiesel)

Search strings for papers in the Italian language: biodiversità AND (biocarburanti OR biocombustibili) AND colza (has led to 190 hits) biodiversità AND (biocarburanti OR biocombustibili) AND (granturco OR granoturco OR mais)

Gomiero AND Paoletti AND biocombustibili (has led to 9 hits)

279

Biomasse AND impatto ambientale AND (bioetanolo OR etanolo OR biodiesel OR agroenergie) (has led to more then 10 pages with results, but we only looked until page 8 because results were overlapping)

(impatto AND ambientale) AND ((biomasse AND oleaginose) OR (colture AND a AND scopo AND energetico))

Search strings for papers in the Polish language: Bioroznorodnosc AND Polska AND Biopaliwa OR Bioetanol OR (Uprawy AND energetyczne) OR (Rosliny AND energetyczne) (has led to 739 hits)

Bioroznorodnosc AND Monokultury AND Biopaliwa OR Bioetanol OR (Uprawy AND energetyczne) OR ( Rosliny AND energetyczne) (has led to 151 hits)

Bioroznorodnosc AND Monokultury AND kukurydzy OR rzepaku OR energetycznych (has led to 171 hits) ochrona AND przyrody AND Monokultury AND kukurydzy OR rzepaku OR energetycznych (has led to 291 hits) Ptaki AND Biopaliwa AND kukurydza OR Rzepak (has led to 99 hits)

Ochrona AND Srodowiska AND biopaliwa AND bioroznorodnosc (has led to 299 hits)

Search strings for papers in the Hungarian language: Magyarorszag AND biouzemanyag gyartas AND hatas AND biodiverzitas AND (kukorica OR repce) (has led to 17 hits)

Fenntarthato AND energianovenyek gyartasa AND Magyarorszagon (has led to 5 hits)

Bioetanol OR biodizel AND hatas AND biodiverzitasra AND Magyarorszagon (has led to 0 hits)

Magyarorszag AND olajnoveny termeles AND hatas AND biodiverzirasra (has led to 0 hits)

Biouzemanyag AND termeles AND Magyarorszagon (has led to 12 hits)

Biouzemanyag OR biodizel OR bioetanol AND kerdes AND Magyarorszagon (has led to 163 hits)

Biouzemanyag AND elovilagra AND gyakorolt AND hatasa AND Magyarorszagon (has led to 14 hits)

280

Kukorica OR repce AND termeles AND biodiverzitasra AND gyakorolt AND hatasa AND Magyarorszagon (has led to 80 hits)

Classification of the relevant literature (28/07/16 - 02/08/16) done by the two people mentioned in section 1.1

R. Klenke identified 3 groups named A, B, and C JabRef codes: #RKA, #RKB, #RKC A: inclusion criteria for quantitative analysis of real data: statistical analysis, direct measurements of diversity/population size/individual counts. B: inclusion criteria for quantitative analysis of modelling results: Modelling/ scenarios with quantitative outcomes comparable to A. C: exclusion criteria: global assessments, reviews, modelling/scenarios not following B, special context for the implementation of bioenergy crops.

B. Frey identified 4 groups named A1, A2, E and F JabRef code : #BFA1, #BFA2, #BFE, #BFF A1: quantitative data = useful for stat analysis, comparison of energy crops’ effects on biodiversity with focus on the biodiversity. The conclusions are useful for ecologists, strong ecological analysis. A2: quantitative data, study of habitat suitability for one specific species, study of one energy crop in different landscapes. Focus on the crop and practices, useful for farmers/ energy actors. E: provides guideline/ advices for scientists, policy-makers and/or farmers. F: Qualitative analysis of BD and assessment of other impacts.

281

Annex 6.3 Detailed results of the literature review

Table A6.2 Number of studies focusing on effects of energy cropping (Maize, Maize and Rape, Rape) on the different taxa.

Effects caused by Effects caused by

Maize Rape

Species Scientific Name - + +/- - + +/-

Taxa 18 species (20 of 18 species (20 of the the farmland bird farmland bird index - 1 index - pheasant pheasant and raven) and raven) Barred Warbler Sylvia nisoria 1 Luscinia svecica Bluethroat 1 cyanecula Common Crane Grus grus 2 2 Common Linnet Linaria cannabina 1 1 Corn Bunting Miliaria calandra 5 3 Cuckoo Cuculus canorus 1 Goldfinch Carduelis carduelis 1 Greenfinch Carduelis cloris 1 Grey Partridge Perdrix perdrix 1

Little Owl Athene noctua 1

Birds Acrocephalus Marsh Warbler 1 palustris Northern Lapwing Vanellus vanellus 1 3 2 Quail Coturnix coturnix 1 1 Red Kite Milvus milvus 2 1 Red-backed Shrike Lanius collurio 4 1 1 1 Skylark Alauda arvensis 8 4 Tree Sparrow Passer montanus 1 Turtle Dove Streptopelia turtur 1 Whinchat Saxicola rubetra 4 2 1 Woodlark Lullula arborea 3 1 Yellow Wagtail Motacilla flava 1 1 Yellowhammer Emberiza citrinella 5 2 Total 42 8 0 16 9 0 Bumblebees Bombus spp. 2 Bumblebees 1 1

Bumble bees Total 1 0 0 0 1 2

Butterflies Butterflies 1

Butterflies

282

Effects caused by Effects caused by

Maize Rape

Species Scientific Name - + +/- - + +/-

Taxa

Total 1

Carabid Beetles Agonum muellerie 1 Amara aenea 1 Anchomenusdorsalis 1 Bembidion lampros 1 Calathus erratus 1

Carabus auratus 1 Carabus cancellatus 1 Carabus granulatus 1 Harpalus affinis 1 Nebria brevicollis 1

Carabid Beetles Poecilus cupreus 1 Pterostichus 1 1 melanarius Pterostichus vernalis 1 Carabidae 1 Total 1 1 10 0 3 0 Allolobophora Allolobophora 3 1 1 chlorotica chlorotica Allolobophora Allolobophora 3 1 1 cupulifera cupulifera Aporrectodea Aporrectodea antipai 1 antipai Aporrectodea Aporrectodea 3 1 1 caliginosa caliginosa Aporrectodea longa Aporrectodea longa 3 1 1 Aporrectodea rosea Aporrectodea rosea 2 1 Apporectodea Apporectodea 1 1 1 icterica icterica Dendrodrilus Dendrodrilus rubidus 1 1 rubidus

Earthworms Fitzingeria platyura Fitzingeria platyura 1 1 Lumbricus Lumbricus castaneus 2 1 1 castaneus Lumbricus rubellus Lumbricus rubellus 2 1 Lumbricus Lumbricus terrestris 2 1 terrestris Octolasion Octolasion cyaneum 1 1 cyaneum Proctodrilus Proctodrilus antipae 1 1 antipae Satchellius Satchellius mammalis 1 1 mammalis

283

Effects caused by Effects caused by

Maize Rape

Species Scientific Name - + +/- - + +/-

Taxa

Total 27 0 0 14 0 6

- Hoverfly Helophilus pendulus 1

Syrphidae Syrphidae 1

flies Hover Total 1 0 0 0 0 1

European Brown Lepus europaeus 1 Hare Field Vole Microtus agrestis 1

Mammals Total 1 0 1 0 0 0

Linyphiid spider Erigone atra 2 Spiders Spiders 1

Spiders Total 1 2 0 0 0 0 Sand Bees Andrena angustior 1 Andrena bicolor 1

Andrena fucata 1 Sweat Bees Halictus rubicundus 1 Wild bees Wild Bees 1 1

Wild Bees Total 1 0 0 0 5 0 Total Overall 75 11 11 31 18 9

284

Figure A6.5 Relative Effect Er of maize cultivation on different taxa found in published or unpublished studies. For birds one result is included using the area weighted habitat suitability (HIS) as response variable for the effect. Single study mode.

