EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 28 / No 1 (February 2023) Wuhan Univ. J. Nat. Sci., 28 1 (2023) 77-87 Full HTML
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 1, February 2023
Page(s) 77 - 87
DOI https://doi.org/10.1051/wujns/2023281077
Published online 17 March 2023

© Wuhan University 2023

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

0 Introduction

Grassland is an important terrestrial ecosystem. In the world, grassland covers 25% of the terrestrial land and accounts for about 25%-30% of total soil carbon storage[1-3]. In China, grassland, as the largest terrestrial ecosystem, covers 41.7% of the national land area[4], and stores 9%-16% of global organic carbon[5]. Soil respiration is defined as the CO2 flux released from the soil surface[6]. Soil respiration is the main way of carbon output in grassland, and is a significant source of greenhouse gases. The global surface temperature could increase 1.4-5.8 ℃ from 1999 to 2100 according to prediction from Intergovernmental Panel on Climate Change (IPCC)[7]. To evaluate the response of soil respiration to global warming accurately is an urgent and challenging task in carbon cycle research[8].

The response of soil respiration to climate change has attracted extensive attentions with lots of significant research results. However, there are still great uncertainties in the response due to insufficient understanding of carbon cycle in terrestrial ecosystems.

First, there are great uncertainties in direction as well as magnitude of feedback between carbon cycle and climate change[9]. Some studies supported that global warming promoted soil respiration, driving positive feedback between terrestrial carbon cycle and climate change[10,11]. Some studies supported that soil respiration acclimated to increasing temperature after variated with climate change over a period of time due to adaptation of microbial activities and exhaustion of labile carbon pools[12]. Other studies suggested soil respiration decreased while temperature increased as a result of limited soil moisture[13,14].

Second, altitude variations drive differences in the response of soil respiration to climate change. The lower the temperature, the higher the temperature sensitivity of soil respiration[15,16]. At different altitudes, long-term temperature differences are responsible for differences in the sensitivity of soil respiration to temperature as well as to climate change. At different altitudes, the response of soil respiration to climate change is different owing to the differences in short-term temperature increase with climate change[17,18], biodiversity, plant growth, species distribution and soil properties. More warming experiments on soil respiration in situ should be conducted so as to further explore the response of soil respiration to climate change.

Mount Wutai, a Buddhist holy place, appeared in world heritage list in 2009. Its highest altitude is 3 061.1 meters above the sea level (ASL)[19]. It retains relatively intact and stable montane meadows above the altitude of 2 000 m[20, 21] and provides an ideal natural laboratory for studying the response of soil respiration to climate change. However, inconvenient transportation and strict entry policy stop conducting long-time field experiments. To our knowledge, soil respiration in the montane meadows, Mount Wutai has not been measured in situ to date. Here, we set up control and warmed plots in situ across an altitude gradient of about 200 m in the subalpine meadow, Mount Wutai. Our study aimed to 1) measure soil respiration and analyze its relationship with environment factors, 2) test whether temporal and spatial differences exist in the response of soil respiration to warming, and 3) predict how soil respiration in the subalpine meadow responds to global warming.

1 Methods

1.1 Study Area

Mount Wutai (113°24'51"-113°44'21"E, 38°50'11"-39°8'22"N) is located in Xinzhou City, Shanxi Province, China (Fig. 1). Based on data over the period of 1998-2019 from the China Meteorological Data Service Center, the annual average air temperature is 2.15 ℃. The maximum and minimum of air temperature are respectively 29.6 ℃ and -32.3 ℃. The annual average precipitation is 673 mm. Rain mainly falls from June to September. The annual average sunshine hours are 2 632 h, the average relative humidity is 60%, and the maximum wind speed is 30.3 m/s. In the subalpine meadow, Mount Wutai, the dominant species are K. pusilla and K. myosuroides. Other species include Carex spp. and Polygonum viviparum[22].

thumbnail Fig. 1 The location of the study area and the sample sites

1.2 Plot Design

In April 2019, we designed and made the open top chambers (OTCs) for warming in situ in our experiment. The making processes are as following: first, two regular hexagons were made from stainless steel square tubes, one for the base of a chamber with side length of 0.75 m, the other for the top of the chamber with side length of 0.40 m; second, two regular hexagons were connected by the stainless steel square tubes with the height between them being 0.65 m; third, glass was fixed to the side of the chamber (Fig. 2).

thumbnail Fig. 2 The open top chamber for warming in the field

In August 2019, three plots (50 m × 50 m) were selected along altitudes in the subalpine meadow, Mount Wutai (Fig. 1). Their geographical coordinates were shown in Table 1. In each plot, twelve subplots (1.5 m × 1.5 m) were established, half of them were set as control plots and the other half were set as warmed plots. On each warmed plot, one open top chamber was placed. In the subplots, in order to support and seal the Li-cor 8100 portable infrared gas analyzer, polyvinyl chloride collars, 8 cm tall and 20 cm in inner diameter, were inserted into soil, keeping the collars 3 cm above soil surface. Before measurements, green plants in the collars were cut to eliminate photosynthesis.

