High seas in the cloud: the role of big data and artificial intelligence in support of high seas governance – The Sargasso Sea pilot - Freestone, David; Bjergstrom, Kieran N.; Gjerde, Kristina M.; Halpin, Patrick; Fleming, Kevin P.; Hudson, Andrew; Rogers, Alex D.; Sapsford, Fae; Tsontos, Vardis M.; Vazquez-Cuervo, Jorge; Vousden, David (Frontiers in Marine Science, 2024)
When the 2023 UN Agreement on Conservation and Sustainable Use of Biodiversity in Areas beyond Nation Jurisdiction comes into force, the Conference of Parties (COP) to the Agreement will have the power to, inter alia, designate marine protected areas (MPAs), and other "area-based management tools" (ABMTs) on the high seas. The designation of large high seas MPAs, which are more than 200 nautical miles from land, will also pose a major challenge to their proponents who will need to plan assessment and management regimes for activities taking place across potentially vast areas of open ocean. This will require exponential access to, and capacity to process, "big data"-from a large number of diverse sources across ecological, economic, scientific, and industrial domains. This article highlights the challenges of future ABNJ governance and the need for a generation shift in sensing and analysis capabilities. It shows how some of these emerging tools have been used in various domains and their advantages as well as some challenges and potential constraints. The experience of the Sargasso Sea project will be used to highlight the role of big data and AI in developing an accurate assessment of a large marine ecosystem (LME).