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# Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics

## Abstract

Storing large amounts of organic carbon, soils are a key but uncertain component of the global carbon cycle, and accordingly, of Earth System Models (ESMs). Soil organic carbon (SOC) dynamics are regulated by a complex interplay of drivers. Climate, generally represented by temperature and moisture, is regarded as one of the fundamental controls. Here, we use 54 forest sites in Switzerland, systematically selected to span near-independent gradients in temperature and moisture, to disentangle the effects of climate, soil properties, and landform on SOC dynamics. We estimated two SOC turnover times, based on bulk soil 14C measurements (τ14C) and on a 6-month laboratory soil incubation (τi). In addition, upon incubation, we measured the 14C signature of the CO2 evolved and quantified the cumulated production of dissolved organic carbon (DOC). Our results demonstrate that τi and τ14C capture the dynamics of contrasting fractions of the SOC continuum. The 14C-based τ14C primarily reflects the dynamics of an older, stabilised pool, whereas the incubation-based τi mainly captures fresh readily available SOC. Mean site temperature did not raise as a critical driver of SOC dynamics, and site moisture was only significant for τi. However, soil pH emerged as a key control of both turnover times. The production of DOC was independent of τi and not driven by climate, but primarily by the content of clay and, secondarily by the slope of the site. At the regional scale, soil physicochemical properties and landform appear to override the effect of climate on SOC dynamics.

## Introduction

Earth System Models (ESMs) have become a primary tool to project future climate1. Given that soil organic carbon (SOC) is one of the largest terrestrial C pool (~3000 Pg C)2; and relevant to terrestrial carbon-climate feedbacks, the adequate representation of soils is critical for these models. SOC dynamics is commonly implemented in ESMs as a function of climate3,4, typically represented by the interplay of temperature and moisture. Soil incubations and short-term (<10 years) field studies have generally indicated that SOC reservoirs are vulnerable to warming5,6. Moisture has also been recognized as important for predicting the evolution of SOC stocks and fluxes, but its influence has been less thoroughly researched compared to temperature7,8. The evaluation of well-established ESMs has shown that they yield widely different projections of SOC stocks and dynamics and that their results poorly fit observations9,10. A major cause of uncertainties relates to the global carbon cycle9 and it has been suggested that they could be partly reduced if the simulation of SOC turnover was improved1,10. For this, the dependences of SOC dynamics on key drivers (e.g. climate, clay content) should be better understood and implemented in carbon models10,11.

A common limitation of observational studies on this topic is the inherent covariation of drivers in time and/or space (e.g. site temperature and moisture, precipitation and soil pH), as this covariation across natural gradients makes it very challenging to disentangle the effect of drivers12,13,14. The complex orography of Switzerland, with the Alps separating the northern and southern regions, generates a large diversity of climates ranging from artic to mediterranean. To unravel the regional-scale effect of temperature and moisture on SOC dynamics, we developed a statistics-based strategy to select 54 study sites spread over Switzerland with maximized orthogonality of temperature and moisture (Fig. 1). We collected field data, ran a laboratory soil incubation and measured 14C contents to test concurrently various putative drivers of SOC dynamics. Our main hypothesis was that climate plays a primary role, followed by soil properties and landform.

We selected the 54 study sites from the forest soils database of the Swiss Federal Institute for Snow, Forest and Landscape Research (WSL) containing information on 1,050 profiles15 (Figs 1 and S4). To represent climate, we included site temperature (1981–2010 average air mean monthly temperature) and moisture (1981–2010 dryness index, details in Methods). For soil properties and landform, we gained pH, clay content (%), slope (%) and orientation (N-E-S-W) data and reduced them to two orthogonal variables (PCo1, PCo2) by principal coordinate analysis. As the Swiss biogeographical regions16 (Fig. 1) differ in biogeophysical characteristics that may also affect SOC stocks and turnover rates, we fitted them as an independent block factor during data analysis. The 54 study sites were selected so that the effects of biogeographic region, temperature, moisture, and the aggregated soil properties and landform-related variables (PCo1, PCo2) were near-orthogonal, i.e. their effects were independent in the multiple linear regression models summarized by analysis of variance17,18 (details in Methods).

