UK Biodiversity Indicators in Your Pocket 2013

This documents supports

C4a. Status of threatend species – status of priority species

For further information on C4a. Status of priority species visit http://jncc.defra.gov.uk/page-4238

For further information on BIYP visit http://www.jncc.gov.uk/page-1824 Indicator C4a. Status of priority species – Technical background report – August 2013

Prepared by the Species Indicator Initiative working group: Jeremy Biggs (Pond Conservation), Tom Brereton (BC), David Brooks (Rothamsted Research), Fiona Burns (RSPB), Natasha Chick (Defra), Mark Eaton (RSPB), Richard Gregory (RSPB), Karen Haysom (BCT), Nick Isaac (BRC - CEH), David Noble (BTO), Deborah Procter (JNCC), Mark Stevenson (Defra), James Williams (JNCC).

1. Introduction

The adjustments to the UK biodiversity indicators set as a result of the adoption of the Strategic Plan for Biodiversity (including the Aichi Targets) at the 10th Conference of Parties of the Convention on Biological Diversity mean there is a need to report progress against Aichi Target 12:

Target 12: By 2020 the extinction of known threatened species has been prevented and their conservation status, particularly of those most in decline, has been improved and sustained.

Previously, the UK biodiversity indicator for threatened species used lead partner status assessments on the status of priority species from three-yearly UK Biodiversity Action Plan (UK BAP) reporting rounds. As a result of the devolution of biodiversity strategies to the UK's four nations, there is no longer a current list of UK priority species, or associated reporting. In order to assess progress towards international biodiversity targets at a UK level, and to improve species’ conservation prospects, a robust system of analysis and reporting for threatened species in the UK is needed. This should also help to meet the reporting requirements of the individual countries.

Although it would be possible to develop indicators separately in each of the four countries and use these for UK reporting (with or without amalgamation), this process would be inefficient because: A. Most of the available datasets are UK wide and so each country would need to separately negotiate with each data provider to develop and update any indicator. B. Many datasets are likely to be too small to produce reliable trends at country level. C. The methods for compiling the indicator at UK level would only need to be developed once (rather than four times). D. If different, bespoke approaches are used to develop indicators at a country level their construction and interpretation may vary in a way that makes compilation at the UK level difficult In addition, there are a number of species, for example, those listed in the Annexes of the Habitats Directive, where a degree of UK coordination is either already required or where coordination is likely to be beneficial to each country.

This paper presents the first iteration of a new indicator to provide a robust measure of the status of threatened species in the UK, with 'species identified as conservation priorities' being taken as a proxy for 'threatened species'. Although biodiversity monitoring in the UK may be as good as anywhere else in the world, and a wide range of data and novel analytical approaches have been used, it should be recognised from the outset that any indicator on the status of priority species will be hampered by short comings in the availability of data.

2. Species List

The UK BAP list has been superseded by the biodiversity lists of the four UK countries (Section 41 of the Natural Environmental and Rural Communities (NERC) Act 2006 in England, Section 42 of the NERC Act in Wales, Northern Ireland priority species list in Northern Ireland and the Scottish biodiversity list in Scotland). As a result, there is no single list of species that represents the UK’s species of conservation priority. The criteria for inclusion in each of the four biodiversity lists are derived from those used to identify the UK BAP priority species list, most recently in 2007, but there has been some divergence in approaches, see Table 1. For example, the Scottish biodiversity list and the Northern Ireland priority species list both have criteria based on rarity alone, whereas the UK BAP criteria did not consider rarity; rare species were only listed if they were considered threatened or declining.

For the purposes of this indicator, an inclusive approach has been taken, whereby any species on one or more of the country biodiversity lists has been considered. A species only has to be included in one of the country lists to be included on the combined list. The Scottish Biodiversity list has a final criterion based on the importance of species to people, however, species designated under this criterion were not considered here. The taxonomic composition of the combined four country list is shown in Table 2.

Some countries have included a small number of taxa below the species level (i.e. sub- species) on their biodiversity lists. Such infra- specific taxa were only retained on the combined four country biodiversity list if the associated species was not included. For example, a sub-species of the willow tit (Poecile montanus) is included on the Welsh list but it is a full species on the Scottish Biodiversity list, thus on the combined list only the full species was retained. Table 1: the biodiversity lists of the four countries of the UK

Country Number of Criteria for species inclusion Taxa England (S41) 943 On the 2007 UK BAP list Hen Harrier Country Number of Criteria for species inclusion Taxa Northern Ireland (NI) 481 1: On the 2007 UK BAP list priority species list 2: Rapid decline of >= 2% per year 3: Decline of >=1 % per year and NI holds >= 50% of Irish, or >=20 % of UK population or Irish/UK population restricted to NI 4: Rare in NI (1-2 sites) and NI holds >=50% of Irish, or >=20% of UK population or Irish/UK population restricted to NI 5: >=20 % of a well recognised sub-species in NI 6: Irish Red data book species 7: Red list Birds of Conservation concern Ireland or UK Scottish Biodiversity 2,090 S1:On the 2007 UK BAP list List S2:International obligation S3:Species defined as 'nationally rare' in GB/UK (<15 10km2), which are present in Scotland S4: Species present in <= 5 km2 or sites in Scotland S5: Decline of >= 25% in 25 years in Scotland S6a: Endemic S6b: Endemic subspecies if also meets another criterion Wales (S42) 567 International importance, IUCN Global Red List or Red listed in >=50% of EU countries where data is available or other source indicating international threat or decline International responsibility >=25% of EU/Global population in Wales and decline >=25% in 25 years in Wales Decline in Wales >=50% in 25 years Other for example decline and very restricted range UK (combined four 2,890 country list)

Table 2: Taxonomic breakdown of combined four country biodiversity list

Group Number of Species

Invertebrates insect - beetle (Coleoptera) 191 insect - butterfly 25 insect - dragonfly (Odonata) 4 insect - hymenopteran 103 insect - moth 174 insect - orthopteran 6 Group Number of Species insect - other 4 insect - riverfly 8 insect - true bug (Hemiptera) 15 insect - true fly (Diptera) 94 other Invertebrate 233 Vertebrates Amphibian 4 Bird 127 Fish 57 marine Mammal 22 terrestrial Mammal 26 Reptile 10 Plants and fungi Vascular plants 409 Alga 254 Stonewort 15 Lichen 546 Bryophytes 301 Fungi 262 Grand Total 2890

3. Data Sources

Robust population time series for as many species on the combined four country biodiversity list as possible were sought by the project team. The majority of these data have previously been published and many are currently used as part of the UK biodiversity indicator set. Population time series of two major types were collated, the first measuring changes in relative species abundance and the second measuring changes in relative frequency of species occurrence. The analysis underlying the time series was in general conducted by the data providers; however, the time series of frequency of species occurrence were generated by the Biological Records Centre (BRC), part of the Centre for Ecology and Hydrology. Details of these analyses and the rules for species inclusion into the data sets are given in the following sections.

3.1. Time series in relative abundance

Tables 3 and 4 provide a summary of the relative abundance datasets included in the indicator. They show the analytical methods used to generate the species time series in each dataset. Although these vary in detail, the underlying method is similar. These datasets are generated largely from data collected by national monitoring schemes. In these schemes data are collected in a robust and consistent manner and the geographical coverage is good, with statistical approaches used to correct for biases in coverage. These datasets are ideal for producing population time series for widespread species; however, in some cases the sample size is insufficient to generate time series for rarer or more range restricted species. Each scheme has a set of criteria to determine whether time series can be generated for each species and if they are sufficiently robust to be included in the published results of the scheme. Table 5 gives an overview of the quality of the data derived from each scheme. Further information about each monitoring scheme and the data analysis and results can be found in the references given at the end of this paper.

