Soil organic matter (SOM) and the carbon and nutrients therein drive
fundamental submicron- to global-scale biogeochemical processes and
influence carbon-climate feedbacks. Consensus is emerging that microbial
materials are an important constituent of stable SOM, and new conceptual
and quantitative SOM models are rapidly incorporating this view. However,
direct evidence demonstrating that microbial residues account for the
chemistry, stability and abundance of SOM is still lacking. Further,
emerging models emphasize the stabilization of microbial-derived SOM by
abiotic mechanisms, while the effects of microbial physiology on microbial
residue production remain unclear. Here we provide the first direct
evidence that soil microbes produce chemically diverse, stable SOM. We show
that SOM accumulation is driven by distinct microbial communities more so
than clay mineralogy, where microbial-derived SOM accumulation is greatest
in soils with higher fungal abundances and more efficient microbial biomass
production.
Introduction
We use model soils to quantitatively assess whether microbial processing of
simple C (i.e., low-molecular-weight) substrates alone, in the absence of
complex plant compounds, can build significant amounts of chemically
diverse, stable SOM. Since our model soils are initially C- and
microbe-free, we eliminate the difficulties of isolating microbial residues
that occur when using natural soils. We use a gradient of substrate-C
inputs to represent different microbial-available energy in order to
facilitate the development of diverging microbial communities and
physiologies that are hypothesized to influence SOM chemistry and
accumulation rates. The substrate gradient includes monomeric and dimeric
sugars since they are abundant energy sources for microbial metabolism in
natural soils33 <https://www.nature.com/articles/ncomms13630#ref33>, but
also because their rapid microbial uptake and intercellular breakdown
should leave little to no unaltered substrate in the soil that would
interfere with the detection of novel SOM molecules formed during the
experiment33 <https://www.nature.com/articles/ncomms13630#ref33>,34
<https://www.nature.com/articles/ncomms13630#ref34>. Further, we include a
more recalcitrant lignin monomeric substrate, as well as plant-derived
dissolved organic C (DOC), a natural analog to test whether substrate
chemical diversity is requisite to generating SOM chemical diversity. We
also compare two clay types (kaolinite or montmorillonite) to investigate
the effects of clay mineralogy on SOM accumulation relative to microbial
communities and substrate chemistry. We characterize the composition of SOM
after 18 months of incubation using high-resolution molecular
fingerprinting by pyrolysis-gas chromatography/mass spectrometry (py-GC/MS)
to establish the chemical fingerprint of newly formed microbial residues
(including cell wall and cytoplasmic materials, metabolites and
extracellular excretions). Our model soils accrue C concentrations between
1 and 1.4% C, with a chemical diversity and stability characteristic of
natural soils. Substrate type has a stronger influence on SOM development
than clay mineralogy. However, this effect appears to be an indirect
consequence of diverging microbial communities, where different substrates
select for distinct microbial communities, with microbial-SOM accumulation
being greatest in soils where fungal abundances are highest and microbial
biomass production is most efficient.
Results
Model soil systems
We created initially C- and microbe-free model soils to study proximal
drivers of SOM development. Model soils were incubated with a natural soil
microbial community inoculum and received weekly C additions of glucose,
cellobiose, syringol (a lignin monomer) or plant-derived DOC, combined with
a nutrient solution for 15 months. Substrate additions were terminated
after 15 months, but the incubation continued for an additional 3 months
(18 months total) without further C inputs in order to maximize endogenous
C recycling.
Following inoculation of model soils, microbial activity was immediately
detectable by a measurable CO2 efflux, and by 6 months post-inoculation
microbial extracellular enzymatic activities, CO2 respiration rates and
living microbial biomass all indicated that an active microbial community
had been established in all treatments (Supplementary Fig. 1
<https://www.nature.com/articles/ncomms13630#s1>). Visually, model soils
progressively resembled natural soil, with significant colour and
structural development (Fig. 1a
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>).
The exception to this was the kaolinite–syringol treatment, which after the
initial 3 months did not exhibit an active microbial community and was thus
removed from the study.
*Figure 1: Soil development and organic matter chemistry.*
[image: Figure 1] <https://www.nature.com/articles/ncomms13630/figures/1>
Images of sugar-treated model soils over time (*a*); the far left panel is
an uninoculated sterile kaolinite and sand mixture, and the far right panel
is the same mixture, inoculated and treated with weekly glucose additions
for 15 months. Relative abundance of chemical compound groups in substrate
(Time 0) and model soils amended with (*b*) sugar, (*c*) syringol and (*d*)
plant dissolved organic carbon (DOC). These are compared to soil collected
from an agricultural field (*e*). Glucose and cellobiose treatments were
averaged since there were no significant differences in their chemistry
(ANOVA: *P*>0.05). Numbers above bars are the total number of identified
compounds.
