Genetic analysis in aid of delineation of Management Units for a wide-ranging marine mammal, the grey seal (Halichoerus grypus), in European waters

doi:10.21203/rs.3.rs-4243253/v1

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Genetic analysis in aid of delineation of Management Units for a wide-ranging marine mammal, the grey seal (Halichoerus grypus), in European waters

https://doi.org/10.21203/rs.3.rs-4243253/v1

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The present study aims at filling genetic structure gaps of grey seals (Halichoerus grypus) in the Northeast Atlantic, where effective Management Units (MUs) must be established to fulfil international obligations set by OSPAR and the EU’s MSFD. Mitochondrial and nuclear genetic markers were analysed for seals from the island of Ireland, southwest England and the German/ Danish North Sea coasts, whereby the integration of previously published data led to the largest genetic dataset analysed for this species to date. Results revealed that individuals from the island of Ireland are part of a single interbreeding population, with Southwest England being a source of migrants to the island of Ireland, and the southern North Sea (Germany, Denmark) being either a source or sharing a common source of migrants to the island of Ireland. Based on observed genetic structure within the Northeast Atlantic, two MUs are proposed: (i) the Faroe Islands, Scotland and the North Sea and (ii) the island of Ireland, southwestern UK (Cornwall) and France. Additionally, Northwest Scotland and the English Channel/Dutch North Sea are proposed as transition zones. Given the species’ high mobility, an adaptive management plan based on an ongoing regional/ European scale monitoring programme is recommended.

Management Unit

Assessment Units

microsatellite loci

mtDNA

non-invasive sampling

conservation

Halichoerus grypus

Knowledge of genetic structure and diversity is key to understanding relationships among individuals over large geographic distances, as well as identifying where and how conservation measures should be applied for a species, population or intraspecific unit when key demographic features should be considered (e.g. Olsen et al., 2017). For example, wide-ranging marine vertebrate species, including pinnipeds (Hoffman and Forcada, 2012), cetaceans (Baker et al., 2013), turtles (Clusa et al., 2018), and sharks (Day et al., 2019), can exhibit strong site fidelity, particularly during breeding, either for one or both sexes. For one such species, the grey seal (Halichoerus grypus), interannual breeding site fidelity is displayed by both sexes, though stronger philopatry has been observed in females (Pomeroy, Twiss and Redman, 2000). Studies on wide-ranging vertebrate species have used multiple lines of evidence to identify intraspecific unit boundaries across large distributional ranges, including genetic applications as well as other approaches, such as telemetry, tagging, photo-identification, or morphology, frequently used in cetaceans (Sveegaard et al., 2015; Fontaine et al., 2017) and sharks (e.g. Green et al., 2019). Partly due to high natal philopatry, intraspecific units for some pinnipeds (mostly otariids) are genetically assessed by sampling breeding colonies (Lopes et al., 2015; Collins et al., 2017). Their genetic structure at foraging sites (outside of the breeding season), and the origin of individuals at these sites, is assessed via genetic analysis including population assignment tests using nuclear data to identify natal origin of individuals (e.g. New Zealand sea lions, Phocarctos hookeri, Collins et al., 2017) and/or mixed-stock analysis (Ream, 2002). However, mixed-stock analysis is more common in other, non-mammal, species such as fish (e.g. Bekkevold et al., 2023). Recent US guidelines for the delineation of Demographically Independent Populations (DIP) of marine mammal species highlighted difficulties for detecting geographic boundaries in wide-ranging and migratory species and advised using either genetic, morphological or telemetry data for unit boundary identification within grey seals, provided robust data are available (Martien et al., 2019). However, it should be noted that these approaches provide different temporal scales of assessment, which may range from evolutionary/ historical (i.e. genetic data) to non-evolutionary/ contemporary processes (i.e. telemetry data). Where robust data are not available, a multidisciplinary approach to delineate geographic boundaries at the intraspecific unit level was recommended (Martien et al., 2019).

Grey seals within European waters are protected under national law (e.g. Wildlife Act 1976 in the Republic of Ireland, UK Wildlife and Countryside Act 1981) and European Union law (e.g. Habitats Directive). Evidence from monitoring surveys indicates increasing numbers of grey seals in Ireland (latest estimate of 7,284–9,365 individuals (NPWS, 2019)), France (Vincent et al., 2017), Germany, Denmark and the Netherlands (Carius et al., 2020; Galatius et al., 2021). While grey seals are reported infrequently in Belgian waters (Galatius et al., 2021), numbers in the UK are increasing/ stable with approx. 157,300 age 1 + individuals (95% CI 144,600 − 169,400) and appear to be reaching carrying capacity (Brasseur et al., 2017; SCOS, 2021). However, a lack of data and limited spatial resolution of genetic analysis across European waters has made it difficult to determine appropriate intraspecific units for the species, for assessment of abundance trends.

Being a top predator, grey seals feature in three biodiversity indicators being developed by the OSPAR Convention (Oslo Paris Convention for the Protection of the Marine Environment of the Northeast Atlantic), including one indicator assessing seal abundance and distribution (Banga et al., 2022a; Banga et al., 2022b; Taylor et al., 2022; ). Such indicators may also be used by Member States for reporting under the EU Marine Strategy Framework Directive (MSFD, 2008/56/EC). These indicators are assessed at the spatial scale of ecologically relevant Assessment Units (AUs) for the species, hence requiring the identification of intraspecific units. It must be considered that while grey seals were historically widespread across the Northeast Atlantic and the North Sea, hunting led to local extinctions in the past centuries (e.g. Wadden Sea, Polish waters and the Kattegat-Skagerrak region) (Härkönen et al., 2007). The species only started recolonising former inhabited areas following legal protection in recent decades, with the first breeding records reported during the late 1970s on the island of Amrum, Germany, for example (Scheibel and Weidel, 1988). In contrast to other areas of the Northeast Atlantic, information regarding seals found around the island of Ireland is very limited (Cronin et al., 2011).