Bird time series are well documented and several data sources are available (Table 3). Some bird species are represented in more than one dataset. The order of the rows in Table 3 shows the hierarchy used, from top to bottom, to ensure that the most appropriate and robust data for each species was included in the indicator.

The majority of species time series start around 1970 and the date of the last available update is 2011. The Rothamsted moth data starts in 1968, but to avoid over representing these time series in the overall indicator, data were only used from 1970 onwards, and the time series were expressed as a proportion of the 1970 value. Some datasets begin later than 1970, for example the butterfly time series begin in 1976. The method of incorporating this variation in time period into the indicator is discussed in the Indicator method section (4) below. Some datasets do not continue until 2010, for example, the Rothamsted moth dataset has currently only been analysed to 2007 and indices for seven bird species surveyed by periodic national surveys end at various points between 2002 and 2008. For these species where the time series did not continue until 2010, the annual estimate was held at the value of the final data point for all years from the end of the time series to 2010. Thus, the 2007 estimate in each Rothamsted moth time series was used as the estimate for 2008, 2009 and 2010.

The steep decline in many moth species has an effect on the indicator as a whole. The last moth data in the indicator is for 2007, and the final values for moth species are then held constant in the overall index until 2010. The impact of this on the assessment has been considered: if moths are excluded from the indicator the short term decrease between 2005 and 2010 is not significant, and the indicator would be assessed as ‘no change’. Over ten years, from 2000 to 2010, the indicator without the moth data would be slightly positive, but not sufficiently so to be assessed as an increase.

Table 3: Summary of the analysis methods and criteria for species selection for bird datasets

Birds Time period (Sample size) Species selection method Analysis method Time series used in Various (45 – split shown in blue Unsmoothed index Various, depending on the original dataset, current bird indicator - below) all those used are described below C5 Statutory Conservation Various (7, 5) These surveys are designed to be in depth surveys for a Linear interpolation was used Agency and RSPB particular species and so have sufficient data to allow to estimate annual values for Annual Breeding Bird population trends to be robustly estimated. years between national Scheme (SCARRABS) surveys.

Common Bird 1970-2011 (1, 28) Smoothed population time Census/Breeding Bird series were generated by Survey (BBS) joint fitting a smoothed curve to trends; the data directly using a generalised additive model (GAM) (Fewster et al. 2000). Thus the model is: log (count) = site effect + smooth (year) where smooth year) represents a smoothing function of the year effect (BTO 2013a). BBS 1995-2011 (4, 5) Data from the BBS surveys were only included for species Unsmoothed time series are recorded in on average over 40 BBS squares in each year of estimated using a similar the survey period. procedure to the CBC/BBS joint trends described above simply without the smoothing parameter, year is taken as a factor (BTO 2013a).

Rare Breeding Birds Various, largely 1970 - 2010 (29, Species where data were known to be biased were excluded Linear interpolation was used Panel 1) (low quality data: RBBP 2010), as were those where to estimate any missing data. individuals were only infrequently present in the UK (taken as species where the maximum count was 10 or less and the median was 3) Seabird Monitoring 1986-2011 (6) Very small colonies and colonies where counting error is For the majority of species a Panel (SMP) and known, or suspected, to exceed 5% are excluded from SMP combination of SMP and Seabird censuses time series. The accuracy of time series obtained using the census data is used. The SMP sample was assessed by comparing them with data two census estimates are from two complete censuses of all breeding seabirds in the used, with linear interpolation UK. A time series was rejected as inaccurate where a for the intervening years. Birds Time period (Sample size) Species selection method Analysis method discrepancy of more than 15% occurred between the SMP The SMP time series is estimate and the census figure (Thompson et al. 1997). anchored to the 2nd census estimate and used in all subsequent years. For a small number of species the census data alone is used. Wetland Bird Survey 1970-2011 (12) There is a system of observer recorded quality of visit As for BBS time series (WeBS) (visibility, areas missed) within WeBS, which excludes poor quality site visits. Only sites that have a good overall level of coverage are used (at least 50% of possible visits undertaken) (BTO 2013b, Maclean and Ausden 2006).

Table 4: Summary of the analysis methods and criteria for species selection for other taxonomic groups

Group Dataset and provider Species selection method Analysis method

Moths Rothamsted Insect Survey Time series were estimated for Site x year Log-linear Poisson regression models in TRIM (Rothamsted Research) species where >500 individuals (Pannekoek and van Strien 1996) were used. One species had been captured over the was analysed using all 411 sites to ensure model sampling period. Only sites that convergence, otherwise only sites with five years data were operated for a minimum of 48 used to estimate time series. To test for biases due to site weeks a year, with at least one turnover linear change estimates from sites running for >=5 year of data (411 sites ) were years (N=199) were compared with those estimated from used, and all but one species sites running a >= 20 years (N=41) over a 35 year period were analysed using a subset of from 1968-2002. The estimates are significantly correlated sites (214) with at least five years (r = 0.90, df = 336, p < 0.001) (Conrad et al. 2004). data (Conrad et al. 2004, 2006; Fox et al. 2013) Moths Butterfly Conservation (BC) Expert opinion (Mark Parsons – Site x year Log-linear Poisson regression models in TRIM Butterfly Conservation) was used (Pannekoek and van Strien 1996) were used. to judge whether the number of sites monitored was sufficient to represent the national time series, given each species’ distribution. Bats National Bat Monitoring A power analysis determined that As BBS time series. In addition, mixed models are used to Programme (Bat Conservation across all surveys, a sample size investigate factors that could influence time series (e.g. bat Trust) of 30-40 repeat sites (surveyed detector make, temperature). Over dispersion is a problem for more than one year) would for bat detector surveys, where a single bat repeatedly flying give sufficient data to calculate past the observer may give rise to a large count of bat robust species time series. This passes. Based on the results of simulations a binomial would provide 90% power to model of the proportion of observation points on each detect a decline of 25% over 25 survey where the species was observed is used. years (0.1 sig. level). Borderline cases are judged based on the quality of the time series, primarily from the confidence limits (Walsh et al. 2001, Bat Conservation Trust 2013). Dormice National dormouse monitoring As BBS time series. Time series are estimated monthly. scheme (PTES) The data for June are used following advice from PTES.

Butterflies UK Butterfly Monitoring Indices are calculated for butterfly Site x year Log-linear Poisson regression models in TRIM Scheme (BRC) species that have been recorded (Pannekoek and van Strien 1996) are used. For years from five or more sites per year. where a transect site has not been recorded, the model The wider countryside butterfly imputes an estimated site index that allows for the general survey has only three counts conditions of the year in question and how favourable the during summer and requires site is. twice as many monitored sites to achieve comparable precision to the 26-week butterfly monitoring scheme. 430 monitoring sites on average are required to achieve 80% power (5% significance level) for detecting a 25% decline in abundance over 10 years.

