Catena 159 (2017) 84–92
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Environmental controls on soil respiration in alpine meadow along a large altitudinal gradient on the central Tibetan Plateau
Jingxue Zhaoa,b,⁎, Ruicheng Lia, Xiang Lib, Lihua Tianc a Department of Ecology, College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China b Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China c Institute of Qinghai-Tibetan Plateau, Southwest Minzu University, Chengdu 610041, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Alpine meadow Soil respiration Temperature sensitivity Tibetan Plateau Environmental controls
Little is known about how environmental factors modify spatiotemporal variations of soil respiration (Rs) and its temperature sensitivity (Q10) in alpine grasslands on the Tibetan Plateau (TP). We conducted an altitudinal experiment across lower and upper limits of alpine meadows on the central TP. Soil respiration and related environmental factors were observed at each of 7 altitudes (from 4400 m to 5200 m) during the growing seasons of 2012 and 2013. Soil temperature (ST) rather than soil moisture (SM) was the major abiotic factor controlling seasonal variation of Rs in alpine grasslands across the seven altitudes. In addition to ST and SM, plant biomass is also an important factor controlling seasonal trends of Rs. The seasonal mean Rs increased with increasing altitude up to 4950 m, and then decreased above 4950 m. Below-ground biomass (BGB), ST and SM have direct eﬀects on Rs, and the altitudinal trends of Rs can be well-predicated from these three variables (R2 = 0.65). The Q10 values of seasonal Rs generally increased with increasing altitude. Alpine meadows generally have higher Q10 values comparing with steppe meadows. Along the altitude gradient, Q10 was negatively correlated with ST, but positively correlated with SM, AGB, BGB and SOC. The results suggested that future climate warming would enhance Rs rates more dramatically in high-altitudes grasslands, and changes in vegetation structure under climate change might have a large impact on Rs in these alpine grasslands.
1. Introduction Soil respiration (Rs) is a major carbon ﬂux from terrestrial ecosystems to the atmosphere (Luo et al., 2001; Davidson et al., 2006), and even small perturbations to the global Rs ﬂux can have the potential to signiﬁcantly alter patterns of both carbon cycling and climate (Cox et al., 2000; Davidson and Janssens, 2006; Piao et al., 2009). Soil respiration is a composite ﬂux of autotrophic root respiration and heterotrophic microbial respiration, interacting with both abiotic and biotic factors. The temporal and spatial variation of Rs is complex and is controlled by diﬀerent environmental factors, such as temperature, water available, substrate available, climate conditions and even human disturbance (Raich and Tufekciogul, 2000; Wan and Luo, 2003; Cao et al., 2004; Davidson and Janssens, 2006; Geng et al., 2012). A better understanding of how Rs respond to environmental factors is essential to improve C cycle models and forecast possible response of ecosystem C cycling to future climate change. Temperature is considered as major factor controlling Rs (Lloyd and Taylor, 1994; Saito et al., 2009). However, the dependence of Rs on soil
temperature may be much less when respiratory substrate or soil water becomes limiting factor (Davidson et al., 1998; Wohlfahrt et al., 2008; Wagle and Kakani, 2014). Especially in arid ecosystems, precipitation and soil moisture are considered to be critical to determining seasonal changes of plant growth and Rs (Jia et al., 2014), which usually also confound the relation of Rs with soil temperature (Qi and Xu, 2001; Wagle and Kakani, 2014; Jiang et al., 2015). Q10 is a common indicator of temperature sensitivity of Rs, which is an important ecological parameter in ecosystem carbon cycle models. Increasing evidences reveal that Q10 is no longer a reﬂection of temperature sensitivity, but an integration of several confounding ecosystem processes (Janssens and Pilegaard, 2003; Davidson and Janssens, 2006). In the ﬁeld study, a large number of studies show that the variation of Q10 value is largely regulated by both biotic and abiotic factors (Xu and Qi, 2001a; Janssens and Pilegaard, 2003; Peng et al., 2009; Xu et al., 2015), and these environmental factors are spatially heterogeneous. The Q10 values resulted from ﬁeld measurements were not a constant value, but varied with geographic locations. However, how these environmental factors controlling the spatial variability of Q10 are still poorly understood
Corresponding author at: Department of Ecology, College of Urban and Environmental Sciences, Peking University, No. 5 Yiheyuan Road., Haidian District, Beijing 100871, China. E-mail address: [email protected]
http://dx.doi.org/10.1016/j.catena.2017.08.007 Received 31 December 2016; Received in revised form 26 July 2017; Accepted 7 August 2017 0341-8162/ © 2017 Elsevier B.V. All rights reserved.
