Climate change severely impacts the grassland carbon cycling by
altering rates of litter decomposition and soil respiration (Rs),
especially in arid areas. However, little is known about the Rs
responses to different warming magnitudes and watering pulses in situ in desert
steppes. To examine their effects on Rs, we conducted long-term moderate
warming (4 years, ∼3∘C), short-term acute
warming (1 year, ∼4∘C) and watering field
experiments in a desert grassland of northern China. While experimental
warming significantly reduced average Rs by 32.5 % and 40.8 %
under long-term moderate and short-term acute warming regimes, respectively,
watering pulses (fully irrigating the soil to field capacity) stimulated it
substantially. This indicates that climatic warming constrains soil carbon
release, which is controlled mainly by decreased soil moisture, consequently
influencing soil carbon dynamics. Warming did not change the exponential
relationship between Rs and soil temperature, whereas the relationship
between Rs and soil moisture was better fitted to a sigmoid function.
The belowground biomass, soil nutrition, and microbial biomass were not
significantly affected by either long-term or short-term warming regimes,
respectively. The results of this study highlight the great dependence of
soil carbon emission on warming regimes of different durations and the
important role of precipitation pulses during the growing season in
assessing the terrestrial ecosystem carbon balance and cycle.
Introduction
The global carbon (C) cycle is a critical component of the earth's
biogeochemical processes and plays a major role in global warming, which is
mainly exacerbated by the elevated carbon dioxide (CO2) concentration
in the atmosphere (e.g., Falkowski et al., 2000; Carey et al., 2016;
Ballantyne et al., 2017; Meyer et al., 2018). Soil respiration (Rs),
mainly consisting of the respiration of live roots and microorganisms, is a key
component of the ecosystem C cycle as it releases ca. 80 Pg of C from the
pedosphere to the atmosphere annually (Boone et al., 1998; Karhu et al.,
2014; L. Liu et al., 2016; Ma et al., 2014; Schlesinger, 1977). The effects of
both soil moisture and temperature on Rs processes and the
eco-physiological mechanism have been reported on extensively; however, it is not
well known how soil moisture modulates the response of Rs to changes in
the duration and intensity of warming, particularly in arid and semiarid
areas, where water and nutrients are both severely limited (e.g., Dacal et
al., 2019; Fa et al., 2018; Reynolds et al., 2015; Ru et al., 2018).
The desert steppe of China is ca. 88 million hm2, accounting for 22.6 %
of all grasslands in China, and is located in both arid and semiarid areas.
More than 50 % of the total area of the steppe faces severe
degradation in terms of the decline of community productivity and soil
nutrient depletion, primarily due to improper land use, such as over-grazing
and adverse climatic changes, including heat waves and drought stresses (Bao
et al., 2010; Kang et al., 2007; Yu et al., 2014). Global surface temperature – mainly caused
by anthropogenic CO2 increase – is expected to increase from 2.6
to 4.8∘ by the end of this century, accelerating land degradation
(IPCC, 2014, 2019). Moreover, the desert steppe ecosystem with low vegetation
productivity is vulnerable to harsh environmental conditions, such as
scarce precipitation and barren soil nutrition. For instance, water deficit
and heat waves during the growing season can markedly decrease plant cover
and productivity in this arid ecosystem (Hou et al., 2013;
Maestre et al., 2012; Yu et al., 2018).
Numerous studies have shown that soil temperature and moisture are the two
main, crucial factors that control Rs; however, it is not well known
how soil moisture status mediates the response of Rs to the changes in
the duration and intensity of climatic warming. Soil temperature is the
primary factor driving temporal Rs variations (e.g., Carey et al., 2016;
Gaumont-Guay et al., 2006; Li et al., 2008; Wan et al., 2005). Generally,
Rs is significantly and positively correlated with soil temperature when
soil moisture is ample (Curiel et al., 2003; Jia et al., 2006; Lin et al.,
2011; Reynolds et al., 2015; Yan et al., 2013). In general, the seasonal
variations of Rs coincide with the seasonal patterns of soil temperature
(Keith et al., 1997; Lin et al., 2011; Wan et al., 2007). For instance, Lin
et al. (2011) reported that 63 % to 83 % of seasonal variations of Rs
are dominantly controlled by soil temperature. Diurnal Rs variations are
highly associated with variations in soil temperature (Drewitt et al., 2002;
Jia et al., 2006; Song et al., 2015). Soil respiration, according to
previous studies, is expected to increase with soil water content (SWC; e.g.,
Chen et al., 2008; Song et al., 2015; Wan et al., 2007; Yan et al., 2013).
