title: Occupancy models for monitoring marine fish: a bayesian hierarchical approach to model imperfect detection with a novel gear combination.
abstract: Occupancy models using incidence data collected repeatedly at sites across the range of a population are increasingly employed to infer patterns and processes influencing population distribution and dynamics. While such work is common in terrestrial systems, fewer examples exist in marine applications. This disparity likely exists because the replicate samples required by these models to account for imperfect detection are often impractical to obtain when surveying aquatic organisms, particularly fishes. We employ simultaneous sampling using fish traps and novel underwater camera observations to generate the requisite replicate samples for occupancy models of red snapper, a reef fish species. Since the replicate samples are collected simultaneously by multiple sampling devices, many typical problems encountered when obtaining replicate observations are avoided. Our results suggest that augmenting traditional fish trap sampling with camera observations not only doubled the probability of detecting red snapper in reef habitats off the Southeast coast of the United States, but supplied the necessary observations to infer factors influencing population distribution and abundance while accounting for imperfect detection. We found that detection probabilities tended to be higher for camera traps than traditional fish traps. Furthermore, camera trap detections were influenced by the current direction and turbidity of the water, indicating that collecting data on these variables is important for future monitoring. These models indicate that the distribution and abundance of this species is more heavily influenced by latitude and depth than by micro-scale reef characteristics lending credence to previous characterizations of red snapper as a reef habitat generalist. This study demonstrates the utility of simultaneous sampling devices, including camera traps, in aquatic environments to inform occupancy models and account for imperfect detection when describing factors influencing fish population distribution and dynamics.
authors.affiliation: National Marine Fisheries Service, Southeast Fisheries Science Center, Beaufort, North Carolina, United States of America; United States Fish and Wildlife Service, Yukon Delta National Wildlife Refuge, Bethel, Alaska, United States of America.
authors.forename: Lewis G
authors.initials: LG
authors.lastname: Coggins
authors.affiliation: National Marine Fisheries Service, Southeast Fisheries Science Center, Beaufort, North Carolina, United States of America.
authors.forename: Nathan M
authors.initials: NM
authors.lastname: Bacheler
authors.affiliation: Biometric Research, and Program for Fisheries and Aquatic Sciences, School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, United States of America.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Gwinn
mesh_terms: D000465:Algorithms; D000818:Animals; D001267:Atlantic Ocean; D001499:Bayes Theorem; D005399:Fishes; D005431:Florida; D005845:Georgia; D008962:Models, Theoretical; D011156:Population Density