Quantifying dispersal between marine protected areas by a highly mobile species, the bottlenose dolphin, Tursiops truncatus

Abstract The functioning of marine protected areas (MPAs) designated for marine megafauna has been criticized due to the high mobility and dispersal potential of these taxa. However, dispersal within a network of small MPAs can be beneficial as connectivity can result in increased effective population size, maintain genetic diversity, and increase robustness to ecological and environmental changes making populations less susceptible to stochastic genetic and demographic effects (i.e., Allee effect). Here, we use both genetic and photo‐identification methods to quantify gene flow and demographic dispersal between MPAs of a highly mobile marine mammal, the bottlenose dolphin Tursiops truncatus. We identify three populations in the waters of western Ireland, two of which have largely nonoverlapping core coastal home ranges and are each strongly spatially associated with specific MPAs. We find high site fidelity of individuals within each of these two coastal populations to their respective MPA. We also find low levels of demographic dispersal between the populations, but it remains unclear whether any new gametes are exchanged between populations through these migrants (genetic dispersal). The population sampled in the Shannon Estuary has a low estimated effective population size and appears to be genetically isolated. The second coastal population, sampled outside of the Shannon, may be demographically and genetically connected to other coastal subpopulations around the coastal waters of the UK. We therefore recommend that the methods applied here should be used on a broader geographically sampled dataset to better assess this connectivity.


| INTRODUC TI ON
The conservation and management of wild animal populations are often achieved through designation of protected areas that are thought to represent important habitats for foraging, breeding, and other fitness-related activities (Palumbi, 2001;Reeves, 2000).
Demographic connectivity, defined as the linking together of local fragmented populations through the dispersal of individuals as larvae, juveniles, or adults (Sale et al., 2005), is an important factor to consider when designating marine protected areas (MPAs), as it has implications for the persistence of metapopulations (reviewed in Botsford et al., 2009). For example, in many marine fish species, larval dispersal and population connectivity determine whether a MPA (or a network of MPAs) contributes to the overall survival and reproduction of the species, thus maintaining sustainable population sizes (Burgess et al., 2014). Dispersal is thus a key variable that conservation biologists need to quantify and consider in order to assess the effectiveness of protected areas (Reeves, 2000). This is particularly relevant in highly mobile and wide-ranging marine species, whose management provision is often restricted to small fixed areas of protection and for which the low cost of movement can facilitate long-range dispersal (reviewed in Forcada, 2009). High levels of mobility can result in substantial gene flow and the homogenization of genetic diversity across a geographic range (Ryman, Lagercrantz, Andersson, Chakraborty, & Rosenberg, 1984;Winkelmann et al., 2013). However, whilst in most marine fish metapopulations dispersal during the larval stage facilitates greater connectivity among habitat patches and reduces the risk of local extinctions (Burgess et al., 2014), marine mammals typically have much lower reproductive rates and their offspring can exhibit a high degree of natal philopatry (Amos, Schlotterer, & Tautz, 1993;Baird, 2000;Sellas, Wells, & Rosel, 2005). This can lead to small isolated populations and a system that is sensitive to changes in environmental conditions, ecological factors, or anthropogenic disturbance. Lowe and Allendorf (2010) distinguished demographic connectivity from genetic connectivity by defining the former as the relative contribution of net immigration and local recruitment to the population growth rate, and the latter as the degree to which evolutionary processes within (sub)populations are affected by gene flow.
Population genetic approaches may provide a tool to measure and quantify the rate and scale of dispersal (i.e., migration) when it is not feasible to assess the movement of individuals by nongenetic capture-recapture methods (Gagnaire et al., 2015). However, when combined together, genetic and nongenetic methods are highly complementary and can provide invaluable information for management of populations. Photo-identification is a cost-effective technique commonly used by marine mammal researchers to identify individuals of several species using the unique natural markings on their body and thus enabling, for example, the estimation of their distribution, association patterns, or abundance via capture-recapture methods (see review by Würsig & Jefferson, 1990). If natural markings cannot be used because of insufficient individual variation, molecular genotyping may provide a usable alternative to photo-identification methods in estimating animal movements (see Palsbøll et al., 1997). Here, both these approaches were applied to quantify the demographic and genetic connectivity between marine protected areas designated for bottlenose dolphins in an area in the north-east Atlantic.
Bottlenose dolphins using the Shannon Estuary SAC have been found to be genetically differentiated from another population inhabiting the coastal waters of counties Galway and Mayo (Mirimin et al., 2011). However, these findings were based on a limited number of samples collected in a relatively small area (ranging about 70 km along the Galway/Mayo coastline) and it is not known whether additional fine-scale structuring exists. Photoidentification studies of dolphins using the Shannon Estuary SAC suggest that these individuals have a high degree of site fidelity (e.g., Englund, Ingram, & Rogan, 2008;; however, the extent of the range of dolphins using Ireland's coastal waters is not yet fully understood. Previous research has shown that at least some of these coastal animals move over great distances (Cheney et al., 2013;Ingram, Englund, & Rogan, 2001O'Brien et al., F I G U R E 2 GPS tracks recorded during boat surveys for bottlenose dolphins on the West coast of Ireland 2009; Oudejans, Ingram, Englund, & Rogan, 2010;Robinson et al., 2012), which could indicate some potential for genetic connectivity between adjacent subpopulations using neighboring coastal SACs, but this has not previously been demonstrated or quantified.
Genetic clustering and kinship-based methods are used here to reexamine the population structure in Irish waters using a larger dataset supplemented with samples collected from a wider coastal area. The contribution of demographic and genetic dispersal to the connectivity between neighboring SACs within Irish waters is quantified using a combination of photo-identification and genetic techniques. In addition, the role of possible drivers for population structuring, including social structure, relatedness, site fidelity, and sex-biased dispersal, are examined. The findings are discussed in the context of conservation and management.