Full size image <https://www.nature.com/articles/ncomms13630/figures/1>
Non-metric multidimensional scaling (NMDS) ordination of the relative
abundance of chemistry compounds at 18 months for substrate and clay
treatments. Open symbols are kaolinite and closed symbols are
montmorillonite. For comparison, unprocessed substrates (Time 0) and an
agricultural field soil are indicated by a star symbol (Stress=8.1, Monte
Carlo: *P*<0.05).
Full size image <https://www.nature.com/articles/ncomms13630/figures/2>
We determined soil organic carbon (SOC) accumulation rates and stability in
the initially C-free model soils. Across all model soils, SOC increased
consistently over time (*P*<0.05; Table 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#t1>).
The efficiency with which substrate-C was converted to total SOC—i.e., the
amount of SOC remaining relative to the total amount of substrate
added—declined from an average across all treatments of 32% ±1.4 at 6
months to 21% ±0.75 at 18 months Using SOC stocks as an integrator of mass
C balance, the majority (>75%) of total substrate-C added was lost via
respiration across all treatments by 18 months (Table 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#t1>).
As a comparison, estimated cumulative respirations represented 50–75% of
added C with the exception of DOC-treated soils (Supplementary Table 4
<https://www.nature.com/articles/ncomms13630#s1>). These estimates are
based on flux measurements, which exhibited rapid and ephemeral responses
to each substrate addition, especially for the monomeric inputs. In
DOC-treated soils, cumulative respiration underestimated C loss, compared
with calculations using SOC stocks. We attribute this to the more
chemically complex DOC treatment having a longer respiration response time
that was not completely captured from fluxes collected immediately
following substrate additions.
*Table 1: Soil carbon accumulation.*
Full size table <https://www.nature.com/articles/ncomms13630/tables/1>
Given its potential influence on SOM accumulation rates and chemistry, we
examined microbial community composition using phospholipid fatty acid
(PLFA) biomarkers. We observed significant differences in broad microbial
groups due to substrate treatment (*P*<0.05; Supplementary Table 3
<https://www.nature.com/articles/ncomms13630#s1> and Supplementary Fig. 3
<https://www.nature.com/articles/ncomms13630#s1>). Montmorillonite,
syringol-treated soils had lower Gram-negative and Gram-positive bacteria
and higher fungal relative abundance compared with sugar-treated soils
(*P*<0.001;
Fig. 3
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f3>).
We also observed an effect of mineralogy on the microbial community; fungal
abundances were generally higher in kaolinite than in montmorillonite
soils, while Gram-negative bacteria were more abundant in montmorillonite
soils (*P*<0.001).
*Figure 3: Influence of substrate and clay type on soil microbial
communities and microbial growth efficiency.*
[image: Figure 3] <https://www.nature.com/articles/ncomms13630/figures/3>
Substrate treatment differences in the relative abundance (RA) of fungi (*a*),
Gram-negative bacteria (*b*), Gram-positive bacteria (*c*) at 15 months,
and microbial carbon use efficiency (CUE) for kaolinite (*d*) and
montmorillonite (*e*) model soils. The CUE values (*d*,*e*) are for 9 and
15 months of incubation. Significant substrate treatment effects are within
clay type or within time and are indicated by different letters
(ANOVA: *P*<0.05).
Error bars represent one s.e. (experimental replication *n*=5).
Full size image <https://www.nature.com/articles/ncomms13630/figures/3>
Non-metric multidimensional scaling (NMDS) ordination of accumulated SOC at
12, 15 and 18 months for sugar- and syringol-treated soils (*a*) and
Pearson correlations for fungal relative abundances and microbial carbon
use efficiency (CUE) (*b*), SOC and CUE (*c*), and fungal relative
abundances and SOC (*d*) at 15 months. NMDS points are categorized *post
hoc* based on soil C concentrations at 18 months: high SOC (closed
circles), medium SOC (closed squares) and low SOC (closed triangles)
(Stress=4.2, Monte Carlo: *P*=0.019, multi-response permutation procedure
for SOC groups: *T*=−10.037, *A*=0,469, *P*<0.0001). NMDS table inset is
Pearson correlation *r* values with ordination Axis 1.
