Introduction

Methane, as a greenhouse gas, contributes 20% to global warming. Its emission from natural sources such as wetlands, hydrothermal vents, and oceans account for more than 40% of the global total methane emission, among which wetland is the largest natural source (Bridgham et al. 2013; Dalal and Allen 2008; Lyu et al. 2018). Coastal wetlands are important source of methane emission and the methane fluxes from coastal wetlands range from 100 to 231 × 102 g a−1 (Tong et al. 2010; Welti et al. 2016). The area of coastal wetlands in China are estimated at more than 1.2 × 104 km2, making up a considerable proportion of the natural wetlands (Huang et al. 2006). Methane emission rates are higher from polluted wetlands than those from unpolluted ones (Purvaja and Ramesh 2000; Zheng et al. 2018).

Methane is produced by methanogens through the process of methanogenesis under anaerobic conditions (Liu and Whitman 2008). Traditionally, methanogens from the phylum Euryarchaeota can be classified into seven orders (Methanococcales, Methanopyrales, Methanobacteriales, Methanomicrobiales, Methanocellales, Methanosarcinales, and Methanomassiliicoccales). Recent genome binning has revealed that the new class of Methanofastidiosa, the new phyla of Verstraetearchaeota, Korarchaeota, and Nezhaarchaeota also have potentials for methanogenesis (McKay et al. 2019; Nobu et al. 2016; Vanwonterghem et al. 2016; Wang et al. 2019b). Methanogens harbor three major pathways of methanogenesis, i.e., hydrogenotrophic, acetoclastic, and methylotrophic pathways (Conrad 2009). All the pathways have a common enzyme, methyl-coenzyme M reductase (MCR), for the final step of methane synthesis (Liu and Whitman 2008). The mcrA gene, which encodes alpha subunit of MCR, is a commonly used gene marker for surveying diversity of methanogens (Yang et al. 2014).

Previous studies based on mcrA gene have observed a vast diversity of methanogens in various environments including animal digestive tract, soil, sediments, and anaerobic digesters (Lu et al. 2015; Mihajlovski et al. 2008; Wilkins et al. 2015). The distribution and environmental preferences of methanogenic groups are also diverse. For example, Methanofastidiosa genomes are obtained from anaerobic sludge digester (Nobu et al. 2016); Methanocellales has been isolated from paddy field soils (Lyu and Lu 2012; Sakai et al. 2008); Methanococcales is preferentially identified in marine habitats (Liu and Whitman 2008); Methanopyrales is the only methanogens so far that could produce methane at temperature higher than 100 °C (Liu 2010); Methanomassiliicoccales has been isolated from human feces (Borrel et al. 2012). These studies indicate that methanogenic community composition could be influenced by environmental factors, such as salinity and temperature.

The river-bay system is located in coastal wetlands connecting land and ocean. Sediments in such environments are under tidal influences and are characterized by an absence of oxygen some or all of the time. In addition, coastal areas are normally densely populated and remarkably disturbed by anthropogenic activities. Due to lack of oxygen and relatively high carbon inputs, coastal sediments are optimal environments for methanogens (Taketani et al. 2010). Methanogenic communities are susceptible to environmental changes across the river-bay continuum where continental freshwater meets oceanic water. Understanding the spatial and seasonal variability of methanogens in the river-bay system is important for recognizing major players involved in methane production. However, few studies have reported the transition of methanogenic communities in the river-bay system.

The Shenzhen River-Bay system is located in subtropical coastal areas in south China. It is an economically developed and densely populated area in China (Wang et al. 2019a). It extends the freshwater-saltwater interface from the Dasha River to Shenzhen Bay and is subjected to tidal influence (Fig. S1). The underlying mechanism of methanogenic community assemblage in such a dynamic and fluctuated system is still not well understood. Since riverine and estuarine sediments in the river-bay system are two types of habitats, some groups of methanogens might be found in distinct niches. In the current study, we analyzed samples collected during two seasons (wet and dry seasons) from the Shenzhen River-Bay system to explore the spatial and seasonal variation of methanogenic abundance and community composition. Our objectives are (i) to explore the transition of methanogen community structure in the Shenzhen River-Bay system, and (ii) to reveal the driving factors for the methanogenic community assemblage in the Shenzhen River-Bay system.

