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suggest labile C traveled through different trophic levels within the soil bacterial community. The microorganisms that metabolized cellulose-C increased in relative abundance over the course of the experiment with the highest number of OTUs exhibiting evidence for $^{13}$C-assimilation after 14to 30 days. Microbes that metabolized cellulose C cellulose-C belonged to cosmopolitan soil lineages that remain uncharacterized including \textit{Spartobacteria}, \textit{Chloroflexi} and \textit{Planctomycetes}. Using an approach that reveals the C assimilation dynamics of specific microbial lineages we describe the ecological properties of functionally defined microbial groups that contribute to decomposition in soil.

of C storage in response to environmental change relative to models that did not consider any microbial physiological diversity. We identified microbial lineages engaged in labile and structural C decomposition that can be defined as copiotrophs or oligotrophs, respectively. We also observed rate differences in turnover of xylose responder biomass relative to cellulose responders which may be important to consider when modeling microbial turnover input to SOM. It's also clear that the characterization of microbes as copiotrophs and oligotrophs may miss other, vital functional types mediating C-cycling in soil. That is, soil-C may travel through multiple bacterial trophic levels where each C transfer represents an opportunity for C stabilization in association with soil minerals or C loss by respiration. Our understanding of soil C dynamics will likely improve as we develop a more granular understanding of the ecological diversity of microorganisms that mediate C transformations in soil. \subsection{Conclusion} % Fakesubsubsection: Microorganisms sequester atmospheric C Microorganisms govern govern\\ C-transformations in soil influencing climate change on a global scale but we do not know the identities of microorganisms that carry out specific transformations. In this experiment microbes from physiologically uncharacterized but cosmopolitan soil lineages participated in cellulose decomposition. Cellulose responders included members of the \textit{Verrucomicrobia} (\textit{Spartobacteria}), \textit{Chloroflexi}, \textit{Bacteroidetes} and \textit{Planctomycetes}. \textit{Spartobacteria} in particular are globally cosmopolitan soil microorganisms and are often the most abundant \textit{Verrucomicrobia} order in soil \citep{Bergmann_2011}. Fast-growing aerobic spore formers from \textit{Firmicutes} assimilated labile C in the form of xylose. Xylose responders within the \textit{Bacteroidetes} and \textit{Actinobacteria} likely became labeled by consuming $^{13}$C-labeled constituents of microbial biomass either by saprotrophy or predation. Our results suggest that cosmopolitan \textit{Spartobacteria} may degrade cellulose on a global scale, plant C may travel through a trophic cascade within the bacterial food web after primary decomposition, and life history traits may act as a filter constraining the diversity of active microorganisms relative to those with the genomic potential for a given metabolism.

produces annually tenfold more CO$_{2}$ than fossil fuel emissions \citep{chapin2002principles}. Despite the contribution of microorganisms to global C flux, many global C models ignore the diversity of microbial physiology \citep{Allison2010,Six2006,Treseder2011}. Further, predictions of climate change feedbacks on soil C flux improve when biogeochemical models explicitly represent microbial physiology \citep{Wieder2013}. However, \citep{Allison2010,Six2006,Treseder2011} and we still know little about the ecophysiology of soil microorganisms, and such microorganisms. Such knowledge should assist the development and refinement of global C models \citep{Bradford2008,Neff_2001,McGuire2010}. \citep{Bradford2008,Neff_2001,McGuire2010,Wieder2013}. % Fakesubsubsection: Most plant C Most plant C is comprised of cellulose (30-50\%) followed by hemicellulose (20-40\%), and lignin (15-25\%) \citep{Lynd2002}. Hemicellulose, being the most soluble, degrades in the early stages of decomposition. Xylans are often an abundant component of hemicellulose, and xylansthemselves include differing amounts of xylose, glucose, arabinose, galactose, mannose, and rhamnose \citep{Saha2003}. Xylose is often the most abundant sugar in hemicellulose, comprising as much as 60-90\% of xylan in some plants (e.g hardwoods

