ABSTRACT Climate change is rapidly affecting species distributions across the globe, particularly in the North Atlantic. For highly mobile and elusive cetaceans, the genetic data needed to

understand population dynamics are often scarce. Cold-water obligate species such as the white-beaked dolphin (_Lagenorhynchus albirostris_) face pressures from habitat shifts due to rising

sea surface temperatures in addition to other direct anthropogenic threats. Unravelling the genetic connectivity between white-beaked dolphins across their range is needed to understand the

extent to which climate change and anthropogenic pressures may impact species-wide genetic diversity and identify ways to protect remaining habitat. We address this by performing a

Sea, while two regional populations in the North Sea and adjacent UK and Irish waters are highly differentiated from all other clades. Our results have important implications for the

conservation of white-beaked dolphins by providing guidance for the delineation of more appropriate management units and highlighting the risk that local extirpation may have on species-wide

genetic diversity. In a broader context, this study highlights the importance of understanding genetic structure of all species threatened with climate change-driven range shifts to assess

the risk of loss of species-wide genetic diversity. SIMILAR CONTENT BEING VIEWED BY OTHERS DISTRIBUTION OF GENETIC DIVERSITY REVEALS COLONIZATION PATTERNS AND PHILOPATRY OF THE LOGGERHEAD

SEA TURTLES ACROSS GEOGRAPHIC SCALES Article Open access 22 October 2020 GENETIC AND DEMOGRAPHIC HISTORY DEFINE A CONSERVATION STRATEGY FOR EARTH’S MOST ENDANGERED PINNIPED, THE

MEDITERRANEAN MONK SEAL _MONACHUS MONACHUS_ Article Open access 11 January 2021 GENOMIC INSIGHTS INTO THE HISTORICAL AND CONTEMPORARY DEMOGRAPHICS OF THE GREY REEF SHARK Article 16 March

2022 INTRODUCTION Understanding within-species connectivity and diversity is essential for informing conservation management and can help in assessing the impact of local extinctions for

species-wide genetic variation (Palsbøll et al. 2007; Pavlova et al. 2017). In the marine environment, limitations to dispersal are more subtle than in terrestrial systems due to the

scarcity of geophysical barriers. Nevertheless, marine species often display genetic structuring influenced by environmental conditions such as physiography, salinity, thermal niches, social

structure, movement patterns, and behavioural specialisation (Craig and Herman 1997; Foote et al. 2011; Hoelzel 2009). Disentangling these patterns can be challenging, yet is critical for

conservation management of species threatened with anthropogenic impacts and environmental shifts driven by global climate change (Palsbøll et al. 2007). The latter is particularly alarming

for species inhabiting cold temperatures, as their available habitat is likely to shift under global warming (Louis et al. 2020; Pauls et al. 2013). This can have strong impacts on

distribution, abundance, and species-wide genetic diversity. For example, when differentiated populations are present in areas subject to strong environmental change with limited

availability to new suitable habitat, extirpation may result in the loss of a significant proportion of species-wide genetic diversity (Razgour et al. 2013). This, in turn, can negatively

affect the ability of the species to adapt to future changes, as high genetic diversity is believed to be a major driver of positively selected mutations (Kardos et al. 2021). The risk of

local extirpation, either driven by climate change or by direct anthropogenic impact, can only be accurately assessed when sufficient information on range-wide population structure of a

species is available. As global recognition of the significance of genetic variation in biodiversity conservation grows and national and international bodies increasingly enforce commitments

to protect ocean habitats (CBD 2022; DeWoody et al. 2021; United Nations 2015), there has never been a more urgent time to explore the connectivity of cold-water marine species to achieve

appropriate conservation management strategies in response to challenges posed by global climate change. The white-beaked dolphin (_Lagenorhynchus albirostris_) is a cold-water obligate

cetacean inhabiting continental shelf, shelf edge and continental slope waters of the temperate and sub-polar North Atlantic (Galatius and Kinze 2016). The species is common in the Canadian

Atlantic, Greenland, Iceland, the Barents Sea, and parts of the North Sea and adjacent UK and Irish waters (Hammond et al. 2013; Hansen and Heide-Jørgensen 2013; Kinze et al. 2018; Lien et

al. 2001; Øien 1996; Pike et al. 2019; Fig. 1a). In past decades a considerable northward shift in its southern distribution has been detected suggesting that white-beaked dolphins in the

North Sea and adjacent UK waters avoid waters with higher sea surface temperatures (SSTs) (IJsseldijk et al. 2018; MacLeod et al. 2007; Waggitt et al. 2020). As SSTs in the North Sea are

projected to further increase, more frequently exceeding the suitable threshold for white-beaked dolphins, the species risks facing a considerable northward-shift in this region (Dieterich

et al. 2019; Evans and Waggitt 2020; Johns et al. 2003; Lambert et al. 2014). Additionally, the species faces numerous direct anthropogenic pressures, such as bycatch in commercial fisheries

