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1 Supporting Information Disturbance Opens Recruitment Sites for Bacterial Supporting Information Disturbance opens recruitment sites for bacterial colonization Vuono et al. SI Materials and Methods Biodiversity analysis. Biodiversity analyses were accomplished in the R environment for statistical computing (http://www.r-project.org/). Briefly, the Vegan package (1) was used to calculate Hill diversities of orders 0≤q≤2, and permutational multivariate analysis of variance (MANOVA or ADONIS) with 999 permutations to test for significant differences in community structure between treatment groups. Hill diversities are interpreted as the effective number of species, or a community of S equally abundant species; these diversity measurements produce ecologically intuitive quantities that possess mathematical doubling properties. Thus, diversity can be compared on a linear scale that implies the magnitude of difference between community assemblages (2, 3). Multivariate cutoff level analysis (MultiCoLA) was performed with Spearman correlation coefficients between full and reduced datasets (singletons excluded) as described by (4). To assess shared bacterial diversity between influent and sludge samples, a dendrogram displaying the clustering pattern of bacterial OTUs was constructed by the complete linkage hierarchical clustering algorithm in hcluster (Damian Eads, code.google.com/p/scipy-cluster) as described by (5). The corresponding heatmap (Figure 4), which displays relative abundance of bacterial OTUs in the dendrogram, was constructed with custom Python scripts from (5) that incorporated Python modules from Matplotlib (6). The software package Explicet v2.9.4 (www.explicet.org) was used to calculate Two-Part statistics (7) and generate figures (8). 1 Null model consideration. The standardized effect size of each community’s mean phylogenetic distance (MPD) was estimated (Picante package in R; (9)) by comparing it to the rarified values of 999 randomized communities generated from null models as described by (10). Because each null model varies in its statistical power to detect niche-based community assembly, three different randomization algorithms were used to test the agreement between them: (1) the taxa.labels algorithm (labels of a phylogenetic distance matrix are shuffled); (2) the independent swap algorithm (the taxa/sample table is shuffled); and (3) the phylogeny.pool algorithm (the phylogenetic distance matrix is randomly sampled to populate the taxa/sample table). In null model 1, diversity was calculated both by weighting the relative number of sequences obtained for each OTU as either 0 or 1 (presence/absence) or as pi, the proportional relative abundance of the OTU in its respective sample. Samples were considered significantly overdispersed or clustered (α = 0.05) if they fell above or below 95% of the randomized communities’ values, respectively. Network analysis. Network-based analysis was accomplished on a subset of the OTU table, where only OTUs with ≥150 sequences were retained for downstream analysis. The number of OTUs retained represented the majority of the influent OTUs (~2.3%) with consistent abundance patterns