Ecosystem stable isotopic values of carbon and nitrogen (δ13C and δ15N) can offer valuable insight into terrestrial ecosystem. However, the factors influencing δ13C and δ15N in alpine grasslands are not clear enough. To address this gap, we measured δ13C and δ15N for soil and Astragalus plants along an altitudinal gradient (3500 — 5000 m) in the south-western Tibetan Plateau, which was unreported by previous studies. As an ecological indicator reflecting nitrogen (N) cycling, soil δ15N values are higher than the mean value of the plateau. Soil δ13C is significantly correlated with soil carbon (C) content, C and N ratio and soil pH. Additionally, we found significant positive correlation between plant δ13C and MAT or MAP. Soil δ15N is negatively correlated with MAP but no relationship was revealed with MAT. In summary, our study highlights the importance of altitude induced MAT changes in the variation of plant δ13C and the independent control of MAP in the variation of both plant δ13C and soil δ15N.
Research Article
Composition and variation of stable carbon and nitrogen isotope in south-western Tibetan Plateau
https://doi.org/10.21203/rs.3.rs-4146943/v1
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Carbon isotopic composition
Nitrogen isotopic composition
Tibetan Plateau
Altitudinal gradient
Climatic gradient
Grasslands are a major part of the global ecosystem, covering 37% of the earth’s terrestrial area (O'Mara 2012; White et al. 2000), which play an important role in the global biogeochemical cycle of C and N. Grasslands provide crucial ecological services, including C sequestration, that contribute substantially to the mitigation of global climate change (Zhou et al. 2017). N has profound impacts on most aspects of ecosystem functions (Vitousek et al. 1997), such as regulating plant growth and its response to increased carbon dioxide (CO2) concentration (Cleveland et al. 2013; Wang and Houlton 2009). Comprehending the patterns of C and N cycling within and across grassland ecosystems is essential for predicting the patterns of ecosystem C sequestration and trace gas emissions to the atmosphere under the backdrop of climate change (Galloway et al. 2008; Goll et al. 2012; Pinder et al. 2012).
Previous studies suggested that ecosystem stable isotopic values of C and N (δ13C and δ15N) can provide insight into terrestrial ecosystem (Nadelhoffer and Fry 1994; Robinson 2001; Staddon 2004). The variation in δ13C values can indicate differences in plant productivity, soil C storage and water use efficiency (McDowell et al. 2010; Staddon 2004), while δ15N in plants and soils can review information about N cycling processes within ecosystems because it is related to the isotope compositions of N inputs and outputs and the internal N transformations. (Högberg 1997). Moreover, δ15N can reflect the degree of the ‘openness’ of the N cycles, where higher values correspond to greater N losses and a more open N cycling (Handley et al. 1999; Vitousek et al. 1997). Thus, accessing patterns of δ13C and δ15N may help understand the process of C and N cycling in ecosystems.
Studies have shown that plant δ13C is primarily determined by plant’s photosynthetic pathway, whereas soil δ13C composition is largely influenced by the plant-derived C (Swap et al. 2004). Variations in carboxylation reactions cause differences in isotopic fractionation between C3 plants and C4 plants (O'leary 1981). Plants with high water-use efficiency (WUE) should result in enriched δ13C relative to those with lower WUE due to the stomatal closure and associated decrease in C isotope discrimination by leaves under arid conditions (Farquhar and Richards 1984).
