biostudies-literatureUnknown16Feng Y<i>Keteleeria evelyniana Mast.</i>, which is widespread in southwestern China, is valuable for studying under different future climate scenarios to assess potential distribution shifts in response to climate warming. Understanding these changes can provide theoretical support for species conservation, rational utilization, ecological restoration, and management of <i>K. evelyniana</i> habitats. The Maxent model was optimized using the package of ENMeval to adjust the Regularization Multiplier (RM) and Feature Class Combinations (FC) parameters. Utilizing 221 effective distribution points and 33 environmental variables, the potential distribution of <i>K. evelyniana</i> in current and future climate scenarios was predicted, with the key environmental variables analyzed. The model with FC = Frontiers in plant science1561031https://www.ebi.ac.uk/biostudies/studies/S-EPMC12090538biostudies-literatureAssessing the impact of climate change on the potential distribution of <i>Keteleeria evelyniana</i> Mast. in southwest China: a Maxent modeling approach.PMC12090538Wang MLi HFeng YDai GQin YLuo CChai YBao RfalseAssessing the impact of climate change on the potential distribution of <i>Keteleeria evelyniana</i> Mast. in southwest China: a Maxent modeling approach.<i>Keteleeria evelyniana Mast.</i>, which is widespread in southwestern China, is valuable for studying under different future climate scenarios to assess potential distribution shifts in response to climate warming. Understanding these changes can provide theoretical support for species conservation, rational utilization, ecological restoration, and management of <i>K. evelyniana</i> habitats. The Maxent model was optimized using the package of ENMeval to adjust the Regularization Multiplier (RM) and Feature Class Combinations (FC) parameters. Utilizing 221 effective distribution points and 33 environmental variables, the potential distribution of <i>K. evelyniana</i> in current and future climate scenarios was predicted, with the key environmental variables analyzed. The model with FC = 2025-01-01T00:00:00Z20252026-05-29T14:34:55.336Z2026-04-08T05:07:19.192ZS-EPMC120905384039528410.3389/fpls.2025.1561031