Perturbation drives changing metapopulation dynamics in a top marine predator

Abstract

Metapopulation theory assumes a balance between local decays/extinctions and local growth/new colonisations. Here we investigate whether recent population declines across part of the UK harbour seal range represent normal metapopulation dynamics or are indicative of perturbations potentially threatening the metapopulation viability, using 20 years of population trends, location tracking data (n = 380), and UK-wide, multi-generational population genetic data (n = 269). First, we use microsatellite data to show that two genetic groups previously identified are distinct metapopulations: northern and southern. Then, we characterize the northern metapopulation dynamics in two different periods, before and after the start of regional declines (pre-/peri-perturbation). We identify source–sink dynamics across the northern metapopulation, with two putative source populations apparently supporting three likely sink populations, and a recent metapopulation-wide disruption of migration coincident with the perturbation. The northern metapopulation appears to be in decay, highlighting that changes in local populations can lead to radical alterations in the overall metapopulation's persistence and dynamics.

1. Introduction

The persistence of spatially distributed species depends on aspects of local population dynamics and on dispersal [1]. Spatial management of a species therefore needs to consider both processes simultaneously. The metapopulation paradigm, where local populations are viewed as relatively discrete spatial entities that interact through migration, has proven very useful in understanding the interplay between dynamics and connectivity (dispersal) in a wide range of species including plants, amphibians, insects, birds, fish and mammals [2–4]. As such, the metapopulation approach is being increasingly applied to management in both the terrestrial and marine environments [3,5].

The dynamics of metapopulations are influenced by natural and anthropogenic factors, such as density-dependent natal dispersal and migration rates; [6–9]; habitat connectivity, loss and fragmentation [9,10]; and environmental heterogeneity [7,11]. At a regional level, it is the balance between local births and deaths, combined with net migration, which drives local population dynamics and persistence. Highly variable habitat quality among patches can also lead to source–sink dynamics [6,12,13]. The key idea is that in good quality regions, mortality is lower than reproduction. Surplus individuals from these ‘source' populations emigrate to lower quality regions, such that even if mortality is higher than natality, these ‘sink' populations can persist. Source–sink metapopulations are of particular interest because they are very susceptible to the effects of localized but abrupt perturbations affecting source populations, which can lead to overall metapopulation decline and eventual extinction [14].

An important implication of metapopulation theory is that, in the absence of exogenous perturbations, a species may persist regionally despite some local population decay and extinctions. A balance between these local decays/extinctions and local growth/new colonizations is expected to maintain the overall metapopulation. Similarly, source–sink dynamics can support sink populations larger than their source over evolutionary timeframes [13]. Therefore, a local population decline or extinction may be simply the manifestation of normal metapopulation dynamics but may also indicate more widespread issues with metapopulation health, particularly when sudden local population declines involve previously stable or growing source populations.

A prime example of changing dynamics in a metapopulation is the UK harbour seal (Phoca vitulina), which has been monitored for decades to provide regional population trends, local movement and genetic datasets [15–18]. The UK-wide abundance of harbour seals is currently 42 100 seals (95% CI: 34 500–52 300), which is comparable to the estimate 20 years ago at 45 550 (95% CI: 37 250–60 750) [17]. In contrast with this stable overall picture, there have been dramatic declines in abundance in key small areas (e.g. 95% decline: 2002–2017 in East Scotland) as well as in large regions, such as Shetland (40% decline: 2001–2006) and the North Coast & Orkney (85% decline: 1997–2016; figure 1). The reasons for these declines, while the populations around the majority of the UK are stable or increasing, are unknown. Factors currently being studied include increased indirect and direct competition (including predation) by grey seals [19] or other marine mammals and exposure to toxins from harmful algae [20]. Harbour seal populations exhibit a combination of structure and connectivity that make them suitable for metapopulation analyses. The species' central place foraging tactics mean that individuals generally feed within 100 km of sites at which they haul out (where they are counted) between foraging trips [16], yet there is evidence for large-scale movements between haul out sites, and between haul out and breeding sites, over 50 km apart [16,18,21,22].

Figure 1.

Map showing the most recent harbour seal count (10 km² resolution; [17]). The Seal Management Units considered in this paper (plus Northeast England SMU, with small population (max count less than 100) and for which we do not have any data) are shown as well as associated trends (line and associated 95% confidence intervals) in August counts (points; y-axis) as a function of year (x-axis), extracted from Thompson et al. [17]. (Online version in colour.)

