Currently, three stocks of sei whales (Balaenoptera borealis) are defined in the North Atlantic; the Nova Scotian, Iceland- Denmark Strait and Eastern North Atlantic stocks, which are mainly based upon historical catch and sighting data. We ana- lyzed mitochondrial control region DNA (mtDNA) sequences and genotypes from 7 to 11 microsatellite loci in 87 samples from three sites in the North Atlantic; Iceland, the Gulf of Maine and the Azores, and compared against the North Pacific using 489 previously published samples. No statistically significant deviations from homogeneity were detected among the North Atlantic samples at mtDNA or microsatellite loci. The genealogy estimated from the mtDNA sequences revealed a clear division of the haplotypes into a North Atlantic and a North Pacific clade, with the exception of one haplotype detected in a single sample from the Azores, which was included in the North Pacific clade. Significant genetic divergence between the North Atlantic and North Pacific Oceans was detected (mtDNA Φ = 0.72, microsatellite Weir and Cockerham’s ϴ = 0.20; ST p < 0.001). The coalescent-based estimate of the population divergence time between the North Atlantic and North Pacific populations from the sequence variation among the mtDNA sequences was at 163,000 years ago. However, the inference was limited by an absence of samples from the Southern Hemisphere and uncertainty regarding mutation rates and generation times. The estimates of inter-oceanic migration rates were low (Nm at 0.007 into the North Pacific and at 0.248 in the oppo- site direction). Although estimates of genetic divergence among the current North Atlantic stocks were low and consistent with the extensive range of movement observed in satellite tagged sei whales, the high uncertainty of the genetic divergence estimates precludes rejection of multiple stocks in the North Atlantic. Keywords Sei whale · Population genetics · Migration · Atlantic Ocean · Pacific Ocean · Northern Hemisphere Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s1059 2-018-1076-5) contains supplementary material, which is available to authorized users. * Léonie A. E. Huijser Biology Department, Woods Hole Oceanographic Institution, email@example.com 86 Water Street, Woods Hole, MA 02543, USA * Per J. Palsbøll The Institute of Cetacean Research, 4-5 Toyomi-cho, firstname.lastname@example.org Chuo-ku, Tokyo 104-0055, Japan National Research Institute of Far Seas Fisheries, 2-12-4 Groningen Institute of Evolutionary Life Sciences, Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-8648, University of Groningen, Nijenborgh 7, 9747 AG Groningen, Japan The Netherlands Marine and Freshwater Research Institute, Program Center for Coastal Studies, 5 Holway Avenue, Provincetown, for Whale Research, PO Box 1390, 121 Reykjavík, Iceland MA 02657, USA Marine and Environmental Sciences Centre and Institute of Marine Research, University of the Azores, 9901-862 Horta, Portugal Vol.:(0123456789) 1 3 1008 Conservation Genetics (2018) 19:1007–1024 Southern, the Pacific and the Atlantic Ocean) likely repre- Introduction sents a distinct stock or subspecies (e.g. Archer et al. 2013; Jackson et al. 2014), and perhaps even different species in The pelagic sei whale (Balaenoptera borealis) has a cos- the case of right whales (Eubalaena spp.; Rosenbaum et al. mopolitan distribution and undertakes seasonal migrations 2000). The sei whale’s annual migration cycle between low between high-latitude summer foraging grounds and low- and high latitudes is similar to the annual migration pattern latitude winter breeding grounds (Mizroch et al. 1984). Sei assumed for many mysticetes resulting in an anti-tropical whales were commercially hunted from the 1950s to 1980s temporal separation between populations in different hemi- after populations of the larger baleen whales were depleted spheres (Mizroch et al. 1984). In addition, the populations by whaling (Mizroch et al. 1984; Prieto et al. 2012). The in the Atlantic and the Pacific are geographically separated current population trends are unknown and the International by continental land masses. Union for the Conservation of Nature (IUCN) estimated the Genetic analysis of sei whale materials began with an current global abundance of sei whales at approximately allozyme study by Wada and Numachi (1991) who com- 20% of pre-whaling levels . Although the International pared the allozyme variation at 45 loci in sei whales sampled Whaling Commission (IWC) placed a moratorium on com- in the Southern Ocean and the North Pacific. The authors mercial whaling in 1986, sei whales are still occasionally reported statistically significant differences in allele frequen- targeted under special permits for scientific whaling and cies between the two hemispheres. A more recent study aboriginal subsistence hunting . compared mitochondrial DNA (mtDNA) control region In 1977, the IWC divided the global sei whale popula- sequence variation in samples collected from sei whales in tion into distinct ‘stocks’ for management purposes. The the two aforementioned ocean basins and the North Atlantic stock divisions were based upon the distribution of catches (Baker et al. 2004). The study revealed that North Atlantic and sightings as well as mark-recapture data, which was the sei whales were genetically distinct from their North Pacific nature of the data available at the time (Donovan 1991). The and Southern Hemisphere conspecifics. In contrast to earlier Southern Hemisphere was divided into six stocks, following findings by Wada and Numachi (1991), Baker et al. (2004) IWC management practice for other baleen whale species. failed to detect a clear differentiation between the Southern Initially, three distinct stocks were proposed in the North Ocean and the North Pacific. Pacific, but these were subsequently combined into a single The population genetic structure of sei whales within stock, due to absence of conclusive evidence for a three- each ocean basin remains poorly understood as well. No stock hypothesis (Donovan 1991). data or analyses of the sei whale population genetic structure In the North Atlantic, sei whales were caught and sighted within the Southern Ocean have been presented so far. In the in eight main areas. However, the IWC did not presume cases of the North Pacific and North Atlantic populations, these areas to represent different stocks and instead divided few analyses and data have been presented. Kanda et al. the North Atlantic sei whales into three stocks: the Nova (2006) failed to detect any spatial or temporal heterogeneity Scotian, Iceland-Denmark Strait and Eastern North Atlantic in the genetic variation at both microsatellite loci and later stocks (Fig. 1). The possible presence of a fourth stock off mtDNA sequences (Kanda et al. 2009) in 790 North Pacific Labrador north of the Nova Scotian stock boundary (Dono- sei whales. Similarly, Daníelsdottír et al. (1991) did not van 1991) was acknowledged, but this stock was never des- detect any temporal heterogeneity at 40 allozyme loci geno- ignated. After the cessation