Table 1

The geographical coordinates of three plots in Mount Wutai

1.3 Field Measurements

Soil respiration was measured by the Li-cor 8100 portable CO2 analyzer (Li-cor Biosciences, Lincoln, Nebraska). Soil moisture at 5 cm was tested by soil moisture probe, 8100-204 Delta-Theta. Soil temperature at 5 cm was tested using the soil temperature probe, 6000-09TC.

1.4 Soil Property Analyses

In September 2020, soil at 0-10 cm was dug with soil drills, put into plastic sealed bags, and then stored in a refrigerator for chemical properties analyses. NH4+-N and NO3--N were measured by flow injection method by Continuous Flow Analyzer (Auto Analyzer 3, Germany). Microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) were tested by fumigation extraction method with Total Organic Carbon Analyzer (multi N/C 3100, Germany).

1.5 Warming Effect on Soil Respiration

Warming effect on soil respiration (WEr) was quantified using WEr=(Rw/Rc-1)×100%, where WEr is warming effect on soil respiration, Rw is soil respiration in warming treatment, Rc is soil respiration in control treatment.

1.6 Statistical Analysis

SPSS 20 was used to analyze statistically. Shapiro-Wilk analysis was used to test whether data distributed normally. The independent sample t test or the nonparametric test was applied to tell if significant differences existed in data between control and warmed plots. Correlation of different variables was analyzed using spearman correlation coefficient. The exponential model and the power model in regression analysis further described soil respiration's relationship with soil temperature and moisture.

2 Results

2.1 Influences of Warming on Soil Properties

At altitudes of 2 500, 2 600 and 2 700 m, NH4+-N was on average 12.50 ± 2.71, 10.91 ± 3.71 and 17.24 ± 2.89 mg/kg in control plots and was on average 17.87 ± 3.91, 14.49 ± 2.47 and 12.10 ± 2.32 mg/kg in warmed plots, respectively. NO3--N varied from 2.91 ± 0.69 to 9.09 ± 2.27 mg/kg in control plots, and from 4.97 ± 3.06 to 9.73 ± 2.20 mg/kg in warmed plots. In control plots, the maximum and minimum of MBC were 961.62 ± 157.48 mg/kg at 2 700 m ASL and 607.83 ± 252.72 mg/kg at 2 600 m ASL, respectively. In warmed plots, the highest value was 913.11 ± 221.53 mg/kg at 2 700 m ASL, and the lowest value was 710.16 ± 288.33 mg/kg at 2 500 m ASL. MBN was higher than 91.27 ± 34.35 mg/kg in control plots, and higher than 98.41 ± 37.84 mg/kg in warmed plots. Warming did not significantly affect NH4+-N, NO3--N, MBC or MBN (Table 2).

Table 2

Soil chemical properties in warmed and control plots at different altitudes

2.2 Warming Responses of Soil Respiration, Temperature and Moisture

At an altitude of 2 500 m, OTCs significantly promoted soil respiration as well as soil temperature (p < 0.05, n = 6). In control and warming treatment, soil respiration peaked in middle July 2020, the value was 6.21±1.530 and 10.09±1.831 μmol·m-2·s-1, respectively. The minimum value was 0.80±0.082 μmol·m-2·s-1 in control treatment and 1.37±0.269 μmol·m-2·s-1 in warming treatment in late October 2019 (Fig. 3(a)). Soil temperature peaked in late August 2020, and the highest value was 11.31±0.329 and 12.20±0.736 ℃ in control and warming treatment, respectively. The lowest soil temperature appeared in late October 2019, and was 0.79±0.213 and 2.53±0.214 ℃ in control and warming treatment respectively (Fig. 3(b)). No significant difference existed (p > 0.05, n=6) between soil moisture in the two treatments over the measurement period except in late October 2019 when OTCs promoted soil moisture significantly (p<0.05, n=6). Soil moisture varied in the range of 35.5%-47.3% in control plots and 36.7%-47% in warmed plots (Fig. 3(c)).

thumbnail Fig. 3 Soil respiration(a), soil temperature(b) and soil moisture(c) in warming and control treatment at 2 500 m ASL

a, b indicate significant differences between warming and control treatment; The data are mean ± standard deviation