From each of the 54 sites, we collected three mineral soil samples from three non-overlapping areas of a plot (~1,600 m2). Each of these samples was a composite obtained by mixing eight 0–20 cm depth soil cores. We determined the bulk soil radiocarbon signature by accelerator mass spectrometry19 on a mixture of material from these three bulk soil composites. Further, we incubated fresh soil samples (sieved ≤2 mm) in microlysimeters, which permitted the flushing of the soils during the incubation to yield water extracts20. For 181 days, we repeatedly measured SOC mineralisation as the evolution of CO2 and the production of dissolved organic carbon (DOC) by determining organic carbon in soil water extracts. We measured 14CO2 evolved upon incubation by accelerator mass spectrometry. We ran the incubation in the dark at constant temperature and moisture (25 °C and water potential of −20 kPa).

SOC dynamics reflects the balance between the long-term above and belowground organic carbon (OC) inputs, as well as SOC loss processes. Assuming system homogeneity, we calculated two separate estimates of turnover time and compared them. First, bulk soil 14C measurements were used to calculate turnover time τ14C, based on near steady-state conditions (i.e. inputs ≈ outputs) and not accounting for lag times21. Second, SOC mineralisation data from the laboratory soil incubation was used to estimate turnover time τi as the ratio of total SOC stock and output flux22 (details in Methods). As the dissolution of organic substrates is a requirement for their transfer across microbial membranes23, sorption to minerals24,25, and large-scale transport across ecosystems and export from soils26,27, DOC constitutes a small but decisive component of the SOC cycle. Thus, we also quantified the DOC flux as the cumulative production throughout the 181-day incubation.

## Results and Discussion

### Turnover of contrasting ‘fractions’ of the SOC continuum

Our data show that the 14C-based τ14C is at least one order of magnitude larger than the incubation-based estimate τi (Fig. 2), reinforcing the marked discrepancy found between both calculation methods in a review by Feng et al.28. The 14C-based τ14C ranges from 67 to 511 years and represents the average turnover time of the mixture of all OC molecules that constitute bulk SOC29. In general, τ14C reflects SOC cycling on time scales ranging from years to millennia, which in mineral soils typically constitutes most of the stock30 and where SOC stabilisation processes prevail. As the terrestrial carbon cycle does not only respond to a mean climate, but also to a time series of weather conditions and extreme events31,32,33, the long-term nature of τ14C makes this metric suited to capture the cumulative effect of such events. On the other side, the incubation-based τi, based on the OC output flux over the course of the incubation, ranges from 5 to 29 years, in agreement with the radiocarbon signature of the CO2 evolved, which carries the atmospheric 14C signature of the last ~5–20 years (Fig. 3). Therefore, τi primarily but not exclusively, reflects plant-derived readily available OC34 not yet stabilised by mineral interactions or occlusion within aggregates35. In addition, our estimates of τ14C and τi comply with the concepts of mean age and mean transit time, respectively. Sierra et al. (2016) presented these metrics to contribute to resolving the misconception in literature between the terms turnover time, age and residence time. Further, the discrepancy between the radiocarbon signature of the bulk soil and of the CO2 evolved (Fig. 3) evidences the heterogeneity of the soil systems. In fact, in a truly homogeneous system τ14C would equal τi (i.e. mean age = mean transit time). Quantitatively, during the 181-day incubation, soils lost 1–9% OC as CO2 and DOC, indicating that under perturbation a labile fraction of SOC will be potentially lost rapidly.

Todd-Brown et al.10 simulated global SOC turnover for the period 1995–2005 with ESMs part of the Coupled Model Intercomparison Project phase 5 (CMIP5). As many of these models do not explicitly report the depth of OC in the soil profile, the authors assumed that simulations referred to the top 1 m. Their calculated turnover times ranged between ~11 and 37 years, shorter than our estimates of τ14C (67–511 years), but matching those of τi (5–29 years). As our samples correspond to the top 20 cm of mineral soil, and τi is based on laboratory soil incubations, our results suggest that at least some ESMs focus on the turnover of the most labile SOC.

Radiocarbon and incubation-based turnover times τ14C and τi were not strongly correlated (Pearson = 0.40, p < 0.01; Fig. 2), suggesting that the OC fractions these estimates reflect may be influenced by different drivers of SOC dynamics. Lastly, part of this discrepancy may be also related to the presence of pyrogenic organic carbon (PyOC). For example, data on PyOC by hydrogen pyrolysis on bulk soil samples of the 54 sites36, show that the two extreme points, with large τ14C (~380 years) but low τi (~6 years) (Fig. 2), lie at the highest end of the range of probability distribution of PyOC (decile 7th and 10th, Fig. S1). When we exclude these two sites from the analysis, the overall trend approaches the curvilinear relationship between mean residence time determined by 14C dating and by a 13C C3–C4 crop experiment in a study by Paul et al.37. The authors of this study concluded that the differences between the two turnover time estimations were because they respectively reflected the dynamics of a slow versus an active pool.

### Regional drivers of SOC turnover times

We did not find an effect of site temperature on τ14C (Tables 1 and S1). Field warming experiments show a direct relationship between temperature and the short-term turnover of SOC, but there is evidence that this increase is ephemeral and that with sustained warming, the CO2 loss from soil tends to decline to ‘prior warming’ levels within a few years (≥10 years)38,39,40. Several mechanisms can explain thermal acclimation: (1) the depletion of microbially accessible OC pools together with the low sensitivity of an older pool41,42,43; (2) increased OC inputs and outputs29 and (3) the thermal acclimation of the microbial community (i.e. physiology, biomass, community composition) and enzymatic activities44,45. As for temperature, site moisture did not significantly influence τ14C (Tables 1 and S1), indicating that acclimation might have also occurred, or that, over long timescales, τ14C was not differently affected by the moisture regimes considered in this study. As such, these results suggest that at the regional scale, the influence of climate on the decadal-centennial turnover of SOC is not apparent, implying that the effect of a changing climate might be similar across climatic gradients.

Although τi is assumed to be linked to climate and land cover type46,47, and despite it captured the dynamics of the most labile SOC fraction (Fig. 3), it was not significantly explained by site temperature (Tables 1 and S1). Therefore, either temperature plays a limited effect on τi, or soils from cold sites are more sensitive to warming than those from warm sites, thus masking the effect of in-situ site temperature on the CO2 evolved during the incubation48. Acclimation processes might have also buffered the response of τi to site temperature; however, as τi averages ~10 years, the time available for acclimation to have occurred is shorter than in the case of τ14C, which averages ~172 years. Overall, these results indicate that at the regional scale, the influence of site temperature on the yearly-decadal turnover of SOC is not evident and that the net effect of warming might be similar across temperature gradients. Furthermore, τi was significantly related to site moisture (Tables 1 and S1) thus, the water balance appears to be more important than temperature for the turnover of the most labile SOC7,29. Our data reveals shorter τi (i.e. higher specific respiration) in dry sites than in moist sites. In the laboratory, when soils were under optimum conditions for decomposition, SOC that had accumulated in the field under dry conditions was mineralised quickly. This pattern evidences that in the field SOC mineralisation was predominantly limited by water availability, and manifests higher sensitivity of enzymes to moisture in dry sites49.

The aggregated indicator of soil properties and landform PCo1 explained a significant part of the variation of τi and τ14C (Tables 1 and S1). Further analyses revealed that soil pH was the underlying driver of τi and, to a lesser extent, of τ14C (Tables 2, S2 and S3). Soil pH is involved in many processes affecting SOC availability and stabilisation and, it is expected that turnover times become shorter with increasing pH values50. The influence of site moisture status on τi but not on τ14C, together with the stronger influence of pH on τi than on τ14C, contribute to explain the lack of correlation between both turnover times (Fig. 2). These results confirm the occurrence of a spatio-temporal gap between the two SOC fractions these turnover times represent and demonstrate that drivers do not equally govern their dynamics (Fig. 4). Additionally, although in temperate forest soils pH is closely linked to the parent material51, we observed a significant but weak correlation between water balance and soil pH (Pearson = −0.35, p < 0.05), highlighting the influence of climate on determining soil pH at the global scale52.

