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      Comparing Habitat Suitability and Connectivity Modeling Methods for Conserving Pronghorn Migrations

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          Abstract

          Terrestrial long-distance migrations are declining globally: in North America, nearly 75% have been lost. Yet there has been limited research comparing habitat suitability and connectivity models to identify migration corridors across increasingly fragmented landscapes. Here we use pronghorn ( Antilocapra americana) migrations in prairie habitat to compare two types of models that identify habitat suitability: maximum entropy (Maxent) and expert-based (Analytic Hierarchy Process). We used distance to wells, distance to water, NDVI, land cover, distance to roads, terrain shape and fence presence to parameterize the models. We then used the output of these models as cost surfaces to compare two common connectivity models, least-cost modeling (LCM) and circuit theory. Using pronghorn movement data from spring and fall migrations, we identified potential migration corridors by combining each habitat suitability model with each connectivity model. The best performing model combination was Maxent with LCM corridors across both seasons. Maxent out-performed expert-based habitat suitability models for both spring and fall migrations. However, expert-based corridors can perform relatively well and are a cost-effective alternative if species location data are unavailable. Corridors created using LCM out-performed circuit theory, as measured by the number of pronghorn GPS locations present within the corridors. We suggest the use of a tiered approach using different corridor widths for prioritizing conservation and mitigation actions, such as fence removal or conservation easements.

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          Most cited references15

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          Analyzing animal movements using Brownian bridges.

          By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding population-level dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal, using discrete location data obtained at relatively short time intervals. The BBMM is based on the properties of a conditional random walk between successive pairs of locations, dependent on the time between locations, the distance between locations, and the Brownian motion variance that is related to the animal's mobility. We describe two critical developments that enable widespread use of the BBMM, including a derivation of the model when location data are measured with error and a maximum likelihood approach for estimating the Brownian motion variance. After the BBMM is fitted to location data, an estimate of the animal's probability of occurrence can be generated for an area during the time of observation. To illustrate potential applications, we provide three examples: estimating animal home ranges, estimating animal migration routes, and evaluating the influence of fine-scale resource selection on animal movement patterns.
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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
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                16 November 2012
                : 7
                : 11
                : e49390
                Affiliations
                [1 ]Conservation Science Program, World Wildlife Fund, Washington D. C., United States of America
                [2 ]Faculty of Environmental Design, University of Calgary, Calgary, Canada
                [3 ]Nicholas School of the Environment, Duke University, Durham, North Carolina, United States of America
                Université de Sherbrooke, Canada
                Author notes

                Competing Interests: The authors have the following interests. This study was partially funded by a Counter Assault, Inc. Grant and by a Student Stipend, #030-00-40-106-4000, from ACA-PetroCanada. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: CL DLU. Performed the experiments: EP AJ. Analyzed the data: EP AJ. Wrote the paper: EP CL AJ DLU.

                Article
                PONE-D-12-15499
                10.1371/journal.pone.0049390
                3500376
                23166656
                607c255d-cfe2-4ff4-af07-2bec81be68ad
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 May 2012
                : 11 October 2012
                Page count
                Pages: 12
                Funding
                Counter Assault, Inc Grant, MT TWS ( http://counterassault.com/), ACCELERATE Grad Research Intern Award, MITACS ( http://www.mitacs.ca/accelerate), Graduate Student Support Award, University of Calgary ( http://evds.ucalgary.ca/), F&W Student Award, Saskatchewan Ministry of Environment ( http://www.environment.gov.sk.ca), AB SRPWF Award, Alberta SRPWF ( http://www.asrpwf.ca/), Glaholt Scholarship, University of Calgary ( http://ucalgary.ca/), Student Stipend, #030-00-40-106-4000, ACA-PetroCanada ( http://www.ab-conservation.com/go/default), Dean's Entrance Scholarship, University of Calgary ( http://ucalgary.ca/), Graduate Research Stipend, #080018, Montana Fish Wildlife and Parks ( http://fwp.mt.gov), and Kathryn Fuller Grant, The Kathryn Fuller Science for Nature ( http://www.worldwildlife.org/science/fellowships/fuller/item22744.html). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Ecology
                Ecological Environments
                Terrestrial Environments
                Ecosystems
                Ecosystem Modeling
                Behavioral Ecology
                Community Ecology
                Conservation Science
                Spatial and Landscape Ecology
                Evolutionary Biology
                Animal Behavior
                Earth Sciences
                Geography
                Cartography
                Gis

                Uncategorized
                Uncategorized

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