Seabird Indicator

Content

Abbreviations...... 2

Introduction...... 3

Short Overview of Indicator ...... 4

Methodology...... 5

Indicator Presentation and Interpretation ...... 6

Indicator Budget ...... 6

Reference...... 7

Attachment 1...... 8

Tables...... 28

Figures ...... 30

1 Abbreviations

CBD Convention on Biological Diversity COP CBD Conference of the Parties to the Convention on Biological Diversity EBMI-F European Biodiversity Monitoring and Indicator Framework EBS Biodiversity Strategy for England EEA European Environmental Agency EU European Union IUCN POSC IUCN Programme Office for the Southern Caucasus McMC Markov chain Monte Carlo OSPAR Oslo-Paris Convention on the Protection of the Marine Environment in the NE Atlantic PA Protected Areas PEBLDS Pan-European Biological and Landscape Diversity Strategy SBS Scottish Biodiversity Strategy SDS Sustainable Development Strategy SMP Seabird Monitoring Programme SST Sea Surface Temperatures USAID United States Agency for International Development WB World Bank

2 Introduction

The past decades of the 20th and the beginning of the 21st centuries were remarkable by the acknowledgment from the world community of the meaning of biodiversity conservation and the understanding of the urgency of taking measures to reduce or even halt the loss of biodiversity. One of the most important initiatives was the development and establishment of a set of biodiversity indicators. Biodiversity indicators are the best tools to measure the progress made on biodiversity conservation. Some of them are used to assess the impacts and threats on biodiversity, others to evaluate sustainability of the use of biodiversity. A set of indicators was designed to measure the influence of different sectors and policies on biodiversity (EEA report 11, 2007).

The history of the development and choosing of priority biodiversity indicators started with the decisions made at the Conference of the Parties to the Convention on Biological Diversity (COP CBD). In 2002 the Parties to the Convention made the commitment to reduce the loss of biodiversity by 2010. To achieve this goal the development and use of biodiversity indicators was recognized to be important. The CBD identified seven focal areas against which a set of indicators are intended to be developed and monitored (CBD 2004). Biodiversity indicators were recognized as best tools to implement the CBD and national biodiversity strategies at local, national, regional and global level. It was suggested that indicators at national level should be developed to assess progress at national level.

At the pan-European level the CBD decision found further continuation in the form of different initiatives, projects or processes. For instance, the European Environmental Agency (EEA) initiated the project Streamlining European 2010 Biodiversity Indicators (SEBI 2010), a joint pan-European activity with countries and other interested bodies. The primary goal of the project is to develop and implement biodiversity initiators for assessing, reporting on and communicating achievement of the 2010 target to halt the loss f biodiversity. SEBI 2010 in a way continues the work started under the Pan-European Biological and Landscape Diversity Strategy (PEBLDS) to develop a European Biodiversity Monitoring and Indicator Framework (EBMI-F) that was integrated into the target within the Kiev Resolution (EEA report 11, 2007).

One of the important global initiatives is Countdown 2010 that is a network of partners hosted by the International Union for Conservation of Nature (IUCN). Countdown 2010 was initiated at the stakeholder conference “Sustaining Livelihoods and Biodiversity: Attaining the 2010 Target in the European Biodiversity Strategy” (Message from Malahide, 2004). It promotes any activities and measures to deal with the degradation of biodiversity at all geographical levels through its partners. It helps governments throughout the world to meet their commitments on the way to reduce and halt the loss of biodiversity. It also aims to assess the progress towards this goal (Countdown 2010, visited in 2008).

The Southern Caucasian states responded to the Countdown 2010 by launching the initiative in Gudauri Conference in 2006 (Georgia). The regional implementation of the Countdown 2010 in Armenia, Azerbaijan and Georgia has been translated into the project “Halting the loss of biodiversity: regional implementation of the Countdown 2010 initiative” funded by the Norwegian government. The project is led by IUCN Programme Office for the Southern Caucasus (IUCN POSC) and aims to achieve better monitoring of biodiversity, the sustainable use and management of the natural resources, improve the network of protected areas and promote ecotourism in the region. One of the components of the project is the development and adaptation of the biodiversity indicators widely used worldwide. The “Seabird Indicator” is one of the priority acknowledged indicators important to be develop by all three states of the Caucasus.

3 Short Overview of Indicator

Title: Abundance and distribution of selected species: sea birds. Focal area: Status and trends of the components of biological diversity. Status: Submitted for review to the governments of Georgia and Azerbaijan. Definition: This indicator shows trends in the abundance of wintering sea birds over time across their national ranges. Key policy question: Which wintering seabird species are being reduced in abundance and distribution, and what actions are being taken to reverse these negative trends? Geographical coverage: National. Temporal coverage: Wintering seabird censuses in Georgia are initiated in 2003. Update frequency: Could be annually, if national monitoring is supported at national level. Identified experts: Academic institutions, rangers of marine Protected Areas (PAs), NGOs, volunteers.

Composite population trend indicators, such as the Seabird Index, provide a tangible basis for measuring progress towards the European target of halting biodiversity loss by 2010, and thus towards the global target of reducing the current rate of biodiversity loss by 2010. The strength of this approach is its simplicity, statistical rigor, sensitivity to change, and ease of update (which is possible annually).

The aim of this indicator is to develop and present a robust analysis of trends in numbers of wintering seabirds at the Black Sea coast of Georgia and Caspian Sea coast of Azerbaijan, and demonstrate their utility as indicators of biodiversity. Where the data allow, the analysis will be disaggregated to show trends in selected species groups and/or by geographical region.

Most seabirds are relatively long-lived, late-maturing species. Hence, it may take several years for environmental changes affecting their breeding performance (e.g. food supply, weather) to have a measurable effect on their breeding population. Therefore, it is proposed that a measure of breeding productivity should also be considered (in case budget allows), as it might provide an early warning of likely future population change.

The purpose of the seabird indicator is to enable policy makers to assess and respond to changes in the environment, and then to review the effectiveness of their actions through time. Birds are excellent barometers for the health of the environment. They occur in many habitats, reflect changes in other animals and plants, are sensitive to environmental change and have great resonance with the public.

Besides, often birds are the focus of volunteer efforts and there is possibility to involve communities in monitoring schemes and action. The indicator complements other trend information on species, sites and habitats.

4 Methodology

Trend information is derived from annually (or at least once per two years) operated surveys of selected species at selected sites. Conventional bird surveying techniques and equipment (binoculars and telescopes) and the general methodologies should be applied (for example, http://www.wetlands.org ). Sites and species are selected based on fragmented bird monitoring data during last 10 years at the Black Sea coast of Georgia.

More detailed methodology for counts, data analyses (including statistical analyses) and models development are presented in the Attachment 1.

Sites: 8 sites are proposed in Georgia for breeding seabird counts: 1) Paliastomi Lake; 2) Partotskali Lake; 3) Sea Coast 1 - Churia r. to Khobistskali r.; 4) Sea Coast 2 - Kulevi to Rioni River Delta; 5) Rioni River Delta; 6) Churia Marshes; 7) Kobuleti Nature Reserve; 8) Chorokhi River Delta.

List of species: 12 species are proposed for this indicator:

English name Scientific name Georgian name Great Cormorant Phalacrocorax carbo didi Cvama Great Crested Grebe Podiceps cristatus didi kokona

Common Teal Anas crecca stvenia ixvi (Wikvara)

Mallard Anas platyrhynchos gareuli ixvi Northern Pintail Anas acuta bolosadgisa (kudsadgisa) ixvi Tufted Duck Aythya fuligula qoCora yvinTia

Common Pochard Aythya ferina wiTelTava yvinTia Common Coot Fulica atra melota Common Tern Sterna hirundo Cveulebrivi Tevziylapia Little Tern Sterna albifrons mcire Tevziylapia Arctic Skua Stercorarius parasiticus viwrokuda Tolia-mekobre

Common Black-headed Gull Larus ridibundus tbis Tolia

Update frequency: The frequency of counts in each category preferable to be annually if funding allows. Coordination and implementation of counts: Species counts can be coordinated by relevant academic institutions or NGOs. It is possible to select for coordination several organizations and distribute tasks by categories and/or by geographical districts. Coordinating organizations should select counters at local levels (rangers, local NGOs and CBOs, local birdwatchers) and provide trainings and necessary materials to them.

Training and materials: It is required to provide the following to local counters: 1) Training in seabird identifications; 2) Small guide-books (or leaflets) with photos (or drawings) of selected species and small identification texts; 3) Binoculars (8x or 10x magnification), telescopes (15 – 45x magnification) with full-size and sturdy tripod and GPSs; 4) notebook and standard recording forms, 5) Maps (including boundaries of count areas).

Data analyses: Initial data analyses should be conduced by the counts coordinating organization(s) and after data must be collated by the responsible national agency for biodiversity monitoring.

5 Indicator Presentation and Interpretation

Attachment 1 provides example of the presentation and interpretation of this indicator.

Indicator Budget

Tables 1 and 2 provide the estimate cost related to developing, producing and updating the indicator in Georgia.

Table 1: Start-up Indicator Budget (trainings, materials, baseline data).

# Categories and Items Unit Cost Number of Unit Total (in US Dollars) 1. Counts coordination 1.1. Management fee 1,000 6 months 6,000 1.2. Cost of counters (travel, fee) 500 8 4,000 2. Materials 2.1. Central computer 2,000 1 2,000 2.2. Binoculars 100 8 800 2.3. Telescopes 500 4 2,000 2.4. GPSs 300 4 1,200 2.5. Guide-books 20 10 200 3. Trainings 200 8 1,600 TOTAL 17,600

Table 2: Annual Indicator Budget.

# Categories and Items Unit Cost Number of Unit Total (in US Dollars) 1. Counts coordination 1.1. Management fee 1,000 6 months 6,000 1.2. Cost of counters (travel, fee) 500 8 4,000 TOTAL 10,000

6 Reference

Baker, H., Stroud, D.A., Aebischer, N.J., Cranswick, P.A., Gregory, R.D., McSorley, C.A., Noble, D.G. and Rehfisch, M.M. (2006). Population estimates of birds in Great Britain and the United Kingdom. British Birds 99: 25–44. Cramp, S., Bourne, W.R.P. & Saunders, D. (1974). The Seabirds of Britain & Ireland. Collins, London. Congdon, P. (2001). Bayesian Statistical Modelling. Wiley, Chichester. Gilbert, G., Gibbons, D. W. & Evans, J. (1998). Bird monitoring methods, a manual of techniques for key U.K. species. Royal Society for the Protection of Birds, The Lodge, Sandy, Beds., UK. Gregory, R.D., Noble, D., Field, R., Marchant, J., Raven, M. & Gibbons, D.W. (2003). Using birds as indicators of biodiversity. Ornis Hungarica 12–13;11–24. Lloyd, C., Tasker, M.L. & Partridge, K. (1991). The status of seabirds in Britain and Ireland. Poyser, London. Mavor, R.A., Parsons, M., Heubeck, M. & Schmitt, S. (2006). Seabird numbers and breeding success in Britain and Ireland, (2005). Peterborough, Joint Nature Conservation Committee. (UK Nature Conservation, No. 30). Mitchell, P.I., Newton, S.F., Ratcliffe, N. & Dunn, T.E. (2004). Seabird Populations of Britain and Ireland. T. & A.D. Poyser, London. Pannekoek, J. & van Strien, A.J. (2001). TRIM 3 Manual. Trends and Indices for Monitoring data (Research paper no. 0102). Statistics Netherlands. Voorburg, The Netherlands. Parsons, M., Mitchell, P.I., Butler, A., Mavor, R., Ratcliffe, N. & Foster, S. (2006). Natural heritage trends: abundance of breeding seabirds in Scotland. Scottish Natural Heritage Commissioned Report No. 222 (ROAME No. F05NB01). Ter Braak, C.J.F., van Strien, A.J., Meijer, R. & Verstrael, T.J. (1994). Analysis of monitoring data for many missing values: which method? In: Hagemeijer, W. & Verstrael, T.J. (Eds.) Bird Numbers 1992: Distribution, monitoring and ecological aspects, pp. 663–673. SOVON, Beek-Ubbergen, The Netherlands. Walsh, P.M., Halley, D.J., Harris, M.P., del Nevo, A., Sim, I.M.W. & Tasker, M.L. (1995). Seabird monitoring handbook for Britain and Ireland. JNCC / RSPB / ITE / Seabird Group, Peterborough.

