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Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation
Cory Merow1, Andrew M Latimer, John A Silander
1Department of Ecology and Evolutionary Biology, University of Connecticut, 75 North Eagleville Road, Storrs, Connecticut 06269-3043, USA. cory.merow@uconn.edu
Ecology
|August 30, 2011
Summary
Entropy maximization (EM) predicts species abundance using functional traits. This method effectively characterizes ecological niches and outperforms other models, offering insights into community composition.
Area of Science:
- Ecology
- Community Ecology
- Trait-based Ecology
Background:
- Functional traits and community composition are key ecological concepts.
- Predicting species relative abundances is crucial for understanding community structure.
- Community-aggregated traits (CATs) offer a way to link species traits to community properties.
Purpose of the Study:
- To evaluate the strengths and limitations of Entropy Maximization (EM).
- To assess the utility of community-aggregated traits (CATs) in ecological studies.
- To determine EM's performance in predicting species abundances across diverse plant communities.
Main Methods:
- Developed a suite of tests to analyze EM and CATs.
- Applied EM to various plant community datasets.
- Compared EM predictions against null models and regression models.
Main Results:
- EM successfully differentiated communities using CATs.
- EM quantified constraints on complex trait relationships, aiding niche characterization.
- EM outperformed null and regression models, particularly for dominant species.
- Predictions were sensitive to trait selection and improved with informative priors.
Conclusions:
- EM is a powerful tool for linking functional traits to community composition.
- CATs are valuable for differentiating communities and understanding ecological niches.
- EM provides robust predictions of species abundance, with guidelines for application provided.
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