# Impacts of nutrient addition on soil carbon and nitrogen stoichiometry and stability in globally-distributed grasslands

Katherine S. Rocci, Kaydee S. Barker, Eric W. Seabloom, Elizabeth T. Borer, Sarah E. Hobbie, Jonathan D. Bakker, Andrew S. MacDougall, Rebecca L. McCulley, Joslin L. Moore, Xavier Raynaud, et al.

► To cite this version:

Katherine S. Rocci, Kaydee S. Barker, Eric W. Seabloom, Elizabeth T. Borer, Sarah E. Hobbie, et al. Impacts of nutrient addition on soil carbon and nitrogen stoichiometry and stability in globally-distributed grasslands. Biogeochemistry, 2022, 159 (3), pp.353-370. 10.1007/s10533-022-00932-w. hal-03784916

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# Impacts of nutrient addition on soil carbon and nitrogen stoichiometry and stability in globally-distributed grasslands

Katherine S. Rocci ID · Kaydee S. Barker ID · Eric W. Seabloom ID · Elizabeth T. Borer ID ·

Sarah E. Hobbie ID · Jonathan D. Bakker ID · Andrew S. MacDougall ID · Rebecca L. McCulley ID ·

Joslin L. Moore ID · Xavier Raynaud ID · Carly J. Stevens ID · M. Francesca Cotrufo ID

Received: 27 November 2021 / Accepted: 11 April 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022

**Abstract** Global changes will modify future nutrient availability with implications for grassland biogeochemistry. Soil organic matter (SOM) is central to grasslands for both provision of nutrients and climate mitigation through carbon (C) storage. While we know that C and nitrogen (N) in SOM can be influenced by greater nutrient availability, we lack understanding of nutrient effects on C and N coupling and

stability in soil. Different SOM fractions have different functional relevance and mean residence times, i.e., mineral-associated organic matter (MAOM) has a higher mean residence time than particulate organic matter (POM). By separating effects of nutrient supply on the different SOM fractions, we can better evaluate changes in soil C and N coupling and stability and associated mechanisms. To this end, we studied responses of C and N ratios and distributions across POM and MAOM to 6–10 years of N, phosphorus (P), potassium and micronutrients ($K_{+\mu}$), and combined $NPK_{+\mu}$ additions at 11 grassland sites spanning 3 continents and globally relevant environmental

Responsible Editor: Myrna Simpson.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10533-022-00932-w.

K. S. Rocci (✉)
Graduate Degree Program in Ecology, Colorado State
University, Fort Collins, CO 80523, USA
e-mail: katie.rocci@colostate.edu

K. S. Rocci · M. F. Cotrufo
Soil and Crop Sciences Department, Colorado State
University, Fort Collins, CO 80523, USA

K. S. Rocci · M. F. Cotrufo
Natural Resource Ecology Laboratory, Colorado State
University, Fort Collins, CO 80523, USA

K. S. Barker
Ecosystem Science and Sustainability Department,
Colorado State University, Fort Collins, CO 80523, USA

E. W. Seabloom · E. T. Borer · S. E. Hobbie
Department of Ecology, Evolution, and Behavior,
University of Minnesota, St. Paul, MN 55108, USA

J. D. Bakker
School of Environmental and Forest Sciences, University
of Washington, Seattle, WA 98195, USA

A. S. MacDougall
Department of Integrative Biology, University of Guelph,
Guelph, ON, Canada

R. L. McCulley
Department of Plant and Soil Sciences, University
of Kentucky, Lexington, CO 40546, USA

J. L. Moore
School of Biological Sciences, Monash University, 25
Rainforest Walk, Clayton, VIC 3800, Australia

X. Raynaud
UPEC, CNRS, IRD, INRA, UMR 7618, Institute
of Ecology and Environmental Sciences - Paris, iEES Paris,
Sorbonne Université, Université de Paris, Paris, France
organic matter is thought to form from the fragmentation of structural plant and microbial compounds (Six et al. 2001; Cotrufo et al. 2015), and unless it is occluded in stable aggregates, it lacks physical protection. MAOM is thought to form from microbial necromass and dissolved organic matter (DOM) sorption to minerals or organic matter occlusion in very fine (<53 µm) aggregates (Kleber et al. 2015; Liang et al. 2017; Wagai et al. 2020). Therefore, it is thought to be less accessible to further microbial decomposition. POM is lost through enzymatic degradation, the rate of which depends on POM chemistry and microbial community and activity, which may be inhibited or limited by temperature, moisture, or nutrient availability (Keiluweit et al. 2017; Bailey et al. 2019; Vaughn and Torn 2019; Ochoa-Hueso et al. 2020). In contrast, MAOM is expected to be lost via desorption (Bailey et al. 2019), leading to a higher mean residence time for MAOM than POM (von Lützow et al. 2007; Kögel-Knabner et al. 2008), when POM is not occluded in aggregates (Heckman, Hicks-Pries et al. 2022).

Because of these different formation and loss processes, changes in C:N stoichiometry of POM and MAOM may be indicative of changes in the mechanisms underlying SOM C:N change. For example, POM C:N may vary due to changes in plant input C:N stoichiometry (Buchkowski et al. 2019). In contrast, MAOM C:N may vary due to changes in C:N stoichiometry of DOM, given the direct sorption pathway of MAOM formation (i.e., ex vivo, sensu Liang et al. 2017), or through changes in the microbial community, given the microbial anabolism pathway of MAOM formation (i.e., in vivo, sensu Liang et al. 2017). There is limited work on drivers of MAOM desorption but recent work contrasts, suggesting organic N may be preferentially desorbed in the rhizosphere (Jilling et al. 2018) and that N-rich molecules are strongly sorbed to minerals (Possigner et al. 2020), making it difficult to hypothesize about desorption-driven changes in MAOM C:N. Because of these more specific controls of MAOM and POM, investigating their C:N stoichiometry can inform the overall response of SOM C:N to nutrient addition. For example, it has been shown that N addition reduces plant C:N (e.g., Rueth and Baron 2002; Han et al. 2014) and P addition desorbs C compounds (Spohn and Schleuss 2019), potentially resulting in decreased POM C:N and MAOM C:N, respectively.

Thus, C:N stoichiometry of SOM fractions could be a powerful tool for better characterizing grassland soil biogeochemistry in response to greater nutrient availability.

