1 Introduction

Rising greenhouse gas (GHG) emissions caused by natural and anthropogenic activities are resulting in a warmer world (IPCC 2007; Ross et al. 2019). Although carbon dioxide (CO2) accounts for sizable proportion of GHG, methane (CH4) was regarded as another remarkable GHG with the potential to drive profound climate change because its molecular warming potential is 28 times greater  than that of CO2 (IPCC 2007, 2021). As an important source and sink of methane, soil harbors methane generation and consumption processes to accomplish methane emissions (Conrad 2009; Zhao et al. 2021b). Methane generation is attributed to methanogenesis, which is mediated by methanogens. Substrates are consumed by methanogens via hydrogenotrophic, acetotrophic, and methylotrophic pathways using a nickel-containing methyl coenzyme M reductase (MCR), which is specific to methanogens (except methanotrophic archaea) (Conrad 2009, 2020). The functional gene mcrA that encodes MCR is commonly used in taxonomic studies of methanogens (Luton et al. 2002; Morris et al. 2002; Shima and Thauer 2005). Methane consumption is accomplished through the methanotroph-mediated oxidation processes. During methane oxidation, CH4 is oxidized to CO2 and H2O through the production of a range of intermediate metabolites that require methane monooxygenase (MMO) (Dedysh et al. 2002). Methane monooxygenases are divided into two structural forms: particulate methane monooxygenase (pMMO) and soluble methane monooxygenase (sMMO) (Dedysh et al. 2002; Liebner and Svenning 2013). Both pMMO and sMMO catalyze methane oxidation by breaking the C–H bond in methane, thereby oxidizing it to methanol and H2O. Most type I methanotrophs are detected by the pmoA gene, which encodes the β subunit of the pMMO protein. Meanwhile, the mmoX gene is selected as a biomarker for a few type II or type I methanotrophs since they utilize mmoX to encode sMMO rather than pMMO (Horz et al. 2001; Liebner and Svenning 2013).

Biochar and livestock dung amendments are applied in the grazed grassland as the critical management measures for soil remediation and fertilization. Recent studies have found that biochar and dung amendments significantly regulated soil methane emission. Biochar was the carbonaceous residue derived from the oxygen-limited pyrolysis of carbon (C)-rich biomass (Lehmann et al. 2011; Lehmann 2019; Zhao et al. 2021a). Biochar regulated both methanogenic and methanotrophic activities by controlling key soil properties (Jeffery et al. 2016; Zhao et al. 2021b). The elevation of soil porosity, aggregation, and water holding capacity due to biochar amendment shaped inappropriate microenvironments for methanogens while these responses of soil parameters promoted or inhibited methanotrophs in different cases (Atkinson et al. 2010; Jeffery et al. 2016; Rasa et al. 2018; Razzaghi et al. 2020). Despite these physical parameters, soil pH and N also increased by the liming effect and N mineralization was enhanced because of biochar amendment. Both methanogen and methanotroph growth depended on the threshold of soil pH and N (Cayuela et al. 2013; Gul and Whalen 2016; Karbin et al. 2016; Buss et al. 2018). The decrease in dissolved organic carbon after biochar amendment limited soil methanogenesis (Zimmerman et al. 2011; Nan et al. 2021). Methane fluxes increased significantly in the dung amended soils (Du et al. 2016; Cai et al. 2017; Cardoso et al. 2019). Fresh dung provided abundant methanogens to enhance soil methanogenic activity. The deposition of soluble C acted as an energy source for methanotrophs (Rastogi et al. 2008; Ho et al. 2013). The dung coverage and the biological crust on the mulch surface created anaerobic soil micro-environments, which were conducive to methanogens rather than methanotrophs (Ma et al. 2006; Singh et al. 2010; Wang et al. 2013). The high moisture of the fresh dung also hindered oxygen availability. Soil microbes decomposed the organic matter in dung to further depleted the oxygen required by methanotrophs (Malyan et al. 2016; Cai et al. 2017; Zhao et al. 2021b). The leaching of NH4+-N and dissolved nitrogen caused by the dung amendment supplied abundant N sources to promote the growthof methanogens and  methanotrophs (Lin et al. 2009; Hartmann et al. 2013; Cai and Akiyama 2016; Additional file 1: 1.1). Conversely, methane oxidation was inhibited with sufficient ammonia because of the exclusive competition between amoA and pmoA  genes (Bédard and Knowles 1989; Gulledge and Schimel 1998). Both biochar and dung amendments might regulate methane emission via interactions between methane metabolism and nitrogen (N) cycling because the processes involved in N cycling could either augment or moderate methanogenesis or methane oxidation (Additional file 1). However, despite previous studies reporting the response of the methane flux, specific mechanisms of the regulation on the soil methanogenic and methanotrophic activities by biochar and dung from the perspectives of functional gene and community assembly remain elusive. Additionally, the linkages and interactions between methane metabolic genes and other functional genes involved in geochemical cycling have not been well studied.