Maize

Spiders: Spiders - Glemnitz et al. (2008) Spiders: Erigone atra - Gevers et al. (2011) Pollinators: Pollinators - Glemnitz et al. (2008) Plants: Cmn. weeds & rare arable herbs - Glemnitz et al. (2008) Mammals: Field vole - Gevers et al. (2011) Mammals: Brown hare - Gevers et al. (2011) Earthworms: Lumbricus spec. - Felten et al. (2011) Earthworms: Lumbricidae, epigaeic - Felten et al. (2011) Earthworms: Lumbricidae, endogaeic - Felten et al. (2011) Earthworms: Lumbricidae, anecic - Felten et al. (2011) Earthworms: Lumbricidae, all groups - Felten et al. (2011) Carabid beetles: Carabid beetles - Glemnitz et al. (2008) Carabid beetles: Bembidion lampros - Gevers et al. (2011) Birds: Yellowhammer - Sauerbrei et al. (2014) Birds: Woodlark - Sauerbrei et al. (2014) Birds: Whinchat - Sauerbrei et al. (2014) Birds: Whinchat - Brand & Glemnitz (2014) Birds: Skylark - Sauerbrei et al. (2014) Birds: Skylark - Gutzler & Helming et al. (2015) Birds: Skylark - Gevers et al. (2011) Birds: Skylark - Brand & Glemnitz (2014) Birds: Red-backed shrike - Sauerbrei et al. (2014) Birds: Red-backed shrike - Brand & Glemnitz (2014) Birds: Red kite - Sauerbrei et al. (2014) Birds: Northern lapwing - Sauerbrei et al. (2014) Birds: Northern lapwing - Brand & Glemnitz (2014) Birds: Little owl - Sauerbrei et al. (2014) Birds: Grey partridge - Gevers et al. (2011) Birds: Corn bunting - Sauerbrei et al. (2014) Birds: Corn bunting - Gutzler & Helming et al. (2015) Birds: Corn bunting - Brand & Glemnitz (2014) Birds: Area weighted HSI - Glemnitz et al. (2015)

-1,5 -1,0 -0,5 0,0 0,5 1,0 1,5

rel. Effect size

285

Figure A6.6 Relative Effect Er of Rape cultivation on different taxa found in published or unpublished studies. For birds one result is included using the area weighted habitat suitability (HIS) as response variable for the effect. Single study mode.

Oilseed rape

Wild bees: Wild bees - Riedinger et al. (2015) Wild bees: Solitary bees - Bourke et al.(2013) Spiders: Erigone atra - Gevers et al. (2011) Pollinators: Pollinators - Glemnitz et al. (2008) Plants: Vascular plants - Bourke et al.(2013) Mammals: Field vole - Gevers et al. (2011) Mammals: Brown hare - Gevers et al. (2011) Hoverflies: Hoverflies - Bourke et al.(2013) Earthworms: Lumbricus spec. - Felten et al. (2011) Earthworms: Lumbricidae, epigaeic - Felten et al. (2011) Earthworms: Lumbricidae, endogaeic - Felten et al. (2011) Earthworms: Lumbricidae, anecic - Felten et al. (2011) Earthworms: Lumbricidae, all groups - Felten et al. (2011) Carabid beetles: Carabid beetles - Bourke et al.(2013) Carabid beetles: Bembidion lampros - Gevers et al. (2011) Bumblebees: Bumblebees - Bourke et al.(2013) Birds: Yellowhammer - Sauerbrei et al. (2014) Birds: Woodlark - Sauerbrei et al. (2014) Birds: Whinchat - Sauerbrei et al. (2014) Birds: Whinchat - Brand & Glemnitz (2014) Birds: Skylark - Sauerbrei et al. (2014) Birds: Skylark - Gutzler & Helming et al. (2015) Birds: Skylark - Gevers et al. (2011) Birds: Skylark - Brand & Glemnitz (2014) Birds: Red-backed shrike - Sauerbrei et al. (2014) Birds: Red-backed shrike - Brand & Glemnitz (2014) Birds: Red kite - Sauerbrei et al. (2014) Birds: Northern lapwing - Sauerbrei et al. (2014) Birds: Northern lapwing - Brand & Glemnitz (2014) Birds: Little owl - Sauerbrei et al. (2014) Birds: Grey partridge - Gevers et al. (2011) Birds: Corn bunting - Sauerbrei et al. (2014) Birds: Corn bunting - Gutzler & Helming et al. (2015) Birds: Corn bunting - Brand & Glemnitz (2014) Birds: Area weighted HSI - Glemnitz et al. (2015)

-1 0 1 2 3 4

rel. Effect size

286

Table A6.4 Detailed information about taxa and species specific effects, related parameters, type of study, reference, and figures and tables with relevant data

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

18 species 18 species (20 of the (20 of the 2016 increase in the acrage rape, farmland farmland farmland positiv rapesee Farmland -06- Table 3, Birds Birds France France of rapeseed in a cereal Analysis bird index - bird index - bird e effect d Bird Index 20- Table 4 cultivated landscape s pheasant pheasant 19-bf and raven) and raven)

2016 -09- 25- expansion of maize 02- Figure Marsh Acrocephalu farmland field versus decrease negativ Farmland Birds Birds Germany Germany maize maize Analysis rk, 2, warbler s palustris bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk

Intrinsic Conceptu Habitat maize al 2016 Table 3, expansion of maize maize & Value, Alauda farmland negativ , Modelling -06- Figure Birds Birds Skylark Germany Brandenburg cultivation landscape rapesee Habitat arvensis bird e effect rape, , 01- 2, level d Suitability, other Predictio 01-bf Figure 4 Available n Habitat

Figure Absolute Analysis 2016 1, Table Alauda farmland expansion of maize negativ breeding based -06- Birds Birds Skylark Germany Germany maize maize 1, Table arvensis bird field e effect pairs for Predictio 03- 2, Table Germany n 02-bf 3

287

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

potential number of 2016 expansion of maize & territories Individual Alauda farmland negativ maize -06- Table3, Birds Birds Skylark Germany Brandenburg maize/rape rapesee per 10 ha = Based arvensis bird e effect , rape 03- Figure 3 cultivation d breeding Model 01-bf pairs per 10 ha

Figure 1, Figure female 3, addition of maize to 2016 abundance / Individual Fugure Alauda farmland Northeast the crop rotation, negativ -06- Birds Birds Skylark Germany maize maize ha = Based 4, arvensis bird Germany loss of rotational set- e effect 01- breeding Model Figure aside 13-bf pairs / ha 5, Table 1, Table 2, Table 6

Figure 1, Figure female 3, 2016 abundance / Individual Fugure Alauda farmland Northeast aggregation of maize negativ -06- Birds Birds Skylark Germany maize maize ha = Based 4, arvensis bird Germany fields e effect 01- breeding Model Figure 13-bf pairs / ha 5, Table 1, Table 2, Table 6

288

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Breeding Table 1, Bird Index Table 2, Change; Table 7, Habitat Figure maize Suitability, 2016 maize & 5, Alauda farmland Northeast expansion of maize negativ , Available -06- Birds Birds Skylark Germany rapesee Figure arvensis bird Germany field landscape level e effect rape, Habitat, 08- d 6, other Habitat 12-bf Figure Change, 7. Table Number of 8, Table Breeding 9 Periods

Table 2, Table 3, Table 4, Breeding , Table Bird Index 2016 6,, habitat quality of Change; Alauda farmland negativ -06- Figure Birds Birds Skylark Germany Brandenburg maize compared to maize maize Habitat arvensis bird e effect 09- 4, cereals Suitability, 03-bf Figure Available 5, Habitat Figure 6, Figure 7

289

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Analysis 2016 Table 1, comparison of Per Sample Alauda farmland negativ rapesee based -08- Table 2, Birds Birds Skylark Hungary Hungary abundance between rape Point arvensis bird e effect d Predictio 15- Table 3, maize and cereals Abundance n 02-fj Table 4

2016 -09- 25- expansion of maize 02- Figure Alauda farmland field versus decrease negativ Farmland Birds Birds Skylark Germany Germany maize maize Analysis rk, 2, arvensis bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk

Figure Absolute 2016 1, Table Athene farmland expansion of maize positiv breeding -06- Birds Birds Little Owl Germany Germany maize maize 1, Table noctua bird field e effect pairs for 03- 2, Table Germany 02-bf 3

2016 -09- 25- expansion of maize 02- Figure Carduelis farmland field versus decrease negativ Farmland Birds Birds Goldfinch Germany Germany maize maize Analysis rk, 2, carduelis bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk

