SNH Commissioned Report 222: Natural heritage trends: abundance ...

COMMISSIONED REPORT

Commissioned Report No. 222

Natural heritage trends: abundance of breeding seabirds in Scotland (ROAME No. F05NB01)

For further information on this report please contact: Simon Foster Scottish Natural Heritage Great Glen House Leachkin Road INVERNESS IV3 8NW Telephone: 01463 725282 E-mail: [email protected]

This report should be quoted as: 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).

This report, or any part of it, should not be reproduced without the permission of Scottish Natural Heritage. This permission will not be withheld unreasonably. The views expressed by the author(s) of this report should not be taken as the views and policies of Scottish Natural Heritage. © Scottish Natural Heritage 2007

COMMISSIONED REPORT

Summary Natural heritage trends:

abundance of breeding seabirds in Scotland Commissioned Report No. 222 (ROAME No. F05NB01) Contractor: Joint Nature Conservation Committee, Royal Society for the Protection of Birds Year of publication: 2007 Background This report presents a novel and robust way of analysing, presenting and updating trends in populations of breeding seabirds in Scotland. This information provides a basis for one of a series of indicators of biodiversity for the Scottish Biodiversity Strategy. The analysis aims to provide a ‘state indicator,’ ie a measure of change of population size of seabirds in their own right, but also investigates the potential for using seabirds as indicators of components of the marine ecosystem.

Main findings l

We established an effective modelling approach to detect changes in abundance of eight species of seabird between 1986 and 2004 and presented these with those of five other species for which the modelling approach was not appropriate.

l

We were unable to detect distinct regional variation in population trends for most species; therefore we conclude that the national (Scottish) trend is generally a suitable scale at which to report.

l

We recommend two seabird indicators: 1. Aggregated trend for 13 species of seabird – the average trend of the constituent species (Figure i). This trend should be used as an indicator of seabird populations in their own right but it should be noted that the constituent species showed considerable variation in how their respective populations varied in size over time. It should not be used 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. 2. Aggregated trend for sandeel–specialist species of seabird – the average trend of five species that rely on sandeels as their main prey during the breeding season (Figure ii). This was found to be the most ecologically appropriate of several multi-species groupings that were investigated. This indicator should be used primarily as a way of communicating the conservation issues surrounding sandeel availability (given that direct measurement of sandeel availability is currently not technically possible). However, given some of the constituent species’ trends were driven by other marine and

Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)

terrestrial influences, we suggest that this indicator be based instead on the trend of a single sandeelspecialist – the black-legged kittiwake – in order to convey a less ambiguous message (Figure iii). l

We recommend further development of the seabird indicators for Scotland, specifically: a)

investigate statistical methods for describing population change in terns and great cormorant;

b)

develop a complementary indicator that presents trends in breeding success of a range of species;

c)

assess the feasibility of increasing the number of seabird species that constitute the seabird indicator.

Figure i

Aggregated trend of breeding abundance of 13 species of seabird in Scotland, 1986–2004. Indices are shown in red, with ‘uncer tainty bands’ equivalent to 95% confident inter vals

Figure ii

Aggregated trend of breeding abundance of nine species of sandeel-specialist seabirds in Scotland, 1986–2004. Indices are shown in red with ‘uncer tainty bands’ equivalent to 95% confident inter vals


Figure iii

Trend in breeding abundance of black-legged kittiwake in Scotland, 1986–2004. Modelled indices are shown in red, with ‘uncer tainty bands’ equivalent to 95% confident inter vals

For further information on this project contact: Simon Foster, Scottish Natural Heritage, Great Glen House, Leachkin Road, Inverness IV3 8NW Tel: 01463 725282 For further information on the SNH Research & Technical Support Programme contact: Policy and Advice Directorate Support Unit, Scottish Natural Heritage, Great Glen House, Leachkin Road, Inverness IV3 8NW Tel: 01463 725000 or [email protected]


Acknowledgements We are grateful to David Elston and Stijn Bierman (both BioSS), who kindly helped in the formulation of a suitable model. Jim Reid ( JNCC), Jeremy Wilson (RSPB), Richard Gregory (RSPB) and Simon Foster (SNH) kindly provided useful comments on an earlier draft of this report and Phil Shaw (SNH) helped to develop the initial proposal.


