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

The deep sea environment comprises diverse flora, fauna and habitats, whose characterisation is key towards our collective understanding of ocean health and resilience. Whereas direct sampling allows for detailed investigation of the vertical variability of seabed characteristics at small spatial scales, optical imaging is suitable for high-resolution assessment of the spatial distribution of habitats and their benthic megafauna across multiple scales. These assessments are typically facilitated by scientific expeditions that survey extensive seabed areas using e.g. continuous imaging techniques, generating huge volumes of high-resolution images for which manual inspection and annotation is costly, non-scalable and therefore infeasible. Transforming these terabyte-scale images (and videos) into actionable insights requires automated workflows that expedite both the generation of baseline information, as well as downstream spatial-ecological analysis. Here, we deployed two A.I workflows to automate the annotation of seabed substrates and megafaunal taxa from still images, which we acquired during seven camera deployments along an 18° N East-West section in the tropical Atlantic north of Cabo Verdes. We manually inspected the auto generated annotations for quality, and subsequently assigned them semantic labels. Thereafter, we used clustering, feature space visualisation and multivariate statistical analysis techniques to classify the seafloor into habitats, estimate megafaunal abundance and spatial distribution patterns, as well as environmental drivers that influence the identified patterns. Our results show that the seabed can be partitioned into seven clearly distinct clusters, with each of these clusters showing visible sub-partitions. Investigations revealed a clear gradient in terms of sediment disturbance due to biogenic activity, with images showing little-to-no sediment disturbance grouping together on one half of the feature space, whereas those images with visibly vigorous signs of sediment reworking clustered on the other half. Our results also show that megafaunal abundance was on average 14 times higher in the Eastern region of our study area, which was approximately 700 metres shallower and closer to shore than the Western region. This observed high abundance could be attributed to higher POC flux that transports more organic matter to the shallower seabed, as well as due to relatively warmer temperatures that enhance metabolic rates of benthic fauna. Our results further reveal geographic hotspots of megafauna in topographically complex features such as the sides of a submarine canyon and the top of seamounts. The complex topography of these features introduces heterogeneity that creates diverse microhabitats and unique niches that megafauna exploit. Finally, we observed that while co-varying depth and longitude variables generally explained the separation between the two main megafaunal communities in our East-West oriented working area, bathymetric drivers like slope and ruggedness had a more pronounced influence in the deeper Western region (-3698m) compared to the shallower Eastern region (-2477m deep). Collectively, these findings demonstrate that the integration of A.I workflows into classical spatio-ecological methods does expedite the transformation of large volumes of marine image datasets into actionable insights, thereby significantly contributing to our understanding, monitoring and sustainable use of ocean resources.