290

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only) 2016 -09- 25- expansion of maize 02- Figure Carduelis farmland field versus decrease negativ Farmland Birds Birds Greenfinch Germany Germany maize maize Analysis rk, 2, cloris bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk 2016 increase of maize & Breeding Coturnix grassland positiv maize -07- Birds Birds Quail Poland Southwest monoculture of rape rapesee Pairs, Analysis Table 2 coturnix nesting bird e effect , rape 25- and maize d Abundance 01-az 2016 -09- 25- expansion of maize 02- Figure Cuculus farmland field versus decrease negativ Farmland Birds Birds Cuckoo Germany Germany maize maize Analysis rk, 2, canorus bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk

land conversion : Available 2016 Figure Dolichonyx grassland increases in percent negativ maize Habitat, -09- Birds Birds Bobolink USA Midwest maize 1, oryzivorus nesting bird cover of maize/soy e effect , soy Number of 04- Figure 3 exceeding 40% Bird Species 01-rk

change land use to : 2016 hay fields, alfalfa Figure Dolichonyx grassland positiv Available -09- Birds Birds Bobolink USA Midwest fields, pastures, and other other 1, oryzivorus nesting bird e effect Habitat 04- unmanaged Figure 3 01-rk grasslands

291

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Intrinsic Habitat maize 2016 Table 3, maize & Value, Yellowhamm Emberiza farmland expansion of maize negativ , -06- Figure Birds Birds Germany Brandenburg rapesee Habitat er citrinella bird field landscape level e effect rape, 01- 2, d Suitability, other 01-bf Figure 4 Available Habitat

Figure Absolute 2016 1, Table Yellowhamm Emberiza farmland expansion of maize negativ breeding -06- Birds Birds Germany Germany maize maize 1, Table er citrinella bird field e effect pairs for 03- 2, Table Germany 02-bf 3

Farmland 2016 maize & Bird Index, Yellowhamm Emberiza farmland expansion of energy negativ maize -07- Table 1, Birds Birds Poland Poland rapesee Rate of Analysis er citrinella bird crops e effect , rape 25- Table 2 d Population 05-az Change

2016 -09- 25- expansion of maize 02- Figure Yellowhamm Emberiza farmland field versus decrease negativ Farmland Birds Birds Germany Germany maize maize Analysis rk, 2, er citrinella bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk

292

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 2, Table 3, Table 4, Breeding , Table Bird Index 2016 6,, habitat quality of Change; Yellowhamm Emberiza farmland negativ -06- Figure Birds Birds Germany Brandenburg maize compared to maize maize Habitat er citrinella bird e effect 09- 4, cereals Suitability, 03-bf Figure Available 5, Habitat Figure 6, Figure 7

2016 -09- Breeding 25- Pairs, expansion of maize & 03- Figure Common farmland positiv maize Abundance, Birds Birds Grus grus Germany Germany maize/rape rapesee Analysis rk, 2, Crane bird e effect , rape Migration, cultivation d 2016 Figure 3 Overwinteri -09- ng 25- 06-rk 2016 -09- Breeding 25- Pairs, expansion of maize & 04- Common farmland positiv maize Abundance, Birds Birds Grus grus France France maize/rape rapesee Analysis rk, Figure 6 Crane bird e effect , rape Migration, cultivation d 2016 Overwinteri -09- ng 25- 05-rk

293

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Intrinsic Habitat maize 2016 Table 3, maize & Value, Red-backed Lanius farmland expansion of maize negativ , -06- Figure Birds Birds Germany Brandenburg rapesee Habitat Shrike collurio bird field landscape level e effect rape, 01- 2, d Suitability, other 01-bf Figure 4 Available Habitat

Figure Absolute 2016 1, Table Red-backed Lanius farmland expansion of maize negativ breeding -06- Birds Birds Germany Germany maize maize 1, Table Shrike collurio bird field e effect pairs for 03- 2, Table Germany 02-bf 3

Breeding Table 1, Bird Index Table 2, Change; Table 7, Habitat Figure maize Suitability, 2016 maize & 5, Red-backed Lanius farmland Northeast expansion of maize positiv , Available -06- Birds Birds Germany rapesee Figure Shrike collurio bird Germany field landscape level e effect rape, Habitat, 08- d 6, other Habitat 12-bf Figure Change, 7. Table Number of 8, Table Breeding 9 Periods

294

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 2, Table 3, Table 4, Breeding , Table Bird Index 2016 6,, habitat quality of Change; Red-backed Lanius farmland negativ -06- Figure Birds Birds Germany Brandenburg maize compared to maize maize Habitat Shrike collurio bird e effect 09- 4, cereals Suitability, 03-bf Figure Available 5, Habitat Figure 6, Figure 7

2016 -09- 25- expansion of maize 02- Figure Red-backed Lanius farmland field versus decrease negativ Farmland Birds Birds Germany Germany maize maize Analysis rk, 2, Shrike collurio bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk

Farmland 2016 maize & Bird Index, Common Linaria grassland expansion of energy negativ maize -07- Table 1, Birds Birds Poland Poland rapesee Rate of Analysis Linnet cannabina nesting bird crops e effect , rape 25- Table 2 d Population 05-az Change

295

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Intrinsic Habitat maize 2016 Table 3, maize & Value, Lullula farmland expansion of maize negativ , -06- Figure Birds Birds Woodlark Germany Brandenburg rapesee Habitat arborea bird field landscape level e effect rape, 01- 2, d Suitability, other 01-bf Figure 4 Available Habitat

Figure Absolute 2016 1, Table Lullula farmland expansion of maize negativ breeding -06- Birds Birds Woodlark Germany Germany maize maize 1, Table arborea bird field e effect pairs for 03- 2, Table Germany 02-bf 3

Table 2, Table 3, Table 4, Breeding , Table Bird Index 2016 6,, habitat quality of Change; Lullula farmland negativ -06- Figure Birds Birds Woodlark Germany Brandenburg maize compared to maize maize Habitat arborea bird e effect 09- 4, cereals Suitability, 03-bf Figure Available 5, Habitat Figure 6, Figure 7

Mean Analysis 2016 Luscinia Northern cultivation of rape in wetland rare positiv rapesee Number of based -06- Birds Birds Bluethroat svecica Germany Upper Rhine wetland/floodplain rape Table 1 species e effect d Territories Predictio 07- cyanecula Valley in Hesse area per Field n 15-bf

296

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

potential number of 2016 expansion of maize & territories Individual Miliaria farmland negativ maize -06- Table3, Birds Birds Corn Bunting Germany Brandenburg maize/rape rapesee per 10 ha = Based calandra bird e effect , rape 03- Figure 3 cultivation d breeding Model 01-bf pairs per 10 ha

Figure Absolute 2016 1, Table Miliaria farmland expansion of maize negativ breeding -06- Birds Birds Corn Bunting Germany Germany maize maize 1, Table calandra bird field e effect pairs for 03- 2, Table Germany 02-bf 3

Breeding Table 1, Bird Index Table 2, Change; Table 7, Habitat Figure maize Suitability, 2016 maize & 5, Miliaria farmland Northeast expansion of maize negativ , Available -06- Birds Birds Corn Bunting Germany rapesee Figure calandra bird Germany field landscape level e effect rape, Habitat, 08- d 6, other Habitat 12-bf Figure Change, 7. Table Number of 8, Table Breeding 9 Periods

297

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 2, Table 3, Table 4, Breeding , Table Bird Index 2016 6,, habitat quality of Change; Miliaria farmland negativ -06- Figure Birds Birds Corn Bunting Germany Brandenburg maize compared to maize maize Habitat calandra bird e effect 09- 4, cereals Suitability, 03-bf Figure Available 5, Habitat Figure 6, Figure 7

Intrinsic Habitat maize 2016 Table 3, maize & Value, Miliaria farmland expansion of maize negativ , -06- Figure Birds Birds Corn Bunting Germany Brandenburg rapesee Habitat calandra bird field landscape level e effect rape, 01- 2, d Suitability, other 01-bf Figure 4 Available Habitat

2016 reimplacement of maize & Milvus farmland Western negativ maize -07- Birds Birds Red Kite Poland fallow/cereals land rapesee milvus bird Wielkopolska e effect , rape 27- with maize or rape d 01-az

Figure Absolute 2016 1, Table Milvus farmland expansion of maize negativ breeding -06- Birds Birds Red Kite Germany Germany maize maize 1, Table milvus bird field e effect pairs for 03- 2, Table Germany 02-bf 3

298

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

potential number of 2016 expansion of maize & territories Individual Yellow Motacilla farmland negativ maize -06- Table3, Birds Birds Germany Brandenburg maize/rape rapesee per 10 ha = Based Wagtail flava bird e effect , rape 03- Figure 3 cultivation d breeding Model 01-bf pairs per 10 ha