| Photo-identification surveys and photograph selection
Boat-based photo-identification surveys were conducted within the Lower River Shannon SAC, Ireland, every year between 1996 to 2008 with the exception of 2004, and in other coastal areas of Ireland (including the West Connacht Coast SAC), in 2001-2005, 2007-2010, and 2013-2014 (Figures 1 and 2). These surveys were mostly conducted during the summer months (May-September), however, some were done in autumn or winter (see Supporting information Table S1 in dryad for the survey information). A bottlenose dolphin "group" was defined as all dolphins within a 100 m radius of each other as per Irvine, Scott, Wells, and Kaufmann (1981) and hereafter "encounters" refer to periods of data collection whilst with dolphin groups. Best effort was made to photograph every individual in the group, and photograph identification of bottlenose dolphins' dorsal fins was examined. For each encounter, the best quality photograph was chosen of each identifiable dolphin and the quality of the photograph was graded from 1 to 4 (1 being the highest quality, 4 being the lowest, see Supporting information Appendix S1) with no consideration concerning the degree of marking of the individual. Each photographed individual was then assigned one of three grades of mark severity (Figure 3), and visually matched against the full catalogue of dolphins photographed during previous encounters.

| Skin tissue sample collection and analysis
The dataset comprising of altogether 97 unique samples included 85 samples already genotyped by Mirimin et al. (2011) Donegal during 2013-2014, a sample from a dolphin that stranded in Co. Cork in 2014, and a sample collected from an animal that was bycaught by a fishing vessel on the continental shelf off south-west of Ireland in 1996. All of the skin biopsy samples in this study were taken using a modified 0.22 caliber rifle (see Krützen et al., 2002) and sampling was carried out during the summer months. The gender of stranded individuals was recorded by inspection of the genital area and reproductive organs, whilst the sex of free-ranging biopsied individuals was determined by multiplex amplification of sex chromosome-specific DNA fragments, following the method described in Rosel (2003).

