Automated underwater image analysis reveals sediment patterns and megafauna distribution in the tropical Atlantic

The deep-sea comprises diverse habitats and species whose characterisation provides crucial insights into the health and resilience of our oceans. Whereas direct sampling enables investigation of the vertical variability of the seafloor at small spatial scales, optical imaging allows for multi-scale assessment of the spatial distribution of (mega)benthos and substrates. However, modern seafloor imaging surveys typically generate thousands of images that are infeasible to manual annotation. Consequently, transforming these terabyte-scale datasets into actionable insights requires automated workflows. Here, we deployed two A.I workflows to automate the annotation of substrates and megafaunal taxa in seafloor images from the tropical North Atlantic. Clustering, feature space visualisation and multivariate statistical analysis techniques were used to classify the seafloor into habitats, estimate megafaunal distribution patterns, and to identify environmental drivers that influence observed patterns. We found that the seabed here formed seven clearly distinct clusters, with visible sub-partitions observed in each cluster. Investigations revealed a gradient of sediment disturbance due to biogenic activity, with images showing little-to-no sediment disturbance mapping to one half of the feature space, whereas images exhibiting visibly vigorous sediment reworking mapping to the other half of the feature space. Also, megafaunal abundances were 14 times higher in the shallower Eastern region of the seabed, potentially due to higher Particulate Organic Carbon flux and relatively warmer temperatures. Moreover, geographic clustering of megafauna was observed in topographically complex features such as slopes of submarine canyons and on top of seamounts, where heterogeneity created diverse microhabitats and unique niches that megafauna could exploit.