A central question in carbon research is how
stabilization mechanisms in soil change over time with soil development and
how this is reflected in qualitative changes in soil organic matter (SOM).
To address this matter, we assessed the influence of soil geochemistry on
bulk SOM composition along a soil chronosequence in California, USA, spanning
3 million years. This was done by combining data on soil mineralogy and
texture from previous studies with additional measurements on total carbon
(C), stable isotope values (δ13C and δ15N), and
spectral information derived from diffuse reflectance infrared
Fourier transform spectroscopy (DRIFTS). To assess qualitative shifts in
bulk SOM, we analysed the peak areas of simple plant-derived (S-POM),
complex plant-derived (C-POM), and predominantly microbial-derived organic matter (OM; MOM) and their changes in abundance across soils with several millennia
to millions of years of weathering and soil development. We observed that
SOM became increasingly stabilized and microbial-derived (lower C : N ratio,
increasing δ13C and δ15N) as soil weathering
progressed. Peak areas of S-POM (i.e. aliphatic root exudates) did not
change over time, while peak areas of C-POM (lignin) and MOM (components of
microbial cell walls (amides, quinones, and ketones)) increased over time
and depth and were closely related to clay content and pedogenic iron
oxides. Hence, our study suggests that with progressing soil development,
SOM composition co-varied with changes in the mineral matrix. Our study
indicates that structurally more complex OM compounds (C-POM, MOM) play an
increasingly important role in soil carbon stabilization mechanisms as the
mineral soil matrix becomes increasingly weathered.
Introduction
Soils harbour the largest and most active terrestrial carbon (C) pool on
earth (Jobbágy and Jackson, 2000; Lal, 2008). Over the past decades,
many of the detailed mechanistic processes determining the fate of soil
organic matter (SOM) in soils have been well studied (Schmidt et al., 2011;
Lehmann and Kleber, 2015; Kleber et al., 2021). Yet, it remains unclear how
these individual processes evolve over time as stabilization mechanisms
depend on the soil matrix properties which in turn are subject to constant
changes through soil formation (Bradford et al., 2016; Bailey et al., 2019;
Kögel-Knabner and Amelung, 2021). Additionally, differences regarding
pedogenic (i.e. soil type, mineralogy, soil microbiome) and environmental
(i.e. climate, vegetation cover) properties not only determine the sizes of
SOM stocks but also govern the abundance of diverse biochemical compounds
(e.g. lignin, polysaccharides, lipids) found in the terrestrial C pool
(Paul, 2016; Hall et al., 2020). Hence, SOM composition (the prevalence of
certain biochemical compounds) may serve as a proxy for ecosystem properties
and soil functioning, as it affects C cycling by the amount of energy
provided to soil microorganisms (Nunan et al., 2015; Barré et al.,
2016). Furthermore, the peak areas related to biochemical SOM compounds have
been successfully used to parameterize fast- and slow-cycling SOM pools
(Todd-Brown et al., 2013; Bailey et al., 2018; Laub et al., 2020; Baldock et
al., 2021).
Despite the central role that SOM composition plays in soil C cycling, we
lack a thorough understanding of how it is influenced by the input of
organic matter (OM) and by changes in soil C stabilization mechanisms
following soil formation (von Lützow et al., 2008; Doetterl et al.,
2018). At initial soil formation stages, the mainly plant-derived input
material determines SOM composition (Khedim et al., 2021). With increasing
soil age, soil properties such as soil mineralogy, texture, and soil
aggregation become increasingly important for the stabilization of
accumulating SOM (Chorover et al., 2004; Mikutta et al., 2006, 2009; Wei et al., 2016). For example, a comparative study of soil
aggregation in relation to soil weathering found micro- and macroaggregates
to become more stable with increasing weathering (Wei et al., 2016). In
addition, there is an emerging consensus that certain SOM compounds are
preferentially bound to mineral surfaces in organo-mineral associations
(OMAs) and are thus highly important C stabilization mechanisms (Kleber et
al., 2021). Many studies reported a high affinity of chemically simple
carbohydrates to minerals (i.e. low molecular weight compounds from root
exudates or of microbial origin) (Kaiser et al., 1997; Calderón et al.,
2011; Kleber et al., 2011; Cotrufo et al., 2015; Lavallee et al., 2019).
