Model selection in occupancy models: inference versus prediction

Occupancy models are a vital tool for applied ecologists studying the patterns and drivers of species occurrence, but their use requires a method for selecting between models with different sets of occupancy and detection covariates. The information-theoretic approach, which employs information criteria such as Akaikes Information Criterion (AIC) is arguably the most popular approach for model selection in ecology and is often used for selecting occupancy models. However, the information-theoretic approach risks selecting models which produce inaccurate parameter estimates, due to a phenomenon called collider bias. Using simulations, we investigated the consequences of collider bias (using an illustrative example called M-bias) in the occupancy and detection processes of an occupancy model, and explored the implications for model selection using AIC and a common alternative, the Schwarz Criterion (or Bayesian Information Criterion, BIC). We found that when M-bias was present in the occupancy process, AIC and BIC selected models which inaccurately estimated the effect of the focal occupancy covariate, while simultaneously producing more accurate predictions of the site-level occupancy probability. In contrast, M-bias in the detection process did not impact the focal estimate; all models made accurate inferences, while the site-level predictions of the AIC/BIC-best model were slightly more accurate. Our results demonstrate that information criteria can be used to select occupancy covariates if the sole purpose of the model is prediction, but must be treated with more caution if the purpose is to understand how environmental variables affect occupancy. By contrast, detection covariates can usually be selected using information criteria regardless of the models purpose. These findings illustrate the importance of distinguishing between the tasks of parameter inference and prediction in ecological modelling. Furthermore, our results underline concerns about the use of information criteria to compare different biological hypotheses in observational studies. Open Research StatementCode to fully reproduce our simulations and analyses is available at: https://zenodo.org/badge/latestdoi/462801230