Contents

Summar y Acknowledgements 1

INTRODUCTION

1

2

AIMS & OBJECTIVES 2.1 Overall aim 2.2 Key objectives

3 3 3

3

METHODS 3.1 Data source 3.1.1 Plot counts and whole colony counts 3.1.2 Species 3.1.3 Missing data 3.2 Approaches to measuring trends in seabird abundance 3.2.1 Chain indices 3.2.2 Statistical models 3.3 A hierarchical model for seabird abundance at individual colonies 3.3.1 The observation model 3.3.2 The latent model 3.4 Applying the heirarchical model to seabird count data 3.5 Computing trends in seabird abundance 3.5.1 Regional intra-specific trends 3.5.2 National intra-specific trends 3.5.3 Multi-species trends 3.5.4 Detecting trends 3.5.5 How well did the model and chain indices fit?

4 4 4 4 4 5 5 6 6 6 7 8 9 9 11 11 12 13

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? 4.1.2 National trends in abundance 4.1.3 Did trends in abundance vary regionally? 4.2 Multi-specific trends in abundance 4.2.1 All species 4.2.2 Ecological groupings 4.2.2.1 Surface feeders 4.2.2.2 Inshore feeders 4.2.2.3 Sandeel specialists 4.2.2.4 Flat-ground nesters 4.2.2.5 Discard, sub-surface and offshore feeders and cliff-nesters

14 14 14 14 16 17 17 18 18 18 19 19 20

Scottish Natural Heritage Commissioned Report No. 222 (ROAME No. F05NB01)

5

6

7

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? 5.1.2 Detecting trends in seabird numbers using the model 5.1.3 Regional variation in trends in abundance 5.1.4 Conservation implications of the trends in abundance 5.2 Multi-species ttrends in abundance 5.2.1 All species 5.2.2 Ecological groupings

40 40 40 42 42 43 44 44 45

CONCLUSIONS AND RECOMMENDATIONS FOR INDICATOR DEVELOPMENT

51

REFERENCES

52

Appendix 1

SBS consultation response

55

Appendix 2

Summary of seabird data available for estimating species-specific trends in abundance and in breeding success in Scotland

59

Technical details of statistical modelling and inference

62

Appendix 3

List of tables Table 3.1 Regional definitions Table 3.2 Number of colonies per species sampled in each Scottish region Table 3.3 Species groupings for multi-species trends in abundance Table 4.1 Overall % change in the population index of 13 seabird species between 1986–2004 and 2000–2004 Table 5.1 Linear regression of the modelled rate of change in numbers (Beta) against colony size, for eight seabird species in Scotland

10 11 12 15 41

List of figures Figure 4.1 Intra-specific trends in abundance of seabirds in Scotland, 1986–2004 22 Figure 4.2 Similarity between regional and colony-specific trends in abundance for each seabird species 34 Figure 4.3 National (ie Scotland) indices of abundance of the multi-species groups of seabirds defined in Table 3.3 37 Figure 5.1 The relationship between the modelled rate of change in numbers and colony size, for eight seabird species in Scotland, 1986–2004 49


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).1 The analysis aims to fulfil 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 19862. 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 (eg 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 (ie Populations of

wild birds) that form the UK’s Headline Indicator H13: Wildlife3 ; and ii) Defra’s Biodiversity Strategy for England (EBS) indicator M1: Populations of coastal birds and

seabirds in England4. 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 (ie colony, region, country – ie 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 modelling approach that, for each species, used observed counts to predict numbers present at colonies during years that no surveys

1

http://www.scotland.gov.uk/library5/environment/sbiiyh-00.asp

2

www.jncc.gov.uk/seabirds

3

http://www.sustainable-development.gov.uk/indicators

4

http://www.defra.gov.uk/wildlife-countryside/ewd/biostrat/#indicators

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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 (ie 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 fulfil 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’, ie 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: l

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);

l

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

l

nest site type (reflecting terrestrial-based influences eg 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 (eg 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


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 (eg intra-specific, multi-specific, national or regional) that most accurately represent changes in seabird biodiversity in Scotland.

