2.1 Site description

This study links information on soil mineralogy and texture (Doetterl et al., 2018) with proxies for microbial transformation and SOM composition to derive a better understanding of long-term SOM dynamics along weathering gradients. The study area is located in the California Central Valley (Merced County, CA, USA), where granitic debris with a minor share of mafic minerals from the Sierra Nevada foothills was deposited on alluvial terraces of the Merced River during interglacial periods (Harden, 1987; White et al., 1996, 2005). The alluvial deposits cover a temporal range of 3 million years, with the youngest terrace only 100 years old (Harden, 1987; Doetterl et al., 2018). We assume that the soil age corresponds to the age of the terrace depositions. The present climatic conditions across the study region are semiarid with a mean annual temperature of 16.3 ^∘C and a mean annual precipitation of 315 mm (Reheis et al., 2012). On the flat, plateau-like terraces, extensive, unfertilized rangeland dominates, which is not affected by wildfires. The vegetation cover is uniform along the chronosequence, consisting of annual Mediterranean grasslands, with common species including Bromus mollis, Festuca spp. (grasses), Erodium spp. (forbs), and legumes (Trifolium spp.) (Jones and Woodmansee, 1979).

2.2 Soil sampling

Soil samples were collected in December 2013 from 1 m³ soil pits located within a diameter of approximately 40 km in the north of Merced County (Fig. 1). The terraces are named based on different quaternary periods that led to the alluvial deposition (Harden, 1987). The two youngest terraces are named Post-Modesto (PM) 24II (0.1 ka) and Post-Modesto (PM) 22 (3 ka). The intermediate terrace Modesto 2.4 m was deposited between 8 and 30 kyr (∅ 19) ago. The two oldest terraces are Riverbank deposits (from 260 to 330 kyr (∅ 295) ago) and Chinahat (3000 kyr old deposits). On each terrace one plot was sampled, and three field replicates were taken per pedogenic horizon (Doetterl et al., 2018). Yet, for the youngest 0.1 ka terrace, soils were only taken to a depth of 30 cm, and in the case of the intermediate Modesto 2.4 m terrace, soil samples were taken from three different soil pits. To convert the pedogenic horizons to homogeneous depth classes (0–10 and 10–30 cm), weighted arithmetic means were calculated based on the contribution of each pedogenic horizon to the respective depth class. All soils were classified as chromic Luvisols except for the 3 ka terrace (PM 22) which was classified as a dystric Fluvisol. The total sample set was comprised of 30 observations.

Figure 1Map of the study area based on a digital elevation model (DEM) retrieved from Shuttle Radar Topography Mission (SRTM, resolution ∼ 30 m). River hydrology data were taken from Lehner et al. (2008).

2.3 Soil analysis

Doetterl et al. (2018) quantified elemental composition, pedogenic oxides, soil pH, cation exchange capacity (CEC), and soil texture. Elemental composition of the soil samples was determined using inductively coupled plasma atomic emission spectrometry (ICP-AES) after borate fusion (Chao and Sanzolone, 1992). Extraction of pedogenic organo-mineral associations and oxy-hydroxides was done using dithionite–citrate–bicarbonate (DCB) at pH 8 (Mehra and Jackson, 1958). The extracted pedogenic oxides (Fe_DCB, Al_DCB) were then quantified using ICP-AES and serve as a measure of how many Fe- and Al-bearing phases formed during soil pedogenesis. The degree of mineral weathering along the chronosequence was assessed based on successive depletion and/or accumulation of geochemical components. For instance, the Fe_total : Si_total ratio indicates how the mostly pedogenically formed Fe_total relates with Si_total, an element that abounds in the felsic parent material and accumulates in the semi-arid environment of the study area (White et al., 2005). Hence, the ratio decreases with increasing soil weathering. Similarly, the Ti : Zr ratio indicates the accumulation of Ti (constituent of many minerals) through mineral weathering relative to Zr (amount largely determined by the parent material). In addition, the increasing share of organo-mineral-associated Fe (Fe_DCB) and Al (Al_DCB) was assessed relative to Fe_total (Fe_total : Fe_DCB) and Al_total, respectively.