In this study, we aimed to investigate the functional gene composition and microbial community assembly of both methanogens and methanotrophs in the biochar and dung amended soils, and to identify the driving factors in these processes. We also discussed the co-occurrence and interactions between methane metabolic genes and other functional gene modules involved in C and N cycling to clarify the critical role of methane metabolic genes in geochemical cycling with biochar and dung amendments. Microcosm incubation, high-throughput sequencing, and Geochip 5.0 gene microarray were performed to explore these responses of functional genes and microbial community to the biochar and dung amendments. We hypothesized that biochar and dung amendments would reshape the functional gene structure and microbial community assembly related to methanogenesis and methane oxidation by altering the soil physicochemical characteristics and impairing the interactions between methane metabolic genes and other biogeochemical genes. This study provides scientific guidance for the implementation of sustainable soil managements to address the challenge of global warming.

2 Materials and methods

2.1 Study area and sample collection

This study employed the soil samples in grazed grassland for the subsequent laboratory incubation. Samples were collected from Research Station of Animal Ecology (44° 18′ N, 116° 45′ E, 1079 m a.s.l) located in the Maodeng Pasture, Inner Mongolia Autonomous Region, China. The sampling area belongs to the typical temperate steppe with a continental temperate semi-arid climate. The details of the sampling grassland are described in Additional file 1: 2.1. Soil cores were collected at a depth of 0–20 cm by using a 3 cm diameter soil auger from 1 m × 1 m sample boxes in selected sample plots, with three replicates.

2.2 Biochar preparation

The biochar in this study employed straws of wheat (Triticum aestivum) as the feedstock. After  oven dried at 60 ℃ for 24 h, the straws were pyrolyzed in an oxygen-free stainless-steel furnace at 400 ℃ for 2 h to prepare the biochar. The collected biochar was sieved by 0.2 mm sieves for the subsequent experiments.

2.3 Microcosm incubation

5 g of air-dried soil was adjusted to the soil with field moisture of 20% and pre-cultured in 100 mL culture flasks at room temperature without light for 7 days. The flasks were then closed at  atmospheric pressure. The experiments were carried out in three replicates for each treatment group: CK control, BC, FC, and BF under the aerobic condition. 0.5 g of biochar was added to BC; 0.5 g of dung was added to FC; 0.5 g of biochar and 0.5 g of dung were amended to BF together. The incubation temperature and time were 25 °C and 18 days, respectively. When the oxygen in the incubation flasks was depleted, soil samples were removed by destructive sampling. The physicochemical properties were determined following previous protocols according to Zhao et al. (2021c) (Additional file 1: 2.2).

2.4 DNA extraction, purification, and sequencing

Soil DNA was extracted from the collected samples using the MoBio PowerSoil isolation kits according to the manufacturer's  instructions (MoBio Laboratories, Carlsbad, CA, USA). Subsequently, a quality and quantification assessment of the purified DNA was conducted by Nano-Drop ND-1000 Spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE). The final DNA samples obtained from three biotopes were diluted and stored at − 80 °C for further analysis. The DNA samples were sequenced using primers such as 189F/682R, 338F/806R, and 524F10extF/Arch958R to obtain the amplicon sequencing of methanotrophs, bacteria, and archaea on the Illumina Hiseq 2500 platform (details of primer sequence  are presented in Additional file 1: 2.3).

