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Updated: Jul 11, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Understanding height-structured competition in forests: is there an R* for light?
Thomas P Adams1, Drew W Purves, Stephen W Pacala
1Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. t.p.adams@sms.ed.ac.uk
Tree species traits influence competition outcomes and forest community structure. Understanding these trade-offs reveals how species coexist or are excluded through light competition dynamics.
Area of Science:
- Ecology
- Forestry
- Theoretical Ecology
Background:
- Tree species exhibit diverse traits, including growth rates, mortality, fecundity, and crown allometry.
- Variation in these attributes can significantly impact interspecific competition and forest community structure.
Purpose of the Study:
- To derive criteria for predicting the outcome of light competition among tree species.
- To develop a comprehensive understanding of how species-specific traits affect community dynamics.
Main Methods:
- Developed theoretical criteria for interspecific competition based on allometric and life-history parameters.
- Introduced a novel whole life-cycle measure of performance to synthesize species differences.
- Analyzed scenarios including general cases, neutral dynamics (differing only in crown transmissivity), and hierarchical competition (differing in all but crown transmissivity).
Main Results:
- Interspecific competition for light can lead to species coexistence, founder control, or competitive exclusion, challenging hierarchical models like R* theory.
- Species differing solely in crown transmissivity exhibit neutral dynamics.
- When species differ in all traits except crown transmissivity, hierarchical competition emerges, with Z* (canopy entry height) as a key parameter.
Conclusions:
- Species-specific traits and trade-offs are critical drivers of forest community structure through light competition.
- Theoretical frameworks can effectively predict competition outcomes by integrating life-history and allometric parameters.
- Light competition dynamics are more complex than previously assumed, allowing for diverse community structures.
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