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
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
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
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)