Response of Soil Respiration and Its Components to Precipitation Exclusion in Vitex negundo Var. Heterophylla Shrubland of the Middle Taihang Mountain in North China

Introduction

In future climate scenarios, the intensity and frequency of precipitation is forecasted to decrease in many areas (IPCC, 2013; Sun et al., 2019). Reductions in precipitation induced by climate change will greatly impact terrestrial carbon (C) cycling, including soil respiration (R_s), especially in arid and semiarid regions (Ahlström et al., 2015; van der Molen et al., 2011). Terrestrial ecosystems release approximately 58 Pg CO₂ per year, which is 10 times more than fossil fuels emissions (Schlesinger and Andrews, 2000; Huang et al., 2018). Accordingly, even small changes in R_s may influence inter-annual fluctuations in C cycling, with subsequent feedbacks on climate change (Bond-Lamberty and Thomson, 2010; Liu et al., 2021).

Responses of Rs to decreasing precipitation have been studied in various ecosystems, but considerable uncertainty remains about the directions and magnitude of the response (Hinko-Najera et al., 2015; Sotta et al., 2007; van Straaten et al., 2010; Wei et al., 2016). Precipitation reduction was reported to suppress R_s in arid and semiarid ecosystems (Talmon et al., 2011) but enhance it in tropical rainforests (Cleveland et al., 2010; Zhang et al., 2015). These contradictory results have been attributed to differences in vegetation types, climatic conditions, and soil microbial activities (Li et al., 2020; Liu et al., 2016). R_s is comprised of autotrophic respiration (R_a), produced by the activities of vegetation roots and associated organisms, and heterotrophic respiration (R_h), associated with decomposition of soil organic matter (SOM) through soil biota (Luo and Zhou, 2006). These two components represent different biological and ecological processes and respond differently to changes in environmental factors including soil temperature (T_s) and soil moisture (M_s) (Liu et al., 2016). For example, reduced precipitation strongly suppressed R_a, but had no effect on R_h in a temperate broadleaved evergreen eucalypt forest (Hinko-Najera et al., 2015). Therefore, identifying differences between R_a and R_h and the factors that control them in different precipitation treatments could help to reduce some of uncertainties associated with climate-carbon feedback forecasts (Liu et al., 2016; Huang et al., 2018; Zhang et al., 2015). However, very few studies have combined manipulating precipitation with partitioning R_s into its component fluxes in semiarid shrublands.

Shrublands are a widely distributed biome type in China (Piao et al., 2009), covering approximately 1.23 million km² of China (Yang et al., 2016). The arid and semi-arid shrublands are important land-cover types in northern China, which are affected by increasing temperatures and changing precipitation patterns (Jia et al., 2016). Moreover, the region of arid and semi-arid shrublands is projected to be characterized by large fluctuations in precipitation and frequent drought periods under future climate change (Liu et al., 2012). Thus, it is important to better understand the response of soil respiration components to changing precipitation by using an artificial precipitation manipulation experiment. In the present study, we established precipitation shelters in mountain shrublands of northern China to identify: 1) the impacts of precipitation exclusion on the different soil respiration component rates and their temperature sensitivity values, and 2) the relation between soil respiration components and soil temperature/moisture.

Materials and Methods

Study Area and Plot Setting

This research was carried out at the Hilly Ecosystem Experimental Station of Taihang Mountain, Chinese Academy of Science (114°15′50″ E, 37°52′44″ N, 350 m a.s.l.) in the Hebei Province of northern China. This region has a semi-arid continental climate with an annual mean atmospheric temperature of 13°C. The lowest average temperature in January is −4°C, and the highest average air temperature in July is 26°C (Zeng et al., 2014). Annual mean precipitation is about 560 mm concentrated from June to September (Shen et al., 2014). Monthly accumulated precipitation and average air temperature during the experimental period are shown in Figure 1. The soil is categorized as Cinnamon soil under the Chinese soil taxonomy, which is equal to Ustalf in the USDA Soil Taxonomy (Zeng et al., 2014). The most abundant shrub is Vitex negundo var. heterophylla, which forms a relatively closed canopy (Shen et al., 2014).

FIGURE 1. Climatic data during the study period from August 2016 to July 2017.