Here we test the hypothesis that the observed regional declines in the UK harbour seal are part of the normal extinction–colonization dynamics of a single metapopulation, or alternatively, a response to a major perturbation driving changes in metapopulation dynamics. In doing so, we identify which local populations are likely to be sources or sinks. Ideally, metapopulation connectivity and source–sink dynamics would be determined using direct measures of demographic parameters and connectivity among and between local populations, respectively (i.e. survival, reproduction, recruitment and dispersal). However, estimates of these demographic parameters are not available for UK harbour seals and typically require datasets that follow individuals throughout their lifespan [23], precluding their estimation in a timeframe relevant for management of the current decline. In this context, there are two main difficulties that make assessing source–sink metapopulation dynamics in long-lived species challenging. First, there is no single approach that can determine whether or not movement of individuals among local populations contributes to local dynamics on the short timescales relevant for conservation. Satellite tracking data can be used to elucidate the level of movement between local populations but cannot determine if dispersing individuals leave descendants in the new location. Genetic data, on the other hand, can estimate per-generation migration rates representing real contributions towards local demography but for long-lived species these estimates may cover a period of several years and therefore may be too coarse-grained to detect sudden changes in migration patterns following a perturbation. Similarly, distinguishing between source and sink populations based on long-term population trends is unfeasible because a sink population may exhibit stable or even increasing census sizes due to the influx of migrants from a source population. On the other hand, genetic data can provide estimates of ‘retention' (proportion of individuals that remain in their local population), which to some extent are indicative of local recruitment but cannot determine if local birth rate exceeds local death rates, as expected in an ‘absolute' sink (cf. [24]). In order to make progress despite these challenges, we adopt a framework, bringing together genetic, location tracking and population trend data, to assess metapopulation identity and connectivity in addition to establishing source–sink dynamics.

We first use population genetics approaches (genetic differentiation index) to establish if all local populations of harbour seals are members of the same metapopulation. Having established that South Eastern UK local populations belong to a metapopulation that extends beyond the British Isles we focus on those found in the North Western and North Eastern UK, all belonging to a separate metapopulation. Specifically, we characterize metapopulation connectivity and source–sink dynamics of local populations in two different time periods, before and after the start of the regional declines (henceforth referred to as pre- and peri-perturbation). Thus, we assess the degree to which local populations are demographically connected by estimating per-generation migration rates pre- and peri-perturbation using multilocus-genotype methods. We seek further support for these results using satellite tracking data providing estimates of short-term movement of adults (greater than 1-year old, non-pups) and pups. Next, we identify putative source populations based on genetic data as those that have both high internal recruitment and display emigration. We then use population trends to further support their ‘source' status as they should also be stable or growing. Finally, we use a similar procedure to identify putative sink populations as those that are net recipients of immigrants and still declining, in which case mortality is likely exceeding reproduction.

The integration of the three data types––genetics, tracking and population trend––allow us to evaluate the viability of putative source and sink populations in the context of changes in connectivity pre- and peri-perturbation and overall trends in abundance. Specifically, we discuss whether or not local population declines and changes in migration patterns are consistent with overall metapopulation persistence or they are indicative of metapopulation decay and potential regional extinction.

2. Results

(a). Dataset summary and grouping definitions

Our final dataset comprises microsatellite genotypes and animal location tracking data from 269 and 380 harbour seals, respectively (table 1). These data were collected from geographical units known as Seal Management Units (SMUs); 11 SMUs covering the UK were established using harbour seal haul out clusters identified from aerial surveys, and tracking and photo-ID studies [16,21,22]. Here, we primarily consider the SMUs that hold significant harbour seal populations (greater than 100 individuals counted on surveys; figure 1 and table 1). We discuss the results in the context of three different types of groupings: metapopulations, metapopulation subunits called local populations and SMUs.

Table 1.

Sampling locations and sample sizes used in this study. Locations are shown as metapopulation (M: defined as northern (N) or southern (S)) and local population inferred in this study, United Kingdom Seal Management Unit (SMU) or Area for European samples, and sample types are the number of genetic samples (n), number of genotypes (n_GEN), number of new genotypes presented here relative to previous work (n_NEW), number of tags on seals aged 1 + (n_TAGS1+) and pups (n_TAGSpups).

