Responses of soil respiration to experimental warming in an alpine steppe on the Tibetan Plateau

Soil respiration (R_S), the second largest C flux in terrestrial ecosystems, is an important regulator of atmospheric CO₂ concentrations (Bond-Lamberty and Thomson 2010, Melillo et al 2017, Bond-Lamberty et al 2018). The annual R_S is 91 Pg C (1 Pg = 10¹⁵ g), which is approximately ten times larger than the anthropogenic C release (Bond-Lamberty and Thomson 2010, Metcalfe et al 2011, Hashimoto et al 2015). Over the past decades, R_S has exhibited an increasing trend because of continuous climate warming (Bond-Lamberty et al 2018). Compared with other regions, temperature increase in high-altitude and high-latitude ecosystems is twice the rate of the global average (IPCC 2013). Considering the large C storage in these regions (Hugelius et al 2014, Ding et al 2016), the faster climate warming may induce large C loss from these cold ecosystems to the atmosphere through R_S and trigger positive C-climate feedback (Schuur et al 2015). R_S generally consists of autotrophic respiration (R_A), produced by plant roots, and heterotrophic respiration (R_H) associated with soil organic matter (SOM) decomposition by soil microbes and fauna (Luo and Zhou 2006). Both components make near equal contribution to R_S (Bond-Lamberty et al 2018), but climate warming may cause distinct changes in R_A and R_H (Wang et al 2014). More importantly, increased R_A may be balanced by plant production, but R_H is not (Hicks Pries et al 2013, 2015), which could then make these two components generate different contribution to the potential positive C-climate feedback. Therefore, a comprehensive understanding of the responses of R_S and its autotrophic and heterotrophic parts to climate warming in cold regions is crucial for an accurate prediction of the direction and magnitude on feedbacks in the terrestrial C cycle.

Previous studies have explored the responses of R_S and its components to experimental warming in different biomes (Zhou et al 2007, Dorrepaal et al 2009, Hicks Pries et al 2015, Melillo et al 2017). By integrating these site-level studies, meta-analyses have been performed to obtain comprehensive response tendencies on a global scale (Rustad et al 2001, Wang et al 2014, Carey et al 2016). These meta-analyses revealed that experimental warming stimulated R_S below a threshold of 25 °C (Carey et al 2016), and also enhanced R_H, but had little impact on R_A (Wang et al 2014). Although responses of R_S and its components under climate warming are better understood, their attention has mainly focused on temperate and tropical ecosystems (Wang et al 2014, Carey et al 2016). Actually, climate warming is expected to result in a more drastic R_S change in cold regions compared with temperate and tropical regions (Jahn et al 2010). However, current studies on warming effects on R_S in high-latitude and high-altitude ecosystems are mainly derived from wet ecosystems, such as wet tundra in arctic regions (Oberbauer et al 2007, Dorrepaal et al 2009, Hicks Pries et al 2015, Natali et al 2015) and alpine meadow on the Tibetan Plateau (Lin et al 2011, Shi et al 2012, Fu et al 2013, Xue et al 2015), with limited evidence from dry ecosystems. Compared with wet ecosystems, warming effects on R_S in the dry ecosystems may be regulated by soil water availability due to its limitation on root and microbial growth and activity (Sheik et al 2011, Suseela and Dukes 2013). Thus, whether warming effects on R_S in cold and dry ecosystems are similar to those in cold and wet ecosystems require further investigation.

The purpose of our study was to explore the mechanisms by which R_S and its components respond to experimental warming in cold and dry ecosystems, and whether these differ from those in wet ecosystems. Specifically, based on a warming experiment in a Tibetan alpine steppe (a typical cold and dry ecosystem), we conducted a three-year field measurement on R_S, R_A, and R_H. We then investigated how changes in soil moisture regulated warming effects on R_S and its components, and performed a meta-analysis to examine whether the finding in our dry alpine steppe differed from that in those cold and wet ecosystems including Tibetan alpine meadow and arctic tundra.

