Modelling seabirds biodiversity through Bayesian Spatial Beta regression models: A proxy to inform marine protected areas in the Mediterranean Sea.

Seabirds are bioindicators of marine ecosystems health and one of the world's most endangered avian groups. The creation of marine protected areas plays an important role in the conservation of marine environment and its biodiversity. The distributions of top predators, as seabirds, have been commonly used for the management and creation of these figures of protection. The main objective of this study is to investigate seabirds biodiversity distribution in the Mediterranean Sea through the use of Bayesian spatial Beta regression models. We used an extensive historical database of at-sea locations of 19 different seabird species as well as geophysical, climatology variables and cumulative anthropogenic threats to model species biodiversity. We found negative associations between seabirds biodiversity and distance to the coast as well as concavity of the seabed, and positive with chlorophyll and slope. Further, a positive association was found between seabirds biodiversity and coastal impact. In this study we define as hot spot of seabird biodiversity those areas with a posterior predictive mean over 0.50. We found potential hot spots in the Mediterranean Sea which do not overlap with the existing MPASs and marine IBAs. Specifically, our hot spots areas do not overlap with the 52.04% and 16.87% of the current MPAs and marine IBAs, respectively. Overall, our study highlights the need for the extension of spatial prioritization of conservation areas to seabirds biodiversity, addressing the challenges of establishing transboundary governance.

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Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.