At family level, 85% of the assignments are coincident between both approaches. OTUs were classified by extracting a consensus from the taxonomic assignments of their individual sequences. The objective was to find the taxon that dominates at the lowest possible taxonomic rank, fulfilling
the following criteria: having more than five sequences in the OTU, and being the only taxon with at selleck chemicals llc least 25% of the sequences of the OTU assigned to it. The usage of either RDP or Greengenes assignments produced coincident assignments for 91% of the instances, and does not alter the results significantly. Unless stated otherwise, the results shown correspond to RDP assignments. Collector’s curves To create collector’s curves for the distribution LB-100 chemical structure of OTUs in environments, a single metasample was created for each environment, pooling together all the sequences from the samples corresponding to it. We simulated the sampling
of the metasample by picking up individual sequences randomly, with non-replacement. To produce the curve, we checked whether another sequence for the corresponding OTU had already been seen or not. The simulated sampling continued until no sequences were left. The full procedure was repeated ten times, and the individual curves were averaged to obtain a final result. Statistical analyses We computed a two-way table with the number of different OTUs per taxa and environment. To assess the level of bacterial biodiversity of the different environment types and the
degree of ubiquity of the taxa considered, we computed Hill biodiversity numbers  using this abundance community matrix for both taxa and environments, respectively. We considered Hill numbers for the scale values 0, 1 and 2 which, for a given environment, for example, correspond to the total number of families, the exponential of the Shannon index of biodiversity, and the inverse Simpson index. Exploratory data analyses revealed that those environments with more samples Galeterone tended to have more OTUs. To remove this ‘size’ effect, we transformed the data by dividing the buy PF-4708671 frequencies in each column by the number of samples in that environment, thus creating a community matrix which contained the average number of OTUs per sample for each taxa and environment type. We then carried out a Detrended Correspondence Analysis (DCA) to explore the variation in the transformed abundance matrix. We also fitted a Bayesian hierarchical model to the original community matrix in order to quantify the affinity between taxa and environments. In the first layer, our model assumes a Poisson distribution for the number of OTUs Yij observed in the taxonomic family i and environment type j.