The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework
Open accessAbstract
Behavioural ecologists often study complex systems in which multiple hypotheses could be proposed to explain observed phenomena. For some systems, simple controlled experiments can be employed to reveal part of the complexity; often, however, observational studies that incorporate a multitude of causal factors may be the only (or preferred) avenue of study. We assess the value of recently advocated approaches to inference in both contexts. Specifically, we examine the use of information theoretic (IT) model selection using Akaike’s information criterion (AIC). We find that, for simple analyses, the advantages of switching to an IT-AIC approach are likely to be slight, especially given recent emphasis on biological rather than statistical significance. By contrast, the model selection approach embodied by IT approaches offers significant advantages when applied to problems of more complex causality. Model averaging is an intuitively appealing extension to model selection. However, we were unable to demonstrate consistent improvements in prediction accuracy when using model averaging with IT-AIC; our equivocal results suggest that more research is needed on its utility. We illustrate our arguments with worked examples from behavioural experiments.
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Metadata
- Authors
- Richards, S. A., Whittingham, M. J., & Stephens, P. A.
- Venue
- Behavioral Ecology and Sociobiology
- Published
- 2010-08-19
- Volume
- 65
- Issue
- 1
- Pages
- 77-89
- ISSN
- 0340-5443 (ISSN-L)
- 1432-0762
- OpenAlex
- W2008618406
- Cited by
- 566
- References
- 38
- FWCI
- 20.20
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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