Spatio-Temporal Evolution and Driving Forces of Habitat Quality in China's Arid and Semi-Arid Regions: An Interpretable Machine Learning Perspective for Ecological Management
文献类型：期刊论文
| 作者 | Liu, Shihao1,2; Huang, Jinchuan2 |
| 刊名 | LAND
 |
| 出版日期 | 2025-09-25 |
| 卷号 | 14期号:10页码:1937 |
| 关键词 | habitat quality landscape scenic index machine leaning PLUS model arid and semi-arid regions |
| DOI | 10.3390/land14101937 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Against the global biodiversity crisis, arid and semi-arid regions are sensitive indicators of terrestrial ecosystems. However, research on their habitat quality (HQ) evolution mechanism faces dual challenges: insufficient multi-scale dynamic simulation and fragmented driving mechanism analysis. To address these gaps, this study takes northern China's arid and semi-arid regions as the object, innovatively constructing a pat-tern-process-mechanism multi-dimensional integration framework. Breaking through single-model/discrete-method limitations in existing studies, it realizes full-process integrated research on regional HQ spatiotemporal dynamics. Based on 1990-2020 Land Use and Land Cover Change (LUCC) data, the framework integrates the InVEST and PLUS models, solving poor continuity between historical assessment and future projection in traditional research. It also pioneers combining the XGBoost-SHAP model and Geographically and Temporally Weighted Regression (GTWR): XGBoost-SHAP quantifies nonlinear interactive effects of natural, socioeconomic, and landscape drivers, while GTWR explores spatiotemporal heterogeneous mechanisms of landscape pattern evolution on HQ, effectively addressing the dual challenges. Results show the following: (1) In 1990-2020, cultivated and construction land expanded, with grassland declining most notably; (2) Overall HQ decreased by 0.82%, with high-value areas stable in the west and northeast, low-value areas concentrated in the central region, and 2030 HQ optimal under the Ecological Protection (EP) scenario; (3) Natural factors contribute most to HQ change, followed by socioeconomic factors, with landscape indices being least impactful; (4) Under future scenarios, landscape Patch Density (PD) has the most prominent negative effect-its increase intensifies fragmentation and reduces connectivity. This study's method integration breakthrough provides a quantitative basis for landscape pattern optimization and ecosystem management in arid and semi-arid regions, with important scientific value for promoting integration of landscape ecology theory and sustainable development practice. |
| URL标识 | 查看原文 |
| WOS关键词 | LAND-USE/COVER CHANGE ; ECOSYSTEM SERVICES ; NATURAL HABITAT ; CLIMATE-CHANGE ; RIVER-BASIN ; AREA ; SIMULATION ; EXPANSION ; CONNECTIVITY ; BIODIVERSITY |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001603056100001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217797]  |
| 专题 | 区域可持续发展分析与模拟院重点实验室_外文论文 |
| 通讯作者 | Huang, Jinchuan |
| 作者单位 | 1.Shandong Agr Univ, Coll Resources & Environm, Tai An 271018, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu, Shihao,Huang, Jinchuan. Spatio-Temporal Evolution and Driving Forces of Habitat Quality in China's Arid and Semi-Arid Regions: An Interpretable Machine Learning Perspective for Ecological Management[J]. LAND,2025,14(10):1937. |
| APA | Liu, Shihao,&Huang, Jinchuan.(2025).Spatio-Temporal Evolution and Driving Forces of Habitat Quality in China's Arid and Semi-Arid Regions: An Interpretable Machine Learning Perspective for Ecological Management.LAND,14(10),1937. |
| MLA | Liu, Shihao,et al."Spatio-Temporal Evolution and Driving Forces of Habitat Quality in China's Arid and Semi-Arid Regions: An Interpretable Machine Learning Perspective for Ecological Management".LAND 14.10(2025):1937. |
入库方式： OAI收割
来源：地理科学与资源研究所
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