The use of Bayesian priors in Ecology: The good, the bad and the not great

Abstract

Bayesian data analysis (BDA) is a powerful tool for making inference from ecological data, but its full potential has yet to be realized. Despite a generally positive trajectory in research surrounding model development and assessment, far too little attention has been given to prior specification. Default priors, a sub-class of non-informative prior distributions that are often chosen without critical thought or evaluation, are commonly used in practice. We believe the fear of being too ‘subjective’ has prevented many researchers from using any prior information in their analyses despite the fact that defending prior choice (informative or not) promotes good statistical practice. In this commentary, we provide an overview of how BDA is currently being used in a random sample of articles, discuss implications for inference if current bad practices continue, and highlight sub-fields where knowledge about the system has improved inference and promoted good statistical practices through the careful and justified use of informative priors. We hope to inspire a renewed discussion about the use of Bayesian priors in Ecology with particular attention paid to specification and justification. We also emphasize that all priors are the result of a subjective choice, and should be discussed in that way.