2.5 Geochip 5.0 gene microarray

The extracted DNA samples were also analyzed by Geochip 5.0 gene microarray (Yang et al. 2013; Li et al. 2021). The purified DNA was labeled with the fluorescent dye Cy5 using random primers. The labeled DNA was purified using the QIAquick PCR purification kit (Qiagen, Valencia, CA, USA) and subsequently dried in a SpeedVac (DNA Speedvac, Model DNA 100, Savant) at 45 °C for 45 min. The dried DNA was suspended in the hybridization buffer to perform hybridization reactions in the MAUI Hybridization Station (BioMicro Systems, Salt Lake City, UT, USA) at 42 °C for 12 h. The hybridized microarrays were scanned using a Scan Array Express microarray scanner (PerkinElmer, Boston, MA, USA) at 90% laser power and 75% photomultiplier gain. The images were then analyzed using ImaGene 6.0 (Biodiscovery, EI Segundo, CA, USA) for the assessment of the signal intensity of each spot. Results with signal-to-noise ratio (SNR = signal mean-background mean/background standard deviation) < 2 were censored with ImaGene markers. Only results that were detected in at least two of the four replicates were applied for the subsequent processes. Signal intensities of all genes were normalized by relative abundance and then subjected to relevant statistical analysis (Yang et al. 2013; Tang et al. 2019).

2.6 Statistical analysis

The statistical analysis was performed in R library using packages of “vegan”, “Hmisc”, “ggtern”, “picante”, “icamp”, “spaa” and “ggplot2” except for the otherwise annotation. All the data of functional genes were obtained according to Geochip 5.0 and the community assembly processes were analyzed based on the Amplicon sequencing.

To examine the environmental impacts on methane metabolic genes, the canonical correlation analysis (CCA) was performed using the “CCA” function in the “vegan” package and embellished by “ggplot2” package with the environmental factors of “TOC”, “nitrate”, “ammonia”, “pH”, and “moisture” for visualization (Dixon 2003; Wickham 2009).

The assembly processes of methanotrophs and methanogens were investigated by “picante” and “icamp” packages based on neutral and null models among samples from four treatments (Kembel et al. 2010; Ning et al. 2020). Mean nearest taxon distance (MNTD) was calculated based on the methanogenic and methanotrophic phylogenetic trees. NTI was calculated by observed MNTD based on the null model. βNTI could be obtained by calculation the MNTD values for 999 randomizations (Webb 2000). When βNTI was > 2 or < − 2, deterministic processes dominated the assembly process while the stochasticity dominated when βNTI was < 2 and > − 2 (Vellend 2010; Stegen et al. 2013; Zhou and Ning 2017). Bray–Curtis-based Raup–Crick index (RCBray) was employed to further identify the stochastic process such as homogenizing dispersal, dispersal limitation, and undominated processes according to the threshold |0.95| (Stegen et al. 2013; Zhou and Ning 2017). The niche  characteristics such as niche breadth and niche overlaps were calculated based on Levin method using the “spaa” package to quantify habitat specialization and elucidate the contribution of assembly processes (Smith and Levins 1970).

The co-occurrence network was performed based on the Spearman correlation matrix calculated by “Hmisc” package (Harrell 2008). After screening the significant genes by false discovery rate (FDR) procedure, the matrix data were visualized on the Cytoscape platform (Benjamini et al. 2006; Bastian et al. 2009). Detailed procedures of co-occurrence network analysis are presented in Additional file 1: 2.4.

3 Results

3.1 The abundance of methane metabolic genes

To assess the amendment effects of biochar and livestock dung on the soil functional genes related to methane metabolisms, we employed intensities of genes such as mcrA, pmoA, and mmoX obtained from Geochip 5.0 to monitor the relative gene abundance. mcrA gene was regarded as the typical methanogenic gene providing MCR. The mcrA gene intensity decreased by 1.47% in the BC group with the biochar amendment while it increased in the FC or BF group by 0.38% and 0.82% with the dung amendment or the dual amendments of biochar and dung compared to the CK control (Fig. 1A). Although the gene intensity of pmoA, which was the most typical methanotrophic gene encoding the pMMO, did not vary significantly between the CK and BC groups, the increasing tendency was still observed after the biochar amendment. The intensity of this pmoA gene also showed decreasing tendency in the FC group compared to the CK group (Fig. 1A; Additional file 1: Table S1). Another crucial gene mmoX produced sMMO to support methane oxidation processes mediated by the other type of methanotrophs. The gene intensity of mmoX dramatically increased by 2.48%, 1.96%, and 2.78% in all the BC, FC, and BF groups compared to the CK control (Fig. 1A). These gene intensity variations indicated that biochar amendment reduced the gene abundance of mcrA and induced the gene abundance of pmoA and mmoX. On the contrary, the dung amendment induced the gene abundance of mcrA and mmoX, and only pmoA abundance was reduced.

