Methane, microbes and models: fundamental understanding of the soil methane cycle for future predictions

Introduction: methane in the context of global warming

Human activities have been identified as a major cause of increased emission of the three main greenhouse gases (GHGs) [carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O)] since the beginning of the industrial era (IPCC, 2007; Eusufzai et al., 2010). CH₄ constitutes the second most significant greenhouse gas after CO₂, and it accounts for up to 20–30% of global warming effect (IPCC, 2007). The Earth's atmosphere had a stable, relatively constant CH₄ concentration of 0.7 p.p.m. (parts per million) until the 19th century when a steady increase brought concentration to 1.745 p.p.m. in 1998 (IPCC, 2007). During the late 1970s and early 1980s, the rate of increase in CH₄ concentration was as high as 1% per year but a minor slowdown started in the mid-1980s. Atmospheric CH₄ has since 2005 stabilized at a value of 1.77–1.78 p.p.m. Recently, a slight imbalance towards an annual increase of about 0.1% per year of CH₄ emission has been calculated (IPCC, 2007). Despite a short lifetime in the atmosphere of approximately 8 years, CH₄ is 25 times more potent than CO₂ as a GHG, over a 100-year horizon and on a mass basis due to its higher efficiency in trapping radiation (Shindell et al., 2009). An increase in the atmospheric concentration of CH₄ to 2.55 p.p.m. and a lifetime in the atmosphere of 8.4 years is predicted by 2050, based on current rates of CH₄ emissions (Lelieveld et al., 1998). Consequently, it is essential to understand the processes responsible for the sources and sinks of CH₄ in order to better control anthropogenic emissions.

Methane is produced by microbial communities as a part of anaerobic respiration. Microbial CH₄ production (methanogenesis) is performed by a specific group of Archaea, the methanogens, and occurs in anoxic environments as a consequence of fermentation, the anaerobic degradation of organic matter. Archaeal methanogens only constitute the last step of fermentation and rely on the presence of a larger bacterial consortium including hydrolytic, fermenting, syntrophic and acetogenic bacteria (Cicerone and Oremland, 1988) (Fig. 1). In contrast, microbial CH₄ consumption (methanotrophy) is mainly achieved by a unique group of Proteobacteria called methanotrophs. The majority of methanotrophs are aerobic organisms. Thus, CH₄ oxidation usually takes place at anoxic/oxic boundaries such as sediments, and in soils in particular, where methanotrophs play an important role in attenuating CH₄ emissions to the atmosphere subsequent to production of methane in the deeper anoxic environments (Conrad, 2009) (Fig. 1).

Methane is naturally emitted from sources such as wetlands, oceans, sediments, plants, termites and geological sources (e.g. gas seepage, hydrates). Wetlands represent the largest source, accounting for 62% of the (natural) CH₄ budget (Fig. 2). Anthropogenic sources include rice agriculture, livestock, landfills and waste treatment, biomass burning and fossil fuel extraction and consumption. During the pre-industrial era, natural sources represented over 90% of total emission whereas man-made emissions dominate present-day CH₄ budgets, accounting for 63% of the total global emissions (Conrad, 2009), and occur mostly in the northern hemisphere (Lelieveld, 2006). Most of the natural CH₄ sources are of microbial origin (Conrad, 1996). In the troposphere, chemical removal of CH₄ from the atmosphere represents 88% of the total sink. It is performed through the photochemical oxidation of CH₄ initiated by the reaction with ●OH radicals (Cicerone and Oremland, 1988). The loss of CH₄ in the stratosphere accounts for about 7% of the sink. Finally, CH₄ is also eliminated from the atmosphere by uptake in upland soils due to microbial oxidation by methanotrophs (estimated at 5% of the microbial sink) (Conrad, 2009).

Knowledge of global CH₄ sources and sinks is essential to devise efficient mitigation strategies to combat global warning. Reducing anthropogenic emissions of CH₄ can have an effect on the global CH₄ budget. This was a primary goal in establishing the Kyoto Protocol of 1997 (UNFCCC, 1998). However, the anthropogenic impact on CH₄ consumption is limited because the terrestrial sink contribution to the global budget is small (only 5–7%), nonetheless, importantly this process will ultimately determine whether the Earth, as a whole ecosystem, is a source or sink for CH₄. It is estimated that more than 50–90% of the CH₄ produced belowground is oxidized before reaching the atmosphere (Reeburgh, 2003; Kvenvolden and Rogers, 2005). This is in conjunction with the sink of atmospheric CH₄ occurring mostly in upland soils, and especially in temperate forests (Ojima et al., 1993; Le Mer and Roger, 2001). Therefore, it is important to better understand the mechanisms involved in CH₄ emissions from soils and CH₄ sinks in soils. This review will highlight the importance of soil microbial communities and their control of net CH₄ flux and propose reasons why they should be included in prediction models to improve the accuracy of climate change simulations.

Methane microbiology

Methane-producing Archaea (methanogens)

Methanogens are strictly anaerobic obligate CH₄-producing Archaea. Methanogenesis represents the terminal step of the anaerobic food chain, converting methanogenic substrates to CH₄ (Hedderich and Whitman, 2006). Methanogens constitute an ancient monophyletic lineage within the Euryarcheota phylum. They are classified into three classes, six orders, 12 families and 35 genera (Table 1). A novel lineage of methanogens, rice cluster I (RC-I), was identified from rice roots by culture-independent approaches (Großkopf et al., 1998; Lueders et al., 2001). However, RC-I members are widely distributed in other habitats around the world (Conrad et al., 2006). They form a distinct clade within the Methanomicrobiales and Methanosarcinales radiation based on the genetic analysis of 16S rRNA and mcrA (encoding methyl-coenzyme M reductase) (Großkopf et al., 1998; Lueders et al., 2001). Recently, members of RC-I were isolated in pure culture and the new Methanocellales order was created (Sakai et al., 2007; 2008; 2010; 2011). Similarly the Methanoregulaceae family was added to the Methanomicrobiales order to accommodate members of the following new genera: Methanoregula (Bräuer et al., 2011; Yashiro et al., 2011), Methanosphaerula (Cadillo-Quiroz et al., 2009) and Methanolinae (Imachi et al., 2008; Sakai et al., 2012) (Table 1).

