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| DOI | 10.1016/j.jclepro.2024.142021 |
| Assessing the quality ecology of endemic tree species in China based on machine learning models and UPLC methods: The example of Eucommia ulmoides Oliv. | |
| 发表日期 | 2024 |
| ISSN | 0959-6526 |
| EISSN | 1879-1786 |
| 起始页码 | 452 |
| 卷号 | 452 |
| 英文摘要 | Climate change significantly affects the suitability distributions of natural populations , especially relict species such as Eucommia ulmoides Oliv. ( E. ulmoides ). However, the driving eco-factors of the distribution of E. ulmoides and the relationship between geo-distribution patterns and quality are still unclear. Here, a new approach was established by combining machine learning models (MaxEnt, Random Forest, and Biomod2 model), ultraperformance liquid chromatography (UPLC) and spatial analysis to address this question. The results reveal that temperature is the most critical variable influencing the distribution of E. ulmoides including Bio1, Bio10, and Bio11, followed by precipitation. Multi -model predictions of suitable habitats for E. ulmoides cover an area ranging from 113.33 x 10 4 km 2 to 285.98 x 10 4 km 2 , mainly distributed in the mountainous areas of southwestern China, including the eastern regions of the Dalou, Wuling, and Qinling-Daba Mountains. Under different future scenarios, suitable habitat shifted northward by 184.90 - 424.88 km, and the contraction area is greater than the expansion area, with the difference ranging from 49.28 x 10 4 km 2 to 98.39 x 10 4 km 2 . Eight common components (e.g., chlorogenic acid and pinoresinol doglucoside) and two endemic components (caffeic acid and geniposidic acid) were found in the leaves of the northern and southern medicinal districts (SDM and NMD). The contents of eight key secondary metabolites of E. ulmoides leaves were better distributed in SDM than in NDM. Among these, Xinyang in Henan, Lu ' an and Fuyang in Anhui, and the southeastern region of Zhejiang are suitable growing areas for the high -quality cultivation of E. ulmoides , which deserve special attention. The seasonal precipitation (Bio15) was negatively correlated with chlorogenic acid and pinoresinol diglucoside while longitude significantly positively correlated with pinoresinol diglucoside. Overall, this study provides a novel assessment pattern for quality assessment and effective protection of E. ulmoides . |
| 英文关键词 | Quality duality; Geographical distribution; Environmental factors; Habitat suitability; Machine learning models; Ultra-performance liquid chromatography |
| 语种 | 英语 |
| WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
| WOS类目 | Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences |
| WOS记录号 | WOS:001228489900001 |
| 来源期刊 | JOURNAL OF CLEANER PRODUCTION
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/303961 |
| 作者单位 | Chinese Academy of Medical Sciences - Peking Union Medical College; Peking Union Medical College; Institute of Medicinal Plant Development - CAMS; Jiangxi University of Traditional Chinese Medicine; Chengdu University of Traditional Chinese Medicine |
| 推荐引用方式 GB/T 7714 | . Assessing the quality ecology of endemic tree species in China based on machine learning models and UPLC methods: The example of Eucommia ulmoides Oliv.[J],2024,452. |
| APA | (2024).Assessing the quality ecology of endemic tree species in China based on machine learning models and UPLC methods: The example of Eucommia ulmoides Oliv..JOURNAL OF CLEANER PRODUCTION,452. |
| MLA | "Assessing the quality ecology of endemic tree species in China based on machine learning models and UPLC methods: The example of Eucommia ulmoides Oliv.".JOURNAL OF CLEANER PRODUCTION 452(2024). |
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