Seal Abundance and Distribution

Overview

This indicator assessment uses estimates of seal numbers from monitoring programmes that count seals on land when they are moulting or breeding, for two species: Atlantic grey seal (Halichoerus grypus) and harbour seal (Phoca vitulina). Assessments were not possible along the Norwegian North Sea coast because not enough annual counts had been conducted. Data for Ireland were not available for this assessment. Sweden provided data but due to differences in formatting and spatial resolution the data could not be used. Furthermore the grey seal population is too small in Swedish waters in the Skagerrak and Kattegat to provide a robust assessment.

Assessments of changes in abundance and distribution were made within discrete geographical areas of coastline, or ‘Assessment Units’ (AUs). The seals counted within each AU are more likely to forage in a similar area of the sea and to be more closely related to one another. Grey seals forage across a much wider area, so their abundance is assessed across the whole of the Greater North Sea and the United Kingdom (UK) part of the Celtic Seas using a single AU (Figure a). The large number of small AUs for harbour seals (Figure b) reflects the fact that they tend to range over shorter distances.

Trends in the abundance of harbour seals counted during the annual moult (every August) were assessed within each AU. Total abundance of grey seals in the single large AU was modelled using summer counts of adult grey seals and counts of pups at grey seal colonies during the autumn and winter for the Wadden Sea area. In addition, trends in the abundance of grey seals counted during their spring moult were assessed in areas where such surveys took place. Trends in both species were assessed over the short term (2009–2014) and over the long term (1992–2014, for most areas).

The presence or absence of seals at monitored haul-out and breeding sites was used to assess changes in the number of sites occupied (‘occupancy rate’) and the number of sites deserted or newly colonised (‘distributional shift’). Changes in occupancy rate and distributional shift were compared between the periods 2003–2008 and 2009–2014 for both species in each of the AUs used for the harbour seal abundance assessment (Figure b). Seal abundance surveys are not designed to detect changes in distribution; they reflect the distribution of seals only at specific times of the year. Therefore, change in distribution is used as a ‘surveillance indicator’ to help interpret changes in abundance. The distribution metrics were not compared to any assessment values.

Seal Monitoring in the North-East Atlantic

Grey seals and harbour seals are surveyed when they come ashore (‘haul-out’) and can be most easily counted. Surveys are usually conducted from the air, but may also be conducted from land or by boat. In most areas, aerial surveyors take photographs of the haul-out sites and animals are later counted from the images (see example in Figure c).

There are two periods during the year when most grey and harbour seal surveys take place: breeding (pupping) and moulting. Harbour seals give birth to pups in early summer and moult after breeding in late summer. Grey seals pup in the autumn or winter and moult in early spring. The frequency of surveys during these periods varies across OSPAR Contracting Parties due to differences in the total number of resident animals, funding, geography and historical development of the monitoring programmes. Repeated surveys of both grey and harbour seals throughout the year are not economically feasible and Contracting Parties use population censuses in one or perhaps two seasons of the year. For grey seals in particular, choosing an ideal single time to survey is difficult because of the potential for discrepancies between the breeding population and the population present during other times of the year (Brasseur et al. 2015; Russell et al. 2013). Table a provides details of current and known seal monitoring programmes in each Assessment Unit.

All Contracting Parties have some form of monitoring in place for harbour seals during their annual summer moulting period, this is when the probability that animals will haul-out and be detectable during a survey is highest. Minimum abundance can be estimated from these counts. Using telemetry data, the proportion of seals hauled-out during the survey window is estimated.

Grey seals are also counted during the harbour seal surveys (except in Norway). The number of grey seals hauled-out during the harbour seal moult can be very variable. In all AUs outside the United Kingdom and Norway, grey seals are also counted during their annual moult period in early spring. The total number of grey seal pups born at colonies during the autumn / winter breeding season is also counted.

Spatial Scope: Assessment Units

The European populations of grey and harbour seals were sub-divided into a number of AUs (Figure a and Figure b). These AUs were based on proposals by the International Council for the Exploration of the Sea (ICES, 2014a) and cover the full distributional range for both species. It is important to note that AUs do not represent demographically independent populations. In addition, survey coverage and monitoring effort is high for both species where they are most abundant. In some AUs monitoring is undertaken in specific areas by local organisations and does not form part of synoptic national survey programmes (see Table a for details of monitoring programmes in each AU).

