Detecting drivers of Ostreopsis blooms through modellistic approach

Blooms of toxic dinoflagellates belonging to the genus Ostreopsis in coastal areas are a topic of increasing interest due to the potential hazard that these species might pose to marine organisms and to human health, and the consequent negative effect on tourism, fishery and aquaculture economy. For the last 15 years, blooms of Ostreopsis species have been observed in temperate and tropical coastal waters in both the northern and southern hemispheres. Despite their ecological, sanitary and economic relevance, particularly in touristic areas, Ostreopsis bloom dynamics are still poorly known and the mechanisms of bloom development are unclear, particularly in terms of which environmental/meteorological variables trigger bloom events. These mechanisms are investigated by modeling the concentration of Ostreopsis cf ovata in seawater in response to an array of environmental and meteorological variables. The best model among multiple linear regressions, generalized linear models, mixed models and generalized linear mixed models is chosen according to its Akaike Information Criterion. This model allows us to define a microalgal bloom event, considering the magnitude of the increase of cells concentration in seawater over time. This definition is in turn applied within a meta-analysis framework to discriminate between bloom and non-bloom conditions and detect which environmental/meteorological variables are driving the bloom event on a global scale. A foreseeable future application of the chosen model is in the field of forecasting Ostreopsis bloom events from a coastal management point of view. This tool would represent one of the major achievements in the framework of M3-HABs, a EU funded project (ENPI-CBCMED program) coordinated by CoNISMa aimed at developing a pan-Mediterranean strategy for monitoring, modeling and implementing mitigation measures to manage Ostreopsis blooms along Mediterranean coasts.