MEPS 615:177-188 (2019)  -  DOI: https://doi.org/10.3354/meps12926

ABSTRACT: Technological advances in underwater video recording are providing novel opportunities for monitoring wild fish. Although extracting data from videos is often challenging, accurate and precise estimates of density for animals whose normal activities are restricted to a bounded area or home range can be obtained from counts averaged across a relatively low number of video frames. However, this method requires that individual detectability (P_ID, the probability of detecting a given animal provided that it is actually within the area surveyed by a camera) be known. Here we propose a Bayesian implementation for estimating P_ID after combining counts from cameras with counts from any other reference method. The proposed framework was demonstrated with a case study of Serranus scriba, a widely distributed and resident coastal fish. Density and P_ID were calculated after combining fish counts from unbaited remote underwater video (RUV) and underwater visual censuses (UVC) as reference methods. The relevance of the proposed framework is that after estimating P_ID, fish density can be estimated accurately and precisely at the UVC scale (or at the scale of the preferred reference method) using RUV only. This method is further validated using computer simulations based on empirical data. We provide a simulation tool kit for comparing the expected precision attainable for different sampling effort and for species with different levels of P_ID. Overall, the proposed method may contribute to substantially enlarge the spatio-temporal scope of density monitoring programmes for many resident fish.

KEY WORDS: Bayesian approach · Fish density · Home range · Individual detectability · Monitoring · Unbaited underwater cameras · Underwater visual census