Developing a more mechanistic understanding of soil respiration is
hampered by the difficulty in determining the contribution of different
organic substrates to respiration and in disentangling autotrophic-versus-heterotrophic and aerobic-versus-anaerobic processes. Here, we use a
relatively novel tool for better understanding soil respiration: the
apparent respiration quotient (ARQ). The ARQ is the amount of CO2 produced
in the soil divided by the amount of O2 consumed, and it changes
according to which organic substrates are being consumed and whether oxygen
is being used as an electron acceptor. We investigated how the ARQ of soil
gas varied seasonally, by soil depth, and by in situ experimental warming
(+4∘C) in a coniferous-forest whole-soil-profile warming
experiment over 2 years. We then compared the patterns in ARQ to those of
soil δ13CO2. Our measurements showed strong seasonal
variations in ARQ, from ≈0.9 during the late spring and summer to
≈0.7 during the winter. This pattern likely reflected a shift from
respiration being fueled by oxidized substrates like sugars and organic
acids derived from root and root respiration during the growing season to
more reduced substrates such as lipids and proteins derived from microbial
necromass during the winter. This interpretation was supported by δ13CO2 values, which were lower, like lipids, in the
winter and higher, like sugars, in the summer. Furthermore,
experimental warming significantly changed how both ARQ and δ13CO2 responded to soil temperature. Wintertime ARQ and δ13CO2 values were higher in heated than in control plots,
probably due to the warming-driven increase in microbial activity that may
have utilized oxidized carbon substrates, while growing-season values were
lower in heated plots. Experimental warming and phenology change the sources
of soil respiration throughout the soil profile. The sensitivity of ARQ to
these changes demonstrates its potential as a tool for disentangling the
biological sources contributing to soil respiration.
Introduction
Despite making extensive measurements of soil respiration
(Bond-Lamberty and Thomson, 2010), scientists lack
methods to disentangle the processes underlying, and substrates contributing
to, soil respiration, which hampers predictions of terrestrial carbon cycle
responses to global change (Phillips et
al., 2017). Mechanistic uncertainty surrounding soil respiration is partly
responsible for the 1000 Pg spread in model predictions of end-of-century
terrestrial carbon–climate feedbacks
(Friedlingstein et al.,
2013). Soil respiration is the flux of CO2 from the soil surface to the
atmosphere, which is dominated by autotrophic respiration from plant roots
and heterotrophic respiration from soil microbes. Heterotrophic respiration,
which has increased globally over the past 3 decades
(Bond-Lamberty et al., 2018), is itself the sum of various
processes using different sources of energy. For example, microbes consume
different organic substrates depending on what molecules are accessible and
whether the microbes are living in the rhizosphere or bulk soil, and
microbes utilize different terminal electron acceptors depending on O2
availability in the microsites in which they reside
(Keiluweit
et al., 2016; Liptzin et al., 2011). The electron donors (the organic
substrates) and the electron acceptors used by soil microbes during
respiration cannot be resolved by measuring the CO2 flux alone.
Previous studies have used measurements of δ13C to partition
respiration into autotrophic and heterotrophic components
(e.g., Dorrepaal et al., 2009),
radiocarbon to partition respiration sources by age (e.g.,
Trumbore, 2000), or both isotopes in combination to more finely separate
respiration among sources
(e.g.,
Hicks Pries et al., 2013; Hopkins et al., 2012). However, isotopes are not
the only way to disentangle soil respiration's various components (Subke et
al., 2006).
Respiration quotients (RQs; the inverse of reported oxidative
ratios, which are based on elemental analyses) and relative isotopic
enrichment of common molecules and substrates for respiration found in soils.
The most common RQ value is listed, followed by the range of potential RQ
values in parentheses. The apparent respiration quotient is based on the
simultaneous measurement of soil CO2 and O2.
a Data from Masiello et al. (2008).
b Data from Bowling et al. (2008).
Our ability to understand soil respiration is limited by measuring only one-half of the respiration equation, the CO2 produced. Simultaneously
measuring the O2 consumed can provide a more mechanistic understanding
of the processes and substrates contributing to soil respiration
(Phillips et al., 2017). The paired
measurements of CO2 and O2 can be used to calculate a respiration
quotient (RQ; Angert and Sherer, 2011). All organic
matter has an oxidative ratio (1 / RQ), which can be calculated based on an
elemental analysis of its C, H, O, and N
(Masiello et al.,
2008). The oxidation state of carbon in carbohydrates is 0 with a
corresponding RQ of 1 based on its elemental structure. More reduced energy
sources such as lipids have lower RQ values (≈0.73), the RQs of
proteins range from 0.67 to 1, and more oxidized sources such as organic acids
have RQ ranges from 1 to 4
(Masiello et al., 2008;
Table 1). The RQ of aerobic respiration therefore changes based on what
substrates are being consumed (Dilly, 2001;
Theenhaus et al., 1997). Anaerobic respiration increases RQ to values
greater than 1, as electron acceptors like Fe(III) and NO3-
replace O2. Thus, RQ can help differentiate between the electron donors
(organic substrates) and terminal electron acceptors used during soil
respiration. We will refer to the “apparent” respiration quotient (ARQ)
because not all ecosystem CO2 or O2 fluxes are due to respiratory
processes (Angert and Sherer, 2011). For example,
fluctuating redox conditions can lead to consumption of O2 during metal
oxidation and drive ARQ below the value of the most reduced organic matter
(Angert et al., 2015).
