Depth-dependent responses of soil microbial respiration to nitrogen addition in a temperate steppe in Inner Mongolia

doi:10.21203/rs.3.rs-3631729/v1

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Depth-dependent responses of soil microbial respiration to nitrogen addition in a temperate steppe in Inner Mongolia

https://doi.org/10.21203/rs.3.rs-3631729/v1

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Background and aims

Over the past few decades, terrestrial ecosystems have experienced rising atmospheric nitrogen (N) deposition, which further impacts the global carbon (C) budget through soil microbial respiration (MR). However, the effects of N deposition on MR are rarely characterized in subsoil (depth > 10 cm) rather than in topsoil (0–10 cm). This study attempted to elucidate how N deposition regulates MR along the soil profile and its underlying mechanism.

Methods

We collected soil samples and determined MR across three soil layers (shallow, medium, and deep) from a decade-long and five-level N addition experiment in a temperate steppe in Inner Mongolia. We further used structural equation modeling to explore how long-term N addition regulates MR through various biotic (plant attributes and microbial community structure) and abiotic (soil properties) factors across the three soil layers.

Results

The overall response of MR to N addition varied with soil depth, shifting from stimulation in the shallow soil layer (standardized total effect of 0.36) to inhibition in the medium and deep soil layers (-0.34 and − 0.31). The identified direct and indirect pathways by which N addition regulates MR significantly differed across soil layers.

Conclusion

As soil depth increases, the suppressive effect of N deposition on MR provides evidence that increasing N deposition may contribute to C accrual in the subsoil in grassland ecosystems.

nitrogen deposition

soil microbial respiration

soil depth

plant community

microbial community

Soil stores large amounts of organic matter (Zhu et al. 2021; Wilcots et al. 2022), and the microbial decomposition of soil organic matter (SOM) represented by microbial respiration (MR) is a primary source of atmospheric carbon (C) dioxide (Post et al. 1982; Bond-Lamberty and Thomson 2010; Sun et al. 2023). Thus, even small changes in MR could have a large influence on climate feedback (Xing et al. 2022a). Over the last few decades, increased atmospheric nitrogen (N) deposition, primarily due to fossil fuel combustion and agricultural fertilization (Galloway et al. 2008; Liu and Greaver 2010), has been an important aspect of anthropogenic global change (Schlesinger 2008). N deposition is widely believed to be a vital regulator of MR by concurrently changing ecosystem productivity (Liu and Greaver 2010; Qu et al. 2022), plant diversity (Ke et al. 2023; Wang et al. 2023a), soil properties (Puspok et al. 2023; Yuan et al. 2023), and microbial community structure (Janssens et al. 2010; Wang et al. 2023a).

The impact of N deposition on MR is contingent upon the specific context. Given the different N addition levels and diverse geographical regions investigated, the resultant effects often exhibit inconsistency. On the one hand, a certain amount of N deposition generally benefits plant growth and promotes net ecosystem productivity, particularly in N-limited regions (Liu and Greaver 2010; Zhang et al. 2019; Qu et al. 2022). An increased input of plant C typically induces the accumulation of soil organic C (Chang et al. 2019; Du et al. 2020) and microbial biomass (Averill and Waring 2018; Xing et al. 2022a). Therefore, N addition may accelerate MR through the stimulation of plant growth (Xing et al. 2022a). On the other hand, excessive N deposition leads to high N availability and may trigger adverse ecological effects such as soil acidification (Schleuss et al. 2019; Ning et al. 2021; Wilcots et al. 2022), which tends to elevate the soil Al³⁺ concentration (Treseder 2008; Liu et al. 2021a). This, in turn, could reduce microbial biomass and activity (Treseder et al. 2018; Ning et al. 2021) and alter microbial community composition (Ramirez et al. 2010; Ma et al. 2022), ultimately leading to a lower MR. Such suppressed MR was observed in a previous synthesis (Janssens et al. 2010) and several long-term N addition experiments in boreal ecosystems (Maaroufi et al. 2019; Jörgensen et al. 2021; Xing et al. 2022b). Moreover, Lu et al. (2011) reported that while the amount of plant residues entering soil increased under N addition, the total organic C in the soil did not increase significantly. This resulted in a neutral effect of N addition on MR. In light of the persistent and accelerating atmospheric N deposition (Sutton and Bleeker 2013; Ke et al. 2023), it becomes imperative to accurately evaluate its impacts on MR to predict future climate change. Such an evaluation necessitates a comprehensive consideration of several factors: long-term N deposition trends (Smith et al. 2015), varying N rates (Xing et al. 2022a), and strong interactions between biotic and abiotic components (Feng et al. 2022).