### Regional drivers of DOC leaching

In the incubation assay, the cumulative DOC leached only accounted for ~1% of total OC losses (i.e. CO2 plus DOC). In agreement with results of previous studies, neither site temperature nor moisture emerged as significant drivers of this flux53,54 (Tables 1 and S1). Our analyses revealed clay content, pH and site slope as strong controls of DOC production. However, as differences in mineralogy often go along with changes in soil pH55, further statistical analyses showed that of the two covarying variables, clay and pH (Pearson = 0.39, p < 0.05), clay was driving the release of DOC (Tables 2, S2 and S3). We found lower DOC losses at higher clay contents, demonstrating the importance of organomineral interactions for the stabilisation of soil organic matter56.

With respect to the site slope, even though laboratory experiments are not able to represent many mechanisms that affect DOC fluxes under field conditions, such as the spatio-temporal variability of water transport, we found that steeper sites yielded smaller DOC amounts. We explain this result by a larger water run-off at steeper sites that limits the accumulation of soluble OC in the profile. Finally, despite organic substrates must be in dissolved form for enzymatic degradation, we did not find a correlation between DOC leaching and τi (Fig. S2). Results from other studies have also indicated that DOC production is not directly related to SOC turnover53,54,57, probably because only a small fraction of the total DOC is readily bio-available58.

To conclude, to our knowledge this is the first study on drivers of SOC dynamics that combines radiocarbon and incubation data adopting an experimental framework that statistically de-correlates the effects of temperature and moisture over a large network of sites. Our results suggest that 14C and incubation-based turnover times primarily capture the dynamics of an older, stabilised and a younger, readily available SOC fraction, respectively. Climatic controls did not affect the long-term 14C-based turnover time, challenging the SOC turnover mechanisms embodied in carbon models in which temperature and moisture are critical for decomposition kinetics. Nevertheless, for the shorter-term incubation-based turnover time, encompassing the decomposition of recently fixed carbon, the legacy effect of site moisture prevailed over the one of temperature. Further, our data revealed a prevailing control of soil pH over both turnover times. The production of DOC was independent of the incubation-based turnover time and neither controlled by climatic drivers, but by clay content. Our data endorses the action of soil physicochemical properties (e.g. pH, clay content) and landform as key drivers of SOC dynamics.

## Methods

### Selection of study sites

We followed a stepwise procedure to select the 54 study sites so that the effects of biogeographic region, site temperature, site moisture, and the aggregated soil properties and landform-related variables (PCo1, PCo2) were near-orthogonal (Fig. S3).

A common limitation of studies on the drivers of SOC dynamics is the co-variation of controls in time and/or space. This makes partitioning their relative influences challenging13,14. For example, if we take a set of random samples, it would be very difficult to statistically distinguish between the effect of decreasing temperature and increasing moisture, as in the temperate zone colder sites also tend to be moister. The knowledge-driven selection of study sites for soil research is not new59, but its application is still far from common practice. In this work, we developed a statistics-based strategy to select, from the soil database of the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL)15, a set of 54 sites spread over Switzerland with minimised co-variation of the putative drivers (i.e. maximising orthogonality/statistical independence). The main hypothesis of this study is that sites air temperature and moisture play a primary role in SOC dynamics, followed by soil properties and landform. Accordingly, the stepwise implementation of the selection strategy was performed as follows with the R software (version 3.3.2)60:

1. 1.

Definition of the exhaustive population within the study area. By May 2014, the WSL soil database contained data on 1,050 profiles covering the main biogeoregions (i.e. Jura, Midland, Pre-Alps, Alps and Southern Alps)16 and a vast range of climatic conditions. 3% of the soil profiles in the database belong to the Long-term Forest Ecosystem Research Programme (LWF, German acronym). LWF sites are of special interest due to the sustained and intensive research activities and availability of data. Studies conducted in forested areas have shown that strong topographic gradients are an important control of erosion and deposition patterns61,62. For this reason, to assume that C losses due to erosion are negligible relative to the rate of SOC mineralisation, we excluded sites, excepting LWF sites, with slopes larger than 50% (26.57 degrees). Once we applied this filter, the working database counted 709 entries.

2. 2.