Soil pH was averaged from two measurements, after 30 min and 24 h, in 0.01 M CaCl₂ with a glass electrode. The potential CEC was measured as the NH${}_{\mathrm{4}}^{+}$ exchanged after saturating cation exchange sites with aluminium acetate with 2 M KCl buffered at pH 7. The share of base saturation (B_sat) was calculated as the availability of all base cations (K, Na, Mg, Ca) relative to the potential CEC. Soil texture was analysed by Doetterl et al. (2018) with a Mastersizer 2000 laser diffraction particle size analyser (Malvern Instruments). Clay (<2 µm), silt (2–63 µm), and sand (63 µm–2 mm) were differentiated based on the World Reference Base (WRB) classification (WRB 2014). In silty soils, clay is under- and silt is overestimated due to the planar geometry of clay minerals (Beuselinck et al., 1998). Therefore, correction factors (see Eq. 1) were employed that correct the percentage of silt based on previous studies (Beuselinck et al., 1998; Miller and Schaetzl, 2012).

Soil samples were ground in a ball mill (8000M Mixer/Mill, with a 55 mL tungsten carbide vial, SPEX SamplePrep, LLC, Metuchen, NJ, USA) prior to SOM analysis. To assess the degree of microbial transformation of SOM, we quantified the stable isotope ratios using an elemental combustion system (Costech ECS 4010 CHNSO Analyzer, Costech Analytical Technologies, Valencia, CA, USA) that was interfaced with an isotope ratio mass spectrometer (DELTA V Plus isotope ratio mass spectrometer, Thermo Fisher Scientific, Inc., Waltham, MA, USA). The stable isotope values were complemented with total C values taken from Doetterl et al. (2018). Stable isotope values for δ¹³C and δ¹⁵N were calculated as the ratio of ¹³C over ¹²C and ¹⁵N over ¹⁴N (R_sample) relative to the Vienna Pee Dee Belemnite (VPDB) standard for δ¹³C and relative to atmospheric N₂ for δ¹⁵N (R_standard). The calculation follows Araya et al. (2017) (see Eq. 2):

The spectroscopic analysis was carried out with a Bruker IFS 66v/S Vacuum FT-IR spectrometer (Bruker Biosciences Corporation, Billerica, MA, USA) to collect diffuse mid-infrared reflectance with a wavenumber range from 4000 to 400 cm⁻¹ (2500–25 000 nm wavelength). In our analysis, 100 co-added scans were performed on each ground sample and averaged for further analysis. Spectra were normalized with the standard normal variate (SNV) method for scatter correction and a potassium–bromide (KBr) background for baseline correction. Subsequently, absorbance peak areas were integrated using a local baseline defined as a straight line connecting the absorbances at the upper and lower wavenumber limits of each functional group (Demyan et al., 2012). Negative peak areas were attributed to noise due to low C stocks and not to excessive signal strength as they only occurred in the subsoil. To account for potential variation in signal strength due to varying C stocks, the peak areas were divided by the respective C stocks. The treatments of the spectra were performed using the OPUS software (version 7.5.18; Bruker Optik GmbH, Ettlingen, Germany).