Table 5: Assessment of robustness of monitoring schemes – Data quality = Red > Amber > Green

Dataset Effort Survey design Field method

Moths Rothamsted moth survey (1968-) 80 Consistent, Non-random Light trap Wider countryside butterfly survey (2007-) 750 Consistent, Random Transect Butterflies UK butterfly monitoring scheme (1976-) 1000 Consistent, Non- random Transect National Dormouse Survey (1993-) 300 Consistent, Known sites Nest box search Breeding bird survey (1995-) 2400 Consistent, Random Transect Mammals Various, field/ roost National Bat monitoring scheme (1997-) 1300 Consistent, Random counts Birds Breeding bird survey (1995-) 3200 Consistent, Random Transect Common bird census (1970-2000) 300 Consistent, Non-random Territory mapping Seabird monitoring programme, (1986 -) Species Consistent, Non-random or Colony counts seabird censuses (1969 ,85,00) specific Total Wetland bird survey (1970-) 3000 Consistent, Non-random Site counts Species Some variation over time, all Site counts and indi- Rare birds breeding panel (1970-) specific or most known sites vidual records Species SCARRABS (1974-) Consistent, stratified random Various, transects specific 3.2. Time series in Frequency of Occurrence, derived from general geological recording

In addition to the time series showing changes in relative species abundance, similar time series showing changes based on general biological recording, were collated. Biological records are observations of species in a known place in space and time. Most records are made by volunteer recorders and whilst these data may be collected following a specific protocol, the majority of records in these datasets are opportunistic. The intensity of recording varies in both space and time (Isaac 2012, Isaac et al. 2013), which is a challenge for estimating robust quantitative trends. Fortunately, a range of methods now exist for producing such trends using unstructured biological records data (e.g. Szabo et al 2010, Hill 2011). In effect these methods identify long-term changes in species distributions; the term ‘frequency of occurrence’ is however technically more accurate, and is therefore used in this technical document. . The frequency of occurrence data have been generated by the Biological Records Centre (BRC), using novel techniques for analysing biological recording data. As part of this work BRC have been using computer simulations to compare different statistical techniques for reducing the influence of biases (such as spatial or temporal variation in observer effort) in recording datasets (Isaac 2012). Analysis is ongoing and therefore robust time series were only available for a sample of taxonomic groups in time for inclusion in the indicators presented here; it is anticipated more will be available for future updates of this indicator, broadening its taxonomic scope.

The datasets included in the indicator have been generated using the ’well-sampled sites model’ (Roy et al. 2012). The input data for each species is a table of all the site visits between 1970 and 2009 for the taxonomic group in question (table 5), with data on the list length (the number of species recorded) and whether the focal species was recorded (1) or not (0). A site visit is defined as a unique combination of date and 1km2 grid cell. These data were then filtered for data quality, first removing all visits with list lengths shorter than the median for the taxonomic group in question. At the second filtering step, grid cells that had visits in less than three years were excluded. The time series for each species was then estimated from a generalised linear mixed effects model, with year as the covariate and grid cell as a random effect (following Roy et al. 2012). This approach has emerged from the simulation study as the most robust and powerful of the available methods (Isaac et al, manuscript in prep).

The annual index for each species is based on the fitted values from the well-sampled sites model for that species. Technically, these fitted values are the probability that the focal species was recorded on an average visit in the year in question. A key assumption of the well-sampled sites model is that species’ detectability has not changed over time. The relevant recording schemes were therefore consulted, and species excluded from the indicator for which this assumption is unsupportable. For example, the speckled bush cricket (Leptophyes punctatissima) was excluded because a large proportion of records come from using bat-detectors, which have only recently been adopted by the community of Orthopteran recorders. The consultation is ongoing, and there may be small changes in future years’ publication as a result.

The frequency of occurrence time series end in 2009. In order to make a direct comparison with the abundance indicator, the series were extended by one year using the same method as for the abundance time series that ended prior to 2010; the 2009 value was held over to 2010.

The following organisations contributed data for this analysis: BWARS (Bees, Wasps and Ants Recording Society), British Dragonfly Society, Butterfly Conservation, andHoverfly Recording Scheme, and the Orthoptera Recording Scheme. 4. Indicator Methods

The two types of population data, abundance and frequency of occurrence were first investigated separately and then the feasibility of combining these two datasets was investigated. The methods of indicator creation were the same regardless of the dataset under consideration. Table 6 gives a summary of the relationship between the number of species on the combined four country biodiversity list (FCL) and the number of these for which population time series are available.

As far as possible, previously published methods of indicator creation were used, both because these are well-established, are likely to have undergone peer review and allow comparison of this indicator with existing species indicators for birds (C5), butterflies (C6) and bats (C8). These methods are described briefly below and references are given for further information.

Table 6: Summary of species time series included in the Species Indicator

Group Data Type Species with Species on Species on FCL with data and data FCL meeting criteria Birds Abundance 198 126 99 Butterflies 56 26 21 Mammals 13 26 11 Moths 355 174 79 Total (Abundance) 210 Moths Frequency of 743 174 105 (overlap of 72 with Occurrence Abundance dataset) Ants 30 10 2 Bees 198 60 37 Wasps 201 33 23 Hoverflies 209 29 2 Dragonflies 39 4 2 Grasshoppers 31 6 3 Total (Frequency of Occurrence) 174 Total 312 (384 – 72 overlap)

The majority of species time series had values estimated for each year. In the few cases where a species year combination was missing, these values were estimated using log-linear interpolation (Collen et al. 2008). Time series were not extrapolated before the first year or after the last. Where time series ended prior to 2010 they were extended by holding the final data value constant in all subsequent years. Since the indicator is focussed on threatened species some of these species are rare and a few time series contained zero counts for one or more years. This was largely in the Rare Breeding Birds Panel data. As the composite indicator is calculated using the geometric mean it is not possible to include zero values. This issue was addressed by adding 1% of the average value of the time series to the whole series of those species’ time series containing zeros (Loh et al. 2005).

Each time series was expressed as a proportion of the first year of the time series, so that the first year equals one hundred. Extremely large or small index values can have a disproportionate influence on composite indicators. Following the methods used in the current wild bird index (C5); any index value greater than 10000 or less than one was set to these values until the index dropped below 10000 again or above one (Noble et al. 2004). One species had a time series that went above 10000 and eight species had time series that dropped below one, these are specified in the species list in Appendix 1.

The period 1970 to 2010 represents the core period covered by the majority of species time series (Figure 1). Fewer than half of the species time series begin before or extend beyond this period and to include these outlying years would mean that the index values estimated for these years would largely reflect data availability rather than biological change.

Each species in the indicator was weighted equally. When creating a species indicator weighting may be used to try to address biases in a dataset, for example if one taxonomic group is represented by far more species than another, the latter could be given a higher weight so that both taxonomic groups contribute equally to the overall indicator. Complicated weighting can, however, make the meaning and communication of the indicator less transparent. Groups with many species on the FCL could be considered more threatened than others and therefore should contribute more to the overall indicator. Although there was some variation between taxonomic groups in the proportion of species on the list for which data were available, this proportion was substantial for all groups where at least some data were available. The main bias on the data is that some taxonomic groups are not represented at all, which cannot be addressed by weighting. For this reason, and to ensure clarity of communication, equal weighting was used.

Figure 1: Number of species contributing to the headline indicator in each year, 1970 to 2010 To create the composite index for a group (by data type or taxonomy) or overall, the geometric mean was calculated from the species time series data (Figure 2). Different species time series had different start dates. This was taken into consideration with the method currently used for the wild bird index (indicator C5); for species time series entering the indicator after the first year, their first year is set to the geometric mean of those species time series already in the indicator. Confidence intervals for each composite indicator were created using bootstrapping (Buckland 2005, Freeman et al. 2001); in each iteration (n = 100) a random sample of species were selected with replication and the geometric mean calculated.

4.1 Headline Indicator - C4ai

The headline indicator (C4ai) was generated by combining 210 time series charting changes in relative species abundance using the methods described in the preceding section. In addition, bars showing the proportion of species showing increases or decreases (of any magnitude, and with no consideration of statistical significance) have been provided. These cover two time periods – ‘long- term’, from 1970 to 2010, and ‘short-term’, from 2005 to 2010.

Figure 2: Change in the relative abundance of priority species in the UK, 1970 to 2010

120 United Kingdom 100 90 100 80

95% Confidence interval max s ) e i

0 70 80 c 0 e 1 p

7 60 e 50 g 9 a 1 t (

n 40 x e e c r d e

n 40 30 I 95% Confidence interval min P 20 20 10 0 0 Long term Short term 1970 1975 1980 1985 1990 1995 2000 2005 2010 Decline Increase

Note: Based on 210 species.