Soil 15N can serve as an indicator of N losses within ecosystems due to various soil N transformations and fluxes, which can lead to isotopic fractionation (Amundson et al. 2003). At the global scale, Amundson et al. (2003) documented a consistent decline in soil δ15N values as mean annual precipitation (MAP) increases and mean annual temperature (MAT) decreases, and a similar trend was confirmed in another study (Craine et al. 2015b). In a latest study, the relationship between soil δ15N values and soil water content (SWC) was characterized as significantly upward-concave. Under warm and moist conditions, the increased hydrologically regulated soil N losses with the increase of SWC. Under drier conditions, increased abiotic N losses like ammonia volatilization and proportion of N supplement through bedrock weathering caused higher δ15N (Lai et al. 2023). Nevertheless, certain regional studies exhibited incongruent trends between ecosystem δ15N variations and climatic variables compared to the global scale. In a study conducted in Inner Mongolia, China, δ15N decreases as both MAT and MAP increase (Cheng et al. 2009). Along temperature gradient on Tibetan Plateau (TP), there was no significant variation in soil δ15N concerning MAT or MAP (Yang et al. 2013). Global patterns offer valuable insights into general trends, but at the regional scale, soil δ15N exhibits significant heterogeneity. To gain a deeper understanding of the underlying mechanisms and the relative significance of various drivers in nitrogen (N) cycling, it may be necessary to narrow down the research focus to a smaller spatial extent. The isotopic composition of plants can serve as an indicator of short-term dynamics in N cycling, whereas soil δ15N may provide insights into long-term dynamics (Craine et al. 2015a). As independent variables, plant δ15N increased with decreasing MAT, and increasing MAP respectively (Amundson et al. 2003) on a global scale. However, plant δ15N is primarily viewed as an indicator of N availability. Across gradients of natural N supply or N availability, plant δ15N values generally rise with increasing N availability (Craine et al. 2015a). Sites with higher N availability tend to exhibit plants with higher N concentration, and plant N concentration is typically positively correlated with plant δ15N. This relationship is often more pronounced at regional scales (Craine et al. 2009).
As the youngest and highest plateau on the Earth (Qiu 2008), the TP vulnerable ecosystem that is highly sensitive to environmental changes (Gao et al. 2010). It serves as a "Pre-warning region" for global climate patterns (Wang et al. 2002). Spanning 700,000 km2, alpine grasslands of the TP are exposed to significant atmospheric N deposition due to regional economic development and long-range transport of atmospheric reactive N, reaching the level of 6.96 kg N ha-1 a-1 (Lu and Tian 2007). In this study, we explored δ13C and δ15N values in plants and soils along a transect of approximately 1000 km across south-western Tibetan Plateau which was never reported before. Given the high altitude of the transect, our objective is to identify patterns of δ13C and δ15N natural abundance in relation to environmental variables and determine if the environmental responses differ from those observed in other grassland transects.
2.1 Study area
This study was carried out on an altitudinal gradient (3500 — 5000 m) in the south-western TP (Fig. 1). The longitudinal range of the transect was 79.8 to 92.2°E and the latitudinal range was 28.5 to 33.2°N. This transect covers a MAP gradient from 131 to 591 mm and a MAT gradient from -6.66 to 9℃ (Fig. 2). Map increased steadily as longitude increased until reaching a high level. After that, MAP showed a decreasing trend. However, MAT didn’t correlate with longitude. Along the transect, the land is dominantly covered mostly by alpine steppe and alpine meadow. Steppe occurs at the dry end of the precipitation gradient, and consists of cold-xerophytic monocots such as Stipa purpurea and Carex moorcroftii. Alpine meadow is distributed at the wet end of the precipitation gradient, and dominated by perennial sedges such as Kobresia pygmaea, K. humilis, and K. tibetica (Yang et al. 2013).
2.2 Field Sampling
We conducted sampling during the summers (July to August) of 2019, and sampled soil and plants from 40 sample sites across the western plateau. We went deep into uninhabited areas to avoid anthropogenic disturbances and if those uninhabited areas were inaccessible, sampling sites were chosen to be 500-1000 m away from major roads. At each site, three 1 m × 1 m quadrats were randomly selected. Leaves of Astragali were collected in each quadrat, and the harvested vegetation in each quadrat was collected for subsequent isotope analysis. We collected a total of 45 astragali leaves on different altitude gradients in 40 sites. Three soil cores (5 cm diameter×10 cm depth) were collected in each subplot using an auger bore and mixed into one sample. They were then sealed in plastic bags and stored below 0°C for laboratory measurements.