We differentiated three functional groups, based on extensive previous research (Ellerbrock et al., 2004; Demyan et al., 2012; Vranova et al., 2013; Parikh et al., 2014; Ryals et al., 2014; Viscarra Rossel et al., 2016; Fissore et al., 2017; Hall et al., 2018): aliphatic C–H bonds, aromatic C = C bonds, and C = O bonds (see Fig. 2). These three functional groups are known to be indicative of three groups of SOM compounds with strongly differing chemical properties and consequently differing bioavailability to microorganisms in soil systems (see Table 1). The wavenumber ranges centred at 2925 (2976–2898 cm⁻¹) and 2850 cm⁻¹ (2870–2839 cm⁻¹) are attributed to antisymmetric and symmetric aliphatic C–H stretching (Parikh et al., 2014) and represent simple plant-derived organic compounds (S-POM), e.g. originating from leaf compounds (i.e. waxes), as well as certain organic acids that are secreted by plant roots (i.e. citrate and oxalate) (Vranova et al., 2013). The second wavenumber range, located at 1512 cm⁻¹ (1550–1500 cm⁻¹), represents aromatic C = C compounds (Parikh et al., 2014). This functional group is related to lignin-derived, complex plant-derived compounds (C-POM) present in lignin constituents of wooden plant parts and aromatic organic acids of root exudates (Ryals et al. 2014). A large wavenumber range at 1620 cm⁻¹ (1660–1580 cm⁻¹) represents a carboxylic C = O stretch of amides, quinones, and ketones, with possible shares of aromatic compounds (Parikh et al., 2014). This peak area is interpreted as microbial-derived organic compounds (MOM) and jointly represents amides and quinones, which constitute microbial cell walls, as well as a ketone contribution attributed to long-chained lipids that can also be found in plant materials (Kögel-Knabner and Amelung, 2014). Wavenumber ranges that overlap with signals from mineral compounds, i.e. between 1400 and 400 cm⁻¹ (Parikh et al., 2014; Margenot et al., 2015, 2017), were excluded from our analysis.

Figure 2Baseline-corrected DRIFTS spectra of each soil age for 0–10 (a) and 10–30 cm (b) soil depth. The wavenumber ranges across which absorbance peak areas were integrated for functional group assignment are indicated with grey bars in the background. The two wavenumber ranges around 2900 cm⁻¹ stand for simple plant-derived OM (S-POM), the one around 1620 cm⁻¹ for microbially associated OM (MOM), and the one at 1525 cm⁻¹ for complex plant-derived OM (C-POM).

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Table 1Overview of wavenumber ranges used to calculate absorbance peak areas from DRIFTS, the respective functional groups related to specific biochemical properties of SOM compounds and compiled information on the origin (Demyan et al., 2012; Parikh et al., 2014; Hall et al., 2018), and the respective SOM properties (C : N ratio, δ¹³C, and δ¹⁵N values) of each functional group (Benner et al., 1987; Austin and Vitousek, 1998; Dijkstra et al., 2006; Xu et al., 2010; Vranova et al., 2013).

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2.4 Statistical analysis

Statistical analysis was carried out using R (Version 4.0.1, R Core Team, 2020). Two-way analysis of variance (ANOVA) was carried out to assess differences across soil age (0.1, 3, 19, 295, 3000 ka) and soil depth (topsoil: 0–10; and subsoil: 10–30 cm) to identify significant trends of bulk SOM composition parameters (n=30). The significance level of the ANOVA results was at p<0.05. Prior to the ANOVA, residuals were tested for normality with a Shapiro–Wilk test, and the homogeneity of variances were checked with Levene's test. We used Tukey's honestly significant difference (HSD) as a post hoc test for pairwise comparison of homogeneous subgroups across soil ages. For the mineralogy and texture data taken from Doetterl et al. (2018), no ANOVA was carried out due to the low number of observations (n=10).

Linear models were formulated to explain the influence of mineralogy and texture variables on the SOM composition (dependent variables: C : N ratio, δ¹⁵N, δ¹³C, S-POM, C-POM, MOM) independent of soil age and soil depth. We controlled for multicollinearity among all variables by calculating variance inflation factors (VIFs) and excluded all variables with VIFs above 5 (Kuhn, 2020). VIFs quantify the collinearity of a variable with the model (Fox and Monette, 1992; Fox and Weisberg, 2019). The VIF of each variable was computed as follows (James et al., 2013) (Eq. 3):

