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# Nutrients cause consolidation of soil carbon flux to small proportion of bacterial community

## Abstract

Nutrient amendment diminished bacterial functional diversity, consolidating carbon flow through fewer bacterial taxa. Here, we show strong differences in the bacterial taxa responsible for respiration from four ecosystems, indicating the potential for taxon-specific control over soil carbon cycling. Trends in functional diversity, defined as the richness of bacteria contributing to carbon flux and their equitability of carbon use, paralleled trends in taxonomic diversity although functional diversity was lower overall. Among genera common to all ecosystems, Bradyrhizobium, the Acidobacteria genus RB41, and Streptomyces together composed 45–57% of carbon flow through bacterial productivity and respiration. Bacteria that utilized the most carbon amendment (glucose) were also those that utilized the most native soil carbon, suggesting that the behavior of key soil taxa may influence carbon balance. Mapping carbon flow through different microbial taxa as demonstrated here is crucial in developing taxon-sensitive soil carbon models that may reduce the uncertainty in climate change projections.

## Introduction

Global climate projections depend on estimates of soil carbon accumulation and decomposition1,2,3, processes driven by microorganisms3,4,5,6. Given the vast diversity of soil microorganisms, different microbial taxa may have individualistic effects on C fluxes in soil7, yet testing this idea has been challenging. Soils hold over twice as much organic carbon (C) as terrestrial vegetation, and soil C turns over much more slowly. Soil microbial communities contain thousands of different heterotrophic microbial taxa that, together, influence soil C content, but the quantitative contributions of individual microbial taxa to the processes governing soil C accumulation and loss are not known. While some soil biogeochemical processes are physiologically specialized and dominated by a few phylogenetically specific groups, processes involved in heterotrophic decomposition are broadly distributed across the bacterial tree of life8. With many taxa contributing to the same process, the functional evenness of heterotrophic decomposition might be expected to be approximately equivalent to the evenness in abundance of heterotrophic decomposers, with each taxon contributing to decomposition in proportion to its abundance. As bacterial abundances are logarithmically distributed9, we might expect that the contributions to soil C may be similarly distributed despite differences in ecosystem or bacterial community composition.

We used a combination of measurements and models to evaluate the contributions of individual bacterial taxa to heterotrophic growth and respiration in four soils along a climate gradient in northern Arizona. Taxon-specific growth rates were measured using quantitative stable isotope probing with 18O-water (qSIP, see “Methods” section)7,10 for soils collected from desert grassland (GL), Piñon-Juniper scrubland (PJ), Ponderosa Pine forest (PP), and mixed conifer forest (MC) sites, as described previously11,12,13. Mean annual temperature for all respective sites: 8.5, 7, 5.5, and 4 °C and mean annual precipitation: 230, 380, 660, and 790 mm12. To determine how taxon-specific contributions to growth and respiration varied with resource availability, measurements were conducted in the laboratory using unamended soil, soil with supplemental glucose, and soil with glucose plus a nitrogen source accessible to microbes, [NH4]2SO4 (carbon+nitrogen). Isotopic signatures of specific 16S sequences were combined with 16S abundances from quantitative PCR to yield quantitative estimates of taxon-specific population size and growth.

## Results and discussion

### Bacterial efficiency and respiration

Taxon-specific productivity (µg C g soil−1 week−1) was modeled as a function of per-capita growth rate, taking into account relative abundance, 16S content per unit soil, as well as 16S copy number and genome size (as as per Li et al.13) to estimate taxon-specific cell size and carbon content (see “Methods” section). We estimated taxon-specific bacterial respiration as a function of taxon-specific growth rate and taxon-specific carbon use efficiency (CUE), using several parameterizations of the growth ~ CUE relationship (Supplementary Fig. 1, see “Methods” section). The relationship between microbial growth and efficiency is complex and difficult to identify based on existing literature14. Among models with different parameterizations, a unimodal relationship between growth rate and CUE was selected with the lowest AIC and further discussed in the methods (Table 1).

