Decomposition causes short-term increases in functional molecular diversity of dissolved organic matter

Domain: physics
Published: 2025-12-10
Editorial outcome: minor_revision

Abstract
The molecular diversity of dissolved organic matter (DOM) in soil depends on the stage of plant litter decomposition and microbial metabolism. Yet the contributions of catabolic and anabolic processes on DOM molecular diversity, and their consequences for organic carbon mineralization, remain unclear. To address this question, we used an 18O-H2O isotope-labelling approach to track microbial transformation of DOM during blue grama grass (Bouteloua gracilis) decomposition and determine how these processes alter molecular diversity. Here, we show that 18O-isotopically labeled compounds indicate that microbially produced DOM increases functional molecular diversity (recognizing compound dissimilarity) during early decomposition (days) but not at later stages (months). Furthermore, carbon mineralization from DOM is most strongly correlated with molecular weight, highlighting the role of chemical properties in regulating microbial decomposition. Our findings suggest that early microbial catabolic and anabolic metabolism enhances DOM molecular diversity, whereas later decomposition favors the accumulation of fewer, recycled microbial compounds.

Introduction
Emergent evidence using new analytical techniques is driving a paradigm shift in our understanding of soil organic matter (SOM) persistence. While soil organic carbon (SOC) was previously considered to result from the accumulation of recalcitrant plant material such as lignin, now microbial decomposition processes are recognized as a key driver for SOC formation and persistence1–4. Microbial uptake and transformation of plant litter is a key step in generating dissolved organic matter (DOM) with longer turnover times in soil2,5. Microbial contributions in the form of microbial necromass (dead cell fragments) and exudates and secretions (metabolites, enzymes, waste products, protective mucus, etc.) are recognized as crucial components to the overall DOM composition4,6. The microbially transformed DOM pool of the smaller, more chemically reactive molecules can promote the persistence of organic carbon (C) in soil; however, it is also the most biologically active pool of organic carbon7–10. We have only recently begun to acknowledge that microbial decomposition influences the molecular richness and diversity of this biologically active pool of DOM in soils11,12. DOM molecular richness provides a simple measure of the number of unique molecules detected in DOM. In contrast, DOM molecular diversity offers a more comprehensive assessment, encompassing both the richness and the relative abundances of individual compounds. DOM molecular diversity can be further extended to account for chemical properties that reflect the similarity of molecules based on specific traits, such as molecular weight, a metric we refer to as DOM functional molecular diversity.

Our previous study showed that microbial decomposition decreased DOM molecular richness and diversity after prolonged decomposition, using soil depth as a proxy for time. The decline in DOM molecular diversity was accompanied by a decrease in compositional similarity across ecosystems. In surface litter layers, DOM composition remained relatively consistent among ecosystems, whereas in subsoil horizons, DOM became increasingly compositionally distinct when comparing differing ecosystems12. Our previous study also found an increase in DOM functional molecular diversity from the litter layer to the mineral A-horizon, which then decreased past baseline into the mineral C-horizon, suggesting microbial decomposition causes an initial increase in DOM functional molecular diversity on shorter timescales12. Therefore, we have focused this study on how the molecular diversity of DOM changes during the early stages of plant litter decomposition.

The molecular richness and molecular diversity of DOM have been shown, as mentioned above, to decrease due to microbial decomposition over long periods of time in entire soil profiles under steady-state11,12. We hypothesized that microbial decomposition initially produces a diverse suite of DOM inputs, yet over long periods of decomposition, molecularly similar products accumulate and therefore decrease DOM molecular diversity11, which in turn may influence further microbial mineralization of dissolved organic C13. Molecular diversity is thought to limit microbial metabolism as the concentration of discrete molecules in a diverse pool falls below the affinity of microbial enzymes and transporters13,14. Whether microbial consumption of plant litter or concomitant production of DOM increases or decreases DOM molecular diversity and how that in turn affects mineralization on short timescales has yet to be thoroughly investigated. Furthermore, it is unknown which compounds that are consumed or produced by microorganisms contribute to changes in DOM molecular diversity.

To fill these knowledge gaps, we tracked the molecular diversity of DOM after a single pulse of plant litter over a relatively short incubation time (<220 days) (Fig. S1). To assess broad changes in the overall DOM composition over time, we used attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. Liquid chromatography–tandem mass spectrometry (LC–MS/MS) data were used to define unique metabolite features based on exact mass and retention time. Putatively identified molecules for each metabolite feature, based on MS/MS similarity [global natural products social molecular networking (GNPS) top-hit] as unique chemical “species”, were used to track changes in DOM composition as well as quantify molecular richness and diversity for eight time points from the incubation (days 1, 2, 8, 32, 60, 100, 140, and 220)15. DOM molecular richness, molecular diversity (Hill Numbers when q=2; annotated as HN(q=2)), and DOM functional molecular diversity (multivariate Rao quadratic entropy; annotated as Rao(multivariate)) indices were calculated from these features by converting peak heights to relative abundance across samples11,12.

To explore in greater detail how microbial products influence changes in DOM molecular diversity, we leveraged 18O-labeled H2O as an isotopic tracer with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) at two different decomposition time points: day 7 and day 60 (Fig. S1). 18O-H2O is taken up by growing microorganisms and incorporated into their biomolecules (e.g., nucleic acids and lipids), but it is also used in numerous other pathways, including enzymatic reactions during metabolism16–18. Prior 18O–H2O tracing experiments have primarily tracked microbial growth and carbon use efficiency using stable isotope probing and Isotope Ratio Mass Spectrometry (IRMS)17,18; however, to our knowledge, previous experiments have not attempted to use 18O-H2O as an isotopic tracer to track microbial-driven DOM composition transformations with mass spectrometry.

DOM from water (polar, hydrophilic compounds), chloroform (non-polar, hydrophobic compounds), and methanol (moderately polar compounds) extractions19 were analyzed using FT-ICR-MS to identify compounds isotopically enriched with 18O. To determine the efficiency of the isotope uptake by microorganisms, we analyzed the water and chloroform-extractable organic matter using IRMS to quantify δ18O. To further identify changes in metabolite abundances, we used gas chromatography mass spectrometry (GC–MS), and to quantify molecular richness, abundance-based molecular diversity (HN(q=2)) and functional molecular diversity (Rao(multivariate)) of DOM, we used LC–MS/MS data.

Here, we used measured changes in DOM richness and molecular diversity in relation to decomposition age and how these changes were driven by microbial extracellular transformation of plant-derived organic matter and production of microbial organic matter using an 18O-isotope labeling method. To address this aim, we focused on two primary questions. First, how do microbial catabolic (consumption of organic compounds for energy) and anabolic (production of biomass and metabolites) metabolic processes differentially contribute to changes in DOM molecular richness and functional molecular diversity during decomposition? Second, how do these microbial processes and their effects on DOM diversity change over short decomposition timescales (<220 days)? Together, these questions aim to clarify the relative roles of microbial breakdown of plant-derived compounds versus the production of new microbial metabolites in shaping the composition and chemical complexity of soil DOM over time.

Results
During the entire incubation period (220 days), the largest shifts in molecular richness and diversity occur during the first 32 days of decomposition and then plateau thereafter. By day 60, molecular richness calculated from compounds identified with LC–MS/MS had declined by 24% from initial values (Figs. 1A, S2A, Table S1, t-test; p-value = 0.006). Abundance-based molecular diversity (HN(q=2)) also declined by 70% over the entire course of the incubation (Figs. 1B, S2B, Table S1, t-test, p-value < 0.05). Functional molecular diversity (Rao(multivariate)) initially increased by 23% during the first 32 days (t-test, p = 0.03) before declining to values similar to the start of the incubation after another 188 days (Figs. 1C, S2C, Table S1). The composition of DOM over time had a larger increase in molecular dissimilarity (Bray–Curtis dissimilarity index) in the first 32 days of decomposition (40%) compared to the increase in dissimilarity from day 32 to day 220 (30%) (Fig. 1D). By day 32, non-purgeable organic carbon (NPOC) concentrations decreased by 60% (Fig. S3, Table S2, pairwise comparison with Bonferroni correction factor; p-value < 0.0001), then declined by an additional 10% for the following 188 days.

The 18O–H2O isotope tracing experiment showed that microbial respiration in the first 7 days amounted to 40% of the total cumulative respiration during the entire 60-day incubation period (Fig. 2A; t-test, p-value = 0.10, Fig. SA). By day 7, NPOC and DON concentrations declined by 58% and 30%, respectively, compared to the abiotic control microcosms (Fig. S4B, C). NPOC and DON concentrations in the biotic microcosms then further declined by 44% and 23%, respectively, from day 7 to day 60 (Fig. 2A, B). Microbial incorporation of ¹⁸O into organic matter, used here as a proxy for microbial activity, declined significantly by day 60 in both water extractable organic matter (WEOM; −25%, p = 0.002) and chloroform extractable organic matter (CEOM; −63%, p < 0.0001) fractions from biotic microcosms relative to day 7 (Fig. 2D). In contrast, abiotic microcosms exhibited negligible ¹⁸O incorporation across both WEOM and CEOM fractions, aligning with the lack of detectable mineralization and stable NPOC and DON concentrations throughout the incubation (Fig. S4D).

To understand microbial contributions to DOM, we analyzed water, methanol, and chloroform extractions using FT-ICR-MS, which capture distinct fractions of soil organic matter. Water extracts (WEOM) primarily polar, hydrophilic compounds (e.g., sugars and amino acids), methanol extracts (MEOM) moderately polar compounds (e.g., phenolics and small organic acids), and chloroform extracts (CEOM) non-polar, hydrophobic compounds (e.g., lipids and waxes)19,20. In the WEOM fraction, a total of 1961 unique peaks were identified using FT-ICR-MS; comprised of 1717 non-isotopically enriched compounds and 244 compounds that were identified as 18O-isotopically enriched when directly compared with the same peaks identified in samples with the natural-abundance H2O (Fig. 3). In WEOM, the average nominal oxidation state of carbon (NOSC) was lower (more reduced) in the pool of 18O-isotopically enriched compounds compared to the non-isotopically enriched compounds (Fig. 4A). While NOSC values of non-isotopically enriched compounds remained stable over time, NOSC values of the 18O-isotopically enriched compounds became 37% more oxidized over time (Fig. 4A, aligned rank transform, p-value < 0.001). H:C ratios of the 18O-isotopically enriched WEOM were higher at both time points than the non-isotopically enriched compounds and declined over time (Fig. 4A).

From day 7 to day 60, the DOM molecular richness and functional molecular diversity of the WEOM showed that the 18O-isotopically enriched compounds declined by 10% and 22%, respectively (Fig. 4B, Table S3, ANOVA, p-values < 0.05). There were also large decreases in molecular richness (−32%, ANOVA, p-value = 0.006), abundance-based molecular diversity (HN(q=2) −38%, ANOVA, p-value = 0.004) in the non-isotopically enriched pool of compounds over time (Fig. 4B). Decreases in DOM molecular richness and abundance-based molecular diversity (HN(q=2)) of non-isotopically enriched compounds indicate extensive microbial consumption and assimilation into biomass, whereas the decline in functional diversity of ¹⁸O-labeled compounds suggests that microbial-derived organic matter is less chemically complex than the mixture of microbial- and plant-derived compounds. DOM functional molecular diversity of WEOM 18O-isotopically enriched compounds at day 7 matched that of non-isotopically enriched compounds but was 22% higher than at day 60 (Table S3; ANOVA, p = 0.007).

In the MEOM fraction, a total of 942 unique peaks were identified, comprised of 891 non-isotopically enriched compounds and 51 18O-isotopically enriched compounds (Fig. S5). The only molecular property of the MEOM fraction that differed across time was the modified Aromaticity Index (AImod) of non-isotopically enriched compounds (Fig. S6A), yet the DOM functional molecular diversity of the MEOM non-isotopically enriched compounds declined by 26% over time (Fig. S6B; ANOVA, p-value < 0.001).

In the CEOM fraction, a total of 4804 unique peaks were identified, which were comprised of 4781 non-isotopically enriched compounds and only 23 18O-isotopically enriched compounds (Fig. S7). The average AI values were significantly lower in the 18O-isotopically enriched compounds in comparison to the non-isotopically enriched peaks for both time points (Fig. S8A; ANOVA, p-value < 0.05). The CEOM fraction did not vary in DOM molecular richness, abundance-based molecular diversity (HN(q=2)), or functional molecular diversity overtime (Fig. S8B, Table S3).

Molecular richness and abundance-based molecular diversity (HN(q=2)) values of 18O-isotopically enriched compounds identified in the WEOM, MEOM, and CEOM were consistently lower than those of non-enriched compounds at both decomposition stages. Conversely, the DOM functional molecular diversity of MEOM and CEOM 18O-isotopically enriched compounds exceeded that of non-isotopically enriched compounds at day 60 (Table S3; ANOVA, p < 0.05).