Thus far, CO2:O2 ratios have been primarily used to understand
large-scale earth system processes, and only few studies have examined
processes within ecosystems. This ratio in atmospheric samples has been used
to estimate (a) the magnitude of the terrestrial carbon sink because carbon
uptake by terrestrial ecosystems is balanced by O2 production, whereas
ocean CO2 uptake is decoupled from O2
(Keeling,
1988; Keeling et al., 1996; Randerson et al., 2006; Worrall et al., 2013),
and (b) anthropogenic impacts on the carbon cycle, based on the principle
that burning of reduced fossil fuels results in a different oxidative ratio
than photosynthesis and subsequent respiration of carbohydrates
(Keeling, 1988). The CO2:O2 ratio of
ecosystem–atmosphere exchanges is an essential quantity in these carbon
cycle calculations. CO2:O2 ratios have been estimated from
measurements of net ecosystem exchange of CO2 and O2
(e.g., Seibt et al.,
2004) and from elemental analysis of biomass
(Hockaday et al., 2015; e.g., Masiello et al., 2008), both of which are
assumed to be similar over multiyear timescales. In early carbon sink
calculations, the oxidative ratio of ecosystem fluxes was assumed to be 1.1
(ARQ = 0.9) based on a single study of temperate soils
(Severinghaus, 1995). However, the few subsequent studies
examining the CO2:O2 ratio of soil respiration fluxes have shown that
soil fluxes can deviate widely from that value.
Soil ARQ from incubations shifts as a result of temperature changes,
substrate additions, and soil management. For example, the ARQ of peat soils
decreased from about 1.1 to about 0.6 when temperatures increased from 0 to
20 ∘C, attributed to changing substrate use
(Chapman and Thurlow, 1998). Glucose additions
to German forest soils increased soil ARQ to 0.95–1.0 from a basal value
around 0.7 (Dilly, 2001; Theenhaus et al.,
1997). Soils under organic agriculture were found to have a greater ARQ
(1.19) than the same soils under conventional agriculture
(0.72; Theenhaus et al., 1997). Soil ARQ in mesocosms
containing pine seedlings changed seasonally and when the pine seedlings
were cut, indicating that the ratio is responsive to changes in vegetation
(Andersen and Scagel, 1997; Scagel and Andersen, 1997).
Lastly, in one of the only studies using in situ measurements, soil ARQ taken from
gas wells across multiple forested ecosystems ranged widely, from 0.14 to
1.23, indicating the influence of abiotic processes that consume O2
(Angert et al., 2015). The wide range in soil ARQ values associated with
different biochemical conditions indicates that the ratio has the potential to
provide insight into the substrates contributing to respiration as well as
into abiotic O2 consumption. Finer-scale research is needed, however,
to explore ARQ values in the same soils under different conditions to learn
what these values indicate about the processes and substrates contributing
to soil respired CO2.
Here we investigated how the ARQ of soil gas in situ varied seasonally, by soil
depth, and by experimental warming in a whole-soil-profile warming
experiment in a well-drained, oxygenated coniferous-forest soil
(Hicks Pries et al., 2017). We characterized soil ARQ at 30
and 90 cm depths in the winter and growing season over 2 years and
compared the patterns in ARQ to monthly patterns in soil profile δ13CO2. We hypothesized that ARQ values would change seasonally
and with warming, reflecting the values of the organic carbon substrates
being consumed by microbes. Like ARQ, the δ13C of soil CO2
is influenced by the use of different organic substrates, since more reduced
substrates tend to have lower δ13C values
(Bowling et al., 2008). By comparing ARQ
values to other indicators of respiration sources, such as δ13C, augmented by what we understand about plant allocation of carbon
substrates belowground, we aim to advance the utility of ARQ as a tracer of
respiration processes.
