rsosroyalsocietypublishingorg

ResearchCite this article Hoffman JI Kowalski GJKlimova A Eberhart-Phillips LJ Staniland IJBaylis AMM 2016 Population structure andhistorical demography of South American sealions provide insights into the catastrophicdecline of a marine mammal populationR Soc open sci 3 160291httpdxdoiorg101098rsos160291

Received 29 April 2016Accepted 23 June 2016

Subject CategoryBiology (whole organism)

Subject Areasgeneticsecology

Keywordspopulation structure anthropogenicexploitation historical demographyphylogeography pinniped

Author for correspondenceJ I Hoffmane-mail j_i_hoffmanhotmailcom

Electronic supplementary material is availableat httpdxdoiorg101098rsos160291 or viahttprsosroyalsocietypublishingorg

Population structure andhistorical demography ofSouth American sea lionsprovide insights into thecatastrophic decline of amarine mammal populationJ I Hoffman1 G J Kowalski12 A Klimova3

L J Eberhart-Phillips1 I J Staniland4 and

A M M Baylis567

1Department of Animal Behaviour University of Bielefeld Postfach 10013133501 Bielefeld Germany2Animal Ecology Group Institute of Biochemistry and Biology University of PotsdamMaulbeerallee 1 14469 Potsdam Germany3Centro de Investigaciones Bioloacutegicas del Noroeste Baja California Sur La Paz Mexico4British Antarctic Survey Natural Environment Research Council High CrossMadingley Road Cambridge CB3 0ET UK5South Atlantic Environmental Research Institute Stanley FIQQ1ZZ Falkland Islands6Falklands Conservation Stanley FIQQ1ZZ Falkland Islands7Department of Biological Sciences Macquarie University Sydney New South Wales2109 Australia

LJE-P 0000-0001-7311-6088

Understanding the causes of population decline is crucial forconservation management We therefore used genetic analysisboth to provide baseline data on population structure andto evaluate hypotheses for the catastrophic decline of theSouth American sea lion (Otaria flavescens) at the FalklandIslands (Malvinas) in the South Atlantic We genotyped259 animals from 23 colonies across the Falklands at281 bp of the mitochondrial hypervariable region and 22microsatellites A weak signature of population structurewas detected genetic diversity was moderately high incomparison with other pinniped species and no evidencewas found for the decline being associated with a strongdemographic bottleneck By combining our mitochondrial datawith published sequences from Argentina Brazil Chile andPeru we also uncovered strong maternally directed populationstructure across the geographical range of the species

2016 The Authors Published by the Royal Society under the terms of the Creative CommonsAttribution License httpcreativecommonsorglicensesby40 which permits unrestricteduse provided the original author and source are credited

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In particular very few shared haplotypes were found between the Falklands and South America andthis was reflected in correspondingly low migration rate estimates These findings do not supportthe prominent hypothesis that the decline was caused by migration to Argentina where large-scalecommercial harvesting operations claimed over half a million animals Thus our study not onlyprovides baseline data for conservation management but also reveals the potential for genetic studiesto shed light upon long-standing questions pertaining to the history and fate of natural populations

1 IntroductionConservation genetics can contribute towards the management of threatened wildlife species innumerous ways [12] For example an important goal of many studies is to elucidate the pattern andstrength of population structure as this can provide insights into factors that affect gene flow as wellas inform conservation practitioners as to where best to invest resources in order to safeguard geneticdiversity [3] A related goal is to quantify the extent to which a focal population is connected to othersby gene flow as this may have implications for the retention of genetic diversity and the movementof beneficial alleles between populations [4] Many conservationists are also concerned about levelsof genetic diversity which are often taken as a proxy for the potential of a population to adapt toenvironmental change [56] However to fully understand contemporary levels of genetic diversityrequires an appreciation of the historical demographic processes that shaped this diversity

The Pinnipedia a group of 33 extant species of marine mammal comprising the true seals eared sealsand the walrus have been subjected to numerous genetic studies This is partly because they provideinteresting case studies for understanding the effects of historical exploitation on contemporary levels ofgenetic diversity and population persistence In particular many populations of eared seal were reducedto low enough densities to be considered extinct [7] yet some are recovering to pre-exploitation numbersand have retained high levels of genetic diversity [8ndash12] whereas others have failed to stage a recovery

The South American sea lion (Otaria flavescens) can be found in breeding colonies along the coasts andoffshore islands of South America from Peru to Uruguay as well as in the Falkland Islands (Malvinas)in the South Atlantic At the Falklands this species experienced a major decline with pup productionfalling from over 80 000 in the 1930s [13] to less than 6000 in 1965 [14] Sea lion numbers continued todecline into the 1990s but have since recovered a little over the past two decades although a range-widesurvey in 2014 reported a pup production of 4500 which is lower than in 1965 and only around 6 ofthe 1930s estimate [15]

A number of hypotheses have been proposed to explain the initial decline and subsequent lack ofrecovery of the South American sea lion population at the Falkland Islands One possibility is thatthe population crash was caused by commercial sealing at the Falklands although historical recordssuggest that around 60 000 animals were killed between 1928 and 1966 which is nowhere near enoughto account for the population decline [1516] However over half a million sea lions were killed over thesame period in Argentina [17] and various modelling exercises have reached different conclusions asto whether the Argentinian population could have sustained the reported level of exploitation withoutreceiving migrants from other localities [1516] This led Thompson et al [16] to propose that the declineof the Falklands population could be explained by combined sealing operations in the Falkland Islandsand Argentina

Several authors have also questioned why the population failed to recover to its original numbersdespite the cessation of commercial sealing in 1966 Various factors could potentially have played arole including predation disease and competition with commercial fisheries [1518] but data are largelylacking with which to evaluate their potential importance Another possibility supported by a recentanalysis of sea surface temperature changes over the past two centuries is that environmental changecould have impacted population growth by altering patterns of food availability [15] Yet anotherpossibility is that the species could have experienced a genetic bottleneck leading to the loss of geneticdiversity and adaptive potential Although it seems unlikely that anthropogenic exploitation would havebeen severe enough to result in an appreciable loss of diversity the census estimates are imperfect and itis also plausible that a bottleneck could have occurred prior to human habitation of the Falklands

South American sea lions have been the focus of a number of previous genetic studies which haveprovided valuable insights into population structure and levels of genetic diversity [19ndash23] Howeverthese studies have tended to focus on specific areas within South America such as Patagonia andUruguay leading Artico et al [19] to call for a range-wide genetic survey Furthermore only one study

