Optimized MaxEnt modeling predicts the distribution change of <i>Chaenomeles</i> sp<i>eciosa</i> (Sweet) Nakai in China under global climate change.

ABSTRACT: 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 speciosa, 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. speciosa 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 (Tempera

SUBMITTER: Shen H 

PROVIDER: S-EPMC12920472 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature