# Fine-scale population structure and connectivity of bottlenose dolphins, Tursiops truncatus, in European waters and implications for conservation

Milaja Nykänen, Marie Louis, Eileen Dillane, Eric Alfonsi, Simon Berrow, Joanne O'Brien, Andrew Brownlow, Pablo Covelo, Willy Dabin, Rob Deaville, et al.

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## To cite this version:

Milaja Nykänen, Marie Louis, Eileen Dillane, Eric Alfonsi, Simon Berrow, et al. . Fine-scale population structure and connectivity of bottlenose dolphins, Tursiops truncatus, in European waters and implications for conservation. Aquatic Conservation: Marine and Freshwater Ecosystems, 2019, 29 (S1), pp.197-211. 10.1002/aqc.3139. hal-02749246

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Fine-scale population structure and connectivity of bottlenose dolphins, Tursiops truncatus, in European waters and implications for conservation

<table><tr><td>Journal:</td><td>Aquatic Conservation: Marine and Freshwater Ecosystems</td></tr><tr><td>Manuscript ID</td><td>AQC-18-0314.R1</td></tr><tr><td>Wiley - Manuscript type:</td><td>Supplement Article</td></tr><tr><td>Date Submitted by the Author:</td><td>n/a</td></tr><tr><td>Complete List of Authors:</td><td>Louis, Marie; Scottish Oceans Institute; Sea Mammal Research Unit; Centre d'Etudes Biologiques de Chize<br/>Nykänen, Milaja; University College Cork School of Biological Earth and Environmental Sciences<br/>Dillane, Eileen; University College Cork School of Biological Earth and Environmental Sciences<br/>Alfonsi, Eric; Océanopolis; BioGeMME, UFR Sciences et Techniques, Université de Brest<br/>Berrow, Simon; Galway-Mayo Institute of Technology, Department of Life & Physical Sciences Galway, IE; Irish Whale and Dolphin Group<br/>O'Brien, Joanne; Galway-Mayo Institute of Technology, Department of Life & Physical Sciences<br/>Brownlow, Andrew; SAC Wildlife Unit<br/>Covelo, Pablo; CEMMA (Coordinadora para o Estudo dos Mamíferos Mariños)<br/>Dabin, Willy; Observatoire PELAGIS, UMS 3462 CNRS/Université La Rochelle<br/>Deaville, Rob; Institute of Zoology, Zoological Society of London<br/>de Stephanis, Renaud; CIRCE, Conservation, Information and Research on Cetaceans; Department of Conservation Biology, Estación Biológica de Doñana (CSIC)<br/>Gally, François; GECC (Groupe d'Etude des Cétacés du Cotentin), Gauffier, Pauline; e) CIRCE (Conservation, Information and Research on Cetaceans), Cabeza de Manzaneda 3, Pelayo, 11390<br/>Ingram, Simon; University of plymouth, School of Marine Science and Engineering<br/>Lucas, Tamara; LIENSs (Littoral Environnement et Sociétés), UMR 7266 CNRS/Université de La Rochelle<br/>Mirimin, Luca; Galway-Mayo Institute of Technology, Department of Life & Physical Sciences Galway, IE<br/>Penrose, Rod; Marine Environmental Monitoring<br/>Rogan, Emer; University of Cork, Zoology, Ecology adn Plant Science Silva, Monica; Center of the Institute of Marine Research (IMAR) & Department of Oceanography and Fisheries, University of the Azores; Laboratory of Robotics and Systems in Engineering and Science (LARSyS); Biology Department, Woods Hole Oceanographic Institution<br/>Simon-Bouhet, Benoit; Centre d'Etudes Biologiques de Chize</td></tr></table>
<table><tr><td></td><td>Gaggiotti, Oscar; Scottish Oceans Institute</td></tr><tr><td>Broad habitat type (mandatory) select 1-2:</td><td>coastal &lt; Broad habitat type, ocean &lt; Broad habitat type</td></tr><tr><td>General theme or application (mandatory) select 1-2:</td><td>genetics &lt; General theme or application, Special Area of Conservation &lt; General theme or application</td></tr><tr><td>Broad taxonomic group or category (mandatory, if relevant to paper) select 1-2:</td><td>mammals &lt; Broad taxonomic group or category</td></tr><tr><td>Impact category (mandatory, if relevant to paper) select 1-2:</td><td></td></tr></table>
1 Fine-scale population structure and connectivity of bottlenose

2 dolphins, *Tursiops truncatus*, in European waters and implications

3 for conservation

4 Milaja Nykänen¹*, Marie Louis²,³,⁴*, Eileen Dillane¹, Eric Alfonsi⁵,⁶, Simon

5 Berrow⁷,⁸, Joanne O'Brien⁷,⁸, Andrew Brownlow⁹, Pablo Covelo¹⁰, Willy Dabin¹¹,

6 Rob Deaville¹², Renaud de Stephanis¹³,¹⁴, François Gally¹⁵, Pauline Gauffier¹⁴,

7 Simon N. Ingram¹⁶, Tamara Lucas¹⁷, Luca Mirimin⁸, Rod Penrose¹⁸, Emer Rogan¹,

8 Mónica A. Silva¹⁹,²⁰,²¹, Benoit Simon-Bouhet⁴, Oscar E. Gaggiotti²

9

10 *these authors contributed equally

11 Correspondence: Milaja Nykänen: milaja.ny@gmail.com - Marie Louis:

12 marielouis17@hotmail.com

13

14 (1) School of Biological, Earth and Environmental Sciences, University College

15 Cork, North Mall, Cork, Ireland

16 (2) Scottish Oceans Institute, Gatty Marine Laboratory, East Sands, St Andrews

17 KY16 8LB, Scotland, UK

18 (3) Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews,

19 Gatty Marine Laboratory, East Sands, St Andrews KY16 8LB, Scotland, UK

20 (4) Centre d'Etudes Biologiques de Chizé, UMR 7372, Université de La Rochelle,

21 France.

22 (5) Océanopolis, Brest, France.

23 (6) BioGeMME, UFR Sciences et Techniques, Université de Brest, France.

24 (7) Irish Whale and Dolphin Group, Kilrush, Co Clare, Ireland.

25 (8) Marine and Freshwater Research Centre, Department of Natural Sciences,

26 School of Science and Computing, Galway-Mayo Institute of Technology, Dublin

27 Road, H91 T8NW Galway, Ireland,

28 (9) SAC Wildlife Unit, Inverness, Scotland.

29 (10) CEMMA (Coordinadora para o Estudo dos Mamíferos Mariños), Gondomar,

30 Spain.
31 (11) Observatoire PELAGIS, UMS 3462 CNRS/Université La Rochelle, France.

32 (12) Institute of Zoology, Zoological Society of London, England.

33 (13) Department of Conservation Biology, Estación Biológica de Doñana (CSIC),
Sevilla, Spain.

35 (14) CIRCE (Conservation, Information and Research on Cetaceans), Pelayo, Spain.

36 (15) GECC (Groupe d'Etude des Cétacés du Cotentin), Cherbourg-Octeville,
France.

38 (16) School of Marine Science and Engineering, Plymouth University, Plymouth PL4

39 8AA, UK,

40 (17) LIENSs (Littoral Environnement et Sociétés), UMR 7266 CNRS/Université de
La Rochelle, France.

42 (18) Marine Environmental Monitoring, Cardigan, Wales, United Kingdom.

43 (19) Center of the Institute of Marine Research (IMAR) & Department of
Oceanography and Fisheries, University of the Azores, Horta, Azores, Portugal.

45 (20) Laboratory of Robotics and Systems in Engineering and Science (LARSyS),
Lisboa, Portugal.

47 (21) Biology Department, Woods Hole Oceanographic Institution, Woods Hole,
Massachusetts, United States of America.

49

50 Abstract

51 1. Protecting species often involves the designation of protected areas, wherein
suitable management strategies are applied either at the taxon or ecosystem level.

53 Special Areas of Conservation (SACs) have been created in European waters under
the Habitats Directive to protect bottlenose dolphins, *Tursiops truncatus*, which
forms two ecotypes, pelagic and coastal.

56 2. The SACs have been designated in coastal waters based on photo-identification
studies that have indicated that bottlenose dolphins have relatively high site fidelity.

58 However, individuals can carry out long-distance movements which suggest
potential for demographic connectivity between the SACs as well as with other areas.

