Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Area of Science:

• Forestry
• Ecology
• Quantitative Biology

Background:

• Accurate prediction of tree growth is crucial for sustainable forest management and understanding forest dynamics.
• Existing models often struggle to account for the hierarchical structure of forest plot data and spatial dependencies.

Purpose of the Study:

• To develop and validate an individual-tree model for predicting 5-year basal area increments of Picea asperata.
• To improve growth modeling by accounting for plot-level random effects and spatial autocorrelation.

Main Methods:

• Utilized a linear mixed-effects modeling approach with random plot effects for 21,898 Picea asperata trees across 779 sample plots.
• Incorporated tree- and stand-level variables (e.g., diameter at breast height, competition indices, elevation) as fixed effects.
• Modeled heteroscedasticity using variance functions and autocorrelation using a first-order autoregressive structure (AR(1)).

Main Results:

• Key predictors for basal area increment included inverse diameter at breast height, basal area of larger trees, tree density, and elevation.
• The exponential function effectively modeled variance structure, and AR(1) significantly corrected autocorrelation.
• The mixed-effects model demonstrated superior performance compared to ordinary least squares regression.

Conclusions:

• Linear mixed-effects models provide a robust framework for individual-tree growth prediction in forestry, effectively handling complex data structures.
• The developed model offers a valuable tool for Picea asperata forest management and ecological research in northwest China.