| DNA extraction, PCR amplification, and genotyping
DNA was extracted from 12 new skin samples using the DNeasy Blood and Tissue kit from Qiagen. A total of 15 nuclear microsatellite loci (see Supporting information Appendix S2) were amplified following polymerase chain reaction (PCR) conditions described in Mirimin et al. (2011). The amplified products were separated on 6% polyacrylamide gels on a Li-Cor 4300 DNA analyzer (Li-Cor Inc, Lincoln, NE, USA) and allele sizes determined by eye in comparison with a 50-530 size standard (Li-Cor) and allele cocktails from F I G U R E 3 Examples of bottlenose dolphin fins showing the three grades of mark severity used in photograph analysis. Each dolphin was graded from one to three as follows: (a) grade M1 marks, consisting of significant fin damage or deep scarring that were considered permanent; (b) grade M2 marking that consist of deep tooth rakes and lesions, with only minor cuts present; (c) fin with grade M3 marks, having only superficial rakes and lesions. Grades M1 and M2 are considered to last many years, enabling long-term identification of these dolphins. In contrast, "superficial" markings (grade M3), such as tooth rakes, may fade and heal within a relatively short period of time and interannual resighting probabilities of these animals are likely to be reduced reference samples. These allele cocktails consisted of mixtures of PCR products from four to five individuals previously genotyped for each locus and allowed alleles in this study to be consistently sized across runs and in line with the samples of Mirimin et al. (2011). Due to the possibility that the same individual dolphin may have been unintentionally biopsied more than once, the uniqueness of the new genotypes was confirmed by calculating the percentage of similarity between the samples in program GIMLET 1.3.3. (Valière, 2002). The same program was also used to calculate the probability of identity (PI), which estimates the power of the set of microsatellite markers to differentiate between two distinct individual samples (Waits, Luikart, & Taberlet, 2001). The error rate involved in genotyping had already been estimated as negligible (<0.01%) by Mirimin et al. (2011), therefore, reestimation of the error was not performed for the new samples because of their low number (n = 12).  (Raymond & Rousset, 1995;Rousset, 2008) and linkage equilibrium using ARLEQUIN (Excoffier & Lischer, 2010) with 10,000 iterations and applying sequential Bonferroni corrections. The above analyses were performed considering the whole dataset as a single unit and separately at population level (identified with Bayesian clustering methods, see below).

| Individual assignment tests
All samples were included in a cluster analysis using STRUCTURE (Pritchard, Stephens, & Donnelly, 2000). The admixture model was run with correlated allele frequencies without including any prior information on the sampling location. Ten independent runs were carried out for each value of K (the number of theoretical populations), with K set to vary from 1 to 6, using 1,000,000 Markov Chain Monte Carlo (MCMC) iterations preceded by 1,000,000 burn-in steps. Convergence of chains (traces of alpha and F ST values) was confirmed visually and the consistency of runs was checked by confirming that the variance in estimated ln Pr(X|K) was smaller within each K compared to the variance between the different Ks, and calculating the average posterior probability for each K. ∆K, which has been argued to be a better predictor of the number of populations, was also calculated following Evanno, Regnaut, and Goudet (2005) in STRUCTURE HARVESTER Web version 0.6.94 (Earl & vonHoldt, 2012). Once K was determined, each individual was assigned to a cluster based on its maximum membership proportion.
As relatedness between individuals can affect population assignment (i.e., including samples of closely related individuals can lead to artificial structuring of populations (Guinand et al., 2006;Anderson & Dunham, 2008), the relatedness coefficient, r, (Queller & Goodnight, 1989) was calculated between all possible dyads within the putative populations identified by the clustering methods using KINGROUP (Konovalov, Manning, & Henshaw, 2004). Then, one member of each dyad with a relatedness coefficient of 0.45 or greater was removed (according to Rosel et al., 2009) and STRUCTURE re-run with this reduced dataset.
In addition, population structuring was inferred using a discriminant analysis of principal components (DAPC) that clusters individuals together based on genetic similarity to find the most likely number of populations. DAPC does not rely on any population genetic model (i.e., does not assume HWE) and is efficient at detecting hierarchical structure (Jombart, Devillard, & Balloux, 2010). DAPC using the package adegenet (Jombart, 2008) in R (R Core Team 2016) was run following the recommendations in the tutorial (Jombart & Collins 2015), and cluster membership probabilities were calculated for each individual. Population differentiation was estimated by calculating pairwise F ST (Weir & Cockerham, 1984) and Jost's D (Jost, 2008) values using the R package diveRsity (Keenan, McGinnity, Cross, Crozier, & Prodöhl, 2013) between populations identified by STRUCTURE, with the whole and the reduced dataset after the removal of close relatives, and the 95% confidence intervals were obtained using 10,000 bootstrap replicates. Population-specific F IS values, expected and observed heterozygosity, mean number of alleles, and allele richness were also calculated using package diveRsity to examine the level of inbreeding. Heterozygote deficiency and excess in each population was tested using Fisher's method implemented in GENEPOP (Raymond & Rousset, 1995;Rousset, 2008) with 10,000 iterations.
As a further check that differentiation was not solely driven by sampling of related individuals or uneven sampling of populations (see Puechmaille, 2016), 10 individuals were randomly selected from each of the two putative coastal populations and the pairwise F ST values (with 95% CI) estimated using the R package diveRsity and repeated 10 times. These pairwise values were compared to F ST values calculated for two sets of ten individuals randomly drawn from within a single coastal population, Coastal Shannon or Coastal mobile.
To supplement this analysis, the power to detect a significant moderate population differentiation, based on an F ST value of ≥0.1 in a sample consisting of the allele frequencies from both coastal populations and using a sample size of ten individuals per "subpopulation" (i.e., Coastal Shannon and Coastal mobile), was calculated by running 1,000 simulations in POWSIM 4.1 ; see also Morin, Martien, & Taylor, 2009).
Sex-biased dispersal between the three populations identified by clustering methods was tested by comparing assignment indices, relatedness, F ST and F IS values separately for males and females using 1,000 permutations in FSTAT 2.9.3 (Goudet, 2001). Following Goudet (2001), it was assumed that sex-biased dispersal within the sampled populations could be detected from gender differences in genetic structuring with the more philopatric sex showing more structure.

