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handle: 10261/245714
The Physalia physalis sighting database could be accessed and downloaded at (Prieto, 2021; https://doi.org/10.20350/digitalCSIC/13920) while the full Physalia-SIM dataset and extraction tools are publicly available at (https://jeodpp.jrc.ec.europa.eu/ftp/public/JRC-OpenData/MMF-BLUE2/PhysaliaSIM/). During recent years, the oceanic siphonophore Physalia physalis has repeatedly entered the Mediterranean Sea through the Strait of Gibraltar, being successively transported and distributed to different regions of that basin. When these floating colonies arrive to coastal areas during peak tourism periods there are large economic and health costs. Their highly venomous nature causes the closure of beaches and coastal attractions, creating a myriad of problems for local and regional authorities throughout the Mediterranean Sea. Many of these problems could be minimized or totally avoided if early warning of P. physalis arrivals to Mediterranean coasts could be issued. In this work, advanced particle tracking Lagrangian models were applied to simulate the dispersion and beaching of P. physalis colonies within the Mediterranean. Observations from two high-presence years (2010 and 2013) were used as calibration dataset and an additional high-abundance record (2018) was employed as validation for the models. The calibrated and validated model set-up was used to construct a statistical inference dataset and extraction tool (Physalia-SIM) that allowed assessing the likelihood of P. physalis arrival to any given coastal region of the Mediterranean Sea (with 97% accuracy) only by knowing their entrance time through the Strait of Gibraltar. The Physalia-SIM is a free-access, easily-useable tool by any stakeholder interested in knowing the probability for P. physalis presence in their particular region of interest. Moreover, this tool can help to provide warning as early as 3–4 months before the actual P. physalis presence is likely to occur. By making use of this prognosis tool, local and regional managers and stakeholders could take the necessary actions in order to minimize the economic and health impacts of the presence of these organisms in their coastlines. This work was performed within the Project Agreement “Sistema de Observación y Predicción de Medusas en el Mar Balear” among Govern des Illes Balears, SOCIB and CSIC (Disposición 15052, BOE núm. 310, 2020). Peer reviewed
Coastal management, Physalia physalis, Mediterranean sea, Mediterranean Sea, Lagrangian modeling, Risk assessment
Coastal management, Physalia physalis, Mediterranean sea, Mediterranean Sea, Lagrangian modeling, Risk assessment
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 18 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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