59
60 3. Connectivity can be studied using genetic markers. Previous studies on the species in this area used different sets of genetic markers and therefore inference on the fine-scale population structure and demographic connectivity has not yet been made at a large scale. A common set of microsatellite markers was used in this study to provide the first comprehensive estimate of genetic structure of bottlenose dolphins in European Atlantic waters.

66 4. As in previous studies, a high level of genetic differentiation was found between coastal and pelagic populations. Genetic structure was defined at an unprecedented fine-scale level for coastal dolphins leading to identification of five distinct coastal populations inhabiting the following areas: Shannon estuary, west coast of Ireland, English Channel, coastal Galicia, east coast of Scotland, and Wales/West Scotland.

71 Demographic connectivity was very low among most populations with less than 10% migration rate suggesting no demographic coupling among them. Each local population should therefore be monitored separately.

74 Keywords: coastal, ocean, population genetics, Special Area of Conservation, mammals, bottlenose dolphins

76
77 **Introduction**

78 Protecting species and their habitats is the goal of conservation biology, and this often
79 includes the designation of protected areas, wherein suitable management strategies
80 are applied either at the taxon or ecosystem level. According to the definition by the
81 World Conservation Union (IUCN), a Marine Protected Area (MPA) is "any area of
82 intertidal or subtidal terrain, together with its overlying water and associated flora,
83 fauna, historical and cultural features, which has been reserved by law or other
84 effective means to protect part or all of the enclosed environment" (Kelleher &
85 Phillips, 1999, 18). The usefulness of static MPAs to preserve biodiversity or to protect
86 a particular species or population has been debated (e.g., Agardy, di Sciara, & Christie,
87 2011; Hartel, Constantine, & Torres, 2015; Hooker & Gerber, 2004; Wilson, 2016),
88 but they remain the primary spatial conservation unit worldwide and are key
89 components of various conservation plans (e.g., the United Nations Plan for
90 Biodiversity (2011–2020), the IUCN World Parks Congress and the European
91 Biodiversity Strategy to 2020 (European Commission, 2011)). In European waters, the
92 Member States of the European Union are required to designate Special Areas of
93 Conservation (SACs) for species listed in Annex II of the Habitats Directive (European
94 Economic Community, 1992), which includes two cetacean species; the harbour
95 porpoise, *Phocoena phocoena*, and the common bottlenose dolphin, *Tursiops
96 truncatus*. These SACs, which are part of the European Natura 2000 strategy, should
97 represent areas essential for the species' life and reproduction. In addition to the
98 protection under the Habitats Directive, as top predators, bottlenose dolphins are
99 considered as one of the indicator species for 'good environmental status' (GES) in
100 coastal waters by the Marine Strategy Framework Directive (MSFD, Council of the
101 European Communities, 2008). The aim of MSFD is to protect the European marine
102 environment by applying a comprehensive ecosystem-based approach to the management of human activities, and by maintaining or restoring the favourable conservation status of a number of species.

105 Previous research using photo-identification has shown that most coastal bottlenose dolphin populations in Europe comprise between 30 and 400 resident individuals with strong site fidelity to their respective coastal site (e.g., Cheney et al., 2013; Ingram & Rogan, 2002; Louis et al., 2015). However, also based on photo-identification studies, some of these individuals are highly mobile travelling distances of hundreds of kilometres around the UK and Ireland (Cheney et al., 2013; Ingram, Englund, & Rogan, 2001; Ingram & Rogan, 2003; O'Brien et al., 2009; Robinson et al., 2012).

112 Nonetheless, the high site-fidelity and the preferential use of some geographical areas indicate that coastal bottlenose dolphins may be very sensitive to changes in local environmental conditions, ecological factors, or anthropogenic disturbance. The sensitivity of bottlenose dolphins to these threats is exacerbated by their position as an apex predator and also by their low reproductive rates (Connor, Wells, Mann, & Read, 2000; Quick et al., 2014). The main threats in coastal environments include pollutants such as xenobiotic chemicals (Jepson et al., 2016; Reif, Schaefer, Bossart, & Fair, 2017), reduced prey availability, habitat degradation, disturbance from vessel traffic (Lusseau, Bain, Williams, & Smith, 2009; Pirotta, Merchant, Thompson, Barton, & Lusseau, 2015; Williams, Bain, Smith, & Lusseau, 2009), entanglement and incidental bycatch, direct hunting, marine construction and anthropogenic noise (Hammond et al., 2012; Meissner et al., 2015; Pirotta et al., 2015). The increased risks of demographic perturbation of dolphin populations due to human activities highlights the need for the design and management of protected areas ensuring that dolphin
126 habitat remains favourable and does not deteriorate. A careful investigation of the
127 population structure and quantification of the genetic and demographic connectivity is
128 also necessary as small isolated populations may require more protection due to their
129 reduced genetic resilience.

130 An important step towards the conservation of bottlenose dolphins was taken under
131 the Habitats Directive by designating SACs across the European North-east Atlantic
132 and Mediterranean coastal waters. These designations were based on photo-
133 identification and habitat use surveys showing long-term site-fidelity (Anon, 2012).

134 Another important step towards their conservation is to evaluate population structure
135 and connectivity of populations between the protected areas as well as with other areas.

136 This is particularly important because of the propensity for some individuals to carry
137 out long-distance movements, which suggests potential demographic connectivity
138 between the populations. It is unclear if such movements can result in migration rates
139 that could lead to correlated population dynamics. There is a paucity of studies
140 assessing the level of migration that will lead to demographic coupling (Waples &
141 Gaggiotti, 2006); a process by which changes in population size in one population are
142 influenced by changes taking place in another population (Hastings, 1993). However,
143 a simulation study by Hastings (1993) indicated that under a simple two-population
144 density-dependent model, a migration rate of 10% or more can lead to coupled
145 dynamics, which would require monitoring the populations as a single
146 management/conservation unit. Thus, a threshold of 10% migration above which two
147 local populations are considered independent management units can be used as an
148 operational criterion to address conservation problems.

149 The most cost-effective approach to evaluate demographic connectivity and fine-scale
<table>
  <tr>
    <td>150</td>
    <td>population subdivision is based on the use of genetic markers and population genetics principles. Thus, a recent workshop on bottlenose dolphin conservation (December 2016; Ó Cadhla & Marnell, 2017) concluded that one of the main priorities for implementing the aforementioned EU directives for this species was a fine-scale population genetics analysis of dolphins inhabiting European waters. This will allow the definition of meaningful management units (MUs), which is essential when setting up strategies for conservation and monitoring, including the estimation of population trends and the evaluation of the impacts of anthropogenic activities. Note that in the past, MUs were frequently defined in genetic terms as genetic management units (GMUs), following Moritz (1994, 374): “populations with significant divergence of allele frequencies at nuclear or mitochondrial loci, regardless of the phylogenetic distinctiveness of the alleles”. However, it is now accepted that MUs comprise demographically independent populations, thus estimating migration rates from a central part of the assessment of suitable MUs (Allendorf, Luikart, & Aitken, 2013; Palsbøll, Berube, & Allendorf, 2007). Furthermore, the criterion underlying GMUs is not entirely appropriate from a demographic point of view because migration rates (m) well below 10% can still lead to an absence of significant allelic differentiation (e.g., if local population size is 100, m > 0.01 will lead to absence of genetic differentiation; c.f. Waples & Gaggiotti, 2006). Nevertheless, population genetics principles can be used to estimate migration rates to implement the 10% migration threshold criterion. In addition, other measures can be used in order to define MUs, such as ecological tracers (e.g., Giménez et al., 2018) or analyses of population viability (Olsen et al., 2014).</td>
  </tr>
</table>
<table>
  <tr>
    <td>173</td>
    <td>Previous genetic studies on bottlenose dolphins worldwide have identified a clear population structuring based on nuclear microsatellites and mitochondrial markers with varying geographical scales (e.g., Allen et al., 2016; Hoelzel, Potter, & Best, 1998; Rosel, Hansen, & Hohn, 2009; Vollmer & Rosel, 2017).</td>
  </tr>
  <tr>
    <td>174</td>
    <td>The same has been found in European waters (Gaspari et al., 2015; Louis, Viricel et al., 2014; Mirimin et al., 2011; Natoli, Peddemors, & Hoelzel, 2003; Nichols et al., 2007; Nykänen et al., 2018; Quérouil et al., 2007), and in some areas this structuring is present even between geographically adjacent populations (e.g., between the Shannon estuary and the rest of the west coast of Ireland, Mirimin et al., 2011; Nykänen et al., 2018).</td>
  </tr>
  <tr>
    <td>177</td>
    <td>Recently, Louis, Viricel et al. (2014) determined that coastal and pelagic bottlenose dolphins in European waters were genetically and ecologically distinct from each other and that further structuring within the two ecotypes existed; the coastal ecotype was divided into the Coastal South population, which included individuals from Normandy and Galicia, and the Coastal North population, consisting of coastal bottlenose dolphins around the UK and Ireland. However, these authors did not have sufficient sample sizes from each local coastal population to fully investigate fine-scale structuring and no samples were available from the population occupying the Shannon Estuary in Ireland. Therefore, it remained unclear whether further fine-scale population structure in coastal waters exists and whether the movement of mobile individuals maintains connectivity between the local populations. Furthermore, the previous studies on bottlenose dolphin population structure (Fernandez et al., 2011; Louis, Viricel et al., 2014; Mirimin et al., 2011; Natoli et al., 2003; Nykänen et al., 2018) have all employed different sets of microsatellite markers, preventing the comparison between studies, and thus giving a fragmented vision of population structure. The purpose of this study, therefore, was to evaluate the population structure of bottlenose dolphins in European waters.</td>
  </tr>
</table>
198 Atlantic waters at a fine-scale level, including samples from the Shannon Estuary and a larger number of samples from west of Ireland and using a common set of microsatellite markers between the studies by Louis, Viricel et al. (2014), Mirimin et al. (2011) and Nykänen et al. (2018). The demographic dispersal between the populations was estimated, and the findings are discussed in light of the conservation of the species in European waters.