The modified aromaticity index (AImod) declined over time in non-isotopically enriched WEOM, MEOM, and CEOM (Figs. 4A, 6A and 8A; aligned rank transform, p-values < 0.05). 18O-isotopically enriched compounds were also consistently less aromatic than non-isotopically enriched ones (Figs. 3, 4 and S5–S8). The average molecular weight of the WEOM 18O-isotopically enriched compounds declined over time and was lower than the non-isotopically enriched compounds at day 60 (Fig. 4A). The chemical composition of 18O-isotopically enriched MEOM and CEOM remained relatively stable over time and did not differ substantially from the non-isotopically enriched fractions (Figs. 6A and 8A). In contrast, the 18O-isotopically enriched WEOM compounds were initially more reduced, less aromatic, and had higher H:C ratios than their non-isotopically enriched counterparts, but became progressively smaller and more oxidized over time. Together, this indicates that microbial activity selectively decreased the DOM functional molecular diversity of the polar WEOM and MEOM fractions of DOM over time, whereas the non-polar CEOM fraction remained largely unchanged. This suggests that shifts in DOM functional molecular diversity are primarily driven by microbial transformations of biologically active, polar, hydrophilic compounds rather than non-polar fractions.

In later decomposition stages (>32 until 220 days), DOM showed an accumulation of compounds characteristic of microbial products. Over time, ATR-FTIR spectra of DOM showed a shift in the amide C=O bonding region (−1660 to −1630 cm−1) and increased peaks in the aliphatic C–H bend regions (−2990 to −2800 and −1470 to −1370 cm−1) (Fig. S9). The most abundant metabolites in the first two days of decomposition were simple sugars (e.g., sucrose, lactose, xylose, ribose, and glucose) (Fig. 5). At the end of the incubation after 220 days, the most abundant metabolites were organic acids such as sebacic acid, abscisic acid, carboxylate, and di-ferulic acid (Fig. 5). Of the calculated molecular properties from the identified metabolites, the number of nitrogen atoms had the greatest variation over time with a 68% increase (Table S2) likely from increased amino sugar inputs. Whereas NOSC, O:C and H:C ratios calculated from LC–MS/MS identified compounds did not vary much over the course of 220 days. DOM metabolites identified using GC–MS from the isotope addition incubation were primarily more molecularly similar at day 60 of decomposition compared to day 7 (Fig. 6). On day 60, microbial metabolites were in greater abundances, such as organic acids commonly identified as fermentation products (e.g., lactic acid, D-gluconic acid, carboxylic acid, and muconic acid) and secondary amines likely produced from the breakdown of necromass-derived proteins (DL-valine, DL-glycine and DL-leucine) (Fig. 6). Microbial metabolites with higher relative abundances at day 60 also consisted of compounds indicative of microbial biosynthesis such as dipeptides (e.g., Ile-Val and Ile-Leu (e.g. aspartic acid, asparagine, and serine) (Fig. 6). PCA ordination showed that the samples obtained at day 60 had less variation in ordination space with a tighter clustering than those taken at day 7. PC1 explained 29.4% of the variance, and PC2 explained 23.2% of the variance and showed a significant difference between day 7 and day 60 (Fig. 7; ANOVA, F-statistic(1,10) = 88.03, p-value < 0.0001). Plant metabolites (e.g., asterric acid, diferulic acid, and hyperoside), simple sugars (e.g., sucrose and D-fructose), and sugar acids (e.g., threonic acid and saccharic acid) occurred in greater abundances at day 7 compared to day 60 (Fig. S10).

Molecular diversity indices, calculated from DOM identified with LC-MS/MS, with molecular sizes < 800 Da, failed to predict DOM mineralization (Fig. S11). Although cumulative carbon dioxide (CO2) respiration varied across the differing sample time points (Fig. S12; ANOVA, p-value = 0.009) and DOM molecular richness, abundance-based molecular diversity (HN(q=2)) and functional molecular diversity varied across time (Fig. 1A), there were no significant trends between molecular diversity and C mineralization over a 10-day incubation period using DOM sourced from the entire incubation comprised of eight time points spanning 220 days (Fig. S11A–C). However, CO2 respiration was negatively correlated with molecular weight (Fig. S11H; R2 = 0.32, p-value = 0.003).

Methods
Plant litter and soil community collection
Soils samples were sourced in 2015 from a clayey aeolian Mollisol from Northwestern Texas, USA (35.31, −101.45; MRLA 77C), with a mean annual temperature of 14 to 27 °C and a mean annual precipitation of 432 to 533 mm41. Blue grama grass (Bouteloua gracilis) plant litter was collected in 2019 from a comparable Mollisol located in New Mexico, USA (35.97, −106.54). Soil samples were frozen and shipped to Cornell University. A soil microbial community was isolated by stirring 50 g of soil in 650 mL of 1× PBS for 24 h, then filtered with 20–24 µm Whatman #1 filters. The resulting community was stirred for an additional four weeks to ensure the community reached a stationary phase. The solution was then washed with 1× PBS three times to remove background DOM, and the cells were stored in a 15% v/v glycerol solution after being adjusted to 0.25 AU at OD600. Aliquots of the soil microbial community were stored at −80 °C prior to use. The soil microbial community was reconstituted by adding 2 mL of the previously frozen stock community to 50 mL of a dilute nutrient broth (1:100) and stirring for one week. Microbial cells were washed three times with 1× PBS and reconstituted to 0.45 AU at OD600 prior to use.

Timeseries microcosm experimental setup
Microcosms were constructed by adding 40 g of acid-washed quartz sand and 1 g of previously autoclaved blue grama grass to 40 mL glass jars. The microcosms were covered with aluminum foil and autoclaved twice, with 24 h between cycles, for 1 h at 120 °C, 0.10 MPa. A 1 L solution nutrient solution was made by adding 10 mL of Wolfe’s trace metal, 10 mL of Wolfe’s vitamin mixture solution42,43 and M2 media ingredients (20× salt, MgSO4, chelated ferrous sulfate, and CaCl2). The nutrient solution was added to the microcosms, followed by the microbial inoculum (1 mL), so that the total water-filled pore space was 60%. Microcosms were placed in mason jars with 5 mL of sterile water in the bottom to maintain humidity in the jars and incubated at room temperature. Microcosms were destructively harvested in triplicate for 14 time points on days 1, 2, 4, 8, 16, 32, 46, 60, 80, 100, 120, 140, 180, and 220. We aimed for frequent sampling at the beginning of the incubation and spaced the sampling time points out later in the incubation to capture changes in molecular richness and diversity properties without missing inflection time points. Microcosms were immediately frozen, lyophilized, and stored at −20 °C until processed further.

18O–H2O isotope microcosm experimental setup and pulse labeling
Microcosms were constructed in the same manner as the time-series microcosms; 20 g of acid-washed quartz sand and 0.5 g of previously autoclaved blue grama grass were added to 40 mL glass jars. The microcosms were covered with aluminum foil and autoclaved twice for 1 hour at 120 °C, 0.10 MPa. A 1 L nutrient solution was made by adding 10 mL of Wolfe’s trace metal, 10 mL of Wolfe’s vitamin mixture solution42,43 and M2 media ingredients (20× salt, MgSO4, chelated ferrous sulfate, and CaCl2). The nutrient solution was added to the microcosms, followed by the microbial inoculum (1 mL), so that the total water-filled pore space was 45%. The microcosms were sealed in mason jars with 5 mL of sterile water at the bottom to maintain humidity.

At two time points, day six and day 59, subsets (1 g in triplicate) from the four treatments (abiotic-16O, abiotic-18O, biotic-16O, and biotic-18O) (Fig. S1) were aseptically transferred from the mesocosms to 15 mL conical tubes. Either 18O–H2O or HPLC-grade water, which we refer to as 16O–H2O even though there is still a natural abundance of 18O in HPLC-grade water, was added to the subsets to bring the total water-filled pore space to 60% in each tube. We selected days 7 and 60 for isotope labeling based on results from the parallel 220-day incubation, allowing us to capture differences in DOM composition and molecular diversity between relatively fast and slow phases of microbial activity. Isotope additions were at a final concentration of 50 atom%, and the subsets were incubated for 24 h, to ensure adequate time for 18O to be utilized during microbial growth while limiting recycling of the 18O-isotope during prolonged decomposition17,44. After 24 h, the subsets were frozen at −20 °C and immediately lyophilized and shipped to the Environmental and Molecular Science Laboratory (EMSL) at the Pacific Northwest National Laboratory (PNNL) for further analysis.

Timeseries sample extraction and analysis
DOM from the time series incubation samples was obtained by shaking 8 g of freeze-dried sample from the destructively harvested microcosms with 32 mL of HPLC-grade water for 1 h. The samples were then centrifuged at 3000 RPM for 30 min and syringe-filtered at 0.20 µm. The resulting extractant was divided into subsets for several/the following purposes. DOC was measured using a Shimadzu TOC-L analyzer (Shimadzu Scientific Instruments, Inc.). Broad DOM composition from the microcosm was analyzed by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) analysis. FTIR spectra were collected using a Bruker Vertex 70 spectrometer (Bruker Optics Inc., Billerica, MA, USA) equipped with a Pike GladiATR accessory (Pike Technologies, Madison, WI, USA) and using a single reflection diamond internal reflection element. Spectra were collected from 4500 to 150 cm−1 with a resolution of 4 cm−1 and averaged from 60 scans per sample. OPUS 7.2 software (Bruker Optics Inc., Billerica, MA, USA) was used to conduct baseline correction and normalization before exporting spectra for interpretation.

Based on DOC concentrations and FTIR spectra, eight time points were selected from the analyzed 14 for LC–MS/MS analysis. Each sample extractant was divided into two 15 mL conical tubes (10 mL each), lyophilized, and then transferred to glass vials using methanol and dried down using ultra-pure nitrogen gas.

18O–H2O sample extraction and analysis
Samples from the 18O-isotope tracing experiment were extracted using a modified Folch method45 by adding 5 mL of LC–MS grade water to 1 g of the sand and litter sample, shaking for 2 h, and then aspirating to collect the water extractable (polar) organic matter fraction. A methanol:water:chloroform solution (4:3:8 mL) was then added to the water-extracted soil, shaken for one hour at room temperature, and then spun at 6000 RPM (4430 xg with an angled rotor) to separate the methanol and chloroform layers. The upper methanol layer was collected and is referred to as the methanol fraction; the lower chloroform layer was also collected to analyze the non-polar compounds. Water fraction and methanol fraction samples were cleaned up using a modified protocol from Dittmar et al.25 by SPE to desalt the samples.

Water and methanol extractable samples were infused into the 21T Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS) by an automated direct injection system at a flow rate of 4 µL min−1 26. Data were acquired in negative mode with a resolving power calculated at 1M and the automated gain control (AGC) set to 1.0E6. The electrospray source was set to 3.4 kV, sheath gas set to 7, and the capillary was set to 275 °C. Samples were co-added for 450 scans from 220 m/z to 900 m/z. Peaks with a signal-to-noise (s/n) ratio of 2 or better were picked from raw spectra using in-house developed software encapsulating Thermo Excalibur libraries.

Chloroform extractable samples were infused into the ScimaX 7T FT-ICR-MS instrument by an automated direct injection system at a flow rate of 4 µl/min. Samples were co-added for 300 scans, 100 m/z to 900 m/z, and peak picked using Bruker DA software with a s/n of 7 or better. Data were calibrated and a monoisotopic formula assigned using the compound identification algorithm (CIA) implemented in software formularity27,46. Formula with enriched incorporation of isotope 18O were determined by direct comparison of biotic and abiotic samples from both samples that were exposed to 18O–H2O and those that had a natural abundance (termed “16O–H2O”) water pulse. The isotopic pattern algorithm (IPA) implemented in Formularity software was used to detect 18O and other isotopologue peaks of formula detected in 18O-enriched and -unenriched H2O samples. Abundance ratios (AR) of 18O isotopologue and monoisotopic peaks in 18O-enriched and -unenriched H2O samples were used to determine the 18O enrichment in the identified formula. In cases when an 18O isotopologue peak was not detected in H2O samples with natural abundance 18O enrichment, AR was calculated from the simulation by ecipex software47. Formulas identified in 18O-H2O samples were then designated as 18O-enriched if their AR differed from the expected AR in formula with naturally abundant 18O by 2 or more standard deviations higher than the mean from samples that were only exposed to water with naturally abundant 18O.

Liquid chromatography–tandem mass spectrometry
DOM from the time series experiment and the two-timepoint non-isotopically labeled parallel incubation (Fig. S1) were analyzed for untargeted metabolites using LC–MS/MS. Untargeted metabolites were analyzed using a Thermo Mass Spectrometer coupled to an ultra-high-processing liquid chromatography (UHPLC) (Thermo Vaquish UHPLC System) instrument using two columns in negative mode; an Accucore Vanquish C18+ column and a SeQuant ZIC pHILIC column. Prior to injection, sample extracts were normalized to 0.39 mg C mL−1 LC–MS grade acetonitrile (ACN) (Optima) 10%, 0.1% LC–MS grade formic acid (FA) (30 µL) for the HILIC column. Quality assurance samples were run before and after experimental samples, and quality control samples were run at the beginning and end of the sequence, as well as after every 10 samples.