However, when the SWC exceeds the optimal point to reach saturated levels,
Rs decreases (Huxman et al., 2004; Kwon et al., 2019; Moyano et al.,
2012, 2013; Wang et al., 2014; Yan et al., 2018). In a study
conducted in a tall grass prairie, water addition dramatically increased
soil CO2 efflux (Liu et al., 2002). Liu et al. (2009) showed a
significant Rs increase after a precipitation pulse in a typical
temperate steppe. Therefore, in arid and semiarid regions, where soil water
is limited, the SWC may control Rs, and regulate the warming effect
(Chen et al., 2008; Curiel et al., 2003; Shen et al., 2015). Furthermore,
the effect of watering pulses depends on the pulse size, antecedent soil
moisture conditions, soil texture and plant cover (Cable et al., 2008; Chen
et al., 2008; Shen et al., 2015; Hoover et al., 2016). For instance, the
results by Huxman et al. (2004) showed that different precipitation pulses
have different effects on carbon fluxes in these arid and semiarid regions;
Sponseller (2007) indicated that CO2 efflux increases with storm
size in a Sonoran Desert ecosystem.
A previous study has indicated that short-term (2-year) warming
(2 ∘C) did not affect significantly respiration rate during the
growing season (T. Liu et al., 2016). However, there is limited information
about long-term (4-year) warming effects on Rs and the underlying
mechanism. In this study, we expect that long-term (4-year)
warming may have more profound effects on Rs relative to previous
2-year short-term warming; and the underlying mechanism under longer-term warming
conditions, and the role of soil water status in Rs responses to climatic
warming are also required to be explored further. Thus, in the present
study, we used a randomized block design with three treatments: control (no
warming, no watering), long-term moderate warming (4 years extending from
2011 to 2014) and short-term acute warming (1 year in 2014). Moreover, a
watering pulse treatment (full irrigation to reach field capacity) was
also established. We present the following hypotheses: (i) both long- and
short-term climatic warming can reduce soil CO2 efflux, in which
decreased soil moisture plays a key role in reducing Rs in the arid
ecosystem; and (ii) the dynamics of Rs in the water-limited ecosystem
can be driven mainly by the combination of soil temperature and soil
moisture, and soil moisture can modulate the response of Rs to warming.
Methods and materialsExperimental site
The experiment was conducted in a desert steppe about 13.5 km from
Bailingmiao in Damao County (110∘19′53.3′′ E, 41∘38′38.3′′ N; 1409 m above sea level), situated in Nei Mongol, northern
China. This area is characterized by a typical continental climate. The mean
annual temperature of this area was 4.3 ∘C with a minimum of
-39.4∘C and a maximum of 38.1 ∘C from 1955 to 2014.
The mean annual precipitation is 256.4 mm and approximately 70 % of the
annual precipitation is distributed in the growth season period occurring
from June to August (Fig. S1 in the Supplement). According to Chinese
classification, the soil type is called “chestnut” (Calcic Kastanozems in
the FAO soil classification) with a bulk density of 1.23 g cm-3 and a pH of 7.4. The area has not been grazed since 1980; the
dominant species is Stipa tianschanica var. klemenzii, accompanied by Cleistogenes squarrosa, Neopallasia pectinata, Erodium stephanianum and Artemisia capillaris (e.g., Hou et
al., 2013; Ma et al., 2018).
