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Article

Responses of Alpine Soil Nitrification and Denitrification Rates to Nitrogen Addition Gradient—The Role of Functional Genes

by
Jiaqi Zhang
,
Yinghui Liu
* and
Jingyi Dong
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(2), 245; https://doi.org/10.3390/agriculture13020245
Submission received: 5 November 2022 / Revised: 14 January 2023 / Accepted: 17 January 2023 / Published: 19 January 2023
(This article belongs to the Special Issue Restoration of Degraded Grasslands and Sustainable Grazing)
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Abstract

:
Increases in nitrogen (N) deposition affect the nitrification and denitrification processes that are regulated by microorganisms. With the aim of understanding what happened to the soil nitrification and denitrification rates under the N application gradients, we set a field experiment treated with N at 6 different rates (0, 2, 4, 8, 16, and 32 g N·m−2·yr−1) in 2014. We determined the physico-chemical properties, abundances and community structures of the nitrifiers and denitrifiers, the net nitrification rate (NNR) and the potential denitrification rate (PDR) of soil samples that were collected in 2020. We found that the abundances of ammonia-oxidizing bacteria amoA (AOB amoA), the sum of ammonia-oxidizing archaea amoA (AOA amoA) and AOB amoA increased with the increase in N application rate, but the abundances of nosZ decreased with that. The microorganisms that dominate the ammonia oxidation process could shift from AOA to AOB under high N application rates. Furthermore, the soil microorganisms respond to the N addition preferentially with the abundance changes rather than the community composition changes. Moreover, the NNR increased with the N input, while the decrease in the PDR was due to the decrease in the pH value caused by high N application. The results also showed that the amoA gene abundance explained most (46.3%) of the variation in the NNR. Moreover, the soil moisture and pH explained 44.0% and 27.1% of the variation in the PDR, respectively. The results demonstrated that the NNR and PDR were mainly explained by functional genes abundances and environmental factors, respectively, in alpine meadow soil under sustained N deposition.

1. Introduction

As nitrogen (N) use in industry and agriculture worldwide has increased continually over recent decades, N deposits from the atmosphere to terrestrial ecosystems have grown rapidly [1]. After a period of rapid socioeconomic transformation, China has implemented programs to monitor and control N pollution. Monitoring data show that N deposition in China began to stabilize around 2000 [2]. However, the current overall tendency toward increases in N deposition worldwide will take some time to change. While N is an essential nutrient for plant and microorganism growth and helps to regulate grassland ecosystem productivity and greenhouse gas emissions [3,4,5], some ecosystems, including alpine meadows, are sensitive to N deposition.
Numerous researchers have studied how the N additions affect the nitrification and denitrification rates, but the findings to date have not been consistent. For example, a study conducted in grassland found that the net nitrification rate (NNR) increased when N was added [6] and the potential nitrification rate (PDR) did not change noticeably when N was added [7]. Furthermore, meta-analysis showed that the N deposition rate could affect the response of gross N transformations to N deposition [8]. However, meta-analysis also found that the N addition could stimulate the nitrification and denitrification rates [9,10], and that the response ratio of the nitrification and denitrification rates were positively correlated with N application duration [11]. At present, we have a limited understanding of how the N applications at different rates affect the nitrification and denitrification rates in alpine meadow soils.
Nitrification and denitrification in soil generate N2O emissions from terrestrial ecosystems that account for about two-thirds of the global N2O emissions [12]. The rates of soil nitrification and denitrification are regulated by the corresponding functional genes [13]. Ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) amoA genes generally control the ammonia oxidization process occupying different ecological niches [14,15], so the sum of AOA and AOB amoA could reflect the same functional microbial genes abundance, while the AOA:AOB amoA ratio could indicate the dominant microbe in the ammonia oxidation process. Nitrite (NO2-) reductase is encoded by the nirK and nirS genes, which control the nitrite (NO2-) reduction process, and nitrous oxide (N2O) reductase, which control N2O reduction process, is encoded by the nosZ gene [13,16,17]. The processes of nitrification and denitrification are controlled by the abundances and community structures of the functional genes [18]. Therefore, it is important to explore the factors that influence the abundances and community structures of the functional genes that drive nitrification and denitrification.
From the meta-analyses, the soil nitrification rate was positively associated with the abundance of the AOB amoA gene [11,19]. Researchers also found that the soil nitrification rate had a positive relation with the AOA amoA gene abundance [20,21] and that soil pH was the best indicator of the PDR [22]. Moreover, studies indicated that soil moisture was positively correlated to the PDR [23]. It has been shown that environmental factors explain more of the changes in the soil PDR than the abundances of denitrifiers [24]. However, the nirK and nirS gene abundances were positively correlated with the denitrification rate in some researches [17,25]. The variable results from these studies show that there is little clarity about whether the rates of nitrification and denitrification in soil can be explained by soil functional genes communities or the soil environmental properties.
The overall aim of this scientific research was to probe into how the nitrification and denitrification rates in alpine meadow soil might vary under the N application gradients. To achieve this overall aim, we set up an N application gradient field experiment on the east of the Qinghai–Tibet Plateau to simulate a range of different N deposition patterns. We assumed that the abundances and community structures of nitrifiers and denitrifiers would change because of the N application gradients. We also hypothesized that the nitrification and denitrification rates in the soil would increase when N was added, and that the variations in the soil nitrification and denitrification rates would be influenced mainly by the functional genes abundances.