Genetic studies of grey seals to date have found significant genetic differentiation using nuclear and/ or mitochondrial markers between major oceanic areas, i.e. the Northwest Atlantic, Northeast Atlantic, and Baltic Sea (Klimova et al., 2014), and to a smaller extent between the Northeast Atlantic and the North Sea (Allen et al., 1995; ICES, 2014; Fietz et al., 2016) and within the Northeast Atlantic (Decker et al., 2017). Based on these findings and telemetry data showing evidence of large-scale individual movements within the species outside the breeding season (Vincent et al., 2017; Carter et al., 2020), a single grey seal AU encompassing waters of the Northeast Atlantic, North Sea and Arctic waters (OSPAR Regions I, II and III) was proposed for assessing abundance trends (Banga et al., 2022). However, significant geographical sampling gaps still exist across their distributional range in the Northeast Atlantic, including Irish waters, and a complete picture of their population genetic structure and connectivity within the region is not presently available.

Understanding population genetic structure is essential for the delineation of discrete intraspecific units either on the level of evolutionary or ecological importance (Waples and Gaggiotti, 2006; Palsbøll, Berube and Allendorf, 2007). While various terminology for such units exists, Management Units (MUs) informed by genetic data identify barriers to gene flow between discrete units so that each unit is considered demographically independent (Palsbøll, Berube and Allendorf, 2007). With increasing sample availability, enabled by technological advances and non-invasive sampling techniques (Steinmetz et al. 2021), genetic data have previously provided evidence-based recommendations for defining MUs of pinnipeds both within and outside European waters (Fietz et al., 2016; Olsen et al., 2017; Carroll et al., 2020; Steinmetz et al., 2023). This study aimed to close existing knowledge gaps by investigating the genetic population structure and diversity of grey seals within the Northeast Atlantic and North Sea, adding new mitochondrial and nuclear data (the island of Ireland, Southwest England, Germany and Denmark) to previously published mitochondrial data for the identification of MUs. Considering current management strategies and currently available data, we hypothesised that grey seals sampled across the island of Ireland would form part of a larger unit in the Northeast Atlantic based on large-scale grey seal movements between the island of Ireland, the UK and the continent.

Licences for all fieldwork carried out under this project were approved by the NPWS (C151/2016, C32/2017, C59/2018). All samples provided by collaborating institutions were collected under their respective licenses.

Grey seal biological samples in Ireland were collected between 2016 and 2018 (Table 1). Key breeding and haul-out sites across Ireland were targeted for sampling during the breeding (November) and moult (April) periods. Tissue samples (scat, moulted/ plucked hair, skin from carcasses) were collected from wild animals using minimally invasive or non-invasive techniques (no seals were captured during the sampling process) and stored at -20˚C until further processing, as per Steinmetz et al., (2021). Additionally, tissue samples (blood, saliva, plucked hair, scat, skin, urine) were obtained from collaborating rehabilitation centres and research institutes in Ireland, the UK (including Northern Ireland), Germany and Denmark (Table 1). Note that samples obtained through collaborators primarily consisted of samples from pups and moulted pups in rehabilitation; and overall, the majority of samples analysed were collected during the breeding season, with fewer samples collected during the moult, and samples from stranded animals collected throughout the year. To enhance the dataset and increase the statistical power of the mitochondrial DNA analysis, previously published grey seal sequence data from the Atlantic, North Sea/ Wadden Sea and Baltic Sea areas (van Bleijswijk et al., 2014; Klimova et al., 2014; Decker et al., 2017) were acquired, resulting in a very large sample size for analysis (n = 2,079).

Table 1

Source of samples/data, ocean, OSPAR region, time of sampling and the sample size for mtDNA and microsatellites shown for each sub-region. Danish samples analysed by the present study were either attributed to GDNS (n = 4) or BAL (n = 2) according to the sample origin where attribution to the respective sub-region was based on population structure as reported by (Fietz et al., 2016). Further, one Baltic individual (born in Lithuania) was available through a German rehabilitation centre and was included in BAL.
Sub-region	Source	Ocean	OSPAR region	Time of sampling	n_mtDNA	n_nuc
Northwest Scotland (NWS)	Klimova et al. 2014	Northeast Atlantic	III	2004	76	-
Island of Ireland (IRL & NI)	this study	Northeast Atlantic, Irish Sea	III	2016–2020	138	108
Southwest England (SWE)	this study	Northeast Atlantic, Irish Sea	II, III	2013–2018	28	15
Northeast England (NEE)	Klimova et al. 2014	(English) North Sea	II	2004	49	-
East Scotland (ESC)	Klimova et al. 2014	Northern UK, North Sea	II	2004	54	-
Orkney Islands (ORK)	Klimova et al. 2014	Northern UK, North Sea	II	2004	1245	-
Faroe Islands (FO)	Klimova et al. 2014	North Atlantic	I	2004	31	-
France (FR)	Decker et al. 2017	English Channel, Northeast Atlantic	II, III, IV	2002–2012	224	-
Dutch Wadden/ North Sea (DWS)	GenBank (Bleijswijk et al. 2014)	North Sea	II	2013	19	-
German/ Danish North Sea (GDNS)	this study	North Sea	II	2014–2019	32	37
Baltic (BAL)	this study, Klimova et al. 2014	Baltic Sea	NA	2011–2019, up to 2014	116	3
Sable Island, Canada (SIC)	Klimova et al. 2014	Northwest Atlantic	NA	2004	67	-

