Ocean Health Science Case Studies

The science case studies will apply data and products developed from earlier work packages in the project (1-3) addressing key science questions relating to open ocean biodiversity. 

Overall lead for this work package (WP4): Dr Dionysios Raitsos.

Phytoplankton Diversity

This case study will investigate phytoplankton taxonomic diversity from pigments related to dynamic ecoregions globally. It will: 
  • Investigate phytoplankton diversity associated with the dynamic seascapes, focussing on taxonomic diversity from pigments.
  • Investigation of dynamic seascapes utility to the understanding of the phytoplankton diversity on carbon budgets - linking with ESA BICEP project
  • Investigation of dynamic seascapes utility to the understanding of the phytoplankton diversity on primary production.
  • Comparison with time series and discrete in situ data
Work package lead: Dr Shubha Sathyendranath (PML)

Southern Ocean Dynamic Seascapes as habitats of krill/salp/copepods

This case study will focus on zooplankton and investigate the following questions:
  • What are the seascapes of existing/proposed Marine Protected Areas (MPAs)?
  • What are the seascapes for krill, salps and copepods?
  • Have these preferred seascapes changed over time?
  • What are the seascapes of krill breeding and predator foraging hotspots?
  • How prevalent are these and are they changing?
  • Can we see a foraging footprint of krill swarms from space?
Work package lead: Dr Angus Atkinson (PML)

Marine Heatwave impacts on phytoplankton indicators and fisheries

This case study will investigate the following:
  • Long term & extreme ocean warming impact on phytoplankton abundance, size & Phenology and Fisheries traits
  • Biodiversity changes within Seascapes during Marine Heatwaves (MHW) in the Atlantic
  • Identify potential vulnerable habitats within Seascapes due to MHWs
Work package lead: Dr Dionysios Raitsos and Dr Sofia Darmaraki (National and Kapodistrian University of Athens)

Artificial Intelligence/Machine Learning and Open Ocean Biodiversity Seascapes

Thsi case study will:
  • Use input datasets (Optical Water Types (OWT), Fronts and Sea Surface Temperature (SST)) and machine learning to incorporate spatiotemporal context into Seascape generation.
  • Compare output with existing products
  • Evaluate accuracy and efficiency of Machine Learning approach.
Work package lead: Dr Dan Clewley (PML)