Even though much progress has been made recently in modeling planktonic communities, uncertainties remain large due to the countless types of planktonic species and the many interacting processes at work. Model formulations, including competition within and among trophic compartments, are often based on empirical relationships, which imply an excessive degree of parameterization and restrictive assumptions. We introduce adaptive modeling as an extension of data assimilation to the selection of model structures. Based on misfits between model predictions and observed data, adaptive modeling identifies model structures that need to be improved, estimates those improvements, and corrects the model accordingly. The model changes and learns from data providing more realistic predictions and selecting the most adequate model formulations that describe the system. We present an application of this new methodology aimed to identify the inter- and intra-specific competition processes that determine the population dynamics of a phytoplankton species (Alexandrium minutum) in a semi-enclosed site and the effects on bloom development

Aquatic Sciences Meeting, Aquatic Sciences: Global And Regional Perspectives - North Meets South, 22-27 February 2015, Granada, Spain

Peer Reviewed