3


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 seabird 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

4


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) Equation 3.1 where the sub-sample of colonies selected in year x was identical to those in year x–1 ie 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 (ie 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 (eg 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 (eg newly available) years. However, there are a number of serious flaws in adopting this rather simplistic approach (eg 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.

5


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. 3.3.1 The obser vation 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 – ie 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).

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Where whole colony counts were available we assumed that: True number of pairs or birds in colony = Observed colony count + Recording error Equation 3.2 and where plot counts are available we assumed that: True number of pairs or birds in colony = (Observed plot count + Recording error) * Plot fraction Equation 3.3 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 package5; 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 sitespecific 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 colonisation 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). 5

http://www.cbs.nl/nl-NL/menu/themas/milieu-natuur-ruimte/natuur/methoden/trim/default.htm

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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 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 (eg 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 (ie 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 LinBUGS6, an open source Linux-based variant of the popular WinBUGS7 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 loglinear trend in abundance over time (ie 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 ‘burnin’ 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 6

http://mathstat.helsinki.fi/openbugs/

7

http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml

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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) Equation 3.4 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 (ie 1986 – the first year for which data were collected for the SMP). For those species datasets to which the model was not fitted – ie great cormorant, common tern, Arctic 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 (ie Scottish Districts 1974–1996) and large-scale marine ecosystem boundaries (ie 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 characterise the spatial variation in population trends of seabirds in Scotland. We assessed this (for species with modelled trends – ie 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 (ie 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 modelled. Firstly, we constructed box-plots of the posterior distributions of three specific trend parameters for each colony:

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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: i)

trends at different colonies within the same region, and

ii) 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. 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, East Lothian, Berwickshire

SE Scotland

Annandale and Eskdale, Nithsdale, Stewartry, Wigtown, Kyle and Carrick, Cunninghame, Inverclyde, Dunbarton, Argyll and Bute

SW Scotland

Lochaber, Skye and Lochalsh, Western Isles, west coast of Ross & Cromarty, north and west coast of Sutherland

NW Scotland

#coastal

districts only

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.

10


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 (ie 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) Equation 3.5 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. 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

6

3

8

5

1

23

little tern 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).

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 modelling and chaining methods.

11


3.5.4 Detecting trends

The modelled 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 equalled 100%. In order to determine whether the modelled population index in a given year was statistically significantly different (ie 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. Table 3.3

Species groupings for multi-species trends in abundance

a) Species included in the present study Surface Sub-surface Sandeel feeder feeder specialist

Discard feeder

Inshore feeder

Offshore Cliff Flat-ground feeder nester nester

northern fulmar

X

X

X

X

northern gannet

X

X

X

X

great cormorant

X

European shag

X

X

X

X

X

X

X

X

Arctic skua

X

great skua.

X

Black-legged kittiwake

X

X

terns: Sandwich, common, Arctic, little

X

X

X

X X X

X X

X

X

common guillemot

X

X

X

X

razorbill

X

X

X

X

b) Species not included in the present study Surface Sub-surface Sandeel feeder feeder specialist

Discard feeder

Inshore feeder

Offshore Cliff Flat-ground feeder nester nester

herrring gull

X

X

X

X

lesser black-backed gull

X

X

X

X

great black-backed gull

X

X

X

X

mew gull, black-headed gull

X

roseate tern

X

Manx shearwater, 2 storm petrels,

X

X

Atlantic puffin

X

black guillemot

X

X X

X

X

X

X

X

X

X X

12

X

X

X

X

X

X

X

X

X


3.5.5 How well did the model and chain indices fit?

The modelled 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 modelled 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 modelled and chain indices from the census results was used as an indicator of how well the modelling and chaining methods estimated the actual change in abundance of each species between 1985–87 and 1998–2002 at the national and regional scales.