Materials and methods

Study sites, sediment sampling, and environmental parameter analysis

The Shenzhen River-Bay system includes Dasha River and Shenzhen Bay. The Dasha River originates in the north of Shenzhen and flows south towards Shenzhen Bay. Shenzhen Bay is a semi-enclosed bay located in the east coast of the Pearl River Estuary. We selected 6 sampling sites from Dasha River (R1, R3–R7) and 10 sampling sites from Shenzhen Bay (E1–E5, E7–E11) along salinity gradients (Fig. S1). The sampling sites were not named consecutively because the sediments of two sites (R2 and E6) were not enough for the following physical and molecular analysis and these two sites were removed. At each sampling site, sediments were collected using a stainless steel sampler in July 2016 and February 2017, representing the wet (W) and dry (D) seasons, respectively. Altogether, 32 sediment samples were collected. All samples were transferred to the laboratory on ice and stored at − 40 °C before analyses.

Water temperature, salinity, and pH were measured in situ by using a multiparameter water quality sonde (EXO2, USA). Sediments were air-dried in the laboratory for a few days until the weight kept unchanged. Sediment ammonium, nitrite, and nitrate were extracted from air-dried sediments with 1 M KCl and determined by a Continuous Segmented Flow Analyzer (SEAL AutoAnalyzer 3 HR, USA). Total organic carbon (TOC) and total nitrogen (TN) contents were measured using TOC Analyzer (Shimadzu, Japan), as previously described (Zhang et al. 2019).

DNA extraction and qPCR

Total DNA was extracted from 0.3 g of sediments using the DNeasy PowerSoil Kit (Qiagen, Germany) according to the manufacturer’s instructions. The quantity and quality of the extracted DNA were examined using NanoDrop® ND-2000c UV-Vis spectrophotometer (NanoDrop Technologies, USA). The DNA samples were stored at − 20 °C and used for later molecular analysis. Abundance of the mcrA genes was determined by quantitative PCR (qPCR) on an iCycler iQ 5 thermocycler (Bio-Rad, USA) using the primer pair mlas-mod-F (5′-GGY GGT GTM GGD TTC ACM CAR TA-3′) and mcrA-rev-R (5′-CGT TCA TBG CGT AGT TVG GRT AGT-3′) (Steinberg and Regan 2008). Each reaction with 25 μl contained 12.5 μl of 2 × SYBR® Premix Ex Taq™ (Takara Biotechnology, Japan), 0.5 μl of each primer (10 μM), and 2 μl of diluted DNA template (1–10 ng). The reaction protocol was as follows: 95 °C for 3 min, 30 cycles of 30 s at 95 °C, 45 s at 55 °C, and 30 s at 72 °C. Standard curves were generated using 10-fold serial dilutions of a plasmid containing the mcrA gene fragments. The PCR efficiency for different assays ranged between 90 and 100%, with R2 value of 0.99.

DNA sequencing and data processing

For amplicon sequencing, mcrA gene fragments were amplified using the primer pair mlas-mod-F and mcrA-rev-R (Steinberg and Regan 2008). The purified amplification products were sequenced on the Illumina MiSeq platform at Novogene Bioinformatics Technology Co., Ltd. (Tianjin, China). Across the 32 samples, the high-throughput sequencing yielded 2,965,757 mcrA gene sequences in total, and the minimum sequence number for individual sample was 10,983.

The paired-end raw reads were merged using the join_paired_ends.py script of Quantitative Insights into Microbial Ecology (QIIME1) (Caporaso et al. 2010). The sequence data set was then analyzed using a modified QIIME2 pipeline (Bolyen et al. 2019). Briefly, chimeras were removed and sequences were trimmed by sequence length (478 bp) using DADA2. After denoising, feature table and feature sequences were generated. Each feature in the feature table will be represented by exactly one sequence. Feature sequences were BLAST against the NCBI-nr database to assign taxonomy. All the samples were randomly rarefied to the minimum number (10,983) of sequences per sample to ensure equal sampling depth.

Statistical analysis

The mcrA gene copies were log-transformed prior to statistical analysis to satisfy the normality assumptions. Shannon index, which was commonly used to represent the alpha diversity, was calculated by diversity function in “vegan” R package. Species abundance distribution (SAD) patterns of methanogenic genera were estimated according to their relative abundance and occurrence. According to the sampling habitats (R = river: Dasha River; E = estuary: Shenzhen Bay) and sampling seasons (W = wet season; D = dry season), the samples were separated into four groups: WR, DR, WE, and DE. The significant differences of the physicochemical properties, abundance, Shannon index, and relative abundances of major orders/families across groups were evaluated by single-factor analysis of variance (ANOVA) followed with Duncan’s test (P < 0.05) by “agricolae” R package. Principal coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) were performed to investigate shifts of the methanogenic community based on the genus level across groups using “vegan” R package. The indicator lineages, which were preferentially found in samples grouped by habitat (river vs. estuary), were calculated by IndVal index in “labdsv” R package (De Cáceres and Legendre 2009). The indicator lineages analysis combined relative abundance and frequency of occurrence of methanogenic genera.