2.3 Laboratory measurements
All plant and soil samples were dried at 75 °C for 48 h, and then ground to a fine powder (final fineness~5 μm) using a laboratory mixer mill (MM400, Retsch). Samples of the ground material were weighed into tin capsules (plant: 2 mg; soil: 20 mg) for further analyses. Soil δ13C and δ15N were measured at the Environmental Stable Isotope Laboratory (ESIL), Institute of Environment and Sustainable Development of Agriculture, Chinese Academy of Agricultural Sciences, using an IsoPrime 100 IRMS (Elementar, UK) coupled with an Elementar Vario (Elementar, UK), and calibrated with USGS40 (δ13Cvpdb=-26.39±0.04‰, δ15Nair=-4.52±0.06‰) and USGS41a (δ13Cvpdb=36.55±0.08‰, δ15Nair=+47.55±0.15‰) as reference materials. For every 12 samples, a laboratory reference-Gelatin from bovine skin (δ13Cvpdb=14.7±0.2‰， δ15N = 6.9 ± 0.2‰) was inserted for calibration and to monitor stability. Soil total C and total N were measured by Elementar Vario cube (Elementar, Germany). The measurement errors were less than ± 0.2‰ for above analyses.
Soil carbon, nitrogen ratios (C:N) were calculated as the quotient of total C and N. Soil pH was measured using PHS-3S pH meter in a 1:2.5 mixture of the homogenized soil material and pure water. Soil samples were immersed and shook for 1 hour in 2 mol/L KCl solution (5.00 g soil and 30.00 ml KCl solution) to extract NH4+-N and NO3−-N. The extract was filtered and injected into (Smartchem200, Alliance, France) to measure the concentration of NH4+-N, and NO3−-N. Soil moisture is measured using the volumetric moisture content method.
2.4 Statistical analysis
We used ordinary least squares (OLS) regression analysis (i.e., type I regression) to investigate the relationship between vegetation δ15N and climatic factors (i.e., temperature) and soil variables (i.e., soil moisture, soil temperature, soil C and N content, and soil C:N ratio).
Further, The R package “MASS” was used to carry out stepwise regression, which provides various functions for multivariate analysis. The stepAIC function from “MASS” performs stepwise model selection based on the Akaike Information Criterion (AIC). At each step, the variable that most improves the model’s fit (i.e., reduces the AIC) is added, or the variable that least contributes to the model's fit is removed. This process continues until no further variables can be added or removed without increasing the AIC.
Structural equation modelling (SEM) was used to explore the direct and indirect effects between variables and δ15N. We constructed the SEM based on the prior knowledge and the SEM was performed with the R package “piecewiseSEM”. All statistical calculation and figure plotting were conducted with R 5.0.0.
3.1 Soil physicochemical properties
The soil C:N ratios at the 0—10 cm depth ranged from 4.34 to 35.13 with a mean of 13.31, but no significant relationship was found between soil C:N ratios and MAP or MAT. The soil pH values of 0—10 cm depth ranged from 4.61 to 7.43 with a mean of 6.66. Soil pH values was marginally correlated with MAT (p=0.067, R2=0.084), MAP (p=0.058, R2=0.091) or soil δ15N values (p=0.05, R2=0.10).
Compared with soil NH4+, NO3- dominated soil inorganic N pool. Soil NH4+ (p<0.05, R2=0.10) and NO3- (p<0.01, R2=0.20) at the 0—10 cm layer was only corelated with soil N content. The δ15N values of total soil N at the 0—10 cm layer along the transect ranged from 2.83 to 11.26‰ with a mean of 7.57. MAP was significantly correlated with the δ15N values of total soil N at the 0—10 cm layer (p<0.05,R2=0.15).