Experimental design
The warming experiment used a randomized block design. The long-term
moderate warming plots were exposed to long-term warming from early
June to late August (the growing season) for 4 years (2011–2014), while
short-term acute warming was manipulated only during the growing season
(June to August) in 2014. The targeted increases in temperatures relative to
ambient temperature (control) are around 3 and 4 ∘C
under the long-term moderate warming (4 years) and short-term acute
warming regimes (1 year), respectively. Watering pulse treatments were
conducted in August in 2014 and 2017. The control plots received no
additional treatments of either temperature or water (they were recognized
as warming or watering control treatments). All of the warmed plots were
heated 24 h d-1 by infrared (IR) lamps (1.0 m long; GHT220-800; Sanyuan
Huahui Electric Light Source Co. Ltd., Beijing, China) at 800 W during
growing seasons in the experiment's years (2011–2014). The IR lamp heights
above the ground were 1.5 and 1.0 m in moderately and acutely warmed
plots, respectively. This facility can effectively mimic different climatic
warming regimes in field in situ, as previously reported (e.g., Hou et al., 2013;
Ma et al., 2018; Yu et al., 2018). The watering pulse plots were fully
irrigated to field capacity to simulate a watering pulse on 19 August 2014,
and 14 August 2017. For the field warming facility, to simulate the shading
effects, the control plots were installed with a “dummy” heater similar to
those used for the warmed plots. There were a total of 15 experimental plots
(2 × 2 m) arranged in a 3×5 matrix with each treatment
randomly replicated once in each block across three experimental blocks; a 1 m buffer for each adjacent plot was made.
Soil temperature and moisture
At the center of each plot, a thermocouple (HOBO S-TMB-M006; Onset Computer
Corporation, Bourne, MA, USA) was installed at a depth of 5 cm to measure
the soil temperature, and a humidity transducer (HOBO S-SMA-M005; Onset
Computer Corporation, Bourne, MA, USA) was installed at a depth of 0 to 20 cm to monitor the soil moisture (v/v). Continuous half-hour measurements
were recorded by an automatic data logger (HOBO H21-002; Onset Computer
Corporation, Bourne, MA, USA).
Soil respiration
Soil respiration was measured with a Li-8100 soil CO2 Flux System
(LI-COR Inc., Lincoln, NE, USA) with the Rs chamber mounted on polyvinyl
chloride (PVC) collars. Fifteen PVC collars (10 cm inside diameter, 5 cm in
height) were inserted into the soil 2 to 3 cm below the surface. They were
randomly placed into the soil in each plot after clipping all plants growing
in the collar placement areas. The collars were initially placed a day
before measurements were begun to minimize the influence of soil surface
disturbance and root injury on Rs (Bao et al., 2010; Wan et al., 2005).
Respirations for the control and all of the warmed plots were measured from
06:00 to 18:00 UTC+8 on 7 and 8 July and on 18, 19, 20 and 21 August 2014.
The Rs for watering pulse treatment was measured after the water
additions on 19 August 2014, and 14, 15, 16 and 17 August 2017. To
stabilize the measurements, Rs was measured only on selected, typical
days (i.e., mildly windy, sunny days). The Rs in all plots was measured
once every 2 h on that day, and each measurement cycle was finished within 30 min to minimize the effects of environmental variables, such as temperature
and light. Thus, a total of six measurement cycles were completed each day.
The soil water content (SWC, 0–10 cm soil depth) in watering plots was
measured using the Field Scout TDR 300 Soil Moisture Meter (Spectrum
Technologies, Inc., Aurora, IL, USA).
Belowground biomass and related soil characteristics
Soil samples of 0 to 10 cm in depth were taken from each collar after the
Rs measurements and then passed through a 1 mm sieve to separate the
roots. The roots were washed and oven-dried at 70 ∘C for 48 h to a
constant weight and then weighed. Subsamples of each soil sample were
separated to determine the gravimetrical water content and soil chemical
properties. Briefly, to determine the soil organic C (SOC) content, we mixed
a 0.5 g soil sample, 5 mL of concentrated sulfuric acid (18.4 mol L-1)
and 5.0 mL of aqueous potassium dichromate (K2Cr2O7; 0.8 mol L-1) in a 100 mL test tube, and then heated them in a paraffin oil pan at
190 ∘C, keeping them boiling for 5 min. After cooling, the three drops of phenanthroline indicator were added and then the sample was
titrated with ferrous ammonium sulfate (0.2 mol L-1) until the color
of the solution changed from brown to purple to dark green (Nelson and
Sommers, 1982; Chen et al., 2008; Edwards and Jfferies, 2013). The soil
ammonium–nitrogen (N; NH4+-N) concentration and the nitrate-N
(NO3--N) concentration were extracted with a potassium chloride
(KCl) solution and measured using a flow injection analyzer (SEAL Auto
Analyzer 3; SEAL Analytical, Inc., Mequon, WI, USA; Liu et al., 2014). Soil
samples (0–10 cm in depth) from each collar were oven-dried at
105 ∘C for at least 48 h and weighed to determine the SWC. The
soil microbial biomass C (MBC) and microbial biomass N (MBN) were measured
using the chloroform–fumigation extraction method and calculated by
subtracting extractable C and N contents in the unfumigated samples from
those in the fumigated samples (Liu et al., 2014; Rinnan et al., 2009). All
extracts were stored at 4 ∘C until further testing commenced.
