Patterns of soil respiration and its temperature sensitivity in grassland ecosystems across China

Soil respiration (R_s), a key process in the terrestrial carbon cycle, is very sensitive to climate change. In this study, we synthesized 54 measurements of annual R_s and 171 estimates of Q₁₀ value (the temperature sensitivity of soil respiration) in grasslands across China. We quantitatively analyzed their spatial patterns and controlling factors in five grassland types, including temperate typical steppe, temperate meadow steppe, temperate desert steppe, alpine grassland, and warm, tropical grassland. Results showed that the mean (±SE) annual R_s was 582.0±57.9 g C m⁻² yr⁻¹ across Chinese grasslands. Annual R_s significantly differed among grassland types, and was positively correlated with mean annual temperature, mean annual precipitation, soil temperature, soil moisture, soil organic carbon content, and aboveground biomass, but negatively correlated with soil pH (p<0.05). Among these factors, mean annual precipitation was the primary factor controlling the variation of annual R_s among grassland types. Based on the overall data across Chinese grasslands, the Q₁₀ values ranged from 1.03 to 8.13, with a mean (±SE) of 2.60±0.08. Moreover, the Q₁₀ values varied largely within and among grassland types and soil temperature measurement depths. Among grassland types, the highest Q₁₀ derived by soil temperature at a depth of 5 cm occurred in alpine grasslands. In addition, the seasonal variation of soil respiration in Chinese grasslands generally cannot be explained well by soil temperature using the van't Hoff equation. Overall, our findings suggest that the combined factors of soil temperature and moisture would better predict soil respiration in arid and semi-arid regions, highlight the importance of precipitation in controlling soil respiration in grasslands, and imply that alpine grasslands in China might release more carbon dioxide to the atmosphere under climate warming.

1 Introduction

Soil respiration (R_s) 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, R_s 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 R_s (Q₁₀), the factor by which R_s 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 R_s 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 R_s 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 R_s 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 R_s 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 R_s to temperature is controlled by temperature effects on autotrophic respiration from roots and heterotrophic respiration from SOC decomposition, the temperature sensitivity of R_s 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 R_s, 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 R_s have been widely conducted in grasslands across China. However, few have been included in global R_s 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, R_s and Q₁₀ may vary among grassland types due to the differences in abiotic and biotic factors, and the patterns of R_s and Q₁₀ across Chinese grasslands may differ from global terrestrial ecosystems and grasslands. However, how the spatial variation of R_s 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 R_s and Q₁₀ in grasslands across China. Our main objectives were to (1) analyze the spatial patterns of R_s and Q₁₀ across various grassland ecosystems in China; (2) compare the differences in R_s and Q₁₀ among grassland types; (3) identify how abiotic and biotic factors drive R_s and Q₁₀ among sites at the regional scale, including geographic variables, climatic factors, soil properties, and biotic factors; and (4) compare the R_s and Q₁₀ in Chinese grasslands with those from previous syntheses at the global and regional scale.

2 Materials and methods

2.1 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 R_s was not less than 12 months so that the annual R_s can be obtained, and modeled annual R_s based on the relationships between R_s 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).

$$\begin{array}{}\text{(1)}& \mathrm{SR}=\mathit{\alpha }\times \exp (\mathit{\beta }\times T),\end{array}$$

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

$$\begin{array}{}\text{(2)}& Q_{\mathrm{10}}=\exp \left(\mathrm{10}\mathit{\beta }\right).\end{array}$$

Several studies measured R_s and its temperature sensitivity in different years, and then these R_s and Q₁₀ values were averaged across years. In this case, only the highest $R_Q^{\mathrm{2}}$ (coefficient of determination for calculating Q₁₀ using Eq. 1) was extracted if more than one $R_Q^{\mathrm{2}}$ were available in the same study. In addition, the Q₁₀ values were estimated by R_s measured at different plant growth stages, and they were further categorized into three types according to the R_s 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 R_s 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 R_s, including annual R_s, growing- and non-growing-season R_s and their proportions to annual R_s, the proportion of autotrophic and heterotrophic respiration to annual R_s, and Q₁₀ values of R_s and their corresponding $R_Q^{\mathrm{2}}$. 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 Q_10−ST5 because of the little difference in soil temperature between 5 and 6 cm.

