Computational Modeling of Phytoplankton Population Dynamics

Our research bridges the gap between observational data and computational modeling to understand phytoplankton population dynamics.

We are developing iinovative computational approaches, including matrix population models, to investigate the effects of environmental change on phytoplankton. These models leverage high-resolution observations from SeaFlow to track key phytoplankton activities including growth, mortality, carbon fixation, respiration, and exudation across various size classes.

Our research has highlighted the importance of light in synchronizing Prochlorococcus growth and mortality, ontributing to the stability of the marine food web. We also identified critical temperature thresholds for their growth and overall productivity. This integrated approach, combining observational datasets with computational modeling, ultimately improves our ability to predict the consequences of changing ocean conditions on marine ecosystem structure and function.