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Empirical evaluation of neutral theory
Brian J McGill1, Brian A Maurer, Michael D Weiser
1Department of Fisheries and Wildlife, Natural Resources Building, Michigan State University, East Lansing 48823, USA. mail@brianmcgill.org
Ecology
|July 28, 2006
Summary
Neutral theory, a model for biodiversity, faces significant challenges. Most empirical tests, including new robust analyses, fail to support its core predictions, suggesting alternative models may better explain ecological patterns.
Area of Science:
- Ecology
- Evolutionary Biology
- Theoretical Ecology
Background:
- Neutral theory is a prominent null model in ecology, proposing that species differences are ecologically irrelevant.
- It makes specific predictions about species abundance distributions and community composition dynamics.
- Previous empirical tests have yielded mixed or negative results, necessitating a re-evaluation.
Purpose of the Study:
- To establish a general framework for rigorously testing neutral theory.
- To compare different versions of neutral theory and their empirical support.
- To develop and apply new, robust tests for neutral theory's core predictions.
Main Methods:
- Reviewed and summarized ten versions of neutral theory, highlighting similarities and differences.
- Analyzed all published empirical tests of neutral theory.
- Developed best practices for testing the zero-sum multinomial (ZSM) distribution against a lognormal null hypothesis, applying these to existing data.
Main Results:
- The vast majority of published empirical tests do not support neutral theory.
- New, robust tests indicate that a lognormal distribution provides a better fit than the ZSM distribution predicted by neutral theory.
- A priori parameterization of neutral theory is not feasible, and its non-curve-fitting predictions are readily falsifiable.
Conclusions:
- There is substantial empirical evidence contradicting the predictions of neutral theory.
- The lognormal distribution is a superior null hypothesis for species abundance.
- Further research should explore alternative ecological models and refine testing methodologies.
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