Wu et al. (2026) Structural Responses of Vegetation Resilience to Background-State and Temperature Asymmetry Across China: An Annual-Scale Causal Analysis

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Forests
• Year: 2026
• Date: 2026-04-01
• Authors: Song Wu, Qingyun Du
• DOI: 10.3390/f17040443

Research Groups

Not available from the provided text.

Short Summary

This study quantified vegetation resilience in mainland China from 2000 to 2024 using kNDVI data, revealing its spatiotemporal patterns, dominant environmental drivers, and dynamic shifts in underlying mechanisms across breakpoints. It found that resilience varies spatially, primarily shaped by persistent climate conditions, with temperature being a key control, and that driver networks undergo significant reorganization over time.

Objective

• To quantify vegetation resilience using AR(1) from kNDVI data over mainland China (2000–2024).
• To assess the spatiotemporal patterns of vegetation resilience.
• To identify the long-term causal drivers of vegetation resilience using Causal Forest.
• To analyze breakpoint-related mechanism shifts in vegetation resilience using non-stationary causal networks.

Study Configuration

• Spatial Scale: Mainland China
• Temporal Scale: 2000–2024

Methodology and Data

• Models used: AR(1) (Autoregressive model of order 1) for resilience quantification, Causal Forest for long-term causal driver assessment, non-stationary causal networks for breakpoint-related mechanism shifts.
• Data sources: kNDVI (kernel Normalized Difference Vegetation Index) data.

Main Results

• Vegetation resilience, quantified via AR(1), varied strongly across space, with higher values concentrated in northern transition belts and inland regions of mainland China.
• Breakpoints in resilience patterns clustered predominantly between 2010 and 2018, exhibiting broad synchronicity nationwide.
• Long-term effects on resilience were dominated by environmental background states; mean variables generally outweighed variability (CV) and memory terms, indicating that persistent climate–environment conditions primarily shaped resilience gradients.
• Temperature emerged as the strongest national-scale control: maximum temperature (TMX) strongly suppressed resilience, while minimum temperature (TMN) tended to enhance it.
• Precipitation and carbon dioxide (CO2) gained importance as drivers regionally.
• Driver networks reorganized markedly across breakpoints, characterized by high edge turnover and heterogeneous lag shifts, suggesting stage-dependent restructuring beyond simple changes in driver strength.

Contributions

• Provides a novel framework that links net effects of drivers with mechanism reorganization to diagnose vegetation resilience under non-stationary conditions.
• Quantifies vegetation resilience over a large spatial and temporal extent (mainland China, 2000–2024) using kNDVI data.
• Identifies the dominant long-term causal drivers of vegetation resilience and their asymmetric impacts (e.g., TMX vs. TMN).
• Reveals the dynamic and stage-dependent restructuring of driver networks across breakpoints, highlighting the non-stationary nature of resilience mechanisms.

Funding

Not available from the provided text.

Citation

@article{Wu2026Structural,
  author = {Wu, Song and Du, Qingyun},
  title = {Structural Responses of Vegetation Resilience to Background-State and Temperature Asymmetry Across China: An Annual-Scale Causal Analysis},
  journal = {Forests},
  year = {2026},
  doi = {10.3390/f17040443},
  url = {https://doi.org/10.3390/f17040443}
}