Full size image <https://www.nature.com/articles/ncomms13630/figures/4>
Discussion
While our approach uses a model system without environmental constraints on
microbial biomass production such as predation and nutrient limitations
present in natural soil, it provides direct evidence that microbial biomass
can produce an abundance of stable, chemically diverse SOM. Moreover, we
are able to identify the microbial community characteristics in these
systems such as fungal abundances and CUE that influence SOM formation. Our
results point to new approaches to rebuild soil C contents that emphasize
the influence of substrate–microbe interactions on the synthesis of novel
SOM constituents that become mineral-stabilized, and will also inform the
necessary validation and parameterization of emerging microbial-explicit C
models that stress the importance of microbial residues in SOM dynamics20
<https://www.nature.com/articles/ncomms13630#ref20>,57
<https://www.nature.com/articles/ncomms13630#ref57>. Most of our
contemporary predictive models and management strategies for SOC are still
based on traditional concepts of plant chemical recalcitrance, yet the
processes that govern microbial-SOM accumulation and stabilization likely
differ from those of plant-derived SOM18
<https://www.nature.com/articles/ncomms13630#ref18>,19
<https://www.nature.com/articles/ncomms13630#ref19>,22
<https://www.nature.com/articles/ncomms13630#ref22>. For example, the
effects of elevated temperatures and soil disturbance may stimulate the
decomposition of plant-stabilized SOM but the consequences for
microbial-stabilized residues are less clear. It is likely that
environmental changes such as soil warming influence microbial-SOM, not
necessarily by destabilizing it, but via alterations in the capacity of the
microbial community for efficient residue production30
<https://www.nature.com/articles/ncomms13630#ref30>. Moreover, our data
provide critical empirical insights to advance new microbial-explicit
biogeochemistry models, including how microbe–substrate interactions
influence SOM dynamics. Thus, our results provide new perspectives on the
origin of stable SOM that will help foster the progress needed to model and
manage soils to promote enhanced SOM accumulation.
Methods
Experimental conditions and sampling
Laboratory mesocosms consisted of 100 g dry wt of 33% kaolinite or
montmorillonite clay mixed with quartz sand. The cation exchange capacity
was 5.2 and 28.6 meg per 100 g soil for the kaolinite and the
montmorillonite soil mixtures, respectively. Metal concentrations for the
kaolinite mixture were 5.6% Al, 1% K, 0.9% Fe, 0.14% Ca, 0.0.11% Na and
0.06% Mg. Montmorillonite mixture metal concentrations were 2.2% Al, 0.8%
K, 2.3% Fe, 0.39% Ca, 0.14% Na and 0.32% Mg. Soil mixtures received weekly
additions of glucose, cellobiose, syringol or switchgrass plant DOC (0.7 mg
C per g dry soil) and biweekly additions of a multi-nutrient Hoagland’s
solution (0.023 mg N per g dry soil). The substrate-C (5 ml of 14,000 ppm
C) and nutrient additions (0.5 ml of 4,666 ppm N) were syringe-injected
throughout the model soils to facilitate uniform substrate application. The
rate of weekly C additions reflects the upper range of natural C inputs to
soil from daily root exudations. Total C inputs from added substrates over
the course of 15 months amounted to 46.9 mg C per g soil and total
inorganic N from nutrient solution was 1.56 mg N per g soil. To create DOC,
we mixed 150 g switchgrass (*Panicum virgatum*) tissue (leaf, stem and
seed) with 2 l H2O and shook for 24 h before filtering. The filtrate was
then partially evaporated until the C concentration was 18 mg C per ml (C:N
20). We created the microbial soil inoculum in a soil slurry (1:100 w/v)
using soil collected from a Michigan bromegrass (*Bromus inermis*) field at
0–7 cm depth. We then syringe-injected 100 μl of inoculum solution (∼0.00001,
g natural soil per g model soil) into the soil mixture. Each soil mesocosm
treatment was replicated five times with five destructive harvests at 6, 9,
12, 15 and 18 months. During incubation, soils were maintained at 25 °C,
45% water holding capacity, and a pH between 4.6 and 5. The pH of
DOC-treated soils, however, increased to 7 after a few months of incubation
and therefore DOC biological data are minimally included in our analyses.
During the 15-month incubation period no soil mixing occurred in order to
better simulate an undisturbed soil environment and to minimize aggregate
destruction.
After 15 months, substrate and nutrient additions ceased but soils were
maintained for an additional 3 months under the previous incubation
conditions with periodic hand mixing to facilitate microbial uptake of any
remaining substrate or unstabilized microbial biomass. Microbial community
activity was monitored weekly by CO2 flux measurements for the first 9
months and then biweekly thereafter, and by microbial hydrolytic and
oxidative potential enzyme activities58
<https://www.nature.com/articles/ncomms13630#ref58> at each mesocosm
harvest.