For samples processed within the current study, extraction of genomic DNA from scats, hair, urine, saliva and skin was carried out as described in Steinmetz et al., (2021). A ≤ 690bp fragment of the mitohondrial control region was amplified, sequenced, inspected and aligned as per Steinmetz et al. (2022). For microsatellites, the following 14 loci were tested for analysis based on their previous application in seal genetic studies, including studies on grey seals: SGPV3, SGPV9, Hgdii, Hg0, Hg6.1, Hg8.10, Hg8.9, Hg6.3, SGPV10, SGPV11, TBPV2, Pvc19, Pvc43 and Pvc78 (Allen et al., 1995; Coltman, Bowen and Wright, 1996; Goodman, 1998). Following optimisation to adapt these markers to screen a subset of samples, three loci (Pvc43 SGPV3, and Hg0) failed to amplify or had poor amplification success rates and were therefore excluded from subsequent analysis. PCR and genotyping were carried out using a 4300 DNA Analyzer (LI-COR), as detailed in Steinmetz et al. (2022). To assess genotyping error rates, genotyping of up to 10% of samples was conducted twice. All microsatellite data were tested in Micro-Checker for allelic dropout and the presence of null alleles, stutter bands, and genotyping inconsistencies (Van Oosterhout et al., 2004).

Genetic diversity at the mitochondrial control region (haplotypic diversity) as well as for microsatellite data (observed and expected heterozygosity) was assessed as per Steinmetz et al. (2022), including an identity analysis of microsatellite data, assessment of deviations from Hardy-Weinberg equilibrium.

To test for mitochondrial population structure within the island of Ireland, five geographic areas were defined a priori based on the geographic distribution of key haul-out sites for which the North of Ireland and Northern Ireland were combined due to low sample availability (Online Resource 1). Genetic differentiation was assessed via pairwise comparisons and AMOVA analysis undertaken in ARLEQUIN v. 3.5 (Excoffier and Lischer, 2010). A priori partitioning of sub-regions using this dataset is detailed in Table 1; and is based on sub-regions used in the aforementioned studies. Additionally, a second dataset was assessed excluding 32 samples known to originate from fully moulted pups(see Online Resource 2 for details), to assess the potential impact of these individuals on genetic structuring results as grey seal pups are known to disperse widely once they are moulted (Brasseur, 2017). In order to assess population differentiation using pairwise comparisons and to partition the variation within and among locations, an AMOVA was performed in ARLEQUIN v. 3.5 (Excoffier and Lischer, 2010). A statistical parsimony network (TCS network) created in tcs (Clement et al., 2002) was visualised in tcsBU (Múrias dos Santos et al., 2016).

Nuclear microsatellite data from previous grey seal studies were not included in the current analysis due to the small number of loci that overlapped between datasets (max. 4–5 loci) and the difficulty of harmonising scoring across datasets gathered by different laboratories with contrasting methodologies. Samples collected for nuclear analysis included three samples originating from the Baltic sub-region (Table 1) and due to this small sample size, this sub-region was excluded from the microsatellite analysis. The final nuclear dataset comprised samples from 163 individuals from three sub-regions: the island of Ireland, Southwest England and the German/ Danish North Sea. Genetic differentiation between and within sub-regions for microsatellite data were assessed by estimating pairwise FST performing an AMOVA, genetically distinct clusters were identified using Bayesian approaches in TESS and STRUCTURE, and Isolation by Distance (IBD) was assessed as detailed in Steinmetz et al. (2022).

For each sub-region, recent demographic reductions, or bottlenecks, in the nuclear dataset were tested for as detailed in Steinmetz et al. (2022). Demographic history of the mitochondrial control region was assessed via Tajima’s D and Fu’s Fs, calculated in ARLEQUIN v. 3.5 (Excoffier and Lischer, 2010).

Migration rates among sub-regions were analysed using BayesAss, which estimates nuclear migration rates for the last two generations based on a gametic disequilibrium signal produced by immigrating individuals or their descendants (Wilson and Rannala, 2003). The median across four runs was taken as described in Carroll et al., (2020) using 10,000,000 iterations per run with a burn-in of 1,000,000 – sampling every 1,000 iterations. As previous studies estimated a generation time for grey seals in the Baltic between 14 and 16 years (Graves et al., 2009; HELCOM, 2013), this approach to assessing nuclear migration rates provides relatively recent information (i.e. within the last few decades), considering that individuals in the current study were sampled for nuclear analysis between 2016 and 2020.

A significance level of 0.04 was employed and sequential Bonferroni correction (Rice, 1989) was used to adjust significance levels for multiple comparisons.

The mitochondrial sequences from the island of Ireland were trimmed to 456bp (n = 123 samples), while the dataset including newly generated data from the island of Ireland, Southwest England, and western North Sea (Germany and Denmark) was trimmed to 350bp (n = 201 samples), to maximise both the number of suitable samples and fragment length. The full dataset was further trimmed to 317bp of the mitochondrial control region to include sequences from the Dutch Wadden/ North Sea, Sable Island, the Faroe Islands, the UK, the island of Ireland, France, the Netherlands, Germany, Denmark and the Baltic (n = 2,079 samples). This fragment corresponded to the mitochondrial control region that overlapped between newly processed samples and previous datasets. The full dataset excluding known fully moulted pups included 2,047 samples (Online Resource 2). Microsatellite data were analysed for a sub-set of 160 individuals sampled during the current study (108 samples from the island of Ireland, 15 from Southwest England, and 37 from Germany and Denmark) and genotyped at 11 loci.