4.1.2 Assessment of change – headline indicator

The assessment is based on a test of statistical significance by comparing the change and 95 per cent confidence intervals between first and last date of the long and short term changes respectively. The overall indicator shows a consistent downward trajectory over its 40 years duration. The final value of the indicator in 2010 is 42 per cent (95% confidence intervals (CI): 30, 50) suggesting that on average those priority species represented in the indicator have declined by more than a half since 1970. To calculate the short-term trend, a change statistic for the 2005 to 2010 period was calculated and the data re-sampled to provide confidence intervals on that change statistic (not shown in Figure 2), and the proportional change for each species over the most recent five and ten years was calculated. The geometric mean of the species level change was calculated and 95% confidence intervals were estimated using bootstrapping. In 2010 the relative abundance of the 210 species included in the indicator had declined by seven per cent relative to their 2005 levels (95% CI: 87,99). The equivalent change between 2000 and 2010 was a five per cent decline (95% CI: 87, 104).

If the confidence interval is entirely below 100 the time series would be assessed as decreasing, if it was entirely above 100 the indicator would be assessed as increasing, if the confidence interval spanned 100 the indicator would be assessed as no significant change. Therefore, both the long- term change (1970 to 2010) and the short-term change (2005 to 2010) were assessed as decreases. There was no change in the indicator between 2000 and 2010.

The steep decline in many moth species has an effect on the indicator as a whole. The last moth data in the indicator is for 2007, and the final values for moth species are then held constant in the overall index until 2010. The impact of this on the assessment has been considered: if moths are excluded from the indicator the short term decrease between 2005 and 2010 is not significant, and the indicator would be assessed as ‘no change’. Over ten years, from 2000 to 2010, the indicator without the moth data would be slightly positive, but not sufficiently so to be assessed as an increase.

This assessment of change over time is currently based upon unsmoothed annual estimates of relative abundance. This means that the percentage change over time can vary substantially depending on the time period assessed. One way to reduce this variation is to make the assessment based on smoothed time series based on generalised additive models (Freeman et al. 2001). These dampen the inter-annual variation in the time series and thus aid the interpretation of important patterns of change. At present this type of information is not available for all the species in the indicator presented here, however, it is hoped it will be possible to work towards generating these data and using this methodology in future iterations of the indicator. 4.2 Change in frequency of occurrence time series

An equivalent indicator was created using the 174 time series of changes in frequency of species occurrence (Figure 3).

Figure 3: Change in priority species (frequency of occurrence time series), with 95 per cent confidence intervals, 1970 to 2010

120 United Kingdom 100 90 100 80

95% Confidence interval max s ) e

i 70 0

80 c 0 e 1

s 60 =

7 60 e 50 9 g 1 a ( t

x 40 e e c d r e n 40

I 30 95% Confidence interval min P 20 20 10 0 0 Long term 1970 1975 1980 1985 1990 1995 2000 2005 2010 Decline Increase

Note: Based on 174 species.

This measure also shows a downward trajectory over its 40 years duration. The final value of the indicator in 2010 is 49 per cent (95% CI 39, 61) suggesting that on average the frequency of occurrence of those priority species represented in the indicator has declined by around a half since 1970. At present these time series are estimated assuming a monotonic rate of change over the full 40 year period, which means it is not possible to disentangle short and long-term change.

4.3 Comparing and combining the two data types

Both types of data give a similar trajectory of change (Figure 4). Depending on the life history, frequency and distribution of a species either type of data may be the most appropriate way to measure changing species status and time series, as each data type for a single species may tell a different story. If these two types of data were combined it would allow measures of change in status for as many species, over as broad a spectrum of taxonomic groups as possible. It is the direction of time series that is most important here, rather than the precise slope of the indicator. Pooling the two types of data would allow provision of a clear, simple measure of change, which is vital as the prime purpose of biodiversity indicators is as communication tools. If the indicator is declining the status of threatened species is, on average, continuing to degrade, and if the indicator is stable or increasing species status is, on average, stabilising or improving. An indicator based on both the time series of relative abundance and those of frequency of species occurrence (312 species) is shown in Figure 5. The only group for which there are time series in both relative abundance and frequency of species occurrence is the moths; 72 species have time series in both data types. Where both abundance and frequency of occurrence data are available abundance trends were used in preference to frequency of occurrence trends.

Figure 4: Change in priority species – comparing Abundance and Frequency of Occurrence data, 1970 to 2010

Note: the number of species included in each line is shown in brackets. Figure 5: Change in priority species - time series with both data types combined, 1970 to 2010.

Note: the number of species included is shown in brackets.

4.4 Treatment of species with time series in both abundance and frequency of occurrence

For the species where a time series in abundance and a time series in frequency of occurrence were both available the abundance date were used in the composite indicator. In order to investigate the influence of this decision on the indicator two further indices for this group of species were estimated: first, a measure based on the abundance time series for each species and second, a measure using the frequency of occurrence time series for each species. Figure 6 shows a strong correlation between the annual index values of these two indicators, showing they exhibit a similar pattern of change. The frequency of occurrence index is somewhat higher over the whole time period assessed than the abundance index. This is similar to the pattern shown in Figure 4 comparing the overall time series for the two data types. Figure 7 shows a comparison between the abundance indicator (Figure 2) and the same indicator but giving precedence to the frequency of occurrence data for those species where both data types were available. There is a strong correlation between the two options, with the latter option exhibiting a shallower decline over time. Figure 6: Comparison between the annual index values for measures derived from abundance time series and frequency of occurrence time series for those moth species where both are available.

Figure 7: Indicator giving precedence to either abundance or frequency of occurrence time series where duplication occurs, 1970 to 2010. 4.5 Change in priority species by taxonomic group

The headline indicator (Figure 2) masks variation within and between taxonomic groups. Figures 8 and 9 show indicators for each taxonomic group separately. These were generated using the same methods as the overall indicator.

Figure 8: Change in relative species abundance, by taxonomic group, 1970 to 2010 160 United Kingdom )

0 140 0 1 Mammals (11) =

9 120 9 1 (

s l ) a 0 100 0 m 1 Birds (99) m = a

) 80 9 0

1 Butterflies (21) ( 0

6 60 n I 7 9 1 (

e 40 i l f r

e Moths (79) t t u 20 B

0 1970 1975 1980 1985 1990 1995 2000 2005 2010

Note: the number of species included in each line is shown in brackets.

This index for birds has remained roughly stable since the 1970s. There are several possible explanations for this. Birds have benefited from more investment in their conservation than other groups and, as a result, some species are increasing due to this. This includes some species increasing rapidly from small numbers, like the marsh harrier (Circus aeruginosus) and the red kite (Milvus milvus) as well as species that have benefited from changes in legislation, like geese which are now protected from hunting. Additionally, the definition of priority species, as provided by the four Country lists, includes all species for which there is specific international obligation for conservation action (owing to the use of this as a criterion for the Scottish priority list). This has resulted in a large number of waterbird species within the indicator, many of which occur in the UK as wintering populations and which have shown substantial increases since the 1970s. The overall stable time series for birds masks some species which are still rapidly declining; for example, turtle dove (Streptopelia turtur) has declined by 97% over the time period assessed.

Mammals are mainly represented here by bats and the composite mammal time series closely resembles indicator C8 (mammals of the wider countryside (bats)). The steep initial increase in the index is driven by an increase in mountain hare (Lepus timidus) and Natterer’s bat (Myotis nattereri). Although the species composition is somewhat different, the butterfly composite index resembles the Habitat Specialist line of indicator C6i (butterflies of the wider countryside), with the steep initial drop in the indicator thought to be attributable to a severe drought in 1976.

Similar variation is seen between composite time series for taxonomic groups with frequency of occurrence data (Figure 9). The majority of groups show on average a decline over the period assessed. Bees, wasps and ants have been subject to fewer declines on average, however, within this group bee species are on average declining whereas wasps are on average increasing.