We compared this model to one without taxonomy-informed genome characteristics (16S content and genome size estimates) and without taxon-specific growth, in which individual bacterial taxa respired in direct proportion to their 16S abundance per unit soil. This comparison served to demonstrate the extent that 16S abundance data of the bacterial community alone can predict soil carbon flux. Across the four soils and nutrient amendment treatments, modeled respiration of individual bacterial taxa was summed over the bacterial assemblage and was compared with measured total soil respiration. When based on measured per-taxon growth rates, modeled bacterial respiration was positively related to total soil respiration (R2 = 0.83, p < 0.001; Fig. 1a). In contrast, when estimated in proportion to a taxon’s abundance alone, modeled bacterial respiration demonstrated a comparatively poor correlation (R2 = 0.02, p = 0.70; Fig. 1b). Although our methods track the incorporation of 18O-labeled water into bacterial DNA, and not carbon explicitly, these results indicate that growth of individual bacterial taxa measured through 18O assimilation can be directly associated with the movement of C through the soil. For all but two soil and treatment combinations, modeled respiration was lower than measured respiration, likely in part owing to non-bacterial contributions to measured total respiration (which were not modeled). When we amended soils with carbon (C) and carbon with nitrogen (C + N) we found elevated soil respiration, patterns which were also observed with modeled bacterial respiration (Fig. 1a). Nutrient amendments also stimulated taxon-specific bacterial respiration (F2,9 = 27.2, p < 0.001) and productivity (F2,9 = 6.96, p = 0.01) leading to higher total C use in these treatments (Fig. 2). Generally, organisms that produced more biomass also respired more (Supplementary Fig. 2).

### Distribution and consolidation of bacterial carbon use

Soils amended with nutrients had higher productivity and respiration; however, in these soils, carbon use was less evenly distributed across the bacterial community, especially in soils provided with carbon and nitrogen. To compare the extent that taxonomic evenness equated to functional evenness (i.e., the extent that both shared similar abundance distributions), we calculated Pielou’s evenness on the relative abundances of bacterial amplicon sequence variants (ASVs) as well as relativized growth and respiration estimates. Bacterial abundances were more evenly distributed than were estimates of bacterial productivity and respiration (Fig. 3a). Similarly, cumulative C use was strongly associated by treatment, with greater consolidation of carbon in C + N soils as shown by a lower proportion of the bacterial community responsible for a greater proportion of carbon flux (Fig. 3b).

Microbial community structure and function are thought to be linked15,16, but most efforts to relate them rely on aggregate community function measurements correlated against summaries of composition, diversity, or interactions (e.g., Creamer et al.17). Interpretation of relative abundances across communities is a common exercise in contemporary studies of microbial ecology. Averaged across all ecosystems, 36 bacterial genera contributed to >50% of sequenced 16S amplicons. Of genera common to all soils, only six were necessary to obtain >50% contributions to C cycling in control and C amended soils while only three were necessary to obtain >50% C cycling in C + N amended soils. Bradyrhizobium (Alphaproteobacteria, Family: Xanthobacteraceae), RB41 (Acidobacteria, Family: Pyrinomonadaceae—Subgroup 4), and Streptomyces (Actinobacteria, Family: Streptomycetaceae) were common to all soils and treatments and in the C + N treated soils, these lineages accounted for the majority of C flux (Fig. 4a; Supplemental Table 1). These taxa also represent globally ubiquitous and abundant lineages as determined across the Earth Microbiome Project database18.

Relative C use in the bacterial community was more consolidated within fewer lineages than the overall distribution of relative abundances might suggest. Averaged across all ecosystems and treatments, 75.7% of bacterial genera used less C than their relative abundance would otherwise predict. We assessed the relationship between relative C use and relative abundance in response to nutrient amendments using linear mixed modeling, accounting for random intercepts (and to limit pseudo-replication) across ecosystems and bacterial genera, and including an offset term to assess significant departure from the 1:1 line. In parallel with changing profiles of diversity, the relationship between taxon-specific bacterial C use and abundance was affected by treatment (F2, 489.11 = 4.926, p = 0.008). Specifically, we estimated that the slope of the relationship between relative C use and relative abundance was slightly but significantly higher than the 1:1 line in C + N amended soils, but not in control and C amended soils (p = 0.02) (Fig. 4b, c). Besides relative abundance, other potential influences on taxon-specific C use estimates were per-capita growth rate and taxon-specific cell mass. Analysis of residual values from the linear mixed model found a significant positive relationship between per-capita growth rate and residual variation in C use (F1, 583 = 14.3, p < 0.001), whereas individual cell size (µg C) was not a significant driver, suggesting that the bacterial taxa that used more soil C in C + N soils did so because they grew and divided faster, not because they had larger cells, and that taxa that used less soil C in C + N soils grew more slowly than in other treatments. In addition, relative abundances likely reflect a mixture of both historical activity and activity as a result of the experimental conditions. A similar mixed model was therefore run using initial relative abundance, reflecting the historical activity of microbial taxa, as a predictor of C use during the incubation. Initial relative abundances were a significant model term (F1, 375 = 83.3, p < 0.001), suggesting that the historical activities of microorganisms can meaningfully influence the trajectories of microbial communities.