Out of the variables presented in Table 2, the Fe_total : Si_total ratio, clay content, Fe_DCB, and Al_DCB were selected as explanatory variables as their inflation factor was below 5. Based on these variables, linear least squares regressions were calculated to explain SOM properties (C : N ratio, δ¹³C, δ¹⁵N, S-POM, C-POM, and MOM). To assess the variable importance of the model parameters we used the absolute t values which are calculated by dividing the parameter estimate (β_i) by the standard error of estimation (SE_i) (James et al., 2013; Kuhn, 2020). The t value is a measure to determine the effect that each parameter has on the regression slope (Grömping, 2015). To give information on the significance of the impact, we added the p value attributed to each predictor (Bring, 1996). The goodness of model fit was quantified with the R² value. The root mean squared error (RMSE), as a measure of prediction error, and the mean absolute error (MAE), as a measure of model bias, were quantified by a Monte Carlo cross-validation following a leave-one-group-out principle. Thus, the RMSE and MAE were calculated as the difference between predicted values based on a randomly selected training set (80 % of the observations) in relation to a randomly selected validation set (20 % of the observations) and relative to the sample size. The RMSE, the MAE, and the R² values were averaged, and respective standard deviations were calculated based on 100 iterations of the cross-validation. In addition, we calculated the relative RMSE (rRMSE) and relative MAE (rMAE). They were calculated as the quotient of the RMSE and the mean of the response variable and multiplied by 100 (to get percentages).

Table 2Overview of soil mineralogy and texture changes with increasing soil age at 0–10 and 10–30 cm depth (n=10). The mineralogy was measured using inductively coupled plasma atomic emission spectrometry (ICP-AES). Texture was quantified with a laser diffraction particle size analyser. Pedogenic oxides were extracted in duplicates with dithionite–citrate–bicarbonate (DCB).

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3.1 Changes in soil properties and SOM composition along the chronosequence

With increasing soil age, the soil mineral matrix was progressively weathered. Weathering and leaching of cations were reflected in decreasing trends of the CEC from 15.2 to 4.5 at 0–10 cm depth (difference between youngest (0.1 ka) and oldest (3000 ka) terrace) and from 14.2 to 2.1 at 10–30 cm depth (see Table 2). Similarly, soil pH showed a trend to decrease from 6.8 to 4.7 in 0–10 cm and 6.8 to 4.3 at 10–30 cm depth (see Table 2). Yet, the gradient observed for CEC and soil pH was not found in base saturation (B_sat) values which did not show any clear pattern with increasing soil age but were consistently higher at the 10–30 cm compared to 0–10 cm depth (except for the youngest 0.1 ka terrace). We observed an increase in Si_total from 241.5 to 393.4 g kg⁻¹ at 0–10 cm and from 280.3 to 389.4 g kg⁻¹ at the 10–30 cm depth with increasing soil age. Al_total and Fe_total contents showed the opposite trend, decreasing with soil age in both depths. However, this decrease was not displayed in the fractions of DCB-extractable Al (Al_DCB) and Fe (Fe_DCB). While Al_DCB stagnated along the age gradient at values ranging from 0.7 to 1.3 g kg⁻¹ at 0–10 cm depth and 0.6 to 1.4 g kg⁻¹ at 10–30 cm depth, Fe_DCB exhibited an increasing trend in both depths. At 0–10 cm depth the values increased from 6.2 to 13.6 g kg⁻¹ and at 10–30 cm depth from 6.1 to 13.5 g kg⁻¹. The higher Fe_DCB values among the DCB-extracted pedogenic oxides reflect the high potential of Fe to be quantitatively important for SOM binding. The Fe_total : Fe_DCB ratio showed the highest values in the soils of the 19 ka terrace (7.6 at 0–10 cm and 6.4 at 10–30 cm depth). The Al_total : Al_DCB ratio was highest in soils of the 295 ka terrace with values of 88.5 at 0–10 cm depth and 106.8 at 10–30 cm depth (see Table 2). Soil texture showed no clear patterns with soil age but instead differed between the 0–10 and 10–30 cm depths (Table 2). Except for the youngest terrace, silt and clay fractions were more abundant at 10–30 cm depth, while the proportion of sand was mostly lower relative to the 0–10 cm depth.

