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      Using high-throughput sequencing to investigate the factors structuring genomic variation of a Mediterranean grasshopper of great conservation concern

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          Abstract

          Inferring the demographic history of species is fundamental for understanding their responses to past climate/landscape alterations and improving our predictions about the future impacts of the different components of ongoing global change. Estimating the time-frame at which population fragmentation took place is also critical to determine whether such process was shaped by ancient events (e.g. past climate/geological changes) or if, conversely, it was driven by recent human activities (e.g. habitat loss). We employed genomic data (ddRAD-Seq) to determine the factors shaping contemporary patterns of genetic variation in the Iberian cross-backed grasshopper Dociostaurus crassiusculus, an endangered species with limited dispersal capacity and narrow habitat requirements. Our analyses indicate the presence of two ancient lineages and three genetic clusters resulted from historical processes of population fragmentation (~18–126 ka) that predate the Anthropocene. Landscape genetic analyses indicate that the limits of major river basins are the main geographical feature explaining large-scale patterns of genomic differentiation, with no apparent effect of human-driven habitat fragmentation. Overall, our study highlights the importance of detailed phylogeographic, demographic and spatially-explicit landscape analyses to identify evolutionary significant units and determine the relative impact of historical vs. anthropogenic factors on processes of genetic fragmentation in taxa of great conservation concern.

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          Inbreeding and extinction in a butterfly metapopulation

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            Adaptive evolutionary conservation: towards a unified concept for defining conservation units.

            Recent years have seen a debate over various methods that could objectively prioritize conservation value below the species level. Most prominent among these has been the evolutionarily significant unit (ESU). We reviewed ESU concepts with the aim of proposing a more unified concept that would reconcile opposing views. Like species concepts, conflicting ESU concepts are all essentially aiming to define the same thing: segments of species whose divergence can be measured or evaluated by putting differential emphasis on the role of evolutionary forces at varied temporal scales. Thus, differences between ESU concepts lie more in the criteria used to define the ESUs themselves rather than in their fundamental essence. We provide a context-based framework for delineating ESUs which circumvents much of this situation. Rather than embroil in a befuddled debate over an optimal criterion, the key to a solution is accepting that differing criteria will work more dynamically than others and can be used alone or in combination depending on the situation. These assertions constitute the impetus behind adaptive evolutionary conservation.
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              Isolation by resistance.

              Brad McRae (2006)
              Despite growing interest in the effects of landscape heterogeneity on genetic structuring, few tools are available to incorporate data on landscape composition into population genetic studies. Analyses of isolation by distance have typically either assumed spatial homogeneity for convenience or applied theoretically unjustified distance metrics to compensate for heterogeneity. Here I propose the isolation-by-resistance (IBR) model as an alternative for predicting equilibrium genetic structuring in complex landscapes. The model predicts a positive relationship between genetic differentiation and the resistance distance, a distance metric that exploits precise relationships between random walk times and effective resistances in electronic networks. As a predictor of genetic differentiation, the resistance distance is both more theoretically justified and more robust to spatial heterogeneity than Euclidean or least cost path-based distance measures. Moreover, the metric can be applied with a wide range of data inputs, including coarse-scale range maps, simple maps of habitat and nonhabitat within a species' range, or complex spatial datasets with habitats and barriers of differing qualities. The IBR model thus provides a flexible and efficient tool to account for habitat heterogeneity in studies of isolation by distance, improve understanding of how landscape characteristics affect genetic structuring, and predict genetic and evolutionary consequences of landscape change.
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                Author and article information

                Contributors
                mariajose.gonzalez@uclm.es
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                7 September 2018
                7 September 2018
                2018
                : 8
                : 13436
                Affiliations
                [1 ]ISNI 0000 0001 2194 2329, GRID grid.8048.4, Grupo de Investigación de la Biodiversidad Genética y Cultural, , Instituto de Investigación en Recursos Cinegéticos – IREC – (CSIC, UCLM, JCCM), Ronda de Toledo, 12, ; E-13071 Ciudad Real, Spain
                [2 ]ISNI 0000 0001 1091 6248, GRID grid.418875.7, Department of Integrative Ecology, , Estación Biológica de Doñana – EBD – (CSIC), Avda. Américo Vespucio, 26, ; E-41092 Seville, Spain
                Author information
                http://orcid.org/0000-0001-8688-7623
                Article
                31775
                10.1038/s41598-018-31775-x
                6128945
                30194365
                3c21fb42-38dc-41d4-9190-4d6a38fca4df
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 May 2018
                : 3 August 2018
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