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      Mixing of porpoise ecotypes in southwestern UK waters revealed by genetic profiling

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          Abstract

          Contact zones between ecotypes are windows for understanding how species may react to climate changes. Here, we analysed the fine-scale genetic and morphological variation in harbour porpoises ( Phocoena phocoena) around the UK by genotyping 591 stranded animals at nine microsatellite loci. The data were integrated with a prior study to map at high resolution the contact zone between two previously identified ecotypes meeting in the northern Bay of Biscay. Clustering and spatial analyses revealed that UK porpoises are derived from two genetic pools with porpoises from the southwestern UK being genetically differentiated, and having larger body sizes compared to those of other UK areas. Southwestern UK porpoises showed admixed ancestry between southern and northern ecotypes with a contact zone extending from the northern Bay of Biscay to the Celtic Sea and Channel. Around the UK, ancestry blends from one genetic group to the other along a southwest--northeast axis, correlating with body size variation, consistent with previously reported morphological differences between the two ecotypes. We also detected isolation by distance among juveniles but not in adults, suggesting that stranded juveniles display reduced intergenerational dispersal. The fine-scale structure of this admixture zone raises the question of how it will respond to future climate change and provides a reference point for further study.

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          ADZE: a rarefaction approach for counting alleles private to combinations of populations

          Motivation: Analysis of the distribution of alleles across populations is a useful tool for examining population diversity and relationships. However, sample sizes often differ across populations, sometimes making it difficult to assess allelic distributions across groups. Results: We introduce a generalized rarefaction approach for counting alleles private to combinations of populations. Our method evaluates the number of alleles found in each of a set of populations but absent in all remaining populations, considering equal-sized subsamples from each population. Applying this method to a worldwide human microsatellite dataset, we observe a high number of alleles private to the combination of African and Oceanian populations. This result supports the possibility of a migration out of Africa into Oceania separate from the migrations responsible for the majority of the ancestry of the modern populations of Asia, and it highlights the utility of our approach to sample size correction in evaluating hypotheses about population history. Availability: We have implemented our method in the computer pro-gram ADZE, which is available for download at http://rosenberglab.bioinformatics.med.umich.edu/adze.html. Contact: szpiechz@umich.edu
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            Revealing cryptic spatial patterns in genetic variability by a new multivariate method.

            Increasing attention is being devoted to taking landscape information into account in genetic studies. Among landscape variables, space is often considered as one of the most important. To reveal spatial patterns, a statistical method should be spatially explicit, that is, it should directly take spatial information into account as a component of the adjusted model or of the optimized criterion. In this paper we propose a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations. This analysis does not require data to meet Hardy-Weinberg expectations or linkage equilibrium to exist between loci. The sPCA yields scores summarizing both the genetic variability and the spatial structure among individuals (or populations). Global structures (patches, clines and intermediates) are disentangled from local ones (strong genetic differences between neighbors) and from random noise. Two statistical tests are proposed to detect the existence of both types of patterns. As an illustration, the results of principal component analysis (PCA) and sPCA are compared using simulated datasets and real georeferenced microsatellite data of Scandinavian brown bear individuals (Ursus arctos). sPCA performed better than PCA to reveal spatial genetic patterns. The proposed methodology is implemented in the adegenet package of the free software R.
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              Genetic differentiation between individuals

              Rousset (2000)
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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society Publishing
                2054-5703
                March 2017
                1 March 2017
                1 March 2017
                : 4
                : 3
                : 160992
                Affiliations
                [1 ]Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen , PO Box 11103 CC, Groningen, The Netherlands
                [2 ]School of Biology, Faculty of Biological Sciences, University of Leeds , Leeds LS2 9JT, UK
                [3 ]Institute of Zoology, Zoological Society of London , London NW1 4RY, UK
                [4 ]Department of Zoology, University of Cambridge , Cambridge CB2 3EJ, UK
                [5 ]EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva , Carouge, Switzerland
                [6 ]INRA, UMR CBGP, 34988 Montferrier-sur-Lez Cedex, France
                [7 ]Scottish Marine Animal Stranding Scheme, SRUC Veterinary Services , Drummondhill, Stratherrick Road, Inverness IV2 4JZ, UK
                [8 ]Animal and Plant Health Agency , Polwhele, Truro, Cornwall TR4 9AD, UK
                Author notes
                Author for correspondence: Michaël C. Fontaine e-mail: mikafontaine@ 123456gmail.com
                Author for correspondence: Simon J. Goodman e-mail: s.j.goodman@ 123456leeds.ac.uk

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.3691933.

                Author information
                http://orcid.org/0000-0003-1156-4154
                Article
                rsos160992
                10.1098/rsos.160992
                5383846
                28405389
                1e1f3918-fa3e-45ec-a221-2bf8ed72d99c
                © 2017 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 3 December 2016
                : 30 January 2017
                Funding
                Funded by: Marie-Curie Fellowship - AGAPE program;
                Award ID: MEST-CT-2004-504318
                Funded by: Natural Environment Research Council (NERC) PhD studentship;
                Award ID: NER/S/A/2001/06405
                Categories
                1001
                70
                197
                60
                Biology (Whole Organism)
                Research Article
                Custom metadata
                March, 2017

                ecotype specialization,molecular ecology,continuous population,dispersal,climate change,admixture

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