MaxEnt Projections of Climate‐Driven Distribution Shifts for Daphniphyllum calycinum in China

ABSTRACT

Understanding how climate change impacts species distribution is crucial for conservation and sustainable resource use. This study projects the distribution shifts of Daphniphyllum calycinum Benth., an ecologically and medicinally important tree species in China, under future climate scenarios using an optimized Maximum Entropy (MaxEnt) model. By integrating 323 occurrence records with 10 environmental variables, we applied a rigorous parameter optimization framework using the ENMeval R package to select optimal settings (regularization multiplier = 2.0, feature classes = HPT), thereby reducing overfitting and improving model transferability. The optimized MaxEnt model achieved high predictive accuracy (AUC = 0.959, TSS = 0.8). Annual mean temperature (Bio1, 35.7%), precipitation of the driest month (Bio14, 33.7%), and annual precipitation (Bio12, 23.7%) were identified as the dominant environmental drivers influencing species distribution. Under current climate conditions, the total suitable habitat covers approximately 110.28 × 10 ⁴  km ² , primarily in southern China. Future projections under three Shared Socioeconomic Pathway (SSP) scenarios (SSP126, SSP370, SSP585) for the 2050s‐2090s indicate a potential expansion of suitable habitat, with the largest gain under SSP585 (i.e., expanding to 161.64 × 10 ⁴  km ² by the 2050s, representing a 46.6% increase relative to the current area). However, this net expansion is accompanied by localized habitat loss and, under certain scenarios, a declining proportion of highly suitable areas, revealing nuanced climate vulnerability. The distribution centroid is projected to shift northwestward by 96–140 km by the 2050s across scenarios, indicating a range shift toward higher latitudes and altitudes. Despite this overall expansion, localized habitat loss under higher emission scenarios reveals climate vulnerability, underscoring the need for proactive conservation in contraction zones. By integrating optimized model parameters with multi‐scenario future projections, this study provides a robust methodological framework and offers spatially explicit guidance for prioritizing in situ conservation and sustainable cultivation of this valuable species under climate change.