Variation partitioning approach (VPA) was used to evaluate the relative importance of habitats, seasons, and environmental factors on methanogenic community using “vegan” R package. Furthermore, redundancy analysis (RDA) was used to explore relationships between methanogenic community and environmental factors using “vegan” R package. Here, environmental factors included water temperature, pH, salinity, and nutrients (ammonia, nitrite, nitrate, TOC, and TN) contents. Multivariate regression tree (MRT) was performed to predict relationships between methanogenic genera and environmental characteristics using “mvpart” R package (De’Ath 2002). The size of tree to be generated was set as 5. Pie chart under every leaf showed the community composition and how the relative abundances of the methanogenic genera contributed to the separation. Relationships between the relative abundances of methanogenic genera and environmental factors were calculated using Spearman’s rank order correlations. All statistical analyses were performed using R software version 3.5.1 (Team 2018).

Results

Environmental parameters

Environmental properties in the Shenzhen River-Bay system varied according to habitat (river vs. estuary) and season (wet vs. dry) (Fig. S2). In general, the concentrations of nutrients (ammonia, nitrate, TOC, and TN) in riverine sediments were significantly higher than those in estuarine sediments (P < 0.001). Nitrite was higher in the wet season than those in the dry season (P < 0.05). Notably, pH and salinity were comparatively higher in estuarine sediments than those in riverine sediments (P < 0.001). In addition, salinity in the dry season was significantly higher than those in the wet season (P < 0.001). Water temperature in the wet season was significantly higher than in the dry season.

Abundance and alpha diversity of methanogens

Quantitative PCR analysis showed that the mcrA gene copies varied significantly according to habitat and season (Fig. 1a). It was significantly higher in the sediments of river than those of estuary (P < 0.05), and significantly higher in the wet season than in the dry season (P < 0.05). Alpha diversity, as measured by Shannon index, showed that there were no significant differences according to habitats and seasons (Fig. 1b).

Fig. 1
figure 1

Abundance and alpha diversity of methanogens. The samples were grouped by different habitats (river and estuary), and seasons (wet and dry seasons). a Log-transformed abundance of the mcrA genes. b Alpha diversity of methanogens, estimated with the Shannon index. Box plots were constructed to show median, interquartile range, and 1.5 × the interquartile range (n = 6 for riverine samples; n = 10 for estuarine samples). The lowercase letters indicate significant differences among groups (one-way ANOVA; Duncan test; P < 0.05). Sample names: W = wet season; D = dry season; R = river, Dasha River; E = estuary, Shenzhen Bay

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Community composition of methanogens

The community composition of methanogens at each sampling site in the Shenzhen River-Bay system is presented in Fig. 2. The mcrA gene sequences were mostly assigned into one class and five orders from the phyla Euryarchaeota. Methanosarcinales, Methanomicrobiales, and Methanobacteriales were the top three abundant orders, contributing 44.4%, 30.6%, and 20.2% of the average relative abundance of methanogenic community, respectively. Methanomassiliicoccales, Methanocellales, and Methanofastidiosa were detected but at relatively low abundance, the average relative abundance of which were 2.4%, 1.9%, and 0.4%, respectively. Furthermore, the relative abundances of different genera were plotted against their occurrence to show the SAD patterns (Fig. S3). Methanosarcina, Methanosaeta, and Methanobacterium were the most abundant and ubiquitous genera in the Shenzhen River-Bay system.

Fig. 2
figure 2

Relative abundance of methanogenic taxa based on mcrA gene sequences. The top 6 methanogenic taxa (class or order) are represented. “Others” refers to the remained minor taxa. Sample names: W = wet season; D = dry season; R = river, Dasha River; E = estuary, Shenzhen Bay

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There were significant differences of the relative abundance of methanogenic taxon according to habitat (river vs. estuary). The relative abundance of Methanomicrobiaceae was significantly higher in estuarine sediments than in the riverine zones, while those of Methanoregulaceae and Methanomassiliicoccales showed the opposite trends (Fig. S4). Specifically, Methanomicrobiaceae contributed 5.0% of the average relative abundance of the riverine community and 18.3% of the estuarine community. Conversely, Methanoregulaceae comprised 20.1% of the average relative abundance of the riverine community and 6.8% of the estuarine community. Methanomassiliicoccales contributed 3.5% of the average relative abundance of the riverine community and 1.7% of the estuarine community.

