Introduction
Soil respiration (Rs) represents carbon dioxide (CO2)
efflux from the soil surface, including autotrophic/root respiration, and
heterotrophic/microbial respiration associated with soil organic matter and
litter decomposition (Boone et al., 1998; Kuzyakov, 2006; Schindlbacher et
al., 2009). As one of the largest fluxes in the global carbon cycle,
Rs plays an important role in regulating ecosystem carbon
cycling, carbon–climate feedback, and climate change (Raich and Schlesinger,
1992; Davidson et al., 2002; Luo and Zhou, 2006; Bond-Lamberty and Thomson,
2010). The temperature sensitivity of Rs (Q10), the factor
by which Rs is multiplied when temperature increases by
10 ∘C, is a key parameter to evaluate the feedback intensity between
soil carbon efflux and climate warming (Reichstein et al., 2005; Davidson and
Janssens, 2006). Knowledge on patterns and control of Rs and
Q10 variation at a large scale is crucial for better understanding and
modeling of the soil carbon cycle in a warmer world (Peng et al., 2009; Wang et al.,
2010).
Temperature and precipitation are commonly believed to be the most important
climatic factors controlling Rs at the large scale, as suggested
by a number of studies (Raich and Schlesinger, 1992; Raich and Potter, 1995;
Chen et al., 2014; Hursh et al., 2017). Soil properties, such as soil organic
carbon (SOC), soil total nitrogen (STN), and soil pH, can also affect
Rs in that they can directly or indirectly affect substrate
quality and quantity, which strongly control soil microbial activity and
heterotrophic respiration (Ryan and Law, 2005; Q. Chen et al., 2010; Chen et
al., 2014; Song et al., 2014). Additionally, biotic factors including
decomposer microbes and roots (together with associated mycorrhizal fungi)
can directly influence soil respiration via heterotrophic and autotrophic
respiration, respectively (Ryan and Law, 2005; Bahn et al., 2010). Previous
studies have shown that Rs increased with total, aboveground, and
belowground net/gross primary production, aboveground biomass (AGB),
belowground biomass (BGB), and leaf area index (Raich and Schlesinger, 1992;
Hibbard et al., 2005; Bahn et al., 2008; S. Chen et al., 2014; Zhao et al.,
2017).
As the response of Rs to temperature is controlled by temperature
effects on autotrophic respiration from roots and heterotrophic respiration
from SOC decomposition, the temperature sensitivity of Rs should
be regulated by plant-related biotic variables and soil-related environmental
variables. Several studies have shown that climatic factors strongly
controlled the spatial variation of Q10, and Q10 generally
decreased with mean annual temperature (MAT) and mean annual precipitation
(MAP) (Raich and Schlesinger, 1992; Kirschbaum, 2000; Peng et al., 2009; Song
et al., 2014). In addition to climatic variables, the spatial variation of
Q10 could be affected by seasonality of plant activity. Previous studies
suggested that plant growth plays an important role in the seasonal variation
of Rs, and thereby the seasonal dynamic changes in plant activity
affect seasonal Q10 (Yuste et al., 2004; Wang et al., 2010).
Furthermore, Q10 is also affected by soil properties, such as soil
temperature, soil moisture, soil pH, SOC, and STN, which can directly
influence root and microbial activities, substrate availability, and nutrient
supply (Zhou et al., 2009; Song et al., 2014; Zhao et al., 2017).