Statistical analysis
All statistical analyses were performed using IBM SPSS Statistics 21.0 (IBM,
Armonk, NY, USA). All the data were normal as tested by the Shapiro–Wilk
method. A one-way analysis of variation (ANOVA) with LSD multiple range
tests was conducted to test the statistical significance of the differences
in the mean values of the soil temperature, soil moisture, Rs,
belowground biomass, SOC, NH4+-N and NO3--N
concentrations, and MBC and MBN concentrations at depths of 0 to 10 cm among
the different treatments. A linear regression analysis was also used to test
the relationship between the SWC and Rs. The relationship between
Rs and the soil temperature in each treatment was tested with an
exponential function.
We used Q10 to express the temperature sensitivity of Rs and
calculated it according to the following equations:
1Rs=aebTs,2Q10=e10b.
Here, Ts is the soil temperature, a refers to the intercept of Rs
when the soil temperature is 0∘, and b is the temperature
coefficient reflecting the temperature sensitivity of Rs and is used to
calculate Q10 (Lloyd and Taylor, 1994; Luo et al., 2001; Shen et al.,
2015).
The relationship between Rs and the SWC was further conducted to fit the
Gompertz function, a sigmoid function (Gompertz, 1825; Yin et al., 2003),
which could express that the linear increase is rapid followed by a leveling-off:
Rs=a⋅e-b⋅(exp(-k⋅SWC))
Here, a is an asymptote; the SWC halfway point of a/2 equals -ln(ln(2)/b)/c.
The turning point of the maximum rate of Rs increase equals ak/e when
the SWC equals ln(b)/k. Thus, from the sigmoid function curve, the
thresholds of the changes in Rs with increasing SWC can be obtained from
the Gompertz function (Gompertz, 1825; Yin et al., 2003).
A non-linear regression model was used to fit the relationship of Rs
with both soil temperature and soil moisture (Savage et al., 2009):
Rs=(Rref⋅Q10(Ts-10)/10)⋅β(SWC0PT-SWC)2,
where Ts is the soil temperature at a soil depth of 5 cm, Rref is
Rs at 10 ∘C and Q10 is a unitless expression in Rs for
each increase in 10 ∘C. SWC is water content in 0 to 20 cm soil
depth, SWC0PT is the optimal water content and β is a
parameter modifying the shape of the quadratic fit.
Following the key factors selected by the stepwise regression method, a path
analysis was used to examine the primary components directly and indirectly
affecting Rs by integrating both the stepwise linear regression module
and Pearson correlation analyses (Gefen et al., 2000). The statistical
significances were set at P<0.05 for all tests, unless otherwise
indicated.
ResultsWarming effects on belowground characteristics
The soil temperatures at a soil depth of 5 cm in the warmed plots were much
higher than those in the control plots (Fig. 1). During growing season, the
mean soil temperatures in the control, the moderately warmed and acutely warmed
plots were 21.9 ∘C (±0.13 SE), 24.5 ∘C (±0.15) and 25.0 ∘C (±0.18), respectively. The moderately
and acutely warmed plots were respectively increased by 2.6 ∘C
(P<0.001) and 3.1 ∘C (P<0.001) compared to those
in the control plots. The SWC in the moderately and acutely warmed plots
(0–20 cm soil profile, defined as ratios of water volume and soil volume)
were significantly reduced (P<0.001) compared to those in the
control plots (Fig. 1), indicating that warming led to marked declines in
the SWC, consequently enhancing drought stress. On 18, 19, 20 and 21 August,
which were the dates that we measured Rs, the daily soil temperatures in
the moderately and acutely warmed plots were around 3 and
4 ∘C higher than those in the control plots, respectively. All
belowground variables (belowground biomass, soil N and microbial
characteristics) were not significantly altered by warming regimes at the
site of this experiment (Table S1 in the Supplement; P>0.05).
However, the organic soil carbon content tended to decrease with long-term
climatic warming.