At an altitude of 2 600 m, warming increased soil respiration. The increases were significant (p < 0.05, n=6) during the measurement period except in late September 2020 (p > 0.05, n = 6). Control plots recorded the highest soil respiration of 5.04 ± 1.285 μmol·m-2·s-1 in middle July 2020 and the lowest value of 0.87 ± 0.159 μmol·m-2·s-1 in late October 2019. Warmed plots recorded the highest soil respiration of 8.27 ± 1.645 μmol·m-2·s-1 in late August 2020 and the lowest value of 1.69 ± 0.377 μmol·m-2·s-1 in late October 2019 (Fig. 4(a)). Warming consistently elevated soil temperature (p < 0.05, n = 6). In control and warming treatment, the highest soil temperature was 9.89 ± 0.177 ℃ and 11.10 ± 0.399 ℃ in late August 2020 and the lowest value was respectively 0.20 ± 0.136 ℃ and 2.05 ± 0.707 ℃ in late October 2019 (Fig. 4(b)), respectively. Soil moisture was above 36% in the two treatments. Not all differences in soil moisture between the two treatments were significant (Fig. 4(c)).

thumbnail Fig. 4 Soil respiration(a), soil temperature(b) and soil moisture(c) in warming and control treatment at 2 600 m ASL

a, b indicate significant differences between warming and control treatment; The data are mean ± standard deviation

At an altitude of 2 700 m, soil respiration increased under OTCs. The effects of OTCs were significant over the measurement period (p < 0.05, n = 6) with the exception of early September in 2019 (p > 0.05, n = 6) (Fig. 5(a)). Except in middle July 2020, soil temperature significantly increased under warming condition (p < 0.05, n = 6). In middle July 2020, soil temperature was 11.46 ± 0.196 ℃ and 11.14 ± 0.323 ℃ under warming and control treatment, respectively (Fig. 5(b)). Under warming condition, soil moisture varied from 39.0% to 48.0%, and under control condition, it varied from 29.2% to 51%. Warming influenced soil moisture significantly (p > 0.05, n = 6) over the measurement period except in early and late September 2019 (p < 0.05, n = 6) (Fig. 5(c)).

thumbnail Fig. 5 Soil respiration(a), soil temperature(b) and soil moisture(c) in warming and control treatment at 2 700 m ASL

a, b indicate significant differences between warming and control treatment; The data are mean ± standard deviation

At 2 500-2 700 m ASL, warming promoted soil respiration and temperature significantly (p < 0.05, n = 6) (Fig. 6(a) and (b)). Soil moisture between the two treatments was not significantly different in early or late September 2019 (p > 0.05, n = 6) while it was significantly different in other measurement time (p < 0.05, n = 6) (Fig. 6(c)). In control and warmed plots, soil respiration was averagely 3.30±2.008 and 5.29±2.851 μmol·m-2·s-1, soil temperature was averagely 6.87 ± 4.018 ℃ and 8.12 ± 3.828 ℃, soil moisture was averagely 44.53% ± 5.939% and 43.30% ± 2.608%, respectively (Fig. 6(a)-(c)).

thumbnail Fig. 6 Soil respiration(a), soil temperature(b) and soil moisture(c) in warming and control treatment at 2 500-2 700 m ASL

a, b indicate significant differences between warming and control treatment; The data are mean ± standard deviation

2.3 Differences in Warming Effects

With OTCs, soil temperature increased by 1.40 ℃ at 2 500 m ASL, 1.38 ℃ at 2600 m ASL and 0.97 ℃ at 2 700 m ASL. Warming averagely increased soil temperature by 1.25 ℃ at 2 500-2 700 m ASL. Warming promoted soil respiration by 1.59 μmol·m-2·s-1 at 2 500 m ASL, 2.26 μmol·m-2·s-1 at 2 600 m ASL, 2.15 μmol·m-2·s-1 at 2 700 m ASL respectively, and by 2.00 μmol·m-2·s-1 on average at 2 500-2 700 m ASL. In late October 2019, warming effect on soil respiration (WEr) reached a maximum of 71.25% at 2 500 m ASL and 373.17% at 2 700 m ASL. At 2 600 m ASL, WEr in late October 2019 was 94.25% that was slightly lower than the highest value of 100% in late September 2019. At 2 500-2 700 m ASL, WEr peaked at 178.31% in late October 2019, the second largest WEr was 72.06% in late September 2019, and other values ranged from 46.39% to 55.36% (Fig. 7).

thumbnail Fig. 7 Warming effect on soil respiration

2.4 Regression Models

At 2 500 m ASL, soil respiration correlated with soil temperature significantly and positively in control and warming treatment (p < 0.01, n = 36). The regression model was y=0.689e0.180x in control treatment (R2 = 0.887, p = 0, n = 36) (Fig. 8(a)) and was y=0.806e0.181x in warming treatment (R2 = 0.807, p = 0, n = 36) (Fig. 8(b)), where y was soil respiration, x was soil temperature, indicating that soil temperature alone accounted for 88.7% and 80.7% of the variation in soil respiration in control and warming treatment, respectively. Soil respiration and soil moisture were negatively correlated (p < 0.05, n = 36) under warming treatment and not correlated (p > 0.05, n = 36) under control treatment.

