Estimating tree growth from complex forest monitoring data

Area of Science:

• Forest ecology
• Quantitative ecology
• Forestry

Background:

• Understanding tree growth is crucial for ecological and management applications.
• Forest inventory data offer long-term insights but present analytical challenges due to complexity.
• Sources of variation include spatial plot effects, repeated measures, and varied sampling intervals.

Purpose of the Study:

• To develop a hierarchical state-space model to estimate tree diameter growth.
• To account for complexities in forest inventory data, including observation error and shared variation.
• To identify limiting factors of tree growth, such as tree size, competition, and resource supply.

Main Methods:

• A hierarchical state-space model was employed within a Bayesian framework.
• The model incorporated potential limiting factors: tree size, competition, and resource supply.
• Diameter growth of white fir (Abies concolor) in the Sierra Nevada was estimated using forest inventory data.

Main Results:

• White fir growth is strongly influenced by tree size and total plot basal area.
• Unexplained variation between individual trees significantly impacts growth.
• Plot-level resource supply variables (light, water, nutrients) showed limited impact on inventory-sized trees.

Conclusions:

• Estimating tree growth using complex forest inventory data is feasible with advanced modeling techniques.
• Tree size and competition are key drivers of white fir growth in the Sierra Nevada.
• The developed approach can be applied to other permanent forest plot networks for enhanced ecological understanding.