3.2 Climatic controls of soil and plant δ13C values along the transect
The mean plant δ13C was -26.14 with less fluctuation while soil δ13C fluctuated greatly. Soil δ13C was significantly correlated with soil C content, C:N ratio and soil pH (Fig. 3). Significant positive correlation between plant δ13C and altitude, MAT or MAP was revealed (Fig. 4). Utilizing stepwise regression analysis, a distinct pattern emerged regarding how MAT and MAP influence plant δ13C (only factors with p<0.05 were included in the equation):
Notably, altitude emerged as an indirect factor influencing plant δ13C. Furthermore, SEM revealed that while both MAT and MAP exert regulatory influence on plant δ13C, the respective weigh of MAT is relatively higher δ13C than MAP. MAT was directly controlled by altitude while MAP wasn’t (Fig. 5).
3.3 Climatic controls of soil and plant δ15N values along the transect
We find that the soil δ15N was significantly and negatively correlated with longitude and significantly and positively correlated with latitude along the transect. Considering the correlation of longitude and latitude with MAT and MAP, we analyzed the correlation between soil δ15N and MAT or MAP respectively. Results showed that soil δ15N was negatively correlated with MAP but no relationship was revealed between soil δ15N and MAT (Fig. 6). Stepwise regression analysis also suggested that MAP be the sole factor influencing soil δ15N. In terms of plant δ15N, only plant C is negatively correlated with plant δ15N.
4.1 Factors controlling plant and soil δ13C along the transect
The utilization of δ13C in plants and soil has become an invaluable technique for evaluating the scale and spatial patterns of plant productivity, water use efficiency, and soil carbon (C) turnover rate. (McDowell et al. 2010; Wu et al. 2018). Plant δ13C values are different among various plant species (Zheng and Shangguan 2007) and photosynthetic pathways (C3, C4,CAM). Generally, δ13C values of C3 species vary between −35‰ and −20‰, averaging −27‰ (O'leary 1981). As C3 plants, plant δ13C values of Astragalus are -26.14±0.61 with few fluctuations.
The unique geographical characteristics of the TP, characterized by high cold and high altitude, have a significant impact on the δ13C composition of plants, surpassing the effects observed in other ecosystems. Our results showed that δ13C are negatively correlated with altitude (Fig. 4). Altitude usually indirectly affects the composition of δ13C in the TP by changing factors such as temperature, pressure, and soil moisture. In this study, by establishing an SEM model, we found altitude affect δ13C indirectly through its control over MAT, and MAP exerts its control over δ13C as an independent variable (Fig. 5). Our results are consistent with previous regional-scale which have reported that plant δ13C are positively correlated with MAT induced by rising altitude. Notably, a study revealed that the average δ13C value of plant leaves in high-altitude areas (average altitude: 2500m-5600m) was 2.7 ‰ higher than that in low-altitude areas (average altitude: 900m or below). The increase in δ13C with altitude is primarily attributed to temperature and pressure fluctuations (Korner et al. 1988). Furthermore, investigations have identified a positive relationship between plant δ13C and altitude beyond a certain threshold, suggesting temperature limitation as a key factor in high-altitude regions (Liu et al. 2007). The positive relationship between plant δ13C and MAT are most likely due to the balance between stomatal conductance and carbon assimilation. In arid and semi-arid regions, plants tend to close some stomata to conserve water, resulting in reduced stomatal conductance and lower CO2 content in the leaves, thus decreasing plant δ13C values (Welker et al. 1993). Additionally, higher MAT often signifies that plants are closer to their optimal photosynthetic temperature, leading to increased photosynthetic rates and consequently higher leaf δ13C values (Schleser et al. 1999).
At global scale, the relationship between precipitation gradients and plant δ13C has generally shown either a negative correlation or no correlation (Craine et al. 2009). We found plant δ13C is positively correlated with MAP, and is supported by some other studies from the TP. Specifically, in the northern TP, a study (Guo and Xie 2006) observed an increase in δ13C with higher precipitation, with a rate of 0.46‰/100 mm. Similarly, in the eastern TP, a similar trend was also found (Li et al. 2009). However, this positive relation can be explained as follow: MAT as a driving factor for δ13C pattern is positively correlated with MAP, thus creating the illusion of a significant positive correlation between δ13C and MAP. Notably, we did not observe such a relationship between MAP and MAT in our study. Hence, further investigation is required to uncover the reasons behind the independent influence of MAP on plant δ13C. Overall, our findings suggest that MAT, influenced by altitude, and MAP as an independent variable, jointly determine the patterns of plant δ13C in the southwestern TP, where high altitudes prevail.
