Assessing Climate Change Impacts on Distribution Dynamics of in China Through MaxEnt Modeling.

a regionally endemic medicinal plant in China, is crucial for ecosystems and traditional medicine. This study evaluates climate change impacts on the geographic spread of by employing an optimized MaxEnt model based on 625 valid occurrence points and various climatic variables. The model was refined with ENMeval in R, selecting optimal feature combinations (FC) and regularization multipliers (RM). The model's predictive performance was evaluated via the AUC metric, and the distribution changes were analyzed across three Shared Socioeconomic Pathways (SSPs) spanning the 2050s, 2070s, and 2090s. The findings indicated that the refined MaxEnt model exhibited strong predictive performance, achieving an AUC of 0.904. The min temperature of coldest month (Bio6) and the standard deviation of temperature seasonality (Bio4) were identified as the principal climatic variables affecting the geographic range of , contributing 68.7% and 20.2%, respectively, under current climatic conditions. Within the SSP1-2.6 pathway, the viable habitat zone remained relatively stable, with retention rates of 86.78%, 86.13%, and 82.03% during the decades of the 2050s, 2070s, as well as 2090s. However, in the context of the SSP5-8.5 pathway, the retention rate significantly decreased to 64.77% by the 2090s, indicating greater habitat instability and expansion needs. The research highlights the critical role of thermal variables in shaping 's distribution and emphasizes the need for adaptive conservation strategies targeting stable or expanding habitats to ensure its long-term survival amid climate change.