2. Materials and Methods

2.1. Study Site and Experimental Design

The N application gradient site was located in Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station in Hongyuan County, China (32°48′ N, 102°33′ E). The site was 3500 m above sea level. The mean annual air temperature was 1.5 °C, and the mean monthly air temperature was lowest in January (−9.7 °C) and highest in July (11.1 °C). The mean annual precipitation was 758.8 mm (1961–2020), and from May to September with about 80% of rain falling in the alpine meadow. In 2020, the mean monthly air temperature was lowest in January (−7.4 °C) and highest in July (10.9 °C). The annual precipitation was 1033 mm, and from May to September with about 76% of rain falling (Figure S2). Soil (0–20 cm deep) has been classified as Gelic Cambisols in the World Reference Base for Soil Resources. The vegetation cover of the research meadow is higher than 90% and the dominant vegetation species in the region include Deschampsia caespitosa, Kobresia setchwanensi, Carex schneideri, Anemone rivularis, and Oxytropis kansuensis. The bulk density of the soil (0–20 cm deep) was 0.89 g·cm−3. Total organic carbon and nitrogen contents of the soil were 37 g C·kg−1 and 3.5 g N·kg−1, respectively [26]. The background N deposition on the eastern Qinghai–Tibetan Plateau was reported as 1.23 ± 0.28 g N·m−2·yr−1 [27].
The experimental area for the N applications was established in early 2014. It contains 30 plots (8 × 8 m) with a 3-m buffer between adjacent plots. The experiment was designed as a randomized complete block and the plots receive the N applications with N at 6 different rates (0, 2, 4, 8, 16, and 32 g N·m−2·yr−1, described here as N0, N2, N4, N8, N16, N32, respectively) and each treatment has 5 replicates. The plots are arranged randomly in the research area (Figure S1). N is applied manually on the surface every month from May to September (the growing season) before rainfall as dry NH4NO3.

2.2. Soil Sampling and Physico-Chemical Analysis

In August 2020, soil samples from surface to 20 cm were collected by a core sampler (D = 5 cm) before N was added in August. Three cores of soil were randomly gathered from each plot and were sieved through a 2-mm mesh. Then, one part of the soil samples was stored at −20 °C for analyzing physico-chemical properties. The other part was stored at −80 ℃ for DNA extraction. The soil moisture (SM) was determined after drying 10 g fresh soil in an oven at 65 °C to constant weight. The pH meter was used to test pH value (SevenCompact, Mettler Toledo, Switzerland). The soil leaching liquor was extracted with 0.5 mol·L−1 K2SO4 (40 mL). Inorganic N contents (NH4+-N, NO3−-N) were analyzed by a continuous flow analyzer (AA3, Bran Luebbe, Hamburg, Germany). TOC element analyzer (Liquid TOCII, Elementar, Frankfurt, Germany) was used to determine dissolved organic carbon (DOC) content. The methods for measuring the soil properties have been described elsewhere in detail [28].
The NNR of the soil was determined by the incubator culture method [29]. Fresh soil (10 g) was placed in a 250 mL triangle beaker and then the soils were cultured in an incubator at 25 °C for 14 days. The soil leaching liquor was extracted with 2 mol·L−1 KCl (50 mL) before and after culturing. The NO3−-N content of the extracted solution was measured with a gas phase molecular absorption spectrometer (AJ−3000 Plus, China). The change in the NO3−-N content per gram of dry soil before and after culturing was used to calculate the NNR of the soil in mg NO3−-N·g−1 soil·d−1.
The PDR was determined by the acetylene gas inhibition method [30]. The soil samples (weight of dry soil converted to 4 g) and ultra-pure water (the total water volume was 13 mL) were placed into a 100-mL anaerobic bottle. The anaerobic bottle was shaken at room temperature for 20 min. Then the anaerobic bottle was pumped to vacuum and filled with 10% (V/V) acetylene (an N mixture was used to balance the air pressure). The acetylene gas was mixed by vibrating at room temperature for 20 min. DEA solution (20 mL), prepared by dissolving 56 mg KNO3-N, 288 mg glucose-C, and 2 mg chloramphenicol in 1 L sterile water, was added to the anaerobic bottle. A sample of gas (20 mL) was extracted before culturing, and another sample of gas (20 mL) was extracted after culturing at room temperature for 2 h. The N2O contents were determined by gas chromatograph (Aglilent 7890A GC System, Santa Clara, CA, USA). The difference between the N2O contents before and after culturing represented the PDR of the soil, measured in ng N2O-N h−1·g−1 dry soil.