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included the Falkland Islands [20] and the sample sizes involved were arguably too small (14 samplesfrom the west and 5 samples from the east) either to be informative about population structure within theFalklands or to allow firm conclusions to be reached about connectivity to the South American mainland

Here we generated sequence data for 259 animals from 23 colonies across the Falklands at 281 bp ofthe mitochondrial hypervariable region and genotyped 22 microsatellites in order to elucidate baselinepatterns of population genetic structure and to explore the historical demography of the populationBy combining our mitochondrial data with previously published sequences from the South Americanmainland we also documented range-wide patterns of maternally directed population structure lookedfor evidence of shared haplotypes between the Falkland Islands and South America and estimatedmigration rates in order to test support for the hypothesis that the population decline was caused bythe hunting of animals that migrated to Argentina

2 Material and methods21 Tissue sample collection and DNA extractionSkin biopsy samples were collected from 277 live pups at 23 breeding colonies across the Falkland Islands(figure 1 electronic supplementary material table S1) Pups were captured by hand and skin sampleswere taken adjacent to the distal phalange of the right hind flipper using a livestock ear notcher Thesamples were stored individually in the preservative buffer 20 dimethyl sulfoxide (DMSO) saturatedwith salt and kept at ndash20degC Total genomic DNA was extracted using a modified phenolndashchloroformprotocol [24]

22 Mitochondrial control region sequencingA 365 bp region of the mitochondrial control region was PCR amplified using ThrPro (5prime-TCCCTAAGACTCAAGGAAGAG-3prime) and Cent (5prime-GAGCGAGAAGAGGTACACTTT-3prime) followingWynen et al [25] Each PCR was carried out in a 10 microl reaction volume containing 10 ng of templateDNA 025 microl Cent 025 microl ThrPro 01 microl Taq polymerase 025 microl dNTPs 715 microl of water and 1 microlof PCR buffer The following PCR profile was used one cycle of 15 min at 94degC 30 cycles of 30 s at94degC 60 s at 60degC 60 s at 72degC and a final cycle of 7 min at 72degC Afterwards 5 microl of PCR productwas purified using Antarctic phosphatase and exonuclease I (New England Biolabs) following themanufacturerrsquos recommended protocol Samples were then sequenced using the Applied BiosystemsBigDyereg TERMINATOR v 31 Cycle Sequencing Kit and analysed on an ABI 3730xl capillary sequencerAll fragments were sequenced in both directions and consensus sequences were generated usingCHROMASPRO v 176 (Technelysium Australia) The sequences were then aligned manually withinBIOEDIT v 71 [26] Sites containing insertionsdeletions or missing data were removed including ahighly variable region enriched for GA repeats that was previously described by Wynen et al [25] All ofthe sequences were then trimmed to the length of the shortest sequence yielding 281 bp of contiguoussequence All nucleotide positions differing from the consensus sequence were inspected to verify basecalls were of high quality

23 Microsatellite genotypingAfter testing 48 microsatellite loci for PCR amplification in eight sea lion individuals we selected 22 lociwith polymorphic and unambiguous banding patterns for genotyping the full sample set (see table 1 fordetails) These were PCR amplified in five separate multiplexed reactions using a Type It Kit (Qiagen) Weused a touchdown PCR profile as follows one cycle of 5 min at 94degC seven cycles of 30 s at 94degC 45 s at60ndash55degC (t = minus1degC) and 60 s at 68degC 30 cycles of 30 s at 94degC 45 s at 55degC and 60 s at 68degC and one finalcycle of 30 min at 60degC Fluorescently labelled PCR products were then resolved by electrophoresis onan ABI 3730xl capillary sequencer and allele sizes were scored using GeneMarker v 195 To ensure highgenotype quality all traces were manually inspected and any incorrect calls were adjusted accordingly

231 Calculation of the genotyping error rate

In order to estimate the rate of microsatellite genotyping error we independently repeat genotyped 20samples The resulting estimate was very low at 043 per locus or 022 per allele (four alleles incorrectout of 1856 comparisons)

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0 500 1000 2000 km km

sampled individuals1ndash56ndash1011ndash2021ndash5051ndash150

10050250

Figure 1 Map of the Falkland Islands showing the locations of South American sea lion breeding colonies from which genetic sampleswere obtained (see table 1 for details) Also shown is a map of South America annotated with the sampling locations corresponding to182 previously published mitochondrial control region sequences Circle sizes are proportional to sample sizes in both panels

24 Generation of summary statisticsThe number of mitochondrial haplotypes the number of polymorphic sites haplotype diversity (h) andnucleotide diversity (π ) were calculated using DNASP v 51 [36] Haplotype frequencies were calculatedusing ARLEQUIN v 20 [37] GENEPOP ON THE WEB [38] was used to test each microsatellite locus fordeviations from HardyndashWeinberg equilibrium We set the dememorization number to 10 000 the numberof batches to 1000 and the number of iterations per batch to 10 000 The resulting p-values were correctedtable-wide for the false discovery rate (FDR) [39] using the program Q-VALUE [40] To calculate numberof alleles the effective number of alleles observed and expected heterozygosities and to test for linkagedisequilibrium we used FSTAT v 2932 [41] and GENEDIVE v 20b23 [42] Finally we used FREENA [43]to estimate null allele frequencies for each locus following the expectation maximization algorithm [44]

25 Population structurePopulation structure within the Falkland Islands was assessed using hierarchical analyses of molecularvariance (AMOVA) conducted within ARLEQUIN These analyses were carried out separately for themitochondrial and microsatellite data at two different hierarchical levels (i) comparing West and EastFalkland and (ii) among the 23 breeding colonies For the mitochondrial data we used the measuresFst [45] and Φst [46] the former quantifying haplotype frequency differences [47] while the latterincorporates haplotype sequence similarity For the microsatellite data we used Fst [45] and Rst [48]the latter being a microsatellite-specific measure that takes account of the stepwise mutation process

In order to test for population structure without knowledge of the sampling locations of individualswe also conducted a Bayesian cluster analysis of the microsatellite dataset using STRUCTURE v 233 [49]This program uses a maximum-likelihood approach to cluster the genotypes into K populations We ranfive independent runs for K = 1 to 10 using 1 000 000 Markov chain Monte Carlo (MCMC) iterations aftera burn-in of 100 000 the correlated allele frequencies model and assuming admixture The most likelynumber of clusters was evaluated using the maximal average value of Ln P(D) a model-choice criterionthat estimates the posterior probability of the data As we have previously found that STRUCTURE resultscan be sensitive to the inclusion of individuals with substantial amounts of missing data we restrictedthis analysis to 233 individuals genotyped at 20 or more loci