Overall, bulk total C decreased significantly with soil depth (ANOVA results were significant at p<0.05) and with soil age at the 0–10 cm depth (see Fig. 3a). However, the decrease was not linear, and the highest C stock was measured at 3 ka (3.6 kg m² C; see Fig. 3a). Furthermore, the C : N ratio was lower at 10–30 cm depth and in the 19, 295, and 3000 kyr old soils compared to the 0.1 and 3 kyr old soils (see Fig. 3b). In contrast, δ¹³C values showed no significant pattern with soil age but increased with soil depth (see Fig. 3c). The δ¹⁵N values increased significantly with soil age but showed no significant difference from 0–10 to 10–30 cm depth (see Fig. 3d). Similarly, the peak areas of S-POM were significantly lower at the 10–30 cm depth and remained stable with increasing soil age (see Fig. 4a). Conversely, peak areas related to C-POM and MOM significantly increased with age at 0–10 cm depth and 10–30 cm depth. Moreover, peak areas were significantly higher at 10–30 cm (see Fig. 4b and c).

Figure 3Mean ± standard deviation of site triplicates of total C (a), the C : N ratio (b), δ¹³C (c), and δ¹⁵N (d) for 0–10 (black) and 10–30 cm soil depth (grey). Significant differences between the soil ages calculated with Tukey's HSD test are indicated with letters (p<0.05). Differences across soil age are indicated with lowercase letters and differences across soil depth in capital letters. Soil age is spaced based on the log-transformed age differences.

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Table 3Overview of the linear regression models describing SOM composition (C : N ratio, δ¹⁵N, and δ¹³C), as well as peak areas of functional groups related to simple plant-derived OM (S-POM), complex plant-derived OM (C-POM) and mainly microbial-derived OM (MOM) based on n=30 observations. Explanatory variables related to the mineral matrix and soil texture were selected (Al_DCB, Fe_DCB, Fe_total : Si_total ratio, clay content). Absolute and relative model fit parameters (root mean squared error (RMSE and rRMSE), mean absolute error (MAE and rMAE), and R² ) with standard deviations were computed following 100 iterations of a Monte Carlo cross-validation. Higher coefficients of variable importance indicate a higher importance of the variable to explain the impact on the regression slope. The significance levels of each model parameter on the linear model are denoted as ${}^{***}$ p<0.001 and ${}^{**}$ p<0.01.

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3.2 Link between soil geochemistry and SOM composition

The DRIFTS peak areas related to S-POM decreased significantly with soil depth, while the C-POM and MOM signals rose significantly with increasing soil age and soil depth (see Fig. 4). This raised the question of whether there were identifiable soil properties driving the relative changes in S-POM, C-POM, and MOM. Therefore, we used a modelling approach to predict bulk SOM composition using the soil mineralogy and texture parameters which were not affected by multicollinearity (see “Methods”). In Table 3, the mean model fit (RMSE and R²), respective relative measures (rRMSE, rMAE), and variable importance coefficients are presented based on the cross-validated results of the linear models with randomly divided training and control sets (see “Methods”).

Figure 4Mean ± standard deviation of site triplicates of the DRIFTS absorbance peak areas (unitless) corresponding to simple plant-derived OM (S-POM; a), complex plant-derived OM (C-POM; b), and mainly microbial-derived OM (MOM; c) for 0–10 (black) and 10–30 cm soil depth (grey). Homogeneous subgroups (p<0.05) across soil age are indicated with letters. Significant differences between soil depth are indicated with letters behind the depth classes in the legend. The subgroups were identified by pairwise comparison (Tukey's HSD) following a two-way ANOVA (n=30). Soil age is spaced based on the log-transformed age differences.