of commercial sei whaling, the typed in 101 sei whales caught off Iceland between 1985 and overall research effort aimed specifically at sei whales was 1988. Population genetic structure across the North Atlantic reduced and most efforts were directed towards the larger basin has yet to be assessed. mysticetes (Prieto et al. 2012). As a result, the original Recent satellite tagging studies (Olsen et al. 2009; Pri- stock boundaries for sei whales in the North Atlantic have eto et al. 2014) have shed some light on possible sei whale remained unchanged, even though it is unclear whether migratory routes in the North Atlantic. Olsen et al. (2009) they reflect an underlying ‘biological’ population structure deployed a satellite radio transmitter on a sei whale off the (Donovan 1991). Azores, which was tracked to the Labrador Sea, revealing As is the case for other cosmopolitan mysticete species, that some sei whales traverse the entire North Atlantic dur- such as fin (Balaenoptera physalus) and humpback whales ing the spring migration. Prieto et al. (2014) later deployed (Megaptera novaeangliae), each major ocean basin (i.e. the satellite radio transmitters on seven sei whales off the Azores during their spring migration, which were all tracked to sum- mer foraging grounds in the Labrador Sea. Signals from two Information available at http://www.iucnr edlis t.org/detai ls/2475/0. transmitters were lost when the tagged whales were mov- Accessed 4 March, 2018. 2 ing toward the Iceland-Denmark Strait. The trajectory of Information available at https ://iwc.int/total -catch es. Accessed 4 March, 2018. these two tagged whales suggests that sei whales can move 1 3 Conservation Genetics (2018) 19:1007–1024 1009 Fig. 1 Map with sampling locations in the North Atlantic and the current IWC stock boundaries among different high-latitude summer foraging grounds, divergence time and migration rates of sei whales in two but whether different sei whale breeding populations also different ocean basins: the North Atlantic and North Pacific utilize the same foraging grounds, remains unknown. The Oceans. To this end, the genetic data on North Atlantic sei exact location of the low-latitude winter breeding grounds is whales from the present study were combined with previ- unknown, although there may be a located ground off north- ously published genetic data collected from North Pacific sei western Africa (Ingebrigtsen 1929; Prieto et al. 2012, 2014). whales (Kanda et al. 2006, 2009). Given the documented long seasonal migrations of sei whales in the North Atlantic (Olsen et al. 2009; Prieto et al. 2014) and wide summer ranges (see above), it is plausible that the genetic heterogeneity among North Atlantic sei Materials and methods whale summer grounds is low as in the case of the North Pacic fi sei whale (Kanda et al. 2006, 2009). Here we present Sample collection the results of the first assessment of the population genetic structure of sei whales in three different locations in the The genetic data from the North Atlantic were obtained North Atlantic, representing two of the three putative stocks; from tissue samples collected from sei whales caught during off Iceland, in the Gulf of Maine and in the Azores. Under special-permit whaling operations off Iceland (1986–1988; the three-stock hypothesis, we expected Iceland and the n = 43), and as skin biopsies obtained from free-ranging Azores to be genetically similar, and different from the Gulf sei whales using a crossbow and biopsy tips (Palsbøll et al. of Maine. However, Iceland and the Gulf of Maine most 1991) in the Gulf of Maine (1999, 2002–2004; n = 18) and likely represent summer foraging grounds and the Azores the Azores (2005, 2008–2010; n = 26) (Fig. 1). Biopsy col- a migratory corridor where whales from different winter lection was conducted under national permits and according breeding grounds potentially mix (Olsen et al. 2009; Pri- to national regulations. The laboratory methods described eto et al. 2014). We estimated the effective population size, below pertain to the North Atlantic samples. 1 3 1010 Conservation Genetics (2018) 19:1007–1024 Data from previous studies Sequencing the mtDNA control region Genetic data from the North Pacific (collected 2002–2007) The first 487 base pairs of the 3′ end of the mtDNA control were obtained from previously published studies (n = 489; region were amplified and the nucleotides sequenced. The Kanda et al. 2006, 2009; Tamura et al. 2009). A single fragment corresponds to positions 15,476–15,963 in the additional Antarctic sei whale mtDNA control region published sei whale mitochondrial genome (Árnason et al. sequence was obtained from GenBank™ (accession num- 1993; Sasaki et al. 2005). The PCR primers used for the ber NC_006929.1; Sasaki et al. 2005). amplification were MT4F (Árnason et al. 1993) and Mn312- R (Palsbøll et al. 1995), as well as BP16071R (Drouot et al. 2004). DNA extraction and sexing For the North Atlantic samples, PCR amplification was performed in a final volume at 15µL containing: 1 µM of Total-cell DNA was extracted using the Qiagen DNeasy™ each PCR primer, 1× Taq DNA polymerase buffer (Fer - Blood and Tissue Kit (Qiagen Inc.) according to the mentas Inc.), 3.2 mM dNTPs, 0.09 units Taq DNA poly- manufacturer’s instructions. The extracted DNA was merase (Fermentas Inc.), and 1 ng of extracted DNA. The re-suspended in 1× TE buffer (10 mM Tris–HCl, 1 mM PCR amplifications were conducted using an MJ Research EDTA, pH 8.0). Samples were sexed using the ZFY/ZFX PTC-100™ thermocycler (MJ Research Inc.) and occurred multiplexing system as described by Bérubé and Palsbøll in 25 reaction cycles, each consisting of a denaturing step (1996a, b). of 30 s at 94 °C, a 30 s annealing step at 54 °C and a 120 s extension step at 72 °C. These 25 cycles were preceded by a single 120 s denaturing step at 94 °C. Genotyping microsatellite loci Unincorporated ddNTPs and PCR primers were removed using the Shrimp Alkaline Phosphate/Exo-I pro- Eleven microsatellite loci were genotyped using the poly- tocol described by Werle et al. (1994). Cycle-sequencing merase chain reaction (PCR; Mullis and Faloona 1987). The of the PCR products obtained by the above described specific loci genotyped were: EV094 and EV037 (Valsecchi amplifications was performed using the BigDye Termina - and Amos 1996), GATA028, GATA053, GATA098 (Palsbøll tor™ ver. 3.1 Cycle Sequencing Kit (Applied Biosystems et al. 1997), GT011 (Bérubé et al. 1998), GT023 and GT211 Inc.) following the manufacturer’s instructions, in both (Bérubé et al. 2000) as well as AC082, CA232 and GT541 directions using the same primers as used for the initial (Bérubé et al. 2005). PCR amplification. The cycle-sequencing products were PCR amplifications of the above microsatellite loci were purified by ethanol/sodium acetate precipitation (Sam- performed in 10 µL reaction volumes containing 1× Taq brook and Russell 2001). The order of labeled sequencing buffer (Fermentas Inc.), 3.2 mM dNTPs, 0.4 units Taq DNA fragments was resolved by capillary electrophoresis on an polymerase (Fermentas Inc.) and 1 ng extracted DNA. The ABI 3730 DNA Genetic Analyzer™ (Applied Biosystems concentration of each PCR primer pair differed among Inc.). loci. The concentrations of the forward and reverse primers were: 0.25 µM for locus EV094, GATA028, GATA053 and GATA098, and 0.50 µM for locus AC082, CA232, EV037, Analysis of microsatellite genotypes GT011, GT023, GT211 and GT541. The PCR amplifica - tions were conducted using a MJ Research PTC-100™ (MJ Quality control and levels of polymorphism Research Inc.) in the case of locus GATA028 and GT023, a MJ Research Dyad™ thermocycler (MJ Research Inc.) Microsatellite alleles were visually checked and sized using for locus AC082, CA232, EV037, EV094, GATA053, GENEMAPPER™ (ver. 4.0, Applied Biosystems Inc.). All GATA098, GT011 and GT541, and a Stratagene Robocy- 87 samples were re-typed once at all 11 loci to estimate a cler™ (Stratagene Inc.) for locus GT211. PCR cycling pro- genotyping ‘inconsistency rate’ per genotype. We estimated files were as described in the original publications of each the number of alleles (A), the expected (H ), the observed het- locus. erozygosity (H ), and the probability of identity (I; Paetkau The experimental conditions employed for the data gener- et al. 1995). I was subsequently employed to detect duplicate ation of the North Pacific samples were described by Kanda samples from the same individuals. H and H were esti- E O et al. (2006, 2009). The microsatellite genotypes from the mated using ARLEQUIN (ver. 184.108.40.206, Excoffier and Lischer two datasets (the North Pacific and North Atlantic) were 2010) and I was estimated using GENALEX (ver. 6.5, Peakall calibrated by re-genotyping the above microsatellite loci in and Smouse 2006, 2012). The 95% confidence interval for 55 North Pacific samples. the mean H and H was estimated by bootstrapping over E O 1 3 Conservation Genetics (2018) 19:1007–1024 1011 loci (10,000 replicates) using the R package POPGENKIT Analysis of mtDNA control region sequences (Paquette 2012) in R (ver. 3.2.5, R Development Core Team 2016). Levels of polymorphism Controlling procedure for multiple comparisons The chromatograms of the mtDNA control region sequences were visually checked using CHROMAS™ (ver. 2.13, The false discovery rate correction developed by Benjamini Technelysium Inc.) and sequences were aligned using and Hochberg (FDR; Benjamini and Hochberg 1995) was CLUSTALW (ver. 1, Thompson et al. 1994) with default applied in all instances when multiple simultaneous tests were parameter settings as implemented in MEGA (ver. 6.06, conducted, using a critical alpha-value at 0.05. Tamura et al. 2013). DNASP (ver. 5.10, Librado and Rozas 2009) was employed to estimate the haplotype (H ) and Assessing deviations from Hardy–Weinberg expectations nucleotide diversity (π; Nei 1987). Coalescent simulations and linkage disequilibrium (Hudson 1990, implemented in DNASP) were employed to estimate the 95% confidence interval for both H and π from Deviations from the expected Hardy–Weinberg genotype 10,000 replicates. proportions and linkage disequilibrium were assessed using Fisher’s exact test (Fisher 1935) implemented in GENEPOP Estimation of mtDNA haplotype sequence genealogy (ver. 4.1.4, Raymond and Rousset 1995; Rousset 2008) using the default analysis parameters and a complete enumeration Nucleotide positions subject to alignment gaps were deleted whenever possible. from the entire dataset. The genetic distances among the haplotypes were estimated and visualized using MEGA (ver. Homogeneity tests and genetic divergence 6.06, Tamura et al. 2013). Genetic distances were estimated using Kimura’s 2-parameter model of nucleotide substitu- The degree of genetic differentiation was estimated as ϴ (Weir tion (Kimura 1980) using a transition–transversion ratio (R) and Cockerham 1984). The probability of ϴ being equal to or estimated from the data. R was estimated at 15 using the larger than the observed value of ϴ under the null hypothesis maximum-likelihood method in MEGA. The mtDNA gene- of a panmictic population was estimated from 10,000 permuta- alogy was estimated using the maximum-likelihood method tions (without replacement) as implemented in ARLEQUIN from the genetic distances estimated as described above. (ver. 220.127.116.11, Excoffier and Lischer 2010). The 95% confidence The consensus genealogy and support for each node was intervals of the observed estimates were obtained from 10,000 inferred from 10,000 bootstrap (over nucleotide positions) bootstrap replicates as implemented in the package DIVER- replicates (Felsenstein 1985). The genealogy was rooted SITY (Keenan et al. 2013) in R (ver. 3.2.5, R Development with the homologous mtDNA control region sequences Core Team 2016). from a North Atlantic fin whale, Balaenoptera physalus, (GenBank™ accession number NC_001321.1; Árnason Bayesian clustering et al. 1991) and a North Pacific Bryde’s whale, B. brydei (GenBank™ accession number NC_006928.1; Sasaki et al. The software STRUCTU RE (v er. 2.3.4, Pritchard et al. 2000; 2005). Furthermore, a neighbor-joining genealogy (Saitou Falush et al. 2007) was employed to assess possible cryptic and Nei 1987; Tamura et al. 2004) was estimated in MEGA population genetic structure. We followed the recommenda- using the same settings as for the maximum-likelihood gene- tion by Wang (2017). In each assessment, we employed the alogy and default settings for tree inference. Haplotype net- admixture and the ‘F’ model, the sample location as a prior, works of both genealogies (without the Antarctic haplotype and 100,000 burn-in Markov chains, followed by 200,000 and the two outgroups) were estimated using the software Markov chains. Fifteen replicates were conducted per value HAPLOTYPE VIEWER (Ewing 2010). of K, ranging from one to five. Lambda was inferred per ‘population’. The remaining estimation parameters were the Homogeneity tests and genetic divergence software default values. The output was summarized using the program CLUMPAK (Kopelman et al. 2015). The most The degree of differentiation was estimated as Φ ST probable value of K was determined from the posterior mean (Excoffier et al. 1992) using ARLEQUIN (ver. 18.104.22.168, likelihood values (Pritchard et al. 2000). Excoffier and Lischer 2010) applying Kimura’s 2-param- eter model (Kimura 1980). The probability of Φ being ST equal to or larger than the observed value of Φ under ST the null hypothesis of a panmictic population was esti- mated from 10,000 permutations (without replacement) 1 3 1012 Conservation Genetics (2018) 19:1007–1024 as implemented in ARLEQUIN. The 95% confidence Tests of mutation‑drift equilibrium and mismatch intervals of the observed estimates were obtained from distributions 10,000 bootstrap replicates as implemented in the pack- age DIVERSITY (Keenan et al. 2013) in R (ver. 3.2.5, R Estimates of Tajima’s D (Tajima 1989) and Fu and Li’s F* Development Core Team 2016). (Fu and Li 1993) and their statistical significance were com- puted using DNASP (ver. 5.10, Librado and Rozas 2009) to assess possible deviations from