2016 -09- 25- expansion of maize 02- Figure Passer farmland field versus decrease negativ Farmland Birds Birds Tree sparrow Germany Germany maize maize Analysis rk, 2, montanus bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk

Figure 1, Figure female 3, addition of maize to 2016 abundance / Individual Fugure Grey Perdrix farmland Northeast the crop rotation, negativ -06- Birds Birds Germany maize maize ha = Based 4, Partridge perdrix bird Germany loss of rotational set- e effect 01- breeding Model Figure aside 13-bf pairs / ha 5, Table 1, Table 2, Table 6

299

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 2, Table 3, Table 4, Breeding , Table Bird Index 2016 6,, habitat quality of Change; Saxicola farmland negativ -06- Figure Birds Birds Whinchat Germany Brandenburg maize compared to maize maize Habitat rubetra bird e effect 09- 4, cereals Suitability, 03-bf Figure Available 5, Habitat Figure 6, Figure 7

Figure Absolute 2016 1, Table Saxicola farmland expansion of maize negativ breeding -06- Birds Birds Whinchat Germany Germany maize maize 1, Table rubetra bird field e effect pairs for 03- 2, Table Germany 02-bf 3

300

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Breeding Table 1, Bird Index Table 2, Change; Table 7, Habitat Figure maize Suitability, 2016 maize & 5, Saxicola farmland Northeast expansion of maize negativ , Available -06- Birds Birds Whinchat Germany rapesee Figure rubetra bird Germany field landscape level e effect rape, Habitat, 08- d 6, other Habitat 12-bf Figure Change, 7. Table Number of 8, Table Breeding 9 Periods

Intrinsic Habitat maize 2016 Table 3, maize & Value, Saxicola farmland expansion of maize negativ , -06- Figure Birds Birds Whinchat Germany Brandenburg rapesee Habitat rubetra bird field landscape level e effect rape, 01- 2, d Suitability, other 01-bf Figure 4 Available Habitat

Analysis 2016 Table 1, comparison of Per Sample Saxicola farmland positiv rapesee based -08- Table 2, Birds Birds Whinchat Hungary Hungary abundance between rape Point rubetra bird e effect d Predictio 15- Table 3, rape and cereals Abundance n 02-fj Table 4

301

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only) 2016 -09- 25- expansion of maize 02- Figure Streptopelia farmland field versus decrease negativ Farmland Birds Birds Turtle dove Germany Germany maize maize Analysis rk, 2, turtur bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk 2016 -09- 25- expansion of maize 02- Figure Barred Sylvia farmland field versus decrease negativ Farmland Birds Birds Germany Germany maize maize Analysis rk, 2, warbler nisoria bird of fallow/set-aside e effect Bird Index 2016 Figure 5 land -09- 25- 01-rk 2016 Tetrax grassland intensification of negativ -06- Birds Birds Little Bustard World World other other tetrax nesting bird agriculture e effect 20- 01-bf 2016 expansion of maize & Northern Vanellus farmland Unspecifie unclear maize -06- Birds Birds Unspecified maize/rape rapesee Editorial lapwing vanellus bird d effect , rape 02- cultivation d 01-bf

Figure Absolute Analysis 2016 1, Table Northern Vanellus farmland expansion of maize positiv breeding based -06- Birds Birds Germany Germany maize maize 1, Table lapwing vanellus bird cultivation e effect pairs for Predictio 03- 2, Table Germany n 02-bf 3

302

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 2, Table 3, Breeding Table 4, Bird Index Conceptu , Table Change; al 2016 6,, habitat quality of Habitat Northern Vanellus farmland negativ Modelling -06- Figure Birds Birds Germany Brandenburg maize compared to maize maize Suitability, lapwing vanellus bird e effect , 09- 4, cereals Available Predictio 03-bf Figure Habitat, n 5, Habitat Figure Change 6, Figure 7

Breeding Table 1, Bird Index Table 2, Change; Table 7, Habitat Conceptu Figure maize Suitability, al 2016 expansion of maize maize & 5, Northern Vanellus farmland Northeast positiv , Available Modelling -06- Birds Birds Germany cultivation landscape rapesee Figure lapwing vanellus bird Germany e effect rape, Habitat, , 08- level d 6, other Habitat Predictio 12-bf Figure Change, n 7. Table Number of 8, Table Breeding 9 Periods

303

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Intrinsic Conceptu Habitat maize al 2016 Table 3, expansion of maize maize & Value, Northern Vanellus farmland positiv , Modelling -06- Figure Birds Birds Germany Brandenburg cultivation landscape rapesee Habitat lapwing vanellus bird e effect rape, , 01- 2, level d Suitability, other Predictio 01-bf Figure 4 Available n Habitat

Figure 2016 European comparison between 1, Amphibian Amphibian Rana farmland unclear Territories, -06- Common UK UK SRC, arable land and other other Analysis Figure s s temporaria species effect Recordings 13- Frog older coppice 2, Table 02-bf 3

Figure 2016 comparison between 1, Amphibian Amphibian Common farmland unclear Territories, -06- Bufo bufo UK UK SRC, arable land and other other Analysis Figure s s Toad species effect Recordings 13- older coppice 2, Table 02-bf 3

Figure 2016 Northern comparison between 1, Amphibian Amphibian Triturus farmland unclear Territories, -06- Crested UK UK SRC, arable land and other other Analysis Figure s s cristatus species effect Recordings 13- Newt older coppice 2, Table 02-bf 3

Figure 2016 comparison between 1, unclear Territories, -06- Reptiles Reptiles Grass Snake Natrix natrix grass snake UK UK SRC, arable land and other other Analysis Figure effect Recordings 13- older coppice 2, Table 02-bf 3

304

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Figure 1, Figure female 3, addition of maize to 2016 abundance / Individual Fugure European Lepus farmland Northeast the crop rotation, negativ -06- Mammals Mammals Germany maize maize ha = Based 4, brown Hare europaeus species Germany loss of rotational set- e effect 01- breeding Model Figure aside 13-bf pairs / ha 5, Table 1, Table 2, Table 6

Figure 1, Figure female 3, addition of maize to 2016 abundance / Fugure Microtus farmland Northeast the crop rotation, unclear -06- Mammals Mammals Field vole Germany maize maize ha = 4, agrestis species Germany loss of rotational set- effect 01- breeding Figure aside 13-bf pairs / ha 5, Table 1, Table 2, Table 6

305

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Figure 1, Figure female 3, 2016 abundance / Individual Fugure Linyphiid farmland Northeast aggregation of maize positiv -06- Arachnida Spiders Erigone atra Germany maize maize ha = Based 4, spider species Germany fields e effect 01- breeding Model Figure 13-bf pairs / ha 5, Table 1, Table 2, Table 6

Figure 1, Figure female 3, addition of maize to 2016 abundance / Individual Fugure Linyphiid farmland Northeast the crop rotation, positiv -06- Arachnida Spiders Erigone atra Germany maize maize ha = Based 4, spider species Germany loss of rotational set- e effect 01- breeding Model Figure aside 13-bf pairs / ha 5, Table 1, Table 2, Table 6

Bavaria 2016 Mecklenburg- comparison of the farmland negativ -07- Arachnida Spiders Spiders Spiders Germany Western abundance in maize maize maize species e effect 05- Pomerania fields and other crops 03-bf Thuringia

306

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

assessment of the 2016 Southern/Sout economic impact of negativ rape, rapesee -06- Insecta Butterflies Butterflies Butterflies butterflies Finland h-Western policies on the e effect other d 02- Finland habitat quality 04-bf

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Pterostichus South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles melanarius Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Carabus South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles cancellatus Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Poecilus South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles cupreus Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

307

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Agonum South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles muellerie Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Carabus South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles auratus Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Carabus South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles granulatus Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Pterostichus South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles vernalis Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

308

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Harpalus South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles affinis Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Amara South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles aenea Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

biodiversity of bioindicator arthropods in maize 2016 of compared to an maize Carabid Carabid Calathus South-West unclear -06- Insecta ecosystem Germany agroforestry system , maize Beetles Beetles erratus Germany effect 08- stability/stre (with maize and other 16-bf ss trees) and other crops

