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How good must models and data be in ecology?
1Ecology Center and Department of Fisheries and Wildlife, Utah State University, 84322-5210, Logan, UT, USA.
Oecologia
|March 18, 2017
Summary
Linear programming (LP) models for diet selection are criticized for parameter sensitivity, but their predictions align well with 400 published tests. Ecological theories should be validated by repeated testing rather than single statistical measures.
Area of Science:
- Ecology
- Mathematical Modeling
- Animal Behavior
Background:
- Linear programming (LP) models are used to predict animal diet selection.
- These models face criticism regarding their sensitivity to imprecise parameter values.
- Despite criticism, LP model predictions often align well with observed dietary patterns.
Purpose of the Study:
- To review the philosophical and statistical criticisms of LP models in diet selection.
- To evaluate the validity of LP model predictions in ecological research.
- To discuss the appropriate methods for testing ecological theories.
Main Methods:
- Review of existing literature on LP models and ecological theory testing.
- Analysis of statistical criticisms concerning parameter sensitivity and error measurement.
- Philosophical discussion on the nature of scientific evidence in ecology.
Main Results:
- LP models' predictions show strong agreement with empirical data across numerous studies (400+ tests).
- Standard statistical error measures may be overly conservative when applied to ecological field data.
- The reliability of ecological theories depends on their robustness across multiple tests.
Conclusions:
- Criticisms of LP models based on parameter sensitivity may be overstated.
- Ecological theories, including LP models, are best validated through repeated, consistent empirical support.
- The focus in ecological theory testing should be on the cumulative evidence from multiple studies.
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