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Model Selection in Occupancy Models: Inference versus Prediction

Stewart, Peter S. and Stephens, Philip A. and Hill, Russell A. and Whittingham, Mark J. and Dawson, Wayne (2023) 'Model Selection in Occupancy Models: Inference versus Prediction.', Ecology .


Occupancy models are a vital tool for ecologists studying the patterns and drivers of species occurrence, but their use often involves selecting among models with different sets of occupancy and detection covariates. The information-theoretic approach, which employs information criteria such as Akaike’s 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, a type of confounding that can arise when adding explanatory variables to a model. 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 than other models in the candidate set. 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 show 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 model’s 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.

Item Type:Article
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Publisher statement:© 2022 The Authors. Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Date accepted:07 November 2022
Date deposited:09 November 2022
Date of first online publication:18 January 2023
Date first made open access:20 January 2023

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