Grey Seal Assessment Units

Grey seals range more widely at sea than harbour seals and may visit multiple distant haul-out sites (McConnell et al., 1999). Immigration of grey seals may account for as much as 35% of the observed population growth in the Dutch Wadden Sea (Brasseur et al., 2015) and hundreds of adults from the United Kingdom visit the area temporarily. Grey seal abundance was therefore assessed across the whole of the Greater North Sea and Celtic Seas as a single AU (Figure a). Assessments were not possible along the Norwegian North Sea coast because not enough annual counts had been conducted. It is important to note that grey seal populations exist along the coast of Ireland but data from Ireland were not available to be included in this assessment. Additionally grey seal populations in Sweden were too small for a robust assessment.

A single large AU was not considered appropriate for assessing the distribution of grey seals because of the substantial loss of local-scale information. Grey seal distribution was therefore assessed using the smaller AUs already defined for harbour seals (Figure b).

Despite their ability to travel long distances, individual mature grey seals of both sexes are usually faithful to particular breeding sites, and may return to within 10–100 m of individual breeding locations (Pomeroy et al., 2000). Insights into the seasonal movement of grey seals in the United Kingdom from telemetry data indicate that grey seal breeding distribution can be considerably different from their foraging distribution during other times of the year (Russell et al., 2013). Grey seal distribution metrics are presented both for the breeding (i.e. colony locations) and non-breeding seasons (i.e. moult) where this information is available.

Harbour Seal Assessment Units

The harbour seal AUs are shown in Figure b. The AUs in the Greater North Sea are broadly similar to those previously defined as OSPAR Ecological Quality Objectives (EcoQO) sub-units. The AUs for harbour seal are much smaller than those used for grey seal because harbour seals tend to undertake shorter excursions from their favoured haul-out sites, and there is some evidence of population structuring in European harbour seals (e.g. Goodman, 1998; Olsen et al., 2014).

In the Celtic Seas, only data from United Kingdom sites were available for assessment. Harbour seal populations do exist along the coast of Ireland, but no data were available because data density could not support the assessment.

Data Collation

All Contracting Parties were asked to provide data on grey seals and harbour seals for the period 1992–2014. Data were received from Denmark, France, Germany, the Netherlands, Norway and the United Kingdom. Counts were provided for individual haul-out sites (both species) and breeding colonies (grey seals only) and summed to give a single number for that year for that AU. Not all Contracting Parties monitor all sites annually. Assessment of trends in abundance of both species was only completed where at least four annual data points were available.

Grey Seal Abundance Estimation

The data provided and used in this assessment comprised: grey seal counts conducted during surveys of harbour seals in their summer moult period (August), counts of grey seal pups made at breeding colonies in the autumn / winter, and counts during the grey seal moult period which follows the breeding season in early spring.

The summer counts of grey seals (made during surveys of harbour seals in their summer moult period) can be variable and are not, on their own, a reliable estimate of the size of the grey seal breeding population. There are three reasons for this: a large proportion of grey seals will not be hauled-out on land and thus counted during the harbour seal moult, and this proportion can vary widely; animals counted in a particular location during the summer are not necessarily those that breed in the same area, because grey seals may travel extensively between breeding and foraging sites (Russell et al., 2013; Brasseur et al., 2015); and in summer more grey seals lie in the midst of harbour seals, potentially leading to confusion between the species and over-estimation or under-estimation is more likely to occur at this time than in other seasons.

The first of these concerns can be addressed by estimating the proportion of time grey seals spent hauled-out during August surveys using telemetry (geolocation) data. However, all of these points have been addressed by some Contracting Parties who use counts made during the harbour seal moult period as estimates of abundance. Such data are not extensively collected in the United Kingdom where a large proportion of the grey seal population is found.

