Comparing the Performance of a Remotely Operated Vehicle, a Drop Camera, and a Trawl in Capturing Deep-Sea Epifaunal Abundance and Diversity

Abstract

Deep-sea ecosystems provide services such as food, minerals, and nutrient recycling, yet baseline data on their structure is often lacking. Our limited knowledge of vulnerable deep-sea ecosystems presents a challenge for effective monitoring and mitigation of increasing anthropogenic threats, including destructive fishing and climate change. Using data from two stations differing in total epifaunal abundance and taxonomic composition, we compared the use of imagery collected by two non-invasive tools [remotely operated vehicle (ROV) and drop camera] and data collected with a trawl system, commonly used to quantify epibenthic megafauna in the deep sea. Imagery and trawl data captured different epifaunal patterns, the former being more efficient for capturing epifauna, particularly Pennatulacean recruits. The image-based methods also caused less disturbance, had higher position accuracy, and allow for analyses of spatial structure and species associations; fine-scale distributions could not be elucidated with a trawl. Abundance was greater for some taxa, and diversity accumulated faster with increasing sample size for the drop camera than the ROV at one station. However, there are trade-offs between these tools, including continuous and discrete sampling for the ROV and drop camera, respectively, which can affect follow-up analyses. Our results can be used to inform monitoring frameworks on the use of appropriate sampling tools. We recommend further research into tool sampling biases and biometric relationships to help integrate datasets collected with different tools.

Heterogeneity on the abyssal plains: A case study in the Bering Sea

“…Apart from these ROV video transects in the northwestern part of the Bering Sea, almost all other biodiversity data regarding deep-sea megafaunal communities in this region have been obtained using extractive gear, such as trawls and corers (Rybakova et al, 2020). Extractive sampling methods are known to underestimate megafauna abundance and richness, and are not directly comparable with our data (Uzmann et al, 1977;Ayma et al, 2016;De Mendonca and Metaxas, 2021). Most other recent comparable studies evaluating epifauna densities using high-resolution image transects have focused on topographic features such as abyssal hills (e.g., Durden et al, 2015;Durden et al, 2020), manganese nodule fields (e.g., Simon-Lledóet al, 2020), or hydrothermal vents (e.g., Thornton et al, 2016).…”

“…Nonetheless, even early imaging methods were considered highly cost effective for surveying megafauna (Uzmann et al, 1977), often defined as those larger than 1 cm or so and that can be recognised in seafloor images (Rybakova et al, 2020). Image-based methods generally have higher positional accuracy and are less destructive (Diaz, 2004;Ayma et al, 2016;De Mendonca and Metaxas, 2021;Mizuno et al, 2022). They can also be implemented at long-term seafloor observatories to collect time-series data (Soltwedel et al, 2005;Taylor et al, 2017).…”

When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification

“…In addition, we observed that the erect 3D structure of these habitatforming species was not efficiently captured in dredges and trawls on the hard bottoms of the Mayotte volcanic island slopes; similarly for penatulids and actinarians which have been observed only in images along sedimented slopes of PNG. This fishing gear selectivity has already been mentioned for hard bottoms (Williams et al, 2015) and for soft bottoms (e.g., pennatulids, actinids) (Rice et al, 1982;Nybakken et al, 1998;de Mendonça and Metaxas, 2021). However, cnidarians and poriferans represent key groups in the benthic ecosystem functioning, because they can host a large diversity of associated fauna (Buhl-Mortensen et al, 2010;Beazley et al, 2013).…”

Machine learning applied to big data from marine cabled observatories: A case study of sablefish monitoring in the NE Pacific

“…Bottom trawling is to date the most reliable stock assessment method for demersal resources (Ovando et al, 2022) but it's temporally intensive application would have a high environmental impact (e.g., Hiddink et al, 2006;Jamieson et al, 2013;Flannery and Przeslawski, 2015;Colloca et al, 2017;Costello et al, 2017;Sciberras et al, 2018;Rousseau et al, 2019;De Mendonca and Metaxas, 2021). Imagery-based sampling methods are still not as widespread (Bicknell et al, 2016), although they are successfully applied for gathering species composition and abundance data, for instance along usage of Baited Remote Underwater Video Stations (BRUVs), stereo-BRUVs (with stereo cameras for accurate fish sizing), and more recently Deep-BRUVS (for deep-water deployments; e.g., Langlois et al, 2018;Withmarsh et al, 2017).…”