Effects of warming on the soil temperature and soil moisture
during the growth peak in 2014 (mean ± SE). Mean daily values were
presented (n=120). The mean values with the same lowercase letters on
the SE bars are not different at P<0.05 according to LSD multiple
range tests (P values and F ratios are shown inside).
Watering pulse effects on Rs
Soil respiration significantly increased with SWC both linearly (R2=0.83; P<0.01) and quadratically (R2=0.88; P<0.01,
Fig. 2a). Moreover, the Gompertz function was well fitted to their
relationship (R2=0.87; RMSE = 4.88; Fig. 2b). From the Gompertz
functional curve, the Rs asymptote value, as an estimated maximum, was
3.76 µmol m-2 s-1 when the
optimal SWC was 22.85 %. In the watering plots, an exponential function
was well fitted to the relationship between soil respiration and the soil
temperatures (R2=0.31; P<0.01), with a temperature
sensitivity (Q10) of 1.70. However, the exponential function was not
well fitted in the control plots (Fig. 3a).
Relationship between Rs and soil water content based on a
linear (blue line) and a quadratic (black line) functional model (a), and
Gompertz functional model (b). Close and open circles denote the data in
2014 and 2017, respectively. The close red circles indicate data used for
the linear Rs response to SWC at low levels. The one open triangle may
be an outlier point due to some errors, but it does not notably affect the
functional fitting when removing it (ref. Fig. S2). Based on Gompertz
functional curve, the Rs asymptote value, as an estimated maximum, is
3.76 µmol m-2 s-1 when the
optimal SWC is 22.85 % (the red line denotes the initial Rs response
to SWC; the blue line denotes Rs= constant value of the maximum
estimated by the asymptote value; and the intersection of the two lines
represents a point (the blue arrow) at which Rs leveled off). Note that we
measured the Rs from 06:00 to 18:00 on these cloudless days with
calm/gentle wind in order to keep other environmental factors such as
soil temperature and radiation relatively stable and constant. The data
were collected in the plots of watering treatments (n=92).
The relationships between soil respiration and soil temperature
under both watering (n= 23–25, mean ± SE; a) and warming treatments (n= 28–33, mean ± SE; b).
Effects of warming regimes on Rs
Warming regimes resulted in marked declines in Rs. Whereas no difference
in Rs was observed in July, during August average Rs values were
1.57, 1.06 and 0.93 µmol m-2 s-1 in the control, moderately warmed and acutely warmed plots,
respectively, indicating that warming regimes resulted in marked declines
(Fig. 4). Changes in Rs differed significantly between the control and
both warmed plots (P<0.01), while the Rs in the two warmed
plots did not significantly differ from each other (P=0.45). The
relationships between the Rs and soil temperature of each treatment were
well fitted by the exponential equations (P<0.05; Fig. 3b). The
Q10 values were 1.88, 2.12 and 1.58 in the temperature-controlled,
moderate warming and acute warming treatments, respectively (Fig. 3b). It indicated
that Rs increased exponentially with temperature in watered plots but
was lower and insensitive to temperature in the control plots (Fig. 3a), and
that long-term warming rather than temporary high temperature reduced
Rs, despite having a positive relationship with soil temperature (Figs. 3b, 4).
Effects of warming regimes on average soil respiration in 2014
(mean ± SE); the mean values with the same lowercase letters on the SE
bars are not different at P<0.05 according to LSD multiple range
tests (P values and F ratios are shown inside).
Interactive effects on Rs from soil temperature and soil water content
Across all watering and warming treatments, generally, high temperature
led to an increase in Rs under ample soil moisture, whereas Rs was
limited under a soil water deficit. As shown in Fig. 5, a non-linear
regression model (Eq. 4) was well fitted to the relationship of
Rs with both soil temperature and soil moisture in the control plots
(R2=0.40, RMSE = 0.60). Based on the function Rs=(0.733⋅1.796(Ts-10)/10)⋅β(0.229-SWC)2, the key parameters
were obtained: Rref, Rs at 10 ∘C, was 0.73 µmol m-2 s-1; Q10, a
unitless expression in Rs for each increase in 10 ∘C, was
1.80; and β, a parameter modifying the shape of the quadratic fit,
was 0.001 (Fig. 5).
