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使用XGBoost-SHAP来了解生态系统服务的权衡效应和生态脆弱地区的驱动机制
Peiyu Du1,2,3, Heju Huai1,2, Xiaoyang Wu3
1Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Frontiers in plant science
|May 13, 2025
概括
水产和土壤保护等生态系统服务下降,而防风和沙子固定在中国温苏增加. 土地使用,降水和温度驱动这些复杂的权衡和协同作用.
科学领域:
- 环境科学 环境科学
- 生态系统管理 生态系统管理
- 空间分析 空间分析
背景情况:
- 了解生态系统服务 (ES) 的变化和相互作用对于区域发展至关重要.
- 评估ES之间的权衡和协同作用对于有效的生态系统管理至关重要.
- 中国新疆的温苏作为分析ES动态的案例研究.
研究的目的:
- 从1990年到2020年,系统地评估生态系统服务 (ES) 和它们的权衡.
- 使用可解释的机器学习量化ES权衡的非线性和值效应.
- 确定影响ES权衡和协同作用的关键驱动因素.
主要方法:
- 生态系统服务的时间序列分析 (1990-2020).
- 应用XGBoost-SHAP模型来识别非线性和值效应.
- 量化水产,土壤保护,防风/沙子固定和息地质量之间的权衡和协同作用.
主要成果:
- 水产和土壤保护呈现下降趋势,而防风/沙子固定增加.
- 息地质量最初有所改善,然后下降.
- 大多数ES对之间主要观察到权衡,除了水产-土壤保护 (协同作用) 之外.
- 土地利用,降水和温度被确定为关键驱动因素,表现出非线性反应和值效应.
结论:
- 该研究强调了生态系统服务驱动因素中的复杂,非线性关系和值效应.
- 可解释的人工智能 (XGBoost-SHAP) 提供了对ES动态的机制性见解.
- 这些发现为生态脆弱地区的生态系统管理提供了宝贵的指导.
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