In most publications, the R_s, Q₁₀, and its $R_Q^{\mathrm{2}}$ of the model were presented, and they were incorporated into our database directly. The R_s, Q₁₀, and $R_Q^{\mathrm{2}}$ 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 R_s were obtained by interpolating the measured R_s 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:

$$\begin{array}{}\text{(3)}& \mathrm{CSR}=\mathrm{\Sigma }(\mathrm{\Delta }{t}_k\times F_{m,k}),\end{array}$$

where CSR is cumulative soil respiration during the season, $\mathrm{\Delta }{t}_k(=t_k-t_{k-\mathrm{1}})$ is the time interval between each field measurement within the season, and F_m,k is the average R_s rate over the interval $t_k-t_{k-\mathrm{1}}$.

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.

Figure 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.

2.2 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 R_s 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 R_s 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 R_s, 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 R_s, 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).

3 Results

3.1 Soil respiration and its controlling factors

3.1.1 Patterns of annual soil respiration across Chinese grasslands

The annual R_s ranged from 122.9 to 2407.1 g C m⁻² yr⁻¹, with a total mean (±SE) of 582.0±57.9 g C m⁻² yr⁻¹. There were significant differences in annual R_s between grassland types (p<0.001), with the highest annual R_s in the warm, tropical grassland and the lowest annual R_s in the temperate desert steppe (Table 1). The proportions of growing-season or non-growing-season R_s varied slightly among different grassland types (p>0.05), but the proportion of R_s in the growing season was significantly higher than that in the non-growing season (p<0.001). Overall, growing-season and non-growing-season R_s consisted of 78.7 % and 21.3 % of the annual R_s, respectively, across all grasslands in China (Table 1). In addition, growing-season R_s was significantly positively correlated with the annual R_s based on a linear regression model (r²=0.923, p<0.001, Fig. S1). At the annual scale, the mean contribution of heterotrophic respiration to R_s was 72.8 % across Chinese grasslands, which was significantly larger than that of autotrophic respiration with a mean of 27.2 % (p<0.01, Fig. S2).

Table 1 The annual soil respiration (R_s) and the proportions of growing-season and non-growing-season R_s to annual R_s in different grassland ecosystems across China. There was no sample for annual R_s in temperate desert steppe, so the data were not presented in this table. The different lowercase letters in each column indicate the significant difference at p=0.05, and different uppercase letters indicate the significant difference between the growing and non-growing season at p=0.001; n represents the number of samples.

3.1.2 Spatial control of abiotic and biotic factors over soil respiration

In the univariate linear regressions, annual R_s significantly increased with MAT, MAP, soil temperature, soil moisture, SOC, and AGB across all grasslands in China, but decreased with soil pH (p<0.05, Fig. 2). In contrast, annual R_s did not correlate well with STN and BGB (p>0.05). The single factor of MAT, MAP, soil temperature, soil moisture, SOC, soil pH, and AGB accounted for 22.4 %, 31.3 %, 20.9 %, 32.0 %, 29.6 %, 20.6 %, and 36.1 % of the spatial variation of annual R_s, respectively (Fig. 2).

Figure 2 Relationships of annual soil respiration (R_s) with abiotic and biotic factors. The dashed lines represent the 95 % confidence interval. When the p value was greater than 0.05, the regression lines were not drawn.

3.2 Temperature sensitivity of soil respiration and its controlling factors

3.2.1 Distributions of Q₁₀ values and their coefficients of determination

Most of the Q₁₀ values (83.0 %) were distributed between 1.5 and 3.8. However, the distributions of Q₁₀ values derived by the five soil depths were different (Fig. 3a–e). The largest relative frequency for Q_10−ST5 and Q_10−ST10 values was within the range of 1.5 to 3.0 (68.5 %) and 1.5 to 3.5 (83.1 %), respectively, while that of Q_10−ST0 was mainly within 1.0–2.0 (88.2 %, Fig. 3). In addition, the distributions of Q_10−ST15 and Q_10−ST20 were relatively uniform (Fig. 3d and e).