Definition of the putative drivers. To represent climate, we included sites temperature (1981–2010 average air mean monthly temperature) and moisture (1981–2010 dryness index). The dryness index is a proxy to site moisture status based on the cumulative number of dry months. A dry month is one on which the precipitation of the month is smaller than its potential evapotranspiration (Penman)63. For soil properties and landform, we reduced the pH value, clay content (%), slope (%) and orientation (N-E-S-W) to two orthogonal variables (PCo1, PCo2) by principal coordinates analysis18,64. For the selection of the sites, all data was sourced from the WSL soil database.

3. 3.

Partition of the population into four equiprobable categories based on main hypothesised drivers. As we hypothesise that climate plays a primary role in SOC dynamics, we partitioned the population into four equiprobable categories based on the median value of site temperature and moisture.

4. 4.

Spatial distribution of the population based on biogeographical regions. Swiss biogeographical regions are the product of a statistical classification based on floristic and faunal distribution patterns, thus they are also connected to geophysical characteristics. To ensure the spatial distribution of the study sites, we subdivided each climatic category upon biogeographical regions. Some combinations of climate-biogeographical region did not occur or were under-represented. For example, we did not find the combination of moist and warm sites in the Alpine region or moist sites in the Southern Alps region.

5. 5.

Partition of the population into four categories based on secondary hypothesised drivers. We reduced pH, clay (%), slope (%) and orientation (N-E-S-W) into two variables (PCo1 and PCo2) product of a principal coordinates analysis performed on the population dataset (PCoA; also known as classical or metric multidimensional scaling)18,64. PCoA is an ordination method that attempts to position objects in a Euclidean space of reduced dimensionality while preserving their distance relationships of dissimilarity (or similarity)64. As a result, in the graphical representation of the Euclidean space, the further are the sites plotted to each other, the more dissimilar they are and vice-versa.

6. 6.

Non-automated selection of study sites from two-dimensional Euclidean spaces defined by PCo1 and PCo2. To select the 54 sites, we positioned the data from each climate-biogeographical region combination into two-dimensional Euclidean spaces defined by PCo1 and PCo2. From here, we selected equidistant sites from the origin of coordinates. Due to missing points, some combinations were not sampled. These gaps are expected to have an only minor influence on the regression analyses because they are spread rather than clustered over the full range of potential combinations.

### Field campaign

Between July and September 2014, we collected from each of the 54 sites three mineral soil samples from three non-overlapping areas of a 40 × 40 m2 plot immediate to the original WSL soil profile. Each of these samples was a composite product of mixing eight 0–20 cm depth soil cores collected with a 5 cm diameter Humax corer. This sampling strategy enabled us to account for soil spatial variability. After collection, we transported the soils in portable fridges to the laboratories where we sieved them by hand (≤2 mm) and stored them at 3.5 °C until further use.

### Laboratory soil incubation

In February 2015, we incubated fresh soil samples (sieved to ≤2 mm; 40 g equivalent dry mass; adjusted to 0.8 g cm−3 bulk density) in sterilised glass microlysimeters20 which permitted the flushing of the samples during the incubation to yield soil water extracts. Microlysimeters were placed into 2-litre airtight glass jars that contained 20 ml of distilled water in a separate beaker to ensure the headspace was moist. After a 10-day pre-incubation, run under the same conditions as the incubation assay, we measured SOC mineralisation for 181 days while keeping soil moisture (−20 kPa) and temperature (25 °C) constant. We adjusted soil moisture with a suction pump and controlled it periodically by weighing the samples. Our goal was not to estimate the turnover time of soils in the field but to establish comparable conditions close to optimum for microbial activity driving SOC mineralisation. As the laboratory incubation ran at 25 °C, it meant an average temperature increase of 17.3 °C compared to field temperatures (range of temperature increase from 13.2–24.0 °C). We intentionally applied this large increase in temperature to override different transient warming responses that might have occurred when warming soil from cold and warm sites48. The CO2-C product of the mineralisation of SOC was captured in 20 ml NaOH (1M) traps placed into the 2-litre glass jars. Subsequently, the amount of C trapped was determined by the change of conductivity of the NaOH65. The amount of dissolved organic carbon (DOC) in the soil water extracts was determined by TOC analysis (DIMA TOC-2000 Dimatec, Essen, Germany). From the beginning of the experiment, NaOH traps were replaced for fresh ones on days 4, 13, 30, 63, 92, 121, 150, and 181. On days 4, 13, 30, 63, 121, and 181, each microlysimeter was also leached with 30 ml of a nutrient solution66 without N or P. Subsequently, after equilibration for 30 minutes, a suction of −20 kPa was applied for 25 minutes to the systems. In a previous study, we verified that leaching and the potential accumulation of mineral nitrogen (NO2, NO3, NH+4) during incubation does not bias SOC mineralisation20. DOC samples were filtered (1.6 µm MGA glass microfibre, Sartorius) before analysis. Incubation data (i.e. SOC mineralised, DOC) were normalised relative to the total organic carbon content of the bulk soil at the beginning of the experiment.

