Topography-human activities-climate jointly shape the multi-scale spatiotemporal patterns of alpine ecosystem services: Evidence from the Zoige Plateau, China

Alpine ecosystems serve as critical bulwarks for regional ecological security and provide irreplaceable ecosystem services (ESs). However, the multi-scale interactions among ESs and their coupled natural-human driving mechanisms remain poorly understood, hindering targeted improvements and collaborative management of ecological functions. This study quantified the spatiotemporal dynamics, trade-offs/synergies, and bundles of four key ESs (carbon storage (CS), habitat quality (HQ), soil conservation (SC), and water yield (WY)) across different scales in China's Zoige Plateau. XGBoost-SHAP machine learning and structural equation modeling (SEM) were used to explore the impacts of topography, climate, and human activities on ESs and their driving pathways. The results revealed the following: (1) Across the three observation years, WY and SC were higher in 2020 than in 2000, whereas CS and HQ showed lower values in 2020. Spatially, WY was higher in the south and lower in the north; high values of CS and HQ were concentrated in the marsh wetlands and forests. (2) Significant trade-offs and synergies existed among ESs. HQ showed synergistic relationships with CS and SC, while WY exhibited trade-offs with CS and HQ, with evident scale effects. Multi-scale ES bundles displayed clear spatial differentiation. (3) Machine learning identified precipitation (PRE) dominated changes in WY, slope significantly promoted SC, and elevation (DEM) contributed the most to CS and HQ. SEM further indicated that DEM directly influenced PRE, potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI), thereby establishing the natural supply base for ESs. Meanwhile, gross domestic product (GDP) acted as an intermediate regulatory factor, significantly mediating the conversion efficiency of these three variables into ESs. This study reveals a nonlinear coupling mechanism in which topography, climate, and socio-economic regulatory factors synergistically shape ESs, providing scientific support for differentiated zoning, synergistic optimization, and sustainable management of alpine ecosystems.

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Publication Details

Journal
Ecological Indicators
Published
2026-07-30
DOI
https://doi.org/10.1016/j.ecolind.2026.115285
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.00

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article

Topography-human activities-climate jointly shape the multi-scale spatiotemporal patterns of alpine ecosystem services: Evidence from the Zoige Plateau, China

Xianglong Xing, Mingye Zhang, Ming Jiang, Shouzheng Tong et al.
Ecological Indicators
Land Use and Ecosystem Services
article

Topography-human activities-climate jointly shape the multi-scale spatiotemporal patterns of alpine ecosystem services: Evidence from the Zoige Plateau, China

Xianglong Xing, Mingye Zhang, Ming Jiang, Shouzheng Tong, Yu An, Kaishan Song, Yifan Li
article en

Abstract

Alpine ecosystems serve as critical bulwarks for regional ecological security and provide irreplaceable ecosystem services (ESs). However, the multi-scale interactions among ESs and their coupled natural-human driving mechanisms remain poorly understood, hindering targeted improvements and collaborative management of ecological functions. This study quantified the spatiotemporal dynamics, trade-offs/synergies, and bundles of four key ESs (carbon storage (CS), habitat quality (HQ), soil conservation (SC), and water yield (WY)) across different scales in China's Zoige Plateau. XGBoost-SHAP machine learning and structural equation modeling (SEM) were used to explore the impacts of topography, climate, and human activities on ESs and their driving pathways. The results revealed the following: (1) Across the three observation years, WY and SC were higher in 2020 than in 2000, whereas CS and HQ showed lower values in 2020. Spatially, WY was higher in the south and lower in the north; high values of CS and HQ were concentrated in the marsh wetlands and forests. (2) Significant trade-offs and synergies existed among ESs. HQ showed synergistic relationships with CS and SC, while WY exhibited trade-offs with CS and HQ, with evident scale effects. Multi-scale ES bundles displayed clear spatial differentiation. (3) Machine learning identified precipitation (PRE) dominated changes in WY, slope significantly promoted SC, and elevation (DEM) contributed the most to CS and HQ. SEM further indicated that DEM directly influenced PRE, potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI), thereby establishing the natural supply base for ESs. Meanwhile, gross domestic product (GDP) acted as an intermediate regulatory factor, significantly mediating the conversion efficiency of these three variables into ESs. This study reveals a nonlinear coupling mechanism in which topography, climate, and socio-economic regulatory factors synergistically shape ESs, providing scientific support for differentiated zoning, synergistic optimization, and sustainable management of alpine ecosystems.

Ecological IndicatorsVol. 190
Northeast Institute of Geography and Agroecology (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 11%
Land Use and Ecosystem Services
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