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      Genetic structure of fragmented southern populations of African Cape buffalo ( Syncerus caffer caffer)

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          Abstract

          Background

          African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19 th century as a result of human demographic expansion, wildlife overexploitation, habitat degradation and cattle-borne diseases. In many areas, ungulate populations are now largely confined within a network of loosely connected protected areas. These metapopulations face gene flow restriction and run the risk of genetic diversity erosion. In this context, we assessed the “genetic health” of free ranging southern African Cape buffalo populations ( S.c. caffer) and investigated the origins of their current genetic structure. The analyses were based on 264 samples from 6 southern African countries that were genotyped for 14 autosomal and 3 Y-chromosomal microsatellites.

          Results

          The analyses differentiated three significant genetic clusters, hereafter referred to as Northern (N), Central (C) and Southern (S) clusters. The results suggest that splitting of the N and C clusters occurred around 6000 to 8400 years ago. Both N and C clusters displayed high genetic diversity (mean allelic richness ( A r ) of 7.217, average genetic diversity over loci of 0.594, mean private alleles ( P a ) of 11), low differentiation, and an absence of an inbreeding depression signal (mean F IS = 0.037). The third (S) cluster, a tiny population enclosed within a small isolated protected area, likely originated from a more recent isolation and experienced genetic drift ( F IS = 0.062, mean A r = 6.160, P a = 2). This study also highlighted the impact of translocations between clusters on the genetic structure of several African buffalo populations. Lower differentiation estimates were observed between C and N sampling localities that experienced translocation over the last century.

          Conclusions

          We showed that the current genetic structure of southern African Cape buffalo populations results from both ancient and recent processes. The splitting time of N and C clusters suggests that the current pattern results from human-induced factors and/or from the aridification process that occurred during the Holocene period. The more recent S cluster genetic drift probably results of processes that occurred over the last centuries (habitat fragmentation, diseases). Management practices of African buffalo populations should consider the micro-evolutionary changes highlighted in the present study.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12862-014-0203-2) contains supplementary material, which is available to authorized users.

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          Most cited references83

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          Arlequin (version 3.0): An integrated software package for population genetics data analysis

          Arlequin ver 3.0 is a software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework. Arlequin 3 introduces a completely new graphical interface written in C++, a more robust semantic analysis of input files, and two new methods: a Bayesian estimation of gametic phase from multi-locus genotypes, and an estimation of the parameters of an instantaneous spatial expansion from DNA sequence polymorphism. Arlequin can handle several data types like DNA sequences, microsatellite data, or standard multi-locus genotypes. A Windows version of the software is freely available on http://cmpg.unibe.ch/software/arlequin3.
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            Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach.

            A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo approach to investigate possible genealogies with branch lengths and with migration events. Properties of the new method are shown by using simulated data from a four-population n-island model and a source-sink population model. Our estimation method as coded in migrate is tested against genetree; both programs deliver a very similar likelihood surface. The algorithm converges to the estimates fairly quickly, even when the Markov chain is started from unfavorable parameters. The method was used to estimate gene flow in the Nile valley by using mtDNA data from three human populations.
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              Estimating F-Statistics for the Analysis of Population Structure

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                Author and article information

                Contributors
                nsmitz@student.ulg.ac.be
                daniel.cornelis@cirad.fr
                p.chardonnet@fondation-igf.fr
                alexandre.caron@cirad.fr
                michel.de_garine-wichatitsky@cirad.fr
                ferran.jori@cirad.fr
                mouton.alice@gmail.com
                alice.latinne@ulg.ac.be
                lmpigneur@gmail.com
                mario.melletti@yahoo.it
                kkanapec@gmail.com
                jonathan.marescaux@unamur.be
                clp2308@gmail.com
                johan.michaux@ulg.ac.be
                Journal
                BMC Evol Biol
                BMC Evol. Biol
                BMC Evolutionary Biology
                BioMed Central (London )
                1471-2148
                1 November 2014
                1 November 2014
                2014
                : 14
                : 1
                : 203
                Affiliations
                [ ]Departement of Life Sciences-Conservation Genetics, University of Liège, Liège, Belgium
                [ ]Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Montpellier, France
                [ ]Fondation Internationale pour la Gestion de la Faune (IGF), Paris, France
                [ ]Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD)-RP-PCP, University of Zimbabwe, Harare, Zimbabwe
                [ ]Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa
                [ ]Department of Biological Sciences, University of Zimbabwe, Harare, Zimbabwe
                [ ]Department of Animal Science and Production, Botswana College of Agriculture, Gaborone, Botswana
                [ ]Institut des Sciences de l’Evolution-CNRS-IRD, Université de Montpellier 2, Montpellier, France
                [ ]Department of Parasitology, Faculty of Veterinary Medicine, Kasetsart University, Bangkok, Thailand
                [ ]Research Unit in Environmental and Evolutionary Biology, University of Namur, Namur, Belgium
                [ ]Independent researcher, Via Di Villa Chigi, Rome, Italy
                [ ]Department of Genetics and Biochemistry, Clemson University, Clemson, USA
                [ ]Wildlife Conservation Society, New York, USA
                Article
                203
                10.1186/s12862-014-0203-2
                4232705
                25367154
                0c5712b4-e6a9-46ef-a698-53a5cf616bc6
                © Smitz et al.; licensee BioMed Central Ltd. 2014

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 18 June 2014
                : 16 September 2014
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2014

                Evolutionary Biology
                syncerus caffer caffer,population genetics,genetic structure,translocation,southern africa,conservation implications

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