neutral evolution. Coalescent Estimation of effective population size, divergence time simulations (Hudson 1990; implemented in DNASP) were and migration rates employed to estimate 95% confidence intervals for D and F* from 10,000 replicates. Furthermore, frequency distributions Effective population size, population divergence time and of the observed pairwise nucleotide site differences (‘mis- migration rates were estimated employing the coalescent match distributions’) per sampling location were computed approach implemented in the software IMA2P (ver. 1.0, using DNASP and compared to the expected distribution Sethuraman and Hey 2016) which applies the Isolation for a population of exponentially expanding size (Watterson with Migration model to genetic data. Compared to other 1975; Slatkin and Hudson 1991; Rogers and Harpending demographic inference methods such as the methods 1992). The degree of statistical deviation from the expected implemented in the software BEAST (Drummond and distribution was evaluated with the χ test (Lindgren 1975). Rambaut 2007) or MIGRATE-N (Beerli and Felsenstein 2001), which allow the estimation of either effective popu- lation size and divergence time or effective population size Data access and migration rate, IMA2P allows the estimation of all three parameters (i.e., effective population size, divergence All North Atlantic mtDNA haplotypes have been time and migration rate). deposited in GenBank™ under accession numbers We applied the HKY model of sequence evolution MH035689–MH035695. Interested readers are encouraged (Hasegawa et al. 1985) and an annual, per-locus muta- to contact the corresponding author(s) for microsatellite −5 tion rate at 2.58 × 10 (based upon a per-site mutation genotypes and access to raw data. −8 rate at 5.30 × 10 from Alter and Palumbi 2009) and a −6 −5 prior range from 4.87 × 10 to 4.87 × 10 . The genera- tion time was 26.19 years; i.e. the average of 29.08 years (Pacifici et al. 2013) and 23.30 years (Taylor et al. 2007). Results The inheritance scalar was set at 0.25. The priors were defined from the posterior distribution Data analysis of microsatellite genotypes from preliminary estimations (see Table S1 and Fig. S1) varying priors of θ (4N µ, where N denotes the effective Duplicate samples and missing data e e population size and µ the generational mutation rate), m −9 (Nm/µ, where Nm denotes the number of migrants per gen- The probability of identity (I) was estimated at 5.0 × 10 for eration) and divergence time (t = T µ, where T denotes the North Atlantic samples (a total of 11 loci, Tables 1 and div div −5 the time since splitting in generations) parameters. The 2) and at 1.1 × 10 for the North Pacific samples (a total of final prior parameter values were set at θ = 250, m = 1.5 7 loci, Table 1). Consequently, the expected number of pairs −7 and t = 10 for the upper bound and zero for the lower of unrelated individuals matching at all loci was at 4.2 × 10 −3 bound for all parameters. The final Markov Chain Monte in the North Atlantic and at 5.3 × 10 in the North Pacific. Carlo (MCMC) sampling comprised 1.0 × 10 steps, with No matching pairs of multi-locus genotypes were observed samples drawn from the posterior every 100 steps and a among the North Pacific samples. A total of three pairs of preliminary burn-in at 1.0 × 10 steps. The Metropolis- matching multi-locus genotypes were detected among the Coupled Markov Chain Monte Carlo (MC ) was applied to North Atlantic samples; two sample pairs from the Gulf of improve the mixing. Stationarity was considered achieved Maine and one pair from the Azores. Also considering the when no perceivable trends were observed in the plot trend samples’ corresponding sex and mtDNA haplotype, these and an effective sample size (ESS) > 500 was obtained for were inferred as duplicate samples from the same individu- all values. In addition, six independent runs, i.e. with dif- als. Accordingly, only data from one sample of each identi- ferent random number seeds, were examined for consist- cal pair were retained in the final dataset. ency in the final parameter estimates. The final parameter The calibration with the North Atlantic dataset (i.e. size- estimates of N , t and 2mN were the average value of the calling of North Pacific alleles after amplification with North e e six replicates (Table S2). Atlantic primers) failed for four North Pacific samples, 1 3 Conservation Genetics (2018) 19:1007–1024 1013 Table 1 Microsatellite diversity Sample EV094 GATA028 GATA053 GATA098 GT011 GT023 GT211 All loci indices in North Pacific and North Atlantic samples Total (n = 569) A 12 11 3 9 4 13 7 59 H 0.69 0.76 0.38 0.71 0.44 0.62 0.28 0.55 (0.43–0.68) H 0.75 0.81 0.43 0.77 0.49 0.70 0.29 0.61 (0.47–0.73) −6 I 0.097 0.062 0.37 0.086 0.31 0.12 0.52 3.6 × 10 North Pacific (n = 485) A 6 11 3 7 4 12 6 49 H 0.69 0.77 0.45 0.72 0.44 0.57 0.30 0.56 (0.44–0.68) H 0.68 0.81 0.48 0.73 0.44 0.61 0.31 0.58 (0.46–0.70) −5 I 0.14 0.061 0.32 0.12 0.35 0.20 0.50 1.1 × 10 North Atlantic (n = 84) A 11 7 1 6 2 7 4 38 H 0.69 0.74 – 0.64 0.48 0.88 0.17 0.60 (0.28–0.72) H 0.75 0.75 – 0.66 0.47 0.76 0.19 0.60 (0.28–0.70) −5 I 0.10 0.10 1.0 0.15 0.40 0.10 0.67 4.5 × 10 Gulf of Maine (n = 16) A 6 6 1 5 2 6 3 29 H 0.81 0.94 – 0.56 0.44 0.81 0.19 0.63 (0.29–0.76) H 0.71 0.79 – 0.77 0.50 0.79 0.18 0.63 (0.29–0.71) −5 I 0.14 0.088 1.0 0.094 0.38 0.089 0.69 2.7 × 10 Iceland (n = 43) A 9 6 1 5 2 6 4 33 H 0.72 0.70 – 0.70 0.46 0.95 0.16 0.62 (0.28–0.74) H 0.76 0.72 – 0.63 0.48 0.76 0.21 0.59 (0.29–0.69) −5 I 0.10 0.13 1.0 0.18 0.39 0.11 0.64 6.5 × 10 Azores (n = 25) A 8 7 1 6 2 4 3 31 H 0.56 0.70 – 0.58 0.52 0.80 0.16 0.55 (0.27–0.66) H 0.73 0.77 – 0.60 0.43 0.75 0.15 0.57 (0.27–0.67) −5 I 0.11 0.096 1.0 0.21 0.43 0.12 0.73 8.2 × 10 n sample size after removal of duplicates and failed genotypes, A number of different alleles, H observed heterozygosity, H expected heterozygosity, I probability of identity I was used to detect duplicate samples in our dataset, thus the original sample sizes were used for estima- tion of the values shown for I. Parenthesis denotes the 95% confidence interval which were thus discarded. No ambiguous genotypes were (GATA053) to 13 (GT023). The mean number of alleles found after re-typing the North Atlantic samples for all loci, was 8.4. Private alleles were detected in both ocean basins, −4 yielding an inconsistency rate of < 5.2 × 10 per genotype. as well as in each of the three North Atlantic sampling loca- The final microsatellite dataset was comprised of 569 tions. When considering all 59 different alleles observed in unique multi-locus genotypes; n = 485 for the North Pacific, the total dataset for 7 loci, 21 (35.6%) of these were private n = 43 for Iceland, n = 16 for the Gulf of Maine and n = 25 to the North Pacific and 10 (16.9%) were private to the North for the Azores. In total, eight genotypes were missing from Atlantic. When considering all 68 different alleles observed the final dataset (i.e. 0.2%). Of the four additional microsat- in the North Atlantic dataset for 11 loci, 1 allele (1.5%) was