### Bacterial carbon use and abundance in response to resource stoichiometry

In general, the relative contributions of individual bacteria to carbon use strongly resembled patterns of relative abundance, where the most abundant genera also utilized the largest proportion of C in the community (Fig. 4). However, while relative abundance was generally predictive of relative C use, it was difficult to predict any individual organism’s contribution to C flux based on relative abundance alone, with differences between abundance and C use estimated to be an order of magnitude or more (Fig. 4b). One notable example was the genus Sphingomonas (Alphaproteobacteria) which had high 16S abundance but contributed minimally to soil C flux (Fig. 4a). Sphingomonas could be distinguished from the top C using genera by a smaller cell mass estimate (31st percentile), a function of genome length, which was lower than Bradyrhizobium (73rd percentile), Streptomyces (88th percentile), RB41 (81st percentile) or the Burkholderia-Caballeronia-Paraburkholderia group (95th percentile), even though its growth rate was comparable.

In glucose-amended soils, the use of native soil C was closely correlated with the use of glucose across the bacterial community (r = 0.96, p < 0.001). In C and C + N treated soils, we performed 13C-glucose amendments in parallel to 18O-water conditions and used per-taxon 13C enrichment to estimate the amount of native (12C) and glucose carbon utilized across the bacterial community. Our results indicate that the organisms that utilized the most glucose were also those that utilized the most native soil carbon. Thus, organisms with the capacity to grow quickly in response to easily accessible carbon substrates are important to the cycling and turnover of existing soil C. To determine the extent that the C:N stoichiometry of labile resources may change microbial C use preferences, we used Levene’s test on the variance in the relationship between 13C use and 12C use, where higher variance in response to nitrogen is indicative of shifts in the type of carbon preferred across bacterial genera. In C + N treated soils, there was significantly more variation around the trend line (F22, 770 = 3.53, p < 0.001, Levene’s test; Fig. 4c), indicating that labile nitrogen addition may disrupt the balance between native soil carbon use and use of a labile carbon substrate.

Despite some differences between 16S abundance and soil C use, across soils, differences in composition of the community significantly predicted the C use profiles (rM = 0.68, p < 0.001, Mantel correlation). The four ecosystems differed in the amount of carbon used by different taxa (R2 = 0.69, p < 0.001, PERMANOVA), patterns that mirrored differences in relative abundance (R2 = 0.69, p < 0.001; Fig. 5). Similarly, bacterial communities changed in response to nutrient amendments, observed both with changes in relative abundance (R2 = 0.14, p = 0.03) and C use (R2 = 0.13, p = 0.05), though these differences were smaller than ecosystem-level separation of community composition and C use.

The strong ecosystem-specific clustering of community composition and C use (Fig. 5) is seemingly at odds with the strong treatment-specific patterns of cumulative C use (Fig. 3b), which suggests that there was a similar response to nutrient addition across all ecosystems regardless of which bacteria were responsible. However, relative abundance and C use were strongly linked (Fig. 4), and we observed that the most important contributors to bacterial C use were consistently represented by the same, abundant lineages across all ecosystems and treatments (Supplemental Table 1). Taken together, these results demonstrate that changing patterns in carbon use were driven by the consistent and abundant portion of the bacterial community which responded to the C + N amendment. Conversely, the importance of any individual lineage that occurred with low abundance towards soil C flux was difficult to determine. Rare taxa are thought to serve as a reservoir or seed bank of microbial function and diversity19. Although rare lineages drove ecosystem-specific patterns in community composition and C use due to the sensitivity of multivariate dissimilarity measures to their high diversity, differences in the composition of rare lineages were negligible, contributing minimally to soil C flux. These non-dominant organisms may be best described as part of the “interchangeable” biosphere, where apart from a few consistent taxa that dominate C flux, the identities of most rare taxa are negligible towards their contributions to C flux.

Generally, dominant lineages became more dominant—in terms of abundance as well as C use—in nutrient amended soils compared to native soil conditions, especially in the Actinobacteria. Consolidation of C use increased in C + N soils for most major phyla, where a proportionally smaller number of taxa was associated with a greater share of overall abundance and C flux in C and C + N treatments (Fig. 6). Several taxa within the Actinobacteria, mostly Streptomyces (Actinomycetaceae), Arthrobacter (Micrococcaceae), and Kribbella (Nocardioidaceae) spp., produced proportionally more 16S rRNA gene copies than other Actinobacteria during the seven-day incubation in C and C + N soils (Supplementary Fig. 3). These taxa were also dominant producers of biomass and CO2 even after correcting for 16S rRNA gene copy number, cell mass, and growth rate (Supplementary Figs. 46). Across nearly all major bacterial phyla, the addition of labile nutrients tended to promote respiration of some lineages relative to others, increasing dominance, and demonstrating that the release of soil carbon as CO2 can be concentrated in a few taxa (Figs. 3 and 6 and Supplementary Figs. 3, 46). These findings complement previous synthesis efforts, which have found key taxa are likely responsible for variability in carbon cycling20. Generally, microbial communities are more resilient to pulses, such as our C and C + N amendments, than longer disturbances (also known as press disturbances)21. It is possible that nutrient addition over longer periods would elicit a different response from abundant and rare bacteria as well as changes in overall soil productivity and respiration.