(Reeves et al. 2013), local unregulated harvesting (Takekawa 2000; Piniarneq 2021), prey depletion (Jackson et al. 2001), anthropogenic noise and chemical contaminants (Stone and Tasker

2006; Galatius, Bossi et al. 2013; Williams et al. 2023). In order to understand the consequences of predicted habitat shifts and other threats, it is necessary to investigate how

white-beaked dolphins are connected across their range. As of now, morphometric studies have described differences in skull characteristics between the western North Atlantic and the North

Sea indicative of separate populations (Mikkelsen and Lund 1994), suggesting some level of population structure. This was confirmed by genetic studies, supporting a distinction between

northeast and northwest Atlantic populations, but also within the northeast Atlantic (Banguera-Hinestroza et al. 2010). Fernández et al. (2016) generated a panel of genome-wide SNPs, yet the

study lacks an assessment of within-species genetic structure. For the conservation management of the white-beaked dolphin, a comprehensive assessment of population structure is needed to

delineate more appropriate management units, as highlighted within the Agreement on the Conservation of Small Cetaceans of the Baltic, Northeast Atlantic, Irish and North Seas (ASCOBANS;

ASCOBANS 2019). Here, we explore species-wide population structure, genetic diversity and contemporary geneflow of the white-beaked dolphin across the North Atlantic with the aim of

providing guidance for improved conservation management of the species. METHODS SAMPLING AND DNA EXTRACTION A total of 169 tissue samples were obtained from preexisting archives and

consisted of stranded (_n_ = 133), by-caught (_n_ = 23) or biopsied (_n_ = 13) white-beaked dolphins sampled between 1992 and 2021. Prior to any further processing, the tissue samples were

stored either dry frozen or in a ≥80% ethanol solution at −80 °C. The geographical distribution of the samples ranged from eastern Canada in the western North Atlantic (_n_ = 3; biopsies),

Iceland in the central North Atlantic (_n_ = 23; bycatch) and the Barents Sea (_n_ = 10; biopsies), Scotland (_n_ = 81; strandings), England (_n_ = 24; strandings), Ireland (_n_ = 3;

strandings), Denmark (_n_ = 4; strandings), Germany (_n_ = 12; strandings), The Netherlands (_n_ = 8; strandings), and France (_n_ = 1; stranding) in the eastern North Atlantic or adjacent

waters (Figs. 1a and 2a). Genomic DNA was extracted using the Maxwell PureFood & GMO Authentication kit on a Maxwell RSC extraction robot. DNA was quantified using a Qubit fluorometer

and tested for high molecular weight DNA content on a 1% agarose gel. DNA concentrations were standardised across samples and subsequently, 166 samples and 18 duplicate samples were

submitted for DArTseq™ (Diversity Arrays Technology, Canberra, Australia). The DArTseq™ assay involves complexity reduction using a pair of restriction enzymes and amplification of the

fragments via PCR. The resulting library is then shotgun-sequenced on an Illumina HiSeq 2500, producing single-end sequenced reads with a length of 130 bp. Due to later acquisition, the

three samples from eastern Canada were submitted to Azenta Life Sciences (Chelmsford, Massachusetts, United States) for Short-Read Non-Human Whole Genome Sequencing (WGS) on an Illumina

NovaSeq to ensure coverage of the same markers retained by the DArTseq™ approach. The raw data produced by Azenta was paired-end shotgun-sequenced with a fragment size of 150 bp. READ

PROCESSING AND MAPPING Single-end DArTseq™ reads were quality-checked using FastQC and barcodes were trimmed using the _process_radtags_ function within STACKS v2.5.4 (Andrews 2010; Catchen

et al. 2013). Similarly, for the paired-end WGS reads FastQC was used for quality control and Trim Galore v0.6.6 was used to remove Illumina adapters. Both the DArTseq™ and WGS reads were

mapped to the chromosomal-level genome assembly of _Lagenorhynchus albirostris_ (Accession number: GCA_949774975.1) using the _bwa mem_ function in BWA v0.7.17 (Li 2013). The output was

assessed for mapping percentage and written to bam files using samtools v1.9 (Li et al. 2009). Following this, the mapped files were sorted (_SortSam_) and all reads were assigned to a read

group (_AddOrReplaceReadGroups_) using Picard Tools (Broad Institute 2019). Additionally, duplicates which can arise during library preparation were tagged and removed (_MarkDuplicates_) in

the WGS reads. The output was indexed, and depth of coverage was calculated using samtools. VARIANT CALLING Analysis of Next Generation Sequencing Data (ANGSD) software was used to detect

variants and calculate genotype likelihoods across the 169 samples (Korneliussen et al. 2014). An initial variant calling step was performed on all samples using base call and mapping

quality filters (-_minMapQ_ 30, -_minQ_ 30, -_SNP_pval_ 1e−6) and the output was written to PLINK format by specifying the _-doPlink_ flag, which translates the variants to called genotypes.

