Bridging process-based and empirical approaches to modeling tree growth

Area of Science:

• Forestry science
• Ecological modeling
• Quantitative biology

Background:

• Traditional tree growth models fall into two categories: process-based and empirical.
• A significant gap exists between these two approaches, limiting comprehensive understanding and application.
• Integrating these methods could offer more robust and versatile modeling capabilities.

Purpose of the Study:

• To develop a unified tree growth model that bridges process-based and empirical approaches.
• To formulate a model grounded in pipe model theory and optimal crown development.
• To enable the model to be fitted and applied using standard inventory data.

Main Methods:

• Formulation of a process-based tree growth model incorporating pipe model theory and optimal control of crown development.
• Expression of tree biomass components using standard inventory variables: tree height, crown height, and stem cross-sectional area.
• Development of growth rate equations based on carbon balance and estimation of model parameters using statistical fitting procedures on inventory data.

Main Results:

• A unified tree growth model was successfully formulated, capable of both process-based and empirical application.
• The model effectively relates physiological and morphological parameters to aggregate parameters estimated from inventory data.
• Demonstrated the potential to bridge the gap between detailed physiological modeling and practical inventory-based assessments.

Conclusions:

• The developed model offers a flexible framework for tree growth analysis, integrating mechanistic understanding with empirical data.
• This approach enhances the utility of standard forest inventory data for understanding tree growth dynamics.
• The unified model provides a pathway for improved ecological forecasting and forest management strategies.