While changes in C:N can indicate changes in the mechanisms driving nutrient addition responses, investigating the distribution of C and N between SOM fractions can provide additional characterization of SOM responses. Specifically, changes in the distribution of C and N between MAOM and POM can indicate changes in SOM stability/vulnerability (i.e., more MAOM=greater resistance to disturbance and global change; Grandy and Robertson 2007; Rocci et al. 2021), turnover time (i.e., more MAOM=longer turnover time; Kögel-Knabner et al. 2008), N requirement for C storage (i.e., more MAOM=higher N requirement; Cotrufo et al. 2019) and dominant drivers of formation and loss. Thus, we interpret greater C and N storage in POM, relative to MAOM, as less stable total C and N pools. Previous work supports disparate responses of POM and MAOM to nutrient addition, and, importantly, that these responses are modified by environmental properties. For example, at both a temperate grassland with sandy soil (Cenini et al. 2015) and an alpine grassland with clay loam soil (Chen et al. 2019), N addition promoted relative C storage in MAOM. In contrast, at a semiarid grassland with sandy soil, N addition promoted increased POM C and N (Song et al. 2014). These results point to the likelihood of climatic and edaphic control of SOM responses to nutrient addition, which is expected to align with the formation and loss pathways associated with POM and MAOM. For example, addition of a specific nutrient in climates where that nutrient is generally limiting (e.g., cool climates have greater N limitation; Fay et al. 2015) or a combination of nutrients (NPK) in a variety of climates (Fay et al. 2015), may more effectively release plant growth limitation, and consequently increase structural input into the POM pool. In contrast, wetter climates may support loss of POM C and N with addition of multiple nutrients (NPK), due to concurrent release of water and nutrient limitation to decomposition (Ochoa-Hueso et al. 2020). In clay-rich soils, greater availability of sorption sites may allow for sorption of increased DOM (Lu et al. 2011b; Schleuss et al. 2019) and microbial necromass (Averill and Waring 2018) following nutrient addition, promoting increased MAOM-C and
-N. However, it is difficult to compare responses of MAOM and POM in studies from different sites with variable methodologies that measure different covariates, preventing clear attribution of variability to environmental, rather than methodological, differences.

The Nutrient Network [NutNet (Borer et al. 2014)] is a global experiment with identical nutrient addition treatments replicated in grasslands experiencing many different edaphic and climatic conditions, allowing for assessment of the influence of site conditions on the response of grassland SOM to addition of N, P, and K plus micronutrients ($K_{+\mu}$). Previous NutNet studies suggest $K_{+\mu}$ availability is important in determining soil C and N, finding increased soil C under $K_{+\mu}$ and NPK$_{+\mu}$ additions (Crowther et al. 2019; Seabloom et al. 2021a) and increased C in the light fraction (conceptually similar to POM) under NPK$_{+\mu}$ addition (Lin et al. 2019). However, studies that examine soil C fractions at NutNet sites that did not include the $K_{+\mu}$ treatments (e.g., Riggs et al. 2015; Keller et al. 2021) have not detected effects of nutrient addition on total soil C. For example, a short term (3–5 years of nutrient addition) study of NutNet sites in the central plains of the US found increased C in macroaggregates under N addition, with no change in total soil C (Riggs et al. 2015). In contrast, a longer-term continental-scale study (9–10 years) of N and P addition, inclusive of sites involved in the short-term study, found no effect of nutrient addition on C or N in SOM or its fractions (Keller et al. 2021). Thus, there is uncertainty around fraction responses to nutrient addition.

To this end, we investigated C and N stoichiometry and distribution in SOM, MAOM, and POM in response to six or more years of N, P, $K_{+\mu}$, and combined (NPK$_{+\mu}$) nutrient addition at 11 NutNet sites on three continents, varying in climate, soil texture, plant productivity, and soil fertility (Table 1). We tested the following hypotheses about responses of SOM-C and -N to greater nutrient availability, and variation in these responses across a wide range of environmental characteristics: (1) Nitrogen in SOM, MAOM, and POM will be more responsive to the addition of N and NPK$_{+\mu}$ than C, reducing C:N. We expect POM C:N stoichiometry to be more responsive to nutrient addition than MAOM C:N stoichiometry. This is because POM C:N will more strongly mirror responses of plant C:N, which has been shown to be sensitive to nutrient addition. Consequently,

C and N will be preferentially stored in POM with added nutrients, due to release of plant growth limitation, preferentially increasing POM C and N (N, $K_{+\mu}$, NPK$_{+\mu}$ addition), and desorption of MAOM-C and -N (P addition; Spohn and Schleuss 2019). (2) We expect decoupling of C:N stoichiometry of SOM, MAOM, and POM in productive systems with non-limiting nutrients and climate (high temperature and precipitation). These systems may allow for luxurious N use by plants, likely reducing plant and, subsequently, POM C:N. Additionally, excess N in DOM may be directly sorbed to MAOM, reducing MAOM C:N. In contrast, in nutrient-limited grasslands, we expect nutrients to increase plant productivity, leading to preferential storage of C and N in POM, with no change in stoichiometry. We expect this to be moderated by soil texture, as sandy soils are limited in their ability to store MAOM C and N. Our study provides insight on differing responses of soil C and N, and SOM fractions, to increased nutrient supply in grasslands spanning gradients of climate, plant productivity, and soil conditions, that can help inform how nutrient availability will regulate soil C and N coupling and stability across grassland environments.

## Methods

### Study sites and soil collection

This study used archived soil samples from 11 NutNet (Borer et al. 2014) sites spanning three continents (Table 1). NutNet is a coordinated, globally-distributed experiment with identical treatments of synthetic nutrient addition in more than 90 grassland sites around the world. NutNet treatments include a control with no nutrients added, and a full factorial combination of N [timed-release urea; $(NH_2)_2CO$], P [triple-super phosphate; $Ca(H_2PO_4)_2$], and $K_{+\mu}$ [potassium sulfate; $K_2SO_4$], each applied annually at $10 \text{ g m}^{-2} \text{ yr}^{-1}$. A micronutrient mix (6% Ca, 3% Mg, 12% S, 0.1% B, 1% Cu, 17% Fe, 2.5% Mn, 0.05% Mo, and 1% Zn) was added at a rate of $100 \text{ g m}^{-2}$ to plots fertilized with $K_{+\mu}$ in the first treatment year only, to prevent potential toxicity. For each treatment and the control, $5 \times 5 \text{ m}$ plots replicated three times were established in a randomized complete block design (Borer et al. 2014).

