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      Genetic evidence challenges the native status of a threatened freshwater fish ( Carassius carassius) in England

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          Abstract

          A fundamental consideration for the conservation of a species is the extent of its native range, that is, regions naturally colonized. However, both natural processes and human‐mediated introductions can drive species distribution shifts. Ruling out the human‐mediated introduction of a species into a given region is vital for its conservation, but remains a significant challenge in most cases. The crucian carp Carassius carassius (L.) is a threatened freshwater fish thought to be native to much of Europe. However, its native status in England is based only on anecdotal evidence. Here, we devise an approach that can be used to empirically test the native status of English fauna. We use this approach, along with 13 microsatellite loci, population structure analyses, and Approximate Bayesian Computation ( ABC), to test hypotheses for the origins of C. carassius in England. Contrary to the current consensus, we find strong support for the human‐mediated introduction of C. carassius into England during the 15th century. This result stimulates an interesting and timely debate surrounding motivations for the conservation of species. We discuss this topic, and the potential for continued conservation of C. carassius in England, despite its non‐native origins.

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          Most cited references100

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          Discriminant analysis of principal components: a new method for the analysis of genetically structured populations

          Background The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. Results We introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Conclusions Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.
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            genepop'007: a complete re-implementation of the genepop software for Windows and Linux.

            This note summarizes developments of the genepop software since its first description in 1995, and in particular those new to version 4.0: an extended input format, several estimators of neighbourhood size under isolation by distance, new estimators and confidence intervals for null allele frequency, and less important extensions to previous options. genepop now runs under Linux as well as under Windows, and can be entirely controlled by batch calls. © 2007 The Author.
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              adegenet 1.3-1: new tools for the analysis of genome-wide SNP data.

              While the R software is becoming a standard for the analysis of genetic data, classical population genetics tools are being challenged by the increasing availability of genomic sequences. Dedicated tools are needed for harnessing the large amount of information generated by next-generation sequencing technologies. We introduce new tools implemented in the adegenet 1.3-1 package for handling and analyzing genome-wide single nucleotide polymorphism (SNP) data. Using a bit-level coding scheme for SNP data and parallelized computation, adegenet enables the analysis of large genome-wide SNPs datasets using standard personal computers. adegenet 1.3-1 is available from CRAN: http://cran.r-project.org/web/packages/adegenet/. Information and support including a dedicated forum of discussion can be found on the adegenet website: http://adegenet.r-forge.r-project.org/. adegenet is released with a manual and four tutorials totalling over 300 pages of documentation, and distributed under the GNU General Public Licence (≥2). t.jombart@imperial.ac.uk. Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                dljeffries86@gmail.com
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                22 March 2017
                May 2017
                : 7
                : 9 ( doiID: 10.1002/ece3.2017.7.issue-9 )
                : 2871-2882
                Affiliations
                [ 1 ] Evolutionary Biology Group School of Biological, Biomedical and Environmental Sciences, Hardy BuildingUniversity of Hull HullUK
                [ 2 ] Salmon and Freshwater TeamCefas Lowestoft SuffolkUK
                [ 3 ] Department of Ecology and Evolution University of LausanneLausanne Switzerland
                [ 4 ] Department of Life and Environmental Sciences Faculty of Science and TechnologyBournemouth University PooleUK
                [ 5 ] Laboratory of Biodiversity and Evolutionary GenomicsUniversity of Leuven LeuvenBelgium
                [ 6 ] Laboratory for Cytogenetics and Genome Research Centre for Human Genetics Genomics CoreUniversity of Leuven (KU Leuven), 3000 LeuvenBelgium
                [ 7 ] Pond Restoratation Research Group Department of Geography Environmental Change Research CentreUniversity College London LondonUK
                Author notes
                [*] [* ] Correspondence

                Daniel L. Jeffries, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.

                Email: dljeffries86@ 123456gmail.com

                Author information
                http://orcid.org/0000-0003-1701-3978
                Article
                ECE32831
                10.1002/ece3.2831
                5415527
                28479988
                5771028a-bd79-44c8-b1dd-9c6c424415f4
                © 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 November 2016
                : 28 January 2017
                Page count
                Figures: 3, Tables: 1, Pages: 13, Words: 10816
                Funding
                Funded by: FSBI
                Funded by: Cefas
                Funded by: Flemish Agency for Nature and Forests
                Award ID: B&G/22/2003
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                ece32831
                May 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.0.9 mode:remove_FC converted:03.05.2017

                Evolutionary Biology
                approximate bayesian computation,introduced species,land bridge,microsatellites,postglacial recolonization

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