Internet Sources: http://www.scotland.gov.uk/library5/environment/sbiiyh-00.asp www.jncc.gov.uk/seabirds http://www.sustainable-development.gov.uk/indicators http://www.defra.gov.uk/wildlife-countryside/ewd/biostrat/#indicators http://mathstat.helsinki.fi/openbugs/ http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml

7 Attachment 1.

Natural Heritage Trends: Abundance of Breeding Seabirds in Scotland

Parsons, M., Mitchell, P.I., Butler, A., Mavor, R., Ratcliffe, N. & Foster, S. Scottish Natural Heritage Commissioned Report No. 222 (ROAME No. F05NB01). 2006

1. INTRODUCTION

This report presents a standardised and robust way of analysing, presenting and updating trends in populations of breeding seabirds in Scotland. This information will provide a basis for developing one of a series of indicators of biodiversity for the Scottish Biodiversity Strategy (SBS). The analysis aims to fulfill the requirements of the SBS Biodiversity State Indicators, as described in the SBS consultation response given in Appendix 1.

The indicator will be derived from data on the abundance and distribution of breeding seabird species in Scotland. These data are available from two main sources: breeding seabird censuses of Britain and Ireland and the Seabird Monitoring Programme (SMP).

Seabird censuses, that involve surveying all or most colonies throughout Britain and Ireland, have been carried out in 1969–70, 1985–88 and 1998–2002 (Cramp et al., 1974, Lloyd et al., 1991, Mitchell et al., 2004). These have produced comparable estimates of coastal breeding populations for 21 of the 24 seabird species currently breeding in Scotland (listed in Appendix 2) and thus provide an indicator of population change at the regional and national level over the 15 to 30-year period (see Mitchell et. al., 2004). While censuses provide an accurate snapshot in time, they do not provide information on patterns of population change during the intervening periods.

The Seabird Monitoring Programme has provided annual estimates of numbers (and breeding success) for a sample of colonies around Britain and Ireland since 1986. Data from the programme proved to be sufficient to provide an indicator of annual change in the abundance of 13 species in Scotland (listed in Appendix 2). Since data are added to the SMP annually (e.g. Mavor et al., 2005), it is envisaged that an indicator using these data could be reviewed within this timeframe. This is the case with other indicators that are already using data from the SMP:

i. UK Government’s Sustainable Development Strategy (SDS) Quality of Life Counts (i.e. Populations of wild birds) that form the UK’s Headline Indicator H13: Wildlife ; and ii. Defra’s Biodiversity Strategy for England (EBS) indicator M1: Populations of coastal birds and seabirds in England.

There are, however inherent features of SMP data that create problems when attempting to measure changes in the abundance of seabirds from year to year at the various geographical scales required in this report (i.e. colony, region, country – i.e. Scotland). These problems mainly stem from the fact that only a sample of colonies in Scotland are surveyed each year and that not all of these colonies are surveyed in a given year, with some colonies being monitored less frequently than others. Hence, comparing counts from one year to the next is less than straightforward. To overcome these problems, we applied a modeling approach that, for each species, used observed counts to predict numbers present at colonies during years that no surveys were conducted. We then summed the annual observed or imputed counts from each colony to produce an estimate of trends in abundance over time at the regional and country (i.e. Scotland) scales for each species.

However, the Scottish Biodiversity Forum’s strategy for ‘Developing an Indicator Set’ (Anon, 2004) infers that a biodiversity indicator for Scotland should consist of a composite of trends for a number of species. Furthermore, the SBS consultation (Appendix 1) assumed that a Scottish Seabird Indicator would primarily consist of a multi-species trend in abundance over time. Hence, this study investigated appropriate methods of combining species-specific trends to fulfill the intended aims of a Scottish Seabird Indicator.

With regard to aims, the SBS consultation concluded that the indicator should be primarily a ‘state indicator’, i.e. a measure of change of population size of seabirds in their own right, as an important element of Scotland’s biodiversity. However, the consultation also recommended that the seabird indicator be ‘disaggregated’ into the following:

8 ƒ geographical region (this may prove essential for interpreting the likely causes of population trends in Scotland as a whole, since trends in breeding numbers and productivity might show regional variation);

ƒ feeding guild (similarly, this may help explain trends in abundance and breeding success, since different guilds vary markedly in their response to environmental change);

ƒ nest site type (reflecting terrestrial-based influences e.g. human disturbance, predation by ground predators, such as American mink (Mustela vison)).

Therefore, in the latter two cases, by investigating the trends of a subset of species that are considered to share ecological traits, we also sought to produce ‘driving force indicators,’ which aim to infer a measure of the ‘health’ of the marine environment and, more specifically, factors responsible for change in state.

Most seabirds are relatively long-lived, late-maturing species. Hence, it may take several years for environmental changes affecting their breeding performance (e.g. food supply, weather) to have a measurable effect on their breeding population. The consultation therefore proposed that a measure of breeding productivity should also be considered, as it might provide an early warning of likely future population change. However, it was agreed between SNH, JNCC and RSPB that the analysis of breeding productivity data was beyond the scope of this report, whilst acknowledging that this should be included in future enhancements to the indicator.

2. AIMS & OBJECTIVES

2.1 Overall aim The aim of this project is to develop and present a robust analysis of trends in numbers of breeding seabirds in Scotland and demonstrate their utility as indicators of biodiversity. Where the data allow, the analysis will be disaggregated to show trends in selected species groups and/or by geographical region.

2.2 Key objectives

2.2.1 Construct a model that will describe intra-specific changes in annual abundance of seabirds in Scotland between 1986 and 2004 at a variety of geographical scales:

a) colony, b) region, c) Scotland.

2.2.2 Identify appropriate geographical areas, both for long-term and annual reporting, based on trends in colony size.

2.2.3 Identify appropriate nesting habitat and feeding guild multi-species groupings.

2.2.4 Generate multi-species trends in abundance for each multi-species grouping (see 2.2.3).

2.2.5 Provide interpretation, where possible, of the resultant trends in terms of their likely causal factors.

2.2.6 Identify those trends in abundance (e.g. intra-specific, multi-specific, national or regional) that most accurately represent changes in seabird biodiversity in Scotland.

9 3. METHODS

3.1 Data source

The Seabird Monitoring Programme database contains counts of 24 species breeding at colonies throughout Scotland since 1986 (see Appendix 2). Methods of counting vary between species (see Walsh et al., 1995 and Gilbert et al., 1998 for full details) and are of breeding pairs or of individuals, depending on the species and circumstances. Data were available for the period 1986–2004.

3.1.1 Plot counts and whole colony counts

The data comprise counts from two distinct sources: a) whole colony counts and b) plot counts. Whole colony counts are generated for all species by a complete survey of a colony. However, it can be overly time-consuming to count all birds in large colonies on a frequent basis, especially those species that do not build clearly-defined nests, such as Common Guillemot (Uria aalge), Razorbill (Alca torda) or Northern Fulmar (Fulmarus glacialis). In such cases, in order to monitor changes in breeding numbers more frequently, counts of representative sub-sections of the colony – ‘plots’ – are conducted instead of (and, sometimes, in addition to) whole colony counts at some colonies. Plots are sections of the colony that are easily demarcated by observers and generally contain no more than 200–300 birds or pairs. For a given colony, a sample of plots is chosen at random and the number of birds or pairs in each plot is counted several times within the breeding season, to estimate counting error and account for daily variation in the number of birds present at a given time (see Walsh et al., 1995).

In the SMP dataset for Scotland, plot count data were available for six species: Northern Fulmar, European Shag (Phalacrocorax aristotelis), Arctic Skua (Stercorarius parasiticus), Great Skua (Stercorarius skua), Razorbill and Common Guillemot. For Arctic and Great Skua, the plot count data consisted of a single count per plot in each year they were surveyed. The plot data for the other four species consisted of single count per colony, equal to the total count of all the plots averaged over a number of replicate survey days during each year (n=2–5). This effectively led to a loss of information regarding variation within and between plots. This loss of information will mean that the resulting estimates of trends for Northern Fulmar, European Shag, Common Guillemot and Razorbill are a good deal more uncertain (less efficient) than if the counts of each individual plot were available, as was the case for Arctic and Great Skua.

3.1.2 Species

Of the 24 species of seabirds breeding in Scotland, data for 13 species were considered sufficient in terms of sample size (number of colonies), geographical spread and period, to provide representative intra-specific trends in abundance at colonies throughout Scotland and within its constituent regions during the period 1986–2004. These species are Northern Fulmar, Northern Gannet (Morus bassanus), European Shag, Great Cormorant (Phalacrocorax carbo), Arctic Skua, Great Skua, Black-legged Kittiwake (Rissa tridactyla), Sandwich Tern (Sterna sandvicensis), Common Tern (Sterna hirundo), Arctic Tern (Sterna paradisaea), Little Tern (Sterna albifrons), Common Guillemot and Razorbill.

3.1.3 Missing data

Out of the 13 species selected, only the data for Sandwich Tern were substantially complete in terms of a count for most colonies in Scotland from every year during 1986–2004. For the 12 other species, only a sample of the population was counted in each year and not all the colonies were counted annually. Consequently, within a species, the sample of colonies surveyed was not completely comparable from one year to the next.

3.2 Approaches to measuring trends in seabird abundance

3.2.1 Chain indices

The standard solution to the problem of estimating time-series trends from incomplete time-series of counts has been to use the ‘chaining’ method. This involved calculating a ‘chain index’ of abundance for each year by comparing data from only those sites counted in consecutive years, as follows:

Index in year x = 100 * (Index in year x–1 ) * (abundance in year x / abundance in year x–1)

10 where the sub-sample of colonies selected in year x was identical to those in year x–1 i.e. data were discarded from those colonies that were not counted in year x and the previous year x–1. Note that the index in the first year of a time-series (i.e. year x=0) is conventionally equal to 100%.

This method has previously been applied to SMP data in order to calculate intra-species trends in abundance of seabirds in the UK and Ireland (e.g. Mavor et al., 2005) and to calculate multi-species trends in seabird abundance for national indicators for the UK and England (see section 1). The key arguments for using this approach were that it was relatively straightforward to implement and to understand, and that indices for past years did not change when data were included for additional (e.g. newly available) years. However, there are a number of serious flaws in adopting this rather simplistic approach (e.g. Ter Braak et al., 1994):

1. The chaining method wastes data that have taken considerable effort and time to collect. Chaining only uses data for the subset of sites at which colony counts have been taken in consecutive years – the approach rejects all data from sites which were only monitored during a few, widely dispersed years. 2. It makes poor use of the auxiliary plot count data, which is available for certain colonies (see section 3.1.1). In this way, chaining leads to an unnecessarily high level of variability within the resulting indices of abundance. 3. It relies heavily on the premise that the set of years in which counts are made at a colony is unrelated to the trends in abundance at that colony. This assumption is invalid for certain seabird species such as northern gannets, for which small colonies are much easier to count than large colonies and therefore, tend to be surveyed more frequently. This would create a bias in the resulting chain index if small colonies increase at a faster rate than large colonies, as is the case with some species (see section 4.1.1). 4. It is difficult to fully quantify levels of variability and uncertainty within the indices of abundance that are generated by the chaining method. 5. The assumptions that underpin the chaining method are not transparent, making it difficult to understand whether the method is appropriate for any specific biological application.