2.1. Field experiment in Tibetan alpine steppe

2.1.1. Experimental site and design

The field experiment was conducted in a typical alpine steppe in Gangca county, Qinghai Province, China (37.30° N, 100.25° E, elevation 3290 m). The mean annual temperature and precipitation at the study site are 0.08 °C and 387 mm, respectively. This experiment site has experienced continuous climate warming over the past three decades, with a mean rate of 0.04 °C per year (Li et al 2019). Dominant plant species of the study site include Stipa purpurea as well as Carex stenophylloides, and the related soil type is Haplic Calcisol (Li et al 2019, Zhang et al 2019).

In May 2013, an enclosure in the study area was established to avoid disturbance from grazing and human activities. The field manipulation contained 10 blocks, with two plots within every block assigned to control and warming treatments. Hexagonal open-top chambers (OTC) were applied to achieve passive warming (Li et al 2019). The size of OTCs was 80 cm along for the top edge, 120 cm along for the bottom edge, and 50 cm height. The transparent material was made of clear acrylic sheeting 2 mm thick and light transmission of over 92%. The OTCs were set out all year round in the field. From May 2014, soil temperature and moisture were monitored simultaneously at a depth of 5 cm in each plot using EM50 data collectors (Decagon, Washington, DC, USA) at 30 min intervals.

2.1.2. Soil respiration measurements

To explore the response of R_S and its components to experimental warming, R_S, R_H, and R_A fluxes were measured based on the deep-collar method used in previous studies (Dorrepaal et al 2009, Hasselquist et al 2012, Suseela and Dukes 2013, Peng et al 2017a). Specifically, R_S and R_H were measured from 09:00 am to 12:00 pm (local time) every 10 days during the growing seasons of 2014–2016, using a detector of LI-8100 soil CO₂ flux chamber (LI-COR, Lincoln, Nebraska, USA). In each plot, R_S measurements were taken using a PVC collar (20 cm in diameter and 5 cm tall) installed 3 cm into the soil. Meanwhile, R_H measurements were taken using a PVC collar (20 cm in diameter and 62 cm tall) which was installed 60 cm into the soil to exclude roots. To eliminate aboveground respiration, small living plants in collars were weeded at least one day before each measurement. R_A was got from calculating the difference between R_S and R_H.

2.1.3. Data analysis of the field measurements

Data from the field measurements were analysed using a repeated measures ANOVA to examine the effects of warming, sampling times and their interactions on R_S, R_H and R_A. Paired t-tests were performed to determine the differences in seasonal average of soil temperature, moisture and R_S, R_H and R_A between warming and control treatments. To explore the warming effects on temperature sensitivity of R_S and its components, we fitted the relationships of R_S, R_H and R_A with soil temperature across the growing season using the following exponential equation (Hui and Luo 2004):

$$\begin{eqnarray}&&R=a{{\rm{e}}}^{bT},\end{eqnarray} \tag{ 1 }$$

where R is soil respiration rate (mol CO₂ m⁻² s⁻¹), T is soil temperature at 5 cm (in °C), a and b are constants. Here, b was used to calculate the respiration quotient (Q₁₀), which characterized the respiration changes under an increase by 10 °C of soil temperature, described as the following equation (Zhou et al 2007):

$$\begin{eqnarray}&&{Q}_{10}={{\rm{e}}}^{10b}.\end{eqnarray} \tag{ 2 }$$

To determine the treatment effects on R_S and its components, linear regressions were performed to determine the relationships of response ratios (RR) of R_S, R_H and R_A with RRs of soil temperature and moisture, where the RRs were calculated as the ratios of variables between warming and control groups. Given that data obtained in different years were not independent in statistic, linear mixed-effects models were performed with fixed effects being explanatory variables and the random effect being experimental year (Li et al 2017, Peng et al 2017b). Three data points for R_S and R_H and two data points for R_A were outliers based on Boxplot Procedures (figure S1 is available online at stacks.iop.org/ERL/14/094015/mmedia; Hoaglin and Iglewicz 1987, Sim et al 2005); therefore, these data points were excluded in the final model. All statistical analyses were conducted using R statistical software v3.4.0 (R Core Team 2018).

