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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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]
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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.
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
1
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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).
6
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
7
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
8
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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:
9
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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 P<0.05
level, inferred from overlap in respective 95% confidence inter vals. 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
+11.0
+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
+9.0
–17.4
razorbill
+19.9
+2.9
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 thestudy period.
The chaining index trend for great cormorant (this species was not modelled) 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 modelled 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.
15
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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 var y regionally?
The scatter plots in Figure 4.2 show that in all the modelled 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 (ie 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.
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 (ie 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 modelled 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 modelled 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 modelled trend in NW Scotland of 225%. The modelled 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 (ie 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
(ie species that were not modelled), 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
16
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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 modelled 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 modelled 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 modelled 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 modelled
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 modelled data, but substantially
underestimated the degree of decline, particularly in years since about 1993.
Figure 4.3a National (ie Scotland) indices of abundance of the all-species group of seabirds,
1986–2004. Modelled indices are shown in red, with ‘uncer tainty bands’
equivalent to 95% confident inter vals, and multi-species chain indices in blue
17
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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.
Figure 4.3b National (ie Scotland) indices of abundance of the sur face feeders group of
seabirds defined in Table 3.3. Modelled indices are shown in red, with
‘uncer tainty bands’ equivalent to 95% confident inter vals, and multi-species chain
indices in blue
4.2.2.2 Inshore feeders
The modelled 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 modelled trend, though this was in part due
to the fact that this grouping is dominated by those species (ie 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).
18
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.3d National (ie Scotland) indices of abundance of the inshore feeders group 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
4.2.2.3 Sandeel specialists
Sandeel specialists (Figure 4.3f) showed a trend very similar to that of surface feeders. The modelled 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).
Figure 4.3f National (ie Scotland) indices of abundance of the sandeel specialist group 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
4.2.2.4 Flat-ground nesters
The modelled 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 modelled indices, in part
19
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
due to the fact that this grouping is dominated by those species (ie 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 modelled 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).
Figure 4.3h National (ie Scotland) indices of abundance of the flat-ground nesters group 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
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 modelled 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 modelled indices.
Figure 4.4c National (ie Scotland) indices of abundance of the sub-sur face feeders group 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
20
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.4e National (ie Scotland) indices of abundance of the of fshore feeders group 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
Figure 4.4g National (ie Scotland) indices of abundance of the discard feeders group 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
Figure 4.4i National (ie Scotland) indices of abundance of the clif f nesters group 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
21
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1 Intra-specific trends in abundance of seabirds in Scotland, 1986-2004. Note: The
red line shows the modelled trend with ‘uncer tainty bands’ equivalent to 95%
confident inter vals, 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 dif ferences 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
22
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
c) Great cormorant (cont.)
d) European shag
23
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
e) Great skua
24
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
e) Great skua (cont.)
f) Arctic skua
g) Black-legged kittiwake
25
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
g) Black-legged kittiwake (cont.)
26
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
g) Black-legged kittiwake (cont.)
h) Sandwich tern
27
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
h) Sandwich tern (cont.)
i) Common tern
28
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
i) Common tern (cont.)
29
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
j) Arctic tern
30
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
j) Arctic tern (cont.)
k) Little tern
31
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
k) Little tern (cont.)
32
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.1
(continued)
l) Common guillemot
m) Razorbill
33
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
I 2000 (x-axis) of the modelled 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 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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.3
(continued)
d) Inshore feeders
e) Offshore feeders
f) Sandeel specialists
38
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 4.3
(continued)
g) Discard feeders
h) Flat-ground nesters
i) Cliff-nesters
39
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
<0.001
Arctic skua
–0.0394
0.0001
<0.01
<0.01
22
0.990
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
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 modelled trends are used in these cases, because
the degree of uncertainty around the annual indices (ie 95% confidence intervals) were measured for the
modelled 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 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 (ie 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.
41
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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, ie 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 modelled 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 modelled 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).
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
42
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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 (eg
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 Conser vation 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
43
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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
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 modelled 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.
