High concentrations of dissolved biogenic methane associated with cyanobacterial blooms in East African lake surface water

The contribution of oxic methane production to greenhouse gas emissions from lakes is globally relevant, yet uncertainties remain about the levels up to which methanogenesis can counterbalance methanotrophy by leading to CH4 oversaturation in productive surface waters. Here, we explored the biogeochemical and microbial community variation patterns in a meromictic soda lake, in the East African Rift Valley (Kenya), showing an extraordinarily high concentration of methane in oxic waters (up to 156 µmol L−1). Vertical profiles of dissolved gases and their isotopic signature indicated a biogenic origin of CH4. A bloom of Oxyphotobacteria co-occurred with abundant hydrogenotrophic and acetoclastic methanogens, mostly found within suspended aggregates promoting the interactions between Bacteria, Cyanobacteria, and Archaea. Moreover, aggregate sedimentation appeared critical in connecting the lake compartments through biomass and organic matter transfer. Our findings provide insights into understanding how hydrogeochemical features of a meromictic soda lake, the origin of carbon sources, and the microbial community profiles, could promote methane oversaturation and production up to exceptionally high rates. Fazi et al. report on an extraordinarily high biogenic methane concentration detected in the surface water of Lake Sonachi, Kenya. Using gas chromatography and microbiome profiling, they determine that these high concentrations are associated with cyanobacterial blooms and help provide insight to methanogenesis in meromictic soda lakes.

M ethane (CH 4 ) is the second most important greenhouse gas in terms of global warming potential, reported as 28-36 times higher than that of CO 2 over the standard 100-year period 1 . The sharp increase in atmospheric CH 4 levels observed since 2007 has been ascribed to biomass burning, fossil fuel combustion, agricultural practices, and accelerated release from biogenic sources 2,3 , although the current observational network cannot unambiguously link recent methane variations to specific sources 4 . In particular, estimates of carbon gas fluxes across the air-water interface showed that freshwater bodies represent a source of methane with a disproportionate contribution to global CH 4 emissions, regardless the relatively small surface they cover 5,6 .
Methane production from lakes is mainly attributed to anoxic sediments, with different emission pathways upward to the surface and the atmosphere (e.g., diffusion, ebullitive and storage fluxes, emissions from aquatic vegetation) 7 . Surface waters can be systematically oversaturated with CH 4 through vertical and lateral transport from bottom and littoral sediments, as found in small and shallow ponds 8 . Methane oversaturation, however, was also reported in lake waters with negligible sediment-to-water exchanges, owing to known pathways of oxic methanogenesis mediated by light-, nutrient-, and salt-dependent microbial metabolisms, along with the occurrence of pelagic micro-anoxic niches [9][10][11][12] . Notably, methanogenic microorganisms were detected in association with either algae or Cyanobacteria in oxygenated epilimnion 13 and experimental cultures of selected Cyanobacteria were likely to produce methane in saturated oxic conditions 14 . A close link between CH 4 release and algal biomass was also confirmed by both the overlap of metalimnetic CH 4 maxima with oxygen oversaturation and chlorophyll maxima 11,12 and the positive relation between surface CH 4 flux rates and chlorophyll-a levels [15][16][17] .
Methane production in surface layers moves the source of CH 4 closer to the water-air interface with a significantly higher contribution to the overall emission 9,12 , but it is not known whether, how, and to what extent methanogenic processes can counterbalance aerobic methanotrophy 18 . Therefore, consistent knowledge gaps persist on the interplays between primary production, organic matter transformation, and methane mobilization mechanisms. In the East African Rift Valley (Kenya), we discovered an unusual high concentration of methane in the oxic layer of a meromictic soda lake. The amount of dissolved CH 4 was exceptionally higher than that reported from natural lakes across a wide range of lake size and type ( Fig. 1 and Supplementary Table 1).
