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Updated: Jun 24, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
A general quantitative theory of forest structure and dynamics
Geoffrey B West1, Brian J Enquist, James H Brown
1Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA.
Summary
This study introduces a quantitative theory for forest structure and dynamics, revealing that forests behave like scaled-up versions of their largest trees. This framework predicts forest properties from individual tree traits using allometric scaling relations.
Area of Science:
- Forest Ecology
- Quantitative Ecology
- Theoretical Ecology
Background:
- Understanding forest structure and dynamics is crucial for predicting ecosystem function.
- Existing models often lack a unified, quantitative framework to link individual tree traits to stand-level properties.
Purpose of the Study:
- To develop a quantitative theory for forest structure and dynamics at demographic and resource steady state.
- To predict emergent properties of forest stands from individual tree traits using allometric scaling.
Main Methods:
- Utilized allometric scaling relations based on metabolism and biomechanics.
- Quantified individual tree resource use, space filling, and growth.
- Scaled individual-level traits to predict forest stand properties like size-frequency distributions and canopy configurations.
Main Results:
- Forest size structure and spatial arrangement emerge from xylem element bundling for water and nutrient transport.
- Identical scaling of geometric and dynamic properties in trees and branches allows forests to be modeled as scaled versions of large trees.
- A small set of parameters predicts numerous structural and dynamical properties of idealized forests.
Conclusions:
- The theory provides a unified quantitative framework for forest structure and dynamics.
- Forests exhibit self-similar scaling properties, mirroring the branching networks within individual trees.
- This approach offers a powerful tool for understanding and predicting forest behavior.
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