Analysis of habitat quality characteristics andinfluencing factors in Anshun City based ongeographic detector

Revealing spatial heterogeneity in regional habitat quality and its potential drivers is important for ecosystem management. Based on land-use data for Anshun City in 2020, this study combined the Habitat Quality module of the InVEST model with a parameter-optimized Geodetector approach to characterize habitat quality patterns and to quantify the explanatory power of candidate drivers. Climatic, edaphic, topographic, and anthropogenic variables were discretized into six classes using the natural breaks (Jenks) method in ArcGIS prior to Geodetector analysis. The results show that the mean habitat quality index for Anshun in 2020 was 0.665, corresponding to a “relatively high–high” level; high-quality habitats were mainly distributed in mountainous areas with dense vegetation. Among the examined drivers, annual temperature range showed the highest explanatory power (q = 0.033), followed by annual precipitation (q = 0.0275) and precipitation in the driest period (q = 0.0251). Slope also exhibited a moderate association with habitat quality heterogeneity (q = 0.0169), whereas the tested soil variables and population density yielded relatively lower explanatory power. Notably, the absolute q-values were low overall (max q = 0.033), indicating that habitat quality patterns likely reflect the combined effects of multiple interacting factors and scale-dependent processes that are not fully captured by the selected predictors and discretization scheme. These findings suggest that climate variability is an important correlate of habitat quality heterogeneity in Anshun, while land-use optimization and targeted ecological restoration in low-quality areas (e.g., urban centers and intensively cultivated lands) may help improve regional ecological resilience under ongoing environmental change.