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precipitation was 338.7 mm and 365.4 mm in 2012 and 2013, respectively. The vegetation types changed from the steppe-meadow dominated by S. capillacea at 4400–4500 m, to the alpine meadow dominated by K. pygmaea at 4650–5200 m. Other coexisting species mainly included A. tapete, Arenaria lancangensis, Potentilla nivea, Carex atrofusca etc. Soils in this region are poorly developed, and soil proﬁle diﬀerentiation is relatively weak and the soil layer is thin. The soil types changed from alpine steppe soil at 4400–4500 m to alpine meadow soil at 4650–5200 m. Detailed information about soil properties can be found in Ohtsuka et al. (2008). Pasture for domestic yaks and sheep is the main land-use type in this region. The stock rate would be higher at lower altitudes because the most severely degraded grasslands were found at 4300–4500 m. In August 2005, seven HOBO weather stations (Onset Inc., Bourne, MA) were set up at 4400 m, 4500 m, 4650 m, 4800 m, 4950 m, 5100 and 5200 m along the slope. Air temperature (1.5 m aboveground) and precipitation were recorded at 30-min intervals. Seven 20 × 20 m plots were set nearby the HOBO weather stations at each of the 7 altitudes. Detailed information is found in Zhao et al. (2016a).
(Hirota et al., 2009; Moriyama et al., 2013). Grasslands occupy extensive areas in terrestrial ecosystems, and considerable attention has been given to carbon cycles in grassland ecosystems due to their potential importance in the global carbon budget and climate change (Adams et al., 1990; Cao et al., 2004; Wang and Fang, 2009). Among these ecosystems, alpine grasslands, which contain enormous stores of carbon belowground, are assumed to release through soil respiration under warming condition (Yang et al., 2008; Piao et al., 2012). Especially on the Tibetan Plateau (TP), where the climatic has become warmer and wetter, and alpine grassland ecosystems will experience more rapid changes in temperature and precipitation than ecosystems at lower elevations (Yang et al., 2014; Pepin et al., 2015; Bosch et al., 2017). Given that temperature and precipitation are key drivers of ecosystem biogeochemical processes, the increase in temperature and precipitation on TP will likely have direct implications for Rs (Rustad et al., 2001; Saito et al., 2009). However, despite large number of manipulative experiments on the response of Rs to changing temperature in alpine grasslands, no consensus has been reached (Kato et al., 2004; Saito et al., 2009; Zhu et al., 2015). Additionally, how the high-altitude ecosystems response to the future climate warming is still uncertain. In mountains areas, altitudinal gradients are generally correlated with climate gradients and changes on vegetation communities over short geographic distances, which are considered well suited to study the long-term eﬀects of climate and vegetation impact on ecosystem processes (Rodeghiero and Cescatti, 2005; Raich et al., 2006; Zimmermann et al., 2010). Thus, knowledge on how environmental factors governing Rs along the altitudinal gradient is necessary to predict the response of alpine grasslands to future climate change. The TP hosts the highest and largest alpine grassland ecosystems worldwide. During recent decades, the TP has experienced a more rapid warming than other regions in the world (Yang et al., 2014; Pepin et al., 2015). How the TP's alpine grasslands response to future climate change has attracted extensive concern and large number of manipulative experiments was conducted to solve the puzzles (Kato et al., 2004; Lin et al., 2011; Jiang et al., 2013; Lu et al., 2013; Peng et al., 2014; Chen et al., 2017). Most of these observations were mainly conﬁned within one site, except for Geng et al. (2012), whose study suggested that belowground biomass and soil moisture well explained the spatial variability of Rs in Tibetan alpine grasslands. Bosch et al. (2016) compared the ability of six regression models in predicting Rs and suggested that regression model based on precipitation performs best in calculating Rs on the TP. However, few reports have addressed on altitudinal variation of Rs and Q10 and their controlling factors on the TP. To ﬁll this gap, we conducted an altitudinal experiment across lower and upper limits of alpine meadows (4400–5200 m) along the south-facing slope of Nyaiqentanglha Mountains on the central TP. Seasonal Rs and related environmental factors were observed at each of 7 altitudes during the growing season of 2012 and 2013. In this study, we used opaque chamber-based CO2 eﬄux (measured by Li-Cor 8100) to explore altitudinal variation of Rs and their driving factors. We aim (1) to test altitudinal changes of Rs and Q10 in alpine grasslands and (2) to clarify the relationships of Rs and Q10 with environmental factors during the growing season.
