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      The largest fish in the world’s biggest river: Genetic connectivity and conservation of Arapaima gigas in the Amazon and Araguaia-Tocantins drainages

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          Abstract

          Arapaima, pirarucu or paiche ( Arapaima gigas) is one of the largest freshwater fish in the world, and has a long history of commercial exploitation in the Amazon region. To estimate levels of genetic variability and historical and recent connectivity in Arapaima, we examined variation in eleven microsatellite DNA markers in individuals from 22 localities in Brazil, Colombia, and Peru. The results of analysis of molecular variance, Bayesian clustering and discriminant analysis of principal components showed that Arapaima in our samples represents two major populations, one in the Amazonas and one in the Araguaia-Tocantins River basins. The Amazonas population is further structured by isolation-by-distance with the hydrologically largely unconnected Amapá locality representing the eastern-most extreme of this continuum; gene flow predominates at distances of less than 1500 km with localities separated by over 2000 km dominated by genetic drift and effectively forming different populations. We saw no evidence of multiple species of Arapaima in the Amazonas basin, and analysis of pairwise genetic divergence ( F ST ) with Mantel tests and correlograms indicated that this largest population exhibits a large-scale pattern of isolation-by-distance, with which results from MIGRATE-N agreed. The degree and significance of genetic divergence indicates that most sampled localities represent demographically independent sub-populations, although we did identify several recent migration events between both proximal and more distant localities. The levels of genetic diversity were heterogeneous across sites, including low genetic diversity, effective population sizes, and evidence of genetic bottlenecks in several places. On average the levels of gene diversity and rarefied allelic richness were higher for localities along the Amazonas mainstem than in the tributaries, despite these being the areas of highest fishing pressure, while the lowest values were found in tributary headwaters, where landscape modification is a significant threat. We recommend that managers consider the regional and local threats to these populations and tailor strategies accordingly, strategies which should ensure the ability of young A. gigas to disperse through floodplain corridors to maintain genetic diversity among otherwise sedentary adult sub-populations.

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          Inference from Iterative Simulation Using Multiple Sequences

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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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              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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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: Writing – original draft
                Role: Data curationRole: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                16 August 2019
                2019
                : 14
                : 8
                : e0220882
                Affiliations
                [1 ] Laboratório de Evolução e Genética Animal/LEGAL, Universidade Federal do Amazonas (UFAM), Manaus, Amazonas, Brazil
                [2 ] Department of Ichthyology, California Academy of Sciences, San Francisco, CA, United States of America
                [3 ] Departamento de Ecologia, Universidade Federal do Rio Grande do Norte (UFRN), Natal, Brazil
                [4 ] Acqua Consultoria Ambiental, Rio de Janeiro, Brazil
                [5 ] Laboratório de Biologia e Genética de Peixes, Instituto de Biociências, Universidade Estadual Paulista (UNESP), Botucatu, São Paulo, Brazil
                [6 ] Departamento de Ciências Biológicas, Faculdade de Ciências, Universidade Estadual Paulista (UNESP), Campus de Bauru, Bauru, Brazil
                [7 ] Instituto de Estudos Costeiros, Universidade Federal do Pará, Campus Universitário de Bragança, Pará, Brazil
                DePaul University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-1416-4351
                Article
                PONE-D-19-00029
                10.1371/journal.pone.0220882
                6697350
                31419237
                efc3c147-6abf-4ee3-8c84-9e66b4f2ee32
                © 2019 Farias et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 January 2019
                : 25 July 2019
                Page count
                Figures: 7, Tables: 5, Pages: 27
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004916, Fundação de Amparo à Pesquisa do Estado do Amazonas;
                Award ID: Tematico 899/03
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004916, Fundação de Amparo à Pesquisa do Estado do Amazonas;
                Award ID: Universal 062.01341/2018
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 557090/2005-9
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 554057/2006-9
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 575603/2008-9
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 42/2012
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 482662/2013-1
                Award Recipient :
                This research was supported by the National Council for Scientific and Technological Development of Brazil (Conselho Nacional de Desenvolvimento Científico e Tecnológico) grants: MCT/CNPq/PPG7 557090/2005-9, CNPq/CT-Amazonia 554057/2006-9 and CNPq/CT-Amazonia 575603/2008-9 to IPF, and CNPq/MPA 42/2012 and CNPq 482662/2013-1 to TH, and the State of Amazonas Research Support Foundation (Fundação de Amparo à Pesquisa do Estado do Amazonas) grants: Tematico 899/03 and Universal 062.01341/2018 to IPF. IPF and TH were supported by a Bolsa de Pesquisa scholarship from CNPq during the study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Data are available at: ( https://github.com/legalLab/publications), in the folder "Farias_et_al_2019". The direct link is: https://github.com/legalLab/publications/tree/master/Farias_et_al_2019.

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