26 Mitochondrial mismatch distributionThe distribution of the observed number of differences between each pair of haplotypes (the lsquomismatchdistributionrsquo) was calculated within ARLEQUIN This resembles a unimodal wave in samples drawn fromrecently expanded populations whereas samples from static or bottlenecked populations tend to exhibit

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Table 1 Details of the 22 microsatellite loci employed in this study and their polymorphism characteristics in 259 Falkland Island sealions Na number of alleles Ae effective number of alleles HE expected heterozygosity HO observed heterozygosity Individuallysignificant HardyndashWeinberg equilibrium (HWE) p-values at α lt 005 are highlighted in bold None of these remained significantfollowing table-wide FDR correction for multiple statistical tests

locus Mastermix reference Na Ae HE HO null allele frequency HWE p-value

Ag-7 3 [27] 6 240 059 056 000018 0046

Ag-1 3 [27] 6 312 069 064 002267 0196

Agaz-10 4 [28] 8 435 077 078 000003 0411

Agaz-1 4 [28] 14 619 084 083 000669 0233

Hg13 1 [29] 8 491 080 077 000827 0547

Hg63 1 [30] 9 370 073 071 001773 0178

OrrFCB8 4 [31] 6 375 073 071 002239 0049

Hg810 1 [30] 21 914 089 086 001203 0276

MHC26 5 [32] 8 592 083 083 000002 0705

MHC4b 5 [32] 5 247 059 054 002925 0288

ZcwE05 3 [33] 9 419 076 083 000000 0447

OrrFCB7 2 [31] 7 316 068 062 004366 0003

Pv9 1 [30] 8 41 076 076 000002 0511

MHC11 4 [32] 4 199 050 049 002601 0005

ZcCgDh18 4 [34] 4 171 041 041 000003 0656

ZcwF07 4 [33] 10 390 074 076 000001 0371

ZcCgDh47 3 [34] 11 395 075 075 000001 0994

Agaz-6 2 [28] 4 163 039 039 000634 0132

Ag-2 2 [27] 3 186 046 052 000001 0075

OrrFCB2 2 [31] 11 217 054 056 001428 0032

ZcCgDhB14 2 [34] 5 241 059 059 000001 0132

Ssl301 3 [35] 11 564 082 085 000009 0055

overall 81 38 068 067 mdash mdash

multimodal distributions [5051] ARLEQUIN was also used to test for deviation of the observed datasetfrom a model of rapid population expansion assuming the same mean number of pairwise differencesas the observed sample [51] We also used DNASP to test for deviations from neutrality with Tajimarsquos D[52] and Fursquos Fs [53] Significant negative values of these statistics indicate an excess of low frequencypolymorphisms a pattern commonly attributed to recent population expansion

27 Bottleneck testsIn order to investigate whether the Falkland Islands population underwent a genetic bottleneck themicrosatellite dataset was tested for heterozygosity excess [5455] using BOTTLENECK v 1202 [56] Onecriticism of this approach is that it can be sensitive to the mutational model assumed [57] Althoughmicrosatellites mainly evolve according to the stepwise mutation model (SMM) in which a single repeatunit is gained or lost [58] multi-step mutations also occur [5960] We therefore specified a range ofmutation models from the strict SMM through two-phase models (TPMs) with varying proportionsof multi-step mutations to the infinite alleles model (IAM) where every new mutation is novel Forour analysis four TPM models were evaluated with 1 5 10 and 30 multi-step mutationsrespectively and a default variance of 30 For each of the mutational models the heterozygosity ofeach locus expected under mutation-drift equilibrium given the observed number of alleles (Heq) wasdetermined using 10 000 simulations and then compared against observed heterozygosity (He) We

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then recorded the number of loci for which He was greater than Heq and determined whether theoverall set of deviations was statistically significant using standardized differences and Wilcoxonrsquossigned rank tests Bottlenecked populations are also expected to exhibit a characteristic lsquomode shiftrsquoin the frequency distribution of alleles away from the L-shaped distribution expected under mutation-drift equilibrium [61] Consequently BOTTLENECK was also used to generate a qualitative descriptor ofwhether the observed allele frequencies at each locus deviate from such a distribution

We also calculated Garza and Williamsonrsquos M-ratio for the microsatellite dataset using the programM_P_VAL [62] The significance of the resulting value was determined by comparison against Mca critical value below which bottlenecks are inferred using the program CRITICAL_M [62] Thisprogram allows the user to modify three parameters that approximate the mutation process in naturalpopulations the proportion of mutations that are larger than a single step (pg) the average size of non-single-step mutations (g) and θ = 4Nemicro (where Ne is the effective pre-bottleneck population size atequilibrium and micro is the mutation rate) We used the default settings of pg = 02 and g = 35 [62] andvaried θ between 1 and 1000 the latter corresponding to an effective pre-bottleneck population size of500 000 assuming a commonly used estimate of the dinucleotide microsatellite mutation rate of 5 times 10minus4

mutants per gamete per generation [63] as suggested by Garza amp Williamson [62]

28 Approximate Bayesian computationTo further investigate the demographic history of the Falklands population we used approximateBayesian computation [6465] as implemented in DIYABC v 210 [6667] We tested support for fourdifferent demographic models describing different patterns of effective population size change over timeThe first scenario that we evaluated (i) represented the null hypothesis of constant effective populationsize The alternative scenarios invoked (ii) population expansion (iii) population reduction and (iv) abottleneck Priors for the timing of events and the magnitude of changes of Ne were loosely basedon knowledge of the demographic history of the species For details of the models and priors usedplease see electronic supplementary material table S2 The microsatellite mutation rate was set between5 times 10minus4 and 5 times 10minus3 substitutions per generation The mitochondrial DNA mutation rate was boundedbetween 812 times 10minus7 and 38 times 10minus6 substitutions per site per generation [968] We used four summarystatistics for microsatellites (mean number of alleles mean genic diversity mean allele size varianceand mean Garza and Williamsonrsquos M) and five summary statistics for the mitochondrial control region(number of haplotypes number of segregating sites Tajimarsquos D the number of private segregating sitesand the mean number of the rarest nucleotide at the segregating site) These statistics were chosen onthe basis of their sensitivity to demographic change For each scenario we simulated 1 times 106 datasetsseparately for the microsatellite data the mitochondrial data and the combined microsatellite andmitochondrial data After that we used a polychotomous-weighted logistic regression on the 4 times 104

simulated datasets closest to the observed dataset to determine the posterior probability for eachscenario In order to evaluate confidence in each scenario we also calculated the posterior predictiveerror