44
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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 eg
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 (eg 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 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 modelled, 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
45
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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 flatground 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, surfacefeeders, 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
46
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
unquestioning assumption of causality could lead to erroneous diagnosis of trends and misallocation
of remedial measures. As we have discussed (and 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.
Figure 4.1 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
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
47
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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.
48
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 5.1 The relationship between the modelled 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 modelled abundance in 1986. A linear trend
line is fitted through the scatter of points
49
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Figure 5.1
(continued)
50
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
6
CONCLUSIONS AND RECOMMENDATIONS FOR INDICATOR DEVELOPMENT
We have established an effective modelling 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 modelling
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 modelling approach was inappropriate, namely terns and great cormorant.
We were unable to detect distinct regional variation in population trends for most species (except for blacklegged 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.
Disaggregations 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 (blacklegged 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.
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.
51
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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.
52
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
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, 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.
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.
53
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.
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.
54
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
APPENDIX 1 – SBS consultation response
BIODIVERSITY STATE INDICATORS QUESTIONNAIRE
Name:
Dr Ian Mitchell (JNCC), Dr Philip Shaw (SNH), Dr Richard Gregory (RSPB)
Organisation:
JNCC, SNH, RSPB
Are you responding on behalf of other partners or just your organisation?
All organisations listed above
INDICATOR
Please specify in the box below the indicator you are responding on below (eg S1a Status of UKBAP priority
species)
S4 Abundance of breeding seabirds
1. Are there any significant corrections, updates or improvements that should be made to the description
of the proposed indicator in the enclosed report?
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. We therefore propose that a measure of breeding
productivity should also be considered, providing an early warning of likely future population change.
The title of the indicator might then change to Abundance and productivity of breeding seabirds.
2. Do you consider the data currently available for Scotland to be sufficiently robust to permit an indicator
to be developed and reported? Please give your reasons.
Trend data are available from two main sources: breeding seabird censuses of Britain and Ireland, that
have taken place at approximately 15-year intervals; and the Seabird Monitoring Programme, which
provides measures of abundance and breeding success annually from a sample of breeding colonies in
Britain and Ireland.
Censuses of breeding seabirds in Britain and Ireland have been carried out in 1969–70 (Operation
Seafarer), 1985–88 (The Seabird Colony Register) and 1998–2002 (Seabird 2000). The latter was
coordinated jointly by the JNCC, the country conservation agencies, RSPB and the Seabird Group
and Shetland Oil Terminal Environmental Advisory Group (SOTEAG), BirdWatch Ireland and Duchas
(in the Republic of Ireland). Together, the three censuses provide comparable estimates of coastal
breeding populations for 20 seabird species between c. 1970 and c. 1987, and for 21 species
between c. 1987 and c. 2000. These data are considered sufficiently robust to provide a long-term
indicator of population change at the regional and national level in about 20 seabird species over the
c. 30-year period.
55
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
In order to provide an ongoing indicator of the current status of seabird populations, data from the
Seabird Monitoring Programme (SMP) should be used. The SMP provides annual (or in some cases
triennial) estimates of colony size and breeding success for a sample of colonies around Britain and
Ireland, dating from 1986. These data are thought to provide a robust indicator of change within the
colonies selected, but less so for colonies of auks, for which breeding numbers and breeding success are
more difficult to assess. In Scotland, however, there has been substantial regional variation in trends in
seabird numbers and breeding success. The degree to which these sample colonies are representative of
Scottish seabird colonies as a whole remains untested. There may be sufficient coverage of colonies
within Scotland to provide an indicator of change in population size of 11 species and in the breeding
success of ten of these. The most extensive coverage of colonies occurs for three of Scotland’s most
abundant breeding seabirds species: Northern Fulmar, Common Guillemot and Black-legged Kittiwake.
3. Could this indicator currently be disaggregated to report trends by (for example) broad habitat types, or
geographical regions within Scotland? Please give your reasons.
Both datasets described in ‘2’ could be disaggregated to report on trends by:
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 show considerable 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).