3.2.2 Statistical models

In the present study, we used an approach based on the construction of an explicit statistical model to describe trends in abundance for each seabird species at each colony within Scotland, and the estimation of various unknown parameters within this model using a generic approach known as Bayesian inference.

The key advantages of adopting a model-based approach were:

1. It made more effective and efficient use of the available data than the chaining method; in particular, it allowed us to include plot counts as well as whole colony counts, and to include data from colonies that were infrequently surveyed. 2. Its flexibility allowed us to identify and, to some extent, choose the scientific assumptions that underpinned our model, and hence estimate parameters that could not be quantified using more ad hoc approaches. 3. It fully quantified the levels of variability within the indices of abundance at the scales of colony, region and Scotland.

The key disadvantages of the model-based approach were that it required the initial development of an appropriate model and it was time-consuming and difficult to implement (both intellectually and computationally).

3.3 A hierarchical model for seabird abundance at individual colonies

We used a statistical model to describe trends in seabird abundance at each seabird colony, and then estimated trends in regional and national abundance by combining the trends of individual colonies. Our model was built on the assumption that the whole colony and plot counts that were recorded had arisen from two distinct sources: an observation process and a hidden (latent) process, both of which involved a random component. This ‘hierarchical’ approach allowed us to link the plot and colony counts with underlying trends in seabird populations, which we assumed to change in a relatively smooth way over time. This assumption of smoothness provided the basis for drawing inferences about those years in which colony and plot counts were both missing.

11

3.3.1 The observation model

The observation model accounted for the uncertainty involved in actually counting the number of pairs or birds (depending on species) that were present at any particular time – i.e. for the recording error. Recording errors arise from the fact that mistakes will inevitably be made by observers – some pairs or birds would more than likely have been missed or counted more than once – and from the fact that the duration of recording varied from visit to visit. We assumed that recording errors for the whole colony and plot counts occurred independently and at random, but for each species we fixed the level of variability in recording error a priori at a level that is regarded as reasonable from a biological perspective. Note that for the majority of species the level of recording error was assumed to be less for the plot counts than for the whole colony counts (Table A3.1, Appendix 3).

Where whole colony counts were available we assumed that:

True number of pairs or birds in colony = Observed colony count + Recording error and where plot counts are available we assumed that:

True number of pairs or birds in colony = (Observed plot count + Recording error) * Plot fraction where ‘plot fraction’ denotes the proportion of pairs or birds in the colony that were contained within the individual plot that was sampled, and was assumed to be unknown but constant over time. This amounted to an assumption that any changes within the population of the entire colony were always reflected within the plots that were selected from that colony, and was one of the most influential assumptions of our model. This assumption did, however, provide a common framework for us to exploit both colony and (where available) plot counts when estimating the underlying trends.

3.3.2 The latent model

The observation described the relationship between the observed count and the true number of pairs or birds present at a colony, whilst the latent model described the trend over time in the true number of pairs or birds. The two models, taken together, allowed us to infer what the true number of pairs or birds may plausibly have been.

We did not make the relatively standard statistical assumptions that trends in the true counts were linear or log-linear, or that trends at different sites were synchronous (as would be made within, for example, the TRIM package; Pannekoek & van Strien, 2001), because we did not have any sound biological basis for making such assumptions. Exploratory analyses of the SMP data suggested that these assumptions were unlikely to be valid. We instead only made the (relatively very weak) assumptions that:

1. some components of the trend over time were common (synchronous) across sites, for example because they arose from a common climate effect; 2. the trend over time could be regarded as a log-linear trend plus some random variation, with any site specific changes in this random variation being relatively gradual (more specifically, we assumed that non-linear asynchronous changes of more than 50% from one year to the next occurred with a probability of 5% or less).

This second assumption was needed in order to ensure that the estimated number of pairs or birds varied smoothly over time, so preventing the uncertainty bands about the indices of abundance from becoming unfeasibly large in years when no counts were recorded. The choice of 50% was somewhat arbitrary, but this assumption appeared to yield plausible results for most species. However, the data for four species – Great Cormorant, Common Tern, Arctic Tern and Little Tern – contained high rates of extinction and colonization and the observed numbers of pairs at some colonies did indeed change by far more than 50% between consecutive years. Hence, this assumption was unlikely to be valid for these species. We attempted to run the model anyway on these species to examine how robust the model’s output was to the violation of the model’s assumptions (see section 3.4).

3.4 Applying the hierarchical model to seabird count data

The observed colony and plot counts were used to estimate the true numbers of pairs or birds at each

12 colony in each year between 1986 and 2004, which were then summed to estimate national and regional indices of abundance. Our model also contained other more subtle unknown values that quantified features such as the plot fractions or the degree of synchroneity between sites. We estimated all of the unknown quantities, or ‘parameters’, simultaneously using a generic statistical approach known as Bayesian inference (e.g. Congdon, 2001), in which we regarded the parameters as random variables and then attempted, through repeated simulation, to generate many different plausible values for these parameters. The repetition allowed us to quantify our uncertainty about the values of the parameters, but did also make this a computationally intensive procedure. We adopted a Bayesian approach because, at least in this instance, it allowed us to fit models that were more realistic than those that can practically be fitted using traditional statistical approaches, and because it also allowed us to more fully take account of the uncertainties involved in statistical estimation.

Within the Bayesian framework, information about the parameter values came both from the data (via the model) and a so-called prior distribution that we had to place upon the parameters. Prior distributions quantified the existing knowledge that we had about the values of the parameters before looking at the data. We attempted to choose prior distributions for most of the parameters of our model in such a way that they had a minimal impact upon the final results (i.e. they are uninformative), but we placed an informative prior distribution upon the variance of changes in log abundance from one year to the next in order to ensure that the resulting trends in seabird numbers varied in a relatively smooth way over time. The assumption of smoothness had a relatively small effect on the eventual output of the model, but allowed us to impute values for years in which counts had not been made, whilst accounting for the inherent uncertainties involved in drawing such inferences about this missing data.

Bayesian inference typically relies on using a sophisticated procedure for simulation known as Markov chain Monte Carlo (McMC), and so requires relatively large amounts of computing power. We fitted our model using LinBUGS, an open source Linux-based variant of the popular WinBUGS software (Spiegelhalter et al., 2004) that provided a powerful and relatively user friendly environment for implementing Bayesian methods.

When the model was run on the data for those species that violated the models’ assumption of a mainly log- linear trend in abundance over time (i.e. Great Cormorant, Common Tern, Arctic Tern and Little Tern – cf. section 3.3.2), it encountered insurmountable convergence problems with the McMC algorithm’ (see Appendix 3). This meant that we were effectively unable to apply the model to count data for these species.

For the remaining eight species we ran the fitting algorithm for 50,000 iterations – following an initial ‘burn- in’ period of 10,000 iterations that we ignored, which appeared to be sufficient to ensure that the relevant parameters had converged to their equilibrium distribution. The time required to run the model was strongly related to the number of colonies, so whilst it was possible to run the model in less than 12 hours for all colonies of three of the species (Northern Gannet, Great Skua and Arctic Skua), it was necessary to run the model for the remaining five species only for a subset of colonies, in order to perform the analysis within a manageable timeframe. For Black-legged Kittiwake, Common Guillemot and Razorbill we ran the model using the 75 colonies with the highest mean observed abundance, whereas for Northern Fulmar and European Shag, we used the 125 colonies with the highest mean observed abundance. These computational limitations could potentially be resolved through better parameterisation of the model.

3.5 Computing trends in seabird abundance

3.5.1 Regional intra-specific trends

Once the ‘true number of pairs or birds in colony’ had been estimated for each species at each colony for each year (1986–2005) using our model (see section 3.3), we summed these within each year over all the colonies in each region and then calculated an annual index of abundance for each region:

Index of abundance in year j = 100 * (True number of pairs or birds at all colonies in year j / True number of pairs or birds at all colonies in base year)

This is the same formula as used for the chaining index (see section 3.2.1). Note that the ‘true number of birds in colony’ is an uncertain quantity, so that these aggregated indices of abundance per region will also be uncertain. Note also that the index of abundance is, by definition, equal to 100% in the base year (i.e. 1986 – the first year for which data were collected for the SMP).

For those species datasets to which the model was not fitted – i.e. Great Cormorant, Common Tern, Arctic

13 Tern, Little Tern (see section 3.4) and Sandwich Tern (see section 3.1.2) – regional indices of abundance were computed by applying the chaining method to observed counts (see section 3.2.1).

Seven regional groupings for seabird colonies in Scotland were defined according to those currently used by the SMP in its annual reporting of results (see Table 3.1; Mavor et al., 2005). However, these regions are relatively ad hoc, based on administrative boundaries (i.e. Scottish Districts 1974–1996) and large-scale marine ecosystem boundaries (i.e. North Sea, Irish Sea, NE Atlantic). While there appears to be some ecological basis to these regional definitions, it is important in the context of developing a regional seabird indicator to determine whether the colonies within a particular region actually exhibit similar trends and thus give clear indication of population change that can be accurately attributed to a distinct geographical area. If this proves not to be the case, we need to know if there is an alternative regional classification that would better characterize the spatial variation in population trends of seabirds in Scotland.

We assessed this (for species with modeled trends – i.e. Northern Fulmar, Northern Gannet, European Shag, Arctic Skua, Great Skua, Black-legged Kittiwake, Common Guillemot and Razorbill) within a Bayesian context by comparing the posterior distributions (i.e. the set of plausible values from 1,000 model iterations – see section 3.4) of trend parameters within and between colonies, and by comparing posterior distributions of equivalent parameters within and between regions. Because no estimates of uncertainty were calculated for the chaining indices, no formal assessment of the strength of ‘regionality’ could be made for the species that were not modeled. Firstly, we constructed box-plots of the posterior distributions of three specific trend parameters for each colony:

a) the estimated abundance in 1986, b) the slope of the log-linear trend, and c) the index of abundance in 2000 (the median year of the Seabird 2000 census).

We visually compared the box-plots of each of these to assess whether or not certain colonies showed significantly different trends in abundance compared with other colonies within the same region. (N.B. Due to their sheer number, these colony-specific box plots are not shown in this report). From examination of the colony-specific box-plots, it became clear that the most comparable trend parameter between colonies was c) the index of abundance in 2000 (I2000). We constructed box-plots of I2000 for each region (Figure 4.2) to visually assess for each species, the similarity between:

a) trends at different colonies within the same region, and b) trends of different regions.

In Figure 4.2, the degree of uncertainty around the estimated I2000 is indicated by the width of the boxes denoting the distance between the 25th–75th percentiles. The wider this distance, the less precise was the estimated regional trend. The level of precision is a direct function of the degree of similarity between the regional trend and the trends of each individual colony within.

This similarity of trend within each region is shown in scatter plots accompanying the box-plots in Figure 4.2. The scatter plots show, within each region, the probability that the colony-specific trend in abundance of each constituent colony is identical to the overall regional trend; a probability of >0.5 indicates that the colony-specific trends and the corresponding regional trends are similar and that there is at least some degree of similarity between them. This probability was derived by summing over the years the squared difference between the annual index of abundance for a colony and the regional index in the same year. If this sum of squared differences is relatively small then this indicates that trend in the colony is relatively synchronous with the region it is being compared to, whereas large values will provide evidence of dissimilarity. If the current regional classification provides a good description of regional variations in abundance trends then we would expect to obtain the small value for the sum of squared differences by comparing colonies against the regions to which they are currently allocated, and to obtain larger values by comparing colonies against regions to which they do not belong. It may be that the trends in colonies on the borders of some regions may be more synchronous with those from the adjacent region.

