Integrating High Resolution Monitoring and Trait-based Modelling to Understand and Predict Phytoplankton Dynamics (AQUASCOPE)

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Plankton community dynamics are controlled by bottom-up (water physics and chemistry) and top-down (natural enemies) drivers. However, the relative importance and direction of these effects on taxa composition and relative abundances are not well established in natural communities: they vary in time and space and also depend on trait-mediated physiological and ecological interactions. The goal of this project is to quantify the effects of interacting controls (e.g. temperature, turbulence, nutrient supply, grazer identity, density and prey selectivity) on lake phytoplankton, within taxonomic and size-based categories, in order to (re)design trait-based theoretical and data-driven models that will allow accurate prediction of plankton food-web changes ---and therefore ecosystem processes and services-- across environmental gradients in space and time. This project features three interconnected work packages (WPs): 1) application of new methods for in situ monitoring, 2) data analysis (exploration of patterns, hypothesis testing and analysis of drivers), and 3) trait-based modelling (development and testing of theories, predictions over space and time). This project will evaluate underwater imaging as a new tool for research and routine lake plankton monitoring. The data to be obtained will allow us to refine concepts and theories in community ecology, particularly of how chemistry, physics and species interactions can the shape dynamics of phytoplankton communities over time and space, using a trait-based framework. Quantitative understanding of drivers and mechanisms that control community structure and abundances will allow us to make forecasts of changes in plankton biodiversity across environmental gradients, and of algal blooms.