2.3. Quantitative PCR and High-Throughput Sequencing

The total soil DNA was extracted by a Power Soil DNA Isolation Kit (MoBio, Carlsbad, CA, USA) within two months since the soil was collected. The concentration of the extracted DNA was determined with a spectrophotometer (Nanodrop Technologies, USA), and then we calculated the microbial DNA concentration per dry soil (ug·g−1) and defined this index as the microbial biomass (MB). The abundances of AOA amoA, AOB amoA, nirS, nirK, and nosZ were quantified by 7500 Fast Real-Time PCR (Applied Biosystems). The 20 μL PCR reaction system and gene primer pairs and amplification conditions are shown in Table S1 and Table S2. The amplification efficiency and R2 values were controlled at 80–110% and 0.995–0.999.
The functional genes were sequenced on a high-throughput sequencing platform (Illumina Miseq PE300) with a measured flux of 60,000 pieces. The amplification primers and the functional gene amplification conditions are shown in Table S3. The sequencing data were inspected and filtered through the existing codes of the Meige Gene cloud platform (http://cloud.magigene.com/login, accessed on 1 July 2021), and then were classified into operational taxonomic units (OTU) with a 98% similarity. The raw reads of five genes were submitted into the NCBI Sequence Read Archive under accession numbers PRJNA878936, PRJNA878640, PRJNA878634, PRJNA876623, PRJNA876569.

2.4. Data Analysis

The differences between all the N application gradients were determined by one-way ANOVA with a significance level of 0.05 (SPSSv20.0). Data were first tested for normality and transformed. The LSD test was used to compare the groups for indicators of homogeneity of variance (NO3−-N, DOC, AOA amoA, AOA amoA + AOB amoA, and AOA amoA:AOB amoA). The Dunnet-T3 test was used to compare between groups for indicators of heterogeneity of variance (SM, NH4+, MB, AOB amoA, nirK, nirK + nirS, NNR, and PDR). Data that could not be normally transformed (pH, nirS, nosZ, and nirK:nirS) were analyzed with the Kruskal–Wallis test. The correlations between the soil properties and the functional gene abundances were determined using the Spearman correlation test (SPSS v 20.0). The bacterial community and soil physico-chemical property data were subjected to redundancy analysis (RDA) in Canoco 5. The factors driving the nitrification and denitrification rates were explored using stepwise regression (SPSSv20.0). The differences in the compositions of the functional gene communities were tested using principal coordinates analysis (PCoA) based on the Bray–Curtis dissimilarity and non-parametric multiple variance analysis (Adonis test) using Rv4.2.0. The figures were drawn using Origin2018.

3. Results

3.1. The Effects of N Application with Different Rates on Soil Physico-Chemical Properties

Summary information about the properties of the soil treated with the different N applications is provided in Figure 1. The pH was lower in the N16 and N32 treatments than in N0 (Figure 1b). The NO3− contents were increased under the high N application rates (N8, N16, and N32 treatments) compared to N0 (Figure 1d). The N16 and N32 treatments facilitated the DOC contents compared to N0 (Figure 1e), and the MB was lower in the N8, N16, and N32 treatments than in N0 (Figure 1f). Further, the SM and the NH4+ contents were similar for all the N application gradients (Figure 1a,c).