thumbnail Fig. 8 Regression models between soil respiration and soil temperature in control(a) and warmed(b) plots at 2 500 m ASL

At 2 600 m ASL, soil respiration and soil temperature were correlated significantly and positively (p < 0.01, n = 36) in the two treatments. Their relationship was further described using y=0.976e0.159x, where y was soil respiration, x was soil temperature, in control treatment (R2 = 0.817, p = 0, n = 36) (Fig. 9(a)) and y=1.390e0.159x, where y was soil respiration, x was soil temperature, in warming treatment (R2 = 0.785, p = 0, n = 36) (Fig. 9(b)). Soil temperature alone accounted for 81.7% and 78.5% of the variance in soil respiration in control and warming treatment, respectively. Soil respiration and soil moisture were correlated significantly and positively in control treatment (p < 0.01, n = 36) and not correlated in warming treatment (p > 0.05, n = 36). In control treatment, their relationship was expressed by y=14.122x2.137 (R2 = 0.367, p = 0, n = 36), where y was soil respiration, x was soil moisture, which meant soil moisture alone explained 36.7% of the variation in soil respiration.

thumbnail Fig. 9 Regression models between soil respiration and soil temperature in control(a) and warmed(b) plots at 2 600 m ASL

At 2 700 m ASL, in control plots soil respiration had a significant positive correlation with soil temperature (p < 0.01, n = 36) as well as soil moisture (p < 0.01, n = 36). The function y=0.757e0.182x (R2 = 0.888, p = 0, n = 36), where y was soil respiration, x was soil temperature, further described soil respiration's relationship with soil temperature (Fig. 10(a)).

thumbnail Fig. 10 Regression models between soil respiration and soil temperature in control(a) and warmed(b) plots at 2 700 m ASL

The function y=14.670x1.898 (R2 = 0.354, p = 0, n = 36), where y was soil respiration, x was soil moisture, expressed its relationship with soil moisture. It was inferred that soil temperature was a stronger controller than soil moisture.

In warmed plots, soil respiration and soil temperature had a significant positive correlation (p < 0.01, n = 36), and their relationship was expressed by y=2.476e0.089x (R2 = 0.476, p = 0, n = 36), where y was soil respiration, x was soil temperature, indicating soil temperature was responsible for 47.6% of soil respiration variation (Fig. 10(b)). Soil respiration was not significantly correlated with soil moisture (p > 0.05, n = 36).

At 2 500-2 700 m ASL, soil respiration had a significant positive correlation with soil temperature in the two treatments (p < 0.01, n = 108). The function y=0.811e0.172x (R2 = 0.853, p = 0, n = 108), where y was soil respiration, x was soil temperature, and y=1.498e0.134x (R2 = 0.612, p = 0, n = 108), where y was soil respiration, x was soil temperature, described their relationship in control and warming treatment respectively (Fig. 11(a) and (b)). It was obvious that 61.2% and 85.3% of the variance in soil respiration were respectively regulated by soil temperature alone with and without warming. There was a significant positive correlation between soil respiration and soil moisture in control treatment (p < 0.01, n = 108) but not in warming treatment (p > 0.05, n = 108). In control treatment, their relationship was described by y=11.764x1.809 (R2 = 0.232, p = 0, n = 108), where y was soil respiration, x was soil moisture, with 23.2% of the variance in soil respiration explained by soil moisture alone.

thumbnail Fig. 11 Regression models between soil respiration and soil temperature in control(a) and warmed(b) plots at 2 500-2 700 m ASL

3 Discussion

3.1 Factors Affecting the Response of Soil Respiration to Warming

Temperature was one of the most important factors regulating soil respiration. Temperature increase stimulated soil respiration[23,24]. Yadugiri and Tiruvaimozhi[25] reported soil respiration almost doubled with soil temperature increased by 1.4 ℃ in a warming experiment with OTCs in tropical montane grasslands. Xu et al[26] observed soil respiration significantly increased as temperature was elevated by 2 ℃ with infrared heaters in a warming experiment for 13 years in a tallgrass prairie in Oklahoma, USA. Lu et al[27] suggested warming accelerated soil respiration by 9.0% and used the response ratio to measure warming effect in a meta-analysis. Similarly, we found that temperature was the most important factor regulating soil respiration and warming with OTCs promoted soil respiration in the subalpine meadow, Mount Wutai in this study.

