Automated discovery of a model for dinoflagellate dynamics

The aim of this paper is to discover a model equation for predicting the concentration of the algal species Peridinium gatunense (Dinoflagellate) in Lake Kinneret. This is a rather difficult task, due to the sudden ecosystem changes that occurred in themid-1990s. Namely, the stable ecosystem (with regular Peridinium blooms until 1993) underwent changes and has transformed into an unstable system, with cyanobacterial blooms now occurring regularly. This shift in the algal succession is expected to influence attempts to model the lake ecosystem. Namely, themodel structure before and after the change is likely to be different. Our modelling experiments were directed to discover a single model equation that can simulate dinoflagellate dynamics in both periods. We apply an automatedmodelling tool (Lagramge), which integrates the knowledgeand the data-drivenmodelling approach. In additionwe include an expert visual estimation of the models discovered by Lagramge to assist in the selection of the optimal model. The dataset used included time-series measurements of typical data from the periods 1988 to 1992 and 1997 to 1999. Using the data and expert knowledge coded in amodelling knowledge library, Lagramge successfully discovered several suitable mathematical models for Peridinium. After the expert’s visual estimation and validation of the models, we propose one optimal model capable of long-term predictions. 2010 Elsevier Ltd. All rights reserved.