Table 1. Taxonomy of major methanogens

Most methanogens are mesophiles; however, several genera of methanogens (Table 1) can be found in extreme environments such as marine geothermal sediments, hot springs and hypersaline sediments. Because methanogens cannot use complex organic compounds, they need the presence of other anaerobes in the environment to degrade these compounds into simple sugars and fatty acids then further fermented by syntrophic bacteria to form acetate, formate, hydrogen (H₂) and CO₂, which constitute the main substrates for methanogenesis (Fig. 1). Acetogens (acetate-producing bacteria) are part of this syntrophic consortium when methanogens consume H₂ and formate efficiently (Stams, 1994). Most methanogens are H₂-consumers because they need H₂ as an electron donor and consequently, they need to closely interact with H₂-producing microorganisms, and also as a way to remove reducing equivalents (Hedderich and Whitman, 2006; Stams and Plugge, 2009).

CH₄-production pathways and key enzymes

The complexity and uniqueness of methanogenesis as a form of anaerobic respiration resides in the requirement of six unusual coenzymes [ferredoxin (Fd), methanofuran (MFR), tetrahydromethanopterin (H₄MPT), coenzyme F₄₂₀ (F₄₂₀), coenzyme M (CoM) and coenzyme B (CoB)]; a multistep pathway and several unique membrane-bound enzyme complexes coupled to the generation of a proton gradient driving ATP synthesis (Ferry, 2010). The three main methanogenic substrates are CO₂, acetate and methyl group containing compounds (such as methanol, methylated amines and methylated sulfides). Therefore, three distinct (hydrogenotrophic, aceticlastic and methylotrophic respectively) pathways for CH₄ production exist (Ferry, 1999; Deppenmeier, 2002) (Fig. S1).

Although the intermediates and enzymatic reactions of the three pathways are different, they share common features in the final steps of CH₄ production (Fig. S1). The hydrogenotrophic and aceticlastic pathways both result in the production of a carrier-bound methyl intermediate. The carrier protein is H₄MPT in the hydrogenotrophic pathway and tetrahydrosarcinapterin (H₄SPT), a derivative of H₄MPT, in the aceticlastic pathway. The transfer of the methyl group to CoM by a specific, membrane-bound methyltransferase (MTR), and the subsequent reduction of methyl-CoM to CH₄ by the key enzyme methyl-coenzyme M reductase (MCR) (Thauer, 1998), is common in all three pathways. The three methanogenic pathways are described in more details in Supporting information.

MCR consists of a dimer of three subunits, α (McrA), β (McrB) and γ (McrG), and contains a unique porphinoid nickel (Ni)-containing active site called coenzyme F₄₃₀ (Gunsalus and Wolfe, 1980). The enzyme's apparent molecular mass is about 300 kDa. Two distinct isoenzymes of methyl-CoM reductase were identified (Steigerwald et al., 1993). The second enzyme, designated MRT for methyl reductase two, has a different substrate affinity (Bonacker et al., 1993). MCR activity is encoded by the mcrBDCGA operon, while the mrtBDGA operon codes for the MRT (Thauer, 1998). The equivalent of the gene mcrC is missing in the mrt operon (Pihl et al., 1994). The products of the genes mcrC (McrC), mcrD (McrD) and mrtD (MrtD) are below 20 kDa. Their function is still unknown (Reeve et al., 1997).

Primary sensors and signal transduction cascades have not been elucidated. However, evidence was found for regulation by trace elements and their availability (Hedderich and Whitman, 2006). This is because many enzymes of methanogenesis contain trace metals (molybdenum, tungsten, selenium, nickel) in their active site. Availability of the substrate H₂ was found to regulate the formation of some key enzymes of methanogenesis including MRC. In Methanothermobacter species, the expression of the two isoenzymes of MCR is differentially regulated by H₂ availability, with isoenzyme I (MCR) predominantly expressed in H₂-limiting conditions (Bonacker et al., 1992; Morgan et al., 1997). The regulation of gene expression in methanogens is still not fully understood and needs further study.

Methane-oxidizing microbes (methanotrophs)

Methanotrophs are Gram-negative aerobic bacteria and generally only grow on CH₄ or methanol as a source of carbon (C) and energy. Methanotrophs are often found at the anoxic/oxic interface of various habitats such as geothermal reservoirs, landfills, soils, peat bogs, wetlands or aquatic environments and sediments, where they consume the CH₄ arising from methanogenesis and are thus able to lower these CH₄ emissions (Whalen et al., 1990; Conrad and Rothfuss, 1991; Conrad, 1996). These methanotrophs can oxidize high concentrations of CH₄ (> 100 p.p.m.), and are sometimes referred to as low-affinity methanotrophs, and many can be cultivated in the laboratory. Other methanotrophs have the ability to oxidize CH₄ at atmospheric (low) levels (∼ 1.8 p.p.m.) and are known as high-affinity methanotrophs (Bender and Conrad, 1992). They have not yet been cultivated although they have been identified in upland soils using molecular and biochemical methods (Holmes et al., 1999; Henckel et al., 2000; Knief et al., 2003). These high-affinity methanotrophs are mainly grouped into two clades, USCα and USCγ, and are distantly related to two groups (respectively, type II and type I) of cultivable methanotrophs (Holmes et al., 1999; Bull et al., 2000; Knief et al., 2003; 2006; Singh and Tate, 2007). The majority of aerobic methanotrophs use CH₄ as their sole source of C and energy (obligate methanotrophs). Facultative methanotrophs also exist (see section below) and can grow on multi-carbon substrates. The majority of extant aerobic methanotrophs are neutrophiles (growing at a pH of ∼ 6.0–8.0) and mesophiles (growing at 20–40°C) (Whittenbury et al., 1970). Nevertheless, several species of methanotrophs have been isolated from extreme ecosystems displaying high and low values of temperature, pH or salinity (Trotsenko and Khmelenina, 2002; Op den Camp et al., 2009) (Table 2).