However, we advise caution over the development of a multi-species indicator for seabirds, since there may
be considerable inter-species variation in the response of populations to changing environmental factors,
even when regional variation and differences in feeding guild and nest site types have been taken into
account. Furthermore, multi-species indicators do not take account of the differential abundance of the
component species. We suggest that certain key single-species indicators would be useful for highlighting
the effects of specific environmental factors on important seabird populations. For example, annual changes
in the population size and breeding success of Black-legged Kittiwakes, Northern Fulmars and Common
Guillemots would represent three of the most abundant seabird species in Scotland, from three different
feeding guilds (ie piscivorous surface feeder, scavenging/plankton surface feeder and piscivorous diver
respectively), as well as the most robust data derived from seabird monitoring in Scotland (see section 2).
4. Could and should this indicator be improved in terms of geographical representativeness and statistical
accuracy and precision in the longer term? What would be the resource implications of making these
improvements?
See comments under ‘2’. A review of the degree to which the SMP provides a representative measure of
change in seabird abundance and breeding success is currently being undertaken by JNCC. This will
consider the geographical spread of survey colonies, and the positioning of sample sites within these
colonies. The review will also investigate the possibility of monitoring population size and breeding
success of burrow-nesting species, which have so far been poorly covered by the SMP. The resource
implications of adding new colonies and species to existing monitoring are unknown at present.
56
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
5. Are you willing to report this indicator (or some modification of it based on your expert
recommendations) for the Scottish Biodiversity Strategy?
The JNCC, on behalf of the contributors to the SMP would be willing to report annually on this indicator
for the Scottish Biodiversity Strategy.
6. Would you be able to report this indicator during the first three-year cycle of the strategy (cycle runs from
2005–2008)? If not, please give reasons. If it is a resource issue, please also see question 7
Yes, although the selection of species used in the indicator needs to be resolved. We would also have
to consider whether the initial report would be based entirely on an analysis of trend data from the three
national censuses, or would include abundance data from SMP colonies. Similarly, we would have to
consider whether annual productivity data should be incorporated into the indicator, and resolve any
issues relating to the representativeness of SMP colonies and survey sites.
7. Would you have any additional resource needs to allow you to report this indicator within these
timescales? If so please outline these.
Would they relate simply to technical/analytical development of the indicator from existing data, or
would there be an ongoing resource need related to reporting?
Additional resources would be required to:
1. Resolve any issues relating to the geographical spread of SMP survey colonies, and the positioning
of sample sites within colonies.
2. Identify appropriate geographical areas, both for long-term and annual reporting.
3. Identify appropriate feeding guilds.
4. Identify appropriate nesting habitat disaggregations.
5. Extract trend data, broken down by geographical area and guild, independently and in combination.
In summary, resources would be required for developing parts of the indicator dependent on SMP data.
Analysis of the three national censuses would require little additional work or resources.
8. Do you consider that trends in the indicator could be interpreted meaningfully in the context of the aims
of the Scottish Biodiversity Strategy? To assist you, the full text of the strategy can be accessed at
http://www.scotland.gov.uk/biodiversity
Different species of seabird respond in quite different ways to changes in the marine environment, which
may in turn vary markedly between different regions of Scotland. Furthermore, breeding numbers of some
species are affected mainly by terrestrial-based factors (eg predation at colonies, nest site availability) or
by factors acting outwith Scotland’s marine environment. Changes in such an indicator may therefore
prove difficult to interpret, in terms of their implications for the marine environment. For this reason, we
suggest that the proposed indicator should be treated as a measure of change in seabird species in their
own right (as an important element of Scotland’s biodiversity), rather than as a measure of the ecological
integrity or ‘health’ of the marine environment.
57
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
9. If the decision was taken to identify a small sub-set of indicators as headline indicators for the Scottish
Biodiversity Strategy, would you recommend that this indicator should be included as a headline
indicator and, if so, why?
For reasons given in ‘8’, it would prove difficult to interpret links between seabird abundance and
changes in the ecology of the marine environment. For this reason we would not recommend S4 as a
headline indicator.
