Optimized MaxEnt modeling predicts the distribution change of Chaenomeles speciosa (Sweet) Nakai in China under global climate change

Climate change is influencing the distribution of medicinal plants, necessitating the need for the development of precise models to predict habitat changes. However, studies on the habitat dynamics of Chaenomeles sp eciosa , an important medicinal herb, under current and future climate scenarios are lacking. In this study, we applied an optimized maximum entropy algorithm integrated with ArcGIS, and 157 occurrence points of C. sp eciosa along with 10 environmental variables to predict its potentially suitable distribution under both current and future climate scenarios (SSP245 and SSP585). The model performed well with an average area under the curve (AUC) of 0.908 and a true skill statistic (TSS) of 0.674. The key factors were Bio_14 (Driest Month), Bio_4 (Temperature Seasonality), elevation, and Srad_10 (October solar radiation). Currently, the species has an estimated total potential distribution range of approximately 328.40 × 10 4 km 2 , and the most suitable habitats are primarily located in central and eastern China. Projections indicate that under future climate scenarios, although the total suitable region increases, the proportion of high-suitability regions notably declines. Core regions are expected to contract as peripheral regions expand, and the distribution centroid will shift nonlinearly within Hubei Province. Therefore, we suggest prioritizing the monitoring of the spatial redistribution of suitable habitats for the future conservation and sustainable use of C. sp eciosa .