An interactive relationship of soil respiration with both soil
temperature (Ts) and soil water content (SWC) based on a nonlinear mixed
model (Rs=(0.733⋅1.796(Ts-10)/10)⋅β(0.229-SWC)2).
The data were used in control plots in the warming experiment. The optimal
SWC of 0.229 was estimated by the Gompertz functional curve (see Fig. 2b).
Effects of multiple factors on Rs: a path analysis
Based on a stepwise regression analysis of the relationships between the
Rs and multiple factors, four key factors were screened: soil
temperature, soil moisture, belowground biomass and SOC. Their effects on
Rs were further determined by path analysis. The results showed that
soil moisture and soil temperature were two major direct factors controlling
Rs (the two direct path coefficients were 0.72 and 0.55, respectively).
SOC had the highest indirect effect on Rs (the indirect path coefficient
was 0.57). Soil moisture highly correlated with Rs (R=0.78, P<0.01; Table S2, Fig. 6), indicating again that the
soil water status may impose the greatest effect on the carbon release from
soil in the desert grassland.
A diagram of the effects of key environmental factors on soil
respiration and their relationships. Blue double-headed arrows represent the
relationships between the key environmental factors; data on the arrows are
correlation coefficients. Black arrows represent the relationships between
soil respiration and the key environmental factors; data on the arrows are
correlation coefficients (bold) and direct path coefficients (italic),
respectively. *P<0.05; **P<0.01, n=12. For other
details, see Table S2.
DiscussionWarming effects on Rs
Previous studies have shown positive Rs responses to increased soil
temperatures below a critical high temperature (e.g., Carey et al., 2016;
Drewitt et al., 2002; Gaumont-Guay et al., 2006; Meyer et al., 2018).
However, in the current study site, the climatic warming finally reduced the
average Rs by 32.5 % and 40.8 % under long-term versus short-term climatic warming conditions in the desert dryland, respectively, which
chiefly confirmed our first hypothesis. In a semiarid grassland on the Loess
Plateau of China, the total Rs was also constrained substantially by a
field manipulative experiment (Fang et al., 2018). This result may have been
caused by the following factors. First, high temperatures may cause thermal
stress on microbes and subsequently reduce microbial respiration (i.e.,
heterotrophic, Rh, Chang et al., 2012; Dacal et al., 2019). For
instance, in an alpine steppe on the Tibetan Plateau, microbial respiration
was significantly reduced when the temperature rose to 30 ∘C (Chang
et al., 2012). Second, in the desert grassland, where water is often
limited, the SWC becomes the primary factor affecting Rs (Table S2; Fig. 6), while warming can cause greater evapotranspiration,
consequently lessening soil moisture (Fig. 1), and finally reducing
Rs (Munson et al., 2009; Wan et al., 2007; Yan et al., 2013). The
decreases in average Rs with warming implicate that the positive
feedback loop could be weakened with increasing length or intensity of warming.
Total respiration (Rs; the sum of root (autotrophic, Ra) and
Rh respiration – the former accounting for ca. 22 % of the total
Rs in the ecosystem; T. Liu et al., 2016) may acclimatize to warming within
an appropriate range of temperature change at an ample soil moisture;
however, it decreases with increasing temperatures above an optimum level.
The mechanisms may be the following: within an appropriate range of temperature
change at an ample soil moisture, climatic warming can enhance both plant
root (Luo et al., 2001; T. Liu et al., 2016) and microbial activities (Tucker et al., 2013), leading to increases in both Ra and Rh, and consequently
the Rs (Luo et al., 2001; Tucker and Reed, 2016; Xu et al., 2019). However,
when warming continues or with increasing temperatures above an optimum
level, root growth can be constrained, directly reducing Ra (Carey
et al., 2016; T. Liu et al., 2016; Luo et al., 2001; Moncrieff et al., 1999; Wan et al., 2007);
limitation to microbial activities may also occur (Tucker et al., 2013; Yu
et al., 2018), decreasing the Rh (Bérard et al., 2011, 2015; Tucker et
al., 2013; Romero-Olivares et al., 2017). In
addition, decreases in soil enzyme pools and their activity under warming may
also contribute to a reduction in Rh (e.g., Alvarez et al., 2018).
