Prediction of historical, current and future potential distribution of Rhus chinensis (Anacardiaceae) based on the optimized MaxEnt model in China

Abstract

The Maximum Entropy (MaxEnt) model is widely employed in ecological and geographical studies to predict potential distribution of species with high accuracy. In this research, we applied an optimized MaxEnt model to forecast the historical, current, and future distribution trends of Rhus chinensis. Our study elucidated the dynamics of species distribution shifts and pinpointed key environmental drivers. The analysis indicated that the min temperature of coldest month, temperature annual range, annual precipitation, and slope predominantly influence the distribution of R. chinensis in the current period. Notably, the most suitable habitat areas for R. chinensis exhibited significant variations across different time periods. During the Mid Holocene, as climate change, the extent of potential suitable habitats expanded markedly relative to the Last Glacial Maximum, showcasing distinct geographical distribution shifts. Under various future climate scenarios, the area deemed suitable for R. chinensis is expected to expand, with the total areas of high suitability increasing more under high-emission scenarios than under low-emission scenarios. The model also identified small, highly suitable areas in Motuo and Chayu in southern Xizang, suggesting new opportunities for the collection and cultivation of R. chinensis. These predictions support efforts to strengthen the conservation of existing resources and promote the sustainable utilization of China’s medicinal plant resources.