Fig. 1
figure 1

A Response of gene intensities of methane metabolic genes to the biochar and dung amendments. Methane metabolic genes were methanogic gene mcrA and methanotrophic genes pmoA and mmoX. Canonical correlation analysis of the relationships between environmental variables and soil methanogenic (B) or methanotrophic (C) genes with biochar and dung amendments. Environmental variables such as soil total organic carbon (TOC), pH, moisture, nitrate and ammonia were presented

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3.2 Environmental factors driving methane metabolism

Canonical Correspondence Analysis (CCA) demonstrated the correlation between environmental factors and the methane metabolic genes with the biochar and dung amendments (Fig. 1B and C). Soil total organic carbon (TOC) and nitrate were the most influential environmental attributes to the methanogenic genes. Particularly, the methanogenic genes in the BC group were strongly positively correlated to soil moisture and negatively correlated to soil pH and ammonia. Methanogenic genes in the FC group were negatively correlated to soil TOC (Fig. 1B). As far as methanotrophic genes were concerned, soil pH, ammonia, moisture, and TOC contributed similarly to the exogenous amendments. Similar to the methanogenic genes, methanotrophic genes in the BC group were still strongly negatively correlated to soil pH and ammonia, and those in the FC group were negatively correlated to soil TOC. A positive correlation between soil moisture and methane oxidation genes in the FC group could be observed (Fig. 1C).

3.3 The community assembly process of methanogens and methanotrophs

The assembly process of methanogen and methanotroph communities based on null models was elucidated after the amplicon sequencing (Fig. 2A). The β-nearest taxon index (βNTI) scores were calculated across all the samples from four treatments. The vast majority and overall proportion of methanogenic and methanotrophic βNTI fell in the range of − 2 to 2, implying the dominantly neutral processes in the methanogen and methanotroph  assemblies. Only a small proportion of methanogenic βNTI was higher than + 2, indicating an inferior driving effect of heterogeneous selection in the methanogen assembly process (Fig. 2A). To further identify the neutral process, the βNTI scores were combined with Bray–Curtis-based Raup–Crick (RCBray). The largest fraction of RCBray fell in the range of − 0.95 to + 0.95 representing the undominated processes such as weak selection, weak dispersal, diversification, and drift rather than probabilistic dispersal dominated the methanogen and methanotroph assembly process. Very few RCBray scores lower than − 0.95 occurred in both methanogen and methanotroph communities. This indicated the inferior effect of dispersal limitation (Fig. 2B). The RCBray scores of methanotroph higher than + 0.95 represented the homogenizing dispersal. Thus, it is the stochastic process, especially undominated processes, to lead the assembly of methanogen and methanotroph community across all the treatments with biochar and dung amendments.

Fig. 2
figure 2

A βNTI of methanogen and methanotroph community assembly. B Bray–Curtis-based Raup–Crick index (RCBray) of methanogen and methanotroph community assembly. C Contribution of various assembly processes to the methanogen and methanotroph assembly. A, B were based on samples across all treatments with biochar and dung amendments; and C was based on samples from CK, BC, and FC groups  , respectively

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When we focused on the methanogen and methanotroph assembly processes of samples from each particular group, quite different results were presented (Fig. 2C). Homogeneous selection rather than stochastic process accounted for 99.32% in the assembly of methanogen communities from the CK group. Similarly, the homogeneous selection also accounted for 99.23% in methanogen assembly in the BC group. In terms of the FC group, homogeneous selection only accounted for 32.60% and undominated processes contributed 65.51%. In the CK group, the assembly of the methanotroph community was mainly driven by undominated processes with 65.51% percentages. Homogeneous selection, homogeneous dispersal, and dispersal limitation contributed to 24.23%, 7.82%, and 6.87%, respectively. In the BC group, dispersal limitation became the main driven process with a contribution of 46.13% percentages. Undominated processes and homogeneous selection accounted for 34.05% and 19.03%. When it comes to the methanotrophs in the FC group, the contribution of undominated processes was 40.14%, also lower than that of the CK group. Homogeneous selection, homogeneous dispersal, and dispersal limitation contributed to 29.62%, 16.62%, and 13.43%, respectively (Fig. 2C). These results highlighted that dung amendment generated undominated processes rather than homogeneous selection as the main driving force in the methanogen assembly and biochar amendment led to more contribution of dispersal limitation in the methanotroph assembly.

