Multimodel inference in ecology and evolution: challenges and solutions

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ID: 290707
2011
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Abstract
Information theoretic approaches and model averaging are increasing in popularity, but this approach can be difficult to apply to the realistic, complex models that typify many ecological and evolutionary analyses. This is especially true for those researchers without a formal background in information theory. Here, we highlight a number of practical obstacles to model averaging complex models. Although not meant to be an exhaustive review, we identify several important issues with tentative solutions where they exist (e.g. dealing with collinearity amongst predictors; how to compute model-averaged parameters) and highlight areas for future research where solutions are not clear (e.g. when to use random intercepts or slopes; which information criteria to use when random factors are involved). We also provide a worked example of a mixed model analysis of inbreeding depression in a wild population. By providing an overview of these issues, we hope that this approach will become more accessible to those investigating any process where multiple variables impact an evolutionary or ecological response.
Reference Key
openalex_W1584343945 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Catherine E. Grueber, Shinichi Nakagawa, Rebecca Laws, Ian G. Jamieson
Journal journal of evolutionary biology
Year 2011
DOI
10.1111/j.1420-9101.2010.02210.x
URL
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/j.1420-9101.2010.02210.x
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