29 Genetic differentiation between the Falkland Islands and South AmericaIn order to facilitate comparisons between the Falkland Islands and the South American mainlandwe collated mitochondrial control region sequence data from published studies for which haplotypefrequency data were available Representative data were obtained for Argentina Brazil Chile and Peru(figure 1 electronic supplementary material table S1) The sequences were downloaded from GenBankand then aligned to the Falkland Islands dataset within BIOEDIT All the sequences were then adjustedto the length of the shortest sequence (254 bp) To provide a broad overview of the relationships amongthe mitochondrial haplotypes we then constructed a median joining network using NETWORK v 4516[69] This program calculates all possible minimum spanning trees for the dataset and then combinesthese into a single minimum spanning network using an algorithm analogous to that proposed byExcoffier amp Smouse [70] Inferred intermediate haplotypes are then added to the network in order tominimize its overall length Finally we used the mitochondrial data to conduct a formal assessment of thestrength of population structure among the Falkland Islands Argentina Brazil Peru and Chile For theFalkland Islands individuals from all 23 colonies were included and treated as one population PairwiseFst and Φst values were calculated and their significance determined using 10 000 permutations ofthe dataset

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210 Estimation of migration ratesFinally we used MIGRATE v 364 [7172] to estimate migration rates and directions based on themitochondrial data MIGRATE uses Bayesian inference to estimate the posterior probability densities ofmigration rates and effective population sizes For this analysis we treated samples from ArgentinaBrazil and Chile as distinct populations but excluded Peru due to the fact that only five sequences wereavailable After several exploratory runs the final simulation was performed with one long chain and100 replicates where 125 000 000 steps were sampled 50 000 were discarded as lsquoburn-inrsquo and 50 000 stepswere recorded Prior values were bound between 00001 and 20 for Θ and between 00001 and 500 for M(mutation scaled migration rate) We used the recommended heating scheme (100 150 300 1 000 000)and estimated the number of migrants per generation by multiplying the estimated mutation-scaledmigration rate by the Θ value

3 ResultsOut of a total of 277 samples collected we successfully sequenced a 281 bp region of the mitochondrialcontrol region and genotyped 22 microsatellites in 259 sea lions from 23 different breeding coloniesaround the Falkland Islands (figure 1 electronic supplementary material table S1) The mitochondrialcontrol region contained 25 variable sites all of which were parsimony informative and a total of 22haplotypes were identified with nucleotide diversity being 0015 and haplotype diversity being 0864The microsatellite loci carried on average 82 alleles none deviated significantly from HardyndashWeinbergequilibrium after correction for multiple testing and there was no evidence for the presence of null alleles(table 1)

31 Population structure within the Falkland IslandsAMOVA was used to determine the proportion of genetic variation attributable to each levelof population substructure separately for both mitochondrial DNA and microsatellites (table 2)Contrasting patterns were obtained for the two markers and these were somewhat dependent on themeasure of genetic differentiation used We did not find that a significant proportion of the variancein the mitochondrial data was partitioned at the uppermost hierarchical level indicating a lack ofdifferentiation between West and East Falkland By contrast among-colony variance components werehighly significant using Fst and approached significance using Φst For microsatellites significantdifferences were found using Fst between West and East Falkland as well as among the 23 coloniesHowever none of the variance components were significant using Rst suggesting that the nuclear signalof population structure is rather weak

Arguably the most powerful tests of population structure need not rely on knowledge of the samplinglocations of individuals We therefore carried out a Bayesian clustering analysis of the microsatellitedataset using STRUCTURE [49] Five runs were conducted for each possible number of clusters (K) from1 to 10 The highest average log-likelihood value was associated with K = 1 (electronic supplementarymaterial figure S1) indicating that STRUCTURE could not detect any population structure within theFalkland Islands

32 Historical demography of the Falklands populationTo provide insights into the recent demographic history of South American sea lions at the FalklandIslands we generated a mismatch distribution from the mitochondrial data (figure 2) and tested fordeviation from a model of rapid population expansion The sum of squared deviations between theobserved and expected distribution and Harpendingrsquos raggedness index were both low and statisticallyinsignificant (SSD = 0009 p = 042 raggedness index = 0019 p = 077) meaning that we could not ruleout a model of population expansion On the other hand however we also could not reject the nullhypothesis of neutrality because Tajimarsquos D and Fursquos Fs values were negative but not statisticallysignificant (D = minus0092 p = 048 and Fs = minus0084 p = 056 respectively)

To test the hypothesis that the population experienced a genetic bottleneck we analysed themicrosatellite data using the heterozygosity excess approach of Luikart amp Cornuet [55] The resultsvaried with the mutational model on which the predicted relationship between heterozygosity andallele number was based (table 3) With models such as the SMM and TPMs with 5 or fewer multi-step mutations [59] no significant excess of heterozygosity was detected Significance was only reached

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Table 2 Results of analyses of molecular variance (AMOVA) based on (ab) the mitochondrial control region and (cd) 22 microsatelliteswith the dataset being partitioned into West and East Falkland and 23 breeding colonies respectively Significant p-values at α lt 005are highlighted in bold

of totalpartition source of variation sum of squares variance variance F p-value

(a) mitochondrial DNA (using Fst)

2 islands among islands 056 minus0001 minus034 minus0003 025

among colonieswithin islands

1249 002 463 005 lt0001

within colonies 9839 042 9571 005 lt0001

23 colonies among colonies 1304 002 453 005 lt0001

within colonies 9839 042 9547 mdash mdash

(b) mitochondrial DNA (usingΦ st)

2 islands among islands 331 0009 046 001 009

among colonieswithin islands

5495 010 507 005 008

within colonies 41969 178 9447 006 002

23 colonies among colonies 5825 010 521 005 002

within colonies 41969 178 9479 mdash mdash

(c) microsatellites (using Fst)

2 islands among islands 1221 003 044 0004 0001

among colonieswithin islands

17672 009 119 001 lt0001

among individualswithin colonies

163065 minus015 minus215 minus002 098

within individuals 186950 722 10051 minus001 089

23 colonies among colonies 18894 009 132 001 100

among individualswithin colonies

163065 minus015 minus216 minus002 098

within individuals 186950 722 10084 minus001 089

(d) microsatellites (using Rst)