Previous studies on the effect of N deposition on MR have mostly focused on topsoil (0–10 cm), where the density of soil microbes is the highest (Xu et al. 2016; Bahram et al. 2018). By comparison, few studies have explored the role of N addition in regulating MR in subsoil (depth > 10 cm). Despite its low C density, subsoil harbors more than half of the total soil C stocks (Rumpel and Kögel-Knabner 2010; Francesca Cotrufo et al. 2021), which is pivotal for global C cycling and climate change (Jobbagy and Jackson 2001). As cumulative N deposition intensifies, the substantial increase in the inputs of reactive N into soil may stimulate C decomposition in subsoil, releasing CO₂ and thereby further accelerating global warming (Jones et al. 2018; Henneron et al. 2022). Apart from this, soils at different layers may receive discrepant amounts and forms of available nutrients (e.g., NO₃⁻-N, NH₄⁺-N, and organic N content) under N deposition (Niu et al. 2021). This implies that the response of subsoil to chronic N deposition may be remarkably different from that of topsoil (Song et al. 2023). However, to date, our understanding of how atmospheric N addition influences MR in subsoil is still limited, hindering a comprehensive prediction of soil C-climate feedback under increasing N deposition scenarios.

Grassland ecosystems account for approximately 40% of the Earth's terrestrial surface (Wang et al. 2023b) and hold nearly 20% of C storage across the globe (Liu et al. 2021b). Ambient N deposition in grassland ecosystems is relatively low (Ma et al. 2022; Xing et al. 2022b), and plant growth is generally N-limited (Du et al. 2020), resulting in high sensitivity to anthropogenic N inputs (Feng et al. 2021). It is therefore important to conduct long-term N addition experiments in grasslands to elucidate the underlying mechanism of the MR response to N addition. Here, to investigate the responses of MR in different soil layers to long-term N deposition, we studied the changes in the soil properties, plant attributes, microbial communities, and MR at different soil depths (0–10 cm, shallow; 10–20 cm, medium; and 20–40 cm, deep) from a 10-year N addition experiment, which was performed in a temperate typical steppe of Inner Mongolia, northern China. Our study had two primary objectives: 1) to examine the responses of MR to N addition across three soil layers and 2) to elucidate the distinct mechanisms driving MR under N addition across different soil layers.

Site description and experimental design

The study was conducted in a typical semiarid steppe ecosystem (42°06′44′′N, 115°29′10′′E; 1450 m above sea level), which is located in Baochang, Inner Mongolia, China. This region is characterized by a dry, monsoon-influenced continental climate with a mean annual temperature of 1.6°C and annual precipitation of 400 mm, with approximately 86% of the precipitation events occurring during the growing season (May-September). In this region, the soil within the top 20 cm is designated chestnut soil (Chinese Soil Taxonomy Research Group, ISSAS, 2001), with 80.50 ± 2.58% sand, 15.3 ± 2.28% silt, and 4.17 ± 0.40% clay. The soil total C and N contents were 23.06 ± 1.98 g kg^− 1 and 2.44 ± 0.14 g kg^− 1, respectively; the soil pH was 8.14 ± 0.17. The annual natural N deposition in this area is approximately 3.43 g N m^− 2 yr^− 1 (He et al. 2016). The plant community in the study area is dominated by Stipa krylovii, Artemisia frigida, and Leymus chinensis.