204 **Materials and methods**

205 Ninety-six samples from Nykänen et al. (2018), were genotyped at 14 microsatellites loci used in Louis, Viricel et al. (2014) (Tut02, Ttr34, Ttr58, Ttr04, Ttr63, Tut01, Ttr19, Tut05, TtrFF6, Tut09, Ttr11, Ttr48, EV37, TexVet7, see characteristics and amplification conditions in Table S1 and Supplementary text S2 of Louis, Viricel et al. (2014). This dataset included 13 samples from Louis, Viricel et al. (2014) which were known to be duplicates based on their sample ID. Additionally, three samples previously genotyped in Louis, Viricel et al. (2014) were used as a scale, or controls, to define allele size. Nine samples from Corsica from Louis, Viricel et al. (2014) were excluded from this study as they are out of the area of interest. Further two samples from Louis, Viricel et al. (2014) and two samples from Nykänen et al. (2018), were excluded as they had less than eight loci genotyped out of 14 loci. The overall dataset used in the present study thus consists of 425 individuals; 344 samples from Louis, Viricel et al. (2014) and 81 samples (excluding the 13 duplicate samples) from Nykänen et al. (2018), with the latter originating mainly from the Shannon and West Ireland populations. The 425 samples include 228 biopsy samples and 197 samples from stranded animals.
221 The biopsy samples were taken in coastal Normandy (English Channel, France,

222 N=90), West Ireland (Connemara-Mayo-Donegal area, N=30), Cork harbour (Ireland,

223 N=4) and Shannon Estuary (Ireland, N=45), offshore Ireland (on the shelf edge, see

224 Figure 1, N=1), the Azores (Portugal, N=19), Gibraltar and Cadiz (Spain, N=39).

225 Fifteen samples of stranded dolphins were matches to photo-identification catalogues

226 of coastal animals from East Scotland (N=10), Normandy (N=2), and the Arcachon

227 estuary (Bay of Biscay, France, N=3). The rest of the stranded animals came from

228 Ireland (N=31), Wales (N=26), Scotland (N=34), France (N=58) and Spain (N=33).

229 The coastal or pelagic origin of all stranded animals was identified using genetic

230 assignments to the same cluster as biopsied individuals (Louis, Viricel et al., 2014).

231 Ecotype assignment was further confirmed for some of the individuals using photo-

232 identification catalogues of known coastal animals as detailed above (N=15), stable

233 isotopes (N=40, Louis, Fontaine et al., 2014) and/or drift prediction models (N=66,

234 Louis, Viricel et al., 2014). For example, all samples of stranded animals from East

235 Scotland were predicted to have died close to shore (Louis, Viricel et al., 2014).

236 Individuals stranded in the English Channel in France were predicted to originate from

237 coastal waters while individuals stranded in the Bay of Biscay were predicted to come

238 both from the shelf and the shelf-edge (Louis, Viricel et al., 2014).

239 *Microsatellite marker quality*

240 The 13 identified duplicate samples between the two studies were used to calculate

241 genotyping error rate by dividing the number of inconsistent genotypes among the

242 duplicates (three) by the total number of genotypes (364 minus six missing genotypes,

243 therefore 358).
244 All individuals were successfully amplified for at least eight loci and there was 1.80% of missing values in the dataset. Microchecker 2.2.3 was used to check for null alleles and scoring errors (Van Oosterhout, Hutchinson, Wills, & Shipley, 2004). Departures from Hardy-Weinberg equilibrium (HWE) and linkage equilibrium were tested using 245 10 000 dememorizations, 1 000 batches and 10 000 iterations per batch in GENEPOP 246 on the web version 4.2 (Raymond & Rousset, 1995; Rousset, 2008). Tests were 247 conducted for the whole dataset and for the finest level of population structure 248 identified by the clustering methods (see below). 249

252 Genetic population structure

253 Population delimitation and assignment of individuals was done using three genetic clustering methods, which were applied to: (i) the full microsatellite dataset (N=425), 254 and (ii) a subset comprising only coastal individuals (N=269). The clustering methods 255 include: two Bayesian methods implemented in STRUCTURE (Pritchard, Stephens, 256 & Donnelly, 2000) and TESS (Durand, Chen, & Francois, 2009) and a multivariate 257 method, Discriminant Analysis of Principal Components (DAPC) (Jombart, Devillard, 258 & Balloux, 2010). TESS was also run considering only the pelagic individuals 259 (N=156). For the coastal dolphins, if any cluster included several sampling locations, 260 TESS was re-run considering only those sampling areas to determine if there was 261 further genetic structuring among them. 262

263 The three different approaches were used to ensure the robustness of the inferred 264 results, as determining the most likely number of clusters can be challenging (Guillot, 265 Leblois, Coulon, & Frantz, 2009). STRUCTURE assigns individuals to clusters by 266 minimizing HWE and linkage disequilibria (Pritchard et al., 2000). TESS implements 267 a probabilistic model similar to STRUCTURE but is spatially explicit as it incorporates
<table>
  <tr>
    <td>268</td>
    <td>the geographic coordinates of the sampled individuals as a priori information (Durand et al., 2009). In contrast to these two Bayesian approaches that use the full data, DAPC uses genetic similarity to cluster individuals and does not make any population genetic model assumptions, i.e., it does not assume clusters are in HWE (Jombart et al., 2010).</td>
  </tr>
  <tr>
    <td>272</td>
    <td>TESS was run using the conditional auto-regressive (CAR) admixture model with a burn-in of 20 000 steps followed by 120 000 Markov Chain Monte Carlo (MCMC) steps. The number of clusters (K) tested varied between two and ten when considering the whole data set and the coastal samples only, and between two and six when analysing the pelagic samples only. In these cases, ten replicate runs for each value of K were performed but six replicates were used in the analyses that excluded closely related individuals. The spatial interaction parameter was left at the default value (0.6) with a linear degree trend. To select the most likely number of clusters, the Deviance Information Criterion (DIC) values were plotted against K and plots of individual membership proportions were examined. Consistency across runs was also checked.</td>
  </tr>
  <tr>
    <td>282</td>
    <td>STRUCTURE was run using the admixture models with correlated and independent allele frequencies, without a priori information. Ten independent runs for number of clusters ranging from one to ten were carried out with a burn-in of 100 000 iterations followed by 500 000 MCMC steps. Convergence of each run was confirmed visually by inspecting the α-parameter and likelihood chains, and the consistency across runs was examined using pophelper (Francis, 2017), a package that implements the functions from software CLUMPP (Jakobsson & Rosenberg, 2007) in R (R Core Team, 2018). If the results between replicate runs differed (a sign of MCMC non-convergence across all runs), STRUCTURE was re-run increasing the number of MCMC steps to 500 000 burn-in followed by 1 000 000 samples. To determine the</td>
  </tr>
</table>
292 most likely *K*, the likelihood (*L*(*K*)), the rate of change in the likelihood (*L*'(*K*)) and *L''*(*K*)) and Δ*K* (Evanno, Regnaut, & Goudet, 2005) were calculated and plotted for each *K* using pophelper (Francis, 2017), and individual membership proportion plots for the run with the highest likelihood were plotted for the most likely values of *K*, following Pritchard *et al.*, (2000).