3.5.2 National intra-specific trends

When computing national indices of abundance for each of the species to which the model was applied (see sections 3.4 and 3.5.1), we modified equation 3.4 to take account of the fact that not all colonies of a particular species in Scotland were surveyed by the SMP (see section 3.1.2), and that the proportion of colonies surveyed varied substantially from region to region (see Table 3.2). Therefore, the country (i.e.

14 Scottish) index of abundance was weighted by the total ‘true number of pairs/birds’ in each region as obtained from actual counts during Seabird 2000, the last seabird census of Britain and Ireland conducted during 1998–2002 (Mitchell et al., 2004) – equation 3.5:

Weighting for colony i = (Number of birds recorded in the region containing colony i within the 2000 census) / (Sum, across all colonies within this region, of true number of birds in year 2000 according to the model)

It is important to note that that the national chain indices calculated (using equation 3.1) for Great Cormorant, Sandwich Tern, Common Tern, Arctic Tern and Little Tern did not include any such regional weighting. However, since most colonies of Sandwich Terns in Scotland were surveyed in each year, the potential for regional bias in the national indices of this species was small.

3.5.3 Multi-species trends

Trends were computed for all species combined and for a number of smaller groupings based on ecological similarities (see section 1) and defined in Table 3.3. Multi-species indices of annual abundance were computed by calculating the geometric mean of indices of abundance for individual species, derived from both modeling and chaining methods.

3.5.4 Detecting trends The modeled annual indices of abundance and trend line were plotted in Figure 4.1. The plotted indices equate to the median value of the iterations of the model and the error bars shown are ‘uncertainty bands’ denoting the 2.5th and 97.5th percentiles of this median value, and can be viewed as the 95% confidence intervals of the median. Note that the abundance index was plotted as a percentage, such that the index in the baseline year of 1986 equaled 100%. In order to determine whether the modeled population index in a given year was statistically significantly different (i.e. at the 0.05 level of probability) from the baseline index in 1986, the ‘uncertainty bands’ were examined: if they fall wholly above or below the 100% line; it indicates a significant increase or decrease, respectively, compared with 1986. Conversely, if uncertainty bands overlap 100% no significant change is inferred.

As mentioned above (section 3.2.1), a major disadvantage of the chaining method was that no measure of certainty/uncertainty about the annual index of abundance could easily be generated, so we could not assess the statistical significance of any trend that may have appeared evident in the chain index.

3.5.5 How well did the model and chain indices fit?

The modeled indices and chain indices for each species were plotted (Figure 4.1) along with the associated indices for the two complete censuses undertaken in 1985–87 and 1998–2002 (Seabird Colony Register and Seabird 2000, respectively), rendered on the same scale for direct comparison. While the modeled and chain indices were derived from annual counts from samples of colonies, the complete censuses provided an actual count estimate of the total national (and regional) populations of all species. Thus, the deviation of the modeled and chain indices from the census results was used as an indicator of how well the modeling and chaining methods estimated the actual change in abundance of each species between 1985–87 and 1998–2002 at the national and regional scales.

4. RESULTS

4.1 Intra-specific trends in abundance

4.1.1 How well did the model and chain indices fit national trends in abundance?

Broadly speaking, the model performed well at a national scale, in that there was a close match and no significant difference between the modeled index of abundance for Scotland in 2000 and the census results (see section 3.5.5) for seven of the eight species (Figure 4.1a, b, e, f, g, l, m). The model slightly underestimated the decline in abundance of European shags that occurred between 1986 and 2000 (Figure 4.1d). However, apparent discrepancies between modeled (and chaining) trends and the census results may have also been partly because, for simplicity, the Seabird 2000 total was plotted at the year 2000 – the middle year of the census, which spanned 1998–2002 (except for gannet, which census was made separately, in 2004). In addition, it is clear that relatively wide confidence intervals of the modeled data in some cases (e.g. Northern Gannet) would tend to lead to a conclusion of no significant difference between

15 this and the census total, even if there was a relatively large difference between the median of the modeled trend and the census total. Since there is no measure of uncertainty for the chain indices, it is impossible to attach any significance to the difference between them and the census results in 2000. However, the national chain indices in 2000 for Northern Fulmar, Arctic Skua, Great Skua and Black-legged Kittiwake, were close to the census results and were not significantly different from the modeled indices (Figure 4.1a, e, f, g). Indeed, for these four species there was no significant difference between the chain indices and modeled indices in most years, except for Great Skua during 1988–92 when the chain indices dipped slightly while the modeled indices showed a steady increase. The chain indices for European shags were very similar to the model in most years and, like the model, overestimated abundance in 2000 compared with the census results (Figure 1d). In stark contrast to the model, the chaining method performed poorly for Northern Gannet, Common Guillemot and Razorbill – the chain indices of all three species were considerably higher in 2000 than expected from the census results and were significantly higher than the modeled indices in most years (Figure 1b, l, m). Of those species that were not modeled, the chaining method worked best for Great Cormorant and Sandwich Tern (Figure 1c, h) – perhaps not surprisingly for the latter, since most of the population in Scotland was surveyed in every year. For both Arctic Tern and Little Tern, the chaining method correctly showed a decline between 1986 and 2000, but over-estimated the extent of the decline (Figure 1j, k). Conversely, the chain indices for Common Tern showed little change between 1986 and 2000, when there had in fact been a decline of 29% in the Scottish population (Figure 1i).

4.1.2 National trends in abundance

Figure 4.1 shows the trends in abundance of each analyses species in Scotland, 1986–2004 and Table 4.1 shows the overall % change in the population index between 1986–2004 and 2000–2004. The trend for Northern Fulmar (Figure 4.1a) was relatively stable during the study period, with a decline between 1999 and 2004, so that the index of abundance in 2004 (85%) was for the first time significantly lower than in 1986. Confidence intervals of the trend for Northern Gannet (Figure 4.1b) were wide; a reflection of the relative infrequency of counts of the larger colonies, and there appeared to be no significant trend over the study period. The chaining index trend for Great Cormorant (this species was not modeled) showed rapid increases from 1986 to the early 1990s, then declines until around 1999, after which the trend started to increase again (Figure 4.1c). The trend for European shag (Figure 1d) declined significantly between 1991 and 1994 and thereafter was stable until 1999, after which the trend showed a significant increase. Great Skua (Figure 4.1e) increased by around one third between 1986 and 2000, but no clear trend has emerged since 2000. Arctic Skuas (Figure 4.1f), conversely, were stable from 1986–1992, but declined thereafter, and particularly since 2000, such that by 2004 the modeled trend indicated a 63% decline since 1986. Black-legged Kittiwake population trend (Figure 4.1g) was fairly stable between 1986 and 1992 but generally declined thereafter, such that by 2004 the index was just 57% – a 43% decline in abundance since 1986. Trends of tern species were calculated from chaining indices only. Sandwich Terns (for which most of the breeding population in Scotland was counted each year) declined by 76% between 1986–1997, but numbers have since largely recovered (Figure 4.1h). In contrast, Arctic Terns (Figure 4.1j) have declined since 1992, and especially rapidly since 2001, though there is uncertainty about the magnitude of the change. The index for Common Tern (Figure 4.1i) revealed no clear trend, but that of Little Tern (Figure 4.1k) appeared to decline somewhat between 1989–1993 and was relatively stable thereafter. Numbers of Common Guillemots (Figure 4.1l) increased by 32% between 1986 and 2000, but showed signs of decline thereafter (though confidence intervals since 2000 were too wide to demonstrate a significant recent decline). Razorbills (Figure 4.1m) were relatively stable over the study period, though they appear to have increased slightly during the early 1990s.

4.1.3 Did trends in abundance vary regionally?

The scatter plots in Figure 4.2 show that in all the modeled species there was a low level of homogeneity in trend in most regions, indicating that in most cases regional trends did not accurately represent the trends at all the colonies within them. Nevertheless, closer examination of the data demonstrated that the trend of a particular colony was usually more similar to the trend of the region it was assigned to than it was to trends of other regions. When this was not the case (i.e. when the trend of a particular colony was more similar to that of another region) there did not appear to be any consistent pattern that would suggest an alternative regional classification would increase the similarity between colony-specific and regional trends. For instance, we did not find any cases where the trend of a colony situated on the edge of a region was more synchronous with the trend of the adjoining region. However there does appear to be a pattern of similarity between regions across several species, in that the similarity of colony-specific and regional trends was consistently higher in Shetland and SE Scotland, but consistently lower in Orkney and NW Scotland.

16 Examination of the box-plots in Figure 4.2 showed clearly that for most species the variation in trend (indicated by I2000) was greater within each region (i.e. between the constituent colonies) than between regions, suggesting there was little distinction between regional trends in abundance.

The only species to exhibit any significant regional variation in trends were Black-legged Kittiwakes and Great Skuas – I2000 of both species was significantly lower compared with other regions (see figure 4.2e & f boxplots). Furthermore similarity of colony-specific trends of both species in Shetland was comparatively high, compared with most other regions (figure 4.2e & f scatter-plots). Indeed the census results showed a much greater decline of Black-legged Kittiwakes in Shetland between c.1986 and c. 2000 (69%) than in any other region and the modeled trends showed a close match with these, although it slightly but significantly underestimated the decline in Shetland. This suggests that the regional distinction of Shetland Black-legged Kittiwakes is real (Figure 4.1g). The modeled trend of Great Skuas in Shetland showed a 12% increase in abundance between 1986 and 2000, lower (but not significantly so) than the 26% increase shown by the census results (Figure 4.1f). The box-plot of I2000 (Figure 4.2e) shows that this increase on Shetland was significantly smaller than the increase in the modeled trend in NW Scotland of 225%. The modeled trend of Great Skuas in Orkney showed a 103% increase in abundance between 1986 and 2000 (Figure 4.1f), however it appears that changes in abundance at the sample of six colonies were unrepresentative of changes in total numbers of Great Skuas on Orkney, which have increased by only 10% during the same period.

Small sample size was responsible for any other apparent regional separation in trend (i.e. of I2000); for example, in N and NW Scotland, single colonies of great and Arctic Skuas respectively, had significantly more positive trends compared with the regional trends of Shetland and Orkney derived from samples of 24 and 23 colonies for each species respectively (Figure 4.2e & f). Likewise, the trend for Northern Gannets in NE Scotland – that was significantly more positive than in the other regions (Figure 4.2b) – was derived solely from the colony at Troup Head.

Although the statistical significance of regional distinctions were not tested for the terns and Great Cormorant (i.e. species that were not modeled), it appeared that some regional differences occurred (see Figures 4.1c, h, i,,j). Changes in abundance of great cormorants in SE and SW Scotland (Figure 4.1c) approximately mirrored the national trend, but in Shetland, the chain indices showed a decline during the early 1990 when numbers were increasing in the other two regions. These regional differences in chain indices of Great Cormorants are confirmed by corresponding difference in the census results. The near disappearance of little terns from SE Scotland during the 1990s was at odds to the other regions where number changed little overall between 1986 and 2004 (Figure 4.1k). The lack of regional weighting (see section 3.5.2) meant that the chain indices in SE Scotland had a disproportionate effect on the national indices of Little Terns, leading to a large discrepancy in the national trends between the chain indices and the census results. The variability in the numbers of terns breeding at any one site was particularly evident in the regional chain indices of Arctic and Common Terns. In some regions, there was substantial year to year variation in index values, none more so than in NW Scotland, were the Arctic Tern regional index ranged between 100 and 1400 and where the index of Common Terns went from 185 in 1994 to 5 in 1995. Hence, there was generally poor agreement between the chain indices and census results in estimating trends in regional abundance of Arctic and Common Terns between 1986 and 2000.

