Du Peiyu, Huai Heju, Wu Xiaoyang, Wang Hongjia, Liu Wen, Tang Xiumei
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Front Plant Sci. 2025 Apr 28;16:1552818. doi: 10.3389/fpls.2025.1552818. eCollection 2025.
Understanding the spatial and temporal variability of Ecosystem services (ES), along with the trade-offs and synergies among different services, is crucial for effective ecosystem management and sustainable regional development. This study focuses on Wensu, Xinjiang, China, as a case study to address these challenges.
ES and their trade-offs were systematically assessed from 1990 to 2020. Explainable machine learning models (XGBoost-SHAP) were employed to quantify the nonlinear effects and threshold effects of ES trade-offs, with specific attention to identifying their driving factors.
(1) From 1990 to 2020, water yield (WY) and soil conservation (SC) exhibited an inverted "N"-shaped downward trend in Wensu County: mean annual WY decreased from 22.99 mm to 21.32 mm, and SC per unit area declined from 1440.28 t/km² to 1351.3 t/km². Conversely, windbreak and sand fixation (WS) showed an "N"-shaped increase from 2.32×10⁷ t to 3.11×10⁷ t. Habitat quality (HQ) initially improved then deteriorated, with values of 0.596, 0.603, 0.519, and 0.507 sequentially. (2) Relationships between WY-WS, WY-HQ, WS-HQ, SC-WS, and SC-HQ were primarily tradeoffs, whereas WY-SC interactions were synergistic. Trade-offs for SC-HQ, WY-HQ, and WS-HQ were stronger, while WY-SC trade-offs were weaker. (3) The XGBoost-SHAP model revealed land use type (Land), precipitation (Pre), and temperature (Tem) as dominant drivers of trade-offs, demonstrating nonlinear responses and threshold effects. For instance, WY-SC trade-offs intensified when precipitation exceeded 17 mm, while temperature thresholds governed WY-HQ trade-off/synergy transitions.
This study advances the identification of nonlinear and threshold effects in ES trade-off drivers. The model's interpretability in capturing these complexities clarifies the mechanisms underlying ES dynamics. Findings are generalizable to other ecologically vulnerable regions, offering critical insights for ecosystem management and conservation strategies in comparable environments.
了解生态系统服务（ES）的时空变异性，以及不同服务之间的权衡和协同作用，对于有效的生态系统管理和区域可持续发展至关重要。本研究以中国新疆温宿县为例，以应对这些挑战。
系统评估了1990年至2020年期间的生态系统服务及其权衡。采用可解释机器学习模型（XGBoost-SHAP）来量化生态系统服务权衡的非线性效应和阈值效应，特别关注识别其驱动因素。
（1）1990年至2020年期间，温宿县的产水量（WY）和土壤保持（SC）呈倒“N”形下降趋势：年平均产水量从22.99毫米降至21.32毫米，单位面积土壤保持量从1440.28吨/平方公里降至1351.3吨/平方公里。相反，防风固沙（WS）呈“N”形增加，从2.32×10⁷吨增至3.11×10⁷吨。栖息地质量（HQ）先改善后恶化，依次为0.596、0.603、0.519和0.507。（2）WY-WS、WY-HQ、WS-HQ、SC-WS和SC-HQ之间的关系主要是权衡关系，而WY-SC之间存在协同作用。SC-HQ、WY-HQ和WS-HQ之间的权衡关系更强，而WY-SC之间的权衡关系较弱。（3）XGBoost-SHAP模型表明土地利用类型（Land）、降水量（Pre）和温度（Tem）是权衡关系的主要驱动因素，呈现出非线性响应和阈值效应。例如，当降水量超过17毫米时，WY-SC之间的权衡加剧，而温度阈值决定了WY-HQ之间的权衡/协同转变。
本研究推进了对生态系统服务权衡驱动因素中非线性和阈值效应的识别。该模型在捕捉这些复杂性方面的可解释性阐明了生态系统服务动态背后的机制。研究结果可推广到其他生态脆弱地区，为类似环境下的生态系统管理和保护策略提供关键见解。
