Tree adaptive growth (TAG) model: a life-history theory-based analytical model for post-thinning forest stand...

Area of Science:

• Forestry and Ecosystem Science
• Ecological Modeling
• Sustainable Forest Management

Background:

• Understanding forest stand dynamics is crucial for predicting wood supply and ecosystem services.
• Managed stands exhibit dynamics, like post-thinning overcompensation, that differ from natural stands.
• Post-thinning overcompensation has been historically overlooked in forest management.

Purpose of the Study:

• To investigate the phenomenon of post-thinning overcompensation in managed forest stands.
• To develop a theory-based model explaining adaptive tree growth responses after thinning.
• To verify if overcompensation is a common outcome under specific growth conditions.

Main Methods:

• Developed the Tree Adaptive Growth (TAG) model, a life history theory-based, state-dependent model.
• Investigated model behavior to identify diverse stand growth patterns.
• Verified that overcompensation is a common outcome when stand growth is sigmoid shaped.

Main Results:

• The TAG model successfully reproduced diverse stand growth patterns observed in empirical data.
• Model results align with predictions from the statistics-based Tree's Compensatory Growth (TreeCG) model.
• Demonstrated that overcompensation is an evolutionarily adaptive response for individual trees post-thinning.

Conclusions:

• The TAG model provides a simple, theory-driven explanation for post-thinning stand dynamics.
• TAG can reproduce diverse growth patterns and aid in addressing silviculture issues.
• The model is broadly applicable across jurisdictions and can integrate factors like fertilization, pruning, and climate change.