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      Multiple estimates of effective population size for monitoring a long-lived vertebrate: an application to Yellowstone grizzly bears

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          Abstract

          Effective population size (N(e)) is a key parameter for monitoring the genetic health of threatened populations because it reflects a population's evolutionary potential and risk of extinction due to genetic stochasticity. However, its application to wildlife monitoring has been limited because it is difficult to measure in natural populations. The isolated and well-studied population of grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem provides a rare opportunity to examine the usefulness of different N(e) estimators for monitoring. We genotyped 729 Yellowstone grizzly bears using 20 microsatellites and applied three single-sample estimators to examine contemporary trends in generation interval (GI), effective number of breeders (N(b)) and N(e) during 1982-2007. We also used multisample methods to estimate variance (N(eV)) and inbreeding N(e) (N(eI)). Single-sample estimates revealed positive trajectories, with over a fourfold increase in N(e) (≈100 to 450) and near doubling of the GI (≈8 to 14) from the 1980s to 2000s. N(eV) (240-319) and N(eI) (256) were comparable with the harmonic mean single-sample N(e) (213) over the time period. Reanalysing historical data, we found N(eV) increased from ≈80 in the 1910s-1960s to ≈280 in the contemporary population. The estimated ratio of effective to total census size (N(e) /N(c)) was stable and high (0.42-0.66) compared to previous brown bear studies. These results support independent demographic evidence for Yellowstone grizzly bear population growth since the 1980s. They further demonstrate how genetic monitoring of N(e) can complement demographic-based monitoring of N(c) and vital rates, providing a valuable tool for wildlife managers.

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          Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows.

          We present here a new version of the Arlequin program available under three different forms: a Windows graphical version (Winarl35), a console version of Arlequin (arlecore), and a specific console version to compute summary statistics (arlsumstat). The command-line versions run under both Linux and Windows. The main innovations of the new version include enhanced outputs in XML format, the possibility to embed graphics displaying computation results directly into output files, and the implementation of a new method to detect loci under selection from genome scans. Command-line versions are designed to handle large series of files, and arlsumstat can be used to generate summary statistics from simulated data sets within an Approximate Bayesian Computation framework. © 2010 Blackwell Publishing Ltd.
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            COLONY: a program for parentage and sibship inference from multilocus genotype data.

            Pedigrees, depicting genealogical relationships between individuals, are important in several research areas. Molecular markers allow inference of pedigrees in wild species where relationship information is impossible to collect by observation. Marker data are analysed statistically using methods based on Mendelian inheritance rules. There are numerous computer programs available to conduct pedigree analysis, but most software is inflexible, both in terms of assumptions and data requirements. Most methods only accommodate monogamous diploid species using codominant markers without genotyping error. In addition, most commonly used methods use pairwise comparisons rather than a full-pedigree likelihood approach, which considers the likelihood of the entire pedigree structure and allows the simultaneous inference of parentage and sibship. Here, we describe colony, a computer program implementing full-pedigree likelihood methods to simultaneously infer sibship and parentage among individuals using multilocus genotype data. colony can be used for both diploid and haplodiploid species; it can use dominant and codominant markers, and can accommodate, and estimate, genotyping error at each locus. In addition, colony can carry out these inferences for both monoecious and dioecious species. The program is available as a Microsoft Windows version, which includes a graphical user interface, and a Macintosh version, which uses an R-based interface. © 2009 Blackwell Publishing Ltd.
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              The genetical theory of natural selection.

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                Author and article information

                Journal
                Molecular Ecology
                Mol Ecol
                Wiley
                09621083
                November 2015
                November 2015
                October 28 2015
                : 24
                : 22
                : 5507-5521
                Affiliations
                [1 ]U.S. Geological Survey; Northern Rocky Mountain Science Center; 2327 University Way, Suite 2 Bozeman MT 59715 USA
                [2 ]Flathead Lake Biological Station; Fish and Wildlife Genomics Group; Division of Biological Sciences; University of Montana; Missoula MT 59812 USA
                [3 ]Wildlife Genetics International; Box 274 Nelson British Columbia V1L 5P9 Canada
                Article
                10.1111/mec.13398
                26510936
                3ad59b0a-b5a8-4332-a226-05a4b04f102a
                © 2015

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

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