The experiment was carried out in June 2015. Based on a randomized block design, three replicate treatments were used to create precipitation gradients, including natural conditions/ambient precipitation (CK), reduced precipitation by 30% (PE30), and reduced precipitation by 60% (PE60). This design resulted in a total of nine plots. Each experimental plot of 10 m × 10 m was established with 10 m spacing between plots. All measurements were carried out at the center (8 m × 8 m) to avoid edge effects. In PE30 and PE60 treatments, precipitation was reduced by 30 and 60%, respectively, by using plastic rainout shelters as described by Sherman et al. (2012). Although the shelters minimally intercepted incoming light, past work has shown that the interception has little effect on plant responses (Zhang et al., 2017).

Measurement of Soil Respiration and Its Components

A mini-trenching method was adopted to measure R_h in the subplots. In each plot, three trenches were excavated (0.5 m high and 0.4 m in diameter) and then the roots were removed. Subsequently, the soil was put into nylon mesh bags (mesh size of 0.038 mm) to avoid roots expanding into the subplots and to allow the movement of soil organic nutrients, microbes, and soil water (Zhang et al., 2014). Finally, the soil was placed back into the trenches. Vegetation was restricted from the area inside each trench by manually cutting plant growth throughout the study period.

In each plot, three PVC collars (inner diameter of 10.4 cm and height of 8 cm) were installed in the soil at 5 cm depth to monitor R_s in the untrenched area. In addition, another three PVC collars were inserted at the center of the trenching areas to measure R_h. In order to remove the impacts of soil collar installation on R_s or R_h measurements, the collars were installed one year before measurements were collected (July 2015). Moreover, living vegetation inside the PVC collars was removed before monitoring (Fang et al., 2018). R_s and R_h were monitored on clear days once every month between August 2016 and July 2017 using a LI-COR 8100 infrared gas analyzer. All measurements of R_s and R_h were carried out from 8:00 to 11:00 a.m. R_a was calculated by subtracting R_h from R_s (Sun et al., 2019). The soil temperature and humidity sensors equipped with the LI-COR 8100 system were used to record T_s and M_s at a soil depth of 10 cm (Shen et al., 2014).

Statistical Analyses

An exponential model was used to calculate the relationship between monthly mean R_s, R_a, and R_h and monthly mean T_s (°C) from three replicates (Sun et al., 2019):

where R stands for R_s, R_a, or R_h (μmol m⁻² s⁻¹); T_s is the soil temperature (°C); a and b are regression parameters; Q₁₀ is the temperature sensitivity of different soil respiration components.

A polynomial function (Sun et al., 2019) was established to analyze the variation between monthly mean R_s, R_a, or R_h and monthly mean M_s (%) as follows:

where R stands R_s, R_a, or R_h (μmol m⁻² s⁻¹); c, d, and e are functional parameters; M_s is the soil moisture (%).

To consider the combined impacts of T_s and M_s on soil respiration components, we also fitted soil respiration components using a two-factor regression model (Zhang et al., 2015) as follows:

Where R represents R_s, R_a, or R_h (μmol m⁻² s⁻¹); f, g, and h are regression parameters; T_s and M_s are the soil temperature (°C) and soil moisture (%) at 10 cm depth, respectively.

According to the method described by Shen et al. (2014), R_s and R_h measurements between respective sampling dates were interpolated and summed to estimate the annual cumulative fluxes for different treatments.

Repeated measures ANOVA analysis was used to examine the significant differences in mean T_s, M_s, R_s, R_a, R_h and R_h/R_s between the CK and treatment plots for various periods. ANOVA was also performed to test the effect of precipitation treatment on the mean soil respiration components, environmental factors, and cumulative soil respiration component fluxes. All analyses were performed using SPSS 13.0 software (SPSS for Windows, Chicago, IL). Significant differences were indicated at the level of p < 0.05. Exponential model 1) and polynomial model 2) and their corresponding coefficients were performed using Sigmplot 12 (Systat Software Inc., CA, United States of America); non-linear regression model 3) and its regression parameters were performed using R 4.1.0 for Windows (https://www.r-project.org).

Results

Soil Temperature and Soil Moisture

Soil temperature varied seasonally, from the lowest of −2.8°C in January 2017 to the highest of 26.9°C in July 2017 (Figure 2A). Mean monthly T_s depth for CK, PE30 and PE60 were 14.1, 13.9 and 13.8°C, respectively, and there was not a significant difference among the three treatments throughout the study period (Figure 2B).

