Nitrogen addition decreases methane uptake caused by methanotroph and methanogen imbalances in a Moso bamboo forest

Forest soils play an important role in controlling global warming by reducing atmospheric methane (CH4) concentrations. However, little attention has been paid to how nitrogen (N) deposition may alter microorganism communities that are related to the CH4 cycle or CH4 oxidation in subtropical forest soils. We investigated the effects of N addition (0, 30, 60, or 90 kg N ha−1 yr−1) on soil CH4 flux and methanotroph and methanogen abundance, diversity, and community structure in a Moso bamboo (Phyllostachys edulis) forest in subtropical China. N addition significantly increased methanogen abundance but reduced both methanotroph and methanogen diversity. Methanotroph and methanogen community structures under the N deposition treatments were significantly different from those of the control. In N deposition treatments, the relative abundance of Methanoculleus was significantly lower than that in the control. Soil pH was the key factor regulating the changes in methanotroph and methanogen diversity and community structure. The CH4 emission rate increased with N addition and was negatively correlated with both methanotroph and methanogen diversity but positively correlated with methanogen abundance. Overall, our results suggested that N deposition can suppress CH4 uptake by altering methanotroph and methanogen abundance, diversity, and community structure in subtropical Moso bamboo forest soils.


Results
Soil properties. N deposition has an important impact on soil physicochemical properties ( Table 1). The highest soil pH (4.9) was recorded from the control treatment; it was significantly higher than that in the other treatments (P < 0.05), especially compared with the N90 treatment (pH 4.2). Concentrations of NO 3 − and NH 4 + and the C/N ratio were higher in the N90 treatment than in the control treatment (P < 0.05). Compared with the control treatment, the higher amount of N addition (N90) significantly decreased moisture and the concentrations of soil organic carbon (SOC) and total nitrogen (TN). The concentration of available phosphorus (AP) increased after N addition compared with the control treatment (P < 0.05).  . Different upper-case letters indicate significant differences (P < 0.05) between treatments for methanotrophs and different lower-case letters indicate significant differences (P < 0.05) between treatments for methanogens. www.nature.com/scientificreports/ the Shannon index only decreased significantly in the N60 treatment (P < 0.05). The Shannon and Chao1 indexes for methanotrophs were positively correlated with soil pH (P < 0.05). In addition, the Chao1 index for methanotrophs was negatively correlated with AP concentration (P < 0.05) and the C/N ratio (P < 0.05) but positively correlated with SOC (P < 0.05) and TN (P < 0.05; Fig. 2) concentration. Pearson's correlation analysis revealed that the Chao1 index for methanogens was positively correlated with soil pH (P < 0.05) and TN concentration (P < 0.05) but negatively correlated with the C/N ratio (P < 0.05).
Methanotroph and methanogen community structure. The number of operational taxonomic units (OTUs) detected varied across the N addition treatments (Fig. S1) www.nature.com/scientificreports/ the four treatments were compared, we found that they shared 231 OTUs. Cluster analysis showed that the methanotroph and methanogen community structure in the control treatment was different from the structure observed in the N deposition treatments (Fig. S2). In addition, ANOSIM showed that there were significant differences between control and N addition treatments for the methanotroph (R = 0.75, P < 0.001) and methanogen (R = 0.58, P < 0.001) community structure (Table S1). The canonical correspondence analysis showed that soil characteristics were related to the methanotroph and methanogen community structure (Fig. 4). Furthermore, a Monte Carlo permutation test showed that soil pH, the C/N ratio, and TN and NH 4 + concentration (P < 0.05) were the primary factors that influenced methanotroph communities (Table 2). For methanogens, soil pH and   www.nature.com/scientificreports/ the C/N ratio were the two most important contributors to the variation in methanogen communities (P < 0.05; Table 2).

Dominant methanotroph and methanogen groups.