SOM chemistry and concentrations
We assessed SOM chemistry using pyrolysis-GC/MS59
<https://www.nature.com/articles/ncomms13630#ref59>. Compound peaks were
analysed and identified with the Automated Mass Spectral Deconvolution and
Identification Systems (AMDIS V 2.65) software, the National Institute of
Standards and Technology (NIST) compound library and published literature59
<https://www.nature.com/articles/ncomms13630#ref59>. Organic matter
compounds are expressed as the % relative abundance of total sample peak
area and classified based on origin (lipids, lignin derivatives,
polysaccharides, proteins, non-protein N-bearing and phenolics).
Identifiable compounds with potentially multiple origins were classified as
unspecified. Aromatic compounds also with an unspecified origin were
classified as aromatics. The SOM chemical diversity was determined based on
the total number of identifiable compounds that were >1% relative abundance.
Total SOC concentrations were determined on an elemental analyser (Costech
ECS 4010). Total microbial biomass carbon (MBC) was determined by the
difference in C concentration (using a Shimadzu TOC-L analyser) in
chloroform fumigated and unfumigated soil subsamples extracted with 0.5 M K2
SO4. We calculated the total SOM conversion efficiency as the amount of
total SOC relative to the total substrate-C added for each destructible
harvest, where the total substrate added is the sum of the weekly substrate
addition within the time period up until harvest.
SOM stability
We determined biological stability in a separate 3-month incubation on soil
subsamples harvested at 6 months. During this separate incubation
experiment, soils received three applications of 13C-labelled 1:1 glutamic
acid/glucose 25 atom % mixture at 50 μg C per g soil. The labelled
substrate enabled us to use a standard isotope mixing model35
<https://www.nature.com/articles/ncomms13630#ref35> to determine the amount
of newly formed C vulnerable to decomposition by an active microbial
community. During the 3-month incubation, soils were frequently hand-mixed
to facilitate SOM mineralization and maintained under the same incubation
conditions as the long-term incubation. We also assessed the chemical
stability of the accumulated SOM with acid hydrolysis fractionation37
<https://www.nature.com/articles/ncomms13630#ref37>. After the full 18
months incubation, 1 g soil was treated with 50 ml 6 M HCl and heated at
100 °C for 18 h. After heating, the acid hydrolysable fraction was decanted
and the remaining non-acid hydrolysable (i.e., stable) residue was rinsed
four times with DI-H2O and isolated by centrifugation36
<https://www.nature.com/articles/ncomms13630#ref36>,37
<https://www.nature.com/articles/ncomms13630#ref37>. The chemically stable
C was then determined based on the differences in C remaining in the
non-acid hydrolysable residues and the initial C content of the sample.
Microbial community composition
We determined differences in microbial community composition from PLFA
biomarkers, identified and quantified at 12 and 15 months60
<https://www.nature.com/articles/ncomms13630#ref60>. We included a
phosphate buffer in the single-phase solvent system (chloroform) to extract
only viable microorganisms. Lipid extracts were fractionated on silicic
acid columns to isolate and collect polar lipids. Polar lipids were
methylated to fatty acid methyl esters with 0.2 M methanolic KOH, purified
and then analysed on a Varian 3800 GC-FID. Peaks were quantified using
internal standards and identification was based on retention time data with
known standards. Fungal relative abundance was determined by the sum of
polyenoic unsaturated fatty acids (18:2ω6 and 18:1ω9c), Gram-positive
relative abundances by the sum of the total branched, saturated fatty acids
(i15:0, a15:0, i16:0, i17:0 and a17:0) and Gram-negative relative
abundances by the sum of monoenoic and cyclopropane unsaturated fatty acids
(16:1ω7c, 16:1ω7t cy17:0, 18:1ω7c and cy19:0). We did not detect any
actinobacterial fatty acids.
Soil microbial CUE and enzyme activities
We characterized microbial CUE at 9 and 15 months by measuring the amount
of a 13C-labelled glutamic acid incorporated into MBC and respired as 13CO2–C
(refs 21 <https://www.nature.com/articles/ncomms13630#ref21>, 30
<https://www.nature.com/articles/ncomms13630#ref30>). Fresh soil subsamples
were amended with 25 atom% labelled 13C glutamic acid (50 μg C per g dry
soil,<1% total soil C) and incubated at 25 °C for 22 h. The 22-h incubation
time was based on preliminary respiration curves to determine the period
when the majority of glutamic acid is utilized but before substrate
recycling begins. Following incubation, soils were extracted for 13C-glutamic
acid incorporation into MBC by extracting and analysing MBC as discussed
previously and determining its isotopic composition on a 1030 TOC Analyzer
(OI Analytical, College Station, TX) interfaced with a PDZ Europa 20–20
isotope ratio mass spectrometer. A 12-ml CO2 sample was also collected for
determining 13CO2–C respiration on a GC-isotope ratio mass spectrometer
(ThermoScientific, Bremen, DE).