Data for grey seal pup counts, which is separately assessed in the Grey Seal Pup Production indicator , are collected by most Contracting Parties. The fact that grey seals form large breeding colonies in the autumn and winter provides an indirect way to estimate abundance by counting the number of pups born or ‘produced’ at a colony. Multiple surveys are conducted and the number of pups is either reported as the maximum of multiple counts (referred to as peak pup counts), or is used in an established statistical model to provide an estimate of the total number of pups produced at that colony during the entire breeding season (referred to as pup production). Data from the United Kingdom were provided in the form of pup production. Peak pup counts (provided by other Contracting Parties) are an under-estimate of pup production. In order to estimate pup production from peak pup counts, a pup production curve was fitted to the United Kingdom pup counts to generate an estimate of pup production and to estimate a scalar between peak counts and pup production. If less than four counts were conducted, this scalar was used to raise the peak pup count to pup production.

Pup production estimates and prior knowledge of life history parameters (e.g. pup survival, adult survival, fecundity) were incorporated into a Bayesian state-space model currently used by the Seal Mammal Research Unit (SMRU) in the United Kingdom to estimate total population size and life history parameters (SCOS, 2014). This model had to be fitted at a sub-regional level because previous work in the United Kingdom has shown that pup survival is density-dependent and acts on a more local scale. Movement of females was also estimated, recognising that some females born in the United Kingdom recruit into the breeding population in continental Europe (Brasseur et al., 2015). Like pup survival, movement was assumed to be density-dependent.

The population model provides estimates of total grey seal abundance for the Greater North Sea (except Norway and Sweden) and the United Kingdom part of the Celtic Seas. These model outputs were then ‘checked’ with another form of information: the total number of grey seals estimated from summer counts in a single year. The total grey seal summer population of the Greater North Sea (excluding Norway because grey seals are not counted here during the summer months) and the United Kingdom part of the Celtic Seas was estimated using summer haul-out counts in 2008. This was the year (2008) with the most comprehensive survey coverage in Scotland, where the majority of the population is found. If no counts were made in 2008 for an area but there were counts made both before and after 2008, the count in 2008 was estimated (interpolated) from these counts. If there were only counts either before or after 2008, the count from the nearest year was used. Lonergan et al. (2011) used telemetry data to estimate that grey seals in the United Kingdom spend 31% of the time hauled-out during the usual survey window. This was used to estimate the total United Kingdom population from observed counts during summer 2008 . The annual estimates produced by the pup production model were refined by inputting the estimated summer population in 2008. For further details of the analytical method, and its development see SCOS (2014).

Baselines                              

Seals have been hunted both illegally and legally for a long time and it is not possible to know the undisturbed state, nor the current carrying capacity that could be attained alongside protection from illegal hunting. Time series data for abundance and distribution of both seal species do not provide an indication of a time when seal populations were not impacted and what it would look like in terms of abundance and distribution. It is therefore not possible to identify a baseline representing un-impacted conditions.

The baseline used was the abundance estimate for each species in 1992, which is the baseline year used by some Member States for seals under the European Union Habitats Directive (Council Directive 92/43/EEC). For harbour seal, data for 1992 were not available in all AUs; in such cases, the start of the data time series was used as the baseline. Indicator assessment values were set as a deviation from the baseline value (Method 3; OSPAR, 2012).

Bearing in mind that the arbitrarily assigned baseline does not necessarily reflect a state without impacts, it is not possible to assess the status of seals in relation to the concept of a “favourable conservation status”. Instead, trends in the seal populations were assessed. A short term rate-based assessment value was also adopted that uses a rolling baseline (Method 1; OSPAR, 2012). Here, the average annual rate of population change over consecutive six-year periods (the reporting cycle for the European Union Marine Strategy Framework Directive; MSFD) was assessed against the next. The rolling baseline provides a means to indicate change in population size compared with the previous six-year assessment period, rather than relying solely on an historical fixed baseline, which probably reflects a point in time when the population is already subject to anthropogenic pressures.

A potential issue with this type of quantitative trend thresholds, known as ‘shifting baselines’ is that each successive assessment uses a different starting point as the basis for comparison. This could result in a substantial cumulative decrease occurring over more than one six-year assessment period not being flagged as a problem, because in each six-year period the rate of decline remained below the assessment value (OSPAR, 2012).

Harbour Seal and Grey Seal Abundance Assessment Values

OSPAR Intermediate Assessment 2017 Indicator Assessment values are not to be considered as equivalent to proposed European Union MSFD criteria threshold values, however they can be used for the purposes of their MSFD obligations by those Contracting Parties that wish to do so.