ellite loci genotyped only in the North Atlantic samples, 6% private to the Gulf of Maine, 5 alleles (7.4%) were private to of genotypes were missing. Iceland and 2 (2.9%) were private to the Azores. Mean H for all seven microsatellite loci was similar Diversity estimates in each ocean basin (Table 1); H was estimated at 0.60 in the North Atlantic (ranging from 0.19 to 0.76) and at Tables 1 and 2 list the diversity estimates observed for the 0.58 in the North Pacific (ranging from 0.31 to 0.81). The microsatellite loci. The number of alleles ranged from 3 mean H was also estimated at 0.60 in the North Atlantic 1 3 1014 Conservation Genetics (2018) 19:1007–1024 Table 2 Measures of diversity for 4 microsatellite loci analysed only Deviations from Hardy–Weinberg expectations and linkage in the North Atlantic samples disequilibrium Sampling area AC082 CA232 EV037 GT541 All loci (incl. the 7 in Table 1) In the total sample (i.e. the combined North Atlantic and North Pacific dataset), significant deviations from the North Atlantic (n = 84) Hardy–Weinberg genotype frequencies were detected at A 6 3 10 11 68 five (EV094, GATA053, GATA098, GT011 and GT023) H 0.62 0.56 0.82 0.87 0.65 (0.42–0.74) of the seven loci after FDR correction (p-values < 0.0036). H 0.68 0.47 0.86 0.84 0.64 (0.41–0.72) No significant deviations from the expected Hardy–Wein - −9 I 0.16 0.39 0.04 0.05 5.0 × 10 berg genotype frequencies were detected in either the North Gulf of Maine (n = 16) Atlantic or the North Pacific datasets after applying the FDR A 6 2 8 7 52 correction. Several instances of statistically significant link - H 0.69 0.69 0.80 0.88 0.68 (0.44–0.77) age disequilibrium were detected among the seven loci after H 0.73 0.50 0.84 0.81 0.66 (0.42–0.72) applying the FDR procedure (p-values < 0.0047) in the com- −9 I 0.13 0.38 0.06 0.08 7.9 × 10 bined North Atlantic and North Pacific dataset. In contrast, Iceland (n = 43) no statistically significant degree of linkage disequilibrium A 5 3 10 11 62 was detected among samples from each ocean basin after H 0.67 0.51 0.84 0.86 0.66 (0.42–0.75) applying FDR correction. H 0.67 0.49 0.85 0.85 0.64 (0.41–0.71) −9 I 0.18 0.37 0.05 0.05 9.4 × 10 Azores (n = 25) Homogeneity tests and genetic divergence A 5 2 9 9 56 H 0.48 0.56 0.82 0.88 0.61 (0.39–0.69) O Pairwise estimates of ϴ ranged from 0.003 (Iceland-Azores H 0.66 0.44 0.90 0.85 0.63 (0.39–0.71) E comparison) to 0.20 (North Atlantic–North Pacific com- −9 I 0.18 0.41 0.03 0.05 8.5 × 10 parison, Table 3). Homogeneity was rejected for all loci separately and combined (p-values < 0.0001) between the n sample size after removal of duplicates and failed genotypes, A North Atlantic and North Pacific Ocean basins. In contrast, number of different alleles, H observed heterozygosity, H expected O E heterozygosity, I probability of identity no significant deviations from homogeneity were detected I was used to detect duplicate samples in our dataset, thus the origi- within the North Atlantic Ocean. nal sample sizes were used for estimation of the values shown for I. Parenthesis denotes the 95% confidence interval Bayesian clustering (ranging from 0.17 to 0.88) and at 0.56 in the North Pacific The most probable value of K in the combined dataset (ranging from 0.30 to 0.77). The estimates of the mean H (i.e. both North Pacific and North Atlantic) was esti- and H at each North Atlantic sampling location were in mated at two from the posterior mean likelihood values the same range as the estimates obtained from the pooled (P (K = 2|D) = ~ 1.0, Table S3). All samples from the same samples in both ocean basins. The mean H and H for all ocean basin were allocated to the same cluster (Fig. 2) E O 11 loci estimated from the North Atlantic samples were at admixture probabilities of 100%. K = 1 was the most also similar (Table 2). Table 3 Pairwise estimates of genetic divergence between sampling locations Between oceans North Pacific North Atlantic North Pacific – 0.72* (0.70–0.73) North Atlantic 0.20* (0.19–0.22) – In the North Atlantic Gulf of Maine Iceland Azores Gulf of Maine – 0.003 (0–0.14) 0 (0–0.08) Iceland 0.013 (0–0.050) – 0 (0–0.08) Azores 0.005 (0–0.047) 0.003 (0–0.03) – Estimates of divergence based upon microsatellite genotypes (i.e. Weir and Cockerham’s ϴ) in italics below the diagonal and upon mtDNA sequences (i.e. Φ ) above the diagonal. *p < 0.05. Parenthesis denotes the 95% confidence interval ST 1 3 Conservation Genetics (2018) 19:1007–1024 1015 Fig. 2 Structure plots for the total dataset (top) and for the North Atlantic dataset (bottom), showing estimated probabilities of assignment to each of two populations (K) for all individu- als’ microsatellite genotypes. Each column represents one individual’s genotype probable estimate for the combined North Atlantic dataset MtDNA genealogy (P (K = 1|D) = ~ 1.0, Table S3). The final alignment of sei, Bryde’s and fin whale mtDNA control region sequences yielded a consensus sequence Data analysis of the mtDNA control region of 491 nucleotides (including alignment gaps). The max- nucleotide sequences imum-likelihood genealogy (Fig. 4) estimated from the aligned sequences was comprised of two clades with sei Levels of polymorphism whale mtDNA sequences supported by a bootstrap value at 90%. One clade contained six mtDNA haplotypes detected The final dataset of mtDNA control region DNA sequences among the North Atlantic samples. The other clade con- was comprised of the first 487 nucleotides at the 3′ end tained all the mtDNA haplotypes detected in the North of the mtDNA control region in 572 samples (n = 488 for Pacific, the only Antarctic mtDNA haplotype as well as the North Pacific due to one failed mtDNA sequence and one North Atlantic mtDNA haplotype. The neighbour- n = 84 for the North Atlantic; each sample representing joining genealogy showed a similar topology and similar a unique multi-locus microsatellite genotype). In total, bootstrap values (Fig. S2). The haplotype networks (see 41 segregating sites which defined 65 different mtDNA Fig. 4 and Fig. S2) were similar with a sister position of sequence haplotypes were identified (Fig. 3), with none North Atlantic haplotype Hap_6. shared between ocean basins. Among the 41 segregating sites, three were segregating for three nucleotides, result- ing in a total of 44 observed substitutions; one inferred Homogeneity tests and estimates of genetic divergence insertion-deletion event, 38 transitions and five trans - versions. There were seven mtDNA haplotypes detected Homogeneity was rejected (Φ = 0.72, p < 0.001) between ST among the North Atlantic samples and 58 among the North the North Atlantic and North Pacific Oceans (Table 3). Pacific samples. The mean haplotype and nucleotide diver - However, no significant deviations from homogeneity sity for each sampling location separately and for all sam- were detected among the three North Atlantic sampling ples together are listed in Table 4. locations. 