Computational protocol: Disturbance Regimes Predictably Alter Diversity in an Ecologically Complex Bacterial System

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[…] QIIME (Quantitative Insights into Microbial Ecology, v.1.7.0; was used to filter reads and cluster operational taxonomic units (OTUs) as described previously (, ). Briefly, we used the open reference OTU picking script ( (), whereby sequences were first clustered with the Greengenes (May 2013) reference database (); OTUs that did not cluster with known taxa (at 97% identity) in the database were then clustered de novo. Singleton sequences were removed prior to downstream analyses. Representative sequences for each remaining OTU were aligned using PyNAST, with a minimum alignment overlap of 75 bp (), and a phylogenetic tree was estimated using FastTree v2.0 (). Taxonomic assignments were made using the RDP classifier (). We computed alpha diversity using the script in QIIME, normalizing sequencing depth across samples. Significant differences in alpha diversity were assessed using a Student’s t test (two-tailed, assuming unequal variances). We used QIIME’s script to compute beta diversity distances between samples and to construct principal coordinate plots using the weighted UniFrac distance metric (), which accounts for both the phylogenetic composition and the relative abundance of taxa. The composition of the heterotroph community was reproducibly characterized at a sampling depth of 430 sequences per sample (see in the supplemental material). We tested for significant beta diversity clustering using the nonparametric multiresponse permutation procedure (MRPP), which determines significant differences in sample groupings in multivariate space. Significant differences in intrareplicate variance were assessed with permutational analysis of multivariate dispersions (PERMDISP). Taxonomic summaries were generated using the script. Plotting was carried out using the Matplotlib graphics library in Python (). […]

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