Beta diversity of methanogenic community and indicator groups for habitats

PCoA revealed that habitat (river and estuary) was a strong structuring factor (Fig. 3). Methanogenic community in estuarine habitat (WE and DE) clearly separated from that in riverine habitat (WR and DR), regardless of the season. PERMANOVA based on Bray-Curtis dissimilarity corroborated that habitat showed a significant influence on methanogenic community composition (P = 0.001, R2 = 0.137).

Fig. 3
figure 3

Principal Coordinates Analysis (PCoA) using Bray-Curtis indices of methanogenic communities at genus level. Sample names: W = wet season; D = dry season; R = river, Dasha River; E = estuary, Shenzhen Bay

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To further identify the methanogenic lineages responsible for the observed community variations according to habitat (river vs. estuary), indicator taxa analysis was conducted. Overall, 7 methanogen genera showed a significant IndVal (P < 0.01) for habitat (Fig. 4). These genera were affiliated to Methanomicrobiales, Methanosarcinales, and Methanomassiliicoccales. Among them, Methanolinea, Methanoregula affiliated to Methanomicrobiales, and Methanomethylovorans affiliated to Methanosarcinales were more abundant in riverine sediments. By contrast, Methanoculleus and Methanogenium affiliated to Methanomicrobiales were more abundant in estuarine sediments. Methanococcoides affiliated to Methanosarcinales only occurred in estuarine sediments and were absent from riverine sediments.

Fig. 4
figure 4

Bubble plot showing the average relative abundance of methanogenic genera in the samples grouped following Fig. 1. The genera represent significant indicator lineages according to the IndVal index (P < 0.05). Sample names: W = wet season; D = dry season; R = river, Dasha River; E = estuary, Shenzhen Bay

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Driving factors of methanogenic community assemblage

VPA revealed that habitat, season, and environmental factors contributed to the variations of methanogenic communities together. Environmental factors explained the largest proportion of variation (29.6%), followed by habitat (11.1%), and season (2.0%). RDA revealed that water temperature, pH, salinity, TN, TOC, ammonia, nitrite, and nitrate were significantly correlated with the variation of methanogenic communities. Among all the measured environmental factors, salinity and ammonia were more strongly associated with methanogenic community variation than other environmental factors (Fig. S5). MRT analysis was carried out to link the methanogenic community composition and environmental effects (Fig. 5). The analysis showed that salinity could divide the MRT into three major branches, one contained samples with freshwater condition (salinity < 1.11‰), one contained samples with saline condition (1.11‰ < salinity < 27.07‰), and the other one contained samples with hypersaline condition (salinity > 27.07‰). The most discriminated groups accounting for the separation were Methanoregula, Methanoculleus, and Methanogenium. Methanoregula was more abundant in freshwater environment, while Methanoculleus and Methanogenium were more abundant in saline environment. The branch with salinity less than 1.11‰ was then spilt by water temperature. The branch with salinity between 1.11‰ and 27.07‰ could be further divided by pH value.

Fig. 5
figure 5

Multivariate regression tree (MRT) showing the relationships between the methanogenic community and environmental factors. Five-split tree is visualized. Statistics information is listed under tree, including the residual error, cross-validated error, and standard error. Pie charts under each leaf represent the methanogenic community composition at genus level. The corresponding residual error and sample numbers under each leaf are also listed

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Next, correlation analyses were performed to examine the relationships between the environmental factors and relative abundances of methanogenic genera (Fig. 6). Spearman’s rank analysis found that pH and salinity were significantly positively correlated with relative abundances of Methanogenium, Methanoculleus, and Methanococcoides (P < 0.05). Moreover, significant positive correlations were found between sediment nutrients concentration (TOC, TN, ammonia, and nitrate) and relative abundances of Methanoregula, Methanomethylovorans, and Candidatus Methanomethylophilus (P < 0.05).