M	local population	SMU/Area	n	nGEN	nNEW	nTAGS1+	nTAGSpups
N	Northern Ireland	Northern Ireland (NIR)	22	20	20	31	0
N	Northwestern (NW)	West Scotland (WS)	106	75	20	61	24
		Western Isles (WI)	17	15	0	20	0
	Total		123	90	20	81	24
N	MFNCO	North Coast & Orkney (NCO)	62	47	9	53	22
		Moray Firth (MF)	40	32	0	39	0
	Total		102	79	9	92	22
N	Shetland	Shetland (SH)	19	14	0	14	0
N	East Scotland	East Scotland (ESC)	36	28	7	33	0
S	—	Southeast England (SEE)	51	24	5	83	0
S	—	South England (SSE)	6	2	2	0	0
S	—	France (FRA)	12	3	0	0	0
	—	Dutch Wadden Sea (DWS)	9	9	0	0	0
	—	Norway (NOR)	15	0	0	0	0
		EUR total	37	12	0	0	0
	total		395	269	63	334	46

(b). The United Kingdom harbour seal comprises two distinct metapopulations

The pattern of genetic diversity and differentiation, as well as tracking data, suggests that the UK harbour seal SMUs fall into two distinct metapopulations: a northern and a southern. First, the Southeast England SMU showed high and significant levels of genetic differentiation against all other UK harbour seal SMUs (F_ST > 0.2; electronic supplementary material (ESM), table S1). By contrast, Southeast England showed only weak differentiation against the European samples. The BayesAss results confirmed this, with estimates of recent migration between components of the two metapopulations typically being ≤1% (ESM, tables S2 and S3), consistent with demographic independence [25]. Therefore, we consider Southeast England and continental Europe part of one southern metapopulation, and all other SMUs part of a northern metapopulation (Northern Ireland and Scottish SMUs) and focus on the latter.

There was significant genetic differentiation between most of the UK harbour seal SMUs within the northern metapopulation based on pairwise F_ST values, although this was not as substantial (F_ST: 0.02–0.14; ESM, table S1) as between the two putative metapopulations (F_ST: 0.18–0.30; ESM, table S1). The exceptions to this general pattern were that there was no significant difference between (i) West Scotland and the Western Isles SMUs, which were pooled to form a Northwest local population, and (ii) North Coast & Orkney SMU, and the neighbouring Moray Firth SMU, which were pooled to form a Moray Firth, North Coast & Orkney (MFNCO) local population. Thus, the F_ST estimates suggest a total of five local populations within the northern metapopulation: Northern Ireland, Northwest, MFNCO, Shetland and East Scotland. Different haul out sites within SMUs and across SMU subunits (e.g. south and central West Scotland) did not show significant differentiation (ESM, table S1). Although no genetic samples were available from Southwest Scotland SMU, we assume this SMU is part of the Northwest local population: there are similar population trends and no spatial differentiation in haul out clusters between Southwest and West Scotland SMUs (figure 1).

The discriminant analysis of principal components (DAPC) clearly separated the northern metapopulation SMUs from Southeast England and continental Europe along linear discriminant 1 (figure 2). The SMUs within the northern metapopulation were characterized by a pattern of isolation-by-distance along linear discriminant 2, but also indicated some east–west division. The isolation by distance was confirmed by a statistically significant (p < 0.001) correlation between F_ST and ‘at-sea' distances between haul sout sites (ESM, figure S1).

Figure 2.

Individual genotypes plotted by LD from the discriminant analysis of principle components conducted with samples grouped by SMU and haul out site (latter in brackets). Mean values for each sampling partition shown by triangle. (Online version in colour.)

The distinctiveness of the northern and southern metapopulations was also supported by tracking data from 334 non-pup seals, showing no movement between metapopulations, but some movement within (ESM, tables S4 and S5). For instance, of the 83 individuals tagged in Southeast England, two travelled to the continent but none to the northern metapopulation. Likewise, in the northern metapopulation, movements were detected among several haul-outs and SMUs, as described below, but there was no movement to the southern metapopulation. Overall, these results provide strong support for defining a northern metapopulation, which excludes the Southeast England SMU.

