AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons

By K. Burnham, David R. Anderson, K. Huyvaert. Behavioral Ecology and Sociobiology, 2011

3,960 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/33e08c84858e01d75d94592e03272dd4073316ff

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In Ecological Modeling

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Citations in the corpus

Formidable females and the power trajectories of socially integrated male vervet monkeys (2017, methodology): “5 of the best model were then used to derive averaged model outcomes (Burnham, Anderson, & Huyvaert, 2011).”
A systematic review on the behavioural responses of wild marine mammals to noise: the disparity between science and policy (2016, methodology): “Models with a delta AICc less than 7 (Burnham et al. 2011) and those with Ei < 10 are presented.”
Applications of structural equation modeling (SEM) in ecological studies: an updated review (2016, methodology): “The selection algorithm can be based on preferable variables and models according to certain statistical criteria (Burnham and Anderson 2002; Burnham et al. 2011).”
Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests (2014, methodology): “To determine which of these explanatory variables were the most decisive in possible changes in species richness, community structure, functional diversity and functional trait attributes we used an information theoretical approach based on the Akaike Information Criterion of Second Order (AICc), wh”
Vertebrate road-kill patterns in Mediterranean habitats: Who, when and where (2015, methodology): “We considered as plausible all models at ΔAIC b 2 from the best model (Burnham et al., 2011).”
Biodiversity consequences of land-use change and forest disturbance in the Amazon:a multi-scale assessment using ant communities (2016, methodology): “To do so, we used multi-model inference, based on a global model with all candidate variables followed by model selection (Burnham et al. 2011).”