MethodsWarming experiment
The whole soil profile warming experiment is located at the University of
California Blodgett Forest Research Station, in the Sierra Nevada foothills
near Georgetown, CA, at 1370 m a.s.l. Mean annual precipitation is
1774 mm, with most of it occurring from November to April, and mean
annual temperature is about 12.5 ∘C (Bird and Torn,
2006). The experiment is in a thinned 80-year-old stand of mixed conifers
including ponderosa pine (Pinus ponderosa), sugar pine (Pinus lambertiana), incense cedar (Calocedrus decurrens), white fir
(Abies concolor), and Douglas fir (Pseudotsuga menziesii). The soils are Holland series: fine loamy, mixed,
superactive, mesic Ultic Haploxeralfs of granitic origin with thick,
>5 cm O horizons, minimal carbonates (Rasmussen et
al., 2005), and a pH that ranges from 5.6 to 6.5
(Hicks Pries et al., 2018). The
warming treatment warmed the soil +4∘C to 1 m depth while
maintaining the natural temperature gradient with depth and temporal
variations in soil temperature as described in Hicks Pries et al. (2017).
Briefly, there were three pairs of control and heated 3 m diameter circular
plots; twenty-two 2.4 m long vertical resistance heater cables in metal conduit
(BriskHeat, Ohio, USA) surrounded the heated plots. To compensate for surface heat
loss, two concentric rings of heater cable at 1 and 2 m in diameter were
installed 5 cm below the soil surface in heated plots. Unheated cables were
installed similarly in control plots. Heating throughout the plot volume was
generally even, ranging from 3.5 to 4.5 ∘C except at 5 cm depth,
where the heated plots were on average only 2.4±1.2∘C
warmer than the control due to a lack of aboveground heating. Soil moisture
was slightly decreased in the warmed plots by an average of 1.5 %–3.5 %
volumetric water content (Hicks Pries et al., 2017).
Sample collection and analysis
Dataloggers (CR1000, Campbell Scientific, Utah, USA) continuously recorded
soil temperature and moisture at 30 min intervals. Temperature was monitored
at 5, 15, 30, 50, 75, and 100 cm depths at a radial distance of 0.75 m from
the center of each plot. Temperature probes consisted of thermistors (Omega 44005) epoxied to PVC rods, placed inside thin-walled steel conduit. To
monitor soil moisture, we used an EnviroSCAN (Sentek, Australia) probe
fitted with capacitance sensors at 10, 30, 50, and 90 cm at a radial
distance of 0.75 m from the center of each plot. We calibrated the soil
moisture measurements by comparing the sensor values at each depth to the
volumetric water content measured in nearby (within 0.5 m) soil cores that
were sampled five times over 2 years.
Each of the six plots has a set of gas wells at 15, 30, 50, 75, and 90 cm.
The gas wells were 6.35 mm diameter stainless-steel tubes inserted into the
soil at a 45∘ angle to the desired depth and topped with straight
swage pipe fittings (Swagelok, Ohio, USA) with septa. For CO2 and δ13CO2 measurements, samples were collected from the wells with
a syringe on a nearly monthly basis from March 2014 to June 2017 (32
months total) and always during morning hours. After clearing the headspace
in each well, a 25 mL gas sample was transferred to an evacuated 20 mL septum-topped glass vial. For analysis, 5 mL samples were injected into the
small sample isotope module of a cavity ring-down spectrometer (CRDS,
Picarro, Santa Clara, California), where they were diluted with ultra-zero
air (without CO2). A four-point calibration curve ranging from 2000 to
20 000 ppm (δ13C=-26.7 ‰) was used to
calculate the CO2 concentration from the CRDS data and to correct for
mass dependency of the δ13C measurement.
In July 2015; February, April, and August 2016; and March and June
2017, we collected additional samples from the 30 (except July 2015) and 90 cm gas wells into 13 mL flasks equipped with O-ring valves (LouwersHanique,
Hapert, Netherlands) to simultaneously measure CO2 and O2
concentrations in order to calculate ARQ. The flasks were analyzed in the
laboratory at the Hebrew University by a closed system (the
Hampadah; Hilman and Angert, 2016). This fully automated system uses an
infrared gas analyzer (IRGA) for CO2 measurement (LI 840A, LI-COR,
Lincoln, NE, USA) and a fuel-cell-based analyzer (FC-10, Sable Systems
International, Las Vegas, NV, USA) for measuring O2. The flasks were
analyzed within 2–3 weeks of collection.
In June 2017, we also ran a set of short (3 h) incubations of root-free
soil and of excised roots collected adjacent to the experimental plots. We
collected four mineral soil cores with a 5 cm diameter hammer corer,
separated the cores into 0–20 and 20–40 cm depths, and removed roots
>1 mm diameter. Roots were collected from four 25 cm × 25 cm × 25 cm soil pits. We rinsed roots with water to remove soil and blotted them
dry before placing them into mason jars. The root-free soil was also placed
into mason jars, and both sets of mason jars were flushed with ambient,
outside air. After a 3 h incubation of the root samples and a 21 h incubation of the soil samples, the headspace was sampled for CO2 and
O2 and analyzed as described above. Incubations were run at room
temperature, which was similar to the field temperature at the time of
collection.