Figure 9: Change by taxonomic group, frequency of occurrence datasets, 1970 to 2010 120 United Kingdom

100 Bees, wasps and ants (62) )

0 80 0 1

7 60 9

1 Moths (105) (

x e d n

20 Other Insects (7)

0 1970 1975 1980 1985 1990 1995 2000 2005 2010

Note: the number of species included in each line is shown in brackets.

5 Future developments

The indicator of relative abundance will be refined in future as further data become available. The basis of assessment, particularly for the short-term change, may also be refined, for example, by investigating statistical smoothing to detect underlying trends and reduce the impact of particularly high or low values at the start or end of a time series. It is anticipated that the general approach of the indicator developed for reporting in 2013 will be retained into the future, however, it is likely that the work will be refined and expanded. Work is currently being undertaken to derive trends from distribution data (general biological recording) for more species using data collected by national recording schemes. Time series should become available for a larger suite of taxonomic groups and therefore more species should be included in the indicator in future years, improving representativity. In addition, analysis will be undertaken to seek to understand the impact of different priority-listing criteria in the UK’s four nations. At present the occurrence time series assume monotonic change over the 40 year time period, in the future it is hoped to be able to assess change over shorter time periods, perhaps by decades.

As the sample size increases it should become possible to investigate other ways to break down the indicator; possibilities include, by countries, upland/lowland, ecosystems/habitats, traits/life history strategies and or by trophic level.

Regardless of advances in statistical techniques there are species on the priority species lists for which monitoring data are sparse. This is for a variety of reasons, including species rarity, difficulty of detection, or those species for which monitoring methods are unreliable or unavailable. In order for the indicator to be representative of all types of species on the biodiversity lists, a method of assessing the changing status of a sample of these remaining data-poor species will need to be considered. References

Bat Conservation Trust (2013) http://www.bats.org.uk/pages/detecting_population_change.html. British Trust for Ornithology (2013a) http://www.bto.org/about- birds/birdtrends/2012/methods/statistical-methods-alerts. British Trust for Ornithology (2013b) http://www.bto.org/sites/default/files/u18/downloads/publications/wituk_methods_0910.pdf. Buckland, S.T., Magurran, A.E., Green, R.E. and Fewster, R.M. (2005) Monitoring change in biodiversity through composite indices. Philosophical Transactions of the Royal Society of London. Series B, 360, 243–254. Collen, B., Loh, J., Whitmee, S., McRae, L., Amin, R. and Baillie, J. (2008) Monitoring Change in Vertebrate Abundance: the Living Planet Index. Conservation Biology, 23, 317-327. Conrad, K.F.,Woiwod, I.P., Parsons, M., Fox, R., Warren, M.S. (2004) Long-term population trends in widespread British moths, Journal of Insect Conservation, 8: 119–136. Conrad, K.F., Warren, M.S., Fox, R., Parsons, M.S., Woiwod, I.P. (2006) Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis, Biological Conservation, 132: 279-291. Freeman, S.N., Baillie, S.R. and Gregory, R.D. (2001) Statistical analysis of an indicator of population trends in farmland birds, BTO Research Report no. 251, Thetford. http://www.bto.org/sites/default/files/u196/downloads/rr251.pdf. Fewster, R.M., Buckland, S.T., Siriwardena, G.M., Baillie, S.R. and Wilson, J.D (2000). Analysis of population trends for farmland birds using generalized additive models. Ecology 81: 1970-1984 Fox, R., Parsons, M.S., Chapman, J.W., Woiwod, I.P., Warren, M.S. and Brooks, D.R. (2013) The State of Britain’s Larger Moths 2013. Butterfly Conservation and Rothamsted Research, Wareham, Dorset, UK. Hill, M.H., (2011) Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods in Ecology and Evolution, 3 (1), 195-205. Isaac, N. J., B. (2012) Extracting trends from biological recording data. National Biodiversity Network Conference. London. doi:10.6084/m9.figshare.428369. Isaac, N. J. B., August, T. A., Harrower, C. and Roy, D.B. (2013) Trends in the Distribution of UK native species 1970-2010. Preliminary report to JNCC. JNCC Report No 488. http://jncc.defra.gov.uk/pdf/488_Web.pdf. Loh, J., Green, R. E., Ricketts, T., Lamoreux, J., Jenkins, M., Kapos, V. and Randers, J. (2005) The Living Planet Index: using species population time series to track trends in biodiversity. Philisophical Transactions of the Royal Society B 360, 289-295.) Maclean, I.M.D. and Austin, G.E. 2006. Wetland Bird Survey Alerts 2004/05: Changes in numbers of wintering waterbirds in the Constituent Countries of the United Kingdom, Special Protection Areas (SPAs) and Sites of Special Scientific Interest (SSSIs). BTO Research Report 458, British Trust for Ornithology, Thetford. Noble, D. G., Newson, S. E. and Gregory, R. D. (2004) Approaches to dealing with disappearing and invasive species in the UK’s indicators of wild bird populations. A report by the BTO and RSPB under contract to Defra (Wild Bird Indicators). Pannekoek,. J., and van Strien, A. J. ( 1996) TRIM – trends and indices for monitoring data. Research paper no. 9634. Statistics Netherlands. Roy, H. E., Adriaens, T., Isaac, N. J. B., Kenis, M., Martin, G. S., Brown, P. M. J. and Hautier, L., et al. (2012) Invasive alien predator causes rapid declines of native European ladybirds. Diversity and Distributions, 18, 717-725. Szabo, J.K., Vesk, P.A., Baxter, P.W.J., and Possingham, H.P (2010) Regional avian species declines estimated from volunteer-collected long-term data using List Length Analysis. Ecological Applications, 20, 2157–2169. Thompson, KR, Brindley, E and Heubeck, M (1997) Seabird numbers and breeding success in Britain and Ireland, 1996. JNCC, Peterborough, (UK Nature Conservation No. 21). van Strien et al. (1997) The statistical power of two butterfly monitoring schemes, JAE, 34: 817- Walsh, A., et al. (2001) The UK's National Bat Monitoring Programme - Final Report 2001, the Bat Conservation Trust, London. http://www.bats.org.uk/pages/nbmp_reports.html. Musgrove, A. J., Austin, G.E., Hearn, R.D., Holt, C.A., Stroud, D.A., Wotton, S.R, (2011), Overwinter population estimates of British waterbirds, British Birds, 104: 364–397. Appendix 1 – Species List

