Wu Yanan, Yan Lanmeng, Shen Hongjian, Guan Rui, Ge Qianqian, Huang Ling, Rohani Emelda Rosseleena, Ou Jinmei, Han Rongchun, Tong Xiaohui
School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
Institute of Systems Biology, Universiti Kebangsaan Malaysi, Bangi, Malaysia.
Front Plant Sci. 2025 May 14;16:1538566. doi: 10.3389/fpls.2025.1538566. eCollection 2025.
Climate change has significantly impacted the distribution patterns of medicinal plants, highlighting the need for accurate models to predict future habitat shifts. In this study, the Maximum Entropy model to analyze the habitat distribution of (Bunge) Regel under current conditions and two future climate scenarios (SSP245 and SSP585). Based on 105 occurrence records and 12 environmental variables, precipitation of the wettest quarter, isothermality, average November temperature, and the standard deviation of temperature seasonality were identified as key factors influencing the habitat suitability for . The reliability of the model was supported by a mean area under the curve (AUC) value of 0.916 and a True Skill Statistic (TSS) value of 0.608. The results indicated that although the total suitable habitat for expanded under both scenarios, the highly suitable area contracted significantly under SSP585 compared to SSP245. This suggests the importance of incorporating climate change considerations into management strategies to address potential challenges arising from future ecosystem dynamics.
气候变化已对药用植物的分布格局产生了重大影响,凸显了需要精确模型来预测未来栖息地变化的必要性。在本研究中,运用最大熵模型分析了( Bunge) Regel在当前条件以及两种未来气候情景(SSP245和SSP585)下的栖息地分布。基于105个出现记录和12个环境变量,最湿润季度降水量、等温性、11月平均温度以及温度季节性标准差被确定为影响 栖息地适宜性的关键因素。该模型的可靠性得到了曲线下平均面积(AUC)值0.916和真技能统计量(TSS)值0.608的支持。结果表明,尽管在两种情景下 总的适宜栖息地都有所扩大,但与SSP245相比,在SSP585情景下高度适宜区域显著收缩。这表明将气候变化考虑因素纳入 管理策略以应对未来生态系统动态变化带来的潜在挑战的重要性。