Within sequences from the island of Ireland, no significant differentiation was observed between any of the five geographic areas using both mtDNA and microsatellite data (Online Resource 3). Hence, all samples collected across the island of Ireland were pooled together as a single sub-region for further analysis.

Genetic diversity

While no identity analysis could be carried out for mitochondrial sequences, the presence of duplicates was unlikely due to sampling strategies and results of the nuclear identity analysis (see below). Overall, 190 haplotypes were identified using the combined dataset, with 9-103 haplotypes and 15–74 polymorphic sites observed within sub-regions (Table 2). The highest number of haplotypes and polymorphic sites was found for seals sampled in Orkney. Haplotype diversity ranged from 0.792 (East Scotland, ESC) to 0.958 (Baltic, BAL). The island of Ireland (IRL & NI) was characterised by high haplotype diversity (0.925). Among the newly generated data, 12 new haplotypes were described, with the remaining 177 haplotypes matching sequence data from previous studies (e.g. Fietz, Graves and Olsen, 2013; Klimova et al., 2014; Decker et al., 2017). Further information regarding specific sequences and GenBank accession codes can be found in the Online Resource 4.

Table 2

Genetic diversity based on a 317bp fragment of the mitochondrial d-loop of grey seals. Shown are the sample number (N), number of haplotypes, the nucleotide (π) and haplotype (h) diversities with standard deviation in brackets and the number of polymorphic sites. Acronyms are shown in Table 1.
Sub-region	N	No. haplotypes	π (SD)	h (SD)	No. polymorphic sites
NWS	76	22	0.013 (0.007)	0.930 (0.013)	28
IRL & NI	138	33	0.015 (0.008)	0.925 (0.011)	31
SWE	28	12	0.013 (0.007)	0.868 (0.042)	18
NEE	49	15	0.011 (0.006)	0.844 (0.036)	26
ESC	54	44	0.008 (0.005)	0.792 (0.041)	15
ORK	1245	103	0.014 (0.007)	0.927 (0.003)	74
FO	31	11	0.012 (0.007)	0.901 (0.028)	16
FR	224	34	0.012 (0.007)	0.919 (0.009)	32
DWS	19	13	0.015 (0.008)	0.924 (0.048)	18
GDNS	32	9	0.010 (0.006)	0.825 (0.035)	17
BAL	116	43	0.020 (0.011)	0.958 (0.007)	35
SIC	67	19	0.013 (0.007)	0.922 (0.014)	24
Total	2079	190	0.013 (0.007)	0.895 (0.024)	96

Analysis of microsatellite data showed no evidence of individuals being sampled more than once, nor for the presence of null alleles based on analysis in Micro-Checker and Cervus. The genotyping error rate of the re-analysed samples was negligible at < 0.01%. Two loci (SGPV9, Pvc19) showed significant deviations from HWE caused by heterozygote excess for global analysis. Analysis on a sub-region level revealed that a maximum of two loci deviated significantly from HWE and these deviations were not consistent across loci. As null alleles however are generally characterised by constant deviations, all loci were retained. The average mean heterozygosity ranged between 0.72 and 0.78 with the lowest observed heterozygosity shown by Southwest England (SWE), while IRL & NI and the German/ Danish North Sea (GDNS) showed higher levels of diversity (Table 3, Online Resource 5). Mean allelic richness was similar between all sub-regions (5.09–5.39) and allelic richness ranged between 1.99 and 7.89, with lower values reported for locus Pvc78. The mean number of alleles ranged from 5.27 for SWE to 8.64 for IRL & NI (Table 3, Online Resource 5).

Table 3

Genetic diversity of grey seals based on 11 microsatellite loci showing the number of alleles (Na), allelic richness (Ar), expected and observed heterozygosity (He, Ho) and the inbreeding coefficient (FIS) as a mean across all employed loci. Acronyms for putative populations/sub-regions are shown in Table 1. Full details for each locus are shown in the supplementary material.
	IRL (n = 108)	SWE (n = 15)	GDNS (n = 37)
N_a (mean ± SD)	8.636 ± 3.009	5.273 ± 1.555	6.545 ± 2.067
A_r (mean)	5.605	5.085	5.392
H_e (mean ± SD)	0.677 ± 0.203	0.635 ± 0.230	0.678 ± 0.197
H_o (mean ± SD)	0.745 ± 0.251	0.718 ± 0.273	0.777 ± 0.236
F_IS (mean)	-0.084	-0.13	-0.146

Population Genetic Structure and Gene Flow

Population genetic structuring based on mitochondrial DNA showed significant sub-structuring (FST = 0.072, p < 0.001), with 7.22% and 92.78% of variance being partitioned among and within sub-regions, respectively. Pairwise comparisons showed genetic differentiation between sub-regions, though a lack of differentiation was observed between the IRL & NI and France (FR), as well as between SWE and the Dutch Wadden/ North Sea (DWS) (Table 4).