In conclusion, we identify the contributions of individual bacterial taxa to soil carbon flux through bacterial production and respiration in their native soil habitats, providing insight into the community dynamics that are missing in microbial carbon models22,23. Our model identified the growth of a few highly abundant bacterial lineages in response to labile nutrient additions, whose pre-existing high abundance in the community allowed them to assimilate ~50% of carbon consumed by or available to bacteria. The well-known pattern of logarithmic bacterial frequency and abundance distributions, thus parallels the high importance of a relatively small subset of bacterial biodiversity in the carbon cycling of any given soil. Given that this pattern is universal in microbial communities9, we expect that such inequality in carbon use is as well. 4 of the 20 most prolific contributors to soil respiration come from poorly understood bacterial groups, one from the Acidobacteria, a phylum often generalized as oligotrophic24. However, the abundance of individual bacterial taxa, alone, was not a sufficient predictor of soil C flux. Thus, the ability to measure in situ growth rates provided by techniques like qSIP has considerable potential to resolve the ecological roles of bacterial lineages that are difficult to culture, or whose functions would otherwise require extensive physiological assays. With regard to soil respiration modeling, we propose that because the majority of bacterial carbon flux could be accounted for by 3–6 common genera from ecosystems with different temperature and precipitation regimes, and that these genera were globally abundant and ubiquitous18, it is worthwhile to determine both the global ubiquity and consistency in carbon process rates, as well as their determining traits, of such highly abundant bacteria in response to climate change. Doing so may reveal a core group of the soil microbial community that act as dominant carbon processors.

## Methods

### Sample collection and incubation

Three replicates of soil samples were collected from the top 10 cm in of plant-free patches in four ecosystems along the C. Hart Merriam elevation gradient in Northern Arizona25 beginning at high desert grassland (1760 m), and followed at higher elevations by piñon-pine juniper woodland (2020 m), ponderosa pine forest (2344 m), and mixed conifer forest (2620 m). Soils were air-dried for 24 h at room temperature, homogenized, and passed through a 2 mm sieve before being stored at 4 °C for another 24 h. Soil incubations were performed on soils with mass of 20 g of dry soil for measurements of CO2 and microbial biomass carbon (MBC), while 2 g of dry soil aliquots were incubated separately (but under equivalent conditions) for quantitative stable isotope probing (qSIP). We applied three treatments to these soils through the addition of water (up to 70% water-holding capacity): water alone (control), with glucose (C treatment; 1000 µg C g−1 dry soil), or with glucose and nitrogen (C + N treatment; [NH4]2SO4 at 100 µg N g−1 dry soil). All samples for qSIP were incubated with 18O-enriched water (97 atom%) and matching controls necessary to calculate the change in 18O enrichment across the microbial community. We applied water at natural abundance (i.e., no 18O-enriched water) to the larger soil samples prepared for measurement of carbon flux. All soils were incubated in the dark for one week. Following incubation, soils were frozen at −80 °C for 1 week prior to DNA extraction.

### Soil, CO2, and microbial biomass measurements

We analyzed headspace gas of soils for CO2 concentration and δ13CO2 three times during the week-long incubation using a LI-Cor 6262 (LI-Cor Biosciences Inc. Lincoln, NE, USA) and a Picarro G2201 (Picarro Inc., Sunnyvale, CA, USA), respectively. Prior to incubation we analyzed soil MBC using the chloroform-fumigation extraction method on 10 g of soil. One sub-sample was immediately extracted with 25 ml of a 0.05 M K2SO4 solution, while a second sub-sample was first fumigated with chloroform (for 5 days), after which it was similarly extracted. Following K2SO4 addition, we agitated soils for 1 h, filtered the extract through a Whatman #3 filter paper, and dried the filtered solution (60 °C, 4 days). Salts with extracted C were ground and analyzed for total C using an elemental analyzer coupled to a mass spectrometer. MBC was calculated as the difference between the fumigated and immediately extracted samples’ soil C using an extraction efficiency of 0.45 (as per Liu et al.26).