Soil primarily originates from plants through leaf and root litter as well as rhizodeposition (Peri et al. 2012). As a result, soil δ13C exhibits correlation with soil C, soil C:N ratio and plant δ13C (Wu et al. 2018; Yang et al. 2015). In this study, soil δ13C showed no correlation with Astragalus δ13C, because semiarid systems are usually covered by C4 plants, and soil δ13C patterns cannot be successfully represented by a single C3 plant. However, soil δ13C of our study was correlated with soil C, soil C:N ratio (C was originated from plants) as other studies, and therefore, soil δ13C is independent of vegetation type and climate.
4.2 Factors controlling plant and soil δ15N along the transect
The responses of N isotope discrimination to various climate factors exhibit complexity (Feng et al. 2020; Luo et al. 2018). At global scale, C4 plants have higher values of δ15N than C3 plants. Plant δ15N values demonstrate a negative correlation with MAP due to the gradual functional group transitions from C4 to C3, resulting in a decrease in plant δ15N values as MAP increases. (Craine et al. 2009). Additionally, when soil nitrogen serves as the dominant nitrogen source for a plant community, plant δ15N values can exhibit a positive correlation with soil δ15N values. This is because the process of plant nitrogen uptake from the soil does not cause significant isotopic fractionation (Wu et al. 2022). In this study, we choose Astragalus to eliminate the differences of δ15N among C3 and C4 species. As a genus from legume, Astragalus can provide an important alternative N source due to their capability for biological N2 fixation under limited N conditions. If the N source is sufficient, the N uptake will be controlled by MAT, resulting in a relatively lower plant δ15N compared with soil δ15N. However, neither MAT nor soil δ15N showed correlation with plant δ15N, indicating Astragalus obtain N from atmosphere. N fixation causes plant δ15N values closer to δ15N of atmosphere (0‰). The mean value of plant δ15N in this study was -0.55, which demonstrate that Astragalus tended to fix N from the atmosphere instead of direct acquisition from the soil. The relatively low plant δ15N deepened the idea that the Tibetan alpine regions are suffering from progressive N limitation (Kou et al. 2020).
Soil δ15N is a valuable and non-invasive indicator that provides an efficient means to assess and monitor N dynamics within an ecosystem (Kahmen et al. 2008; Wang et al. 2014). Its application holds potential for advancing our understanding of the larger-scale nitrogen cycle within ecosystems (Yang et al. 2013). Generally, soil δ15N values fluctuate within the range of -6‰ to 16‰ (Piccolo et al. 1994). In our study, the average soil δ15N was determined to be 7.75, which aligns with the global range. To be specific, our results showed that soil δ15N in south-western TP is higher than that of grasslands across China, even much higher than mean values on TP 10 or 20 years ago (Table 2).
Soil δ15N values are influenced by multiple processes, including as gaseous N loss, net plant N uptake, N leaching, atmospheric N deposition and biological N fixation (Högberg 1997; Robinson 2001). Among these processes, gaseous N loss (16—30‰) and net plant N uptake (5—10‰) affect soil δ15N values most, comparing to N leaching (1‰), atmospheric N deposition and biological N fixation (-2—0‰) (Houlton and Bai 2009; Wang et al. 2014) and was confirmed to be the key processes determining soil δ15N values on TP (Kou et al. 2020). Among our study sites, desert steppes with sparse vegetation are scattered throughout south-western TP and thus net plant N uptake is an unimportant process compared to gaseous N loss. Processes of NH3 volatilization and denitrification control gaseous N loss from the system. As a rule, denitrification is important under anaerobic conditions such as aquatic systems and wet terrestrial systems and is sensitive to temperature (Högberg 1997). However, we observed there is no correlation between soil δ15N and MAT and a study also showed that in arid and semiarid systems, denitrification is usually less important than NH3 volatilization (Xu, 2007). On the other hand, precipitation is one of the most important factors that determine plant production and N uptake and assimilation in arid and semiarid ecosystems (Cheng et al. 2009). Less productive ecosystems have a lower capability to immobilize available N sources and there by NH3 volatilization can easily occur instead of letting N immobilized into plants. Studies also indicate that vegetation can take up air NH3 so that net loss of N through NH3 volatilization can be bring back to soil-plant system under high vegetation-density systems (Frank et al. 2004). This can potentially explain the negative correlation between MAP and soil δ15N values along the transect.
