Exploring spatial-temporal driving factors for changes in multiple ecosystem services and their relationships in West Liao River Basin, China

Ecosystem services (ES) are the direct and indirect benefits people obtain from ecosystems, serving as a bridge linking ecological systems and social-economic systems. The quantitative assessment of the dynamic changes in ES and their relationships and the identification of the driving forces behind them have recently become a research hotspot. However, several research gaps remain challenging, such as the lack of an analytical framework for selecting relevant driving factors and the need for an innovative approach that integrally estimates the impacts of driving factors on the changes in ES and the relationships between ES. In this study, we modify the social-ecological system framework as the analytical basis and suggest a series of principles for selecting relevant driving factors, we then adopt the path analysis model to simultaneously and consistently quantify the contributions of driving factors to ES changes and their relationships. Using the West Liao River Basin (WLRB) as a case study, the results show the spatial-temporal variations in three ES and six driving factors from 2000 to 2020, divided into four periods. The estimation of path analysis model confirm two hypotheses that different driving factors exerted differential effects on changes in multiple ES in four periods for the whole WLRB and in three sub-basins for the period 2015–2020. In addition, the path analysis exhibits the quantitative relationships between food production, water yield, and soil conservation, which vary temporally and spatially in different periods and different sub-basins. The identification of driving factors is helpful for supporting policy-making to construct a coupled self-adjusted social-ecological for the benefit of the public. [Display omitted] •Using modified social-ecological system framework and suggesting choosing principles for driving factors•Adopting path analysis model to quantify the impacts of driving factors on ES changes and their relationships•Revealing temporally and spatially differential effects of different driving factors on multiple ES changes•Identifying spatial-temporal relationship between food production, water yield, and soil conservation