At the methanotroph genus level, four genera were most abundant (Methylococcus, Methylocapsa, Methylosinus, and Methylocystis) and presented relative abundances > 1% in all treatments (Fig. S2). Methylocapsa and Methylococcus were the two most abundant genera across all treatments and together accounted for 80.32-97.24% of the pmoA gene sequences. The relative abundance of Methylocapsa in the N addition treatments (N30 and N90) was significantly higher than that in the control treatment (Fig. 5), whereas Methylococcus showed the opposite trend. The relative abundance of Methylocapsa was negatively correlated with soil pH and TN concentration (P < 0.05) but positively correlated with the C/N ratio and NH 4 + concentration (P < 0.05), whereas Methylococcus presented the opposite trend (P < 0.05; Fig. 2). The relative abundance of Methylosinus and Methylocystis was negatively correlated with SOC, microbial biomass carbon (MBC), and NO 3 − concentrations (P < 0.05) but positively correlated with soil AP and dissolved organic carbon (DOC) concentrations (P < 0.05; Fig. 2).  (Fig. S2). The N addition treatments significantly decreased the relative abundance of Methanoculleus (Fig. 5). The relative abundance of Methanothrix was positively correlated with the C/N ratio and NH 4 + (P < 0.05) concentration, whereas Methanolobus presented the opposite trend. Methanoculleus was positively correlated with soil pH (P < 0.05) and SOC (P < 0.05) and TN (P < 0.05) concentrations but negatively correlated with soil AP concentration (P < 0.05; Fig. 2). CH 4 flux. CH 4 flux in the N addition treatments was significantly higher (39.2-58.8%) than that in the control treatment (Fig. 6). The CH 4 flux was positively correlated with methanogen abundance and the relative abundance of Methylocapsa but negatively correlated with the Shannon and Chao1 indexes for both methanotrophs and methanogens, the relative abundance of Methanoculleus and Methylococcus, and pH (P < 0.05; Fig. 2).

Discussion
Effect of N addition on methanotroph abundance, diversity, and community structure. N addition did not significantly affect the abundance of the pmoA gene in the soil from the Moso bamboo forest; this did not support the first hypothesis that N addition would decrease methanotroph abundance. However, previous studies have found that N addition reduces the abundance of the pmoA gene in rice soils 34 and temperate forest soils 39 . These reductions may be the result of high NH 4 + concentrations reducing methanotroph activity through inhibition or competition for MMO 16 . Nitrite toxicity owing to the nitrification of NH 4 + may also inhibit methanotroph activity 62 . In our study, the NH 4 + soil concentration was significantly and negatively correlated with methanotroph abundance, which supports the aforementioned conclusion that a high NH 4 + concentration inhibits methanotroph abundance. Low levels of N addition (N30 treatment) did not significantly affect soil NH 4 + concentration in the present study, which indirectly indicates that N addition has no effect on pmoA abundance.
The Chao1 index presents species richness information and is sensitive to changes in rare species 63 , whereas the Shannon index accounts for both species abundance and evenness 64 . The effect of N addition on both the Shannon and Chao1 indexes was negative, suggesting an overall decline in soil methanotroph diversity. Although few studies have focused on methanotroph diversity in forest soils 15 , some studies have demonstrated that N addition significantly decreases soil microbial diversity [65][66][67][68] . A meta-analysis found that N addition decreases soil microbial (bacteria and fungi) diversity among different ecosystems 33 owing to a decrease in soil pH 69,70 . Our results also found that soil pH was lower in the N addition treatments and positively correlated with methanotroph diversity, which supports the conclusion that N addition reduces methanotroph diversity. The underlying mechanism may be that soil pH influences the growth of some microbial functional groups 71 . Low pH leads to the leaching of magnesium and calcium and the mobilization of aluminum 72 . When this occurs, some microbes may suffer magnesium-or calcium-limitation or aluminum toxicity, which result in decreased microbial diversity 33,66 .
