Bridging the gap in deep seafloor management: Ultra fine-scale ecological habitat characterization of large seascapes

Abstract

The United Nations' sustainable development goal to designate 30% of the oceans as marine protected areas by 2030 requires practical management tools, and in turn ecologically meaningful mapping of the seafloor. Particularly challenging is the mesophotic zone, a critical component of the marine system, a biodiversity hotspot, and a potential refuge. Here, we introduce a novel seafloor habitat management workflow, integrating cm-scale synthetic aperture sonar (SAS) and multibeam bathymetry surveying with efficient ecotope characterization. In merely 6 h, we mapped ~5 km2 of a complex mesophotic reef at sub-metric resolution. Applying a deep learning classifier on the SAS imagery, we classified four habitats with an accuracy of 84% and defined relevant fine-scale ecotones. Visual census with precise in situ sampling guided by SAS images for navigation were utilized for ecological characterization of mapped units. Our preliminary fish surveys indicate the ecological importance of highly complex areas and rock/sand ecotones. These less abundant habitats would be largely underrepresented if surveying the area without prior consideration. Thus, our approach is demonstrated to generate scalable habitat maps at resolutions pertinent to relevant biotas, previously inaccessible in the mesophotic, advancing ecological modeling and management of large seascapes.