Seasonal patterns of biodiversity in Mediterranean coastal lagoons

Aim Understanding and quantifying the seasonal patterns in biodiversity of phytobenthos, macro-zoobenthos and fishes in Mediterranean coastal lagoons, and the species dependence upon environmental factors. Location The study was carried out in the Stagnone di Marsala e Saline di Trapani e Paceco, the largest coastal lagoon system in the central Mediterranean Sea (Sicily, Italy), a Special Protection Area located along one of the central ecological corridors joining Africa and Europe. Methods The coastal lagoon system was selected as a model ecosystem to investigate the seasonal variations in biodiversity indices and dominance-diversity relationships in phytobenthos, macro-zoobenthos and fishes, and how seasonal variations in temperature, salinity, depth, inorganic and organic suspended matter affect the abundance of the species constituting these communities. Models of ecosystem structure, describing the interactions among functional groups and environmental variables, were also developed using confirmatory path analysis and artificial neural networks to exemplify their application in predicting temperature-driven alterations. Results Wide seasonal variations in biodiversity indices and dominance-diversity relationships across the communities of the coastal lagoon system were observed, driven by the dynamics in climate and resource availability. The effects of the environmental variables on taxon abundances varied in relation to the community, with the widest responses elicited in phytobenthos and fishes. Temperature was the main variable affecting taxon abundances in macro-zoobenthos and was also the major driver of shallow water ecosystem structure. Main conclusions This research shed light on the seasonal variations in biodiversity of Mediterranean coastal lagoons, elucidating also the tight dependence of phytobenthos, macro-zoobenthos and fish diversity upon environmental factors. The findings and the methodological approach proposed may be crucial in developing models able to predict future climate-driven alterations in communities inhabiting these important and threatened ecosystems.