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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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          Most cited references 75

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          Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

          The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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            Inference of population structure using multilocus genotype data.

            We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci-e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/ approximately pritch/home. html.
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              genepop'007: a complete re-implementation of the genepop software for Windows and Linux.

              This note summarizes developments of the genepop software since its first description in 1995, and in particular those new to version 4.0: an extended input format, several estimators of neighbourhood size under isolation by distance, new estimators and confidence intervals for null allele frequency, and less important extensions to previous options. genepop now runs under Linux as well as under Windows, and can be entirely controlled by batch calls. © 2007 The Author.
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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
                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
                © 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/.

                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)
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                © The Author(s) 2019

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                conservation biology, evolutionary biology, molecular ecology

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