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      Inferring fine-scale spatial structure of the brown bear ( Ursus arctos) population in the Carpathians prior to infrastructure development

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          Abstract

          Landscape genetics is increasingly being used in landscape planning for biodiversity conservation by assessing habitat connectivity and identifying landscape barriers, using intraspecific genetic data and quantification of landscape heterogeneity to statistically test the link between genetic variation and landscape variability. In this study we used genetic data to understand how landscape features and environmental factors influence demographic connectedness in Europe’s largest brown bear population and to assist in mitigating planned infrastructure development in Romania. Model-based clustering inferred one large and continuous bear population across the Carpathians suggesting that suitable bear habitat has not become sufficiently fragmented to restrict movement of individuals. However, at a finer scale, large rivers, often located alongside large roads with heavy traffic, were found to restrict gene flow significantly, while eastern facing slopes promoted genetic exchange. Since the proposed highway infrastructure development threatens to fragment regions of the Carpathians where brown bears occur, we develop a decision support tool based on models that assess the landscape configuration needed for brown bear conservation using wildlife corridor parameters. Critical brown bear corridors were identified through spatial mapping and connectivity models, which may be negatively influenced by infrastructure development and which therefore require mitigation. We recommend that current and proposed infrastructure developments incorporate these findings into their design and where possible avoid construction measures that may further fragment Romania’s brown bear population or include mitigation measures where alternative routes are not feasible.

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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

                Contributors
                ancutacotovelea@yahoo.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 July 2019
                1 July 2019
                2019
                : 9
                : 9494
                Affiliations
                [1 ]National Institute for Research and Development in Forestry Marin Dracea, Brasov, 500040 Closca Street 13, Romania
                [2 ]ISNI 0000 0001 2159 8361, GRID grid.5120.6, Faculty of Silviculture and Forest Engineering, , Transilvania University of Brasov, ; Brasov, 500123 Beethoven Lane 1, Romania
                [3 ]ISNI 0000 0001 0807 5670, GRID grid.5600.3, Cardiff School of Biosciences, Sir Martin Evans Building, , Cardiff University, ; Museum Avenue, Cardiff, CF10 3AX United Kingdom
                [4 ]GRID grid.501486.e, Center for Large Landscape Conservation, ; 303 W Mendenhall St #4, Bozeman, MT 59715 USA
                Article
                45999
                10.1038/s41598-019-45999-y
                6602936
                31263171
                67fdb74f-e86d-474b-a7a9-f714b55143a8
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 December 2018
                : 20 June 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100005802, Autoritatea Natională pentru Cercetare Stiintifică (National Authority for Scientific Research);
                Award ID: BiodivERsA3-2015-147-BearConnect
                Award ID: PN09460210
                Award ID: BiodivERsA3-2015-147-BearConnect
                Award ID: PN09460210
                Award ID: BiodivERsA3-2015-147-BearConnect
                Award ID: PN09460210
                Award ID: BiodivERsA3-2015-147-BearConnect
                Award ID: PN09460210
                Award ID: BiodivERsA3-2015-147-BearConnect
                Award ID: PN09460210
                Award Recipient :
                Funded by: This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI – UEFISCDI, project number BiodivERsA3-2015-147-BearConnect, within PNCDI III(part of the BiodivERsA project BearConnect)
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                evolutionary biology,molecular ecology,conservation biology
                Uncategorized
                evolutionary biology, molecular ecology, conservation biology

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