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      Conservation genetics and potential geographic distribution modeling of Corybas taliensis, a small ‘sky Island’ orchid species in China

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          Abstract

          Background

          Corybas taliensis is an endemic species of sky islands in China. Its habitat is fragile and unstable, and it is likely that the species is threatened. However, it is difficult to determine the conservation priority or unit without knowing the genetic background and the overall distribution of this species. In this study, we used double digest restriction-site associated DNA-sequencing (ddRAD-seq) to investigate the conservation genomics of C. taliensis. At the same time, we modeled the extent of suitable habitat for C. taliensis in present and future (2030 and 2090) habitat using the maximum-entropy (MaxEnt) model.

          Results

          The results suggested that the related C. fanjingshanensis belongs to C. taliensis and should not be considered a separate species. All the sampling locations were divided into three genetic groups: the Sichuan & Guizhou population (SG population), the Hengduan Mountains population (HD population) and Himalayan population (HM population), and we found that there was complex gene flow between the sampling locations of HD population. MT was distinct genetically from the other sampling locations due to the unique environment in Motuo. The genetic diversity (π, H e ) of C. taliensis was relatively high, but its contemporary effective population size ( N e ) was small. C. taliensis might be currently affected by inbreeding depression, although its large population density may be able to reduce the effect of this. The predicted areas of suitable habitat currently found in higher mountains will not change significantly in the future, and these suitable habitats are predicted to spread to other higher mountains under future climate change. However, suitable habitat in relatively low altitude areas may disappear in the future. This suggests that C. taliensis will be caught in a ‘summit trap’ in low altitude areas, however, in contrast, the high altitude of the Himalaya and the Hengduan Mountains are predicted to act as ‘biological refuges’ for C. taliensis in the future.

          Conclusions

          These results not only provide a new understanding of the genetic background and potential resource distribution of C. taliensis, but also lay the foundation for its conservation and management.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12870-023-04693-y.

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          Most cited references119

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          WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

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            Biodiversity hotspots for conservation priorities.

            Conservationists are far from able to assist all species under threat, if only for lack of funding. This places a premium on priorities: how can we support the most species at the least cost? One way is to identify 'biodiversity hotspots' where exceptional concentrations of endemic species are undergoing exceptional loss of habitat. As many as 44% of all species of vascular plants and 35% of all species in four vertebrate groups are confined to 25 hotspots comprising only 1.4% of the land surface of the Earth. This opens the way for a 'silver bullet' strategy on the part of conservation planners, focusing on these hotspots in proportion to their share of the world's species at risk.
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              Fast model-based estimation of ancestry in unrelated individuals.

              Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
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                Author and article information

                Contributors
                wbsun@mail.kib.ac.cn
                Journal
                BMC Plant Biol
                BMC Plant Biol
                BMC Plant Biology
                BioMed Central (London )
                1471-2229
                2 January 2024
                2 January 2024
                2024
                : 24
                : 11
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, Yunnan Key Laboratory for Integrative Conservation of Plant Species with Extremely Small Populations, Kunming Institute of Botany, , Chinese Academy of Sciences (CAS), ; Kunming, Yunnan 650201 China
                [2 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, , Chinese Academy of Sciences (CAS), ; Kunming, Yunnan 650201 China
                [3 ]University of Chinese Academy of Sciences, ( https://ror.org/05qbk4x57) Beijing, 100049 China
                [4 ]GRID grid.9227.e, ISNI 0000000119573309, Kunming Botanical Garden, Kunming Institute of Botany, , Chinese Academy of Sciences (CAS), ; Kunming, Yunnan 650201 China
                Article
                4693
                10.1186/s12870-023-04693-y
                10759615
                38163918
                c7565d14-813b-4d2e-a24d-567d93d25413
                © The Author(s) 2023

                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/. 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
                : 15 June 2023
                : 15 December 2023
                Funding
                Funded by: Weibang Sun
                Award ID: 2019QZKK0502
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Plant science & Botany
                corybas taliensis,ddrad-seq,population genetic,maxent,suitable habitat,conservation management

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