297 The DAPC analysis was performed using the package adegenet 2.1.1 (Jombart, 2008) in R following the recommendations in Jombart (2012). The most likely number of clusters was determined with the *K*-means method using the decrease in Bayesian Information Criterion (BIC) value and, in the absence of an “elbow” (a clear drop followed by a sharp increase) in the BIC curve, by plotting and inspecting the membership proportions for the values of *K* with the lowest BIC values. Maximum number of clusters tested was set to ten, and the linear discriminant analysis was performed on 80% of the retained principal components. Scatter-plots were produced for varying *K*. The membership proportion plots were checked for concordance with the number of estimated clusters and whether membership proportions to those clusters were high (>80%).

308 Results of analyses characterizing population structure presented in the main text are based on TESS because they were the most consistent between runs, and importantly, were more concordant with photo-identification studies (see details in the Results section) than those of the two other genetic clustering methods.

312 The inclusion of closely related individuals can affect population structure analyses (Anderson & Dunham, 2008). Therefore the Queller and Goodnight's (Queller & Goodnight, 1989) relatedness coefficient (*R*) was estimated among individuals using COANCESTRY (Wang, 2011) within each population identified by TESS. TESS was
316 re-run after removing one individual from each pair of individuals showing a

317 relatedness coefficient larger or equal to 0.45 as in Rosel et al. (2009).

318 *Nuclear genetic differentiation and diversity*

319 Genetic differentiation, i.e., pairwise $F_{ST}$ (Weir & Cockerham, 1984) and Jost's $D$

320 (Jost, 2008), between the populations inferred by TESS, were estimated using the R-

321 package *diveRsity* (Keenan, McGinnity, Cross, Crozier, & Prodöhl, 2013). The level

322 of significance was assessed using 1 000 bootstrap samples. For each population, the

323 mean number of alleles (NA), allelic richness (AR), inbreeding coefficient ($F_{IS}$),

324 observed heterozygosity (*Ho*) and expected heterozygosity (*He*), were calculated, also

325 in *diveRsity*. Program *CONVERT* (Glaubitz, 2004) was used to count private alleles.

326 Diversity indices were also calculated separately for each locus.

327 *Recent migration rates*

328 Recent migration rates (i.e., within the last two generations) between the populations

329 identified by TESS at the finest level of genetic structuring were estimated using

330 BayesAss (Wilson & Rannala, 2003). Following Rannala (2013), preliminary runs

331 were first performed to tune up MCMC parameters ensuring proposal acceptance rates

332 around 30%. Ten runs were performed with a burn-in of $1 \times 10^6$ iterations followed by

333 $2 \times 10^7$ MCMC iterations and a sampling frequency of 1 000. Trace files were plotted

334 using Tracer (Rambaut & Drummond, 2007) to check for convergence and mixing.

335 Consistency of the results between the runs was also checked.

336
337 **Results**

338 The genotyping error rate between the two datasets was 0.0084. Significant departures
339 from Hardy-Weinberg equilibrium (HWE) were detected for loci EV37 and Ttr34 in
340 one population each (Appendix S1). As deviation was significant in only one out of
341 seven populations for each locus, these loci were kept in the analyses. Linkage
342 disequilibrium was not significant for any of the pairwise comparisons within each
343 population. No null alleles or scoring errors were found.

344 *Individual assignment methods*

345 The most likely number of clusters when running TESS on the whole dataset was six.
346 The delimitation of the six clusters (see Figure 2, Appendices S2a and S2b) and the
347 whole data set (i.e., including both coastal and pelagic individuals) is as follows. The
348 first cluster was mainly composed of coastal dolphins from East Scotland (ten of which
349 were identified as resident based on photo-identification catalogue), Wales and a few
350 individuals from West Scotland and Galicia. The second cluster consisted of
351 individuals biopsy sampled in the Connemara-Mayo-Donegal area on the west coast
352 of Ireland (i.e., the West Ireland population). The third cluster included coastal
353 biopsies from the Shannon Estuary and four biopsy samples from dolphins sampled in
354 Cork harbour, Ireland (i.e., the Shannon population). The fourth cluster was composed
355 of coastal dolphins sampled in the English Channel, in particular in the Gulf of Saint-
356 Malo, three stranded dolphins previously photo-identified as part of a small group that
357 used to reside in the Arcachon estuary (Bay of Biscay, France) and a few individuals
358 from Galicia (i.e., the English Channel population). The fifth cluster included stranded
359 samples from the west coasts of the United Kingdom, Ireland, France and northern
360 Spain, and biopsy samples from the Northeast Atlantic and around the Azores (i.e. the
361 Pelagic Atlantic population). The last cluster was composed of individuals sampled in

362 the Strait of Gibraltar and the Gulf of Cadiz (i.e., the Gibraltar-Cadiz population).

363 When considering K=2, populations (e) and (f) (in Figure 2), were grouped together in
364 one cluster and all remaining populations in the other (TESS, results not shown). This

365 result highlights the hierarchical structuring of the species into coastal (populations (a)
366 to (d)) and pelagic (populations (e) and (f)). Indeed, populations (e) and (f) consist of

367 biopsies from individuals sampled in deep waters of the Azores, North Atlantic and
368 Strait of Gibraltar (plus some samples from the Gulf of Cadiz) as well as samples from

369 stranded animals from the West coasts of Scotland, Ireland, France and Spain.

370 In order to study in more detail the structuring among the coastal individuals, TESS
371 was re-run with coastal samples only. In this case, the most likely number of clusters

372 was five (Figure 3A). The DIC plot indicated a plateau at K=5 or K=6 (Appendix S3a)

373 but examination of membership proportions indicated that there were actually only
374 five clusters in all of the replicate runs with K=6 (Figure 3A, Appendix S3b). Thus,
375 this finer scale analysis uncovered an additional cluster among the coastal dolphins.

376 More precisely, the cluster comprising individuals sampled in Scotland, Wales and
377 Galicia (Figure 2a) was divided into two clusters: one comprising individuals from

378 Scotland and Wales (Figure 3A(a)) and another including individuals from Galicia
379 (i.e., the Galicia population, Figure 3A(e)). All remaining coastal individuals (Figure

380 2b-d) were clustered as before (see Figure 3A(b-d)).

381 To further explore fine-scale structuring, TESS was re-run for only the individuals of
382 the Scotland-Wales cluster (Figure 3A(a)), as this population encompassed several

383 geographical areas. Further population structure was found within this area (see Figure
384 3B and Appendices S4a and S4b) with the first cluster including individuals from
385 Wales and a few individuals from West Scotland (Wales-West Scotland population,

386 Figure 3B(a)) and a second cluster consisting of individuals from East Scotland (East

387 Scotland population, Figure 3B(b)).

388 When running TESS on the pelagic samples only, no further genetic structuring was

389 found but the best number of clusters was two, corresponding to the Pelagic Atlantic

390 and the Gibraltar-Cadiz populations (Appendices S5a and S5b), inspection of the

391 admixture plots for K=2 to K=6 also indicated that there were only two clusters, results

392 shown for K=2 and K=3 in S5b.

393 Additional analyses with STRUCTURE and DAPC provide general support for TESS

394 results but were either less stable due to convergence problems (STRUCTURE) or

395 were not completely congruent with results of photo-identification studies. When all

396 samples (N=425) were included in STRUCTURE runs, the Evanno-method (ΔK,

397 Evanno et al., 2005) detected only the uppermost hierarchical structure i.e., the

398 division into coastal and pelagic at K=2 (Appendix S9d). Nevertheless, using the L(K)

399 criterion proposed by Pritchard et al. (2000), the most likely K was six (Appendix S9a),

400 in concordance with TESS. Beyond this, no new genetic clusters emerged in the

401 membership proportion plots (Appendix S10). The full description of STRUCTURE

402 results when including all samples and only the coastal samples is given in Appendices

403 S7 and S11, respectively. In contrast to TESS, STRUCTURE could not detect the

404 samples from Galicia as a separate cluster when only coastal samples were included.

405 When all samples were included in the DAPC analyses, the best number of clusters

406 was found at K=6 (Appendices S15-S16). The clusters were almost identical to those

407 found with TESS and STRUCTURE, dividing the Irish samples into Shannon and

408 West coast populations and separating the biopsy samples from Cadiz-Gibraltar from
409 the Azores biopsies and stranded samples thought to be from a pelagic origin.