(c). Finer temporal scale connectivity of northern United Kingdom harbour seal metapopulation

We assessed connectivity of local populations and SMUs from genetic, movement and demographic perspectives, using the microsatellites, location tracking and population trends, respectively.

(i). Genetic estimates of demographic connectivity

As the Southeast England SMU and European samples appear part of a distinct metapopulation, we consider here a BayesAss analysis with the harbour seal dataset covering only the northern metapopulation (ESM, table S6).

Overall, migration connects the northern local populations, however, in many cases connectivity has sharply reduced in the peri-perturbation generation compared with the pre-perturbation generation. This is evidenced by a lack of first-generation migrants, but a substantial proportion of individuals with migrant ancestry, during the peri-perturbation period (i.e. descended from parents that migrated before the perturbation). Median estimates of recent gene flow (past two generations), inferred from the BayesAss analysis, are shown in ESM, table S6. Convergence was shown by traces and similar results across independent runs, with effective sample sizes for each parameter greater than 250.

The Northern Ireland and Northwest local populations are highly connected, as shown by the high proportion of immigrants from the latter (0.26, 95% HPD:0.19–0.31) and the substantial degree of recent migration based on individual ancestry. By contrast, the Northwest local population appears to be mostly local recruits, based on individual ancestry data and the high proportion of non-migrants (0.91, 95% HPD:0.85–0.97).

There is indication that the MFNCO local population has been a source of migrants to the Northwest local population based on ancestry and migration rates (0.05, 95% HPD:0.01–0.12), but with a decline from four likely second-generation migrants to two first-generation migrants in the past two generations. This suggests a decline in migration peri-perturbation compared with pre-perturbation. The MFNCO local population also has a high proportion of non-migrants (0.95, 95% HPD: 0.88–0.99) and no evidence of immigrants in the past one generation. By contrast, there is evidence of past gene flow from the East Scotland local population based on the migrant ancestry of two individuals. Furthermore, the MFNCO local population is contributing migrants to both the Shetland (0.20, 95% HPD: 0.11–0.29) and East Scotland local populations (0.09, 95% HPD: 0.01–0.18) based on migration rates over the past two generations. For East Scotland, the gene flow from the MFNCO local population in the current generation shows a decrease from the previous generation, again supporting a decline in migration peri-perturbation compared with pre-perturbation. For Shetland, there is also evidence of migration from the Northwest local population, as indicated by individual ancestry data and relatively high migration in recent years (0.08, 95% HPD: 0.01–0.16), despite a moderate and significant F_ST value of 0.07 between the two regions.

(ii). Regional patterns of movement

The location tracking data of seals (non-pups) in the non-breeding season broadly supports the distinctiveness of the five local populations; in total, 21 (6.3%) tagged seals moved between SMUs, of which only one (less than 1%) moved local populations. None of the individuals tagged in Northern Ireland (n = 33), Shetland (n = 14) or East Scotland (pre or peri-perturbation; n = 33) moved between local populations. There was movement between the three SMUs comprising the Northwest local population, particularly between West Scotland and Southwest Scotland (ESM, table S5). There was also significant movement within the MFNCO local population, from the Moray Firth to North Coast & Orkney (n = 4/39), with 1/53 (1/34 tagged in southern Orkney) going in the opposite direction. The only movement between local populations was of one individual tagged in 2003 in northern Orkney, MFNCO local population (of 19), which moved to Shetland.

Pup-tracking data indicated a higher degree of connectivity between SMUs and local populations compared with non-pups, as expected from the dispersing demographic class; in total 11/46 (26%) moved SMUs, of which nine (19%) changed local population. Of the 24 pups tagged in Lismore, West Scotland SMU in the Northwest local population, four (17%) moved SMUs within the local population (two to Southwest Scotland and two to Western Isles) and one (4%) moved to Ireland. The latter pup moved to a region from which we do not have any genetic samples so cannot assess the location's position in the metapopulation. However, as the seal moved to within 50 miles of the Northern Irish border, its movement could represent a shift between local populations. Of the 22 pups tagged in Orkney, MFNCO local population, seven (27%) moved to Shetland and one (4%) moved to West Scotland (Northwest local population).