3.2. The Effect of Different N Application Rates on the Abundances of Nitrifiers and Denitrifiers

The five gene abundances responded differently to the N applications (Figure 2). The AOA amoA gene abundance was similar in all the N application gradients (Figure 2a). The AOB amoA gene abundances were facilitated in the high N application rates (N8, N16, and N32) compared to N0 (Figure 2b, p < 0.05). The amoA gene abundances (the sum of AOA and AOB amoA) were higher in all the N application gradients than in N0 (Figure 2c, p < 0.05). We found that AOA:AOB amoA ratio > 1 under N0 treatments, while AOA:AOB amoA ratio < 1 under N2 to N32 treatments. The AOA:AOB amoA ratio was significantly higher in the control than in the N2, N8, N16, and N32 treatments (Figure 2d, p < 0.05). Additionally, the AOA:AOB amoA ratio was lower for the high N application rates (N16, and N32) than the low N application rates (N2 and N4) applications. The nirK, nirS, and nir gene abundances (the sum of nirK and nirS) were similar for all 6 N application gradients (Figure 2e,f); however, the nosZ gene abundance was lower for the N32 treatment than for the N0 (Figure 2i). The nirK:nirS ratio ranged from 1.12 to 1.19 and did not vary significantly between the N treatments (Figure 2h).

3.3. The Effects of the Different N Addition Rates on the Compositions of the Nitrifier and Denitrifier Communities

We identified the differences in the community structures in the soils treated with the five levels of N and the control using principal coordinates analysis (PCoA) based on the Bray–Curtis dissimilarity. The Adonis test was used to highlight significant differences between the pairs of the different N treatments. The results showed that the community structures between the pairs of the N treatments were not significantly different (Table S4).
The AOA amoA functional group was mainly comprised of Nitrososphaerales, Nitrosopumilales, and Nitrosocaldales (Figure 3a). The Nitrosospira in the AOB amoA functional group were mainly from Cluster-1, Cluster-3b, and Cluster-8b (Figure 3b). The nirK and nirS functional groups were mainly comprised of α-, β-, and γ-proteobacteria (Figure 4a,b). The nosZ functional groups were mainly comprised of Rhizobiales, Pseudomonadales, Rhodospirillales, Burkholderiales, Rhodobacterales, Campylobacterales, and Alteromonadales (Figure 4c).

3.4. Relationships between the Soil Physical and Chemical Properties and the Abundances and Community Structure of Nitrifier and Denitrifier

The AOB amoA and amoA gene abundances had a negative relation with the pH, but were positively correlated with the N application rate, NO3− contents, and DOC contents (p < 0.05, Table 1). The nirK, nirS, and nir gene abundances had a positive relation with the soil SM (p < 0.05, Table 1). The nosZ gene abundance and MB had a positive relation with the pH but had a negative relation with the N application rate, NO3− contents, and DOC contents (p < 0.05, Table 1).
The results from the RDA to explore the main influencing factors of the nitrifier and denitrifier bacterial communities are shown in Figure 5. The pH (F = 18.1, p = 0.002), NO3− (F = 3.4, p = 0.048), and DOC (F = 3.6, p = 0.030) were the main environmental influences on the AOA amoA bacterial communities (Figure 5a, p < 0.05) and explained 68.5% of the total variation. The NO3− contents (F = 6.7, p = 0.014) and the soil moisture (F = 8.7, p = 0.010) were the main environmental influences on the AOB amoA bacterial communities (Figure 5b, p < 0.05) and explained 45.4% of the total variation. The pH (F = 5.7, p = 0.002) was the main influence on the nirS bacterial communities (Figure 5d, p < 0.05) and explained 36.3% of the total variation. The DOC contents (F = 5.9, p = 0.008), NH4+ contents (F = 5.4, p = 0.006), and the soil moisture (F = 4.6, p = 0.028) were the main environmental influences on the nosZ bacterial communities (Figure 5e, p < 0.05) and explained 45.3% of the total variation. However, the nirK bacterial communities were not influenced by the environmental factors (Figure 5c).

3.5. Soil Nitrification and Denitrification Rates in the Soils Treated with Different N Applications

The NNR ranged from 17.38 to 224.29 ug NO3−-N·d−1·g−1 soil. The NNR was higher for all the N application rates than for the control (N0) (Figure 6a, p < 0.05) and was highest for the N16 treatment. The PDR ranged from 112.62 to 577.99 ng N2O-N·h−1·g−1 dry soil. When compared with the control, the different N applications had no significant effect on the PDR (Figure 6b, p < 0.05), and only the PDR in the N32 treatment was lower than that in the control (29.37% lower).
The results from stepwise multiple regression to show the main environmental and microbial influences on nitrification and denitrification are presented in Table 2. The gene abundance of amoA explained most (46.3%) of the variation in the NNR (Table 2, p < 0.001), while the soil moisture and pH explained most (44.0% and 27.1%, respectively) of the variations in the PDR (Table 2, p < 0.001).