4.2 Multi-specific trends in abundance

Figure 4.3 shows the national indices of abundance of the multi-species groupings of seabirds, as defined in Table 3.3. Trends obtained from modeled data and from chain indices are shown for comparison (but note that for terns and Great Cormorant chain indices were used in place of the modeled trend, because the latter was thought to be an inaccurate description of trend – see section 3.4 and 3.5.3).

4.2.1 All species

The all-species trend as revealed by the modeled data (Figure 4.3a) was broadly stable from 1986 until 1991 and thereafter declined to 1998, after which further and rapid declines occurred, such that by 2004 the index reached its lowest level in the time series. It should be noted that the error bars of the modeled trend shown (95% confidence intervals) reflect both the variation between the indices of the constituent species’ trends and the degree of uncertainty of the estimates of the individual species’ trends. The trend revealed by the chain index showed a similar pattern to that of the modeled data, but substantially underestimated the degree of decline, particularly in years since about 1993.

17

4.2.2 Ecological groupings

Figure 4.3b–i shows multi-species trends in abundance disaggregated into ecological groupings (as described in Table 3.3).

4.2.2.1 Surface feeders

The trend for surface-feeders (Figure 4.3b) was similar to that of all species combined (Figure 4.3a), although the magnitude of decline was greater for the surface-feeders, such that the index in 2004 was equivalent to 57% of the value in 1986. Again, the chain index underestimated the degree of decline. Constituent species that showed a dissimilar trend to the group as a whole included: Northern Gannet and Great Skua (which tended to increase through the study period – Figure 4.1b & f), and Sandwich and Common Tern (which showed recent increases – Figure 4.1h & i). Arctic Skua and Arctic Tern showed greater declines than the group as a whole (Figure 4.1e & j), while the trend for Black-legged Kittiwake (Figure 4.1g) was similar to the group’s trend.

4.2.2.2 Inshore feeders

The modeled trend for inshore feeders (Figure 4.3d) showed a large and significant decline since about 1991, and especially in 2002–4, such that in 2004 the index of abundance was equivalent to 51% of the 1986 value. The trend in chain indices was very similar to the modeled trend, though this was in part due to the fact that this grouping is dominated by those species (i.e. terns and Great Cormorant) for which the model performed poorly and as a result chain indices were used instead for these species. The group’s trend appeared to be driven largely by the trends of Arctic Tern (also the other tern species) and Arctic Skua. Indeed, European Shag and Great Cormorant displayed rather different trends in the last 5 years, the former showed a marked recovery in abundance since 1999 (Figure 4.1d), with the latter species increasing or stable since 1999 (Figure 4.1c).

4.2.2.3 Sandeel specialists

Sandeel specialists (Figure 4.3f) showed a trend very similar to that of surface feeders. The modeled annual indices were stable during 1986–1991, but then declined steadily until the mid 1990s, thereafter remaining stable until 2001 when a very rapid decline followed. The index in 2004 was equivalent to 56% of the 1986 value. The trend in chain indices closely mirrored this pattern but underestimated the degree of decline. The group’s trend was driven largely by the trends of Black-legged Kittiwake, Arctic Tern and Arctic Skua. Common Guillemot showed a quite different trend, increasing until 2000 (but with a possible decline thereafter – Figure 4.1l), European Shag numbers have increased since 1999 (Figure 4.1d) and those of Sandwich Tern have increased since 1997 (Figure 4.1h).

4.2.2.4 Flat-ground nesters

The modeled indices for flat-ground nesters (Figure 4.3h), which in general are more prone to predation by mammalian predators than are cliff-nesters, showed a marked decline since 1986, although were fairly stable during the mid 1990s, but rapidly declined during 2002–2004, such that the index for 2004 was equivalent to 0.45 of the 1986 value. The chain indices were very similar to the modeled indices, in part 19 due to the fact that this grouping is dominated by those species (i.e. terns) for which the model performed poorly and as a result chain indices were used instead. Furthermore, there was a similarity between the trend in modeled and chain indices for both species of skua (Figure 4.1e and f). The grouped trend was most influenced by the trends of Arctic Tern and Arctic Skua; the trend for Great Skua was very different, having increased throughout the period (Figure 4.1f), while Sandwich Tern and Common Tern showed increases since 1997 and 2001, respectively (Figure 4.1h & i).

4.2.2.5 Discard, sub-surface and offshore feeders and cliff-nesters

Within all of these multi-species groups, there was a high degree of variation in the annual modeled indices between the constituent species and as a result, the confidence intervals of the combined indices were very large and there were no detectable change (Figure 4.4c, e, g & i). However, the trends did tend to be positive, or at least stable, rather than exhibiting an overall decline as in the previous multi-species groupings. The annual chain indices of each of these groups appeared to substantially over-estimate annual abundance and diverged markedly from the modeled indices.

18 5. DISCUSSION

5.1 Intra-specific trends in abundance

5.1.1 How well does the model fit and is it a practical method to describe trends?

The model generally proved to be a good predictor of trends in seabird populations between complete censuses in Scotland, although, as expected, it performed poorly in those species whose numbers fluctuate markedly from year to year, namely the four tern species and Great Cormorant.

There was the potential for there to be biases in the modeled trends, for example if the data used were not representative of the true range of colony sizes of the biological population. Indeed, it was necessary for expediency (see Section 3.4) to limit analysis to the larger colonies for Northern Fulmar, European Shag, Black-legged Kittiwake, Common Guillemot and Razorbill. However, the modeled trends were largely representative of the true populations, as revealed by comparisons with the complete census data, as there was close agreement between the modeled trend and the complete census results.

The trends obtained using the chaining method did show marked deviation from the trend revealed from the complete census, and there are reasons to believe that the data used to compute the chain indices may have been biased towards the smaller colonies. This is because the chaining method uses, for a given colony, only counts made in consecutive years; for logistical reasons these colonies tend to be the smaller and therefore more easily counted ones. Such biases in colony size can greatly affect the resultant trend in abundance due to the phenomenon of density-dependent population change, which is thought to occur in some seabird species (e.g. Moss et al., 2002). In simple terms, density dependence may mean, for example, that larger colonies show a lower rate of change in abundance (i.e. the change in numbers as a proportion of colony size) over time than do smaller colonies (including newly established colonies). Figure 5.1 shows the rate of change in abundance at individual colonies between 1986–2000 (computed from modeled data) plotted against colony size (in 1986) on a natural log scale. Species that showed significant density dependent population growth – in which small colonies tended to increase in size at a faster rate than large colonies – were Northern Gannet, European Shag, Common Guillemot and Razorbill (Table 5.1). A result of this density dependence is that the combination of trends in abundance of individual colonies is likely to create a bias in the resulting composite trend (e.g. regional, national). A sample biased towards small colonies will over-estimate trends at the regional or national scale, whereas a sample biased towards large colonies, will under-estimate trends at larger scales. The species for which the chaining index performed least well were Northern Gannet, Common Guillemot and Razorbill, and it is likely that density- dependent colony size change accounted for some of the poor fit in these cases.

A mechanism for the density-dependent effect is that in newly-established (i.e. small) colonies there is more available habitat for breeding and more room for expansion than at larger and longer-established colonies (Moss et al., 2002). Expansion of large colonies may also be limited by other density-dependent processes such interference or competition for a limited food source in surrounding waters (e.g. Lewis et al., 2001).

A further source of potential bias in the trends obtained from the chaining indices was that no regional weighting was applied. This may have been important if a disproportionate part of the population was sampled in a given region over and above that expected from the actual distribution of birds, thus biasing the national trend obtained. Such potential bias was corrected for in the modeled trends.

Biases resulting from density-dependent effects and regional variation in abundance significantly reduced the accuracy of the chaining method, but did not affect the accuracy of the Bayesian model, which is therefore the preferred method for describing changes in the size of populations of most species of seabird in Scotland (where sufficient data are available). However, there was relatively little apparent difference in accuracy between the trends computed using both methods for Northern Fulmar, European Shag, Great Skua, Arctic Skua, Black-legged Kittiwake, but we recommend that the modeled trends are used in these cases, because the degree of uncertainty around the annual indices (i.e. 95% confidence intervals) were measured for the modeled indices, but not for the chain indices.

The model performed poorly for terns and Great Cormorant, since these species characteristically show very marked annual fluctuations in colony size and indeed some colonies are relatively ephemeral. Breeding numbers of European Shag are known also to exhibit marked year-to-year fluctuations (Harris & Wanless 2004), but the model performed quite well for this species. This was probably because although European Shag numbers at a particular colony can fluctuate markedly, this is usually a result of deferred breeding

19 from one year to the next or to mass mortality events, both of which may occur after prolonged winter storms that create poor feeding conditions. These phenomena may be expected to exert their effects synchronously within a given region, unlike the fluctuations of tern or cormorant colonies, where movements tend to be due to site-specific events, such as disturbance or predation.

In contrast to the model, the chaining method proved to be much less sensitive to large inter-annual fluctuations in colony size (>50%) and consequently produced reasonably accurate trends for Great Cormorants and terns. However, there is still the problem of bias in the chain indices as a result of selecting only those data from sites that were surveyed in consecutive years (see above). However, this is probably less of an issue for the tern species in Scotland, where there are fewer missing data (i.e. years when sites were not surveyed) than for Great Cormorant. Hence it should be possible in the future to improve the accuracy trends in tern abundance in Scotland using chain indices by exploring the effect of different site weightings upon the performance of the chaining index in order to address any site-specific biases contained within the data. The chaining method can also be enhanced by placing confidence intervals about the annual indices (using bootstrapping). However in order to improve the accuracy of the chaining for measuring trends in abundance of Great Cormorants, there would need to be some mechanism for smoothing trends to take account of the missing data.

While the model developed in this study performed well for eight out of the 13 species examined, there are two key problems inherent in its application. The first is that the level of uncertainty around colony specific estimates of annual abundance – and, therefore, the uncertainty around regional and national trends – become increasingly large in years more distant from those in which a large proportion of the national population was counted, i.e. census years. This is true for most species during the years subsequent to the Seabird 2000 census. However, as more data are collected in future years, the uncertainty around the trends During previous years will decrease as the intra-specific models are updated with new data. Secondly, there are computational challenges in running the model, especially for those species that are distributed fairly equally over a large number of colonies. For example, the model may take 100 or more hours of computational time to run for a single species. We addressed this problem by limiting the sample size of colonies, selecting the largest first, with little detriment to the fit of the model, while saving a great deal of computational time.

5.1.2 Detecting trends in seabird numbers using the model

The ability of the modeled data to detect a statistically significant level of change for a given species at a given spatial scale is governed by many factors, including observer count error, inter-colony and interregional variation in trend, frequency at which a colony is counted in the time-series, and the proportion of the total population that is counted in a given year. If we seek to improve our ability to detect change then we could feasibly aim to improve on the last two parameters. Recent work has indicated that for Common Guillemot we are better able to detect change in the size of a colony monitored using sample plot counts if we increase the number of plots sampled, effectively increasing the proportion of the ‘population’ counted (Sims et al., in press). Similar ‘power analyses’ are required for a wider range of species if we are to enhance our sampling strategy.

5.1.3 Regional variation in trends in abundance

For the modeled species, we were unable to detect significant regional variation in trend in abundance, except in the case of Great Skua in Shetland and of Black-legged Kittiwake in Shetland and NE Scotland. The variation in trend within each region was in most species much greater than any variation between regions.

There was also no evidence to suggest that an alternative regional classification would have helped tease out any geographical variation in trend other than between colonies. However, differences between regional populations have been evident for demographic features other than colony size. Annual monitoring by the SMP of breeding success has shown significant regional differences in some years, For instance in 2004, breeding success of most species of seabird in Shetland and Orkney was extremely low, while colonies on the west coast of Scotland fared much better (Mavor et al., 2005). In fact the more rapid decline in Blacklegged Kittiwake numbers in Shetland compared with those in Orkney may be a direct result of successive years of poor breeding success that have occurred in Shetland, throughout the 1990s and since 2000, whereas in Orkney breeding success was generally higher than in Shetland (Mavor et al., 2005). Also, intense predation by Great Skuas probably exacerbated kittiwake declines in Shetland (Heubeck, 2000).