N addition significantly influenced the methanotroph community structure and the relative abundance of type-I and type-II methanotrophs. This result supports the first hypothesis of this study and is consistent with the findings of Zhang et al. 39 and Jang et al. 43 , who found that N addition significantly affects methanotroph community structure. These studies demonstrated that N addition affects the community structure of soil microbes by changing the inorganic N concentration, the C/N ratio, and pH in soils 33,73,74 . In our study, the changes in soil TN and NH 4 + concentrations, the C/N ratio, and pH owing to N addition influenced the methanotroph genera present and subsequently altered the composition of the microbial community. One possible explanation for this result is that soil microbial communities are directly influenced by soil pH given that most microbial taxa exhibit a relatively narrow pH tolerance for growth 69,75 . For example, a decrease in optimum growth of only 25% would lead to a population being rapidly outcompeted by other microbial populations that were not growth-impeded 69 . These narrow pH optima for microbes would explain the strong relationship between microbial community www.nature.com/scientificreports/ composition and soil pH. Furthermore, previous studies found that different methanotrophs have different pH optima 11,51 . The C/N ratio also plays an important role in the regulation of microbial community structure 76 , which may be attributed to microorganisms using substrates with different C/N ratios 77 .
In the present study, we found that the soil methanotroph community was dominated by type-II methanotrophs (Methylocapsa, Methylosinus, and Methylocystis) in all treatments. In particular, Methylocapsa was the most abundant indicator of methanotroph species and accounted for 77.5% of all sequences. Previous studies have reported that type-II methanotrophs are the predominant group in forest soils 43,78 , which may be the result of the abundance of type-I and type-II methanotrophs being affected by the concentration of CH 4 79 . Type-II methanotrophs have been found to dominate under low CH 4 concentrations, whereas type-I methanotrophs have been found to dominate under high CH 4 concentrations 43 . Bender and Conrad 80 demonstrated that forest soils are exposed to low CH 4 concentrations. Therefore, type-II methanotrophs are the predominant group in Moso bamboo forest soils. In addition, some studies have shown that Methylocapsa is a member of USC-α in forest soils with an acidic pH 81,82 . In this study, the relative abundance of Methylocapsa was significantly and negatively correlated with soil pH (P < 0.01), which was consistent with the finding of Täumer et al. 38 that there is a negative correlation between the pH and USC-α. However, the relative abundance of Methylococcus was strongly and positively correlated with soil pH (P < 0.01), which indicates that type-I methanotrophs were not able to adapt to the lower pH conditions of the soil in the N addition treatments. These results demonstrated that pH played an important role in altering the community composition of soil methanotrophs. Overall, the effects of N addition on methanotroph community structures in Moso bamboo forest soils were consistent with the results from temperate forest soils 39,43 . These results indicate that the response of methanotroph community structures to N addition in a subtropical forest ecosystem is similar to that in different forest ecosystems.
Effect of N addition on methanogen abundance, diversity, and community structure. N addition significantly increased the abundance of the mcrA gene but decreased methanogen diversity, which partly supports the second hypothesis. Aronson et al. 15 observed that the abundance of the mcrA gene is greater with N treatments than with control treatment in a pine forest soil. High N concentrations stimulate multiple microbial processes and provide more substrate for methanogens compared with low N concentrations 53 . DOC could partly act as the substrate and affect soil microbial activity 83 . In this study, N addition (N30 treatment) promoted an increase in the DOC concentration, which could explain the increase in methanogen abundance under conditions of N addition. Furthermore, our previous studies found that N addition increases the leaf photosynthetic rate 84 , soil MBC 66 , soil respiration rate 23 , and the decomposition rates of both leaf litter 47 and fine roots 65 , which indirectly supports the aforementioned conclusion. Our study also found that the soil SOC and TN concentrations were lower in the N addition treatments than in the control treatment and were negatively correlated with methanogen abundance, which partly supports the idea that N addition significantly increases mcrA gene abundance. Moreover, the Shannon and Chao1 indexes for methanogens sharply declined with decreasing soil pH (from pH 4.9 to 4.2) in the N addition treatments, which is likely owing, in part, to a small fraction of methanogens not being able to survive in low-pH soil. For example, the relative abundance of Methanoculleus was lower in the N addition treatments and was positively correlated with both the pH and the Chao1 and Shannon indexes for methanogens (Fig. 2). This result supports the conclusion that low pH resulted in a decrease in the relative abundance of some methanogens.