Soil moisture affected the response of soil respiration to warming. Reynolds et al[28] observed warming promoted soil respiration under sufficient soil water, but insufficient soil water reduced respiration. Zhang et al[13] and Chen et al[29] showed warming decreased soil moisture, and consequently soil respiration reduced in grassland in Inner Mongolia and the Qinghai-Tibet Plateau in China. Zhang and Hong[30] found soil respiration did not increase under warming condition with infrared heating devices due to low soil moisture in the desert steppe, Inner Mongolia, China. Yu et al[31] suggested soil respiration was more sensitive to warming in wetter environments. In our study, moderate soil moisture ranging from 35.6% to 48.5% had no inhibitory effect on the response of soil respiration to warming. Because of higher evaporation under warming, soil moisture was prone to be higher in control plots than that in OTC plots over experiment period except in late October 2019. In late October 2019, snow melted in OTCs due to warming which increased soil moisture while snow still covered the ground outside the OTCs (Fig. 12). Similarly, Wu[32] speculated that soil thawing due to warming might promote soil moisture temporarily in a desert steppe.

thumbnail Fig. 12 A warmed plot without snow and a control plot(a) with snow in the subalpine meadow(b), Mount Wutai in late October 2019

Labile carbon pool affected the response of soil respiration to warming. Soil contains two carbon pools. One is labile carbon pool, which is sensitive to temperature, the other is recalcitrant carbon pool, which is less or not sensitive to temperature[17, 33, 34]. Due to exhaustion of labile carbon pool, soil respiration would become less responsive after a period of time, showing acclimatization to warming. Luo et al[35] quantified acclimatization of soil respiration to warming for the first time and speculated that acclimatization might weaken positive feedback between climate change and terrestrial carbon cycle. Liu et al[36] revealed that warming reduced decomposable carbon and soil moisture resulting in a weaker response of heterotrophic respiration to warming in alpine meadows, the Qinghai-Tibet Plateau. However, soil respiration did not show acclimatization to warming in our study. The subalpine meadow in Mount Wutai was a natural pasture, the grazing cattle from Grain Buds to White Dew according to China's Twenty-Four Solar Terms could increase belowground biomass or carbon allocation to roots while decrease aboveground plant biomass, thereby increasing labile carbon input to soil[14,37]. MBC in soil reflected the size of soil microbial biomass[38], and belonged to labile carbon pool for soil nutrient transformation. In our study, MBC in soil was relatively high and was not significantly different between treatments. We speculated sufficient labile carbon pool due to grazing might be responsible for responsive soil respiration.

Available nitrogen affected the response of soil respiration to warming, too. Plants could only absorb and utilize available nitrogen. Both NH4+-N and NO3--N were the main forms of available nitrogen in soil and were main nutrients for plants[39]. Available nitrogen could affect plant grow and then affect soil respiration[40]. In this study, comparatively high NH4+-N and NO3--N indicated sufficient available nitrogen. Therefore, the response of soil respiration to warming could not be inhibited by available nitrogen.

3.2 Differences in Warming Effects

Generally, warming effect on soil respiration was stronger in late October 2019 in this study. It averaged 178.31% across the selected altitudes. In late October 2019, the weather was so cold that no green grass left. Soil respiration was low due to limited microbe and root activities in the cold weather. Experimental warming with OTCs at this time of the year effectively increased soil temperature and mainly promoted heterotrophic respiration, resulting in higher warming effect on soil respiration.

During the growing season, warming could improve soil microbial activity and plant root physiological activity, thereby increasing soil heterotrophic and autotrophic respiration[41]. Warming-induced increases in soil respiration were largest in middle July, 2020 in this study. Because at this time of the year the biological vitality was vigorous, and both heterotrophic and autotrophic respiration increased greatly with temperature increasing.

An altitude gradient was characterized by obvious climatic changes within a short distance. Microbial composition along an altitude gradient varied as temperature changed[42-44]. Patterns of microbial diversity at 2 900- 3 055 m were different from those at lower altitudes in Mount Wutai[45]. We speculated that soil respiration at higher altitudes (2 800-3 061 m) in the alpine meadow, Mount Wutai should have distinctive features compared with this study. In order to fully understand soil respiration in montane meadow, it is necessary to conduct more warming experiments in higher elevations in future.

3.3 Regression Relationships

Li et al[46] reported a significant linear regression relationship existed between soil temperature and soil respiration under both warming and control conditions, and their correlation was weaker under warming condition than control condition. Tian et al[47] showed that 76.9% and 25.8% of variation in soil respiration were controlled by soil temperature and soil water content alone in the Abies nephrolepis forest in Mount Wutai. In our study, soil temperature explained more variations of soil respiration under control condition than those under warming condition which indicted warming decreased the reliance of soil respiration on soil temperature. Warming also reduced the dependence of soil respiration on soil moisture in this study. Based on regression models, soil respiration was mainly influenced by soil temperature in the subalpine meadow, Wutai Mountain. Therefore, global warming should promote soil respiration in the subalpine meadow, Wutai Mountain in the future.

