Chen et al. (2026) Climate Change and Ecological Restoration Synergies Shape Ecosystem Services on the Southeastern Tibetan Plateau

Identification

• Journal: Forests
• Year: 2026
• Date: 2026-01-12
• Authors: Qian Hong, Dongyan Pang, Qinying Zou, Yanbing Wang, Chao Liu, Xiaohu Sun, Shu Guang Zhu, Yixuan Zong, Xiao Zhang, Jianjun Zhang
• DOI: 10.3390/f17010102

Research Groups

• State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102200, China
• School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

Short Summary

This study investigated how multi-factor interactions and their spatial variability shape ecosystem services on the southeastern Tibetan Plateau from 2000 to 2020, revealing that synergistic effects of climate and restoration drivers nonlinearly enhance explanatory power more than individual factors.

Objective

• To clarify whether multi-factor interactions produce nonlinear enhancements in ecosystem service (ES) explanatory power and how these driver–response relationships vary across heterogeneous terrains.
• To quantify spatiotemporal patterns of four key ecosystem services (water yield, soil conservation, carbon sequestration, and habitat quality) from 2000 to 2020 using multi-source remote sensing data.
• To utilize geographic detectors to quantify the nonlinear enhancements produced by driver interactions.
• To reveal how these driving mechanisms vary spatially across the southeastern Tibetan Plateau’s complex elevational gradients.

Study Configuration

• Spatial Scale: Southeastern Tibetan Plateau (STP), covering 149,100 square kilometers (1.491 x 10^11 square meters), located between 91°45′52′′–98°52′52′′ E and 27°48′42′′–31°34′7′′ N, with a spatial resolution of 30 meters.
• Temporal Scale: From 2000 to 2020.

Methodology and Data

• Models used:
  • InVEST model (version 3.13.0) for Water Yield, Soil Conservation (SDR module), and Habitat Quality.
  • CASA model for Net Primary Productivity (NPP) to assess Carbon Sequestration.
  • Principal Component Analysis (PCA) for dimensionality reduction of driving factors.
  • Geo-detector model for quantifying spatial stratified heterogeneity and driver interactions.
  • Geographically Weighted Regression (GWR) for analyzing spatially varying relationships.
• Data sources:
  • Climate Data: Annual mean precipitation (1 km resolution, 2000–2020), Annual mean temperature (1 km resolution, 2000–2020), Potential evapotranspiration (1 km resolution, 2000–2020) from National Earth System Science Data Center and National Tibetan Plateau Data Center.
  • Soil Data: Harmonized World Soil Database version 1.1 (HWSD) (1 km resolution).
  • Land Use Data: China Multiperiod Land Use Land Cover Remote Sensing Monitoring Dataset (CNLUCC) (1 km resolution, 2000–2020).
  • Topographic Data: GDEMV3 Digital Elevation Model (DEM) (30 m resolution, 2020).
  • Vegetation Data: MODIS Vegetation Index Products (MOD13A3) and Global GIMMS NDVI3g v1 dataset (1 km resolution, 2000–2020) for Normalized Difference Vegetation Index (NDVI) and Net Primary Productivity (NPP).
  • Driving Factors (with SI units):
    • Climate: Annual mean Precipitation (meter), Annual mean temperature (Kelvin), Potential evapotranspiration (meter per second cubed, as stated in paper).
    • Vegetation: Normalized Difference Vegetation Index (dimensionless).
    • Topography: Digital Elevation Model (meter), Slope (radian).
    • Socioeconomic: Gross domestic product density (count per square meter), Population density (monetary value per square meter).
    • Land use type: (categorical).
  • USLE Factors (with SI units):
    • R (Rainfall erosivity factor): J·m−1·s−2
    • K (Soil erodibility factor): kg·s·J−1·m−1
    • LS (Slope length and steepness factor): dimensionless
    • C (Vegetation cover factor): dimensionless
    • P (Conservation practice factor): dimensionless

Main Results

• Ecosystem Service Trends (2000–2020):
  • Water Yield (WY) decreased from 0.3579 m·yr−1 to 0.28388 m·yr−1, a decline of 0.0037 m·yr−1.
  • Soil Conservation (SC) increased from 7.924 kg·m−2·yr−1 to 8.779 kg·m−2·yr−1, an increase of 0.043 kg·m−2·yr−1.
  • Carbon Sequestration (CS) rose from 0.22566 kg·m−2 to 0.25911 kg·m−2, an increase of 0.00167 kg·m−2·yr−1.
  • Habitat Quality (HQ) improved from 0.57 to 0.66 (dimensionless), an increase of 0.09.
• Decadal Variations:
  • WY declined by 0.00401 m·yr−1 during 2000–2010, moderating to 0.00339 m·yr−1 in 2010–2020.
  • SC declined by 0.245 kg·m−2·yr−1 in 2000–2010, followed by a recovery with an increase of 0.331 kg·m−2·yr−1 in 2010–2020.
  • CS increased by 0.00199 kg·m−2·yr−1 during 2000–2010.
• Dominant Driving Factors and Synergies:
  • ES variations are predominantly shaped by potent synergies, where interactive explanatory power consistently surpasses individual drivers.
  • Hydrothermal coupling (precipitation ∩potential evapotranspiration) reached a q-value of 0.52 for WY and SC.
  • Climate–vegetation synergy (precipitation ∩normalized difference vegetation index) achieved a q-value of 0.76 for CS.
  • By 2020, the synergy between land use and climate reached a q-value of 0.742 for CS and 0.205 for HQ, while the DEM∩LUT interaction rose to 0.368 for SC.
• Spatial Non-stationarity:
  • Geographically Weighted Regression (GWR) revealed distinct spatial dependencies.
  • Southeastern regions experienced strong negative effects of land use type and elevation on WY.
  • Northwestern areas showed a positive elevation associated with WY but negative effects on SC and HQ.
• Land Use Change: Forest area increased from 25.97% in 2000 to 27.24% by 2020, primarily through conversion from grassland, which contracted from 52.89% to 50.64%.

Contributions

• Clarifies that multi-factor interactions produce nonlinear enhancements in ES explanatory power, and that these driver–response relationships exhibit significant spatial non-stationarity across heterogeneous terrains.
• Provides a robust empirical basis for formulating differentiated conservation strategies tailored to the heterogeneous terrains of the southeastern Tibetan Plateau.
• Highlights the critical importance of accounting for spatial non-stationarity in driver–ecosystem service relationships when designing conservation strategies for vulnerable alpine ecosystems.
• Develops a spatially explicit framework to unravel the complex dynamics of key ecosystem services.
• Offers a transferable approach for zonal environmental governance in fragile ecosystems worldwide, emphasizing the integration of broad patterns with local heterogeneity for sustainability.

Funding

• State Grid Corporation Headquarters’ Science and Technology Project: Research on design and construction technology of UHV engineering in high altitude area with fragmented and steep terrain (5200-202356401A-2-4-KJ).

Citation

@article{Chen2026Climate,
  author = {Chen, Xiaofeng and Hong, Qian and Pang, Dongyan and Zou, Qinying and Wang, Yanbing and Liu, Chao and Sun, Xiaohu and Zhu, Shu Guang and Zong, Yixuan and Zhang, Xiao and Zhang, Jianjun},
  title = {Climate Change and Ecological Restoration Synergies Shape Ecosystem Services on the Southeastern Tibetan Plateau},
  journal = {Forests},
  year = {2026},
  doi = {10.3390/f17010102},
  url = {https://doi.org/10.3390/f17010102}
}