Methanogen community structure, like that of the methanotrophs, was influenced by N addition, which supports the second hypothesis of this study. Moreover, our results showed that soil pH was strongly correlated with methanogen community structure in Moso bamboo forest soils (P < 0.01). Some studies have found similar results 68,70 . For example, Lin et al. 68 found that pH strongly controls microbial community structure in soils with N fertilization treatments. This result was attributed to most microbes having relatively narrow pH optima 69 . Our results also showed that soil pH was significantly and positively correlated with the relative abundance of Methanoculleus but negatively correlated with the amount of mcrA (Fig. 2), which supports the conclusion that soil pH plays a dominant role in determining the structure of methanogen communities. However, other soil physicochemical factors may also play important roles in determining soil microbial community patterns and cannot be ruled out. The soil C/N ratio also significantly influenced the methanogen community structure, which is consistent with the results of Wan et al. 85 , who found that the soil C/N ratio is the major determining factor of the structure of microbial communities in subtropical coniferous and broadleaf forest plantation soils. The soil C/N ratio can reflect the quality of the substrate for soil microorganism growth 85 . In general, microbial biomass and activity are constrained by the availability and quality of C and nutrients, which may shift the structure of microbial communities 86 . In fact, a few studies on methanogens have been performed in forest soils within the context of atmospheric N deposition 15,35 . Aronson et al. 15 found that N addition increases methanogen abundance in the poorly drained pine forest soil but does not impact methanogen abundance in a well-drained site. In our study, we showed that N addition significantly influenced methanogen abundance, diversity, and community structure in Moso bamboo forest soils. The differences in these results may be attributed to the evaluation of different forest soil types, drainage conditions, and N addition rates. As such, it is important to study the effects of N addition on methanogens in a variety of forest soils.
Effect of N addition on CH 4 flux. The oxidation of CH 4 from the atmosphere is an important function in forest ecosystems 5 . Our results indicate that N addition significantly decreased CH 4 uptake in the Moso bamboo forest, which supports the third hypothesis of this study and is consistent with the results of previous studies that have shown a negative effect of N addition on CH 4 oxidation in forest soils 18,87 . Mo et al. 48 and Zhang et al. 49 also observed that CH 4 uptake in monsoon evergreen broadleaf forest soils is significantly reduced by N deposition in southern China. The decrease in CH 4 uptake with N addition is probably owing to increased methanogen www.nature.com/scientificreports/ and decreased methanotroph abundances 7 . It has been found that the abundance of USC-α is positively correlated with CH 4 uptake in forest soils 38 . Aronson and Helliker 87 found that large amounts of available N inhibit methanotrophs in non-wetland soil systems. Similarly, we found that N addition decreased methanotroph diversity and altered the community structure of methanotrophs. Pearson's correlation analysis demonstrated that methanotroph diversity (Shannon and Chao1 indexes) was strongly correlated with CH 4 flux, which agrees with the findings of Schnyder et al. 32 , who deduced that the diversity of methanotrophic communities is important for CH 4 oxidation. The result also provides direct evidence for the loss of microbial diversity with increasing N deposition rates, which results in altered ecosystem functions. Moreover, Shang et al. 29 showed that methanogen activity is enhanced by N addition, which results in the production of more CH 4 . In our study, N addition significantly increased methanogen abundance, which was positively correlated with CH 4 flux. Our results indicate that N deposition resulted in the suppression of CH 4 uptake in Moso bamboo forest soils, thereby contributing to an increased concentration of atmospheric CH 4 . In addition, abiotic soil factors, such as pH, directly and indirectly influence CH 4 flux 88 by altering methanotroph and methanogen abundance, diversity, and community structure.