Table 4

Pairwise genetic differentiation, shown as F_ST values, between grey seal sub-regions based on mtDNA. Acronyms are shown in Table 1. Asterix indicates a significant p value after sequential Bonferroni correction (*p < 0.0007). Data gained by the present study is highlighted in bold.
	NWS	IRL & NI	SWE	NEE	ESC	ORK	FO	FR	DWS	GDNS	BAL	SIC
NWS	0
IRL & NI	0.037*	0
SWE	0.049	0.017	0
NEE	0.029*	0.036*	0.064*	0
ESC	0.059*	0.062*	0.106*	-0.010	0
ORK	0.006	0.030*	0.048*	0.023*	0.047*	0
FO	-0.001	0.059*	0.068*	0.072*	0.128*	0.012	0
FR	0.023*	0.006	0.025	0.022*	0.043*	0.017*	0.051*	0
DWS	-0.000	0.021	0.048	-0.009	0.027	-0.010	0.024	0.014	0
GDNS	0.029	0.041*	0.098*	0.031	0.049	0.048*	0.079*	0.032	0.039	0
BAL	0.072*	0.074*	0.088*	0.111*	0.151*	0.095*	0.060	0.105*	0.061*	0.089*	0
SIC	0.277*	0.314*	0.337*	0.353*	0.409*	0.275*	0.255*	0.342*	0.265*	0.351*	0.189*	0

The parsimony network for the newly generated mtDNA data indicated the isolation of seals sampled in the Baltic Sea, while showing a grouping of seals sampled in IRL & NI and SWE (Fig. 1a). Samples from GDNS and SWE however share only a single common haplotype in addition to a less frequent one. When assessing the full dataset, the network showed that seals sampled in Sable Island and the Baltic Sea were genetically distinct from those in other sub-regions, and samples from France shared haplotypes with samples from northern UK sub-regions (Northwest Scotland, Northeast England, East Scotland, Orkney Islands), as well as with samples collected in IRL & NI and SWE (Fig. 1b). Further, samples from France, IRL & NI as well as SWE all included haplotypes not observed in northern UK sub-regions (Fig. 1b). The most common haplotypes identified by previous studies were also observed in samples from IRL & NI, SWE and GDNS. Furthermore, four haplotypes were observed in IRL & NI, SWE and/or FR, but not in any other sub-region. IRL & NI showed eight unique haplotypes all of which were different by a single mutation to haplotypes previously identified (Fietz, Graves and Olsen, 2013; Klimova et al., 2014; van Bleijswijk et al., 2014; Decker et al., 2017; Cammen et al., 2017). Similarly, one and three haplotypes observed in GDNS and SWE, respectively, were unique and showed the same pattern in that they displayed a single mutation difference to previously described haplotypes.

Regarding microsatellite data, the AMOVA results showed a low yet highly significant FST (FST = 0.01, p < 0.0001) with 1.11% and 98.89% of the variance partitioned among and within sub-regions (IRL & NI, SWE, GDNS), respectively. Pairwise comparisons of the three sub-regions identified significant differentiation between SWE and the other sub-regions, whereas no significant differentiation was observed between IRL & NI and GDNS (Table 5).

Table 5

Pairwise genetic differentiation between grey seal sub-regions based on microsatellite data. Acronyms are shown in Table 1. Asterix indicates a significant p value after sequential Bonferroni correction (*p < 0.017).
	IRL & NI (n = 108)	SWE (n = 15)	GDNS (n = 37)
IRL & NI	0
SWE	0.03351*	0
GDNS	-0.00014	0.02482*	0

Analysis using the two Bayesian clustering approaches identified a single genetic cluster both with (TESS) and without (STRUCTURE) taking geographic origin into account. No evidence for isolation by distance (IBD) was found using the full microsatellite dataset.

Demographic analysis

The BOTTLENECK analysis did not identify any signs of a recent genetic bottleneck, in any of the three sub-regions based on nuclear microsatellite data. Tajima’s D values and Fu’s Fs values for demographic analysis of the mitochondrial control region were all negative (Table 6), with Tajima’s D results being significant for ORK and Fu’s Fs results being significant for NWS, IRL & NI, ORK, FR, DWS and BAL (Table 6).

Table 6

Tajima’s D and Fu’s Fs neutrality tests for each grey seal sub-region based on the 317bp fragment of the mitochondrial d-loop for the mitochondrial assessment of population demographics.
	Tajima’s D	p	Fu’s Fs	p
NWS	-0.897	0.211	-6.251	0.023*
IRL & NI	-0.494	0.365	-11.511	0.006*
SWE	-0.478	0.350	-2.130	0.171
NEE	-1.406	0.069	-3.493	0.096
ESC	-0.778	0.226	-1.687	0.250
ORK	-1.495	0.026*	-24.682	0.000*
FO	-0.207	0.479	-1.257	0.300
FR	-0.761	0.251	-12.676	0.005*
DWS	-0.427	0.383	-4.624	0.027*
GDNS	-0.818	0.219	-0.311	0.479
BAL	-0.081	0.549	-19.404	0.000*
SIC	-0.654	0.269	-4.401	0.074

Migration analysis showed that the island of Ireland received migrants both from the German/Danish North Sea (0.31, 95% HPD: 0.28, 0.34) and the Southwest of England (0.28, 95% HPD: 0.23, 0.33) during the last two generations. Internal recruitment within the sub-regions ranged from 0.676 to 0.979, with the highest internal recruitment observed within IRL & NI.