### Quantitative stable isotope probing

We performed DNA extraction and 16S amplicon sequencing on 18O-incubated qSIP soils11,12,13. The procedures targeted the V4 region of the 16S gene as specified by the Earth Microbiome Project (EMP, http://www.earthmicrobiome.org) standard protocols27,28. We used PowerSoil DNA extraction kits following manufacture instructions to isolate DNA from soil (MoBio laboratories, Carlsbad, CA, USA). We quantified extracted DNA using the Qubit dsDNA High-Sensitivity assay kit and a Qubit 2.0 Fluorometer (Invitrogen, Eugene, OR, USA). To quantify the degree of 18O isotope incorporation into bacterial DNA, we performed density fractionation and sequenced 15–18 fractions separately following methods modified from the canonical publication7. We added 1 µg of DNA to 2.6 mL of saturated CsCl solution in combination with a gradient buffer (200 mM Tris, 200 mM KCL, 2 mM EDTA) in a 3.3 mL OptiSeal ultracentrifuge tube (Beckman Coulter, Fullerton, CA, USA). The solution was centrifuged to produce a gradient of increasingly labeled (heavier) DNA in an Optima Max bench top ultracentrifuge (Beckman Coulter, Brea, CA, USA) with a Beckman TLN-100 rotor (127,000 × g for 72 h) at 18 °C. We separated each sample from the continuous gradient into approximately 20 fractions (150 µL) using a modified fraction recovery system (Beckman Coulter). We then measured the density of each separate fraction with a Reichart AR200 digital refractometer (Reichert Analytical Instruments, Depew, NY, USA) and retained fractions with densities between 1.640 and 1.735 g cm−3. We cleaned and purified DNA in these fractions using isopropanol precipitation, quantified DNA using the Quant-IT PicoGreen dsDNA assay (Invitrogen) and a BioTek Synergy HT plate reader (BioTek Instruments Inc., Winooski, VT, USA), and quantified bacterial 16S gene copies using qPCR (primers: Supplementary Table 1) in triplicate. We used 8 µL reactions consisting of 0.2 mM of each primer, 0.01 U µL−1 Phusion HotStart II Polymerase (Thermo Fisher Scientific, Waltham, MA), 1× Phusion HF buffer (Thermo Fisher Scientific), 3.0 mM MgCl2, 6% glycerol, and 200 µL of dNTPs. We amplified DNA using a Bio-Rad CFX384 Touch real-time PCR detection system (Bio-Rad, Hercules, CA, USA) with the following cycling conditions: 95 °C at 1 min and 44 cycles of 95 °C (30 s), 64.5 °C (30 s), and 72 °C (1 min).

We sequenced the 16S V4 region (primers: EMP standard 515F—806R; Supplementary Table 1) on an Illumina MiSeq (Illumina, Inc., San Diego, CA, USA). Sequences were amplified using the same reaction mix as qPCR amplification but cycling at 95 °C for 2 min followed by 15 cycles of 95 °C (30 s), 55 °C (30 s), and 60 °C (4 min). In addition to post-incubation soils, we extracted, amplified, and sequenced DNA of the bacterial community at the start of the incubation.

### Sequence processing and qSIP analysis

The raw sequence data of forward and reverse reads (FASTQ) were processed within the QIIME 2 environment (release 2018.6)29,30, denoising sequences with the available DADA2 pipeline31. We clustered the remaining sequences into amplicon sequence variants or ASVs (at 100% sequence identity) against the SILVA 132 database32 using an open-reference Naïve Bayes feature classifier33. We removed global singletons and doubleton ASVs, non-bacterial lineages, and samples with less than 4000 sequence reads. Removal of global singletons and doubletons resulted in the removal of 2241 unique ASVs from the feature table yielding 115,647 out of 117,888 (a retention of 98% of all ASVs) as well as the loss of 4018 sequences leaving 37,765,678 (a retention >99% of all sequences). We combined taxonomic information and ASV sequence counts with per-fraction qPCR and density measurements using the phyloseq package (version 1.24.2), in R (version 3.5.1)34. Because high-throughput sequencing produces relativized measures of abundance, we converted ASV sequencing abundances in each fraction to the number of 16S rRNA gene copies per g dry soil based on the known amount of dry soil added and the amount of DNA in each soil sample. All data and analytical code have been made publicly accessible35.

To perform qSIP analysis and calculate per-capita growth rates of each ASV, we used our in-house qsip package (https://github.com/bramstone/qsip) based on previously published research7,10. Because rare and infrequent taxa are more likely to be lost in samples with poor sequencing depth with their absences affecting DNA density changes, we invoked a presence or absence-based filtering criteria on ASVs prior to calculation of per-capita growth rates. Within each ecosystem, we kept only ASVs that appeared in two of the three replicates of a treatment (18O, C, and C + N) and at that appeared in at least five of the fractions within each of those two replicates. ASVs filtered out of one treatment were allowed to appear in another if they met the frequency threshold.