| Migration rates
Recent migration rates (the proportion of migrants per population) within the last two generations were estimated using BAYESASS (Wilson & Rannala, 2003). The migration rates were calculated between the populations identified by STRUCTURE and DAPC, and then reestimated with the individual biopsied in the Shannon Estuary but genetically assigned to Coastal mobile population grouped together with the Shannon dolphins. The MCMC mixing parameters of migration rates, allele frequencies, and inbreeding coefficients, were adjusted as recommended by Rannala (2007), during preliminary runs to obtain acceptance rates of around 30%. Ten runs with a burn-in of 1,000,000 iterations followed by 10,000,000 MCMC iterations sampling every 1,000 iterations were performed.
Convergence and mixing of chains were confirmed by plotting trace files using TRACER (Rambaut et al., 2014), and the consistency of runs was checked.

| Effective population size
An estimate of contemporary effective population size (N e ) for the Coastal Shannon population was derived using LDNe, a method that uses linkage disequilibrium (Waples & Do, 2008). This method has performed best in situations with little to no migration (<1%) (Gilbert & Whitlock, 2015) and adequately with migration rates of up to ~5%-10% (Waples & England, 2011). Allele frequencies of <0.02 were excluded from the analyses to avoid bias caused by rare alleles (Louis, Viricel et al., 2014;Waples & Do, 2010). As some of the samples were collected over a 15-year time period (in the Shannon Estuary) and the data are thus likely to be biased downward due to overlapping generations (Waples, 2010), the estimate of N e was inflated by 15% as in Louis, Viricel et al. (2014). N e could not be calculated for the Coastal mobile or the Pelagic populations, due to small sample size (Tallmon et al., 2010).