20 In those species for which the chain index was used to describe trends, some apparently marked regional differences in trend were observed. Large declines during the 1990s in the number of breeding Great Cormorants in Shetland compared with Scotland as a whole have not been clearly diagnosed, though they appear not to be due to food shortage, since breeding success has been high (Okill et al., 1992). Adult mortality in Caithness breeders was about 20% higher than elsewhere in Britain, but the causes of this (or if the same is true of Shetland populations) remain uncertain (Budworth et al., 2000). The cause of the near local disappearance of breeding Little Terns from south-east Scotland in the late 1990s, compared with other regions of Scotland, seems likely due to predation by foxes rather than poor food availability (Pickerell, 2004).

Seabirds are long-lived and delay breeding until they are several years old, so changes in breeding success will only be manifested in colony size several years subsequently. Colony size is also a complex function of many other factors such as adult survival, post-fledgling survival, age at first breeding and breeding likelihood. Each of these is in turn affected by an array of environmental and other external factors such as disease, food availability, predation and weather that may all be operating on a variety of spatial or temporal scales. So while some factors would be expected to operate on a predictable spatial scale (e.g. food availability), the complex interaction with other factors may mean that the resulting pattern of change in abundance and colony size is masked. Frederiksen et al., (2005) found a much clearer inter-regional variation in breeding success of Black-legged Kittiwakes using SMP data. In fact the SMP regional classification for Scotland proved appropriate for distinguishing large scale variation in breeding success across Scotland, except they found no reason to distinguish between SE and NE Scotland. Frederiksen et al., (2005) suggested that breeding success trends in most regions were distinct from each other because they each depended on a separate sandeel stock on which to feed their chicks. The exceptions were Orkney and Shetland, where trends in annual breeding success were correlated, as both regions rely on the sandeels from the same spawning stock.

Incorporating SMP breeding success data into the Scottish Seabird Indicator would better enable us to discern environmental effects on seabird populations at a finer temporal and spatial scale. This would increase our ability to better understand the process underlying change in Scottish seabird abundance.

5.1.4 Conservation implications of the trends in abundance

The trends revealed by this analysis have shed new light on the population trends of seabirds in Scotland, therefore facilitating further understanding of the potential causes of population change. This study has enabled an insight into the changes that occurred over the intervening years between the censuses of the SCR and Seabird 2000; it has also made it possible to infer population changes in the period since Seabird 2000. For example, it has already been shown from the complete censuses that the population of European Shag declined between the mid 1980s and 2000 (Mitchell et al., 2004); this study supports other work, showing that the decline occurred mainly in 1993 and 1994 (Figure 4.1d), when a year of widespread non- breeding preceded a large mortality event of adult shags, caused by severe winter storms (Harris et al., 1998). Moreover, it has revealed the nature of the recovery since Seabird 2000: the abundance index increased by 33% between 2000 and 2004. Recent data show, however, that the breeding population decreased in 2005, following winter storms (Mavor et al., 2006).

We have shown that the population trends of many seabirds in Scotland are in decline, most notably, perhaps, those species that feed largely on sandeels, including Arctic Tern, Arctic Skua, and Black-legged Kittiwake. These trends are related to declining availability of sandeels in the North Sea, which have been most keenly felt in Shetland colonies of these and other species (see also 5.3.1) but also, in recent years, in Orkney and on east coast colonies of Scotland and, in 2005, in northwest Scotland also, which had previously not encountered widespread or significant problems with food availability (Mavor et al., 2006).

The problems are not restricted to overall abundance of available prey: Wanless et al., (2004) showed that the size of sandeels caught by (and available to) Atlantic puffins nesting on the Isle of May in SE Scotland decreased significantly between 1973 and 2002. Increases in sea surface temperatures (SST) over the last few decades led to a ‘regime shift’ in the plankton communities in the North Sea around the mid 1980s (Beaugrand et al., 2003) and consequently, a reduction in sandeel recruitment (Arnott & Ruxton, 2002).

One study found significant negative correlations with SST and over-winter survival and breeding success of Black-legged Kittiwakes on the Isle of May (Frederiksen et al., 2004). That study also showed that the presence during 1990–99 of the currently closed sandeel fishery over the Wee Bankie (near to the Isle of May), was significantly associated with low breeding success of Black-legged Kittiwakes, predicting that if

21 SST in the North Sea were to increase in the future and if the sandeel fishery were to be reinstated, the Black-legged Kittiwake population on the Isle of May (and perhaps other nearby colonies) would decline dramatically. Therefore, while fisheries may play a part in determining the availability of sandeels, fundamental changes in the marine ecosystem are being shown to exert effects upon seabird survival, breeding success and, in turn, population size.

Some of the greatest impacts on seabird population size come from terrestrial sources. The presence of land predators can limit the extent of safe nesting habitat available to ground nesting species, such as terns, (and other species, not included in the present analysis: Atlantic Puffin (Fratercula arctica), petrels, gulls, and Black Guillemots (Cepphus grylle). The invasion into the west of Scotland by American mink has led to local extirpation of Common and Arctic Terns from many islands in that region (Craik,1997). Avian predators and competitors have also played a role in determining population size of some seabirds; for example, the marked decline of Arctic Skuas in the Northern Isles has been linked – in addition to sandeel shortages – to the spread and increase in population size of Great Skuas, which compete with the smaller species for territories and predate their young (Furness and Ratcliffe, 2004). Furthermore, migratory species may be affected by factors operating outside the UK.

The most recent population estimates of seabirds for Great Britain (in Baker et al., 2006) came from the census results of Seabird 2000. However, the present study has shown that the trends of some seabird species have changed significantly since Seabird 2000. For example the modeled population index of Arctic Skua (whose Great Britain breeding population is wholly in Scotland) fell by 48% between 2000 and 2004, while that of Black-legged Kittiwake (with c.75% of the GB population in Scotland) fell by 33% over the same period. Therefore, now that we have reliable population trends for seabirds in Scotland it may be necessary to revise our assessments of their population status – particularly for rapidly declining species such as Arctic Skua – in advance of the next complete seabird census (which, following previous periodicity, would be around 2015). Furthermore, assessments of species’ rate of change and degree of threat, such as the IUCN Red List of Threatened Species (IUCN 2001), the UK Biodiversity Action Plan, and ‘Birds of Conservation Concern’ (Gregory et al., 2002), should be informed by the seabird trends of this study and future developments of it.

5.2 Multi-species trends in abundance

5.2.1 All species

The all-species trend showed a significant decline, from around 1992–2004, by 30% compared with 1986. This indicates that, on average, the number of species in decline outweighed the number of species that increased or that were stable, and/or that the magnitude of the declines outweighed the increases.

In fact, two stages of decline were evident: between 1992 and 1998, followed by a stable period until 2001, after which further declines occurred. The recent period of decline is largely driven by the trends of Arctic Tern, Arctic Skua and Black-legged Kittiwake, which, as discussed in 5.1.4 have been strongly affected by changes in sandeel availability.

The trend for the all-species grouping is the geometric mean of the index values of the 13 constituent species. It is therefore an ecologically diverse group, the constituent species of which show very different trends, resulting in wide confidence intervals for the estimates. If we are to use the trend as an indicator of the state of seabird populations in their own right, as opposed to an indicator of the marine environment, for example, then the grouping is perhaps appropriate, though care is needed in its interpretation. In this respect, it is relevant to note that the multi-species trends are not weighted according to the absolute or relative abundance of the constituent species: in other words the commoner species are given equal weighting to the rarer species (not weighting is the traditional approach that has been taken in most other indicators e.g. Gregory et al., 2003). Therefore, because those species that increased or were stable between the mid 1980s and 2000 were also the most numerous in absolute terms (e.g. Northern Fulmar, Common Guillemot, Northern Gannet), the number of individual pairs of seabirds actually increased, by about 5%, over the period between the SCR and Seabird 2000. Our ability to detect change, at least over a short period, is weakened with a diverse grouping, however, due to large confidence limits. Nevertheless, the all-species grouping does appear to show a significant downward trend over the period 1986–2004. We would urge further caution in the interpretation of the all-species group trend, because the magnitude of trend of individual species is subsumed by the generality of the indicator. For example, unless we look at the individual trends for Arctic Skua or Arctic Tern – which showed far greater declines than the average of 30% (Figure 4.1) – then we risk overlooking the most serious declines and drawing simplistic overall conclusions

22 about seabird populations. Conversely, some species, such as Great Skua (Figure 4.1), showed increasing trends that are not apparent from the trend of the all-species group.

The all species group comprised trends of 13 species (8 of which were modeled, 5 of which were produced from the chain index method), but at present exclude the 11 other seabird species that breed in Scotland (Table 3.3). In most cases, these were omitted from the analysis because data on their numbers were sparse, with very small proportions of the total Scotland population being counted in any given year (other than during the complete censuses, in 1985–88 and 1998–2002). Of the omitted species, the Larus gulls are the largest group (5 species), then the petrels (3 species), auks (2 species) and terns (1 species, roseate tern, which is a very scarce breeder in Scotland but has been well counted and could be included in the analysis in future). There is, as yet, no satisfactory method of producing annual indices of abundance for species with very sparse data.

The use of an all-species grouping to infer changes in aspects of the marine environment (other than seabird numbers) is more problematic, given what we know about the diversity of factors that control a single species of seabird, let alone a large group of ecologically different species, and is not recommended for that purpose.

5.2.2 Ecological groupings

There are sound ecological and practical reasons for disaggregating the all-species grouping into smaller groupings. Firstly, if we select a group of species whose populations we believe are controlled by similar factors – for example food source, feeding niche, nesting habitat niche – then that group could potentially be used as an indicator of the factor in question and, importantly, may indicate appropriate conservation action. Secondly, there is a basis for expecting species with similar ecologies to display similar trends and therefore, from a statistical perspective, we are more likely to be able to detect trends in smaller, more ecologically coherent, groups. Eight ecological groupings were analysed, based upon feeding method (surface feeder, sub-surface feeder, discard feeder), feeding location (inshore feeder, offshore feeder), main prey type (sandeel specialist), and nesting habitat (cliff nester, flat-ground nester). We found no discernable trend in four of these groupings: sub-surface feeders, offshore feeders, discard feeders and cliff-nesters, and a moderately clear trend for surface-feeders (though with fairly large confidence intervals). Three groupings, however, showed a clear (downward) trend: inshore feeders, sandeel specialists and flat ground nesters. Of particular note was the sandeel specialist group, which showed small confidence limits despite being in other respects a fairly diverse grouping, containing cliff-nesters, flat-ground nesters, surface feeders, subsurface feeders, inshore and offshore feeders. Although a more sophisticated analysis of the data is required to establish the ‘discriminant factor’ that explains most of the variation in trend (which is beyond the scope of this report), we saw that the other groups that showed a clear trend – inshore feeders and flat-ground nesters – contained a high proportion of sandeel specialist species (see Table 3.3). However, caution is needed in the interpretation of the ecological groupings; one should not infer, because species groupings show a significant trend, that the grouping variable is the driving force behind the trend. For example, inshore feeders showed strong commonality in trends, but in reality these were unlikely to be driven by inshore feeding conditions but rather due to coincidental, but unrelated, causes. While Arctic Tern and skua declines were probably related to inshore sandeel availability, European shags declined on the east coast due to storms; terns and European shags on the west coast declined due to mink predation (the latter locally also due to Brown Rat (Rattus norvegicus) predation, and terns on the East coast due to fox (Vulpes vulpes) predation, whereas Great Cormorants, overall, remained stable but may have declined in northern Scotland due to persecution (Sellers, 2004). The flat-ground nester group comprised all terns and both skuas, so the interpretation of changes in the populations of this group needs to consider the points raised under the inshore feeders; there are clearly factors other than predation at play in the species that comprised this group. Furthermore, it should be remembered that these three groups combined species which spend the winter in very different areas, so factors that determine adult survival – and hence breeding population size – during the non-breeding season are therefore likely to differ widely between these species. For example, the sandeel specialist group and inshore feeder group combine European Shag that largely stay in or near UK waters during the non-breeding season, with the terns and Arctic Skua that all winter in west Africa or even farther afield, while the flat-ground nester group combines the terns and Arctic Skua (which share similar wintering grounds) with Great Skua that winters largely in the Bay of Biscay and western Mediterranean (Wernham et al., 2002). In summary, superficially these indicators seemed to tell us something about inshore foraging conditions, sandeel availability or predation intensity when, on closer inspection, it was revealed that some of the constituent species’ trends were driven by very diverse terrestrial and marine influences. Therefore, an unquestioning assumption of causality could lead to erroneous diagnosis of trends and misallocation of remedial measures. As we have discussed (and