Table 2. Taxonomy of aerobic methanotrophs

To date, aerobic methanotrophs are known to have representatives in two phyla, three orders and four families. A total of 21 genera and 56 species have been identified (Table 2). Based on morphological, physiological and genetic differences (refer to Supporting information for more details), extant CH₄-oxidizing bacteria have traditionally been divided into two groups: type I and type II. Nowadays, they are referred to as Gammaproteobacteria and Alphaproteobacteria respectively (Table 2). Type I methanotrophs (referred to as gammaproteobacterial methanotrophs in this review) belong to the Methylococcaceae family in the Gammaproteobacteria. Type II methanotrophs (Alphaproteobacteria, or alphaproteobacterial methanotrophs here) are divided into two families: the Methylocystaceae and the Beijerinckiaceae (Bowman et al., 1993; Op den Camp et al., 2009). Among the Methylococcaceae, the genera Methylovulum (Iguchi et al., 2011) and Methylomarinum (Hirayama et al., 2013) were recently added, as well as two new Methylomonas species [M. paludis (Danilova et al., 2012) and M. koyamae (Ogiso et al., 2012)] (Table 2). Furthermore, two genera of filamentous methanotrophs occur: Clonothrix and Crenothrix (Stoecker et al., 2006; Vigliotta et al., 2007). Although 16S rRNA-based studies group them with gammaproteobacterial methanotrophs, Crenothrix shows a very divergent pmoA gene (encoding for a key polypeptide of pMMO) (Stoecker et al., 2006). Newly isolated microorganisms expanded the phylogeny of alphaproteobacterial methanotrophs with the addition of the genus Methyloferula (Vorobev et al., 2010) and a new member of the Methylocystaceae family, Methylocystis bryophila (Belova et al., 2013). Finally, CH₄-oxidizing bacteria of the Verrucomicrobia phylum were recently identified and the three species that were isolated from geothermal habitats in Italy, New Zealand and Russia (Dunfield et al., 2007; Pol et al., 2007; Islam et al., 2008) were classified under the Methylacidiphilum genus of the Methylacidiphilales order (Op den Camp et al., 2009) (Table 2). These methanotrophic Verrucomicrobia represent a unique group of methanotrophs capable of growing at very low pH value although they share many characteristics of methanotrophic Proteobacteria, especially of alphaproteobacterial methanotrophs (Hou et al., 2008). It appears that verrucomicrobial methanotrophs can fix CO₂ to use as sole source of carbon while CH₄ is used for energy generation (Khadem et al., 2011; Sharp et al., 2012). More details on the biochemistry, physiology and genetics of methane oxidation by this group have recently been uncovered (Hou et al., 2008; Khadem et al., 2010; 2011; 2012a,b,c).

Anaerobic oxidation of methane (AOM) is carried out by a tight/physical association of anaerobic methanotrophic (ANME) Archaea and sulfate-reducing bacteria (SRB), Desulfosarcina and Desulfococcus of the Deltaproteobacteria class (Hinrichs et al., 1999; Boetius et al., 2000; Knittel and Boetius, 2009). AOM involves the use of sulfate as an electron acceptor to oxidize CH₄ via a process of reversed methanogenesis (Thauer and Shima, 2008). AMNE Archaea are close relatives of the methanogenic Archaea (Methanosarcinales and Methanomicrobiales) and are divided into three main clusters: ANME-1, ANME-2(a, b and c) and ANME-3 (Knittel and Boetius, 2009). Although originally thought to be obligate methanotrophs, a recent analysis suggested that the impact of the ANME-1 group on the global methane budget should be re-evaluated as these organisms were able to revert to methanogenesis in CH₄-producing sediments (Lloyd et al., 2011). AOM coupled to denitrification was described from enriched cultures (Raghoebarsing et al., 2006). In particular, the bacterium Methylomirabilis oxyfera is able to anaerobically reduce nitrite to form its own supply of O₂ via a new intra-aerobic pathway (Ettwig et al., 2009; 2010). The presence of small concentrations of O₂ (2–8%) has an overall inhibitory effect on this organism (Luesken et al., 2012). Genomic analysis revealed that this newly discovered anaerobic denitrifying methanotroph does not use reversed methanogenesis to oxidize CH₄ but rather the typical aerobic methanotrophic mechanism and despite the apparent absence of intracytoplasmic membrane system (Wu et al., 2011; 2012). Apart from using sulfate and nitrite as electron acceptors, AOM was also been found to be dependent on manganese and iron in marine environments (Beal et al., 2009).

Oxidation of CH₄

Aerobic methane oxidation is catalysed by methane monooxygenase (MMO). MMO exists in two forms: a soluble form (sMMO) and a membrane-bound, or particulate, form (pMMO) (Prior and Dalton, 1985; Lipscomb, 1994; Hakemian and Rosenzweig, 2007; Semrau et al., 2010) (detailed information in Supporting information). Although these two enzymes catalyse the same reaction, their mechanism and origin are very different (Holmes et al., 1995; Semrau et al., 2010). The oxidation of CH₄ into methanol is followed by the formation of formaldehyde by the enzyme methanol dehydrogenase (MDH). Formaldehyde constitutes the central point of methanotroph metabolism. Two pathways were identified for its assimilation and these were originally used by Whittenbury and colleagues (1970) to separate methanotrophs into type I and type II (Fig. S2). Gammaproteobacterial (type I) methanotrophs use the ribulose monophosphate (RuMP) pathway whereas alphaproteobacterial (type II) methanotrophs assimilate formaldehyde through the serine pathway (Lawrence and Quayle, 1970; Trotsenko and Murrell, 2008). Only a portion (∼ 50%) of carbon at the oxidation level of formaldehyde is assimilated into cellular biomass (Prior and Dalton, 1985). The remainder is converted into formate and ultimately into CO₂ to generate reducing power for the initial oxidation of CH₄ and for biosynthetic reactions and energy generation (Fig. S2). A third pathway for carbon assimilation in some methanotrophs is the fixation of CO₂ via the Calvin–Benson–Bassham (CBB) cycle by verrucomicrobial methanotrophs (Op den Camp et al., 2009) (Fig. S2). Other methanotrophs (Methylocaldum, Methylococcus, Methylogaea) also possess RubisCO activity (see Table 3) but it is not known if, or how, these bacteria use the CBB pathway in nature. Other differences between proteobacterial and verrucomicrobial methanotrophs are detailed in Table 3 and can be found in Supporting information.