| Analyses of social structure and site fidelity
To test possible drivers of population structure and connectivity, indices of social structure, site fidelity, and kinship were examined among the coastal bottlenose dolphins (Shannon and Mobile). Longterm photo-identification data are not available for the "pelagic" dolphins in this area. Social structure analyses were performed in SOCPROG 2.4 compiled version (Whitehead, 2009). The dataset was limited to photographs of sufficient quality (grades 1-3) and to individuals with permanent and obvious markings (mark severity grade M1, Figure 3) in order to identify individuals between several years, and only dolphins photographed in at least five separate encounters were included to reduce bias caused by rarely seen individuals (Whitehead, 2008). Individuals photographed together during an encounter were considered associated with each other, so an encounter was chosen as the grouping variable in SOCPROG. "Day" was chosen as the sampling period.
The strength of association between pairs of individuals (i.e., dyads) was measured using two indices of the frequency of cooccurrence: the half-weight association index (HWI) and the simple ratio (Cairns & Schwager, 1987;Ginsberg & Young, 1992). The simple ratio index is suitable when association is defined by the presence in the same group during a sampling period (Ginsberg & Young, 1992). However, the HWI can be more appropriate when not all individuals within a group have been identified (Ginsberg & Young, 1992), as is often the case with dolphin photoidentification studies due to individuals reacting differently to the presence of the research vessel. As both indices gave almost identical results and were considered good representations of social structure by the high cophenetic correlation coefficient (CCC) values (CCC HWI: 0.874, CCC simple ratio: 0.887), only the results derived using the HWI are presented. NETDRAW (Borgatti, 2002) was used to visualize a social network diagram using the network statistics calculated in SOCPROG. Permutation tests (Bejder, Fletcher, & Bräger, 1998;Whitehead, 1999) with 20,000 steps were used to test whether the observed association patterns were different than expected from random associations and to identify dyads with significantly larger or smaller association indices.
The standardized lagged association rate (SLAR) was used to test if temporary or long-lasting social bonds existed between individuals, and compared to the null association rate (expected if all individuals are associating at random). The SLAR was fitted separately to the individuals encountered within and outside of the Shannon Estuary as the data showed that these groups did not associate with each other. Mathematical models representing simulated social structures, that is whether individuals had constant companionships or casual associates during the study (Whitehead, 1995), were fitted to the SLARs. The best fitting models were chosen based on the lowest quasi-Akaike information criterion (QAIC) value (see Whitehead, 2007). To investigate movements of dolphins between different coastal areas and to estimate the amount of time identified individuals resided within each area, Lagged identification rates (LIRs) within and between all study areas were calculated in SOCPROG (Whitehead, 2009). Markov movement models (expected LIRs) of emigration/mortality and emigration + reimmigration (Whitehead, 2001) were fitted to estimate the probabilities of individuals moving from one area to another, and QAIC values were used to identify the best fitting model. 100 bootstrap replicates were used to estimate the standard error for the LIRs.

| Relatedness, associations, and spatial overlap
A Mantel test in R package ade4 (Dray & Dufour, 2007) was used to investigate whether associations reflected kinship bonds, and whether a correlation existed between the strength of pairwise association (HWI) and relatedness between all biopsied dyads that had been encountered at least three times. To examine whether there was a correlation between spatial overlap and relatedness kernel utilization distribution (KUD) was calculated for individually identified dolphins that were encountered at least five times using R package adehabitatHR (Calenge, 2006),

| RE SULTS
Twelve new individuals, including ten coastal biopsies and two within the Pelagic population were out of HWE (Supporting information Appendix S2). STRUCTURE was therefore run with and without these three loci.

| Individual assignment tests
The most likely number of clusters (i.e., populations), K, identified by STRUCTURE based on the highest Pr(X|K) and using the ad hoc method by Evanno et al. (2005)   There was significant differentiation in allele frequencies (based on both F ST and Jost's D) between the pelagic and the two coastal populations and between the two coastal populations (defined with STRUCTURE), and this difference persisted after removing close relatives from the dataset ( Table 1)

| Sex-biased dispersal and migration rates
No evidence of sex-biased dispersal was found in any of the indices used (Supporting information Appendix S10). The inferred migration rates (the proportion of migrants per population) calculated with BAYESASS were nonsignificant as zero was included in the range of 95% confidence intervals in each comparison (Table 2).
When looking at individual posterior probabilities of migrant ancestry, two individuals from the Coastal mobile population and one from the Pelagic population had >50% probability of being either first-or second-generation migrants from other populations. Two individuals from the Coastal mobile population ("tt-09-12" and "12-09-2014_Tt2") were second-generation migrants from the Coastal Shannon population with 64% and 79% probability, respectively.
One individual assigned to the Pelagic population by STRUCTURE ("bnd204") had a 37% probability of being a first-generation migrant and a 46% probability of being a second-generation migrant from the Coastal mobile population. When the individual that was biopsied in the Shannon Estuary but genetically assigned to Coastal mobile population ("tt-05-03") was grouped together with other Shannon individuals, it had a 19% probability of being a first-generation migrant and a 70% probability of being a second-generation migrant from the Coastal mobile population.