Two assessment values were used to assess grey and harbour seal abundance in each AU in the Greater North Sea in relation to the baselines above:

Assessment Value 1:

No decline in seal abundance of > 1% per year in the previous six-year period (a decline of approximately 6% over six years).

This uses a rolling baseline based on the previous six-year period which seeks to identify if seal populations are maintained in a healthy state, with no decrease in population size with regard to the baseline (beyond natural variability) and to identify if efforts are needed to restore populations, where they have deteriorated due to anthropogenic influences, to a healthy state.

To estimate the annual increase or decrease in the number of animals counted within the previous six-year reporting round, a trend was fitted to the sum of all available data in each AU for the period 2009–2014. Generalised linear models (GLMs) were fitted to count data with a quasi-Poisson error distribution and log link. Annual growth rate (%) and 80% confidence intervals were estimated for each AU. Although no formal hypothesis testing was conducted, 80% confidence intervals were calculated to reflect the choice to set the statistical significance level, α, equal to 0.20 or 20%.

Assessment Value 2:

No decline in seal abundance of >25% since the fixed baseline in 1992 (or closest value).

To avoid the problem of shifting baselines when using the rolling baseline applied in assessment value 1, an assessment value relating to a fixed baseline is needed (assessment value 2). The baseline chosen was 1992, as used by some Member States for seals under the European Union Habitats Directive (Council Directive 92/43/EEC) (or if such data are not available, the start of the data series). The 25% chosen for the second assessment value currently approximates to 1% a year since 1992. Testing shows that there is sufficient monitoring to assess against this assessment value with confidence.

Where a shorter timescale is assessed, the 25% decline since the baseline is therefore not equivalent to those AUs where data do extend to 1992 (for example, a 25% decline since 2003 describes a more rapid contraction in the population than a 25% decline since 1992).

To determine the change in the abundance of seals since the baseline year, generalised linear models (GLMs) or generalised additive models (GAMs) were fitted to the sum of count data within an AU with a quasi-Poisson error distribution and log link using all available annual survey data in the range 1992 to 2014. The percentage change in abundance since baseline year (Δbaseline) and 80% confidence intervals were calculated from fitted values. Although no formal hypothesis testing was conducted, 80% confidence intervals were calculated to reflect the choice to set the significance level, α, equal to 0.20 or 20% (Formula A).

The use of the two assessment values aims to provide an indicator that would warn against both a slow but long-term steady decline (the problem of ‘shifting baselines’ associated with only having a rolling baseline) and against a recovery followed by a subsequent decline (potentially missed with a fixed baseline set below reference conditions). The two assessment values together would be able to act as a trigger for investigation of any necessary management measures to promote a steady recovery and subsequent slowing of growth as carrying capacity is approached.

Statistical Power of Assessments

There are many ways in which the number of seals counted during any one year could vary, aside from representing true changes in population size. These include variation in weather, or a disturbance at a haul-out site prior to counting. It is therefore advisable to examine the variability in survey counts and to incorporate this variability into trend or population size change estimates. The International Council for the Exploration of the Sea (ICES) Working Group for Marine Mammal Ecology (WGMME) (ICES, 2014b) provided general advice on the need to understand the statistical power of current and proposed monitoring programmes. In the present context, statistical power is the percentage confidence in not missing a significant decline. Statistical power depends on the sample size (number of surveys), the level of statistical significance set (α-level), variance in the counts, and the magnitude of the trend, that is, -1% and -25%. The ICES WGMME (ICES, 2014b) recommended that monitoring should achieve a minimum of 80% power – which equates to a 20% chance of making a Type II error (i.e. the frequency with which a true decline would not be detected). The same group also recommended that the threshold for detection of a ‘significant’ trend be relaxed from the traditional α = 0.05 to α = 0.20. The α parameter, or significance level, equates to the probability of concluding that a significant trend exists when in fact it does not (Type I error). An α value of 0.2 and power of 80% means there is equal probability of making an incorrect conclusion (either Type I or Type II error) about the detection of a trend.