Chromatographic features and MS/MS spectra were extracted using Mzmine 2 prior to annotation via spectral matching using Global Natural Products Social Molecular Networking (GNPS) libraries15,48 and the feature-based molecular networking (FBMN) workflow49,50. An Mzmine workflow was used to generate a list of features (m/z and retention time values were obtained from extracted ion chromatograms containing chromatographic peaks within a narrow m/z range (<10 ppm) and filtered to remove isotopes. For each feature, the most intense fragmentation spectrum was uploaded to GNPS. When a sample mass spectrum matches one deposited within the GNPS database, a putative identification is made. Library hits were filtered based on accurate mass <15 ppm for negative mode and <5 ppm for positive mode, cosine score > 0.70, and number of matching ions = 3. In addition, features were filtered to meet the following requirements: retention time greater than 1 min, fold-change greater than extraction controls > 50 times. Only the top one hit from the GNPS networking library was selected to remain in the dataset. Features were also dropped when all peak heights across samples had an intensity of less than 100,000. Global Natural Products Social Molecular Networking (GNPS) was used to annotate features with matching molecules within the GNPS library51. Library hits were validated based on three metrics (accurate mass <15 ppm, retention time within 1 min, and MS/MS fragment matching). Only the top one GNPS hit was selected to remain in the dataset. Molecular formula of each identified peak was derived from associated SMILES strings using the R package mzAnnotation52. Compounds were also annotated with superclass designations derived from the MS-BUDDY application53.

Isotope ratio mass spectrometry
To determine microbial activity and uptake of the 18O isotope, water and methanol extracts from the 18O–H2O isotope labeling experiment were analyzed using a high-temperature conversion elemental analyzer (Thermo Finnigan) isotope ratio mass spectrometer (TC-EA IRMS, Thermo Finnigan Delta V Plus IRMS)17,44. This method quantifies the content of 18O in organic molecules within extractable organic matter, providing an indicator of the extent to which substrates have been metabolized by bacterial cells17. Incorporation of the ¹⁸O label, reflected in δ¹⁸O values, was used to distinguish biotic and abiotic incorporation of the ¹⁸O label, providing an indicator of microbial anabolic activity across early and late decomposition stages. Samples were weighed into silver capsules (EA Consumables, 5×3.5 mm), measured in triplicate, and standardized using USGS42, USGS43, and a benzoic acid in-house standard. IRMS isotope data are reported as δ-notation relative to Vienna Standard Mean Ocean Water (VSMOW) standard, where δ18O is defined as
δ18O = (Rsample / Rstandard − 1) × 1000 (1)
and R represents the ratio of the heavy to light isotopes (18O/16O) for the sample and standard, with values denoted in δ-notation as per mil values (‰).

Gas chromatography mass spectrometry
We used GC-MS to identify changes in low-molecular-weight DOM compounds (e.g., sugars, amino acids, and fatty acids) between the two time points (day 7 and day 60). Dried metabolite extracts from samples are derivatized using a modified version of the protocol used to create the FiehnLib spectral library54. Samples undergo methoximation to protect carbonyl groups and reduce tautomeric isomers. Followed by silylation with N-methyl-N-trimethylsilyltrifluoroacetamide and 1% trimethylchlorosilane (MSTFA) to derivatize hydroxy and amine groups to trimethylsilated (TMS) forms. An Agilent GC 7890A coupled with a single quadrupole MSD 5975C (Agilent Technologies) is used for the collection of GC/MS data. Data is collected over a mass range of 50–550 m/z. A standard mixture of fatty acid methyl esters (FAMEs) (C8–C28) was analyzed with samples for RI alignment. The GC oven was held at 60 °C for 1 min after injection, followed by a temperature increase by 10 °C min−1 to a maximum of 325 °C, at which point it was held for 5 min. GC–MS raw data files were processed using Metabolite Detector software, version 2.5 beta. Agilent files were converted to CDF format using Agilent Chemistation. Files were then converted to binary files using the metabolite detector. Retention indices (Ris) of detected metabolites were calculated based on analysis of the Fatty acid methyl esters standard mixture, followed by chromatographic deconvolution and alignment. Metabolites were initially identified by matching experimental spectra to an augmented version of FiehnLib54. All metabolite identifications were manually validated with the NIST v20 GC–MS library. The summed abundances of the three most abundant fragment ions of each identified metabolite were integrated across the GC elution profile (automatically determined by Metabolite Detector); fragment ions due to trimethylsilylation (that is, m/z 73 and 147) were excluded from the determination of metabolite abundance. Features resulting from GC column bleeding were removed from the data before further data processing and analysis.

DOM re-mineralization across molecular diversity gradient
Acid-washed sand (10 g) was added to glass jars (40 mL) and autoclaved for two cycles 24 h apart. Previously extracted and lyophilized samples were reconstituted using methanol so that equal amounts of C (0.3 mg) were pipetted into the microcosms (Fig. S1). The methanol was then removed by gently flowing nitrogen gas over the microcosms to stimulate evaporation of the methanol while leaving the transferred DOM coating on the sand minerals. The soil microbial community was revitalized by adding a 2-mL aliquot of the previously frozen (−80 °C) stock soil microbial community to 50 mL of a dilute nutrient broth (1:100) and allowed to grow for one week. After a week, the microbial community cells were washed three times with 1× PBS in preparation for inoculation. At the start of the incubation, 2 mL of a 1 L solution comprised of 10 mL of Wolfe’s trace metal, 10 mL of Wolfe’s vitamin mixture solution42,43, and M2 media ingredients (20× salt, MgSO4, chelated ferrous sulfate and CaCl2) was added to each microcosm to provide adequate vitamins and trace metals for cellular growth. A nitrogen treatment group received excess nitrogen (0.07 mg N) in the form of ammonium chloride in the Wolfe’s mixture. The nitrogen treatment group was used to identify if correlations between mineralization and molecular diversity were caused by changes in nitrogen concentrations (e.g., nutrient dilution hypothesis)14. The Wolfe’s mixture was followed by 1 mL of the PBS rinsed microbial community stock solution, which brought the microcosms up to 60% water-filled pore space. The microcosms were sealed in mason jars alongside 10 mL of 0.03 M KOH solution in scintillation vials55. The KOH solution was measured with an EC meter over the course of 10 days, and cumulative CO2 emissions were calculated from standard curves using pure CO2. C mineralization was analyzed using a two-way analysis of variance (ANOVA) to test the effects of incubation time point of DOM extraction (day), ammonium chloride nitrogen treatment, and their interaction. Significant differences among treatment combinations were further evaluated using pairwise comparisons with Tukey’s honest significant difference (HSD) method.

Molecular diversity calculations
DOM molecular richness, abundance-based molecular diversity, and functional molecular diversity of DOM were calculated from both LC–MS/MS and FT-ICR-MS data. In alignment with our previous study12, we calculated molecular richness using the sum of identified molecular features in each sample and abundance-based molecular diversity using Hill Numbers (q=2)56,57 for a pool of molecules that favors common individuals (HN(q=2)), defined as
HNq=2(p) = 1 / Σ pi^2 (2)
where pi is the relative abundance of a given molecule, and when the q quotient is equal to 2, which estimates the effective number of species with more weight given to common species57–61. Rao’s quadratic entropy was calculated with a multivariate distance matrix using the standardized molecular properties of molecular weight (Da), the nominal oxidation state of carbon (NOSC), O:C and H:C molar ratios, and the number of nitrogen atoms (Rao(multivariate)). Rao’s quadratic entropy is defined by the equation,
Rao = Σ Σ dij pi pj (3)
where dij is the dissimilarity between molecule i and molecule j (or the inter-species diversity), pi is the relative abundance of molecule i, and pj is the relative abundance of molecule j. Molecular dissimilarity is calculated using a multivariate distance matrix of the standardized molecular properties of molecular weight (Da), the nominal oxidation state of carbon (NOSC), O:C and H:C molar ratios, and the number of nitrogen atoms (Rao(multivariate))58,59. The multivariate distance matrix enables us to incorporate multiple important features of compounds that likely influence the behavior of that molecule in the soil, or its ability to be further decomposed by microorganisms. The Bray–Curtis Dissimilarity Index was calculated using the bcdist() function from the vegan package in Rstudio60, as a measure of molecular β-diversity, or the similarity of compounds across time.

Molecular richness and abundance-based molecular diversity were calculated using peak intensities as presence/absence and relative abundance, respectively, of unique annotated metabolite features. Relative abundance of peaks was calculated across sample or time. LC–MS/MS features from the C18 and HILIC columns that were annotated to a chemical identifier and associated molecular formula based on GNPS spectral matching were combined into one dataset and used to calculate richness or diversity measures. The associated molecular formula, based on simplified molecular input line entry system (SMILES) strings, was used to calculate molecular properties based on functional molecular diversity including molecular weight, NOSC, O:C and H:C ratios, and number of N atoms.

18O–H2O experiment statistical analyses
IRMS. IRMS analysis of water and chloroform extracts was used to verify the uptake of 18O on day 7 and day 60. To assess differences in δ¹⁸O values between days 7 and 60 within each extraction type (water and lipid), we performed independent two-sample Welch’s t-tests using the t_test() function from the rstatix package in R (Version 4.3.1). P-values were adjusted for multiple comparisons using the Bonferroni correction. Statistical significance was defined as p < 0.05 after adjustment.

FT-ICR-MS. To identify 18O-enriched vs. non-enriched organic compounds in water and methanol, we used FT-ICR-MS. Chloroform extractable organic samples yielded few isotopically labeled compounds and, therefore, were not used to identify differences in isotopically labeled compounds (Fig. S5). Relative abundances of 18O-enriched and non-isotopically enriched peaks identified using FT-ICR-MS for the biotic samples are displayed using a Van Krevelen diagram with H:C and O:C ratios on the x- and y-axis respectively (Figs. 4 and S4). DOM molecular diversity was calculated using relative abundances of 18O-enriched and non-isotopically enriched peaks from the biotic samples (Fig. 4). To compare means of DOM molecular diversity indices and molecular properties, t-tests were performed for day 7 and day 60 (capital letters) and enriched and non-isotopically enriched samples (lowercase letters). Groups that do not share the same letters are considered significantly different (Tables S3 and S4).

GC–MS. Raw metabolite peak intensity data were converted to relative abundances after metabolite names were standardized, and duplicate metabolites were consolidated. Relative abundances were averaged for day 7 and day 60 and visualized using the pheatmap() function (Fig. 6A). Hierarchical clustering of metabolites was performed using Euclidean distance and complete linkage, and metabolites were grouped into four clusters. Principal component analysis (PCA) of GC–MS metabolites was performed using the factoextra61 package in R to determine clustering of biotic samples for the two time points (Fig. 6B).

LC–MS/MS. The relative abundances for each unique metabolite with LC–MS/MS is displayed using a heatmap to show changes in compound abundances for the two time points (day 7 and day 60) (Fig. S8A). Molecular formulas were derived from MS-BUDDY53 and used to calculate various molecular properties such as the nominal oxidation state of carbon (NOSC)62, the double bond equivalent (DBE)63, O:C and H:C ratios, and molecular weight (Fig. S8B). Comparison of molecular diversity means from the two time points was done with pairwise t-tests.

