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

• Applicant: Professor Dr. Agostino Merico
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management ; Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions ; Oceanography
• Term: from 2019 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 412375259

• Final Report Year: 2023

Final Report Abstract

The goal of this project was to understand how top-down and bottom-up mechanisms, and their trait-mediated effects, control compositions and relative abundances of phytoplankton communities in lake environments. We measured environmental drivers and relative phytoplankton abundances in natural lakes at high frequency, and focused in particular on how taxa densities and key morphological traits (i.e., size) relate to nutrients, disturbance and natural enemies. We studied taxa and community dynamics, taxa-environment and trait-environment relationships. We tested hypotheses in community ecology with plankton ecological models to understand the mechanisms of plankton community dynamics and predict community changes across space and time. The goals of this proposal have been fully met.

Publications

• Grazing strategies determine the size composition of phytoplankton in eutrophic lakes. Limnology and Oceanography, 69(4), 933-946.
  To, Sze‐Wing; Acevedo‐Trejos, Esteban; Chakraborty, Subhendu; Pomati, Francesco & Merico, Agostino