Current monitoring programmes vary in the level of statistical power achievable. To carry out a full study of retrospective power to detect changes in the observed population trends, detailed information about the between- and within-year variability in all survey counts would be necessary. A full assessment of power was not undertaken here; this has already been done for the Wadden Sea and Southern Scandinavia where comprehensive coordinated survey efforts throughout the year provide some of the most robust estimates of trends in seal counts (Meesters et al., 2007; Teilmann et al., 2010). In other areas, however, the survey area is too large or complex so comprehensive and repeated surveys have not been feasible (such as in many parts of the Scottish coast) and the power to detect change in these units is reduced (SCOS, 2014).

Because 80% statistical power is not feasible to achieve in most areas, confidence intervals (CIs) were used to provide a relevant measure of confidence in the assessment. Simply said, they describe the frequency with which the true, unobservable, population parameter (here, the mean count) could be expected to fall within the intervals described by an upper and lower confidence limit. Where the confidence intervals encompass the assessment value the data do not provide conclusive evidence for the calculated value being above or below the assessment value.

Assessing Changes in Harbour Seal and Grey Seal Distribution

Describing the distribution of seals from surveys that are designed primarily to assess abundance is problematic because these are designed for when the seals are on land. Any distribution metric based on these data will have inherent limitations arising from three main issues:

1. Spatial coverage: Seal abundance surveys necessarily census animals seen hauled-out on land and do not address the distribution at sea. To estimate at-sea usage, long-term telemetry data are necessary (e.g. Jones et al., 2013).
2. Sampling effort: Ideally in studies of distributional change, a complete and standardized survey is conducted repeatedly in the area of interest. The areas of interest for this indicator assessment are the AUs. Those shown in Figure a and Figure b are not all surveyed completely on an annual basis due to geographical and / or financial constraints. Surveys have been prioritised towards those areas of known and high seal occurrence. Statistically, this could lead to a bias in seal distribution metrics due to preferential sampling.
3. Temporal coverage: the surveys cover narrow time windows during key life-stages such as moulting, breeding and pupping. The distribution of seals can be different between these stages. Grey seals, for example, may completely vacate breeding areas for the rest of the year. The present analysis assesses changes in moulting distribution for harbour seals, and changes in breeding colony distribution for grey seals.

These general limitations are applicable to most studies of animal abundance and distribution (Fortin et al., 2005). Despite these limitations, survey data may be useful to detect large-scale contractions in population distributions in terms of reduced use or abandonment of haul-outs or breeding areas, depending on the spatial resolution with which presence / absence data are reported.

To explore changes in seal distribution from available survey data, it was necessary to further sub-divide the AU into subareas. The borders of sub-areas were (arbitrarily) prescribed by the data provider, but with the intention to aggregate haul-out sites by seaward proximity and likelihood that seals travel between sites rather than aligning with any pre-existing municipal boundaries. In cases where haul-out sites rather than geographic areas were defined, these were taken to be areas / sites routinely used by the population to haul-out. Using presence or absence of seals within these spatial units, two metrics of change in distribution were calculated (following an approach developed for marine bird roost sites and breeding colonies – Humphreys et al., 2015):

1. Distributional pattern– percentage change in occupancy between two periods for a given spatial unit:

2. Shift in occupancy – an index to describe the overall shift in the seasonal distribution of seals between sub-areas or grid cells over time:

The shift index value is between 0 and 1: a value of 0 indicates that there has been a complete shift in the spatial units occupied; a value of 1 indicates there has been no shift.

The shift index was not calculated for grey seal breeding colonies because female seals usually return to the same colony year after year and the colony location does not shift much over time.

A similar set of assessment values was suggested for seal distribution but as meaningful changes in seal distribution are currently difficult to detect and assess from abundance surveys, this aspect of the Indicator will be considered as a ‘surveillance indicator’: the metric(s) are described but not quantitatively assessed against an assessment value.

Grey seal and harbour seal are also assessed under the Habitat Directive Article 17 reporting in 2013 (EU, 2013).