(iii). Demographic trends

We looked at demographic connectivity by assessing published trends in population trajectories and identifying which SMUs have similar trends. The Northwest local population SMUs have shown stable population trends over the time period covered by the study (Western Isles, West and Southwest Scotland). However, Northern Ireland, a separate local population, has exhibited a constant gradual decline (figure 1 and table 2). The SMUs in the MFNCO local population underwent a sudden change in dynamics in the early 2000s. For instance, North Coast & Orkney were stable until 2001, whereas the subsequent survey in 2006 showed a dramatic decline in abundance. Moray Firth stabilized in the early 2000s after a period of decline. Both East Scotland and Shetland underwent a similar change in dynamics in the early 2000s, but where Shetland looks to be stabilizing at a depleted level, East Scotland continues to decline.

Table 2.

Summary distinctiveness and inferred source–sink dynamics between local populations of UK harbour seals, based on movement of non-pups, genetic and demographic data. The demographic distinction criteria are the proportion of non-migrants from BayesAss and likely dispersal is from BayesAss and pup movement data. These are considered in the context of the trend and abundance for each unit to suggest putative sources and sinks. Depleted trend means there was a period of decrease and then stabilization a lower abundance level. For local population abbreviations see table 1.

(d). Contemporary dynamics of the northern harbour seal metapopulation

The combined analyses allow us to assess the levels of movement, as well as genetic and demographic connectivity, within the metapopulation on different timescales and using different aspects of the harbour seal's biology (figure 3 and table 2). On the UK west coast, our data suggest that the Northwest local population (Southwest Scotland, West Scotland, Western Isles SMUs) is a source population, as it has a high level of retention and substantial emigration to Northern Ireland (table 2). Furthermore, population abundance for Northern Ireland has been steadily decreasing over the study period, despite receiving immigrants from the Northwest local population, suggesting that it is a sink population.

Figure 3.

Inferred source–sink dynamics of UK harbour seal northern metapopulation. Black lines delineate the SMUs and coloured lines indicate inferred local populations, with arrows indicating movement from putative sources to putative sinks. Dots represent approximate locations of telemetry tag deployment and/or genetic sampling. (Online version in colour.)

We also hypothesize that the MFNCO is a source population. This local population appears to have a high level of internal recruitment, based on the BayesAss analysis. Furthermore, the genetic and pup movement data suggests that there is emigration from MFNCO to other local populations. In particular, it seems that MFNCO is a likely source population to both Shetland and East Scotland. Both latter two local populations have a substantial proportion of individuals with MFNCO ancestry based on BayesAss, and the pup-tracking data showed considerable movement from the MFNCO to Shetland (table 2, ESM, table S5). Finally, MFNCO, Shetland and East Scotland share similar population trends, in that they have dramatic declines subsequent to the perturbation. In previous generations, there appears to have been migration from East Scotland into both Shetland and the MFNCO local populations; however, there is little evidence of this in the past one generation spanning the perturbation. This decline in emigration coincides with the precipitous decline in the East Scotland local population.

Overall, the results show that the northern harbour seal metapopulation is highly connected and contains two probable source local populations: MFNCO and Northwest. The decline in the MFNCO local population appears to have reduced connectivity between these local populations as well. For example, there are four individuals (4%) from the Northwest local population (n = 90) that are most likely second-generation migrants from the MFNCO local population, but only two individuals are likely to be first-generation migrants. This could represent a decline in connectivity over the past one generation during which the MFNCO population exhibited a pronounced decline.

3. Discussion

A key tenet of the metapopulation paradigm is that local population decay and decline do not necessarily threaten metapopulation persistence if there is a concomitant balance with new colonization and growth. Such balanced patterns of extinction and colonization have been empirically shown in butterflies Melitaea cinxia [26] and Speyeria nokomis apacheana [27], as well as the American pika Ochotona princeps [28]. Here we have shown that the northern UK harbour seal metapopulation has been subject to a recent perturbation that has impacted local population connectivity in a way that appears to go beyond the expectations of a simple extinction–colonization equilibrium. The disruption of migration we see at a local level seems to have wider impacts on the metapopulation, rarely before seen in empirical studies. This change in connectivity could eventually lead to genetic isolation and genetic erosion over time [29], which can be very difficult to detect in the short term given the long generation time of the species [30]. Predicting long-term consequences, therefore, requires the use of a thorough population viability analysis integrating both local population demography and migration [31]. The migration estimates we provide could be used in such a model once reliable estimates of survival and fecundity are obtained.

