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      Genetic diversity and population structure of the endangered species Paeonia decomposita endemic to China and implications for its conservation

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          Abstract

          Background

          Paeonia decomposita, endemic to China, has important ornamental, medicinal, and economic value and is regarded as an endangered plant. The genetic diversity and population structure have seldom been described. A conservation management plan is not currently available.

          Results

          In the present study, 16 pairs of simple sequence repeat (SSR) primers were used to evaluate the genetic diversity and population structure. A total of 122 alleles were obtained with a mean of 7.625 alleles per locus. The expected heterozygosity ( H e) varied from 0.043 to 0.901 (mean 0.492) in 16 primers. Moderate genetic diversity ( H e = 0.405) among populations was revealed, with Danba identified as the center of genetic diversity. Mantel tests revealed a positive correlation between geographic and genetic distance among populations ( r = 0.592, P = 0.0001), demonstrating consistency with the isolation by distance model. Analysis of molecular variance (AMOVA) indicated that the principal molecular variance existed within populations (73.48%) rather than among populations (26.52%). Bayesian structure analysis and principal coordinate analysis (PCoA) supported the classification of the populations into three clusters.

          Conclusions

          This is the first study of the genetic diversity and population structure of P. decomposita using SSR. Three management units were proposed as conservation measures. The results will be beneficial for the conservation and exploitation of the species, providing a theoretical basis for further research of its evolution and phylogeography.

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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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            STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

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              GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update

              Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s D est and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. Contact: rod.peakall@anu.edu.au
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                Author and article information

                Contributors
                wsqmah@163.com
                Journal
                BMC Plant Biol
                BMC Plant Biol
                BMC Plant Biology
                BioMed Central (London )
                1471-2229
                9 November 2020
                9 November 2020
                2020
                : 20
                : 510
                Affiliations
                GRID grid.440732.6, ISNI 0000 0000 8551 5345, Ministry of Education Key Laboratory for Ecology of Tropical Islands, Key Laboratory of Tropical Animal and Plant Ecology of Hainan Province, College of Life Sciences, Hainan Normal University, ; Haikou, 571158 China
                Article
                2682
                10.1186/s12870-020-02682-z
                7650209
                33167894
                ef796fca-a351-4a53-a077-cdb249ad821b
                © The Author(s) 2020

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 3 December 2019
                : 1 October 2020
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 31670345
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31860085
                Funded by: Hainan Provincial Natural Science Foundation
                Award ID: 318MS047
                Funded by: Hainan Provincial Natural Science Foundation
                Award ID: 2019RC172
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Plant science & Botany
                conservation strategy,genetic diversity,genetic relationships,paeonia decomposita,population structure,simple sequence repeat (ssr)

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