[Simulation of Larix gmelinii tree volume growth based on random effect]

Area of Science:

• Forestry science
• Quantitative ecology
• Biometrics

Context:

• Dahurian larch (Larix gmelinii) plantations are significant in Heilongjiang Province.
• Accurate tree volume growth modeling is crucial for sustainable forest management.
• Existing models may not fully capture individual tree and plot-level variations.

Purpose:

• To fit nonlinear tree volume growth models for Dahurian larch using the Logistic growth model.
• To evaluate and compare the prediction precision of models considering individual tree effects and plot effects.
• To identify the optimal model structure for predicting tree volume growth.

Summary:

• Stem analysis data from 80 Dahurian larch trees were used with the Nonlinear Mixed-Effects (NLME) procedure.
• Random effect models, particularly those with parameters b1, b2, and b3 for individual tree effects, and b1 for plot effects, demonstrated superior performance.
• Models incorporating both individual tree and plot effects outperformed the basic Logistic model, with individual tree effects yielding higher precision.

Impact:

• The developed random effect models accurately represent mean tree volume growth trends and individual variations.
• Enhanced model fitting and prediction precision contribute to improved forest inventory and yield forecasting.
• Model validation confirms the utility of random effects for capturing heterogeneity in tree growth, with potential for further refinement through parameter calibration.