Grasslands in China cover 29 %–41 % of its total land area (Shen et
al., 2016) and have significant effects on the regional climate and carbon cycle
(Ni, 2002). In China, grasslands are widely distributed throughout the
country, and the different climate gradients and landforms in China support a
number of grassland types, including tropical, warm, temperate, and alpine
grassland. (Chen et al., 2002; Shen et al., 2016). Specifically, the
temperate arid and semi-arid grasslands in Inner Mongolia and the alpine
meadow and steppe in Qinghai-Tibet Plateau comprise the main body of
temperate and alpine grasslands, respectively (Shen et al., 2016). In the
past two decades, a large number of case studies on Rs have been
widely conducted in grasslands across China. However, few have been included
in global Rs and Q10 syntheses (Raich and Schlesinger, 1992;
Wang et al., 2010; Bond-Lamberty and Thomson, 2010; Chen et al., 2014; Hursh
et al., 2017), largely because most studies were published in Chinese
journals. Given the diverse grassland types, especially alpine grasslands
distributed in China, Rs and Q10 may vary among grassland
types due to the differences in abiotic and biotic factors, and the patterns
of Rs and Q10 across Chinese grasslands may differ from
global terrestrial ecosystems and grasslands. However, how the spatial
variation of Rs and Q10 changes with abiotic and biotic
factors across Chinese grasslands and their differences among grassland types
still remain poorly understood.
In this study, we synthesized all the available data relating to
Rs and Q10 in grasslands across China. Our main objectives
were to (1) analyze the spatial patterns of Rs and Q10
across various grassland ecosystems in China; (2) compare the differences in
Rs and Q10 among grassland types; (3) identify how abiotic
and biotic factors drive Rs and Q10 among sites at the
regional scale, including geographic variables, climatic factors, soil
properties, and biotic factors; and (4) compare the Rs and
Q10 in Chinese grasslands with those from previous syntheses at the
global and regional scale.
Materials and methods
Data collection
Peer-reviewed journal articles and published theses (including those
available online) before 1 December 2017 were searched using the Web of
Science and China National Knowledge Infrastructure (CNKI, available online:
http://epub.cnki.net, last access: 1 December 2017) with the following
search term combinations: (soil respiration OR soil CO2 flux OR
soil CO2 efflux OR soil CO2 emission OR soil carbon flux
OR soil carbon efflux OR soil carbon emission) AND (grassland OR steppe OR
meadow OR grass). Additional searches with the same keywords were conducted
on ScienceDirect (Elsevier Ltd., Amsterdam, Nederland), Springer Link
(Springer International Publishing AG, Berlin, Germany), and the Wiley Online
Library (John Wiley & Sons Ltd., Hoboken, USA). Furthermore, previous
global and regional syntheses on the similar topic were also screened to
check Chinese grassland data, such as Peng et al. (2009), Wang and
Fang (2009), Bond-Lamberty and Thomson (2010), Wang et al. (2010), Q. Chen et
al. (2010), and Chen et al. (2014).
To ensure data consistency and accuracy, the following six criteria were
applied to select appropriate studies: (1) experimental studies were
conducted in the field; (2) experiments with the addition of nitrogen
(fertilizer) treatments, increased or decreased precipitation, warming,
elevated CO2, simulated acid rain, clipping, and grazing were
removed; (3) the study must contain soil respiration or Q10 with a clear
record of grassland type and experimental duration; (4) the investigation
time for measuring Rs was not less than 12 months so that the
annual Rs can be obtained, and modeled annual Rs
based on the relationships between Rs rate and temperature were
not included; (5) the investigation time for estimating Q10 value was
not less than 4 months; and (6) Q10 values were calculated by the
van't Hoff equation (Van's Hoff, 1898).
SR=α×exp(β×T),
where SR is the measured soil respiration rate, T is the measured soil
temperature at a given depth, and the coefficients α and β are
fitted parameters. The Q10 values were calculated as
Q10=exp(10β).
