Understanding information criteria for selection among capture-recapture or ring recovery models

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Abstract
We provide background information to allow a heuristic understanding of two types of criteria used in selecting a model for making inferences from ringing data. The first type of criteria (e.g. AIC, AlCc QAICc and TIC) are estimates of (relative) Kullback-Leibler information or distance and attempt to select a good approximating model for inference, based on the principle of parsimony. The second type of criteria (e.g. BIC, MDL, HQ) are 'dimension consistent' in that they attempt to consistently estimate the dimension of the true model. These latter criteria assume that a true model exists, that it is in the set of candidate models and that the goal of model selection is to find the true model, which in turn requires that the sample size is very large. The Kullback-Leibler based criteria do not assume a true model exists, let alone that it is in the set of models being considered. Based on a review of these criteria, we recommend use of criteria that are based on Kullback-Leibler information in the biological sciences.