Gan Zhengkun, Ma Jun, Liu Xinyu, Luo Jiaxin, Li Junke, Pu Lili, Jiang Guihua, Lian Yan
State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xinjiang Uyghur Autonomous Region Academy of Surveying and Mapping, Urumqi, Xinjiang, China.
Front Plant Sci. 2025 Jul 7;16:1600491. doi: 10.3389/fpls.2025.1600491. eCollection 2025.
is a traditional medicinal plant known for its high content of bioactive coumarins. With climate change potentially affecting both species distribution and secondary metabolite accumulation, there is a pressing need to integrate ecological and chemical data to guide future cultivation and resource utilization strategies.
This study combined the Maximum Entropy (MaxEnt) ecological modeling approach with chemometric analysis to (i) predict the suitable habitat distribution of under current and future climate scenarios and (ii) evaluate the correlation between environmental variables and coumarin accumulation.
(1) The key environmental variables influencing the distribution of were identified as BIO_13 (precipitation of the wettest month), BIO_11 (mean temperature of the coldest quarter), and elevation (DEM). (2) Presently, the highly suitable regions for cultivation are mainly in Sichuan, Henan, and Hebei provinces. (3) Under future climate scenarios, the highly suitable habitats are expected to expand and shift geographically, especially toward Henan and Jiaozuo, with parts of Hubei, Shaanxi, and Shandong transitioning into highly suitable zones. (4) Chemometric analyses revealed that samples from highly suitable areas contained significantly higher total coumarin content than those from medium-suitability regions. (5) A strong correlation was observed between key environmental factors (especially BIO_11 and DEM) and the relative content of five major coumarin components.(6) Spatial mapping of chemical composition indicated distinct regional differences in coumarin distribution, suggesting the potential for geoherbalism-based classification.
The integration of ecological modeling with chemical analysis provides a powerful framework for understanding the impact of environmental variables on both the distribution and chemical quality of . These findings offer valuable guidance for targeted cultivation and resource management under future climate change conditions.
是一种传统药用植物，以其高含量的生物活性香豆素而闻名。随着气候变化可能影响物种分布和次生代谢产物积累，迫切需要整合生态和化学数据，以指导未来的种植和资源利用策略。
本研究将最大熵（MaxEnt）生态建模方法与化学计量分析相结合，以（i）预测当前和未来气候情景下的适宜生境分布，以及（ii）评估环境变量与香豆素积累之间的相关性。
（1）确定影响分布的关键环境变量为BIO_13（最湿月降水量）、BIO_11（最冷月平均温度）和海拔（数字高程模型）。（2）目前，种植的高度适宜区域主要在四川、河南和河北省。（3）在未来气候情景下，高度适宜的栖息地预计将扩大并在地理上发生转移，特别是向河南和焦作转移，湖北、陕西和山东的部分地区将转变为高度适宜区域。（4）化学计量分析表明，来自高度适宜区域的样本总香豆素含量明显高于中度适宜区域的样本。（5）观察到关键环境因素（特别是BIO_11和数字高程模型）与五种主要香豆素成分的相对含量之间存在很强的相关性。（6）化学成分的空间映射表明香豆素分布存在明显的区域差异，这表明基于道地药材分类的潜力。
生态建模与化学分析的整合为理解环境变量对分布和化学质量的影响提供了一个强大的框架。这些发现为未来气候变化条件下的定向种植和资源管理提供了有价值的指导。