Further, Rs decreases with warming under a water deficit (Moyano et al.,
2013; Wang et al., 2014; and see below). Together, the declines in both root
and microbial respirations finally reduce the Rs. Nevertheless, the
drastic declines in Rs under both long- and short-term climatic
warming regimes in the desert dryland ecosystem may be driven by multiple
factors, including the ecosystem type, time and soil features (T. Liu et al.,
2016; Wan et al., 2007; Meyer et al., 2018; Thakur et al., 2019). It implies
that the effects of multiple factors should be considered in assessing the
carbon balance between ecosystem and atmosphere.
Interactive effect of soil water status and temperature
As stated above, in an arid ecosystem, soil water deficit is a primary
factor inhibiting soil carbon release (Table S2; Fig. 6; T. Liu
et al., 2016; Munson et al., 2009; Yan et al., 2013). Thus, Rs linearly
increases with increasing soil moisture. However, it could be leveled off or
decreased when soil moisture exceeds an optimal level for the soil carbon
release (Huxman et al., 2004; Moyano et al., 2013; Wang et al., 2014). Thus,
the relationship between Rs and SWC may be well fitted to the Gompertz
functional curve model, a sigmoid function (Gompertz, 1825; Yin et al.,
2003), which can be confirmed by the present results in the native arid
desert ecosystem (Fig. 2b). The mechanisms mainly are that an increase in SWC
may rapidly increase microbial activities (Cable et al., 2008; Meisner et
al., 2015; Wu and Lee, 2011), and enhance root growth (Xu et al., 2014),
leading to a linear increase in Rs. However, when soil moisture reaches
an ample level, microbial activities may also reach a maximum, where the
limiting effects of substrate occur (Skopp et al., 1990), finally
maintaining a stable change in Rh. A similar response to watering appears
for root growth (Xu et al., 2014), and also similarly leading to a stable
change in Ra. Thus, Rs can be leveled off at an increased and stable
level. Moreover, the decrease in Rs at a saturated SWC level may be
ascribed to inhibitions of both root systems and microbial activities under
the anaerobic environment (Drew, 1997; Huxman et al., 2004; Kwon et al.,
2019; Sánchez-Rodríguez et al., 2019; Yan et al., 2018). The model
concerning the relationship Rs with a broad range of SWC is helpful to
assess and predict the dynamics in soil carbon release in natural arid
ecosystems.
As indicated by Tucker and Reed (2016), soil water deficit can shrink the
Rs itself and its response to temperature, suggesting that the changes in
Rs may be determined simultaneously by both soil temperature and water
status (Janssens et al., 2001; Yan et al., 2013; Sierra et al., 2015).
Moreover, in the present experiment, the interactive effects of both factors
were tested based on the relationship of Rs with both soil temperature
and soil moisture in a non-linear regression model (Savage et al., 2009).
The model utilized was well fitted but marginally so (R2=0.40, RMSE = 0.596; Fig. 5), indicating that both the soil temperature and SWC
coordinated the changes in Rs. However, this interaction may also be
affected simultaneously by other abiotic and biotic factors, such as soil
nutrition availability and soil microbe activity (e.g., Camenzind et al.,
2018; Han et al., 2006; Karhu et al., 2014; Thakur et al., 2019; Zhang et al., 2014).
Key factors and the influence path
As noted above, Rs is affected by several abiotic and biotic factors.
The current results showed that soil moisture and soil temperature were two
major direct factors, and SOC was only an indirect factor controlling
Rs (Table S2, Fig. 6). Importantly, soil moisture,
with both the highest direct path coefficients (0.7) and correlation
coefficient (0.8) for Rs, may become the most important factor affecting
Rs in this desert steppe. These findings agree with the previous
results: improved soil water status had a significantly positive effect on
Rs (e.g., Chen et al., 2008; T. Liu et al., 2016; Xu et al., 2016).
Furthermore, soil moisture conditions can mediate the relationship
between soil temperature and Rs, thus affecting Rs temperature
sensitivity; SWC becomes the main factor controlling Rs, especially in
arid ecosystems, such as desert steppes, where the available soil water is
limited (Conant et al., 2000; Curiel et al., 2003; Fa et al., 2018; Jassal
et al., 2008; Roby et al., 2019). Thus, under both the long- and
short-term climatic warming regimes, soil moisture could modulate the
response of Rs to warming. The changes in Rs might be driven by both
soil temperature and soil moisture as two key factors, and SOC as an
indirect factor, thus mostly confirming our second hypothesis. The findings
again implicate that multiple factors together coordinate Rs, and
provide new insight into how to control soil carbon release in arid
ecosystems. The models on the Rs changes should consider multiple-factor
effects of soil carbon dynamics when assessing and predicting carbon cycle,
and its climate feedback.
