1. Soil sampling
An Oxisol and Mollisol, which are both characterized by redox
fluctuations under field conditions, were sampled in March 2017 in a
perhumid tropical forest near the El Verde field station of the
Luquillo Experimental Forest (18°17´N, 65°47´W), Puerto Rico and an
agricultural field in north-central Iowa (41°75´ N, 93°41´W), USA,
respectively. The Oxisol was from an upland valley in the Bisley
watershed, with mean annual precipitation and temperature of 3800 mm
and 24 °C, respectively. Soil was formed from volcaniclastic
sediment (Buss et al., 2017). The Oxisol experiences
O2 fluctuations on scales of hours to weeks
due to variations in rainfall and biological
O2 demand (Liptzin et al., 2011). Soil was
randomly sampled from the A horizon (0–10 cm) by compositing six
replicate soil cores without disturbing microaggregate structure (no
sieving), and then shipped overnight to Iowa State University. The
Mollisol was sampled from a topographic depression that experiences
periodic flooding (Logsdon, 2015) in the Walnut Creek watershed,
with mean annual precipitation of 820 mm and mean monthly
temperature ranging from -13.4 °C (January) to 29.4 °C (July)
(Hatfield et al., 1999). This very poorly drained soil was formed
from till following the Wisconsin glaciation and developed under
tallgrass prairie and wetland vegetation, and is described as mucky
silt loam (fine, montmorillonitic, mesic Cumulic Haplaquoll). This
site was cultivated with corn (Zea mays) and
soybean (Glycine max) rotated on an annual
basis. We collected soils from the plow layer A horizon (0–20 cm)
following corn cultivation. Six soil cores (10.2 cm diameter) were
randomly sampled in a 50 × 50-m region and then composited.
2. Laboratory incubations
We amended soils with finely ground leaf tissue of
Andropogon gerardii (big bluestem, a
C4 grass), which ameliorated short-term C
limitation of microbial metabolism (Chacon et al., 2006) and
provided an isotopic contrast with extant C. Soils were gently mixed
after coarse roots, organic debris and macrofauna (worms) were
manually removed. Field moisture capacity was determined by
saturating soils and then measuring gravimetric water content
following 48 h of drainage (1.01 g H2O
g-1 soil for the Oxisol and 0.46 g
H2O g-1 soil for
the Mollisol). Aliquots of litter (500 mg) were gently homogenized
with fresh soil subsamples (5 g dry mass equivalent), and deionized
water was added to achieve field moisture capacity. Each replicate
was incubated in an open 50 mL centrifuge tube placed in a glass jar
(946 mL) and sealed with a gas-tight aluminum lid with butyl septa
for headspace gas purging and sampling.
Replicates from each soil were incubated under five headspace
treatments in the dark at 23 °C for 384 days, including a static
oxic control and four fluctuating-O2
treatments. Carbon mineralization data from the static oxic controls
were previously published in a companion experiment that compared
the impacts of long-term oxic vs. anoxic conditions on soil C
cycling (Huang et al., 2020). The
fluctuating-O2 treatments consisted of either
2, 4, 8, or 12 days of anoxic conditions followed by 4 days of oxic
conditions, cycles which were repeated for the duration of the
experiment. The fluctuating-O2 treatments are
denoted by the length of the anoxic phase (2-day, 4-day, 8-day, and
12-day treatments, respectively). There were five replicates for
each headspace treatment (total n = 50). To achieve anoxic and oxic
phases according to the above treatments, each jar was flushed with
humidified N2 or
CO2-free air, respectively, at 500 mL
min-1 for 15 min immediately following
headspace sampling for CO2 and
CH4 measurements. Sample masses were recorded
and additional water was added as necessary at approximately
eight-day intervals to replace moisture loss during headspace
flushing.
3. Analysis of CO2 and
CH4 production
Gas samples (5 mL) were collected immediately prior to headspace
flushing for measurements of CO2
concentration and δ13C values using a
tunable diode laser absorption spectrometer (TDLAS, TGA200A,
Campbell Scientific, Logan, Utah, USA) (Hall et al., 2017).
Measurements were conducted daily for the first month and every two
days thereafter in the control and
fluctuating-O2 treatments. Additional gas
samples (20 mL) were collected at four-day intervals to measure
CH4 concentration by gas chromatography (GC)
with a flame ionization detector (GC-2014, Shimadzu, Columbia, MD).
CH4 production over two-day intervals was
estimated from the average of consecutive four-day measurements (for
the 2-day treatment, 4-day averages were calculated between adjacent
measurements with the same sequence of anoxic/oxic phase
transition). We also measured δ13C values
of CH4 by TDLAS every four days in order to
achieve C isotope mass balance and account for the effects of
CH4 production on the
δ13C values of CO2
due to methanogenesis and methane oxidation (Huang & Hall, 2018;
Whiticar, 1999). We chemically removed CO2
from each gas sample and then combusted CH4
to CO2 (Huang & Hall, 2018). For the
4-day, 8-day and 12-day treatments, the
δ13C values of CH4
were measured at two-day intervals prior to 84 d and subsequently at
four-day intervals. The δ13C values of
CH4 were interpolated over two-day intervals
using the same method for CH4 production
estimates. The CO2-equivalent greenhouse gas
emission was calculated over a 20-y time scale by multiplying
CH4 mass by 84 (1g CH4
= 84 g of CO2 equivalent) and adding to the
CO2 mass (Myhre et al., 2013). Net
N2O production was negligible in our
experiment, determined by periodic measurements of
N2O by gas chromatography concomitant with
CH4 measurements.