Conclusions
The present study provides evidence that N deposition may influence methanotroph and methanogen abundance, diversity, and community structure by decreasing pH in Moso bamboo forest soil. Furthermore, N addition significantly decreased methanotroph and methanogen diversity, which may influence their ecosystem functions, such as CH 4 uptake. Increasing the soil pH should be an effective intervention option to alleviate the effect of N deposition on methanotrophs and methanogens. In this study, we ignored the potential role of anaerobic methanotrophs and soil characteristics (horizon layering, hydrology, and oxygen availability) over soil depths.
In a further study, we will investigate the effect of N addition on soil anaerobic methanotrophs and soil characteristics of different depths in the Moso bamboo plantation. Besides, the long-term effect of N deposition on methanotrophs and methanogens, the CH 4 emission rate, and the associated underlying mechanisms should be evaluated in future studies.

Materials and methods
Experimental site and design. The field site was established in Qingshan Town, Hangzhou City (30° 14ʹ N, 119° 42ʹ E), Zhejiang Province, China. The soil type is classified as a ferrosol derived from granite 23,50 . Moso bamboo is an economically important bamboo species in Southeast China and the most important source of non-wood forest products in China 47 . The Moso bamboo forest at the study site was originally established in the late 1970s from a native evergreen broadleaf forest in sites of similar topography 65 . The Moso bamboo forest, with 11 understory herbal species, achieves a mean height of 0.1 m. Forest floor coverage is 5% with a total herbal biomass of 14.6 kg ha −1 . The forest is influenced by a subtropical monsoon climate, with a mean annual temperature of 15.6 °C and mean annual precipitation of 1420 mm. The initial soil characteristics are summarized in Table S2. Twelve (3 replicates per treatment × 4 treatments) randomly scattered plots (20 m × 20 m per plot) were established in November 2012. Adjacent plots were separated by a 20-m buffer zone. Four distinct N treatments were defined: Control (0 kg N ha −1 yr −1 ), N30 (30 kg N ha −1 yr −1 ), N60 (60 kg N ha −1 yr −1 ), and N90 (90 kg N ha −1 yr −1 ). The N-addition treatments were designed to simulate single (N30), double (N60), or triple (N90) ambient N deposition rates (30 kg N ha −1 yr −1 ) in the region 46 . NH 4 NO 3 was used to simulate N deposition given that the N that is typically deposited through natural and anthropogenic processes is mainly in the form of NH 4 + and NO 3 −89,90 , which account for 56.1% and 43.9% of wet N deposition in China, respectively 91 . Different concentrations of NH 4 NO 3 solution (mixed with 10 L of water) were sprayed over the plots each month starting from January 2013 to March 2018. Each control plot received 10 L of water.
Soil sampling and physicochemical analysis. For each plot, bulk soil (0-20 cm depth) was collected in early March 2018 from ten randomly selected points and mixed to form one composite sample. The samples were transported to the laboratory in a constant temperature box (4 ℃) containing ice within hours of being collected. After visible stones, roots, and litter were removed using forceps, the soil samples were gently broken apart along natural-break points and thoroughly mixed. One portion of the soil sample was passed through a 2.0-mm sieve and stored at − 80 °C for subsequent DNA extraction, quantitative PCR, and high-throughput sequencing. Another portion of the soil was passed through a 2.0-mm sieve and subsequently divided into two parts