4 Conclusion

Our experimental warming with OTCs in situ increased soil respiration and soil temperature in the subalpine meadow, Mount Wutai. Soil temperature was the main factor affecting soil respiration, and warming reduced the reliance of soil respiration on soil temperature as well as soil moisture. Therefore, in the future, global warming should promote soil respiration and alter its relationships with environmental factors in the subalpine meadow, Mount Wutai.

Acknowledgments

The authors gratefully thank graziers in the grassland, Mount Wutai for their help in the field work.

References

  1. Guo Z X, Ma W Y, Zhao C Y, et al. The effect of snow on soil respiration rate in subalpine meadows of the Qilian Mountains [J]. Acta Ecologica Sinica, 2019, 39(9): 3297-3308(Ch). [Google Scholar]
  2. Wang Y H, Gong J R, Liu M, et al. Effects of grassland-use on soil respiration and litter decomposition [J]. Chinese Journal of Plant Ecology, 2015, 39(3): 239-248(Ch). [CrossRef] [Google Scholar]
  3. Akiyama T, Kawamura K. Grassland degradation in China: Methods of monitoring, management and restoration [J]. Grassland Science, 2007, 53(1): 1-17. [CrossRef] [Google Scholar]
  4. Ministry of Ecology and Environment of the People's Republic of China. Bulletin of China's ecological environment [EB/OL]. [2022-05-31]. https://www.mee.gov.cn. [Google Scholar]
  5. Hu Z D, Liu S R, Shi Z M, et al. Diel variations and seasonal dynamics of soil respirations in subalpine meadow in western Sichuan Province, China[J]. Acta Ecologica Sinica, 2012, 32(20): 6376-6386 (Ch). [CrossRef] [Google Scholar]
  6. Bond-Lamberty B, Thomson A. Temperature-associated increases in the global soil respiration record [J]. Nature, 2010, 464(7288): 579-582. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  7. Houghton J T, Ding Y, Griggs D J, et al. Climate Change 2001: The Scientific Basis [M]. Cambridge: Cambridge University Press, 2001. [Google Scholar]
  8. Chen X W, Post W M, Norby R J, et al. Modeling soil respiration and variations in source components using a multi-factor global climate change experiment [J]. Climatic Change, 2011, 107(3): 459-480. [NASA ADS] [CrossRef] [Google Scholar]
  9. Melillo J M, Steudler P A, Aber J D, et al. Soil warming and carbon-cycle feedbacks to the climate system [J]. Science, 2002, 298(5601): 2173-2176. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  10. Carey J C, Tang J W, Templer P H, et al. Temperature response of soil respiration largely unaltered with experimental warming [J]. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(48): 13797-13802. [CrossRef] [PubMed] [Google Scholar]
  11. Crowther T W, Todd-Brown K E O, Rowe C W, et al. Quantifying global soil carbon losses in response to warming [J]. Nature, 2016, 540(7631): 104-108. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  12. Noh J N, Kuribayashi M, Saitoh T M, et al. Different responses of soil, heterotrophic and autotrophic respirations to a 4-year soil warming experiment in a cool-temperate deciduous broadleaved forest in central Japan [J]. Agricultural and Forest Meteorology, 2017, 247: 560-570. [NASA ADS] [CrossRef] [Google Scholar]
  13. Zhang L X, Yang J, Gao Q Z, et al. Effects of simulated warming and precipitation enhancement on soil respiration of stipa krylovii steppe [J]. Chinese Journal of Agrometeorology, 2013, 34(6): 629-635(Ch). [Google Scholar]
  14. Sharkhuu A. Soil and ecosystem respiration responses to grazing, watering and experimental warming chamber treatments across topographical gradients in northern Mongolia [J]. Geoderma, 2016, 269: 91-98. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  15. Kirschbaum M U F. Soil respiration under prolonged soil warming: Are rate reductions caused by acclimation or substrate loss? [J]. Global Change Biology, 2004, 10(11): 1870-1877. [CrossRef] [Google Scholar]
  16. Chen S T, Wang J, Zhang T T, et al. Climatic, soil, and vegetation controls of the temperature sensitivity (Q10) of soil respiration across terrestrial biomes [J]. Global Ecology and Conservation, 2020, 22: e00955. [CrossRef] [Google Scholar]
  17. Chen J, Cao J J, Liu Y, et al. The response and uncertainties of soil respiration to experimental warming [J]. Journal of Earth Environment, 2013, 4(4): 1415-1421 (Ch). [Google Scholar]
  18. Li X J. Substrate availability and soil microbes drive temperature sensitivity of soil organic carbon mineralization to warming along an elevation gradient in subtropical Asia [J]. Geoderma, 2020, 364: 114198. [NASA ADS] [CrossRef] [Google Scholar]
  19. Hou W Z. Records of Wutai Mountain [M]. Taiyuan: Shanxi People's Publishing House, 2003(Ch). [Google Scholar]
  20. Dong K H. Resources and utilization of mountain meadow on the Wutai Mount [J]. Grassland of China, 1994, (3): 29-32(Ch). [Google Scholar]
  21. Cao Y L, Cui H T, Liu H Y, et al. Remote sensing analysis of alpine landscape of Wutai mts., Shanxi Province [J]. Acta Geographica Sinica, 2001, 56(3): 297-306(Ch). [Google Scholar]
  22. Zhang Y P, Jiang Y, Liu Q R, et al. Impacts of grazing on herbage quality of the alpine and subalpine meadows within Wutai Mountain [J]. Acta Ecologica Sinica, 2011, 31(13): 3659-3667(Ch). [Google Scholar]