NI priority England Scotland biodiversity Capped Halted Scientific_Name Common_Name Group Datatype species S41 list at.10000 at 1 list Accipiter gentilis Northern goshawk birds abundance N N Y NA NA Acrocephalus palustris marsh warbler birds abundance Y N N NA NA Acrocephalus scirpaceus reed warbler birds abundance N Y N NA NA Alauda arvensis skylark birds abundance Y Y Y NA NA Alcedo atthis kingfisher birds abundance N Y N NA NA Anas acuta pintail birds abundance N N Y NA NA Anas clypeata shoveler birds abundance N N Y NA NA Anas querquedula garganey birds abundance N Y Y NA NA Anser albifrons subsp. European white- albifrons fronted goose birds abundance Y Y N NA NA Anser albifrons subsp. Greenland white- flavirostris fronted goose birds abundance N Y Y NA NA Anthus trivialis free pipit birds abundance Y Y Y NA NA Apus apus swift birds abundance N Y Y NA NA Aquila chrysaetos golden eagle birds abundance N Y Y NA NA Aythya ferina pochard birds abundance N Y Y NA NA Aythya fuligula tufted duck birds abundance N N Y NA NA Botaurus stellaris great bittern birds abundance Y Y Y NA NA Branta bernicla subsp. dark-bellied brent bernicla goose birds abundance Y N N NA NA Branta bernicla subsp. Nearctic light-bel- Hrota lied brent goose birds abundance N N Y NA NA Svalbard barnacle Branta leucopsis goose birds abundance N Y N NA NA Bucephala clangula goldeneye birds abundance N N Y NA NA Burhinus oedicnemus stone-curlew birds abundance Y N N NA NA Calidris alpina dunlin birds abundance N Y Y NA NA Calidris canutus knot birds abundance N N Y NA NA Caprimulgus europaeus nightjar birds abundance Y Y Y NA NA Carduelis cabaret lesser redpoll birds abundance Y Y Y NA NA Carduelis cannabina linnet birds abundance Y Y Y NA NA Carduelis spinus siskin birds abundance N Y N NA NA Charadrius hiaticula ringed plover birds abundance N N N NA NA Circus aeruginosus marsh harrier birds abundance N Y N Y NA Circus cyaneus hen harrier birds abundance Y Y Y NA NA Corvus cornix hooded crow birds abundance N Y N NA NA Coturnix coturnix quail birds abundance N N Y NA NA Crex crex corncrake birds abundance Y Y Y NA NA Cuculus canorus cuckoo birds abundance Y Y Y NA NA Cygnus columbianus Bewick's swan birds abundance Y Y Y NA NA Cygnus cygnus whooper swan birds abundance N Y Y NA NA Dendrocopos minor lesser spotted subsp. comminutus woodpecker birds abundance Y N N NA NA Emberiza calandra corn bunting birds abundance Y Y N NA NA Emberiza cirlus cirl bunting birds abundance Y N N NA NA Emberiza citrinella yellowhammer birds abundance Y Y Y NA NA Emberiza schoeniclus reed bunting birds abundance Y Y Y NA NA Falco columbarius merlin birds abundance N Y N NA NA Falco peregrinus peregrine falcon birds abundance N Y N NA NA NI priority England Scotland biodiversity Capped Halted Scientific_Name Common_Name Group Datatype species S41 list at.10000 at 1 list Falco subbuteo hobby birds abundance N Y N NA NA Falco tinnunculus kestrel birds abundance N Y N NA NA Ficedula hypoleuca pied flycatcher birds abundance N N N NA NA Gavia arctica black-throated diver birds abundance N Y Y NA NA Gavia stellata red-throated Diver birds abundance N Y N NA NA Haliaeetus albicilla white-tailed eagle birds abundance N Y Y NA NA Jynx torquilla wryneck birds abundance N Y N NA NA Lagopus lagopus red grouse birds abundance Y Y Y NA NA Lanius collurio red-backed shrike birds abundance N Y N NA Y Larus argentatus herring gull birds abundance Y Y Y NA NA Limosa lapponica bar-tailed godwit birds abundance N Y N NA NA Limosa limosa black-tailed godwit birds abundance Y Y Y NA NA Locustella luscinioides Savi's warbler birds abundance Y N N NA NA grasshopper Locustella naevia Warbler birds abundance Y Y Y NA NA Lullula arborea woodlark birds abundance Y N N NA NA Melanitta nigra common scoter birds abundance Y Y Y NA NA Milvus milvus red kite birds abundance N Y N NA NA Motacilla flava yellow wagtail birds abundance Y Y Y NA NA Muscicapa striata spotted flycatcher birds abundance Y Y Y NA NA Numenius arquata curlew birds abundance Y Y Y NA NA Pandion haliaetus osprey birds abundance N Y N NA NA Panurus biarmicus bearded tit birds abundance N Y N NA NA Passer domesticus house sparrow birds abundance Y Y Y NA NA Passer montanus tree sparrow birds abundance Y Y Y NA NA Perdix perdix grey partridge birds abundance Y Y N NA NA Pernis apivorus honey buzzard birds abundance N Y N NA NA red-necked Phalaropus lobatus phalarope birds abundance N Y Y NA NA Philomachus pugnax ruff birds abundance N Y N NA NA Phylloscopus sibilatrix wood warbler birds abundance Y Y Y NA NA Pluvialis apricaria golden plover birds abundance N Y Y NA NA Podiceps auritus slavonian grebe birds abundance N Y N NA NA Podiceps nigricollis black-necked grebe birds abundance N Y Y NA NA Poecile montanus willow tit birds abundance Y Y N NA NA Poecile palustris subsp. palustris/dresseri marsh tit birds abundance Y Y N NA NA Porzana porzana spotted crake birds abundance N Y N NA NA Prunella modularis dunnock birds abundance Y Y Y NA NA Pyrrhocorax pyrrhocorax chough birds abundance N Y Y NA NA Pyrrhula pyrrhula bullfinch birds abundance Y Y Y NA NA Scolopax rusticola woodcock birds abundance N Y N NA NA Stercorarius parasiticus Arctic skua birds abundance N Y Y NA NA Sterna dougallii roseate tern birds abundance Y Y Y NA NA Sterna hirundo common tern birds abundance N Y N NA NA Sterna paradisaea Arctic tern birds abundance N Y N NA NA Sterna sandvicensis sandwich tern birds abundance N Y N NA NA Sternula albifrons little tern birds abundance N Y Y NA NA Streptopelia turtur turtle dove birds abundance Y Y Y NA NA Sturnus vulgaris starling birds abundance Y Y Y NA NA NI priority England Scotland biodiversity Capped Halted Scientific_Name Common_Name Group Datatype species S41 list at.10000 at 1 list Tetrao tetrix black grouse birds abundance Y Y N NA NA Tetrao urogallus capercaillie birds abundance N Y N NA NA Tringa glareola wood sandpiper birds abundance N Y N NA NA Tringa totanus redshank birds abundance N N Y NA NA Troglodytes troglodytes subsp. fridariensis Fair Isle wren birds abundance N Y N NA NA Turdus iliacus redwing birds abundance N Y Y NA NA Turdus philomelos song thrush birds abundance Y Y Y NA NA Turdus pilaris fieldfare birds abundance N N Y NA Y Vanellus vanellus lapwing birds abundance Y Y Y NA NA Argynnis adippe high brown fritillary butterfly abundance Y N N NA NA Northern brown ar- Aricia artaxerxes gus butterfly abundance Y Y N NA NA pearl-bordered fritil- Boloria euphrosyne lary butterfly abundance Y Y N NA NA small pearl- Boloria selene bordered fritillary butterfly abundance Y Y N NA NA Coenonympha pamphilus small heath butterfly abundance Y Y Y NA NA Coenonympha tullia large heath butterfly abundance Y Y Y NA NA Cupido minimus small blue butterfly abundance Y Y Y NA NA Erynnis tages dingy skipper butterfly abundance Y Y Y NA NA Euphydryas aurinia marsh fritillary butterfly abundance Y N Y NA NA Duke of Burgundy Hamearis lucina fritillary butterfly abundance Y N N NA NA Hipparchia semele grayling butterfly abundance Y Y Y NA NA Lasiommata megera wall brown butterfly abundance Y Y