1 3 1016 Conservation Genetics (2018) 19:1007–1024 Fig. 3 Frequency of mtDNA control region haplotypes per sampling location Table 4 Measures of diversity Sampling location H π Tajima’s D Fu and Li’s F* and neutrality estimated from the mtDNA control region North Pacific 0.79 (0.55–0.90) 3.8 (1.0–9.1) − 0.67 − 0.35 sequences North Atlantic 0.52 (0.047–0.81) 1.1 (0.070–3.1) − 1.7 − 3.3* Gulf of Maine 0.48 (0–0.82) 0.95 (0–2.9) − 0.68 − 0.74 Iceland 0.48 (0–0.79) 0.91 (0–2.7) − 0.52 − 0.05 Azores 0.61 (0.15–0.86) 1.6 (0.15–4.3) − 1.9* − 2.8* H average haplotype diversity, π nucleotide diversity per locus *p < 0.05. Parenthesis denotes the 95% confidence interval Estimation of effective population sizes, divergence time into the North Pacific population (95% credible interval: and migration rates 0–1.47, Table 5). The parameter , which can be viewed as a proxy for long- Tests of neutrality and mismatch distributions term historic effective population sizes, was estimated at 6.2 (95% credible interval: 2.2–14) and 53 (95% credible inter- The observed estimates of Tajima’s D and Fu and Li’s F* for val: 39–73) for the North Atlantic and North Pacific samples, the separate and pooled sampling locations were all nega- respectively; a difference of almost one order of magnitude tive (Table 4), suggestive of population expansion. How- (Table 5). The divergence time between the North Atlan- ever, F* was only statistically significant for the Azores’ tic and North Pacific populations was estimated at ~ 163 sample (p < 0.05) and for the pooled North Atlantic sample thousand years ago (kya, 95% credible interval: 57–386 kya; (p < 0.02) and D was only significant for the Azores’ sample Table 5). The number of effective migrants 2mN from the (p < 0.05). The observed mismatch distributions (Fig. 5) cor- North Pacific population into the North Atlantic popula- responded to the expected frequency distributions of pair- tion was estimated at 0.248 (95% credible interval: 0–1.97, wise nucleotide site differences in an exponentially growing Table 5) and at 0.007 from the North Atlantic population population. 1 3 Conservation Genetics (2018) 19:1007–1024 1017 Fig. 4 Maximum-likelihood haplotype network (left) and genealogy (right) of mtDNA control region haplotypes. Each node in the haplotype network represents a haplotype and node sizes are proportional to haplotype frequencies. Each line segment between nodes repre- sents one nucleotide difference. For convenience, only the North Atlantic haplotype designations and the designations of the four most frequently occurring North Pacific haplotypes are shown. The haplotype tree is drawn to scale, with branch lengths in the evolutionary distance unit of number of base substitutions per site. Only bootstrap values above 60% are shown Table 5 Estimates of relative θ θ θ 2mN 2mN T (kya) NA NP A e (NP → NA) e (NA → NP) DIV effective population sizes, divergence times and migration 6.2 (2.2–14) 53 (39–73) 14 (0–220) 0.248 (0–1.97) 0.007 (0–1.47) 163 (57–387) rates NA North Atlantic, NP North Pacific, A ancestral population, θ 4N µ, N denotes the equivalent effective e e population size for a diploid autosomal locus, µ generational mutation rate per locus. 2mN number of effective migrants per generation, → denotes the direction of migration. T population divergence time DIV in kya Parenthesis denotes the 95% credible interval 1 3 1018 Conservation Genetics (2018) 19:1007–1024 Fig. 5 Frequency distributions of the observed pairwise nucleotide are given. None of the observed mismatch distributions deviated sig- site differences, or mismatch distributions, for the North Pacific, the nificantly from the expected distribution. The observed frequency North Atlantic and the separate North Atlantic sampling locations, distribution for the combined North Atlantic and North Pacific data- compared to expected frequency distributions for a population of set is also given, but no expected distribution can be shown because exponentially expanding size (red dotted line). For each distribution assumptions (i.e. panmictic population) for their estimation do not comparison, the χ value and number of degrees of freedom (d.f.) hold sequences, and this study augmented the conclusion with Discussion nuclear microsatellite genotypes. At an initial glance our results were consistent with the Differentiation within the North Atlantic Ocean notion of a single panmictic population of sei whales in at least the western and central North Atlantic (but see Low levels of genetic differentiation below) which appear to have undergone a historic pop- ulation expansion. Our results also supported the infer- We failed to detect any significant genetic heterogeneity ence drawn by Baker et al. (2004) that sei whales in the among the three distinct sampling locations in the North North Atlantic and North Pacific Ocean are genetically Atlantic (the Gulf of Maine, off Iceland and the Azores) distinct. The previous results were based solely on mtDNA at nuclear or mtDNA loci. These findings suggested an 1 3 Conservation Genetics (2018) 19:1007–1024 1019 absence of genetic population structure within the western equilibrium. In other words, the 95% confidence intervals of and central North Atlantic. Pairwise estimates of Φ and our divergence estimates included levels of divergence that ST ϴ at mtDNA and nuclear loci were low, most close to zero both support a single stock (i.e. F ~ 0) and multiple stocks ST (Table 3). The program STRU CTU RE also failed to iden- (i.e. 3–8 migrants per generation). Along the same vein, the tify significant levels of genetic structure within the North failure of STRUCTU RE to detect more than one cluster in Atlantic (and North Pacific). In other words, our analyses the North Atlantic does not negate the presence of multi- did not yield any results supporting the current designation ple stocks given the relatively low migration rates possible of two of the three sei whale management units in the North given the observed outcome. From a conservation point of Atlantic by the IWC (Fig. 1). Samples from the third, East- view, genetic differentiation alone might therefore not be a ern North Atlantic stock would provide for a more complete sufficient criterion to delineate useful management stocks. assessment, but according to the IUCN, sei whales seem to have been depleted in that area with no signs of recovery . Possible historic population expansion However, low levels of genetic differentiation do not necessarily imply a single stock of sei whales in the North The degree of population genetic divergence estimated as Atlantic but could have other causes (Palsboll et al. 2010). F does not necessarily ree fl ct contemporary gene flow (i.e. ST Firstly, our samples originated from two summer feeding migration) but is heavily influenced by population history. grounds; namely, the Gulf of Maine and Iceland, and from The negative values of the observed estimates of Tajima’s D a migratory corridor; the Azores. The sei whales utilizing and Fu and Li’s F* were indicative of a historic population these areas may have formed mixed assemblages of sei expansion, which makes sense given the geological history whales from different breeding