58
59
Y
N
N
Northern fulmar
Fulmarus glacialis
Manx shearwater
Puffinus puffinus
European storm-petrel
Hydrobates pelagicus
N
N
Y
N
N
Y
N
N
Y
trends in
productivity
trends in
abundance
1969–70 to
1985–88
1985–88 to
1998–2002
b) SMP data – are
data sufficient for
estimating annual
trends for Scotland?
a) Census data – are
data sufficient for
calculating long-term
trends in Scotland?
L
L
w
Extent of
distribution
in Scotland1
There are 59 island colonies situated in the Northern Isles and
off N & W Scotland. The first complete survey of all colonies
was conducted in 1998–2002.
95% of the Scottish population breeds at four colonies in
Lochaber, at three colonies elsewhere in W Scotland and at one
small colony in Shetland. The first complete survey of all colonies
was conducted in 1998–2002.
notes
in the third column under ‘trends in abundance’ were included in the analyses described in this report.
to generate data that provides an accurate indication year-to-year trends in abundance and/or breeding success. Those species that contain a ‘YES’ or ‘YES/NO’
UK since 1986 (see sections 1 and 3.1 above). The table below indicates those species for which there has been sufficient coverage of colonies in Scotland
b) SMP data – the Seabird Monitoring Programme has collected data on abundance and breeding success annually from a sample of colonies throughout the
in Mitchell et al., (2004)
2004). By comparing estimates from these three censuses, a trend (ie percentage change) can de derived for a period of 15–30 years. Such trends are given
conducted of breeding seabirds in Britain and Ireland during 1969–70 (Cramp et al., 1974), 1985–88 (Lloyd et al., 1990) and 1998–2002 (Mitchell et al.,
a) Census data – these are estimates of the total numbers of pairs or individuals of a particular species breeding in Scotland during one of three censuses
from two sources: a) Census data and b) SMP data.
The table below illustrates for each species of seabird breeding in Scotland (24 in total) whether or not it is possible to derive trends from existing data derived
APPENDIX 2 – Summar y of seabird data available for estimating species-specific trends in abundance and in breeding
success in Scotland
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
N
N
N
N
Y
Y
Arctic skua
Stercorarius parasiticus
Great skua
Stercorarius skua
Black-headed gull
Larus ridibundus
Mew gull
Larus canus
Lesser black-backed gull
Larus fuscus
Herring gull
Larus argentatus
Y
Great cormorant
Phalacrocorax carbo
Y
Y
Northern gannet
Morus bassanus
European shag
Phalacrocorax aristotelis
N
60
Y
Y
N
Y
Y
Y
Y
Y
Y
N
N
N
N
N
Y
Y
?
?
N
N
?
?
Y
Y
Y/N2
Y
Y
N
Y
N
trends in
productivity
trends in
abundance
1969–70 to
1985–88
1985–88 to
1998–2002
b) SMP data – are
data sufficient for
estimating annual
trends for Scotland?
a) Census data – are
data sufficient for
calculating long-term
trends in Scotland?
(continued)
Leach’s storm-petrel
Oceanodroma leucorhoa
Appendix 2
w
w
w
w
L
L
w
w
L
L
Extent of
distribution
in Scotland1
43% of the Scottish population breeds inland, but inland colonies
were not surveyed during 1969–70 and 1985–88 censuses.
Poor coverage by SMP of colonies in Scotland.
85% of the Scottish population breeds inland, but inland colonies
were not surveyed during 1969–70 census. Poor coverage by
SMP of colonies in Scotland.
Confined mainly to Northern Isles and NW Scotland. Coverage
during 1969–70 census was incomplete.
Confined mainly to Northern Isles and NW Scotland. Coverage
during 1969–70 census was incomplete.
Limited to 12 colonies on islands widely spaced around
Scotland.
95% of the Scottish population breed on St Kilda; elsewhere are
confined to 5 other colonies in the Western Isles and one small
colony in Shetland. The first complete survey of all colonies was
conducted in 1998–2002.
notes
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
61
Y
Y
N
Y
Common guillemot
Uria aalge
Razorbill Alca torda
Black guillemot
Cepphus grylle
Atlantic puffin
Fratercula arctica
w = widespread, L = limited
Y
Little tern
Sterna albifrons
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y/N2
N
N
Y
N
N
N
?
