Ren Jun, Li Suhang, Zhang Yawen, Yang Qiong, Liu Jiaojiao, Fan Jing, Xiang Yangzhou
School of Geography and Resources, Guizhou Education University, Guiyang, China.
School of Pharmacy, Lanzhou University, Lanzhou, China.
Front Plant Sci. 2025 Jul 8;16:1601585. doi: 10.3389/fpls.2025.1601585. eCollection 2025.
This study aimed to assess the impact of climate change on the potential distribution of the endangered medicinal plant in China. We sought to identify key bioclimatic variables influencing its distribution, predict current and future suitable habitats, and evaluate shifts in these habitats under different climate scenarios. We constructed a dataset comprising 405 distribution records of and 9 major environmental factors. The MaxEnt model, integrated with GIS software, was employed to predict the potential distribution under current (1970-2000) and future periods (2050s, 2070s, and 2090s). Model optimization was conducted using the ENMeval package to adjust regularization multiplier and feature combination parameters, ensuring enhanced predictive accuracy. The optimized MaxEnt model demonstrated high predictive precision with an AUC value of 0.917. The minimum temperature of the coldest month, mean diurnal range, and annual precipitation were identified as the key environmental variables influencing distribution, with contribution rates of 72.7%, 11.6%, and 4.2%, respectively. The suitable habitat was predicted to expand by 2050s under the SSP1-2.6 scenario but showed a reduction in highly suitable areas under more severe scenarios like SSP5-8.5. Centroid shift analyses indicated a northwestward migration of suitable habitats. These results from this study suggest that climate change poses significant risks to the distribution of , with potential shifts in both the extent and quality of suitable habitats. Our findings highlight the importance of considering climate change projections in conservation planning and underscore the need for adaptive strategies to ensure the sustainability of this medicinally valuable species. The study provides a scientific basis for the conservation and sustainable use of in the context of climate change.
本研究旨在评估气候变化对中国濒危药用植物潜在分布的影响。我们试图确定影响其分布的关键生物气候变量，预测当前和未来的适宜栖息地，并评估在不同气候情景下这些栖息地的变化。我们构建了一个数据集，包括该植物的405条分布记录和9个主要环境因素。结合GIS软件的MaxEnt模型被用于预测当前（1970 - 2000年）和未来时期（2050年代、2070年代和2090年代）的潜在分布。使用ENMeval软件包进行模型优化，以调整正则化乘数和特征组合参数，确保提高预测精度。优化后的MaxEnt模型显示出较高的预测精度，AUC值为0.917。最冷月最低温度、平均日较差和年降水量被确定为影响该植物分布的关键环境变量，贡献率分别为72.7%、11.6%和4.2%。在SSP1 - 2.6情景下，预计到2050年代适宜栖息地会扩大，但在更严峻的情景如SSP5 - 8.5下，高度适宜区域会减少。质心迁移分析表明适宜栖息地向西北方向迁移。本研究的这些结果表明，气候变化对该植物的分布构成重大风险，适宜栖息地的范围和质量都可能发生变化。我们的研究结果突出了在保护规划中考虑气候变化预测的重要性，并强调了采取适应性策略以确保这种具有药用价值物种可持续性的必要性。该研究为气候变化背景下该植物的保护和可持续利用提供了科学依据。