Fig. 6
figure 6

Heat map representing Spearman’s rank order correlations between the methanogenic genera and environmental properties. Significant correlations are labeled with asterisk (P < 0.05)

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Discussion

In the current study, we systematically investigated the transition of methanogenic abundance and community composition in the Shenzhen River-Bay system. The presented findings provided novel evidence that salinity was a major factor driving the methanogen community assemblage in the river-bay system. Salinity and nutrients’ contents regulated the relative abundance of a few particular methanogenic groups. For example, Methanoregula were positively influenced by nutrients’ contents, whereas Methanogenium and Methanoculleus were positively influenced by salinity. These results implied that particular methanogenic groups had environmental preference.

Quantitative PCR analysis revealed that mcrA gene copies ranged from 104 to 108 per g sediment and differed according to habitats and seasons. The abundance of methanogens was significantly higher in the riverine sediments than that in the estuarine sediments. One of the possible reasons is that methanogenic abundance is associated with nutrients availability (Zhou et al. 2014). Human activities, such as industrial and domestic wastewater, discharge relatively high contents of nutrients in riverine sediments, which might provide substrates for methanogenesis (Liu et al. 2018). Higher concentration of nutrients in the riverine sediments we measured in the current study supported the above opinion. We also found that the methanogenic abundance was higher in the wet season than in the dry season. It might be because of a higher temperature in the wet season than in the dry season, which promoted the growth of methanogens (Chafee et al. 2017; Lu et al. 2015).

We identified diverse methanogens, including one class and five orders from the phylum Euryarchaeota, in the Shenzhen River-Bay system. Methanosarcinales, Methanomicrobiales, and Methanobacteriales were the three dominant methanogen orders identified in the current study. This was consistent with previous reports that Methanomicrobiales and Methanosarcinales were abundant in coastal sediments (Chen and Yin 2013). Methanomassiliicoccales, Methanocellales, and Methanofastidiosa were also detected, but their relative abundance were low, indicating that these methanogens contribute less to methane production in the river-bay system. Methanomassiliicoccales has been isolated from human feces and considered to be the seventh order of methanogens according to phylogenetic analysis (Borrel et al. 2013; Dridi et al. 2012). It has been reported that Methanomassiliicoccales contain two main clades: environmental clade and gastro-intestinal tract (GIT) clade (Söllinger and Urich 2019). Methanomassiliicoccales in river-bay system has to face higher variations in environmental parameters such as salinity, oxygen exposure, and nutrient availability than Methanomassiliicoccales in the GIT clade, which might explain the low abundance in the river-bay system. Methanocellales has been isolated from paddy field soils (Lyu and Lu 2012; Sakai et al. 2008). Methanocellales are key methanogen groups in rice soils, and they are at a small fraction in other environments including riverine sediments (Angel et al. 2012; Li et al. 2012). Methanofastidiosa is a newly described class of methanogens. There is no pure culture of this class for now. The first draft genomes retained from the anaerobic digester were reported in 2016 (Nobu et al. 2016). Methanofastidiosa is suspected to produce methane using methylated thiol as substrates strictly. In addition, genome annotations revealed that Methanofastidiosa lack abilities to fix carbon/nitrogen and synthesize many amino acids (Nobu et al. 2016). Since they require exogenous organic carbon as a carbon source for growth, there is no competitive advantage over other methanogens in the river-bay system.

In the current study, methanogenic communities were clustered according to habitat (river vs. estuary). Salinity was the major environmental factor regulating the methanogenic community assemblage, as indicated by RDA and MRT analysis. It is in agreement with previous studies disclosing that salinity is a primary factor shaping microbial communities (Wang et al. 2019a; Wen et al. 2017). Effects of salinity on growth and metabolisms of methanogens depend on the level of salinity and the pathways of methanogenesis. Low salinity (0.1–0.2‰) is essential to methanogens, probably because of its role in the oxidation of NADH and in the formation of ATP (Lu et al. 2019). In contrast, high salinity (larger than 8‰) inhibits hydrogenotrophic and acetotrophic methanogens but not methylotrophic methanogens. It is because high levels of salinity favor growth of sulfate reducing bacteria (SRB). Due to higher affinity for H2 and acetate, SRB have a thermodynamic advantage over hydrogenotrophic and acetotrophic methanogens. Methylotrophic methanogens could coexist with SRB because they use methyl compounds as substrates (Xiao et al. 2017).

