Benthic habitat mapping from a machine learning perspective on the Cape St Francis inner shelf, Eastern Cape, South Africa

Abstract We have developed an algorithm to map benthic habitats on the continental shelf of South Africa, integrating marine geophysics and biological science. Multibeam bathymetry, backscatter and Remotely Operated Vehicle (ROV) footage were collected on the inner continental shelf of Cape St Francis and the hydroacoustic data were processed using machine learning clustering techniques. The k-means clustering algorithm was used to map the distribution of sediment at different depths. ROV footage was classified using the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) substrata classification scheme. Eight ROV dives along the three transects located off Seal Point, Cape St. Francis Point and within the bay were collected, and ranged from 30 to 80 m in depth. The most common Phyla in order from greatest to least abundance were; Cnidaria, Mollusca, Echinodermata, Chordata (fish species), Arthropoda (Subphylum Crustacea), Bryozoa, Porifera and Chordata (Class Ascidiacea), these were identified both on rocky substrate and sand. The first benthic habitat map of the Cape St. Francis area revealed ten different habitat types, accounting for geology, topography, and sediment cover. This work builds on an ongoing method development that incorporates broader habitat types from a range of substrates, enhancing the robustness of the algorithm, and will aid in improving our current understanding of the relationships between biota and physical habitats along the continental shelf of South Africa.