Impact of moulted pups on mitochondrial analysis

Grey seal pups that were known to be fully moulted were excluded to test for a potential impact on mitochondrial genetic structuring (see Online Resource 2 for details). Mitochondrial diversity remained similar for affected sub-regions (IRL & NI, SWE, GDNS) with minimal decreases in the number of observed haplotypes and polymorphic sites while haplotype and nucleotide diversity remained stable, with minimal changes up- or downwards (Table 2-A in the Online Resource 2). Based on pairwise population differentiation (FST) between sub-regions, grey seals sampled in GDNS (n = 19) showed higher genetic differentiation to UK sub-regions and in two cases, namely to ESC and NEE, this differentiation was now significant (Table 4-A in the Online Resource 2). Though no significant genetic differentiation was observed between GDNS and NWS, as before. Genetic differentiation between GDNS and IRL & NI was lower, and no longer significant, after excluding known moulted pups. Values for Tajima’s D and Fu’s Fs tests of neutrality remained largely similar but changed for SWE and GDNS whereby SWE showed positive values for both indices and GDNS showed a positive Fu’s Fs value – though none of these were significant (Table 6-A in the Online Resource 2).

Genetic diversity

All sampled sub-regions were characterised by high mitochondrial diversity, including the three newly assessed sub-regions (island of Ireland, Southwest England, German/ Danish North Sea). Samples from the island of Ireland displayed very high diversity, similar to Northwest Scotland, the Orkney Islands and France, whereas grey seals sampled in the German/ Danish North Sea and Southwest England had lower yet nonetheless high levels of diversity, similar to those of East Scotland and Northeast England. Considering that no restriction to maternal gene flow was detected (see below), the lower levels of diversity observed in the latter regions may potentially be attributed to sampling biases resulting from sampling stranded individuals and the varying representation of age-sex groups between sub-regions, and/or differences in effective population size between sub-regions. Effective population size is known to affect genetic diversity of populations whereby populations with larger effective population size show evidence for higher genetic diversity (e.g. Peart et al., 2020). This correlation is in line with observed diversity patterns where seals sampled for instance in Scotland show very high genetic diversity and the area is home to large numbers of breeding seals (SCOS, 2021). In comparison, breeding grey seal numbers in Germany and Denmark, though increasing, are much lower, as grey seals only recently recolonised this sub-region. Further, both mitochondrial and nuclear diversity were found to be higher in comparison to the other resident seal species in European waters, the harbour seal (Andersen et al., 2011; Olsen et al., 2017, Steinmetz et al, 2022), again reflecting the typical pattern of lower genetic diversity for small and/ or geographically constrained populations (e.g. of harbour seals) in contrast to higher observed diversity in larger and/ or wider ranging populations (e.g. of grey seals).

Nuclear diversity was also high across the three newly sampled sub-regions assessed in the current study (island of Ireland, Southwest England, Germany/ Denmark) with the lowest diversity being found for Southwest England. However, as the sample size was relatively small for this sub-region (n = 15), results may not reflect the true level of variation. Consistent with the relatively high diversity observed in the current study, FIS values were close to zero for all three sub-regions, indicating no evidence of inbreeding. Besides current levels of diversity, no recent population bottlenecks, or isolation by distance, were detected. A similar level of nuclear diversity within the species has been reported for other sub-regions within the Northeast Atlantic and Baltic Sea (Allen et al., 1995; Graves et al., 2009; Wood et al., 2011; Klimova et al., 2014; Fietz et al., 2016).

Population genetic structure and gene flow

Previously, Klimova et al. (2014) reported the existence of three genetically distinct metapopulations, namely the Northwest Atlantic (including the Sable Island population), the Eastern Atlantic (comprising of the northern UK, including the northern North Sea and the Faroe Islands) and the Baltic Sea, results confirmed by mitochondrial analysis within the current study. Additionally, our results indicated the existence of two intraspecific units within the Eastern Atlantic metapopulation, previously hypothesised by Decker et al. (2017) based on the genetic distinctiveness of seals sampled in France from those sampled in the northern UK. The existence of two intraspecific units is largely supported by pairwise differentiation, though Bayesian clustering (STRUCTURE and TESS) indicated the presence of only one genetic metapopulation within these data, as could be expected given that such approaches do not perform well when differentiation is low.

With regard to the newly processed data, both mitochondrial and nuclear gene flow indicated that grey seals sampled in the island of Ireland belong to the same interbreeding (panmictic) population. Though contemporary patterns of genetic diversity and differentiation are the result of past rather than present segregation patterns, these findings reflect current knowledge of large-scale movements of grey seals in Irish waters based on telemetry data. For instance, individuals tagged in the Southwest of Ireland during the moult (February) were found to use waters all along the west coast, with movements observed to extend into Northern Ireland and Northwest Scotland (Cronin, Pomeroy and Jessopp, 2013; Jessopp, Cronin and Hart, 2013), and seals tagged in the Northwest of Ireland (April) were also found to travel to Scotland (Luck, 2020). On the other hand, individuals tagged in the Southeast (February, April) displayed movements that primarily stayed within the Irish Sea, along the South and extending across to Wales (Cronin et al., 2016).