For all remaining ASVs (1081 representing less than 1% of all ASVs but 58% of all sequence reads), we calculated per-capita gross growth (i.e., cell division) rates observed in each replicate using an exponential growth model10. We applied these per-capita rates to the number of 16S rRNA gene copies to estimate the production of new 16S rRNA gene copies of each ASV per g dry soil per week using the following equation:

$$\frac{{\rm{d}}{N}_{{\rm{i}}}}{{{\rm{d}}t}}={N}_{{\rm{i,t}}}-{N}_{{\rm{i,t}}}{e}^{-{g}_{{\rm{i}}}t},$$
(1)

Where Ni,t is the number of 16S rRNA gene copies of taxon i at time t (here after 7 days) and gi represents the per-capita growth rate (calculated as a daily rate). See Supplementary Fig. 3 for results on the production of 16S gene copies.

### Calculation of 16S rRNA gene copy numbers and cell mass

In parallel to taxonomic assignment, we compared quality-filtered 16S sequences against a database of 12,415 complete prokaryote genomes obtained from GenBank. From these genomes, we extracted data on 16S rRNA gene copy number, total genome size, and 16S gene sequence. We used BLAST to find matches against this database to the ASVs generated from QIIME 2 to make per-taxon assignments of 16S rRNA gene copy number and total genome size13. For ASVs that did not find an exact match, we assigned 16S rRNA gene copy number values and genome sizes based on the median values observed in the most specific possible taxonomic rank. We estimated the mass of individual cells for each population using published allometric scaling relationships between genome length and cellular mass from West and Brown:36

$${{{\log }}}_{10}({M}_{{\rm{i}}})=\frac{{{{\log }}}_{10}\left({G}_{{\rm{i}}}\right)-9.4}{0.24},$$
(2)

where Mi indicates cellular mass (g) and Gi indicates genome length (bp) for taxon i. We obtained this relationship by digitizing Fig. 436 using DataThief III and re-fitting the trend line in log–log space. We estimated that 20% of the cellular mass was carbon37. To validate this approach, cellular mass estimates and initial 16S copy number measurements were used to estimate population-level biomass C values which were summed and compared to initial community-level MBC. We found that these values overestimated initial MBC by an order of magnitude. As such, cellular carbon mass was divided by 10 in our final calculations. We applied cellular mass and 16S copy number estimates to the production of 16S copies to estimate the production of biomass carbon for each taxon during the incubation period (t):

$${P}_{{\rm{i}}}=\frac{{\rm{d}}{N}_{{\rm{i}}}/{{\rm{d}}t}}{C_{{\rm{i}}}}\cdot {M}_{{\rm{i}}}\cdot 0.2,$$
(3)

where Pi indicates production of biomass carbon (µg C g dry soil−1 week−1) and Ci indicates 16S copy number per cell for taxon i. The 0.2 coefficient represents an estimate that 20% of cellular mass is composed of carbon.

### Efficiency and respiration modeling

We estimated rates of respiration using qSIP-informed growth rates and community-level carbon use efficiency (CUE). CUE estimates were based on the incorporation of 18O-water into DNA as a measure of gross biomass production38,39 and measured CO2 in headspace gas from soil incubations. We calculated the production of 18O-labeled biomass carbon (18P) at the community-level for each sample by summing the products of per-taxon 18O enrichment (excess atom fraction, EAF) and relative abundance:

$${\, }^{18}{P}=\mathop{\sum }\limits_{i=1}^{n}({\,}^{18}{{{\rm{EAF}}}}_{{\rm{i}}}\cdot {y}_{{\rm{i}}})\cdot {\rm{DN}}{\rm{A}}_{0}\cdot f\left({{\rm{MB}}}{\rm{C}}_{0} \sim {\rm{DN}}{\rm{A}}_{0}\right),$$
(4)

where 18P indicates the gross production of 18O-labeled microbial biomass carbon per gram of dry soil per week, 18EAFi indicates the enrichment of DNA of taxon i and yi indicates its relative abundance, DNA0 indicates the concentration of DNA per gram of dry soil prior to incubation, and MBC0 indicates the microbial biomass carbon per gram of dry soil prior to incubation. Here, the MBC0 ~ DNA0 function indicates the linear relationship between MBC and DNA concentration. We used the output from Eq. 4 to calculate community CUE for each sample:

$${{\rm{CUE}}}=\frac{{\,}^{18}{{P}}}{(\!{\,}^{18}P+R)},$$
(5)

where R indicates the total CO2 respired per gram dry soil per week.