At each NutNet site, two to three 2.5 cm diameter and 10 cm deep soil cores were collected from
<table><caption>**Table 1** Nutrient Network sites used in analysis of soil organic matter (SOM) responses to nutrient addition</caption><thead><tr><th>Site name</th><th>Country, continent</th><th>Years of treatment (YT)</th><th>Soil pH (Y0)</th><th>MAT (°C; Y0)</th><th>MAP (mm; Y0)</th><th>WRB classification, % sand (Y0)</th><th>Aboveground biomass (g m<sup>-2</sup>; Y0-YT)</th><th>Soil N (%; Y0)</th><th>Soil P (ppm; Y0)</th><th>Soil K (ppm; Y0)</th></tr></thead><tbody><tr><td>Burrawan</td><td>Australia, Australia</td><td>8</td><td>5.6</td><td>18.4</td><td>682</td><td>VR, 82.2%</td><td>238.4</td><td>0.084</td><td>17.1</td><td>72.5</td></tr><tr><td>Bogong</td><td>Australia, Australia</td><td>7</td><td>4.5</td><td>6.5</td><td>1507</td><td>LC, 63.7%</td><td>524.9</td><td>0.602</td><td>46.0</td><td>188.0</td></tr><tr><td>Cereep</td><td>France, Europe</td><td>6</td><td>6.1</td><td>11</td><td>643</td><td>LC, 73.1%</td><td>N/A</td><td>0.134</td><td>126.0</td><td>181.0</td></tr><tr><td>Lancaster</td><td>England, Europe</td><td>8</td><td>4.8</td><td>8.1</td><td>1297</td><td>GL, 50.2%</td><td>120.2</td><td>1.070</td><td>35.3</td><td>106.0</td></tr><tr><td>Val Mustair</td><td>Switzerland, Europe</td><td>8</td><td>5.7</td><td>0.5</td><td>1093</td><td>LP, 57.6%</td><td>219.9</td><td>0.576</td><td>44.4</td><td>108.9</td></tr><tr><td>Cedar point biological station</td><td>USA, North America</td><td>9</td><td>6.6</td><td>9.2</td><td>457</td><td>KS, 70.2%</td><td>177.9</td><td>0.113</td><td>33.1</td><td>285.0</td></tr><tr><td>Chichaqua bot-toms</td><td>USA, North America</td><td>10</td><td>6.1</td><td>9.1</td><td>850</td><td>PH, 88.3%</td><td>395.3</td><td>0.061</td><td>62.3</td><td>93.3</td></tr><tr><td>Cowichan</td><td>Canada, North America</td><td>9</td><td>5.6</td><td>9.7</td><td>807</td><td>PH, 32.7%</td><td>507.8</td><td>0.399</td><td>38.7</td><td>88.3</td></tr><tr><td>Elliot Chap-paral</td><td>USA, North America</td><td>8</td><td>5.7</td><td>16.7</td><td>242</td><td>LC, 54.2%</td><td>285.8</td><td>0.152</td><td>16.8</td><td>261.0</td></tr><tr><td>Smith Prairie</td><td>USA, North America</td><td>9</td><td>6.1</td><td>9.8</td><td>614</td><td>AN, 78.0%</td><td>373.7</td><td>0.522</td><td>75.6</td><td>179.0</td></tr><tr><td>Spindletop</td><td>USA, North America</td><td>9</td><td>6.4</td><td>12.5</td><td>1136</td><td>LC, 29.3%</td><td>375.3</td><td>0.255</td><td>230.0</td><td>79.6</td></tr></tbody></table>

Soil data represent ambient conditions (pre-treatment sampling detailed in Supplementary Material A). Climate data were extracted from the WorldClim dataset using R statistical software based on latitude and longitude of sites. WRB soil classifications were extracted from the FAO digital coverage using ArcGIS based on latitude and longitude of sites. Years of treatment (YT) pertains to the time the soil used for SOM measurements was collected.

MAT mean annual temperature, MAP mean annual precipitation, WRB World Reference Base soil classification, aboveground biomass, mean peak annual biomass in the control plots, N nitrogen, P phosphorus, and K potassium, Y0 pre-treatment measurements made in year 0 of the treatment. For control plot aboveground biomass, values were averaged over Y0-YT. For WRB, VR Vertisols, LC Luvisols, Cambisols, GL Gleyisols, Histosols, Fluvisols, LP Leptosols, Regosols, KS Kastanozems, Solonetz, PH Phaeozems, AN Andosols

<figure><img src="image_2.png" /></figure>
each plot before treatments were applied (pre-treatment soils). Soils were collected from the surface with vegetation removed prior to sampling and roots were removed by 2 mm sieving. A depth of 10 cm was chosen to capture the most biologically and chemically active area of the soil. Once treatments were initiated, soils were collected and handled with the same method every 3–5 years and sent to the University of Minnesota where they were air-dried and archived (post-treatment soils). Because SOM C and N has been shown to be unaltered when air-dried and stored for long periods (Blake et al. 2000), we used post-treatment archived soils for measurement of SOM fractions and used pre-treatment soil data to characterize the ambient environment of each grassland. We analyzed archived post-treatment soil samples from sites that had received a minimum of 6 years of nutrient addition, with the assumption that the treatment had sufficient time to affect SOM pools (Hagedorn et al. 2003; Ferreira et al. 2018), leaving us with soil samples from 25 NutNet sites. We conducted the SOM fractionation (see Soil Analyzes) on a set of 11 of those sites that represented the widest range of climate, productivity, and soil texture and fertility (e.g., concentrations of soil N, P, and K) of all NutNet grassland sites (Supplementary Fig. 1; Table 1). Soil texture (% sand) and nutrients (ambient soil N, P, and K) were measured on pre-treatment soils (detailed in the Supplementary Material A). Only % sand was used as it was the direct complement of % silt + clay. Live plant biomass was estimated annually (except at Cereep, which did not separate live and dead biomass) from clipped samples of live peak aboveground biomass from two 0.1 m² quadrats per plot. Samples were dried to constant mass at 60 °C.

We assessed the effects on SOM fractions of addition of N, P, and $K_{+\mu}$ individually, and in combination ($NPK_{+\mu}$), along with control plots, giving a total of 5 treatments. We did not include pairwise factorial combinations of N, P, and $K_{+\mu}$ (e.g., NP, $NK_{+\mu}$, etc.) in our analysis. Treatments were replicated in 3 blocks at each site providing a total of 163 observations at the plot scale, because Elliot Chapparal and Cowichan were each missing archived soils for a single control plot.

## Soil analyses

To separate MAOM and POM, we performed a physical fractionation procedure, modified from Cotrufo et al. (2019). In brief, 10 g of archived soil for each replicate, treatment, and site (n=163), were dispersed in DI water with glass beads (Balesdent 1987) to break up aggregates by shaking at low speed on a reciprocal shaker for 18 h. For the Lancaster and Cedar Point soils, 5.5 g soil was used, because they were resistant to dispersion and in low availability, respectively. Lancaster soils were also shaken at fast speed for 2 h and at low speed for 16 h to increase dispersion. Any remaining aggregates were manually disrupted during wet sieving. Following shaking, the slurry was then rinsed onto a 53 µm sieve with the fraction passing through (<53 µm) representing MAOM and the fraction remaining on the sieve (>53 µm) representing POM. We ensured the sum of POM and MAOM mass following fractionation was 100±5% of the starting weight of the soil sample (mean; range=99.5%; 96.3–104.1%). There are many fractionation approaches to characterize SOM pools but this was chosen as the most appropriate to separate SOM into two meaningful fractions (POM and MAOM) with distinct controls while being an efficient approach for our large sample number (Poeplau et al. 2018; Cotrufo et al. 2019). However, we acknowledge that all SOM fractionation schemes have limitations, and that this method may allow for inclusion of small-sized structural material (conceptually POM) in our MAOM pool (<53 µm), as well as some large-sized protected material (conceptually MAOM) in our POM pool (>53 µm). Nevertheless, we contend that the inclusion of conceptually incorrect material in these pools is small and that our physical pools have average properties expected of their conceptual matches (e.g., higher C and N and lower C:N in MAOM), as has also been shown by Poeplau et al. (2018) for this fractionation method. Following physical fractionation, we measured total C and N individually in SOM (bulk soil), MAOM, and POM (n=489) on either a LECO Tru-SPEC elemental analyzer (Leco Corp., St. Joseph, MI) or a Costech elemental analyzer (Costech Analytical Technologies, Inc., Valencia, CA), as for ambient soil N, above. Samples and standards were crosschecked between instruments to ensure values were comparable. All soils were free of carbonates, as evidenced
by effervescence class assessment with 1 M HCl (Soil Survey Staff 2014), so total soil C is defined as organic.