Y NA NA Leptidea sinapis wood white butterfly abundance Y N N NA NA Limenitis camilla white admiral butterfly abundance Y N N NA NA Maculinea arion large blue butterfly abundance Y N N NA NA Melitaea athalia heath fritillary butterfly abundance Y N N NA NA Plebejus argus silver-studded blue butterfly abundance Y N N NA NA Pyrgus malvae grizzled skipper butterfly abundance Y N N NA NA white-letter hair- Satyrium w-album streak butterfly abundance Y N N NA NA Thecla betulae brown hairstreak butterfly abundance Y N N NA NA Thymelicus acteon Lulworth skipper butterfly abundance Y N N NA NA red banded sand frequency of Ammophila sabulosa wasp bwa occurrence N Y N NA NA frequency of Ancistrocerus parietum wall mason wasp bwa occurrence N Y N NA NA frequency of Andrena cineraria grey mining bee bwa occurrence N Y N NA NA frequency of Andrena coitana a bee bwa occurrence N N Y NA NA frequency of Andrena denticulata a bee bwa occurrence N N Y NA NA frequency of Andrena fuscipes a bee bwa occurrence N N Y NA NA frequency of Andrena helvola a mining bee bwa occurrence N Y N NA NA frequency of Andrena marginata a mining bee bwa occurrence N Y N NA NA frequency of Andrena nigroaenea a bee bwa occurrence N N Y NA NA frequency of Andrena praecox a bee bwa occurrence N N Y NA NA frequency of Andrena ruficrus a mining bee bwa occurrence N Y N NA NA a spider-hunting frequency of Anoplius concinnus wasp bwa occurrence N Y N NA NA NI priority England Scotland biodiversity Capped Halted Scientific_Name Common_Name Group Datatype species S41 list at.10000 at 1 list frequency of Anthidium manicatum wool-carder bee bwa occurrence N Y N NA NA fork tailed flower frequency of Anthophora furcata bee bwa occurrence N Y N NA NA brown-banded frequency of Bombus humilis carder-bee bwa occurrence Y N N NA NA frequency of Bombus muscorum moss carder bee bwa occurrence Y Y Y NA NA red-shanked frequency of Bombus ruderarius carder-bee bwa occurrence Y Y N NA NA frequency of Cerceris quadricincta a wasp bwa occurrence Y N N NA NA Cerceris quinquefasci- frequency of ata a wasp bwa occurrence Y N N NA NA a spider-hunting frequency of Ceropales maculata wasp bwa occurrence N Y N NA NA frequency of Colletes daviesanus a bee bwa occurrence N Y N NA NA frequency of Colletes fodiens a bee bwa occurrence N Y N NA NA frequency of Crabro peltarius a solitary wasp bwa occurrence N Y N NA NA Crossocerus megaceph- frequency of alus a digger wasp bwa occurrence N Y N NA NA Crossocerus quad- 4-spotted digger frequency of rimaculatus wasp bwa occurrence N Y N NA NA melancholy black frequency of Diodontus tristis wasp bwa occurrence N Y N NA NA a spider-hunting frequency of Dipogon subintermedius wasp bwa occurrence N Y N NA NA a spider-hunting frequency of Dipogon variegatus wasp bwa occurrence N Y N NA NA frequency of Ectemnius cephalotes a digger wasp bwa occurrence N Y N NA NA frequency of Ectemnius continuus a digger wasp bwa occurrence N Y N NA NA frequency of Epeolus variegatus a cuckoo bee bwa occurrence N Y N NA NA frequency of Eucera longicornis long-horned bee bwa occurrence Y N N NA NA a spider-hunting frequency of Evagetes crassicornis wasp bwa occurrence N Y N NA NA frequency of Formica aquilonia Scottish wood ant bwa occurrence N N Y NA NA frequency of Formica fusca negro ant bwa occurrence N Y N NA NA frequency of Hedychridium ardens a ruby-tailed wasp bwa occurrence N Y N NA NA frequency of Hylaeus brevicornis a bee bwa occurrence N Y Y NA NA frequency of Hylaeus hyalinatus a bee bwa occurrence N N Y NA NA Lasioglossum angusti- frequency of ceps a bee bwa occurrence Y N N NA Y frequency of Lasioglossum fulvicorne a mining bee bwa occurrence N Y N NA NA Lasioglossum nitidiuscu- frequency of lum a bee bwa occurrence N N Y NA NA frequency of Lasioglossum rufitarse a solitary bee bwa occurrence N N Y NA NA Lasioglossum smeath- frequency of manellum a mining bee bwa occurrence N Y N NA NA frequency of Lasioglossum villosulum shaggy mining bee bwa occurrence N Y N NA NA frequency of Lindenius albilabris a digger wasp bwa occurrence N Y N NA NA frequency of Mutilla europaea large velvet ant bwa occurrence N Y N NA NA frequency of Nomada errans a bee bwa occurrence Y N N NA Y Fabricius' nomad frequency of Nomada fabriciana bee bwa occurrence N Y N NA NA NI priority England Scotland biodiversity Capped Halted Scientific_Name Common_Name Group Datatype species S41 list at.10000 at 1 list frequency of Nomada goodeniana a bee bwa occurrence N N Y NA NA frequency of Nomada leucophthalma a nomad bee bwa occurrence N Y N NA NA frequency of Nomada obtusifrons a nomad bee bwa occurrence N Y N NA NA frequency of Nomada striata a bee bwa occurrence N N Y NA NA Odynerus melanoceph- frequency of alus a mason-wasp bwa occurrence Y N N NA NA gold-fringed mason frequency of Osmia aurulenta bee bwa occurrence N Y N NA NA common spiny dig- frequency of Oxybelus uniglumis ger wasp bwa occurrence N Y N NA NA frequency of Pompilus cinereus leaden spider wasp bwa occurrence N Y N NA NA a spider-hunting frequency of Priocnemis schioedtei wasp bwa occurrence N Y N NA NA frequency of Sphecodes ferruginatus a bee bwa occurrence N N Y NA NA frequency of Sphecodes gibbus a bee bwa occurrence N Y Y NA NA frequency of Sphecodes pellucidus a solitary bee bwa occurrence N N Y NA NA frequency of Stelis punctulatissima a cuckoo bee bwa occurrence N Y N NA NA Tachysphex pompili- a spider-hunting frequency of formis wasp bwa occurrence N Y N NA NA Lepus europaeus brown hare mammals abundance Y Y N NA NA Lepus timidus mountain hare mammals abundance Y Y Y NA NA Muscardinus avellanari- us dormouse mammals abundance Y N N NA NA Myotis daubentonii Daubenton's bat mammals abundance N Y N NA NA Myotis nattereri Natterer's bat mammals abundance N Y N NA NA Nyctalus noctula noctule mammals abundance Y Y N NA NA Pipistrellus pipistrellus pipistrelle bat mammals abundance N Y N NA NA Pipistrellus pygmaeus soprano pipistrelle mammals abundance Y Y Y NA NA brown long-eared Plecotus auritus bat mammals abundance Y Y Y NA NA Rhinolophus fer- greater horseshoe rumequinum bat mammals abundance Y N N NA NA Rhinolophus hipposider- lesser horseshoe os bat mammals abundance Y N N NA NA basil thyme case- Coleophora tricolor bearer moths abundance Y N N NA NA Eustroma reticulatum netted carpet moths abundance Y N N NA NA Hydraecia osseola subsp. hucherardi marsh mallow moths abundance Y N N NA NA Idaea ochrata subsp. cantiata bright wave moths abundance Y N N NA NA Pyropteron chrysidi- formis fiery clearwing moths abundance Y N N NA NA Siona lineata black-veined moth moths abundance Y N N NA NA Thalera fimbrialis sussex emerald moths abundance Y N N NA NA Acronicta psi grey dagger moths both Y Y Y NA NA Acronicta rumicis knot grass moths both Y Y Y NA NA Agrochola helvola flounced chestnut moths both Y Y Y NA NA Agrochola litura brown-spot pinion moths both Y Y Y NA NA Agrochola lychnidis beaded chestnut moths both Y Y Y NA NA green-brindled Allophyes oxyacanthae crescent moths both Y Y Y NA NA Amphipoea oculea ear moth moths both Y Y Y NA NA Amphipyra tragopoginis mouse moth moths both Y Y Y