for soil physicochemical property analysis. A part of each fresh sample was stored at 4 °C for the analysis of MBC, DOC, inorganic N (NH 4 + and NO 3 − ), and soil moisture. MBC was estimated using the chloroform fumigation-extraction method 92,93 . DOC was extracted with distilled water, passed through a 0.45-mm filter, and evaluated using a TOC analyzer (TOC-VCHP, Shimadzu, Kyoto, Japan). NH 4 + and NO 3 − were extracted with 2 M KCl and measured using a SmartChem 200 Discrete Analyzer (Alliance Instruments, Frepillon, France). Fresh soil samples were weighed and then dried in an oven at 105 °C to a constant weight to determine gravimetric soil moisture 94 . The other parts were air-dried and stored at room temperature (25 °C). Air-died soils were used to determine soil pH, SOC, TN, and AP. Soil pH was measured using a pH meter (FE20, Mettler-Toledo, Zurich, Switzerland) after a soil-water (1:2.5 dry w/v) mixture was created and shaken for 30 min. SOC and TN concentrations were measured using a Vario Max element analyzer (Elementar, Hanau, Germany). AP was extracted with 0.0125 M H 2 SO 4 in 0.05 M HCl and its concentration was determined using the molybdenum blue method 84 -GGNGAC TGG GAC TTC TGG-3ʹ) and 650R (5ʹ-ACG TCC TTA CCG AAGGT-3ʹ) 95 and mlas-mod-F (5ʹ-GGY GGT GTMGGDTTCACMCARTA-3ʹ) and mcrA-rev-R (5ʹ-CGT TCA TBGCG TAG  TTVGGR TAG T-3ʹ) 96 were used for pmoA and mcrA gene amplification, respectively. Functional methanotroph and methanogen genes were quantified using qPCR in a CFX connect Real-Time Detection System (Bio-Rad Laboratories Inc., Hercules, CA, USA). The DNA sample was used for qPCR after a tenfold dilution. There was a single dissolution curve peak. The qPCR reaction mixture contained 10 μL of 2 × ChamQ SYBR Color qPCR Master Mix, 2 μL of each primer (10 μM), 1 μL of DNA template (1-10 ng), and 7 μL of ddH 2 O. Amplification was initiated by denaturation at 95 °C for 3 min, followed by 35 cycles of denaturation at 95 °C for 20 s, annealing at 60 °C for 30 s, and extension at 72 °C for 20 s, and the plate was read at 80 °C. To generate a standard curve, individual clones with accurate inserts were cultured in Luria-Bertani medium and the plasmid DNA was extracted, purified, and quantified. Plasmid DNA was prepared in a tenfold dilution series to yield a standard curve covering six orders of magnitude (10 2 to 10 8 copies) per assay 35 . The qPCR assay was performed in triplicate for each replicate. The qPCR amplification average efficiencies were 97% and the R 2 was 0.996.
High-throughput sequencing and bioinformatics. PCR amplification was performed for each soil DNA extract, using the above-mentioned primers (A189f. and 650R and mlas-mod-F and mcrA-rev-R), in triplicate and combined into a single composite sample. This is because these primers are widely used to study upland soils and cover the most methanotrophs and methanogens 35,38,95,96 . The specificity of the primer, which had been checked by Primer-BLAST, was good. The amplicon size of pmoA and mcrA was 500 and 469 bp, respectively. The PCR products were subsequently purified with AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA) and quantified by Qubit (Invitrogen Corporation, Carlsbad, CA, USA). The PCR amplicon pools were prepared for sequencing and library quality was assessed using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA) and the Library Quantification Kit for Illumina (Kapa Biosciences, Woburn, MA, USA). Finally, high-throughput sequencing for pmoA/mcrA genes was carried out using a 2 × 300 bp paired-end Illumina MiSeq PE300 at LC-Bio Technology Co., Ltd, Hang Zhou, Zhejiang Province, China.
