Scale dependency of trade-offs/synergies analysis of ecosystem services based on Bayesian Belief Networks: A case of the Yellow River Basin

In recent years, climate change and human activities have reduced various ecosystem services (ESs). Analyzing trade-offs/synergies among ESs is crucial for ecosystem sustainability and human well-being. Many studies overlook potential scale effects by focusing on a single scale. We addressed this by evaluating ESs - grain production (GP), habitat quality (HQ), carbon sequestration (CS), soil conservation (SC), and water yield (WY) - in the Yellow River Basin (YRB) from 1990 to 2020, using Bayesian belief networks to analyze their drivers and relationships across scales. The study revealed a steady increase in GP, while both CS and SC exhibited fluctuating upward trends, and HQ and WY experienced downward trends. Significant spatial heterogeneity was observed, with GP decreasing from east to west, and WY, CS, and SC decreasing from northeast to southwest. HQ values were generally high but lower in specific downstream and upstream locations. Key factors influencing these ESs included land use, slope, population, evapotranspiration, NDVI, and precipitation. As the spatial scale expanded, the influence of these factors diminished. The relationships among ESs primarily reflected synergies. However, with the spatial scale increased, trade-offs/synergies among ESs weakened, and several relationships transitioned between trade-offs and synergies. This study offers insights for optimizing ecosystem management by understanding the scale dependency of relationships among ESs.