\citep{Bunnell2013}). Microbes that respire labile C in the form of sugars proliferate during the initial stages of decomposition \citep{Garrett1951,Alexander1964}, and metabolize as much as 75\% of sugar C during the first 5 daysof decomposition \citep{Engelking2007}. In contrast, cellulose decomposition proceeds more slowly with rates increasing for approximately 15~days while degradation continues for 30-90~days \citep{Hu1997,Engelking2007}. It is hypothesized that different microbial

Though microorganisms mediate 80-90\% of the soil C-cycle \citep{ColemanCrossley_1996,Nannipieri_2003}, and microbial community composition can account for significant variation in C mineralization \citep{Strickland_2009} , \citep{Strickland_2009}, terrestrial C-cycle models rarely consider the community composition of soils \citep{Zak2006,Reed2007}. Rates of soil C transformations are measured without knowledge of the organisms that mediate these reactions \citep{Nannipieri_2003} leaving the importance of community

diagnostic genes for specific functions are available (e.g. denitrification \citep{Cavigelli2000}, nitrification \citep{Carney2004,Hawkes2005,Webster2005}, methanotrophy \citep{Gulledge1997}, and nitrogen fixation \citep{Hsu2009}). However, thecomplexity of soil C transformations and the lack of diagnostic genes for describing these soil-C transformations has limited progress in characterizing the contributions of individual microbes to decomposition. Remarkably, we still lack basic information on the physiology and ecology of the majority of organisms that live in soils. For example, contributions to soil processes remain uncharacterized forentire and cosmopolitan bacterial phyla in soil such as \textit{Acidobacteria}, \textit{Chloroflexi}, \textit{Planctomycetes}, and \textit{Verrucomicrobia}. These phyla combined can comprise 32\% of soil microbial communities (based on surveys of the SSU rRNA genes in soil) \citep{Janssen2006,Buckley2002}. % Fakesubsubsection:Characterizing the functions Characterizing the functions of microbial taxa has relied historically on culturing microorganisms and subsequently characterizing their physiology in the laboratory, and on environmental surveys of genes diagnostic for specific processes. However, But, most microorganisms are difficult to grow in culture \citep{Janssen2006} and many biogeochemical processes lack suitable diagnostic genes. Nucleic acid stable-isotope probing (SIP) links genetic identity and activity without the need to grow microorganisms in culture and has expanded

applications of SIP have targeted specialized microorganisms such as methanotrophs \citep{radajewski2000stable}, methanogens \citep{lu2005}, syntrophs \citep{lueders2004}, or microbes that target pollutants \citep{derito2005}. Exploring the soil soil-C cycle with SIP has proven to be more challenging becauseit SIP has lacked the resolution necessary to characterize the specific contributions of individual microbial groups to the decomposition of plant biomass. High throughput DNA sequencing technology, however, improves the resolving power of SIP \citep{Aoyagi2015}. % Fakesubsubsection:High throughput sequencing Coupling SIP with high throughput DNA sequencing now enables exploration of

relative abundance in non-fractionated DNA, demonstrated signal consistent with higher atom \% $^{13}$C in labeled DNA, and had lower estimated \textit{rrn} copy number (Figure~\ref{fig:shift}). In the non-fractionated DNA, cellulose responders had lower relative abundance (3.8 (1.2 x 10$^{-3}$ (s.d. 1.2 3.8 x 10$^{-3}$)) than xylose responders (3.5 x 10$^{-3}$ (s.d. 5.2 x 10$^{-3}$)) (Figure~\ref{fig:xyl_count}, P-value~$=$~1.12 x 10$^{-5}$, Wilcoxon Rank Sum test). Six of the ten most common OTUs observed in the non-fractionated DNA responded to xylose, and, seven of the ten most abundant responders to xylose or cellulose in the non-fractionated DNA were xylose responders although ``OTU.6'' annotated as \textit{Cellvibrio} a cellulose responder at day 14 was the responder found at highest relative abundance (3.3 \% (approximately 3\% or SSU rRNA genes at day~14). day~14, Figure~\ref{fig:example}). % Fakesubsubsection:DNA buoyant density increases as the amount DNA buoyant density (BD) increases in proportion to atom \% $^{13}$C.

