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The remarkable seasonal variations of CH4 fluxes are shown in Fig. 2. The Dajiuhu subalpine peatland was the source of CH4 (Table 1), with an average CH4 release rate of 18.50 nmol m–2 s–1. The maximum monthly release rate was 42.64 nmol m–2 s–1 in August, which was 13.8 times than that of in March (3.09 nmol m–2 s–1). The monthly average release rate remained steady from December 2018 to May 2019 and then rose from May to August. The cumulative emissions of CH4 were highest in summer (4681.16 mg m–2) and lowest in spring (587.02 mg m–2).
Figure 2. Daily and monthly variations of CH4 fluxes (nmol m−2 s−1) from December 2018 to November 2019.
Quarter Month Monthly average
CH4 fluxes
(nmol m–2 s–1)Quarterly average
CH4 fluxes
(nmol m–2 s–1)CH4 quarterly
cumulative emissions
(mg m–2)Proportion of
annual cumulative
emissions (%)Winter 2018.12 9.45 6.46 803.92 4.34 8.61 2019.01 5.85 2.69 2019.02 3.83 1.59 Spring 2019.03 3.09 4.62 587.02 1.42 6.29 2019.04 4.71 2.09 2019.05 6.05 2.78 Summer 2019.06 28.09 36.81 4681.16 12.48 50.16 2019.07 39.41 18.10 2019.08 42.64 19.58 Autumn 2019.09 40.40 25.92 3261.16 17.95 34.94 2019.10 24.76 11.37 2019.11 12.65 5.62 Mean value 18.50 18.50 2333.32 − − Total − − 9333.26 100 100 Table 1. CH4 emissions in different time scales of Dajiuhu subalpine peatland.
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The topological structure of the methanogenic networks in four seasons was different (Fig. 3a and Table S1 in the Electronic Supplementary Materials, ESM). The maximum number of nodes and edges appeared in spring (nodes: 621, edges: 32159). An obvious reduction occurred in summer (nodes: 512, edges: 8030) when the smallest average path length, average clustering coefficient, and the largest average degree of the spring network were observed. In the summer network, the average path length and the average clustering coefficient were both the largest. The tightest connections in spring could be inferred, while the lowest degree of connectivity occurred in summer. The modularity value of networks was larger in summer, and the smallest values occurred in spring. The proportion of negative edges to the total edges in the winter, spring, summer, and autumn networks were 29.90%, 73.26%, 47.19%, and 50.63%, respectively.
Figure 3. Ecological molecular networks of the methanogenic community (a). Each node represents an OTU, sorted in color by modularity class, the size of each node is proportional to the number of connections, the red lines represent the positive (cooperative) interactions and the green lines represent the negative (competitive) interactions. The methanogenic Zi-Pi plot of the topological role of each OTU (b).
The topological roles of the OTUs in the four seasonal networks also showed seasonal changes (Fig. 3b and Table S2 in the ESM). Although network hubs are considered as super generalists (acting as both module hubs and connectors) (Olesen et al., 2007), no network hub was observed in the networks. There were 12, 3, 8, and 2 module hubs in the winter, spring, summer, and autumn networks, respectively. The nodes of connectors in the spring and summer networks were 17 and 1, respectively. Most of the module hubs and connectors of the winter, spring, and autumn networks belonged to Methanoregula formicica, whereas most in the summer networks were Methanocella arvoryzae.
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The differences in the topological structure were shown in the four seasonal methanogenic networks (Fig. 4 and Table S1 in the ESM). The maximum number of nodes and edges appeared in spring (nodes: 811, edges: 377775), while minimum values emerged in summer (nodes: 589, edges: 2715). The average degree and network density of the summer network were the smallest, while there were larger values in the spring. There was also a decrease in the average path length and the average clustering coefficient in the spring network than in the summer. The highest degree of connectivity occurred in spring, and the lowest occurred in summer, which could be inferred by these topological parameters. The modularity value of networks was largest in summer and smaller in spring. The proportion of negative edges in the four networks was 47.79%, 7.70%, 28.18%, and 69.62%, respectively. We conclude that the competition among methanotrophs was weakest in spring.
Figure 4. Ecological molecular networks of the methanotrophic community (a) and the methanotrophic Zi-Pi plot of the topological roles of OTU (b).