3.4 Niche breadth and niche overlap of methanogens and methanotrophs

To deeply clarify the ecological role of methanogens and methanotrophs, the niche breadth and niche overlap were investigated as the important parameters of niche characterization (Fig. 3). Niche breadth described the capacity of species to obtain resources with various niches (Sexton et al. 2017). The niche breadth value of methanogen was maintained at a quite low level and even lower than the entire archaea community (Fig. 3A). This narrow niche breadth indicated poor environment adaptivity and niche-discriminatory growth of methanogens. The niche breadth value of methanotrophs is higher than that of methanogens but also lower than that of the entire bacteria community (Fig. 3A). This result indicated that methanotrophs were more adaptive compared to methanogens but still pone to be shaped by niche selection compared to the other bacteria.

Fig. 3
figure 3

A Niche breadth of the methanotrophs, bacteria, methanogens and archaea with biochar and dung amendments. All the niche breadths were calculated based on samples across all treatments. B Niche overslaps between microbes involved in methane metabolism and nitrogen cycling. Microbes involved in methane metabolism were divided into methanogens and methanotrophs; Microbes involved in nitrogen cycling were related to DNRA, nitrogen fixation, ammonification, nitrification, and denitrification

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Since several processes in N cycling were profoundly influential to methanogenesis and methane oxidation, the niche overlaps based on the Levin method between microorganisms mediating these processes and methanogens or methanotrophs were also calculated. Figure 3B  depicts the prominently high niche overlaps between microbes mediating dissimilatory nitrogen reduction (DNRA) or ammonification and methane metabolic microbes. The niche overlap analysis employed functional genes involved in methanogenesis, methane oxidation, DNRA, ammonification, nitrogen fixation, nitrification, and denitrification to reveal the niche competition between microbes related to these processes of methane metabolism or nitrogen cycling. This niche overlaps implied the methane metabolic microbes competed for more resource attributes with microbes mediating DNRA or ammonification than diazotrophs, denitrifiers, and nitrifiers. All these microbes related to the N cycle overlapped more with  methanotrophs than with methanogens across all the treatments. This result further proved only  methanotrophs rather than methanogens could compete resources with microbes related to N cycles in various niches.

3.5 Amendment effect of biochar and dung on N cycling genes

Significant variation of functional genes involved in the N cycling among the CK and BC, FC, or BF groups could be observed (Fig. 4). nifH gene representing N fixation, ureC gene representing ammonification, amoA gene representing nitrification, nirB gene representing DNRA, narG, nirS/K, nosZ  genes representing denitrification all increased more in the FC than in the BC group. The higher value of gene intensity shift in FC than in BC reflected the stronger effect of dung amendment than that of biochar amendment. Referring to all these genes, the sums of variation in the BC and FC were all lower than those in the BF (Fig. 4).

Fig. 4
figure 4

Response of the functional genes involved in nitrogen cycling to biochar and dung  amendments. The values along with the genes represent the intensity variation of genes in groups after biochar, dung and biochar-dung amendments. The three percentage values indicate the ratio of the gene intensity increase in BC, FC, or BF group to the gene intensity in CK group

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3.6 Co-occurrence network of functional genes involved in C and N cycling

There were substantial and strong positive correlations (r > 0.9) among gene modules involved in C and N cycling such as methane metabolism, N cycling, C fixation, and C degradation in the CK group. A few strongly negative correlations (r > 0.9) could be observed between the C fixation module and the other three modules (Fig. 5A). After the amendments of biochar and dung, plenty of weak positive correlations (r < 0.9) occurred among the four modules. The strong negative correlations (r > 0.9) among the modules dramatically decreased. Only the negative correlation between modules of C fixation and C degradation rarely occurred (Fig. 5B). The topo-properties of this co-occurrence network depicted the decreasing cohesion parameters such as clustering coefficient, network density, and network centralization in the BF group compared to the CK group. Simultaneously, the homogenous  parameters such as network heterogeneity increased in the BF group (Table 1). These results indicated that biochar and dung amendments both weakened the association and diversity of the functional genes involved in C and N cycling.