Sample calculations and statistics
To calculate ARQ, we used Eq. (1) from Angert et al. (2015):
ARQ=-0.76ΔCO2ΔO2,
where ARQ is the apparent respiratory quotient, ΔCO2 (ppmv) is
the difference between CO2 concentrations in the soil pore-space gas
and ambient (i.e., 0.5 to 1 m a.g.l.) samples, ΔO2 (ppmv)
is the difference of the soil pore-space O2 concentration and ambient
O2 concentration, and 0.76 is the ratio of CO2 to O2
diffusivity in air (Massman, 1998). The negative sign is for
convenience, so the ARQ value will typically be positive because the
difference in O2 concentration is always negative. For the jar
incubations we used the same equation without the 0.76 factor. Ambient
CO2 concentrations were measured in the field at the time of sampling
with the CRDS, while the ambient O2 concentration was assumed to be
20.95 % (Rumble, 2019). To relate the δ13C value of
soil pore-space CO2 to the δ13C of CO2 production, we
corrected the pore-space δ13C value for diffusion, since
13C diffuses slower in air than 12C, and thus the measured value
does not accurately represent the value of production. For the correction,
we used Eq. (2) from Bowling et al. (2015):
δproduction=Csδs-4.4-Caδa-4.41.0044(Cs-Ca),
where Cs is the soil pore-space CO2 concentration (ppmv), δs (‰) is the isotopic composition of soil pore-space CO2, and Ca and δa are the CO2
concentration and isotopic composition of ambient air, respectively. The
ambient CO2 concentrations and δ13C values needed for
these corrections were measured in the field at the time of sampling with
the CRDS.
To investigate the effects of season, warming treatment, and soil depth on
ARQ and δ13C, we ran multiple regressions in R (R
Development Core Team, 2019). Because ARQ was not sampled from both depths
on all dates, we ran separate regressions for each depth (30 and 90 cm) and
then ran a regression that included a depth effect while dropping the first
sampling date. In all regressions, treatment and sampling date (as a factor)
were fixed effects. Following Zuur et al. (2009), we used a
full model with all fixed effects and their interactions to optimize the
random effects and autocorrelation structure based on the Akaike information criterion (AIC). For both
versions, we used the individual gas well as a random effect, and a temporal
autocorrelation did not improve the model, nor did an autocorrelation
function graph indicate that one was needed. We chose the significant fixed
effects by performing a series of pairwise model comparisons using AIC and
the F test, dropping the least significant variables each time until only
variables that improved the model fit remained. The p values reported are
those from the t tests of the summary.lme function of best-fit model. We
report conditional R2 values calculated using the rsquared command in
the piecewiseSEM package.
To investigate seasonal patterns in δ13CO2, we had more
data in terms of both length of time and temporal density of sampling and
were thus able to treat the month as a continuous variable. We fit a sine
function and tested models including the first and second harmonics of the
month effect as well as linear fixed effects of depth, treatment, and a
depth-by-treatment interaction. Graphical exploration indicated that the
sinusoidal pattern differed slightly by year, so we also added a year effect
to the second harmonic of the month effect. As above, we used the full fixed-effect model to test the best random and autocorrelation structure.
Individual gas well depth was used as a random effect, and a correlation
structure did not improve the model.
To test relationships between ARQ and δ13CO2, and both ARQ
and δ13CO2 individually versus soil temperature and
VWC (volumetric water content), we ran mixed-model regressions with the individual gas
well as a random effect. For the soil–climate relationships, we used the AIC and
the F test to investigate whether the warming treatment and its interaction
with soil temperature or VWC were significant fixed effects. We tested the
need for autocorrelation structures based on AIC, and none improved the
models. For all models, we graphically checked the residuals for violations
of normality and heterogeneity of variance. For δ13CO2
analyses, we dropped the 15 cm depths due to their unusually low δ13C value (<-32 ‰) after correction (Eq. 2), which indicated potential intrusion of atmospheric air during sampling
that led to an overcorrection. We used one-way ANOVA to compare the ARQ of
soil and root incubations and the ARQ of two soil depths we incubated. All
statistics were performed in R version 3.4.1, and regressions were done using the
lme function (R Development Core Team, 2019).
Mean (± SE) apparent respiration quotient (ARQ, n=12
except n=6 for July 2015; a) and corrected δ13CO2 (n=24
per date; b) in soil pore air averaged across all depths and treatments by
sampling month. The relationship between ARQ and δ13CO2
values during the months when they were sampled simultaneously (c). The line
shows the fit of a linear regression (p<0.0001, n=64,
R2=0.20).