2.2. Field sampling and measurements of respiration The CO2 eﬄux without aboveground vegetation measured in dark chamber was considered as soil respiration (Rs). During the growing seasons (June to September) of 2012 and 2013, diurnal variation (08:00–18:00, local time) of Rs at 2–3 h intervals was measured once a month using the opaque chamber of Li-8100 103 automatic soil CO2 ﬂux system (LI-COR Biosciences, Lincoln, NE, USA). The sampling frequency of Re measurements for each month during the growing season was low because of diﬃculty in the ﬁeld campaign at high altitudes. Given that the altitudinal measurements were made under similar weather conditions of sunny days, the sampling procedure would be ﬁne for a comparison along altitudes at a certain point in time. At beginning of measurement, ﬁve chloride collars (diameter: 20 cm; height: 5 cm) were inserted 3 cm into the soil for measurement of respiration at each of the 7 altitudes. All the collars were installed at least 24 h prior to the measurements in order to reduce disturbance. Before Rs measurement, the plants within those collars were clipped to the ground level and collected in the envelopes, and the dry matter weight was calculated to reﬂect dynamics of aboveground biomass during growing season. To determine the diﬀerences in soil temperature (ST) and moisture (SM) across altitudes, ST and SM at 5 cm were measured simultaneously with Rs using a digital temperature sensor (Type E, OMEGA Engineering, Inc., Stamford, CT, USA) and a Time Domain Reﬂectometer with a handheld push probe (Type ML2x, Delta-T Devices Ltd., Burwell, Cambridge, United Kingdom) attached to the Li-8100 system. 2.3. Measurements of plant biomass and soil organic carbon We set up ﬁve quadrats (0.5 × 0.5 m) at each of the 7 altitudes. In total, 35 quadrats were sampled at seven altitudes from 4400 to 5200 m. The maximum above-ground biomass (AGB) was harvested in each quadrat in mid-August of 2012 and 2013, which was dried in an oven at 65 °C for 48 h. At the same time, below-ground biomass (BGB) was measured by collecting ﬁve soil cores (diameter: 5.0 cm; depth: 30 cm) in each quadrat. BGB samples were washed oﬀ the soil by a 2mm sieve and dried at 65 °C for 48 h. We collected top soil samples (0–10 cm in depth) with a soil auger (diameter: 3.0 cm) from each quadrat in mid-August of 2012 and 2013. After removal of any visible roots, soil samples were air-dried at room temperature and sieved for measuring soil organic carbon (SOC) (Nelson and Sommers, 1982). Brieﬂy, 0.5 g of sieved soil samples were digested with 5 ml of 1 N K2Cr2O7 standard solution, and then mixed with 10 ml of concentrated H2SO4 at 180 °C for 5 min with an oil bath furnace, followed by titration of the digests with standardized FeSO4 (Shang et al., 2012; Chen
2. Materials and methods 2.1. Study site This study was conducted along the south-facing slope of the Nyaiqentanglha Mountains (30°30′–30°32′N, 91°03′E), which was located in the zonal ecotone between alpine Stipa steppe and alpine Kobresia meadow on the central TP (Fig. 1). According to the meteorological observation at Damxung station, the mean air temperature for the year was 1.7 °C and the annual precipitation was 479 mm. The mean air temperature was 2.7 °C and 2.4 °C, and the annual 85
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Fig. 1. Location of the study site on the central Tibetan Plateau and photograph of the 7 sampling sites surveyed within southeast-facing slope of the Nyainqentanglha Mountains along an altitudinal gradient from 4400 to 5200 m.
Fig. 2. Seasonal variations in (a) daily mean air temperature (Tair) and precipitation (PPT), (b) soil temperature (ST) and moisture (SM), (c) above-ground biomass (AGB), and (d) soil respiration (Rs) at each of 7 altitudes during the growing season of 2012 and 2013. Bars indicated SE of mean, n = 5.
relate ST, SM, AGB, BGB and SOC, based on the theoretical knowledge of the major factors controlling the spatial pattern of Rs at large scale (Geng et al., 2012). A χ2 goodness-of-ﬁt test was employed to test whether the model was a reasonable explanation of the observed patterns (P < 0.05 indicated a poor ﬁt). The index of root mean squared error of approximation (RMSEA) was used as an additional test to conﬁrm the adequate model ﬁt (RMSEA < 0.05 and high P-values indicated a close ﬁt). It is diﬃcult to set replicate altitudes at such high altitude regions, and only a ﬁeld site at each altitude was set in this study. However, as is descripted above, we set up ﬁve replicate quadrats (0.5 × 0.5 m) at each of the 7 altitudes, and that would be ﬁne for
et al., 2015). 2.4. Statistical analysis One-way analysis of variance (ANOVA) and the Tukey-HSD test were applied to assess the diﬀerences in ST, SM, AGB, BGB, SOC, Rs and Q10 among diﬀerent altitudes. We also analyzed the simple linear relationship between Rs and Q10 and the related environmental factors (i.e. ST, SM, AGB, BGB and SOC). In addition, we used structural equation modeling (SEM) to partition the total eﬀect of variables on Rs into direct eﬀects and indirect eﬀects. A path model was developed to 86
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a comparison along altitudes at a certain point in time. In addition, our path model totally has 13 variable parameters, and we totally got 70 data (5 × 7 × 2) in the path model which can meet the model required data amount based on Rules of thumb for sample size (James Grace). The temperature sensitivity (Q10) of Rs was calculated according to the Van't Hoﬀ equation,
changes in AGB well associated with the seasonal variation in Rs at each altitude, suggesting that plant growth and respiratory substrate also lead an important role in controlling seasonal variation in Rs.