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The C : N ratio was best explained by the Fe_total : Si_total ratio which was the most important variable contributing significantly to the regression slope (p<0.01; see Table 3). However, the R² showed high variability and was comparatively low in relation to the other models (R²: 0.32 mean ± 0.27 SD). The rRMSE of 17.9 % and the rMAE of 16.1 % indicated a good prediction error compared to the other models (see Table 3). The δ¹⁵N values were most influenced by clay content, the Fe_total : Si_total ratio, and Al_DCB. However, despite the high model fit, no model parameter was significant. Similar results were obtained for the R² values of δ¹³C. In this model all parameters were significant (p<0.001), and based on the absolute values of the variable importance coefficients, Fe_DCB, the Fe_total : Si_total ratio, and clay were more important than Al_DCB (see Table 3). The rRMSE and rMAE were slightly higher for δ¹⁵N compared to the C : N ratio model (rRMSE: 24.2 %; rMAE: 20.3 %) and low for δ¹³C (rRMSE: −2.2 %; rMAE: −2.2 %). Regarding the peak areas related to S-POM, the most important significant variable was clay (p<0.01; see Table 3). Yet, the explained variance was low and had a high standard deviation (R²: 0.39 ± 0.33). This is also reflected in a high rRMSE and rMAE of >100 % (see Table 3). Conversely, the linear models explaining the variance of the peak areas linked to C-POM and MOM had high R² values (R²: 0.78 ± 0.10 and 0.57 ± 0.27, respectively) indicating a good fit. In both cases, clay content was the only significant model parameter (p<0.01). Despite the high variable importance of the Fe_total : Si_total ratio in the C-POM model, this parameter was not significant (see Table 3). The rRMSE (C-POM: 56.3 %; MOM: 61.7 %) and rMAE (C-POM: 45.8 %; MOM: 55.1 %) were high in both cases.

4.1 Assessing chemical alteration of soil mineralogy, texture, and SOM composition over time and depth

Older soils are increasingly weathered, and soil fertility decreases, as indicated by the lower soil pH and CEC (see Table 2). Further, the increasing dominance of Si with ongoing mineral weathering indicates increasing amounts of low reactive clay-sized silicates, i.e. kaolinite, which offer fewer binding sites to SOM (White et al., 2005). At the same time, significantly lower total C in strongly weathered soils were accompanied by an increasing trend of pedogenic iron oxides, i.e. Fe_DCB, that likely contributed to the unaltered base saturation values. Similar effects were observed in previous soil chronosequence studies, highlighting the importance of iron-bearing mineral phases for SOM stabilization in strongly weathered soils (Chorover et al., 2004; Mikutta et al., 2009). In this study, the increasing trends in pedogenic iron oxides co-varied with proxies that indicate increasingly stabilized and microbially transformed SOM (decreasing C : N ratio, increasing δ¹³C and δ¹⁵N values) and a shift towards a C-limited soil environment (Coyle et al., 2009; Cotrufo et al., 2021). The stronger microbial-derived origin of SOM is reflected in the increasing δ¹³C and δ¹⁵N values that develop due to isotopic discrimination of microorganisms against heavier ¹³C and ¹⁵N (Dijkstra et al., 2006, 2008; Brunn et al., 2016; Kramer et al., 2017).

Soil fertility and SOM stabilization capacity of soils decreased along the chronosequence, thus leading to lower total C stocks on old terraces and stronger signs of microbially transformed SOM following reduced C input (Doetterl et al., 2018). Additionally, we observed significant changes in the peak areas of different functional groups of different origins and complexity regarding their chemical structures. While peak areas of simple plant-derived OM (S-POM) were less affected over time but significantly decreased with depth, peak areas of microbial-derived (MOM) and complex plant-derived OM (C-POM) significantly increased over time and with depth (see Fig. 4). We related S-POM to aliphatic compounds from litter and/or root exudates, C-POM compounds to aromatic structures of aboveground woody debris and/or aromatic root constituents, and MOM to long-chained lipids of predominantly microbial origin (see Table 1).

