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

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 temperature seasonality (Bio7, 30.8% contribution) and dry-season precipitation (Bio17, 21.5%) as dominant habitat filters. Spatial projections revealed a previously undocumented habitat paradox: while high suitability core areas may expand by 138.73 km² under optimal scenarios, total suitable habitat could contract by 53.89 km² due to climate-driven edge effects. Three key innovations emerge from this work: First, we established a novel protocol for urban biodiversity assessment that integrates climatic, topographic and anthropogenic variables. Second, we demonstrated the critical role of microclimate buffering in maintaining habitat refugia, particularly in the southwestern Guandu and northwestern Chenggong districts. Third, we developed a decision-support framework that identifies priority conservation zones based on habitat stability thresholds. These findings advance ecological theory by quantifying the impacts of urban heat islands on species distributions, providing actionable tools for city planners. The habitat stability maps and climate-resilience indicators developed in this study are currently being implemented in Kunming's urban green space master plan, demonstrating the immediate practical relevance of this research.