Timeseries statistical analyses
LC–MS/MS. Mean Bray–Curtis dissimilarity index values for each time point were calculated using identified LC-–MS/MS features with the bcdist() function from the vegan package60 (Fig. 1). The relative abundances for each unique metabolite with LC–MS/MS is displayed using a heatmap to show changes in compound abundances over time (Fig. 2). Rows were clustered by averaged relative abundance of compounds across time. Row annotations further indicate superclass groups derived from MS-BUDDY53; non-annotated compounds are left blank. Comparison of molecular richness, abundance-based, and functional molecular diversity index means from the eight time points for the timeseries data was done with pairwise t-tests using a Bonferroni correction factor to account for multiple comparisons using the emmeans package in RStudio. Compact letter displays were generated for the eight time points using the multcomp package in R; means from time points that share the same letters are not statistically different (p-value < 0.05). We performed linear regressions to determine if calculated molecular richness, abundance-based, and functional molecular diversity index means or molecular properties predicted mineralization rates of DOM (Fig. S12).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study systematically explored the regulatory mechanism of microbial metabolic activities on the dynamic changes in the molecular diversity of soil dissolved organic matter (DOM). By combining stable isotope (18O-H2O) labeling technology and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), the mechanism by which microbial decomposition dynamically regulates the molecular diversity of DOM was revealed. The study found that: (1) In the initial stage of microbial activity (≤32 days), the functional diversity of DOM was significantly increased through substrate differential metabolism; (2) Substrate depletion led to molecular convergence, verifying the \"metabolic splitting effect\" hypothesis; (3) The negative correlation between the molecular weight distribution and the carbon mineralization rate suggested that diversity might not directly regulate carbon stability but was rather a comprehensive manifestation of molecular properties. The experiment design is innovative with high data richness. However, the current version has several problems such as insufficient logical rigor, lack of technical details, and excessive inference of conclusions, which may affect the reliability of the conclusions. The authors can supply data, improve the discussion, and standardize the writing to solve above problems, the core conclusions of this study will be more convincing and provide a key paradigm for cross-scale research on the mechanism of soil carbon turnover. The specific modification suggestions are as follows:\n1. I believe that molecular weight is a crucial factor in DOC mineralization. The authors stated that molecular characteristics probably affect mineralization rates much more than the number of compounds in a single C functional group clade. So, I wonder why the authors didn't think about other molecular properties, like aromaticity, and how they relate to the mineralization rate. A multiple regression analysis is helpful to explain the results. In addition, detailed discussion on the low R2 values is required. This will help explain that the findings are exploratory.\n2. The authors found a significant increase in molecular richness and diversity in the early stage (at 2 days), following by a decrease in molecular richness due to changes in oxygen levels. However, as shown in Fig. 1a, the increase in molecular richness only occurred at 2 days, and such data composition seems rather insufficient. How did the authors consider the selection of sampling time?\n3. The 18O-H2O labeling might introduce errors through abiotic pathways, like the oxygen exchange between water molecules and organic matter. But the authors didn't verify the specificity of the labeling. For instance, it is not determined whether the labeling only reflects microbial activity.\n4. The filtering criteria for characteristic peaks of FT-ICR-MS and LC-MS/MS, such as a signal-to-noise ratio (S/N) of 2 and peak intensity thresholds, are too lenient. This may lead to the introduction of false positive peaks, resulting in calculation errors in molecular diversity. For example, the molecular diversity of the 18O-labeled peaks in Figure 3 may include instrumental noise.\n5. The manuscript assumes that the increase in functional diversity in the early stage is a direct result of microbial metabolic diversity, but it fails to rule out the influence of the initial complexity of the substrate. For example, it doesn't consider that the molecular diversity of plant litter itself may be gradually released.\n6. The conclusion of this manuscript, that there is no significant correlation between molecular diversity and CO₂ mineralization, does not match the conclusion suggested by the title \"Microbial decomposition increases molecular diversity.\" Moreover, the author has previously reported in an article that \"Decomposition decreases molecular diversity and ecosystem similarity of soil organic matter,\" which creates an ambiguity with the title of this manuscript.\nSpecify comments:\n1. The main text mentions \"Fig. S5\" and \"Fig. S6\", but the supplementary materials do not indicate the existence of S5 and S6 (it is necessary to confirm whether there is an omission in numbering). In addition, the figures in the main text are repeated twice, and the information of some figures is inconsistent. Please confirm the correct content.\n2. The formula for δ18O does not use a unified symbol. In the original text, \"δ18O\" and \"Δ18O\" are used interchangeably, and it is necessary to unify them as either δ or Δ.\n3. In Line 375, \"ml\" should be unified into \"mL\".\n4. The use of \"et al.\" in the references with multiple authors is not unified.", "reviewer_2": "This study examines how microbial decomposition impacts various metrics of molecular diversity over time. A series of experiments were conducted using a range of advanced methods and measurements. While the general topic, the microbial impact on molecular properties of DOM is both interesting and timely, it lacks a clear introduction to the significance of molecular diversity, and the motivation behind the study. Furthermore, in my opinion, the different experiments and methods were not well synthesized being able to give an overall implication.This becomes very obvious in the abstract, in which the concluding sentences refers to potential experimental and measurement limitations instead of presenting implications of the findings.\nMain points:\n- (to my knowledge) This is the second data driven study from the Lehmann lab (see “Decomposition decreases molecular diversity and ecosystem similarity of soil organic matter” Davemport et al. 2023), which does not support the very influential perspective paper (“Persistence of soil organic carbon caused by functional complexity” Lehmann et al. 2020). I think, more room should be given to discuss these non-supportive results also under the possibility that this is not caused by limitations of the experiment and measurements, but to consider that the results of the experiment are realistic.\n- the substantial amount of data has not been analyzed in a manner that allows for the identification of novel findings or mechanisms related to microbial litter decomposition. In particular, the stable isotope experiment potentially offers a way in identifying microbial metabolites that are produced during the decomposition. Simply analyzing the molecular diversity of the labeled formulas does not provide significantly more information than the results of the long-term experiment. Plotting the enriched peaks in a van-krevelen diagram (Fig. 3) can be a first step. However, it would be more informative how the molecular properties, such as size or NOSC differ from the non-labeled peaks and even more insightful, if the peaks can be related to specific metabolites or metabolic pathways (similar to the results presented in Fig 4B).\n- Throughout the manuscript, the language and use of terminology is not very accurately and are rather loosely applied (a few examples: having of enzymes instead of expressing them, Bray Curtis distance instead of dissimilarity, richness and/or evenness despite their clear distinctions and implications)\n- It may be related to the formatting requirements of the journal - however, there is a need for revising parts of the manuscript for a better understanding. The current version is difficult to understand when reading the sections consecutively, in particular, the reasoning for some experiments (N-addition, both fractions of the chloroform fumigation, …), introductions of abbreviation, their inconsistent use etc.\nMinor Points:\nTitle:\nI acknowledge the very limited space for the title, however, it is misleading, as it does not properly reflect the results.\nAbstract:\n- Lines 25-28: The abstract lacks motivation and the \"big picture\" behind the study.\n- Lines 28-30: Specify what was incubated during the long-term incubations and how it was done. Provide details.\n- Lines 35-36: Suggest indicating the direction of this relationship.\n- Lines 36-38: Surprisingly, no implications or conclusions are drawn from the results at the end of the abstract. Is the most obvious implication of the study that there are technical and experimental limitations?\nIntroduction:\n- Lines 46-47: Verify if these are the correct references for this statement.\n- Lines 50-59: A more general and compelling introduction is needed to explain why the molecular diversity of DOM is important. The authors inquire if a more molecularly diverse DOM is retained longer in mineral association, yet mineral association is not investigated in this study.\n- Line 62: Decide on using either DOM or SOM consistently.\n- Lines 64-67: Add a reference or explanation for the assumption made.\n- Line 65: The phrase \"long periods of decomposition\" sounds awkward, as decomposition is a process, not a measure of time. Please rephrase and use commas for clarity.\n- Lines 62, 64, 68-70: The importance of molecular diversity is not well introduced. Consider adding a paragraph to explain its significance.\n- Line 75: Ensure consistency with the term \"long-term,\" as used in the abstract.\n- Line 85-87: Redundant with lines before?\nLine 92: LC has been described, for what was the GC used?\nResults:\n- Structure: The structure is not optimal for a Nature journal. Detailed information is missing, and the methods section is at the end. More information in the introduction section would be beneficial.\n- Line 112: What does the abbreviation HN stand for? Please introduce it properly.\n- Lines 113, 115: Clarify what \"S2\" refers to.\n- Lines 116-118: This sentence is difficult to understand. Please rephrase for clarity.\n- Lines 125-127: The interpretation of the two fractions of CFE is not well introduced for those unfamiliar with it. Please clarify its relevance.\n- Line 127: There is no Figure 2E.\n- Section starting from line 129: This section is very technical. It is not well described (here or earlier) what can be concluded or assessed with the two CFE fractions.\n- Line 136: There is inconsistent use of terms like “multivariate functional molecular diversity,” “multivariate functional diversity,” and “functional molecular diversity.”\n- Line 141: Does the MetOH-extractable OM only comprise lipids? Provide more examples or remove the statement.\n- Lines 152-161: The takeaway of this paragraph is unclear. Please clarify and rephrase.\nLines 199-200: likely only day 140 differs from day 100. Was a posthoc test done? The N-treatment is also not clear (no introduction, motivation,...), ANOVA is not specified in the statistics section of the methods.\nLines 202-203: In my opinion it is difficult to deduce a causal relationship or a dependency from this relation, collinearity is possible.\nDiscussion:\nThe discussion on microbial activity is quite superficial, with many speculations lacking references. There is no concluding remark on the study's revelations or its benefits to the field, yet the discussion is lengthy.\n- Lines 215-217: There are several issues with this sentence. Where in the results section are DON and nutrient effects are shown? Was carbon use efficiency assessed? Higher activity and carbon use efficiency (CUE) are not mutually exclusive. How is CUE related to nutrients/nitrogen? Please rephrase or provide sufficient references.\n- Lines 225-228: Is this based on your own data or another study’s data? Please rephrase for clarity.\n- Lines 230-232: The relevance of the previous study is unclear, as it was based on spatial differences (i.e., depth), while your current study is based on temporal differences. Please rephrase or remove for clarity.\n-- Lines 243-245: This is a strong statement. If statistics are missing, please add a test (e.g., anosim) if this is based only on visually inspecting the ordination in Fig 4.\n- Lines 245-269: The same information is repeated twice in the paragraph. Please rephrase to reduce redundancy. There are many assumptions but no discussion on how to evidence or prove those claims, or on future study focus (e.g., using sequencing data to confirm postulated microbial community turnovers). Please back with statistical results or appropriate references.\n- Line 270: Clarify what the “laboratory microcosm-induced phenomenon” is.\n- Lines 306-312: The definition isn’t clear. Refer to Roulet & Moore 2006 (https://doi.org/10.1038/444283a) and rephrase for clarity.\n- Lines 315-318: There are contradictory statements. You state higher microbial activity increases functional diversity, then state it reduces functional diversity, as mentioned in the previous subsection. Which is correct? Please rephrase for clarity and add references to support your assumptions, especially since your experimental approach does not allow inference of microbial activity patterns solely based on 18O observations.\n- Lines 323-324: Is this a general pattern or context-dependent? Please clarify.\n- Lines 335-340: These lines might fit better in the introduction.\n- Lines 337-340: Why were the parameters not included in the model if they are known to have an effect? Please clarify.\n- Lines 343-345: The response and size cannot be disentangled with your experimental setup. Please clarify.\nMethods:\n- Software and Packages: There is no mention of the software used for statistics and analysis, nor the packages utilized (assuming it was done in R). Please include this information.\n- Factual Redundancies: There are many factual redundancies in the methods section.\n- Line 468: Avoid using an abbreviation immediately after introducing it, i.e., \"(ACN) ACN.\"\n- Lines 475-476: Define what is meant by a “narrow m/z range” by providing specific numbers.\n- Lines 492-503: It is unclear how isotope ratio translates to microbial activity, please explain and include a reference for this.\n- Line 506: What is Feihnlib.1? Please prodive a reference.\n- Line 536: Clarify whether Wolfe’s trace metal solution was also added. You mention adding the vitamin solution only, but refer to adding trace metals, which would require both Wolfe’s solutions. Provide appropriate details.\n- Line 537: Explain the purpose of the nitrogen treatment group and provide additional details.\n- Line 548: Bray-Curtis is a dissimilarity matrix. Please rephrase for accuracy.\n- Line 554: Ensure consistency with units, as \"Da\" is used as a unit in line 198.\nFigures, Tables, and Supplement:\n- Figure 1: Not all abbreviations are explained. Please ensure all abbreviations are defined. Furthermore, there is a “log-like” distance between the sampling steps. Why not plott the time in log scale on the x-axes (A,B,C).\n- Figure 2: Not all abbreviations are explained. Please ensure all abbreviations are defined. Is it necessary to show the abiotic samples in the main text?