The Zoo and Phytoplankton EOV Products Virtual Lab is developed by the Flanders Marine Institute (VLIZ), in collaboration with the Faculty of Science and Engineering at Sorbonne University and GeoHydrodynamics and Environment Research (GHER) at the University of Li├Ęge.

This demonstrator aims to provide a methodology to generate:

  • zooplankton products based on in situ observations of the abundance of different zooplankton species in a region encompassing the North-East Atlantic;
  • global ocean three-dimensional (3D) key phytoplankton products of chlorophyll-a (Chla) concentration, as a proxy for total phytoplankton biomass and phytoplankton functional types, as a proxy for phytoplankton diversity;
  • a mechanistic model using near real-time data to quantify the relative contributions of the bottom-up and top-down drivers in phytoplankton dynamics.

The Zoo and Phytoplankton EOV demonstrator provides a description of the current state of the plankton communities and forecasts their evolution, representing valuable information for the modelling, assessment and management of the marine ecosystem. It is useful for a variety of communities:

  • Fisheries advisory organisations can use these plankton products to study the availability of food resources for fish stocks and assess the effects on fish stocks.
  • Marine policy officers will have the needed support to address threats such as food insecurity, as foreseen under the EU Biodiversity Strategy for 2030.
  • Fundamental research (e.g. researchers and consultants from environmental agencies) contributing to the understanding of the environmental conditions and top-down factors at new scales of observations (e.g. regional/global, seasonal and time series).


A complete workflow using the DIVAnd software tool (Data Interpolating Variational Analysis in n dimensions) to create interpolated maps of zooplankton abundances. The service is provided as a set of Jupyter notebooks that describe the full procedure to create the final, gridded products. The main dataset for this service is the abundance data, consisting of a set of positions (longitude, latitude) and the bathymetry and distance to nearest coastline datasets used for the interpolation.

A service implementing a methodology to produce phytoplankton EOV products, i.e. global 3D products, by using machine learning-based methods. An artificial neural network (Multi-Layer Perceptrons, MLPs) is trained to produce a vertical distribution of Chla concentrations.

A workflow to run a mechanistic model analysis, using near-real-time data to quantify the relative contributions of the bottom-up and top-down drivers in phytoplankton dynamics. The Nutrient, Phytoplankton, Zooplankton and Detritus (NPZD) model used in this demonstrator has been created by Soetaert and Herman (2009). The workflow is provided in R markdown documents and uses parallel computing on the VRE.