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Title: Coralligenous and maerl habitats : predictive modelling to identify their spatial distributions across the Mediterranean Sea
Authors: Martin, Corinne Sophie
Giannoulaki, Marianna
De Leo, Francesco
Scardi, M.
Salomidi, Maria
Knittweis, Leyla
Pace, Marie Louise
Garofalo, Germana
Gristina, Michele
Ballesteros, Enric
Bavestrello, Giorgio
Belluscio, Andrea
Emma, Cebrian
Gerakaris, Vasilis
Pergent, Gerard
Pergent-Martini, Christine
Schembri, Patrick J.
Terribile, Kimberly
Rizzo, Lucia
Ben Souissi Souilah, Jamila
Bonacorsi, Marina
Guarnieri, Giuseppe
Krzelj, Maja
Macic, Vesna
Punzo, Elisa
Valavanis, V.
Fraschetti, Simonetta
Keywords: Ecology -- Mediterranean Sea
Algae -- Mediterranean Sea
Biodiversity -- Mediterranean Sea
Issue Date: 2014
Publisher: Nature Publishing Group
Citation: Martin, C. S., Giannoulaki, M., De Leo, F., Pace, M. L., Scembri, P. J., Terrible, K.,…& Fraschetti, S. (2014). Coralligenous and maerl habitats : predictive modelling to identify their spatial distributions across the Mediterranean Sea. Scientific Reports, 4, 5073.
Abstract: Bioconstructions such as coralligenous outcrops and maërl beds are typical Mediterranean underwater seascapes. Fine-scale knowledge on the distribution of these sensitive habitats is crucial for their effective management and conservation. In the present study, a thorough review of existing spatial datasets showing the distribution of coralligenous and maërl habitats across the Mediterranean Sea was undertaken, highlighting current gaps in knowledge. Predictive modelling was then carried out, based on environmental predictors, to produce the first continuous maps of these two habitats across the entire basin. These predicted occurrence maps for coralligenous outcrops and maërl beds provide critical information about where the two habitats are most likely to occur. The collated occurrence data and derived distribution model outputs can help addressing the challenge of developing basin-wide spatial plans and to guide cost-effective future surveys and monitoring efforts towards areas that are presently poorly-sampled.
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