Gao et al. (2026) Divergent Responses of Leaf Area Index to Abiotic Drivers Across Abies Forest Types in China

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

Identification

• Journal: Forests
• Year: 2026
• Date: 2026-01-12
• Authors: Zichun Gao, Xi Luo, Yiwen Zhang, Yunxiang Han
• DOI: 10.3390/f17010103

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This study investigated the spatial heterogeneity of Leaf Area Index (LAI) in 17 Abies forest types across China's alpine ecosystems and its sensitivity to environmental factors. It found that temperature is the dominant positive driver and elevation the strongest negative driver for overall Abies LAI, but the primary environmental controls vary significantly among specific forest types.

Objective

• To disentangle the direct and interactive effects of climate, soil, topography, and human footprint (HFP) on Leaf Area Index (LAI) across 17 distinct Abies forest types in China's alpine ecosystems.

Study Configuration

• Spatial Scale: Regional scale, covering 17 distinct Abies forest types within China’s alpine ecosystems.
• Temporal Scale: Not explicitly mentioned, but implies a contemporary assessment of drivers influencing LAI.

Methodology and Data

• Models used: Random Forest (RF), Structural Equation Modeling (SEM).
• Data sources: Not explicitly mentioned, but includes data on climate, soil, topography (Elevation/DEM), and human footprint (HFP), used to model Leaf Area Index (LAI).

Main Results

• Temperature was identified as the dominant positive driver for the overall Abies forests (Total effect = 2.197).
• Elevation (DEM) exerted the strongest negative regulation on LAI for the overall Abies forests (Total effect = −0.335).
• Driver dominance varied substantially among forest types: climatic water availability was the primary constraint for Abies georgei var. smithii forest (Type 55), while DEM determined LAI in Abies fargesii forest (Type 49).
• Human footprint (HFP) could exert positive effects on LAI in specific communities (e.g., Abies densa forest, Type 58), potentially due to understory compensation under moderate disturbance.

Contributions

• Provides novel insights into the spatial heterogeneity of LAI and its sensitivity to environmental filtering across diverse Abies forest types in China.
• Disentangles the direct and interactive effects of climate, soil, topography, and human footprint on LAI using advanced statistical modeling.
• Highlights the necessity of type-specific management strategies for alpine Abies forests.
• Offers a theoretical basis for predicting alpine forest dynamics in the context of changing environments.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Gao2026Divergent,
  author = {Gao, Zichun and Zhang, Huayong and Luo, Xi and Zhang, Yiwen and Han, Yunxiang},
  title = {Divergent Responses of Leaf Area Index to Abiotic Drivers Across Abies Forest Types in China},
  journal = {Forests},
  year = {2026},
  doi = {10.3390/f17010103},
  url = {https://doi.org/10.3390/f17010103}
}