Predicting current and future potential distribution of Changnienia amoena in China under global climate change
Abstract Changnienia amoena is a terrestrial orchid endemic to China and holds significant ornamental and medicinal value. Understanding the current and future potential geographic distribution patterns of C. amoena under climate change is crucial for its effective conservation and sustainable devel...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-02231-4 |
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| author | Xingjian Liu Qimeng Sun Tingting Li Shu’an Wang Jiahao Shen Yueqi Sun Mimi Li |
| author_facet | Xingjian Liu Qimeng Sun Tingting Li Shu’an Wang Jiahao Shen Yueqi Sun Mimi Li |
| author_sort | Xingjian Liu |
| collection | DOAJ |
| description | Abstract Changnienia amoena is a terrestrial orchid endemic to China and holds significant ornamental and medicinal value. Understanding the current and future potential geographic distribution patterns of C. amoena under climate change is crucial for its effective conservation and sustainable development. This study uses 48 distribution records and 19 environmental variables to simulate and predict the potential distribution and spatial pattern changes of C. amoena under different future gas emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for both the 2050s and 2090s. The dominant environmental variables influencing its distribution were also identified. The MaxEnt model yielded an AUC of 0.990 and CBI of 0.959, indicating extremely high predictive accuracy. The key environmental variables influencing the distribution of C. amoena include the minimum temperature of coldest month (Bio06), annual precipitation (Bio12), isothermality (Bio03), land use classification, slope, topsoil USDA texture classification, elevation, and topsoil calcium carbonate. Among these, temperature and precipitation have relatively significant impacts on the distribution of C. amoena. Under the SSP1-2.6 scenario, the suitable habitat for C. amoena shows a slight contraction, while under the SSP2-4.5 and SSP5-8.5 scenarios, the suitable habitat shifts and expands significantly towards the northwest, higher latitude and altitude areas. This research has important scientific significance and practical guidance value for the in-situ conservation, ex-situ cultivation, and sustainable utilization of C. amoena. |
| format | Article |
| id | doaj-art-50eb34d6a1c34746a6e1d51a7d7d96dd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-50eb34d6a1c34746a6e1d51a7d7d96dd2025-08-20T03:48:19ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-02231-4Predicting current and future potential distribution of Changnienia amoena in China under global climate changeXingjian Liu0Qimeng Sun1Tingting Li2Shu’an Wang3Jiahao Shen4Yueqi Sun5Mimi Li6Institute of Botany, Jiangsu Province and Chinese Academy of SciencesInstitute of Botany, Jiangsu Province and Chinese Academy of SciencesNingbo Yinzhou Planning & Design InstituteInstitute of Botany, Jiangsu Province and Chinese Academy of SciencesInstitute of Botany, Jiangsu Province and Chinese Academy of SciencesInstitute of Botany, Jiangsu Province and Chinese Academy of SciencesInstitute of Botany, Jiangsu Province and Chinese Academy of SciencesAbstract Changnienia amoena is a terrestrial orchid endemic to China and holds significant ornamental and medicinal value. Understanding the current and future potential geographic distribution patterns of C. amoena under climate change is crucial for its effective conservation and sustainable development. This study uses 48 distribution records and 19 environmental variables to simulate and predict the potential distribution and spatial pattern changes of C. amoena under different future gas emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for both the 2050s and 2090s. The dominant environmental variables influencing its distribution were also identified. The MaxEnt model yielded an AUC of 0.990 and CBI of 0.959, indicating extremely high predictive accuracy. The key environmental variables influencing the distribution of C. amoena include the minimum temperature of coldest month (Bio06), annual precipitation (Bio12), isothermality (Bio03), land use classification, slope, topsoil USDA texture classification, elevation, and topsoil calcium carbonate. Among these, temperature and precipitation have relatively significant impacts on the distribution of C. amoena. Under the SSP1-2.6 scenario, the suitable habitat for C. amoena shows a slight contraction, while under the SSP2-4.5 and SSP5-8.5 scenarios, the suitable habitat shifts and expands significantly towards the northwest, higher latitude and altitude areas. This research has important scientific significance and practical guidance value for the in-situ conservation, ex-situ cultivation, and sustainable utilization of C. amoena.https://doi.org/10.1038/s41598-025-02231-4 |
| spellingShingle | Xingjian Liu Qimeng Sun Tingting Li Shu’an Wang Jiahao Shen Yueqi Sun Mimi Li Predicting current and future potential distribution of Changnienia amoena in China under global climate change Scientific Reports |
| title | Predicting current and future potential distribution of Changnienia amoena in China under global climate change |
| title_full | Predicting current and future potential distribution of Changnienia amoena in China under global climate change |
| title_fullStr | Predicting current and future potential distribution of Changnienia amoena in China under global climate change |
| title_full_unstemmed | Predicting current and future potential distribution of Changnienia amoena in China under global climate change |
| title_short | Predicting current and future potential distribution of Changnienia amoena in China under global climate change |
| title_sort | predicting current and future potential distribution of changnienia amoena in china under global climate change |
| url | https://doi.org/10.1038/s41598-025-02231-4 |
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