Dealing with overdispersed count data in applied ecology

Summary The ability to identify key ecological processes is important when solving applied problems. Increasingly, ecologists are adopting Akaike's information criterion (AIC) as a metric to help them assess and select among multiple process‐based ecological models. Surprisingly, however, it is still unclear how best to incorporate AIC into the selection process in order to address the trade‐off between maximizing the probability of retaining the most parsimonious model while minimizing the number of models retained. Ecological count data are often observed to be overdispersed with respect to best‐fitting models. Overdispersion is problematic when performing an AIC analysis, as it can result in selection of…

• IR
  Innovative Research Group Project of the National Natural Science Foundation of China