In this study, we measured the radiocarbon content of (1) bulk soil organic carbon and (2) CO2–C evolved from bulk soil. Measurements were performed on a MIni CArbon DAting System (MICADAS, Ionplus, Switzerland)19 featuring a gas ion source67 and coupled to an Elemental Analyser (EA vario MICRO cube, Elementar, Germany) at the Laboratory of Ion Beam Physics, ETH Zurich.

1. (1)

We measured the radiocarbon signature of bulk soil organic carbon on mixtures of the three composite samples collected at each of the 54 study sites. Prior analysis, we acidified (HCl 37% Trace-Metal purity, 60 °C, 72 h) and neutralized (NaOH pellets, 72 h, 60 °C) soils to remove potential inorganic carbon. Samples were combusted in the EA and the gas generated measured in the MICADAS.

2. (2)

We measured the radiocarbon signature of the CO2 evolved on mixtures of the NaOH traps of the three composites containing the CO2-C evolved during the last 31 days of the 181-day incubation. We selected the last sampling period of the incubation in order to minimise the effect of soil disturbance as affected by the incubation preparation. After mixing, we acidified the NaOH samples with H3PO4 (85% purity) and measured the gas generated in the MICADAS. Incubated blanks (i.e. jars without soil) were used to correct the radiocarbon signatures and to incorporate a total uncertainty based on a mixing model assuming constant contamination68.

Radiocarbon values are reported in Δ14C. We corrected the standards OX-I (SRM-4990, NIST) and OX-II (SRM-4990C, NIST) for the decay undergone from 1950 to 2014, year of field sampling.

### 14C and incubation-based turnover times

Various observational techniques have been developed to evaluate SOC dynamics and the magnitude and variability of these estimations is largely method dependent28,37,69,70. In fact, one single satisfactory technique does not exist and each of them may be suited to capture the dynamics of SOC at different timescales70,71. Turnover time is a metric of SOC dynamics that at steady-state often refers to the ratio of total carbon stock to input or output flux72. In a homogeneous system at steady-state, it indicates the time it would take for a reservoir to empty if there were no further inputs73. When data are not interpreted adequately, the assumption of a single homogeneous pool can lead to misconception. Soils with similar mean turnover time may present distinct distributions of turnover among pools, thus running the risk of overlooking the dynamics of smaller temperature-sensitive labile SOC fractions74. We acknowledge that soils constitute a heterogeneous mixture of organic C compounds cycling along a continuum of timescales (i.e. from minutes to millennia). Therefore, although in this study we assumed system homogeneity, we calculated two separate estimates of turnover time based on bulk soil 14C measurements (τ14C) and on a 6-month laboratory soil incubation (τi), capturing the dynamics of contrasting ‘fractions’ of the SOC continuum.

1. 1.

14C-based turnover time. 14C data is applied in open systems to estimate the rate of exchange of carbon between reservoirs34,72. This technique takes advantage of the 14C produced in the explosion of thermonuclear weapons during the early 1960s75. The degree to which bomb 14C is found in SOC provides a direct measure of the extent to which soils have incorporated C from the atmosphere34. In this work, we followed an approach that models a single homogeneous reservoir at steady-state, incorporating bomb 14C since 1950 and that does not account for lag times (i.e. time gap between C fixation in photosynthesis and release via soil microbial mineralisation)21,72,73. This method provides two possible solutions, but as our samples are 0–20 cm depth mineral soils, we always adopted the solution after the bomb 14C peak in 1964.