Several studies measured Rs and its temperature sensitivity in
different years, and then these Rs and Q10 values were
averaged across years. In this case, only the highest RQ2
(coefficient of determination for calculating Q10 using Eq. 1) was
extracted if more than one RQ2 were available in the same study. In
addition, the Q10 values were estimated by Rs measured at
different plant growth stages, and they were further categorized into three
types according to the Rs measurement period, including growing-season Q10, non-growing-season Q10, and annual Q10. If these
three types of Q10 were all available, only the annual Q10 was
selected in our database. Within these constraints, 54 measurements of annual
Rs rate and 171 estimates of Q10 value were obtained from
108 published experimental studies across Chinese grasslands (Table S1 in the
Supplement). Our database contained 14 variables associated with
Rs, including annual Rs, growing- and non-growing-season Rs and their proportions to annual Rs, the
proportion of autotrophic and heterotrophic respiration to annual
Rs, and Q10 values of Rs and their corresponding
RQ2. Here, the growing season was from May to October, and the
non-growing season was from November to April in the following year. The Q10
values were divided into five soil depths with different soil temperatures
(ST0, soil surface temperature; ST5, soil temperature at 5 cm; ST10, soil
temperature at 10 cm; ST15, soil temperature at 15 cm; and ST20, soil
temperature at 20 cm) for the same site. In one study, the Q10 was
derived by soil temperature at a depth of 6 cm, and then it was treated as
Q10-ST5 because of the little difference in soil temperature between
5 and 6 cm.
In most publications, the Rs, Q10, and its RQ2 of
the model were presented, and they were incorporated into our database
directly. The Rs, Q10, and RQ2 values were
recalculated according to the available information if these values were not
directly provided in some publications. The growing-season, non-growing-season, and annual Rs were obtained by interpolating the measured
Rs rate between respective sampling dates for each seasonal
measurement period of the year, and then computing the sum to obtain the
corresponding values (Frank and Dugas, 2001; Sims and Bradford, 2001) as
follows:
CSR=Σ(Δtk×Fm,k),
where CSR is cumulative soil respiration during the season, Δtk(=tk-tk-1) is the time interval between each
field measurement within the season, and Fm,k is the average
Rs rate over the interval tk-tk-1.
In addition, for each study site, we also recorded other supporting
information from the original publications, including grassland type,
geographic variables (longitude and latitude), climatic factors (MAT and
MAP), soil properties (soil temperature, soil moisture, soil pH, SOC, and
STN), and biotic factors (microbial biomass carbon (MBC), AGB, and BGB).
Missing climatic information was obtained using NASA Surface meteorology and
Solar Energy according to the location of the case study site, and
the other missing information was obtained from the related references
according to the study site and described experiment design. Several studies
provided the soil organic matter content, which was converted to SOC by
multiplying a conversion factor of 0.50 (Pribyl, 2010). In the case of
gravimetrical soil moisture being provided, it was converted to volumetric
soil moisture according to soil bulk density. Given that BGB was measured in
different soil depths, only BGB measured in 0–40 and 0–50 cm soil depths
was selected because roots were mainly distributed in 0–50 cm and there was
a minor difference between 0–40 and 0–50 cm. The distributions of selected
experimental sites are shown in Fig. 1.
The site location of soil respiration studies selected in this study
across Chinese grasslands. Publisher's remark: Please note that the above
figure contains disputed territories.
Data analysis
In this study, grasslands were divided into five groups, including temperate
typical steppe, temperate meadow steppe, temperate desert steppe, alpine
grassland, and warm, tropical grassland. If grassland type was not provided
directly, it was determined according to the dominant plant species reported
in selected publications and the classification of grassland ecosystems in
China (Chen et al., 2002). Detailed statistical parameters for the five
grassland types were presented in Table S2.
One-way analysis of variance (ANOVA) was used to examine whether annual
Rs and Q10 values differed among grassland types, measuring
periods, or measuring methods. In the case of homogeneity of variances, the least
significant differences (LSD) test was applied; otherwise, the Dunnett T3
test was applied. A paired-samples t-test was performed to compare the
differences between growing-season and non-growing-season Rs and
between autotrophic respiration and heterotrophic respiration. The reason for
using a paired-samples t-test was that these two corresponding variables were
interconnected as they were from the same study sites. In addition, we used
two statistical methods to explore the differences for Q10 among
measurement depths. The paired-samples t-test was used to compare Q10
among different measurement depths from same sites, whereas the ANOVA was
used to compare Q10 among different measurement depths from all sites.