Table 3. Some characteristics of known aerobic methanotrophs

Gram-negative, aerobic	Type I		Type II		Verrucomicrobia
Phylum/class	Gammaproteobacteria		Alphaproteobacteria		Verrucomicrobia
Genera	Methylobacter, Methylomicrobium, Methylomonas, Methylosarcina, Methylosphaera, Methylohalobius, Methylothermus, Methylosoma, Methylomarinum, Methylovulum, Clonothrix, Crenothrix	Methylocaldum, Methylococcus, Methylogaea	Methylocystis, Methylosinus	Methylocapsa, Methylocella, Methyloferula	Methylacidiphilum
Methanotrophy	Obligate		Obligate	Obligate Facultativea	Obligate
ICM formation	Bundles of vesicular disks distributed throughout the cell and perpendicular to cell periphery		Methylocystaceae – membrane stacks packed in parallel to cell periphery Methylocapsa – membrane vesicles packed parallel to only one side of cell membrane Methylocella – vesicular membranes connected to ICM		No but carboxysome-like structures or vesicular membranes
Formaldehyde assimilation	RuMP pathway	RuMP pathway; low levels of enzymes of the serine pathway	Serine pathway	Serine pathway RuMP pathwayb	A variant of the serine pathway
MMO activity	pMMO; sMMOc		pMMO; sMMO	pMMOd; sMMOe	pMMO
pmoA genotype affiliation associated with oxidation of atmospheric CH4f	USCγ; cluster 1, cluster 3 Methylococcaceae		Methylocystaceae	USCα; cluster 5 Beijerinckiaceae	Methylacidiphilaceae
Major PLFA biomarkers	14:0; 16:0; 16:1ω7c; 16 ω5t 18:1ω7g	16:0; 16:1ω7c	18:1ω8c 18:2 ω6c,12c; 18:2 ω7c,12ch	18:1ω7c	i14:0; a15:0; 18:0
Nitrogen fixation	Methylosphaera, Methylosoma, Methylogaea and Methylococcus only		Yes		Yes
RubisCO activity	No	Yes	Methyloferula only		Yes
G+C (mol%)	43–63	57–65	62–67	56–63	41–46

• ^a Only in Methylocella and Methylocapsa aurea.
• ^b Only in Methyloferula.
• ^c Only in Methylomonas, Methylomicrobium, Methylovulum and Methylococcus.
• ^d Not in Methylocella or Methyloferula.
• ^e Not in Methylocapsa.
• ^f Kolb (2009).
• ^g Only in Methylohalobius.
• ^h Bodelier and colleagues (2009).
• Data were compiled from Op den Camp and colleagues (2009), Semrau and colleagues (2010) and recent literature.
• Abbreviations: RuMP, Ribulose monophosphate; pMMO, particulate methane monooxygenase; sMMO, soluble MMO; USC, upland soil cluster; ICM, intracytoplasmic membrane.

Both forms of methane monooxygenase (sMMO and pMMO) require O₂ but they use a different electron donor/acceptor systems (Lieberman and Rosenzweig, 2004; Hakemian and Rosenzweig, 2007; Semrau et al., 2010) (Fig. S2). sMMO is a very versatile enzyme capable of oxidizing a wide range of alkanes, aliphatics and aromatic compounds (Colby et al., 1977). pMMO is present in all methanotrophs, except in Methylocella and Methyloferula spp. (Dedysh et al., 2000; Dunfield et al., 2003; Vorobev et al., 2010). A number of methanotrophs of the genera Methylomonas, Methylomicrobium, Methylovulum, Methylococcus, Methylocystis and Methylosinus contain both pMMO and sMMO (Table 3).

Methylocella and Methyloferula species use sMMO to grow on methane and are facultative methanotrophs, also growing on multi-carbon compounds such as acetate, pyruvate, succinate, malate and ethanol (Dedysh et al., 2005; Rahman et al., 2011). The evolutionary reasons behind obligate methanotrophy are still unknown (Chung et al., 1988; Dedysh et al., 2005; Theisen and Murrell, 2005; Trotsenko and Murrell, 2008; Chen et al., 2010a; Semrau et al., 2011). Recently, other facultative alphaproteobacterial methanotrophs have been isolated, Methylocapsa aurea (Dunfield et al., 2010), M. bryophila and Methylocystis strain SB2 (Im and Semrau, 2011; Yoon et al., 2011; Belova et al., 2013). Like Methylocella, these microorganisms are mostly found in acidic soils and all belong to the Beijerinckiaceae family (Table 3). A detailed inventory of the known facultative methanotrophs, the different pathways for acetate assimilation and their ecological applications (particularly in the field of bioremediation) has been recently reviewed (Im and Semrau, 2011; Semrau et al., 2011; Yoon et al., 2011).

Although there is some understanding of the genetics of methanotrophs (operon structure, gene copy number and variation, regulation by copper – see Supporting information for more details), a number of important knowledge gaps still exist in the microbiology, biochemistry and genetics of methane oxidation (Box 1 ).

Box 1. Research gaps in methane microbiology

• (1) Production of CH₄ has been observed in marine systems where methanogens could not be detected by molecular methods (Biddle et al., 2008; Valentine, 2011). However, recent work has confirmed that methanogens are omnipresent and become more active when the environmental conditions become favourable (Conrad, 2009). Furthermore, it is believed that some methanogens are aero-tolerant (Conrad, 2009) and can thrive in the pelagic zone and oxic water column of many water bodies (Reeburgh, 2007; Grossart et al., 2011). Their identification in ecological studies, in particular in soils, will be key findings.
• (2) Metagenome sequencing suggests that the vast majority of methanogens are still to be cultivated (Biddle et al., 2008).
• (3) One of the greatest challenges for the scientific community is to isolate high-affinity methanotrophs which can oxidize CH₄ at atmospheric concentration. This is a crucial step required for understanding the phenotypes and physiological capabilities of methanotrophs.
• (4) Further detailed studies on gene regulation and how methanogens and methanotrophs respond, at the molecular level, to changes in environmental conditions are needed. Additional studies are required to identify the mechanisms responsible for anaerobic methane oxidation and also how N₂O is formed under anaerobic conditions.
• (5) Information on ecology and niche adaptation of methanogens and methanotrophs will provide key information to utilizing microbial traits in predictive models.
• (6) Deciphering the role of facultative methanotrophs in the global budget of CH₄ is needed.
• (7) Newly discovered methanotrophs outside of the Proteobacteria phylum (Op den Camp et al., 2009) highlights the increased diversity of methanotrophs. Emerging metagenomics, transcriptomics and proteomics techniques can now be used to help identify novel methanogens and methanotrophs from many different environments. The success of such approach can be achieved, for example, by combining enrichment methods, such as stable-isotope probing (SIP), with metagenomics (Chen and Murrell, 2010; Chen et al., 2010b). Another important technique will be the combination of fluorescence in situ hybridization (FISH) to stable-isotope Raman spectroscopy, or Raman-FISH (Huang et al., 2007; Neufeld et al., 2007b).