Saline lakes are distributed worldwide, with an estimated total volume (104 × 10 3 km 3 ) comparable to that of freshwater lakes (124 × 10 3 km 3 ) 19 . In particular, soda lakes, characterized by saline alkaline waters in which Na + and carbonate species are the dominant ions, are common in regions with volcanic bedrock, including the eastern branch of the East African Rift and several lake basins across the globe 20,21 .
There is a growing debate about the relevance of tropical aquatic ecosystems in terms of methane emissions. The focus has now moved to Africa, because in situ measurements are poorly documented and frequent cloud cover reduces satellite data densities/estimates 22,23 . East African soda lakes are characterized by high salinity, high constant solar radiation, warm temperature, and high steady pH, promoting high primary production with high amount of autochthonous derived dissolved organic matter and diverse haloalkaliphilic microbial communities 19,24,25 . They are considered as model environments for a deeper understanding of microbially-driven processes, including all possible methanogenic pathways, which were found to be concurrently active up to nearly salt-saturation conditions 20,26 .
The objectives of this study were to (i) uncover the contribution of geogenic and biogenic sources to the bulk of dissolved CH 4 , (ii) identify key microbial players and interactions at different lake compartments (i.e., oxic/anoxic water layers and sediments). We tested the hypothesis that, regardless of geogenic sources, the oversaturation of methane in oxic lake waters can be promoted by microbial community structuring and cell-to-cell interactions up to unexpectedly high levels.

Results
Water stratification and geochemical characteristics. Lake Sonachi showed a maximum depth of 4.5 m and a pH ranging between 9.47 and 9.61. During sampling at the center of the lake, the meromictic stratification was found at −3.5 m, with a chemocline separating the mixolimnion (surface waters) from the monimolimnion (bottom waters, BW). The vertical profiles of δD-H 2 O and δ 18 O-H 2 O changed consistently below the chemocline, showing the influence of water evaporation and the lack of mixing between mixolimnion and monimolimnion (Supplementary Figs. 1 and 2). The chemocline was apparent from abrupt changes in electrical conductivity, redox potential, pH, inorganic and organic solutes, and dissolved organic matter (DOM) (Supplementary Fig. 3; Supplementary Tables 2 and 3). The redox potential (Eh 0(25°C) ) showed high values in surface waters and an abrupt decrease in BW.
Water temperature and oxygen in the water column ranged between 20.5 and 22.4°C, and 0.1 and 3.6 mg L −1 , respectively. In surface waters, the vertical profiles of temperature and oxygen revealed the presence of a thermocline and oxycline at −1.5 m, discriminating between the shallow oxic surface waters (OSW) and anoxic surface waters (ASW) (Supplementary Fig. 3). Oxygen concentrations reached saturation value in OSW, when taking into account water salinity, temperature and elevation. BW were characterized by high concentrations of Na + , HCO 3 − , CO 3 2-(up to 5276, 9523 and 2196 mg L −1 , respectively), with remarkable concentrations of K + , Cl − , SO 4 2-, F − , Si and reduced sulfur species. In contrast, Ca 2+ concentration was low (<4.64 mg L −1 ), as well as dissolved inorganic nitrogen, mainly represented by NH 4 + NH 3 (<0.03 mg L −1 ) (Supplementary Table 2). Dissolved organic carbon (DOC) did not change in OSW and ASW (97.3 ± 6.8 mg L −1 ) but increased significantly in BW (up to 593 mg L −1 ). The analysis of solid phase extracted DOM (SPE-DOM) showed the signal of Reported values derive from lakes distributed worldwide across a range of size (i.e., area) and climatic region (i.e., arctic, boreal, Mediterranean, temperate, tropical). Alkaline and meromictic lakes are highlighted. Regression line (p < 0.001) and confidence interval (0.99) are reported. Extended data are reported in Supplementary Table 1. DOM autochthonous production together with photodegradation. In particular, there was low aromaticity and high relative abundance of reduced and saturated compounds with a remarkable contribution of oxygen impoverished aliphatic-like molecules (Supplementary Fig. 3 and Supplementary Table 3).
