Entwicklung eines Individual-Tree Basal Area Increment Modells mit einem linearen Mixed-Effects-Ansatz

Overview

This study presents a protocol for developing an individual-tree basal area increment model using linear mixed-effects modeling. It employs complex statistical techniques to analyze hierarchical data structures found in forestry, aiming to enhance predictions of forest growth.

Key Study Components

Research Area

• Forest growth modeling
• Statistical analysis of hierarchical data
• Tree basal area increment assessment

Background

• Linear mixed-effects models can efficiently analyze data with complex structures.
• These models have potential applications in improving forest growth predictions.
• Heteroscedasticity and autocorrelation are crucial considerations in model fitting.

Methods Used

• Development of a basal area increment model
• Application of nlme package in R software
• Selection of random effects and fitting models using maximum likelihood

Main Results

• Identification of the best model via AIC, BIC, and likelihood tests.
• Significant improvement in model performance compared to basic models.
• Final model showed reduced heteroscedasticity and enhanced residuals.

Conclusions

• The study demonstrates the effectiveness of linear mixed-effects models in forestry.
• This methodology is relevant for researchers aiming to improve growth models in forest ecosystems.

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