This initial dataset was inspected for levels of missing data and distribution of heterozygosity using the _--het_ and --_missing_ functions within PLINK v.1.09 (Purcell et al. 2007). Seven

samples showed missing data ≥ 30% and three samples displayed above-average heterozygosity suggesting cross-contamination issues. These samples were removed from the workflow. Furthermore,

pairwise relatedness between individuals was calculated by combining output from the PLINK _--genome_ function and output from the programme NGSrelate (Korneliussen and Moltke 2015). Two

pairs of samples showed a pairwise relatedness coefficient (_PI_HAT_) above 0.5 corresponding to first-degree relatedness (parent-offspring or full-siblings) and the sample with the lower

genotyping rate of each pair was removed from subsequent analyses (Supplementary Fig. S1). The dataset for investigation of population structure thus comprised 157 individuals and the

genotype likelihood calculation in ANGSD was repeated with additional filters on read depth (-_setMinDepth 785_, -_setMaxDepth 3140_) corresponding to a minimum depth of coverage of 5X and a

maximum depth of coverage of 20X per locus per individual to avoid potential bases arising from sequencing errors following recommendations by O’Leary et al. (2018). Furthermore, we

identified variants that were located in an interspersed repeat region using the programme RepeatMasker and excluded those from the variant calling by specifying the remaining sites using

the _-sites_ flag in ANGSD. Further filtering of the multilocus genotypes was conducted in PLINK using a minor allele count of 2 to remove variants generated through uncertainties in base

calling during sequencing. We examined the patterns of linkage disequilibrium decay in our data and observed a relatively steep decline in linkage disequilibrium in the initial portion of

your linkage disequilibrium decay graph drawn by the programme ngsLD (Fox et al. 2019). This suggests stronger linkage patterns among nearby SNPs and therefore, we used the --_indep_

function in PLINK to prune loci affected by linkage disequilibrium with a window size of 50 kb, a step size of 5 and a variant inflation factor of 2. The final dataset comprised 1092 Single

Nucleotide Polymorphisms (SNPs) for all downstream population genetic analyses. POPULATION STRUCTURE We investigated population structure using a number of different approaches. First, we

performed a Principal Components Analysis (PCA) in PCAngsd (Meisner and Albrechtsen 2018). PCA is a dimensionality reduction approach, summarising genetic variation into Principal Components

(PCs), which can be projected into axes to visualise genetic clustering. This approach is not influenced by geographic information. Eigenvalues of the first 20 PCs were inferred from the

covariance matrix generated by PCAngsd. In R, population structure was visualised by plotting PCs one and two and PCs two and three (R Core Team 2022). To investigate patterns of fine-scale

and sex-mediated population structure, the PCA was also performed with putative population assignments based on sampling site and for each sex separately, respectively. Second, _K_-means

clustering was conducted to estimate the number of ancestral populations in the dataset using a maximum likelihood approach in NGSadmix (Skotte et al. 2013) and a Bayesian approach in

Structure both with and without a-priori population assignments. A-priori assignments were informed by the clustering retained from the PCA. We investigated the most likely number of genetic

clusters present in the dataset by calculating both DeltaK and Log Likelihood from the NGSadmix output and DeltaK using the Evanno method from the Structure output (Evanno et al. 2005;

Supplementary Figs. S2 and S3). We examined the fit of the admixture proportions derived from the NGSadmix algorithm to its model assumptions by correlating the residual differences between

called and predicted genotypes with the EvalAdmix software (Garcia-Erill and Albrechtsen 2020; Supplementary Fig. S4). The individual admixture proportions for each _K_ and the correlation

of residuals were plotted in R. Finally, in order to investigate the significance of genetic population structure in the dataset, we grouped the samples into the populations informed by the

approaches above and calculated the Weir and Cockerham pairwise fixation index (_F__ST_) with 10,000 bootstraps using functions embedded in the _DartR_ package (Weir and Cockerham 1984;

Mijangos et al. 2022). Additionally, we grouped the samples by sampling sites (i.e., by country) and calculated pairwise _F__ST_ to investigate patterns of fine-scale population structure.

CONTEMPORARY GENE FLOW We estimated the proportion and direction of contemporary geneflow between genetic populations and between sampling sites using the BA3-SNPS extension of the software

BayesAss, which enables computation of large genomic datasets (Mussmann et al. 2019; Wilson and Rannala 2003). In the first instance, initial runs were performed using the BA3-SNPs-autotune

function to determine the optimal combination of the mixing parameters _deltaM_ (mixing parameter for migration rates), _deltaA_ (mixing parameter for allele frequencies) and _deltaF_

(mixing parameter for inbreeding coefficients). These parameters were set to _deltaM_ = 0.1563, _deltaA_ = 0.3250 and _deltaF_ = 0.0500. Five separate runs of BA3-SNPS were performed on

different seeds with 10,000,000 MCMC iterations and 1,000,000 burn-ins on sampling intervals of 1000. Chain convergence of the runs was assessed in R and significance of the retained

migration rates was assessed by a 95% confidence interval calculated as mean migration rate ±1.96 x mean standard deviation (Supplementary Fig. S5). The proportion and directionality of

geneflow between populations was visualised in R. ISOLATION BY DISTANCE We tested the correlation of geographic and genetic distance by performing redundancy analysis and an ANOVA test on