NA NA Apamea anceps large nutmeg moths both Y Y N NA NA NI priority England Scotland biodiversity Capped Halted Scientific_Name Common_Name Group Datatype species S41 list at.10000 at 1 list Apamea remissa dusky brocade moths both Y Y Y NA NA Arctia caja garden tiger moths both Y Y Y NA NA Asteroscopus sphinx the sprawler moths both Y N Y NA NA centre-barred sal- Atethmia centrago low moths both Y Y Y NA NA Blepharita adusta dark brocade moths both Y Y Y NA NA Brachylomia viminalis minor shoulder-knot moths both Y Y Y NA NA Caradrina morpheus mottled rustic moths both Y Y Y NA NA Celaena haworthii Haworth's minor moths both Y Y Y NA NA Celaena leucostigma the crescent moths both Y Y Y NA NA Chesias legatella the streak moths both Y Y Y NA NA Chesias rufata broom-tip moths both Y Y N NA NA Chiasmia clathrata latticed heath moths both Y Y Y NA NA Cymatophorima diluta oak lutestring moths both Y Y N NA NA Dasypolia templi brindled ochre moths both Y Y Y NA NA Diarsia rubi small square-spot moths both Y Y Y NA NA Diloba caeruleocephala figure of eight moths both Y Y Y NA NA Ecliptopera silaceata small phoenix moths both Y Y Y NA NA Ennomos erosaria September thorn moths both Y Y N NA NA Ennomos fuscantaria dusky thorn moths both Y N N NA NA Ennomos quercinaria August thorn moths both Y Y Y NA NA grey mountain car- Entephria caesiata pet moths both Y Y Y NA NA Epirrhoe galiata galium carpet moths both Y Y Y NA NA Eugnorisma glareosa autumnal rustic moths both Y Y Y NA NA Eulithis mellinata the spinach moths both Y Y N NA NA Euxoa nigricans garden dart moths both Y Y Y NA NA Euxoa tritici white-line dart moths both Y Y N NA NA Graphiphora augur double dart moths both Y Y Y NA NA Hemistola chryso- prasaria small emerald moths both Y Y N NA NA Hepialus humuli ghost swift moths both Y Y Y NA NA Hoplodrina blanda the rustic moths both Y Y Y NA NA Hydraecia micacea rosy rustic moths both Y Y Y NA NA Idaea dilutaria silky wave moths both Y N N NA NA Lycia hirtaria brindled beauty moths both Y Y Y NA NA Macaria wauaria the v-moth moths both Y Y N NA NA Malacosoma neustria the lackey moths both Y Y N NA NA Melanchra persicariae dot moth moths both Y Y Y NA NA Melanchra pisi broom moth moths both Y Y Y NA NA Melanthia procellata pretty chalk carpet moths both Y N N NA NA Mesoligia literosa rosy minor moths both Y Y Y NA NA shoulder-striped Mythimna comma wainscot moths both Y Y Y NA NA Orthonama vittata oblique carpet moths both Y Y Y NA NA Orthosia gracilis powdered quaker moths both Y Y Y NA NA Pelurga comitata dark spinach moths both Y Y Y NA NA Perizoma albulata grass rivulet moths both Y Y Y NA Y Rhizedra lutosa large wainscot moths both Y Y N NA NA Scopula margine- punctata mullein wave moths both Y Y Y NA NA NI priority England Scotland biodiversity Capped Halted Scientific_Name Common_Name Group Datatype species S41 list at.10000 at 1 list Scotopteryx chenopodi- ata shaded broad-bar moths both Y Y Y NA NA Spilosoma lubricipeda white ermine moths both Y Y Y NA NA Spilosoma luteum buff ermine moths both Y Y Y NA NA Stilbia anomala the anomalous moths both Y Y Y NA NA Tholera cespitis hedge rustic moths both Y Y Y NA NA Tholera decimalis feathered gothic moths both Y Y Y NA NA round-winged Thumatha senex muslin moths both N Y N NA NA Timandra comae blood-vein moths both Y Y N NA NA Trichiura crataegi pale eggar moths both Y Y Y NA NA Tyria jacobaeae cinnabar moths both Y Y Y NA NA Watsonalla binaria oak hook-tip moths both Y Y N NA NA Xanthia gilvago dusky-lemon sallow moths both Y Y N NA NA Xanthia icteritia the sallow moths both Y Y Y NA NA Xanthorhoe decoloraria red carpet moths both Y Y Y NA NA dark-barred twin- Xanthorhoe ferrugata spot moths both Y Y Y NA NA Xestia agathina heath rustic moths both Y Y Y NA NA neglected or grey Xestia castanea rustic moths both Y Y Y NA NA frequency of Adscita statices forester moths occurrence Y Y Y NA NA frequency of Aleucis distinctata sloe carpet moths occurrence Y N N NA NA small dark yellow frequency of Anarta cordigera underwing moths occurrence N Y N NA Y light crimson under- frequency of Catocala promissa wing moths occurrence Y N N NA NA frequency of Chortodes brevilinea Fenn's wainscot moths occurrence Y N N NA NA frequency of Chortodes extrema concolorous moths occurrence Y N N NA NA frequency of Cosmia diffinis white-spotted pinion moths occurrence Y N N NA NA frequency of Cossus cossus goat moth moths occurrence Y Y N NA NA frequency of Cyclophora pendularia dingy mocha moths occurrence Y N N NA NA frequency of Cyclophora porata false mocha moths occurrence Y N N NA NA frequency of Dicycla oo heart moth moths occurrence Y N N NA NA frequency of Endromis versicolora Kentish glory moths occurrence N Y N NA NA yellow-ringed car- frequency of Entephria flavicinctata pet moths occurrence N N Y NA NA frequency of Eriogaster lanestris small eggar moths occurrence N N Y NA NA frequency of Hadena albimacula white spot moths occurrence Y N N NA NA shoulder-striped frequency of Heliothis maritima clover moths occurrence Y N N NA Y frequency of Lithostege griseata grey carpet moths occurrence Y N N NA NA netted mountain frequency of Macaria carbonaria moth moths occurrence N Y N NA NA frequency of Minoa murinata drab looper moths occurrence Y N N NA NA lunar yellow under- frequency of Noctua orbona wing moths occurrence Y Y N NA NA frequency of Paracolax tristalis clay fan-foot moths occurrence Y N N NA NA frequency of Parasemia plantaginis wood tiger moths occurrence N N Y NA NA Pechipogo strigilata common fan-foot moths frequency of Y N N NA NA NI priority England Scotland biodiversity Capped Halted Scientific_Name Common_Name Group Datatype species S41 list at.10000 at 1 list occurrence frequency of Perizoma blandiata pretty pinion moths occurrence N N Y NA NA frequency of Polia bombycina pale shining brown moths occurrence Y N N NA Y frequency of Rheumaptera hastata argent and sable moths occurrence Y Y Y NA NA frequency of Scotopteryx bipunctaria chalk carpet moths occurrence Y N N NA NA frequency of Shargacucullia lychnitis striped lychnis moths occurrence Y N N NA NA Trichopteryx poly- frequency of commata barred tooth-striped moths occurrence Y Y N NA NA frequency of Trisateles emortualis olive crescent moths occurrence Y N N NA NA frequency of Tyta luctuosa four-spotted moth moths occurrence Y N N NA NA frequency of Xestia ashworthii Ashworth’s rustic moths occurrence N N N NA NA frequency of Xylena exsoleta sword-grass moths occurrence N Y Y NA NA other in- frequency of Aeshna isosceles Norfolk hawker sects occurrence Y N N NA NA other in- frequency of Anasimyia transfuga a hoverfly sects occurrence N Y N NA NA other in- frequency of Cheilosia chrysocoma a hoverfly sects occurrence N Y N NA NA other in- frequency of Coenagrion mercuriale Southern damselfly sects occurrence Y N N NA NA other in- frequency of Decticus verrucivorus wart-biter sects occurrence Y N N NA NA other in- frequency of Metrioptera brachyptera bog bush-cricket sects occurrence N Y N NA NA large marsh other in- frequency of Stethophyma grossum grasshopper sects occurrence Y N N NA NA