Fig. 5
figure 5

Co-occurrence network of functional genes involved in carbon and nitrogen cycling without (A) or with (B) biochar and dung  amendments. Nodes represent individual genes; edges represent significant spearman correlations (P < 0.01); strong positive correlations (ρ > 0.9), weak positive correlations (ρ < 0.9), and strong negative correlations (ρ > 0.9) were presented with colours of light blue, grey and blue, respectively; genes are divided into 4 modules representing methane metabolism, nitrogen cycling, carbon fixation and carbon degradation with different colours

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Table 1 Topological parameters of the co-occurrence network of functional genes involved carbon and nitrogen cycling
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4 Discussion

As important measures for grassland management, biochar and dung amendments showed enormous potential to shape the soil functional genes and microbial community. Because grasslands are the greatest terrestrial sink for the atmospheric methane, the microbiomes involved in the methane metabolism in grazed grassland soils with biochar and dung amendments have been thoroughly investigated (Gul et al. 2015; Palansooriya et al. 2019). We examined the soil functional gene structure and microbial community assembly using Geochip 5.0 and high throughput sequencing, respectively.

4.1 Biochar effects on methane metabolic genes

The abundance of the methanogenesis gene mcrA and methane oxidation gene pmoA decreased and increased, respectively, with biochar amendment (Fig. 1A). This might be mainly attributed to the alteration of soil porosity by biochar amendment. Biochar could improve soil aeration by providing the numerous internal particle pores and interparticle voids to the surrounding soils (Atkinson et al. 2010; Sohi et al. 2010; Hardie et al. 2013). The soil porosity might also be improved by the modification of the inherent pore size distribution because biochar created accommodation macropores in the surrounding soil in addition to directly contributing its own pores (Chia et al. 2012; Hardie et al. 2013; Guo et al. 2015). These accommodation macropores also decreased the soil bulk density (Devereux et al. 2013; Peake et al. 2014; Nan et al. 2021). Oxygen fluxes correspondingly increased in soils with high porosity and low density. Methanogenic activities were inhibited because this oxygen availability was harmful to Methanosarcina, which acted as the primary methanogenic taxa in this study (Kim et al. 2017; Nan et al. 2021). Instead, the dominant methanotrophs belong to USCγ with an extremely high preference for O2 and high affinity for CH4. Methanotrophic activities were enhanced because of the higher substrates supply such as CH4 and O2 in the biochar amended soil (Scheutz et al. 2009; Bohn et al. 2011; Cong et al. 2018).

pH might be another vital parameter for biochar regulation on the methanogenic and methanotrophic gene abundance. The pH of the biochar in this study reached 9.6. After the biochar amendment, the pH of the slightly alkaline soil in this study further increased due to the liming effect of biochar (Jeffery et al. 2016; Si et al. 2018). Methanogens were inhibited since the optimum pH range of Methanosarcina fell in 6.5–7.1 (Fig. 1B). Methanotrophic activities were also limited because the optimum pH of methanotrophs ranged from 5.0 to 6.5 (Jeffery et al. 2016; Nan et al. 2021). Incidentally, pH elevation mitigated the Al3+ availability to protect methanotrophs from the Al3+ toxicity (Tamai et al. 2007). Biochar amendment resulted in NH4+-N input by enhancing N mineralization. When soil ammonia was sufficient, either growth of methanogens or methanotrophs might be limited (Bédard and Knowles 1989; Gulledge and Schimel 1998; Zhao et al. 2021b). In addition, biochar amendment might diminish methanogen growth by reducing substrates for methanogens such as soil dissolved organic carbons (DOCs) (Zimmerman et al. 2011). Decreases in soil DOC and methane emissions from biochar amended soil have been observed in several previous studies (Han et al. 2016; Zheng et al. 2016). Altogether, the high porosity in the biochar amended soil resulted in lower methanogenic and higher methanotrophic gene abundance, and the altered pH and DOCs further diminished the methanogenic gene abundance.