Euclidean distance matrices of geographic distances (_km_) and pairwise fixation indices (_F__ST_) calculated between sampling sites using the _vegan_ package in R. To achieve this, we

calculated the minimum marine distance between sampling sites using a workflow described in Assis et al. (2013). Genetic distances were transformed to a continuous scale as

\({GD}=\frac{{F}_{{ST}}}{(1-{F}_{{ST}})}\) to allow for correlation with geographic distances. The correlation of geographic distances and corresponding fixation indices was subsequently

visualised in R. MULTILOCUS HETEROZYGOSITY To investigate variation in genetic diversity across populations, we calculated the multilocus heterozygosity (_MLH_) across the 1092 SNPs as

described in Stoffel et al. (2016) per population using the package _InbreedR_. Based on the detected population structure in the dataset, we grouped the individuals into their corresponding

populations. Additionally, we visualised _MLH_ distribution across the entire sample set. INVESTIGATION OF A CONTACT ZONE Upon initial inspection of the observed structure, each analysis

was repeated within a more localised approach in the North Sea and adjacent UK and Irish waters, to investigate finer-scale structure and the putative existence of a region of strong

admixture in east Scotland in greater detail. For calculation of fixation indices, estimation of migration rates and heterozygosity, this was achieved by removing the admixed individuals

from east Scotland to retain unbiased estimates. RESULTS DATA QUALITY The percentage of reads that mapped to the reference genome was 100% in almost all the samples. The mean coverage of all

covered regions in the genome across all DArTseq™ samples was 12.75X and across the three WGS samples it was 8–10X across the entire genome. The initial number of variants detected in the

unfiltered dataset was 542,232 SNPs across 169 individuals, which was reduced to 1092 highly informative SNPs across 157 individuals. Comparison of the 18 duplicate pairs ensured no

genotyping errors were present our analyses. POPULATION STRUCTURE We investigated the population structure present in the dataset using complexity reduction, maximum likelihood and Bayesian

approaches combined with estimation of pairwise fixation indices and isolation-by-distance analysis. Combining results from all analyses, we observed both significant broad-scale and

fine-scale population structure across the range of the white-beaked dolphin. Mapping genetic origin against sampling location shows a clear differentiation of geographically isolated

populations into four genetic clusters (Fig. 1b, c). Samples collected in both Iceland and the Barents Sea were assigned to the same genetic clade with a clear separation from the British

Isles and the North Sea along the first PC axis. Similarly, the three individuals sampled in eastern Canada (WNA) were separated further along PC1, forming a separate cluster (Fig. 1b).

Interestingly, NGSadmix and STRUCTURE analysis did not identify the WNA samples as a separate genetic cluster and grouped them together with the Icelandic and Barents Sea samples (Fig. 1d,

Supplementary Figs. S6 and S7). Finer structure could be identified with separation of white-beaked dolphins sampled around west Scotland and Ireland and the coastlines of the North Sea

along the third PC axis (Fig. 1c). This was further corroborated by NGSadmix and Structure analyses (Fig. 1d, Supplementary Figs. S6 and S7). A subsequent assessment of finer-scale and

sex-mediated structure by a separate PCA confirms the overall structure detected by the previous approaches, demonstrating no clear difference in population structure between male and female

white-beaked dolphins, implying the absence of sex-mediated dispersal (Supplementary Fig. S8). Based on these results, naming conventions for the genetic clusters are introduced as the

following regions: Western North Atlantic (WNA), Iceland and Barents Sea (ICE&BAS), west Scotland and Ireland (WSI) and North Sea (NS). Additionally, based on the pattern observed

between NS and WSI, subsequent analyses were also performed with the eastern Scottish (E_SCOT) individuals as a separate group to investigate the influence a potential contact zone in this

region may have on the estimation of population genetic parameters (Fig. 2a). We find that the majority of individuals previously grouped with NS but clustering with WSI were indeed from

eastern Scotland (Fig. 2b). Admixture proportions for the three regions separately further visualise that eastern Scotland seems to be a contact zone between individuals of the southern and

central North Sea and individuals sampled in Ireland and the west coast of Scotland (Fig. 2c). Pairwise fixation indices between the four populations retained by the PCA confirm that the

samples from the western North Atlantic were significantly differentiated from the North Sea and western Scotland and Ireland samples (_F__ST_WNAvs.NS_ = 0.05943055, _p_ = 0.000;

_F__ST_WNAvsWSI_ = 0.078774106, _p_ = 0.000), but displayed a lower, yet significant, differentiation to the Iceland and Barents Sea samples (_F__ST_WNAvs.ICE&BAS_ = 0.01886975, _p_ = 

0.002). Similarly, the animals sampled in Iceland and the Barents Sea were significantly differentiated from the North Sea (_F__ST_ICE&BASvsNS_ = 0.03946870, _p_ = 0.000) and western

