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      Population structure and historical demography of South American sea lions provide insights into the catastrophic decline of a marine mammal population

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          Abstract

          Understanding the causes of population decline is crucial for conservation management. We therefore used genetic analysis both to provide baseline data on population structure and to evaluate hypotheses for the catastrophic decline of the South American sea lion ( Otaria flavescens) at the Falkland Islands (Malvinas) in the South Atlantic. We genotyped 259 animals from 23 colonies across the Falklands at 281 bp of the mitochondrial hypervariable region and 22 microsatellites. A weak signature of population structure was detected, genetic diversity was moderately high in comparison with other pinniped species, and no evidence was found for the decline being associated with a strong demographic bottleneck. By combining our mitochondrial data with published sequences from Argentina, Brazil, Chile and Peru, we also uncovered strong maternally directed population structure across the geographical range of the species. In particular, very few shared haplotypes were found between the Falklands and South America, and this was reflected in correspondingly low migration rate estimates. These findings do not support the prominent hypothesis that the decline was caused by migration to Argentina, where large-scale commercial harvesting operations claimed over half a million animals. Thus, our study not only provides baseline data for conservation management but also reveals the potential for genetic studies to shed light upon long-standing questions pertaining to the history and fate of natural populations.

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          Most cited references31

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          Detection of reduction in population size using data from microsatellite loci.

          We demonstrate that the mean ratio of the number of alleles to the range in allele size, which we term M, calculated from a population sample of microsatellite loci, can be used to detect reductions in population size. Using simulations, we show that, for a general class of mutation models, the value of M decreases when a population is reduced in size. The magnitude of the decrease is positively correlated with the severity and duration of the reduction in size. We also find that the rate of recovery of M following a reduction in size is positively correlated with post-reduction population size, but that recovery occurs in both small and large populations. This indicates that M can distinguish between populations that have been recently reduced in size and those which have been small for a long time. We employ M to develop a statistical test for recent reductions in population size that can detect such changes for more than 100 generations with the post-reduction demographic scenarios we examine. We also compute M for a variety of populations and species using microsatellite data collected from the literature. We find that the value of M consistently predicts the reported demographic history for these populations. This method, and others like it, promises to be an important tool for the conservation and management of populations that are in need of intervention or recovery.
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            Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations.

            We consider the distribution of pairwise sequence differences of mitochondrial DNA or of other nonrecombining portions of the genome in a population that has been of constant size and in a population that has been growing in size exponentially for a long time. We show that, in a population of constant size, the sample distribution of pairwise differences will typically deviate substantially from the geometric distribution expected, because the history of coalescent events in a single sample of genes imposes a substantial correlation on pairwise differences. Consequently, a goodness-of-fit test of observed pairwise differences to the geometric distribution, which assumes that each pairwise comparison is independent, is not a valid test of the hypothesis that the genes were sampled from a panmictic population of constant size. In an exponentially growing population in which the product of the current population size and the growth rate is substantially larger than one, our analytical and simulation results show that most coalescent events occur relatively early and in a restricted range of times. Hence, the "gene tree" will be nearly a "star phylogeny" and the distribution of pairwise differences will be nearly a Poisson distribution. In that case, it is possible to estimate r, the population growth rate, if the mutation rate, mu, and current population size, N0, are assumed known. The estimate of r is the solution to ri/mu = ln(N0r) - gamma, where i is the average pairwise difference and gamma approximately 0.577 is Euler's constant.
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              Unified framework to evaluate panmixia and migration direction among multiple sampling locations.

              For many biological investigations, groups of individuals are genetically sampled from several geographic locations. These sampling locations often do not reflect the genetic population structure. We describe a framework using marginal likelihoods to compare and order structured population models, such as testing whether the sampling locations belong to the same randomly mating population or comparing unidirectional and multidirectional gene flow models. In the context of inferences employing Markov chain Monte Carlo methods, the accuracy of the marginal likelihoods depends heavily on the approximation method used to calculate the marginal likelihood. Two methods, modified thermodynamic integration and a stabilized harmonic mean estimator, are compared. With finite Markov chain Monte Carlo run lengths, the harmonic mean estimator may not be consistent. Thermodynamic integration, in contrast, delivers considerably better estimates of the marginal likelihood. The choice of prior distributions does not influence the order and choice of the better models when the marginal likelihood is estimated using thermodynamic integration, whereas with the harmonic mean estimator the influence of the prior is pronounced and the order of the models changes. The approximation of marginal likelihood using thermodynamic integration in MIGRATE allows the evaluation of complex population genetic models, not only of whether sampling locations belong to a single panmictic population, but also of competing complex structured population models.
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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society
                2054-5703
                July 2016
                27 July 2016
                27 July 2016
                : 3
                : 7
                : 160291
                Affiliations
                [1 ]Department of Animal Behaviour, University of Bielefeld , Postfach 100131, 33501 Bielefeld, Germany
                [2 ]Animal Ecology Group, Institute of Biochemistry and Biology, University of Potsdam , Maulbeerallee 1, 14469, Potsdam, Germany
                [3 ]Centro de Investigaciones Biológicas del Noroeste Baja California Sur , La Paz, Mexico
                [4 ]British Antarctic Survey, Natural Environment Research Council , High Cross, Madingley Road, Cambridge CB3 0ET, UK
                [5 ]South Atlantic Environmental Research Institute , Stanley FIQQ1ZZ, Falkland Islands
                [6 ]Falklands Conservation , Stanley FIQQ1ZZ, Falkland Islands
                [7 ]Department of Biological Sciences, Macquarie University , Sydney, New South Wales 2109, Australia
                Author notes
                Author for correspondence: J. I. Hoffman e-mail: j_i_hoffman@ 123456hotmail.com
                Author information
                http://orcid.org/0000-0001-7311-6088
                Article
                rsos160291
                10.1098/rsos.160291
                4968474
                27493782
                e114a193-a0fa-45a6-a14e-024f6ba5780e
                © 2016 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 29 April 2016
                : 23 June 2016
                Funding
                Funded by: Falkland Islands Government
                Funded by: Joint Nature Conservation Council
                Funded by: National Geographic Society http://dx.doi.org/10.13039/100006363
                Funded by: Rufford Small Grants
                Funded by: Sea World and Busch Gardens Conservation Fund
                Funded by: Seventh Framework Programme http://dx.doi.org/10.13039/501100004963
                Award ID: PCIG-GA-2011-303618
                Funded by: Shackleton Scholarship Fund
                Award ID: Centenary Award
                Funded by: Winifred Violet Scott
                Categories
                1001
                197
                60
                Biology (Whole Organism)
                Research Article
                Custom metadata
                July, 2016

                population structure,anthropogenic exploitation,historical demography,phylogeography,pinniped

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