Evaluation of a Nested Patch Occupancy Model Applied to PIT Tagged Salmon in a Branching River Network

River systems are highly impacted by anthropogenic influences, such as, river channelization, agricultural runoff, urbanization, and dam construction. These impacts have, in part, led to management concerns regarding the diversity of species that use these systems for rearing, migration, and reproduction (e.g., salmon, sturgeon, lamprey, and eels). In an effort to assess the efficacy of restoration actions, and in order to improve monitoring for species of concern, managers have turned to PIT (passive integrated transponder) tag studies with in-stream detectors to monitor movements of tagged individuals throughout river networks. We propose a flexible Bayesian analytic framework that models the movement of tagged individuals in a nested PIT tag detector river network. This model structure allows for imperfect observations by modeling the location of each tagged individual with an underlying state variable. We apply our model framework to data from steelhead (Oncorhynchus mykiss) in the Upper Columbia River basin in 2012, and evaluate model precision/variance as a function of population tagging rates and detection array densities within the river system.