309

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Figure 1, Figure female 3, addition of maize to 2016 abundance / Individual Fugure Carabid Carabid Bembidion farmland Northeast the crop rotation, positiv -06- Insecta Germany maize maize ha = Based 4, Beetles beetles lampros species Germany loss of rotational set- e effect 01- breeding Model Figure aside 13-bf pairs / ha 5, Table 1, Table 2, Table 6

Mean species land conversion :from 2016 Anchomenu richness, Carabid Carabid farmland South-East conventional crop positiv rapesee -06- Insecta s Ireland rape abundance Analysis Figure 2 Beetles beetles species Ireland (grassland, tillage e effect d 07- dorsalis and diversity crop) to rape 18-bf (Chao's Index)

Mean species land conversion :from 2016 richness, Carabid Carabid Pterostichus farmland South-East conventional crop positiv rapesee -06- Insecta Ireland rape abundance Analysis Figure 2 Beetles beetles melanarius species Ireland (grassland, tillage e effect d 07- and diversity crop) to rape 18-bf (Chao's Index)

310

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Mean species land conversion :from 2016 richness, Carabid Carabid Nebria farmland South-East conventional crop positiv rapesee -06- Insecta Ireland rape abundance Analysis Figure 2 Beetles beetles brevicollis species Ireland (grassland, tillage e effect d 07- and diversity crop) to rape 18-bf (Chao's Index)

Figure 3 A Trend 2016 Figure 3 attractiveness of Cover of Wild bees Würzburg, positiv rapesee -06- B Insecta Wild Bees Wild bees pollinator Germany Rape and effects on rape rape and Analysis (40spp) Bavaria e effect d 07- Species bee productivity Density of 22-bf list on Bumblebees appendi x B

Figure 3 A Trend 2016 Figure 3 attractiveness of Cover of Bumblebe Bumblebee Würzburg, positiv rapesee -06- B Insecta Bumblebees pollinator Germany Rape and effects on rape rape and Analysis es (6spp) Bavaria e effect d 07- Species bee productivity Density of 22-bf list on Bumblebees appendi x B

Nidda increase in the acrage 2016 Plant- Bumblebe Bombus catchment of Rape in unclear rapesee -06- Insecta Bumblebees pollinator Germany rape Pollinator Analysis es spp. spanning, surrounding effect d 07- Interaction Hesse landscapes 12-bf

311

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Mean species land conversion :from 2016 richness, Bumblebe Bombus conventional crop unclear rapesee -06- Insecta Bumblebees pollinator Ireland South-East rape abundance Analysis Figure 2 es spp. (grassland, tillage effect d 07- and diversity crop) to rape 18-bf (Chao's Index)

Number of Dundenheim, 2016 flower visitation in a flower Apis Offenburg, unclear -06- Insecta Honey Bee Honey Bee pollinator Germany double-cropping other other visiting Analysis mellifera Baden- effect 02- system individuals Wurttemberg 07-bf of species

Number of Dundenheim, 2016 flower visitation in a flower Lasioglossu Offenburg, unclear -06- Insecta Wild Bees Sweat Bees pollinator Germany double-cropping other other visiting Analysis m spp. Baden- effect 02- system individuals Wurttemberg 07-bf of species

Number of Dundenheim, 2016 flower visitation in a flower Offenburg, unclear -06- Insecta Wild Bees Masked Bees Hylaeus spp. pollinator Germany double-cropping other other visiting Analysis Baden- effect 02- system individuals Wurttemberg 07-bf of species

312

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Number of Dundenheim, 2016 flower visitation in a flower Offenburg, unclear -06- Insecta Hoverflies Syrphidae Syrphidae pollinator Germany double-cropping other other visiting Analysis Baden- effect 02- system individuals Wurttemberg 07-bf of species

Mean species land conversion :from 2016 richness, Andrena South-East conventional crop positiv rapesee -06- Insecta Wild Bees Sand Bees pollinator Ireland rape abundance Analysis Figure 2 angustior Ireland (grassland, tillage e effect d 07- and diversity crop) to Rape 18-bf (Chao's Index)

Mean species land conversion :from 2016 richness, Andrena South-East conventional crop positiv rapesee -06- Insecta Wild Bees Sand Bees pollinator Ireland rape abundance Analysis Figure 2 bicolor Ireland (grassland, tillage e effect d 07- and diversity crop) to Rape 18-bf (Chao's Index)

Mean species land conversion :from 2016 richness, Andrena South-East conventional crop positiv rapesee -06- Insecta Wild Bees Sand Bees pollinator Ireland rape abundance Analysis Figure 2 fucata Ireland (grassland, tillage e effect d 07- and diversity crop) to Rape 18-bf (Chao's Index)

313

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Mean species land conversion :from 2016 richness, Halictus South-East conventional crop positiv rapesee -06- Insecta Wild Bees Sweat Bees pollinator Ireland rape abundance Analysis Figure 2 rubicundus Ireland (grassland, tillage e effect d 07- and diversity crop) to Rape 18-bf (Chao's Index)

Mean species land conversion :from 2016 richness, Helophilus South-East conventional crop unclear rapesee -06- Insecta Hoverflies Hoverfly pollinator Ireland rape abundance Analysis Figure 2 pendulus Ireland (grassland, tillage effect d 07- and diversity crop) to Rape 18-bf (Chao's Index)

Number of Dundenheim, 2016 natural flower visitation in a flower Ladybird Offenburg, unclear -06- Insecta Coccinelidae Coccinelidae enemy of Germany double-cropping other other visiting Analysis Beetles Baden- effect 02- pollinators system, comparison individuals Wurttemberg 07-bf of species

Bavaria 2016 Mecklenburg- comparison of the maize Carabid negativ Species, -07- Figure 3 Insecta Carabidae Carabidae predators Germany Western abundance in maize , maize Analysis Beetles e effect Number 05- Figure 4 Pomerania fields and other crops other 03-bf Thuringia

314

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Bavaria 2016 Mecklenburg- comparison of the maize Bumblebe negativ Species, -07- Figure 3 Insecta Bumblebees Bumblebees pollinator Germany Western abundance in maize , maize Analysis es e effect Number 05- Figure 4 Pomerania fields and other crops other 03-bf Thuringia

Bavaria 2016 Mecklenburg- comparison of the maize negativ Species, -07- Figure 3 Insecta Wild Bees Wild Bees Wild Bees pollinator Germany Western abundance in maize , maize Analysis e effect Number 05- Figure 4 Pomerania fields and other crops other 03-bf Thuringia

Bavaria 2016 Mecklenburg- comparison of the maize negativ Species, -07- Figure 3 Insecta Hoverflies Syrphidae Syrphidae pollinator Germany Western abundance in maize , maize Analysis e effect Number 05- Figure 4 Pomerania fields and other crops other 03-bf Thuringia

315

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Biomass of natural enemies, comparison of pest 2016 Southern Species Earthwor Earthwor Corn Helicoverpa common control between negativ -06- USA Wisconsin and maize maize number of Analysis ms ms earworm zea crop pest maize and perennial e effect 09- Michigan natural crops 01-bf enemies, Egg predation

Abundance 2016 comparison of maize maize Earthwor Earthwor Lumbricus Lumbricus anecic Western negativ (Number/m -06- Table 2, Germany with other , maize Analysis ms ms terrestris terrestris species Germany e effect 2), Biomass 09- Figure 1 energetical crops other (g/m2) 05-bf

Table 1, Figure 1 comparison of the Kenner Flur (copy), earthworms 2016 and Ehranger maize Number of Figure Earthwor Earthwor Lumbricus Lumbricus anecic population between negativ -07- Germany Flur , maize Species, Analysis 2, ms ms terrestris terrestris species maize and e effect 04- Rhineland other Abundance Figure cereals/grassland/fall 06-bf Palatinate 3, ow land/Miscanthus Figure 5 (copy),

316

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 1, Figure 1 comparison of the Kenner Flur (copy), earthworms 2016 and Ehranger Number of Figure Earthwor Earthwor Lumbricus Lumbricus anecic population between negativ rape, rapesee -07- Germany Flur Species, Analysis 2, ms ms terrestris terrestris species rape and e effect other d 04- Rhineland Abundance Figure grassland/fallow 06-bf Palatinate 3, land/Miscanthus Figure 5 (copy),

Abundance 2016 comparison of maize maize Earthwor Earthwor Lumbricus Lumbricus epigeic Western negativ (Number/m -06- Table 2, Germany with other , maize Analysis ms ms rubellus rubellus species Germany e effect 2), Biomass 09- Figure 1 energetical crops other (g/m2) 05-bf