In 2011, a field experiment with various N addition levels was established to examine the potential effects of N deposition on grassland ecological processes. It comprises 15 plots (each 3 m in width × 6 m in length), consisting of five different levels of N addition (0, 2, 5, 10, and 25 g N m^− 2 yr^− 1), denoted as N0, N2, N5, N10, and N25, respectively. Adjacent plots were separated by a 1-m buffer zone between plots to avoid N fertilizer transfer. Each plot was randomly assigned one of the five N levels, resulting in three replications for each level. N additions were applied two times in early June and July of each year to the surface of each plot as dilute sodium nitrate (NaNO₃) solution using a backpack sprayer. The control plots without N fertilizer (i.e., N0) received equal amounts of water. The water applied to each plot was kept consistent, amounting to approximately 2 mm of local precipitation. Following the estimated annual N deposition rate in northern China (0.042 g N m^− 2 yr^− 1), N2, N5, and N10 simulate N deposition levels for the next 50, 100, and 200 years, respectively. In turn, N25 simulates the effects of N fertilization in agricultural fields (He et al. 2016).

Field sampling and measurement

In July 2021, the plant community structure (including plant species richness, abundance, height, and coverage) was surveyed in a randomly selected 0.5 m × 0.5 m quadrat within each plot. Meanwhile, another 0.5 m × 0.5 m quadrat was selected in the buffer zone of each plot, and all aboveground plant individuals in the quadrat were harvested after investigating the community structure. We dried the harvested aboveground live (green) material of individual plant species at 65°C for 48 h, weighed them, and then converted them to the plot-level aboveground biomass (AGB, g m^− 2) of our corresponding studied plots. Plant diversity (as measured by the Shannon‒Wiener index) for each plot was calculated based on the field investigation (Risser and Rice 1971).

Three randomly located soil cores (8 cm diameter) were taken at depths of 0–10, 10–20, and 20–40 cm for each plot, and subsamples from the same soil layer were composited. In total, 45 soil samples (15 plots × 3 soil layers) were taken in this study. After the removal of stones and visible roots, the soils were sieved with a 2-mm mesh and thoroughly mixed. Each soil sample was then divided into three equal parts. One part was designated fresh soil samples and stored at 4°C for incubation experiments. The second part was air-dried in preparation for subsequent soil property analysis (Table 1), while the third part was freeze-dried and stored at -80°C for assessing soil microbial diversity. All live roots extracted from soil samples were carefully rinsed in deionized water. Fine roots (diameter < 2 mm) were manually picked, dried at 65°C for 48 h, and then weighed to determine the plot-level belowground biomass (BGB, g m^− 2). We also measured fine root properties after weighing (Table 1). Further details on the methods of soil and root physiochemical properties determinations are provided in the Text S1.

Microbial community diversity and C-degrading functional groups

To profile soil microbial communities, we amplified the V3-V4 hypervariable region of the 16S rRNA gene for soil bacteria and the internal transcribed spacer region for soil fungi, and then sequenced the PCR products on the Illumina HiSeq 2500 platform (Illumina). To ensure a standardized sampling effort, we rarefied all samples to achieve an even number of sequences per sample for bacteria (52,293) and fungi (38,704). These sequences were clustered into 15,328 bacterial operational taxonomic units (OTUs) and 3,124 fungal OTUs at a 97% similarity level (Wang et al. 2022). We finally calculated the richness (total number of unique OTUs), diversity (as measured by the Shannon‒Wiener index), and evenness (using Pielou’s evenness index) for both soil bacteria and fungi according to the number of OTUs obtained. The sequencing process of this study was performed at Magi Gene Technology (Guangzhou, China).

