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      Landscape genomics to the rescue of a tropical bee threatened by habitat loss and climate change

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          Abstract

          Habitat degradation and climate change are currently threatening wild pollinators, compromising their ability to provide pollination services to wild and cultivated plants. Landscape genomics offers powerful tools to assess the influence of landscape modifications on genetic diversity and functional connectivity, and to identify adaptations to local environmental conditions that could facilitate future bee survival. Here, we assessed range‐wide patterns of genetic structure, genetic diversity, gene flow, and local adaptation in the stingless bee Melipona subnitida, a tropical pollinator of key biological and economic importance inhabiting one of the driest and hottest regions of South America. Our results reveal four genetic clusters across the species’ full distribution range. All populations were found to be under a mutation–drift equilibrium, and genetic diversity was not influenced by the amount of reminiscent natural habitats. However, genetic relatedness was spatially autocorrelated and isolation by landscape resistance explained range‐wide relatedness patterns better than isolation by geographic distance, contradicting earlier findings for stingless bees. Specifically, gene flow was enhanced by increased thermal stability, higher forest cover, lower elevations, and less corrugated terrains. Finally, we detected genomic signatures of adaptation to temperature, precipitation, and forest cover, spatially distributed in latitudinal and altitudinal patterns. Taken together, our findings shed important light on the life history of M. subnitida and highlight the role of regions with large thermal fluctuations, deforested areas, and mountain ranges as dispersal barriers. Conservation actions such as restricting long‐distance colony transportation, preserving local adaptations, and improving the connectivity between highlands and lowlands are likely to assure future pollination services.

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          Most cited references56

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          A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems.

          Bees provide essential pollination services that are potentially affected both by local farm management and the surrounding landscape. To better understand these different factors, we modelled the relative effects of landscape composition (nesting and floral resources within foraging distances), landscape configuration (patch shape, interpatch connectivity and habitat aggregation) and farm management (organic vs. conventional and local-scale field diversity), and their interactions, on wild bee abundance and richness for 39 crop systems globally. Bee abundance and richness were higher in diversified and organic fields and in landscapes comprising more high-quality habitats; bee richness on conventional fields with low diversity benefited most from high-quality surrounding land cover. Landscape configuration effects were weak. Bee responses varied slightly by biome. Our synthesis reveals that pollinator persistence will depend on both the maintenance of high-quality habitats around farms and on local management practices that may offset impacts of intensive monoculture agriculture. © 2013 Blackwell Publishing Ltd/CNRS.
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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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              Comparative population genomics in animals uncovers the determinants of genetic diversity.

              Genetic diversity is the amount of variation observed between DNA sequences from distinct individuals of a given species. This pivotal concept of population genetics has implications for species health, domestication, management and conservation. Levels of genetic diversity seem to vary greatly in natural populations and species, but the determinants of this variation, and particularly the relative influences of species biology and ecology versus population history, are still largely mysterious. Here we show that the diversity of a species is predictable, and is determined in the first place by its ecological strategy. We investigated the genome-wide diversity of 76 non-model animal species by sequencing the transcriptome of two to ten individuals in each species. The distribution of genetic diversity between species revealed no detectable influence of geographic range or invasive status but was accurately predicted by key species traits related to parental investment: long-lived or low-fecundity species with brooding ability were genetically less diverse than short-lived or highly fecund ones. Our analysis demonstrates the influence of long-term life-history strategies on species response to short-term environmental perturbations, a result with immediate implications for conservation policies.
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                Author and article information

                Contributors
                r.jaffe@ib.usp.br
                Journal
                Evol Appl
                Evol Appl
                10.1111/(ISSN)1752-4571
                EVA
                Evolutionary Applications
                John Wiley and Sons Inc. (Hoboken )
                1752-4571
                10 April 2019
                June 2019
                : 12
                : 6 ( doiID: 10.1111/eva.2019.12.issue-6 )
                : 1164-1177
                Affiliations
                [ 1 ] Instituto Tecnológico Vale Belém Brazil
                [ 2 ] Departamento de Ecologia Universidade de São Paulo São Paulo Brazil
                [ 3 ] Departamento de Biociências Universidade Federal Rural do Semi‐Árido Mossoró Brazil
                [ 4 ] Instituto de Ciências Biológicas Universidade Federal do Pará Belém Brazil
                [ 5 ] Department of Integrative Biology University of Texas Austin Texas
                [ 6 ] Departamento de Genética e Biologia Evolutiva Universidade de São Paulo São Paulo Brazil
                [ 7 ] Departamento de Genética, Faculdade de Medicina de Ribeirão Preto Universidade de São Paulo Ribeirão Preto Brazil
                [ 8 ] Unidade Acadêmica de Serra Talhada Universidade Federal Rural de Pernambuco Serra Talhada Brazil
                [ 9 ] Centro de Agroecologia Rio Seco Universidade Estadual de Feira de Santana Amélia Rodrigues Brazil
                [ 10 ] Departamento de Zootecnia Universidade Federal do Ceará Fortaleza Brazil
                [ 11 ] Departamento de Sistemática e Ecologia Universidade Federal da Paraíba João Pessoa Brazil
                [ 12 ] Escola Politécnica da Universidade de São Paulo Universidade de São Paulo São Paulo Brazil
                [ 13 ] Embrapa Meio‐Norte Teresina Brazil
                Author notes
                [*] [* ] Correspondence

                Rodolfo Jaffé, Instituto Tecnológico Vale, Belém, Brazil.

                Email: r.jaffe@ 123456ib.usp.br

                [†]

                These authors contributed equally.

                Author information
                http://orcid.org/0000-0002-2101-5282
                http://orcid.org/0000-0001-7554-2785
                http://orcid.org/0000-0001-5326-0891
                http://orcid.org/0000-0002-0063-2185
                http://orcid.org/0000-0003-4139-0562
                http://orcid.org/0000-0003-1477-101X
                http://orcid.org/0000-0003-3395-4444
                http://orcid.org/0000-0002-7078-5048
                http://orcid.org/0000-0002-2413-966X
                http://orcid.org/0000-0002-9932-2207
                http://orcid.org/0000-0001-9830-1204
                http://orcid.org/0000-0003-4931-3924
                http://orcid.org/0000-0001-5953-3758
                http://orcid.org/0000-0003-0054-3438
                http://orcid.org/0000-0002-1079-2158
                Article
                EVA12794
                10.1111/eva.12794
                6597871
                31293629
                8a5add75-51a6-4095-a886-36956e97db11
                © 2019 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 September 2018
                : 14 March 2019
                : 19 March 2019
                Page count
                Figures: 6, Tables: 5, Pages: 14, Words: 10339
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico
                Award ID: 300714/2017-3
                Award ID: 301616/2017-5
                Award ID: 302976/2015-9
                Award ID: 305126/2013-0
                Award ID: 306932/2016-4
                Award ID: 311531/2014-8
                Award ID: 478982/2013-5
                Funded by: Research Center on Biodiversity and Computing
                Funded by: Empresa Brasileira de Pesquisa Agropecuária
                Award ID: 02.11.01.029.00.00
                Funded by: Instituto Tecnológico Vale
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
                Award ID: 15/2014
                Award ID: 88887.156652/2017-00
                Funded by: National Science Foundation
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                eva12794
                June 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.5 mode:remove_FC converted:28.06.2019

                Evolutionary Biology
                deforestation,environmental associations,gene flow,isolation by resistance,local adaptation,pollination,single nucleotide polymorphism,stingless bees

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