Rs = αe bT ,
The growing-season average Rs ranged from 1.05 to 2.81 μmol m− 2 s− 1. The seasonal mean Rs increased with increasing altitude up to 4950 m, and then decreased above 4950 m (Fig. 3a). At the peak of the growing season, Rs was also well related with ST, SM, AGB, BGB and SOC along the altitudinal gradient (Fig. 4a–e). It is evident that, from SEM (Fig. 5 and Table 4), Rs can be well-predicated from BGB, ST and SM (R2 = 0.65). However, AGB and SOC only had strong indirect positive eﬀects on Rs, even though there were signiﬁcant relationships between these two variables and Rs (Fig. 4c–e). Above-ground biomass (0.339) and SOC (0.441) only had indirect effects on Rs through their direct eﬀects on BGB (Table 4).
3.1. Microclimates, plant biomass and soil organic carbon
3.4. Temperature sensitivity of Rs
Across the 7 altitudes, air temperature showed a typical pattern that peaked in June and declined in September in 2012 and 2013 (Fig. 2a). Most of the precipitation events concentrated in the beginning of the growing season (from mid-June to early-July) and in the latter part of the growing season (from later August to mid-September) (Fig. 2a). During the growing season of 2012 and 2013, mean ST in the growing season generally decreased with increasing altitude, whereas SM typically increased with increasing altitude (Fig. 2b and Table 1). Vegetation types varied from alpine steppe-meadow to typical alpine meadow along the altitudinal gradient. Additionally, species composition also varied greatly among altitudes, with higher altitudes dominant by sedges and lower altitudes dominant by grasses (Table 2). Plant biomass varied dramatically and showed an increasing level from May to August, before decreasing in September (Fig. 2c). At the peak of the growing season, the AGB and BGB increased with increasing altitude up to 4950 m, and then decreased above 4950 m (Table 1). A similar altitudinal pattern was found for SOC (Table 1). Moreover, we found that AGB showed a strong correlation with BGB (R2 = 0.81, P < 0.001) and SOC (R2 = 0.59, P < 0.001) at the peak of the growing season in our study sites.
The Q10 values of Rs ranged from 1.27 to 2.80 along the altitude gradient. Across the 7 altitudes, the Q10 values showed a similar altitudinal pattern in both 2012 and 2013, and generally increased with increasing altitude (Fig. 3b). Typical alpine meadows which were major located at higher altitudes (4650–5200 m) have higher Q10 values comparing with steppe meadows that located at lower altitudes (4400–4500 m). Across altitudes, Q10 were positively correlated to AGB, BGB and SOC (Fig. 4h–j). Additionally, Q10 was negatively correlated with ST, but positively correlated with SM, AGB, BGB and SOC (Fig. 4f–g).
3.3. Altitudinal variation of Rs
in which Q10 was calculated as,
Q10 = e10b. Where, α is the intercept of Rs when T is 0 °C and b reﬂects the temperature sensitivity of Rs. All statistical analyses were performed using SPSS 19.0 for Windows, and all signiﬁcant diﬀerences were taken at P < 0.05.