| Social structure and site fidelity
When testing for preferred and avoided companionships between and within the two coastal populations, the mean HWI in the real data was found to be significantly higher compared to the HWI of a permuted random dataset (mean: p < 0.01, SD: p < 0.0001, and TA B L E 2 Inferred (posterior) mean migration rates (with 95% HPDI) between the different Irish bottlenose dolphin populations identified by STRUCTURE and DAPC, given as proportion of migrants per population CV: p < 0.0001) indicating significant preferred short-(within sampling period) and long-term (between sampling periods) companions.
Moreover, the proportion of nonzero elements was larger in the random data compared to real data which suggests that some individuals may avoid others (Whitehead, 2009), both within each population and between the two coastal populations (Figure 6). The latter comes as no surprise as the two populations have not been documented associating with each other. Pairwise associations within the Coastal Shannon population were best described by the standardized lagged association rate (SLAR) model "casual acquaintances" (Supporting information Appendix S11a), by which dyads remain associated for a period of time, dissociate and may, or may not, reassociate (Whitehead, 2015;Whitehead, Waters, & Lyrholm, 1991). Within the Coastal mobile population, on the other hand, the model "constant companions and casual acquaintances" best explained the data, with "constant companions" remaining associated with each other throughout the length of the study (Whitehead, 2015;Whitehead et al., 1991) (Supporting information Appendix S11b). The mean HWI within the belonging to the Coastal mobile population were never photographed in the Shannon Estuary during the study period so their LIR in the Shannon Estuary was also zero. The LIR within the Shannon stayed fairly constant for approximately 100 days, followed by some fluctuations in the rate (Figure 7a). Two competing models had substantial support explaining the data, with the emigration/mortality model having the lowest AIC value, followed by emigration + reimmigration + mortality model (Supporting information Appendix S12). LIR associated with the Coastal mobile population was best explained by the emigration/ mortality model (Figure 7b, Supporting information Appendix S12).

| Relatedness, spatial overlap, and associations
When only the biopsied individuals with a sufficient number of photo-identification encounters (≥3) were considered, a significant correlation was found between the relatedness coefficient (Queller & Goodnight, 1989) and HWI (r = 0.345, p = 0.0001) when the data from the two coastal populations were combined. However, this is likely attributed to the correlation of zero values in the combined dataset as no correlation was found between the two indices when testing for this separately for each population (Coastal Shannon: r = 0.028, p = 0.363; Coastal mobile: r = 0.0004, p = 0.480). Of fifteen dyads with F I G U R E 6 Social network diagram of bottlenose dolphins encountered on at least five occasions during the data collection 1996-2014. Boxes represent a social cluster of individuals encountered in the Shannon Estuary, and circles a cluster of the "mobile" dolphins encountered on the west and north-west coast of Ireland. The length of the line in the network diagram inversely represents the strength of the association between a dyad calculated as half-weight index (HWI) significant associations (i.e., who associated with each other significantly more or less than with other individuals), none had relatedness coefficient ≥0.45, but three dyads had coefficient values close to 0.25 indicating possible half-siblings or cousins. No correlation was found between relatedness and spatial overlap within the Coastal Shannon (r = 0.076, p = 0.193) or the Coastal mobile population (r = 0.042, p = 0.417). Overall, these results indicate that close kinship may not strongly promote overall social associations in these two populations.

