Consistency Is Critical for the Effective Use of Baited Remote Video

Abstract

Baited remote underwater videos (BRUV) are popular marine monitoring techniques used for the assessment of motile fauna. Currently, most published studies evaluating BRUV methods stem from environments in the Southern Hemisphere. This has led to stricter and more defined guidelines for the use of these techniques in these areas in comparison to the North Atlantic, where little or no specific guidance exists. This study explores metadata taken from BRUV deployments collected around the UK to understand the influence of methodological and environmental factors on the information gathered during BRUV deployments including species richness, relative abundance and faunal composition. In total, 39 BRUV surveys accumulating in 457 BRUV deployments across South/South-West England and Wales were used in this analysis. This study identified 88 different taxa from 43 families across the 457 deployments. Whilst taxonomic groups such as Labridae, Gadidae and Gobiidae were represented by a high number of species, species diversity for the Clupeidae, Scombridae, Sparidae, Gasterosteidae and Rajidae groups were low and many families were absent altogether. Bait type was consistently identified as one of the most influential factors over species richness, relative abundance and faunal assemblage composition. Image quality and deployment duration were also identified as significant influential factors over relative abundance. As expected, habitat observed was identified as an influential factor over faunal assemblage composition in addition to its significant interaction with image quality, time of deployment, bait type and tide type (spring/neap). Our findings suggest that methodological and environmental factors should be taken into account when designing and implementing monitoring surveys using BRUV techniques. Standardising factors where possible remains key. Fluctuations and variations in data may be attributed to methodological inconsistencies and/or environment factors as well as over time and therefore must be considered when interpreting the data.