We used the community CUE values from each sample (Eq. 5) to constrain/as upper and lower limits our estimates of per-taxon CUE. For a group of three replicates from a given ecosystem and treatment, we used the minimum and maximum observed community-level CUE values as the acceptable range of per-taxon CUE values. These constraints were used to control the shape of the function of per-taxon CUE and growth rate, though functions were modeled both with and without constraints (i.e., per-taxon CUE values were bounded only by 0 and 0.7). The range of community-level CUE values for each treatment were 0.18–0.53 for control soils, 0.04–0.13 for carbon amended soils and 0.03–0.08 for carbon and nitrogen amended soils and did not vary much between ecosystems. As a result of uncertainty in the literature about the relationship between growth rate and CUE14, several different relationships were postulated to model per-taxon CUE as a function of per-taxon growth rate: linear increase, linear decrease, exponential decrease, unimodal with peak CUE at growth rate of 0.5, and unimodal with peak CUE at a growth rate of 0.05 (the median of all per-taxon growth rates in the data). Comparisons between functions were made by calculating AIC values from per-taxon respiration, summed, and regressing against measured respiration values. Likewise, for each function, we tested how well per-taxon CUE estimates reconstructed community-level CUE by weighting the CUE value of each taxon by its relative abundance, summing, and regressing against community-level CUE. To select the best per-taxon CUE function, AIC values from both scaling efforts were combined. To make AIC values comparable, all respiration and CUE terms were z-transformed prior to regression scaling. To reflect our priority of estimating per-taxon respiration, AIC values from the respiration scaling regression models were multiplied by two and summed with AIC values from CUE scaling such that AICTotal = 2(AICResp) + AICCUE. Across these comparisons, the best estimate of per-taxon CUE was the unimodal function of growth rate, constrained by community-level CUE and peaking at growth rates of 0.5 (Table 1), such that:

$${{\rm{CUE}}}_{{\rm{i}}}=-4({{\rm{CUE}}}_{{\rm{E}}{\rm{:}}{\rm{T}}{\rm{:}}{{\rm{range}}}})\cdot {\left({g}_{{\rm{i}}}-0.5\right)}^{2}+({{\rm{CUE}}}_{{\rm{E}}{\rm{:}}{\rm{T}}{\rm{:}}{\max }}),$$
(6)

where CUEi indicates per-taxon CUE, CUEE:T:max indicates the maximum CUE values observed for a group of replicates within a given ecosystem and treatment (E:T). With this function, higher per-capita growth rate values were parameterized to produce higher CUE values initially and then decrease reflecting a growth-CUE tradeoff14, here bound by the difference in maximum and minimum CUE values. We applied per-taxon CUE estimates from Eq. 6 to per-taxon growth rates to yield estimates of per-taxon respiration:

$${r}_{{\rm{i}}}={r}_{{\rm{g,i}}}+{r}_{{\rm{m,i}}}=\left(\frac{{g}_{{\rm{i}}}}{{{\rm{CUE}}}_{{\rm{i}}}}-{g}_{{\rm{i}}}\right)+\left(\frac{{g}_{{\rm{i}}}}{{{\rm{CUE}}}_{{\rm{i}}}}-{g}_{{\rm{i}}}\right)\cdot \beta,$$
(7)

where ri indicates per-capita respiration for taxon i, rg,i indicates growth-related respiration, rm,i indicates maintenance-related respiration, and β is a constant of 0.01 that represents the maintenance requirements as a proportion of total energy use40. We used these values of per-taxon, per-capita respiration rates to estimate per-taxon respiration per gram of dry soil per week:

$${R}_{{\rm{i}}}={P}_{{\rm{i}}}\cdot {r}_{{{\rm{g,i}}}}+{P}_{{\rm{i}}}\cdot {r}_{{{\rm{m,i}}}},$$
(8)

where Ri indicates respiration of CO2–C (µg C g dry soil−1 week−1) for taxon i.

In addition to per-taxon respiration estimates based on 18O enrichment, we used another model for comparison. Here, respiration was calculated based on 16S abundance alone:

$${R}_{{\rm{i}}}={N}_{{\rm{i}}}\cdot f(R \sim N+0),$$
(9)

where Ni indicates final 16S abundance for taxon i, R indicates microbial respiration of CO2-C (µg C g dry soil−1 week−1) and N indicates total 16S abundance at the end of the incubation. Here, the R ~ N function indicates the linear relationship, with an intercept of 0, between CO2 respiration and 16S gene concentration across all samples.

