TRAITZOO adopts a trait-based approach for exploiting recent technological advances in high throughput observation of marine mesozooplankton (omics, imaging) and machine learning for data analysis and modelling, in order to study MZP functional diversity, trait biogeography and its crucial links to ecosystem functioning.

Through this project we seek to provide the first comprehensive characterization of the impact of environmental and anthropogenic drivers on mesozooplankton functional traits and functional diversity. More specifically, our 3 objectives are: O1) To develop ML methods to quantify key MZP traits relevant for energy transfer and trophic quality (e.g., lipid content, colour, trophic regime) from imaging and omics; O2) To estimate functional traits of MZP communities from large datasets available at various spatio- temporal scales (local, regional, global) and describe the distribution of MZP functional traits and diversity in relation with abiotic drivers and ecosystem functioning (i.e., biomass production, carbon export); O3) To develop new functional trait-based models of MZP using data-driven trait information.