\n- Figure 3: Consider comparing statistics within the same treatment across days 7 and 60, instead of between enrichment treatments, as this may reveal differences better. Also, is the scale \"relative abundance\" correct? There are many peaks (in particular in the non-enriched panels) with a relative abundance higher than 0.5. It's really interesting that the result of the HN increase is enriched from early to late, while it decreases in non-labeled HN from early to late stages. I suggest to elaborate on this.\n- Figure 4: Add a color scale legend for the heatmap for clarity (same in Fig. S5, S6). Is somewhere indicated what \"relative\" means? (relative among compound/metabolites considered ore among all annotated formulas found in the samples). Please give more information on the cluster diagram (same in Fig. S5, S6)\n- Figure 5: Not all abbreviations are explained. Please ensure all abbreviations are defined.\n- Tables S1 - S4: Clarify what the numbers in the brackets represent in the table legend.\nOther Remarks:\n- Consistency in Number Style: Ensure a uniform style for writing numbers (e.g., \"60 days\" vs. \"sixty days\"). Use the same style consistently throughout the manuscript.\n- Orthography: Double-check for spelling mistakes and orthography issues, as there are several at present.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the reviewer for the concise summary of the study and the main findings we have attempted to convey. Due to the specific comments below, we believe we have improved the rigor, readability and reproducibility of our study. We have heavily revised this manuscript with the guidance of the specific comments provided and revised and added data products based on new analyses. While the core findings remain the same, we have included better visualizations and data analyses of the FT-ICR-MS data along with a more detailed (but less redundant) discussion of the results and broader implications, which make them more defensible and clearer. Thank you for helping us to accomplish that.\nThe specific modification suggestions are as follows:\n1. I believe that molecular weight is a crucial factor in DOC mineralization. The authors stated that molecular characteristics probably affect mineralization rates much more than the number of compounds in a single C functional group clade. So, I wonder why the authors didn't think about other molecular properties, like aromaticity, and how they relate to the mineralization rate. A multiple regression analysis is helpful to explain the results. In addition, detailed discussion on the low R2 values is required. This will help explain that the findings are exploratory.\nAuthor’s response: We chose to report regressions with the nominal oxidation state of carbon (NOSC), O:C and H:C ratios, the number of nitrogen atoms, and molecular weight with mineralization rates because these are the variables we selected to be used in a multi-variate functional diversity calculation. We felt these characteristics encompassed the traits of molecules that would impact their mineralizability. Other characteristics, such as an aromaticity index or double bond equivalent (DBE) are helpful indices, but in this application are redundant since they are calculated using similar molecular traits (O:C and H:C ratios, N atoms, etc.).\nWe agree with the reviewer that a multiple regression analysis can be helpful, however due to our limited sample size we do not have enough statistical power to do anything beyond repeated linear regressions. We added clarifications to the manuscript as shown below and in the manuscript.\nWe have added more discussion regarding the low R-squared values.\nAuthor’s changes: (Ln. 346-362): “Reactive low–molecular weight DOM may also form complexes with enzymes that limit their mineralization. Organo-organic complexes may also decrease by reducing the DOC solubility or enzyme efficiency. For example, aquatic DOM was found to have larger polyphenolic molecules that formed complexes with enzymes (e.g., phosphatases) and thus reduced the efficiency of enzymes32. It is also possible that molecular weight may be a proxy for a chemical property or interaction mechanism within DOM that we cannot measure directly and cannot incorporate as a key characteristic in DOM molecular diversity calculations. Advances in our technical ability to quantify DOM molecular diversity and the fine-scale measurement of CO respiration are needed to resolve the effects of molecular diversity on mineralization.\nWe should also consider that molecular diversity alone may not have a measurable impact on DOC mineralization rates as previously proposed14. Soil microbial communities exhibit high functional similarity, with overlapping ecological roles among individual microorganisms, enabling them to simultaneously use a broad suite of substrates in C-poor environments33–36. Chemostat experiments have shown that E. coli can utilize at least six sugar substrates at once34,35,37. This suggests that molecular characteristics – such as oxidation state or molecular weight – may play a greater role in regulating mineralization rates rather than the sheer number of compounds within a functional group.”\n2. The authors found a significant increase in molecular richness and diversity in the early stage (at 2 days), following by a decrease in molecular richness due to changes in oxygen levels. However, as shown in Fig. 1a, the increase in molecular richness only occurred at 2 days, and such data composition seems rather insufficient. How did the authors consider the selection of sampling time?\nAuthors’ response: We conducted the microcosm incubation for 220 days and harvested samples on days 1, 2, 4, 8, 16, 32, 46, 60, 80, 100, 120, 140, 180, and 220 (Ln. 395-396). We aimed for frequent sampling in the beginning of the incubation and spaced the sampling time points out later in the incubation, as is commonly done. We then measured DOC concentrations and OM composition (using FTIR-ATR) from these 14 time points to select eight time points to measure using LC-MS/MS (Ln. 431-432). See Fig. S2 & S3 for DOC concentrations and FTIR-ATR spectra. We further used this data to identify two time points (day seven and 60) to conduct isotope labeling. These selections were our best approach to do targeted MS/MS analysis to capture changes in molecular richness and diversity properties without missing inflection time points. We added information to the manuscript to clarify that procedure.\nAuthors’ changes: (Ln. 399-402): “We aimed for frequent sampling in the beginning of the incubation and spaced the sampling time points out later in the incubation to capture changes in molecular richness and diversity properties without missing inflection time points.”\n(Ln. 418-419): “We selected days seven and 60 for isotope labeling based on results from the parallel 220-day incubation, allowing us to capture differences between relatively fast and slow phases of microbial activity.”\n3. The 18O-H2O labeling might introduce errors through abiotic pathways, like the oxygen exchange between water molecules and organic matter. But the authors didn't verify the specificity of the labeling. For instance, it is not determined whether the labeling only reflects microbial activity.\nAuthors’ response: We agree there might be errors via abiotic pathways with the 18O-H2O label, which is why we had included both abiotic and biotic treatments using either 18O-H2O or natural abundance H2O (Fig. S1). Using these treatments, we were able to directly compare assigned formula to identify which peaks were biotic-specific that picked up the label. We have clarified this within the main text to direct the reader to this method.\nAuthors’ changes: (Ln. 465-475): “Formula with enriched incorporation of isotope 18O were determined by direct comparison of biotic and abiotic samples from both samples that were exposed to 18O-H O and those that had a natural abundance (termed “16O-H O”) water pulse.\nIsotopic Pattern Algorithm (IPA) implemented in Formularity software was used to detect 18O and other isotopologue peaks of formula detected in 18O-enriched and -unenriched H O samples.\nAbundance ratios (AR) of 18O isotopologue and monoisotopic peaks in 18O-enriched and -unenriched H O samples were used to determine the 18O enrichment in identified formula. In cases when an 18O isotopologue peak was not detected in H O samples with natural abundance 18O enrichment, AR was calculated from the simulation by ecipex software49. Formula identified in 18O-H O samples were then designated as 18O-enriched if its AR differed from the expected AR in formula with naturally abundant 18O by 2 or more standard deviations higher than the mean from samples that were only exposed to water with naturally abundance of 18O.”\n4. The filtering criteria for characteristic peaks of FT-ICR-MS and LC-MS/MS, such as a signal-to-noise ratio (S/N) of 2 and peak intensity thresholds, are too lenient. This may lead to the introduction of false positive peaks, resulting in calculation errors in molecular diversity. For example, the molecular diversity of the 18O-labeled peaks in Figure 3 may include instrumental noise.\nAuthors’ response: We have updated the methods section to clarify that both the 21T FTICR and 7T were used, with different s/n ratios. It is also unlikely that these selected peak filters introduced false peaks or instrument noise in FT-ICR-MS measurements. Described 21T FT-ICR instrument uses FT acquisition computer from an LTQ-FT Ultra (ThermoFisher Scientific, San Jose, CA, USA) (Ipsen, 2014). Peaks were extracted using Thermo Xcalibur 2.2 ((ThermoFisher Scientific, San Jose, CA, USA) libraries with calculated “S/N” of 2 based on direct ratio of peak intensity and instrument recorded noise information. Although we do not routinely record time domain transient signal, in our previous tests and estimates, on this instrument, cut-off of 2 would correspond to standard signal processing S/N of more than 7σ. Xcalibur software also automatically removed artifact harmonic peaks.\nIpsen, A. Efficient Calculation of Exact Fine Structure Isotope Patterns via the Multidimensional Fourier Transform. Anal Chem 86, 5316–5322 (2014).\nAuthors’ changes: (Ln. 436-447): “Water and methanol extractable samples were infused into the 21T Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS) by an automated direct injection system at a flow rate of 4 µL min-1 46. Data were acquired in negative mode with a resolving power calculated at 1M and the automated gain control (AGC) set to 1.0 E6. The electrospray source set to 3.4 kV, sheath gas set to 7 and the capillary was set to 275°C.\nSamples were co-added for 450 scans from 220 m/z to 900 m/z. Peaks with signal-to-noise (s/n) ratio of 2 or better were picked from raw spectra using in-house developed software encapsulating Thermo Excalibur libraries. Chloroform extractable samples were infused into the ScimaX 7T FT-ICR-MS instrument by an automated direct injection system at a flow rate of 4ul/min. Samples were co-added for 300 scans, 100 m/z to 900 m/z, and peak picked using Bruker DA software with a s/n of 7 or better. Data were calibrated and monoisotopic formula assigned using Compound Identification Algorithm (CIA) implemented in software Formularity47,48.”\n5. The manuscript assumes that the increase in functional diversity in the early stage is a direct result of microbial metabolic diversity, but it fails to rule out the influence of the initial complexity of the substrate. For example, it doesn't consider that the molecular diversity of plant litter itself may be gradually released.\nAuthors’ response: Thank you for pointing this out. We recognize that we failed to accurately describe that in addition to inputs from microbial sources, that the degradation of plant material also contributes to functional molecular diversity. We have added a conceptual diagram to better illustrate how the additive nature of both plant litter and microbial sources may contribute to increased functional molecular diversity.\nAuthors’ changes:\n(Ln. 258-262): “As microbial activity peaked, we suspect both newly released compounds cleaved from plant litter, and a variety of microbial substances—including enzymes, antibiotics, and metabolic waste—contribute to the increased functional molecular diversity of the DOM pool (Fig. 7).”\nSee Fig. 7.\n6. The conclusion of this manuscript, that there is no significant correlation between molecular diversity and CO₂ mineralization, does not match the conclusion suggested by the title \"Microbial decomposition increases molecular diversity.\" Moreover, the author has previously reported in an article that \"Decomposition decreases molecular diversity and ecosystem similarity of soil organic matter,\" which creates an ambiguity with the title of this manuscript.\nAuthors’ response: This is an important point, to reduce ambiquity we have changed the title to add the word “Functional” to clarify the difference. We were also careful to use the term “Short-term” in this title since we are investigating changes in molecular diversity on vastly different time scales (in situ soil depth profiles with likely hundreds to thousands of years in the PNAS paper that the referee mentioned (titled “Decomposition decreases molecular diversity and ecosystem similarity of soil organic matter”) vs 220 day lab incubation in the paper under review here). The title of the manuscript also does not indicate a relationship between molecular diversity and mineralization of soil carbon; just that decomposition initially (short-term) increases functional diversity of DOM which is what our data show.\nAuthors’ changes: (Ln. 1-2): “Decomposition Causes Short-term Increase in Functional Molecular Diversity of Dissolved Organic Matter ”\nSpecify comments:\n1. The main text mentions \"Fig. S5\" and \"Fig. S6\", but the supplementary materials do not indicate the existence of S5 and S6 (it is necessary to confirm whether there is an omission in numbering). In addition, the figures in the main text are repeated twice, and the information of some figures is inconsistent. Please confirm the correct content.\nAuthors’ response: We thank the reviewer for pointing out issues with references to figures and tables, we have done our best to ensure there are no discrepancies in this revised text.\n2. The formula for δ18O does not use a unified symbol. In the original text, \"δ18O\" and \"Δ18O\" are used interchangeably, and it is necessary to unify them as either δ or Δ.\nAuthors’ response: We have updated the text to use to δ consistently. Thank you for the catch.\n3. In Line 375, \"ml\" should be unified into \"mL\".\nAuthors’ response: We have revised, thank you.\n4. The use of \"et al.\" in the references with multiple authors is not unified.\nAuthors’ response: Citations are converted to et al., when there are more than five authors; fewer than five authors retains the full list of co-authors, as outlined in the guide to authors.\nReviewer #2 (Remarks to the Author):\nThis study examines how microbial decomposition impacts various metrics of molecular diversity over time. A series of experiments were conducted using a range of advanced methods and measurements. While the general topic, the microbial impact on molecular properties of DOM is both interesting and timely, it lacks a clear introduction to the significance of molecular diversity, and the motivation behind the study. Furthermore, in my opinion, the different experiments and methods were not well synthesized being able to give an overall implication.This becomes very obvious in the abstract, in which the concluding sentences refers to potential experimental and measurement limitations instead of presenting implications of the findings.\nAuthors’ response: We thank the reviewer for conducting a thorough examination of our study and providing helpful feedback to greatly improve our manuscript. We have revised the introduction and abstract to better convey the studies’ motivation and significance. We have also revised the methods section to include more details regarding statistical methods used and experimental design. We have further directly addressed the reviewers’ concerns that we did not discuss these findings within the context of Lehmann et al., 2020. We hope we have satisfied the reviewer’s concerns and issues with inconsistencies with terminology throughout the manuscript, as these surely have improved the manuscript.