  Technological advances in underwater video recording are opening novel
opportunities for monitoring wild fish. However, extracting data from videos is
often challenging. Nevertheless, it has been recently demonstrated that
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. The method, however,
requires that individual detectability (PID, the probability of detecting a
given animal provided that it is actually within the area surveyed by a camera)
has to be known. Here we propose a Bayesian implementation for estimating PID
after combining counts from cameras with counts from any reference method. The
proposed framework was demonstrated using Serranus scriba as a case-study, a
widely distributed and resident coastal fish. Density and PID were calculated
after combining fish counts from unbaited remote underwater video (RUV) and
underwater visual censuses (UVC) as reference method. The relevance of the
proposed framework is that after estimating PID, 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 key statement has been extensively
demonstrated using computer simulations yielded by real empirical data.
Finally, we provide a simulation tool-kit for comparing the expected precision
attainable for different sampling effort and for species with different levels
of PID. Overall, the proposed method may contribute to substantially enlarge
the spatio-temporal scope of density monitoring programs for many resident
fish.