410 However, in contrast to TESS, DAPC also failed to delineate the samples from Galicia

411 into a separate cluster when only coastal samples were included. The full description

412 of results when including only the coastal samples is given in Appendix S17.

413 As mentioned previously (Methods), the inclusion of closely related individuals can

414 bias results of genetic clustering methods. The presence of related individuals varied

415 among the geographic areas considered in this study. They were almost inexistent

416 among pelagic samples as no closely related individuals (with a relatedness coefficient

417 of ≥0.45) were found in the Gibraltar-Cadiz population and only three pairs were found

418 in the pelagic Atlantic population. In contrast, relatedness among coastal individuals

419 varied from 1.15% in the English Channel population to 5.13% in the Galicia

420 population. Therefore, TESS was re-run for the coastal populations only taking out

421 one individual from each close kin pair (N=79). The results were similar to the runs

422 including close relatives and also indicated five clusters corresponding to the Shannon,

423 the West Ireland, the English Channel, the East and West Scotland-Wales and the

424 Galicia populations (Appendices S6a and S6b). As the results with and without

425 including close relatives were similar, we conclude that the results from TESS

426 presented in Figures 2 and 3 are highly reliable and can be used to draw inferences on

427 migration rates.

428 Results from genetic clustering methods such as TESS can be used as a first approach

429 to study migration. More precisely, individuals assigned to a population different from

430 the geographical area where they were sampled can be considered as likely migrants.

431 One individual sampled in Galicia had a high assignment probability to the English

432 Channel population (0.75), another individual sampled in the English Channel was
433 assigned to the West Ireland population (with 0.86 probability) and a third individual
434 sampled in the Shannon estuary was assigned to the West Ireland population (with
435 0.77 probability). These results suggest that there is some connectivity between
436 dolphin populations and further analyses with BayesAss are warranted.

437 *Nuclear genetic differentiation and diversity*

438 All nuclear F<sub>ST</sub> and Jost's D pairwise comparisons between the eight populations
439 identified with TESS were significant. The highest level of differentiation was between
440 pelagic and coastal populations (Table 1). When considering F<sub>ST</sub>, the lowest level of
441 differentiation was detected between the two pelagic populations. In terms of Jost's D
442 the lowest differentiation was between Wales and East Scotland. The Shannon
443 population was the most differentiated from the pelagic populations followed by the
444 West Ireland population for both indices.

445 Nuclear genetic diversity (Allele Richness (AR) and Observed Heterozygosity (Ho))
446 was significantly lower in most coastal populations than in the pelagic populations,
447 with Kruskal-Wallis *P*<0.01 in all AR coastal-pelagic comparisons except between
448 English Channel and Gibraltar-Cadiz (*P*=0.07), and *P*<0.05 in all *Ho* comparisons
449 except between Gibraltar-Cadiz and English Channel (*P*=0.12), Gibraltar-Cadiz and
450 Galicia (*P*=0.18) and Pelagic Atlantic and Galicia (*P*=0.09) (Table 2, Appendix S20
451 for values per loci per populations). Among the coastal populations, allele richness
452 was highest in the English Channel population and lowest in the Shannon population,
453 between which the difference was significant (Kruskal-Wallis *P*<0.05). The highest
454 number of private alleles was found in the pelagic Atlantic population (N=26), two
455 private alleles were found in the Gibraltar/Cadiz and the English Channel populations
456 and one private allele in the East Scotland and in the West Scotland-Wales
457 populations. A significant heterozygote deficiency was detected in the Shannon, West

458 Ireland and Galicia populations (Table 2).

459 *Recent migration rates*

460 Estimates of recent migration rates were highly consistent between runs; therefore, the

461 results presented here are based on a randomly chosen run (Table 3). Estimated

462 migration rates were very low between most populations, around <1% per generation,

463 with an upper bound for the 95% credibility interval of less than 10% and a lower

464 bound of 0 (Table 3). The only exceptions were mean migration rate of 18.1% between

465 Galicia and East Scotland, and mean rate of 25.7% from East Scotland to Wales-West

466 Scotland. However, these higher migration rates need to be interpreted with caution as

467 individuals from Scotland, Wales and Galicia consisted of stranded animals. As

468 mentioned before, although a portion of the East Scotland individuals were matched

469 to photo-identification catalogues of known coastal animals, there is uncertainty about

470 the origin of strandings in Wales and West Scotland.

471

472 **Discussion**

473 This study presents, to date, the most comprehensive analysis of the genetic structure

474 of bottlenose dolphins in the North-east Atlantic as it includes samples collected from

475 an unprecedentedly wide geographical area, which unlike previous studies, were

476 analysed using a common set of microsatellite markers. The results of this study,

477 therefore, have the potential to be used to identify management units in this area and

478 thus offer a significant contribution to the conservation of the species in European

479 waters.
480 A first level of genetic differentiation was found between coastal and pelagic populations, as in a previous study (Louis, Viricel et al., 2014). The results from TESS in terms of assignments to coastal and pelagic ecotypes were identical to that of Louis, Viricel et al. (2014) with only one exception. One individual that stranded in the Bay of Biscay which belonged to the Pelagic Mediterranean population in Louis, Viricel et al. (2014) clustered with the coastal English Channel population in this study. No further genetic structure was found within the two pelagic populations (i.e., Pelagic Atlantic and Gibraltar-Cadiz).

488 Fine-scale population structure among coastal bottlenose dolphins from different geographical locations corresponded to the different local populations that inhabit the Shannon estuary, west of Ireland, the English Channel, Galicia, East Scotland, and Wales/West Scotland. Previous studies only identified large-scale population structure in coastal waters due to uneven sampling of each local coastal population (Louis, Viricel et al., 2014) or fine-scale population structure in small geographic regions (Fernandez et al., 2011; Mirimin et al., 2011, Nykänen et al., 2018). Thus, the results of this study highlight the need for and the power of broad scale collaboration when working on the conservation of highly mobile species that can move across national borders. The level of resolution was only possible thanks to broad international collaborations, sample sharing and careful calibration of allele scoring to overcome the difficulties of comparing genotypes across studies.

500 Although results of all three genetic clustering methods were generally consistent, their comparison with independent results from photo-identification studies highlighted differences in performance among them. More explicitly, the coastal population assignments inferred with TESS were concordant with photo-identification studies
504 indicating geographically isolated populations with site fidelity. This was not always

505 the case for STRUCTURE and DAPC results. This highlights the power of clustering

506 methods using a priori spatial information to infer complex population structure.

507 Indeed, although STRUCTURE was able to identify the same six clusters as TESS

508 when all samples were considered, it was unable to identify further fine-scale

509 structuring among coastal samples. Comparing the two approaches to infer the number

510 of clusters using STRUCTURE, supports the idea that Evanno's method is well

511 adapted to identify the first level of structuring under hierarchical scenarios (Waples

512 & Gaggiotti 2006) such as those observed in bottlenose dolphins. DAPC also supports

513 the subdivision of bottlenose dolphins into six management units but assignment of

514 individuals to populations was less consistent with those of the two other methods. In

515 particular, one of the genetic clusters was composed of individuals sampled from many

516 different geographic locations. As Jombart et al. (2010) indicate, DAPC uses a purely

517 statistical criterion aimed at identifying the minimum number of groups that best

518 explain total observed variation while at the same time maximising between group

519 variation. Thus, as opposed to TESS and STRUCTURE, it does not take into

520 consideration Hardy-Weinberg and linkage equilibrium, which explains the observed

521 differences with the two other methods in the individual assignments to populations.

522 The use of stranded animals could be considered as a limitation of this study. However,

523 we are relatively confident in the inferences made, even though strandings constitute

524 almost half of all samples. Firstly, a drift-prediction model (Peltier et al., 2012) was

525 applied in a previous study (Louis, Viricel et al., 2014), to estimate the most likely area

526 of death. The estimated origin with the drift prediction model was consistent with the

527 genetic results separating coastal and pelagic bottlenose dolphins (Louis, Viricel et al.,
528 2014). For example, in East Scotland, all individuals were estimated to have died very close to shore. The coastal and pelagic assignments of a subset of the samples were confirmed using stable isotopes (Louis, Fontaine et al., 2014). In addition, 15 stranded animals were known to be part of the resident coastal populations and their genetic assignments matched photo-identification studies (Louis, Viricel et al., 2014). Nevertheless, we acknowledge that there is uncertainty in the origin of the stranded animals when there is no further evidence such as photo-identification and drift modelling. This is the case for the coastal populations of Galicia and Wales/West Scotland and caution is therefore required when interpreting these results.

