Spatial heterogeneity and driving factors of ecosystem service trade-offs in Beijing’s ecological conservation area: Insights for spatial planning and management

Abstract Context Although rapid urbanization affects ecosystems’ ability to provide multiple ecosystem services (ESs), potentially intensifying trade-offs or fostering synergies among them, the spatial variation of urbanization’s impact on ES trade-offs remains underexplored. The urban–rural gradient effectively depicts the shift from human-dominated landscapes to more natural ones, providing insight into the spatial heterogeneity of urbanization’s influence on ES trade-offs. This understanding is vital for refining ES management and improving regional landscape sustainability. Additionally, conventional regression methods are insufficient for uncovering the intricate nonlinear mechanisms driving ES trade-offs and identifying priority optimizing areas. Objectives To investigate the spatiotemporal dynamics, underlying drivers, and priority optimization areas of ES trade-offs along the urban–rural gradient, thereby facilitating coordinated development of regional human–environment systems. Methods This study quantitatively analyzed the spatial differentiation of ESs and their trade-offs along the urban–rural gradient, and then integrated optimal parameters-based geographical detector and Bayesian belief network models to identify the underlying drivers of ES trade-offs and priority optimization areas. Results (1) The average values of water yield (WY), food production (FP), soil conservation (SC), and leisure recreation (LR) increased, while those of carbon sequestration (CS) and habitat quality (HQ) slightly declined during 2000–2020. Spatially, along the gradient from urban to rural ecological areas, WY and LR progressively decreased, whereas CS, SC and HQ steadily improved, and FP initially rose before declining. (2) Except for CS_HQ, the other 14 pairs of ES trade-offs exhibited pronounced spatial heterogeneity along the urban–rural gradient. Urban and urban–rural transition areas displayed notable trade-offs between cultural services and other services, while rural ecological areas demonstrated obvious trade-offs between regulating and provisioning services. (3) Land use type was the predominant driver influencing ES trade-offs. Scenario simulation further reveals that the transition zone between rural ecological areas and rural agricultural areas is a key area where trade-offs are likely to intensify, making it a priority area for ES optimization. Conclusions This study underscores the need to identify spatial heterogeneity and priority optimization areas of ES trade-offs, thereby enhancing tailored ecosystem management and advancing landscape sustainability.