4. Discussion 4.1. Factors aﬀecting seasonal variation of Rs Temperature is considered as an important control over the seasonal variation of respiration rate in many types of grassland (Saito et al., 2009; Zeng et al., 2014; Peri et al., 2015). However, within a season, the temperature dependency may be much less, especially when other factors become limiting (Davidson et al., 1998; Wohlfahrt et al., 2008; Wagle and Kakani, 2014). In the present study, the patterns of seasonal change in Rs generally followed ST throughout the experimental period along the altitude gradient, which was consistent with previous studies in alpine grasslands (Saito et al., 2009; Wang et al., 2010; Lin et al., 2011; Shen et al., 2015) and in other ecosystem (Wang et al., 2015). However, ST explained more of the seasonal variation of Rs at higher altitudes than lower altitudes. Our data also indicated that ST and SM contributed diﬀerently to the regulation of the seasonal patterns of Rs along the altitudinal gradient, and other site-speciﬁc factors might be responsible for the observed variation in Rs, especially at lower-altitude sites. In the alpine regions, high-altitude grasslands were likely suﬀered longer freezing period, and these extreme low temperatures could also
3.2. Factors aﬀecting seasonal changes in Rs Soil respiration increased exponentially with ST at − 5 cm, and ST alone could explain 20.8%–66.1% of the seasonal variation in Rs along the altitude gradient, when all data were pooled together (Table 3). However, such correlation was weaker at the lower altitudes compared with the higher altitudes. Seasonal variation in Rs showed signiﬁcant relationship to SM only at 4400 m, 4500 m and 4650 m, but SM explained only 0.8%–23.7% of Rs variation based on a linear model (Table 3). Moreover, AGB varied dramatically over the growing season and showed a similar seasonality pattern as Rs (Fig. 2c–d). The seasonal
Table 1 Altitudinal variations in growing season air temperature (GST), growing season precipitation (GSP), soil temperature (ST), soil moisture (SM), aboveground biomass (AGB), belowground biomass (BGB) and soil organic carbon (SOC). Values (mean ± SE) are means of total ten plots in 2012 and 2013 at each altitude. Diﬀerent letters indicate a signiﬁcant diﬀerence between altitudes (P < 0.05). Altitude (m)
4400 4500 4650 4800 4950 5100 5200
30.51 30.52 30.53 30.53 30.53 30.54 30.54
91.07 91.06 91.06 91.05 91.05 91.05 91.05
9.80 9.04 8.05 7.23 5.85 4.68 3.98
277.72 308.57 335.34 384.91 422.50 461.73 390.30
18.18 18.83 16.60 14.57 14.56 12.93 12.79
± ± ± ± ± ± ±
SM (%) 0.18d 0.10d 0.34c 0.48b 0.49b 0.14a 0.27a
11.50 14.11 15.77 19.87 23.89 25.47 23.77
± ± ± ± ± ± ±
0.15a 0.16b 0.50b 0.38c 0.93d 0.54d 0.55d
AGB (g m− 2)
BGB (g m− 2)
SOC (g kg− 1)
34.82 ± 4.79a 50.93 ± 4.53a 104.43 ± 13.12b 178.33 ± 12.39c 238.60 ± 11.78d 206.07 ± 10.67cd 103.88 ± 3.20b
467.51 ± 30.81a 991.68 ± 50.54a 3294.44 ± 292.74b 6475.70 ± 264.27d 9424.84 ± 267.26f 7747.85 ± 289.75e 4932.10 ± 59.51c
20.23 ± 2.76a 33.54 ± 3.66ab 38.90 ± 1.16b 60.76 ± 3.14c 113.42 ± 4.38e 83.40 ± 6.18d 74.73 ± 3.33cd
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Table 2 Cover (%) of major species at each of seven sites along altitudinal gradient. Values are the average cover for two years at the peak growing season. “–” represents that there is no species at the related site. Species
Kobresia pygmaea Carex thibetica Franch Kobresia robusta Kobresia humilis Stipa capillacea Stipa purpurea Poa litwinowiana Trisetum tibeticum Androsace tapete Androsace bryophylla Oxytropis glacialis Potentilla saundersiana Saussurea haoi Artemisia wellbyi Anaphalis szechuaensis Leontopodium pusillum Stellera chamaejasme Thalictrum alpimum Polygonum viviparum Pedicularis bella Lmmiophlomis rotata Euphorbia altotibetica Rhodiola tibetica Pleurospermum hedinii Microula tibetica Gentiana nubigena Heteropappus boweri Lancea tibetica Taraxacum tibetanum Comastoma falcatum Potentilla bifurca Primula pumilio
Sedges Sedges Sedges Sedges Grasses Grasses Grasses Grasses Cushions Cushions Legumes Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs Herbs
– 9.05 10.00 13.94 37.01 13.86 10.18 16.78 – – 9.85 – – 15.44 16.9 5.00 32.08 – – – – – – 12.06 5.64 5.00 – – – – 12.66 –
– 13.51 12.48 16.39 34.39 20.86 15.13 18.17 – – 28.06 5.00 18.06 14.28 25.5 26.88 11.50 – – – – – – – 6.20 – 10.00 – 15.63 5.00 5.00 5.00
35.37 11.50 10.42 20.49 26.50 25.00 16.14 – – – 12.53 10.92 7.50 8.08 – 20.71 – 7.50 – – 5.00 – – 11.00 5.81 5.00 – 6.67 13.11 7.50 9.46 5.00
41.92 11.31 5.83 12.22 21.11 15.00 12.33 – 21.75 10.91 13.39 9.90 14.44 10.00 13.5 11.50 – 8.39 9.35 – 13.59 7.00 5.00 16.92 6.67 14.17 – – 13.79 13.75 15.00 6.13
