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      Using PVA and captive breeding to balance trade-offs in the rescue of the island dibbler onto a new island ark

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          Abstract

          In the face of the current global extinction crisis, it is critical we give conservation management strategies the best chance of success. Australia is not exempt from global trends with currently the world’s greatest mammal extinction rate (~ 1 per 8 years). Many more are threatened including the dibbler ( Parantechinus apicalis) whose remnant range has been restricted to Western Australia at just one mainland site and two small offshore islands—Whitlock Island (5 ha) and Boullanger Island (35 ha). Here, we used 14 microsatellite markers to quantify genetic variation in the remaining island populations from 2013 to 2018 and incorporated these data into population viability analysis (PVA) models, used to assess factors important to dibbler survival and to provide guidance for translocations. Remnant population genetic diversity was low (< 0.3), and populations were highly divergent from each other (pairwise F STs 0.29–0.52). Comparison of empirical data to an earlier study is consistent with recent declines in genetic diversity and models projected increasing extinction risk and declining genetic variation in the next century. Optimal translocation scenarios recommend 80 founders for new dibbler populations—provided by captive breeding—and determined the proportion of founders from parental populations to maximise genetic diversity and minimise harvesting impact. The goal of our approach is long-term survival of genetically diverse, self-sustaining populations and our methods are transferable. We consider mixing island with mainland dibblers to reinforce genetic variation.

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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/~pritch/home.html.
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              STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

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                Author and article information

                Contributors
                daniel.white@uwa.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 July 2022
                13 July 2022
                2022
                : 12
                : 11913
                Affiliations
                [1 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, School of Biological Sciences, , The University of Western Australia, ; 35 Stirling Highway, Crawley, WA 6009 Australia
                [2 ]GRID grid.452589.7, ISNI 0000 0004 1799 3491, Department of Biodiversity, Conservation and Attractions, ; Locked Bag 104, Bentley Delivery Centre, Perth, WA 6983 Australia
                [3 ]GRID grid.452589.7, ISNI 0000 0004 1799 3491, Department of Biodiversity, Conservation and Attractions, ; 120 Albany Highway, Albany, WA 6330 Australia
                Author information
                http://orcid.org/0000-0002-8967-5201
                http://orcid.org/0000-0002-9870-1969
                http://orcid.org/0000-0002-4023-7761
                http://orcid.org/0000-0002-3334-8041
                http://orcid.org/0000-0003-0744-984X
                Article
                14150
                10.1038/s41598-022-14150-9
                9279492
                35831431
                a7975ca7-eaaf-417a-b3d1-4f6c78b3ffb8
                © The Author(s) 2022

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 March 2021
                : 25 April 2022
                Funding
                Funded by: Australian Government’s National Environmental Science Programme through the Threatened Species Recovery Hub
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                genetic markers,conservation biology,ecological genetics,population dynamics
                Uncategorized
                genetic markers, conservation biology, ecological genetics, population dynamics

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