23 notwithstanding the caveats presented above), disaggregated groups of species may, on the face of it, make more useful indicators of driving forces of seabird numbers than the all-species group. Disaggregated groups are less useful as an overall indicator of seabird populations as, by definition, each comprises a smaller, more ecologically, similar group of species. We would recommend, given the importance of sandeels in the diet of many seabird species, and recognising the difficulty of inferring simple causative factors from multi-species groupings, that the population size of a single sandeel-specialist should be presented as an indicator. Black-legged Kittiwake (Figure 4.1) would be a good candidate, given that it is particularly reliant on sandeels (and on these being available at the sea surface) and that unlike other sandeel specialist such as terns, it tends not to be also affected by mammalian predators, because it nests on inaccessible cliff ledges. This indicator should be used primarily as a way of highlighting and communicating the conservation issues surrounding sandeel availability (given that direct measurement of sandeel availability is currently not technically possible). However, it would be misleading to infer, from the national trend in Black-legged Kittiwake numbers, that sandeel availability has varied in direct proportion or even in any closely-related way. The use of a single-species (or, indeed, multispecies) indicator should not substitute the monitoring and research of a range of individual species and their ecologies. Interpretation of indicators that present data on seabird population size would be substantially enhanced by complementing them with an indicator of the demographic parameters that control population size, such as breeding success or adult survival rate. Of greatest value in terms of an indicator would be the use of breeding success data, as there are very much more data available than for survival rate. Indeed, the use of breeding success of Black-legged Kittiwakes as an indicator of sandeel availability in the North Sea is under development by the Oslo-Paris Convention on the Protection of the Marine Environment in the NE Atlantic (OSPAR). Breeding success is known to be more immediately responsive to changes in, say, food availability, than is population size and would therefore make a more sensitive ecological indicator. In this way, annual updates of the breeding success indicator would present biologically meaningful year-on year changes, whereas significant changes in population size tend to be manifested only over longer periods of time. Indeed, changes in breeding success could be used as an ‘early warning’ of potential future population change. We would therefore recommend that breeding success data be incorporated into future development of the seabird indicator set.

6. CONCLUSIONS AND RECOMMENDATIONS FOR INDICATOR DEVELOPMENT

We have established an effective modeling approach to detecting changes in abundance of eight species of seabirds and have presented these changes along with those of five other species for which the modeling approach was not appropriate. These trends can be updated annually, with relatively little effort or expense.

We recommend further investigation into statistical methods for describing population change in those species for which the new modeling approach was inappropriate, namely terns and Great Cormorant. We were unable to detect distinct regional variation in population trends for most species (except for Black- legged Kittiwake and Great Skua and possibly also Great Cormorant and Little Tern), so we conclude that the national (Scottish) trend is generally a suitable scale at which to report.

The all-species grouping should be interpreted with caution, given variations in the trends of the constituent species. While there may be some use in presenting the trend as an indicator of seabird populations in their own right (as an important element of Scotland’s biodiversity), we would not recommend its use to infer anything about the marine environment, given the diversity of species contained in the group and the complexity of factors responsible for population change.

Disaggregation of the all-species indicator, according to a priori ecological groupings, showed apparently clear trends for three groupings. Most ecologically appropriate, we suggest, as a biological indicator, is the trend of sandeel-specialists, though there are significant problems with the interpretation of the multi-species groupings. Therefore, we instead recommend that the population size of a single sandeel-specialist (Black- legged Kittiwake) should be presented as an indicator of sandeel availability.

We recommend that a complementary indicator be developed, that would present trends in breeding success of a range of species, including sandeel specialists. This would greatly enhance the interpretation of trends in numbers and perhaps provide an ‘early warning’ of potential future population change.

We recommend investigating the feasibility of increase the number of seabird species to be included in analyses of population size trend and, hence, in the indicator itself. These include the Larus gulls, the petrels and Manx Shearwater. In addition, roseate tern could be usefully included in the indicator with little further work.

24

We recommend that the approach of this report and its recommendations be applied also on a United Kingdom scale, in order to place the trends for Scotland in a wider geographical context. In addition, updating UK (or Great Britain)-wide trends for seabirds is required to inform updates of population estimates for statutory conservation purposes.

25 7. REFERENCES

Anon. (2004). Scotland’s Biodiversity – It’s in Your Hands. A strategy for the conservation and enhancement of biodiversity in Scotland: Developing an Indicator set. Indicators Working Group, Scottish Biodiversity Forum, Edinburgh. Arnott, S.A. & Ruxton, G.D. (2002). Sandeel recruitment in the North Sea: demographic, climatic and trophic effects. Mar. Ecol. Prog. Ser. 238: 199–210. Baker, H., Stroud, D.A., Aebischer, N.J., Cranswick, P.A., Gregory, R.D., McSorley, C.A., Noble, D.G. and Rehfisch, M.M. (2006). Population estimates of birds in Great Britain and the United Kingdom. British Birds 99: 25–44. Beaugrand, G., Brander, K.M., Lindley, A., Souissi, S. & Reid, P.C. (2003). Plankton effect on cod recruitment in the North Sea. Nature 426: 661–664. Budworth, D., Canham, M., Clark, H., Hughes, B., & Sellers, R.M. (2000). Status, productivity, movements and mortality of Great Cormorants Phalacrocorax carbo breeding in Caithness, Scotland: a study of a declining population. Atlantic Seabirds, 2: 165–180. Craik, J.C.A. (1997). Long-term effects of North American Mink Mustela vison on seabirds in western Scotland. Bird Study 44:303–309. Cramp, S., Bourne, W.R.P. & Saunders, D. (1974). The Seabirds of Britain & Ireland. Collins, London. Congdon, P. (2001). Bayesian Statistical Modelling. Wiley, Chichester. Frederiksen, M., Wanless, S., Harris, M.P., Rothery, P. & Wilson, L.J. (2004). The role of industrial fishery and oceanographic changes in the decline of North Sea black-legged kittiwakes. J. Appl. Ecol. 41:1129– 1139. Frederiksen, M., Wright, P. J., Harris, M.P., Mavor, R., Heubeck, M. & Wanless, S. (2005). Regional patterns of kittiwake Rissa tridactyla breeding success are related to variability in sandeel recruitment. Mar. Ecol. Prog. Ser. 300: 201–211. Furness, R.W. & Ratcliffe, N. (2004). Arctic skua Stercorarius parasiticus. Pp. 160–172 in: Mitchell, P.I., Newton, S.F., Ratcliffe, N. & Dunn, T.E. (2004). Seabird Populations of Britain and Ireland. T. & A.D. Poyser, London. Gilbert, G., Gibbons, D. W. & Evans, J. (1998). Bird monitoring methods, a manual of techniques for key U.K. species. Royal Society for the Protection of Birds, The Lodge, Sandy, Beds., UK. Gregory, R.D., Noble, D., Field, R., Marchant, J., Raven, M. & Gibbons, D.W. (2003). Using birds as indicators of biodiversity. Ornis Hungarica 12–13;11–24. Gregory, R.D., Wilkinson, N.I., Noble, D.G., Robinson, J.A., Brown, A.F., Hughes, J., Proctor, D.A., Gibbons, D.W. & Galbraith, C.A. (2002). The population status of birds in the United Kingdom, Channel Islands and Isle of Man; an analysis of conservation concern 2002–2007. British Birds 95: 410–450. Harris, M.P., Wanless, S. & Elston, D.A. (1998). Age-related effects of a non-breeding event and a winter wreck on the survival of Shags Phalacrocorax aristotelis. Ibis 140: 310–14. Harris, M.P. & Wanless, S. (2004). European Shag Phalacrocorax aristotelis. In Seabird Populations of Britain and Ireland. Mitchell, P.I., Newton, S.F., Ratcliffe, N. & Dunn, T.E. (Eds.). T. & A.D. Poyser, London. Heubeck, M. (2000). Population trends of kittiwake Rissa tridactyla, black guillemot Cepphus grille and common guillemot Uria aalge in Shetland, 1978–98. Atlantic Seabirds 2: 227–244. IUCN (2001). IUCN Red List Categories and Criteria: Version 3.1. IUCN Species Survival Commission. IUCN, Gland, Switzerland and Cambridge, UK. Lewis, S., Sherratt, T.N., Hamer, K.C. & Wanless, S. (2001). Evidence of intra-specific competition for food in a pelagic seabird. Nature 412, 816–819. Lloyd, C., Tasker, M.L. & Partridge, K. (1991). The status of seabirds in Britain and Ireland. Poyser, London. Mavor, R.A., Parsons, M., Heubeck, M. & Schmitt, S. (2005). Seabird numbers and breeding success in Britain and Ireland, (2004). Peterborough, Joint Nature Conservation Committee. (UK Nature Conservation, No. 29.) Mavor, R.A., Parsons, M., Heubeck, M. & Schmitt, S. (2006). Seabird numbers and breeding success in Britain and Ireland, (2005). Peterborough, Joint Nature Conservation Committee. (UK Nature Conservation,

26 No. 30.) Mitchell, P.I., Newton, S.F., Ratcliffe, N. & Dunn, T.E. (2004). Seabird Populations of Britain and Ireland. T. & A.D. Poyser, London. Moss, R., Wanless, S. & Harris, M.P (2002). How small Northern Gannet colonies grow faster than big ones. Waterbirds 25, 442–8. Okill, J.D., Fowler, J.A., Ellis, P.M. & Petrie, G.W. (1992). The diet of Cormorant Phalacrocorax carbo chicks in Shetland in 1989. Seabird 14: 21–26. Pannekoek, J. & van Strien, A.J. (2001). TRIM 3 Manual. Trends and Indices for Monitoring data (Research paper no. 0102). Statistics Netherlands. Voorburg, The Netherlands. Parsons, M., Mitchell, P.I., Butler, A., Mavor, R., Ratcliffe, N. & Foster, S. (2006). Natural heritage trends: abundance of breeding seabirds in Scotland. Scottish Natural Heritage Commissioned Report No. 222 (ROAME No. F05NB01). Pickerell, G. (2004). Little tern Sterna albifrons Pp. 339–349 in: Mitchell, P.I., Newton, S.F., Ratcliffe, N. & Dunn, T.E. (2004). Seabird Populations of Britain and Ireland. T. & A.D. Poyser, London. Sellers, R. (2004). Great Cormorant Phalacrocorax carbo. In Seabird Populations of Britain and Ireland. Mitchell, P.I., Newton, S.F., Ratcliffe, N. & Dunn, T.E. (Eds.). T. & A.D. Poyser, London. Sims, M., Wanless, S., Harris, M.P., Mitchell, P.I. & Elston, D.A. In press. Evaluating the power of monitoring plot designs for detecting long-term trends in the numbers of common guillemots. J. Appl. Ecol. Spiegelhalter, D., Thomas, A., Best, N. & Lunn, D. (2004). WinBUGS User Manual: Version 2.0. MRC Biostatistics Unit, Cambridge. Ter Braak, C.J.F., van Strien, A.J., Meijer, R. & Verstrael, T.J. (1994). Analysis of monitoring data for many missing values: which method? In: Hagemeijer, W. & Verstrael, T.J. (Eds.) Bird Numbers 1992: Distribution, monitoring and ecological aspects, pp. 663–673. SOVON, Beek-Ubbergen, The Netherlands. Walsh, P.M., Halley, D.J., Harris, M.P., del Nevo, A., Sim, I.M.W. & Tasker, M.L. (1995). Seabird monitoring handbook for Britain and Ireland. JNCC / RSPB / ITE / Seabird Group, Peterborough. Wanless, S., Wright, P.J., Harris, M.P. & Elston, D.A. (2004). Evidence for decrease in size of lesser sandeels Ammodytes marinus in a North Sea aggregation over a 30-yr period. Mar. Ecol. Prog. Ser. 279: 237–246. Wernham, C.V., Toms, M.P., Marchant, J.H., Clark, J.A., Siriwardena, G.M. & Baillie, S.R. (eds.) 2002. The Migration Atlas: movements of the birds of Britain and Ireland. T. & A.D. Poyser, London.