The most remarkable feature of lake waters was the exceptionally high concentration of CH 4 in OSW. The highest level was measured at BW (615 µmol CH 4 L −1 ), which decreased rapidly to 201 µmol L −1 in ASW. This decrease stopped abruptly at OSW, where CH 4 stabilized at 151-156 µmol L −1 , leading to an estimated water-air CH 4 diffusive flux between 460 and 1137 mg C m −2 d −1 ( Table 1). The δ 13 C-CH 4 values ranged from −78 in BW to −16 in SW ‰ vs. V-PDB (Fig. 2).
SPE-DOM analysis showed that 27.5% of all detected aliphaticlike molecules covaried together with CH 4 . On the contrary, 34% of the assigned aromatic-like ones were inversely related to CH 4 concentration (Fig. 3, Supplementary Table 5).
Microbiome profiling. In surface waters, the archaeal community was dominated by members of the classes Altiarchaeia, Methanobacteria (genera Methanobacterium, Methanothermobacter), and Methanomicrobia (genera Methanoculleus, Methanosaeta and Methanosarcina) ( Fig. 4a and Supplementary Table 6). Remarkably, a fraction of archaeal Amplicon Sequence Variance (ASV) at 2-3 m depth was not identified using known databases (8-11% of total reads). Cyanobacteria of the class Oxyphotobacteria were identified as the most abundant lineage (on average 60% of total reads), mainly represented by the genus Cyanobium PCC-6307 ( Fig. 4b and Supplementary Table 7). In particular, among the four main ASVs belonging to Cyanobium (ASV4-7) found in the mixolimnion, ASV7 reached up to 27% of total reads. Other Cyanobacteria belonged to Synechocystis PCC-6803 (ASV1-3) with relative abundance reaching up to 4% of total reads (Supplementary Table 8). Fimbriimonadia (family Fimbriimonadaceae), Deinococci (only represented by the genus Truepera), and Actinobacteria (genus Table 1 Values of CO 2 and CH 4 fluxes (ΦCO 2 and ΦCH 4 ) estimated by applying published empirical relationships for the determination of k 600,i , at T = 21°C and wind speed U 10 = 2 m s −1 .       Table 7). In BW, Bacteroidia of the family ML635J-40 aquatic group reached a relative abundance higher than 80% of total reads. The euryarchaeotal Thermococci of the family Methanofastidiosaceae (genera Candidatus Methanofastidiosum) represented the second most abundant group (>10%).

Bottom waters & Sediments Surface waters
In sediments, the euryarchaeotal genera Methanobacterium, Methanolinea, Methanosaeta and Methanocalculus dominated the community along with members of the classes Bathyarchaeia and Thermoplasmata. The sediment bacterial community showed high dominance of Chloroflexi, mainly represented by Dehalococcoidia of the genus SCGC-AB-539-J10 in subsurface sediments (up to 93.0% of total reads). Clostridia (genus Dethiobacter) showed high percentages in surface sediments (28.2%) (Fig. 4a, b and Supplementary Tables 6 and 7).
The microbial communities retrieved above and below the chemocline were consistently different in terms of phylogenetic structure (one-way PERMANOVA, Bray-Curtis similarity index, p = 0.016). No statistical differences were found either between OSW and ASW (p = 0.59) or between BW and sediments (p = 0.33). The Principal Coordinate Analysis (PCoA) ordination plot allowed the relatively closer associations among all identified taxa of Bacteria and Archaea to be visualized (Fig. 4c).
Quantitative assessment of microbial community structure. The Chlorophyll-a (Chl-a) signal concentration ranged from 93.6 ± 5.5 µg L −1 in surface waters to 28.5 ± 12.0 µg L −1 in BW. The qPCR assays revealed a high abundance of genes involved in the CH 4 production pathway in both the water column and sediments. In particular, abundance of the mcrA gene in the water column increased with increasing depth, showing the lowest value at 0.5 m (190 ± 42 gene copies cm −3 ) and the highest at 4.5 m (5.1 × 10 3 ± 1.0×10 3 gene copies cm −3 ). The mcrA gene was highly abundant in sediments with values ranging between 5.0 × 10 6 ± 3.7×10 5 and 2.2 × 10 7 ± 1.0 × 10 3 gene copies cm −3 (Fig. 5).
