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Benthic diversity patterns and predictors: A study case with inferences for conservation.
Vassallo, Paolo; Paoli, Chiara; Aliani, Stefano; Cocito, Silvia; Morri, Carla; Bianchi, Carlo Nike.
Affiliation
  • Vassallo P; DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, I-16132 Genova, Italy.
  • Paoli C; DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, I-16132 Genova, Italy.
  • Aliani S; ISMAR (Institute of Marine Sciences), CNR, Forte Santa Teresa, I-19036 Pozzuolo di Lerici, SP, Italy.
  • Cocito S; ENEA (Italian Agency for New Technologies, Energy and Sustainable Economic Development), Marine Environment Research Centre, I-19100 La Spezia, Italy.
  • Morri C; DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, I-16132 Genova, Italy.
  • Bianchi CN; DiSTAV (Department of Earth, Environmental and Life Sciences), University of Genoa, Corso Europa 26, I-16132 Genova, Italy. Electronic address: carlo.nike.bianchi@unige.it.
Mar Pollut Bull ; 150: 110748, 2020 Jan.
Article in En | MEDLINE | ID: mdl-31784263
ABSTRACT
Understanding which drivers cause diversity patterns is a key issue in conservation. Here we applied a spatially explicit model to predict marine benthic diversity patterns according to environmental factors in the NW Mediterranean Sea. While most conservation-oriented diversity studies consider species richness only and neglect equitability, we measured separately species richness, equitability, and 'overall' diversity (i.e., the Shannon-Wiener H' function) on a dataset of 890 benthic species × 209 samples. Diversity values were predicted by means of Random Forest regression, on the basis of 10 factors depth, distance from the coast, distance from the shelf break, latitude, sea-floor slope, sediment grain size, sediment sorting, distance from harbours and marinas, distance from rivers, and sampling gear. Predictions by Random Forests were accurate, the main predictors being latitude, sediment grain size, depth and distance from the coast. Based on predicted values, diversity hotspots were identified as those localities where indices were in the 15% top segment of ranked values. Only a minority of the diversity hotspots was included within the boundaries of the protection institutes established in the region. Marine protected areas are often created in sites harbouring important coastal habitats, which risks neglecting the diversity hidden in the sedimentary seafloor. We suggest that marine protected areas should accommodate portions of sedimentary habitat within their boundaries to improve diversity conservation.
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Full text: 1 Database: MEDLINE Main subject: Ecosystem / Biodiversity Type of study: Prognostic_studies / Risk_factors_studies Language: En Year: 2020 Type: Article

Full text: 1 Database: MEDLINE Main subject: Ecosystem / Biodiversity Type of study: Prognostic_studies / Risk_factors_studies Language: En Year: 2020 Type: Article