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In the present work, culture-based and culture-independent investigations were performed to determine the microbiota structure of the coelomic fluid of Mediterranean sea urchin Paracentrotus lividus individuals collected from two distinct geographical sites neighboring a high-density population bay and a nature reserve, respectively. Next Generation Sequencing analysis of 16S rRNA gene (rDNA) showed that members of the Proteobacteria, Bacteroidetes and Fusobacteria phyla, which have been previously reported to be commonly retrieved from marine invertebrates, dominate the overall population of microorganisms colonizing this liquid tissue, with minority bacterial genera exhibiting remarkable differences among individuals. Our results showed that there is a correlation between microbiota structure and geographical location of the echinoderm collection site, highlighting over-representation of metagenomic functions related to amino acid and bioactive peptides metabolism in specimens inhabiting the nature reserve. Finally, we also described the developmental delay and aberrations exhibited by sea urchin embryos exposed to distinct bacterial isolates, and showed that these defects rely upon hydrophilic compound(s) synthesized by the bacterial strains assayed. Altogether, our findings lay the groundwork to decipher the relationships of bacteria with sea urchins in their aquatic environment, also providing an additional layer of information to understand the biological roles of the coelomic fluid.

The GIS-based open source software r.slope.stability computes broad-scale spatial overviews of shallow and deep-seated slope stability through physically-based modelling. We focus on the landslide-prone 90 km2 Collazzone area, central Italy, exploiting a comprehensive set of lithological, geotechnical and landslide inventory data available for that area. Inevitably, the geotechnical and geometric parameters are uncertain, particularly for their three-dimensional variability. Considering the most unfavourable set of geotechnical parameters (worst case scenario, appropriate for engineering purposes) is less useful to obtain an overview of the spatial probability (susceptibility) of landslides over tens of square kilometres. Back-calculation of the parameters based on topographic and geotechnical considerations would better suit for such a purpose, but obtaining one single parameter combination would require information on one of the parameters. Instead, we estimate the slope failure probability by testing multiple combinations of the model parameters sampled deterministically. Our tests indicate that (i) the geotechnical parameterization used allows to reproduce the observed landslide distribution partly (a challenge consists in the appropriate treatment of the variation of the geotechnical parameters with depth); (ii) the evaluation outcome depends strongly on the level of geographical aggregation; and (iii) when applied to large study areas, the approach is computing-intensive, and requires specific strategies of multi-core computing to keep computational times at an acceptable level.

The GIS-based open source software r.slope.stability computes broad-scale spatial overviews of shallow and deep-seated slope stability through physically-based modelling. We focus on the landslide-prone 90 km2 Collazzone area, central Italy, exploiting a comprehensive set of lithological, geotechnical and landslide inventory data available for that area. Inevitably, the geotechnical and geometric parameters are uncertain, particularly for their three-dimensional variability. Considering the most unfavourable set of geotechnical parameters (worst case scenario, appropriate for engineering purposes) is less useful to obtain an overview of the spatial probability (susceptibility) of landslides over tens of square kilometres. Back-calculation of the parameters based on topographic and geotechnical considerations would better suit for such a purpose, but obtaining one single parameter combination would require information on one of the parameters. Instead, we estimate the slope failure probability by testing multiple combinations of the model parameters sampled deterministically. Our tests indicate that (i) the geotechnical parameterization used allows to reproduce the observed landslide distribution partly (a challenge consists in the appropriate treatment of the variation of the geotechnical parameters with depth); (ii) the evaluation outcome depends strongly on the level of geographical aggregation; and (iii) when applied to large study areas, the approach is computing-intensive, and requires specific strategies of multi-core computing to keep computational times at an acceptable level.