Predicting crown width using nonlinear mixed-effects models accounting for competition in multi-species secondary...

Area of Science:

• Forestry Science
• Ecology
• Quantitative Biology

Background:

• Crown width (CW) is a key tree variable for forest growth modeling and management.
• Accurate CW prediction is crucial for effective forest inventory and evaluation.

Purpose of the Study:

• To develop nonlinear mixed-effects models for predicting individual tree crown width (CW) in multi-species secondary forests.
• To incorporate tree species as a random effect and account for competition effects on CW.

Main Methods:

• A simple power function was used for the basic CW model.
• Nonlinear mixed-effects models were developed using diameter at breast height (DBH), height to crown base (HCB), tree height (TH), and competition indices (CI).
• Both distance-independent (Sum of Relative DBH - SRD) and distance-dependent (Sum of Hegyi index for fixed number competitors - SHGN) competition indices were evaluated.

Main Results:

• The developed models explained over 50% of CW variation without significant residual trends.
• Spatially explicit models showed a larger effect on CW but are computationally complex.
• Spatially non-explicit models provide a practical alternative due to minimal differences in fit statistics.

Conclusions:

• Nonlinear mixed-effects models incorporating species and competition improve CW prediction in secondary forests.
• Spatially non-explicit models offer a viable and less complex alternative to spatially explicit models for practical forestry applications.
• The developed models can aid in the evaluation and management of secondary forests.