Furthermore, some indicator genera of methanogens were observed to show preferences for freshwater or saline environments, explaining the observed community structure variation. The relative abundances of Methanomethylovorans, Methanolinea, and Methanoregula were significantly higher in freshwater riverine sediments (Fig. 4). Methanomethylovorans, a methanogen belonging to Methanosarcinales, have been isolated from freshwater sediment (Lomans et al. 1999). Methanolinea and Methanoregula, affiliated to the order Methanomicrobiales, are detected from diverse habitats including digester sludge, wastewater treatment systems, rice field soils, and riverine sediments (Chen and Yin 2013; Kuroda et al. 2015; Sakai et al. 2012; Yashiro et al. 2011). The relative abundance of Methanoregula was positively and significantly correlated to nutrient contents, which might explain its preference for riverine sediments. The current results also showed that the relative abundance of Methanomassiliicoccales was significantly higher in riverine sediments during wet season. Methanomassiliicoccales could utilize methyl-compounds such as methanol, methylamine, and methyl thiol for methane production (Borrel et al. 2014). The upstream riverine zone (sites R3) had a larger input of nutrients due to discharges of wastewater from the nearby Xili wastewater treatment plants, thus Methanomassiliicoccales had relatively higher abundance in the riverine sediments.

In contrast, the relative abundances of Methanococcoides, Methanoculleus, and Methanogenium were significantly higher in saline estuarine sediments (Fig. 4). Methanococcoides, a genus in Methanosarcinales, was absent from riverine sediments, suggesting that Methanococcoides is indicator group of saline environments. It has been reported that Methanococcoides is a predominant lineage in marine sediments and barely exist in riverine sediments (Wen et al. 2017; Xiao et al. 2017). It might be explained by that Methanococcoides are methylotrophic methanogens and could compete with sulfate reducer in saline environments (L’Haridon et al. 2014). Methanoculleus and Methanogenium, belonging to the order Methanomicrobiales, were significantly more abundant in estuary than in riverine sediments. It is in agreement with the previous analyses that Methanoculleus and Methanogenium are commonly detected in marine and estuarine environments, but rarely found in river habitats (Romesser et al. 1979; Wen et al. 2017; Weng et al. 2015). They are hydrogenotrophic methanogens and well adapt to low H2 concentration. Therefore, the above genera might have an advantage over other methanogens in saline environments.

It is observed that some methanogenic genera such as Methanosarcina, Methanosaeta, and Methanobacterium were abundant and ubiquitous in all the samples, indicating they had high adaptation to various salinity conditions including freshwater to saline environments. Methanosarcina could utilize multiple substrates including H2 + CO2, acetate, and methyl compounds for methanogenesis, which gave them an advantage to exist in different environments (Thauer et al. 2008). Methanosaeta was acetoclastic methanogens and was frequently detected in wetlands (Narrowe et al. 2017; Welte and Deppenmeier 2014). It has been reported that Methanobacterium were detected in diverse environments, such as freshwater sediments and anaerobic digester (Kern et al. 2015; Schirmack et al. 2014) Methanobacterium was proposed to be involved in syntrophic methane production with syntrophs such as Geobacter, Desulfovibrio, Pelobacter, Pelotomaculum, and Syntrophomonas (Losey et al. 2017; Yin et al. 2019), which might favor the widespread of Methanobacterium. In addition, we used traditional mcrA gene primer pair that targeted Euryarchaeotal methanogens in this study. It has low specificity to ANaerobic MEthanotrophic (ANME) archaea and the novel methanogens in other phyla such as Verstraetearchaeota, Korarchaeota, and Nezhaarchaeota, potentially not reaching the highest diversity of methanotrophs and methanogens (McKay et al. 2017). Therefore, the response obtained could slightly vary if the groups not considered here were taken into account. Further studies should be conducted to design new primers and investigate the novel methanogens in future.

In conclusion, we found that abundance of methanogens was significantly higher in the riverine than estuarine sediments. Community of methanogens was separated by habitat (river vs. estuary). According to RDA and MRT analysis, salinity was the major factor regulating methanogenic community assemblage. Interestingly, the indicator groups for riverine and estuarine environments were identified, suggesting some methanogenic groups were environment-specific. Methanomethylovorans, Methanolinea, Methanoregula, and Methanomassiliicoccales preferentially dwelled in riverine sediments, while Methanococcoides, Methanoculleus, and Methanogenium preferred existing in estuarine sediments. Collectively, the current study contributes to the understanding of the spatial and seasonal patterns of methanogens and addresses the underlying mechanisms explaining methanogenic community assemblage in the Shenzhen River-Bay system.