  Occupancy models are typically used to determine the probability of a species
being present at a given site while accounting for imperfect detection. The
survey data underlying these models often include information on several
predictors that could potentially characterize habitat suitability and species
detectability. Because these variables might not all be relevant, model
selection techniques are necessary in this context. In practice, model
selection is performed using the Akaike Information Criterion (AIC), as few
other alternatives are available. This paper builds an objective Bayesian
variable selection framework for occupancy models through the intrinsic prior
methodology. The procedure incorporates priors on the model space that account
for test multiplicity and respect the polynomial hierarchy of the predictors
when higher-order terms are considered. The methodology is implemented using a
stochastic search algorithm that is able to thoroughly explore large spaces of
occupancy models. The proposed strategy is entirely automatic and provides
control of false positives without sacrificing the discovery of truly
meaningful covariates. The performance of the method is evaluated and compared
to AIC through a simulation study. The method is illustrated on two datasets
previously studied in the literature.
