Liu Qi, Qiao Jiajun, Li Mengjuan, Huang Mengjiao
College of Geography and Environmental Science, Henan University, Kaifeng 475004, China.
College of Geography and Environmental Science, Henan University, Kaifeng 475004, China.
Sci Total Environ. 2024 Jan 15;908:168486. doi: 10.1016/j.scitotenv.2023.168486. Epub 2023 Nov 10.
Accurately understanding ecosystem service (ES) interactions and an analysis of the complex, multiscale driving mechanisms are foundational prerequisites for implementing effective multiscale ES management. This study dives into the spatial and temporal variations of ES interactions in the Yellow River Basin across four spatial scales. The eXtreme Gradient Boosting (XGBoost) model is later deployed to pinpoint the key drivers of ecosystem services and their indirect pathways to ESs are illuminated utilizing Partial Least Squares-Structural Equation Modeling (PLS-SEM). The results indicate that (1) The synergistic effect between ES pairs in the Yellow River Basin surpasses that of trade-offs. Various types of ecosystem service bundles have transformed into each other from 2000 to 2020, and the spatial patterns of ES interactions bear resemblances at different scales. (2) The factors driving habitat quality (HQ), carbon sequestration (CS), and landscape aesthetics (LA) are mainly the landscape configuration and biophysical conditions. The factor driving food production (FP) is mainly the level of urbanization, whereas soil conservation (SC) and water yield (WY) are mainly subject to climate. (3) When biophysical conditions and level of urbanization serve as mediating variables in pathways, driving factors invariably have negative indirect effects on ESs. When landscape configuration serves as a mediating variable, biophysical conditions positively influence HQ and CS, and negatively impact FP, WY, and LA. Conversely, the level of urbanization negatively affects all ESs. (4) The combination of XGBoost and PLS-SEM offers a comprehensive and innovative lens for analyzing ESs driving mechanisms. Based on our findings, scientific management of ESs should account not only for the direct impacts of driving elements but also for their scale and indirect effects.
准确理解生态系统服务（ES）相互作用并分析复杂的多尺度驱动机制是实施有效的多尺度ES管理的基本前提。本研究深入探讨了黄河流域ES相互作用在四个空间尺度上的时空变化。随后运用极端梯度提升（XGBoost）模型来确定生态系统服务的关键驱动因素，并利用偏最小二乘结构方程模型（PLS-SEM）阐明其对生态系统服务的间接影响路径。结果表明：（1）黄河流域生态系统服务对之间的协同效应超过权衡效应。2000年至2020年期间，各类生态系统服务束相互转化，不同尺度下生态系统服务相互作用的空间格局具有相似性。（2）驱动栖息地质量（HQ）、碳固存（CS）和景观美学（LA）的因素主要是景观格局和生物物理条件。驱动粮食生产（FP）的因素主要是城市化水平，而土壤保持（SC）和产水量（WY）主要受气候影响。（3）当生物物理条件和城市化水平作为路径中的中介变量时，驱动因素对生态系统服务总是具有负向间接影响。当景观格局作为中介变量时，生物物理条件对HQ和CS有正向影响，对FP、WY和LA有负向影响。相反，城市化水平对所有生态系统服务均有负向影响。（4）XGBoost和PLS-SEM的结合为分析生态系统服务驱动机制提供了一个全面且创新的视角。基于我们的研究结果，对生态系统服务的科学管理不仅应考虑驱动因素的直接影响，还应考虑其尺度和间接影响。