Y
Y/N2
Y
Y
N
Y
Y
?
Y/N2
N
Y
Y
N
w
w
w
w
w
w
w
L
L
w
w
Extent of
distribution
in Scotland1
SMP coverage is limited to the Northern Isles. Survey methods
used during 1969–70 census were not comparable with those
used in the two subsequent censuses.
SMP breeding success coverage of only 3 colonies.
SMP breeding success coverage of only 8 widely spaced
colonies.
80% of the Scottish population breeds in the Northern Isles,
where coverage was incomplete during 1969–70 census.
Just a few pairs now nest in Scotland.
Confined to 8 colonies in Orkney and E Scotland and one small
colony in NW Scotland. SMP monitors numbers annually at all
Scottish colonies.
notes
High rates of extinction and colonisation meant that trends in annual abundance could be described only by using a chain index method and not by using the Bayesian
model developed in this study.
2
1
N
Y
Roseate tern
Sterna dougallii
Arctic tern
Sterna paradisaea
Y
Sandwich tern
Sterna sandvicensis
Y
Y
Black-legged kittiwake
Rissa tridactyla
Common tern
Sterna hirundo
Y
trends in
productivity
trends in
abundance
1969–70 to
1985–88
1985–88 to
1998–2002
b) SMP data – are
data sufficient for
estimating annual
trends for Scotland?
a) Census data – are
data sufficient for
calculating long-term
trends in Scotland?
(continued)
Great black-backed gull
Larus marinus
Appendix 2
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
APPENDIX 3 – Technical details of statistical modelling and inference
Modelling: the obser vation model
Assume that we have data for i = 1,…,L colonies and j = 1,…,M years. Let Cij denote the colony count for
year j at colony i, and let Pijk denote the plot count for year j in plot k at colony i. If Tij denotes the (unknown)
true number of birds in year j at colony i then we assume that colony and plot counts are both normally
distributed about this:
The unknown “plot fraction” λij denotes the proportion of birds in colony i that are contained within plot j,
and is assumed to be constant across years. The coefficients of variation for the colony and plot counts, O|C
and O|P respectively, are fixed a priori at either 0.025 or 0.05 (the choice varies between species: 0.05
for colony counts for great skua, arctic skua, fulmar, guillemot & razorbill and for plot counts for great skua
and arctic skua, 0.025 for colony counts for shag, kittiwake, little tern, cormorant, gannets, arctic tern and
common tern and for plot counts for fulmar, guillemot, razorbill, shag, kittiwake & little tern). The values of
0.025 and 0.05, which were provided by JNCC/RSPB, correspond to a biological assumption that the
level of recording error is likely (with 95% probability) to be less than either 5% or 10% respectively.
Modelling: the latent model
The parameters Ti1,…,TiM correspond to the (unknown) true numbers of birds for colony i, and we are
interested in estimating trends in these parameters. We consider a multiplicative model of the form
where uij represents the index of abundance for year j at colony i and (i denotes the abundance in year 1
(1986). This implies that Ti1 = (i, and so that ui1 = 0.
The indices of abundance are able to quantify relative changes in abundance in a scale-free way. We
model uij using a linear model
where
denotes the log-linear trend over time for abundance at colony i (a random effect). The parameters γj denote
common year effects for year j, and we assume that the differences of these year effects are iid, with
distribution.
62
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
The parameters εij represent site-specific “noise” about the log-linear trend & common year effects, and again
we assume that the differences of these year effects are iid, with distribution.
In order to ensure that the parameters γj and εij have the correct interpretation we impose the additional
constraints that,
so that the noise and common year effects represent deviations about colony-specific and common log-linear
trends respectively.
Inference & computation
We fit the models within a Bayesian context, using LinBUGS to sample from the posterior distribution of the
model parameters via Markov chain Monte Carlo (or, more specifically, via Gibbs’ sampling). LinBUGS
(www.math.helsinki.fi/openbugs/) is a Linux-based variant of the popular WinBUGS software (www.mrcbsu.cam.ac.uk/bugs/), and provides a powerful and relatively user friendly computational environment for
implementing Bayesian methods.