59.34 12.99 9.32 12.14 – – 8.25 – 22.41 10.00 13.43 10.69 10.75 10.83 – – – 8.44 14.78 6.08 18.09 7.50 – – – 12.82 – 5.97 18.75 – – 6.20
61.89 9.93 11.20 9.30 – – 10.36 – 27.01 30.51 14.27 10.85 13.77 – – 5.00 – 8.65 – 6.84 – – 5.00 – – 7.54 – 7.31 – 6.25 – 5.53
45.39 15.01 15.00 16.67 – – 11.66 – – 35.77 – 11.67 7.92 – 13.00 11.06 – – – 7.08 – – 7.22 11.50 – 5.71 – 7.02 – 5.00 – 6.18
confounded by precipitation and SM (Wagle and Kakani, 2014; Jiang et al., 2015). In this study, we found that the seasonal patterns of Rs at low altitudes were positively related with SM, which was in agreement with ﬁndings in similar alpine grasslands (Wei et al., 2012; Jiang et al., 2015). In addition, it was reported that low precipitation and the relatively higher temperature may lead to drought stress at lower altitudes (Wang et al., 2013), and the low SM could possibly constrain root and microbial activity by stressing plants and soil microorganisms, and hence might cause a decrease in Rs (Wen et al., 2006; Liu et al., 2009). As it was noted before, Rs is composed of both autotrophic respiration and heterotrophic respiration. Evidences suggest that autotrophic root respiration closely relates with root biomass and accounts a large proportion of the total respiration (Raich and Tufekciogul, 2000; Geng et al., 2012). In this study, however, we only determined the AGB for each collar during the observation. Soil respiration was strongly related to the amount of AGB at each altitude, and such linear relationship between Rs and AGB was also reported in other alpine grasslands (Geng et al., 2012; Huang et al., 2013; Jiang et al., 2015). At the peak of the growing season, the AGB and BGB showed similar altitudinal pattern, and we found that AGB showed a strong correlation with BGB (R2 = 0.81, P < 0.001). Plant biomass varied dramatically over the growing season which could result in a greater availability of labile carbon, and probably contributed to a stronger inﬂuence on Rs (Wan and Luo, 2003; Han et al., 2014). Thus, the plant biomass also exerts great control over seasonal changes of total respiration in alpine grasslands. Comparing with soil temperature, soil water content and plant biomass, soil organic carbon changes quite small at the seasonal scale. Thus, temperature, precipitation and vegetation character together exert great control over seasonal changes of total respiration in alpine grasslands.
Table 3 Regression models (Rs = αebT; Rs = αM + b) for relationships of soil respiration (Rs) to soil temperature (ST) and soil moisture (SM) at each of 7 altitudes during the growing season. NS, no signiﬁcant diﬀerence; *P < 0.05; **P < 0.01; ***P < 0.001. Values in parentheses indicated SE of mean, n = 5. Altitude (m)
Re = αe 4400 4500 4650 4800 4950 5100 5200
0.482 0.665 0.664 0.702 0.778 0.508 0.501
Re = αM + b 4400 4500 4650 4800 4950 5100 5200
(0.034) (0.050) (0.045) (0.076) (0.035) (0.038) (0.029)
0.039 0.039 0.062 0.066 0.077 0.101 0.102
(0.004) (0.004) (0.004) (0.006) (0.002) (0.004) (0.003)
0.256 0.208 0.319 0.303 0.379 0.661 0.651
(0.032) (0.030) (0.029) (0.050) (0.030) (0.030) (0.029)
** ** *** *** *** *** ***
0.03 (0.006) 0.076 (0.009) 0.067 (0.002) 0.037 (0.006) 0.051 (0.010) 0.033 (0.007) 0.010 (0.003)
0.722 0.522 1.096 1.313 1.601 1.486 2.469
(0.046) (0.068) (0.060) (0.089) (0.213) (0.161) (0.123)
0.090 0.237 0.101 0.049 0.075 0.034 0.008
(0.026) (0.025) (0.007) (0.011) (0.019) (0.014) (0.003)
* ** * NS NS NS NS
prevent diﬀusion of substrate and depresses microbial activity (Liu et al., 2002; Moriyama et al., 2013). Thus, alpine grasslands at those high altitudes might be more responsible to future climate change. In addition, precipitation was also an important factor driving ecosystem processes in arid grasslands, and rainfall events strongly aﬀect the phenology and growth of alpine plants (Li et al., 2013; Wang et al., 2013). Precipitation and SM were considered critical in determining the seasonal changes of Rs in arid ecosystems (Qi and Xu, 2001; Wagle and Kakani, 2014), and the relationship between Rs and ST could be 88
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Fig. 5. Eﬀects of abiotic and biotic factors on altitudinal variations of seasonal mean Rs calculated by the structural equation model (χ2 = 6.877, P = 0.332; RMSEA = 0.046, P = 0.440; n = 70) of path analysis based on the observed data at 7 altitudes during 2012–2013. Eﬀects were calculated according to the standardized path coeﬃcients. ST, soil temperature; SM, soil moisture; AGB, above-ground biomass; BGB, below-ground biomass; SOC, soil organic carbon; Rs, soil respiration.
