title: Bayesian inference in camera trapping studies for a class of spatial capture-recapture models.
abstract: We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.
authors.affiliation: U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA. aroyle@usgs.gov
authors.forename: J Andrew
authors.initials: JA
authors.lastname: Royle
authors.forename: K Ullas
authors.initials: KU
authors.lastname: Karanth
authors.forename: Arjun M
authors.initials: AM
authors.lastname: Gopalaswamy
authors.forename: N Samba
authors.initials: NS
authors.lastname: Kumar
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003710:Demography; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D046010:Tigers / Q000502:physiology*; D014741:Video Recording