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Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China
Paeonia (Paeoniaceae), a culturally and economically important plant genus, has an isolated taxonomy while the evolution of this genus is unclear. A plant species endemic to southwest China, Paeonia mairei is precious germplasm for evolution-related research and cultivar improvement, and its conserv...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997482/ https://www.ncbi.nlm.nih.gov/pubmed/32047503 http://dx.doi.org/10.3389/fpls.2019.01717 |
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author | Chen, Qihang Yin, Yijia Zhao, Rui Yang, Yong Teixeira da Silva, Jaime A. Yu, Xiaonan |
author_facet | Chen, Qihang Yin, Yijia Zhao, Rui Yang, Yong Teixeira da Silva, Jaime A. Yu, Xiaonan |
author_sort | Chen, Qihang |
collection | PubMed |
description | Paeonia (Paeoniaceae), a culturally and economically important plant genus, has an isolated taxonomy while the evolution of this genus is unclear. A plant species endemic to southwest China, Paeonia mairei is precious germplasm for evolution-related research and cultivar improvement, and its conservation is urgent. However, little is known about its patterns of habitat distribution and responses to climate change. Using 98 occurrence sites and data of 19 bioclimatic variables, we conducted principal component analysis and hierarchical cluster analysis to delineate different climatic populations. Maximum entropy algorithm (MaxEnt) was applied to each population to evaluate the importance of environmental variables in shaping their distribution, and to identify distribution shifts under different climate change scenarios. We also applied MaxEnt to all of the P. mairei presence sites (P_Whole) to evaluate the need to construct separate species distribution models for separate populations rather than a common approach by treating them as a whole. Our results show that local adaptation exists within the distribution range of P. mairei and that all presence sites were clustered into a western population (P_West) and an eastern population (P_East). Two variables (precipitation of the driest month and temperature seasonality) are important when shaping the distribution of P_West, and another two variables (mean diurnal range and mean temperature of the wettest quarter) are important for P_East. Both populations are likely to shift upward under climate change, while P_East may lose most current suitable areas while P_West may not. P_Whole produced a narrower area compared to the combination of P_West and P_East but a suitable area (south Chongqing) may have been missed in the prediction. Accordingly, a population-based approach in constructing a species distribution model is needed to provide a detailed appreciation of the distribution of P. mairei, allowing for a population-based conservation strategy. In this case, it could include assisted migration to new and suitable distribution areas for P_West and in situ conservation in high elevation regions for P_East. The results of our study could be a useful reference for implementing the long-term conservation and further research of P. mairei. |
format | Online Article Text |
id | pubmed-6997482 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69974822020-02-11 Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China Chen, Qihang Yin, Yijia Zhao, Rui Yang, Yong Teixeira da Silva, Jaime A. Yu, Xiaonan Front Plant Sci Plant Science Paeonia (Paeoniaceae), a culturally and economically important plant genus, has an isolated taxonomy while the evolution of this genus is unclear. A plant species endemic to southwest China, Paeonia mairei is precious germplasm for evolution-related research and cultivar improvement, and its conservation is urgent. However, little is known about its patterns of habitat distribution and responses to climate change. Using 98 occurrence sites and data of 19 bioclimatic variables, we conducted principal component analysis and hierarchical cluster analysis to delineate different climatic populations. Maximum entropy algorithm (MaxEnt) was applied to each population to evaluate the importance of environmental variables in shaping their distribution, and to identify distribution shifts under different climate change scenarios. We also applied MaxEnt to all of the P. mairei presence sites (P_Whole) to evaluate the need to construct separate species distribution models for separate populations rather than a common approach by treating them as a whole. Our results show that local adaptation exists within the distribution range of P. mairei and that all presence sites were clustered into a western population (P_West) and an eastern population (P_East). Two variables (precipitation of the driest month and temperature seasonality) are important when shaping the distribution of P_West, and another two variables (mean diurnal range and mean temperature of the wettest quarter) are important for P_East. Both populations are likely to shift upward under climate change, while P_East may lose most current suitable areas while P_West may not. P_Whole produced a narrower area compared to the combination of P_West and P_East but a suitable area (south Chongqing) may have been missed in the prediction. Accordingly, a population-based approach in constructing a species distribution model is needed to provide a detailed appreciation of the distribution of P. mairei, allowing for a population-based conservation strategy. In this case, it could include assisted migration to new and suitable distribution areas for P_West and in situ conservation in high elevation regions for P_East. The results of our study could be a useful reference for implementing the long-term conservation and further research of P. mairei. Frontiers Media S.A. 2020-01-28 /pmc/articles/PMC6997482/ /pubmed/32047503 http://dx.doi.org/10.3389/fpls.2019.01717 Text en Copyright © 2020 Chen, Yin, Zhao, Yang, Teixeira da Silva and Yu http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Chen, Qihang Yin, Yijia Zhao, Rui Yang, Yong Teixeira da Silva, Jaime A. Yu, Xiaonan Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China |
title | Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China |
title_full | Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China |
title_fullStr | Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China |
title_full_unstemmed | Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China |
title_short | Incorporating Local Adaptation Into Species Distribution Modeling of Paeonia mairei, an Endemic Plant to China |
title_sort | incorporating local adaptation into species distribution modeling of paeonia mairei, an endemic plant to china |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997482/ https://www.ncbi.nlm.nih.gov/pubmed/32047503 http://dx.doi.org/10.3389/fpls.2019.01717 |
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