FIGURE 2. Seasonal variation in (A) soil temperature (T_s) at 10 cm depth, and (C) soil moisture (M_s) at 10 cm depth from August 2016 to July 2017. Data are the mean ± SD (n = 3). Monthly mean values of (B)T_s and (D)M_s throughout the study period. Letters on the top of the bars indicate significant difference among treatments at level of p < 0.05. CK: ambient precipitation; PE30: 30% reduced precipitation; PE60: 60% reduced precipitation.

Soil moisture in the three precipitation gradients displayed similar seasonal variation (Figure 2C). On average, M_s was 10.2% in CK and decreased by 5.0 and 17.8% in PE30 and PE60, respectively (Figure 2D). In addition, soil in PE60 was significantly drier than the soil in the control (p < 0.05).

R_s and Its Components

During the study period, R_s, R_h, and R_a showed similar seasonal variation to T_s and M_s, with maximum values occurring during summer (July) and minimum values occurring in the winter (Figure 3A,C,E). However, the ratio of Rh/R_s showed an opposite pattern with the lowest value (approximately 60%) during the peak season (Figure 3G). The average R_s was 1.48 ± 0.14 μmol m⁻² s⁻¹ in CK, and was reduced by 8.5 and 18.8% in PE30 and PE60, respectively; the reduction in R_s was significant for PE60 (p < 0.05) (Figure 3B). The average R_h was 1.07 ± 0.09, 0.98 ± 0.08, and 0.88 ± 0.04 μmol m⁻² s⁻¹ in CK, PE30, and PE60, respectively. The mean R_a was 0.41 ± 0.08 μmol m⁻² s⁻¹ in CK, with a reduction of 7.5% in PE30 and 19.9% in PE60, respectively (Figure 3D,F). The one-way ANOVA showed that the average R_h was significantly higher in CK than in PE60 (Figure 3D). In contrast, R_a was not significantly altered by changing precipitation (Figure 3F). In addition, precipitation exclusion did not significantly alter the ratio of Rh/R_s (Figure 3H). R_h was significantly and positively correlated to R_s under the three precipitation manipulation treatments (Figure 4). The model implied that R_h approached zero with R_s, which made sense biologically as R_h occurred in the soil when R_s > 0 (Bond-Lamberty et al., 2004). Annually, reducing precipitation significantly decreased the cumulative R_s by 47.8 g C m⁻² in PE30 and 106.0 g C m⁻² in PE60, respectively (Figure 5). Moreover, the contribution of R_h to R_s was altered by different precipitation treatments, with a larger R_h/R_s ratio in CK than in PE30 and PE60.

FIGURE 3. Seasonal variations in (A) total soil respiration (R_s) and its (C) heterotrophic (R_h) and (E) autotrophic (R_a) components and (G) the ratio of R_h/R_s (%) from August 2016 to July 2017. Data are the mean ± SD (n = 3). Monthly mean values of (B)R_s, (D)R_h, (F)R_a and (H) ratio of R_h/R_s (%) throughout the study period. Letters on the top of the bars indicate significant difference among treatments at level of p < 0.05. CK: ambient precipitation; PE30: 30% reduced precipitation; PE60: 60% reduced precipitation.

FIGURE 4. Relationship between soil respiration (R_s) and its heterotrophic (R_h) component during the study period. (A) CK: ambient precipitation; (B) PE30: 30% reduced precipitation; (C) PE60: 60% reduced precipitation.

FIGURE 5. The ratio of heterotrophic respiration (R_h) and autotrophic respiration (R_a) to cumulative soil respiration components fluxes (g C m⁻²) from August 2016 to July 2017. Letters on the top of the bars indicate significant difference among treatments at level of p < 0.05. CK: ambient precipitation; PE30: 30% reduced precipitation; PE60: 60% reduced precipitation.