## Data analyses

The response variables of interest for this study were C:N stoichiometry in SOM, MAOM, and POM, and the proportion of C and N in MAOM (fMAOM-C=MAOM-C/SOM-C; fMAOM-N=MAOM-N/SOM-N; Supplementary Table 1). These responses are informed by C and N concentration (g of C or N kg⁻¹ soil) data, which can be found in the Supplementary Material. Because plot-resolved bulk density values were not available for all sites, we were unable to evaluate C and N stocks. We evaluated (1) how SOM C and N stoichiometry and distribution responded to nutrient treatment, (2) how SOM responses to nutrient treatments were modified by site-specific environmental variables, and (3) the relative importance of each predictor variable (combining treatment and environmental variables as predictors in the same model). At the plot level, we analyzed ambient soil N (%), P (ppm), and K (ppm) concentrations, measured at the start of the experiment. At the site-level, we analyzed mean annual precipitation (MAP; mm yr⁻¹), mean annual temperature (MAT; °C), average peak season aboveground biomass at the control plots (g m⁻²), and sand content (% sand), which each had one value per site. Climate data were extracted from the WorldClim dataset using R statistical software based on latitude and longitude of sites. Separate models were fit for site and plot level variables, as these strongly covaried (Supplementary Fig. 2), such that variance inflation factors reached concerning values between 4 and 5 (up to 4.8; O'Brien 2007; Zuur et al. 2007) when these were analyzed together. We were also interested in unique effects of ambient soil nutrients (plot-level) as compared to biophysical effects (site-level).

Statistical analyses were carried out using RStudio in R version 4.0.2 (R Core Team 2019). We followed the data exploration procedures outlined in Zuur et al. (2010) and found that many of our response variables and some of our predictor variables had outliers that led to non-homogeneous variances. Data for SOM, MAOM and POM-C and -N concentration, C:N, and ambient soil P concentration were all natural log transformed to mitigate outliers and meet the

assumptions of the linear model. Additionally, environmental variables were standardized to a mean of 0 and standard deviation of 0.5, as the variables differed widely in scale, allowing for direct comparison of response estimates (Grueber et al. 2011; Rasmussen 2018). The responses of fMAOM-C and -N were modeled with beta regression (glmmTMB package; Brooks et al. 2017) because they are continuous proportion data (Douma and Weedon 2019). SOM, MAOM, and POM C and N concentrations and stoichiometry (C:N) were modeled with the normal distribution. Our analyses follow our hypotheses and the layout of data analyses presented in the above paragraph: (1) To assess overall nutrient effects, we created mixed models (lme4 package; Bates et al. 2015) with SOM, MAOM, or POM C:N or fMAOM-C or -N as response variables, nutrient treatment as the predictor variable and block nested within site as the random variable, to account for the blocking structure. We used analysis of variance (ANOVA; car package; Fox and Weisberg 2019) and pairwise comparisons between all nutrient treatments and the control (emmeans package; Lenth 2020) to determine overall effect of treatment and individual nutrient effects, respectively. (2) We determined how nutrient addition effects varied with environmental factors by using the same model structure as for (1) but with the mean percent change in a SOM component under nutrient addition (Eq. 1) as the response variable and environmental variables as the predictors. Mean percent changes were calculated as a back-transformed log response ratio for ease of interpretation using the equation:

Mean Percent Change

$$= \left( \exp \left( \ln \left( \bar{X}_{\text{trt}} \right) - \ln \left( \bar{X}_{\text{cntrl}} \right) \right) - 1 \right) \times 100 \quad (1)$$

where X̄trt is the mean value under the nutrient treatment (i.e. N, P, K+, or NPK+μ) and X̄cntrl is the mean value under the control, with no nutrients added. For this analysis we used the normal distribution, since mean percent change approximates a normal distribution (Hedges et al. 1999). (3) Finally, we determined the relative importance of the treatment and environmental factors by carrying out multi-model averaging with all environmental variables and the treatment following Grueber et al. (2011) using the MuMln package (Barton 2020). We used the natural
averaging method to avoid shrinkage towards zero of predictors that only appear in low-weighted models (Grueber et al. 2011) and a model inclusion criterion of 2AICc (Burnham and Anderson 2002). Data for Cereep could not be included in multi-model averaging because of missing aboveground biomass data. For all hypothesis tests and pairwise comparisons, significance was determined at p<0.05.

## Results

### Data distribution

Our sites spanned a variety of soil textures and nutrient concentrations, climate factors, and productivities (Table 1), leading to a diversity of SOM-C and -N stoichiometries, concentrations, and distributions between SOM components. Carbon stored per unit N (median; range), i.e., C:N stoichiometry, was highest in POM (14.3; 10.3–36.1) and lowest in MAOM (10.6; 7.4–18.3), with SOM falling between (12.0; 7.1–22.4). SOM-C had a median (range) value of 26.6 g OC kg⁻¹ soil (3.5–278.0 g OC kg⁻¹ soil), with most of that C stored in MAOM (17.0; 1.9–187.3 g OC kg⁻¹ soil) as opposed to POM (9.9; 1.6–99.0 g OC kg⁻¹ soil). Similarly, SOM-N (2.7; 0.2–18.3 g N kg⁻¹ soil) was mostly found in MAOM (1.7; 0.2–12.7 g N kg⁻¹ soil) as opposed to POM (0.7; 0.1–6.12 g N kg⁻¹ soil). Consequently, proportions of C and N in MAOM (fMAOM-C and -N) had median values of 0.62 (0.18–0.93) and 0.65 (0.23–1.06), respectively.

### Overall responses to nutrient addition

The effect of nutrient treatment on C stored per unit N was subtle and only significant for MAOM [$\chi^2$ (4, N=163)=18.0, p=0.001] and POM [$\chi^2$ (4, N=163)=10.4, p=0.035] but not for total SOM [$\chi^2$ (4, N=163)=5.9, p=0.210; Fig. 1]. Overall SOM C:N was likely unaffected by treatment, because the nutrient treatments caused proportional increases in both SOM-C and -N (Table 2; Supplementary Material B2). For SOM fractions, addition of N (p=0.002), P (p=0.028), and NPK$_{+\mu}$ (p=0.048) decreased MAOM C:N. Similarly, addition of NPK$_{+\mu}$ (p=0.059) or of N (p=0.055) decreased POM C:N, but not significantly.