Microbial community diversity and C-degrading functional groups

Table 1

Microbial respiration and relevant abiotic/biotic factors (soil properties, plant attributes, microbial community structure, and C-degrading functional groups) and their abbreviations.
Variables	Abbreviation (unit)
Microbial respiration	MR (µg C g^− 1 soil d^− 1)
Soil properties
soil water holding capacity	WHC (g g^− 1 soil)
soil gravimetric water content	SWC (g g^− 1 soil)
soil pH	pH
soil clay content	sand (%)
soil silt content	silt (%)
soil sand content	slay (%)
soil available phosphorus	AP (mg kg^− 1 soil)
soil total carbon content	soil C (%)
soil total nitrogen content	soil N (%)
soil carbon and nitrogen ratio	soil C:N
soil ammonium nitrogen concentration	NH₄⁺-N (mg kg^− 1 soil)
nitrate nitrogen concentration	NO₃⁻-N (mg kg^− 1 soil)
total dissolved carbon concentration	DC (mg C L^− 1)
total dissolved nitrogen concentration	DN (mg C L^− 1)
total dissolved carbon and nitrogen ratio	DC:DN
total dissolved organic carbon concentration	DOC (mg C L^− 1)
Plant attributes
fine root total carbon content	root C (%)
fine root total nitrogen content	root N (%)
fine root carbon and nitrogen ratio	root C:N
total amount of carbon in the fine root	FRBC (g m^− 2)
total amount of nitrogen in the fine root	FRBN (g m^− 2)
Microbial community structure
soil fungal to bacterial ratio	F:B
Microbial C-degrading functional groups
decreased carbon-degrading bacteria	DCB
increased carbon-degrading bacteria	ICB
decreased carbon-degrading fungi	DCF
increased carbon-degrading fungi	ICF

Microbial community diversity and C-degrading functional groups

Based on the sequencing results, we identified six bacterial and four fungal phyla reported to be closely involved in C degradation (Fig. S1) (Pankratov 2012; Tláskal et al. 2017). We calculated the relative abundance of OTUs for these phyla in each soil sample, which is the ratio of the number of OTUs for these phyla to the total number of OTUs in the sample. From linear regression of MR against the relative abundance of these C-degrading phyla (Fig. S2), we classified them into four C-degrading functional groups: decreased C-degrading bacteria (DCB), increased C-degrading bacteria (ICB), decreased C-degrading fungi (DCF), and increased C-degrading fungi (ICF). For each group, the relative abundance of all involved phyla was summed to give the group-level relative abundance that was used in the subsequent analysis.

Soil microbial respiration

We performed a laboratory incubation experiment under varying temperature conditions (i.e., 10-15-20-25-30-25-20-15-10°C) to measure the MR for 45 soil samples by a 32-channel MR automatic measurement system (Zhang et al. 2022). This system incorporated a LI-840A CO₂/H₂O infrared gas analyzer (IRGA; LI-COR Inc., Lincoln, NE, USA), an air pump (LI-COR Inc.), a laptop (Surface Pro 3, Microsoft, USA), a gas exchange system and an artificial weather box (IGS100, Thermo). The MR values (µg C g^− 1 soil d^− 1) were calculated as the mean daily microbial decomposition rates of soil C across different temperatures.

Statistical analysis

We used two-way analysis of variance (ANOVA) to assess the individual and interaction effects of soil depth and N addition on the MR. This analysis was also applied for soil properties, microbial community structure, and C-degrading functional groups. The Mantel test was then performed to examine the linkage between soil properties, plant attributes and microbial community structure.

We finally constructed structural equation models (SEMs) to quantitatively discern the direct and indirect relationships between N addition and MR through various factors across different soil layers (Grace 2009). To make the SEM more concise and intuitive, we used random forest analysis to identify the variables with the greatest explanatory power on MR (Breiman 2001; Yuan et al. 2023). Random forest is a versatile machine-learning algorithm that is used for making predictions and involving both linear and non-linear main effects, and interaction effects (Strobl et al. 2007). It is relatively insensitive to multicollinearity and overfitting; we incorporated all the previously mentioned variables in this step. The importance of the selected variables on MR was quantified using the percentage increase in mean square error (%lnMSE). We chose the top ten important variables that most decreased the mean square error (MSE) over random permutations (Fig. S3). The ten selected variables were used as starting variables for the subsequent analyses. The random forest algorithm was performed using the R package “randomForest” (Liaw and Wiener 2002).