throughout the study period. The resulting OTU table was passed to the ‘make_otu_network.py’ script in QIIME to visualize clustering of samples based on shared OTUs (http://qiime.org/scripts/make_otu_network.html). The resulting output of nodes and edge files were passed to Cytoscape; Node attributes included OTU identification, the OTU abundance, taxonomic affiliation and whether the OTUs were shared between sample types 2 (influent and sludge). Edge attributes specified sample type, influent or sludge. OTU and sample nodes were clustered with the stochastic spring-embedded algorithm (11). References 1. Oksanen Aj et al. (2012) Vegan: community ecology package. Compute. 2. Hill MO (1973) Diversity and Evenness : A Unifying Notation and Its Consequences. Ecology 54:427–432. 3. jost L (2006) Entropy and diversity. Oikos 113:363–375. 4. Gobet A, Quince C, Ramette A (2010) Multivariate Cutoff Level Analysis (MultiCoLA) of large community data sets. Nucleic Acids Res 38:e155. 5. Pepe-Ranney C, Berelson WM, Corsetti F a, Treants M, Spear jR (2012) Cyanobacterial construction of hot spring siliceous stromatolites in Yellowstone National Park. Environ Microbiol 14:1182–97. 6. Hunter jD (2007) Matplotlib: A 2D graphics envirionment. Comput Sci Eng 9:90–95. 7. Wagner BD, Robertson CE, Harris jK (2011) Application of two-part statistics for comparison of sequence variant counts. PLoS One 6:e20296. 8. Robertson CE et al. (2013) Explicet: Graphical user interface software for metadata- driven management, analysis, and visualization of microbiome data. Bioinformatics 29:3100–3101. 9. Kembel SW et al. (2010) Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26:1463–4. 10. Armitage DW, Gallagher KL, Youngblut ND, Buckley DH, Zinder SH (2012) Millimeter-scale patterns of phylogenetic and trait diversity in a salt marsh microbial mat. Front Microbiol 3:293. 11. Shannon P et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–504. 3 Supplemental Information Table and Figures Table S1. Rarefied MPDSES values (mean ± standard deviation) for the entire set of bacterial OTUs found within sewage influent and activated sludge libraries for three null models: The taxa.labels algorithm (presence/absence and proportional abundance), the independent swap algorithm, and the phylogeny.pool algorithm. Sequence Day of Taxa.labels Ind. Swap Phylo. Pool Library experiment Treatment Group Pres/Abs Abund. Weight Influent 10 30 Day SRT -4.47 ± 0.71 0.11 ± 0.20 -1.41±0.07 -4.54±0.60 36 -5.32 ± 0.76 -0.53 ± 0.19 -1.53±0.10 -5.15±0.60 103 12 Day SRT -5.52 ± 0.32 -1.38 ± 0.30 -1.63±0.08 -5.56±0.36 138 -1.37 ± 0.95 2.48 ± 0.19 -0.85±0.22 -1.35±0.96 165 3 Day SRT -5.73 ± 0.63 -1.51 ± 0.23 -1.72±0.16 -5.62±0.55 169 -4.13 ± 0.14 0.33 ± 0.41 -1.46±0.02 -4.22±0.35 177 -6.53 ± 0.70 -1.99 ± 0.93 -1.84±0.12 -6.82±0.77 186 Biomass Recovery -5.49 ± 1.20 -0.45 ± 1.06 -1.66±0.18 -5.49±1.08 190 -7.32 ± 0.62 -1.12 ± 0.28 -2.05±0.10 -7.37±0.76 201 -4.56 ± 1.98 -0.14 ± 0.81 -1.52±0.43 -4.65±1.96 214 20d Recovery -3.71 ± 0.71 0.33 ± 1.25 -1.31±0.14 -3.76±0.66 228 -7.20 ± 1.60 -3.71 ± 0.64 -1.92±0.18 -7.07±1.73 243 30d Recovery -3.95 ± 0.68 0.08 ± 0.15 -1.40±0.16 -3.98±0.59 256 -6.73 ± 0.56 -3.25 ± 0.93 -1.85±0.12 -6.85±0.47 270 -6.19 ± 