Direct input of monitoring data into a mechanistic ecological model as a way to identify the phytoplankton growth‑rate response to temperature variations

Abstract

We present an approach (knowledge-and-data-driven, KDD, modeling) that allows us to get closer to understanding the processes that afect the dynamics of plankton communities. This approach, based on the use of time series obtained as a result of ecosystem monitoring, combines the key features of both the knowledge-driven modeling (mechanistic models) and data-driven (DD) modeling. Using a KDD model, we reveal the phytoplankton growth-rate fuctuations in the ecosystem of the Naroch Lakes and determine the degree of phase synchronization between fuctuations in the phytoplankton growth rate and temperature variations. More specifcally, we estimate a numerical value of the phase locking index (PLI), which allows us to assess how temperature fuctuations afect the dynamics of phytoplankton growth rates. Since, within the framework of KDD modeling, we directly include the time series obtained as a result of feld measurements in the model equations, the dynamics of the phytoplankton growth rate obtained from the KDD model refect the behavior of the lake ecosystem as a whole, and PLI can be considered as a holistic parameter.