  23. Rustad L, Campbell J, Marion G, et al. A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming [J]. Oecologia, 2001, 126(4): 543-562. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  24. Wu Z T, Dijkstra P, Koch G W, et al. Responses of terrestrial ecosystems to temperature and precipitation change: A meta-analysis of experimental manipulation [J]. Global Change Biology, 2011, 17(2): 927-942. [NASA ADS] [CrossRef] [Google Scholar]
  25. Yadugiri V, Tiruvaimozhi. Soil respiration in a tropical montane grassland ecosystem is largely heterotroph-driven and increases under simulated warming [J]. Agricultural and Forest Meteorology, 2019, 276/277: 107619. [Google Scholar]
  26. Xu X, Shi Z, Li D J, et al. Plant community structure regulates responses of prairie soil respiration to decadal experimental warming [J]. Global Change Biology, 2015, 21(10): 3846-3853. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  27. Lu M, Zhou X H, Yang Q, et al. Responses of ecosystem carbon cycle to experimental warming: A meta-analysis [J]. Ecology, 2013, 94(3): 726-738. [CrossRef] [PubMed] [Google Scholar]
  28. Reynolds L L, Johnson B R, Pfeifer-Meister L, et al. Soil respiration response to climate change in Pacific Northwest prairies is mediated by a regional Mediterranean climate gradient [J]. Global Change Biology, 2015, 21(1): 487-500. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  29. Chen J, Luo Y Q, Xia J Y, et al. Differential responses of ecosystem respiration components to experimental warming in a meadow grassland on the Tibetan Plateau [J]. Agricultural and Forest Meteorology, 2016, 220: 21-29. [NASA ADS] [CrossRef] [Google Scholar]
  30. Zhang Y, Hong M. Response of soil respiration to experimental warming and nitrogen addition in Inner Mongolia desert steppe [J]. Acta Agrestia Sinica, 2014, 22(6): 1227-1231(Ch). [Google Scholar]
  31. Yu C Q, Wang J W, Shen Z X, et al. Effects of experimental warming and increased precipitation on soil respiration in an alpine meadow in the northern Tibetan Plateau [J]. Science of the Total Environment, 2019, 647: 1490-1497. [NASA ADS] [CrossRef] [Google Scholar]
  32. Wu Q. (2019) Long-Term Effects of Warming and Nitrogen Addition on the Stability of Plant Community in a Desert Steppe [D]. Hohhot: Inner Mongolia Agricultural University, 2019(Ch). [Google Scholar]
  33. Davidson E A, Trumbore S E, Amundson R. Soil warming and organic carbon content [J]. Nature, 2000, 408(6814): 789-790. [CrossRef] [PubMed] [Google Scholar]
  34. Grace J, Rayment M. Respiration in the balance [J]. Nature, 2000, 404(6780): 819-820. [CrossRef] [PubMed] [Google Scholar]
  35. Luo Y Q, Wan S Q, Hui D F, et al. Acclimatization of soil respiration to warming in a tall grass prairie [J]. Nature, 2001, 413(6856): 622-625. [CrossRef] [PubMed] [Google Scholar]
  36. Liu H K, Lv W W, Wang S P, et al. Decreased soil substrate availability with incubation time weakens the response of microbial respiration to high temperature in an alpine meadow on the Tibetan Plateau [J]. Journal of Soils and Sediments, 2019, 19(1): 255-262. [Google Scholar]
  37. Gao Y H, Schumann M, Chen H, et al. Impacts of grazing intensity on soil carbon and nitrogen in an alpine meadow on the eastern Tibetan Plateau [J]. Journal of Food Agriculture & Environment, 2009, 7: 749-754. [Google Scholar]
  38. Wu J S, Lin Q M, Huang Q Y, et al. Soil Microbial Biomass—Methods and Application [M]. Beijing: China Meteorological Press, 2006(Ch). [Google Scholar]
  39. Liao S X, Ren Y T, Yuan X B, et al. Effects of fencing on contents of soil ammonium nitrogen and nitrate nitrogen in the Loess Plateau, northern China [J]. Pratacultural Science, 2016, 33(6): 1044-1053(Ch). [Google Scholar]
  40. Zhang X Y, Yan W D, Zheng W, et al. Effects of nitrogen deposition on soil respiration of Pinus elliottii [J]. Chinese Agricultural Science Bulletin, 2012, 28(22): 5-10(Ch). [Google Scholar]
  41. Ma Z L, Liu M, Zhao W Q. Influencing mechanisms of warming on the soil respiration components in an alpine scrubland [J]. Ecology and Environmental Sciences, 2019, 28(3): 636-642(Ch). [Google Scholar]
  42. Zeng X M, Feng J, Chen J, et al. Microbial assemblies associated with temperature sensitivity of soil respiration along an altitudinal gradient [J]. Science of the Total Environment, 2022, 820: 153257. [NASA ADS] [CrossRef] [Google Scholar]
  43. Ren C J, Zhou Z H, Guo Y X, et al. Contrasting patterns of microbial community and enzyme activity between rhizosphere and bulk soil along an elevation gradient [J]. Catena, 2021, 196: 104921. [CrossRef] [Google Scholar]
  44. Frindte K, Pape R, Werner K, et al. Temperature and soil moisture control microbial community composition in an Arctic-alpine ecosystem along elevational and micro-topographic gradients [J]. The ISME Journal, 2019, 13(8): 2031-2043. [CrossRef] [PubMed] [Google Scholar]
  45. Luo Z M, Liu J X, Bao J B, et al. Elevational distribution patterns and assembly mechanisms of soil fungal community in Mount Wutai, Shanxi, China [J]. Acta Ecologica Sinica, 2020, 40(19): 7009-7017(Ch). [Google Scholar]
  46. Li Y, Hasbagan G, Hu G Z, et al. Effects of warming on respiration in alpine meadow in the Qinghai-Tibetan Plateau [J]. Acta Ecologica Sinica, 2020, 40(1): 266-273(Ch). [Google Scholar]
  47. Tian Z J, Liu X Y, Chen C L, et al. Temporal and spatial variations of soil respiration of Abies nephrolepis forest in Wutai Mountains [J]. Chinese Journal of Ecology, 2020, 39(12): 3952-3960(Ch). [Google Scholar]