537 The fine-scale population structure likely results from natal philopatry, possibly driven by vertically (mother to offspring) and horizontally (between non-filial conspecifics) learned foraging behaviours during the juvenile life stage, site fidelity and social structure (Foote et al., 2016; Kopps et al., 2014; Whitehead, 2017; see further discussion in Louis, Viricel et al., 2014; Louis, Fontaine et al., 2014; Nykänen et al., 2018). Photo-identification studies indicated site fidelity to relatively restricted geographical areas (Cheney et al., 2014; Ingram & Rogan, 2002; Louis et al., 2015), however, individuals can undertake movements of a few hundreds of kilometres, i.e., around Ireland (O'Brien et al., 2009) and the East coast of Scotland (Cheney et al., 2013) but these movements can still be considered occurring at a relatively small scale. There is also some evidence of larger scale movements between the North Sea and the Atlantic, as reported in Robinson et al. (2012), and this provides further potential for genetic and demographic connectivity between the populations; seven transient dolphins were first sighted in the Moray Firth, East Scotland and later re-sighted in the Hebrides, West Scotland. Five of these animals were later recorded in coastal Irish
<table>
  <tr>
    <td>552</td>
    <td>waters and some of these movements represent travelled distances of over 1 200 km</td>
  </tr>
  <tr>
    <td>553</td>
    <td>(Robinson et al., 2012). These individuals are believed to be part of the West Ireland</td>
  </tr>
  <tr>
    <td>554</td>
    <td>population based on photo-identification catalogue kept on the coastal Irish dolphins.</td>
  </tr>
  <tr>
    <td>555</td>
    <td>However, photo-identification fieldwork mainly occurs during the summer in most</td>
  </tr>
  <tr>
    <td>556</td>
    <td>areas. Therefore, movements outside this season may be overlooked.</td>
  </tr>
  <tr>
    <td>557</td>
    <td>Despite the above-mentioned movements, our results are consistent with a very low</td>
  </tr>
  <tr>
    <td>558</td>
    <td>degree of connectivity among the studied populations. In other words, the different</td>
  </tr>
  <tr>
    <td>559</td>
    <td>populations are relatively isolated from each other. Estimated migration rates among</td>
  </tr>
  <tr>
    <td>560</td>
    <td>populations were very low, and less than 1% per generation for most of the pairwise</td>
  </tr>
  <tr>
    <td>561</td>
    <td>comparisons. Although there is no consensus on the level of migration that leads to</td>
  </tr>
  <tr>
    <td>562</td>
    <td>demographic coupling (Waples & Gaggiotti, 2006; Palsboll et al., 2007), the estimated</td>
  </tr>
  <tr>
    <td>563</td>
    <td>migration rates are well below the rate of 10%, which according to a simulation study</td>
  </tr>
  <tr>
    <td>564</td>
    <td>can lead to coupled dynamics (Hastings, 1993). Demographic connectivity is therefore</td>
  </tr>
  <tr>
    <td>565</td>
    <td>very low among local populations. The only exceptions are the migration rates of 18%</td>
  </tr>
  <tr>
    <td>566</td>
    <td>from East Scotland to Galicia and 26% from East Scotland to Wales, the latter of which</td>
  </tr>
  <tr>
    <td>567</td>
    <td>could be explained by the relatively short distance between the two sites. However, as</td>
  </tr>
  <tr>
    <td>568</td>
    <td>these populations included only stranded dolphins and although all the East Scotland</td>
  </tr>
  <tr>
    <td>569</td>
    <td>samples were estimated to originate from the North Sea according to a drift-prediction</td>
  </tr>
  <tr>
    <td>570</td>
    <td>model used in a previous study (Louis, Viricel et al., 2014), there is some uncertainty</td>
  </tr>
  <tr>
    <td>571</td>
    <td>about the origin of the individuals sampled in Wales and Galicia. As migration rate</td>
  </tr>
  <tr>
    <td>572</td>
    <td>estimates have been shown to be dependent on the number of clusters chosen to best</td>
  </tr>
  <tr>
    <td>573</td>
    <td>represent the population structure and thus the number of individuals in each cluster</td>
  </tr>
  <tr>
    <td>574</td>
    <td>(Olsen et al., 2014), great care has to be taken that the sampling strategy used is robust</td>
  </tr>
  <tr>
    <td>575</td>
    <td>and the samples are representative of each population. The present study is based on</td>
  </tr>
</table>
576 the most comprehensive set of genetic samples available but there is still a need for

577 more biopsy samples from Wales and Scotland to further support our results.

578

579 The different populations were characterised in terms of genetic diversity,

580 differentiation and migration rates. This information and existing estimates of

581 abundance will help to evaluate the conservation status and vulnerability of the

582 populations. Indeed, small isolated population are at risk of losing heterozygosity and

583 genetic resilience due to genetic drift (Lacy, 1987) and are thus more vulnerable to

584 stochastic environmental changes or anthropogenic stressors than larger populations.

585 Genetic diversity was higher in the pelagic populations than coastal populations as in

586 previous studies (Hoelzel et al., 1998; Louis, Viricel et al., 2014). This is consistent

587 with higher abundance of the pelagic populations compared to the coastal populations.

588 The Gibraltar-Cadiz population included around 700 photo-identified individuals

589 (Giménez et al., 2018) and the Pelagic Atlantic abundance estimates (from Scotland to

590 Spain) are several tens of thousands of individuals (Hammond et al., 2013, 2009) while

591 the abundances of coastal dolphins do not exceed ~400 individuals (see below).

592 Genetic diversity indices for the coastal populations were concordant with abundance

593 estimates using mark-recapture methods. The population of the English Channel,

594 which is the largest coastal population with abundance estimates of around 400

595 individuals (Louis et al., 2015), had the highest genetic diversity. The Shannon

596 population had the lowest genetic diversity and is the smallest with abundance

597 estimated between 110 and 140 dolphins (Berrow, 2012; Englund, Ingram, & Rogan,

598 2008; Ingram & Rogan, 2002, 2003; Rogan, Gkarakouni, Nykänen, Whitaker, &

599 Ingram, 2018). Abundance estimates were around 200 individuals in East Scotland
600 (Cheney et al., 2013), ca. 190 for the West Irish population (Nykänen, 2016) and 150 to 250 individuals in Wales (Pesante, Evans, Baines, & McMath, 2008). Any local perturbation or global change could thus have drastic negative effects on these coastal populations due to their small size, low genetic diversity and low connectivity uncovered in this study.

605 This study filled a major knowledge gap on the fine-scale population structure of bottlenose dolphins in European Atlantic waters, which was considered as the main research priority for the protection of this species (Ó Cadhla & Marnell, 2017). As previously found in Louis, Viricel et al. (2014), coastal and pelagic populations are distinct and should be monitored separately. In terms of the coastal populations, the high genetic differentiation and negligible migration rates found in this study highlight the need to separately monitor each local population (the English Channel, the Shannon, the West Ireland, the East Scotland, the Galicia and the Wales-West Scotland populations) as they correspond to different management units. However, we cannot rule out further fine-scale population division in Wales and West Scotland due to the small sample sizes (N=16 and N=5, respectively), and the fact that all came from stranded dolphins as the use of samples of stranded animals only may lead to underestimated population structure (Bilgmann, Möller, Harcourt, Kemper, & Beheregaray, 2011).

619 The small population sizes of the coastal populations and their isolation may render them vulnerable to anthropogenic impacts. These results highlight the need to protect their habitat through protected areas such as the SACs where anthropogenic activities are appropriately managed, ensuring their ecological suitability to the populations utilizing them. Regular monitoring of population dynamics (e.g., abundance, survival,
624 calving rate) using photo-identification, as undertaken in some of the areas, is therefore
625 recommended to evaluate population trends. Population viability analyses, as applied
626 to harbour seals, *Phoca vitulina*, in Southern Scandinavia in Olsen et al. (2014), could
627 also help inform whether the populations are of sufficient size for long-term population
628 viability in the absence of immigration.

629 In this study, management units were identified by first clustering individuals into
630 putative populations based on individuals' genotypes and then by estimating their
631 connectivity. Populations not sampled in this study (i.e., the Sado Estuary, Portugal,
632 and the Iroise Sea, France) could be genotyped in the future using the same set of
633 markers as in this study to further clarify their connectivity with the other European
634 Atlantic populations. The Atlantic pelagic population showed no genetic
635 differentiation over a large geographical range from West Scotland to the Azores.

