A generalised volumetric method to estimate the biomass of photographically surveyed benthic megafauna

• Published in: Progress in Oceanography
• Main Authors: Benoist, Noëlie M.A., Bett, Brian J., Morris, Kirsty J., Ruhl, Henry A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2019
• Subjects: Northeast Atlantic
• Online Access: http://nora.nerc.ac.uk/id/eprint/525711/; https://nora.nerc.ac.uk/id/eprint/525711/1/main.pdf; https://doi.org/10.1016/j.pocean.2019.102188

Biomass is a key variable for understanding the stocks and flows of carbon and energy in the environment. The quantification of megabenthos biomass (body size ≥ 1 cm) has been limited by their relatively low abundance and the difficulties associated with quantitative sampling. Developments in robotic technology, particularly autonomous underwater vehicles, offer an enhanced opportunity for the quantitative photographic assessment of the megabenthos. Photographic estimation of biomass has typically been undertaken using taxon-specific length-weight relationships (LWRs) derived from physical specimens. This is problematic where little or no physical sampling has occurred and/or where key taxa are not easily sampled. We present a generalised volumetric method (GVM) for the estimation of biovolume as a predictor of biomass. We validated the method using fresh trawl-caught specimens from the Porcupine Abyssal Plain Sustained Observatory (northeast Atlantic), and we demonstrated that the GVM has a higher predictive capability and a lower standard error of estimation than the LWR method. GVM and LWR approaches were tested in parallel on a photographic survey in the Celtic Sea. Among the 75% of taxa for which LWR estimation was possible, highly comparable biomass values and distribution patterns were determined by both methods. The biovolume of the remaining 25% of taxa increased the total estimated standing stock by a factor of 1.6. Additionally, we tested inter-operator variability in the application of the GVM, and we detected no statistically significant bias. We recommend the use of the GVM where LWRs are not available, and more generally given its improved predictive capability and its independence from the taxonomic, temporal, and spatial, dependencies known to impact LWRs.