Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors

Environmental DNA (eDNA) is a survey tool with rapidly expanding applications for assessing presence of a species at surveyed sites. eDNA methodology is known to be prone to false negative and positive errors at the data collection and laboratory analysis stage. Existing models for eDNA data require augmentation with additional sources of information to overcome identifiability issues of the likelihood function and do not account for environmental covariates that predict the probability of species presence or the proba-bilities of error. We present a novel Bayesian model for analysing eDNA data by proposing informative prior distributions for logistic regression coefficients that allow us to overcome parameter identifiability, while performing efficient Bayesian model-selection. Our methodology does not require the use of trans-dimensional algorithms and provides a general framework for performing Bayesian variable selection under informative prior distributions in logistic regression models.