Results
Both ARQ and δ13CO2 had similar, strong seasonal patterns
(Fig. 1a and b). ARQ values were higher during the growing season (0.89±0.01, n=42) and lower during the winter (0.70±0.02,
n=23). In ARQ regression analyses for both depths, there was a significant
effect of date (p<0.0001, n=59), with February 2016 and March 2017
differing significantly from July 2015 (90 cm only), April and August
2016, and June 2017. Similarly, δ13C was higher during the
summer (June through October, -27.97±0.06, n=311) and lower during
the winter and spring (November through May, -29.01±0.04, n=447).
While individual dates were not compared statistically for δ13CO2, the vast improvement in model fit using month as a sine
function instead of a linear function or factor (ΔAIC = 114) is
strong statistical evidence for a seasonal effect (Fig. 2b). ARQ and δ13CO2 were significantly related according to the mixed-effect
regression model (Fig. 1c; p<0.0001, n=64, R2=0.20).
However, the patterns in ARQ and δ13CO2 did not match
during April.
Apparent respiration quotient (ARQ) by sampling date for heated
(black) and control (grey) treatments at 30 cm (circles) and 90 cm (triangles) depths (n=3 per date and depth combination; a). ARQ differed
significantly among treatments during the winter at 30 cm. Corrected δ13CO2 for all depths (30, 50, 70, and 90 cm) and months sampled (b). The lines represent the predicted fit of a sinusoidal regression (see
text) for an average soil depth in control (grey) and heated (black)
treatments (n=758).
The mean ± SE (number of samples) of corrected δ13CO2 and ARQ of soil pore-space by depth averaged over all
time points.
Both ARQ and δ13CO2 differed by warming treatment (Fig. 2)
and by depth (Table 2). For the ARQ of 30 cm depths, there was a significant
treatment-by-date interaction (p=0.051, n=30) whereby heated plots had
greater ARQ values during the winter months (February 2016 and March 2017;
Fig. 2a). For the ARQ of 90 cm depths, the best-fit model did not include a
significant treatment effect or treatment-by-date interaction (Fig. 2a;
n=35). For δ13CO2 across all depths, treatment was a
significant effect (p=0.0065, n=758), with warmed soil on average having
a slightly higher δ13CO2 (-28.33±0.05) than the
control soil (-28.83±0.06; Fig. 2b). The treatment-by-depth
interaction was not significant for δ13CO2 and was not
included in the best-fit model. Looking at depth only (Table 2), the ARQ at 30 cm was marginally significantly greater than the ARQ at 90 cm by 0.07 units
(p=0.099, n=59), while δ13CO2 increased with depth,
from -28.98 at 30 cm to -28.34 at 90 cm (p=0.0089, n=758).
The relationships of apparent respiration quotient (ARQ) by soil
temperature (a; n=65) and soil moisture (b; n=60) and δ13CO2 by soil temperature (c; n=565) and soil moisture
(d; n=535). Gray and black points represent data from control and heated
gas wells, respectively. The lines show the fit of a mixed-model regression
between each variable, where individual gas well was treated as a random
effect. Separate grey (control) and black (heated) lines indicate that there was
a significant effect of warming treatment on the relationship between the
response variable and soil temperature or volumetric water content.
Both ARQ and δ13CO2 showed strong relationships with soil
climate that were significantly affected by the warming treatment (Fig. 3).
We tested relationships with soil temperature and soil moisture individually
because of the strong negative correlation between temperature and moisture
in this Mediterranean climate (Pearson's r=-0.76 to -0.78). ARQ increased
significantly with increasing soil temperatures (p<0.0001, n=65,
R2=0.52; Fig. 3a), with values increasing faster in control plots than
in warmed plots (p=0.0051). ARQ decreased with increased soil moisture
(p<0.0001, n=60 due to missing VWC values, R2=0.24; Fig. 3b), and the decrease was faster in the control than in the warmed plots.
δ13CO2 became higher with increasing soil temperatures
(p<0.0001, n=375, R2=0.33; Fig. 3c), with values again
increasing faster in the control than in the warmed plots (p=0.02).
δ13CO2 decreased with increased soil moisture (p<0.0001, n=345 due to missing VWC values, R2=0.30; Fig. 3d), and
treatment did not have a significant effect.
Our incubations of roots (n=4) and of root-free soil (n=4 per depth
increment) indicated that heterotrophic and autotrophic respiration had
significantly different ARQ values, at least during the summer when we
performed the incubations. Roots had a greater ARQ (0.87±0.03) than
root-free soil (0.78±0.02; one-way ANOVA, p=0.029).
Furthermore, the ARQ of the soil incubations significantly declined with depth,
from 0.82±0.01 at 0–20 cm to 0.74±0.02 at 20–40 cm (one-way
ANOVA, p=0.0053).