Scotland and Ireland (_F__ST_ICE&BASvs.WSI_ = 0.05496347, _p_ = 0.000), while the latter two regions displayed a weak, but statistically significant differentiation (_F__ST_NSvs.WSI_ = 

0.006011101, _p_ = 0.000). When excluding eastern Scottish samples, the fixation index between the North Sea and western Scotland and Ireland clades increases (_F__ST_NSvs.WSI_ = 

0.008685244, _p_ = 0.000), indicative of a large proportion of admixed individuals from both populations in this region. The pairwise _F__ST_ values calculated between sampling sites are

visualised in Fig. 3a and exact values can be obtained from Supplementary Table S2. CONTEMPORARY GENE FLOW Estimation of the proportion and direction of geneflow between populations

performed in BayesAss suggested little introgression from the western North Atlantic population into any of the other populations (Fig. 3c). Likewise, the Iceland and Barents Sea population

and the two populations of the North Sea and western Scotland and Ireland showed very little evidence of gene flow in either direction. However, a higher proportion of gene flow was detected

between Iceland and the Barents Sea and western North Atlantic populations in the direction of the western North Atlantic, specifically from Iceland to western North Atlantic with an

estimated 0.166 migrants per generation (see Supplementary Table S1 for details). Similarly, there is a strong signal of unidirectional geneflow from Iceland into the Barents Sea (_m_ = 

0.222). Furthermore, a high level of introgression was detected between the two geographically neighbouring North Sea and western Scotland and Ireland populations with the majority of

geneflow being facilitated by the North Sea population (_m_ = 0.306). Interestingly, although the estimated migration rate remained similar when excluding eastern Scottish samples (_m_ = 

0.3265), the direction of geneflow reversed to a unidirectional influx from western Scotland and Ireland to the North Sea. All other migration rates did not exceed 0.06 migrants per

generation and were therefore considered low. ISOLATION BY DISTANCE We investigated patterns of isolation by distance (IBD) by correlating geographic distance with pairwise fixation indices

(genetic distance) between all sampling sites. The redundancy analysis (RDA) showed a moderate but statistically significant correlation between geographic and genetic distance (_r__2_ = 

0.3989931, _p_ = 0.001). The IBD curve confirms that most datapoints fit within the confidence interval, but some points outside the general trend suggest deviations from IBD in both

directions that is, stronger connectivity than expected under pure IBD (below curve) and stronger differentiation than expected under pure IBD (above curve; Fig. 3b). GENETIC DIVERSITY To

assess genetic variation across the dataset, we calculated individual multilocus heterozygosity (_MLH_). We found _MLH_ was normally distributed with a mean of 0.147 across all samples (min 

= 0.079, max = 0.173, Supplementary Fig. S9). When comparing _MLH_ between genetic populations, we found little difference in heterozygosity between all sampled populations, but the western

North Atlantic population displays slightly higher yet non-significant _MLH_ compared to the other populations (Fig. 3d). The admixed eastern Scottish individuals did not inflate the

heterozygosity estimates of the North Sea population as assessed by excluding these in a separate estimate (_MLH_ = 0.1444 with E_SCOT vs. _MLH_ = 0.1447 with E_SCOT removed). DISCUSSION

Exploring the extent of genetic connectivity and differentiation across the range of the white-beaked dolphin is essential for conservation management, particularly given the numerous

anthropogenic impacts on dolphin populations such as bycatch, accumulation of chemical contaminants and traditional hunts, as well as the putatively rapidly progressing effects of increasing

SSTs threatening cold-water obligate species with habitat shifts. Using a combination of reduced representation and whole-genome sequencing, we investigated population structure, gene flow

and genetic diversity in white-beaked dolphins from ten different sampling locations. We detect both broad-scale structure across the North Atlantic and fine-scale structure in the eastern

North Atlantic and adjacent waters. The results of this study allow for a more informed delineation of management units for conservation and highlight the importance of population genomics

in biodiversity conservation of species facing changes in their habitat amid global climate change. Principal Components Analysis of 157 white-beaked dolphins detected a pattern of four

differentiated clusters. Three of the four populations were also detected using _K_-means clustering approaches, but western North Atlantic samples were continuously grouped with Icelandic

and Barents Sea individuals. This is likely due to limitations of the programmes Structure and NGSadmix to detect structure when gene flow is high and sample sizes are small (Waples and

Gaggiotti 2006). The evaluation of the fit of our data to the admixture algorithm confirms that this method may not be able to disentangle the full ancestral history, likely due to gaps in

sampling coverage across the species’ range and associated assumptions the algorithm makes based on the provided data set. Statistical evaluation of panmixia using Weir and Cockerham’s

pairwise fixation indices reject the null hypothesis of continuous genetic connectivity between Iceland and Barents Sea and western North Atlantic, supporting the existence of four

differentiated populations as shown in the PCA. We therefore conclude that _K_ = 4 comprising of the western North Atlantic, Iceland and Barents Sea, North Sea, and west Scotland and

Ireland, is the most likely number of populations in our dataset. Pairwise _F__ST_ values were highest between western North Atlantic and west Scotland and Ireland and North Sea populations.

