Estimating the density of resident coastal fish using underwater cameras: accounting for individual detectability

TL;DR

This paper introduces a Bayesian method to estimate fish density from underwater videos by accounting for individual detectability, enabling more accurate and scalable monitoring of resident coastal fish populations.

Contribution

It develops a Bayesian framework to estimate individual detectability and fish density from underwater videos, combining counts with reference methods for improved accuracy.

Findings

Accurate density estimates achieved using the proposed Bayesian method.

Method validated through computer simulations with empirical data.

Provides a toolkit for optimizing sampling effort based on species detectability.

Abstract

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.