| D ISCUSS I ON
Understanding the scale of dispersal is an important consideration for the conservation and management of marine species (Lotterhos, 2012). By combining genetic and photo-identification data, spatial dispersal and genetic dispersal over both short and long temporal scales have been elucidated in unprecedented detail for bottlenose dolphins in Irish waters. Dispersal can be gametic, that is, via gene flow during temporary interactions and spatial overlap, and therefore only detected by genetic methods.
Dispersal can also be demographic, that is, the permanent movement of individuals from one location to another, detectable over the short-term using photo-identification of naturally marked individuals and over the past few generations using genetic methods supporting the division of the samples into one "pelagic" and two "coastal" clusters. In addition, Jost's D values and DAPC indicated the presence of a hierarchical population structure with the largest genetic difference occurring between the "pelagic" and "coastal" populations. Furthermore, social structure analyses using long-term photo-identification data revealed that the two coastal populations were not only genetically, but also socially, distinct. This kind of social separation has been previously reported between the "pelagic" and "coastal" bottlenose dolphins (Oudejans, Visser, Englund, Rogan, & Ingram, 2015).
The results also suggest that both coastal populations show a similar degree of site fidelity to their respective areas and are likely to have nonoverlapping core home ranges, at least during the seasons that photo-identification work was conducted. The gradual decline in the lagged identification rates (LIRs) toward the end of the study period reflects a decrease in site fidelity that is likely explained by mortality and/or emigration. These results highlight that a high degree of site fidelity, especially evident in the Shannon Estuary SAC where data have been collected for over 12 years, is a key driver of fine-scale population structure among coastal populations. A high degree of site fidelity among resident populations of bottlenose dolphins to certain local areas has been found in other parts of the world (Bristow & Rees, 2001;Möller, Allen, & Harcourt, 2002;Simoes-Lopes & Fabian, 1999). This residency, found especially in embayments, coupled with genetic differentiation between dolphins residing in adjacent coastal habitats, has led a number of authors to suggest that variability in these habitats accompanied by the ability F I G U R E 7 Lagged identification rate (LIR) for bottlenose dolphins encountered ≥5 times (a) in the Shannon Estuary and (b) outside the Shannon Estuary in the coastal waters of Ireland during the study period 1996-2014. The graph describes the probability that a dolphin photographed at time 0 will be identified again at time X within the area. Data points are represented as green circles (with SE), and the best fitting model (see Supporting information Appendix S12) is displayed as the black solid line. Time lag (number of days) is given on logarithmic scale of local populations to accommodate it by the development of different foraging strategies (e.g., Barros & Wells, 1998;Smolker, Richards, Connor, Mann, & Berggren, 1997), may have shaped the fine-scale population structure among these dolphins (Hoelzel et al., 1998;Chilvers & Corkeron, 2001;Natoli et al., 2005;Möller, Wiszniewski, Allen, & Beheregaray, 2007;Sargeant, Wirsing, Heithaus, & Mann, 2007;Richards et al., 2013;Allen et al., 2016). In addition, there is growing evidence that cultural transmission occurs within dolphin social communities in the form of social learning (e.g., Krützen et al., 2005;Mann, Stanton, Patterson, Bienenstock, & Singh, 2012) which may facilitate the evolution of specialist foraging behaviors, which in turn has the potential to maintain population structure between adjacent communities.
In this study, there is evidence of significant companionships within the two coastal populations, and it is possible that social bonds promote and maintain the observed social and genetic separation of these populations. The observed companionships did not seem to be linked to relatedness, but close associates were found both among kin and nonkin individuals, similar to a recent study by Louis et al. (2018). In contrast, close associations were linked to relatedness among females in a population of Indo-Pacific bottlenose dolphins (Möller, Beheregaray, Allen, & Harcourt, 2006), and support for relatedness in male groups has been documented in alliances of this genus (Krützen et al., 2003), as well as among shortbeaked common dolphins (Dephinus delphis) in southern Australia, with greater relatedness found between males within schools than between schools (Zanardo, Bilgmann, Parra, & Möller, 2016). It is unfortunate that there were insufficient combined photo-identification and genetic data to fully investigate possible sex-specific patterns in the relatedness and associations among the two coastal Irish populations, partly due to genetic sampling being biased toward males (especially in the Coastal Shannon population) and partly because of the fact that the biopsy sampled animals did not necessarily have enough photo-identification encounters for further social analyses.