0.71 -3.87 ± 1.11 -1.76±0.13 -6.17±0.77 284 -8.71 ± 1.05 -5.45 ± 0.91 -2.26±0.20 -8.81±1.09 Sludge 3 30 Day SRT 0.93 ± 0.62 1.43 ± 0.21 -0.09±0.20 0.90±0.65 10 0.85 ± 0.67 1.11 ± 0.30 -0.13±0.17 0.84±0.62 19 0.61 ± 0.34 1.61 ± 0.12 -0.21±0.07 0.64±0.37 30 1.31 ± 0.56 1.74 ± 0.22 0.06±0.11 1.29±0.52 43 0.14 ± 0.87 0.65 ± 0.24 -0.35±0.25 0.10±0.91 52 1.13 ± 0.51 0.98 ± 0.25 -0.04±0.13 1.13±0.47 108 12 Day SRT 2.67 ± 0.27 2.15 ± 0.17 0.52±0.10 2.78±0.27 127 3.63 ± 0.26 2.74 ± 0.19 0.68±0.15 3.67±0.19 138 2.90 ± 0.53 2.64 ± 0.09 0.48±0.15 2.91±0.47 149 2.90 ± 0.31 2.40 ± 0.16 0.55±0.08 3.00±0.32 165 3 Day SRT 2.81 ± 0.55 1.83 ± 0.03 0.58±0.13 2.78±0.55 169 3.05 ± 0.53 1.68 ± 0.03 0.53±0.18 3.02±0.57 177 3.00 ± 0.81 1.58 ± 0.15 0.57±0.14 2.99±0.77 186 Biomass Recovery 2.76 ± 0.43 1.79 ± 0.05 0.39±0.14 2.76±0.46 190 3.23 ± 0.48 1.92 ± 0.09 0.53±0.04 3.31±0.44 201 2.67 ± 0.41 1.33 ± 0.25 0.26±0.10 2.72±0.35 214 20d Recovery 2.99 ± 0.28 1.94 ± 0.13 0.52±0.07 3.01±0.23 228 2.40 ± 0.68 1.70 ± 0.17 0.39±0.17 2.46±0.77 243 30d Recovery 2.30 ± 0.66 1.59 ± 0.25 0.32±0.14 2.36±0.64 256 1.17 ± 0.44 1.12 ± 0.34 0.11±0.11 1.23±0.42 270 2.08 ± 0.91 1.11 ± 0.06 0.31±0.26 2.07±1.00 284 2.25 ± 0.51 1.32 ± 0.17 0.35±0.13 2.24±0.38 298 0.99 ± 0.53 1.22 ± 0.02 -0.04±0.15 1.01±0.54 311 0.99 ± 0.83 1.56 ± 0.08 -0.09±0.18 0.94±0.76 Bold values indicate significant (p < 0.05) phylogenetic clustering (negative values) or overdispersed (positive values) based on rank test 4 Table S2. Taxonomic classification of OTUs that were detected to be significantly enriched in the bioreactor from the influent by the Two-Part test. OTU P-Value -Log(P-Value) Actinobacteria/Actinobacteria/Actinomycetales 0.0010 3.02 Actinobacteria/Actinobacteria/Actinomycetales/.../Leucobacter 0.0000 4.56 Actinobacteria/Actinobacteria/Actinomycetales/.../Phycicoccus 0.0034 2.46 Actinobacteria/Actinobacteria/Actinomycetales/.../Tetrasphaera 0.0042 2.37 Bacteroidetes/Bacteroidia/Bacteroidales/.../Bacteroides 0.0003 3.57 Bacteroidetes/Bacteroidia/Bacteroidales/.../copri 0.0003 3.57 Bacteroidetes/Bacteroidia/Bacteroidales/.../distasonis 0.0009 3.06 Bacteroidetes/Bacteroidia/Bacteroidales/.../ovatus 0.0009 3.05 Bacteroidetes/Bacteroidia/Bacteroidales/.../Parabacteroides 0.0041 2.39 Bacteroidetes/Bacteroidia/Bacteroidales/.../Prevotella 0.0002 3.63 Bacteroidetes/Bacteroidia/Bacteroidales/.../uniformis 0.0063 2.20 Bacteroidetes/Bacteroidia/Bacteroidales/Rikenellaceae 0.0017 2.77 Bacteroidetes/Cytophagia/Cytophagales/.../Leadbetterella 0.0048 2.32 Bacteroidetes/Flavobacteriia/Flavobacteriales/.../Chryseobacterium 0.0001 3.91 Bacteroidetes/Flavobacteriia/Flavobacteriales/.../Flavobacterium 0.0098 2.01 Bacteroidetes/Flavobacteriia/Flavobacteriales/.../succinicans 0.0079 2.10 Bacteroidetes/Saprospirae/Saprospirales/Chitinophagaceae 0.0002 3.72 Firmicutes/Bacilli/Lactobacillales/.../Lactococcus 0.0013 2.88 Firmicutes/Bacilli/Lactobacillales/.../Streptococcus 0.0024 2.62 Firmicutes/Bacilli/Lactobacillales/.../Trichococcus 0.0001 4.00 Firmicutes/Bacilli/Lactobacillales/Enterococcaceae 0.0000 4.37 Firmicutes/Clostridia/Clostridiales/.../Anaerovibrio 0.0037 2.43 Firmicutes/Clostridia/Clostridiales/.../Blautia
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