2 islands among islands 66031 minus000 minus000 minus000 012

among colonieswithin islands

13 11592 738 146 001 011

among individualswithin colonies

116 78492 minus367 minus072 minus001 055

within individuals 130 06600 50219 9926 001 045

23 colonies among colonies 13 77624 739 146 002 098

among individualswithin colonies

116 78492 minus367 minus072 minus072 055

within individuals 130 06600 50219 9926 001 045

with the less conservative TPM with 30 multi-step mutations A mode shift in the allele frequencydistribution was not found

For populations with large pre-bottleneck sizes the ratio of the number of alleles to the allelic sizerange has been suggested to be more informative about bottleneck history than heterozygosity excess[73] Consequently we also calculated the M-ratio of Garza amp Williamson [62] The resulting value of

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020

016

012

008

004

01 3 5 7 9 11 13 15

no mismatches

freq

uenc

y

Figure 2 Histogram showing the observed distribution of pairwise differences among mitochondrial haplotypes obtained from theFalkland Islands For comparison the line with black points represents the expected distribution under a model of sudden populationexpansion

Table 3 The number of loci with heterozygosity excess and test probabilities obtained using a range of mutational models within theprogram BOTTLENECK [56] Significant p-values atα lt 005 are highlighted in bold

no loci with standardized Wilcoxonrsquos test p-valuemutational heterozygosity sign test differences test Wilcoxonrsquos test for heterozygositymodel excess p-value p-value p-value excess (one tailed)

IAM 21 lt00001 lt00001 lt00001 lt00001

TPM70 17 00550 01033 00029 00014

TPM90 11 02635 01166 06327 06949

TPM95 8 00270 00044 00501 09769

TPM99 5 00006 lt00001 00022 09990

SMM 4 00001 lt00001 00004 09999

088 was above the 07 threshold proposed by Garza amp Williamson [62] as well as the critical M valuesobtained through simulation (063ndash081) implying a lack of support for a bottleneck

Finally we analysed the mitochondrial and microsatellite data within an approximate Bayesiancomputation (ABC) framework to evaluate statistical support for the following four demographicscenarios (i) constant population size (ii) population expansion (iii) population reduction and (iv)a population bottleneck (see electronic supplementary material table S2 for further details) The bestsupported scenarios were population expansion for the mitochondrial dataset stable population size forthe microsatellite dataset and a bottleneck for the combined mitochondrial and microsatellite dataset(table 4) However the posterior probabilities associated with the best supported scenarios were low(049 039 and 036 respectively) and the corresponding posterior predictive error estimates werehigh (055 058 and 035 respectively) indicating that this analysis is not very informative about thedemographic history of the population

33 Relationship to the South American mainlandWe analysed the relationship between sea lion colonies from the Falkland Islands and the SouthAmerican mainland by comparing our mitochondrial data with 182 previously published mitochondrialsequences from Argentina Brazil Chile and Peru (figure 1 electronic supplementary material table S1)For visualization we generated a median joining network (figure 3) This revealed very strong maternallydirected population structure across the geographical range of the species which is reflected by largeand highly significant pairwise Fst and Φst values (electronic supplementary material table S3) From

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ArgentinaBrazilChileFalklandsPeru

Figure 3 Median joining network showing the phylogenetic relationships among 53 mitochondrial haplotypes obtained from theFalkland Islands Argentina Brazil Chile and Peru Each line joining two circles corresponds to a single nucleotide substitution withcoloured circles representing observed haplotypes and black circles representing hypothetical haplotypes that were not observed in thesample Circle size is proportional to the relative frequency of each of the observed haplotypes

Table 4 Posterior probability estimates for each competing scenario in the approximate Bayesian computation analysis based on threedatasets Posterior predictive errors are also shown for each analysis

posterior probability

combinedmitochondrial mitochondrial DNA

scenario DNA microsatellites and microsatelites

constant population size 034 039 028

population expansion 049 005 029

population reduction 016 028 006

bottleneck 001 026 036

posterior predictive error 055 058 035

this figure it can also be seen that the Atlantic coast (Argentina and Brazil) and the Pacific coast (Chileand Peru) of South America are deeply divergent with the Falkland Island haplotypes being moreclosely related to the former In addition two haplotypes were found to be shared between the FalklandIslands and Chile and one between the Falkland Islands and Argentina indicating a limited degree ofmaternally directed gene flow between the Falklands and the South American mainland

34 Migration rates and directionsFinally we used the program MIGRATE [71] to estimate rates and directions of gene flow fromthe mitochondrial data Peru was excluded from this analysis due to the sample size being only

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0015

0010

0005

0

0015

0010

0005

freq

uenc

yfr

eque

ncy

freq

uenc

yfr

eque

ncy

0

0015

0010

0005

0

0015

0010

0005

0

0 025 050number of migrants per generation

to Chile

from

to Falklands

to Argentina

to Brazil

075 100

Chile Falklands Argentina Brazil(a)

(b)

(c)

(d)

Figure 4 Migration rate estimates calculated from the mitochondrial control region using MIGRATE [7172] Depicted are distributions ofmigration rate estimates fromChile (blue) the Falklands (pink) Argentina (green) andBrazil (orange) towards (a) Chile (b) the Falklands(c) Argentina and (d) Brazil

Table 5 Modal estimates of gene flow among sea lion populations from the Falklands Argentina Brazil and Chile calculated from themitochondrial control region using MIGRATE [7172] Results are given as the number of migrants per generation from each of theareas on the left (row headings) into the areas on the right (column headings) 95 Confidence intervals are given in parentheses

region Falklands Argentina Brazil Chile

Falklands 041 (031ndash051) 072 (059ndash083) 029 (019ndash042)

Argentina 031 (020ndash041) 079 (067ndash091) 011 (000ndash023)

Brazil 023 (011ndash033) 065 (053ndash075) 011 (001ndash020)

Chile 039 (027ndash051) 031 (021ndash042) 054 (044ndash064)

five individuals The resulting migration rate estimates were low with all of the pairwise regionalcomparisons yielding estimates of less than one migrant per generation (figure 4 table 5) A tendency wasalso observed for Chile and the Falklands to receive fewer migrants from the other localities (figure 4a

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and b respectively) than Argentina and Brazil (figure 4c and d respectively) The two highest migrationrate estimates were from Argentina to Brazil (green distribution in figure 4d) and from the Falklands toBrazil (pink distribution in figure 4d) possibly reflecting a combination of geographical proximity andprevailing currents (see Discussion)