Lowe and Allendorf (2010) described genetic connectivity as the exchange of alleles through gene flow between populations, and demographic connectivity as the dispersal of individuals from one population to another thus contributing to underlying population demographic processes and parameters (e.g., survival, mortality, abundance). Gene flow maintains genetic variation in populations, enhancing adaptive potential to environmental variation (Yamamichi & Innan, 2012). Even small amounts of gene flow can prevent the accumulation of large genetic differences between populations of low effective size (Palumbi, 2003;Slatkin, 1987 (Berrow, 2012;Berrow, Holmes, & Kiely, 1996;Englund, Ingram, & Rogan, 2007;Englund et al., 2008;Ingram & Rogan, 2002, it is possible that this dolphin has not (yet) genetically contributed to dispersal of gametes into the Coastal Shannon population. In contrast, close kinship was found between "tt05-03" and an individual sampled within the Coastal mobile population. Thus, "tt05-03" appears to be an example of demographic dispersal from the Coastal mobile population to the Coastal Shannon population. Nonetheless, considering that this individual (one of 46 biopsied dolphins in the Shannon Estuary) represents <3% demographic dispersal between the coastal Irish populations, it seems unlikely that the contribution to the demographic processes are significant. However, this largely depends on the management targets set to the population in question and the power to detect changes in abundance, survival, or other demographic processes.
No evidence for sex-biased dispersal was found in this study.
However, the sampling was biased toward males (due to efforts to sample marked animals), with more than double the amount of samples compared to females; thus these results should be treated with caution. Both Mirimin et al. (2011) and Louis et al. (2014a) found two haplotypes that were shared between "coastal" and "pelagic" dolphins based on the mitochondrial control region, but the sequencing of the entire mitochondrial genome revealed no shared haplotypes between these two "ecotypes" suggesting limited female dispersal between coastal and pelagic populations (Moura et al., 2013;Nykänen, 2016). However, two mitogenome haplotypes were shared between the Coastal Shannon and Coastal mobile populations (Nykänen, 2016), suggesting either that some movement between these populations exists via female-mediated gene flow, or that the shared haplotypes are a consequence of shared ancestry and recent divergence between the two populations.
Two individuals strongly assigned to the Coastal mobile population were identified as likely second-generation migrants originating from the Coastal Shannon population. However, whilst individual assignment methods, such as STRUCTURE, are believed to perform well at identifying migrant individuals (Putman & Carbone, 2014), BAYEASS was found to be less reliable in calculating individual migrant probabilities (Faubet, Waples, & Gaggiotti, 2007); thus, these results should be interpreted with caution. Nevertheless, BAYEASS was found to perform well at estimating overall migration rates between populations over a few generations at migration rates up to 0.1 (Faubet et al., 2007). Whether these dispersal events further translated into gene flow is uncertain and warrants more sampling effort especially within the Coastal mobile population. To date, only ~12% of this population occurring in Irish waters has been sampled, based on a median multisite abundance estimate of 189 dolphins derived for a wide area extending to the west and north-west coast of Ireland (Nykänen, 2016). Overall, despite some evidence for low levels of demographic dispersal, it appears that connectivity between populations is too low to prevent the buildup of genetic differentiation. Nichols et al. (2007) and Louis et al. (2014a)  North" metapopulation, and previous research has shown that at least some of these mobile coastal animals travel over distances at the scale of hundreds of kilometers (Cheney et al., 2013;Ingram et al., 2001O'Brien et al., 2009;Robinson et al., 2012). If they do indeed comprise part of the "Coastal North" metapopulation extending beyond Irish waters, transnational cooperation, monitoring and management may be needed. Six individuals from the west coast of Ireland have been matched on an ad hoc basis to photo-identification catalogues comprised of animals ranging in the coastal waters of Scotland (Robinson et al., 2012) but there is a need for a consistent collaborative effort to better integrate photo-identification catalogues from different regions/countries (e.g., Ireland, Wales, Scotland, France, Cornwall). Such collaboration would provide better insights into demographic dispersal, ranging patterns and the abundance of this putative metapopulation.
In addition, genetic dispersal within the metapopulation needs to be quantified through increased sampling effort over a larger area extending beyond country boundaries and using a common set of genetic markers that are comparable between laboratories.
The present study supports the delineation of the three populations occurring in Irish waters as separate management units based on the low genetic, social, and demographic dispersal between the populations, thus validating the current designation of separate SACs for the two coastal populations. The study also highlights the importance of distinguishing genetic and demographic connectivity so that gene flow can be differentiated from immigration that has no subsequent genetic contribution from the migrant to the local population.
Even though the genetic connectivity between the different popula- wrote the manuscript. All authors approved the final manuscript.

DATA ACCE SS I B I LIT Y
Analysis input files used for TESS, DAPC, STRUCTURE, diveRsity, and SOCPROG are deposited in Dryad (Supporting information Data S1-S4).