More practically, we demonstrated that there are two distinct harbour seal metapopulations in the UK using genetic data. This confirms previous analyses that showed harbour seals from Southeast UK clustered with samples from France and the Netherlands [18]. In addition, it builds on this work by demonstrating that the previously identified Northwest UK genetic cluster represents two local populations (Northern Ireland and Northwest) and the Northeast UK cluster represents three local populations (Shetland, MFNCO and East Scotland) resulting in five local populations in a northern metapopulation linked by gene flow and dispersal [18].

Furthermore, we have identified putative source–sink dynamics for the northern metapopulation while evaluating if migration patterns had changed between the generations pre- and peri-perturbation. Identifying source–sink metapopulation dynamics and the associated connectivity pattern is of fundamental importance for the management of marine systems [32]. However, this is a particularly difficult task in the case of long-lived vertebrate species. No single type of data (population trends, location tracking, genetics) on its own can achieve this goal. Previous studies have used genetic data indicating asymmetric gene flow to support source–sink dynamics (e.g. [33,34]) but this is not sufficient evidence as net recipients of individuals could still be self-supporting populations. Our integrative approach, combining genetic, location tracking and population trend information, provides a framework for assessing source–sink metapopulation dynamics in future studies. Specifically, we have placed estimates of local population connectivity from genetic and movement data, as well as genetic migrant ancestry information, in the context of population trends to infer whether local populations could be self-supporting sources or immigration-dependent sinks.

Through this methodology, we show that the putative key source population of MFNCO has decreased in abundance by perhaps 50% with a concomitant reduction in migration to East Scotland and the Northwest population in the past one generation that spans the start perturbation. Extrinsic factors, such as epizootics, can periodically cause declines and impact pinniped population dynamics [35]. In the case of the Scottish harbour seal, environmental change, including exposure to toxins from harmful algae [36], and competition and predation [17], are hypothesized to be contributing to changes in the population dynamics, but there is no evidence for infectious disease [37]. Future work should examine habitat loss or fragmentation, which previous studies focused on other species suggest can accelerate metapopulation fragmentation and result in regional extinctions [38].

Intrinsic factors, such as density-dependent emigration, have been shown as an important determinant of grey seal metapopulation dynamics [6,39]. If similar mechanisms operate in harbour seals, then the decline in abundance in the MFNCO local population could have led to a concurrent decline in density-dependent emigration to previously connected local populations such as the rapidly declining East Scotland [40]. As pups of the year are thought to be the dispersing age class in harbour seals [41–43], facilitating connectivity across the metapopulation, future work should focus on assessing their patterns of movement and recruitment.

According to genetic and pup-tracking data, seals continue to migrate from MFNCO to Shetland peri-perturbation, likely key to the persistence of the Shetland local population. Rather than a regional issue, the decline in the MFNCO local population has had a ripple effect across much of the northern metapopulation. Indeed, the apparent decline in migration from MFNCO to the Northwest local population could have impacts that are yet to be detected or determined. The Northwest local population is also a likely source population for Northern Ireland, as demonstrated by genetic estimates of migration rates and, potentially, pup dispersal.

Although the evidence we provide for source–sink dynamics and changes in connectivity are convincing, there are important caveats to consider. Ultimately, our framework uses a range of proxies instead of direct estimates of demographic parameters, such as population trends as an indication that mortality is greater than survival, in the absence of high levels of emigration. As noted earlier, direct estimates of these parameters are needed to definitively assess source–sink and metapopulation dynamics. Furthermore, while we have used genetic data from across the harbour seal's UK distribution to estimate genetic differentiation, migration rates and migrant ancestry, our sample sizes from some locations were small. Future work should assess migration rates using both larger genetic sample sizes and numbers of markers. We also only had pup-tracking data from two of five northern metapopulation local populations. However, our inferences from multiple lines of evidence––genetic, pup and non-pup movement data––were consistent, providing confidence in our results. We hope that this prompts other scientists to examine extant datasets on other species for similar analytical opportunities.

