Drivers and dominant pathways for ecosystem service trade-offs in the Luo River Basin at the local optimal scale

Amid shrinking ecological space and deteriorating basin ecosystems, understanding drivers and pathways of ecosystem service trade-offs remains a key challenge for ecological civilization. This research examines the Luo River Basin. Based on trade-offs of water yield, carbon sequestration, soil conservation, and habitat quality from 2000 to 2023 at multiple scales (1 km grid, township, county), the Optimal Multivariate Geographical Detector (OMGD) was used to identify the local optimal scale. A Bayesian network model was applied to analyze driving factors and dominant influence paths. Findings show (1) Trade-off intensity among ecosystem services diminishes with increasing scale, with spatial heterogeneity stabilizing. At the county scale, the area with intensified trade-offs between carbon sequestration and soil conservation accounts for 95.80 %. (2) OMGD recognizes the county scale as locally optimal (average q-value 0.69), with explanatory power 46.8 % and 102.9 % higher than township and grid scales. (3) Precipitation, NDVI (Normalized Difference Vegetation Index), and nighttime light act as dominant shared drivers, displaying marked spatial heterogeneity across different service trade-offs, mountain areas are mainly driven by precipitation and NDVI, while urban areas are influenced by nighttime light. (4) Among 12 key pathways, the slope - NDVI link is central (mutual information 0.730). Combined with sensitivity analysis, it provides a governance entry point integrating natural and human systems. The study proposes a targeted ecological management framework for the Luo River Basin and analogous areas, emphasizing county-based planning, vegetation recovery, and integrated natural-human system strategies, supporting enhanced ecosystem service synergy and sustainable governance.