\nMain points:\n- (to my knowledge) This is the second data driven study from the Lehmann lab (see “Decomposition decreases molecular diversity and ecosystem similarity of soil organic matter” Davemport et al. 2023), which does not support the very influential perspective paper (“Persistence of soil organic carbon caused by functional complexity” Lehmann et al. 2020). I think, more room should be given to discuss these non-supportive results also under the possibility that this is not caused by limitations of the experiment and measurements, but to consider that the results of the experiment are realistic.\nAuthors’ response: We acknowledge that we did not give enough direct attention to acknowledging the apparent contrast of our findings with a previous paper from our group (as outlined in the previous responses, the apparent contrasts can be reconciled by the vastly different time periods of the two studies,- short-term in the paper here under review (220 days) which shows a transient effect of decomposition over the first few weeks, and very long periods of time (hundreds to thousands of years in soil profiles) in the PNAS paper from 2023 that the referee mentions). We have added more direct discussion on this topic as suggested.\nAuthors’ changes:\n(Ln. 258-267): “These findings support the idea that functional molecular diversity is driven by both catabolic and anabolic metabolism, unlike the hypotheses from Liang et al. (2017) that predominantly catabolic metabolic processes would favor a more chemically diverse suite of compounds24. Our results provide important fine points to the framework of Lehmann et al. (2020) by showing that DOM functional molecular diversity only initially increases with the addition of microbial organic matter. However, we show here that functional molecular diversity declines once microbial organic matter becomes the dominant fraction, consistent with the findings of reduced molecular diversity over very long periods of time found in soil profiles (Davenport et al., 2023). This pattern produces a bell-shaped curve of functional diversity over decomposition time; where functional molecular diversity is generally low when the DOM pool is composed of either primarily plant organic matter or microbial organic matter (Fig. 7).”\n(Ln. 355-362): “We should also consider that molecular diversity alone may not have a measurable impact on DOC mineralization rates as previously proposed14. Soil microbial communities exhibit high functional similarity, with overlapping ecological roles among individual microorganisms, enabling them to simultaneously use a broad suite of substrates in C-poor environments33–36. Chemostat experiments have shown that E. coli can utilize at least six sugar substrates at once34,35,37. This suggests that molecular characteristics – such as oxidation state or molecular weight – may play a greater role in regulating mineralization rates rather than the sheer number of compounds within a functional group.”\n- the substantial amount of data has not been analyzed in a manner that allows for the identification of novel findings or mechanisms related to microbial litter decomposition. In particular, the stable isotope experiment potentially offers a way in identifying microbial metabolites that are produced during the decomposition. Simply analyzing the molecular diversity of the labeled formulas does not provide significantly more information than the results of the long-term experiment. Plotting the enriched peaks in a van-krevelen diagram (Fig. 3) can be a first step. However, it would be more informative how the molecular properties, such as size or NOSC differ from the non-labeled peaks and even more insightful, if the peaks can be related to specific metabolites or metabolic pathways (similar to the results presented in Fig 4B).\nAuthors’ response: We agree that using more detailed DOM molecular properties derived from the FT-ICR-MS labelling experiment would help identify mechanisms relating to microbial decomposition. This data enabled us to discuss the different metabolic pathways engaged during early and late decomposition stages that were driving changes in functional molecular diversity. We have added this data in an updated figure to highlight what was buried in the SI data tables.\nAuthors’ changes: See new Fig. 4.\nResults section (Ln. 180-196): “In WEOM, the average nominal oxidation state of carbon (NOSC) was lower (more reduced) in the pool of 18O-isotopically enriched compounds compared to the non-isotopically enriched compounds (Fig. 4A). While NOSC values of non-isotopically enriched compounds remained stable over time, NOSC values of the WEOM 18O-isotopically enriched compounds became 37% more oxidized over time (Fig. 4A, aligned rank transform, p-value < 0.001). The modified aromaticity index (AI ) declined over time in WEOM, MEOM, mod and CEOM non-isotopically enriched compounds (Fig. 4A, Fig. 7A, Fig. 9A; aligned rank transform, p-values < 0.05). 18O-isotopically enriched compounds were also consistently less aromatic than non-isotopically enriched ones (Fig. 3-4, Fig. S6-S9). The average molecular weight of the WEOM 18O-isotopically enriched compounds declined over time and was lower than the non-isotopically enriched compounds at day 60 (Fig. 4A). H:C ratios of the WEOM 18O-isotopically enriched compounds were higher at both time points than the non-isotopically enriched compounds, and also declined over time (Fig. 4A). The chemical composition of MEOM and CEOM 18O-isotopically enriched compounds remained relatively stable over time and did not differ substantially from the non-isotopically enriched fractions. In contrast, the WEOM 18O-isotopically enriched compounds initially more reduced, less aromatic, and had higher H:C ratios than their non-isotopically enriched counterparts, but became progressively smaller and more oxidized over time. ”\nDiscussion section (Ln. 239-254): “Microbial decomposition involves the transformation of plant litter–derived organic compounds through extracellular depolymerization and intracellular processes of catabolism (energy release) and anabolism (biomass synthesis). Anabolic pathways assimilate organic carbon into cellular constituents to support microbial biomass growth, whereas catabolic pathways degrade organic compounds—often following extracellular depolymerization—primarily to generate energy22,23. Liang et al. proposed that microbial metabolism dominated by catabolic pathways with high rates of extracellular depolymerization (ex vivo turnover) generates a greater complexity of organic compounds compared to decomposition processes dominated by anabolic metabolic pathways (in vivio turnover), which would cause a convergence of organic matter composition over time23. Our results, however, indicate that both catabolic and anabolic metabolic processes were high during early decomposition (day seven), with microorganisms likely allocating proportionally more energy toward anabolic activity. A predominance of anabolic metabolism in the early decomposition is evident by the greater number of 18O-isotopically labeled compounds and higher δ18O concentrations in the early decomposition time point (day seven) compared to the later time point (day 60) (Fig. 3-4). ”\n- Throughout the manuscript, the language and use of terminology is not very accurately and are rather loosely applied (a few examples: having of enzymes instead of expressing them, Bray Curtis distance instead of dissimilarity, richness and/or evenness despite their clear distinctions and implications)\nAuthors’ response: We apologize for the lack of attention to consistent use of terminology used throughout the manuscript and have made changes throughout the manuscript to ensure consistency and clarity in our definitions.\n- It may be related to the formatting requirements of the journal - however, there is a need for revising parts of the manuscript for a better understanding. The current version is difficult to understand when reading the sections consecutively, in particular, the reasoning for some experiments (N-addition, both fractions of the chloroform fumigation, …), introductions of abbreviation, their inconsistent use etc.\nAuthors’ response: We have ensured all abbreviations are defined before their use throughout the manuscript and have changed section headings to aid in the flow of the manuscript. We also clarified why a nitrogen treatment group was used for the mineralization experiment.\nAuthors’ changes: (Ln. 562-564): “The nitrogen treatment group was used to identify if correlations between mineralization and molecular diversity were caused by changes in nitrogen concentrations (e.g., nutrient dilution hypothesis)17.”\nMinor Points:\nTitle:\nI acknowledge the very limited space for the title, however, it is misleading, as it does not properly reflect the results.\nAuthors’ response: We have updated the title to clarify the functional molecular diversity changes that happen quickly and are also temporary (i.e., short-term).\nAuthors’ changes: (Ln. 1-2): “Decomposition Causes Short-term Increase in Functional Molecular Diversity of Dissolved Organic Matter”\nAbstract:\n- Lines 25-28: The abstract lacks motivation and the \"big picture\" behind the study.\nAuthors’ response: We have fully revised the abstract to better describe the motivation and findings of the study, as also mentioned in the response to comments by referee 1.\nAuthors’ changes: (Ln. 25-41): “Molecular diversity of dissolved organic matter (DOM) in soil depends on the stage of plant litter decomposition and microbial metabolic capacity. The relative contributions of catabolic and anabolic processes to DOM molecular diversity, and their consequences for organic carbon mineralization, remain poorly understood. To address this question, we incubated blue grama grass (Bouteloua gracilis) in quartz-sand microcosms and applied a novel targeted 18O-H O isotope labelling approach to track how microbial production 2 and consumption of DOM alters its molecular diversity. 18O-isotopically labeled compounds revealed that microbially produced DOM exhibited greater functional molecular diversity during early decomposition (day seven) than at later stages (day 60). Over a 220-day incubation, functional molecular diversity initially increased by 23% (t-test, p-value = 0.03) within the first 32 days then declined by 22% over the remaining 188 days as the number of unique metabolites decreased. Carbon mineralization was not strongly predicted by molecular diversity but was negatively correlated with average molecular weight (R-squared = -0.23, p = 0.009), highlighting the role of chemical properties in regulating microbial decomposition. Our findings suggest that early microbial activity enhances DOM functional molecular diversity through simultaneous catabolic and anabolic metabolism, whereas later decomposition after relatively short timeframes (i.e., weeks) favors the accumulation of fewer, recycled microbial compounds.”\n- Lines 28-30: Specify what was incubated during the long-term incubations and how it was done. Provide details.\nAuthors’ response: We have updated the abstract to include incubation details within our word limit.\nAuthors’ changes: (Ln. 28-31): “To address this question, we incubated blue grama grass (Bouteloua gracilis) in quartz-sand microcosms and applied a novel targeted 18O-H O isotope 2 labelling approach to track how microbial production and consumption of DOM alters its molecular diversity.”\n- Lines 35-36: Suggest indicating the direction of this relationship.\nAuthors’ response: We have updated the abstract to include a direction of the relationship.\nAuthors’ changes: (Ln. 35-38): “Carbon mineralization was not strongly predicted by molecular diversity but was negatively correlated with average molecular weight (R-squared = -0.23, p = 0.009), highlighting the role of chemical properties in regulating microbial decomposition.”\n- Lines 36-38: Surprisingly, no implications or conclusions are drawn from the results at the end of the abstract. Is the most obvious implication of the study that there are technical and experimental limitations?\nAuthors’ response: We have revised the abstract to include broader implications and findings rather than focusing on technical limitations as aptly suggested.\nAuthors’ changes: (Ln. 38-41): “Our findings suggest that early microbial activity enhances DOM functional molecular diversity through simultaneous catabolic and anabolic metabolism, whereas later decomposition after relatively short timeframes (i.e., weeks) favors the accumulation of fewer, recycled microbial compounds.”\nIntroduction:\n- Lines 46-47: Verify if these are the correct references for this statement.\nAuthors’ response: We verified these are the references we would like to use for this statement but added Kallenbach et al., 2016 as well.\n- Lines 50-59: A more general and compelling introduction is needed to explain why the molecular diversity of DOM is important. The authors inquire if a more molecularly diverse DOM is retained longer in mineral association, yet mineral association is not investigated in this study.\nAuthors’ response: We agree that we do not investigate the role of mineral association in this study and therefore have removed this statement. We have further revised the introduction for clarity of intent of the study.\nAuthors’ changes: (Ln. 52-67): “The microbially-transformed DOM pool of then smaller, more chemically reactive, molecules can promote the persistence of organic carbon (C) in soil, however it is also the most biologically active pool of organic carbon8–11. We’ve only recently begun to acknowledge that microbial decomposition influences the molecular diversity of this biologically active pool of DOM in soils12,13. Our previous study showed that microbial decomposition decreased DOM molecular richness and diversity after prolonged decomposition, using soil depth as a proxy for time. The decline in DOM molecular diversity was accompanied by a decrease in compositional similarity across ecosystems. In surface litter layers, DOM composition remained relatively consistent among ecosystems, whereas in subsoil horizons, DOM became increasingly compositionally distinct when comparing differing ecosystems13. Our previous study also found an increase in DOM functional molecular diversity from the litter layer to the mineral A-horizon, which then decreased past baseline into the mineral C-horizon, suggesting microbial decomposition causes an initial increase in DOM functional molecular diversity on shorter timescales13. Therefore, we have focused this study on how molecular diversity of DOM changes during early stages of plant litter decomposition.”\n(Ln. 73-78): “Molecular diversity is thought to limit microbial metabolism as the concentration of discrete molecules in a diverse pool falls below the affinity of microbial enzymes and transporters14,15. Whether microbial consumption of plant litter or concomitant production of DOM increases or decreases DOM molecular diversity and how that in turn affects mineralization on short time scales has yet to be thoroughly investigated. Furthermore, it is unknown which compounds that are consumed or produced by microorganisms contribute to changes in DOM molecular diversity.”\n- Line 62: Decide on using either DOM or SOM consistently.\nAuthors’ response: We have revised the manuscript to consistently use DOM, however where we reference our 2023 PNAS paper findings we use SOM as that is the terminology used in that paper due to the methodology of extraction.\n- Lines 64-67: Add a reference or explanation for the assumption made.\nAuthors’ response: We have added Lehmann et al., 2020, Davenport et al., 2023 and Davenport et al., 2025 as references for this statement.\n- Line 65: The phrase \"long periods of decomposition\" sounds awkward, as decomposition is a process, not a measure of time. Please rephrase and use commas for clarity.\nAuthors’ response: We have revised as suggested.\nAuthors’ changes: (Ln. 56-58): “Our previous study showed that microbial decomposition decreased DOM molecular richness and diversity after prolonged decomposition, using soil depth as a proxy for time.”