Ecology of methanotrophy and methanogenesis

Both abiotic and biotic environmental factors are considered as strong regulators of CH₄ flux (Lessard et al., 1994; Castro et al., 1995). Methanotrophic activity is lowered while methanogenesis is increased when soil moisture increases above field capacity due to a decrease in O₂ availability (Czepiel et al., 1995; Sitaula et al., 1995). The atmospheric CH₄ sink mainly occurs in upland forest soils and this function is attributed to high-affinity methanotrophs (Kolb, 2009). In temperate forest and poorly drained soils, when moisture ranged from 60% to 100% water-filled pore space (WFPS), CH₄ oxidation rates decreased (Whalen and Reeburgh, 1990; Bender and Conrad, 1995). The authors attributed this to limited O₂ availability and soil gas diffusivity, in particular because CH₄ transport in water is 10 000 times slower than in air (Whalen and Reeburgh, 1992; Castro et al., 1995). In contrast, when moisture content is very low (< 12% v/v), CH₄ consumption is also inhibited as observed in desert and landfill cover soils (Whalen et al., 1990; Striegl et al., 1992). It was suggested that inhibition of atmospheric CH₄ oxidation was caused by increased osmotic stress and desiccation (Conrad, 1996; Jäckel et al., 2001). The effects of temperature on CH₄ oxidation are inconsistent, indicating that soil methanotrophs may adapt to different temperatures (Hanson and Hanson, 1996) maybe because they thrive over a large range of temperatures (Table 2). Although temperature changes had little effect on overall CH₄ consumption, temperatures < 10°C and > 40°C seem to decrease significantly methanotrophic activity in forest and landfill cover soils, possibly due to the inhibition of mesophilic methanotroph activity (Castro et al., 1995; Semrau et al., 2010). Interestingly, methanogens are highly sensitive to variations in temperature and pH (discussed by Le Mer and Roger, 2001). Thus, they may influence more the net CH₄ flux than any other abiotic factors affecting methanogenesis. Similarly to temperature, soil pH does not seem to be a strong controller of CH₄ oxidation since comparable values were measured between pH 3.5 and pH 8 (Born et al., 1990). Again, methanotrophs seem to be adapted to a range of soil pH due to the existence of acido-, neutro- and alkaliphiles (Table 2). However, soil alkalinity and acidity were found to decrease net CH₄ fluxes in grassland and forest soils (Hütsch et al., 1994; Amaral et al., 1998; Reay et al., 2001). More recently, a meta-analysis on conifer and deciduous forest soils revealed that soil pH can influence the community structure of methanotrophs Kolb (2009). The author observed that gammaproteobacteria-related methanotrophs and members of the upland soil cluster γ (USCγ) were predominantly found in neutral soils, while alphaproteobacteria-related methanotrophs and members of the USCα thrived in acidic soils, suggesting clear niche adaptations. Also, the recent discovery of Methylacidiphilum proved that CH₄ consumption at very acidic pH (< 1) is possible (Op den Camp et al., 2009). A number of articles have provided in-depth reviews of soil methanogenesis and its role in CH₄ flux and reported a strong impact of temperature and soil pH on both methanogens and methanogenesis (Chin et al., 1999; Le Mer and Roger, 2001; Conrad et al., 2006; Conrad, 2009).

Soil characteristics such as porosity, WFPS and bulk density are closely linked to soil water content and therefore influence O₂ availability and CH₄ diffusion in soils (Ball et al., 1997; Le Mer and Roger, 2001; Smith et al., 2003). Soil texture and mineralogy are believed to be strong regulators of CH₄ flux (Castro et al., 1995; Le Mer and Roger, 2001). Soils containing some types of clay protect organic matter from mineralization (Oades, 1988) and soils with high clay content can retain CH₄ by preventing diffusion (Sass et al., 1994). In summary, soil moisture, WFPS and porosity are recognized as important drivers of CH₄ flux rates. Temperature has a positive impact on methanogenesis but is not a major driver of methanotrophy. Elevated temperature and levels of CO₂ may have direct and/or indirect effect on CH₄ flux and global warming, as discussed previously (Singh et al., 2010).

Study of the ecology and niche adaptation of methanotrophs/methanogens is still in its infancy and further research is necessary to unravel these important aspects of the ecology of functional microbial communities. A recent report confirmed that methanogens are ubiquitous in aerobic soils and can actively produce CH₄ as soon as conditions change to anoxic (Angel et al., 2012). The authors concluded that Methanosarcina and Methanocella appear to be universal members of the biomes in aerated soils. Niche partitioning among methanogenic Archaea has also been observed. Kemnitz and colleagues (2004) found that soils which are never or rarely flooded were dominated by Crenarchaeota, Methanomicrobiaceae, Methanosarcinaceae. Uncultivable Rice cluster IV and VI were present in frequently and permanently flooded soils, while Methanosaetaceae was detected only in soils which were frequently or permanently flooded and where the acetate concentration was < 30 μm, which indicated this taxon is particularly adapted for low acetate concentration under severe anoxic conditions (Kemnitz et al., 2004). This would suggest strong niche adaptation of methanogens under field conditions. However, in-depth studies are required to define ecological niches for different methanogens explicitly considering abiotic environmental drivers, land-use history and management practices, and climatic conditions.