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Soil (a), mineral-associated (b), and particulate (c) organic matter (SOM, MAOM, and POM) carbon (C) versus nitrogen (N) concentrations under nutrient addition treatments (+N=nitrogen, +P=phosphorus, +K=potassium + micronutrients, +NPK=combined) at 11 globally-distributed grasslands (Table 1). There were only significant pairwise comparisons for MAOM at p<0.05, which are denoted with a “*” in the upper left of the MAOM plot. The main effect of nutrient treatment was not significant for SOM C:N [$\chi^2$ (4, N=163)=5.9, p=0.210], but was for MAOM [$\chi^2$ (4, N=163)=18.3, p=0.001] and POM [$\chi^2$ (4, N=163)=10.4, p=0.035] C:N</figcaption></figure>

of POM C:N to nutrient treatment was likely driven by changes in soil N concentration rather than soil C concentration, as POM-N was significantly increased by N and NPK addition. MAOM-N was also significantly affected by nutrient treatment but was only higher under N addition as compared to K addition, indicating a more subtle response of MAOM C:N (Table 2; Supplementary Material B2). The nutrient treatment significantly affected fMAOM-C and -N [C: $\chi^2$ (4, N=163)=10.1,
<caption>**Table 2** Means and standard errors (in parentheses) of soil total, mineral, and particulate organic matter (SOM, MAOM, and POM) carbon (C) and nitrogen (N) concentrations and</caption>

ratios in response to nutrient treatments at 11 globally-distributed grasslands (Table 1)

<table><thead><tr><th>Treatment</th><th>Control</th><th>Nitrogen (N)</th><th>Phosphorus (P)</th><th>Potassium + micronutrients (K<sub>+μ</sub>)</th><th>Combined (NPK<sub>+μ</sub>)</th></tr></thead><tbody><tr><td colspan="6"><em>SOM component</em></td></tr><tr><td>SOM C:N</td><td>12.1 (0.5)</td><td>12.0 (0.5)</td><td>12.6 (0.5)</td><td>12.5 (0.5)</td><td>12.1 (0.4)</td></tr><tr><td>SOM-C*</td><td>45.3 (10.1)</td><td>51.6 (10.7)</td><td>45.5 (9.1)</td><td>46.1 (10.2)</td><td>50.5 (10.8)</td></tr><tr><td>SOM-N*</td><td>3.3 (0.6)</td><td>3.9 (0.6)</td><td>3.4 (0.6)</td><td>3.3 (0.5)</td><td>3.8 (0.7)</td></tr><tr><td>MAOM C:N*</td><td>12.1 (0.6)</td><td>11.1 (0.4)</td><td>11.2 (0.4)</td><td>11.7 (0.5)</td><td>11.4 (0.5)</td></tr><tr><td>MAOM-C</td><td>31.5 (7.9)</td><td>33.0 (7.9)</td><td>30.8 (7.2)</td><td>28.1 (6.6)</td><td>29.9 (7.6)</td></tr><tr><td>MAOM-N*</td><td>2.25 (0.4)</td><td>2.6 (0.5)</td><td>2.5 (0.4)</td><td>2.1 (0.4)</td><td>2.4 (0.5)</td></tr><tr><td>POM C:N*</td><td>16.7 (0.8)</td><td>15.2 (0.9)</td><td>15.8 (0.7)</td><td>16.0 (0.8)</td><td>15.0 (0.6)</td></tr><tr><td>POM-C</td><td>15.7 (2.8)</td><td>20.1 (3.7)</td><td>15.7 (2.4)</td><td>19.7 (3.7)</td><td>21.1 (4.0)</td></tr><tr><td>POM-N*</td><td>0.9 (0.1)</td><td>1.3 (0.2)</td><td>1.0 (0.1)</td><td>1.2 (0.2)</td><td>1.4 (0.2)</td></tr><tr><td>fMAOM-C*</td><td>0.62 (0.03)</td><td>0.59 (0.03)</td><td>0.62 (0.03)</td><td>0.59 (0.03)</td><td>0.56 (0.03)</td></tr><tr><td>fMAOM-N*</td><td>0.63 (0.3)</td><td>0.65 (0.04)</td><td>0.69 (0.03)</td><td>0.64 (0.03)</td><td>0.60 (0.03)</td></tr></tbody></table>

Proportions of C and N in MAOM are denoted as fMAOM-C and fMAOM-N, respectively. An asterisk (*) indicates a significant treatment effect at *p*<0.05

<figure><img src="image_4.png" /><figcaption>**Fig. 2** Proportion of carbon and nitrogen in MAOM (fMAOM-C and -N) under nutrient addition treatments (+N = nitrogen, +P = phosphorus, +K = potassium + micronutrients, +NPK = combined) at 11 globally-distributed grasslands (Table 1). Means with 95% confidence intervals (n=33 for treatments and n=31 for control) are shown as large dots with whiskers and individual observations are behind the large dots. Significantly different pairwise comparisons, at *p*<0.05, are denoted with a “*”</figcaption></figure>

*p*=0.039 and N: $\chi^2$ (4, N=163)=12.7, *p*=0.013, respectively; Fig. 2]. Addition of NPK$_{+μ}$ decreased the proportion of C in MAOM (*p*=0.029), relative to the control, and the proportion of N in MAOM, relative to P addition (*p*=0.011; Fig. 2).

