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      How spatio-temporal habitat connectivity affects amphibian genetic structure

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          Abstract

          Heterogeneous landscapes and fluctuating environmental conditions can affect species dispersal, population genetics, and genetic structure, yet understanding how biotic and abiotic factors affect population dynamics in a fluctuating environment is critical for species management. We evaluated how spatio-temporal habitat connectivity influences dispersal and genetic structure in a population of boreal chorus frogs ( Pseudacris maculata) using a landscape genetics approach. We developed gravity models to assess the contribution of various factors to the observed genetic distance as a measure of functional connectivity. We selected (a) wetland (within-site) and (b) landscape matrix (between-site) characteristics; and (c) wetland connectivity metrics using a unique methodology. Specifically, we developed three networks that quantify wetland connectivity based on: (i) P. maculata dispersal ability, (ii) temporal variation in wetland quality, and (iii) contribution of wetland stepping-stones to frog dispersal. We examined 18 wetlands in Colorado, and quantified 12 microsatellite loci from 322 individual frogs. We found that genetic connectivity was related to topographic complexity, within- and between-wetland differences in moisture, and wetland functional connectivity as contributed by stepping-stone wetlands. Our results highlight the role that dynamic environmental factors have on dispersal-limited species and illustrate how complex asynchronous interactions contribute to the structure of spatially-explicit metapopulations.

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          Most cited references51

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          Effects of habitat loss and fragmentation on amphibians: A review and prospectus

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            microsatellite analyser(MSA): a platform independent analysis tool for large microsatellite data sets

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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
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                08 September 2015
                2015
                : 6
                : 275
                Affiliations
                [1] 1Department of Ecology & Evolutionary Biology, University of Toronto Toronto, ON, Canada
                [2] 2Department of Natural Resources Management, Texas Tech University Lubbock, TX, USA
                [3] 3Department of Zoology and Physiology, University of Wyoming Laramie, WY, USA
                [4] 4Program in Ecology, University of Wyoming Laramie, WY, USA
                [5] 5School of Biological Sciences, Washington State University Pullman, WA, USA
                [6] 6Graduate Degree Program in Ecology, Department of Biology, Colorado State University Fort Collins, CO, USA
                [7] 7Department of Biology, Central Connecticut State University New Britain, CT, USA
                [8] 8Fort Collins Science Center, U.S. Geological Survey Fort Collins, CO, USA
                [9] 9Department of Ecosystem Science and Management, University of Wyoming Laramie, WY, USA
                Author notes

                Edited by: Andrew Shirk, University of Washington, USA

                Reviewed by: Peter J. Prentis, Queensland University of Technology, Australia; Jeffrey Ryan Row, University of Waterloo, Canada

                *Correspondence: Alexander G. Watts, Department of Ecology & Evolution, University of Toronto, 25 Harbord St., Toronto, ON M5S 3G5, Canada alexander.watts@ 123456mail.utoronto.ca ;
                Melanie A. Murphy, Department of Ecosystem Science and Management, Program in Ecology, University of Wyoming, 1000 E. University Ave., Laramie, WY 82071, USA melanie.murphy@ 123456uwyo.edu

                This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2015.00275
                4561841
                26442094
                f38d195b-bb5d-48aa-87e4-9a8e60d27d12
                Copyright © 2015 Watts, Schlichting, Billerman, Jesmer, Micheletti, Fortin, Funk, Hapeman, Muths and Murphy.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 May 2015
                : 14 August 2015
                Page count
                Figures: 2, Tables: 4, Equations: 1, References: 78, Pages: 13, Words: 9023
                Categories
                Genetics
                Original Research

                Genetics
                boreal chorus frog (pseudacris maculata),functional connectivity,dispersal,gravity model,landscape genetics,metapopulation dynamics,spatio-temporal dynamics

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