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      Everything you always wanted to know about gene flow in tropical landscapes (but were afraid to ask)

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          Abstract

          The bulk of the world’s biodiversity is found in tropical regions, which are increasingly threatened by the human-led degradation of natural habitats. Yet, little is known about tropical biodiversity responses to habitat loss and fragmentation. Here we review all available literature assessing landscape effects on gene flow in tropical species, aiming to help unravel the factors underpinning functional connectivity in the tropics. We map and classify studies by focus species, the molecular markers employed, statistical approaches to assess landscape effects on gene flow, and the evaluated landscape and environmental variables. We then compare qualitatively and quantitatively landscape effects on gene flow across species and units of analysis. We found 69 articles assessing landscape effects on gene flow in tropical organisms, most of which were published in the last five years, were concentrated in the Americas, and focused on amphibians or mammals. Most studies employed population-level approaches, microsatellites were the preferred type of markers, and Mantel and partial Mantel tests the most common statistical approaches used. While elevation, land cover and forest cover were the most common gene flow predictors assessed, habitat suitability was found to be a common predictor of gene flow. A third of all surveyed studies explicitly assessed the effect of habitat degradation, but only 14 of these detected a reduced gene flow with increasing habitat loss. Elevation was responsible for most significant microsatellite-based isolation by resistance effects and a single study reported significant isolation by non-forested areas in an ant. Our study reveals important knowledge gaps on the study of landscape effects on gene flow in tropical organisms, and provides useful guidelines on how to fill them.

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          Estimating landscape resistance to movement: a review

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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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              Circuit theory predicts gene flow in plant and animal populations.

              Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                13 February 2019
                2019
                : 7
                : e6446
                Affiliations
                [1 ]Instituto Tecnológico Vale , Belém, PA, Brazil
                [2 ]Instituto de Ciências Biológicas, Universidade Federal do Pará , Belém, Pará, Brazil
                [3 ]Departamento de Botânica, Museu Paraense Emílio Goeldi , Belém, Pará, Brazil
                [4 ]CONACYT, Red de Diversidad Biológica del Occidente Mexicano, Instituto de Ecología, A.C. , Michoacán, Mexico
                [5 ]Departamento de Ecologia, Universidade de São Paulo , São Paulo, Brazil
                Article
                6446
                10.7717/peerj.6446
                6377592
                67ebf3cd-880f-44b9-934b-190049721728
                ©2019 Monteiro et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 28 August 2018
                : 15 January 2019
                Funding
                Funded by: Instituto Tecnologico Vale, CAPES
                Award ID: 88882.161651/2017-01
                Award ID: 15/2014
                Award ID: 88887.156652/2017-00
                Funded by: CNPq
                Award ID: 300714/2017-3
                Award ID: 131105/2016-7
                Award ID: 301616/2017-5
                This study was funded by Instituto Tecnologico Vale, CAPES (88882.161651/2017-01–Waléria Pereira Monteiro, 15/2014–Jamille Costa Veiga and 88887.156652/2017-00–Carolina da Silva Carvalho), and CNPq (300714/2017-3–Éder Cristian Malta Lanes, 131105/2016-7–Amanda Reis Silva and 301616/2017-5–Rodolfo Jaffé). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Biodiversity
                Conservation Biology
                Ecology
                Genetics
                Spatial and Geographic Information Science

                functional connectivity,landscape genetics,isolation by resistance,matrix permeability,tropical biodiversity

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