Responses to nutrient addition as modified by environmental factors

Generally, soil C:N tended to increase with nutrient addition at sites with low ambient soil nutrients, and tended to decrease at sites with high ambient soil nutrients. Specifically, SOM C:N responses to N, P, and K$_{+μ}$ addition were all negatively related to ambient soil P (Table 3; Fig. 3). In addition, SOM C:N responses to N and NPK$_{+μ}$ addition were negatively related to ambient soil K concentration (Table 3) and SOM C:N responses to P addition were negatively related to ambient soil N concentration (Table 3; Fig. 3). Similarly, MAOM C:N responses to P and NPK$_{+μ}$ addition were negatively related to ambient soil N (Table 3; Fig. 3). MAOM C:N response to NPK$_{+μ}$ was also negatively related to ambient soil K concentration (Table 3; Fig. 3). Nitrogen was likely responsible for some SOM C:N responses, as the SOM-N response to K$_{+μ}$ was positively related to ambient soil P, whereas other nutrient relationships with C and N concentrations were more subtle and not directly connected to SOM and MAOM C:N responses (Supplementary Material B3). In contrast, the response of POM C:N to nutrient addition was not related to ambient soil nutrient concentrations (Fig. 3). Both POM-C and -N responses to N and P additions were negatively related to ambient soil P,
<table><caption>**Table 3** Chi-square estimate and (p-value) from type 3 Wald chi-squared tests for models of the response of total soil, mineral-associated, and particulate organic matter (SOM, MAOM, and POM) carbon-to-nitrogen ratio (C:N) to nutrient addition, as modified by environmental variables at 11 globally-distributed grasslands (Table 1)</caption><thead><tr><th rowspan="2"></th><th colspan="3">SOM C:N</th><th colspan="3">MAOM C:N</th><th colspan="3">POM C:N</th></tr><tr><th colspan="3">Df=1; n=31</th><th colspan="3">Df=1; n=31</th><th colspan="3">Df=1; n=31</th></tr><tr><th></th><th>N</th><th>P</th><th>K</th><th>NPK</th><th>N</th><th>P</th><th>K</th><th>NPK</th><th>N</th><th>P</th><th>K</th><th>NPK</th></tr></thead><tbody><tr><td>Ambient soil N (%)</td><td>2.7 (0.097)</td><td><strong>4.1 (0.042)</strong></td><td>0.3 (0.615)</td><td>2.0 (0.158)</td><td>2.2 (0.138)</td><td><strong>4.1 (0.043)</strong></td><td>0.4 (0.520)</td><td><strong>8.0 (0.005)</strong></td><td>0.2 (0.660)</td><td>0.1 (0.722)</td><td>0.0 (0.831)</td><td>0.2 (0.680)</td></tr><tr><td>Ambient soil P (ppm)</td><td><strong>7.7 (0.006)</strong></td><td><strong>6.4 (0.011)</strong></td><td><strong>4.6 (0.032)</strong></td><td>1.6 (0.204)</td><td>0.4 (0.528)</td><td>0.0 (0.877)</td><td>0.0 (0.914)</td><td>1.6 (0.211)</td><td>1.4 (0.231)</td><td>0.1 (0.774)</td><td>0.2 (0.624)</td><td>2.0 (0.153)</td></tr><tr><td>Ambient soil K (ppm)</td><td><strong>9.5 (0.002)</strong></td><td>1.8 (0.186)</td><td>0.7 (0.394)</td><td><strong>4.4 (0.036)</strong></td><td>0.2 (0.682)</td><td>0.2 (0.634)</td><td>0.6 (0.444)</td><td><strong>6.2 (0.013)</strong></td><td>0.1 (0.752)</td><td>0.2 (0.644)</td><td>3.4 (0.066)</td><td>0.1 (0.803)</td></tr><tr><td>MAT (°C)</td><td><strong>4.3 (0.038)</strong></td><td>3.8 (0.052)</td><td>0.2 (0.639)</td><td>0.4 (0.522)</td><td>0.2 (0.655)</td><td>2.0 (0.156)</td><td>0.4 (0.527)</td><td>1.4 (0.243)</td><td><strong>5.1 (0.024)</strong></td><td>1.0 (0.310)</td><td>0.4 (0.505)</td><td>1.2 (0.280)</td></tr><tr><td>MAP (mm yr<sup>-1</sup>)</td><td>1.5 (0.215)</td><td>0.3 (0.580)</td><td>0.0 (0.901)</td><td>0.5 (0.465)</td><td>0.7 (0.390)</td><td>0.3 (0.613)</td><td>0.1 (0.744)</td><td>0.0 (0.877)</td><td>0.0 (0.937)</td><td>0.5 (0.466)</td><td>1.1 (0.305)</td><td>0.0 (0.922)</td></tr><tr><td>Sand content (%)</td><td>1.6 (0.206)</td><td>2.0 (0.157)</td><td>0.1 (0.780)</td><td>2.2 (0.137)</td><td>0.5 (0.472)</td><td>0.3 (0.567)</td><td>0.0 (0.982)</td><td>0.9 (0.341)</td><td>0.2 (0.659)</td><td>0.0 (0.994)</td><td>0.4 (0.535)</td><td><strong>5.0 (0.025)</strong></td></tr><tr><td>Aboveground biomass (g m<sup>-2</sup>)</td><td>0.0 (0.950)</td><td>0.1 (0.752)</td><td>0.0 (0.883)</td><td>0.0 (0.904)</td><td>0.2 (0.645)</td><td>1.9 (0.164)</td><td>0.6 (0.432)</td><td>0.7 (0.408)</td><td>1.0 (0.329)</td><td>2.7 (0.097)</td><td>1.3 (0.263)</td><td>0.0 (0.922)</td></tr></tbody></table>

Columns for N, P, K, and NPK represent responses to nitrogen, phosphorus, potassium+micronutrients and their combined addition. Bolded relationships are significant at $p<0.05$

likely leading to no change in POM C:N (Supplementary Material B3). Instead, addition of NPK$_{+\mu}$ tended to decrease POM C:N at sites with low sand content (Table 3). Climate also modified this response. In particular, SOM and POM C:N increased more with N addition with increasing MAT (Table 3).

Addition of all nutrients decreased the proportion of C and N in MAOM relatively consistently across environmental variables. The response of fMAOM-C to nutrient addition was not related to any environmental factor. The response of fMAOM-N to N addition was positively related to MAT and the response to N and K$_{+\mu}$ addition were negatively related to ambient soil P (Supplementary Table 6). These responses were subtle and could not be directly connected to MAOM and POM-C and -N responses (Supplementary Material B3).

## Relative importance of explanatory variables

To indicate the overall strongest drivers of SOM, MAOM, and POM C:N stoichiometry and C and N distribution (fMAOM), we performed multi-model averaging using all environmental variables and the treatment variables. Soil N was included in the averaged model for all soil components except POM C:N. Soil texture and MAT also emerged as consistently strong (high estimate) predictors across our response variables, whereas other predictor variables were only related to changes in a subset of SOM components or had relatively low estimates (Table 4). Multi-model averaging for SOM, MAOM, and POM C and N concentrations followed similar patterns as above (Supplementary Table 5). Treatment was only included in models of the distribution of C and N between MAOM and POM (fMAOM-C and -N).

## Discussion

We found that six or more years of experimental nutrient addition, particularly of N and NPK$_{+\mu}$, promoted N accrual more strongly than C accrual in MAOM and POM and reduced average stability of SOM-C, as evidenced by reduced fMAOM-C. However, responses of C:N stoichiometry to chronic nutrient addition were subtle—total SOM C:N was unaffected by nutrient treatment—and depended on environmental context. Generally, for SOM and

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3** Relationships between ambient soil nitrogen (N; **a**, **d**, **g**), phosphorus (P; **b**, **e**, **h**), and potassium (K; **c**, **f**, **i**) concentrations and mean percent change (MPC) in soil, mineral-associated, and particulate organic matter [SOM (**a**–**c**), MAOM (**d**–**f**), and POM (**g**–**i**)] carbon to nitrogen ratios (C:N) under N (blue), P (green), K (micronutrients, yellow) and NPK (micronutrients, pink) addition at 11 globally-distributed grasslands (Table 1). Linear trendlines are reported only for significant relationships from ANOVA. Statistical information for linear trendlines are reported here. For SOM C:N fertilized with each individual nutrient, soil P estimates, p-values, and R² were, +N: −11.7, p=0.010,</figcaption></figure>

and r²=0.18; +P: -13.7, p=0.017, and r²=0.16; +K: -7.4, p=0.083, and r²=0.14, for SOM C:N fertilized with N and NPK+μ, soil K estimates, p-values, and R² were, +N: -0.14, p=0.005, and r²=0.22, +NPK+μ: -0.08, p=0.046, and r²=0.02, and for SOM C:N fertilized with P, soil N estimate, p-value, and R² were, -26.8, p=0.052, and r²=0.10. For MAOM C:N fertilized with P and NPK+μ, soil N estimates, p-values, and R² were, +P: -19.5, p=0.085, and r²=0.21; +NPK+μ: -19.5, p=0.028, and r²=0.26 and for NPK+μ, soil K estimate, p-value, and R² were -0.08, p=0.043, and r²=0.21. Chi-squared estimates and p-values from ANOVA analysis are in Table 3

MAOM, addition of specific nutrients increased C relative to N at sites with low ambient soil fertility; whereas for POM this occurred at warm sites with sandy soils following N and NPK<sub>+μ</sub> addition. In contrast, responses of fMAOM-C were consistent across the wide range of climatic and edaphic conditions represented in this study. These results suggest that increased availability of NPK<sub>+μ</sub> could consistently promote C storage in the less stable POM pool but that the response of soil C per unit N is dependent on SOM fraction, nutrient identity, and local environmental conditions.