Discussion
There are many factors that can affect ARQ; however, our evidence indicates
that the strong seasonal patterns in ARQ and δ13CO2 were likely
driven by changes in the amount of root-derived organic substrates providing
energy for heterotrophic microbial respiration and changes in the
contributions of autotrophic root respiration. This interpretation is
supported by previous soil ARQ studies, our incubations, and the scientific
understanding of how plant carbon inputs change seasonally. The seasonal
range in ARQ from ≈0.9 during the growing season to ≈0.7
during the winter may reflect a shift in the molecules fueling respiration
from more oxidized substrates like sugars and organic acids derived from
roots in the summer to more reduced substrates in the winter such as lipids
and proteins derived from microbial necromass. Previous incubations found
that glucose additions increased ARQ (Dilly,
2001; Theenhaus et al., 1997). Other studies attributed a decline in ARQ
during the time course of incubation to the depletion of labile carbon
sources (Angert et al.,
2015; Severinghaus, 1995). Our short-term incubations demonstrated that root
respiration has a greater ARQ than microbial respiration from root-free
soils. During the growing season, root respiration and exudation increase,
which should increase ARQ, as seen in our data. In the eastern US deciduous
forests, root exudation rates tend to be lower in the winter and spring than
in the summer and fall
(Abramoff and Finzi,
2016; Phillips et al., 2008). Mass-specific fine-root respiration rates were
greater during the growing season (up to 8 nmol CO2 g-1 s-1)
than in the winter (<1 nmol CO2 g-1 s-1), and total
belowground carbon flux was greatest from May to October
(Abramoff and Finzi, 2016). Though these root
studies were not from the western United States, eddy covariance data from a
coniferous forest near our study site found that primary production was
greatest during the summer months, from June to mid-September
(Goldstein et al., 2000).
Beyond the results of our root and root-free soil incubations, there is
additional evidence that root and rhizosphere respiration should have a
greater ARQ than microbially derived respiration. For example, respiration of
root tips is driven by sugar content and has an RQ of 1.0
(Saglio and Pradet, 1980). Furthermore, recent
metabolomic analysis of root exudates identified sugars, carboxylic acids,
amino acids, and phenolics as the main metabolites (Zhalnina
et al., 2018), most of which are relatively oxidized energy sources with
relatively high respiratory quotients. Thus, we would expect greater ARQ
values during the summer due to higher root activity. When trees are
dormant, the lack of fresh inputs from roots may lead to more recycling of
organic carbon within microbial biomass, wherein proteins and lipids are the
first and third largest constituents by weight, making up to 55 % and from
10 % to 35 % of a typical bacterial cell's dry mass, respectively
(Kleber and Reardon, 2017; Neidhardt, 1987). Lipids and
proteins tend to be reduced and have the lowest RQ values of common organic
substrates, likely explaining the lower wintertime ARQ values in our soils.
The seasonal pattern in δ13CO2 reinforces our
interpretation that changes in respiration carbon sources were driving
changes in ARQ. Soil δ13CO2 was more enriched in the
summer and became more depleted in the winter by up to
2 ‰. In a comprehensive review of carbon isotopes in
terrestrial ecosystems, Bowling et al. (2008) showed that plant lipids tend to be
more depleted in 13C, while sugars and organic acids tend to be more
enriched in 13C relative to bulk leaf δ13C. While these
numbers are based on plant lipids, if we assume that microbial lipids are
similarly depleted relative to other organic compounds, an increase in
microbial necromass as an organic matter source relative to root-derived
sources during the winter would cause the observed fluctuation in δ13CO2. Furthermore, a chemical fractionation of soil organic
matter found that the water-soluble fraction, which includes sugars, was
3 ‰–4 ‰ more enriched than the acid-insoluble pool
(Biasi et al., 2005). While the interpretation of respiration
δ13C by itself in C3 ecosystems can be difficult due to
the small per mill differences among carbon sources
(e.g., Bowling et al., 2015), the simultaneous use
of ARQ and 13CO2 helps strengthen interpretations.
Seasonality encompasses changes to phenology and soil climate, among other
factors. Both ARQ and δ13C had significant positive
relationships with soil temperature. In addition to the importance of plant
phenology described above, temperature could have direct effects on
respiration sources. Specifically, warmer temperatures can increase root
exudation rates (Yin et al., 2013) and the
relative contribution of autotrophic-derived, if not directly autotrophic,
respiration to total soil respiration. In two subarctic ecosystems, warming
increased the proportion of ecosystem respiration derived from autotrophs
(which, using natural abundance radiocarbon as a tracer, included
heterotrophic respiration of root exudates) relative to heterotrophs
(Hicks Pries et al., 2015). However, temperatures can affect ARQ
beyond changing the contributions of autotrophic sources.