4 DiscussionWe conducted a genetic analysis of South American sea lion populations around the Falkland Islandsand combined our data with previously published mitochondrial sequences to allow a comparisonto be made between the Falklands and the South American mainland Within the Falkland Islandswe found moderately high levels of genetic diversity at both types of marker and no evidence fora recent genetic bottleneck Population structure was also rather weak in comparison to the strongmaternally directed population structure observed across the broader species range Finally migrationrate estimates were very low in all pairwise comparisons involving Argentina Brazil Chile and theFalklands which is at odds with the hypothesis that the decline was caused by historical exploitationin Argentina

41 Population structure within the Falkland IslandsTests for population structure within the Falkland Islands uncovered somewhat mixed results dependingon the marker and genetic distance measure used For this reason and for consistency with previousstudies of this species [202123] our preferred approach was to partition the total genetic variance usingAMOVA For the mitochondrial DNA we found that the uppermost hierarchical level of populationstructure was not statistically significant implying a lack of differentiation between West and EastFalkland However around 5 of the genetic variance could be attributed to among-colony differencesindicating the presence of shallow population structure This variance component was highly significantusing Fst but failed to reach significance using Φst suggesting that the overall result may be drivenby haplotype frequency differences

A similarly weak and rather inconsistent pattern was also obtained for the microsatellite data despiteour having used 22 loci For this class of marker a significant proportion of the genetic variance wasattributable to differences between West and East Falkland as well as among colonies but only usingFst and not Rst Similarly Bayesian cluster analysis of the microsatellite dataset failed to uncover anyevidence for population structure within the Falklands Taken together these results are suggestive ofthe presence of very weak population structure within the Falkland Islands as a simulation study foundthat STRUCTURE often struggles to find sub-populations when Fst is below around 002 [74]

These findings are in line with previous studies of this species on the South American mainlandFor instance Tuacutenez et al [21] found weak among-colony differences in the mitochondrial controlregion in north-central Patagonia whereas Crespo et al [23] found no mitochondrial differences amongpopulations sampled between Southern Brazil and Tierra del Fuego In the only study of this speciesto have used both mitochondrial and nuclear markers Feijoo et al [20] found significant differencesamong colonies at the former but not the latter This was interpreted as reflecting sex-biased dispersala pattern that is common among pinnipeds as males are often the dispersing sex [75ndash77] This would beconsistent with a tagging study of South American sea lions that documented long-distance movementsof reproductively active adult males between Patagonia and Uruguay [78]

Our results are somewhat less straightforward to interpret as genetic differences appear to be presentamong colonies but whether or not these achieve statistical significance depends on the genetic distancemeasure used Moreover taking our results at face value one might conclude that although geneticdifferences among colonies are found at both class of marker a difference between West and EastFalkland is only apparent in the nuclear and not the mitochondrial genome Such a pattern mightbe genuine although this would be contrary to expectations based on strong female philopatry andmale dispersal Alternatively it could be possible that different markers vary in their power to detectpopulation structure [79] To distinguish between these explanations as well as to resolve populationstructure more clearly it would be desirable to increase both sample sizes and genetic resolution

Regardless of the exact pattern of population structure South American sea lions at the FalklandIslands show much weaker structuring than is evident over similar spatial scales in several other relatedpinniped species including the Galaacutepagos fur seal Arctocephalus galapagoensis [77] the Galaacutepagos sealion Zalophus wollebaeki [80] and the Australian sea lion Neophoca cinerea [81] This could have positiveimplications for conservation as there appears to be adequate gene flow between sea lion colonies

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within the Falklands both to counteract inbreeding and to maintain adaptive potential The level ofstructure we observe is also insufficient to recommend the delimitation of evolutionary significantunits (ESUs) which are often used to objectively define units below the level of the species thatshould be prioritized for conservation [82ndash84] This is because ESUs should typically be reciprocallymonophyletic for mitochondrial haplotypes and significantly divergent at nuclear loci [83] Howevera case could potentially be made for defining ESUs over a broader geographical scale due to the deepmitochondrial sequence divergence found among the Falkland Islands Brazil Argentina Chile and Peru(see below)

42 Testing hypotheses for population declineThe first hypothesis we tested was that a historical bottleneck could have been involved in the failureof the sea lion population to recover from commercial exploitation Although census data suggest thathuman exploitation may not have been severe enough to have depleted genetic diversity these data areimperfect and effective population sizes are typically at least an order of magnitude smaller than censussizes particularly in polygynous species such as sea lions where only a fraction of adults contributetowards successive generations [85] Moreover pinniped numbers are known to be highly responsive tochanges in food or habitat availability [86ndash89] meaning that a bottleneck in the more distant past cannotbe ruled out

To test for a bottleneck we analysed genetic data from the Falkland Islands using threecomplementary approaches Although the results are perhaps not as clear cut as one might hope anumber of lines of evidence suggest that sea lions at the Falklands probably did not experience anappreciable loss of genetic diversity First we detected moderately high levels of genetic diversity at bothmitochondrial DNA and microsatellites in comparison with values reported for 18 different pinnipedspecies (see table 7 in [77]) Second BOTTLENECK only found support for a bottleneck with the IAMand TPM with 30 multi-step mutations while the more conservative TPMs with 5 or fewer multi-step mutations did not detect a significant excess of heterozygosity Although this is difficult to interpretbecause the true proportion of multi-step mutations is unknown a clear contrast can be drawn withthe Antarctic fur seal (Arctocephalus gazella) which experienced a severe bottleneck that is reflected in asignificant excess of heterozygosity with the TPM with 5 multi-step mutations [90] Third the empiricalvalue of the M-ratio was not consistent with a bottleneck and fourth ABC analysis also failed to findconvincing support for a bottleneck scenario In contrast again with the Antarctic fur seal study whereABC analysis conclusively favoured a bottleneck we found that none of the four scenarios were robustlysupported either by the mitochondrial or microsatellite datasets on their own or when the two datasetswere combined One explanation for this stems from a recent analysis of the numbers and sexes of sealions hunted in the Falklands from 1928 to 1966 [15] Although over 60 000 animals were taken themajority of these are thought to have been adult males suggesting that any effects on breeding femalesmay have been relatively minor

A prominent hypothesis for the collapse of the sea lion population at the Falkland Islands is that large-scale commercial harvesting operations in Argentina could have been responsible [15161891] Howeverfor hunting in Argentina to have accounted for the 95 decline in pup production at the Falkland Islandsfrom approximately 80 000 in the mid-1930s to around 4500 today a large fraction of the reproductivelyactive female population of the Falklands would have had to have migrated to Argentina Baylis et al[15] argued that this is unlikely because females of this species lactate for around 11 months leavingvery little time for them to undertake long migrations However as the migration of subadults cannot beruled out we approached this hypothesis from a genetic perspective

To explore patterns of genetic connectivity across the geographical range of the species we analysedmitochondrial data from the Falklands Argentina Brazil Chile and Peru Only four shared haplotypeswere found among these five regions indicating very strong mitochondrial structuring over a continentalscale While this is consistent with a previous study of this species that compared Brazil with Peru [19]ours is the first study to document range-wide mitochondrial relationships thereby revealing a broadtendency for restricted long-distance maternally directed dispersal Such a pattern is broadly in line withprevious large-scale studies of other pinniped species including the sympatric South American fur sealArctocephalus australis [92]

We also found limited evidence of haplotype sharing between the Falkland Islands and the SouthAmerican mainland with only two haplotypes being common to the Falklands and Chile and one beingshared by the Falklands and Argentina By implication maternally directed gene flow appears to berestricted between the Falklands and South America This clearly goes against the notion that migration

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14

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to Argentina could have been responsible for the decline of the Falklands population although ourgenetic data would be unable to detect migration if the majority of migrants had been culled prior tobreeding This however seems unlikely for two main reasons First if female migration was a previouslyunappreciated aspect of this species biology one would expect this to be reflected in widespreadhaplotype sharing due to the migration that would have taken place prior to commercial harvestingin Argentina Second the sea lion population in Argentina was estimated to have been almost twicethe size of the Falklands population in the 1930s [93] It is difficult to reconcile why sea lions from theFalklands would migrate to compete with a larger population of sea lions particularly when consideringthat the Falkland Islands are within closer proximity to the Patagonian Shelf slope a region of enhancedbiological activity and productivity [94]

Consistent with the rarity of shared haplotypes analysis of the global mitochondrial dataset withinMIGRATE yielded very low migration rate estimates for all of the pairwise regional comparisons lendingfurther support to the notion of restricted long-distance female dispersal We also found some evidenceto suggest that migration rates towards Chile and the Falkland Islands may be lower on average thanmigration rates towards Argentina and Brazil although some degree of caution is warranted due to thefact that all of the estimates are very low Interestingly the two highest estimates were from Argentina toBrazil and from the Falklands to Brazil One possible interpretation of this could be that the prevailingnorthwards flow of the Malvinas current [95] may facilitate migration towards Brazil It could beworthwhile investigating this further through biologging studies or targeted genetic studies

Finally because we do not have any microsatellite data from the South American mainland wecannot exclude the possibility of male-mediated gene flow from the Falkland Islands towards ArgentinaHowever it is unlikely that the harvesting of adult males would significantly influence populationgrowth as pinniped population dynamics are typically most sensitive to the survival of adult andsubadult females [8996] Moreover although movements of reproductively active adult males havebeen documented within South America Baylis et al [15] found no evidence of males tagged at theFalklands migrating to Argentina [15] Further biologging and genetic studies could shed light on thistopic although the latter would require tissue samples to be collected from representative locationsaround South America

In conclusion our study failed to find evidence of a strong bottleneck consistent with the conclusionof Baylis et al [15] based on historical records that anthropogenic impacts on the Falkland Island sealion population may not have been as severe as previously thought Our results are also at odds withthe hypothesis that the decline could have resulted from females migrating to Argentina where large-scale commercial harvesting operations were responsible for the deaths of over half a million sea lions Ittherefore seems likely that local factors at the Falkland Islands such as increased fisheries competitiondisease predation andor environmental change may have played a role Of these Baylis et al [15]argued that commercial fisheries are unlikely to have been an important driver of the decline becauseintensive near shore trawl fisheries were not developed until the 1980s They instead showed that seasurface temperatures at the Falklands increased significantly during the period of steepest declinesuggesting a potential role of bottom-up trophic forcing on the sea lion population Our results areconsistent with this hypothesis in as far as commercial harvesting is unlikely to have caused the declineThus to better understand the decline it would seem appropriate for future studies to explore otherpotentially contributing factors such as the impact of environmental variation on sea lion populations

Ethics Research was approved by the Falkland Islands Government Environmental Planning Department andconducted under research permits nos R142013 and R142014 issued by the Falkland Islands GovernmentData accessibility Mitochondrial DNA sequences are available from GenBank (accession nos KU729822ndashKU729843)Microsatellite genotypes are available via Dryad at httpdxdoiorg105061dryadd826hAuthorsrsquo contributions JIH conceived of the study designed the study supervised the laboratory work and data analysisand wrote the manuscript GJK carried out the molecular laboratory work conducted microsatellite scoring andsequence alignments participated in data analysis and drafting of the manuscript AK performed the data analysisand participated in drafting of the manuscript LJE-P participated in data analysis and drafting of the manuscriptIJS conceived of the study and designed the study AMMB conceived of the study designed the study conductedtissue sampling and participated in drafting of the manuscript All authors read and approved the final manuscriptCompeting interests The authors declare no competing interestsFunding The genetic work was funded by a Marie Curie FP7-Reintegration-Grant within the 7th European CommunityFramework Programme (PCIG-GA-2011ndash303618) Fieldwork was funded by the Shackleton Scholarship Fund(Centenary Award) Rufford Small Grants Sea World and Busch Gardens Conservation Fund Joint NatureConservation Council National Geographic Winifred Violet Scott and the Falkland Islands GovernmentAcknowledgements We are grateful to P Brickle R Cordiero J Fenton and N Rendell for facilitating fieldwork andR Orben for assistance with sample collection

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15

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References1 DeSalle R Amato G 2004 The expansion of

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32 Wells DA Hoffman JI Amos W SubmittedImmunity and olfaction linked microsatellites inpinnipeds

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34 Hernandez-Velazquez FD Galindo-Sanchez CETaylor MI De La Rosa-Velez J Cote IM Schramm YAurioles-Gamboa D Rico C 2005 New polymorphicmicrosatellite markers for California sea lions(Zalophus californianus)Mol Ecol Notes 5 140ndash142(doi101111j1471-8286200400858x)

35 Huebinger RM Louis Jr EE Gelatt T Rea LDBickham JW 2007 Characterization of eightmicrosatellite loci in Steller sea lions (Eumetopiasjubatus)Mol Ecol Notes 7 1097ndash1099(doi101111j1471-8286200701790x)

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53 Fu YX 1997 Statistical tests of neutrality ofmutations against population growth hitchhikingand background selection Genetics 147 915ndash925

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62 Garza JC Williamson EG 2001 Detection of reducedpopulation size using data frommicrosatellite loci

Mol Ecol 10 305ndash318 (doi101046j1365-294x200101190x)