Bacteria and Archaea represented respectively 72.3 ± 9.0% and 17.6 ± 6.6% of the total DAPI-stained cells in the water column. The sediments showed percentages of 53.9 ± 1.6% and 46.1 ± 1.6% for Bacteria and Archaea, respectively. Cyanobium-like cells represented on average 91.1 ± 8.6% of total Cyanobacteria. The majority of the microbial biomass in water was part of the particulate OM (on average >77% of total cell counts), as assessed by flow cytometry comparing unfiltered and GFF-filtered (0.7 µm pore size) aliquots. In particular, >99% of the total autofluorescent pigmented cells were removed by GFF filtration. The concentration of total microbial aggregates was in the range of 2.6 × 10 6 to 9.4 × 10 6 aggregates cm −3 and decreased with depth, along with the percentage of micrometric aggregates (Fig. 5).
The visual inspection by confocal microscopy confirmed the occurrence of microbial cells clustered in micrometric aggregates (Fig. 6). A close association between Archaea and Bacteria, including Cyanobacteria, was visualized within suspended microbial aggregates by epifluorescence and confocal microscopy ( Supplementary Fig. 4).

Discussion
This study reports the accumulation of an unusual high amount of biogenic methane in surface oxic waters of the meromictic soda lake Sonachi, occurring together with a high availability of autochthonous dissolved organic matter and abundant cyanobacteriabacteria-methanogens interacting cells. The amount of dissolved CH 4 was 1-3 orders of magnitude higher than that reported in oxic layers of other natural lakes, regardless of their geochemical setting, morphology (e.g., area, depth), and trophic status (e.g., Chl-a, DOC) ( Fig. 1, Supplementary Fig. 5, Supplementary Table 1). The measured values were comparable only to those reported from high latitude lakes in winter when CH 4 released from sediments is trapped at the water-ice interface 27 or during hypolimnion overturn episodes 28 .
Dissolved CH 4 concentrations were exceptionally high in OSW and corresponded to an estimated water-to-air CH 4 flux of up to 1137 mg C m −2 d −1 (Table 1). Recent findings have highlighted regional hot-spot methane emissions in South Sudan (the Sudd swamp), Southern Africa (wetlands in Zambia, Angola and Botswana), Congo (floodplains of the River Congo), and around lakes Victoria, Kyoga and Albert 22,29 . Our study has provided evidence that highly productive soda lakes from the East African Rift might also be remarkable CH 4 sources in tropical settings. The water-to-air CH 4 flux, calculated according to the thin boundary layer model from dissolved CH 4 concentration, appeared to be among the largest diffusive estimates from a lake, with values of the same order of those reported for rice fields 30 , the Amazon floodplain 31 , and wetlands 32 .
It is worth noting that our estimation is probably a conservative approximation because direct flux measurements of CH 4 ebullition were not performed in this study. The formation of visible bubbles was not observed and the measured total dissolved gas pressure did not exceed hydrostatic pressure along the depth profile (Supplementary Table 4). However, bubbling from sediments and bottom waters might be triggered by system perturbations (e.g., promoting sediment resuspension), temperature increase (resulting in lowered CH 4 solubility and increased methanogenesis 33 ), and water level decline (resulting in decreased The vertical profile of CH 4 showed a sharp decrease approaching the surface, indicating the prevalence of methane oxidation processes, as confirmed by the strong 13 C enrichment in ASW. With this rate of decrease, CH 4 should become depleted at an approximate depth of 1.2 m. However, the decrease in δ 13 C-CH 4 values observed in OSW suggested the occurrence of an additional source of CH 4 likely balancing methanotrophy. Previous studies reporting 13 C depletion in oxic waters from other systems similarly invoked the occurrence of oxic CH 4 production 12,34 . Notably, the 13 C depletion reported by these studies was about half of the isotopic variation found in OSW 12,34 , thus indicating an outstanding CH 4 production in oxic conditions in Lake Sonachi. Although lateral transport from the littoral zone has been similarly invoked to explain CH 4 supersaturation in surface water 28,29 , such hypothesis seemed to be unlikely in the case of the endorheic crater Lake Sonachi, where horizontal water movements were largely hindered by (i) the conic lake morphology that reduces the action of winds and (ii) the lack of in-and out-flowing waters.