Throughout the quaternary period, the climate and vegetation undoubtedly varied. Nevertheless, we found that S-POM did not decrease along the chronosequence, thus indicating a steady supply by aboveground (light fraction of litter compounds) and belowground input (low-molecular weight root exudates; Nardi et al., 2005) particularly in the topsoil layer. The significant decreases with depth might be related to a lower stabilization potential of aliphatic organic acids with increasing soil depth (Vranova et al., 2013). Yet, it is noteworthy that below the main rooting zone (>16 cm depth) increases in aliphatic compounds have been observed in similar grassland soils which was explained by increasing stabilization on mineral surfaces (Feng et al., 2007). The latter is further supported by the high affinity of S-POM, in particular citrate and oxalate, to bind with Fe and Al oxides (Fe_DCB tended to increase with soil age) in acidic soils (Clarholm et al., 2015). The significant increase in MOM peak areas in parallel to the constant S-POM peak areas along the chronosequence could indicate that an increasing fraction of microbially processed and transformed OM gains importance in bulk SOM with soil age (Feng et al., 2007). Previous studies showed a strong correlation between the MOM peak area and organo-mineral associations (Kaiser et al., 2007; Demyan et al., 2012; Kaiser et al., 2012). Thus, the observed increases in our study hint towards an increasing importance of OMAs with progressing soil weathering. C-POM signals, interpreted as lignin compounds from woody plant parts, behaved similarly to MOM and significantly increased with soil age and depth. In grassland soils, increases in signals related to lignin compounds with depth were explained by the increasing dominance of root-derived OM input in subsoils (Feng et al., 2007). Yet, increases in lignin compounds were also observed following a preferential association with iron oxides which is supported by the increase in pedogenic Fe_DCB with soil age (Hall et al., 2016; Zhao et al., 2016), as well as kaolinite which is supported by the increasing clay contents in our samples with soil depth (Li et al., 2019).

4.2 Mineralogical control of SOM dynamics over geological time

As indicated above, the changes in the degree of decomposition (as indicated by the C : N ratio, δ¹³C, and δ¹⁵N) and SOM composition (peak areas of S-POM, MOM, and C-POM) must be contemplated in the light of simultaneously occurring changes in the mineral matrix along the chronosequence. Therefore, here we discuss to which extent the relationship between soil mineralogy, texture, and SOM (de-)composition is reflected in linear models and how these findings can be embedded in our current understanding of SOM dynamics.

The properties related to SOM decomposition showed differences in the model fit (see Table 3). The C : N ratio showed the lowest model fit of all SOM composition properties. Low and variable R² values highlighted that the mineral matrix did not strongly affect the C : N ratio of bulk SOM even though SOM was progressively decomposed with increasing soil age. The only variable that contributed significantly to the explanation of the C : N ratio was the Fe_total : Si_total ratio. The decreasing trend of the Fe_total : Si_total ratio with soil age indicated that during soil weathering processes, low reactive silicates, i.e. quartz or 1:1 layer type of clay minerals (kaolinite), abound (White et al., 2005). The second most important predictor variable, Fe_DCB, reflects an increasing importance of pedogenic iron oxides in SOM stabilization as these compounds retain very high reactive surface areas (Kleber et al., 2021). Our findings show that the properties of the mineral matrix play a subordinate role in shaping the C : N ratio which is more strongly driven by the increasing microbial origin of the SOM (Sollins et al., 2009).

In contrast to the C : N ratio, linear models with δ¹³C and δ¹⁵N values as response variables showed a good model fit (R²>0.60, Table 3). The δ¹³C values were strongly related to Fe_DCB, Fe_total : Si_total, and clay, while δ¹⁵N values were explained by clay, Fe_total : Si_total, and Al_DCB (although not significantly). In both cases, the high importance of clay content as predictor supports empirical findings that SOM with a higher share of microbial-derived OM (high δ¹⁵N; high MOM signal) and increasingly decomposed OM (low C : N ratio; high δ¹⁵N, less negative δ¹³C) is predominantly found within the heavy (clay) fraction of soils (Conen et al., 2008; Sollins et al., 2009; Clemente et al., 2011; Lawrence et al., 2015). It has been previously demonstrated that SOM bound to iron oxides was enriched in δ¹³C and exhibited more MOM (Zhao et al., 2016). Likely the imprint of δ¹³C enrichment with increasing microbial transformation of the SOM manifested itself more clearly in bulk SOM as the overall C stocks decreased in the oldest soils (Yang et al., 2015).