Our study uniquely considers population trend, location tracking and genetic data over a multi-generational timescale for a long-lived mammalian species and provides convincing evidence of source–sink metapopulation dynamics for this top predator. The results suggest that the Southeast England SMU can be assessed and managed independently from those in Scotland and Northern Ireland, with implications for the broader management of the species across Europe. Management across the northern metapopulation should consider connectivity patterns identified here. Continued research into habitat preference for UK harbour seals, combined with patterns of connectivity described here and vital rate estimates, will contribute considerably in the near future to the debate on the metapopulation and habitat paradigms for understanding declines of species [44]. More broadly, this work shows that changes in migration and connectivity at a local level can impact wide-scale dynamics, which has important implications for management of the diverse array of terrestrial and marine species that exist as metapopulations. For example, most conservation frameworks assess changes in abundance over time (e.g. the IUCN red list, [45]). This work suggests that changes in migration rates and connectivity could foreshadow changes in abundance. Monitoring and identification of reduced connectivity may prompt conservation measures to be put in place that could forestall decline, or could be assessed retrospectively, as has been done here. As anthropogenic activities cause more widespread environmental degradation and habitat fragmentation [46], understanding connectivity could be an important factor in maintaining both populations and biodiversity in the future.

4. Materials and methods

(a). Data collection

(i). Microsatellite genotyping

In the UK, the Sea Mammal Research Unit (SMRU) and the University of Aberdeen collected skin samples during live-capture of harbour seals across 20 sampling sites from 2003 to 2012, using methods described in Sharples et al. [16]. In addition to the UK samples, 36 harbour seal samples were included from Norway, Dutch Wadden Sea and France as described in Olsen et al. [18]. DNA was extracted using a salt-saturated technique [47]. Fourteen microsatellite loci were amplified and genotyped by Xelect Ltd (St Andrews, UK). All genotyping previously conducted [18] was repeated to ensure complete comparability across the dataset, but was augmented with more loci and UK samples to increase the power of our analyses (ESM, table S7).

(ii). Tracking data

Tracking data provide two sources of information: regional movements of individuals aged one year and older within the non-breeding season and movements of pups. We determined movement behaviour in non-pup seals using Argos satellite relay data loggers or GPS/GSM phone tags (developed and supplied by the SMRU Instrumentation Group) deployed from 2001 to 2017 on 334 harbour seals in eight of the SMUs (table 1; see ESM, table S5). We also considered the movements of tagged pups as an indication of dispersal; juveniles are more dispersive than adults in pinnipeds, and the movements of these pups in the first few months of life may be indicative of where they will recruit into the breeding population. However, data were only available from two locations: 46 pups tagged in Orkney, North Coast & Orkeny SMU and West Scotland SMU [42]. The tags (SPOT tags, Wildlife Computers, Redmond, WA, USA) were deployed on flipper tags and thus do not fall off during the annual moult. Pup tag duration was 31–424 days (mean: 155 days) and non-pup tag duration was 28–243 days (mean 95 days).

(b). Data analysis

(i). Metapopulation delimitation

We inferred the metapopulation membership of local populations by estimating genetic differentiation between SMUs, and between sampled haul out sites and subunits within SMUs, using microsatellite data. We calculated pairwise F_ST values [48] using GENEPOP [49], with significance assessed using the exact G test in the same program (100 000 dememorization steps, 1000 batches each with 10 000 iterations). Furthermore, we investigated isolation-by-distance across the UK by regressing F_ST/(1-F_ST) with the log of the ‘at-sea' distance between haul out sites using the ISOLDE program implemented in GENEPOP [50]. To infer recent connectivity, we also ran the program BayesAss ([51], see next section). Finally, we conducted DAPC using the R package adegenet [52] to investigate the genetic differences between SMUs in a multivariate statistical framework.

(ii). Northern metapopulation connectivity before and during perturbation

In order to understand migration and genetic connectivity over recent timescales, we used program BayesAss [51]. The program estimates immigration rates over the past two generations using gametic disequilibrium signal generated by immigrant individuals or their descendants (for details see ESM). The patterns of connectivity, in terms of migration rates and ancestry of individuals, were used to infer connectivity over the past two generations. As samples were collected between 2003 and 2012 and the harbour seal is estimated to have a 14.8-year generation span [53], the approximate timings for the migration estimates are 1993 to 2007 (taking midpoint of the samples) for the past one generation, clearly spanning the recent decline (peri-perturbation), and 1977 to 1992 for the second generation, clearly preceding the recent decline (pre-perturbation).

