title: Model selection and assessment for multi-species occupancy models.
abstract: While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction.
authors.affiliation: Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, 80523, USA.
authors.forename: Kristin M
authors.initials: KM
authors.lastname: Broms
authors.affiliation: Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, 80523, USA.|Colorado Cooperative Fish and Wildlife Unit, U.S. Geological Survey, Fort Collins, Colorado, 80523, USA.|Department of Statistics, Colorado State University, Fort Collins, Colorado, 80523, USA.
authors.forename: Mevin B
authors.initials: MB
authors.lastname: Hooten
authors.affiliation: Aquatic Wildlife Research Group, Colorado Parks and Wildlife, Fort Collins, Colorado, 80523, USA.
authors.forename: Ryan M
authors.initials: RM
authors.lastname: Fitzpatrick
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D044822:Biodiversity; D003120:Colorado; D003247:Conservation of Natural Resources; D017753:Ecosystem; D005399:Fishes / Q000145:classification / Q000502:physiology*; D015233:Models, Statistical
keywords: Bayesian hierarchical models; South Platte River Basin; biodiversity; cross-validation; plains fish; species distribution maps