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      Idiosyncratic responses to climate-driven forest fragmentation and marine incursions in reed frogs from Central Africa and the Gulf of Guinea Islands

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          Abstract

          Organismal traits interact with environmental variation to mediate how species respond to shared landscapes. Thus, differences in traits related to dispersal ability or physiological tolerance may result in phylogeographic discordance among co-distributed taxa, even when they are responding to common barriers. We quantified climatic suitability and stability, and phylogeographic divergence within three reed frog species complexes across the Guineo-Congolian forests and Gulf of Guinea archipelago of Central Africa to investigate how they responded to a shared climatic and geological history. Our species-specific estimates of climatic suitability through time are consistent with temporal and spatial heterogeneity in diversification among the species complexes, indicating that differences in ecological breadth may partly explain these idiosyncratic patterns. Likewise, we demonstrated that fluctuating sea levels periodically exposed a land bridge connecting Bioko Island with the mainland Guineo-Congolian forest and that habitats across the exposed land bridge likely enabled dispersal in some species, but not in others. We did not find evidence that rivers are biogeographic barriers across any of the species complexes. Despite marked differences in the geographic extent of stable climates and temporal estimates of divergence among the species complexes, we recovered a shared pattern of intermittent climatic suitability with recent population connectivity and demographic expansion across the Congo Basin. This pattern supports the hypothesis that genetic exchange across the Congo Basin during humid periods, followed by vicariance during arid periods, has shaped regional diversity. Finally, we identified many distinct lineages among our focal taxa, some of which may reflect incipient or unrecognized species.

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          Neighbor-net: an agglomerative method for the construction of phylogenetic networks.

          We present Neighbor-Net, a distance based method for constructing phylogenetic networks that is based on the Neighbor-Joining (NJ) algorithm of Saitou and Nei. Neighbor-Net provides a snapshot of the data that can guide more detailed analysis. Unlike split decomposition, Neighbor-Net scales well and can quickly produce detailed and informative networks for several hundred taxa. We illustrate the method by reanalyzing three published data sets: a collection of 110 highly recombinant Salmonella multi-locus sequence typing sequences, the 135 "African Eve" human mitochondrial sequences published by Vigilant et al., and a collection of 12 Archeal chaperonin sequences demonstrating strong evidence for gene conversion. Neighbor-Net is available as part of the SplitsTree4 software package.
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            USING CIRCUIT THEORY TO MODEL CONNECTIVITY IN ECOLOGY, EVOLUTION, AND CONSERVATION

            Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.
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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

                Journal
                Molecular Ecology
                Mol Ecol
                Wiley
                09621083
                October 2017
                October 2017
                August 24 2017
                : 26
                : 19
                : 5223-5244
                Affiliations
                [1 ]Department of Vertebrate Zoology; National Museum of Natural History; Smithsonian Institution; Washington DC USA
                [2 ]Museum of Vertebrate Zoology; University of California, Berkeley; CA USA
                [3 ]Department of Ecology and Evolutionary Biology; Cornell University; Ithaca NY USA
                [4 ]Grupo de Ecología y Evolución de Vertebrados; Instituto de Biología; Universidad de Antioquia; Medellín Colombia
                [5 ]Département d'Ecologie et Biodiversité des ressources Aquatiques; Centre de Surveillance de la Biodiversité; Kisangani Democratic Republic of the Congo
                [6 ]Museum für Naturkunde - Leibniz Institute for Evolution and Biodiversity Science; Berlin Germany
                [7 ]Florida Museum of Natural History; University of Florida; Gainesville FL USA
                [8 ]Department of Herpetology; California Academy of Sciences; San Francisco CA USA
                [9 ]African Amphibian Conservation Research Group; Unit for Environmental Sciences and Management; North-West University; Potchefstroom South Africa
                [10 ]Flora Fauna & Man, Ecological Services Ltd.; Tortola British Virgin Islands
                [11 ]Biodiversity and Conservation Biology Department; University of the Western Cape; Bellville South Africa
                [12 ]Abteilung Biologie; Institut für Integrierte Naturwissenschaften; Universität Koblenz-Landau; Koblenz Germany
                [13 ]Department of Biological Sciences; University of Texas at El Paso; El Paso TX USA
                [14 ]Institute of Vertebrate Biology; Czech Academy of Sciences; Brno Czech Republic
                [15 ]Department of Zoology; National Museum; Prague Czech Republic
                [16 ]Section of Freshwater Biology; Department of Biology; University of Copenhagen; Copenhagen Denmark
                [17 ]Center for Macroecology, Evolution and Climate; Natural History Museum of Denmark; Copenhagen Denmark
                [18 ]Laboratoire d'Herpétologie; Département de Biologie; Centre de Recherche en Sciences Naturelles; Lwiro Democratic Republic of the Congo
                [19 ]Biogeography Department; Trier University; Trier Germany
                [20 ]Department of Biology; Drexel University; Philadelphia PA USA
                [21 ]Royal Belgian Institute of Natural Sciences; Brussels Belgium
                [22 ]Department of Biology; University of Texas; Arlington TX USA
                [23 ]North Carolina Museum of Natural Sciences; Raleigh NC USA
                [24 ]Centre for Tropical Biodiveristy & Climate Change; College of Science and Engineering; James Cook University; Townsville Qld Australia
                [25 ]Division of Research and Innovation; eResearch Centre; James Cook University; Townsville Qld Australia
                [26 ]Institut National de Recherche en Sciences Exactes et Naturelles; Brazzaville République du Congo
                Article
                10.1111/mec.14260
                28753250
                67996f05-8052-4640-84c4-7e0f8265585d
                © 2017

                http://doi.wiley.com/10.1002/tdm_license_1.1

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