Multimodel inference in ecology and evolution: challenges and solutions

By C. Grueber, Shinichi Nakagawa, R. Laws, Ian G. Jamieson. Journal of Evolutionary Biology, 2011

2,203 citations recorded by the source corpus.

Subfields: Ecology, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/2bdfe2138c756fee135f9b82107b7c8d2f403bd0

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Citations in the corpus

Global patterns of ecologically unequal exchange: Implications for sustainability in the 21st century (2021, background): “…for the purpose of testing a set of hypotheses from a particular theory as we have aimed for here, but it has to be kept in mind that a candidate modeling approach and multimodel inference would likely be more useful for predictive purposes (Burnham and Anderson, 2004; Grueber et al., 2011).”
Vertebrate community composition and diversity declines along a defaunation gradient radiating from rural villages in Gabon (2017, methodology): “approach and implemented model averaging using the MUMIN package (Grueber et al. 2011; Barto n 2016).”
Deadwood enrichment in European forests – Which tree species should be used to promote saproxylic beetle diversity? (2016, positive): “The subset of models was then averaged using parameter estimate and Akaike weight of each model with the model.avg function of thepackage(Grueber et al., 2011).”
Ecologists overestimate the importance of predictor variables in model averaging: a plea for cautious interpretations (2014, methodology): “This is called themodel selection procedure and is most frequently based on Aka€ıke Information Criterion (AIC) in ecological studies (Grueber et al. 2011, Aho, Derryberry & Peterson 2014, but see Bolker 2008, p. 219).”
A brief introduction to mixed effects modelling and multi-model inference in ecology (2018, methodology): “Consequently, Grueber et al (2011) recommend always fitting both random slopes and intercepts where possible.”
The impact of high temperatures on foraging behaviour and body condition in the Western Australian Magpie Cracticus tibicen dorsalis (2015, background): “These models were then carefully scrutinised; any terms with a confidence interval that did not intersect zero were considered to have a significant influence on the data (sensu Grueber et al. 2011).”
A general and simple method for obtaining R2 from generalized linear mixed‐effects models (2013, methodology): “Commonly used information criteria include Akaike Information Criterion (AIC) (Akaike 1973), Bayesian information criterion (BIC), (Schwarz 1978) and the more recently proposed deviance information criterion (DIC), (Spiegelhalter et al. 2002; reviewed in Claeskens & Hjort 2009; Grueber et al. 2011; ”
A global analysis of traits predicting species sensitivity to habitat fragmentation (2017, methodology): “To synthesize and present the results of the optimal model, we evaluated the importance of individual parameters by running the model on a centred and scaled dataset (Kutner et al., 2005; Grueber et al., 2011).”