To avoid overfitting caused by high collinearity among the selected variables, we excluded one variable from any pair of predictor variables with a correlation coefficient (Pearson’s r) larger than 0.70 (Fig. S4) (Dormann et al. 2013; Luo et al. 2019; Zhang et al. 2019). Only the retained variables were then included in the initial SEM. Given that no variable was removed for the medium soil layer (all Pearson’s r values were less than 0.7, Fig. S4b), we proceeded with a principal component analysis (PCA) to reduce the dimensions for soil properties (i.e., WHC, AP, soil N, DN, and silt), simplifying our analyses and facilitating interpretations (Yuan et al. 2023). The first two components of the PCA result (soil_PC1 and soil_PC2) captured 72% of the total variance and were thereafter used to characterize the soil properties of the medium soil layer for subsequent SEM analyses (Fig. S5).

The results from correlation analyses revealed that NH₄⁺-N, fungal diversity, AGB, ICF, soil C:N, and WHC were introduced into the final SEM in the shallow soil layer. Comparatively, ICF, root C, fungal diversity, ICB, soil_PC1, and soil_PC2 were considered in the medium soil layer. In the deep soil layer, we constructed the SEM by including soil pH, sand, fungal diversity, plant diversity, ICB, and bacterial richness.

Our conceptual SEMs were constructed based on the assumption that N addition influences MR indirectly by regulating soil–plant–microbe interactions (Fig. 1). To examine whether the direction and magnitude of the N addition effect on MR changed with soil depth, we performed separate SEM analyses for each soil layer. The fits of SEMs were evaluated using chi-squared (χ²) tests and associated p values (Grace 2009). SEM analyses were carried out using IBM SPSS Amos 26 (Amos Development Corporation). All statistical analyses, except for the SEM, were performed by R version 4.2.2 (R Core Team, 2021).

Impacts of N addition on MR and biotic and abiotic factors along the soil profile

The two-way ANOVA revealed significant variations in MR across different N addition levels and soil depths (Table 2). Notably, the interaction effect between N addition and soil depth on MR was not significant. Further analysis showed that the shallow soil layer exhibited the highest MR of 10.35 ± 0.60 µg C g^− 1 soil d^− 1, while the deep soil layer had the lowest MR of 4.67 ± 0.33 µg C g^− 1 soil d^− 1 (Table S1, Fig. S6). Among the studied driving factors, all of them varied significantly across soil layers except for soil C, SWC, DN, DC:DN, fungal diversity, and fungal evenness. N addition treatments significantly altered pH, soil C, soil C:N, NO₃⁻-N, DC, DN, DC:DN, DOC, bacterial richness, fungal richness, and ICF. Only soil sand and silt were affected by the interactions between N addition and soil depth (Table 2).

Table 2

Results of two-way ANOVA reveal the effects of N addition and soil depth, and their interactions on MR, soil properties, soil microbial community structure and C-degrading functional groups. Corresponding F values are shown alongside their respective significance levels (*p < 0.05; **p < 0.01; ***p < 0.001).
Variables	N addition	Soil depth	N addition × soil depth
MR	2.92*	63.18***	2.13
Soil properties
AP	0.87	48.49***	0.64
pH	7.60***	46.27***	1.1
sand	0.89	93.36***	2.76*
silt	0.76	83.35***	2.69*
clay	1.84	69.32***	1.09
WHC	1.22	69.31***	1.1
soil N	0.97	117.18***	1.23
soil C	5.20**	2.57	1.26
soil C:N	5.82**	14.41***	1.69
SWC	0.37	0.32	1.31
NH₄⁺-N	1.86	24.98***	1.22
NO₃⁻-N	11.09***	3.40*	1.01
DC	4.05***	3.31*	1.48
DN	9.40***	1.6	0.98
DC:DN	22.74***	0.05	1.67
DOC	3.16*	14.95***	1.62
Microbial community structure
bacterial richness	22.37***	38.72***	0.89
bacterial diversity	2.44	28.91***	0.28
bacterial evenness	0.71	24.24***	0.25
fungal richness	14.31***	52.04***	1.18
fungal diversity	2.51	2.24	0.44
fungal evenness	0.62	0.16	0.62
F:B	1.4	9.04***	1.3
Microbial C-degrading functional groups
DCB	1.81	112.87***	0.96
ICB	1.24	92.87***	0.42
DCF	0.75	10.73***	0.31
ICF	3.09*	8.88***	1.29

