Optimizing remote underwater video sampling to quantify relative abundance, richness, and corallivory rates of reef fish

Abstract Remote underwater videos (RUVs) are valuable for studying fish assemblages and behaviors, but analyzing them is time-consuming. To effectively extract data from RUVs while minimizing sampling errors, this study developed optimal subsampling strategies for assessing relative abundance, richness, and bite rates of corallivorous fish across eight geographically dispersed reef sites on the Great Barrier Reef and in the Torres Strait. Analyzing 40 frames per 60-min video yielded precise and accurate estimates of the mean number of individuals per frame (i.e., MeanCount), with systematic sampling (one frame every 90 s) proved as effective as or better than random sampling, depending on the survey sites. However, this approach underestimated species richness by ~ 40%, missing the less common species. For estimating bite rates, 30 min or 15 feeding events were optimal, with no significant gains in precision and accuracy with further effort. These strategies enhance data standardization and process efficiency, reducing the time required for MeanCount and bite rate estimates by nine and two times, respectively, compared to full video annotation.