This approach estimates the amount of C allocated to biomass per unit of C
substrate consumed and represents a proxy for microbial CUE. We calculated
CUE as [MB13C/(MB13C+13CO2-C) × 100], where MB13C and 13CO2-C were
determined using a standard isotope mixing model equation29
<https://www.nature.com/articles/ncomms13630#ref29> and represent the
amount of substrate incorporated into MBC and the substrate-C respired as CO
2, respectively. We used glutamic acid to estimate microbial community CUE
across all substrate-treated soils since it is taken up directly into
microbial cells. This approach allows us to more directly compare CUEs
across treatments since substrate-C allocation towards enzyme production is
minimized and we can better approximate the efficiency of new biomass
growth rather than substrate degradation efficiency32
<https://www.nature.com/articles/ncomms13630#ref32> (Supplementary Note 2
<https://www.nature.com/articles/ncomms13630#s1>).
The potential activities of microbial extracellular enzymes were measured
at 6, 9, 12 and 15 months of incubation. We used a standard fluorometric
method to assess hydrolytic enzyme activity by adding 1 g soil dry weight
homogenized in a slurry with 50 mM sodium acetate buffer58. The buffer
solution was adjusted to the average soil pH of 4.9. Soil homogenates were
added to black, 96-well microplates with compound-specific fluorescing
substrates bound to methylumbelliferone. Oxidative enzyme activity (phenol
oxidase and peroxidase), associated with lignin breakdown, was measured
spectrophotometrically using clear 96-well microplates.
Statistical and data analyses
Data were analysed using a linear mixed-model two-way analysis of variance
(ANOVA) to determine differences in SOM chemistry, SOC concentration, SOC
conversion efficiency, SOC stability, PLFAs and CUE between clay mineralogy
and substrate type. Treatment replicate (*n*=5) was used as a random
effect, and clay and substrate were treated as fixed effects. Since the
experimental design was unbalanced (there was no syringol treatment within
kaolinite soils), analyses for differences in main effects of substrate and
mineralogy and their interactions did not include syringol. A one-way ANOVA
was used to examine the effect of substrate within a clay treatment,
allowing us to include syringol-treated soils in our comparisons. In many
analyses the glucose and cellobiose treatments were not statistically
different (*P*>0.05). In these cases, glucose and cellobiose data were
averaged and reported as a ‘sugar’ treatment. Pair-wise comparisons between
treatments were determined by Tukey’s HSD (*P*<0.05). We used Pearson’s
correlation analysis to examine relationships between SOC concentrations,
microbial CUE, microbial community composition and SOM chemistry. All ANOVA
and correlation analyses were performed in SAS v.9.3 (SAS Institute, 1999)
using PROC MIXED and PROC CORR. When necessary, data were normalized using
log transformation before analysis of variance analyses. Significance for
all analyses was determined at a probability level of *P*<0.05 unless
otherwise stated. Variation around means are reported or shown as standard
error.
We also used non-metric multidimensional scaling (NMDS) (PC-ORD; version
4.14) to explore differences in SOM chemistry and community composition.
Pyrolysis-gas chromatography/mass spectrometry compound relative abundances
and PLFA biomarkers were relativized to maximum and used separately in NMDS
matrices. We also used NMDS to determine relevant parameters influencing
SOC concentrations for use in our correlation analyses, where SOC
concentrations at 9, 12, 15 and 18 months were grouped *a priori* into low,
medium and high SOC concentrations. The Sorensen (Bray-Curtis) index was
used as a distance measure for all ordinations. Final NMDS solutions were
considered acceptable if Monte Carlo simulations had stress values <20 and
a solution stability of <0.005. We determined significant groupings by the
Monte Carlo stress reduction (*P*<0.05) and multi-response permutation
procedure (*P*<0.05).
Data availability
The data that support the findings of this study are available on request
from the corresponding author (C.M.K.).
Additional information
*How to cite this article:* Kallenbach, C. M. *et al*. Direct evidence for
microbial-derived soil organic matter formation and its ecophysiological
controls. *Nat. Commun.* *7,* 13630 doi: 10.1038/ncomms13630 (2016).