Warming effects on the variables belowground
Elevated temperature has been shown to increase or decrease root
productivity and biomass, depending on experimental sites and vegetation
types (Bai et al., 2010; Fan et al., 2009; Litton and Giardina, 2008; Wan et
al., 2004). The decreased availability of soil nutrients apparently limits
root growth, finally inducing root mortality and weakening responses to the
elevated temperature (Eissenstat et al., 2000; Johnson et al., 2006; Wan et
al., 2004; Zhang et al., 2014). In our experiment, no significantly
different changes occurred in either soil NH4+-N or
NO3--N concentrations among the three treatments (Table S1), and these might be linked to the non-significant response of
belowground biomass to increasing temperature. Microbial biomass and its
activities in soil depend on the root biomass, SWC and soil N conditions
(Liu et al., 2014; Rinnan et al., 2007; Zhang et al., 2008,
2014). Warming regimes had no significant effects on either MBC or MBN in
the current study (Table S1), which might be due to the lack
of any difference in the changes in basic soil nutrition status, such as the
N conditions, among the three warming treatments. This result is consistent
with those of Zhang et al. (2005) and Liu et al. (2015). Moreover, in the
present study, SOC concentrations were not significantly affected by
climatic warming (Table S1), which is inconsistent with the
findings of previous studies (Jobbágy and Jackson, 2000; Prietzel et
al., 2016). However, there might be a decreasing trend evident with
long-term warming. For instance, Crowther et al. (2016) reported a loss of
approximately 30±30 Pg of C in the upper soil horizons at
1 ∘C warming in global soil C stocks and projected a loss of 203±161 Pg of C under 1 ∘C of warming over 35 years. The C losses
from soil moving into the atmosphere may result in positive feedback
regarding global warming (Bradford et al., 2016; Dacal et al., 2019;
Jenkinson et al., 1991; T. Liu et al., 2016; Martins et al., 2016). However, SOC was shown to have exerted an indirect
effect via path analysis (Fig. 6). For this difference, therefore, more
evidence needs to be provided to address the issue (Xu et al., 2019).
In conclusion, we determined the responses of Rs to field experimental
long- versus short-term climatic warming and watering pulses in a desert
steppe ecosystem. We found the following: (i) both long- and short-term
warming significantly reduced Rs during the peak growth season; (ii) soil
moisture was the main factor controlling Rs in desert grassland; (iii) Rs was significantly and exponentially increased with soil temperature,
while soil moisture conditions can mediate the relationship between soil
temperature and Rs, thus affecting its temperature sensitivity; and (iv) belowground biomass, soil nutrition variables and soil microbial
characteristics showed no significant changes after either long-term or
short-term climatic warming. These findings may be useful in assessing and
predicting the dynamics of soil CO2 fluxes, particularly the feedback of
warming to climatic change, and finally optimizing C management work in arid
and semiarid regions under the changing climate. However, the patterns of
the changes in soil C fluxes and the underlying mechanism in response to
climatic change are markedly complicated at various spatial–temporal scales
during the growing season – from site and regional to global scales, and from
daily, seasonal and yearly to decade scales – and still need to be
investigated further (e.g., Ballantyne et al., 2017; Dacal et al., 2019;
Meyer et al., 2018; Romero-Olivares et al., 2017).
Data availability
The final derived data presented in this study are available at
10.5281/zenodo.3546062 (Yu et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-781-2020-supplement.
Author contributions
ZX and GZ conceived and designed this study. HY, ZX and YS conducted this
experiment and analyzed the data. All authors wrote and proofread this
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We greatly
thank Feng Zhang, Yuhui Wang, Bingrui Jia, Hui Wang, Minzheng Wang and He Song
for their loyal help during the present study. The authors also greatly
appreciate Martin De Kauwe and the four anonymous reviewers for their constructive comments.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 31661143028 and 41775108) and China Special Fund for Meteorological Scientific Research in the Public Interest (Major projects) (grant no. GYHY201506001-3).
Review statement
This paper was edited by Martin De Kauwe and reviewed by four anonymous referees.
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