The high CH 4 concentration occurred in a context of unlimited availability of inorganic carbon, high DOC and Chl-a values, high and steady temperatures. DOC was rich in oxygen-poor saturated-like compounds, thus reflecting the autochthonous microbial origin together with photodegradation 25 . Literature reports showed that labile autochthonous phytoplanktonic OM enhanced methane production in freshwater lake sediments 35,36 . Moreover, DOM photooxidation can release molecules acting as electron acceptors and carbon sources in CH 4 production 37 .
High CH 4 concentrations in oxic waters were related to chlorophyll peaks and current observations suggest a link between planktonic primary producers and methanogens, putatively mediated by DOM release from pelagic microbial primary producers 13,17 .
Concomitantly, Lake Sonachi was also a remarkable net CO 2 sink, with a CO 2 uptake rate comparable to those reported for other eutrophic lakes in temperate areas 38,39 . The CO 2 inward flux reflected high pH and chlorophyll concentration (high primary productivity in the mixolimnion). In an earlier study spanning 15 months, Melack 40 reported a net daily oxygen production exceeding the nightly oxygen consumption (respiration) in six out of nine cases, and concluded that Lake Sonachi should be a net CO 2 sink during most of the year. Here, the δ 13 C-TDIC value measured in bottom waters (up to 10.7‰ vs. V-PDB) was in line with that reported elsewhere 41 , one of the highest δ 13 C-TDIC values reported for natural lakes, to the best of our knowledge. These high values pointed to biomass-dependent carbon fractionation through CO 2 fixation by chemosynthetic organisms and CO 2 consumption due to methanogenesis, also observed in pore waters from other alkaline lakes from East Africa 42 . The process strongly affected the isotopic composition of CO 2 in bottom waters. Thus, the possible occurrence of CO 2 from mantle/magmatic degassing and/or from carbonate-rich sediments 43 cannot be recognized by the isotopic signature of CO 2 , clearly excluding the input of geogenic gases. Moreover, CO 2 migrating upward due to diffusion is affected by other consumption processes related to photosynthesis and dissolution as carbonate ions. Both these processes caused a 13 C increase in the residual CO 2 , which was counteracted by the production of 12 C-rich CO 2 from CH 4 oxidation. Such a complex superimposition of processes may explain the vertical profile of the δ 13 C-CO 2 values (Fig. 2).
