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Multimodel inference in ecology and evolution: challenges and solutions

Journal of evolutionary biology, 2011-04, Vol.24 (4), p.699-711 [Peer Reviewed Journal]

2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology ;2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology. ;ISSN: 1010-061X ;EISSN: 1420-9101 ;DOI: 10.1111/j.1420-9101.2010.02210.x ;PMID: 21272107

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Title:
Multimodel inference in ecology and evolution: challenges and solutions
Author: GRUEBER, C.E ; NAKAGAWA, S ; LAWS, R.J ; JAMIESON, I.G
Subjects: Akaike Information Criterion ; Biological Evolution ; Data Interpretation, Statistical ; Ecology ; Ecology - methods ; evolution ; Evolutionary biology ; generalized linear mixed models ; inbreeding ; inbreeding depression ; Information theory ; lethal equivalents ; model averaging ; Models, Biological ; random factors ; standardized predictors
Is Part Of: Journal of evolutionary biology, 2011-04, Vol.24 (4), p.699-711
Description: 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.
Publisher: Oxford, UK: Blackwell Publishing Ltd
Language: English
Identifier: ISSN: 1010-061X
EISSN: 1420-9101
DOI: 10.1111/j.1420-9101.2010.02210.x
PMID: 21272107
Source: MEDLINE
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Multimodel inference in ecology and evolution: challenges and solutions

2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology ;2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology. ;ISSN: 1010-061X ;EISSN: 1420-9101 ;DOI: 10.1111/j.1420-9101.2010.02210.x ;PMID: 21272107

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