Jiang Wei, Fu Bojie, Gao Guangyao, Lv Yihe, Wang Cong, Sun Siqi, Wang Kai, Schüler Stefan, Shu Zhongguo
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No.18 Shuangqing Road, 100085 Beijing, China.
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, No.18 Shuangqing Road, 100085 Beijing, China.
Sci Total Environ. 2023 Dec 15;904:166716. doi: 10.1016/j.scitotenv.2023.166716. Epub 2023 Sep 1.
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.
生态系统服务（ES）是人们从生态系统中获得的直接和间接利益，是连接生态系统和社会经济系统的桥梁。对生态系统服务动态变化及其关系进行定量评估，并识别其背后的驱动因素，最近已成为一个研究热点。然而，仍有几个研究空白具有挑战性，例如缺乏选择相关驱动因素的分析框架，以及需要一种创新方法来整体评估驱动因素对生态系统服务变化及其关系的影响。在本研究中，我们修改了社会生态系统框架作为分析基础，并提出了一系列选择相关驱动因素的原则，然后采用路径分析模型同时并一致地量化驱动因素对生态系统服务变化及其关系的贡献。以西辽河流域（WLRB）为例，结果显示了2000年至2020年期间三个生态系统服务和六个驱动因素的时空变化，分为四个时期。路径分析模型的估计证实了两个假设，即不同的驱动因素在四个时期对整个西辽河流域以及2015 - 2020年期间三个子流域的多种生态系统服务变化产生了不同的影响。此外，路径分析展示了食物生产、产水量和土壤保持之间的定量关系，这些关系在不同时期和不同子流域在时间和空间上有所不同。识别驱动因素有助于支持政策制定，构建一个有利于公众的耦合自我调节社会生态系统。