### Diversity, compositional, and statistical analysis

For patterns of evenness in bacterial carbon use and relative abundance, we used Pielou’s evenness which is the quotient of Shannon’s diversity and the observed richness. For each sample, we applied Pielou’s evenness to bacterial abundances as well as bacterial carbon use (relativized to sum to one, in both cases).

We created a linear mixed model to test the relationship between the carbon use (the sum of biomass production and respiration) and relative abundance of bacterial genera from the dominant phyla, which accounted for >90% of all C flux. Here, we averaged carbon use and relative abundance for all replicates in a given ecosystem and treatment. We used the lme4 R package (version 1.1-20)41 and obtained p-values using the Satterthwaite method in the lmerTest R package (version 3.1-0)42. To limit pseudo-replication, we accounted for differences in carbon use across ecosystems and due to bacterial Genus by implementing random intercepts. We selected for the optimal random and fixed components by dropping individual terms and comparing models with likelihood ratio tests, disregarding models that failed to converge. Our final model fit was:

$${{{\log }}}_{10}({C}_{{\rm{i}}}) \sim {{{\log }}}_{10}\left({y}_{{\rm{i}}}\right)\ast T+\left(1|E\right)+(1|{{\rm{Genus}}}),$$
(10)

where Ci indicates the relativized carbon use for taxon i (averaged across all three replicates in a given ecosystem and treatment), yi indicates the relative abundance of taxon i (averaged across all three replicates), T indicates soil treatment, and E indicates ecosystem.

For differences in composition, we created species abundance tables using the traditional abundances, as well as measures of carbon use (growth and maintenance respiration) of each ASV in each sample. To account for differences in absolute abundances and flux rates between sites, we relativized all abundance tables. We summarized compositional differences using Bray–Curtis dissimilarities then identified multivariate centroids for all replicates in a site by treatment group. We tested the effect of site and nutrient amendment on the resulting group centroids using PERMANOVA tests implemented with the adonis function in the vegan package (version 2.5-3)43. We related compositional shifts in relative abundance to those in relativized growth and maintenance using Mantel tests with the mantel function in vegan.

To test for changes in the type of soil C preferred by microbial genera (either 13C-labeled glucose or 12C soil carbon) in response to nitrogen addition, we used Levene’s test with the car package (version 3.0-10)44. Specifically, we analyzed the relationship between 13C use and 12C use (both relativized) on bacterial genera across all replicates and in C and C + N treatments using a linear model. We then extracted model residuals and tested whether variance was significantly different across treatments by focusing on the interaction between individual replicates and treatment. This produced a significance test describing treatment-level differences in 13C–12C use.

### Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

Sequence data and sample metadata have been previously deposited in the NCBI Sequence Read Archive under the project number PRJNA521534. All other data have been made available at https://github.com/bramstone/bacterial-carbon-flux-qSIP (https://doi.org/10.5281/zenodo.4592585).

## Code availability

All statistical and modeling analyses have been made available at https://github.com/bramstone (https://doi.org/10.5281/zenodo.4592585).

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## Acknowledgements

This research was supported by grants from the Department of Energy’s Biological Systems Science Division Program in Genomic Science (Nos. DE-SC0016207 and DE-SCSC0020172), and by the National Science Foundation (No. DEB-1645596). Research conducted at Lawrence Livermore National Laboratory was supported by the U.S. Department of Energy Office of Science, via awards SCW1679 and SCW1590, conducted under the auspices of DOE Contract DE-AC52- 07NA27344. Research conducted at Pacific Northwest National Laboratory was supported by the U.S. Department of Energy Office of Science, via awards FWP 68907 and FWP 74475, conducted under the auspices of DOE Contract DE-AC05-76RL01830.

## Author information

Authors

### Contributions

B.W.S. analyzed data and wrote first draft of manuscript. J.L. performed bioinformatic analyses on prokaryote genomes. B.J.K., S.J.B., P.D., K.S.H., M.H., X.J.L., R.L.M., J.P.R., S.J.B., E.S. and B.A.H. contributed to project scope, experimental design, and interpreted findings. P.D., E.M.M. and B.A.H. contributed to organization and framing of manuscript. X.J.L., M.H. and R.L.M. collected and processed samples. M.H. and R.L.M. performed laboratory work and generated data. B.W.S. created supplemental Fig. 1. All authors contributed meaningfully to revisions.

### Corresponding author

Correspondence to Bram W. Stone.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

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Stone, B.W., Li, J., Koch, B.J. et al. Nutrients cause consolidation of soil carbon flux to small proportion of bacterial community. Nat Commun 12, 3381 (2021). https://doi.org/10.1038/s41467-021-23676-x

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