## Responses to increased nutrient availability

Effects of nutrient addition on soil stoichiometry were subtle, with SOM C:N unaffected by increased nutrient availability. This lack of change was driven by similar increases in SOM-C and -N in response to nutrient addition (Table 2) and aligns with previous NutNet work (Crowther et al. 2019; Keller et al. 2021; Seabloom et al. 2021a) and with meta-analyses (Lu et al. 2011a, b; Yue et al. 2016, 2017; Rocci et al. 2021), which all find increased soil C and N with nutrient addition. While there are certainly
<table><caption>**Table 4** Top model (<2 AICc) estimates and standard errors (in parentheses) from multi-model averaging for bulk soil, mineral-associated, and particulate organic matter (SOM, MAOM, and POM) carbon (C) and nitrogen (N) stoichiometries (C:N), and distributions (fMAOM) with all environmental variables and the treatment at 11 globally-distributed grassland nutrient addition experiments (Table 1)</caption><thead><tr><th>Soil component</th><th>Number of models</th><th>Percent sand</th><th>Above-ground biomass</th><th>MAT</th><th>MAP</th><th>Ambient soil N</th><th>Ambient soil P</th><th>Ambient soil K</th><th>Nutrient addition</th></tr></thead><tbody><tr><td>SOM C:N</td><td>3</td><td></td><td></td><td></td><td></td><td>0.26 (0.08)</td><td>-0.21 (0.06)</td><td></td><td></td></tr><tr><td>MAOM C:N</td><td>8</td><td>0.24 (0.09)</td><td></td><td>0.22 (0.09)</td><td>0.29 (0.12)</td><td>0.34 (0.12)</td><td>-0.24 (0.12)</td><td></td><td></td></tr><tr><td>POM C:N</td><td>2</td><td></td><td></td><td></td><td></td><td></td><td>-0.26 (0.10)</td><td>-0.28 (0.10)</td><td></td></tr><tr><td>fMAOM-C</td><td>3</td><td>-0.23 (0.09)</td><td>0.31 (0.09)</td><td>1.14 (0.10)</td><td>-0.26 (0.13)</td><td>1.04 (0.13)</td><td>0.28 (0.10)</td><td>0.25 (0.09)</td><td>N: -0.19 (0.12)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>P: -0.05 (0.12)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>K: -0.03 (0.12)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>NPK: -0.31 (0.12)</td></tr><tr><td>fMAOM-N</td><td>3</td><td>-0.70 (0.12)</td><td>0.14 (0.11)</td><td>1.16 (0.13)</td><td>0.59 (0.14)</td><td>0.39 (0.13)</td><td>0.22 (0.10)</td><td></td><td>N: 0.20 (0.15)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>P: 0.44 (0.16)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>K: 0.11 (0.16)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>NPK: -0.12 (0.15)</td></tr></tbody></table>

MAT mean annual temperature, MAP mean annual precipitation

<figure><img src="image_7.png" /></figure>
exceptions to this general finding of increased soil C and N with nutrient addition (e.g., Mack et al. 2004; Boot et al. 2016), these findings might be particu- larly expected given the relatively long period of treatment (6–10 years) and relatively high nutrient addition rate for this study, as soil C responds more positively at higher N addition durations and amounts (Xu et al. 2020). We might expect individual C and N responses to be stronger on decadal time scales, potentially leading to altered C:N ratios, as has been found for fertilized retired farmland (Seabloom et al. 2021b). However, given the heterogeneity of the total SOM pool, it is important to unpack these findings by analyzing distinct fractions of SOM. Because POM and MAOM are thought to have distinct pathways of formation and loss, they can indicate both potential mechanisms underlying responses and the stability of SOM following global change.

In contrast to the total SOM pool, and aligned
with our hypotheses, we found that greater nutrient
(specifically N, P, and NPK<sub>+µ</sub>) availability reduced
MAOM and POM C:N. Since these findings were
likely driven by increases in MAOM and POM N, our
results suggest nutrient addition promotes MAOM
and POM formation or loss pathways that increase
N, relative to C. MAOM formation is thought to
occur through two main pathways; efficient microbial
resource use efficiency and subsequent necromass
sorption (i.e., in vivo) and exo-enzymatic breakdown
of polymeric residues followed by direct DOM sorp-
tion (i.e., ex vivo; sensu Liang et al. 2017). Both of
these pathways could reduce MAOM C:N with N
addition. Addition of N tended to reduce plant C:N
at our sites (Supplementary Fig. 3), which aligns with
previous NutNet work (Anderson et al. 2018), and is
expected to favor MAOM formation via the in vivo
pathway (Cotrufo et al. 2013). Since microbial necro-
mass is more N-rich than plant inputs (Cleveland and
Liptzin 2007), this may decrease MAOM C:N. Direct
addition of labile N may also form MAOM through
the ex vivo pathway. A previous NutNet study of 6
globally-distributed grasslands, including one of our
sites, found that N addition increases dissolved N con-
centrations (Schleuss et al. 2021), and N compounds
are thought to sorb strongly to mineral surfaces (Poss-
inger et al. 2020), thus potentially increasing MAOM-
N. Carbon stored per unit N in MAOM was also
reduced under P addition, which aligns strongly with
our expectations, given findings of C desorption with

P addition (Spohn and Schleuss 2019). The drivers for MAOM C:N responses to N and P addition can likely be extended to explain reduced MAOM C:N under NPK<sub>+µ</sub> addition. Responses of POM C:N were more variable than MAOM C:N but nevertheless tended to decline with N and NPK<sub>+µ</sub> addition. At our sites, addition of N and NPK<sub>+µ</sub> increased standing above-ground biomass and NPK<sub>+µ</sub> addition also reduced live aboveground plant C:N (Supplementary Fig. 3), in line with other NutNet studies (Fay et al. 2015; Anderson et al. 2018). Thus, reduced POM C:N is likely due to greater inputs of lower C:N plant material with N and NPK<sub>+µ</sub> addition, although responses of live plant biomass may be different than plant litter (Seabloom et al. 2021a). We lack root data for all of our sites but a study at one of our sites (Cowichan) suggested root biomass and C:N responses would mirror aboveground plant responses (Ziter and MacDougall 2013), potentially adding greater low quality plant input belowground, also potentially reducing POM C:N. Thus, our results suggest increasing nutrient—especially N—availability in grasslands will likely store soil C at a higher N cost, which could be a benefit in areas experiencing rapidly increasing N deposition (Ackerman et al. 2019) but may be problematic for management that depends on fertilization.

Beyond studying C relative to N, we also studied
content of C and N in MAOM relative to POM, to
assess how the average stability of soil C and N may
change with greater nutrient availability. Addition of
NPK<sub>+µ</sub> reduced the relative distribution of C and N
in MAOM versus POM (fMAOM-C and -N) as com-
pared to the control and P, respectively. These find-
ings align with previous NutNet work that suggests
grassland biomass production is co-limited by N,
P, and K, as adding these together increases above-
ground plant growth (Fay et al. 2015; Carroll et al.
2021), as we also found in our sites (Supplementary
Fig. 3). This is expected to favor POM, rather than
MAOM, formation. Addition of NPK<sub>+µ</sub> may be more
effective at promoting N storage in POM than P addi-
tion because P addition alone did not increase average
aboveground biomass in our sites (Supplementary
Fig. 3). Overall, our findings of lower fMAOM-C
with NPK<sub>+µ</sub> addition suggest that providing limit-
ing nutrients to grasslands will shift distributions of
soil C towards storage in POM, potentially making
them more susceptible to loss due to warming (Rocci
et al. 2021) and management (Grandy and Robertson 2007).

