State-of-the-art and recent progress in phytoplankton succession modelling

Abstract

Dynamic phytoplankton succession models are an essential instrument to improve scientific knowledge on the development of algal blooms characterized by a specific composition and to support water quality management decisions. The peculiar structure and formulation of these models generate questions that differ from the ones found in modelling eutrophication and are related to simulation of multiple phytoplankton groups. In this work, a classification of phytoplankton models simulating several algal groups is provided. Coupled succession models, explicitly describing nonlinear interactions between physical and biological processes and capturing the response of phytoplankton community to environmental changes, are analyzed in detail. Approaches, actual achievements, and developments of succession models are examined. In particular, we discuss the level of discrimination adopted, number and type of algal groups simulated, biomass unit employed, type of model evaluation used, and efficacy of prediction achieved. Simulations of multiple phytoplankton group behaviour still produce significant deviations over time or in magnitude compared to the patterns observed. Frequently, goodness-of-fit estimation is only graphical and statistics adopted do not allow a direct comparison between different models. To facilitate comparisons we propose the use of a common statistic that would be applied, separately, to all the phytoplankton groups differentiated in each model. Each model’s level of complexity in relation to prediction ability is also analyzed. Through this work we aspire to orient upcoming works and encourage others to apply mechanistic succession models, including the description of physical and biological relationships, specific phytoplankton behaviour and interactions between phytoplankton groups.