4. Discussion

4.1. Impacts of N Applications on the Abundances of Soil Nitrifiers and Denitrifiers

In our research, the N application could promote the net nitrification rate, which produces H+ and decreases the soil pH value (Fig1, b). Meanwhile, N could increase the abundances of nitrifiers (the sum of AOA and AOB amoA), and the pH value had a negative relationship with the abundance of AOB amoA and the sum of AOA and AOB amoA (Table 1), which indicated that the N addition could improve the ability of soil to convert ammonium to nitrate and caused the result that the soil ammonium concentration was not increased with the N inputs. Furthermore, soil acidification caused by high N application could decrease the nosZ abundance (Figure 2, p < 0.05), demonstrating that the microorganisms were not always limited by N and that high N supplements might negatively impact the soil microbial abundance [31], which in turn may decrease the reduction in N2O to N2 and increase the greenhouse gas N2O emission.
Meanwhile, we found that the response of the AOB amoA gene abundance to the N application was more susceptive than the AOA amoA gene. All the N addition gradients had no effect on the AOA amoA gene abundance, but the high N application rates increased the AOB amoA gene abundance compared to the N0 treatment. These results are consistent with an earlier study of an alpine meadow [32]. The different responses of AOA and AOB may reflect their different physiological characteristics, metabolic patterns, and habitat preferences, as they are located in different ecological niches [33]. AOA has a smaller volume and a larger specific surface area than AOB, and the ammonia monooxygenase (AMO) of AOA has a stronger affinity for ammonia nitrogen than that of AOB, so the AMO of AOA can reach saturation at a lower NH4+ concentration, meaning that AOA does not change significantly when N is applied [14]. Meanwhile, we found that the AOA:AOB amoA ratio was > 1 under the N0 treatments, which means that AOA dominates the soil ammonia-oxidizing process, while the AOA:AOB amoA ratio was < 1 under the N2 to N32 treatments, which means that AOB dominates the soil ammonia-oxidizing process. Additionally, the AOA:AOB amoA ratio was lower for the high N application rates (N8, N16, and N32) compared to the low N application rate (N2 and N4) applications, which suggests that the microorganisms that dominate the ammonia oxidation process could shift from AOA to AOB under the N application rates.
Comparison with the control (N0) showed that the N applications had no effect on nirK, nirS, and nir (Figure 2). This finding is consistent with a meta-analysis by Dong et al. [34], who found that denitrifying functional genes (nirK and nirS) in grassland were stable under the N additions, which suggests that the functional gene abundances of the denitrifying microorganisms were not sensitive to the N additions in alpine meadows. The nirK:nirS ratio ranged from 1.12 to 1.19 under the N treatments and did not differ for the different N additions, which displays that nirK dominated the denitrification process numerically and the relative dominance of nirK and nirS did not change under the N applications.
We found that the AOB amoA gene abundance had a positive relation with the NO3− contents (Table 1, p < 0.01), but was not related to the NH4+ contents (Table 1). That was due to the fact that the N inputs could significantly promote the nitrification rate but not the denitrification rate in our research. The nitrification process could convert ammonium into nitrate and caused the result that the soil nitrate but not the ammonium concentration had a positive relationship with N application rates. Furthermore, the nirK, nirS, and nosZ abundances had a positive relation with the SM (Table 1, p < 0.01), as denitrification prefers anaerobic conditions [13]; when the SM is high, soil porosity could decrease and anaerobic conditions could develop.

