Unveiling spatiotemporal evolution and driving factors of ecosystem service value: interpretable HGB-SHAP machine learning model - Xu, Xiangming; Zhang, Xinyi; Qin, Linghua; Li, Rui (Frontiers in Environmental Science, 2025)
IntroductionThe ecosystem service value (ESV) is a critical element in the preservation of ecological barriers. The objective of this study is to elucidate the nonlinear correlation between ESV and the key factors that contribute to enhancing the accuracy and reliability of ecosystem service value assessment.MethodsIn this study, ESV were evaluated based on grid and county scales. Furthermore, the driving factors of ESV were explored using the explainable machine learning method.ResultsThe findings are as follows: (1) The net ESV of the Gangjiang Upstream Basin (GUB) has undergone a decline from 1990 to 2000, with climate regulation and hydrological regulation collectively accounting for approximately 50% of all functions. (2) A mere 0.69% of the areas exhibited an increase in the level of ESV, while 11.19% demonstrated a decline by 2020, based on the grid scale. The ESV exhibited a slight increase in two counties, while it demonstrated a decrease in the remaining 16 counties at the county scale. The ESV exhibited a substantial positive spatial correlation, manifesting as the presence of considerable high-high and low-low clustering regions. (3) The interpretable machine learning analysis revealed a consistently strong negative correlation between ESV and human activity intensity (HAI), fractional vegetation cover (FVC), and elevation across the entire observed range. In contrast, the soil organic matter (SOC) demonstrated a non-linear, highly significant positive correlation with ESV.DiscussionThis paper addresses the observed decline in the value of ecosystem services of GUB by proposing a series of strategies designed to enhance ESV in the region. Furthermore, drawing on research findings related to the driving factors and thresholds of ESV, this paper presents specific measures that can serve as references for managers.