Similarly, the distributions of $R_Q^{\mathrm{2}}$ for Q₁₀ derived by the five soil depths also differed from each other (Fig. 3f and g). The $R_Q^{\mathrm{2}}$ values for Q_10−ST5 and Q_10−ST10 were mainly distributed in 0.6–0.9 and 0.5–0.7, respectively, while those for Q_10−ST15 and Q_10−ST20 were mainly distributed in 0.3–0.6. The $R_Q^{\mathrm{2}}$ value for Q_10−ST0 was distributed uniformly (Fig. 3f). Overall, only 35.6 % of $R_Q^{\mathrm{2}}$ values for Q₁₀ were within the range of 0.7–1.0.

Figure 3 Histogram plots for Q₁₀ values (a–e) and the coefficient of determination for Q₁₀ ($R_Q^{\mathrm{2}}$, f–j) across Chinese grasslands. (a, f) soil surface temperature; (b, g) soil temperature at a depth of 5 cm; (c, h) soil temperature at a depth of 10 cm; (d, i) soil temperature at a depth of 15 cm; (e, j) soil temperature at a depth of 20 cm. n represents the number of samples.

3.2.2 Patterns of Q₁₀ values across Chinese grasslands

Across all grasslands, the overall Q₁₀ values ranged from 1.03 to 8.13, with a mean (±SE) of 2.60±0.08. Specifically, the mean (±SE) of Q₁₀ values derived by ST0, ST5, ST10, ST15, and ST20 was 1.73±0.08, 2.80±0.14, 2.56±0.12, 2.64±0.33, and 2.81±0.31, respectively (Fig. 3a–e). The paired-samples t-test demonstrated that Q₁₀ significantly differed between two adjacent depths in the top 15 cm soil (p<0.05), whereas no difference occurred below 15 cm depth (p>0.05; Fig. 4). Generally, the overall Q₁₀ and paired Q₁₀ increased with soil temperature measurement depth (Figs. 4 and S3). In terms of grassland types, there were significant differences for Q₁₀ derived by ST5 and ST10 among grassland types, respectively (p<0.05, Fig. 4b and c). For Q₁₀ derived by ST5, it was highest in alpine grassland, while for Q₁₀ derived by ST10, the highest value was in warm, tropical grassland. In addition, there were not enough Q₁₀ values derived by ST0, ST15, and ST20 to meet the demand of statistical analysis, so their differences among grassland types were not examined.

Figure 4 Comparisons of Q₁₀ values among soil temperature measurement depths (a) and among grassland types (b, c). (a) Q₁₀ values derived by soil temperature at depths of 0, 5, 10, 15, and 20 cm, respectively. (b) Q₁₀ values derived by soil temperature at a depth of 5 cm. (c) Q₁₀ values derived by soil temperature at a depth of 10 cm. TTS, TMS, TDS, ALG, and WTG represent temperate typical steppe, temperate meadow steppe, temperate desert steppe, alpine grassland, and warm, tropical grassland, respectively. Error bars in (a) represent standard errors. In the box plot (b, c), the “+” symbols represent mean values, horizontal lines inside box represent medians, box ends represent the 25th and the 75th quartiles, vertical lines represent 2.5th and 97.5th percentiles, and n represents the number of samples. Different lowercase letters indicate significant differences among soil depths or grassland types at p=0.05.

3.2.3 Spatial control of environmental factors over Q₁₀

The relationships of Q_10−ST5 and Q_10−ST10 with abiotic and biotic factors were presented in Fig. 5. Among these abiotic and biotic factors, Q_10−ST5 significantly positively correlated with SOC, AGB, and BGB, whereas negatively correlated with soil temperature (p<0.05, Fig. 5). In contrast, Q_10−ST10 significantly correlated with MAP, soil temperature and SOC (p<0.05, Fig. 5). In addition, combined MAP and MAT, and combined soil temperature and soil moisture affected Q_10−ST5, and jointly accounted for 22.1 % and 13.9 % of the spatial variation of Q_10−ST5 across Chinese grasslands (Table S4).

4 Discussion

4.1 Spatial patterns and controlling factors of annual soil respiration

4.1.1 Annual soil respiration among grassland types

Figure 5 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 R_s among various grassland types in China (p<0.01, Fig. S4). The significant difference in mean annual R_s 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 R_s rate.

4.1.2 Control of environmental factors over annual R_s

Across Chinese grasslands, annual R_s 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 R_s, suggesting that these two factors are more important in predicting R_s in arid and semi-arid ecosystems under climate change.