Indeed, migration between these regions was very low in our data and a significant correlation of geographic and genetic distance suggests isolation by distance contributing largely to the

observed differentiation. This is in agreement with previous genetic studies using mtDNA and microsatellite loci and a morphometric study describing distinct differences in skull

characteristics between dolphins from the North Sea and the western North Atlantic (Banguera-Hinestroza et al. 2010; Mikkelsen and Lund 1994). In direct contrast to the differentiation

between the western North Atlantic and the North Sea, western Scotland and Ireland is the relatively strong connectivity between western North Atlantic and Iceland and Barents Sea

populations, despite an apparent hiatus in distribution between the two latter areas (Pike et al. 2019). Remarkably, a complete homogeneity of genotypes from Iceland and the Barents Sea was

found and is indicative of frequent (0.222 migrants per generation) long-distance individual migration events between the two regions as corroborated by our analysis on contemporary gene

flow and in line with observed movement capabilities of the species (Rasmussen et al. 2013). Similarly, a relatively high migration rate (0.166 migrants per generation) was found between

Iceland and the western North Atlantic, yet genetic distinction is persistent but weak. Abundance estimates from Iceland and the Barents Sea suggest large population sizes (Byrd et al. 2020;

Øien 1996; Pike et al. 2019) which could be obscuring the presence of differentiation between two otherwise demographically separate clades (Waples 1998). Regular geneflow between

populations and large population sizes could also contribute to the higher heterozygosity values in the western North Atlantic, though this could also be an artifact of different sequencing

techniques in this population and the possibility of introducing batch effects in the estimates (Lou and Therkildsen 2022). Within the eastern North Atlantic and adjacent waters, we detected

a clear separation of Icelandic and Barents Sea white-beaked dolphins from individuals sampled around western Scotland and Ireland and the North Sea, as well as a regional separation of

individuals sampled off the coast of the North Sea and those sampled off western Scotland and Ireland with a region of strong admixture in eastern Scotland. This result was in part

anticipated and consistent with results from a previous study by Banguera-Hinestroza et al. (2010) who compared Barents Sea samples to the British Isles and North Sea. The introduction of

samples from Iceland in our study gives a new dimension to the overall pattern of structure found in this species, as the minimal marine distance between sampling sites in Iceland and, for

example, the Netherlands is comparable to the distance between Iceland and the Barents Sea (~2200 km) yet genetic distances and migration rates are in stark contrast. Hence, there is a

strong implication that ecological factors could influence population structure in the eastern North Atlantic and adjacent waters. This is further supported by the consistent pattern of

regional structure found between the North Sea and west Scotland and Ireland. The region of strong admixture between white-beaked dolphins of the North Sea and of western Scotland and

Ireland, located at the eastern Scottish coast, brings up interesting questions about the factors driving this pattern. Ecological differences may in part be responsible for the detected

differentiation between the two neighbouring clades, and the occurrence of a contact zone could reflect a response to environmental change and resulting change in behaviour suggesting the

contact zone is a recent phenomenon. An alternative explanation to the pattern could be a retrieval of a separate refugium population to the southern North Sea during the most recent LGM,

which has been argued as a potential driver for regional structure in marine species of the North Atlantic (Hewitt 2000; Hoarau et al. 2007). The limited understanding of white-beaked

dolphin ecology, life history and habitat use hampers the interpretation of drivers of the observed population structure. Notably, seasonal migration from higher latitudes in the winter to

lower latitudes in the summer have been observed in various regions (Canning et al. 2008; Fall and Skern-Mauritzen 2014; Pike et al. 2019). This could be influenced by numerous factors such

as responses to migratory prey, site fidelity to certain areas during mating season, competition from other species or predator avoidance. Regarding diet, white-beaked dolphins have been

reported to target higher level trophic gadoid fish with some regional variation across their range based on stomach content analyses (Dong et al. 1996; Jansen et al. 2010; Fall and

Skern-Mauritzen 2014; Schick et al. 2020; Samarra et al. 2022). However, studies using stable isotopes show a clear preference for pelagic squids in the western North Atlantic versus a

preference for higher trophic level fish in the eastern North Atlantic and Iceland (Samarra et al. 2022; Plint et al. 2023; Kiszka and Caputo, unpublished data). This difference may in part

explain the elevated genetic distance that we found in our IBD analysis between the two sites of the North Atlantic. Within the eastern North Atlantic, seasonal occurrence during summer

months has been argued to possibly result from site fidelity in both the North Sea and west Scotland and Ireland, potentially driving the fine-scale structure as observed in our study