*Publisher’s note*: Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
*Disclaimer*
Any opinions, findings and conclusions or recommendations expressed in this
material are those of the authors and do not necessarily reflect the views
of the National Science Foundation or the DOE.
Change history
- 24 September 2018
In the originally published version of this Article, financial support
was not fully acknowledged. The PDF and HTML versions of the Article have
now been corrected to include support from the NSF Long-term Ecological
Research Program (DEB 1637653) at the Kellogg Biological Station and from
Michigan State University AgBioResearch.
References
1. 1.
Lehmann, J. & Kleber, M. The contentious nature of soil organic matter.
*Nature* *528*, 60–68 (2015).
-
McGill, W. B., Shields, J. A. & Paul, E. A. Relation between carbon and
nitrogen turnover in soil organic fractions of microbial origin. *Soil
Biol. Biochem.* *7*, 57e63 (1975).
Kögel-Knabner, I. The macromolecular organic composition of plant and
microbial residues as inputs to soil organic matter. *Soil Biol. Biochem.*
*34*, 139–162 (2002).
Paul, E. A. The nature and dynamics of soil organic matter: plant inputs,
microbial transformations, and organic matter stabilization. *Soil Biol.
Biochem.* *98*, 109–126 (2016).
Amelung, W., Brodowski, S., Sandhage-Hofmann, A. & Bol, R. Combining
biomarker with stable isotope analyses for assessing the transformation and
turnover of soil organic matter. *Adv. Agron.* *100*, 155–250 (2008).
Miltner, A., Bombach, P., Schmidt-Brücken, B. & Kästner, M. SOM genesis:
microbial biomass as a significant source. *Biogeochemistry* *111*, 41–55
(2012).
Gleixner, G. Soil organic matter dynamics: a biological perspective derived
from the use of compound-specific isotopes studies. *Ecol. Res.* *28*,
683–695 (2013).
Baldock, J. A. & Skjemstad, J. O. Role of the soil matrix and minerals in
protecting natural organic materials against biological attack. *Org.
Geochem.* *31*, 697–710 (2000).
Grandy, A. S. & Neff, J. C. Molecular C dynamics downstream: the
biochemical decomposition sequence and its impact on soil organic matter
structure and function. *Sci. Total Environ.* *404*, 297–307 (2008).
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The
Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant
litter decomposition with soil organic matter stabilization: do labile
plant inputs form stable soil organic matter? *Glob. Change Biol.* *19*,
988–995 (2013).
Grandy, A. S., Neff, J. C. & Weintraub, M. N. Carbon structure and enzyme
activities in alpine and forest ecosystems. *Soil Biol. Biochem.* *39*,
2701–2711 (2007).
Hedges, J. I. *et al.* The molecularly-uncharacterized component of
nonliving organic matter in natural environments. *Org. Geochem.* *31*,
945–958 (2000).
Conant, R. T. *et al.* Temperature and soil organic matter decomposition
rates- synthesis of current knowledge and a way forward. *Glob. Change
Biol.* *17*, 3392–3404 (2011).
Kleber, M. *et al.* Old and stable soil organic matter is not necessarily
chemically recalcitrant: implications for modeling concepts and temperature
sensitivity. *Glob. Change Biol.* *17*, 1097–1107 (2011).
Wieder, W., Grandy, A. S., Kallenbach, C. M. & Bonan, G. B. Integrating
microbial physiology and physiochemical principles in soils with the
MIcrobial-MIneral Carbon Stabilization (MIMICS) model. *Biogeoscience* *11*,
3899–3917 (2014).
Kallenbach, C. M., Grandy, A. S., Frey, S. D. & Diefendorf, A. F. Microbial
physiology and necromass regulate agricultural soil carbon accumulation. *Soil
Biol. Biochem* *9*, 279–290 (2015).
von Lützow, M. *et al.* Stabilization of organic matter in temperate soils:
mechanisms and their relevance under different soil conditions—a review. *Eur.
J. Soil Sci.* *57*, 426–445 (2006).
Gillabel, J., Cebrian‐lopez, B., Six, J. & Merckx, R. Experimental evidence
for the attenuating effect of SOM protection on temperature sensitivity of
SOM decomposition. *Glob. Change Biol.* *16*, 2789–2798 (2010).
Castellano, M. J., Mueller, K. E., Olk, D. C., Sawyer, J. E. & Six, J.
Integrating
plant litter quality, soil organic matter stabilization and the carbon
saturation concept. *Glob. Change Biol.* *21*, 3200–3209 (2015).