Table 1, Figure 1 comparison of the Kenner Flur (copy), earthworms 2016 and Ehranger maize Number of Figure Earthwor Earthwor Lumbricus Lumbricus epigeic population between negativ -07- Germany Flur , maize Species, Analysis 2, ms ms rubellus rubellus species maize and e effect 04- Rhineland other Abundance Figure cereals/grassland/fall 06-bf Palatinate 3, ow land/Miscanthus Figure 5 (copy),

317

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 1, Figure 1 comparison of the Kenner Flur (copy), earthworms 2016 and Ehranger Number of Figure Earthwor Earthwor Lumbricus Lumbricus epigeic population between negativ rape, rapesee -07- Germany Flur Species, Analysis 2, ms ms rubellus rubellus species rape and e effect other d 04- Rhineland Abundance Figure grassland/fallow 06-bf Palatinate 3, land/Miscanthus Figure 5 (copy),

Table 1, Figure 1 comparison of the Kenner Flur (copy), earthworms 2016 and Ehranger maize Number of Figure Earthwor Earthwor Lumbricus Lumbricus epigeic population between negativ -07- Germany Flur , maize Species, Analysis 2, ms ms castaneus castaneus species maize and e effect 04- Rhineland other Abundance Figure cereals/grassland/fall 06-bf Palatinate 3, ow land/Miscanthus Figure 5 (copy),

318

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 1, Figure 1 comparison of the Kenner Flur (copy), earthworms 2016 and Ehranger Number of Figure Earthwor Earthwor Lumbricus Lumbricus epigeic population between negativ rape, rapesee -07- Germany Flur Species, Analysis 2, ms ms castaneus castaneus species rape and e effect other d 04- Rhineland Abundance Figure grassland/fallow 06-bf Palatinate 3, land/Miscanthus Figure 5 (copy),

Figure Number of 1, 2016 Kenner Flur comparison of rape Species, Figure Earthwor Earthwor Lumbricus Lumbricus epigeic unclear rape, rapesee -06- Germany Rhineland with other Abundance, Analysis 2, ms ms castaneus castaneus species effect other d 09- Palatinate energetical crops Dominance, Figure 06-bf Frequency 3, Table 4

Figure Number of 1, 2016 Kenner Flur comparison of maize maize Species, Figure Earthwor Earthwor Lumbricus Lumbricus epigeic negativ -06- Germany Rhineland with other , maize Abundance, Analysis 2, ms ms castaneus castaneus species e effect 09- Palatinate energetical crops other Dominance, Figure 06-bf Frequency 3, Table 4

Abundance 2016 comparison of maize maize Earthwor Earthwor Aporrectode Aporrectode endogeic Western negativ (Number/m -06- Table 2, Germany with other , maize Analysis ms ms a caliginosa a caliginosa species Germany e effect 2), Biomass 09- Figure 1 energetical crops other (g/m2) 05-bf

319

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Figure Number of 1, 2016 Kenner Flur comparison of rape Species, Figure Earthwor Earthwor Aporrectode Aporrectode endogeic unclear rape, rapesee -06- Germany Rhineland with other Abundance, Analysis 2, ms ms a caliginosa a caliginosa species effect other d 09- Palatinate energetical crops Dominance, Figure 06-bf Frequency 3, Table 4

Figure Number of 1, 2016 Kenner Flur comparison of maize maize Species, Figure Earthwor Earthwor Aporrectode Aporrectode endogeic negativ -06- Germany Rhineland with other , maize Abundance, Analysis 2, ms ms a caliginosa a caliginosa species e effect 09- Palatinate energetical crops other Dominance, Figure 06-bf Frequency 3, Table 4

Table 1, Figure 1 comparison of the Kenner Flur (copy), earthworms 2016 and Ehranger maize Number of Figure Earthwor Earthwor Aporrectode Aporrectode endogeic population between negativ -07- Germany Flur , maize Species, Analysis 2, ms ms a caliginosa a caliginosa species maize and e effect 04- Rhineland other Abundance Figure cereals/grassland/fall 06-bf Palatinate 3, ow land/Miscanthus Figure 5 (copy),

320

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Table 1, Figure 1 comparison of the Kenner Flur (copy), earthworms 2016 and Ehranger Number of Figure Earthwor Earthwor Aporrectode Aporrectode endogeic population between negativ rape, rapesee -07- Germany Flur Species, Analysis 2, ms ms a caliginosa a caliginosa species rape and e effect other d 04- Rhineland Abundance Figure grassland/fallow 06-bf Palatinate 3, land/Miscanthus Figure 5 (copy),

Abundance 2016 comparison of maize maize Earthwor Earthwor Aporrectode Aporrectode anecic Western negativ (Number/m -06- Table 2, Germany with other , maize Analysis ms ms a longa a longa species Germany e effect 2), Biomass 09- Figure 1 energetical crops other (g/m2) 05-bf

Figure Number of 1, 2016 Kenner Flur comparison of rape Species, Figure Earthwor Earthwor Aporrectode Aporrectode anecic unclear rape, rapesee -06- Germany Rhineland with other Abundance, Analysis 2, ms ms a longa a longa species effect other d 09- Palatinate energetical crops Dominance, Figure 06-bf Frequency 3, Table 4

Figure Number of 1, 2016 Kenner Flur comparison of maize maize Species, Figure Earthwor Earthwor Aporrectode Aporrectode anecic negativ -06- Germany Rhineland with other , maize Abundance, Analysis 2, ms ms a longa a longa species e effect 09- Palatinate energetical crops other Dominance, Figure 06-bf Frequency 3, Table 4

321

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Aporrectode Aporrectode anecic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms a longa a longa species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Aporrectode Aporrectode anecic population between negativ rape, rapesee -07- Germany Flur Redundancy ms ms a longa a longa species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

Abundance 2016 comparison of maize maize Earthwor Earthwor Aporrectode Aporrectode Western negativ (Number/m -06- Table 2, Germany with other , maize Analysis ms ms a antipai a antipai Germany e effect 2), Biomass 09- Figure 1 energetical crops other (g/m2) 05-bf

Abundance 2016 comparison of maize maize Earthwor Earthwor Aporrectode Aporrectode endogeic Western negativ (Number/m -06- Table 2, Germany with other , maize Analysis ms ms a rosea a rosea species Germany e effect 2), Biomass 09- Figure 1 energetical crops other (g/m2) 05-bf

322

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Aporrectode Aporrectode endogeic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms a rosea a rosea species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Aporrectode Aporrectode endogeic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms a rosea a rosea species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Apporectode Apporectod endogeic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms a icterica ea icterica species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Apporectode Apporectod endogeic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms a icterica ea icterica species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

323

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Figure Number of 1, 2016 Kenner Flur comparison of rape Species, Figure Earthwor Earthwor Apporectode Apporectod endogeic unclear rape, rapesee -06- Germany Rhineland with other Abundance, Analysis 2, ms ms a icterica ea icterica species effect other d 09- Palatinate energetical crops Dominance, Figure 06-bf Frequency 3, Table 4

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Allolobophor Allolobopho endogeic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms a chlorotica ra chlorotica species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Allolobophor Allolobopho endogeic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms a chlorotica ra chlorotica species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

Abundance 2016 comparison of maize maize Earthwor Earthwor Allolobophor Allolobopho endogeic Western negativ (Number/m -06- Table 2, Germany with other , maize Analysis ms ms a chlorotica ra chlorotica species Germany e effect 2), Biomass 09- Figure 1 energetical crops other (g/m2) 05-bf

324

Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Figure Number of 1, 2016 Kenner Flur comparison of rape Species, Figure Earthwor Earthwor Allolobophor Allolobopho endogeic unclear rape, rapesee -06- Germany Rhineland with other Abundance, Analysis 2, ms ms a chlorotica ra chlorotica species effect other d 09- Palatinate energetical crops Dominance, Figure 06-bf Frequency 3, Table 4

Figure Number of 1, 2016 Kenner Flur comparison of maize maize Species, Figure Earthwor Earthwor Allolobophor Allolobopho endogeic negativ -06- Germany Rhineland with other , maize Abundance, Analysis 2, ms ms a chlorotica ra chlorotica species e effect 09- Palatinate energetical crops other Dominance, Figure 06-bf Frequency 3, Table 4

