Spatial patterns and temporal variability of seagrass connectivity in the Mediterranean Sea

Aim: The endemic seagrass Posidonia oceanica is a key component of the coastal seascapes of the Mediterranean Sea, where it provides crucial ecosystem services and promotes the assembly of diverse ecological communities. Although protection policies exist, P. oceanica meadows have been steadily declining in the recent past because of human activities and climate change. Here, we quantitatively analyse basin-wide patterns of seagrass connectivity over a 30-year-long period and identify connectivity hotspots that may serve as priority targets for conservation actions. Location: Mediterranean Sea. Time period: 1987–2016. Major taxa studied: The seagrass P. oceanica. Methods: A biophysical Lagrangian approach is used to simulate dispersal of seagrass fruits operated by marine currents. Connectivity metrics (self-retention, indegree and outdegree) are evaluated on top of Lagrangian simulations to identify the most ecologically connected areas. Time series of local connectivity scores are analysed to study temporal variability and possibly detect trends at different spatial scales. Results: Spatio-temporal variability is an important component of seagrass connectivity in the Mediterranean. Connectivity hotspots are unevenly distributed in all of its four main sub-basins, and along both European and African coastlines. Although statistically significant local trends in connectivity are generally quite infrequent across the whole basin, they appear to be relatively more prevalent in connectivity hotspots. The interannual variability of average connectivity scores seems to be at least partially linked to meteorological fluctuations. Main conclusions: The present study represents a step forward in the application of a quantitative, scalable and replicable methodological framework for the prioritization of seagrass conservation actions in the Mediterranean large marine ecosystem, a challenging environment characterized by complex socio-economic boundary conditions and high sensitivity to the localized effects of global climate change.