No abstract

Acidobacteria are ubiquitous and abundant members of soil bacterial communities. However, an ecological understanding of this important phylum has remained elusive because its members have been difficult to culture and few molecular investigations have focused exclusively on this group. We generated an unprecedented number of acidobacterial DNA sequence data using pyrosequencing and clone libraries (39 707 and 1787 sequences, respectively) to characterize the relative abundance, diversity and composition of acidobacterial communities across a range of soil types. To gain insight into the ecological characteristics of acidobacterial taxa, we investigated the largescale biogeographic patterns exhibited by acidobacterial communities, and related soil and site characteristics to acidobacterial community assemblage patterns. The 87 soils analyzed by pyrosequencing contained more than 8600 unique acidobacterial phylotypes (at the 97% sequence similarity level). One phylotype belonging to Acidobacteria subgroup 1, but not closely related to any cultured representatives, was particularly abundant, accounting for 7.4% of bacterial sequences and 17.6% of acidobacterial sequences, on average, across the soils. The abundance of Acidobacteria relative to other bacterial taxa was highly variable across the soils examined, but correlated strongly with soil pH (R ¼ À0.80, Po0.001). Soil pH was also the best predictor of acidobacterial community composition, regardless of how the communities were characterized, and the relative abundances of the dominant Acidobacteria subgroups were readily predictable. Acidobacterial communities were more phylogenetically clustered as soil pH departed from neutrality, suggesting that pH is an effective habitat filter, restricting community membership to progressively more narrowly defined lineages as pH deviates from neutrality.

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

Recent studies have highlighted the surprising richness of soil bacterial communities; however, bacteria are not the only microorganisms found in soil. To our knowledge, no study has compared the diversities of the four major microbial taxa, i.e., bacteria, archaea, fungi, and viruses, from an individual soil sample. We used metagenomic and small-subunit RNA-based sequence analysis techniques to compare the estimated richness and evenness of these groups in prairie, desert, and rainforest soils. By grouping sequences at the 97% sequence similarity level (an operational taxonomic unit [OTU]), we found that the archaeal and fungal communities were consistently less even than the bacterial communities. Although total richness levels are difficult to estimate with a high degree of certainty, the estimated number of unique archaeal or fungal OTUs appears to rival or exceed the number of unique bacterial OTUs in each of the collected soils. In this first study to comprehensively survey viral communities using a metagenomic approach, we found that soil viruses are taxonomically diverse and distinct from the communities of viruses found in other environments that have been surveyed using a similar approach. Within each of the four microbial groups, we observed minimal taxonomic overlap between sites, suggesting that soil archaea, bacteria, fungi, and viruses are globally as well as locally diverse.

In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.