Effects of Soil Temperature and Moisture on Soil Respiration Components

Both R_s and its R_h and R_a components increased exponentially with T_s (p < 0.01) for the three precipitation treatments (Table 1). T_s interpreted 68.2–78.9% of the variations in R_s, R_h, and R_a (Table 1). The Q₁₀ values of R_s, R_h, and R_a varied from 3.35 to 4.57, 2.12 to 2.20, and 3.60 to 4.35, respectively. Moreover, the Q₁₀ of R_s and R_a both significantly increased with reduced precipitation, while the Q₁₀ of R_h did not. A significant quadratic relationship was observed between soil respiration components and M_s (p < 0.01) measured at 10 cm depth (Table 2). M_s explained 76.1–78.6% of the variation in R_s, 71.8–74.5% in R_h, and 76.9–81.6% in R_a, respectively (Table 2); thus, M_s was a better predictor of soil respiration components in the three precipitation treatments. Moreover, simultaneously considering both T_s and M_s explained 56.6–77.9% of the variation in soil respiration components (p < 0.01 or p < 0.05) (Table 3), indicating that the inclusion of T_s did not improve the explanation of soil respiration components compared to the model based on M_s only.

TABLE 1. Impacts of soil temperature (T_s, °C) on the variation of different soil respiration components (R, μmol m⁻² s⁻¹). R², p, and Q₁₀ values are reported.

TABLE 2. Impacts of soil moisture (M_s, %) on the variation of different soil respiration components (R, μmol m⁻² s⁻¹). R² and p values are reported.

TABLE 3. Combined impacts of soil temperature (T_s, °C) and soil moisture (M_s, %) on the variation of different soil respiration components (R, μmol m⁻² s⁻¹). R² and p values are reported.

Discussion

Segmentation of Soil Respiration

The average R_s in this study ranged from 1.20 ± 0.09 μmol m⁻² s⁻¹ to 1.48 ± 0.14 μmol m⁻² s⁻¹, which was in the range of values reported for other shrubland ecosystems (de Dato et al., 2010; Shi et al., 2020; Talmon et al., 2011). The relative contribution of R_h to R_s was 72.6, 72.3 and 71.9% in CK, PE30 and PE60, respectively. These were consistent with the values reported by Cheng et al. (2015) and Huang et al. (2018), but were higher than those reported in other ecosystems (Comstedt et al., 2011; Huang et al., 2016; Saiz et al., 2005). The trenching method has been widely utilized to distinguish R_h from R_s in many ecosystems (Hanson et al., 2000; Kukumägi et al., 2017; Liu et al., 2016). Nevertheless, it should be noted that a long period of time (i.e., more than 6 months) might be needed to completely remove the influence of dead roots on R_h (Xu et al., 2015; Lei et al., 2017). In order to eliminate the impacts of dead root decomposition on R_h measurements, we inserted collars into the trenches almost one year before the measurement of soil respiration components. However, the trenching method may lead to low estimates of R_h due to removal of inputs from root exudates and dead roots (Yi et al., 2007; Fang et al., 2018).

Effects of Precipitation Manipulation on R_s and Its Components

Precipitation can affect R_s and its components by changing soil humidity, which directly influences the substrates for heterotrophic respiration as well as the autotrophic respiration of roots and microorganisms (Wang et al., 2014b; Liu et al., 2018; Sun et al., 2019). Consistent with several previous works (Balogh et al., 2016; Borken et al., 2006; Suseela et al., 2012), a decrease in R_s and its R_h and R_a components was also observed under the precipitation exclusion treatments in the present study, and this response can be explained by a number of abiotic and biotic mechanisms. First, reductions in R_h, and hence R_s, were possibly caused by lower soil moisture due to decreased precipitation (Yang et al., 2020). Reduced precipitation might inhibit R_h and R_s by impeding the diffusion of unstable substrates, and thus decreasing the rates of soluble substrates absorption by microorganisms (Yan et al., 2011). Second, lower fine root growth caused by lower soil moisture levels might also explain the effect of decreased precipitation on R_s and its components (Hinko-Najera et al., 2015). Third, lower soil moisture in decreased precipitation treatments might increase the amount of CO₂ that accumulates in soil pores (Liu et al., 2019).

However, many other studies have reported varying responses of soil respiration to reduced precipitation (Deng et al., 2018; Zhang et al., 2015). Davidson et al. (2008) found no effect of reduced precipitation on soil respiration, indicating that belowground carbon allocation may not have been significantly impacted by reduced precipitation. In addition, Cleveland et al. (2010) and Zhang et al. (2015) reported that reducing precipitation increased soil respiration in tropical rainforest experiments because the soils responded to the increase of dissolved organic matter concentrations or soil O₂ availability. The reasons for these inconsistent results may be due to the fact that they were conducted in diverse ecosystems and measurements were conducted on different temporal scales (Wang et al., 2014b). Therefore, it is necessary to quantitatively assess the changes of R_s and its components under different intensities and frequencies of precipitation exclusion in different ecosystems.

