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      Introduction history overrides social factors in explaining genetic structure of females in Mediterranean mouflon

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          Abstract

          Fine‐scale spatial genetic structure of populations results from social and spatial behaviors of individuals such as sex‐biased dispersal and philopatry. However, the demographic history of a given population can override such socio‐spatial factors in shaping genetic variability when bottlenecks or founder events occurred in the population. Here, we investigated whether socio‐spatial organization determines the fine‐scale genetic structure for both sexes in a Mediterranean mouflon ( Ovis gmelini musimon ×  Ovis sp.) population in southern France 60 years after its introduction. Based on multilocus genotypes at 16 loci of microsatellite DNA ( n = 230 individuals), we identified three genetic groups for females and two for males, and concurrently defined the same number of socio‐spatial units using both GPS‐collared individuals ( n = 121) and visual resightings of marked individuals ( n = 378). The socio‐spatial and genetic structures did not match, indicating that the former was not the main driver of the latter for both sexes. Beyond this structural mismatch, we found significant, yet low, genetic differentiation among female socio‐spatial groups, and no genetic differentiation in males, with this suggesting female philopatry and male‐biased gene flow, respectively. Despite spatial disconnection, females from the north of the study area were genetically closer to females from the south, as indicated by the spatial analysis of the genetic variability, and this pattern was in accordance with the common genetic origin of their founders. To conclude, more than 14 generations later, genetic signatures of first introduction are not only still detectable among females, but they also represent the main factor shaping their present‐time genetic structure.

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          Most cited references72

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          Gene flow and the geographic structure of natural populations.

          M Slatkin (1987)
          There is abundant geographic variation in both morphology and gene frequency in most species. The extent of geographic variation results from a balance of forces tending to produce local genetic differentiation and forces tending to produce genetic homogeneity. Mutation, genetic drift due to finite population size, and natural selection favoring adaptations to local environmental conditions will all lead to the genetic differentiation of local populations, and the movement of gametes, individuals, and even entire populations--collectively called gene flow--will oppose that differentiation. Gene flow may either constrain evolution by preventing adaptation to local conditions or promote evolution by spreading new genes and combinations of genes throughout a species' range. Several methods are available for estimating the amount of gene flow. Direct methods monitor ongoing gene flow, and indirect methods use spatial distributions of gene frequencies to infer past gene flow. Applications of these methods show that species differ widely in the gene flow that they experience. Of particular interest are those species for which direct methods indicate little current gene flow but indirect methods indicate much higher levels of gene flow in the recent past. Such species probably have undergone large-scale demographic changes relatively frequently.
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            Advances in our understanding of mammalian sex-biased dispersal.

            Sex-biased dispersal is an almost ubiquitous feature of mammalian life history, but the evolutionary causes behind these patterns still require much clarification. A quarter of a century since the publication of seminal papers describing general patterns of sex-biased dispersal in both mammals and birds, we review the advances in our theoretical understanding of the evolutionary causes of sex-biased dispersal, and those in statistical genetics that enable us to test hypotheses and measure dispersal in natural populations. We use mammalian examples to illustrate patterns and proximate causes of sex-biased dispersal, because by far the most data are available and because they exhibit an enormous diversity in terms of dispersal strategy, mating and social systems. Recent studies using molecular markers have helped to confirm that sex-biased dispersal is widespread among mammals and varies widely in direction and intensity, but there is a great need to bridge the gap between genetic information, observational data and theory. A review of mammalian data indicates that the relationship between direction of sex-bias and mating system is not a simple one. The role of social systems emerges as a key factor in determining intensity and direction of dispersal bias, but there is still need for a theoretical framework that can account for the complex interactions between inbreeding avoidance, kin competition and cooperation to explain the impressive diversity of patterns.
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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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                Author and article information

                Contributors
                elodie.portanier@gmail.com
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                16 November 2017
                November 2017
                : 7
                : 22 ( doiID: 10.1002/ece3.2017.7.issue-22 )
                : 9580-9591
                Affiliations
                [ 1 ] Laboratoire de Biométrie et Biologie Evolutive CNRS Université Claude Bernard Lyon 1 Université de Lyon Villeurbanne France
                [ 2 ] Unité Faune de Montagne Office National de la Chasse et de la Faune Sauvage Juvignac France
                [ 3 ] VetAgro Sup ‐ Campus Vétérinaire de Lyon Université de Lyon Marcy l'Etoile France
                Author notes
                [*] [* ] Correspondence

                Elodie Portanier, Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard Lyon 1, Université de Lyon, Villeurbanne, France.

                Email: elodie.portanier@ 123456gmail.com

                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-6566-6263
                Article
                ECE33433
                10.1002/ece3.3433
                5696436
                764dea25-493e-4b99-abaf-0f6aa74b20cf
                © 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
                : 06 June 2017
                : 15 August 2017
                : 15 August 2017
                Page count
                Figures: 4, Tables: 5, Pages: 12, Words: 10887
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                ece33433
                November 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.6 mode:remove_FC converted:20.11.2017

                Evolutionary Biology
                introduction,large herbivores,ovis,socio‐spatial organization,spatial genetic structure

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