By combining hydrogeochemical features, the origin of carbon sources, and microbial community profiles, we developed a conceptual model of major C-cycle related processes, as mediated by key microbial taxa (Fig. 7). In OSW, the abundance and identity of methanogenic Archaea, along with the occurrence of mcrA gene, provided evidence of microbial methanogenesis (Fig. 7, box 4) in waters with high primary production (Fig. 7, box 1). The genera Methanobacterium, Methanoculleus, and Methanothermobacter were the most abundant putative hydrogenotrophs, as most of the known members of Methanobacteria and Methanomicrobia 44 . Notably, the acetoclastic methanogen Methanosaeta co-occurred with fermentative Izimaplasmataceae, as also reported elsewhere 45 . Members of Methanofastidiosacea, detected in all samples, could also contribute to CH 4 production through the H 2 -utilizing methylotrophic pathway 46,47 . The highly methane productive system was likely fed by carbon fixation, mediated by well-known photosynthetic Cyanobacteria (i.e., Cyanobium PCC-6307 and Synechocystis PCC-6803) and anoxigenic photosynthetic bacteria (e.g., Rhodobacteraceae) in the illuminated surface waters. CO 2 fixation could be also carried out by members of the class Altiarchaeia. Representatives of the 'Candidatus Altiarchaeum hamiconexum' were found as dominant primary producers in anaerobic environmental conditions in which CO 2 fixation can be mediated by a novel variant of Acetyl-CoA pathway 48 . The high abundance of Cyanobacteria across the water column may suggest a direct involvement of photosynthetic bacteria in CH 4 production. This is possibly due to cellular release of precursors of methylated compounds produced to cope with high salinity. It is worth noticing that Synechocystis PCC-6803 was reported to contain only the bidirectional hydrogenase that seems insensitive to oxygen 49 . Moreover, there is emerging evidence that some Cyanobacteria may directly produce CH 4 by demethylation, completely bypassing the involvement of heterotrophic microorganisms. Moreover, members of Bathyarchaeota could contribute to methanogenesis but, by means of the reversible Mcr complex, they could also mediate methanotrophic processes 50 . Bathyarchaeota are reported to form the backbone of the archaeal community, often co-occurring with Methanomicrobia 51 . Methanotrophic pathways could additionally be linked to dissimilatory sulfur reduction through the sulfide-dependent anaerobic oxidation of CH 4 to methanol mediated by members of Korarchaeota 52 , herein retrieved only in surface waters (Fig. 7, box 2).
In addition, the occurrence of heterotrophic and fermentative bacterial taxa along the water column confirmed that lake functioning was fundamentally based on OM degradation processes  (Fig. 7, box 3, 5). Notably, fermentation was putatively mediated by Bacteroidetes of the genus ML635J-40 aquatic group. In anaerobic reactors setup with sediment from a soda lake, the supplied Spirulina-derived substrate was mainly hydrolyzed by Bacteroidetes from the ML635J-40 aquatic group 57 .
The high relative abundance of sulfur (non-sulfate) reducing bacteria (i.e., Dethiobacter spp.) suggested that high salinity could prevent sulfate reduction, thus lowering competition for H 2 and organic substrates with methanogens (Fig. 7, box 7).
Phenotypic characteristics and structural patterns of the microbial community can play a fundamental role in lake functioning, in addition to the phylogenetic diversity with putative functional assignments found here. Microbial communities can show heterogeneous behavior while adapting to a changing environment to optimize resource utilization, even when cells are genetically identical 58,59 . In particular, the dynamics of microbial aggregates could provide underinvestigated clues supporting methanogenesis in oxic waters. Cell density and proximity could lead to direct interactions among Bacteria, Cyanobacteria, and Archaea, promoting methane production 13,60 . Aggregate settling can also increase OM availability in bottom waters and sediments, since clustered cells can move across the chemocline more rapidly than single cells 61 . The breakdown of settled aggregates, induced by abrupt water chemistry changes, could accelerate the release of intracellular methylated compounds, further supporting methanogenesis below the chemocline through fermentative and acetogenic processes.
In conclusion, our findings provide insights towards understanding how hydrogeochemical features, the origin of carbon sources, and microbial community profiles could lead to an exceptionally high concentration of dissolved biogenic methane in a meromictic soda lake. As lake functioning is influenced by water stratification and primary production under oxic and anoxic conditions, both genotypic and phenotypic microbial community changes can affect methane fluxes, with direct yet overlooked consequences for greenhouse gas emissions and climate feedbacks under accelerating global trends of lake eutrophication.