Mitochondrial findings relating to our newly generated data were broadly consistent with nuclear results with the exception of two findings of pairwise differentiation: (i) mitochondrial (i.e. maternal) gene flow versus significant nuclear differentiation between the island of Ireland and Southwest England, and (ii) significant mitochondrial differentiation versus nuclear evidence for gene flow between the island of Ireland and the German/ Danish North Sea. However, this is likely due to differences in sample size for the two marker types and the inclusion of fewer sub-regions in the nuclear analysis. Further, results from nuclear migration analysis were consistent with mitochondrial findings, but not consistent with nuclear differentiation findings. Specifically, migration analysis identified gene flow between the island of Ireland and Southwest England - whereby Southwest England was found to be a source of migrants for the island of Ireland. Further, nuclear migration analysis found the German/ Danish North Sea to be a source of migrants for the island of Ireland, though this observation is likely due to other sub-regions missing from the nuclear dataset, and these two areas may rather share a common source population. Overall, no mitochondrial structuring was observed between the island of Ireland and Southwest England, France, and the Dutch Wadden/ North Sea, indicating that movements across these areas are reflective of effective migration rates with exchange of breeding migrants. Observed genetic differentiation reflecting past gene flow of this species align with contemporary movement patterns identified from telemetry data within the sub-region, showing movements of grey seals tagged in France (tagged within and outside of breeding) to southern England and Ireland (Vincent et al., 2017), as well as movements of seals tagged in England to the island of Ireland (Carter et al., 2020). In line with Decker et al.’s hypothesis, France and Southwest England showed similar patterns of differentiation to Scotland, the North Sea and the Faroe Islands, while gene flow between Southwest England and France was observed. Such patterns of differentiation to Scotland and the Faroe Islands were also observed for the island of Ireland. Overall, mitochondrial findings indicate that samples from the island of Ireland, Southwest England and France are distinct from other sub-regions except the Dutch Wadden/ North Sea which showed gene flow to all other sub-regions.

Results of the current study support the previous identification of the Orkney Islands as a source population for other sub-regions in Scotland and Northeast England (Klimova et al., 2014), as well as reported gene flow between the Isle of May/ North Rona and the Danish North Sea based on both mitochondrial and nuclear analyses (Fietz et al., 2016). While it has been suggested that the UK grey seal population may be approaching carrying capacity, causing a southward shift in their distribution towards the southern North Sea (Brasseur et al., 2017; SCOS, 2021), seals breeding in the English North Sea have also been reported foraging in Dutch coastal waters with intraspecific competition suggested as a key driver for such transient behaviour (Brasseur et al., 2017). Interestingly, population differentiation between the German/ Danish North Sea and Northeast England as well as East Scotland was significant when excluding known moulted pups, which could be interpreted as a confounding effect due to the inclusion of “vagrant” non-breeding individuals in genetic analyses (e.g. some of the moulted pups sampled in the German/ Danish North Sea may have originated from sub-regions within the UK, and dispersed southward into the GDNS following moulting). In contrast, there was still no significant differentiation between Northwest Scotland and the German/ Danish North Sea after removing moulted pups from the dataset. The former sub-region included samples originating from North Rona, an area that was previously shown to be a source for the Danish North Sea (Fietz et al., 2016). Hence, this area may be a more established source also for the German North Sea. The observation that the island of Ireland and the German/ Danish North Sea were no longer significantly differentiated when removing known moulted pups from the mitochondrial dataset may be an artefact of the large discrepancy in sample size after removal of samples from the dataset, especially regarding the German/ Danish North Sea sub-region. Another explanation, is that both sub-regions may share a common source within the UK, such as Northwest Scotland. The overall lack of genetic differentiation between sub-regions within the North Sea using the whole dataset is not surprising, given the high dispersal potential of the species, also reflected by the observed contemporary movement patterns that have been identified by telemetry studies. These include movements of weaned pups between Heligoland (Germany) and the Wash in Southeast England, the Netherlands and Denmark (Peschko et al., 2020), movements of adult grey seals between the Netherlands (tagged in March, September) and offshore Dutch, Belgian, German (Heligoland) and UK waters, and the observed breeding behaviour of these adult seals in Southeast England (Brasseur et al., 2017). These patterns may suggest that Germany, Denmark and the Dutch Wadden/ North Sea were likely colonised from Scotland and Northeast England, while France was likely colonised from Southwest England – and potentially Wales, which was not included here but from where tagged young grey seals were found stranded during the 1950s in France (Härkönen et al., 2007).

Despite some discrepancies in levels of genetic differentiation between marker types and/ or datasets, findings from the present study provide evidence of low yet significant genetic structuring within the Northeast Atlantic, supporting the hypothesis that the island of Ireland, southwestern England and France show sufficient genetic differentiation to Scotland and the North Sea to constitute a separate MU. It is worth noting that the present study employed a conservative approach so that the presented significance in differentiation (at the p = 0.05 level) was considered sufficient evidence for the delineation of this separate MU. Further, FST can be an imperfect measure of differentiation particularly when using relatively small numbers of loci. In this regard, using a large number of markers (e.g. Single Nucleotide Polymorphisms, SNPs) has been found to perform better than using microsatellite markers for the analysis of population structure in some cases, specifically where differentiation is low (e.g. Lah et al., 2016), and is recommended for future analysis. Considering the high dispersal potential and the lack of physical boundaries in the marine environment, care needs to be taken when identifying “hard” geographic boundaries between discrete units/ regions and thus it may be appropriate to identify ‘transition zones’ as has been suggested for other marine mammals (Sveegaard et al., 2015). Such a transition zone may exist within the waters off Northwest Scotland, where patterns of genetic differentiation and gene flow connect this region to sub-regions of both proposed MUs (e.g. Southwest England, but also other sites in the UK and southern North Sea), as well as within the English Channel and Dutch North Sea. Scarcity of samples from the latter transition zone limit inference in this regard, although genetic differentiation showed the Dutch North Sea to be connected through gene flow to all sub-regions within both MUs. The identified barrier within the English Channel itself may also be an artefact of the rather low abundance of grey seals along the shores of the English Channel. If the growth of abundance in this area continues, more genetic exchange is likely.

