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In bacterial libraries, sequences were distributed into 636 total OTUs; 58\% of quality controlled sequences fell into the top 25 OTUs in order of decreasing sum of relative abundance across all samples. 23S plastid rRNA gene sequences were distributed into 359 total OTUs; 71\% of sequences fell into the top 25 OTUs sorted by mean relative abundance across all samples. Rank abundance curves for each mesocosm specific pair of planktonic and biofilm samples showed planktonic communities to be more sharply skewed in both the algal and bacterial datasets (Figure 9).   We used the an  RNA-Seq differential expression statistical framework to find OTUs enriched in the given sample classes (R package DESeq2  developed by \citet{deseq} \citet{deseq})  (for review of RNA-Seq differential expression statistics applied to microbiome OTU count data see \citet{24699258}). We use the term "differential abundance" (coined by \citet{24699258}) to denote OTUs that have different mean proportions across sample classes. We are particularly interested in two sample classes: 1) environment type (biofilm or planktonic) and, 2) high carbon (C:P = 500) versus not high carbon (C:P = 10, C:P = 100 and C:P = control). A differentially abundant OTU, for instance, would have a mean proportion in one class that is statistically different from its mean in another. This differential abundance could mark an enrichment in either class and the direction of the enrichment is apparent in the sign (positive or negative) of the metric used summarize the mean proportion difference. Here we use log$_{2}$ of the mean proportion ratio (means are derived from OTU proportions for all samples in each given class) as our differential abundance metric. It is also important to note that the DESeq2 R package we are using to calculate the differential abundance metric "shrinks" the metric in inverse proportion to the information content for each OTU. In this way the magnitude of the differential abundance metric will be high only for OTUs which we have strong confidence of true differential abundance and the metric can be used to effectively rank OTUs by magnitude of the sample class affect (i.e. OTUs with high proportion mean differences but also high within sample class proportion variance will not produce misleadingly large differential abundance metric values). The DESeq2 RNA-Seq statistical framework has been shown to improve power and specificity when identifying differentially abundant OTUs across sample classes in microbiome experiments \cite{24699258}. To probe how