Conversely, chemically more complex compounds (i.e. lignin or phenols from
woody plant parts), formerly considered to be recalcitrant OM, were found to
be less associated with minerals (Spielvogel et al., 2008; Kleber et al.,
2011; Lavallee et al., 2019). However, recent studies reported a
preferential binding with iron oxides in the particular case of lignin
(Kramer et al., 2012; Hall et al., 2016).
OMAs are preferentially found in the fine-grained, heavy soil fraction
(<2µm = clay) which is therefore often regarded as the most
important fraction to explain how SOM persists in soils (Six et al., 2000;
Kleber et al., 2021). Recent conceptualizations argue that the
organo-mineral interaction is subject to dynamic changes through mineral
weathering (Cotrufo et al., 2013; Lehmann and Kleber 2015; Kleber et al.,
2021). With ongoing soil weathering, primary silicates originating from the
parent material are replaced by newly formed secondary clay minerals (White
et al., 1996). The sorptive capacity of clay minerals decreases as 2:1 layer
type silicates (e.g. smectite) are substituted by 1:1 layer type silicates
(e.g. kaolinite) (Sposito et al., 1999). In contrast, iron oxides,
particularly nano-sized iron (Fe)- and aluminium-bearing (Al) oxides, gain
importance with increasing soil weathering as they conserve high specific
surface areas. As soil acidification progresses with soil weathering, the
importance of these oxides to stabilize OM compounds becomes more pronounced
(Kleber et al., 2021). Hence, the potential of different SOM stabilization
mechanisms changes dynamically as a function of mineral weathering (Mikutta
et al., 2009). Given that different SOM compounds have differing binding
preferences, mineral weathering is thus likely to affect SOM composition.
Therefore, a promising approach to gain further insights into the controls
of SOM composition is the observation of changes in SOM composition along
pedogenic gradients.
The aim of our study was to assess how bulk SOM composition changed along an
undisturbed soil chronosequence spanning 3 million years, located under
rangeland vegetation in the Mediterranean climatic conditions of California,
USA. To this end, we used the C : N ratio and the stable isotope signatures
δ15N and δ13C as proxies for the degree of
microbial transformation of the bulk SOM and the selected peak areas of
diffuse reflectance infrared
Fourier transform spectroscopy (DRIFTS) measurements as proxies for SOM composition. We then combined this
information with previously published data on soil mineralogy and texture
from the same samples (Doetterl et al., 2018) in order to identify which
drivers are most important in explaining shifts in SOM composition with soil
development. Based on previous studies, we hypothesized that absorbance peak
areas related to simple plant-derived OM (S-POM) would decrease with
increasing soil formation as binding sites for the formation of OMAs are
reduced. Consequently, the protection of S-POM compounds from microbial
degradation and transformation decreases. The peak areas of complex
plant-derived OM (C-POM) were expected to increase with soil age due to
preferential association with pedogenic iron oxides (Hall et al., 2016; Zhao
et al., 2016; Huang et al., 2019). Finally, MOM was expected to strongly
increase with soil age since microbial uptake of OM and the subsequent
stabilization in soils leads to an increasing share of microbial-derived SOM
(Cotrufo et al., 2013, 2015).
Material and methodsSite 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).
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.
Map 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).
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 (FeDCB,
AlDCB) 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
Fetotal : Sitotal ratio indicates how the mostly pedogenically
formed Fetotal relates with Sitotal, 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 (FeDCB)
and Al (AlDCB) was assessed relative to Fetotal
(Fetotal : FeDCB) and Altotal, 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
NH4+ exchanged after saturating cation exchange sites with
aluminium acetate with 2 M KCl buffered at pH 7. The share of base
saturation (Bsat) 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 (<2µm),
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).
%silt=100-(%estimated clay+%estimated sand)
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 (Rsample) relative to the
Vienna Pee Dee Belemnite (VPDB) standard for δ13C and relative
to atmospheric N2 for δ15N (Rstandard). The calculation follows Araya et al. (2017) (see Eq. 2):
δ[‰]=Rsample-RstandardRstandard×1000.
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.
Baseline-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).
Overview 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).
