The Role of Sea Surface Processes in Anchovy Larvae Distribution in the Strait of Sicily (Central Mediterranean).
Falcini, Federico1; Palatella, Luigi1; Cuttitta, Angela2; Bignami, Francesco1; Patti, Bernardo2; Santoleri, Rosalia1; Fiorentino, Fabio2; Mazzola, Salvatore2
1ISAC-CNR, ITALY; 2IAMC-CNR, ITALY
The European Anchovy (Engraulis encrasicolus, Linnaeus, 1758) is one of the most important resources of the Mediterranean Sea. Despite its relevance, the anchovy population off the Mediterranean coasts exhibits a patchy distribution and, moreover, the influence of environment on its variability is poorly known. We here use data from ichthyoplankton-surveys carried out during the peak spawning season in order to analyze anchovy larvae distribution and aging in the Strait of Sicily, with respect to sea surface currents and hydrographic patterns. The Strait of Sicily dynamics is characterized by upwelling regions, fronts, vortices, and filaments. To investigate the role of these mesoscale features on the anchovy larvae, ichthyoplankton observations were paired to remote sensing data (such as sea surface temperature, chlorophyll, primary production, surface wind speed as well as light attenuation, absorption, and particle backscattering coefficients), and Lagrangian and Eulerian numerical simulations for larval transport and ocean currents, respectively.
The analysis shows and quantifies how the Atlantic Ionian Stream (AIS, a meandering current of Atlantic origin) path and variability, as well as the upwelling-induced south Sicilian coastal current, have consequences for anchovy spawning and larvae distribution. Surface currents transport anchovy larvae towards the Sicilian coast’s south-eastern tip, where larvae are then retained in a frontal structure. However, significant cross-shore transport events due to relatively cold filament-like baroclinic instabilities generated by wind-induced coastal upwelling were also observed. Larval age distribution qualitatively agrees with this transport pattern. All these dynamics are well recognized by the remote sensing data we used.