Abundance 2016 comparison of maize maize Earthwor Earthwor Allolobophor Allolobopho endogeic Western negativ (Number/m -06- Table 2, Germany with other , maize Analysis ms ms a cupulifera ra cupulifera species Germany e effect 2), Biomass 09- Figure 1 energetical crops other (g/m2) 05-bf

Figure Number of 1, 2016 Kenner Flur comparison of rape Species, Figure Earthwor Earthwor Allolobophor Allolobopho endogeic unclear rape, rapesee -06- Germany Rhineland with other Abundance, Analysis 2, ms ms a cupulifera ra cupulifera species effect other d 09- Palatinate energetical crops Dominance, Figure 06-bf Frequency 3, Table 4

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Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

Figure Number of 1, 2016 Kenner Flur comparison of maize maize Species, Figure Earthwor Earthwor Allolobophor Allolobopho endogeic negativ -06- Germany Rhineland with other , maize Abundance, Analysis 2, ms ms a cupulifera ra cupulifera species e effect 09- Palatinate energetical crops other Dominance, Figure 06-bf Frequency 3, Table 4

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Allolobophor Allolobopho endogeic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms a cupulifera ra cupulifera species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Allolobophor Allolobopho endogeic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms a cupulifera ra cupulifera species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Fitzingeria Fitzingeria anecic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms platyura platyura species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

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Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Fitzingeria Fitzingeria anecic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms platyura platyura species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Satchellius Satchellius epigeic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms mammalis mammalis species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Satchellius Satchellius epigeic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms mammalis mammalis species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Dendrodrilus Dendrodrilu epigeic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms rubidus s rubidus species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

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Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Dendrodrilus Dendrodrilu epigeic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms rubidus s rubidus species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Proctodrilus Proctodrilus endogeic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms antipae antipae species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Proctodrilus Proctodrilus endogeic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms antipae antipae species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

comparison of the Kenner Flur earthworms 2016 and Ehranger maize Ordination Earthwor Earthwor Octolasion Octolasion endogeic population between negativ -07- Germany Flur , maize Redundancy Analysis ms ms cyaneum cyaneum species maize and e effect 04- Rhineland other Analysis cereals/grassland/fall 06-bf Palatinate ow land/Miscanthus

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Crops (maize File Scientific Natural Type of Figures Class Taxa Species Country Region Factor Effect Crops and Parameter Nam Name Habitats Study in Ch 9 rapesee e d only)

comparison of the Kenner Flur earthworms 2016 and Ehranger Ordination Earthwor Earthwor Octolasion Octolasion endogeic population between negativ rape, rapesee -07- Germany Flur Redundancy Analysis ms ms cyaneum cyaneum species rape and e effect other d 04- Rhineland Analysis grassland/fallow 06-bf Palatinate land/Miscanthus

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Annex 7 Decision Tree for Selection of Data and Analytical Methods for the Assessment of Environmental Effects on Biodiversity

A. Introduction

This document presents part of the results of a project for the European Commission considering methods and evidence to evaluate the possible relationships between agriculture and biodiversity in European Union member states59. The conclusions in respect of approaches to identify whether appropriate data exist to inform about a specific question and the best analytical approaches to use to answer it were summarized as a decision tree. The aim of this decision tree (and the glossary in Section E below) is to act as a guide to inform the selection of appropriate data and a suitable method of analysis in order to answer a question of interest. In Part 1 (Figure 2-1), the structure guides choices of data sets for the biodiversity “response” and the agricultural or land-use “predictor” that define the question, which is assumed to concern the relationship between the response and the predictor, together with any other data that may be required. Throughout, failures of the data chosen to meet the needs implied by the question (i.e. where the data for the predictor or the response would be insufficient to produce a reliable answer) indicate that it is necessary to reconsider the data selection, or to revise the question being asked. This aims to avoid expectations of the evidence provided by the analysis exceeding what is actually feasible.

In general, it is expected that the processes outlined in Part 2 would be followed, in practice, by expert analysts who would not need to use the tree as a guide, but it should inform others with an interest in the data or the results as to the logic behind analyses that are conducted. Part 1, however, should both be more accessible to the lay reader and useful to identify the forms of information that analysts will require in order to conduct projects of interest to commissioning bodies.

In the notes below, guidance is provided as to how to use the decision tree and on the meaning of the specific branches and nodes within it.

59 Siriwardena, G. and Tucker, G. (eds) (2017) Service contract to support follow-up actions to the mid- term review of the EU biodiversity strategy to 2020 in relation to target 3A – Agriculture. Report to the European Commission, Institute for European Environmental Policy, London.

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Figure 1 Decision tree, part 1: selection and format of datasets

Biodiversity and Agriculture Start QUESTION

Evidence Assessment Decision 1

Tree – Part 1: Data Preparation Identify predictor and response data sets needed to answer the

question

How many What areas or Spatial Scale Temporal Scale years’ data are needed to locations should answer be considered to 2 3 question? answer question?

question Y Tests are limited to Y effects on static distributions and 4

Y Y Y

N N Is the spatial scale large Is the temporal scale enough to ensure effects

large enough to avoid are not due to local confounding annual dispersal/changes? variation? Y

Y N

Y Cycle back towards start of decision tree (usually

answer that currently Y being considered.

End point of Go to analysis tree dataset selection tree

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B. Decision Tree Part 1: Data preparation

This part of the decision tree guides the process of data selection and aims to ensure the data chosen can be used to answer the question at the correct spatial and temporal scales, whether this means using different data or altering the question to get maximal value from the data available. This is important because scale issues can be critical in determining whether analyses will actually deliver the evidence that they are intended to deliver. For example, population change measures need to be collected over a sufficient period of time that changes can be expected to be observed. Then, spatially, biodiversity data need to be available at a spatial scale that is appropriate for the taxon concerned. For example, if the scale of data collection is too small, whether a species is recorded may be subject to an unacceptable level of random chance, or detection may reflect factors such as choice of foraging location rather than the presence of a breeding individual. In addition, the data on biodiversity and potential drivers (such as agricultural land‐use or policy measures) need to cover overlapping, mutually representative locations and time periods. From another perspective, it is the periods and locations of overlap that determine what an analysis combining the datasets will inform about.

The numbered sections below refer to numbered areas on the decision tree.

1. Question

The first step is to define the question being asked. This must be:

a) Specific, i.e. defining time period, area, species, response variable and landscape, as required.

b) Answerable, i.e. data exist that can answer the question in a meaningful way, without giving potentially misleading results.

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trends in Germany between 1990 and 2010 associated with variation in cropping patterns between survey areas?”. From the question definition, the decision tree provides guidance on identifying datasets that may be used to answer the question: do they cover the appropriate species, area, and predictors, etc., that are required by the question?

2. Temporal scale

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real effects of the cropping regime associated with the existence of specific crops, unless short-term, field-level effects are of interest. Answering ‘no’ to any of the questions in box 2 indicates that it is necessary to go back to the data selection stage, where a number of options are available to progress (see 4 below).

In parallel to considering questions relating to temporal scale issues around spatial scale need to be considered (see 3 below).

3. Spatial scale

In addition to the temporal scale of the datasets, the spatial scale must also be examined before analysis can be carried out. In box 3, this is addressed via a series of questions about the spatial coverage and resolution of the datasets. These are based around the following principles: a) The data selected should encompass or be representative of the areas and landscape defined in the question. Spatial coverage is more likely to be representative of the whole area of interest if the data derive from a random sample. If sampling was not random, spatial representation should be checked.

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to be available a fine spatial resolution. Spatial scale is also important for predictor data, particularly if static data are being used. In the case of cropping data, the scale might often need to be large enough that a whole rotation is represented, in order to explain local patterns of biodiversity. Conversely, very fine‐scale data are required to inform about the locations of different field boundary habitats, for example, which are typically only up to a few metres wide, so land‐cover information needs to be sufficiently high in resolution to detect them. Answering ‘no’ to any of the questions in box 3 indicates that it is necessary to return to the data selection stage, where a number of options are available to progress (see 4 below).