The relationship between decline in seal populations and human activities is unclear. In those areas where marked and prolonged declines have been detected (Orkney, Shetland, East Scotland, Lonergan et al., 2007, 2013; SCOS, 2014; Hanson et al., 2015) major research initiatives are already in place to further characterise the demographic parameters driving the change and to investigate potential causes. Another consideration is the historical and present dynamic between grey seals and harbour seals, which have considerably overlapping coastal and at-sea distributions. In some areas, the present abundance of harbour seals may be higher, in part, because historical hunting pressure on grey seals drove some local populations to extinction, potentially reducing the competitive pressure on harbour seals. As grey seal populations recover, harbour seals may face increased competitive pressure from grey seals that could have a detrimental effect on their abundance. While direct effects of human activities on seal population growth (hunting) have been greatly reduced or removed in most areas, other human pressures such as pollution and underwater noise could influence future growth by determining the level of carrying capacity.

Improving Estimates of Grey Seal Abundance

The estimates of grey seal population size and trajectories from the Bayesian population model are preliminary. Combining data across different areas and monitoring programmes resulted in two key assumptions having to be made: (1) that the proportion of grey seals hauled-out during the August survey windows does not vary geographically and (2) that peak pup counts could be scaled to production using a single scalar based on United Kingdom data. In reality the value of this scalar will depend on factors such as whether the true peak count was observed and the shape of the pup production. Referring to assumption (1): the only published proportion estimate available is based on United Kingdom telemetry data. Other telemetry datasets (for example, from France, Ireland, Netherlands, and Denmark) could be examined to refine this assumption in the future.

It is possible that haul-out habitat (substrate) could influence haul-out behaviour and this would be important to incorporate into the model. In addition, the estimates produced by the model are sensitive to the prior distributions input to the model (SCOS-BP 14/02). ‘Priors’ are probability distributions used in Bayesian statistical models that describe parameters before some evidence is taken into account. The priors which have been found to be most appropriate for the United Kingdom grey seal population model (SCOS-BP 14/02) were used here with the exception of the movement parameters which are not in the current United Kingdom population model. Further model runs with different priors on these parameters will ensure that the population predictions are robust to changes in the priors on the movement parameters.

Reducing Uncertainty in Seal Abundance Estimates

Uncertainty in quantitative assessment can arise from a lack of data or due to the dynamic fluctuations of seal populations that occur naturally. Historically, national monitoring programmes were designed to detect and report changes in abundance and distribution at a national scale. Aggregating these data across OSPAR regions could provide an opportunity to place regional / national population trends at a more appropriate scale. Gaining this understanding would make it easier to assess what additional data may be required to improve confidence in the assessments.

Understanding the Implications of the Grey Seal Population Increase

The grey seal population appears to be still increasing. It is likely that the population is recovering from a time when populations were significantly depleted by human activities. Hunting, pollution and overfishing are all likely to have reduced populations in the 20th century. Although an increase in grey seal abundance can be seen as a positive sign, this does not indicate that there are no anthropogenic pressures (such as from fisheries bycatch, resource competition, noise, habitat loss, disturbance or pollutants) only perhaps that there is less pressure than there was previously. More research is needed if direct links between population and human activities are to be established. Even with no pressure from human activities, the grey seal population cannot keep increasing indefinitely as a growing population will eventually approach its natural carrying capacity. What this carrying capacity might be for grey seals, or what the consequences of population change will be for harbour seals is not yet known.

Improving the Power to detect Trends in Harbour Seal Abundance

Power to detect the growth rate specified in assessment value 1 (-1% per year) was retrospectively calculated using the formulae from Thomas (1997) where λ (the non-centrality parameter) is a function of the specified effect size (here, the assessment growth rate of -1% per year), the sum of squares and variance estimated from the fitted model. The total sample size was calculated as the product of the number of years of survey data and the typical number of replicate surveys performed in the Assessment Unit (AU). Using this method, the statistical power to detect a harbour seal population rate of decline of 1% per year was low (around 20%) in some AUs, especially where monitoring occurs on a less than annual basis. This assessment value may need to be revised for future assessments and / or monitoring programmes improved to enhance the power to detect trends. In the present assessment, the confidence intervals provide a robust assessment of uncertainty in the magnitude of observed trends.

Improving the Power to detect Changes in Distribution of Seals

Both distribution metrics are sensitive to the number of sub-divisions within the spatial units defined within each AU by the Contracting Party. In an AU with relatively few total sub-divisions, an absence of animals in one sub-division will equate to a large change in occupancy (Δ_occupancy) between the reporting rounds. In addition, variation in survey effort between reporting period can confound assessment of this metric.

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