et al., 2013; Wang et al., 2013). A similar altitudinal trend was found in SOC and Rs with plant biomass at present study (Fig. 3). We found a strong positive correlation of Rs with AGB, BGB and SOC at the peak of the growing season, which can be expected because plant biomass provides the original substrates driving soil microbial activities (Bahn et al., 2008). Thus, plant biomass and SOC content plays an important role in altitudinal trends of Rs. Additionally, livestock grazing is the major use for alpine grasslands at our study sites and grazing could also be a major reason for the pattern of Rs we observed along the altitudinal gradient. Although we did not measure grazing intensity along the altitudinal gradient, the grazing pressure should be much higher at low altitudes because the base camp for summer pasturing was mainly located at 4400 m. Our previous study have showed that the stimulations of AGB and BGB due to grazing exclusion generally decreased along altitudinal gradient from 4400 to 5100 m, which suggested that grazing
Fig. 3. Altitudinal variations in (a) growing-season mean soil respiration rate (Rs) and (b) temperature sensitivity (Q10) of Rs in 2012 and 2013. Diﬀerent letters indicate a signiﬁcant diﬀerence among altitudes (P < 0.05). Bars indicated SE of mean, n = 5.
4.2. Altitudinal changes of Rs Recent studies have reported that plant growth was mainly limited by drought at low altitudes, but by low temperature at high altitudes, in the same sites, resulting in a unimodal pattern of vegetation biomass (Li
Fig. 4. Relationships between (a–e) soil respiration and environmental factors and (f–k) temperature sensitivity of Rs and environmental factors during the peak growing season of alpine grasslands. ST, soil temperature; SM, soil moisture; AGB, above-ground biomass; BGB, below-ground biomass; SOC, soil organic carbon; Rs, soil respiration; Q10, temperature sensitivity of Rs.
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higher Q10 values at the higher altitudes. In addition to soil temperature and moisture, the spatial distribution of Q10 could be confounded by other factors, such as plant biomass, litter input, soil carbon storage and substrate availability (Yuste et al., 2004; Davidson et al., 2006; Gershenson et al., 2009). Previous study has indicated that ecosystems with high carbon storage might be more closely coupled with climate than those with low carbon storage (Luo et al., 2001). In this study, Q10 was well related to SOC and plant biomass, suggesting that Q10 was aﬀected by carbon substrate availability along the altitude gradient. Compared with lower altitudes, relatively high plant biomass and low soil carbon content leading to more substrate supply for respiration which could attribute to higher Q10 values at higher altitudes. Furthermore, the nutrient conditions and plant communities also aﬀected the temperature sensitivity of Rs (Moriyama et al., 2013; Gutiérrez-Girón et al., 2015). The quantity and quality of substrates supply might evolve diﬀerently among altitudes with diﬀerent vegetation types, which could potentially confound the temperature dependence of Rs (Zhao et al., 2016b). In our study, typical meadow is located mainly at higher altitudes and contains higher SOC, which also showed higher Q10 values compared with steppe-meadow. These ﬁndings indicate that, on the TP, changes in vegetation structure under future climate change might also have a large impact on Rs in the alpine grasslands.