4. Partitioning of mineralized C sources (see associated pdf
document with equations)
5. Soil chemical analyses
We measured net Fe reduction and dissolved organic carbon (DOC)
released by water extractions in additional replicate samples from
each soil and headspace treatment during the initial 48 d. Three
replicates per treatment were destructively sampled every four days
for the control and at the end of each anoxic/oxic phase for the
fluctuating-O2 treatments. Soil subsamples
were extracted in 0.5 M hydrochloric acid (HCl) for net Fe reduction
and nanopure water for DOC in a 1:60 dry soil-to-solution ratio.
Iron concentrations in 0.5 M HCl extractions (denoted
Fe(II)HCl and
Fe(III)HCl) were determined colorimetrically
by ferrozine (Huang & Hall, 2017a). The DOC concentrations were
measured on a Shimadzu TOC-L analyzer (Columbia, MD).
At the end of this experiment (384 d), soil subsamples were analyzed
for dissolved organic C (DOC) concentrations in water
(DOCH2O) and several sequential extractions.
The first extraction was sodium sulfate
(DOCNa2SO4), which releases C from weak
polyvalent cation bridges (Ye et al., 2018), followed by sodium
dithionite (DOCNa2S2O4), which releases C
sorbed or co-precipitated with reducible Fe phases (Wagai &
Mayer, 2007), and finally sodium pyrophosphate
(DOCNa4P2O7), which releases C in
organo-metal/mineral complexes (Coward et al., 2017). The
DOCNa2SO4 values were corrected for
DOCH2O measured on separate soil subsamples
(n = 5) extracted by nanopure water in a 1:60 dry soil-to-solution
ratio. For the additional sequential extractions, subsamples were
first extracted by 0.5 M
Na2SO4 at a
soil-to-solution ratio (g mL-1) of 0.0056
for 1 h, followed by 0.266 g
Na2S2O4
(0.05 M) and 30 mL deionized water for 16 h. Then, to dissolve any
sulfide-associated elements, soils were extracted in 0.05 M HCl for
1 h, prior to extraction with 0.1 M
Na4P2O7
for 16 h (Huang et al., 2019). Following each extraction, slurries
were centrifuged at 20,000 g for 10 min and supernatant solutions
were stored at 4 oC prior to analysis.
The DOC concentrations and their δ13C
values were analyzed by measuring CO2 and
δ13C produced from sample oxidation by
boiling with persulfate in serum vials followed by injection of the
headspace gas on TDLAS (Huang & Hall, 2017b). The soluble
litter- and soil-derived C in each extraction was estimated as the
product of DOC concentration and the respective fractional
contributions from litter and soil calculated using the isotope
mixing models described above.
Two replicate soil subsamples from each treatment after the 384-d
incubation were analyzed by 13C nuclear
magnetic resonance (NMR) spectroscopy to assess organic C molecular
composition. Soil was pre-treated with hydrochloric acid (HCl, 10%)
and hydrofluoric acid (HF, 10%) to remove calcium carbonate and
mineral phases (including paramagnetics) to increase the NMR
sensitivity. Briefly, 2–3 g of finely-ground soil was shaken with 30
ml HCl (10%) for 30 min, centrifuged and decanted. The residues were
then shaken with 40 ml of mixed HF (10%) and HCl (10%) for 8 h,
repeated four times. Each sample was washed with distilled water
three times after HF treatment, and then
N2-dried at 50 oC
prior to chemical analysis.
Samples were analyzed by a 300 MHz Bruker AVANCE III NMR
spectrometer equipped with a 4 mm magic angle spinning (MAS) probe
(Bruker BioSpin, Billerica, MA) at Baylor University (Waco, TX). The
NMR analytical details were reported previously (Cusack et al.,
2018). Resulting spectra were divided into seven C functional
groups, and their relative contributions were quantified by
integrating the signal intensities. The chemical shift regions: 0–45
ppm, 45–60 ppm, 60–95 ppm, 95–110 ppm, 110–145 ppm, 145–165 ppm,
165–215 ppm were assigned to alkyl C, N-alkyl and methoxyl C,
O-alkyl C, Di-O-alkyl C, aromatic C, phenolic C, amide and carboxyl
C, respectively. The C oxidation states and the percentages of six
biomolecular SOM constituents (carbohydrate, protein, lignin, lipid,
carbonyl and char) were estimated by a molecular mixing model
constrained by C and N concentrations after the acid pre-treatment
(Baldock et al., 2004).
6. Process-based model simulation
We also used a Microbial-ENzyme Decomposition (MEND) model (Wang et
al., 2019) with a new CH4 module to simulate
C mineralization responses to fluctuating O2.
We first parameterized the MEND model using data from the control
only and then using data from all treatments. Please see more
details on the process-based model simulation are provided in
sections of Materials and Methods and Supplementary Methods in the
paper of Huang et al. (2021).
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