Data from: Model selection with overdispersed distance sampling data

Eric J. Howe, Stephen T. Buckland, Marie-Lyne Després-Einspenner & Hjalmar S. Kühl
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...
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