Influence of biotic and abiotic factors on bacterial and fungal community composition across soil layers

As revealed in the Mantel test, the shifts in bacterial and fungal community compositions were tightly linked to some key soil properties and plant attributes (Fig. 2). In the shallow soil layer, N addition, plant diversity, and AGB significantly influenced the alterations in bacterial community composition (all p < 0.01), while N addition, DC, DOC, DC:DN, plant diversity, and AGB had a significant effect on the alterations in fungal community composition (all p < 0.05, Fig. 2a). In the medium soil layer, the alterations in bacterial community composition showed strong correlations with N addition, plant diversity and AGB (all p < 0.01); the changes in fungal community composition were strongly associated with N addition, pH, NH₄⁺-N, DC, DOC, plant diversity, and AGB (all p < 0.05, Fig. 2b). In the deep soil layer, N addition, soil C, soil N, soil C:N, and pH led to significant alterations in bacterial community composition (all p < 0.05), while only silt significantly (p < 0.01) affected the alterations in fungal community composition (Fig. 2c).

Pathways of N addition regulating MR across soil layers

In the shallow soil layer, the SEM accounted for 81% of the variation in MR (Fig. 3a). N addition indirectly affected MR, notably through its positive effects on AGB (standardized path coefficient of 0.82) and WHC (0.54), while concurrently diminishing fungal diversity (-0.38). Direct positive relationships were observed between MR and WHC (0.40), AGB (0.34), and fungal diversity (0.27) (Fig. 3a). Soil NH₄⁺-N (0.84) exhibited a direct as well as an indirect impact on MR through fungal diversity (-0.83) and soil C:N (0.54). Taken together, NH₄⁺-N, WHC, and N addition were the three strongest contributors that governed the variation in MR, with standardized total effects of 0.66, 0.40, and 0.36, respectively (Fig. 3b).

In the medium soil layer, the SEM accounted for 75% of the variation in MR (Fig. 3c). In detail, soil_PC2 (0.38) strongly influenced MR directly and indirectly via fungal diversity (0.59) and root C (0.57), which subsequently shifted ICF (0.38) (Fig. 3c). Soil_PC2, ICF and N addition, with respective standardized total effects of 0.68, 0.51, and − 0.34, eventually outweighed all other identified factors (Fig. 3d).

In the deep soil layer, the SEM accounted for only 25% of the variation in MR (Fig. 3e). N addition exerted a significant indirect effect on MR by decreasing plant diversity (-0.85) and increasing soil pH (0.66). Lower plant diversity and higher soil pH tended to reduce fungal diversity (0.36 and − 0.50, respectively) (Fig. 3e). Fungal diversity was ultimately the most important predictor for MR, with a standardized total effect of 0.43, followed by N addition and pH with standardized total effects of -0.31 and − 0.28, respectively (Fig. 3f).