All Tables

Table 1

The geographical coordinates of three plots in Mount Wutai

In the text
Table 2

Soil chemical properties in warmed and control plots at different altitudes

In the text

All Figures

thumbnail Fig. 1 The location of the study area and the sample sites
In the text
thumbnail Fig. 2 The open top chamber for warming in the field
In the text
thumbnail Fig. 3 Soil respiration(a), soil temperature(b) and soil moisture(c) in warming and control treatment at 2 500 m ASL

a, b indicate significant differences between warming and control treatment; The data are mean ± standard deviation
In the text
thumbnail Fig. 4 Soil respiration(a), soil temperature(b) and soil moisture(c) in warming and control treatment at 2 600 m ASL

a, b indicate significant differences between warming and control treatment; The data are mean ± standard deviation
In the text
thumbnail Fig. 5 Soil respiration(a), soil temperature(b) and soil moisture(c) in warming and control treatment at 2 700 m ASL

a, b indicate significant differences between warming and control treatment; The data are mean ± standard deviation
In the text
thumbnail Fig. 6 Soil respiration(a), soil temperature(b) and soil moisture(c) in warming and control treatment at 2 500-2 700 m ASL

a, b indicate significant differences between warming and control treatment; The data are mean ± standard deviation
In the text
thumbnail Fig. 7 Warming effect on soil respiration
In the text
thumbnail Fig. 8 Regression models between soil respiration and soil temperature in control(a) and warmed(b) plots at 2 500 m ASL
In the text
thumbnail Fig. 9 Regression models between soil respiration and soil temperature in control(a) and warmed(b) plots at 2 600 m ASL
In the text
thumbnail Fig. 10 Regression models between soil respiration and soil temperature in control(a) and warmed(b) plots at 2 700 m ASL
In the text
thumbnail Fig. 11 Regression models between soil respiration and soil temperature in control(a) and warmed(b) plots at 2 500-2 700 m ASL
In the text
thumbnail Fig. 12 A warmed plot without snow and a control plot(a) with snow in the subalpine meadow(b), Mount Wutai in late October 2019
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.