636 However, pelagic bottlenose dolphin populations may show ecological differences
637 even in the absence of genetic divergence and this should be considered when
638 allocating management units. For example, the bottlenose dolphins of the Gibraltar
639 Strait and of the Gulf of Cadiz, although potentially presenting no genetic structure,
640 showed differences in ecology detected using different ecological tracers (i.e., stable
641 isotopes and contaminant loads) and individual monitoring through photo-
642 identification, leading to the delineation of two ecological, management units
643 (Giménez et al., 2018). This type of ecological differentiation may be detected using
644 next generation sequencing data covering the whole genome of the species, which
645 would allow testing for adaptive differentiation (Funk, McKay, Hohenlohe, &
646 Allendorf, 2012). We recommend combining genetic data with ecological data (e.g.,
647 the use of stable isotopes) and individual monitoring, where available, to determine
648 the geographical scales most relevant to monitoring and the protection of populations

649 of any species.

650

651 Data accessibility: The data are available on dryad at the following link: (to be

652 uploaded at the revisions stage).

653

654 Acknowledgments

655 We thank Andrew Foote for advice and comments, Amélia Viricel, Hélène Peltier

656 and Christophe Guinet for their help during ML's PhD study. We also thank

657 everyone involved in data collection: Conor Ryan (GMIT, IWDG), Nigel Monaghan

658 and Ruth Carden (National Museum of Ireland), Barry McGovern (SAC Inverness)

659 and Julie Béesau, Gill Murray-Dickson and Paul Thompson (University of

660 Aberdeen), GECC and Réseau National Echouages volunteers, Fabien Demaret,

661 Ghislain Doremus, Vincent Ridoux and Olivier Van Canneyt (Pelagis), Sami Hassani

662 (Océanopolis), Angela Llavona, Ruth Fernandez (CEMMA) and Paula Mendez-

663 Fernandez (CEMMA and LIENSs), Philippe Verborgh and Ruth Esteban (CIRCE),

664 Joan Giménez (EBD-CSIC) and all volunteers involved in sample collection from all

665 the organisations. All samples were taken under the relevant permits. UK samples

666 were collected under the aegis of the UK Cetacean Strandings Investigation

667 Programme, which is funded by Defra and the Devolved Administrations of Scotland

668 and Wales. Data from offshore Irish waters were collected on the Cetaceans on the

669 Frontier cruise thanks to National Marine Research Vessels Ship-Time Grant Aid

670 Programme 2010 funded under the Science Technology and Innovation Programme

671 of National Development Plan 2007-2013. Biopsy samples taken in Ireland were

672 carried out under licence from the National Parks and Wildlife Service (NPWS) Nos.

673 C104/2011 and DER/Dolphin2012-10 (licence numbers C111/2013 and C043/2014)

674 and the Health Products Regulatory Authority (licence number: AE19130/P014),

675 with biopsy sampling funded by the NPWS and by a grant to ER from Science

676 Foundation Ireland. Samples from Galicia were obtained with the support of

677 Dirección Xeral de Conservación da Natureza-Xunta de Galicia, cofinanced with
678 European Regional Development Funds (ERDF/FEDER). Southern Spain samples

679 were collected thanks to LIFE 'Conservación de Cetáceos y tortugas de Murcia y

680 Andalucía' (LIFE 02 NAT/E/8610). MAS was supported by an FCT postdoctoral

681 grant (SFRH/BPD/29841/2006). Data collection in the Azores was funded by

682 projects TRACE (PTDC/MAR/74071/2006) and MAPCET (M2.1.2/F/012/2011).

683 Funding for sample collection in France and analyses was provided by Fondation

684 Total, Agence de l'Eau Seine-Normandie, Fonds de Dotation pour la Biodiversité,

685 Agence des Aires Marines Protégées, Association Nationale de la Recherche et de la

686 Technologie, Direction Régionale de l'Environnement, de l'Aménagement et du

687 Logement, Ministère de l'Ecologie, du Développement Durable et de l'Energie and

688 Conseil Général de la Manche. ML was supported by a Fyssen post-doctoral

689 fellowship, Fondation Total, a bridge funding from the School of Biology of the

690 University of St Andrews and People's Trust for Endangered Species. MN was

691 supported by NPWS, the Crawford-Hayes Fund, MASTS (Marine Alliance for

692 Science and Technology for Scotland) and Project FishKosm that is funded by the

693 Department of Agriculture, Food and the Marine's Competitive Research Funding

694 programmes. We would also like to thank the editors, Morten Tange Olsen and two

695 unnamed reviewers for helpful comments, which helped improve this manuscript.

696

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1024

1025

1026

1027

1028

1029

1030

1031

1032

1033

1034

1035

1036

1037

1038

1039
1040 Tables

1041

1042 Table 1. Pairwise fixation indices based on 14 microsatellite loci (given as average with 95% Highest Probability Density Interval (HPDI)) between the different populations. The samples were divided into populations based on results from TESS.
1043
1044
1045 Values above the diagonal are F<sub>ST</sub>-values and values below the diagonal are Jost's D values.
1046

<table><thead><tr><th></th><th>Shannon</th><th>Wales/West Scotland</th><th>East Scotland</th><th>West Ireland</th><th>English Channel</th><th>Galicia</th><th>Gibraltar/Cadiz</th><th>Pelagic Atlantic</th></tr></thead><tbody><tr><td>Shannon</td><td>-</td><td>0.136<br>(0.096-0.180)</td><td>0.164<br>(0.130-0.200)</td><td>0.129<br>(0.104-0.153)</td><td>0.090<br>(0.079-0.103)</td><td>0.226<br>(0.178-0.283)</td><td>0.232<br>(0.215-0.249)</td><td>0.227<br>(0.212-0.241)</td></tr><tr><td>Wales/West Scotland</td><td>0.090<br>(0.061-0.121)</td><td>-</td><td>0.066<br>(0.037-0.103)</td><td>0.128<br>(0.092-0.169)</td><td>0.082<br>(0.058-0.111)</td><td>0.125<br>(0.083-0.176)</td><td>0.173<br>(0.154-0.197)</td><td>0.178<br>(0.159-0.198)</td></tr><tr><td>East Scotland</td><td>0.141<br>(0.104-0.176)</td><td>0.036<br>(0.010-0.075)</td><td>-</td><td>0.149<br>(0.119-0.183)</td><td>0.090<br>(0.067-0.114)</td><td>0.091<br>(0.062-0.131)</td><td>0.150<br>(0.129-0.172)</td><td>0.164<br>(0.147-0.183)</td></tr><tr><td>West Ireland</td><td>0.074<br>(0.056-0.095)</td><td>0.089<br>(0.064-0.116)</td><td>0.115<br>(0.086-0.148)</td><td>-</td><td>0.115<br>(0.099-0.131)</td><td>0.178<br>(0.132-0.234)</td><td>0.208<br>(0.190-0.227)</td><td>0.190<br>(0.177-0.206)</td></tr><tr><td>English Channel</td><td>0.065<br>(0.052-0.079)</td><td>0.082<br>(0.054-0.115)</td><td>0.081<br>(0.054-0.110)</td><td>0.104<br>(0.085-0.125)</td><td>-</td><td>0.094<br>(0.065-0.134)</td><td>0.134<br>(0.122-0.146)</td><td>0.146<br>(0.136-0.157)</td></tr><tr><td>Galicia</td><td>0.174<br>(0.123-0.234)</td><td>0.098<br>(0.042-0.164)</td><td>0.054<br>(0.027-0.090)</td><td>0.111<br>(0.067-0.164)</td><td>0.093<br>(0.060-0.142)</td><td>-</td><td>0.140<br>(0.115-0.173)</td><td>0.139<br>(0.116-0.170)</td></tr><tr><td>Gibraltar/Cadiz</td><td>0.386<br>(0.346-0.426)</td><td>0.289<br>(0.246-0.331)</td><td>0.260<br>(0.220-0.310)</td><td>0.347<br>(0.308-0.388)</td><td>0.286<br>(0.254-0.320)</td><td>0.259<br>(0.214-0.312)</td><td>-</td><td>0.034<br>(0.026-0.043)</td></tr><tr><td>Pelagic Atlantic</td><td>0.457<br>(0.423-0.492)</td><td>0.359<br>(0.318-0.398)</td><td>0.345<br>(0.310-0.384)</td><td>0.379<br>(0.347-0.414)</td><td>0.351<br>(0.324-0.377)</td><td>0.335<br>(0.281-0.401)</td><td>0.097<br>(0.072-0.126)</td><td>-</td></tr></tbody></table>