Ho and colleagues (2013) tried to conceptualize how methanotrophs could adopt differing life strategies based on their functional and ecological characteristics. Using a three-dimensional (Competitor–Stress tolerator–Ruderal) functional classification framework, the authors suggested that gammaproteobacterial methanotrophs are composed of competitor and ruderal species because they respond rapidly to disturbances and are usually active in many environments. Alternatively, alphaproteobacterial methanotrophs are stress tolerant species due to their persistence, stability and ability to use versatile substrates, in response to fluctuating environmental conditions (Ho et al., 2013). Further studies to demonstrate if existing ecological theories, usually applied to macroorganisms such as plants (Tilman et al., 2006), can explain microbial regulation of CH₄ fluxes are also needed. No such study has been reported for methanogenesis. Limited evidence is available in the literature for methanotrophy and is contradictory. For example, Levine and colleagues (2011) demonstrated that the complimentary theory explains CH₄ fluxes, i.e. that an increase in biodiversity (e.g. methanotroph diversity) increases function (e.g. CH₄ oxidation) but other reports contradict this finding. It was suggested that a few dominant species regulate methanotrophy in soils (Singh et al., 2007; Kolb, 2009; Nazaries et al., 2011), which may suggest that the selection theory (only a selected number of species performs the main function) better explains methanotrophy (Nazaries et al., 2013). It is possible that both theories can explain CH₄ flux but in different environmental conditions. For example, in disturbed sites, the complementary theory may be more applicable because both community and environmental conditions are undergoing change and, as a result, higher number of species will be allowed to occupy a large number of niches, which in turn will ensure greater activity [gammaproteobacterial methanotrophs are competitors–ruderals as in the Competitor–Stress tolerator–Ruderal concept (Ho et al., 2013)]. On the other hand, in established ecosystem, niches are well defined and one or few species will occupy those niches and therefore, will control the physiological activities and CH₄ flux. However, distinguishing which theory (complementary versus selection) explains microbial regulation of CH₄ flux at the global scale is key information for both incorporation of microbial data in predictive models and future conservation policy on soil biodiversity.

Effect of change in land management and land use

Influence of land management

The use of inorganic/organic fertilizers is an important practice for land management. Conflicting reports on the precise effect of nitrogenous fertilizers (ammonia- or nitrate-based) on CH₄ flux in soils exist, showing either no effect, inhibition or stimulation (Bodelier and Laanbroek, 2004; Mohanty et al., 2006). However, several studies have shown that inorganic fertilizers inhibit methanotrophs while stimulate methanogens in many environments such as rice paddies, grassland and forests (Conrad and Rothfuss, 1991; Hütsch et al., 1994; Hanson and Hanson, 1996). This is attributed to possible competitive inhibition of MMO by ammonium (NH₄⁺), or through the toxicity of nitrate (NO₃⁻) (Bodelier, 2011). Long-term application of NH₄⁺-N fertilizer decreased soil CH₄ sink strength, whereas NO₃⁻-N did not (Willison et al., 1995). Conversely, organic fertilizers (compost but not manure) had no effect on methanotrophy in comparison with inorganic fertilizers (Seghers et al., 2005). Thus, the form of fertilizer (organic versus inorganic) as well as the form of N (NH₄⁺-N versus NO₃⁻-N) are important in determining rates of CH₄ sink potential.

It is well documented that nitrate addition suppresses methanogenesis in soils (Klüber and Conrad, 1998) by either competition or toxic effects (Conrad, 2007). Competitive suppression of methanogenesis by nitrate is believed to be affected by the nitrate-induced release of sulfate and ferric ions and thus allowing successful competition from sulfate and iron reducers for available H₂ – an important substrate for CH₄ production (Klüber and Conrad, 1998). An alternative mechanism of nitrate-induced inhibition of methanogenesis proposes that intermediate products of denitrification (nitrite, nitric oxide and N₂O) can be toxic to all microbes including methanogens (Roy and Conrad, 1999). However, suppression of CH₄ emissions due to the application of nitrate-based fertilizers has not been reported under field conditions. It is argued that this is because nitrate is either rapidly assimilated by plants or denitrified and released in the atmosphere (Conrad, 2007). However, most of the literature suggests that ammonium-based fertilizers (e.g. urea) may decrease CH₄ emission from soils; however, the mechanism of this inhibition is not known but may occur due to its direct impact on methanogenesis or methanotrophy. Indirectly, fertilizers can increase plant productivity, which in turn increases the supply of organic substrates for methanogenesis (Conrad, 2007; Conrad et al., 2008).

In a meta-analysis, Liu and Greaver (2009) estimated that N-fertilizers (urea, NH₄⁺ and NO₃⁻) application could cause a 97% increase in CH₄ emission and 38% decrease in CH₄ uptake by soils across a range of non-forest and forest ecosystems. In another meta-analysis of 33 studies, Aronson and Helliker (2010) reported that lower N applications could stimulate CH₄ uptake and thus reduce CH₄ emissions. However, the addition of > 100 kg N ha⁻¹ year⁻¹ (urea, NH₄⁺ or NO₃⁻) resulted in significant inhibition of CH₄ uptake and the consequent increase in CH₄ emissions in all ecosystems (Fig. 3). This inhibition was dependent on type of fertilizer, rate, biome and management practices (Aronson and Helliker, 2010). Microbial investigations suggest that gammaproteobacterial methanotrophs, which are more active in wetlands, are stimulated by the addition of N-fertilizers (Qiu et al., 2008; Shrestha et al., 2010). On the other hand, the activity of alphaproteobacterial methanotrophs, which are dominant in upland soils, could be inhibited by N additions (Mohanty et al., 2006). Recent work based on the meta-analysis of 33 published studies suggested that competitive inhibition of methanotrophs was not a dominant mechanism controlling CH₄ emissions from N-fertilized fields. It was concluded that CH₄ emissions were more strongly influenced by fertilizer-induced crop yields and simultaneous increase in the release of carbon substrates for methanogenesis (Banger et al., 2012). Overall, the effects of the application of nitrogen-based compounds on methanotrophy and methanogenesis are controversial and further understanding is required, in particular at the molecular level.

The effects of biocides (insecticides, pesticides and herbicides) on CH₄ flux were also investigated. Some lowered CH₄ oxidation rates (Mosier et al., 1991; Topp, 1993; Boeckx et al., 1998; Priemé and Ekelund, 2001) whereas others did not have an effect (Hütsch, 1996; Seghers et al., 2003b; 2005). Few studies evaluated the impact of fertilizer and herbicides on abundance and community structure of methanogens and methanotrophs. Seghers and colleagues (2003a,b; 2005) concluded that application of organic fertilizer improved CH₄ oxidation rates and the abundance of methanotrophs, in particular alphaproteobacterial methanotrophs, whereas the combined application of fertilizers and herbicides had no effect. The herbicide Butachlor was reported to inhibit both methanogenesis and methanotrophy, indicating that some herbicides may have an impact on the soil CH₄ cycle (Mohanty et al., 2004). Other agricultural practices such as tillage and ploughing can reduce CH₄ sink, although tillage seems to be less damaging while direct seeding improve CH₄ oxidation (Dalal et al., 2008).