Table 4 Direct and indirect eﬀects of abiotic and biotic driving factors on altitudinal variations of seasonal mean Rs calculated by the structural equation model of path analysis. Eﬀects were calculated according to the standardized path coeﬃcients. Model Rs BGB AGB SOC ST SM BGB AGB SOC ST SM AGB SOC SM SOC SM
0.612 – – 0.399 0.574
– 0.339 0.441 −0.128 0.365
0.612 0.339 0.441 0.271 0.938
0.554 0.295 − 0.209 –
– 0.426 – 0.596
0.554 0.721 − 0.209 0.596
pressure may decrease with increasing altitude (Zhao et al., 2016a). Compared with the higher altitudes, overgrazing is likely to result in reducing plant biomass and litter input as well as causing a reduction in substrate supply (Ohtsuka et al., 2008; Moriyama et al., 2013), which in turn possibly lead to lower Rs at the lower altitudes. In grassland ecosystem, ST and SM directly control plant growth and microbial activity, which in turn feed back to autotrophic and heterotrophic respiration (Davidson et al., 2006). In this study, ST generally decreased with increasing altitude, whereas SM typically increases with increasing altitude, which was in agreement with ﬁndings in similar alpine grasslands (Li et al., 2013; Wang et al., 2013). We found that Rs were well related with ST and SM along the altitude gradient. At lower altitudes, relatively lower soil moisture as well as higher temperature may lead to drought stress when sometime evaporation exceeds precipitation (Li et al., 2013; Wang et al., 2013), which may cause the decline of Rs. On the contrary, the robust low temperature would also have limited impact on Rs. Previous research showed that ST decreased with increasing altitude, and the decomposition rate at higher altitude was depressed by low temperatures (Coûteaux et al., 2002; Ohtsuka et al., 2008). Additionally, low temperature at higher altitude may also the reasons that vegetation suﬀering from water or nitrogen limitation at higher altitude sites (Luo et al., 2005; Wang et al., 2013). Microbial activity and root respiration also can be decreased due to water deﬁcit which in turn directly lead to decline of total respiration (Chang et al., 2012; Wang et al., 2014). Moreover, soil freezing period is longer at higher altitudes, and this freezing prevents diﬀusion of substrate and depresses microbial activity (Moriyama et al., 2013), which could possibly constrain Rs at those altitudes.
4.4. Limitations of the study Natural climate gradient is well used to study spatial variation in ecosystem processes (Körner, 2007), and altitudinal gradients are considered well suited to study the long-term eﬀects of climate and vegetation impact on ecosystem processes (Raich et al., 2006; Zimmermann et al., 2010). The observations in this study only conducted at one ﬁeld site in each altitude, which make our ﬁndings might be site speciﬁc in the alpine grasslands. In addition, the sampling frequency of Re measurements for each month during the growing season was low because of diﬃculty in the ﬁeld campaign at high altitudes. However, given that the altitudinal measurements were made under similar weather conditions of sunny days, the sampling procedure would be ﬁne for a comparison along altitudes at a certain point in time. Moreover, pasture for livestock is a common land use type of the alpine grasslands (Wu et al., 2008). Grazing intensity, vegetation characteristic and soil factors and as well as climatic usually co-vary with altitudes (Ohtsuka et al., 2008; Wang et al., 2013; Li et al., 2016), which may have signiﬁcant inﬂuence on altitudinal variations of Rs. Although our previous study suggested that grazing intensity may decrease with increasing altitude (Zhao et al., 2016a), a precise observation on how grazing intensity varies along altitudinal gradient and its eﬀect on Re is still needed. In the future, to make our ﬁndings more validity and transferability, long term and continuous observations of Rs at replicate altitudes should be conducted. Meanwhile, other biotic factors, including altitudinal variations of litter decomposition, microbial community, and diﬀerent soil carbon pools might also result in the changes of Re, which also deserved our further studies.
4.3. Temperature sensitivity of Rs Our results showed that the temperature sensitivity of Rs varied greatly across altitudes, and the Q10 values ranged from 1.27 to 2.80, which fall within the range of Q10 values reported by previous studies in alpine regions (Lin et al., 2011; Peng et al., 2009; Fu et al., 2013). Moreover, the higher altitudes tended to have higher Q10 values than lower altitudes, indicating that temperature sensitivity is greater in high-latitude ecosystems than that in low-latitude ecosystems. Generally, soil temperature and moisture are considered as the principal environmental factors controlling Q10 (Xu and Qi, 2001b; Nakano et al., 2008). In the present study, Q10 values were negatively correlated with average ST, whereas positively with SM, similar to the previous studies (Atkin and Tjoelker, 2003; Chen and Tian, 2005; Peng et al., 2009). Compared with lower altitudes, relatively low temperature and high precipitation should be the major environmental factors involved in the
5. Conclusion The seasonal mean Rs increased with increasing altitude up to 4950 m, and then decreased above 4950 m. Across the 7 altitudes, Rs were positively correlated to AGB, BGB and SOC. During the growing season, ST rather than SM were the major abiotic factor controlling the seasonal variation of Rs in alpine grasslands. In addition to ST and SM, plant biomass also leads an important way in controlling altitudinal trends of Rs. The Q10 values of seasonal Rs generally increased with increasing altitude. Additionally, Q10 was negatively correlated with ST, but positively correlated with SM, plant biomass and SOC. The results suggested that future climate warming would enhance Rs rates more dramatically in high-altitudes grasslands, and changes in 90
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