Revealing the evolution and driving factors of the landscape pattern-ecosystem service-human well-being cascade: Social network and geodetector based analysis

Unraveling the evolution of "landscape pattern (LP) - ecosystem service (ES) - human well-being (HW)" (LEH) cascade process is crucial for optimizing LPs, mitigating ES degradation, and enhancing HW. This study focuses on Qinghai Province, China, employing social network analysis and Geodetector to explore the evolution and driving mechanism of LEH cascade. The results demonstrated that, ES synergies increased and trade-offs decreased in Qinghai between 2000 and 2020. The LEH cascade network grew more cohesive and interconnected, with system boundaries as interactions strengthened key factors -including the number of patches, soil conservation, and residential savings balance-emerged as crucial nodes for sustaining network's functionality. Single-factor detection showed that drivers of HW were best explained by soil organic carbon content (q = 0.42). For ESs, population density (q = 0.34) was the key factor, while LP was primarily influenced by factors like topographic roughness, albeit with weaker explanatory power. Factor interactions provided greater explanatory power for LP, ESs, and HW than any single factor. Both dual-factor and nonlinear enhancements significantly improved the model's explanatory power. The population density-fractional vegetation cover pair showed the strongest synergistic effect on ESs (q = 0.94), indicating nonlinear enhancement, while the temperature-precipitation pair was most explanatory for HW(q = 0.88). The study offers a valuable reference for the systematic understanding of the LEH cascade and provide a scientific foundation for the sustainable management of ecosystems and the enhancement of HW.