Nonlinear Relationships and Synergies Between Wetland Landscape Patterns and Ecosystem Services Using Explainable Machine Learning: A Case Study of Wuhan, China

Under rapid urbanization, fragmentation and declining connectivity of wetland landscapes may be accompanied by changes in ecosystem service (ES) supply and the balance among services. However, nonlinear associations between wetland landscape patterns and wetland ecosystem services (WESs), including candidate turning-point ranges, remain insufficiently understood. Taking Wuhan, China, as the study area, we quantified spatiotemporal changes in wetland landscape patterns and four core WESs—water yield, water purification, soil conservation, and habitat quality—from 2000 to 2020. We constructed an ecosystem service composite benefit index (ESS), an overall trade-off index (EST), and a synergistic benefit index (ESB), and used XGBoost and SHAP to identify nonlinear associations of landscape pattern metrics with WESs and ESB in 2020. The results showed that (1) Wuhan’s total wetland area decreased from 2000 to 2020, landscape fragmentation intensified, aggregation and connectivity declined, and multiple WESs showed downward trends in and around built-up areas; (2) the five landscape pattern metrics had marked nonlinear associations with WESs and ESB, with the mean shape index (SHAPE), aggregation index (AI), and largest patch index (LPI) contributing strongly to the models; and (3) the SHAP curves showed relatively stable ESB turning points near SHAPE 1.40 and AI 96%, while the central LPI crossing was near 61%. These values can serve as reference thresholds for wetland planning and restoration in Wuhan. This study provides quantitative evidence for coordinating multiple urban wetland ecosystem services from a landscape-pattern perspective.