(Reeves et al. 1999; Canning et al. 2008; Brereton et al. 2013; Galatius, Jansen et al. 2013). Contrastingly, long-term photo ID monitoring of Icelandic dolphins suggest no strong signal of

site fidelity (Bertulli et al. 2015). Altogether, further studies on white-beaked dolphin movement, diet, behaviour, and ecology of different populations are needed to explore potential

drivers of the observed population structure and inform a more targeted management approach. IMPLICATIONS FOR CONSERVATION MANAGEMENT Our findings could have significant implications for

conservation management at both regional and North Atlantic basin-wide scales by providing new evidence on fine-scale population structure of white-beaked dolphins. Currently, the species

receives varying degrees of management; in the western North Atlantic, white-beaked dolphins are considered a single stock across their western range (Byrd et al. 2020). In Icelandic and

Norwegian waters, the species receives no targeted management, while in its southern distribution it is managed as a single management unit (MU) comprising of the North Sea and the waters

extending beyond the western coast of Ireland and the UK (IAMMWG 2015). Evans and Teilmann (2009) compiled all available information on the species and recommended four MUs comprising of the

Labrador shelf, Icelandic waters, the Barents Sea and the North Sea and adjacent waters. Our results largely support this delineation, but based on our genetic analysis, our recommendations

differ slightly. The Labrador shelf population is only represented by three samples in our study from eastern Canada. These samples represent a differentiated clade in our analyses,

generally supportive of the distinction of this region as a separate MU. However, a more in-depth assessment of structure is needed in this region, covering larger areas, and increasing

sample size. An important aim for future studies will be the introduction of samples from Greenland, especially in the light of increasing rates of removals through traditional hunts and the

uncertainty on the sustainability of these hunts (Piniarneq 2021). Our findings also reveal strong connectivity between white-beaked dolphins sampled in Iceland and the Barents Sea, and

strong differentiation between the Iceland and Barents Sea and the North Sea and west Scotland and Ireland. Populations in higher latitudes such as the Iceland and Barents Sea population are

unlikely to experience habitat loss due to increasing SSTs and may in fact find more available habitat as sea ice retreats (Stafford et al. 2022). However, it may still be useful to assess

white-beaked dolphins in these regions regarding their distribution, habitat use and behaviour as well as impact of anthropogenic activities to investigate their responses to potential

environmental change and learn more about this populations’ ecology. It is recommended that the genetic connectivity between Iceland and the Barents Sea should be considered in future

assessments of this population. Most strikingly, our findings on fine-scale structure between the North Sea and western Scotland and Ireland warrant reconsideration of current local

management (IAMMWG 2015). In this part of their range, white-beaked dolphins appear to strongly associate with SSTs below 12–13 °C (MacLeod et al. 2007) and therefore are likely to be

especially vulnerable to increasing SSTs (Evans and Waggitt 2020). Furthermore, this region has been identified as a high- risk area for strong anthropogenic impact from climate change,

pollution, and fishing (Davidson et al. 2012). Populations of white-beaked dolphins on the edge of their southern distribution are therefore likely to be impacted by climate-change

associated habitat shifts (Lambert et al. 2014), in addition to numerous direct threats (Stone and Tasker 2006; Bearzi et al. 2006; Reeves et al. 2013; Galatius, Bossi et al. 2013; Williams

et al. 2023). A recent northward-shift in their distribution based on strandings data (IJsseldijk et al. 2018; Williamson et al. 2021) and predictive habitat modelling (Lambert et al. 2014)

is indicative of an ongoing contraction of suitable habitat around the British Isles and North Sea. The responses of the two local populations are difficult to predict. Possible scenarios

range from a retreat to small pockets of suitable habitat, leading to small vulnerable populations, the total extirpation of the species in the area or a northward-shift and subsequently

increased connectivity into waters currently occupied by dolphins of the genetically differentiated population around Iceland and the Barents Sea. Our assessment of local structure in this

region indicates that eastern Scotland may currently be a contact zone for dolphins from the two southern clades, suggesting that further admixture could weaken the observed structure over

time. Future genetic monitoring of these regions could help to predict how those populations may interact and what the genetic consequences could be. The potential risk of local extirpation

of these two southern populations, and consequently the loss of a significant proportion of species-wide genetic diversity, should be emphasised in future management plans. Furthermore,

formal assessment of the impact of factors that may cause additional mortality such as bycatch, pollution, and marine development should be a priority in future conservation efforts. In a

wider context, our study provides an example of the importance of assessing population genomics in marine species facing pressures from climate change and human impact. As the relevance of

genetic diversity as a pillar of biodiversity conservation for long-term species survival gains acknowledgement from international and national policymakers (United Nations 2015; CBD 2022),

detailed knowledge on the population structure and genetic variability is urgently needed. Using these data to understand the dynamics of these species can help in identifying vulnerable

populations and assess the risk for the loss of species-wide genetic diversity by local depletion and continued human impact. DATA AVAILABILITY Raw sequence reads are available at the

European Nucleotide archive (ENA) under the accession number PRJEB71584. CODE AVAILABILITY All code for analyses and plotting can be accessed at https://github.com/MarcGose/WBD_PopGen.

