An Integrated Framework for Diagnosing Ecological Resilience Degradation in a High-Density Urban Agglomeration: Evidence from the Guangdong–Hong Kong–Macao Greater Bay Area

Ecological security pattern (ESP) planning typically emphasizes ecological structure and connectivity, but may provide limited information on long-term functional degradation occurring within ecologically important areas. To address this gap, this study develops a baseline-referenced framework for diagnosing ecological resilience degradation (ERD) by integrating ecosystem service value (ESV) dynamics, minimum cumulative resistance (MCR) modeling, and explainable machine learning (XGBoost–SHAP). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, ERD was operationally defined as long-term functional degradation within ecologically important components of the 2000 baseline network, with net ESV decline from 2000 to 2020 used as the functional-degradation signal. Two complementary ERD patterns were distinguished: source erosion and corridor interruption. The results showed that ERD exhibited a pronounced core–periphery pattern, with higher degradation intensity concentrated along the Guangzhou–Foshan–Dongguan–Shenzhen urban development corridor. Integrated ERD covered 5452.69 km2, of which source erosion accounted for 4238.32 km2 (77.73%) and corridor interruption for 1214.37 km2 (22.27%). Source erosion represented the dominant ERD pattern in terms of spatial extent. The XGBoost model showed moderate predictive performance under spatial block cross-validation (mean validation R2 = 0.638 ± 0.043), and SHAP analysis indicated that vegetation condition, proximity to water bodies, nighttime light, and elevation were among the most influential factors associated with spatial variation in ERD intensity. NDVI contributions shifted from positive to negative around 0.65, while the distance-to-water response changed most rapidly within 200–300 m. These values are interpreted as empirical transition ranges in the model response. Overall, the proposed framework links long-term ecosystem functional degradation with baseline ecological network position and provides a spatially explicit basis for identifying functionally degraded ecological areas and supporting differentiated spatial prioritization and ecological management.