4.2 Dung effects on methane metabolic genes

Soil porosity was also the primary factor controlled by the dung effect to regulate methanogenesis and methane oxidation (Fig. 1A). The dung amendment harbored biological crusts in the dung-soil patch to create an anoxic interface. The high moisture of the fresh dung also hindered the oxygen flux in the dung amended soil. The microbial degradation of the abundant organic matters from the dung further depleted oxygen. Thus, more anoxic compartments occurred after the dung amendment. These anoxic compartments created microenvironments that promote anaerobic methanogenesis and inhibit aerobic methanotrophic activities (Chadwick et al. 2000; Ma et al. 2006; Cai et al. 2017). Leaching of ammonia nitrogen (NH4+-N) and dissolved organic N (DON) simulated by dung allowed methanotrophs to obtain sufficient N (Cai and Akiyama 2016). The N-enriched environments could mitigate the limiting effect of C on microorganisms through higher litter inputs, thereby increasing the activity of methanogens (Banger et al. 2012; Siciliano et al. 2013). Under conditions of adequate N, the methanotrophic gene pmoA compete with the amoA gene, leading to further mitigation of methane oxidation (Bédard and Knowles 1989; Gulledge and Schimel 1998). Nitrite and nitrate oxidized from ammonia were also toxic to methanotrophs (Bédard and Knowles 1989; Gulledge and Schimel 1998; Bykova et al. 2006; Nazaries et al. 2013). More importantly, the methanogens in fresh dung from the intestine of livestock further enhanced the methanogenic activity (Zhao et al. 2021c). Notably, the abundance of another methanotrophic gene mmoX increased after the dung amendment. Methylosinus with mmoX were found in the incubated soils. These methanotrophs utilizing either pMMO or sMMO favored methane-enriched and oxygen-impoverished environment (Tentori and Richardson 2020). The anaerobic compartments in soils with dung amendment produced considerable endogenous CH4. The abundance of mmoX gene increased correspondingly with Methylosinus. Notably sMMO was inactive with the presence of copper ion and pMMO activity was facilitated in such environment. This was also consistent with the divergent abundance change of pmoA and mmoX  genes after the dung amendment with copper input in this study. Unfortunately, how these reciprocally regulations of Cu on sMMO and pMMO formed is not confirmed (Semrau et al. 2010). Methanotrophs could synthesize a copper chelate called methanobactin to uptake copper (Semrau et al. 2020). How this copper uptaking interact in the methane oxidation was controversial until pMMO was proposed to employ a mono-copper as the only center to catalyze the methane oxidation, so the uptaken copper might provide extra binding sites for methane consumption (Ross et al. 2019). On the contrary, sMMO is a multicomponent soluble di-iron monooxygenase (Semrau et al. 2020). This might explain the divergent response of pmoA and mmoX  genes to the copper input. Therefore, the disruption of aeration conditions, ascending N and Cu input, accounted for the increase and decrease in methanogenic and methanotrophic gene abundance, respectively.

4.3 Regulation of exogenous amendment on methanogenic and methanotrophic community assembly

The narrow niche breadth of methanogen made methanogens niche-discriminatory specialists rather than generalists in various environments (Fig. 3A) (Sexton et al. 2017; Xu et al. 2021). The poor environment adaptability led the  methanogen community to hardly exist in various niches but be a strong competitor in a limited niche. The methanogen assembly process was therefore shaped by homogeneous selection without exogenous amendment (Fischbach and Segre 2016; Delgado-Baquerizo et al. 2017; Zhou and Ning 2017). Nonetheless, the undominated processes replaced homogeneous selection to primarily drive methanogen assembly with dung amendment (Fig. 2B). This change might be attributed to the exogenous methanogen input by fresh dung from the livestock intestine. Livestock intestine formed an extreme environment to harbor enormous methanogens which contributed a considerable proportion of the methanogen community in dung amended  soils (Zhao et al. 2021c). When these methanogens were excreted outside the intestinal tracts with fresh dung, environmental conditions such as pH, moisture, temperature stress, and aeration drastically fluctuated. This environmental randomness poses difficulty in shaping methanogen populations by environment selection. Instead, undominated processes such as weak selection and dispersal dominated the assembly process.