We used the tracking data to investigate connectivity through the patterns of movement of UK harbour seals. For both pups and non-pups, we calculated the proportion of animals tagged in each unit that moved between local populations, SMUs or areas within SMUs (north, central and south subunits of West Scotland SMU), identifying movements using haul out locations rather than at sea locations. Of the SMUs which have shown decline, only for East Scotland were there data that could reliably represent both pre- (n = 10/33 tagged in late 2001/early 2002) and peri-perturbation (ESM, table S5). The movements from the Moray Firth represent peri-perturbation (tags deployed from 2004 onwards). For Shetland, all tags were deployed in late 2003/early 2004; the gap in the surveys synonymous with the 40% drop in population size. For North Coast & Orkney, 14/53 tags (14/19 of those deployed in northern Orkney) were deployed during the gap in surveys (late 2003/early 2004), with the remainder tagged peri-perturbation (2011–2017). All tags deployed in Southeast England were deployed after the 2002 PDV epidemic (2003 onwards).

We also examined population trend data to describe and categorize the trajectories of the different SMUs as increasing, decreasing, stable or depleted (defined as a decline and then stabilization) using published information [17]. Briefly, harbour seal population trend data was compiled from the 10 SMUs, within which greater than 50 individuals have been counted during a survey. Counts were conducted during the annual moult when the highest proportion of the population is hauled out (c. 0.72%; [54]), ensuring compatibility of data across survey years and regions and as described more thoroughly in [17]. Although the overall trend for UK harbour seals is stable or even increasing abundance, the SMUs exhibit strikingly different dynamics (figure 1).

(iii). Identifying changes in source–sink dynamics

We made inferences about the source–sink dynamics of the harbour seal metapopulations from the genetic and short-term movement estimates in the context of the trends in abundance [17] (figure 1). Specifically, we summarized whether the available data suggested local populations were (i) genetically distinct, based on the estimates of pairwise F_ST and migration rates, (ii) linked by movement of non-pups and (iii) demographically distinct, based on the proportion of non-migrants from BayesAss and dispersal inferred from the pup-tracking data (where available).

Finally, we considered whether these data suggested that the local populations were putative sources or sinks in the context of the trend and abundance data. A local population was considered: (i) putative source region if genetic and demographic (pup movement) data results indicated high internal recruitment and substantial degree of emigration to other regions and non-pup-tracking data indicated low rates of movement or (ii) a putative sink region if genetic and demographic data indicated substantial recruitment from outside the local population and showed a trend similar to its source population. The long-term viability of putative source populations was considered in the context of their population size and trend data [17]. Ultimately, we consider whether the totality of the evidence suggests a broadscale metapopulation decline or a regional decline.

Ethics

All procedures were carried out under Animal (Scientific Procedures) Act, 1986 Home Office Licences issued to SMRU (PIL nos. 60/3303, 60/4009 and 70/7806).

Data accessibility

Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.j9kd51c8j [55].

Authors' contributions

E.L.C., O.E.G., A.H., A.O. and D.J.F.R. designed the research; A.O., A.H. and D.J.F.R. performed research; E.L.C., M.T.O., O.E.G. and D.J.F.R. analysed data and all authors wrote the paper.

Competing interests

We declare we have no competing interests.

Funding

Fieldwork and tag deployment were supported by Dept of Business, Energy and Industrial Strategy, Beatrice Offshore Windfarm Ltd, Crown Estate, Highlands & Islands Enterprise, Marine Current Turbines Limited, Marine Scotland Science, Moray Firth Offshore Renewables Limited, Natural Environment Research Council (NERC) and Scottish Natural Heritage (SNH). Genetic analysis was funded by SNH, the Scottish Government and NERC (grant no. SMRU 10/001). Data collection in the Thames was funded by BBC Wildlife Fund and SITA Trust. O.E.G. was supported by the Marine Alliance for Science and Technology for Scotland, funded by the Scottish Funding Council (grant no. HR09011). E.L.C. was supported by a Newton Fellowship (Royal Society of London), Marie Curie Fellowship (EU Horizon2020) and a Rutherford Discovery Fellowship (Royal Society of New Zealand). A.J.H. and D.J.F.R. were supported by NERC (grant no. SMRU 10/001).