4.2. Impacts of N Applications on the Nitrifier and Denitrifier Community Compositions

The nitrifier and denitrifier community compositions in these soils were not affected by any N application gradients, indicating that the nitrifying and denitrifying groups in this alpine meadow soil were stable and resistant to changes in nutrient conditions. Meanwhile, considering that the N addition could significantly change gene abundances of nitrifiers and denitrifiers, the results suggest that the soil microorganisms respond to the N addition preferentially with the abundance changes rather than the community composition changes. Other studies have reported similar findings. For example, the community compositions of the AOA amoA, AOB amoA, nirK, and nirS functional groups in both temperate and subtropical forests did not change after N was applied for 3 years (10 g N·m−2·yr−1) [25]. However, after N was applied to temperate steppe soils for 8 years, the community compositions of the AOB amoA and nosZ functional groups changed significantly, but the community compositions of the AOA amoA, nirK, and nirS functional groups did not change [7]. Moreover, another study found that the AOB amoA group shifted, and the AOA amoA group did not shift, when N was added over a period of 3 years (10 g N·m−2·yr−1) [32]. These studies show that the N application duration and rate may be important for shaping the nitrifying and denitrifying groups. Moreover, the responses of the nitrifying and denitrifying groups to the N applications may vary by ecosystem type.
The RDA results highlighted that the pH was the main environmental influence on the AOA amoA bacterial communities (Figure 5a, p < 0.05), which is consistent with the results of previous research [35]. In acidic conditions, NH3 might be transformed to NH4+, and the bioavailability of the NH3 might affect how the different AOA amoA communities adapt [36]. This study showed that NO3− was the main environmental influence on the AOB amoA bacterial communities (Figure 5b, p < 0.05); other studies, however, reported that NH4+ was the strongest influence on the structure of the AOB amoA group [35]. This difference may be evidence that ammonium was converted into NO3− after the nitrification reaction, as NO3− had a strong influence on the community structure of the AOB amoA group. The nirS bacterial communities were strongly influenced by the pH (Figure 5d, p < 0.05), but the nirK bacterial communities were not influenced by the environmental factors (Figure 5c). This shows that the two functional groups had different sensitivities to the soil properties [37,38]. The DOC, NH4+, and the soil moisture were the main environmental influences on the nosZ bacterial communities (Figure 5e, p < 0.05), which suggests that the N additions might affect the community structure of the nosZ functional group through changing the microbial energy and substrate.

4.3. Impacts of N Applications on Nitrification and Denitrification Rates

The N additions triggered different changes in the NNR and PDR. The NNR was higher for all the N application rates than for the control (N0) (Figure 6a, p < 0.05). The increase in the nitrification rate may reflect the weaker competition of plants rather than the microorganisms for N when the inorganic N substrate is added [6]. The PDR for the N32 treatment was 29.37% less than that of the N0 treatment (Figure 6b, p < 0.05), which shows that the soil denitrification processes remained stable when low N was applied. Research also found that the N applications had no effect on the PDR in forest and grassland soil [25,39]. The decrease in the PDR under the N32 treatment may reflect the complexity of the PDR process in soil, from their meta-analysis, concluded that the soil PDR was affected by the N contents, MB, pH, water content, texture, and temperature of the soil [40]. That the PDR of the N32 soil was lower than in the other soils may reflect the acidic conditions.
From their meta-analysis, the AOB amoA abundance had a positive correlation with the potential nitrification rate in numerous ecosystem types [19]. Conversely, studies found that the nitrification rate was related to the AOA amoA abundance [21], while research showed that AOA and AOB both contribute to ammonia oxidation [20]. Here, we found that the sum of the AOA amoA and AOB amoA gene abundances explained most of the NNR (Table 2, p < 0.001), which suggests that the functional gene abundance was the most important influence on the soil nitrification rate. Moreover, we found that the PDR was strongly influenced by the soil moisture and pH (Table 2, p < 0.001). These results are coincident with a range of factors. The soil pH was the best indicator of the PDR [22]. The denitrification rate was also affected by the soil moisture, through influencing the fluidity and aeration of the soil solutes. When the soil moisture is high, the nitrification rate decreases and the denitrification rate increases [23]. In this study, the nitrifier abundances were more sensitive to the N applications than the denitrifier abundances. The nitrification rate was mainly explained by the nitrifier abundances, while the denitrification rate was mainly explained by environmental factors. The results may indicate that the abundance of the denitrifying functional genes does not respond significantly to the N addition, and that environmental factors may be an important explanation for the denitrification rate. The results were different with a meta-analysis that showed that the narG gene abundance was an important control factor of the denitrification rate under the N deposition [11], which may reflect that the environmental factor may be an important explanation for the denitrification rate when the abundance of the denitrifying functional genes does not respond significantly to the N addition.

5. Conclusions

Our results show that the nitrifiers and NNR were more sensitive to N fertilization than the denitrifiers and PDR in alpine meadows, which indicates that soil acidification was mainly affected by the nitrification process producing H+. The N application could change the microorganisms that dominate the ammonia-oxidizing process from AOA to AOB. High N application decreased the nosZ gene abundance, which may increase N2O emission to the atmosphere. Moreover, the community compositions of AOA amoA, AOB amoA, nirS, nirK, and nosZ were not affected by the N addition, which indicates that the soil microorganisms respond to the N addition preferentially with the abundance changes rather than the community composition changes. Our study suggests that the nitrification rate was mainly explained by the nitrifier abundance (46.3%), while the denitrification rate was mainly explained by soil environmental properties (71.1%). These findings will provide a theoretical basis for future model simulation of the nitrification and denitrification processes and suggest that functional gene abundances and soil environmental properties need to be considered more in the simulation of nitrification and denitrification, respectively.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture13020245/s1. Figure S1: Nitrogen application gradient experimental design; Figure S2: Mean monthly temperature and monthly precipitation of the filed site in 2020; Table S1: PCR reaction system; Table S2: Primers and amplification conditions of PCR for functional gene abundances; Table S3: Primers and amplification conditions of PCR for functional gene species composition; Table S4: Primers and amplification conditions of PCR for functional gene species composition.