Figure 2 Examples of patterns of co‐location of predictor (P) and response (R) datasets within a broader region (oval area) In (a), predictors and responses are collected for the same survey locations and are equally representative of the region. In (b), each comes from a different location, but they are matched in the sense that each can be considered to represent the region. In (c), predictor data are drawn from a larger spatial scale than the response, but the response location is assumed to be representative of the wider region. Hybrid combinations of these patterns are also suitable, but the suitability of situations like (b) and (c) depend on the survey locations being representative of one another, or on the homogeneity of the region, and this should be verified. a) b)

P R

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Figure 13-3 Examples of the ways in which predictor (P, in small rectangles) and response (R, in ovals) variables may coincide within statistical regions in practice Regions are presented as a hypothetical nest of NUTS3 areas within a NUTS2 region. Where predictor and response sampling does not overlap perfectly, assumptions about the mutual representativeness of the areas concerned are required, the accuracy of which should be verified. Where predictor and response areas overlap by differing amounts, or differ in size, or multiple areas of one type overlap with one of the other, corrections of data values by area, such as taking averages weighted by the extent of an overlap, or modelling solutions, such as allowing for repeated measures, are likely to be necessary.

R P

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4. Data selection

a) Expand the data selection to ensure appropriate temporal or spatial coverage.

b) Search for other datasets that may be more appropriate to the question.

c) If no other datasets can sufficiently answer the question, redefine the question itself. It is then necessary to progress through the tree again, with the new data or revised question.

5. Final stages of data selection tree

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C. Decision Tree Part 2: Choice of analytical approach

The numbered sections below refer to numbered areas on the decision tree.

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Figure 4 Decision tree, part 2: choice of statistical analyses Datasets for analysis Biodiversity and Agriculture Evidence Assessment

Decision Tree – Part 2: Choice of Analysis

Response variables Explanatory variables

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7. Continuous explanatory variables

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Figure 5 Example possible shapes of biodiversity response to a predictor variable The solid line shows a simple linear response, the short-dashed line a monotonic, but non-linear, increasing response and the long-dashed line a peaking response.

Biodiversity

response

variable

Predictor variable

8. Categorical explanatory variables

9. Combining explanatory variables

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10. Derived response data

There are multiple forms of biodiversity response data. First, the only available data may be data derived from surveys and other analyses, such as regional summaries, trends or levels of conservation status. Where possible, it is always preferable to analyse raw data or data that are processed only as far as providing the best information for the spatial unit of interest. For example, multiple survey visits in a single year might best be combined in an appropriate way for the taxon involved before analysis, rather than analyses of environmental relationships working with the raw data. However, if such data are not available, it may be necessary to use derived summary data, such as regional trends, which can be analysed with the modelling frameworks described below (see 13). Options are more limited for highly derived data such as qualitative status records (see 14), although it may be appropriate to recast “good” and “poor” condition or “secure” and “threatened” status, for example, as a binary variable (levels of 1 and 0, respectively), when more sophisticated analyses can be conducted (see 13).

11. Unstructured response data

12. Structured response data

13. Chi-square tests

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causal relationship. For example, protected sites might be more likely to be in better condition than unprotected ones than would be the case if condition levels were randomly distributed between these categories. A chi-square test assesses this difference in distribution formally. This test is simple and quick to conduct, but only allows a simple comparison of frequencies of occurrence, so analyses incorporating control parameters, for example, are not possible. It is also increasingly difficult to interpret when there are more categories amongst which data are distributed.

14. Generalized Linear (Mixed) Models

GLMs and GLMMs encompass the analytical procedures that are used most commonly with biodiversity data. Any combination of continuous and categorical variables, and interactions between them, can be incorporated, including variables included only as controls for variation that is not of interest (such as weather conditions, when the influences of land- use are the focus of the study). Controls can be treated in the same way as other predictor variables (GLMs) or considered as random effects (GLMMs), which can provide more confidence that the results are widely applicable, as opposed to being limited to the sampled locations.

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computing resources permit (note that this method can be prohibitively computer- intensive).

15. Bayesian occupancy models Recent developments in biodiversity modelling are allowing greater inference than has been possible before to be gleaned from unstructured data, by making use of Bayesian modelling approaches (van Strien et al. 2010, Isaac et al. 2014). These are currently still in development and have been developed with a view to revealing temporal trends, but may be applicable to questions relating to environmental variation in due course.

D.Key Links in the Decision Tree

The decision tree as a whole is intended to describe the process of identifying suitable data and analytical processes to answer defined questions, but there are key links or sensitive points in the tree that require particular attention because they reflect where problems with analyses are commonly found. These points are listed below:

 Spatial scale of predictor and response data (point 3). Issues of confidentiality around data supply and other constraints mean that agricultural and land-use data are often available only at large spatial scales, such as of that of the NUTS3 region. This means that the only data available to match with site-level biodiversity data, for example, are regional agricultural data. This means that all sampling or monitoring sites within a region are assigned the same agricultural data, so that (i) there is no variation at this scale from which to estimate effects and (ii) the data are likely to be inaccurate as measures of the real values of the variables involved at the site scale.

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in summer, so relating their numbers or presence may mean that larger areas need to be considered to derive appropriate variables to describe the important environmental information.

 Derived data (point 9). Data ownership and access constraints may mean that raw or site- level data on biodiversity, in particular, are not available. Some analyses can generally be done with derived or summary data, but analyses lose power when variability declines and more factors tend to be confounded at larger scales (e.g. land-use variables and geographical location), as described above. Data at smaller scales are always preferable, although they do require higher capacity computer resources, simply because data sets are larger.

E. Glossary

Association

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Two variables are associated if some of the variability of one can be accounted for by the other. Association does not imply causation, unlike a statistical effect. Correlation is a specific type of association.

Bias The extent to which a sample is unrepresentative of the target population.

Binomial distribution A probability distribution for independent events for which there are only two possible outcomes, such as a coin flip.

Categorical variable A variable which has two or more qualitative categories; these have no intrinsic ordering. It can take on one of these categories. This differs from a continuous variable.

Confidence interval A confidence interval is an interval which has a known and selected probability (generally 95% or 99%) of containing the true value.

Control variable A variable whose effect is not of interest as a predictor of the response variable in a model, but which may be an influence upon it and may confound or obscure effects of interest.

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Hence, the variable is included in the model, but the results referring to it may not be reported.

Dependent variable See Response variable.

Driver In ecology, any factor that directly or indirectly causes a change in an ecosystem or a parameter describing the status of a species.

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squared distributions. If the calculated value exceeds the corresponding reference value from an F table at a specific confidence value, the variances are not equal.

Fixed and random effect predictors See Generalised linear mixed model.

Free choice sampling Sampling sites are chosen by surveyors as they see fit. In some cases this may distort results, e.g. if sites are chosen for high populations of the surveyed species.

Independent Two variables or events are independent if they have no influence on each other (i.e. are not correlated).

Independent variable See Predictor variable.

Linear mixed model See Generalized linear mixed model.

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Metadata Metadata are data that define and describe other data.

Multicollinearity The condition occurring when two or more of the predictor variables in a regression equation are correlated.

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Model averaging uses the AIC values to calculate average parameter estimates across the full model set, weighted by the support given to each mode by the data.

P value See significance.

Pearson’s χ2 goodness-of- fit See Chi-squared test.

Predictor variable Predictors (also called independent, explanatory or input variables) are variables used to predict or to explain the value(s) of one or more dependent variables.

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the components. PCA is commonly used to reduce a set of candidate predictor variables down to a smaller set of uncorrelated variables, prior to modelling analyses.

Random intercept Feature of a model in which the intercept is a random variable. See Linear mixed model.

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Score test An equivalent to a likelihood ratio test for comparing models accounting for repeated measures that are fitted using generalized estimating equations.

Semi-random sampling Sampling sites are selected randomly from within a restricted area.

Significance The statistical significance of a result is an estimated measure of the probability that it could have arisen by chance, according to an assumed or data-derived distribution and, hence, of the degree to which it is “true” in the sense of being representative of the population. P- values are often used to assess significance: a p-value is the probability of error involved in accepting the result as valid. Conventionally, only a p-value of less than 0.05 is considered to show significance, i.e. to indicate that the relationship has not arisen by chance.

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indeed, at location C). Thus, inference about likely or historical temporal change is gleaned from contemporary spatial variation.

Standard deviation The standard deviation is a widely used measure of the variability of a sample. It is computed by taking the square root of the variance.

Stochastic An effect arising due to random chance.

Transect In ecology, a fixed path along which a species is sampled or observed, often in the context of recording numbers of the species.

Variance The variance is a widely used measure of variability in a sample. It is defined as the mean squared deviation of scores from the mean. It is also the square of the standard deviation.

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