\n- Lines 62, 64, 68-70: The importance of molecular diversity is not well introduced. Consider adding a paragraph to explain its significance.\nAuthors’ response: We have revised the introduction to molecular diversity to add clarity and significance.\nAuthors’ changes: (Ln. 67-78): “DOM molecular diversity—the richness and/or evenness of individual molecular compounds—has been shown, as mentioned above, to decrease due to microbial decomposition over long periods of time in entire soil profiles under steady-state12,13. We hypothesized that microbial decomposition initially produces a diverse suite of DOM inputs, yet over long periods of decomposition molecularly similar products accumulate and therefore decrease DOM molecular diversity12, which in turn may influence further microbial mineralization of dissolved organic C14. Molecular diversity is thought to limit microbial metabolism as the concentration of discrete molecules in a diverse pool falls below the affinity of microbial enzymes and transporters14,15. Whether microbial consumption of plant litter or concomitant production of DOM increases or decreases DOM molecular diversity and how that in turn affects mineralization on short time scales has yet to be thoroughly investigated. Furthermore, it is unknown which compounds that are consumed or produced by microorganisms contribute to changes in DOM molecular diversity.”\n- Line 75: Ensure consistency with the term \"long-term,\" as used in the abstract.\nAuthors’ response: Thank you for noticing this inconsistency, we have revised where appropriate.\n- Line 85-87: Redundant with lines before?\nAuthors’ response: We have revised this section to remove redundances.\nAuthors’ changes: (Ln. 79-111): “To fill these knowledge gaps, we tracked molecular diversity of DOM after a single pulse of plant litter over a relatively short incubation time (<220 days) (Fig. S1). To assess broad changes in the overall DOM composition over time, we used attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. Liquid chromatography-tandem mass spectrometry (LC–MS/MS) data were used to define unique metabolite features based on exact mass and retention time. Putatively identified molecules for each metabolite feature, based on MS/MS similarity [Global Natural Products Social Molecular Networking (GNPS) top--hit] as unique chemical “species”, were used to track changes in DOM composition as well as quantify molecular richness and diversity for eight time points from the incubation (days 1, 2, 8, 32, 60, 100, 140, and 220)16. DOM molecular richness, molecular diversity (Hill Numbers when q=2; annotated as HN ), and DOM functional molecular (q=2) diversity (multivariate Rao quadratic entropy; annotated as Rao ) indices were (multivariate) calculated from these features by converting peak heights to relative abundance across samples12,13.\nTo explore in greater detail how microbial products influence changes in DOM molecular diversity, we leveraged 18O-labeled H O as an isotopic tracer with Fourier Transform Ion 2 Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) at two different decomposition time points: day seven and day 60 (Fig. S1). 18O-H O is taken up by actively growing microorganisms 2 and incorporated into their biomolecules (e.g., nucleic acids and lipids), but it is also used in numerous other pathways including enzymatic reactions during metabolism17–19. Prior 18O-H O 2 tracing experiments have primarily tracked microbial growth and carbon use efficiency using stable isotope probing and Isotope Ratio Mass Spectrometry (IRMS)18,19, however to our knowledge previous experiments have not attempted to use 18O-H O as an isotopic tracer to 2 track microbial driven DOM composition transformations with mass spectrometry.\nTo determine microbial products during very early and later stages (seven and 60 days) of decomposition we pulsed labeled microcosms of plant litter and a soil microbial community with 18O-H O. DOM from water (polar, hydrophilic compounds), chloroform (non-polar, hydrophobic 2 compounds) and methanol (moderately polar compounds) extractions20 were analyzed using FT-ICR-MS to identify compounds isotopically enriched with 18O. To determine the efficiency of the isotope uptake by microorganisms we analyzed the water and chloroform extractable organic matter using IRMS to quantify δ18O. To further identify changes in metabolite abundances we used gas chromatography mass spectrometry (GC-MS) and to quantify molecular richness, abundance-based molecular diversity (HN ) and functional molecular diversity (q=2) (Rao ) of DOM, we used LC-MS/MS data.”\n(multivariate)\nLine 92: LC has been described, for what was the GC used?\nAuthors’ response: We used GC to identify general changes in DOM composition at two time points in addition to LC-MS/MS. We have added a clarifying statement in the introduction and in the methods.\nAuthors’ changes: (Ln. 108-111): “To further identify changes in metabolite abundances we used gas chromatography mass spectrometry (GC-MS) and to quantify molecular richness, abundance-based molecular diversity (HN ) and functional molecular diversity (q=2) (Rao ) of DOM, we used LC-MS/MS data.”\n(multivariate)\n(Ln. 524-525): “We used GC-MS to identify changes in low-molecular weight DOM compounds (e.g., sugars, amino acids, and fatty acids) from the two time points (day seven and day 60).”\nResults:\n- Structure: The structure is not optimal for a Nature journal. Detailed information is missing, and the methods section is at the end. More information in the introduction section would be beneficial.\nAuthors’ response: We have updated the introduction to add information that is missing. We have also revised the methods section to include detailed information. We have left the methods section at the end as that is the order requested in the online guide for authors.\n- Line 112: What does the abbreviation HN stand for? Please introduce it properly.\nAuthors’ response: We have defined the three diversity indices in the introduction as suggested.\nAuthors’ changes: (Ln. 88-91): “DOM molecular richness, molecular diversity (Hill Numbers when q=2; annotated as HN ), and DOM functional molecular diversity (multivariate Rao (q=2) quadratic entropy; annotated as Rao ) indices were calculated from these features by (multivariate) converting peak heights to relative abundance across samples12,13”\n- Lines 113, 115: Clarify what \"S2\" refers to.\nAuthors’ response: Table S2 points to supplemental table 2 with calculated molecular richness, abundance-based diversity and functional molecular diversity\n- Lines 116-118: This sentence is difficult to understand. Please rephrase for clarity.\nAuthors’ response: We have revised the sentence as suggested.\nAuthors’ changes: (Ln. 126-128): “Functional molecular diversity (Rao ) initially (multivariate) increased by 23% during the first 32 days (t-test, p = 0.03) before declining to values similar to the start of the incubation after another 188 days (Fig. 1C, Fig. S5C, Table S1).”\n- Lines 125-127: The interpretation of the two fractions of CFE is not well introduced for those unfamiliar with it. Please clarify its relevance.\nAuthors’ response: As suggested, we introduced the relevance of the fractions measured with FT-ICR-MS.\nAuthors’ changes:\n(Ln. 104-107\n): “DOM from water (polar, hydrophilic compounds), chloroform (non-polar, hydrophobic compounds) and methanol (moderately polar compounds) extractions20 were analyzed using FT-ICR-MS to identify compounds isotopically enriched with 18O.”\n(Ln. 141-146): “To understand microbial contributions to DOM, we analyzed water, methanol, and chloroform extractions using FT-ICR-MS, which capture distinct fractions of soil organic matter. Water extracts (WEOM) primarily polar, hydrophilic compounds (e.g., sugars and amino acids), methanol extracts (MEOM) moderately polar compounds (e.g., phenolics and small organic acids), and chloroform extracts (CEOM) non-polar, hydrophobic compounds (e.g., lipids and waxes)20,21.”\n- Line 127: There is no Figure 2E.\nAuthors’ response: We have updated Fig. 2E to include the E panel.\nAuthors’ changes: See Fig. 2.\n- Section starting from line 129: This section is very technical. It is not well described (here or earlier) what can be concluded or assessed with the two CFE fractions.\nAuthors’ response: As suggested we have revised this section to convey significance of the findings.\nAuthors’ changes:\n(Ln.154-158): “Decreases in molecular richness and abundance-based molecular diversity (HN ) of non-isotopically enriched compounds indicate extensive microbial consumption and (q=2) assimilation into biomass, whereas the decline in functional diversity of ¹⁸O-labeled compounds suggests that microbial-derived organic matter is less chemically complex than the mixture of microbial- and plant-derived compounds.”\n(Ln. 166-170): “Microbial activity selectively decreased the functional molecular diversity of the WEOM and MEOM fractions of DOM over time, whereas the non-polar CEOM fraction remained largely unchanged. This suggests that shifts in functional molecular diversity are primarily driven by microbial transformations of biologically active, polar, hydrophilic compounds rather than non-polar fractions.”\n(Ln. 190-195): “The chemical composition of 18O-isotopically enriched MEOM and CEOM remained relatively stable over time and did not differ substantially from the non-isotopically enriched fractions. In contrast, the 18O-isotopically enriched WEOM compounds initially more reduced, less aromatic, and had higher H:C ratios than their non-isotopically enriched counterparts, but became progressively smaller and more oxidized over time.”\n- Line 136: There is inconsistent use of terms like “multivariate functional molecular diversity,” “multivariate functional diversity,” and “functional molecular diversity.”\nAuthors’ response: We have revised to use the uniform term of “functional molecular diversity” where appropriate.\n- Line 141: Does the MetOH-extractable OM only comprise lipids? Provide more examples or remove the statement.\nAuthors’ response: We have revised the statement regarding lipids as the MEOM pool of compounds does contain lipids but other compounds as well.\nAuthors’ changes: (Ln. 144-146): “Water extracts (WEOM) primarily polar, hydrophilic compounds (e.g., sugars and amino acids), methanol extracts (MEOM) moderately polar compounds (e.g., phenolics and small organic acids), and chloroform extracts (CEOM) non-polar, hydrophobic compounds (e.g., lipids and waxes)20,21.”\n- Lines 152-161: The takeaway of this paragraph is unclear. Please clarify and rephrase.\nAuthors’ response: We have revised and added concluding statements to this results section for clarity.\nLines 199-200: likely only day 140 differs from day 100. Was a posthoc test done? The N-treatment is also not clear (no introduction, motivation,...), ANOVA is not specified in the statistics section of the methods.\nAuthors’ response: We revised the methods section to include the motivation for the nitrogen treatment and add the anova and post-hoc analysis. The reviewer is correct that the only significant differences were between day 100 and 140.\nAuthors’ changes:\n(Ln. 561-563): “The nitrogen treatment group was used to identify if correlations between mineralization and molecular diversity were caused by changes in nitrogen concentrations (e.g., nutrient dilution hypothesis)15.”\n(Ln. 567-571): “C mineralization was analyzed using a two-way analysis of variance (ANOVA) to test the effects of incubation time point of DOM extraction (day), ammonium chloride nitrogen treatment, and their interaction. Significant differences among treatment combinations were further evaluated using pairwise comparisons with Tukey’s Honest Significant Difference (HSD) method.”\nLines 202-203: In my opinion it is difficult to deduce a causal relationship or a dependency from this relation, collinearity is possible.\nAuthors’ response: We agree there are confounding factors that may be controlling for the relationship between molecular weight and mineralization. What is interesting is that the multivariate functional molecular diversity index incorporates molecular weight as a chemical trait in the distance matrix used in Rao’s quadratic entropy – but does not appear to influence its relationship with mineralization. We have put little emphasis on our mineralization findings due to these issues.\nDiscussion:\nThe discussion on microbial activity is quite superficial, with many speculations lacking references. There is no concluding remark on the study's revelations or its benefits to the field, yet the discussion is lengthy.\nAuthors’ response: We have heavily revised the discussion section to explore in greater detail the findings from our study. We have also added more remarks regarding the overall significance of the study in relation to the field, especially in relation to Liang et al., 2017 and Lehmann et al. 2020. We have also added a conceptual diagram to visualize the importance of our findings (Fig. 7).\nAuthors’ changes: See major discussion changes Ln. 234-372, Fig. 7.\n- Lines 215-217: There are several issues with this sentence. Where in the results section are DON and nutrient effects are shown? Was carbon use efficiency assessed? Higher activity and carbon use efficiency (CUE) are not mutually exclusive. How is CUE related to nutrients/nitrogen? Please rephrase or provide sufficient references.\nAuthors’ response: DON is shown in Fig. 2C. CUE was not assessed in this study, therefore we have removed this sentence.\n- Lines 225-228: Is this based on your own data or another study’s data? Please rephrase for clarity.\nAuthors’ response: During the revision of the discussion this concept was removed from the manuscript.\n- Lines 230-232: The relevance of the previous study is unclear, as it was based on spatial differences (i.e., depth), while your current study is based on temporal differences. Please rephrase or remove for clarity.\nAuthors’ response: We have added a clarifying statement to make it clear that we can compare temporal differences to depth differences in soil profiles when we consider soil depth as a proxy for time.\nAuthors’ changes: (Ln. 299-301): “Although changes in DOM composition could be explained by the GASP laboratory microcosm-induced phenomenon, we found similar DOM composition patterns to those found in soil profiles, where soil depth can be used as a proxy for duration of decomposition.”\n-- Lines 243-245: This is a strong statement. If statistics are missing, please add a test (e.g., anosim) if this is based only on visually inspecting the ordination in Fig 4.\nAuthors’ response: We have revised as suggested.\nAuthors’ changes: (Ln. 278-280): “After prolonged decomposition (> 60 until 220 days), the molecular composition changed very little as the DOM become more similar (Fig. 1B, Fig. 6B; ANOVA, F-statistic =88.03, p-value <0.0001).”\n(1,10)\n- Lines 245-269: The same information is repeated twice in the paragraph. Please rephrase to reduce redundancy. There are many assumptions but no discussion on how to evidence or prove those claims, or on future study focus (e.g., using sequencing data to confirm postulated microbial community turnovers). Please back with statistical results or appropriate references.\nAuthors’ response: We have revised this section to remove redundancies and add findings from our recent study published in SBB (Davenport et al., 2025).\nAuthors’ changes: (Ln. 281-291): “It is also likely that a shift toward a greater proportion of fermenting microorganisms during early decomposition would produce more low molecular weight organic acids in comparison to a microbial community predominantly using aerobic metabolic pathways. A shift back toward a slower growing aerobic-driven microbial community due to DOC depletion after 30 days would result in far less dramatic change in DOM decomposition and diversity over time. Our recent work also found that the predicted genome size of a microbial population was strongly positively correlated with DOM functional molecular diversity, suggesting that microbial communities with flexible metabolic pathways produce generates a greater variety of microbial organic matter inputs12. However, it remains unknown which specific microbial pathways most strongly influence DOM molecular diversity.”