Ji et al. (2026) Local forest structure modifies realized climate vulnerability under macroclimatic forcing: Attribution from a national forest inventory

Identification

• Journal: Agricultural and Forest Meteorology
• Year: 2026
• Date: 2026-10-12
• Authors: Wenxu Ji, Hanyue Zhang, Fei Yang, Qian Li, Biao Zhang, Hao Li, Tiantian Ma, Zhihao Wang, Yan Zhang, Yang Bai, Weiyi Li, Zhongke Feng, Zhichao Wang, Shuo Feng
• DOI: 10.1016/j.agrformet.2026.111510

Research Groups

• College of Forestry, Beijing Forestry University, Beijing, China
• College of Science, Beijing Forestry University, Beijing, China
• Shanghai Academy of Landscape Architecture Science and Planning, Shanghai, China
• Aerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou, China
• Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, China
• Department of Land, Air, and Water Resources, University of California at Davis, CA, USA
• Xing'an league institute of forestry, Ulanhot City, Inner Mongolia, China
• Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing, China

Short Summary

This study developed an attribution framework using two decades of China's National Forest Inventory data to estimate the conditional effect of climate drivers on tree growth, demonstrating that local ecological conditions significantly modify realized climate vulnerability beyond macroclimatic exposure.

Objective

• To estimate the conditional effect of individual climate drivers on annual tree diameter at breast height (DBH) growth across observed spatial climatic gradients, after adjustment for measured local ecological factors, thereby characterizing realized climate vulnerability and quantifying individual-level heterogeneity in climate sensitivity.

Study Configuration

• Spatial Scale: National (China), covering 33 dominant tree species (groups) with nearly one million individual tree records.
• Temporal Scale: Two decades of data from China's National Forest Inventory.

Methodology and Data

• Models used: Structural Causal Models (SCM), Double Machine Learning (DML).
• Data sources: China’s National Forest Inventory (NFI) comprising nearly one million individual tree records.

Main Results

• Species-level average responses masked substantial individual-level divergence in climate-growth responses.
• Trees exposed to similar macroclimatic conditions exhibited contrasting climate-growth responses depending on local topography and stand structure.
• Local ecological conditions modified climate-growth responses in non-linear and context-dependent ways.
• Topographic factors were more influential under thermal stress, while stand structural attributes were more influential under hydrological stress.
• Density-associated empirical ranges were identified where growth-based climate vulnerability was relatively lower under observed stand structural conditions.

Contributions

• Advances forest vulnerability assessment from exposure-based prediction toward conditional attribution of heterogeneous climate-growth responses.
• Provides empirical evidence relevant to context-dependent forest adaptation strategies under global environmental change.
• Introduces an attribution framework integrating Structural Causal Models and Double Machine Learning for estimating conditional effects of climate drivers on tree growth.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Ji2026Local,
  author = {Ji, Wenxu and Zhang, Hanyue and Yang, Fei and Li, Qian and Zhang, Biao and Li, Hao and Ma, Tiantian and Wang, Zhihao and Zhang, Yan and Bai, Yang and Li, Weiyi and Feng, Zhongke and Wang, Zhichao and Feng, Shuo},
  title = {Local forest structure modifies realized climate vulnerability under macroclimatic forcing: Attribution from a national forest inventory},
  journal = {Agricultural and Forest Meteorology},
  year = {2026},
  doi = {10.1016/j.agrformet.2026.111510},
  url = {https://doi.org/10.1016/j.agrformet.2026.111510}
}