Effects of Precipitation Manipulation on Modeled Soil Respiration Components

Soil temperature exhibited seasonal variation, which primarily accounted for the temporal variation of soil respiration components (Fang et al., 2018). In this study, both R_s and its components rose exponentially with the increase of T_s in the three precipitation treatments, consistent with previous findings from shrublands (Lellei-Kovács et al., 2016; Sun et al., 2021) and other ecosystems (Rey et al., 2002; Zhang et al., 2015). Mechanisms underlying the response of soil respiration components to changes in T_s may include the availability of nutrients and substrates, the adaptation of roots to different soil environments, and the alteration of the microbial community (Wei et al., 2016). In addition to T_s, M_s has also been considered an important variable that controls the variation of R_s and its components (Saiz et al., 2005; Sun et al., 2019). Our results provided evidence that the precipitation manipulation treatments significantly decreased M_s and had a much stronger effect on both R_s and its components. M_s might limit soil respiration components by stressing the distribution of assimilates in the plant-soil system, microorganisms, and enzymatic activities in the rhizosphere (Escolar et al., 2015; Sanaullah et al., 2011). According to the M_s-based quadratic function, both R_s and its components may become depressed when M_s becomes either too high or too low (Liu et al., 2018). We simulated the soil respiration components with a two-factor model (Eq. 3) (Table 3) which turned out to be weaker than the M_s-based model, suggesting that precipitation exclusion amplified the effects of soil water limitation on soil respiration (Sun et al., 2019).

Q₁₀ is recognized as an important parameter to evaluate temperature adaptation of R_s (Luo and Zhou, 2006; Fang et al., 2018). In our work, the values of Q₁₀ ranged from 2.12 to 4.57, which was consistent with the range (0.65–5.18) of other ecosystems (Rey et al., 2002; Zou et al., 2018; Sun et al., 2021). Previous studies have suggested that drought might change the sensitivity of R_s to temperature and disrupt the coupling between temperature and humidity (Selsted et al., 2012; Wang et al., 2014b). Soil-water deficit adequately weakened the sensitivity of R_s to T_s, leading to the decrease of Q₁₀ (Rey et al., 2002; Wang et al., 2014b; Liu et al., 2016). In contrast, our study found that reducing precipitation increased the Q₁₀ values of different soil respiration components, which was consistent with previous results in a grassland ecosystem (Sun et al., 2019). The Q₁₀ values of R_a were higher than those of R_h, reflecting a tighter relationship between Q₁₀ and plant root activities (Sun et al., 2019; Zou et al., 2018). In addition, the results also suggested that R_h was less sensitive than R_a to the precipitation exclusion treatments, indicating that drought might have a weaker feedback mechanism of SOM decomposition on climate change (Sun et al., 2018).

Conclusion

The present study provided unique data for exploring the impacts of precipitation exclusion on R_s and its components in a semiarid mountain shrubland of northern China. Precipitation exclusion significantly depressed R_s and its R_h and R_a components. R_s and its components were all exponentially related with T_s and quadratically related with M_s. The temperature sensitivity (Q₁₀) of R_s and R_a were both significantly increased by decreased precipitation. In addition, decreasing the intensity of precipitation decreased the contribution of R_h to R_s. We estimated an annual C reduction release of 47.8 and 106.0 g C m⁻² in response to treatments that decreased precipitation by 30 and 60%, respectively. Our findings are critical for understanding and forecasting possible changes in the release of carbon by semiarid shrublands in response to climate change. Further work with long-term experiments is necessary to evaluate the influence of precipitation manipulation treatments on soil respiration components and how the responses may vary along under future drought events.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

HS was involved with experiment plan, data collection and analysis, manuscript formation and manuscript editing. LZ, HM, and TZ collected the data. ZZ and YZ edited the manuscript.

Funding

The present study was funded by the Natural Science Foundation of Hebei Province (Grant No. C2019302018), the Natural Science Foundation of China (Grant No. 31500448), the Youth Talent Project of Hebei Province, and the Talent Training Program of Hebei Academy of Sciences (Grant No. 2020G16).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank Elizabeth Tokarz at Yale University for her assistance with polishing up the manuscript.

References