Compared with ANOVA, the advantages of the paired-samples t-test was that it
avoided the effects of unequal spatial distribution of samples from different
depths on Q10 and only compared the effects of measurement depth. The
univariate regression analysis was used to identify the relationships between
annual Rs, Q10, and a given biotic or abiotic factor
mentioned above, except for MBC because of its limited sample size. The
multiple linear regression analyses were also performed to identify the
comprehensive effects of environmental variables (including MAT, MAP, soil
temperature, and soil moisture as they had relatively enough sample sizes) on
annual Rs, and Q10 derived by ST5 and ST10. Correlations
among these factors were calculated with the Pearson correlation
coefficients. All statistical analyses were performed using the software IBM
SPSS Statistics 20.0 (IBM Corporation, New York, USA).
Discussion
Spatial patterns and controlling factors of annual soil
respiration
Annual soil respiration among grassland types
Relationships of temperature sensitivity of soil respiration
(Q10) with abiotic and biotic factors. The black and gray points
represent Q10 derived by soil temperature at depths of 5 and 10 cm,
respectively, and the black and gray lines represent their corresponding
relationships with environmental factors. When the p value was greater than
0.05, the regression lines were not drawn.
In arid and semi-arid ecosystems, such as grassland and desert, MAP might
play a key role in controlling carbon cycling. Our results also suggested
that MAP significantly controls mean annual Rs among various
grassland types in China (p<0.01, Fig. S4). The significant difference in
mean annual Rs might be mainly attributed to the differences in
AGB, BGB, and microbial activity induced by precipitation across various
grassland types. Previous studies suggested that grasslands with higher MBC
had larger heterotrophic respiration (Colman and Schimel, 2013; Ding et al.,
2016). Meanwhile, a regional study demonstrated that microbial biomass
increased with MAP in grasslands (Y. Chen et al., 2016), which was also found
in this study (Table S3). Altogether, these suggested that the regions with
high MAP would have larger heterotrophic respiration. Additionally, a
previous study demonstrated that both AGB and BGB increased with MAP across
Chinese grasslands (not including warm, tropical grasslands) (Ma et al.,
2014). In this case, autotrophic respiration would be higher in the
grasslands with high plant biomass. Collectively, the grasslands with high
MAP would have a relatively higher Rs rate.
Control of environmental factors over annual Rs
Across Chinese grasslands, annual Rs was strongly related to MAT,
MAP, soil temperature, and soil moisture, which was consistent with previous
results obtained from global terrestrial ecosystems (Raich and Schlesinger,
1992; Raich and Potter, 1995; Chen et al., 2014; Hursh et al., 2017), global
grasslands (Wang and Fang, 2009), and Chinese forests (Song et al., 2014; Xu
et al., 2015). Compared with MAT and soil temperature, MAP and soil moisture
explained more spatial variation of annual Rs, suggesting that
these two factors are more important in predicting Rs in arid and
semi-arid ecosystems under climate change.
In addition, spatial variation of annual Rs was also controlled
by soil properties, such as SOC and soil pH. The relationships between annual
Rs and SOC as well as pH were also observed in global, regional, and local terrestrial ecosystems (S. Chen et al., 2010; Chen et al., 2014;
Song et al., 2014; Xu et al., 2016). Since Rs involves the
process of converting organic carbon into inorganic carbon, the soil
CO2 emission from microbial decomposition of soil organic carbon is
ultimately determined by the supply of C substrate (Wan et al., 2007).
