Submerged Carbonate Banks Aggregate Pelagic Megafauna in Offshore Tropical Australia

The conservation of marine biodiversity is firmly embedded in national and international policy frameworks. However, the difficulties associated with conducting broad-scale surveys of oceanic environments restrict the evidence base available for applied management in pelagic waters. For example, the Oceanic Shoals Australian Marine Park (AMP) was established in 2012 in a part of Australia’s continental shelf where unique topographic features are thought to support significant levels of biodiversity, yet where our understanding of ecological processes remains limited. We deployed midwater baited remote underwater video systems (midwater BRUVs) in the Oceanic Shoals AMP to provide the first non-extractive baseline assessment of pelagic wildlife communities in the area. We used these observations and high-resolution multibeam swaths of the seafloor to explore potential relationships between prominent geomorphological features and the (i) composition, (ii) richness, and (iii) total abundance of pelagic communities. We documented 32 vertebrate species across three sampling areas, ranging from small baitfish to large sharks and rays, and estimated that up to twice as many taxa may occur within the region as a whole. This highlights the Oceanic Shoals AMP as a reservoir of biodiversity comparable to other documented offshore oceanic hotspots. Our results also confirm the AMP as a possible distant foraging destination for IUCN red listed sea turtles, and a potential breeding and/or nursing ground for a number of charismatic cetaceans. Model outputs indicate that both species richness and abundance increase in proximity to raised geomorphic structures such as submerged banks and pinnacles, highlighting the influence of submarine topography on megafauna distribution. By providing a foundational understanding of spatial patterns in pelagic wildlife communities throughout a little studied region, our work demonstrates how a combination of non-destructive sampling techniques and predictive models can provide new opportunities to support decision-making under data shortage.