Author Contributions

All authors contributed to the study conception and design. Experimental design was performed by Y.L. and J.Z., data collection and analysis were performed by J.Z. and J.D. The first draft of the manuscript was written by J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [Grant No. 2019QZKK0608] and National Natural Science Foundation of China [31770519].

Data Availability Statement

Data is contained within the article and Supplementary Material.

Acknowledgments

We would like to thank the reviewers and editor for proofreading and providing helpful suggestions on the manuscript. We thank Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station for providing access to the study site.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. Boxplots showing the physico-chemical properties of the soils with different N application rates. (a) Soil moisture (SM), (b) pH, (c) NH4+ contents, (d) NO3− contents, (e) DOC contents, and (f) microbial biomass (MB). In all, 25–75% represents the interquartile range (IQR). The different lowercase letters indicate significant differences among all the N application treatments (p < 0.05). N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1 application, and N32 is the 32 g N·m−2·yr−1 application.
Figure 1. Boxplots showing the physico-chemical properties of the soils with different N application rates. (a) Soil moisture (SM), (b) pH, (c) NH4+ contents, (d) NO3− contents, (e) DOC contents, and (f) microbial biomass (MB). In all, 25–75% represents the interquartile range (IQR). The different lowercase letters indicate significant differences among all the N application treatments (p < 0.05). N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1 application, and N32 is the 32 g N·m−2·yr−1 application.
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Figure 2. Boxplot of the abundances of the functional genes of the soil nitrifiers and denitrifiers under different N application rates. (a) gene abundance of AOA amoA, (b) gene abundance of AOB amoA, (c) gene abundance of amoA, (d) the ratio of AOA:AOB amoA, (e) gene abundance of nirK, (f) gene abundance of nirS, (g) gene abundance of (nirK + nirS), (h) the nirK:nirS ratio, and (i) nosZ. In all, 25–75% represents the interquartile range (IQR). Different lowercase letters indicate significant differences among all the N application treatments (p < 0.05). N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1 application, and N32 is the 32 g N·m−2·yr−1 application.
Figure 2. Boxplot of the abundances of the functional genes of the soil nitrifiers and denitrifiers under different N application rates. (a) gene abundance of AOA amoA, (b) gene abundance of AOB amoA, (c) gene abundance of amoA, (d) the ratio of AOA:AOB amoA, (e) gene abundance of nirK, (f) gene abundance of nirS, (g) gene abundance of (nirK + nirS), (h) the nirK:nirS ratio, and (i) nosZ. In all, 25–75% represents the interquartile range (IQR). Different lowercase letters indicate significant differences among all the N application treatments (p < 0.05). N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1 application, and N32 is the 32 g N·m−2·yr−1 application.
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Figure 3. The relative abundances of nitrifier community compositions and principal coordinates analysis (PCoA) based on the Bray–Curtis distance. (a) AOA amoA, and (b) AOB amoA. N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1 application, and N32 is the 32 g N·m−2·yr−1 application.
Figure 3. The relative abundances of nitrifier community compositions and principal coordinates analysis (PCoA) based on the Bray–Curtis distance. (a) AOA amoA, and (b) AOB amoA. N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1 application, and N32 is the 32 g N·m−2·yr−1 application.
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Figure 4. The relative abundances of nitrifier community compositions and principal coordinates analysis (PCoA) based on the Bray–Curtis distance. (a) nirK, (b) nirS, (c) nosZ. N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1 application, and N32 is the 32 g N·m−2·yr−1 application.
Figure 4. The relative abundances of nitrifier community compositions and principal coordinates analysis (PCoA) based on the Bray–Curtis distance. (a) nirK, (b) nirS, (c) nosZ. N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1 application, and N32 is the 32 g N·m−2·yr−1 application.
