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Twenty fractions from a SIP gradient fractionation for each treatment at each time point were sequenced. Using NMDS analysis from weighted unifrac distances the relationship between all the fractions from all treatments and time points are projected into the ordination space (Fig 1). Each point on the NMDS represents the microbial community from a single fraction where the size of the point is representative of the density of that fraction (the smaller the point, the less dense) and the colors represent the treatments (Fig1A) or days (Fig1B). NMDS1 explains the greatest amount of variation in the data (put in the amount of variation explained here\%) based on density. \textsuperscript{13}C-labeled organisms are expected be to found in the denser fractions (large points, Fig x). NMDS2 explains the second greatest amount of variation in the data (put in percent\%) which correlates to fractions of greater density (1.73-1.74...figure out exact densities) that contain \textsuperscript{13}C-labeled OTUs (herein called 'responders') indicated by the separation of these fractions relative to the control. The microbial communities becoming labeled in the \textsuperscript{13}C-xylose treatment are different from those becoming labeled in the \textsuperscript{13}C-cellulose treatment (Fig 1A). When those same samples are labeled by the day harvested (Fig1B), there is an observable time signature of fractions becoming labeled at days 1,3, and 7 for the xylose treatment and days 14 and 30 for the cellulose treatment. In combination, these data demonstrate that different microbial community members are responsible for the consumption of these two different substrates and that xylose is consumed quickly, whereas, cellulose decomposition takes longer. This supports the hypothesis of a microbial community succession during the decomposition process. Furthermore, this demonstrates the sensitivity of this technique by being able to detect \textsuperscript{13}C-label incorporation in samples with low C additions (blah mg g\textsuperscript{-1} soil).   Using biplots, potentially 13C-responsive OTUs were resolved. Targeting potentially responsive OTUs, their relative abundance in all fractions can be traced for an experimental treatment and compared to its relative abundance in the control fractions from the same time point (Fig Y). Using biplots we can tease out members that cause the greatest shift in experimental treatment versus control. We then generate C utilization charts to demonstration discrete OTUs in control versus treatment.    Using fractions from within a range of high density (1.7125-1.755 g/ml), relative abundances of phyla in the experimental treatments were compared to the respective relative abundances in the control treatment to calculate the log2-fold change (Fig2). Firmicutes show the strongest response on day 1 in the xylose treatment with a steady decline in subsequent time points. Proteobacteria demonstrate the second highest repsonse at day 1 and continue to increase in response up to day 7, followed by decline by days 14 and 30. Demonstrates the boom-bust of phyla with time. Bacteriodetes are the strongest responders on day 3 and proteobacteria are the strongest responders on day 7. Actinobacteria and planktomycetes fluctuate in responsiveness within the first 7 days then decline thereafter.    
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Discuss the short-comings  While we are currently able to resolve highly responsive OTUs which has provided greater taxonomic resolution than previous techniques (cloning, TRFLP), the next step is to find better ways to resolve taxa that are partially responsive which we  are currently difficult not able  to dicern differentiate  from noise. PCoA demonstrates microbial succession and based on xylose and cellulose falling out separately, it indicates different microbial community members are responsible for C degradation. Using X ordination we can tease out members that cause Yet, given  the greatest shift in treatment versus control. We then generate C utilization charts ability  to demonstration discrete OTUs in control versus treatment. Bulk community shifts over time.   unlabeled resolve partially responsive taxa, the ecology would still be difficult to discern. For example, a generalist utilizing many substrates including the 13C-labeled substrate may exhibit the same partial labeling that a specialist utilizing both the 13C-substrate and the same  substrate (unlabeled) that is  inherent in the system soil. Additionally, partially labeled taxa could be further down the trophic cascade including predators or secondary consumers of waste products from primary consumer microbes that were highly labeled.  Discuss interpretations of plots -compare to literature of what we already knew about cellulose (or xylose) degradation, microbial succession, or the application of realistic C substrate additions to soil and how that gives us a more natural view of what we might expect to see in nature. Boast about what this technique can do/ how it’s added to our field. Discuss the short-comings  While we are currently able to resolve highly responsive OTUs which has provided greater taxonomic resolution than previous techniques (cloning, TRFLP), the next step is to find better ways to resolve taxa that are partially responsive resulting in a less dramatic effect from the microbial community than highly responsive taxa.