Lower temperatures increase the thermodynamic favorability of the oxidation
of reduced carbon in compounds like lipids (LaRowe
and Van Cappellen, 2011), which could also explain the decrease in ARQ
values at lower temperatures. For δ13C, it is likely that
phenological changes to organic carbon sources were more important than
temperature per se. Several soil incubation studies show that increases in
temperature cause respired δ13CO2 to decrease by about
0.12 ‰–0.35 ‰ for each 1 ∘C rise in
temperature – the opposite of the relationship we found
(Andrews et al., 2000;
Biasi et al., 2005; Hicks Pries et al., 2013). In these incubations, which
were devoid of new organic carbon inputs, unlike in situ conditions, the
shift was attributed to changes to the microbial community that affected
carbon source preferences (Andrews et al., 2000; Biasi et
al., 2005). Furthermore, in a Mediterranean climate, phloem sap from trees
has been shown to become more enriched in 13C during the summer
(Merchant et al., 2010), matching our pattern
in soil δ13CO2.
While ARQ and δ13CO2 increased with soil temperature,
experimental warming slowed that rate of increase so that both ARQ and
δ13CO2 values were generally greater in the control than
in the heated treatment at the warmest soil temperature. Concurrently,
during the colder months, experimental warming caused greater ARQ values (as
at 30 cm depths in February 2016 and March 2017) and slightly higher δ13CO2 relative to the controls. The increase in ARQ and δ13CO2 with experimental warming during the colder soil
temperatures of winter indicates proportionately more respiration of
relatively oxidized, labile organic substrates in the heated treatment.
Perhaps enhanced root growth and exudation in the heated treatment
(Yin et al., 2013) could result in the
increased availability of labile organic substrates, but this increase
occurred in winter, when trees were less active, and was not seen during the
growing season. The increase in ARQ and δ13CO2 could also
be the result of preferential decomposition of more highly oxidized, labile
substrates by a more active microbial population during the winter.
Experimental warming increased microbial activity at all soil depths;
warming increased CO2 production by 34 % to 37 % overall, with about
40 % of the warming response occurring below 15 cm in the soil profile
(Hicks Pries et al., 2017). A warming-induced increase in
the consumption of labile substrates could lead to exhaustion of the labile
pool and eventually smaller warming-induced SOC (soil organic carbon) losses, as seen at Harvard
Forest (Melillo et al., 2002,
2017). In fact, the trend towards decreased ARQ and δ13CO2 values during the warmer soil temperatures of the growing season could be
due to a depletion of the labile SOC pool during the winter. Another
potential explanation for lower values during the growing season could be a
reduction in the proportion of soil respiration derived from roots. In one
warming study, root respiration was less sensitive to warming relative to
heterotrophic respiration (Hartley et al.,
2007). The warming treatment dried the soil slightly at Blodgett
(Hicks Pries et al., 2017), which could stress roots during
California's essentially rainless growing season. Future measurements of
CO2 production, ARQ, and δ13CO2 in trenched and
untrenched plots could help distinguish these possibilities.
Soil temperature and soil moisture were so strongly negatively correlated
due to our study site's Mediterranean climate that it is difficult to
separate their effects. The ARQ and δ13CO2 were negatively
correlated with volumetric water content, which was greatest when soil
temperatures were coldest. Volumetric water content has the potential to
control ARQ in several ways. First, increased soil moisture reduces O2
availability, which could increase ARQ values >1, as CO2 is
produced without O2 consumption. However, during our study the soil
remained oxic (soil O2 averaged 20 % and the minimum was 17.38 %).
The negative relationship between ARQ and soil moisture indicates that
anaerobic respiration was not a driver, and we only measured one ARQ value
greater than 1 (1.03) during our study. However, diffusion rates slow
with higher soil moisture, which could make detection of high ARQ values
difficult if anoxic conditions occur within microaggregates. In anoxic
microaggregates, iron (II) is produced anaerobically, which is subsequently
oxidized to iron (III) as the aggregate dries and becomes aerobic, a process
that consumes O2 without producing CO2, resulting in low ARQ
values that can be detected as drying soils increase diffusion
(Angert et al., 2015). In our
soils, which tend to contain relatively high amounts of iron oxides
(Rasmussen et al., 2005), iron oxidation could explain the
15 % of ARQ values that were less than the reduced organic matter value of
0.7. Lastly, since CO2 is more soluble in water than is O2, more
CO2 relative to O2 is expected to dissolve in soil water, which
would reduce ARQ values at higher moisture contents. However, different
dissolution rates and iron oxidation do not fully explain our data, as the
wide variability in ARQ values (0.44 to 0.94) at high volumetric water
contents (0.27 to 0.31) can be best explained by time of year (Fig. S1 in the Supplement),
which again points to phenology as the main driver; the greater ARQ values
are from April and June, while the lower values are from February and March.