2.2. Meta-analysis of field experiments in Tibetan alpine meadow and arctic ecosystems

To explore how R_S in cold regions responded to warming over a large scale, we synthesized published studies reporting warming effects on R_S from the Tibetan alpine meadow and arctic ecosystems using Web of science (http://apps.webofknowledge.com/). To avoid publication bias, selected papers had to meet the following criteria: (i) in situ warming experiments were conducted using one of the three frequently used methods (e.g. OTCs, infrared heater and soil heating cable); (ii) R_S was measured for at least one complete growing season; and (iii) the mean value, standard deviation (or standard error) and number of replicate were reported. Based on the above criteria, 11 observations were collected in the form of 'control-warming' pairs from seven studies in the Tibetan alpine meadow, and 23 observations were identified from 10 studies in the arctic and subarctic zones (table S1). The background information, including latitude, longitude, mean annual temperature, mean annual precipitation, experiment duration, and changes in soil temperature as well as moisture in the studies, was also recorded (table S1).

To evaluate warming effects on R_S, the RR was calculated as described by Hedges et al (1999):

$$\begin{eqnarray}&&{\rm{RR}}=\,{\rm{ln}}\left({X}_{t}/{X}_{c}\right)=\,{\rm{ln}}\left({X}_{t}\right)-\,{\rm{ln}}\left({X}_{c}\right),\end{eqnarray} \tag{ 3 }$$

where X_t and X_c are the mean values of control and treatment groups, respectively. Moreover, variance (v₁) of individual studies was calculated based on sample size and standard deviation:

$$\begin{eqnarray}&&{v}_{1}=\displaystyle \frac{{S}_{t}^{2}}{{n}_{t}{\bar{X}}_{t}^{2}}+\displaystyle \frac{{S}_{c}^{2}}{{n}_{c}{\bar{X}}_{c}^{2}},\end{eqnarray} \tag{ 4 }$$

where n_t and n_c, and s_t and s_c are the sample size and standard deviation for treatment and control groups, respectively. Due to the limited number of data points, the overall mean RRs and the corresponding 95% confidence intervals (CI) were calculated using resampling methods (e.g. bootstrapping; Adams et al 1997). If the 95% CI did not overlap with 1, treatment effects on R_S were considered significant. To further explore whether the mean effect size was different between the Tibetan alpine meadow and arctic ecosystems, total heterogeneity (Q_t) was partitioned into within-group heterogeneity (Q_w) and between-group heterogeneity (Q_b). The significant Q_b implies that the RRs differ between the two ecosystems (Hedges et al 1999). All above analyses were conducted using MetaWin 2.1 software (Rosenberg et al 1997).

3.1. Warming effects on R_S and the regulating factors in our field study

During the growing seasons of 2014–2016, warming treatments significantly increased soil temperature in this alpine steppe by 1.4, 1.6, and 2.0 °C, respectively, while the corresponding soil moisture significantly decreased by 2.5, 3.4, and 2.7%, respectively (figure S2). Experimental warming had no significant effects on R_S, R_H, and R_A at the seasonal scale (table 1), although R_S and its components exhibited seasonal pattern (figure 1). Similarly, there were no significant changes in seasonal means of the three parameters (R_S, R_H, and R_A) between warmed and control plots (figure 1 inserts). However, warming significantly reduced the Q₁₀ of the three parameters during the three growing seasons, except for that of R_A in 2016 (figures 2(d), (h), (l)).

Table 1.  Results of repeated ANOVA on the effects by warming (W), date (D) and their interactions on R_S, R_H and R_A during the growing seasons between 2014 and 2016.

		2014		2015		2016
	Effect	F	P	F	P	F	P
RS	W	2.08	0.17	3.7	0.07	3.16	0.09
	D	82.59	<0.001	70.09	<0.001	78.09	<0.001
	W × D	5.62	<0.001	9.24	<0.001	3.99	<0.01
RH	W	4.25	0.05	2.2	0.16	0.45	0.51
	D	42.42	<0.001	34.23	<0.001	54.76	<0.001
	W × D	3.31	0.01	3.66	<0.01	5.01	<0.01
RA	W	<0.001	0.99	1.01	0.32	1.93	0.18
	D	25.43	<0.001	22.84	<0.001	3.58	0.02
	W × D	4.37	<0.01	3.81	<0.01	0.7	0.55

Note: Values in bold denote statistically significance.