Distinct responses of MR to N addition across soil layers

Up to now, the dynamics of C in subsoils have largely been ignored under N deposition scenarios. In this study, based on a decade-long N addition experiment in a temperate grassland, we explored the response of soil C decomposition to N addition in different soil layers. We found that the N addition effect on MR varied distinctly across different soil layers (Fig. 3). Specifically, N additions stimulated MR in topsoil (Fig. 3b), which was consistent with Zhang et al. (2021) and Yuan et al. (2023) but different from Xing et al. (2022a) and Tian et al. (2015). Moreover, we found that MR in subsoil decreased in response to N addition, which, however, has rarely been studied previously. To the best of our knowledge, we provide the first empirical evidence concerning the responses of soil C decomposition to N addition along soil depth, which will advance our current understanding of the effects of atmospheric N deposition on grassland soil C dynamics.

Underlying mechanisms of N addition effects on MR

Across soil layers, the mechanisms through which N addition affected MR were different. In the shallow soil layer, N addition had dual effects on MR. On the one hand, N addition accelerated MR through its positive effect on AGB (Fig. S7) and WHC (Fig. 3a). The increase in AGB was predominantly due to the increased N availability in the grassland ecosystem. Increased plant C input into the soil provides microbes with more decomposable substrate, thus promoting MR (Chang et al. 2019; Du et al. 2020). Indeed, the important contribution of AGB to higher MR indicates that the shallow soil layer may be more susceptible to the fluctuation caused by the aboveground parts of plants (Niu et al. 2021). Similarly, a higher WHC is capable of maintaining or promoting soil microbial activity by increasing soil air-filled porosity (Jörgensen et al. 2021). On the other hand, N addition suppressed MR through its negative effect on fungal diversity (Fig. 3a). This finding supports the assertion of a decline in soil fungi involved in the utilization of recalcitrant C compounds under N fertilization (Zhou et al. 2017; Liu et al. 2018). In this layer, the overall positive effect of N addition on MR was greater than the negative effect (Fig. 3a), indicating that considering multiple factors simultaneously allows for a more accurate assessment of the impact of N addition on MR.

In the medium soil layer, we found a whole cascade chain for the N-induced changes in soil–plant–microbe interactions in driving MR (Fig. 3c). Originally, N addition negatively and indirectly affected root C via soil_PC2 (mostly contributed by AP, silt and DN, Fig. S5). Such a negative effect on root C may be explained by existing evidence suggesting that under N fertilization, plants could allocate fewer photosynthates belowground by decreasing the exudation of labile root exudates (Högberg et al. 2010; Rappe-George et al. 2017). Furthermore, the reduction in root C restricted soil microbial activity, leading to a lower ICF (Fig. 3c). This negative path aligned with previous studies, which demonstrated that reduced belowground labile C input from plants might further aggravate soil microbial C limitation (Rappe-George et al. 2017; Ning et al. 2021; Feng et al. 2022; Jiang et al. 2024). Finally, a lower ICF (increased C-degrading fungi) led to a decline in MR (Fig. 3c). Moreover, all indirect effects of N addition on other factors (such as fungal diversity and ICF) were driven by its direct effects on soil_PC2 (Fig. 3c). Such a result, to some degree, was consistent with previous evidence showing that shifts in soil microbial communities are largely caused by changes in soil properties in the context of N deposition (Leff et al. 2015; Xing et al. 2022c). Overall, soil properties played a predominant role in the response of MR to N addition in the medium soil layer (Fig. 3d), presumably because the effect of N-induced soil property changes on the MR outweighed the effects of N-induced changes in soil microbial communities (Niu et al. 2021).