Additionally, soil pH can directly regulate the activities of microbes and
C-acquiring enzymes (Turner, 2010). In neutral and alkaline soils, microbial
biomass tended to decrease with soil pH (Ding et al., 2016). Therefore, this
led to a negative correlation between Rs and soil pH in Chinese
grasslands because most of grasslands in China are distributed in neutral and
alkaline soils. Further, S. Chen et al. (2010) demonstrated that annual
Rs significantly increased with soil total nitrogen at a global
scale. Meanwhile, some case studies revealed the similar relationship between
growing-season Rs and soil total nitrogen among different
grassland types and vegetation communities (Q. Chen et al., 2010; Wang et
al., 2015; Xu et al., 2016) at local scales, while annual Rs did
not correlate well with STN in this study. Given that SOC and STN are closely
associated with one another (Table S3), the insignificant correlation of
Rs with STN might be due to the fact that soil total nitrogen
might not represent nitrogen availability for plants and microbes well.
Therefore, how STN influences Rs across Chinese grasslands at
a regional scale should be studied further.
Furthermore, as a source of autotrophic respiration, BGB can directly
influence Rs, which has been observed in ecosystems at global and
local scales (Q. Chen et al., 2010; Chen et al., 2014). However, no
significant correlation between BGB and Rs was observed in the
present study, which might be attributed to the limited sample size (n=6)
and the uncertainty in measuring BGB (due to inconsistent or insufficient
sampling depth). In grassland ecosystems, BGB generally increased with AGB
(Ma et al., 2014), and this relationship was also observed in this study (p<0.10, Fig. S5). Therefore, given the significant correlation between AGB
and Rs in Chinese grasslands (Fig. 2), BGB may also have the
potential to control annual Rs across Chinese grasslands,
although this should be investigated further based on accurate quantification
of BGB and Rs across a large number of sites.
Spatial patterns and controlling factors of Q10 values
RQ2 for Q10 in Chinese grasslands
In this study, only 37.3 % of RQ2 for Q10 was larger than
0.7, indicating that most of the seasonal variation of the Rs rate
cannot be well explained by soil temperature using the van't Hoff equation
(Eq. 2). Compared with the results obtained from Chinese forests (Xu et al.,
2015), the van't Hoff equation (Eq. 2) was not very suitable to describe the
relationships between Rs rate and soil temperature in most
Chinese grasslands. This might be associated with the difference in soil
moisture between these two ecosystems because besides temperature, soil
moisture may strongly influence the apparent Q10 (Subke and Bahn, 2010).
Previous studies have suggested that in humid and semi-humid regions the
effect of soil moisture on Rs is weak, whereas in arid and
semi-arid regions, Rs is significantly influenced by soil
moisture (Jia et al., 2006; Li et al., 2011; B. Wang et al., 2014; M. Wang et
al., 2014). Moreover, some studies showed that soil moisture and temperature
had an interactive effect on the seasonal variation of the Rs rate
(Davidson et al., 1998; Jia et al., 2006; M. Wang et al., 2014; Liu et al.,
2016), indicating that the two-variable equations could better explain the
variation in Rs than the single variable of temperature. Our
results also showed that, in general, RQ2 for Q10 closely
increased with MAP and soil moisture (p<0.05, Fig. S6), indicating that
the RQ2 for Q10 tended to be larger in the regions with abundant
precipitation. Collectively, for ecosystems (e.g., grassland and desert) in
arid and semi-arid regions, Rs could be better estimated by the
combined factors of soil temperature and moisture. By comparison, 46.6 %
of RQ2 for Q10-ST5 was distributed in 0.7–1.0, which was
higher than those values derived by soil temperature at other depths, suggesting
that the seasonal variation of Rs can be better explained by soil
temperature at a depth of 5 cm across Chinese grasslands.
Q10 among soil depths and grassland types
In Chinese grasslands, the estimated Q10 generally increased with soil
temperature measurement depth, which was consistent with previous synthesis
study about Chinese ecosystems (Peng et al., 2009). The differences for
Q10 among measurement depths might be due to the seasonal amplitudes of
temperature at different soil depths (Pavelka et al., 2007; Graf et al.,
2008).