In addition, spatial variation of annual R_s was also controlled by soil properties, such as SOC and soil pH. The relationships between annual R_s 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 R_s 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 R_s 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 R_s significantly increased with soil total nitrogen at a global scale. Meanwhile, some case studies revealed the similar relationship between growing-season R_s 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 R_s 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 R_s 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 R_s across Chinese grasslands at a regional scale should be studied further.

Furthermore, as a source of autotrophic respiration, BGB can directly influence R_s, 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 R_s 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 R_s in Chinese grasslands (Fig. 2), BGB may also have the potential to control annual R_s across Chinese grasslands, although this should be investigated further based on accurate quantification of BGB and R_s across a large number of sites.

4.2 Spatial patterns and controlling factors of Q₁₀ values

4.2.1 $R_Q^{\mathrm{2}}$ for Q₁₀ in Chinese grasslands

In this study, only 37.3 % of $R_Q^{\mathrm{2}}$ for Q₁₀ was larger than 0.7, indicating that most of the seasonal variation of the R_s 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 R_s 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 R_s is weak, whereas in arid and semi-arid regions, R_s 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 R_s 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 R_s than the single variable of temperature. Our results also showed that, in general, $R_Q^{\mathrm{2}}$ for Q₁₀ closely increased with MAP and soil moisture (p<0.05, Fig. S6), indicating that the $R_Q^{\mathrm{2}}$ 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, R_s could be better estimated by the combined factors of soil temperature and moisture. By comparison, 46.6 % of $R_Q^{\mathrm{2}}$ for Q_10−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 R_s can be better explained by soil temperature at a depth of 5 cm across Chinese grasslands.

4.2.2 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 Q_10−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 Q_10−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).

4.2.3 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 Q_10−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 Q_10−ST5. Additionally, both univariate and multiple regression analyses demonstrated that there were no significant relationships between Q_10−ST10 and abiotic and biotic factors (not shown), indicating that the Q_10−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 R_s 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.

4.3 Comparisons of R_s and Q₁₀ between Chinese grasslands and the global ecosystems

4.3.1 Comparisons of annual R_s

The annual R_s 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 R_s 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 R_s rate across Chinese grasslands was within the lowest and highest R_s across global grasslands.

Across Chinese grasslands, the proportions of R_s 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 R_s, 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 R_s varied with ecosystem type and depended on the magnitude of total R_s (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 R_s during the growing season and heterotrophic respiration were an important part of the annual R_s in Chinese grasslands, respectively, and should be given enough attention.

Table 2 Comparisons of annual soil respiration and Q₁₀ between Chinese grasslands and other syntheses. The numbers in parentheses represent the number of samples.

4.3.2 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 Q_10−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).

4.4 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 R_s 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 R_s 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 R_s in the selected experiments, and previous studies have suggested that measurement methods affected the results of R_s 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 R_s, Q_10−ST5, and Q_10−ST10 among the three measurement methods (Fig. S7). Given that only one sample of annual R_s was measured by alkali absorption, the effects of measurement methods on R_s could be neglected. Therefore, including data measured by the alkali absorption method in our synthesis does not meaningfully change the results of R_s 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 R_s. Our results showed that the measurement period did not significantly affect Q_10−ST10, but significantly affected Q_10−ST5 (Fig. S7). In terms of Q_10−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.

5 Conclusions

Chinese grasslands cover a vast area, have high spatial heterogeneity, and include various grassland types. By synthesizing all the available data relating to R_s and Q₁₀, we analyzed their spatial patterns and driving factors in grasslands across China. Our results showed that annual R_s and Q₁₀ varied greatly within and among grassland types. Across Chinese grasslands, the mean annual R_s 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 R_s, whereas soil pH negatively affected annual R_s. Among these environmental factors, MAP played an important role in controlling R_s variation across Chinese grasslands. Moreover, the combined factors of MAP and MAT accounted for 22.1 % of the variation of Q_10−ST5 across Chinese grasslands. The Q_10−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 R_s 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, R_s and Q₁₀, at an annual scale, especially measuring autotrophic and heterotrophic respiration separately, limit our in-depth knowledge on the key drivers of R_s 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 R_s and its temperature sensitivity in relatively arid grassland ecosystems.