Material and methods
Study site. Lake Sonachi (meaning 'barren bull' from Masaai and previously referred to as Crater Lake) is a endorheic meromictic volcanic soda lake, located at about 90 km NW of Nairobi at 1884 m a.s.l., within the Eastern Rift Valley in central Kenya to the immediate South West of the freshwater Lake Naivasha (Supplementary Fig. 6). The lake surface area is around 0.18 km 2 , with a maximum depth of~5 m. Local climate is warm and semiarid, with evaporation exceeding precipitation on an annual basis. Protection from wind by steep crater walls (rising up from 30 to 115 m above the lake surface) and vegetation (mainly Vachellia xanthophloea) limit water mixing. The hydrological balance is maintained by precipitation (~680 mm/year in the crater catchment) and evaporation (~1870 mm/year). Furthermore, the occurrence of subsurface inflow from the nearby Lake Naivasha was proposed according to synchronous lake-level changes among the two lakes and other hydrological evidences. Chemical stratification and meromixis were documented across 8 years of periodic measurements and attributed to several local factors, including basin morphometry, diurnal periodicity of winds and thermal stratification, seasonal/yearly rainfall variations, and biological decomposition 62,63 .
Sampling procedures and field measurements. Water temperature, pH, electrical conductivity, dissolved O 2 and Oxidation Reduction Potential were measured by means of multiparameter YSI sensors at regular depth intervals of 0.5 m along one single vertical profile in the deepest part of the lake, immediately before and after sampling (i.e., at 11.30 a.m. and 3.15 p.m.). Water and dissolved gas sampling was carried out down a vertical profile from surface to bottom (0, 0.5, 1, 2, 3, 4, and 4.5 m depth) using the single hose method 64 . Two unfiltered aliquots were collected in 125 mL polyethylene bottles for the analysis of major anions and stable isotopes. Two filtered (0.45 µm) and acidified (0.5 mL ultrapure HCl and HNO 3 , respectively) aliquots were collected in 50 mL polyethylene bottles for the analysis of major cations and trace species, respectively. For the analysis of total reduced sulfur species (ΣS 2− ), 8 mL of unfiltered water were collected in 15 mL plastic tubes after the addition of 2 mL of a Cd-NH 3 solution 65 . For the isotope analysis of Total Dissolved Inorganic Carbon (TDIC), 6 mL of unfiltered water were collected in a pre-evacuated 12 mL glass vial filled with 2 mL H 3 PO 4 and equipped with a pierceable septum. Dissolved gases were sampled in a pre-evacuated 250 mL glass flask, equipped with a Teflon stopcock, connected to the Rilsan ® tube 64 . For DOM analysis, 50 mL filtered (combusted GFF Whatman filters) and acidified (HCl) aliquots were stored in pre-combusted glass bottles. For the analysis of microbial diversity, lake water (500 mL) was collected at each depth, filtered with 0.2 µm pore-size polycarbonate filters (type GTTP; diameter, 47 mm; Millipore, Eschborn, Germany) and stored at −20°C. For the analysis of community composition by CARD-FISH, a further aliquot (15 mL) was fixed with a formaldehyde solution (Sigma Aldrich; final concentration 1%). Sub-aliquots of 5-10 mL were filtered at low vacuum levels (<0.2 bar) onto 0.2 µm pore-size polycarbonate filters. Filters were stored at −20°C until further processing. Unfiltered and (GFF Whatman) filtered (2 mL) aliquots were fixed as above and stored at 4°C for cytometric analysis. Sediments collected by a grab from the bottom of the lake were divided into sub-aliquots, either (i) directly stored at −20°C (5 mL), or (ii) fixed with ethanol (Sigma Aldrich; final concentration 50%) and stored at −20°C (50 mL) until further processing.
Trace elements were analyzed by inductively coupled plasma optical emission spectrometry using a PerkinElmer Optima 8000 (analytical error <10%). Reduced sulfur species were analyzed as SO 4  DOM characterization. DOC and dissolved organic nitrogen were determined by oxidative combustion and IR analysis using a Shimadzu total organic carbon analyzer coupled to a Total Nitrogen unit. An elemental formula data set from Fourier transform ion cyclotron resonance mass spectrometry was available and reported earlier for the same sampling campaign 25 . The inter sample ranks (for components which were present in all seven depths) which were calculated in that study were used for the calculation of the Spearman's rank correlation with the methane concentrations. The calculation of the rank correlation coefficients and the assignment of levels of significance to elemental formula components and its visualization in van Krevelen diagrams were described elsewhere 68 .