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Figure 5. Redundancy analysis (RDA) showing the correlations between nitrifiers (AOA amoA (a), AOB amoA (b)) and denitrifiers (nirK (c), nirS (d) and nosZ (e)) bacterial communities and the soil physico-chemical properties. * Significant at the 0.05 probability level. SM is the soil moisture. DOC is dissolved organic carbon. MB is microbial biomass.
Figure 5. Redundancy analysis (RDA) showing the correlations between nitrifiers (AOA amoA (a), AOB amoA (b)) and denitrifiers (nirK (c), nirS (d) and nosZ (e)) bacterial communities and the soil physico-chemical properties. * Significant at the 0.05 probability level. SM is the soil moisture. DOC is dissolved organic carbon. MB is microbial biomass.
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Figure 6. Boxplot of the soil nitrification and denitrification rates under different N application rates. (a) Net nitrification rate (NNR), and (b) potential denitrification rate (PDR). In all, 25−75% represents the interquartile range (IQR). The different lowercase letters indicate significant differences among all the different N application treatments (p < 0.05). N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1application, and N32 is the 32 g N·m−2·yr−1application.
Figure 6. Boxplot of the soil nitrification and denitrification rates under different N application rates. (a) Net nitrification rate (NNR), and (b) potential denitrification rate (PDR). In all, 25−75% represents the interquartile range (IQR). The different lowercase letters indicate significant differences among all the different N application treatments (p < 0.05). N0 is the control (0 g N·m−2·yr−1 application), N2 is the 2 g N·m−2·yr−1 application, N4 is the 4 g N·m−2·yr−1 application, N8 is the 8 g N·m−2·yr−1 application, N16 is the 16 g N·m−2·yr−1application, and N32 is the 32 g N·m−2·yr−1application.
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Table
Table 1. Spearman’s correlation coefficients for the relationships between the nitrifier and denitrifier abundances and the soil physical and chemical properties.
Table 1. Spearman’s correlation coefficients for the relationships between the nitrifier and denitrifier abundances and the soil physical and chemical properties.
AOAAOBamoAnirKnirSnirnosZMB
N−0.1850.819 **0.941 **−0.333−0.05−0.316−0.671 **−0.940 **
SM−0.0060.217−0.0350.433 *0.530 **0.467 **0.368 *0.328
pH0.161−0.846 **−0.922 **0.3040.0340.2950.582 **0.876 **
NH4+0.2140.070.0150.013−0.085−0.037−0.150.113
NO3−−0.10.788 **0.902 **−0.1260.205−0.098−0.555 **−0.789 **
DOC−0.1360.696 **0.729 **−0.236−0.175−0.221−0.486 **−0.684 **
* Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. N is the nitrogen application rate. SM is the soil moisture. DOC is dissolved organic carbon. MB is microbial biomass. amoA is (AOA amoA +AOB amoA). nir is (nirK + nirS).
Table
Table 2. Results of multiple regression analyses applied to data obtained from all N treatments to evaluate the relative importance of microbiological and environmental factors for the net nitrification activities (NNA) and potential denitrification activities (PDA).
Table 2. Results of multiple regression analyses applied to data obtained from all N treatments to evaluate the relative importance of microbiological and environmental factors for the net nitrification activities (NNA) and potential denitrification activities (PDA).
Factor (n = 30)Standardized Betapss
NNRamoA abundance0.694<0.0010.463
Total explained 0.463
PDRSM0.555<0.0010.44
pH0.535<0.0010.271
Total explained 0.711
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Zhang, J.; Liu, Y.; Dong, J. Responses of Alpine Soil Nitrification and Denitrification Rates to Nitrogen Addition Gradient—The Role of Functional Genes. Agriculture 2023, 13, 245. https://doi.org/10.3390/agriculture13020245

AMA Style

Zhang J, Liu Y, Dong J. Responses of Alpine Soil Nitrification and Denitrification Rates to Nitrogen Addition Gradient—The Role of Functional Genes. Agriculture. 2023; 13(2):245. https://doi.org/10.3390/agriculture13020245

Chicago/Turabian Style

Zhang, Jiaqi, Yinghui Liu, and Jingyi Dong. 2023. "Responses of Alpine Soil Nitrification and Denitrification Rates to Nitrogen Addition Gradient—The Role of Functional Genes" Agriculture 13, no. 2: 245. https://doi.org/10.3390/agriculture13020245

APA Style

Zhang, J., Liu, Y., & Dong, J. (2023). Responses of Alpine Soil Nitrification and Denitrification Rates to Nitrogen Addition Gradient—The Role of Functional Genes. Agriculture, 13(2), 245. https://doi.org/10.3390/agriculture13020245

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