SOM typeWavenumberFunctional groupOriginSOM propertiescentre (range) [cm-1]S-POM2925 (2976–2898) +2850 (2870–2839)Aliphatic C–H stretchConstituent of litter, component ofmany root exudates (i.e. citrate, oxalate)Intermediate C : Nratio, δ13C and δ15Ndepleted compared tosoil but highercompared to C-POMC-POM1525 (1550–1500)Aromatic C = C stretchMostly lignin, related to wooden plantparts, also found in root constituentsand aromatic root exudates(i.e. phthalic or vanillic)High C : N ratio, δ13C and δ15NdepletedMOM1620 (1660–1580)Amide, quinone, ketone C = O stretch, aromatic C = C, and/or carboxylate C–O stretchConstituents of microbial cell walls(amides and quinones) andlong-chained lipids (ketones)Low C : N ratio, δ13C and δ15NenrichedStatistical 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):
VIFβ^=11-RXj|Xj2.
Out of the variables presented in Table 2, the Fetotal : Sitotal
ratio, clay content, FeDCB, and AlDCB 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 (βi) by the standard error of estimation (SEi) (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).
Overview 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).
Soil depth [cm]0–100–100–100–100–1010–3010–3010–3010–3010–30Soil age [ka]0.131929530000.13192953000Altotal [g kg-1]82.76581.163.929.392.273.487.164.936.7Fetotal [g kg-1]36.535.338.418.223.137.139.643.718.425.3Sitotal [g kg-1]241.5293.1267.2338.6393.4280.3306.2273348389.4Fetotal : Sitotal0.150.120.140.050.060.130.130.160.050.06AlDCB [g kg-1]1.311.10.71.310.91.30.61.4FeDCB [g kg-1]6.29.56.66.213.66.110.47.26.513.5Fetotal : FeDCB5.93.77.62.91.76.13.86.42.81.9Altotal : AlDCB65.166.286.988.522.3102.880.384.7106.826.2Ti : Zr21.6819.6226.8513.8210.8618.0714.2224.4214.610.13Clay [%]15.29.111.213.711.14.912.917.41918.3Silt [%]58.462.664.350.368.163.169.269.353.369.2Sand [%]26.428.224.53620.83217.913.427.712.5CEC [meq/100 g]15.213.424.74.64.514.29.920.82.12.1Bsat [%]86.6109.481.1103.758.374.3116.695.5131.285.3pH6.86.65.96.14.76.86.265.84.3ResultsChanges 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 (Bsat) 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 Sitotal 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.
Altotal and Fetotal 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 (AlDCB) and Fe (FeDCB). While
AlDCB 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, FeDCB
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 FeDCB values among the DCB-extracted pedogenic oxides
reflect the high potential of Fe to be quantitatively important for SOM
binding. The Fetotal : FeDCB 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
Altotal : AlDCB 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).
Mean ± 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.
Overview 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 (AlDCB, FeDCB,
Fetotal : Sitotal 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.
C : N ratioδ15Nδ13CS-POMC-POMMOMRMSE2.0 ± 0.51.2 ± 0.50.6 ± 0.22.9 ± 1.03.2 ± 0.720.3 ± 5.7rRMSE17.924.4-2.2128.956.361.7MAE1.8 ± 0.41.0 ± 0.40.6 ± 0.12.5 ± 0.92.6 ± 0.718.1 ± 5.5rMAE16.120.3-2.2111.145.855.1R20.32 ± 0.270.64 ± 0.240.61 ± 0.320.39 ± 0.330.78 ± 0.100.57 ± 0.27Coefficients of variable importance AlDCB1.31.33.3**2.31.01.1FeDCB1.80.65.2***2.11.01.6Fetotal : Sitotal3.2**1.45.0***1.45.00.8Clay0.41.54.1***2.2**2.8**3.7**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”).
Mean ± 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.
The C : N ratio was best explained by the Fetotal : Sitotal 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
Fetotal : Sitotal ratio, and AlDCB. 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, FeDCB, the
Fetotal : Sitotal ratio, and clay were more important than
AlDCB (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 Fetotal : Sitotal 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.