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The insignificant responses of R_S, R_H, and R_A in this alpine steppe were likely associated with the lowered soil moisture (figure S2), particularly for R_S and R_H. Consistent with the above results of insignificant warming-induced changes in R_S, R_H and R_A, no obvious relationships were observed between RRs of R_S and its components and RR of soil temperature across the three years (R_S: r² = 0.02, P = 0.23; R_H: r² = 0.02, P = 0.32; R_A: r² = 0.06, P = 0.07; figures 3(a), (c), (e)). However, results from the linear mixed-effects models showed that RRs of R_S and R_H were positively correlated with the RR of soil moisture (R_S: r² = 0.27, P < 0.001; R_H: r² = 0.30, P < 0.001; figures 3(b), (d)), although it had little effects on R_A (r² = 0.05, P = 0.11; figure 3(f)).

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3.2. Responses of R_S in Tibetan alpine meadow and arctic ecosystems

Meta-analysis revealed that the mean R_S exhibited a significantly positive response under experimental warming in both Tibetan alpine meadow and arctic ecosystems. Specifically, in the Tibetan alpine meadow, a significant R_S response was observed in seven out of 11 individual observations, accounting for 64% of the total. The mean RR of R_S was 1.13 and the corresponding 95% bootstrap CI ranged from 1.04 to 1.23 (figure 4(a)). In arctic ecosystems, a significant R_S response to warming was observed in 16 out of 23 study sites, accounting for 70% of the total. The mean effect size of R_S was 1.23 and the corresponding 95% CI ranged from 1.14 to 1.34 (figure 4(b)). Q_b test indicated that no significant warming effects on R_S occurred between the two ecosystems (P = 0.09).

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Our results showed that R_S and its components exhibited insignificant responses to experimental warming in the Tibetan alpine steppe (figure 1). Generally, climate warming should increase R_S in cold regions due to the alleviated temperature limitation (Schuur et al 2015). Yet reductions in soil moisture which accompany warming can also affect R_S responses (Carey et al 2016). In our field study, the warming-induced changes in R_S were significantly related to changes in soil moisture (figure 3(b)). Although slight decline in soil moisture can still increase growth and activity of soil organisms, and thus stimulate R_H, severe soil drying would depress R_H (figure 3(d)), eventually leading to unchanged R_H. However, the non-significant R_A response cannot be explained by the changes in soil temperature or moisture (figures 3(e), (f)), indicating that other factors may regulate this process, and the potential mechanisms require further investigation. Altogether, the unchanged R_H and R_A resulted in a non-significant R_S response to experimental warming in this alpine steppe.

Although investigating R_H and R_A can provide mechanical understanding of the R_S response to warming (Bond-Lamberty et al 2018), the root exclusion method, which has been widely used for distinguishing R_H and R_A (Dorrepaal et al 2009, Hasselquist et al 2012, Suseela and Dukes 2013, Peng et al 2017a), may create some artifacts. Deep collar installation could temporarily stimulate R_H due to the decomposition of dead roots. Considering that this transient increase was reported to last for approximately five months (Zhou et al 2007), we started the respiration measurements one year after collars were installed to minimize this limitation. Live root exclusion in deep collars also affected the presences of root exudates, which would in turn affect R_H. Despite these inevitable uncertainties, we found that the ratio of R_H to R_S was between 57% and 72% during the experimental period, within the previously reported range (25%–90%) for global grasslands (Subke et al 2006), indicating that the method is feasible to partition R_H and R_A in our experiment.