In the deep soil layer, fungal diversity was the primary factor associated with MR under N addition (Fig. 3e). This suggests a tighter interaction of soil fungi in linking soil and plant properties to MR, which resonates with findings from other studies in China’s grasslands (Chen et al. 2018; Wang et al. 2022; Jiang et al. 2023). Soil bacteria exerted a limited role in regulating MR, which may be explained by the fact that bacteria, potentially being less stable, could be out-competed by fungi that are more resistant to the specific environment of subsoils (Chen et al. 2018; Liu et al. 2021a). Our results showed that the negative effect of N addition on MR in the deep soil layer primarily resulted from the N-induced changes in fungal diversity (Fig. 3e). Specifically, N addition indirectly regulated both plant diversity and pH, each of which further affected MR through fungal diversity (Fig. 3e, Fig. S7). Such a decline in plant diversity with increasing N input be might due to increasing light (Eskelinen et al. 2022) and nutrient (Band et al. 2022) competition or ion toxicity (Tian et al. 2016). This is particularly concerning, as it stresses the potential vulnerability of the ecological communities in our surveyed grassland and underscores the imperative to curb anthropogenic N deposition. Furthermore, the decreasing trend in fungal diversity in the deep layer as plant diversity decreased might be attributed to microbial C limitation (Geisseler et al. 2016; Ning et al. 2021; Feng et al. 2022; Jiang et al. 2024). Especially, we found that N addition led to elevated soil NO₃⁻-N and higher soil pH (Fig. S8), which contrasts with the commonly reported trend of lower soil pH under N fertilization (Schleuss et al. 2019; Ning et al. 2021; Ma et al. 2022). Such a discrepancy might be attributed to our use of NaNO₃ as the N fertilizer. When NaNO₃ is applied to the soil, plants tend to absorb more anions (NO₃⁻) than base cations (Na⁺). The remaining cations in the soil can cause it to become alkaline or weakly alkaline (Kissel et al. 2020), which in turn leads to a higher soil pH (Fig. 3e).

We acknowledge that there are still potential uncertainties remaining in this study. For instance, a relatively small number of sampling points could lead to a bias in MR estimation under N deposition. In addition, we observed that the captured total variation in MR (interpreted as R²) markedly decreased with increasing soil depth (Fig. 3). While the mechanisms underlying the effect of N addition in determining MR in both shallow and medium soil layers are relatively clear (R² = 0.81 and 0.75, respectively), that in the deep layer is considerably more complex (R² = 0.25, with the majority of unexplained variation remaining). This complexity deserves further exploration in our future work to improve the accuracy of predicting soil C sink in grasslands.

In summary, our findings revealed distinct MR responses to increasing N addition rates in different soil layers in a temperate steppe. N addition stimulated MR in the shallow soil layer, while it suppressed MR in both the medium and deep soil layers. This implies that the assessment of N deposition effects on MR may have considerable uncertainty if only topsoil is taken into consideration. In particular subsoil contains a higher proportion of recalcitrant SOC stocks that are potentially vulnerable to rapid climate change (Davidson and Janssens 2006; Balesdent et al. 2018). Given the decreasing MR within subsoil under N addition, future atmospheric N deposition might favor deep C sequestration in grassland ecosystems.

Funding

This study was funded by the Guangdong Basic and Applied Basic Research Foundation of Guangdong Province (2022A1515110163) and Forest Science and Technology Innovation Program of Guangdong Province (2023KJCX017).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

YJ and BZ conceived the idea and designed the experiment; YJ, BZ, and JL carried out the field work and data samplings; YJ and WW analyzed the data and wrote the first draft, with substantial input from BZ and XW. All authors contributed to the writing and editing of the final manuscript.

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Depth-dependent responses of soil microbial respiration to nitrogen addition in a temperate steppe in Inner Mongolia

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Abstract

Background and aims

Methods

Results

Conclusion

Figures

Introduction

Methods and materials

Site description and experimental design

Field sampling and measurement

Microbial community diversity and C-degrading functional groups

Microbial community diversity and C-degrading functional groups

Microbial community diversity and C-degrading functional groups

Soil microbial respiration

Statistical analysis

Results

Impacts of N addition on MR and biotic and abiotic factors along the soil profile

Influence of biotic and abiotic factors on bacterial and fungal community composition across soil layers

Pathways of N addition regulating MR across soil layers

Discussion

Distinct responses of MR to N addition across soil layers

Underlying mechanisms of N addition effects on MR

Conclusion

Declarations

References

Supplementary Files

Status:

Version 1