## Responses to nutrient addition along environmental gradients

While C:N stoichiometry responses indicated that, on average, soil N, and C to a lesser extent, increased with greater nutrient availability, we found that nutrient addition in sites with low ambient nutrients may allow for more C stored per unit N added. Some of these relationships (Fig. 3b, d, f) were aligned with our expectation that adding a nutrient when it has low ambient availability may promote more coupled C and N responses, but only for SOM and MAOM. Thus, these relationships were likely mediated through, for example, increased microbial necromass inputs to MAOM with N addition (Fig. 3d; Averill and Waring 2018) rather than through plant productivity, as we would expect for POM. However, many of these relationships were not expected and indicated interactions between different nutrients. For example, positive responses of SOM C:N to N addition at low ambient soil P and K suggest that adding a non-limiting nutrient may preserve existing soil C and N through reduced microbial acquisition of those elements (Schleuss et al. 2019). In contrast, responses of POM C:N to the addition of N and NPK$_{+\mu}$ did not vary with soil fertility but rather with MAT and soil texture, respectively, which is likely related to the formation pathway of POM. Warmer temperatures are generally associated with higher plant tissue C:N (Reich and Oleksyn 2004; Anderson et al. 2018). Since aboveground biomass increased with N addition at our sites (Supplementary Fig. 3), N addition in warm grasslands may promote more structural (C-rich, nutrient poor) plant inputs to the POM pool (Cotrufo et al. 2015). Our finding of increased POM C:N with NPK$_{+\mu}$ addition in sandy soils is strongly in line with findings for total soil C in a previous NutNet study (Crowther et al. 2019) and for fertilized retired farmland (Seabloom et al. 2021b). Notably, we corroborate this finding for the POM pool only, which may be due to relatively smaller MAOM pool size in sandy soils (Supplementary Fig. 4). Because our fractionation method defines POM C as the C in the sand-sized fraction, this is not a surprising finding, but, nevertheless,

suggests that increased soil C with fertilization of sandy soils may be occurring in the less stable POM pool. Overall, findings of environmental influence on responses of SOM, MAOM, and POM C:N align with relatively high estimates for ambient soil N, MAT, and sand content for top models of SOM components C and N (Table 4). In addition, they emphasize the importance of site edaphic and climatic conditions, as found in other NutNet studies (Keller et al. 2021), in determining the strength and direction of soil responses to nutrient addition (Supplementary Table 7, Supplementary Fig. 5). These results confirm and extend previous NutNet results to show that findings of increased soil C in sandy versus low nutrient sites may be realized in POM and MAOM, respectively, with implications for the stability of soil C.

In contrast to C:N stoichiometry responses to environmental gradients, NPK$_{+\mu}$ addition consistently reduced C content in MAOM, relative to POM, across a wide range of climatic and edaphic conditions. This may be because plant growth is often co-limited by multiple nutrients (Fay et al. 2015), so addition of NPK$_{+\mu}$ is able to reduce plant growth limitation under a wide range of baseline conditions, thus increasing C content preferentially in POM. Responses of fMAOM-N were slightly less consistent but were subtle and could not be directly related to POM or MAOM-N responses (Supplementary Table 3). It is possible that increased fMAOM-N with N addition at warmer sites could be due to greater microbial anabolism and decomposition in these conditions (Lu et al. 2013; Averill and Waring 2018; Li et al. 2019), increasing MAOM-N and decreasing POM-N, respectively. However, a study at 21 globally-distributed NutNet sites found increased decomposition rate with N addition in cool, not warm, sites (Ochoa-Hueso et al. 2020), suggesting reduced POM-N from heightened decomposition is not likely to drive the fMAOM-N response. The generally uniform response of increased C content in POM relative to MAOM under nutrient addition may represent a consistent soil response that could be considered in management decisions and soil modeling.
**Conclusions**

Because nutrient availability is expected to increase with global environmental change, it is important we evaluate not just the total soil C responses to nutrient addition, but also the coupling of C and N and the distribution of C and N between soil components, to improve our understanding of future grassland biogeochemistry. While C and N were coupled in the total SOM pool, evaluating MAOM and POM allowed us to find more subtle decreases in C:N after N, P, and NPK$_{+\mu}$ addition, suggesting that increased nutrient supplies may cause C and N to decouple in MAOM and POM. Furthermore, MAOM and POM responded differently to nutrient addition across environmental gradients. This revealed that nutrient addition-driven increases in soil C may be driven by increases in MAOM C:N in low fertility soils and by POM C:N in sandy soils. By evaluating distribution of C and N between MAOM and POM, we were able to show that addition of macro- and micronutrients in combination (NPK$_{+\mu}$) drives C accumulation preferentially in POM, suggesting reduced soil C stability. Our results highlight that more mechanistic and practical insight can be gained by investigating coupled C and N and SOM heterogeneity in addition to bulk soil C.

**Acknowledgements** We thank Aaron Prairie for his assistance in the lab and the Nutrient Network organizers and participants for being willing to share their data, soils, and ideas. We especially thank Brooke Osbourne, Sumanta Bagchi, Sally Power, and Adrienne Keller for their helpful comments on earlier versions of this manuscript. This work was generated using data from the Nutrient Network (http://www.nutnet.org) experiment, funded at the site-scale by individual researchers. Coordination and data management have been supported by funding to E. Borer and E. Seabloom from the National Science Foundation Research Coordination Network (NSF-DEB-1042132) and Long Term Ecological Research (NSF-DEB-1234162 and NSF-DEB-1831944 to Cedar Creek LTER) programs, and the Institute on the Environment (DG-0001-13). We also thank the Minnesota Supercomputer Institute for hosting project data and the Institute on the Environment for hosting Network meetings. Soil analyses were supported, in part, by USDA-ARS Grant 58-3098-7-007 to ETB.

**Author contributions** KSR and MFC developed and framed the research questions. KSR analyzed the data and KSB contributed to data analyses. EWS and ETB coordinate the Nutrient Network and their lab funded and generated the pre-treatment soil dataset. SEH manages the permits for the archived soils used in this manuscript. RLM, ASM, CJS, XR, JLM, and JDB were site coordinators for sites whose data are used in this

manuscript. KSR wrote the manuscript. All authors contributed to writing and approved the final manuscript.

**Funding** This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. #1650114. This work has benefited from technical and human resources provided by CEREEP-Ecotron IleDeFrance (CNRS/ENS UMS 3194) as well as financial support from the Regional Council of Ile-de-France under the DIM Program R2DS bearing the reference I-05-098/R. It has received support under the program "Investissements d'Avenir" launched by the French government and implemented by ANR with the reference ANR-11-INBS-0001 AnaEE France and ANR-10-IDEX-0001-02 PSL.

**Data availability** The data for this study are available at the Environmental Data Initiative (https://doi.org/10.6073/pasta/b8cdf07124285fa2b511ef27882c421e).

**Code availability** Analyses were conducted in R Studio (https://rstudio.com/) and code is available from the corresponding author upon request.

**Declarations**

**Conflict of interest** The authors report no conflict of interest.

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