calculated for each OTU its mean BD weighted by relative abundance to determine its ``center of mass'' within a given density gradient. We then quantified for each OTU the difference in center of mass between control gradients and gradients from $^{13}$C-xylose or $^{13}$C-cellulose treatments (see SI for the detailed calculation). calculation, Figure~\ref{fig:c1}). We refer to the change in center of mass position for an OTU in response to $^{13}$C-labeling as $\Delta\hat{BD}$. $\Delta\hat{BD}$ can be used to compare relative differences in $^{13}$C-labeling between OTUs. $\Delta\hat{BD}$ values, however, are not comparable to the BD changes observed for DNA from pure cultures both becuase because they are based on relative abundance in density gradient fractions (and not DNA concentration) and because isolated strains grown in uniform conditions generate uniformly labeled molecules while OTUs composed of heterogeneous strains in complex environmental samples do not. Cellulose responder $\Delta\hat{BD}$ (0.0163 g mL$^{-1}$ (s.d.~0.0094)) was greater than that of xylose responders (0.0097 g mL$^{-1}$ (s.d.~0.0094)) (Figure~\ref{fig:shift}, P-value~$=$~1.8610 x 10$^{-6}$, Wilcoxon Rank Sum test). % Fakesubsubsection:We predicted the rrn We predicted the \textit{rrn} gene copy number for responders as described

Percentage of added $^{13}$C remaining in soil over time.C losses are likely due to microbial respiration.

NMDS analysis of SSU rRNA gene composition differences between non-fractionated DNA alone (right panel) and in the context of SIP gradient fractions (left panel). Non-fractionated DNA SSU rRNA gene composition changed with time but not with treatment (right panel) and variance of non-fractionated DNA SSU rRNA gene composition wasmuch less than variance introduced by density fractionation (left panel). Distance in SSU rRNA gene composition was quantified with the weighted UniFrac metric.

Estimated rRNA operon \textit{rrn} copy number for xylose and cellulose responders. The leftmost panel contrasts estimated \textit{rrn} copy number for cellulose (13CCPS) and xylose (13CXPS) responders. The right panel shows estimated \textit{rrn} copy number versus time of first response for xylose responders. Colors denote the phylum of the OTUs (see legend).

Raw data from example responders highlighted in the main text. text (see Results). The left column shows DNA-SIP density fraction relative abundances for $^{13}$C-xylose or $^{13}$C-cellulose gradients in addition to control gradients for each of the chosen OTUs. Time is indicated by the color of the relative abundance profile (see legend). Gradient profiles are shaded by treatment where orange represents ``control'' profles, blue ``$^{13}$C-cellulose'', and green ``$^{13}$C-xylose.'' The right column shows the relative abundance of each OTU in non-fractionated DNA (i.e. the DNA that was subsequently fractionated on the density gradient). Enrichment in the heavy end of the gradient in $^{13}$C treatments $^{13}$C-treatments indicates an OTU has $^{13}$C-labeled DNA.

Maximum enrichment at any point in time in heavy fractions of $^{13}$C-treatments relative to control (expressed as LFC) shown for $^{13}$C-cellulose versus $^{13}$C-xylose treatments. Each point represents an OTU. Blue points are cellulose responders, green xylose responders,and red are responders to both xylose and cellulose, Grey and gray points are OTUs that did not repspond to either substrate. Line indicates a slope of one.

differences in the sequence composition of gradient fractions is correlated to fraction density, isotopic labeling, and time. SSU rRNA gene compositon was profiled for fractions for each density gradient. $^{13}$C-labeling of DNA is apparent as because the SSU rRNA gene composition of gradient fractions from $^{13}$C and control gradients treatments differ at high density. Each point on the NMDS plot represents one gradient fraction. SSU rRNA gene composition differences between gradient fractions were quantified by the weighted Unifrac metric. The size of each point is positively correlated with density and colors indicate the treatment (A) or day (B).

Whiskers extend to 1.5 times the IR, and the box extends one IR about the median (solid line)). Each day in the right column shows all responders (i.e. OTUs that responded to xylose at any point in time). Greater enrichment in high density fractions of the $^{13}$C-xylose treatment relative to control indicates DNA is $^{13}$C-labeled.