Liang, C., Cheng, G., Wixon, D. L. & Balser, T. C. An absorbing Markov
chain approach to understanding the microbial role in soil carbon
stabilization. *Biogeochemistry* *106*, 303–309 (2010).
Manzoni, S., Schaeffer, S. M., Katul, G., Poporato, A. & Schimel, J. P. A
theoretical analysis of microbial eco-physiological and diffusion
limitations to carbon cycling in drying soils. *Soil Biol. Biochem.* *73*,
69–83 (2014).
Kaiser, M., Zederer, D. P., Ellerbrock, R. H., Sommer, M. & Ludwig, B. Effects
of mineral characteristics on content, composition and stability of organic
matter fractions separated from seven forest topsoils of different
pedogenesis. *Geoderma* *263*, 1–7 (2016).
Bradford, M. A., Keiser, A. D., Davies, C. A., Mersmann, C. A. & Strickland,
M. S. Empirical evidence that soil carbon formation from plant inputs is
positively related to microbial growth. *Biogeochemistry* *113*, 271–281
(2013).
Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of
soil microbial efficiency and its feedback to climate. *Nat. Clim. Change*
*3*, 395–398 (2013).
Geyer, K. M., Kyker-Snowman, E., Grandy, A. S. & Frey, S. D. Microbial
carbon use efficiency: accounting for population, community, and
ecosystem-scale controls over the fate of metabolized organic matter.
*Biogeochemistry* *127*, 173–188 (2016).
Fischer, H., Ingwersen, J. & Kuzyakov, Y. Microbial uptake of
low-molecular-weight organic substances out-competes sorption in soil. *Eur.
J. Soil Sci.* *61*, 504–513 (2010).
Baldock, J. A., Oades, J. M., Vassallo, A. M. & Wilson, M. A. Incorporation
of uniformly labelled 13C-glucose carbon into the organic fraction of a
soil. Carbon balance and CP/MAS ′3C NMR measurements. *Aust. J. Soil Res.*
*27*, 725–746 (1989).
Ineson, P., Cotrufo, M. F., Bol, R., Harkness, D. D. & Blum, H. Quantification
of soil carbon inputs under elevated CO2:C-3 plants in a C-4 soil. *Plant
Soil* *187*, 345e350 (1996).
Plante, A. F., Fernández, J. M., Haddix, M. L., Steinweg, J. M. & Conant,
R. T. Biological chemical and thermal indices of soil organic matter
stability in four grassland soils. *Soil Biol. Biochem.* *43*, 1051–1058
(2011).
Paul, E. A., Collins, H. P. & Leavitt, S. W. Dynamics of resistant soil
carbon of Midwestern agricultural soils measured by naturally occurring 14
Cabundance. *Geoderma* *104*, 239–256 (2001).
Pronk, G. J., Heister, K., Ding, G. C., Smalla, K. & Kögel-Knabner, I.
Development
of biogeochemical interfaces in an artificial soil incubation experiment;
aggregation and formation of organo-mineral associations. *Geoderma* *189*,
585–594 (2012).
Vogel, C., Heister, K., Buegger, F., Tanuwidjaja, I., Haug, S., Schloter, M.
& Kögel-Knabner, I. Clay mineral composition modifies decomposition and
sequestration of organic carbon and nitrogen in fine soil fractions. *Biol.
Fert. Soils* *51*, 427–442 (2015).
Hemkemeyer, M., Pronk, G. J., Heister, K., Kögel-Knabner, I., Martens,
R. & Tebbe,
C. C. Artificial soil studies reveal domain-specific preferences of
microorganisms for the colonisation of different soil minerals and particle
size fractions. *FEMS Microbiol. Ecol.* *90*, 770–782 (2014).
Steinbach, A. *et al.* Clay minerals and metal oxides strongly influence
the structure of alkane-degrading microbial communities during soil
maturation. *ISME J.* *97*, 1687–1691 (2015).
Golchin, A., Clarke, P. & Oades, J. M. The heterogeneous nature of
microbial products as shown by solid-state 12C CPMAS NMR spectroscopy.
*Biogeochemistry* *34*, 71–97 (1996).
Nguyen, C. & Guckert, A. Short-term utilisation of 14C-glucose by soil
microorganisms in relation to carbon availability. *Soil Biol. Biochem.*
*33*, 53*–*–60 (2001).
Jones, D. L., Dennis, P., Owen, A. & Van Hees, P. Organic acid behavior in
soils—misconceptions and knowledge gaps. *Plant Soil* *248*, 31*–*–41
(2003).