populations and therefore do of the North Atlantic Ocean. The Gulf of Maine and the seas not show population structure. North Atlantic minke whales off Iceland were inaccessible to baleen whales during the last (Balaenoptera acutorostrata) present a similar problem of glacial maximum (LGM, 26.5–19 kya; Clark et al. 2009). cryptic population structure. For instance, where Daníels- The ice caps and summer sea ice extent have since retreated dottír et al. (1992) and Andersen et al. (2003) were able to making the current summer foraging areas, such as the Gulf detect some differentiation between minke whales from West of Maine and the waters off Iceland, accessible to sei whales. Greenland, the Central and Northeast Atlantic, Anderwald Our results suggested an expansion of the North Atlantic et al. (2011) were not. The optimal sampling scheme would sei whale population(s) after the LGM during the retreat include the identification and sampling of sei whale breed- of the summer sea ice as previously reported in case of the ing grounds as well as additional migratory corridors and North Atlantic fin whale (Balaenoptera physalus; Bérubé feeding areas. et al. 1998) and minke whale (B. acutorostrata; Pastene et al. Secondly, it is important to consider the uncertainty of the 2007; Anderwald et al. 2011). Albeit all values being nega- divergence estimates as well as the assumptions underlying tive, estimates of Fu and Li’s F* were only significant for equating divergence estimates with contemporary connectiv- the pooled North Atlantic sample and for the Azores, and ity. The upper bounds of 95% confidence intervals estimated Tajima’s D only for the Azores (Table 4). However, when for the point estimates of F among the North Atlantic the single individual from the Azores with haplotype Hap_6 ST sampling locations ranged from 0.08 to 0.14 and from 0.03 (see below) was excluded, all estimates of D and F* became to 0.05 for mtDNA and microsatellite data, respectively. statistically insignificant. The observed frequency distribu- Applying Wright’s drift-migration equilibrium, the rela- tions of pairwise nucleotide site differences (Fig. 5) fitted the tion between F and Nm, i.e. F = 1/(4 Nm + 1), implies expected distribution for an exponentially growing popula- ST ST that these upper bounds would correspond to between 3 tion. Thus, we found a trend toward population expansion, and 8 migrants per generation (females for mtDNA). The but statistically insignificant, likely due to low statistical failure of the program STRU CTU RE to detect more than power from low sample sizes, and limited sequence varia- a single cluster among the North Atlantic samples should tion in the sei whale mtDNA control region. similarly be interpreted with caution. Several in silico Among the seven mtDNA haplotypes detected in the assessments (e.g. Latch et al. 2006; Waples and Gaggiotti North Atlantic, six differed from each other by a single sub- 2006) of the program have shown that STRUCTU RE f ails to stitution, suggesting a recent coalescence of these lineages detect more than one cluster when the degree of population consistent with the presumed recent population expansion. genetic divergence is below 0.05–0.025, which corresponds The seventh mtDNA haplotype (Hap_6) was detected in a to 5–10 migrants per generation assuming drift-migration single sample taken in the Azores. The haplotype differed from the remaining six North Atlantic mtDNA haplotypes by twelve substitutions and was placed as a sister group to 3 the North Pacific haplotypes in the genealogy estimated in Information available at http://www.iucnr edlis t.org/detai ls/2475/0. our study (Fig. 4). However, the bootstrap support for this Accessed 4 March, 2018. 1 3 1020 Conservation Genetics (2018) 19:1007–1024 haplotype’s branch was low leaving its position rather uncer- 2011; Pomilla et al. 2014), although none of these studies tain. Increased outgroup sampling and additional markers have demonstrated any discernible effects on change in pre- (i.e. the partial or complete mitogenome) may provide a whaling population genetic divergence. more strongly supported topology. Although anecdotal at this point, the seventh divergent mtDNA haplotype might Timing and level of gene flow between the North represent a recent immigrant maternal lineage, e.g. from the Atlantic and North Pacific Oceans South Atlantic, or represent a rare North Atlantic mtDNA lineage. More data and samples are required to discern We detected high and significant degrees of genetic diver - among these two possibilities. gence between the samples from the North Atlantic and Population expansion reduces the rate of genetic drift and North Pacific oceans (Table 3). Haplotype diversity was high hence the rate of population genetic divergence compared for the North Pacic fi and intermediate for the North Atlantic to constant-sized populations (e.g. Rogers and Harpending (Table 4). The estimates of nucleotide diversity were low 1992; Kimmel et al. 1998; Waxman 2012). This effect would but within the range reported for other rorquals (e.g. Bérubé be even stronger if the ‘new’ populations were founded from et al. 1998; Anderwald et al. 2011). The global haplotype the same historical population (e.g. Avise et al. 1988). In genealogy revealed a clear separation of the North Atlantic other words, a recent population history and expansion of sei and North Pacific haplotypes (Fig. 4). The single Antarc- whales in the North Atlantic may have contributed to the low tic mtDNA haplotype included in our analysis clustered levels of spatial population genetic divergence observed in together with the North Pacific mtDNA haplotypes, which our study. Basic population genetic theory relates the degree was consistent with previous findings by Baker et al. (2004). of genetic divergence among populations to the number of The inter-oceanic migration rate estimates pointed to migrations, more precisely the product of the effective pop- migration rates of only ~ 1 migrant per four generations or ulation size (N ) and the probability that an individual is less (Table 5). Divergence time estimates suggested that an immigrant (m; Wright 1951; Slatkin and Barton 1989). the North Atlantic and North Pacific sei whale populations In other words, the number of immigrants per generation separated ~ 163 kya during the penultimate Pleistocene gla- (i.e. 2mN ) determines the degree of genetic divergence ciation; the Illinoian glaciation (140–350 kya; Lisiecki and among populations, meaning that populations with large Raymo 2005a, b). This is known to be one of the coldest N ’s will diverge at a slower rate compared to populations glacial periods over the last million years (Colleoni et al. with smaller N ’s. Consequently, expanding populations 2016). The extent of sea ice during colder conditions might will diverge at decreasing rates compared to similar-sized have facilitated the population divergence between the North non-expanding populations, all other factors being equal. Atlantic and North Pacific sei whales, as has been suggested One example