Ebrahimi et al. (2026) Editorial: Forest growth in a changing climate: insights from predictive modeling and adaptive strategies

Identification

• Journal: Frontiers in Forests and Global Change
• Year: 2026
• Date: 2026-04-10
• Authors: Aziz Ebrahimi, Alireza Rahemi
• DOI: 10.3389/ffgc.2026.1807606

Research Groups

• Hardwood Improvement and Regeneration Center (HTIRC), Purdue University, West Lafayette, IN, United States
• Department of Agricultural Sciences, Morehead State University, Morehead, KY, United States

Short Summary

This editorial synthesizes diverse research on forest growth in a changing climate, highlighting the context-dependent nature of forest responses to environmental stressors. It emphasizes the critical role of integrated predictive modeling and adaptive strategies for understanding and managing forest ecosystems under future climate scenarios.

Objective

• To promote the integration of empirical data with predictive and mechanistic models to examine forest growth, resilience, regeneration, and ecosystem functions across various biomes in the context of a changing climate.

Study Configuration

• Spatial Scale: Global to regional, encompassing boreal, temperate, subtropical, and alpine forest systems across diverse geographical locations (e.g., Caucasus, southwestern Hungary, Altai Mountains, northern high-latitude regions, Zhejiang Province, southeastern Tibet, District Sialkot in Pakistan, Delaware and Barnegat Bays in USA, Southwest China, Xinjiang in China).
• Temporal Scale: Historical (dendrochronological records), contemporary observations, and future climate projections.

Methodology and Data

• Models used: Predictive and mechanistic models, ecological niche modeling (e.g., Maximum Entropy - MaxEnt), optimized InTEC model, gradient-boosted linear regression, statistical models for tree-ring data.
• Data sources: Empirical observations, dendrochronological data (tree-ring width indices), satellite imagery (e.g., MODIS NDVI), extreme climate indices, experimental study data (e.g., rainfall exclusion, UV-B radiation), species occurrence records, environmental variables (climatic, edaphic), vegetation analyses.

Main Results

• Forest growth responses to climate change are highly context-dependent, primarily influenced by water balance, climatic extremes, and biotic interactions.
• Climatic sensitivity varies with stand age, with younger stands often showing higher responsiveness to favorable temperature and moisture regimes.
• Site-specific hydrological controls and species-level differences are crucial for accurate predictive growth models.
• Multi-proxy approaches, combining dendrochronology and remote sensing, enhance the detection of growth decline and improve future projections.
• Forest resilience is significantly determined by recovery capacity following extreme growth-reduction events, rather than solely initial suppression.
• Climate stressors induce species-specific physiological responses (e.g., photosynthesis, stomatal conductance, transpiration, secondary metabolite production).
• Even modest drought can disrupt tree growth and nutrient dynamics through interacting abiotic and biotic mechanisms.
• The climate sensitivity of net ecosystem productivity varies with forest age, highlighting the importance of age-dependent processes in carbon-sink potential assessments.
• Natural forest regeneration is influenced by light availability, water resources, wind, soil properties, topography, and groundcover.
• Biodiversity assessments are critical for sustainable management, identifying key environmental drivers and anthropogenic pressures (e.g., population growth, housing development, farming, grazing).
• Coastal forests exhibit complex, non-linear growth responses to climate variability and rising tidal flooding, with outcomes varying by species and local conditions.
• Ecological niche modeling predicts shifts in habitat suitability for alpine species (e.g., Rhododendron delavayi) and potential distribution of forest pathogens (e.g., Cytospora chrysosperma) under future climate scenarios.

Contributions

• This editorial synthesizes a diverse collection of studies, providing a comprehensive overview of current understanding regarding forest growth in a changing climate.
• It highlights the critical need for integrating empirical observations with predictive and mechanistic modeling to disentangle complex drivers of forest dynamics.
• It emphasizes the context-dependent nature of forest responses, underscoring the importance of water balance, climatic extremes, and recovery processes.
• The editorial outlines key knowledge gaps and future research directions, advocating for deeper integration of physiological mechanisms, multi-proxy datasets, and demographic processes to improve forest forecasts and guide adaptive management strategies.

Funding

No specific funding for the editorial itself is mentioned.

Citation

@article{Ebrahimi2026Editorial,
  author = {Ebrahimi, Aziz and Rahemi, Alireza},
  title = {Editorial: Forest growth in a changing climate: insights from predictive modeling and adaptive strategies},
  journal = {Frontiers in Forests and Global Change},
  year = {2026},
  doi = {10.3389/ffgc.2026.1807606},
  url = {https://doi.org/10.3389/ffgc.2026.1807606}
}