Data and R markdown to replicate analyses in “Optimizing remote underwater video sampling to quantify relative abundance, richness, and corallivory rates of reef fish”
2025
Hsu, Tsai-Hsuan Tony | Gordon, Sophie | Ferrari, Renata | Hoey, Andrew S. | Figueira, Will F.
Remote underwater videos (RUVs) are valuable for studying fish assemblages and behaviours, 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-minute video yielded precise and accurate estimates of the mean number of individuals per frame (i.e., MeanCount), with systematic sampling (one frame every 90 seconds) 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 minutes 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. If used in full or in part, please cite the data and the original publication: Hsu TH.T, Gordon S, Rerrari R, Hoey A.S, Figueira W.F (2025) Optimizing remote underwater video sampling to quantify relative abundance, richness, and corallivory rates of reef fish (accepted in Coral Reefs).
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Este registro bibliográfico ha sido proporcionado por University of Sidney