An Evaluation of Deep-Sea Benthic Megafaunal Communities in the Northern Gulf of Mexico Using Industrial ROVS and Video Imagery

Stephanie M Sharuga and Mark C Benfield, Louisiana State University and Agricultural & Mechanical College, Baton Rouge, United States
Abstract:
The Deepwater Horizon oil spill in 2010 created a need for more thorough studies of deep-sea benthic biota, especially in soft-sediment areas of the Northern Gulf of Mexico (GoM). These benthic environments are increasingly vulnerable as demand and exploitation of resources in these areas grow. A 15°, 250 m long radial transect survey design was developed for use with industrial remotely operated vehicles (ROVs) to quantify benthic megafaunal communities in the vicinity of the MC252 well. Further, a customized database system was developed to explore natural and anthropogenic factors potentially responsible for influencing benthic megafaunal characteristics in this area. Biotic and abiotic characteristics were extracted from ROV videos collected one year after the Deepwater Horizon spill at seven study sites ranging from 2-39 km away from MC252, and located at depths from 850-1500 m. Seafloor environments differed amongst the sites, with differences found to be related to location and depth. Benthic megafauna in ten taxonomic categories were evaluated in order to compare benthic community characteristics, including density and diversity. Overall, community composition was found to be primarily related to depth and, to a lesser degree, site location. Results from this study suggest that depth, location, and the abiotic environment (ex. seafloor features, including anthropogenic disturbance) play important roles in the abundances and diversity of deep-sea benthic megafauna in the Northern GoM and should be considered when conducting environmental studies. This study demonstrates the utility of industrial-based deep-sea imaging platforms as a readily accessible option for collecting valuable information on deep-sea environments. These platforms exhibit excellent potential for use in determining baseline data and evaluating ecosystem changes and/or recovery.