\n- Line 270: Clarify what the “laboratory microcosm-induced phenomenon” is.\nAuthors’ response: We have revised this to add the GASP concept. See above.\n- Lines 306-312: The definition isn’t clear. Refer to Roulet & Moore 2006 (https://doi.org/10.1038/444283a) and rephrase for clarity.\nAuthors’ response: We have revised for clarity and added the citation recommended.\nAuthors’ changes: (Ln. 318-321): “These constraints mean that detected compounds (100–900 Da) represent only a subset of DOM, despite soil dissolved organic carbon being operationally defined as compounds that pass through a <0.45 µm filter27, and untargeted approaches often yield incomplete positive matches from annotation libraries.”\n- Lines 315-318: There are contradictory statements. You state higher microbial activity increases functional diversity, then state it reduces functional diversity, as mentioned in the previous subsection. Which is correct? Please rephrase for clarity and add references to support your assumptions, especially since your experimental approach does not allow inference of microbial activity patterns solely based on 18O observations.\nAuthors’ response: We have reorganized the discussion section and moved this type of conclusion elsewhere. However, we do show that microbial activity initially increases functional molecular diversity but with continued decomposition functional molecular diversity ultimately declines. Together the respiration measurements, DOC concentrations, ATR-FTIR composition, FT-ICR-MS, LC-MS/MS and GC-MS data support the conclusion that the DOM is primarily plant matter at the start of the incubation and then is dominated by microbially transformed DOM at the end of the incubations.\n- Lines 323-324: Is this a general pattern or context-dependent? Please clarify.\nAuthors’ response: This may be a general pattern but could also be dependent upon the nutrient status of the soil or the microbial community metabolic capacity. We have added more context here for this section.\nAuthors’ changes: (Ln. 325-330): “In the context of these technological limitations, we hypothesized that higher molecular diversity would inhibit mineralization of dissolved organic C in soil by lowering energetic returns to microbes from producing the necessary enzymes for diverse substrates14,28. Greater molecular diversity also dilutes the concentration of individual molecules reducing the likelihood that microorganisms can chemically sense and respond to them15. However, it is unclear whether the proposed constraints imposed by molecular diversity depend on nutrient availability.”\n- Lines 335-340: These lines might fit better in the introduction.\nAuthors’ response: We have left this context in place, and added corresponding information to the introduction.\n- Lines 337-340: Why were the parameters not included in the model if they are known to have an effect? Please clarify.\nAuthors’ response: Key assumptions of the model included concentration dependencies, direct assimilation of dissolved organic matter regardless of chemical traits, enzymes can only assimilate a single corresponding substrate, and that microbes will only make enough of a specific enzyme to recoup energetic costs of doing so. In this sense, the model does not consider functional molecular diversity, just molecular richness and abundance-based diversity.\nAuthors’ changes: (Ln. 364-366): “Likely for simplicity, this model excluded key molecular properties, such as oxidation state and molecular weight, even though microbial metabolism is well known to be constrained by thermodynamic and kinetic factors29,38.”\n- Lines 343-345: The response and size cannot be disentangled with your experimental setup. Please clarify.\nAuthors’ response: As suggested, we have simplified and revised this concluding statement.\nAuthors’ changes: (Ln. 366-369): “. Our results indicate that DOM functional molecular diversity is shaped by both microbial catabolic and anabolic metabolic pathways at different decomposition stages, but these processes may have a smaller direct influence on mineralization rates than previously assumed.”\nMethods:\n- Software and Packages: There is no mention of the software used for statistics and analysis, nor the packages utilized (assuming it was done in R). Please include this information.\nAuthors’ response: We have fully revised the methods section to include R packages for each type of analysis.\nAuthors’ changes: (Ln. 373-641): See revised Materials and Methods section.\n- Factual Redundancies: There are many factual redundancies in the methods section.\nAuthors’ response: We have revised the methods section to remove factual redundancies.\n- Line 468: Avoid using an abbreviation immediately after introducing it, i.e., \"(ACN) ACN.\"\nAuthors’ response: We have revised this statement.\nAuthors’ changes: (Ln. 482-484): “Prior to injection, sample extracts were normalized to 0.39 mg C mL-1 using LCMS grade acetonitrile (ACN) (Optima) 10%, 0.1% LCMS grade formic acid (FA) (30 µL) for the HILIC column.”\n- Lines 475-476: Define what is meant by a “narrow m/z range” by providing specific numbers.\nAuthors’ response: We have added the ppm range.\nAuthors’ changes: (Ln. 489-492): “An Mzmine workflow was used to generate a list of features (m/z and retention time values were obtained from extracted ion chromatograms containing chromatographic peaks within a narrow m/z range (<10 ppm) and filtered to remove isotopes.”\n- Lines 492-503: It is unclear how isotope ratio translates to microbial activity, please explain and include a reference for this.\nAuthors’ response: We have updated this section with references to explain how IRMS measurements can indicate microbial activity.\nAuthors’ changes: (Ln. 512-514): “This method quantifies the content of 18O in organic molecules within extractable organic matter, providing an indicator of the extent to which substrates have been metabolized by bacterial cells17.”\n- Line 506: What is Feihnlib.1? Please prodive a reference.\nAuthors’ response: This was the spectral library used; we have updated the text with a citation.\nAuthors’ changes: (Ln. 524-525): “Dried metabolite extracts from samples are derivatized using a modified version of the protocol used to create the FiehnLib spectral library55.”\n- Line 536: Clarify whether Wolfe’s trace metal solution was also added. You mention adding the vitamin solution only, but refer to adding trace metals, which would require both Wolfe’s solutions. Provide appropriate details.\nAuthors’ response: We have clarified and updated the text.\nAuthors’ changes: (Ln. 556-559): “At the start of the incubation, 2 mL of a 1 L solution comprised of 10 mL of Wolfe’s trace metal, 10 mL of Wolfe’s vitamin mixture solution40,41 and M2 media ingredients (20X salt, MgSO , chelated ferrous sulfate and CaCl was added to each 4 2 microcosm to provide adequate vitamins and trace metals for cellular growth.”\n- Line 537: Explain the purpose of the nitrogen treatment group and provide additional details.\nAuthors’ response: We have explained the purpose of the nitrogen treatment group.\nAuthors’ changes: (Ln. 559-563): “A nitrogen treatment group received excess nitrogen (0.07 mg N) in the form of ammonium chloride in the Wolfe’s mixture. The nitrogen treatment group was used to identify if correlations between mineralization and molecular diversity were caused by changes in nitrogen concentrations (e.g., nutrient dilution hypothesis)15.”\n- Line 548: Bray-Curtis is a dissimilarity matrix. Please rephrase for accuracy.\nAuthors’ response: We have revised the methods section as necessary.\nAuthors’ changes: (Ln. 577-579): “The Bray-Curtis Dissimilarity Index was calculated using the bcdist() function from the vegan package in Rstudio57, as a measure of molecular -diversity, or the similarity of compounds across time.”\n- Line 554: Ensure consistency with units, as \"Da\" is used as a unit in line 198.\nAuthors’ response: We have ensured consistency with molecular weight units as noted.\nFigures, Tables, and Supplement:\n- Figure 1: Not all abbreviations are explained. Please ensure all abbreviations are defined. Furthermore, there is a “log-like” distance between the sampling steps. Why not plott the time in log scale on the x-axes (A,B,C).\nAuthors’ response: In response to this helpful suggestion, we have revised Figure 1 to plot the molecular diversity indices against time using a logarithmic scale and used polynomial fits to plot regressions. We have also defined all abbreviations were necessary.\nAuthors’ changes: (Figure 1; Ln. 833-839):” Fig. 1. DOM molecular diversity relationship with time. Molecular richness (A), molecular diversity (Hill numbers ) (B), and multivariate (q=2) functional molecular diversity using molecular weight, NOSC, number of N atoms, O:C and H:C ratios (Rao ) (C) are plotted against time of decomposition (day of incubation) on a (multivariate) logarithmic scale. Mean Bray-Curtis Dissimilarity Index values for each ecosystem comparing eight time points (1, 2, 8, 32, 60, 100, 140 and 220 days) (D). Polynomial fits (black lines), confidence intervals (shaded grey areas), and R-squared values are displayed for molecular diversity indices over time.”\n- Figure 2: Not all abbreviations are explained. Please ensure all abbreviations are defined. Is it necessary to show the abiotic samples in the main text?\nAuthors’ response: We have defined all unexplained abbreviations and moved the abiotic samples to the SI.\n- Figure 3: Consider comparing statistics within the same treatment across days 7 and 60, instead of between enrichment treatments, as this may reveal differences better. Also, is the scale \"relative abundance\" correct? There are many peaks (in particular in the non-enriched panels) with a relative abundance higher than 0.5. It's really interesting that the result of the HN increase is enriched from early to late, while it decreases in non-labeled HN from early to late stages. I suggest to elaborate on this.\nAuthors’ response: Following the suggestion of the referee, we have included new figures showing the changes in molecular richness, abundance-based molecular diversity and functional molecular diversity over time and compared these indices used linear mixed effects ANOVA models. We have also elaborated on these differences in molecular diversity changes over time within the enriched and non-enriched fractions throughout the discussion.\nAuthors’ changes: See Fig. 4, Fig. S7, Fig. S9.\n- Figure 4: Add a color scale legend for the heatmap for clarity (same in Fig. S5, S6). Is somewhere indicated what \"relative\" means? (relative among compound/metabolites considered ore among all annotated formulas found in the samples). Please give more information on the cluster diagram (same in Fig. S5, S6)\nAuthors’ response: We have added legends to the heatmaps, while also improving the data analysis to show the top 3-4 clusters with the heatmaps and molecular super class categories. We have also updated the methods section to include details for these approaches.\nAuthors’ changes: See Fig. 5, Fig. 6, Fig. S10.\n- Figure 5: Not all abbreviations are explained. Please ensure all abbreviations are defined.\nAuthors’ response: We have thoroughly checked the manuscript for all abbreviations to ensure they are defined.\n- Tables S1 - S4: Clarify what the numbers in the brackets represent in the table legend.\nAuthors’ response: We have clarified in the table titles that the numbers in the parentheses are standard errors.\nOther Remarks:\n- Consistency in Number Style: Ensure a uniform style for writing numbers (e.g., \"60 days\" vs. \"sixty days\"). Use the same style consistently throughout the manuscript.\nAuthors’ response: We have revised the manuscript for consistency with number style as appropriately suggested.\n- Orthography: Double-check for spelling mistakes and orthography issues, as there are several at present.\nAuthors’ response: We have thoroughly edited for spelling errors for this revised manuscript and apologize for these initial oversights.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nAuthors’ response: We thank this reviewer for the time spent co-reviewing our paper."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed my comments, and those of the other reviewers. I believe this will be a widely cited paper. Nevertheless, some minor issues need to be addressed before final publication:\n1. Lines 35-37: While the statement suggests a standard linear regression was used, the coefficient of determination (R-squared) mathematically cannot be negative. Please verify the reported statistic and ensure consistency with the data presented in Lines 231-232.\n2. The reference to \"Fig. S10H\" in Lines 231-232 appears to be inaccurate and should likely be \"Fig. S12H\". Such errors are frequent throughout the manuscript; for instance, Fig. S5 is cited before Fig. S3. This inconsistent numbering significantly hinders the reading process.\n3. Figure 3: The current color scheme makes the information in the figure difficult to discern, significantly hindering readability. Please adjust the figure for better clarity.", "reviewer_2": "The authors have substantially revised the manuscript to address the issues raised in my initial review, and the revision presents this important study more clearly. A few remaining points should be addressed before the paper may be accepted for publication:\nL37: Please remove the negative sign when reporting the R2 value.\nL49, L53, and References: References 4 and 7 appear to be identical.\nL56: Richness can be seen, among other metrics, as a measure of diversity. To avoid confusion, I suggest either using “diversity” alone instead of “richness and diversity,” or briefly elaborating on the distinction here.\nL102: Consider removing “actively,” as microbial growth is by definition active.\nL109–111: The first sentence of this paragraph repeats information from the previous paragraph; I suggest removing it here.\nL199–123: It is unusual to end the Introduction with a summary of results. Please consider removing this and instead presenting hypotheses or specific questions.\nL188–192: This sentence reads like implications and/or discussion. Please rephrase or move it to the appropriate section.\nThere are two additional points I did not catch in the first review round—my apologies for the oversight:\n- Microbial activity is used to explain shifts in DOM diversity and to interpret incorporation of the 18O2 label (e.g., L166, L188, L236). In the incubation experiment, days 7 and 60 were selected to “capture differences between relatively fast and slow phases of microbial activity” (L417). In L509 ff., you describe using 18O2 uptake to determine microbial activity. Could you please disentangle this more clearly to avoid the impression that microbial activity is both a predictor of and a response to label uptake? Moreover, Fig. 2A shows CO2 production during the labelling experiment. Respiration is a reasonable proxy for microbial activity; however, the figure suggests more CO2 is produced by day 60 than by day 7. So, does this mean the microbial activity is higher at 60 days than on 7 days or is this cumulative CO2 over the sampling interval? Also, reporting respiration rates (e.g., CO2 production per hour) could be useful in illustrating the fast and slow phases of microbial activity.\n- A key message of the manuscript is that carbon mineralization and molecular weight are negatively correlated. NOSC is not highlighted, yet its change from day 7 to day 60 is much stronger (Fig. S10B). Have you considered that these metrics may be correlated—i.e., more microbially processed compounds could be larger and more oxidized at the same time? I think this might be disentangled statistically using a linear model including both predictors with Type I sums of squares. Changing the fitting sequence, when the variables are correlated, can tell which is the more important driver of the carbon mineralization.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter