Estimating relative abundance and species richness from video surveys of reef fishes

Abstract

Abstract-Underwater video sampling has become a common approach to index fish abundance and diversity, but little has been published on determining how much video to read. We used video data collected over a period of 6 years in the Gulf of Mexico to examine how the number of video frames read affects accuracy and precision of fish counts and estimates of species richness. To examine fish counts, we focused on case studies of red snapper (Lutjanus campechanus), vermilion snapper (Rhomboplites aurorubens), and scamp (Mycteroperca phenax). Using a bootstrap framework, we found that fish counts were unbiased when at least 5 of 1201 video frames within a 20-min video were read. The relative patterns of coefficients of variation (CVs) were nearly identical among species and declined as an inverse power function. Initial decreases in CVs were rapid as the number of frames read increased from 1 to 50. However, subsequent declines were modest, decreasing only by ~50% when the number of frames read increased by 300%. Estimated species richness increased asymptotically as the number of frames read increased from 25 to 200 frames, and reading 50 frames documented 86% of the species observed across all 1201 frames. Lastly, we used a generalized additive model to show that the most likely species to be missed were fast-swimming fishes that are solitary or form relatively small schools. Our results indicate that the most efficient use of resources (i.e., maximum information gained at the lowest cost) would be to read ~50 frames from each video.

Temporal and spatial variation in adult and juvenile mobile fauna associated with natural and artificial coastal habitats

“…Although BRUV has been criticised for recording relative abundance (MaxN) and not density, it has shown to be a cost-effective non-destructive method which overcomes problems with diver-based fish avoidance (Schramm et al 2020). MaxN was used rather than other metrics (Whitmarsh et al 2017) such as "meancount" (Bacheler and Shertzer 2015) as this has shown to over inflate zero observations (Campbell et al 2015). MaxN accounts for both fauna attracted to the bait and the presence of the camera frame, alongside species which are just passing through the field of view.…”

Spatial Distribution of Reef Fish Species along the Southeast US Atlantic Coast Inferred from Underwater Video Survey Data

“…In our study, video was able to provide presence-absence data for a variety of economically important priority species, but frequency of occurrence should be considered a minimum estimate because video does not always document all species present at a site due to incomplete detection [ 9 , 11 ]. Reading the entire 20 minutes of underwater video (or more) would likely have increased the frequency of occurrence of reef fish species [ 21 ], but would have been more costly. Using bait also likely resulted in higher number of species seen compared to unbaited video [ 39 ].…”

“…due to their significance as an invasive species in the SEUS. We note that one downside of our video-reading approach is that we may not document all species present at each site [ 9 , 21 ], so therefore we view our results as a conservative estimate of the distribution of reef fish species in the SEUS.…”

The relationship between fish abundance and benthic community structure on artificial reefs in the Mid-Atlantic Bight, and the importance of sea whip corals Leptogorgia virgulata

“…Therefore, fish abundance was estimated for all fish present and not separated by species. To estimate fish abundance on artificial reefs, we used a modified method of the fish MeanCount method, which is defined as the mean number of individuals observed in a series of frames throughout a viewing interval (Bacheler & Shertzer, 2014). To maintain independence between frames and statistical independence, 12 frames were randomly selected from the line transect surveys and the number of fish within each frame were counted.…”