is depicted in Fig. S3, which illustrates the for other species and populations during the Pleistocene gla- pronounced difference in estimates of F in constant and ciations (e.g. Hewitt 2000, 2004). ST expanding populations in the time following a population The estimates of , a proxy for effective population size, divergence. indicated that the median effective population size of the North Pacific sei whale population was much larger (approx- Eec ff ts of whaling on population structure imately nine times) compared to the North Atlantic popula- tion (Table 5). This was also reflected in the differences in It is possible that whaling of sei whales may have influenced haplotype and nucleotide diversities between the two oceans the contemporary population genetic structure among North (Table 4). Looking at heterozygosity alone (Tables 1, 2) Atlantic sei whales. However, the possible effects could we saw no indication of a genetic bottleneck in the North either increase or decrease post-whaling population genetic Atlantic preceding the presumed population expansion after structure (Baker and Clapham 2004). For instance, differen - the LGM, which could have explained the differences in tial rates of post-whaling recovery among populations could genetic diversity between the two oceans. However, provid- lead to source-sink dynamics and hence reduce pre-whaling ing detailed insight into the demographic history of both population genetic divergence. In contrast, severe reductions populations is reserved to future studies. of abundance in some populations might result in reduced Although the estimates of can be converted into esti- levels of gene flow among populations and elevated rates of mates of effective female population sizes, we refrained genetic drift which increase pre-whaling divergence. Among from doing so given that the interpretation of such an the baleen whales there are examples of both rapid post- estimate is far from straightforward (as reviewed by Pals- whaling recolonization, i.e. source-sink dynamics (e.g. Best bøll et al. 2013). Similarly, the inferred population diver- 1993; Clapham et al. 1999; George et al. 2004; Rugh et al. gence time should not be taken too literally. Direct gene 2005), as well as slow or absent post-whaling recovery, i.e. flow between the North Atlantic and North Pacific after increased isolation (e.g. Clapham et al. 1999; Wade et al. the rise of the Panama Isthmus (~ 3.5 million years ago; 1 3 Conservation Genetics (2018) 19:1007–1024 1021 Maine were conducted under U.S. National Oceanic and Atmospheric e.g. Coates et al. 1992) has only been possible through Administration research permits 633–1483 and 633–1778 and licenses the Northwest Passage during a few brief periods with obtained from the Canadian Department of Fisheries and Oceans. The elevated temperatures. Our divergence time estimate was Icelandic samples were archived from sei whales taken in 1986–1988 likely heavily influenced by past periods of gene flow as a part of a special permit issued by the government of Iceland in compliance with the rules of the International Whaling Commission. between the hemispheres, as well as the mtDNA muta- The North Pacific samples were archived from sei whales taken in tion rate and generation time employed in our estimation 2000–2016 during the JARPNII program under a permit issued by the (Avise et al. 1988). The inclusion of samples from the Government of Japan. Southern Hemisphere would likely result in very different estimates. Open Access This article is distributed under the terms of the Crea- tive Commons Attribution 4.0 International License (http://creat iveco mmons.or g/licenses/b y/4.0/), which permits unrestricted use, distribu- tion, and reproduction in any medium, provided you give appropriate Concluding remarks credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. In conclusion, while our results did not seem to support the current division by the IWC of North Atlantic sei whales into three different stocks, the uncertainty in our References estimates was sufficiently high that we could not rule out Alter SE, Palumbi SR (2009) Comparing evolutionary patterns and the presence of multiple stocks either. The available sat- variability in the mitochondrial control region and cytochrome ellite tagging data suggests that sei whales travel across b in three species of baleen whales. J Mol Evol 68(1):97–111 wide latitudinal and longitudinal ranges, which might Andersen LW, Born EW, Dietz R, Haug T, Øien N, Bendixen C explain the low levels of genetic divergence estimated in (2003) Genetic population structure of minke whales Balae- noptera acutorostrata from Greenland, the North East Atlantic this study. In order to aid further efforts in the manage- and the North Sea probably reflects different ecological regions. ment and conservation of sei whales, we propose addi- Mar Ecol Prog Ser 247:263–280 tional sampling across the species’ entire range, including Anderwald P, Daníelsdottír AK, Haug T, Larsen F, Lesage V, breeding and feeding grounds and migratory corridors, as Reid RJ, Víkingsson GA, Hoelzel AR (2011) Possible cryp- tic stock structure for minke whales in the North Atlantic: well as increased sample sizes. The low levels of variation implications for conservation and management. Biol Conserv in the North Atlantic sei whale suggest that increasing the 144(10):2479–2489 number of loci may also enhance the precision of esti- Archer FI, Morin PA, Hancock-Hanser BL, Robertson KM, Les- mates of divergence and gene flow (e.g. single nucleotide lie MS, Bérubé M, Panigada S, Taylor BL (2013) Mitog- enomic phylogenetics of fin whales (Balaenoptera physalus polymorphism, or SNP, genotypes from genotyping-by- spp.): genetic evidence for revision of subspecies. PLoS ONE sequencing approaches). 8(5):e63396. https ://doi.org/10.1371/journ al.pone.00633 96 Árnason Ú, Gullberg A, Widegren B (1991) The complete nucleotide Acknowledgements We would like to thank Pauline Gauffier, Yvonne sequence of the mitochondrial DNA of the fin whale, Balaeno- Verkuil and Vania Rivera for assistance with the laboratory and data ptera physalus. J Mol Evol 33(6):556–568 analyses. We would also like to thank David Mattila and other field Árnason Ú, Gullberg A, Widegren B (1993) Cetacean mitochondrial personnel involved in the collection of the samples. The Center for DNA control region: sequences of all extant baleen whales and Information Technology of the University of Groningen is acknowl- two sperm whale species. Mol Biol Evol 10(5):960–970 edged for IT support and access to the Peregrine high performance- Avise JC, Ball RM, Arnold J (1988) Current versus historical popu- computing cluster. We thank the anonymous referees for their con- lation sizes in vertebrate species with high gene flow: a com- structive comments on a draft of this paper. This study was in part parison based on mitochondrial DNA lineages and inbreeding funded by: the University of Groningen; Fundação para a Ciência e theory for neutral mutations. 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