In terms of grassland types, the highest Q10-ST5 value was in the
alpine grassland and the lowest in the temperate desert steppe and typical
steppe (Fig. 4). This difference could be associated with soil properties and
climatic conditions. For example, it is well known that the alpine grasslands
are usually distributed in high-altitude regions (above 3000 m), where the
climate is relatively colder and SOC is relatively higher than the other
grassland types (Table S2). However, the temperate desert steppes and typical
steppes are mainly distributed in north China, with relatively high MAT and
low MAP that may lead to low Q10. Moreover, as shown in Fig. 4, the
highest Q10-ST10 value occurred in warm, tropical grassland, which might
be associated with the abundant substrate supply in this grassland type
because high substrate availability can enhance apparent Q10 of soil
respiration (Davidson et al., 2006; Zhu and Cheng, 2011).
Control of environmental factors over Q10
Generally, the Q10 derived by either ST5 or ST10 did not correlate well
with climatic factors, which was inconsistent with previous results at global
and regional scales (Chen and Tian, 2005; Peng et al., 2009; Wang et al.,
2010; Song et al., 2014; Xu et al., 2015). However, we found that Q10
derived by soil temperature at depths of 5 and 10 cm decreased closely
with increasing soil temperature, partly supporting the idea that Q10
tends to be higher in colder regions. Additionally, the positive
relationships of Q10-ST5 with SOC, AGB, and BGB indicated that
soil properties and plant biomass can also profoundly influence the spatial
variation of Q10. Previous studies suggested that higher plant biomass and
SOC can lead to more substrate supply for soil respiration and then result in
higher Q10 values because apparent Q10 increased with increasing
substrate availability (Gershenson et al., 2009; Zhao et al., 2017).
The extremely low R2 value for the relationship of Q10 with
climatic variables suggested that the single factor of temperature,
precipitation, or soil moisture poorly controls the spatial variation of
Q10 in Chinese grasslands. Therefore, the variation of Q10 in
Chinese grasslands should be controlled by multiple factors due to the
complex and diverse environments among grasslands at a large scale. Multiple
linear regression analyses also showed that combined MAT and MAP, and
combined soil temperature and moisture could better explain the variation of
Q10 derived by ST5 (Table S4), indicating their integrative effects on
the spatial variation of Q10-ST5. Additionally, both univariate
and multiple regression analyses demonstrated that there were no significant
relationships between Q10-ST10 and abiotic and biotic factors
(not shown), indicating that the Q10-ST10 might not have a clear
spatial pattern or its variation might be controlled by other factors.
In addition to the environment variables discussed above, seasonality of
plant activity could also affect the spatial variation of Q10 at a large
scale. Plant activity can directly affect Rs via control of root
respiration, and can indirectly affect SOC decomposition by microbes via
regulation of the rhizosphere priming effect (see Wang et al., 2010). In this study,
the dataset covered various climatic regions, and accordingly, seasonal
amplitudes of plant activity among grassland types were also different. A
previous global synthesis using NDVI (normalized difference vegetation index)
as an indicator of plant activity demonstrated that seasonal amplitude of
plant activity dominated the variation of seasonal Q10 among different
sites (Wang et al., 2010). Therefore, the seasonal amplitude of plant
activity might be an important factor explaining the spatial variation of
Q10 across Chinese grasslands, and should be studied further.
Comparisons of Rs and Q10 between Chinese grasslands and the global
ecosystems
Comparisons of annual Rs
The annual Rs varied largely within and among the grassland types
across China (Table 1), with a mean value of
582.0 g C m-2 yr-1, which was much lower than those in global
terrestrial ecosystems and in Chinese forests (Table 2). For these results,
the main biomes in their dataset were forests, which had relatively high
precipitation and net primary productivity (Hursh et al., 2017), leading to
relatively higher Rs than grasslands (Table S2). Compared with
global grasslands, our result was much lower or higher than the results
obtained from S. Chen et al. (2010), Chen et al. (2014) and Wang and
Fang (2009), but approximately consistent with Hursh et al. (2017). These
differences might be associated with data sources and distributions of case
study sites. In general, the mean annual Rs rate across Chinese
grasslands was within the lowest and highest Rs across global
grasslands.