Furthermore, there was a stronger relationship between observed and
predicted ARQ in the temperature model than in the soil moisture model.
Experimental warming affected the relationship between ARQ and soil
moisture. ARQ was greater in the heated treatment when soil moisture was
high (winter) and lower in the heated treatment when soil moisture was low
(growing season). Soil water sampled from lysimeters had a greater
concentration of dissolved organic carbon in the warming treatment than in
the control (unpublished data), which could deliver oxidized substrates to
microbes during the winter rainy season.
The reasons for δ13CO2 decreasing with increasing
volumetric water content are not clear. Based on kinetics, we would expect
that as more CO2 dissolves in water, the soil air should become
enriched in 13CO2 because dissolution discriminates against the
heavy isotope, and increasingly so at lower temperatures
(Zhang
et al., 1995), but our data were not consistent with this explanation.
Another possibility is that advective transport of atmospheric CO2
through the soil is more likely at lower soil moisture content. While
intrusion of atmospheric CO2 would increase the δ13C of
soil air, it reduces the effective diffusion fractionation to <4.4 ‰, leading to overcorrected and thus unrealistically
low δ13C values, of which we did have several.
Depth was the only parameter by which ARQ and δ13CO2 did
not change in concert with one another. ARQ decreased with depth, while
δ13CO2 increased. The decrease in ARQ with depth, which
was more dramatic in the root-free soil incubations than in soil air
(difference of 0.08 versus 0.03), is likely due to decreased plant inputs
with fewer fine roots and less root exudation at depth
(Hicks
Pries et al., 2018; Tückmantel et al., 2017). The enrichment of soil
δ13CO2 likely reflects the near-universal enrichment of
soil organic carbon with depth due to catabolic carboxylation reactions
(as microbial byproducts and
necromass become a larger proportion of soil organic matter; Ehleringer et
al., 2000; Torn et al., 2002) or the Suess effect (the continuing depletion
of atmospheric CO2 over time due to the burning of fossil fuels). In
our soils, there was about a 2 ‰ enrichment in bulk soil
organic δ13C with depth
(Hicks Pries et al., 2018).
Conclusions
Here we have shown, for the first time, annual patterns in the soil ARQ and
how ARQ is affected by experimental warming. We inferred that seasonal
patterns in ARQ were likely due to changes in the dominant substrates
providing the energy for soil respiration, with root-derived sugars and
organic acids being the dominant substrates during the growing season and
microbial necromass being the dominant substrate during the winter. These
inferences of organic substrates were supported by soil δ13CO2 measurements, which showed clear patterns despite our study
system containing only C3 plants. We recognize that direct experimental
evidence of how ARQ changes with sources is needed before our inferences of
substrate use can be proven. However, our data indicate that ARQ measurements can
help to disentangle the biological sources contributing to soil respiration
and to understand how sources are shifting due to global change. This
application of ARQ worked well in our soils, which were well-drained,
oxygenated, and lacked carbonates. The interpretation of soil ARQ values
becomes more complex if those conditions are not met
(Angert et al., 2015). The
autotrophic and heterotrophic source separation in our incubations indicates
that ARQ has the potential to be used to partition soil respiration in a similar
manner to natural abundance δ13C
(e.g.,
Dorrepaal et al., 2009; Hicks Pries et al., 2013). To enable further
applications of ARQ, more characterization is needed of the controls of the
ratio, including incubation studies of sterile and “live” soils under
aerobic and anaerobic conditions and co-located measurements of ARQ fluxes
and the oxidative ratio of organic matter sources as in Masiello et al. (2008). Such future
investigations will help determine whether ARQ deserves a prominent place
alongside natural abundance isotopes in the ecosystem ecology and
biogeochemistry toolkit.
Code and data availability
Data (https://doi.org/10.15485/1596312, Hicks Pries et al., 2020) are publicly available on ESS-DIVE (http://ess-dive.lbl.gov/). The R code used for the statistics and to
generate the figures in this paper is available as a Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-3045-2020-supplement.
Author contributions
CHP, AA, and MST conceived of the study. Field measurements were conducted
by CHP and CC. Lab analyses were conducted by CHP and BH. Statistical
analyses were conducted by CHP. CHP wrote the paper, with feedback from
all authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to acknowledge Rachel Porras for her
assistance in running the isotopic samples and Bryan Curtis and Biao Zhu for
their contributions to setting up the warming experiment.
Financial support
This research was supported as part of the Terrestrial
Ecosystem Science Program by the Office of Science,
Office of Biological and Environmental Research, of the US Department
of Energy under grant no. DE-AC02-05CH11231.
Review statement
This paper was edited by Michael Bahn and reviewed by two anonymous referees.
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