The topological nodes varied in the four seasonal networks (Fig. 4b and Table S3 in the ESM). Only one network hub was found in the spring network, which belonged to Methylocystaceae. The nodes of module hubs in the winter, spring, summer, and autumn networks were 19, 9, 12, and 8, respectively, and there were 2, 2, and 8 connectors in the winter, spring, and summer networks, respectively. Most of the module hubs and connectors belong to Methylocystaceae, Methylocapsa aurea, and Candidatus Methyloumidiphilus alinensis. The module hubs and connectors in the spring and summer network were mainly attached to Methylocystaceae, while module hubs in the autumn network were affiliated with the Methylocapsa aurea.
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The interactions between the methanogenic and methanotrophic communities caused the seasonal variations (Fig. 5). The number of edges in the four networks was 252, 456, 185, 233 in sequence; the number of negative edges among them was 128, 225, 102, and 122 in sequence. These data imply that strong competition and cooperation existed between methanogens and methanotrophs in spring and summer, respectively. The majority of the methanogen connections were associated with Methanoregula formicica (winter: 178, spring: 228, summer: 128, autumn: 135), followed by Methanocella arvoryzae (winter: 48, spring: 103, summer: 50, autumn: 90). Methanocella arvoryzae played a pivotal role, evidenced by its numerous positive connections with other species; however, Methanoregula formicica was more competitive in summer. For the methanotrophs, Methylocystaceae accounted the majority of the connections (winter: 64, spring: 35, summer: 114, autumn: 64) in the networks. Methylocapsa aurea (winter: 27, spring: 22, summer: 35, autumn: 65) and Candidatus Methyloumidiphilus alinensis (winter: 66, spring: 64, summer: 7, autumn: 12) accounted for a large part otherwise. Numerous negative connections with the other species, Methylocapsa aurea and Candidatus Methyloumidiphilus alinensis, deemed them as competitors in spring, while Methylocystaceae contributed to maintaining community stability.
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The correlation analysis was carried out to demonstrate the correlations among environmental factors, methane metabolic microbials, and CH4 fluxes (Fig. 6, Table S4, and Table S5 in the ESM). Among the environmental factors measured, total nitrogen (TN) had the most significant influence on methanogens and methanotrophs, whereas the
${\rm{NH}}_4^+ $ -N and${\rm{NO}}_2^- $ -N also exerted obvious effects. The CH4 fluxes were positively correlated with soil temperature (r = 0.757) (r: Pearson correlation coefficient) but negatively correlated with the water table (r = –0.539), TN (r = –0.231),${\rm{NH}}_4^+ $ -N (r = –0.425),${\rm{NO}}_2^- $ -N (r = –0.336), and${\rm{NO}}_3^{-}$ -N (r = –0.202). The cumulative CH4 emissions were positively correlated with the average clustering coefficient of the methanogenic community (r = 0.759), the diameter (r = 0.871) and average path length (r = 0.641) of the methanotrophic community, and the proportions of negative edges between methanogenic and methanotrophic communities (r = 0.961).Figure 6. Correlations among environmental factors, methane metabolic microbials, and methane fluxes. In order from A to K are: CH4 fluxes, water table, pH, Oxidation-Reduction Potential (Eh), total nitrogen (TN),
${\rm{NH}}_4^+ $ -N,${\rm{NO}}_2^-$ -N,${\rm{NO}}_3^- $ -N, total organic carbon (TOC), soil temperature (Ts), and soil water content (SWC).
Quarter | Month | Monthly average CH4 fluxes (nmol m–2 s–1) | Quarterly average CH4 fluxes (nmol m–2 s–1) | CH4 quarterly cumulative emissions (mg m–2) | Proportion of annual cumulative emissions (%) | |
Winter | 2018.12 | 9.45 | 6.46 | 803.92 | 4.34 | 8.61 |
2019.01 | 5.85 | 2.69 | ||||
2019.02 | 3.83 | 1.59 | ||||
Spring | 2019.03 | 3.09 | 4.62 | 587.02 | 1.42 | 6.29 |
2019.04 | 4.71 | 2.09 | ||||
2019.05 | 6.05 | 2.78 | ||||
Summer | 2019.06 | 28.09 | 36.81 | 4681.16 | 12.48 | 50.16 |
2019.07 | 39.41 | 18.10 | ||||
2019.08 | 42.64 | 19.58 | ||||
Autumn | 2019.09 | 40.40 | 25.92 | 3261.16 | 17.95 | 34.94 |
2019.10 | 24.76 | 11.37 | ||||
2019.11 | 12.65 | 5.62 | ||||
Mean value | 18.50 | 18.50 | 2333.32 | − | − | |
Total | − | − | 9333.26 | 100 | 100 |