Influence of land use

Changes in land use, such as afforestation and deforestation, can influence the CH₄ source and sink strength. From several studies, the following trend can be observed (in decreasing order of sink strength): woodland > non-cultivated upland > grassland > cultivated soils (Hütsch et al., 1994; Willison et al., 1995; Dobbie and Smith, 1996; Le Mer and Roger, 2001). More specifically, an effect of land-use change on CH₄ sink rates was associated with tree species (Reay et al., 2001; Menyailo and Hungate, 2003; Borken and Beese, 2006; Saggar et al., 2007; Menyailo et al., 2010). Different tree species have varied effects on CH₄ sinks but a common trend was that soils under hardwood species (aspen, beech, birch, oak) consumed more CH₄ than soils under coniferous species (larch, pine, spruce). Interestingly, other studies observed that soils under alder did not oxidize significant amounts of CH₄ (Reay et al., 2001; 2005). This was correlated with high nitrification rates and it was suggested that the high concentrations of nitrate measured were the result of N₂ fixation by the symbiotic Frankia sp. in the root nodules and subsequent conversion of ammonia to nitrate by ammonia-oxidizing bacteria.

Arable soils are mainly considered as a net source of CH₄ due to the impact of farming practices and use of fertilizers on soil abiotic and biotic characteristics (IPCC, 2007). Deforestation and conversion of forest and grassland to arable soils are known to increase CH₄ emission from soils (Zerva and Mencuccini, 2005; Tate et al., 2006; Dörr et al., 2010). Recently, a study suggested that land-use change from upland (soybean) cultivation to long-term rice cropping also significantly increased CH₄ emission and this change in emission was linked to a lower (iron) electron availability in rice fields (Eusufzai et al., 2010). Compared with arable soils, set-aside soils shows little or no difference in CH₄ oxidation rates, indicating that recovery from agricultural use is not immediate (Dobbie and Smith, 1996; Hütsch, 1998). A study reported that, in Denmark and Scotland, over 100 years were necessary to reach pre-cultivation levels of CH₄ oxidation rates after land-use change from agriculture to woodland (Priemé et al., 1997). Modelling studies supported this estimate for all Northern European soils (Smith et al., 2000). However, mechanisms that regulate recovery are still debated. Afforested soils showed increased uptake of atmospheric CH_4, due partly to a change in soil abiotic properties (Hiltbrunner et al., 2012), and party to a shift in the community structure from type I to type II methanotrophs when shifting land use from pasture to forests (Singh et al., 2007; 2009). In particular, there is evidence which suggest that increased CH₄ sinks following afforestation/reforestation were associated with an increased dominance of particular methanotrophs, members of USCα clade, distantly related to type II methanotrophs (Nazaries, 2011; Nazaries et al., 2011). This was observed in sites spread across New Zealand and Scotland (Singh et al., 2007; Nazaries et al., 2011; 2013). In contrast, forest clear-felling and deforestation changed the soil from being a sink for CH₄ into a net source (Keller et al., 1990; Keller and Reiners, 1994; Zerva and Mencuccini, 2005). This change was linked to a shift from type II methanotrophs of the Beijerinckiaceae towards type I methanotrophs of the Methylococcaceae occurring after conversion of forest to farmland (Dörr et al., 2010). However, none of these studies included analyses of methanogens, the population of which may have also shifted and could influence the rate of CH₄ flux independently and in association with methanotrophs. A relationship between methanogen activity and methanogenesis was reported in peat soils (Freitag and Prosser, 2009) and was further correlated with the ratio of methanogen and methanotroph activities with CH₄ flux rate (Freitag et al., 2010).

It can be assumed that changes in methanotrophic/methanogenic activity and atmospheric CH₄ sinks are also driven by environmental and ecological factors. Lower CH₄ flux has been linked to higher CH₄ uptake and lower production rates, which were mostly correlated with lower water content and lack of chemical fertilization in soils and sediments (Hiltbrunner et al., 2012). In addition, competition between alpha- and gammaproteobacterial methanotrophs occurs through copper, CH₄, O₂ and nitrogen availability in soils. The findings above will have a significant impact on future global methane emissions as arable land area is set to increase and management practices will change in the future to meet human needs for food (Nelson et al., 2010).

Climate change and its impact on the methane cycle and activities of methanotrophs and methanogens

The vast majority of the literature suggests that climate change will increase global CH₄ fluxes. Increasing concentrations of CO₂ and elevated temperature both impact a number of soil biotic and abiotic characteristics, including the activities of methanogens and methanotrophs, and water content in soil pores (van Groenigen et al., 2011). These changes in soil properties will have both direct and indirect impacts on CH₄ production and consumption. In a recent meta-analysis, van Groenigen and colleagues (2011) estimated a significant increase in CH₄ emissions under elevated CO₂ from both natural and managed wetlands including rice paddies, which together are estimated to contribute to 53% of total global CH₄ emissions (Fig. 4). No change in upland CH₄ consumption was predicted (Fig. 4). van Groenigen and colleagues (2011) also estimated that increased production of CH₄ and N₂O under elevated CO₂ conditions will negate up to 17% of the climate change mitigation potential previously predicted for terrestrial carbon sink under future atmospheric CO₂ concentration. Previous studies suggested up to 30% reduction in CH₄ consumption by temperate forest soils in response to increasing atmospheric CO₂ concentration, which is equivalent to a 10% reduction in the sink strength of temperate forest soils (Phillips et al., 2001). Similarly, more than 50% decrease in CH₄ consumption in grassland under elevated CO₂ was reported (Ineson et al., 1998) (Fig. 4).

There are several mechanisms to explain increased CH₄ emission from soil under elevated CO₂ (Singh et al., 2010). For example, plant-mediated increases in soil moisture due to changed transpiration rates can have an impact on O₂ and CH₄ availability, which in turn will favour methanogenesis over methanotrophy. Additionally, elevated CO₂ (eCO₂) increases primary productivity and rhizodeposition, which produce the main substrates for methanogenesis and, in combination with increased anaerobic conditions and elevated temperature (eT), will promote methanogenesis at the expense of methanotrophy. However, contrasting findings on the role of soil moisture in relation to CH₄ flux under eCO₂ have been reported (Singh et al., 2010). It is also possible that change in CH₄ flux under eCO₂ is mediated through an indirect impact on microbial physiology and activity and this has been highlighted in recent reports on feedback responses to climate change (Singh et al., 2010). The effect of warming on CH₄ cycle is less known, both at the process level and at the global scale, although methanotrophy and methanogenesis are mutually expected to increase with rising temperature as a physiological response. However, it has been estimated that up to 78% increase in global CH₄ emission could occur if temperatures increased by 3.4°C (Shindell et al., 2004). Increased temperature was also observed to cause a decline in methanotroph abundance in soil (Mohanty et al., 2007). However, the global effect of elevated temperature on methanotrophy is difficult to predict because different ecosystems will respond differently to climate change (Singh et al., 2010). Soil moisture is arguably one of the most important factors in CH₄ flux (Le Mer and Roger, 2001) as it stimulates methanogenesis and inhibits methanotrophy. This is because elevated temperature increases evaporation and alters rainfall patterns, which results in changed soil moisture content. As a consequence, areas which will become wetter under future climate conditions will have higher moisture, creating anaerobic conditions which increases CH₄ production, and at the same time reduces CH₄ consumption by reducing O₂ availability for methanotrophs. Therefore, future modifications in precipitations (rainfall intensity, pattern, frequency) linked to climate change will have a serious impact on the global CH₄ budget.