Compared to the methanogens, the wider niche breadth of methanotrophs indicated that they were more environmentally adaptive and tended to be generalists in various biotopes (Fig. 3A). The higher niche overlaps between methanotrophs and microbes involved in N cycling also proved that methanotrophs could compete for resources with other species in much more extensive niches (Sexton et al. 2017). This environment tolerance resulted in stochastic processes rather than deterministic processes dominated the methanotroph assembly process (Fig. 2B). Stochastic processes increased the community redundancy of methanotrophs because other microbes overlapping methanotrophic niches could serve the same functions in the ecosystem (Fig. 3B) (De Vrieze et al. 2020). Although the passive dispersal of microorganisms was always random due to small sizes, microbial dispersal sometimes showed strong environmental patterns owing to dispersal limitation (Martiny et al. 2006; Hanson et al. 2012; Nemergut et al. 2013; Peay et al. 2016). When grassland soil was amended with biochar, the significant modification of the internal pores and interparticle void structure might be the limitation to eliminate the randomness of microbial dispersal (Zhou and Ning 2017; Ning et al. 2020). Thus, dispersal limitation became the primary driving force for the methanotroph assembly process in the biochar amended soils.

4.4 Interactions between methane metabolic genes and other functional genes involved in C and N cycling

Both biochar and dung amendments enhanced ecological processes such as N fixation, ammonification, nitrification, denitrification, and DNRA involved in N cycling (Fig. 4). Dung amendment showed stronger effects on these N cycling processes. Dung amendment increased N sequestration within the soil to participate in long-term N cycling. Dung deposition enhances soil organic C availability and microbial activity, thereby stimulating the mineralization of pre-existing N and microbial N fixation in the soil (Cai and Akiyama 2016; Cai et al. 2017). At the same time, invertebrates such as dung beetles, earthworms, termites, and maggots degraded the fresh dung to mineralize the organic N and residual organic N, which were also aminated by invertebrates (Mendez et al. 2013; Evans et al. 2019). Nitrification converted NH4+ to NO3− in soils and the ammoxidation gene amoA completes the first step of nitrification by oxidizing NH4+ to NH2OH. The ammoxidation microorganisms with amoA typically include ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA). Dung deposition induced soil NH4+, which stimulates ammoxidation (Hartmann et al. 2013; Cardenas et al. 2016). AOB in grassland soils were reported to increase with the dung amendment (Schauss et al. 2009; Yamamoto et al. 2012; Xue et al. 2016). AOA abundance also increased following dung amendment, utilizing additional labile C (Cai et al. 2017). The aforementioned N fixation, N mineralization, and nitrification prominently altered soil NH4+. Sufficient NH4+ inhibited methanotrophic activities through exclusive competition with CH4 (Bédard and Knowles 1989; Gulledge and Schimel 1998). Significant niche overlaps were found between methanotrophs and microbes involved in these processes (Fig. 3B). Sums of these gene variations in BC and FC were lower than those in BF. This implied synergy effects of biochar and dung on N cycling (Fig. 4).

After biochar and dung amendments, the occurrence of numerous weak positive correlations in the co-occurrence network of C and N cycling genes suggested less common preference for environmental conditions or niche-overlapping among species with these functional genes. The disappearance of strong negative correlations indicated competition or niche-partitioning among these species involved in C and N cycling (Fig. 5) (Faust and Raes 2012; He et al. 2021). These results were consistent with the decreasing association parameters in the topological parameters of the co-occurrence network. The increasing network heterogeneity depicted the decreasing diversity among these functional genes (Table 1). The decreasing complexity and connectivity of the network implied that the species with these genes were less interconnected after applying biochar and dung amendments and the efficiency of resource and information transfer was lessened (Morrien et al. 2017; He et al. 2021). Hence, biochar and dung amendments might result in lower stability of the microbial community that participated in C and N cycling (Mougi and Kondoh 2012).

5 Conclusion

Dung amendment promoted methanogenic genes and inhibited methanotrophic genes while biochar amendment inhibited methanogenic genes and promoted methanotrophic genes via controlling soil porosity, pH and ammonia. Dung amendment led undominated  processes rather than homogeneous selection to dominate the methanogen assembly and biochar amendment raised the contribution of dispersal limitation in methanotroph assembly. The two exogenous amendments weakened the interconnection between methane metabolic genes and other functional genes involved in C and N cycling. This study provided an integrated understanding of the microbial coexistence patterns and community assembly related to methane emission with typical management in grasslands. Deeper investigation on the interactions among the functional modules involved in biogeochemical cycling would better optimize soil utilization in the context of global climate change.