Across Chinese grasslands, the proportions of Rs during the growing
season ranged from 76.2 % to 86.8 %, which were 2.2–5.6 times higher
than those during the non-growing season. Microbial activity and plant growth is
constrained by temperature and precipitation during the non-growing season,
leading to lower decomposition of soil organic carbon and root respiration.
In addition, as a whole, heterotrophic respiration contributed 72.8 % of
the annual Rs, 2.7 times that of autotrophic respiration, which was
close to that of global terrestrial ecosystems and grasslands (Wang and Fang,
2009; Chen et al., 2014) and Chinese forests (Song et al., 2014). Previous
studies suggested that the proportions of heterotrophic respiration to total
Rs varied with ecosystem type and depended on the magnitude of
total Rs (Subke et al., 2006). However, the limited samples (n=7) limited our comparisons among these grassland types. Generally,
our findings and previous studies suggested that both Rs during
the growing season and heterotrophic respiration were an important part of the annual Rs
in Chinese grasslands, respectively, and should be given enough attention.
Comparisons of annual soil respiration and Q10 between Chinese
grasslands and other syntheses. The numbers in parentheses represent the
number of samples.
Scope
Annual Rs(g C m-2 yr-1)
Q10-ST5
Q10-ST10
Reference source
Global terrestrial ecosystems
910.0 (657)
S. Chen et al. (2010)
870.0 (1195)
Chen et al. (2014)
791.2 (1741)
Hursh et al. (2017)
2.40 (77)
3.10 (46)
Wang et al. (2010)
Global grasslands
448.9 (46)
2.13 (41)
Wang and Fang (2009)
745.0 (179)
S. Chen et al. (2010)
840.0 (113)
Chen et al. (2014)
599.1 (163)
Hursh et al. (2017)
Chinese terrestrial ecosystems
2.03 (64)
2.61 (33)
Peng et al. (2009)
Chinese forests
919.7 (139)
2.46 (107)
Song et al. (2014)
2.51 (145)
Xu et al. (2015)
Chinese grasslands
582.0 (54)
2.80 (73)
2.56 (59)
This study
Comparisons of Q10
The overall mean Q10 of 2.60 derived by soil temperature at all
measurement depths was similar to 2.40 and 2.54 in global terrestrial
ecosystems (Raich and Schlesinger, 1992; Lenton and Huntingford, 2003). The
Q10 derived by ST5 varied from 1.39 to 8.13, with a mean of 2.80,
which was higher than that of global and Chinese terrestrial ecosystems,
Chinese forests, and particularly global grasslands (Table 2). The difference
may be partly due to the distribution of grasslands in China and the
grassland types. Chinese grasslands are mainly distributed in the high-latitude (temperate grassland) and high-altitude (Qinghai–Tibet Plateau
alpine grassland) regions, and Q10 takes relatively higher values in
cold regions than in warm regions (Chen and Tian, 2005; Wang et al., 2010).
In addition, in this study, averaged Q10-ST5 was highest in
alpine grassland with a mean of 3.30, implying that grasslands in alpine
regions may release more carbon dioxide under climate warming. However, there
were no alpine grasslands in the global database. Collectively, this may lead
to higher Q10 values in Chinese grasslands. In terms of Q10 derived
by ST10, the mean value for Chinese grasslands was close to Chinese
terrestrial ecosystems, but much lower than the global ecosystems (Table 2).
Uncertainties
In order to ensure data consistency and minimize error, only field
experiments in accordance with the six aforementioned criteria were selected.
