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      Large-Scale Genetic Structuring of a Widely Distributed Carnivore - The Eurasian Lynx ( Lynx lynx)

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          Abstract

          Over the last decades the phylogeography and genetic structure of a multitude of species inhabiting Europe and North America have been described. The flora and fauna of the vast landmasses of north-eastern Eurasia are still largely unexplored in this respect. The Eurasian lynx is a large felid that is relatively abundant over much of the Russian sub-continent and the adjoining countries. Analyzing 148 museum specimens collected throughout its range over the last 150 years we have described the large-scale genetic structuring in this highly mobile species. We have investigated the spatial genetic patterns using mitochondrial DNA sequences (D-loop and cytochrome b) and 11 microsatellite loci, and describe three phylogenetic clades and a clear structuring along an east-west gradient. The most likely scenario is that the contemporary Eurasian lynx populations originated in central Asia and that parts of Europe were inhabited by lynx during the Pleistocene. After the Last Glacial Maximum (LGM) range expansions lead to colonization of north-western Siberia and Scandinavia from the Caucasus and north-eastern Siberia from a refugium further east. No evidence of a Berinigan refugium could be detected in our data. We observed restricted gene flow and suggest that future studies of the Eurasian lynx explore to what extent the contemporary population structure may be explained by ecological variables.

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          How to track and assess genotyping errors in population genetics studies.

          Genotyping errors occur when the genotype determined after molecular analysis does not correspond to the real genotype of the individual under consideration. Virtually every genetic data set includes some erroneous genotypes, but genotyping errors remain a taboo subject in population genetics, even though they might greatly bias the final conclusions, especially for studies based on individual identification. Here, we consider four case studies representing a large variety of population genetics investigations differing in their sampling strategies (noninvasive or traditional), in the type of organism studied (plant or animal) and the molecular markers used [microsatellites or amplified fragment length polymorphisms (AFLPs)]. In these data sets, the estimated genotyping error rate ranges from 0.8% for microsatellite loci from bear tissues to 2.6% for AFLP loci from dwarf birch leaves. Main sources of errors were allelic dropouts for microsatellites and differences in peak intensities for AFLPs, but in both cases human factors were non-negligible error generators. Therefore, tracking genotyping errors and identifying their causes are necessary to clean up the data sets and validate the final results according to the precision required. In addition, we propose the outline of a protocol designed to limit and quantify genotyping errors at each step of the genotyping process. In particular, we recommend (i) several efficient precautions to prevent contaminations and technical artefacts; (ii) systematic use of blind samples and automation; (iii) experience and rigor for laboratory work and scoring; and (iv) systematic reporting of the error rate in population genetics studies.
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            A spatial statistical model for landscape genetics.

            Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST<0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.
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              Back to the future: museum specimens in population genetics.

              Museums and other natural history collections (NHC) worldwide house millions of specimens. With the advent of molecular genetic approaches these collections have become the source of many fascinating population studies in conservation genetics that contrast historical with present-day genetic diversity. Recent developments in molecular genetics and genomics and the associated statistical tools have opened up the further possibility of studying evolutionary change directly. As we discuss here, we believe that NHC specimens provide a largely underutilized resource for such investigations. However, because DNA extracted from NHC samples is degraded, analyses of such samples are technically demanding and many potential pitfalls exist. Thus, we propose a set of guidelines that outline the steps necessary to begin genetic investigations using specimens from NHC.
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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
                2 April 2014
                : 9
                : 4
                : e93675
                Affiliations
                [1 ]Centre for Ecological and Evolutionary Synthesis (CEES), Dept. of Biosciences, University of Oslo, Oslo, Norway
                [2 ]A. N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Leninsky pr. 33, Moscow, Russia
                Instituto de Higiene e Medicina Tropical, Portugal
                Author notes

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

                Conceived and designed the experiments: EKR SN NCS. Performed the experiments: EKR SN. Analyzed the data: EKR PT. Contributed reagents/materials/analysis tools: EKR SN PT NCS. Wrote the paper: EKR SN PT NCS.

                Article
                PONE-D-13-40675
                10.1371/journal.pone.0093675
                3973550
                24695745
                1357d6cc-fa1a-4ff3-9fd6-ed94cd0ba332
                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
                : 4 October 2013
                : 10 March 2014
                Page count
                Pages: 11
                Funding
                The study was funded by the Research Counsel of Norways project RCN 179569/V40, Centre for Ecological and Evolutionary Synthesis. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Biodiversity
                Evolutionary Ecology
                Spatial and Landscape Ecology
                Biogeography
                Evolutionary Biology
                Evolutionary Systematics
                Phylogenetics
                Animal Phylogenetics
                Organismal Evolution
                Animal Evolution
                Population Genetics
                Gene Flow
                Haplotypes
                Zoology
                Mammalogy
                Ecology and Environmental Sciences
                Conservation Science
                Terrestrial Environments

                Uncategorized
                Uncategorized

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