Implications for and recommendations for conservation management

Based on the genetic findings within the current and previous studies, it is recommended to assign samples from the island of Ireland to the same MU, and to designate the following international grey seal MUs: (i) Northwest Atlantic (i.e. Sable Island); (ii) the island of Ireland, southwestern UK (Cornwall), and France; (iii) Faroe Islands, Scotland and the North Sea; and (iv) the Baltic Sea region (Fig. 2). Results from the present genetic study thereby suggest the delineation of two separate AUs within the Northeast Atlantic (incl. the North Sea) as outlined above for assessing abundance trends. This stands in direct contrast to the single AU currently used within these OSPAR Regions, which is based on assumptions dictated by the wide-ranging movement patterns observed by photo-ID and telemetry studies (e.g. Gerondeau et al., 2007; Vincent et al., 2017). However, at the same time those studies (i.e. Vincent et al., 2017) supported a hypothesised potential barrier to gene flow in the English Channel (Decker et al., 2017). The existence of transition zones indicates significant exchanges between the two MUs of the Northeast Atlantic that have to be considered for conservation management.

Despite a substantially widespread effort in obtaining samples from grey seals around the island of Ireland, some areas remain underrepresented in the present study due to logistical and jurisdictional constraints hampering sampling at certain haul-out sites (e.g. Donegal, Northern Ireland) and low seal abundance in some areas (e.g. East Ireland) (Cadhla et al., 2013; Morris and Duck, 2019). Thus, future efforts should aim at increasing sampling effort in these areas, with particular relevance to resolving potential differentiation between the north of Ireland, Northern Ireland and western Scotland, regions between which grey seals are known to move frequently (e.g. Carter et al., 2020), as well as grey seals occurring along the Welsh coast, a sub-region not assessed within the current study. Furthermore, grey seals breeding at a certain colony can move to other haul-out sites at other times of the year which are also used by seals from different breeding colonies (Brasseur et al., 2017). Thus, the temporal (breeding versus foraging) origin of samples has important implications for the assessment of population structure in grey seals. As the temporal origin was not known for all samples and the temporal sampling distribution of the current study was uneven, it was not possible to separate data into two datasets in order to assess the specific impact of this parameter. It is hence recommended that future investigations into genetic population structure of grey seals should take this into account, in addition to employing a multidisciplinary approach as described in Steinmetz et al. (2022).

Based on available evidence, it is proposed to delineate two tentative MUs where both the English Channel and the Dutch Wadden/ North Sea, and waters off Northwest Scotland should be treated as transition zones (i.e. zones that display genetic connectivity to both MUs), until further genetic analysis is undertaken, and to use these two intraspecific units as AUs for future OSPAR indicator assessments. Within the English Channel, it is recommended to utilise the ICES fisheries management division boundary (between VIIe and VIId), dividing the western and eastern English Channel, for boundary delineation between the two proposed MUs within the Northeast Atlantic, until further genetic data become available. There are indications of the differential use of the English Channel by grey seals tagged in France (tagged between April-August) and the Netherlands (tagged in March-May and in September), with individuals primarily using the western and eastern English Channel, respectively (Vincent et al., 2017; Brasseur et al., 2017), supporting this division. Both nuclear migration and mtDNA analysis indicated gene flow between the Faroe Islands/ Scotland/ the North Sea MU and the island of Ireland/ Southwestern UK (Cornwall)/ France MU. Such exchanges between the two MUs could indicate that the genetic distinctiveness of the two proposed MUs may gradually decrease over time due to increased admixture, and boundaries may shift or even eventually disappear. Continued monitoring and assessment of population structuring is advised, and while the occurrence of grey seals in the English Channel is low (Russell, Jones and Morris, 2017; Carter et al., 2020), increasing future sampling effort in this region is recommended along with the inclusion of samples from Wales, western Scotland, the Wash, the Thames Estuary, Belgium and southern Norway.

Funding

This research was funded by the Galway-Mayo Institute of Technology (KS scholarship, now Atlantic Technological University), by the National Parks and Wildlife Services (NPWS, grant ref SPU G07-2017) and by the Department of Housing, Local Government and Heritage. The provided data from SMRU (sampling and genetic analysis) was funded by NERC (Natural Environment Research Council).

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Authors' contributions

KS, LM, SM and OO conceptualised the study, obtained funding and conducted fieldwork. KS and LM performed laboratory work. JB, MC and MJ provided crucial samples. JH, JJ, AG, SH and PP provided access to previous data to combine datasets for analysis. KS, LM and SM contributed to writing the original version of the manuscript. LM and SM provided study supervision. KS performed analysis. KS, LM, SM, OO, MJ, JH, JJ, AG, and PP contributed revisions to the original manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

Many thanks to NPWS staff for providing advice on fieldwork as well as for collecting samples during their own surveys. Our thanks extend to the following students for help with fieldwork and/or laboratory work: Daniel Brady, Shraveena Venkatesh, Sofia Wunder and Javier Burgoa Cardas. Many thanks also to boat operators for taking us safely out to fieldwork sites. As data from fieldwork were corroborated through collaborations, we wish to thank the following collaborators for providing samples: University College Cork, Seal Rescue Ireland, the Department of Agriculture, Environment, and Rural Affairs (DAERA), the Cornish Seal Sanctuary, the Seehundstation Norddeich, and the Seehundstation Friedrichskoog.

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Genetic analysis in aid of delineation of Management Units for a wide-ranging marine mammal, the grey seal (Halichoerus grypus), in European waters

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