However, the interannual variation in Rs and Q10 might be
very large for grassland at one site, which was associated with the
interannual variation in annual precipitation and mean temperature (Peng et
al., 2014; Wang et al., 2016). Therefore, the interannual variation of
Rs would impact the accuracy of the results. Additionally, three
methods, including a static closed chamber, a dynamic closed chamber, and alkali
absorption, were widely applied to measure Rs in the selected
experiments, and previous studies have suggested that measurement methods
affected the results of Rs rate and Q10 value (Bekku et al.,
1997; Yim et al., 2002; Peng et al., 2009). However, in this study, there
were generally no significant differences for Rs,
Q10-ST5, and Q10-ST10 among the three measurement
methods (Fig. S7). Given that only one sample of annual Rs was
measured by alkali absorption, the effects of measurement methods on
Rs could be neglected. Therefore, including data measured by the
alkali absorption method in our synthesis does not meaningfully change the
results of Rs and Q10.
Furthermore, Q10 values measured during three periods, including the growing
season, the non-growing season, and the whole year, were selected as long as the
investigation time was longer than 4 months. The seasonal dynamics of
plant growth and microbial activity may influence autotrophic and
heterotrophic respiration, and thus the Q10 of Rs. Our results
showed that the measurement period did not significantly affect
Q10-ST10, but significantly affected Q10-ST5
(Fig. S7). In terms of Q10-ST5, the significant difference
between annual Q10 and non-growing-season Q10 across all sites was
mainly caused by alpine grasslands, in which annual Q10 was much higher
than non-growing-season Q10 (Fig. S7). The seasonal amplitude of
plant activity at an annual scale is likely much greater than that at a
non-growing-season scale in alpine regions. Therefore, the different investigation time
and measurement period for estimating Q10 would inevitably affect the
accuracy of results.
In this study, the selected experiments were mainly conducted in temperate
and alpine grasslands, so the limited data obtained from desert and tropical and
subtropical grasslands might lead to some uncertainties in these ecosystems.
Moreover, grassland management practices, such as land use/cover change,
intensity and pattern of livestock grazing, and fencing, can have a significant
effect on soil carbon emission (Chen et al., 2013; Y. Zhang et al., 2015;
J. Chen et al., 2015, 2016). In the past three decades, several ecological
projects relating to grassland have been implemented in China, and have
observably increased the grassland area and altered the land cover (H. Zhang
et al., 2015). To some extent, these changes can also impact our findings.
Conclusions
Chinese grasslands cover a vast area, have high spatial heterogeneity, and
include various grassland types. By synthesizing all the available data
relating to Rs and Q10, we analyzed their spatial patterns
and driving factors in grasslands across China. Our results showed that
annual Rs and Q10 varied greatly within and among grassland
types. Across Chinese grasslands, the mean annual Rs and Q10
were 582.0 g C m-2 yr-1 and 2.60, respectively. MAT, MAP, soil
temperature, soil moisture, SOC, and AGB all significantly positively affected
annual Rs, whereas soil pH negatively affected annual
Rs. Among these environmental factors, MAP played an important
role in controlling Rs variation across Chinese grasslands.
Moreover, the combined factors of MAP and MAT accounted for 22.1 % of the
variation of Q10-ST5 across Chinese grasslands. The
Q10-ST5 in Chinese grasslands was much higher than that in global
ecosystems, mainly attributed to the higher Q10 value in alpine
grasslands. These findings together advance our understanding of the spatial
variation and environmental control of Rs and Q10 across
Chinese grasslands, and also improve our ability to predict soil carbon
efflux under climate change at a regional scale. However, the few experiments
measuring soil and microbial variables, Rs and Q10, at an annual
scale, especially measuring autotrophic and heterotrophic respiration
separately, limit our in-depth knowledge on the key drivers of Rs
and Q10 in grasslands across China. Therefore, more field measurements
are needed to verify the relationships found here and to reveal how
environmental variables control Rs and its temperature
sensitivity in relatively arid grassland ecosystems.