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      Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections

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          Significance

          Forecasts of species vulnerability and extinction risk under future climate change commonly ignore local adaptations despite their importance for determining the potential of populations to respond to future changes. We present an approach to assess the impacts of global climate change on biodiversity that takes into account adaptive genetic variation and evolutionary potential. We show that considering local climatic adaptations reduces range loss projections but increases the potential for competition between species. Our findings suggest that failure to account for within-species variability can result in overestimation of future biodiversity losses. Therefore, it is important to identify the climate-adaptive potential of populations and to increase landscape connectivity between populations to enable the spread of adaptive genetic variation.

          Abstract

          Local adaptations can determine the potential of populations to respond to environmental changes, yet adaptive genetic variation is commonly ignored in models forecasting species vulnerability and biogeographical shifts under future climate change. Here we integrate genomic and ecological modeling approaches to identify genetic adaptations associated with climate in two cryptic forest bats. We then incorporate this information directly into forecasts of range changes under future climate change and assessment of population persistence through the spread of climate-adaptive genetic variation (evolutionary rescue potential). Considering climate-adaptive potential reduced range loss projections, suggesting that failure to account for intraspecific variability can result in overestimation of future losses. On the other hand, range overlap between species was projected to increase, indicating that interspecific competition is likely to play an important role in limiting species’ future ranges. We show that although evolutionary rescue is possible, it depends on a population’s adaptive capacity and connectivity. Hence, we stress the importance of incorporating genomic data and landscape connectivity in climate change vulnerability assessments and conservation management.

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          Most cited references21

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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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            Uncertainty in ensemble forecasting of species distribution

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              Genomic signals of selection predict climate-driven population declines in a migratory bird

              The ongoing loss of biodiversity caused by rapid climatic shifts requires accurate models for predicting species' responses. Despite evidence that evolutionary adaptation could mitigate climate change impacts, evolution is rarely integrated into predictive models. Integrating population genomics and environmental data, we identified genomic variation associated with climate across the breeding range of the migratory songbird, yellow warbler (Setophaga petechia). Populations requiring the greatest shifts in allele frequencies to keep pace with future climate change have experienced the largest population declines, suggesting that failure to adapt may have already negatively affected populations. Broadly, our study suggests that the integration of genomic adaptation can increase the accuracy of future species distribution models and ultimately guide more effective mitigation efforts.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                21 May 2019
                6 May 2019
                6 May 2019
                : 116
                : 21
                : 10418-10423
                Affiliations
                [1] aBiological Sciences, University of Southampton , Southampton SO17 1BJ, United Kingdom;
                [2] bSchool of Biological Sciences, University of Bristol , Bristol BS8 1TQ, United Kingdom;
                [3] cDepartment of Biology, Colorado State University , Fort Collins, CO 80523-1878;
                [4] dInstitute of Aquaculture, University of Stirling , Stirling FK9 4LA, United Kingdom;
                [5] eEstación Biológica de Doñana, Consejo Superior de Investigaciones Cientifica (CSIC) , 41092 Seville, Spain;
                [6] fInstitut des Sciences de l’Evolution de Montpellier (ISEM), University of Montpellier , 34095 Montpellier, France;
                [7] gGroupe Chiroptères de Midi-Pyrénées (GCMP) , 31076 Toulouse, France;
                [8] hZoological Institute and Museum, University of Greifswald , 17489 Greifswald, Germany;
                [9] iEvolutionary Genomics, University of Copenhagen , 1350 Copenhagen, Denmark;
                [10] jCentre d’Écologie Fonctionnelle et Évolutive (CEFE), Université de Recherche Paris Sciences et Lettres (PSL), École Pratique des Hautes Études (EPHE), Université de Montpellier , 34293 Montpellier, France
                Author notes
                1To whom correspondence should be addressed. Email: Orly.Razgour@ 123456soton.ac.uk .

                Edited by Nils Chr. Stenseth, University of Oslo, Oslo, Norway, and approved April 5, 2019 (received for review December 4, 2018)

                Author contributions: O.R. designed research; O.R., J.B.T., J.J., C.I., S.J.P., and A.A. performed research; B.F., J.B.T., and S.M. contributed new reagents/analytic tools; O.R., B.F., M.B., and R.N.-F. analyzed data; and O.R. wrote the paper.

                Author information
                http://orcid.org/0000-0003-3186-0313
                http://orcid.org/0000-0002-1206-7654
                Article
                201820663
                10.1073/pnas.1820663116
                6535011
                31061126
                27edc01c-143d-41c5-a346-02f83c6b2f6a
                Copyright © 2019 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 6
                Funding
                Funded by: NERC Environmental Bioinformatics Centre (NEBC) 100008668
                Award ID: NE/M018660/1
                Award Recipient : Orly Razgour
                Categories
                Biological Sciences
                Evolution

                global climate change,genetic adaptations,ecological niche models,conservation genomics,evolutionary rescue

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