Internet Sources: http://www.scotland.gov.uk/library5/environment/sbiiyh-00.asp www.jncc.gov.uk/seabirds http://www.sustainable-development.gov.uk/indicators http://www.defra.gov.uk/wildlife-countryside/ewd/biostrat/#indicators http://mathstat.helsinki.fi/openbugs/ http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml

27 Tables

Table 3.1 Regional definitions

Scottish district (1974–1996) Region Shetland Shetland Orkney Orkney Caithness, east coast of Sutherland, east coast of Ross & Cromarty, Inverness N Scotland Nairn, Moray, Banff and Buchan, Gordon, City of Aberdeen, Kincardine and Deeside NE Scotland Angus, City of Dundee, North-East Fife, Kirkcaldy, Dunfermline, West Lothian, City of Edinburgh, SE Scotland East Lothian, Berwickshire Annandale and Eskdale, Nithsdale, Stewartry, Wigtown, Kyle and Carrick, Cunninghame, Inverclyde, SW Scotland Dunbarton, Argyll and Bute Lochaber, Skye and Lochalsh, Western Isles, west coast of Ross & Cromarty, north and west coast NW Scotland of Sutherland

Table 3.2 Number of colonies per species sampled in each Scottish region

Shetland Orkney North NE SE SW NW Total Northern Fulmar 57 21 9 5 0 1 32 125* Northern Gannet 4 3 0 1 1 2 4 15 Great Cormorant 9 10 10 5 9 30 32 105 European Shag 23 15 4 8 8 21 46 125* Arctic Skua 16 7 0 0 0 0 1 24 Great Skua 14 6 1 0 0 0 5 26 Black-legged Kittiwake 6 14 6 21 7 4 17 75* Sandwich Tern 0 0 0 2 3 1 0 6 Common Tern 3 0 7 14 12 21 14 71 Arctic Tern 5 6 6 8 9 7 2 43 Little Tern 6 3 8 5 1 23 Common Guillemot 7 15 7 10 8 8 20 75* Razorbill 5 14 6 12 7 7 24 75*

*total number of colonies was limited for these species to those colonies with the highest abundance, in order to reduce the time taken to run the model (see section 3.4).

Table 3.3 Species groupings for multi-species trends in abundance a) Species included in the present study Surface Sub- Sandeel Discard Inshore Offshore Cliff Flat- feeder surface specialist feeder feeder feeder nester ground nester feeder Northern Fulmar X X X X

Northern Gannet X X X X

Great Cormorant X X X

European Shag X X X X

Arctic Skua X X X X Great Skua. X X X X Black-Legged X X X X Kittiwake Terns: Sandwich, X X X X Common, Arctic, Little

Common Guillemot X X X X Razorbill X X X X

28 b) Species not included in the present study Surface Sub- Sandeel Discard Inshore Offshore Cliff Flat-ground feeder surface specialist feeder feeder feeder nester nester feeder

Herrring Gull X X X X X X Lesser Black-backed X X X X X Gull Great Black-backed X X X X X X Gull Mew Gull, Black- X X X headed Gull Roseate Tern X X X X Manx Shearwater, 2 X X X X Storm Petrels, Atlantic Puffin X X X X X Black Guillemot X X X X

Table 4.1 Overall % change in the population index of 13 seabird species between1986–2004 and 2000– 2004. Note: * indicates a significant change at the P<0.05 level, inferred from overlap in respective 95% confidence intervals. For the terns and cormorant it was not possible to determine the level of statistical significance of change. Figures in brackets indicate those species in which there was a wide disparity between the trends of the chain index and complete census results, and should therefore be viewed with caution.

Species % change 1986–2004 % change 2000–2004 Northern Fulmar –29.0 * –23.7 Northern Gannet 84.8 37.8 Great Cormorant 15.1 1.3 European Shag 11 31.4 Arctic Skua –63.0 * –46.8* Great Skua +34.3* 2.2 Black-legged Kittiwake –42.7* –33.0* Sandwich Tern –27.8 –22.9 Common Tern 16.1 –14.6 Arctic Tern (–95.5) (–83.3) Little Tern (–54.3) 0 Common Guillemot 9 –17.4 Razorbill 19.9 2.9

Table 5.1 Linear regression of the modeled rate of change in numbers (Beta) against colony size, for eight seabird species in Scotland, 1986–2004 (see plot in Figure 5.1). Regression equation: Ln(Beta) = b * (Ln (colony size)) + a, where colony size equals the modeled abundance in 1986

a b R2 F df P Northern Fulmar 0.0365 –0.0054 0.02 2.46 123 0.119 Northern Gannet 0.1805 –0.0138 0.36 4.95 9 0.053 European Shag 0.1343 –0.0333 0.26 43.83 122 <0.001 Arctic Skua –0.0394 0.0001 <0.01 <0.01 22 0.99 Great Skua 0.0606 –0.0055 0.07 1.85 24 0.186 Black-Legged Kittiwake 0.0533 –0.0089 0.04 3.25 73 0.076 Common Guillemot 0.1016 –0.0102 0.13 10.69 73 0.002 Razorbill 0.0946 –0.0126 0.12 9.77 73 0.003

29 Figures

Figure i Aggregated trend of breeding abundance of 13 species of seabird in Scotland, 1986–2004. Indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals

Figure ii Aggregated trend of breeding abundance of nine species of sandeel-specialist seabirds in Scotland, 1986–2004. Indices are shown in red with ‘uncertainty bands’ equivalent to 95% confident intervals

Figure iii Trend in breeding abundance of black-legged kittiwake in Scotland, 1986–2004. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals

30 Figure 4.3a National (i.e. Scotland) indices of abundance of the all-species group of seabirds, 1986–2004. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and multi-species chain indices in blue

Figure 4.3b National (i.e. Scotland) indices of abundance of the surface feeders group of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and multi-species chain indices in blue

Figure 4.3d National (i.e. Scotland) indices of abundance of the inshore feeders group of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and chain indices in blue

31 Figure 4.3f National (i.e. Scotland) indices of abundance of the sandeel specialist group of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and chain indices in blue

Figure 4.3h National (i.e. Scotland) indices of abundance of the flat-ground nesters group of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and chain indices in blue

Figure 4.4c National (i.e. Scotland) indices of abundance of the sub-surface feeders group of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and chain indices in blue

32 Figure 4.4e National (i.e. Scotland) indices of abundance of the offshore feeders group of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and chain indices in blue

Figure 4.4g National (i.e. Scotland) indices of abundance of the discard feeders group of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and chain indices in blue

Figure 4.4i National (i.e. Scotland) indices of abundance of the cliff nesters group of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and chain indices in blue

33 Figure 4.1 Intra-specific trends in abundance of seabirds in Scotland, 1986-2004. Note: The red line shows the modeled trend with ‘uncertainty bands’ equivalent to 95% confident intervals, and the black line shows chain indices. Y-axis is the population index expressed as a percentage, where index=100 in 1986, the first year data was available. The total abundance in Scotland and in each region as measured during two censuses: SCR in c.1986 and Seabird 2000 in c.2000 are presented as indices for comparison. Regional trends shown for species in which significant regional differences in trend occurred; also shown for tern species and great cormorant (in which trends were produced using chain index only) a) Northern Fulmar

b) Northern Gannet

c) Great Cormorant

34 Figure 4.1 (continued) c) Great Cormorant (cont.)

d) European Shag

35 Figure 4.1 (continued) e) Great Skua

36 Figure 4.1 (continued) f) Arctic Skua

g) Black-legged Kittiwake

37 Figure 4.1 (continued) g) Black-legged Kittiwake (cont.)

38 Figure 4.1 (continued) h) Sandwich Tern

39 Figure 4.1 (continued) i) Common Tern

40 Figure 4.1 (continued) j) Arctic Tern

41 Figure 4.1 (continued) j) Arctic Tern (cont.)

k) Little Tern

42 Figure 4.1 (continued) k) Little Tern (cont.)

43 Figure 4.1 (continued) l) Common Guillemot

m) Razorbill

Figure 4.2 Similarity between regional and colony-specific trends in abundance for each seabird species (see sections 3.5.1 and 4.2). Box-plots show the parameter I2000 (x-axis) of the modeled trend in abundance for each region (y-axis). The boxes denote the 25th-75th percentiles about the median (denoted as a vertical line) and the range is indicated by ’whiskers’, with outliers as dashes. The scatter plots show, for each colony, the probability that the trend in abundance is identical to that of the overall trend for the region in which it is assigned – a probability of >0.5 indicates that the colony-specific trends and the corresponding regional trends are similar. On both plots region is denoted by a number: 1 = Shetland, 2 = Orkney, 3 = North Scotland, 4 = NE Scotland, 5 = SE Scotland, 6 = SW Scotland, 7 = NW Scotland. Note: only modeled species analysed in this way.

a) Northern Fulmar b) Northern Gannet

c) European Shag d) Arctic Skua

44 e) Great Skua f) Black-legged Kittiwake

g) Common Guillemot h) Razorbill

Figure 4.3 National (i.e. Scotland) indices of abundance of the multi-species groups of seabirds defined in Table 3.3. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals, and chain indices in blue a) All species

e) Offshore feeders

45 f) Sandeel specialists

Figure 4.3 (continued) d) Inshore feeders

e) Offshore feeders

46 f) Sandeel specialists

Figure 4.3 (continued) g) Discard feeders

h) Flat-ground nesters

47 i) Cliff-nesters

Figure 4.1 Trend in breeding abundance of black-legged kittiwake in Scotland, 1986–2004. Modeled indices are shown in red, with ‘uncertainty bands’ equivalent to 95% confident intervals

48 Figure 5.1 The relationship between the modeled rate of change in numbers and colony size, for eight seabird species in Scotland, 1986-2004. Note the y-axis is the rate of change between 1986-2004, expressed on a natural log scale. Colony abundance (natural log scale) is taken from the modeled abundance in 1986. A linear trend line is fitted through the scatter of points

49

IUCN Programme Office for the Southern Caucasus 41 Vaja-Pshavela Ave., VI floor Tbilisi 0177, Georgia Phone: (995 32) 47 30 70, 47 30 71 Fax: (995 32) 39 57 05 E-Mail: [email protected] Web: www.iucn.org/caucasus

50