Model selection with overdispersed distance sampling data

1. Distance sampling (DS) is a widely-used framework for estimating animal abundance. DS models assume that observations of distances to animals are independent. Non-independent observations introduce overdispersion, causing model selection criteria such as AIC or AICc to favour overly complex models, with adverse effects on accuracy and precision. 2. We describe, and evaluate via simulation and with real data, estimators of an overdispersion factor (c ̂), and associated adjusted model selection criteria (QAIC) for use with overdispersed DS data. In other contexts, a single value of c ̂ is calculated from the “global” model, i.e., the most highly-parameterized model in the candidate set, and used to calculate QAIC for all models in the set; the resulting QAIC values, and associated ΔQAIC values and QAIC weights, are comparable across the entire set. Candidate models of the DS detection function include models with different general forms (e.g., half-normal, hazard rate, uniform), ...