Effect of physical geographic and socioeconomic processes on interactions among ecosystem services based on machine learning

A thorough understanding of the interactions of ecosystem services (ESs) can enable effective ES planning and management to curtail their degradation and enhance restoration. So it is essential to explore the ecosystem processes and driving mechanisms of ES relationships. Here, the objective of this study is to investigate the mechanisms of multiple ES interactions and provide a decision-making reference for ES management. A dimension reduction to 11 ESs was performed using principal component analysis, and a machine learning approach based on a Bayesian belief network (BBN) was used to identify the effects of physical geographic and socioeconomic processes on ES trade-offs and synergies. Considering the Nansihu Lake Basin, China, as a study area, 11 ESs, namely nutrient retention (nitrogen and phosphorus), aquatic products, habitat quality, pollination, carbon storage, landscape esthetic quality, water yield, food provision, biomass production, and erosion control, mapped in 2018 were quantified. Four principal components, namely synergies between nutrient retention, aquatic production, and habitat quality; synergies between pollination, carbon storage, and landscape esthetic quality; trade-offs between water yield and food provision, biomass production; and erosion control, were extracted from multiple ESs. BBN sensitivity analysis revealed that among the socioeconomic factors (land use type, population density, and night-time light), climate (precipitation, temperature), topogeography (digital elevation model, slope), and soil characteristics (soil types and soil texture), land use exhibits the most critical effect on ES interactions. The response of ES capacities and interactions to land use, climate, and soil management were predicted through BBN scenario analysis, and the results revealed that critical decision-making can optimize multiple ESs. The results of this study can provide a guideline of ES interactions for sustainable management to maximize ES capacities and limit ES trade-offs.