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      Circuit-theory applications to connectivity science and conservation : Circuit Theory

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          Abstract

          Conservation practitioners have long recognized ecological connectivity as a global priority for preserving biodiversity and ecosystem function. In the early years of conservation biology, ecologists extended principles of island biogeography to assess connectivity using measures of source patch proximity and other metrics derivable from binary maps of habitat. From 2006–2008, the late Brad McRae introduced circuit theory to many ecologists and conservation biologists as an alternative approach to model gene flow and the dispersal or movement routes of organisms. He posited concepts and metrics from electrical circuit theory as a robust way to quantify movement across multiple possible paths in a landscape, not just a single least-cost path or corridor. Here we discuss applications of circuit theory and related tools to the science and practice of connectivity conservation. We begin with a brief introduction to the foundations of circuit theory and a synthesis of recent publications from multiple geographies. We then sample and explore the diverse array of applications of circuit theory and the open-source software Circuitscape , focusing on how these tools have been used to understand genetic structuring, the movement and dispersal paths of organisms, habitat corridors and barriers, and the impacts of humans and climate change on connectivity. Finally, we consider the impact that circuit theory is likely to have on conservation science and practitioners, as well as the maintenance and restoration of connectivity for species and fundamental ecological processes around the globe.

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          Most cited references80

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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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            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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                Author and article information

                Journal
                Conservation Biology
                Conservation Biology
                Wiley
                08888892
                November 27 2018
                Affiliations
                [1 ]Conservation Science Partners Inc.; 11050 Pioneer Trail, Suite 202 Truckee CA 96161 U.S.A.
                [2 ]Landscape Conservation Initiative; Northern Arizona University; Box 5694 Flagstaff AZ 86011 U.S.A.
                [3 ]Julia Computing; 45 Prospect Street Cambridge MA 02139 U.S.A.
                [4 ]School of Forestry; Northern Arizona University; Box 15018 Flagstaff AZ 86011 U.S.A.
                [5 ]The Nature Conservancy - North America Region; 1101 West River Parkway, Suite 200 Minneapolis MN 55415 U.S.A.
                [6 ]U.S. Geological Survey; Northern Rocky Mountain Science Center; 38 Mather Drive West Glacier MT 59936 U.S.A.
                [7 ]School of Environmental and Forest Sciences; University of Washington; Box 352100 Seattle WA 98195 U.S.A.
                [8 ]U.S. Fish & Wildlife Service; Science Applications; 101 12th Avenue, Number 110 Fairbanks AK 99701 U.S.A.
                [9 ]Department of Human Genetics, Department of Ecology and Evolution; University of Chicago; 920 East 58th Street Chicago IL 60637 U.S.A.
                [10 ]The Nature Conservancy; 201 Mission Street San Francisco CA 94105 U.S.A.
                [11 ]U.S. Environmental Protection Agency; 200 Southwest 35th Street Corvallis OR 97330 U.S.A.
                Article
                10.1111/cobi.13230
                6727660
                30311266
                33af0a13-666a-4ec8-a43e-724346814a85
                © 2018

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

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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