In contrast to the observations in the dry alpine steppe, our results also revealed that experimental warming significantly increased R_S in the Tibetan alpine meadow and arctic ecosystems (figure 4). The different responses of R_S to warming among various ecosystems may be caused by the following three aspects. First, regional differences in environmental factors, including temperature and water availability, may have led to different R_S responses. It has been reported that mean annual temperature in the Tibetan alpine steppe (0.80 °C) was higher than those in the Tibetan alpine meadow (0.15 °C, Chen et al 2019). Moreover, background information of synthesized studies indicated that the sites in Tibetan alpine meadow had a higher temperature range than those in arctic ecosystems (table S1). This temperature difference may have led to lower R_S response levels to experimental warming in our field experiment, since R_S in cold regions often show higher temperature sensitivity (Carey et al 2016, Hursh et al 2017). In addition, soil moisture in the Tibetan alpine steppe (10.9%) was lower than the Tibetan alpine meadow (22.3%, Chen et al 2019), and the soil moisture in arctic tundra were likely to remain unchanged under experimental warming (table S1). Consequently, R_S in the Tibetan alpine meadow and arctic tundra are not severely impacted by the limited water supply, and may be stimulated by experimental warming.

Second, SOC differences across regions may contribute to different patterns of R_S under warming. SOC density (C amount per area) within the 1 m depth was reported as 4.5 kg C m⁻² in the Tibetan alpine steppe, lower than 9.0 kg C m⁻² in the Tibetan alpine meadow (Yang et al 2008, Ding et al 2016) and 34.8 kg m⁻² in arctic tundra (Ping et al 2008). It has been suggested that the responses of R_S to climate warming are directly related to SOC stock, with larger SOC triggering stronger R_S response (Crowther et al 2016). Consequently, elevated temperature may lead to higher R_S levels in the Tibetan alpine meadow and arctic ecosystems than in the Tibetan alpine steppe. In addition, SOC quality is another important factor controlling R_S response. Laboratory incubation experiments have shown that SOM is highly decomposable in both Tibetan alpine meadow and arctic tundra (Schadel et al 2014, Treat et al 2014, Chen et al 2016a), which may contribute to more sensitive R_S changes under climate warming in these regions (Eberwein et al 2015).

Third, differences in plant and microbial growth may cause the heterogeneity in R_S response among various regions. Previous research revealed that the average aboveground biomass in the Tibetan alpine steppe was 50.1 g m⁻² (Yang et al 2009), while that in the Tibetan alpine meadow and arctic tundra was 90.8 and 315.4 g m⁻², respectively (Yang et al 2009, Howard et al 2012). The higher aboveground biomass may be responsible for larger R_A fluxes in the Tibetan alpine meadow and arctic ecosystems. In addition to plants, soil microorganisms are also of great importance in determining the response of R_S to warming (Allison et al 2010, Wieder et al 2013). Soil microbial biomass in the Tibetan alpine steppe was reported to be 13.5 mmol C kg⁻¹, lower than that in the Tibetan alpine meadow (33.5 mmol C kg⁻¹; Chen et al 2016b) and arctic tundra (340.5 mmol C kg⁻¹; Xu et al 2013). The lower soil microbial biomass indicates that soil microbes in the Tibetan alpine steppe may respire less CO₂ under warming. Overall, the larger plant and microbial biomass may contribute to a significant warming-induced R_S increase in the Tibetan alpine meadow and arctic ecosystems.

In summary, our study demonstrated that changes to R_S under climate warming in the Tibetan alpine steppe were different from those reported in Tibetan alpine meadow and arctic ecosystems. Although warming significantly increased R_S in the Tibetan alpine meadow and arctic tundra, it exerted no significant effect on R_S in the Tibetan alpine steppe. This finding does not support the traditional view that climate warming would induce a significant increase in R_S in cold regions, suggesting that a more complex C-climate feedback mechanism exists in cold regions. Therefore, future evaluations on terrestrial C-feedback, even in warming-sensitive ecosystems, should call for a comprehensive consideration of the heterogeneous ecosystem responses and complex regulating mechanisms.

The data that support the findings of this study are available from the corresponding author upon reasonable request. We appreciate the editor and two anonymous reviewers for their critical comments on an early version of this manuscript. We thank Dr Tianfeng Han for his help in field measurements, and acknowledge the financial support from the National Natural Science Foundation of China (31825006 and 41877046), Key Research Program of Frontier Sciences, Chinese Academy of Sciences (QYZDB-SSWSMC049), and Chinese Academy of Sciences-Peking University Pioneer Cooperation Team.