Prior distributions
In order to use Bayesian methods we must first define prior distributions upon the hyperparameters of our
model. For the plot fraction and overall abundance parameters we attempt to use prior distributions that are
vague, and so bring little prior information into the analysis. Specifically, we assume that:
λij ~ U(0,1), and log (i ~ U(–3, 13).
We use the priors for the remaining parameters to ensure that the trends over time are relatively smooth,
taking:
( ~ N(0, 1/100), log (( ~ N(–3, 1/2), log (( ~ N(–3, 1/2), and log (( ~ U(–15, –3/2).
Investigations using short model runs indicated that our results are largely insensitive to the prior distributions
for θ, log (( and log (( (so long as we do not use highly informative uniform priors), but are sensitive to the
upper endpoint for the distribution of log ((. Taking a value of –3/2 amounts to making an assumption that
year-to-year changes in the noise term of more than 50% are unlikely to occur (or, more specifically, have
roughly a 5% chance of occuring). If the parameter log (( is given a vague prior then the overall variability
in the resulting indices of abundance is enormously large, but the synchronous component of the trend
become very small – placing an informative uniform prior on log (( forces some (weak) degree of
synchroneity across sites, and is necessary in order to ensure that the model is not overparameterised. The
priors on θ, log (( and log (( are chosen in such a way that strong trends are unlikely, a priori, but are not
impossible.
63
Scottish Natural Heritage Commissioned Report No. 222 ( ROAME No. F05NB01)
Initial values
Initial values for the Markov chains are generated in an automatic way, and, because the number of
parameters within the model is so large, we have unfortunately not been able to assess the sensitivity of the
results to the selection of the initial values.
The initial values for Tij, and for missing values of Cij, are taken equal to the mean of the observed colony
counts for colony i. The initial values for log (i are taken equal to 5, and for (ik are taken equal to 0.5. Initial
values for log ((, log (( and log (( are all taken equal to –3, whilst those for (, (i, (j – (j–1 and (ij – (i(j–1) are
all taken equal to zero.
Running LinBUGS
We run the model separately for each of the twelve seabird species. In each case we generate a total of
55000 simulations, reject the first 5000 values as burn-in, and then use only every 50th value thereafter: in
this way we aim to obtain 1000 values which provide independent realisations from the posterior
distribution for the parameters of the model. We attempt to use simple visual checks to check that each of
the Markov chains has indeed converged to a stationary distribution, and use sample autocorrelation values
to try and verify that the thinning procedure has been sufficient to remove most of the dependence from the
series of simulated parameter values. We can only do this for a small subset of relevant parameters,
however, because the model generates an enormous quantity of output. The results of these diagnostic
checks suggest that most of the parameters of interest at the individual colony level do indeed appear to
have converged to a stationary distribution, and that the thinning procedure has yield a set of parameter
values that are only weakly correlated. Unfortunately, the “true number of birds” parameters indicate lack of
convergence for certain colonies – this usually occurs when there are only two colony counts and no plot
counts, as is often the case for the most highly abundant colonies, and as a consequence the regional and
national indices indicate substantive lack of convergence for some of the seabird species. These problems
could, at least in principle, be overcome by running the model for a larger number of iterations – however,
the computational burden of this would be probably be unfeasibly high, and would still not overcome the
more fundamental problem that the assumptions which underpin the model are simply insufficiently strong to
enable meaningful interpolation of trends at sites with extremely sparse data.
Table A3.1 Values of obser ver error used in hierarchical model of seabird abundance
Species
Whole colony counts
Plot counts
northern fulmar
10%
5%
northern gannet
5%
NA
great cormorant
5%
NA
European shag
5%
5%
Arctic skua
10%
10%
great skua
10%
10%
black-legged kittiwake
5%
NA
common tern
5%
NA
Arctic tern
5%
NA
little tern
5%
NA
common guillemot
10%
5%
razorbill
10%
5%
64