Habitat-quality dynamics and reversal-risk diagnostics in Southwest China using a productivity–hydrology enhanced RSEI framework

Habitat quality in mountainous Southwest China is shaped by strong terrain gradients, climate heterogeneity, and intensifying human pressures, yet existing remote-sensing ecological indices inadequately represent the productivity and hydrological functions critical to mountain ecosystems. Here, habitat quality is assessed as a remote-sensing-based regional proxy rather than as a direct measure of biodiversity or species-specific habitat suitability. We develop a Productivity–Hydrology Enhanced Remote Sensing Ecological Index (PHE-RSEI) that retains the PCA-based latent-gradient logic of the conventional RSEI while replacing NDVI with EVI and incorporating net primary productivity and water retention to represent productivity and hydrological regulation in mountain ecosystems, and couple it with an integrated diagnostic framework comprising spatial autocorrelation, Theil–Sen/Mann–Kendall trend analysis with FDR-BH correction, Hurst-exponent persistence diagnostics, GeoShapley-based explainable machine learning, and PLS-SEM pathway decomposition. Applied across 1.38×106 km2 for 2000–2020, PHE-RSEI reveals that habitat quality improved overall: Good + Excellent area rose from 28.0% to 56.5%. However, the improvement was spatially uneven and potentially unstable: 84.1% of the region showed positive trends, yet only 17.7% were statistically significant, and a mean Hurst exponent of ∼0.42 indicates that 67.2% of the improving area faces reversal risk. Driver attribution shows that terrain and climate jointly structure habitat-quality heterogeneity through nonlinear, spatially varying pathways, while human-activity proxies remain a stable net stressor. These findings support persistence-aware ecological zoning that distinguishes fragile high-elevation blocks, stable ecological-function belts, and reversal-prone transition zones, providing actionable guidance for programs such as the Grain-for-Green and Ecological Red Line frameworks.