Roth, V. N., Dittmar, T., Gaupp, R. & Gleixner, G. The molecular
composition of dissolved organic matter in forest soils as a function of pH
and temperature. *PloS ONE* *10*, e0119188 (2015).
Monreal, C. M., Schulten, H. R. & Kodama, H. Age, turnover and molecular
diversity of soil organic matter in aggregates of a Gleysol. *Can. J. Soil
Sci.* *77*, 379–388 (1997).
Schmidt, S. K. *et al.* The earliest stages of ecosystem succession in
high-elevation (5000 metres above sea level), recently deglaciated soils. *P.
R. Soc. Lond. B Biol.* *275*, 2793–280 (2008).
Castle, S. C. *et al.* Biogeochemical drivers of microbial community
convergence across actively retreating glaciers. *Soil Biol. Biochem.* *101*,
74–84 (2016).
Wickings, K., Grandy, A. S., Reed, S. C. & Cleveland, C. C. The origin of
litter chemical complexity during decomposition. *Ecol. Lett.* *15*,
1180–1188 (2010).
Wallenstein, M. D. *et al.* Litter chemistry changes more rapidly when
decomposed at home but converges during decomposition–transformation. *Soil
Biol. Biochem.* *57*, 311–319 (2013).
Feng, X., Simpson, A. J. & Simpson, M. J. Chemical and mineralogical
controls on humic acid sorption to clay mineral surfaces. *Org. Geochem.*
*36*, 1553–1566 (2005).
Watterl-Koekkoek, E. J., Van Genuchten, P. P., Buurman, P. & van
Lagen, B. Amount
and composition of clay-associated soil organic matter in a range of
kaolinitic and smectitic soils. *Geoderma* *99*, 27–49 (2001).
Throckmorton, H. M., Bird, J. A., Dane, L., Firestone, M. K. & Horwath, W.
R. The source of microbial C has little impact on soil organic matter
stabilisation in forest ecosystems. *Ecol. Lett.* *15*, 1257–1265 (2012).
Campbell, E. E. & Paustian, K. Current developments in soil organic matter
modeling and the expansion of model applications: a review. *Environ. Res.
Lett.* *10*, 123004 (2015).
Saiya-Cork, K. R., Sinsabaugh, R. L. & Zak, D. R. The effects of long term
nitrogen deposition on extracellular enzyme activity in an Acer saccharum
forest soil. *Soil Biol. Biochem.* *34*, 1309–1315 (2002).
Grandy, A. S., Strickland, M. S., Lauber, C. L., Bradford, M. A. & Fierer,
N. The influence of microbial communities, management, and soil texture on
soil organic matter chemistry. *Geoderma* *150*, 278–286 (2009).
Guckert, J. B., Antworth, C. P. & Nichols, P. D. Phospholipid, ester-linked
fatty acid profiles as reproducible assays for changes in prokaryotic
community structure of estuarine sediments. *FEMS Microbiol. Ecol.* *31*,
147–158 (1985).
This work was supported by the National Science Foundation Doctoral
Dissertation Improvement Grant to C.M.K. (DEB-1311501). Partial funding for
this research was also provided by the Department of Energy’s Great Lakes
Bioenergy Research Center (DOE Office of Science, DE-FC02-07ER64494 and
Office of Energy Efficiency and Renewal, DE-ACO5-76RL01830) and by the New
Hampshire Agricultural Experiment Station (scientific contribution number:
2605; USDA-NIFA Hatch project: 230340). We are grateful to Cory Cleveland
and Phil Robertson for providing comments on previous manuscript drafts.
Support for this research was also provided by the NSF Long-term Ecological
Research Program (DEB 1637653) at the Kellogg Biological Station and by
Michigan State University AgBioResearch. Disclaimer Any opinions, findings
and conclusions or recommendations expressed in this material are those of
the authors and do not necessarily reflect the views of the National
Science Foundation or the DOE.
Author information
Affiliations
1. Department of Natural Resources and the Environment, University of
New Hampshire, Durham, New Hampshire 03824, USA
- Cynthia M. Kallenbach
- , Serita D. Frey
- & A. Stuart Grandy
2. Soil and Crop Sciences Department, Colorado State University, Fort
Collins, Colorado 80523, USA
- Cynthia M. Kallenbach
Contributions
C.M.K. and A.S.G. conceptualized the study. C.M.K. collected the data and
performed all the statistical analyses. All authors contributed to
manuscript development and revisions.
Competing interests
The authors declare no competing financial interests.
Corresponding author
[permaculture] Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls | Nature Communications,
Lawrence London, 12/03/2018