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64 Beaumont MA ZhangW Balding DJ 2002Approximate Bayesian computation in populationgenetics Genetics 162 2025ndash2035

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66 Cornuet JM Santo F Beaumont MA Robert CPMarin J-M Balding DJ Guillemaud T Estoup A2008 Inferring population history with DIYABC auser-friendly approach to approximate Bayesiancomputations Bioinformatics 24 2713ndash2719(doi101093bioinformaticsbtn514)

67 Cornuet JM Ravigne V Estoup A 2010 Inference onpopulation history and model checking using DNAsequence and microsatellite data with the softwareDIYABC (v 10) BMC Bioinform 11 104 (doi1011861471-2105-11-401)

68 Phillips CD Trujillo RG Gelatt TS Smolen MJMatson CW Honeycutt RL Patton JC Bickham JW2009 Assessing substitution patterns rates andhomoplasy at HVRI of Steller sea lions EumetopiasjubatusMol Ecol 18 3379ndash3393 (doi101111j1365-294X200904283x)

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70 Excoffier L Smouse PE 1994 Using allelefrequencies and geographic subdivision toreconstruct gene trees within a species molecularvariance parsimony Genetics 136 343ndash359

71 Beerli P 2006 Comparison of Bayesian andmaximum-likelihood inference of populationgenetic parameters Bioinformatics 22 341ndash345(doi101093bioinformaticsbti803)

72 Beerli P Palczewski M 2010 Unified framework toevaluate panmixia and migration direction amongmultiple sampling locations Genetics 185 313ndash326(doi101534genetics109112532)

73 Williamson-Natesan EG 2005 Comparison ofmethods for detecting bottlenecks frommirosatellite loci Conserv Genet 6 551ndash562(doi101007s10592-005-9009-5)

74 Latch EK Dharmarajan G Glaubitz JC Rhodes Jr OE2006 Relative performance of Bayesian clusteringsoftware for inferring population substructure andindividual assignment at low levels of populationdifferentiation Conserv Genet 7 295ndash302(doi101007s10592-005-9098-1)

75 Andersen LW Born EW Gjertz I Wiig O Holm LEBendixen C 1998 Population structure and geneflow of the Atlantic walrus (Odobenus rosmarusrosmarus) in the eastern Atlantic Arctic based onmitochondrial DNA and microsatellite variationMol Ecol 7 1323ndash1336 (doi101046j1365-294x199800455x)

76 Burg TM Trites AW Smith MJ 1999 Mitochondrialand microsatellite DNA analyses of harbour sealpopulation structure in the northeast Pacific OceanCan J Zool 77 930ndash943 (doi101139z99-057)

77 Lopes F Hoffman JI Valati VH Bonatto S Wolf JBWTrillmich F Oliveira LR 2015 Fine-scale matrilinealpopulation structure in the Galapagos fur seal and

its implications for conservation managementConserv Genet 16 1099ndash1113 (doi101007s10592-015-0725-1)

78 Giardino GV Mandiola MA Bastida J Denuncio PEBastida RO Rodriguez DH 2015 Travel for sexlong-range breeding dispersal and winter hauloutfidelity in southern sea lion malesMamm Biol 8189ndash95(doi101016jmambio201412003)

79 Hoffman JI Dasmahapatra KK Amos W Phillips CDGelatt TS Bickham JW 2009 Contrasting patterns ofgenetic diversity at three different genetic markersin a marine mammal metapopulationMol Ecol 182961ndash2978 (doi101111j1365-294X200904246x)

80 Wolf JBW Harrod C Brunner S Salazar S TrillmichF Tautz D 2008 Tracing early stages of speciesdifferentiation ecological morphological andgenetic divergence of Galapagos sea lionpopulations BMC Evol Biol 8 150ndash164(doi1011861471-2148-8-150)

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82 Ryder OA 1986 Species conservation andsystematics the dilemma of subspecies Trends EcolEvol 1 9ndash10 (doi1010160169-5347(86)90059-5)

83 Moritz C 1994 Defining lsquoevolutionarily significantunitsrsquo for conservation Trends Ecol Evol 9 373ndash375(doi1010160169-5347(94)90057-4)

84 Avise JC 1989 A role for molecular geneticists in therecognition and conservation of endangeredspecies Trends Ecol Evol 4 279ndash281 (doi1010160169-5347(89)90203-6)

85 Palstra FP Ruzzante DE 2008 Genetic estimates ofcontemporary effective population size what canthey tell us about the importance of geneticstochasticity for wild population persistenceMolEcol 17 3428ndash3447 (doi101111j1365-294X200803842x)

86 Matthee CA Fourie F Oosthuizen WH Meyer MATolley KA 2006 Mitochondrial DNA sequence dataof the Cape fur seal (Arctocephalus pusillus pusillus)suggest that population numbers may be affectedby climatic shiftsMar Biol 148 899ndash905(doi101007s00227-005-0121-3)

87 De Bruyn M Hall BL Chauke LF Baroni C Koch PLHoelzel AR 2009 Rapid response of a marinemammal species to Holocene climate and habitatchange PLoS Genet 5 e10000554 (doi101371journalpgen1000554)

88 Klimova A Fietz K Olsen MT Harwood J Amos WHoffman JI 2014 Global population structure anddemographic history of the grey sealMol Ecol 233999ndash4017 (doi101111mec12850)

89 Forcada J Hoffman JI 2014 Climate change selectsfor heterozygosity in a declining fur seal populationNature 511 462ndash465 (doi101038nature13542)

90 Hoffman JI Grant SM Forcada J Phillips CD 2011Bayesian inference of a historical genetic bottleneckin a heavily exploited marine mammalMol Ecol20 3989ndash4008 (doi101111j1365-294X201105248x)

91 Thompson D Duck CD McConnell B Garrett J 1998Foraging behaviour and diet of lactating femalesouthern sea lions (Otaria flavescens) in theFalkland Islands J Zool 246 135ndash146(doi101111j1469-79981998tb00142x)

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92 Oliveira LR Hoffman JI Hingst-Zaher E Majluf P

Muelbert MMC Amos W Morgante JS 2008Morphological and genetic evidence for twoevolutionary significant units (ESUs) in the SouthAmerican fur seal Arctocephalus australis ConservGenet 9 1451ndash1466 (doi101007s10592-007-9473-1)

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95 Matano RP Palma ED Piola AR 2010 The influenceof the Brazil and Malvinas currents on the

southwestern Atlantic shelf circulation Ocean Sci 6983ndash995 (doi105194os-6-983-2010)

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