Similar to the models explaining SOM decomposition variables, the linear models on DRIFTS peak areas had varying model fits (see Table 3). The model for S-POM had a lower model fit (R²=0.39) compared to the models explaining C-POM and MOM (R²=0.78 and 0.57, respectively). In all models, the clay content was the key explanatory variable which hints at different behaviours in top- and subsoils as consistently higher shares of clay were found at the 10–30 cm depth (see Table 2). The fact that S-POM was not significantly influenced by the Fe_total : Si_total ratio suggests that the contribution of S-POM to OM is less related to weathering-induced changes in the mineral matrix but is rather determined by above- and belowground vegetation input, i.e. through litter components or root exudates (Vranova et al., 2013; Mueller et al., 2013). Lower signals of S-POM in subsoils compared to topsoils (see Fig. 4) indicate that the mineral stabilization of S-POM compounds in subsoils is lower or that S-POM compounds are less abundant at these depths (Vranova et al., 2013). Still, the higher variable importance of Al_DCB and Fe_DCB to explain S-POM potentially indicates that certain compounds, likely aliphatic organic acids derived from root exudates, are preferentially attached to minerals (Clarholm et al., 2015).

In the case of C-POM, the decreasing Fe_total : Si_total ratio with increasing soil age had the highest variable importance coefficient but was not significant to the model. The high variable importance coefficient is in line with other studies that found functional groups that are considered chemically more complex (i.e. through an aromatic ring structure) to be increasingly stabilized relative to other functional groups as soils become more weathered, as overall SOM stocks deplete, and as protection through reactive mineral surfaces declines in strongly weathered soils (Angst et al., 2018). Lignin compounds represented by C-POM were shown to be better stabilized due to attachment on mineral surfaces, such as calcite (Grünewald et al., 2006), kaolinite (Li et al., 2019), and Fe oxides (Kramer et al., 2012; Hall et al., 2016; Zhao et al., 2016; Huang et al., 2019). Yet, the coefficients of variable importance of the C-POM model did not show a particular importance of Fe_DCB and Al_DCB (see Table 3). Potentially the more linear decrease in the Fe_total : Si_total ratio might mask less consistent increases in Fe_DCB or the varying behaviour of Al_DCB (see Table 2). Clay content was the only significant predictor, and it was previously shown that kaolinite accumulates in the mineral matrix with increasing soil age and depth along the present chronosequence (White et al., 2005). Hence, the mineral stabilization of C-POM in heavy soil fractions might be more strongly linked to the presence of clay minerals than iron oxides (Li et al., 2019; Grünewald et al., 2006).

The peak areas related to MOM were significantly explained by the clay content (see Table 3). This is consistent with the significant model parameters for S-POM and C-POM. The clay fraction is known to harbour the vast majority of soil microorganisms and to stimulate their activity (i.e. Stotzky and Rem, 1966; Sollins et al., 2009). Therefore, it is logical that the increasingly microbial-derived imprint in the bulk SOM is significantly linked to the clay fraction. Further, the Fe_DCB parameter had a higher variable importance coefficient which might be linked to the increasing importance of pedogenic iron oxides and MOM along the soil chronosequence. This might be caused by the increasing importance of metal surfaces in stabilizing bulk SOM but also soil microorganisms or microbial necromass in particular (Kaiser et al., 1997, Zhao et al., 2016). Yet, these interpretations are more speculative due to the heterogeneity of soil microorganisms, and the vast range of possible interactions requires further investigation.