$${\Delta }^{14}{C}_{t+h}=k\,h\,{\Delta }^{14}{C}_{atm,t+h}+{\Delta }^{14}{C}_{t}(1-h(k+\lambda ))$$

Where:

• Δ14C is the soil sample radiocarbon content

• k is the SOC decomposition rate constant (year−1) and its inverse corresponds to the turnover time, τ = 1/k (year)

• h is the time step used in the calculation (1/12 for monthly time step)

• Δ14Catm is the radiocarbon content of the atmospheric C

• t + h corresponds to the time for which the model runs (month)

• t refers to a previous time step (month)

• λ is the radiocarbon decay constant and corresponds to the inverse of Godwin mean life (1/8267 = 1.21 × 10−4 year−1)

1. 2.

Incubation-based turnover time. Under the assumptions of one homogenous pool, we calculated turnover time as the ratio of total C stock to input or output flux22.

### pH, total organic carbon (TOC) & other data

We measured soil pH (CaCl2) on 40 °C dried composite subsamples. Part of these subsamples was also milled and subjected to acid fumigation to quantify TOC by Elemental Analysis (vario MICRO cube, Elementar, Germany). The rest of data were sourced from the WSL soil database15,76.

### Data analysis

As the 54 study sites were selected to maximise the orthogonality of the drivers, their effects were independent in the sequential fitting of multiple linear regression models summarized by analysis of variance17,18. As Swiss biogeographical regions are indirectly connected to biogeophysical characteristics, which may covary and confound the effect of the drivers of interest, we introduced them as a blocking factor in the regression models. Analyses of τi exclude sites 6, 12 and 24. Sites 6 and 24 were removed because NaOH traps became saturated (i.e. ≥80% of their uptake capacity was reached). By removing saturated samples, we guarantee a linear relationship between the conductivity of the alkali solution and avoid isotopically fractionated samples. Although the inclusion or exclusion of site 12 did not determine what drivers emerged significant, we rejected this site to improve the distribution of the residuals. On the other side, analyses of τ14C exclude site 40 (Eschenbach, Luzern, Midland) because we consider that the large turnover time obtained for this site (~1140 years) was due to the accidental collection of soil >20 cm depth. Both, τi and τ14C were log transformed to improve the distribution of residuals. We used DOC and drivers data untransformed. Analyses were performed with the R software (version 3.3.2)60.

## Data Availability

Data necessary to interpret, replicate and build upon this study are shared via a public repository (DOI 10.5281/zenodo.2526673). For access to the complete forest soil database of the Swiss Federal Institute for Forest, Snow and Landscape Research (n = 1050), you may contact S. Zimmermann (stephan.zimmermann@wsl.ch) or L. Walthert (lorenz.walthert@wsl.ch). List of figures that contain associated raw data: Figs 13 and S1S4.

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## Acknowledgements

We thank the Swiss National Science Foundation (project-no. 143026) for funding this study in the frame of the National Research Programme 68. (http://www.nrp68.ch/en). We thank the Geography Department of the University of Zurich for their support and the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and the Swiss Long-term Forest Ecosystem Research Programme (LWF) for giving us access to the research sites and to the soil database.

## Author information

Authors

### Contributions

Conceptualization: B.G.D., S.A., P.A.N. Data curation: B.G.D., S.Z., L.W. Formal analysis: B.G.D., P.A.N. Funding acquisition: S.A., P.A.N. Investigation: B.G.D. Methodology: B.G.D., S.A. Project administration: B.G.D. Resources: S.A., P.A.N. Software: B.G.D. Supervision: B.G.D., S.A. Validation: B.G.D., S.A., P.A.N. Visualization: B.G.D. Writing – original draft: B.G.D. Writing – review & editing: B.G.D., S.A., M.S.S., P.A.N., N.H., C.M., L.W., S.Z., L.W., F.H.

### Corresponding authors

Correspondence to B. González-Domínguez or S. Abiven.

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González-Domínguez, B., Niklaus, P.A., Studer, M.S. et al. Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics. Sci Rep 9, 6422 (2019). https://doi.org/10.1038/s41598-019-42629-5

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• DOI: https://doi.org/10.1038/s41598-019-42629-5

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