Ecosystem service trade‐offs and synergies and their drivers in severely affected areas of the Wenchuan earthquake, China

Abstract The suddenness and destructive power of earthquake disasters pose significant threats to ecosystems. Understanding the changes in post‐earthquake ecosystem services and the complex relationships of their trade‐offs and synergistic effects is crucial for post‐disaster recovery and achieving a holistic and sustainable balance of human well‐being. However, quantifying post‐disaster ecosystem services and these intricate relationships, as well as probing the mechanisms behind their formation from a spatial perspective, remain challenging. This study presents a comprehensive framework that integrates spatial statistical methods, the InVEST model, geodetectors, and the Multiscale Geographically Weighted Regression model (MGWR) to quantify trade‐offs and synergistic effects among ecosystem services before and after earthquakes, and to investigate the spatial heterogeneity of these relationship effects with various driving factors. Results indicate (1) Typical ecosystem services, that is, water yield, soil conservation, carbon storage, and habitat quality, have more weak trade‐off effects and fewer synergistic effects. These relationship intensities vary considerably spatially, demonstrating some agglomerative distribution, suggesting that the ecosystem has not yet recovered to a good state after the quake; (2) The impact of different factors on the trade‐offs and synergies between ecosystem services varies. Interaction effects of driving factors are stronger than singular effects. Relationships between soil conservation and other ecosystem services are primarily influenced by topography and climate. Relationships involving carbon storage are mainly affected by vegetation and climate, while those concerning habitat quality are attributed mostly to human and climatic factors; (3) In the modeling process, considering the different spatial scales of driving factors, MGWR outperforms the Geographically Weighted Regression in explaining the relationship between ecosystem services and driving factors. Moreover, several spatially feasible and targeted ecological measures are proposed to enhance the synergy among ecosystem services. By focusing on the changes in ecosystem services, this study reveals the intrinsic mechanisms of trade‐offs and synergies between ecosystem services, offering insights for related studies in other regions.