Kullback-Leibler information as a basis for strong inference in ecological studies

By K. Burnham, David R. Anderson. 2001

927 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/982fd011a9478cf18a7f1a2a98b9f882775f80ce

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Citations in the corpus

Evaluating resource selection functions (2002, methodology): “Here we assign a 0 to sites where the organism is absent and a 1 to sites where present; and a set of predictor variables is selected a priori using hypothesized habitat relations based on the ecology of the organism ( Burnham and Anderson, 2001 ).”
Predator control allows critically endangered lizards to recover on mainland New Zealand. (2012, methodology): “Model selection was based on the corrected Akaike Information Criterion (AICc, an adjustment to the AIC to account for bias due to small sample size; Burnham & Anderson 2001).”
Information theory and hypothesis testing: a call for pluralism (2005, background): “Scientists must move away from the bias towards reporting only positive results, in order to avoid bias in subsequent meta-analyses ITMC employs ‘some simple rules of thumb’ ( Burnham & Anderson 2001 ) for assessing the relative merits of competing models.”
Sea urchin grazing and kelp re-vegetation in the NE Atlantic (2009, methodology): “All the predictors were included in a full model and significant predictors were selected according to the Aikake Selection Criterion (AIC; see Burnham & Anderson 2001).”
Sea ice affects the population dynamics of Adélie penguins in Terre Adélie (2006, background): “DAICc can easily be interpreted following this scale (Burnham and Anderson 2001): models having DAICc £ 2 are strongly plausible, those where 4 £ DAICc £ 7 are considerably less plausible whereas models having DAICc ‡ 10 are improbable.”
Why you should use capture-recapture methods when estimating survival and breeding probabilities: on bias, temporary emigration, overdispersion, and common toads (2002, background): “For comparison we also present 1AICc and AICc weights, which assume no overdispersion ( O c D 1). K is the number of estimated parameters for models assuming O c D 1. Add C1 to K when assuming O c D 4 ( Burnham and Anderson, 2001 ).”
Exploring the model space and ranking a best class of models in surface-wave dispersion inversion: Application at European strong-motion sites (2012, methodology): “AIC or similar information-theoretic approaches do not require particular assumptions on the experimental data and they are used for problems of model decision in various fields of environmental, hydrology, and numerical analyses (Mutua, 1994; Burnham and Anderson, 2001; Zhao et al., 2008).”
Invasive Exotic Plants in the Tropical Pacific Islands: Patterns of Diversity (2009, methodology): “Using the island data base, we compared performances of seven linear regression models in PROC MIXED (SAS Institute, Inc. 2003) on the basis of Akaike’s Information Criterion for small samples (AICc) (Burnham & Anderson 2001, 2002).”
BEHAVIOR OF AMPHIBIANS ON THE ROAD IN RESPONSE TO CAR TRAFFIC (2005, methodology): “We obtained the estimates and standard errors for each variable with model-averaging techniques, as described in detail in Burnham and Anderson (2001, 2002) and Mazerolle (in press).”
A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion (2010, background): “Burnham and Anderson (2001, 2002, 2004) emphasise the importance of justifying the inclusion of each parameter, and each model within the candidate set.”
Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework (2010, methodology): “To account for model uncertainty, Burnham and Anderson (2002) and Lukacs et al.”
Developing multiple hypotheses in behavioral ecology (2010, methodology): “…using Akaike’s information criterion (AIC), in which its wide use in ecology and evolutionary biology is largely due to influential works by David Anderson and Kenneth Burnham (Anderson et al. 2000, 2001; Burnham and Anderson 2001; Anderson and Burnham 2002; Burnham and Anderson 2002, 2004).”