The obtained sequencing data were processed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline 97 . Sequence data, including raw data and clean data, were filtered using Mothur. The proportion of chimeric sequences of pmoA and mcrA was 4.8% and 6.5%. The non-chimeric pmoA and mcrA gene reads were then checked for frameshift errors using the "FrameBot" tool [98][99][100] . The above analysis resulted in a total of 424,628 (ranging from 31,030 to 40,328 sequences per sample) and 775,842 (ranging from 36,438 to 40,328 sequences per sample) high-quality sequences of pmoA and mcrA in all samples, respectively (Table S3). To standardize the results, we resampled each sample using the sequence number of the sample with the least sequences and calculated the diversity indices based on this normalized data set 101,102 . The remaining high-quality sequences were clustered into OTUs at a 97% identity threshold using UCLUST. The taxonomic information of each OTU was annotated using the taxonomically determined reference sequences from the National Center for Biotechnology Information (NCBI v20180310) using BLAST 35,38,39 . The specific parameter settings of BLAST were as follows: the minimum identity was 70%, the minimum query coverage was 70%, the maximum E-value was 10 -5 , and the E-value interval multiple was 10 times. The detailed parameters and classification methods have been described by Liu et al. 35 . Alpha diversity was assessed by calculating the Chao1 63 and Shannon 64 indexes in QIIME (Version 1.8.0). Furthermore, QIIME was used to calculate the weighted UniFrac, and unweighted pair group method with arithmetic mean clustering was conducted on the weighted UniFrac based on a previously published protocol 66 . All sequence data in this study are deposited in the Sequence Read Achieve database of NCBI under accession number SRP255341. CH 4 measurement. CH 4 samples were collected once each month on a clear day using a widely applicable static chamber and measured using gas chromatography techniques 103 . The sampling process has been described in a previous study 23 . In brief, the static chambers were made of opaque polyvinyl chloride panels, including a square base box (40 × 40 × 10 cm) and a U-shaped groove (50 mm wide and 50 mm deep) at the top edges that held a removable top (40 × 40 × 40 cm). In each plot, three boxes were installed 10 cm below the soil surface. The chamber tops were placed onto the base boxes during gas sampling, and the grooves were filled with water to act as an air seal. A small fan was installed inside the top of each chamber to generate turbulence during sampling. Sampling was conducted between 9:00 am and 11:00 am to minimize the influence of diurnal variation. Gas samples (60 mL) were extracted from the chamber at 0, 10, 20, and 30 min using polyurethane syringes and stored in gas sampling bags (Delin Ltd., Dalian, China). The CH 4 concentrations were determined using a gas chromatograph (GC-2014; Shimadzu Corporation, Kyoto, Japan) within 1 day of sample collection. The CH 4 flux was calculated using the following formula 103 : where F (mg m −2 h −1 ) is the soil CH 4 flux; dc dt is the slope of the linear regression between the change in the CH 4 concentration (dc) and the time (dt) in the chamber; M and V 0 are the molar mass and molar volume of CH 4 under standard conditions, respectively; T is the absolute air temperature during sampling; and V (m 3 ) and A www.nature.com/scientificreports/ (m 2 ) are the effective volume and bottom area of the chamber, respectively. Owing to the malfunction of the gas chromatograph in March 2018, the data of CH 4 flux for that month were abnormal and, thus, were eliminated. The CH 4 flux data collected in late February 2018, 12 days before the soil sampling in March, were used to analyze the correlation between CH 4 flux and the abundance, diversity, and community structures of methanotrophs and methanogens in the present study.
Statistical analysis. A one-way analysis of variance (ANOVA) was performed to assess the differences in the number of gene copies, Chao1 index, Shannon index, and CH 4 flux among the different treatments. Posthoc multiple comparisons were conducted using the least significant difference (LSD) method. All data were tested for homogeneity of variance and normality of distribution prior to conducting the ANOVA. The relative abundance of the major genera was analyzed using STAMP software (v2.1.3) with a correction for multiple comparisons using the Bonferroni method. Pearson's correlation analysis was used to test the association among soil physicochemical variables, alpha diversity, the relative abundance of the major genera, and CH 4 flux, across all treatments. All these analyses were performed using SPSS v. 18.0 (SPSS Inc., Chicago, IL, USA). R software (Version 3.4.1) was utilized to conduct the following analyses. First, correlations between soil physicochemical variables and OTUs were calculated with the vegan package using a Monte Carlo permutation test, canonical correspondence analysis (CCA), and analysis of similarities (ANOSIM). Venn diagrams for graphical descriptions of unique and shared OTUs between different ecosystems were generated using the VennDiagram package.