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           6                                                                             Ann Microbiol (2015) 65:1–13

           particular, one main cluster including samples from Praja and  products, suggesting that most of first amplification amplicons
           Lido Burrone only, while another one including all samples  were due to amoA-related sequences.
           from Faraglioni and some samples from Lido Burrone, were
           identified. In agreement with CCA, in this latter cluster, all
           upper-line samples from Faraglioni and Lido Burrone grouped  Diversity of 16S rRNA gene amplicon libraries
           together at the Jaccard similarity >0.50. Finally, it is interest-  from Faraglioni beach
           ing to notice, that in both CCA and UPGMA, several triplicate
           samples from the same point along the Y-axis clustered to-  Since T-RFLP analysis showed a pattern of community diver-
           gether at theJaccard similarity > 0.60 (i.e., Faraglioni mid-line,  sity along the Y-axis, we focused on one locality only for an
           Lido Burrone mid-line, Praja upper-line, Praja shore-line, and  in-deep taxonomic investigation by massive sequencing of
           Faraglioni sore-line). To quantitatively evaluate the contribu-  16S rRNA gene amplicons. We selected the locality of
           tion of Y-axis position and of locality (single beach) an  Faraglioni because of the lower anthropic impact and higher
           AMOVA was carried out on T-RFLP profiles. A two-levels  organic carbon present, with respect to the other two localities.
           hierarchical partition (among sampling points and within  A total of 380,080, 391,008, and 382,584 reads for shore-line,
           sampling points partitions) showed that most of the variance  mid-line and upper-line samples, respectively, were analyzed.
           is due to differences between sampling points (73.37%,  Results (Table 3,Fig.S3) indicated differences in the coverage
           P<0.0001). On a three-levels hierarchical analysis  of diversity of samples along the Y-axis. In particular, upper-
           (Table 2), localities and Y-axis similarly contributed to  line was the most diverse, while mid-line and shore-line
           bacterial community differentiation (8.9 and 8.5% of variance,  samples were less diverse. In order to have an idea of the
           respectively).                                     number of unseen taxa present in the samples, the nonpara-
             To evaluate the functionality of bacterial communities in  metric estimator Chao I was also calculated (Table 3),
           terms of performance, some of the steps of the biogeochemical  allowing to show an underestimation for all the three samples
           cycle of nitrogen and of carbon were checked. In particular,  along the Y-axis, which accounted for ∼44 Families.
           three gene fragments, for which “universal” primers have  Considering higher taxonomic levels (order and class), the
           been developed, were chosen: i) nifH (encoding the nitroge-  difference between observed and predicted values of
           nase reductase subunit), ii) nosZ (encoding the nitrous oxide  richness were clearly reduced. In general, Richness and
           reductase gene), and iii) pmoA/amoA (the conserved internal  inverse Simpson indices were in agreement with the
           fragment of particulate methane monooxygenase and ammo-  above mentioned coverage data, showing a profile of
           nia monooxygenase genes). Amplicons were detected only for  increasing diversity along the Y-axis from shore-line to
           pmoA/amoAwith primer pair A189F/A682R in Faraglioni and  upper-line, while Evenness and Shannon-Weaver indices
           Praja shore line samples. Semi-nested reamplification with  have a similar profile and did not allow for resolving
           A189F/mb661 primer pair did not produced amplification  the variability on the Y-axis.


           Table 2 Analysis of Molecular Variance (AMOVA) of T-RFLP profiles for three localities (Lido Burrone, Praja, Faraglioni) and three environments for
           locality (shore-, mid-, upper-line)
           Source of variation            d.f.   Sum of squares  Variance components  Percentage of variation  P-value

           a) Localities
             Among localities             2      25.704         0.37860            8.87                <0.2 (n.s.)
             Among environments within locality  6  56.667      2.77778            65.09               <0.0001
             Within locality              18     20.000         1.11111            26.04               <0.0001
             Total                        26     102.370        4.26749            F ST =0.73963
           b) Environments
             Among environments           2      25.926         0.37037            8.57                <0.2 (n.s.)
             Among locality within environment  6  57.778       2.83951            65.71               <0.0001
             Within environment           18     20.000         1.11111            25.71               <0.0001
             Total                        26     103.704        4.32099            F ST =0.71875

           AMOVAwas performed attributing the following two types of groupings: Computation a) is made with T-RFLP profiles grouped according to localities.
           Computation b) is made with T-RFLP profiles grouped according to the environment. For each grouping the percent of the total variance observed was
           attributed to the two hierarchical partitions. Data show the degrees of freedom (d.f.), the sum of squared deviation, the variance component estimate, the
           percentage of total variance contributed by each component, the F ST statistics (Fixation index), and the probability (P) of obtaining a more extreme
           component estimate by chance alone estimated computing 10,000 permutations; n.s., not significant
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