Liu et al. (2025) Exploring the dynamic relationships and mechanisms driving a long-term sequence of ecosystem services in mountains based on ecosystem service bundles

Identification

• Journal: Ecological Indicators
• Year: 2025
• Date: 2025-11-30
• Authors: Heng Liu, Yuzhong Zheng, Lu Zhou, Yuan Hao, Diwei Tang, Binbin Cao, Yangxiang Huang
• DOI: 10.1016/j.ecolind.2025.114486

Research Groups

• Hubei Key Laboratory of Biologic Resources Protection and Utilization, Hubei Minzu University, China
• Gongshuihe National Wetland Park Management Center of Xuan’en, China
• School of Arts and Communication, China University of Geosciences, China
• Administration Bureau of Hubei Qizimeishan National Nature Reserve, China

Short Summary

This study analyzed the long-term spatiotemporal dynamics, trade-offs, synergies, and driving mechanisms of four key ecosystem services (HQ, NPP, SC, WY) in China's Wuling Mountain Area from 2000-2020, identifying four ecosystem service bundles and proposing differentiated management strategies.

Objective

• To assess four key ecosystem services (Habitat Quality, Net Primary Productivity, Soil Conservation, and Water Yield) in the Wuling Mountain Area (WMA) from 2000 to 2020 and identify ecosystem service bundles (ESB).
• To analyze the trade-offs and synergies among ES pairs in the overall WMA and within individual ESB from a spatiotemporal perspective.
• To identify the factors influencing multiple ES in the WMA and in different ESB, with a focus on detecting potential threshold effects of dominant drivers.
• To propose refined spatial management strategies for the WMA based on the findings.

Study Configuration

• Spatial Scale: Wuling Mountain Area (WMA), south-central China (107°4′ to 112°2′ E and 25°52′ to 31°24′ N), covering approximately 171,800 km². Data standardized to 1000 m spatial resolution.
• Temporal Scale: Long-term data from 2000 to 2020 (21 years).

Methodology and Data

• Models used:
  • InVEST model (Habitat Quality, Sediment Delivery Ratio, Annual Water Yield modules)
  • K-means clustering (for ESB identification)
  • Pearson correlation analysis (for trade-offs and synergies)
  • eXtreme Gradient Boosting (XGBoost) model (for driving factors)
  • Partial Dependence Plots (PDP) (for threshold effects)
  • Linear regression trend analysis (for ES variations)
• Data sources: Multi-source datasets, standardized to 1000 m spatial resolution.
  • Land use (30 m resolution, 7 types, 2000–2020)
  • Meteorological data (temperature, precipitation, potential evapotranspiration, monthly, 1000 m, 2000–2020)
  • Vegetation index (NDVI, MODIS MOD13A2, 16-day, 1000 m, 2000–2020)
  • Net Primary Productivity (NPP, MODIS MOD17A3HGF, 500 m, 2000–2020)
  • Topographic data (Elevation, slope, 90 m DEM, 2000)
  • Soil dataset (texture, organic matter, rooting depth, 1000 m, 2012)
  • Human footprint dataset (8 pressure variables, 1000 m, 2000–2020)

Main Results

• Ecosystem Service Trends (2000-2020): All ES showed significant spatial heterogeneity. Habitat Quality (HQ) exhibited a slight overall downward trend (mean slope = -0.002), while Net Primary Productivity (NPP), Soil Conservation (SC), and Water Yield (WY) all showed increasing trends (mean slopes of 2.19 gC/m² per year, 1.17 t/hm² per year, and 5.39 mm per year, respectively).
• Ecosystem Service Bundles (ESB): Four distinct ESB were identified:
  • Core Ecological Bundle (CEB): 28.20% of the area, characterized by high overall ES, especially HQ (mean 0.90), dominated by forestland (91.85%).
  • Water Abundance Bundle (WAB): 21.66% of the area, with substantially higher WY (mean 0.50) but relatively lower other ES, mixed forestland (54.67%) and cropland (42.05%).
  • Balanced Production Bundle (BPB): 28.16% of the area, showing high levels of NPP (mean 0.55) and SC (mean 0.38), reflecting balanced ecological and productive functions.
  • Ecological Transition Bundle (ETB): 21.98% of the area, with high HQ (mean 0.75) but low NPP (mean 0.38), SC (mean 0.17), and WY (mean 0.27), representing a transitional zone.
• ES Relationships (Trade-offs/Synergies): Relationships were dynamic and spatially heterogeneous.
  • Across the WMA, HQ generally showed trade-offs with NPP, SC, and WY. In contrast, NPP-SC, NPP-WY, and SC-WY mostly displayed synergistic relationships.
  • Temporally, HQ-NPP and HQ-SC shifted from synergies to trade-offs, while NPP-WY shifted from trade-offs to synergies.
  • Relationships varied significantly across different ESB, with trade-offs concentrated in the northwestern WMA and synergies prevailing in the southeastern areas.
• Driving Factors and Threshold Effects: Both natural (climate, vegetation, topography) and socio-economic (land use, human activities) drivers jointly influenced ES changes, often exhibiting nonlinear threshold effects.
  • HQ declined significantly in CEB when the proportion of cropland area exceeded 14%.
  • In WAB, NPP increased sharply when NDVI exceeded 0.73, and SC significantly accelerated when the slope exceeded 5.27°.
  • Slope was consistently the most critical factor influencing SC across all ESB.

Contributions

• Provides a robust, long-term (21-year) spatiotemporal analysis of ecosystem service dynamics and interactions in a mountainous region, addressing limitations of short-term studies.
• Identifies and characterizes four distinct ecosystem service bundles (ESB) in the Wuling Mountain Area, offering a novel classification for regional ecological functions.
• Explores the dynamic evolution of trade-offs and synergies among ES at both regional and ESB scales, revealing complex interaction patterns.
• Utilizes advanced machine learning (XGBoost and Partial Dependence Plots) to identify key natural and socio-economic driving factors, including nonlinear relationships and critical threshold effects, which are crucial for targeted management.
• Proposes differentiated and refined spatial management strategies tailored to the specific ecological characteristics and challenges of each ESB, providing scientific support for functional zoning and sustainable development.

Funding

• National Natural Science Foundation of China (Grant No. 42367070)

Citation

@article{Liu2025Exploring,
  author = {Liu, Heng and Zheng, Yuzhong and Zhou, Lu and Hao, Yuan and Tang, Diwei and Cao, Binbin and Huang, Yangxiang},
  title = {Exploring the dynamic relationships and mechanisms driving a long-term sequence of ecosystem services in mountains based on ecosystem service bundles},
  journal = {Ecological Indicators},
  year = {2025},
  doi = {10.1016/j.ecolind.2025.114486},
  url = {https://doi.org/10.1016/j.ecolind.2025.114486}
}