TP is distinguished by its high altitude and the biogeochemical cycles of C and N of south-western TP were poorly understood before. In this study, we demonstrated the important role of altitude by affecting MAT, which resulted in the variation of δ13C. Furthermore, MAP also acted as an independent variable in the determination of δ13C. In terms of soil δ15N, we also found soil δ15N from this region is higher than any other part of TP and it was strongly controlled by MAP. Covered mostly by steppe and desert steppe and these ecosystems are threatened by drought and decrease in precipitation (Yao et al. 2022) , our findings will provide scientific references for future research on C and N biogeochemical cycles in high altitude areas.
Acknowledgement
This study was supported by the National Natural Science Foundation of China (42075164; U21A20185), the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0606), and the National Key R&D Program: Intergovernmental Cooperation in International Science and Technology Innovation (2022YFE0119300).
Funding: Listed in the acknowledgement.
Conflicts of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethics approval: Not applicable.
Consent to participate: Not applicable.
Consent for publication: Not applicable.
Availability of data and material: All data will be available by emailing with corresponding author.
Code availability: Not applicable.
Author’s contributions: Songbo Qu did formal analysis, data imaging and wrote the original draft. Xu-Ri originally formulated the idea, provided funds and developed methodology and reviewed the original draft. Jiaoneng Yu, Ribu Shama, Haojing Lei and Tian-Liang performed the experiments and analyzed the data. Sainbuyan Bayarsaikhan, Azzaya Jukov and Munkhbat Tsendeekhuu conducted fieldwork and provided editorial advice. Almaz Borjigidai provided funds, reviewed the original draft and provided editorial advice.
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Table 1. Plant and soil isotopic composition, C and N content, soil NH4+, NO3-, and soil pH along the transect
|
δ13C (‰) |
TC (%) |
δ15N (‰) |
TN (%) |
C:N |
NH4+ (mg/kg) |
NO3- (mg/kg) |
pH |
|
|
Plant |
-26.14±0.61 |
41.09±3.36 |
-0.55±1.14 |
3.25±0.67 |
13.11±2.54 |
/ |
/ |
/ |
|
Soil |
-18.38±7.34 |
1.35±1.52 |
7.75±2.7 |
0.08±0.05 |
13.31±7.71 |
2.32±2.3 |
6.02±6.1 |
6.66±0.67 |
Table 2. Comparisons of MAT, MAP and soil δ15N values between grasslands in China and this study
|
Site |
MAT (℃) |
MAP (mm) |
Soil δ15N (‰) |
Reference |
||
|
Min |
Max |
Mean |
||||
|
South-western TP |
-1 |
514.6 |
2.8 |
11.3 |
7.75 |
This study |
|
Loose plateau |
9.1 |
520 |
0.97 |
8.97 |
5.65 |
(Shan et al. 2019) |
|
Inner Mongolia |
3.9 |
323 |
-2.5 |
7.4 |
4.7 |
(Cheng et al. 2009) |
|
TP in 2000s |
1.61 |
413.6 |
/ |
/ |
5.2 |
(Kou et al. 2020) |
|
TP in 2010s |
/ |
/ |
/ |
/ |
5.8 |
(Yang et al. 2013) |