Spatiotemporal Prediction of Ideal Butterfly Habitats in Kun-Ming's Urban Green Areas: Enabled by Maxent and ArcGIS.

ABSTRACT: This study presents groundbreaking insights into urban butterfly conservation by developing an integrated modelling framework to predict habitat suitability under climate change. The research addresses three critical gaps in current knowledge: (1) the lack of robust methodologies for assessing subtropical urban Lepidoptera habitats, (2) insufficient understanding of microclimate-mediated edge effects in fragmented landscapes and (3) the absence of predictive tools for climate-adaptive conservation planning. Our analytical approach combined Maxent species distribution modelling with high-resolution GIS sensitivity analysis, incorporating 64 georeferenced occurrence records and 22 environmental variables. The optimised model achieved exceptional predictive accuracy (AUC = 0.966), identifying

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12518168 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature