Assessing relative contributions of climate and socio-environmental factors to ecosystem health through space-time interpretable machine learning
文献类型：期刊论文
| 作者 | Meng, Xiaoliang6; Wu, Junyi6; Xie, Yichun5; Bai, Yongfei3; Zhou, Chenghu2; Li, Yuchen1,4 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2025-09-01 |
| 卷号 | 178页码:114087 |
| 关键词 | Relative contributions of coupled climate and socioenvironmental factors Interpretable machine learning Fine-grained spatial and temporal analysis Each feature's contribution to individual prediction (EFCTIP) Localized EFCTIP |
| ISSN号 | 1470-160X |
| DOI | 10.1016/j.ecolind.2025.114087 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | The biggest challenge of sustainability is improving ecosystem production services while preserving other functions at large scales. One solution is to seek a delicate equilibrium from complex nonlinear trade-offs between socioeconomic development, climate change, and environmental protection to maximize ecosystem services. This solution's thrust is understanding these factors' relative contributions (RC) to long-term ecosystem changes. Here, we developed a novel analysis based on space-time interpretable machine learning (IML) to assess the RC from two climate factors, four land uses, four environmental pollution indicators, and 28 socioeconomic variables from 2001 to 2018 on grassland changes across the Inner Mongolia Plateau. Compared with econometric panel regression analysis and hierarchical linear mixed models, which are popular in ecological and geographical studies, the IML models generated exciting insights and illustrated several advantages. The IML models identified that no driving factors have maintained consistent impacts on ecosystem health in space and time. The strengths and directions of their RC varied and depended on their regional and local interactions. The IML models revealed fine-grained space-time RC variations, with each feature contributing to individual predictions (EFCTIP), and EFCTIP varying regionally and locally, which no other approaches can achieve. Traditional models' system-wide interpretations are misleading. The IML models support the concurrent spatiotemporal examination of ecosystem health and feature engineering to mitigate limitations from multicollinearity and non-linearity. This study has significant policy implications for grassland management in Inner Mongolia and can be applied to other ecosystems. |
| URL标识 | 查看原文 |
| WOS关键词 | ARTIFICIAL-INTELLIGENCE ; PANEL-DATA ; EARTH ; COEVOLUTION |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001595233400002 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217467]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Xie, Yichun |
| 作者单位 | 1.Univ Cambridge, Sch Clin Med, MRC Epidemiol Unit, Cambridge CB2 0QQ, England; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; 3.Chinese Acad Sci, Inst Bot, Beijing, Peoples R China; 4.Univ Leeds, Inst Spatial Data Sci, Sch Geog, Leeds LS2 9JT, England 5.Eastern Michigan Univ, Dept Geog & Geol, Ypsilanti, MI 48197 USA; 6.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Meng, Xiaoliang,Wu, Junyi,Xie, Yichun,et al. Assessing relative contributions of climate and socio-environmental factors to ecosystem health through space-time interpretable machine learning[J]. ECOLOGICAL INDICATORS,2025,178:114087. |
| APA | Meng, Xiaoliang,Wu, Junyi,Xie, Yichun,Bai, Yongfei,Zhou, Chenghu,&Li, Yuchen.(2025).Assessing relative contributions of climate and socio-environmental factors to ecosystem health through space-time interpretable machine learning.ECOLOGICAL INDICATORS,178,114087. |
| MLA | Meng, Xiaoliang,et al."Assessing relative contributions of climate and socio-environmental factors to ecosystem health through space-time interpretable machine learning".ECOLOGICAL INDICATORS 178(2025):114087. |
入库方式： OAI收割
来源：地理科学与资源研究所
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