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      Microhabitat Types Promote the Genetic Structure of a Micro-Endemic and Critically Endangered Mole Salamander ( Ambystoma leorae) of Central Mexico

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          Abstract

          The reduced immigration and emigration rates resulting from the lack of landscape connectivity of patches and the hospitality of the intervening matrix could favor the loss of alleles through genetic drift and an increased chance of inbreeding. In order for isolated populations to maintain sufficient levels of genetic diversity and adapt to environmental changes, one important conservation goal must be to preserve or reestablish connectivity among patches in a fragmented landscape. We studied the last known population of Ambystoma leorae, an endemic and critically threatened species. The aims of this study were: (1) to assess the demographic parameters of A. leorae and to distinguish and characterize the microhabitats in the river, (2) to determine the number of existing genetic groups or demes of A. leorae and to describe possible relationships between microhabitats types and demes, (3) to determine gene flow between demes, and (4) to search for geographic locations of genetic discontinuities that limit gene flow between demes. We found three types of microhabitats and three genetically differentiated subpopulations with a significant level of genetic structure. In addition, we found slight genetic barriers. Our results suggest that mole salamander’s species are very sensitive to microhabitat features and relatively narrow obstacles in their path. The estimates of bidirectional gene flow are consistent with the pattern of a stepping stone model between demes, where migration occurs between adjacent demes, but there is low gene flow between distant demes. We can also conclude that there is a positive correlation between microhabitats and genetic structure in this population.

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          Most cited references16

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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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            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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              Spatial inference of admixture proportions and secondary contact zones.

              Genetic admixture of distinct gene pools is the consequence of complex spatiotemporal processes that could have involved massive migration and local mating during the history of a species. However, current methods for estimating individual admixture proportions lack the incorporation of such a piece of information. Here, we extend Bayesian clustering algorithms by including global trend surfaces and spatial autocorrelation in the prior distribution on individual admixture coefficients. We test our algorithm by using spatially explicit and realistic coalescent simulations of colonization followed by secondary contact. By coupling our multiscale spatial analyses with a Bayesian evaluation of model complexity and fit, we show that the algorithm provides a correct description of smooth clinal variation, while still detecting zones of sharp variation when they are present in the data. We also apply our approach to understand the population structure of the killifish, Fundulus heteroclitus, for which the algorithm uncovers a presumed contact zone in the Atlantic coast of North America.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                30 July 2014
                : 9
                : 7
                : e103595
                Affiliations
                [1]Estación Biológica Sierra Nanchititla, Facultad de Ciencias, Universidad Autónoma del Estado de México, Toluca, Estado de México, México
                Institute of Biochemistry and Biology, Germany
                Author notes

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

                Conceived and designed the experiments: AS OMV MZG. Performed the experiments: AS OMV CRV MZG. Analyzed the data: AS OMV CRV MZG. Contributed reagents/materials/analysis tools: AS OMV CRV MZG. Contributed to the writing of the manuscript: AS OMV MZG.

                Article
                PONE-D-14-12483
                10.1371/journal.pone.0103595
                4116214
                25076052
                998f7cb8-42ef-4e9b-9fe0-4685b70ae472
                Copyright @ 2014

                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
                : 27 March 2014
                : 1 July 2014
                Page count
                Pages: 11
                Funding
                This work was supported by grant number 3527/2013MT ( http://www.uaemex.mx/) from the Universidad Autónoma del Estado de Mexico to OMV. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Evolutionary Biology
                Genetics
                Conservation Genetics
                Molecular Biology
                Zoology
                Earth Sciences
                Marine and Aquatic Sciences
                Aquatic Environments
                Ecology and Environmental Sciences
                Habitats
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. All data are within paper.

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