13


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 modelled 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 modelled (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 was censused separately, in 2004). In addition, it is clear that relatively wide confidence intervals of the modelled data in some cases (eg northern gannet) would tend to lead to a conclusion of no significant difference between this and the census total, even if there was a relatively large difference between the median of the modelled 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 modelled indices (Figure 4.1a, e, f, g). Indeed, for these four species there was no significant difference between the chain indices and modelled indices in most years, except for great skua during 1988–92 when the chain indices dipped slightly while the modelled 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 modelled indices in most years (Figure 1b, l, m). Of those species that were not modelled, 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.

14


Table 4.1

Overall % change in the population index of 13 seabird species between 1986–2004 and 2000–2004. Note: * indicates a significant change at the P0.5 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 modelled species analysed in this way.

a)) Northern fulmar 1

0.9 0.8

0.7

Probability

0.6

0.5

0.4 0.3

0.2 0.1

0 0

1

2

3

4

5

6

7

4

5

6

7

Re gion

b) Northern Northerngannet gannet b) 1

0.9 0.8

0.7

Probability

0.6

0.5

0.4 0.3

0.2 0.1

0 0

1

2

3 Re gion

c) European European Shag c) shag 1

0.9 0.8

0.7

Probability

0.6

0.5

0.4 0.3

0.2 0.1

0 0

Figure 4.2 (cont.)

1

2

3

4 Re gion

34

5

6

7


Figure 4.2

(continued)

d) Arctic skua 1

Arctic Skua

0.9

0.8 0.7

Probability

0.6 0.5

0.4 0.3

0.2 0.1

0 0

1

2

3

4

5

6

7

Re gion

e) e) Great Greatskua skua 1

Great Skua

0.9 0.8

0.7

Probability

0.6

0.5

0.4 0.3

0.2 0.1

0 0

1

2

3

4

5

6

7

4

5

6

7

Re gion

f)f) Black-legged kittiwake Black-legged kittiwake

1

Kittiwake

0.9

0.8 0.7

Probability

0.6 0.5

0.4 0.3

0.2 0.1

0 0

1

2

3 Re gion

35


Figure 4.2

(continued)

g) Common guillemot h) Razorbill 1

Guillemot

0.9

0.8 0.7

Probability

0.6 0.5

0.4 0.3

0.2 0.1

0 0

1

2

3

4

5

6

7

Re gion

h) Razorbill Razorbill h) 1

Razorbill

0.9

0.8 0.7

Probability

0.6 0.5

0.4 0.3

0.2 0.1

0 0

1

2

3

4 Re gion

36

5

6

7


Figure 4.3 National (ie Scotland) indices of abundance of the multi-species groups of seabirds defined in Table 3.3. Modelled indices are shown in red, with ‘uncer tainty bands’ equivalent to 95% confident inter vals, and chain indices in blue

a) All species

b) Surface-feeders

c) Sub-surface feeders

37


Figure 4.3

(continued)

d) Inshore feeders

e) Offshore feeders

f) Sandeel specialists

38


Figure 4.3

(continued)

g) Discard feeders

h) Flat-ground nesters

i) Cliff-nesters

39


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 modelled 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 modelled trends were largely representative of the true populations, as revealed by comparisons with the complete census data, as there was close agreement between the modelled 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 (eg Moss et al., 2002). In simple terms, density dependence may mean, for example, that larger colonies show a lower rate of change in abundance (ie 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 modelled 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 (eg 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 (ie 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 (eg 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 modelled trends.

40


Table 5.1

Linear regression of the modelled 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 modelled 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