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In terms of taxonomic richness (both measured by number of OTUs and phylogenetic diversity,) there was no significant difference between diet types (Fig) (non-parametric Kruskal-Wallis test with compare_alpha_diversity.py, P>0.6). We also tested for the significant variation in frequency of individual OTUs between diet types using the Kruskal-Wallis test as implemented in the group_significance.py script. Before conducting these tests, we removed OTUs that were not present in at least 3 of the 15 samples. None of the OTUs were significantly different between the three diet types.   ##Metagenome Prediction with PICRUSt  With PICRUSt, one can calculate a metric (NSTI) that measures how closely related the average 16S sequence in an environmental sample is to an available sequenced genome. When this number is low, PICRUSt is likely to perform well in predicting the genomes of the organisms in an environmental sample (i.e., a metagenome). The average NSTI for our 15 meals was 0.03(??), which is on par with the NSTI for the Human Microbiome Project samples (??), for which a massive effort has been made to obtain reference genome sequences. The most significant difference in KEGG functional categories was for “other N-glycan degradation (KO??, *p*= \(8.21^-3^\)).” \(8.21^(-3)\)).”