From global climate variability to local blooms: the role of ENSO in the bloom dynamics of Ostreopsis cf. ovata in the Mediterranean
Over the last two decades, blooms of the benthic dinoflagellate Ostreopsis cf. ovata have become a recurring phenomenon along the Mediterranean coast, with ecological and health implications linked to the production of ovatoxins and the exceeding of risk thresholds. Although sea surface temperature is one of the main factors regulating the development of these blooms, the role of large-scale climatic drivers in modulating their intensity and interannual variability remains unclear.
This study analysed twenty years of monitoring data (2006–2026) collected along the Genoa coastline, integrating epiphytic and planktonic concentrations of O. cf. ovata with ENSO (El Niño-Southern Oscillation) indices, with the aim of determining whether the different phases of the phenomenon are associated with variations in the frequency and intensity of blooms and in the exceeding of the operational thresholds established for public health monitoring. The hypothesis is that El Niño and La Niña events, through their influence on atmospheric circulation, the position of subtropical high-pressure systems and the persistence of high-pressure conditions in the Mediterranean basin, may alter the local thermal and meteomarine regime, creating conditions that are differently favourable to the development of blooms.
The time series of abundances were compared with the intensity and duration of the various ENSO phases, assessing the relationships with sea surface temperature anomalies, the frequency of marine heatwaves and the number of occasions on which alert and emergency thresholds were exceeded.
The aim is to determine whether a global climate index could serve as an early indicator of the likelihood of O. cf. ovata blooms occurring in the north-western Mediterranean. The identification of any statistically significant relationships could help to improve seasonal forecasting of bloom events and to complement monitoring systems that are currently based solely on local environmental parameters.