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<table><caption>Table 2. Nuclear diversities over all loci for each population inferred by TESS.</caption><thead><tr><th>Population</th><th>N (mean)</th><th>A</th><th>%</th><th>AR</th><th>Ho</th><th>He</th><th>HWE</th><th>F<sub>IS</sub></th><th>F<sub>IS</sub> Low</th><th>F<sub>IS</sub> High</th><th>PA</th></tr></thead><tbody><tr><td>Shannon</td><td>52</td><td>52</td><td>31.19</td><td>3.06</td><td>0.44</td><td>0.42</td><td>0.997</td><td>-0.0604*</td><td>-0.1215</td><td>-0.0014</td><td>0</td></tr><tr><td>Wales/West Scotland</td><td>20</td><td>57</td><td>35.86</td><td>3.36</td><td>0.46</td><td>0.48</td><td>0.839</td><td>0.0362</td><td>-0.0744</td><td>0.1386</td><td>1</td></tr><tr><td>East Scotland</td><td>31</td><td>54</td><td>35.94</td><td>3.42</td><td>0.52</td><td>0.54</td><td>0.982</td><td>0.0297</td><td>-0.0565</td><td>0.1067</td><td>1</td></tr><tr><td>West Ireland</td><td>36</td><td>53</td><td>34.82</td><td>3.30</td><td>0.51</td><td>0.47</td><td>1.000</td><td>-0.0754*</td><td>-0.1358</td><td>-0.0204</td><td>0</td></tr><tr><td>English Channel</td><td>111</td><td>93</td><td>57.45</td><td>4.75</td><td>0.60</td><td>0.60</td><td>0.971</td><td>0.0115</td><td>-0.0244</td><td>0.0470</td><td>2</td></tr><tr><td>Galicia</td><td>13</td><td>58</td><td>36.90</td><td>3.76</td><td>0.60</td><td>0.55</td><td>0.419</td><td>-0.0892*</td><td>-0.2011</td><td>-0.0046</td><td>0</td></tr><tr><td>Gibraltar/Cadiz</td><td>49</td><td>113</td><td>72.10</td><td>6.10</td><td>0.73</td><td>0.74</td><td>0.463</td><td>0.0141</td><td>-0.0172</td><td>0.0462</td><td>2</td></tr><tr><td>Pelagic Atlantic</td><td>106</td><td>148</td><td>92.43</td><td>7.07</td><td>0.75</td><td>0.77</td><td>0.998</td><td>0.0266</td><td>0.0030</td><td>0.0497</td><td>26</td></tr></tbody></table>

N=mean number of individuals used for each locus, A=number of alleles observed, %=percentage of total alleles, AR=allelic richness, Ho=observed heterozygosity, He=expected heterozygosity, are given as per locus per population sample. Hardy-Weinberg Equilibrium (HWE)=corrected p-values (chi-square test for goodness-of-fit), F<sub>IS</sub>=F<sub>IS</sub> values for each loci and population sample (overall), F<sub>IS</sub> Low/High=bias corrected 0.025% and 92.5% percentiles of the confidence interval. *denotes significance in F<sub>IS</sub> values as the 95% CI does not overlap zero, PA=number of Private Alleles.
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<table><thead><tr><th>Population \ Source</th><th>Shannon</th><th>Wales/West Scotland</th><th>East Scotland</th><th>West Ireland</th><th>English Channel</th><th>Galicia</th><th>Gibraltar/Cadiz</th></tr></thead><tbody><tr><td>1 Shannon</td><td>0.959 (0.931 - 0.987)</td><td>0.005 (0.000 - 0.016)</td><td>0.007 (0.000 - 0.019)</td><td>0.006 (0.000 - 0.018)</td><td>0.006 (0.000 - 0.018)</td><td>0.005 (0.000 - 0.016)</td><td>0.005 (0.000 - 0.016)</td></tr><tr><td>2 Wales/West-Scotland</td><td>0.012 (0.000 - 0.036)</td><td>0.678 (0.657 - 0.700)</td><td><strong>0.257 (0.206 - 0.307)</strong></td><td>0.010 (0.000 - 0.029)</td><td>0.013 (0.000 - 0.037)</td><td>0.010 (0.000 - 0.029)</td><td>0.010 (0.000 - 0.029)</td></tr><tr><td>3 East Scotland</td><td>0.010 (0.000 - 0.030)</td><td>0.008 (0.000 - 0.025)</td><td>0.935 (0.892 - 0.978)</td><td>0.009 (0.000 - 0.027)</td><td>0.011 (0.000 - 0.030)</td><td>0.009 (0.000 - 0.025)</td><td>0.009 (0.000 - 0.026)</td></tr><tr><td>4 West Ireland</td><td>0.054 (0.000 - 0.112)</td><td>0.008 (0.000 - 0.022)</td><td>0.009 (0.000 - 0.028)</td><td>0.898 (0.833 - 0.964)</td><td>0.008 (0.000 - 0.024)</td><td>0.008 (0.000 - 0.022)</td><td>0.008 (0.000 - 0.022)</td></tr><tr><td>5 English Channel</td><td>0.008 (0.000 - 0.021)</td><td>0.003 (0.000 - 0.008)</td><td>0.007 (0.000 - 0.019)</td><td>0.005 (0.000 - 0.013)</td><td>0.969 (0.948 - 0.990)</td><td>0.003 (0.000 - 0.008)</td><td>0.003 (0.000 - 0.008)</td></tr><tr><td>6 Galicia</td><td>0.015 (0.000 - 0.043)</td><td>0.014 (0.000 - 0.041)</td><td><strong>0.181 (0.104 - 0.257)</strong></td><td>0.015 (0.000 - 0.043)</td><td>0.062 (0.000 - 0.127)</td><td>0.684 (0.652 - 0.716)</td><td>0.014 (0.000 - 0.041)</td></tr><tr><td>7 Gibraltar/Cadiz</td><td>0.006 (0.000 - 0.017)</td><td>0.006 (0.000 - 0.018)</td><td>0.006 (0.000 - 0.018)</td><td>0.006 (0.000 - 0.017)</td><td>0.006 (0.000 - 0.018)</td><td>0.006 (0.000 - 0.017)</td><td>0.941 (0.902 - 0.981)</td></tr><tr><td>8 Iberian Atlantic</td><td>0.003 (0.000 - 0.009)</td><td>0.003 (0.000 - 0.009)</td><td>0.003 (0.000 - 0.009)</td><td>0.003 (0.000 - 0.009)</td><td>0.004 (0.000 - 0.011)</td><td>0.003 (0.000 - 0.009)</td><td>0.008 (0.000 - 0.022)</td></tr></tbody></table>

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**Figure legends**

Figure 1. Map of samples used in the study and their origin; stranding - samples collected from stranded bottlenose dolphins, biopsy - samples collected from skin biopsies, and catalogued - samples collected from stranded animals that had been matched to a photo-identification catalogue of known coastal dolphins. The grey contours represent 200m and 1000m depth contours.

Figure 2. Map of individual assignment probabilities per population identified by TESS using the whole dataset (N=425) and *K*=6. The color scale bar indicates the assignment probabilities, (a) East and West Scotland, Wales and Galicia, (b) West Ireland, (c) Shannon estuary, Ireland, (d) English Channel, France, (e) pelagic Atlantic, (f) Gibraltar-Cadiz.

Figure 3. Results of TESS analyses involving only coastal samples. A) Map of individual assignment probabilities per population using all coastal samples (N= 269) and *K*=5: (a) East Scotland and Wales (b) West Ireland, (c) Shannon estuary, Ireland, (d) English Channel, France, (e) Galicia, Spain. (B) Map of individual assignment probabilities per population using only the samples from coastal Wales, West and East Scotland (N=53) and *K*=2: (a) West Scotland and Wales (b) East Scotland. The color scale bar indicates the assignment probabilities.
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<figure><img src="image_4.png" /><figcaption>Figure 1. Map of samples used in the study and their origin; stranding - samples collected from stranded bottlenose dolphins, biopsy - samples collected from skin biopsies, and catalogued - samples collected from stranded animals that had been matched to a photo-identification catalogue of known coastal dolphins. The grey contours represent 200m and 1000m depth contours.</figcaption></figure>

208x181mm (300 x 300 DPI)
<figure><img src="image_5.png" /><figcaption>b)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>c)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>d)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>e)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>f)</figcaption></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>
A) Coastal samples - K = 5

<figure><img src="image_12.png" /></figure>

B) Wales and all Scotland coastal samples - K = 2

<figure><img src="image_13.png" /></figure>