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migration rates using multilocus genotypes. Genetics 163:1177–1191 PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS Samples were collected, stored, and curated by

many organisations involved in this study. We thank all volunteers and staff of the Scottish Marine Animal Stranding Scheme (SMASS) and of the Cetacean Strandings and Investigation

Programme (CSIP) at Zoological Society of London for their effort in collecting samples from Scotland, England, and Wales. We wish to thank all individuals in Germany who helped to collect

carcasses, perform necropsies, and conduct further investigations. The study was partly funded by the German Federal Ministry for Environment, Nature Conservation and Nuclear Safety, the

German Federal Ministry for Research and Education and Ministry of Energy Transition, Agriculture, Environment, Nature and Digitalisation of Schleswig-Holstein (MELUND) and samples from

Germany were transferred under CITES permits DE 207-02 and GB 034. Post-mortem investigations and tissue sampling in the Netherlands is conducted at the Faculty of Veterinary Medicine of

Utrecht University, commissioned by the Ministry of Agriculture, Nature and Food Quality. We are thankful for the help of stranding network volunteers in reporting and retrieving the animals

for post-mortem investigation, and staff and students of Utrecht University for assisting the examinations. Dutch samples were transferred under CITES permit 610516/01. Samples from eastern

Canada (Saint Pierre et Miquelon) were collected under permit #431 from Préfecture de Saint Pierre et Miquelon through a grant from the Office Français de la Biodiversité (COPEMAM) to

Université de La Rochelle and Florida International University and transferred under CITES permit 615900/01. This is contribution #1674 of the Institute of Environment at Florida

International University. In Ireland, data were reported through the Irish Whale and Dolphin group stranding scheme and collection and storage of samples was facilitated by the Irish

Cetacean Genetic Tissue Bank at the National Museum of Ireland and was funded in part by the Heritage Council and the Ireland-Wales Interreg programme. We wish to thank staff and volunteers

of the Fisheries and Maritime Museum for providing samples from Denmark. Samples from Denmark, Ireland, Iceland, and Norway were transferred under CITES permits DK 014 and GB 034. Many

thanks to Ruth Fernandez and Kyle Ewart for their contribution with communication to sample providers and sample transfer logistics. We thank Gísli Víkingsson for organising access to

Icelandic dolphin samples. Gísli passed away in the summer of 2022. We want to thank the editor, Morten Tange Olsen, and two anonymous reviewers for their insightful comments on the

manuscript. For the purpose of open access, the author has applied for a CC-BY public copyright licence to any author accepted manuscript version arising from this submission. FUNDING

Funding was provided through a Ph.D. studentship from the Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh. AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * Royal (Dick) School of Veterinary Studies and the Roslin Institute, University of Edinburgh, Edinburgh, UK Marc-Alexander Gose, Emily Humble & Rob Ogden * Scottish Marine

Animal Stranding Scheme, School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Science, University of Glasgow, Glasgow, UK Andrew Brownlow, 

Mariel ten Doeschate & Nicholas J. Davison * Irish Whale and Dolphin Group (IWDG), Kilrush, Ireland Dave Wall * School of Biological, Earth & Environmental Sciences, University

College Cork, Cork, Ireland Emer Rogan * Marine and Freshwater Research Institute, Hafnarfjörður, Iceland Guðjón Már Sigurðsson * Institute of Environment, Department of Biological Sciences,

Florida International University, North Miami, FL, USA Jeremy J. Kiszka * Fisheries and Maritime Museum, Esbjerg, Denmark Charlotte Bie Thøstesen * Division of Pathology, Department of

Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands Lonneke L. IJsseldijk * Institute of Marine Research (IMR), Bergen, Norway Nils

Øien * Institute of Zoology, Zoological Society of London, London, UK Rob Deaville * Institute for Terrestrial and Aquatic Wildlife Research, University of Veterinary Medicine Hannover

Foundation, Hannover, Germany Ursula Siebert Authors * Marc-Alexander Gose View author publications You can also search for this author inPubMed Google Scholar * Emily Humble View author

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can also search for this author inPubMed Google Scholar * Mariel ten Doeschate View author publications You can also search for this author inPubMed Google Scholar * Nicholas J. Davison View

author publications You can also search for this author inPubMed Google Scholar * Nils Øien View author publications You can also search for this author inPubMed Google Scholar * Rob

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Scholar * Rob Ogden View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS MAG, RO, AB and EH designed the study. AB, DW, ER, GMS, JJK, CBT, LLI,

MtD, NJD, NØ, RD and US provided samples. MAG conducted all laboratory work and formal analysis, with substantial input from EH. MAG, RO and EH interpreted the results. MAG wrote the main

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authors declare no competing interests. ETHICS APPROVAL AND CONSENT TO PARTICIPATE The study was performed in concordance with the Animals Scientific Procedures Act (1986) and has been

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E., Brownlow, A. _et al._ Population genomics of the white-beaked dolphin (_Lagenorhynchus albirostris_): Implications for conservation amid climate-driven range shifts. _Heredity_ 132,

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