Bacterial diversity of 15 extra virgin olive oils, obtained from different Italian varieties, including Frantoio, Coratina, Bosana, and Semidana, was analyzed in this study. All bacterial isolates were genotyped using RAPD and REP-PCR method and grouped by means of cluster analyses. Sequencing of 16S rDNA of 51 isolates, representative of 36 clusters, led to the identification of Bacillus spp., Brevibacillus spp., Micrococcus spp., Staphylococcus spp., Pantoea spp., Kocuria spp., Lysinbacillus spp., and Lactobacillus spp., most of which reported for first time in olive oils. Phenotypic characterization of the 51 isolates, some of which ascribed to potentially probiotic species, indicate that two of them have beta-glucosidase activity while 37% present lipolytic activity. Preliminary evaluation of probiotic potential indicates that 31% of the isolates show biofilm formation ability, 29% acidic pH resistance, and 25% bile salt resistance. Finally, 29% of the isolates were sensitive to antibiotics while the remaining 71%, that include bacterial species well-recognized for their ability to disseminate resistance genes in the environment, showed a variable pattern of antibiotic resistance. The results obtained underline that microbial diversity of extra virgin olive oils represents an unexpected sink of microbial diversity and poses safety issues on the possible biotechnological exploitation of this microbial biodiversity.

Modeling phenological phases in a Mediterranean environment often implies tangible challenges to reconstructing regional trends over heterogenous areas using limited and scattered observations. The present investigation aimed to project phenological phases (i.e., sprouting, blooming, and pit hardening) for early and mid–late olive cultivars in the Mediterranean, comparing two phenological modeling approaches. Phenoflex is a rather integrated but data-demanding model, while a combined model of chill and anti-chill days and growing degree days (CAC_GDD) offers a more parsimonious and general approach in terms of data requirements for parameterization. We gathered phenological observations from nine experimental sites in Italy and temperature timeseries from the European Centre for Medium-Range Weather Forecasts, Reanalysis v5. The best performances of the CAC_GDD (RMSE: 4 days) and PhenoFlex models (RMSE: 5–9.5 days) were identified for the blooming and sprouting phases of mid–late cultivars, respectively. The CAC_GDD model was better suited to our experimental conditions for projecting pit hardening and blooming dates (correlation: 0.80 and 0.70, normalized RMSE: 0.6 and 0.8, normalized standard deviation: 0.9 and 1.0). The optimization of the principal parameters confirmed that the mid–late cultivars were more adaptable to thermal variability. The spatial distribution illustrated the near synchrony of blooming dates between the early and mid–late cultivars compared to other phases.