The real limitations in estimating feedback responses to climate change have been that most studies have investigated impact of only one climate treatment (eCO₂ or eT). Due to increasing evidence, from both modelling and experimental research, of the interacting effects of climate treatments, it is pertinent that future field-based experimentation should include individual and interactive impacts of at least three major climate variables (CO₂, temperature and precipitations). Additionally, the IPCC (2007) predicted a significant increase in the intensity and frequency of extreme weather events (e.g. flash flooding, extreme drought, prolonged heat waves). Such scenarios necessitate setting up next-generation climate change experiments, which should account not only for climate treatments but also for interacting effects with extreme weather events (Fig. 5). Such design is extremely important for CH₄ cycle because both process and functional microflora are mostly impacted by soil temperature and water levels. Future field studies need to include detailed work on different pathways (both process and microflora) of methanogenesis and methanotrophy and how they will be impacted under individual and interactive climate variables and extreme events (Fig. 5). Quantification of these responses will be key data for the accurate prediction of CH₄ flux under future climatic conditions and for improved national CH₄ inventory.

Modelling

Several predictive models are used to model CH₄ flux and there are significant concerns regarding the predictive values of these models, due to the type and nature of the data used for the development and validation of these models. For example, Glatzel and Bareth (2006) used a bottom-up approach to estimate regional CH₄ emissions, accounting for the interaction between land use and soil type. However, because of the global over-estimation of the results, the authors acknowledged the need to link ecosystem approaches to process-based models, in which specific soil processes such as C and N cycles and their impact on net CH₄ fluxes are considered. The process-based model DNDC (DeNitrification and DeComposition) requires detailed information on site climate (daily precipitation and temperature), soil properties (texture, porosity, clay content, moisture) and land management (crop, fertilization) (Li et al., 1992a; 1992b; Li, 2000). This model was applied to estimate global N₂O emissions from arable sites (Li et al., 1996; Butterbach-Bahl et al., 2001). Recently, the DNDC model was adapted for New Zealand (‘NZ-DNDC’) for modelling N₂O emissions from grazed pastures (Saggar et al., 2004) and was also successfully applied to model CH₄ consumption (Saggar et al., 2007). However, a consensus exists for these estimations by predictive models that need to incorporate biological processes and microbial communities (Davidson and Janssens, 2006; Singh et al., 2010). No current models account for the role of microorganisms in CH₄ flux. Currently, temperature, precipitation and soil moisture are used to predict CH₄ emissions. Microbial data are not considered due to the complexity of the soil microbial communities which are regarded as a black box in the majority of global change models (Bodelier, 2011). This is because microbes are incredibly diverse, as well as temporally and spatially dynamic. Many microbes are not cultivable under current laboratory regimes and therefore their physiological capabilities are unknown. The above issues make the parameterization of microbial data challenging. However, including microbial data in models for improved prediction of CH₄ flux from terrestrial ecosystems are necessary for the following reasons. (i) Soil methane flux seems to be strongly linked to functional microbial communities. (ii) Current models assume that changes in CH₄ flux are related to changes in soil abiotic properties without considering changes in the microbial communities. However, since most studies suggest that global change has a direct impact on soil microbial community composition (Singh et al., 2010; Kim et al., 2012; Nie et al., 2013), current models cannot predict changes in flux rates associated with changes in the physiological capabilities of the ecosystem also associated with microbial communities. (iii) It is argued that if historical temperature and precipitations play a role in the ability of microbial adaptations then incorporating microbial data into global change model will be essential to improve their predictions (Docherty and Gutknecht, 2012; Wallenstein and Hall, 2012). (iv) Ecological functions that are carried out by specific and specialized group of microbes, as in the case of CH₄ production and consumption, will be more sensitive to a change in community composition (Schimel and Gulledge, 2004; Singh et al., 2010). This makes data (gene expression/abundance, PLFA profile, etc.) from methanogens and methanotrophs ideal for parameterization and their incorporation into predictive models.

The above discussion provides compelling reasons to include microbial data; however, to successfully parameterize and include microbial data in models for a better predictive value, several coordinated steps will be required. First of all, modellers will need long-term data on the global change impact on microbial community, diversity and functions. This is key for the successful inclusion of microbial data into predictive models, because on the short term, soil moisture and temperature can influence the process rate by influencing the metabolic rates of existing microbes. However, on the long term, microbes can adapt to changing climate and reduce the process rates to near ambient conditions (Docherty and Gutknecht, 2012). There is already evidence on acclimation of microbial respiration to increasing temperature (Bradford et al., 2008) but we do not know if the same is true for CH₄ flux. Second, microbial data need to be detailed and should include the time needed to determine environmental factors that influence community composition or metabolic rates and the time needed for such change to occur. Finally, data from multi-factorial treatments in field and glasshouse experimental procedures need to be detailed. Not only do modellers need microbial process data from integrated global change experiments including land-use change, N deposition and climate change in combination with extreme weathers (Fig. 5) but they also require data at the genetic and expression level. Most of the genes involved in CH₄ cycle are well characterized. Emerging -omic technologies (metagenomics, transcriptomics and proteomics) provide unprecedented opportunities to (semi)-quantitatively characterize shifts in total community, active communities and physiological capabilities of an ecosystem (Box 1). These technologies also have the ability to distinguish treatment effects from spatial and temporal dynamics within microbial communities, which is key for upscaling these data to the ecosystem level.

Current limitations and future perspectives