Discussion
Our study assessed to what extent SOM composition changes as driven by soil
weathering. We found that soil mineralogical and texture properties were
able to explain a large part of variance in SOM composition parameters
(Table 3). SOM was increasingly processed as indicated by a significant
decrease in the C : N ratio and δ15N over time and significant
decreases in δ13C with soil depth. These developments were
accompanied by significant increases in the peak areas related to complex
plant-derived OM (C-POM) and microbial-derived OM (MOM), as well as constant peak
areas related to simple plant-derived OM (S-POM) (see Fig. 5). This suggests
that changes in soil mineralogy following weathering likely exert control on
SOM composition.
Conceptual model explaining the influence of soil development with
increasing soil age and depth on mineralogy and texture based on our
findings. These properties (i.e. Fetotal : Sitotal, FeDCB) may
then drive the dynamics of SOM stocks, properties, and composition. SOM
stocks reflect the total C stock, SOM decomposition proxies include δ13C, δ15N, and the C : N ratio, and the signals of the
selected DRIFTS peak areas are represented individually. Soil weathering
proxies and clay content were not tested since only one value per soil age
and depth was available (n=10). SOM decomposition proxies and peak areas
were tested by a two-way ANOVA (n=30) with Tukey's HSD as the post hoc test (p<0.05): * shows significant trends, ** significant trends for C : N
and δ15N but not for δ13C, and *** significant trends
for C : N and δ13C but not for δ15N. The outcomes of
the ANOVA are visualized in Figs. 3 and 4 and the values of soil geochemistry in
Table 2.
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. FeDCB,
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 (FeDCB 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 FeDCB 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).
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 Fetotal : Sitotal ratio. The decreasing trend
of the Fetotal : Sitotal 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, FeDCB, 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 FeDCB, Fetotal : Sitotal, and clay,
while δ15N values were explained by clay,
Fetotal : Sitotal, and AlDCB (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
Fetotal : Sitotal 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 AlDCB and
FeDCB 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 Fetotal : Sitotal 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 FeDCB and AlDCB (see Table 3). Potentially the more linear decrease in the Fetotal : Sitotal
ratio might mask less consistent increases in FeDCB or the varying
behaviour of AlDCB (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 FeDCB 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.
Conclusions
The present observational study provides new information on qualitative
changes in the SOM composition in relation to soil geochemical dynamics
driven by mineral weathering from a soil chronosequence covering a gradient
of 3 million years. With progressing soil development SOM stocks decreased,
and we observed an increasingly microbially transformed bulk SOM (lower C : N
ratio, higher δ13C and δ15N values).
Simultaneously, the spectroscopic fingerprint of SOM (reflected in the peak
areas related to S-POM, C-POM, and MOM) shifted as peak areas of
structurally more complex compounds increased (C-POM and MOM). These
qualitative changes in SOM composition co-occurred with changes in soil
geochemistry and texture induced by soil formation. Hence, changes in soil
texture (clay content) and an increasingly kaolinite- and iron-oxide-rich,
strongly weathered mineral matrix might discriminate in favour of
structurally more complex SOM compounds (C-POM and MOM). Our study shows
that soil mineralogy plays an important role in shaping SOM composition
during soil weathering across large timescales. We therefore recommend
further studies to assess this link in contrasting soil mineralogies or
vegetation types in order to gain a better understanding of how mineral
matrix and C input affect SOM composition across larger geographical areas.
Code availability
The code used for the statistical analysis is available on Zenodo (10.5281/zenodo.6366361, Mainka, 2022).
Data availability
The dataset and the data description files underlying the statistical analysis can be accessed on Zenodo (10.5281/zenodo.6366361, Mainka, 2022).
Author contributions
AAB and SD designed the experiments. MM performed the measurements, analysed
the data, and wrote the manuscript. DW, GG, MG, and LS reviewed and edited
the manuscript. LS created the map (Fig. 1). All authors contributed to
writing the paper (lead author: MM).
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank the working group of Asmeret A. Berhe for logistic help and support in the laboratory.
Financial support
This research has been supported by the Bayerisch-Kalifornischen Hochschulzentrum (Projekt 22 (grant no. 2016-1)).
Review statement
This paper was edited by Sara Vicca and reviewed by Jeroen Meersmans and Xavier Dupla.
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