Gas composition. The chemical composition of dissolved inorganic gases in the headspace of the sampling flask (CO 2 , N 2 , Ar, H 2 , He) was determined using a Shimadzu 15A gas chromatograph equipped with a Thermal Conductivity Detector, whereas CH 4 was analyzed using a Shimadzu 14A gas chromatograph equipped with a Flame Ionization Detector. Instrument specifications and analytical procedures were described elsewhere 64 . The analytical error of the GC analysis was ≤5 %. Assuming that gases in the headspace of the sampling flasks were in equilibrium with the liquid, the number of moles of each gas species in the liquid (n i l) was computed on the basis of those measured in the flask headspace (n i g) by means of the Henry's law constants 69 . The total moles of each dissolved gas species (n i t) was given by the sum of n i l and n i g. The partial pressures of each gas species were computed based on the total mole values according to the ideal gas law. The isotopic composition of CH 4 (expressed as δ 13 C-CH 4 in ‰ vs. V-PDB) collected in the headspace of the sampling flask was analyzed by Wavelength-Scanned Cavity Ring Down Spectroscopy (WS-CRDS) using a Picarro G2201-i analyzer. The isotopic composition of dissolved CO 2 (expressed as δ 13 C-CO 2 in ‰ vs. V-PDB) was calculated from measured δ 13 C-TDIC assuming the attainment of chemical and isotopic equilibria among dissolved carbon species, as follows (Eq. (1)): where HCO 3 − , CO 3 2− , CO 2 , and TDIC concentrations were expressed in mmol L −1 ; the equilibrium isotopic enrichment factors ε HCO3-CO2 and ε CO3-HCO3 were calculated according to previous methods 69,70 , as follows (Eqs. (2) and (3)): where T is temperature in degrees Kelvin.
Water-air CO 2 and CH 4 diffusive fluxes. The water-air CO 2 and CH 4 diffusive exchange fluxes (ΦCO 2 and ΦCH 4 , respectively) were calculated, according to the thin boundary layer (TBL) model 71 , from dissolved gas concentrations measured in surface water (0.5 m depth) and gas transfer velocities (k i , in cm h −1 ), as follows (Eq. (4)): where C i,w was the dissolved gas concentration measured in surface water (in mol L −1 ), C i,eq was the dissolved gas concentration calculated assuming equilibrium with the atmosphere (based on Bunsen coefficients compiled by Wanninkhof 72 as a function of temperature and salinity), and β was the chemical enhancement applicable for CO 2 only (see below). The k i values were estimated as follows (Eq. (5)): where Sc i was the Schmidt number (i.e., the ratio of kinematic viscosity of water and the diffusion coefficient of the gas), k 600,i was the transfer coefficient for each gas normalized to 600, and the power dependence x was dependent upon the roughness of the water surface (−0.67 or −0.5 for wind speed <3 m s −1 or >3 m s −1 , respectively; 73 ). Sc i and k 600,i values are specific for each gas species and depend on temperature and wind speed, respectively. The Sc i values were determined using the fourth-order polynomial fit proposed by Wanninkhof 72 (Eqs. (6) and (7)): Sc CH 4 ¼ 2101:2 À 131:54T þ 4:4931T 2 À 0: where T was the temperature in°C. The k 600,i values were calculated from local wind speed using the empirical relationships reported in Table 1, where T was the temperature measured in surface water (⁓21°C) and U 10 was the wind speed at a height of 10 m. During sampling, wind speed was low, but the exact velocity was not measured. Nevertheless, according to Melack 40 and Melack and MacIntyre 74 , wind speeds at~2 m above water surface at Lake Sonachi were frequently low (<2 m s −1 ) and averaged 2-4 m s −1 , with gusts only occasionally exceeding 6 m s −1 . Consequently, an average U 10 value of 2 m s −1 was adopted for estimating gas diffusive fluxes.
Since pH in Lake Sonachi was ≥9.5, CO 2 was expected to undergo hydration and hydroxylation reactions (i.e., CO 2 þ H 2 O ¼ H 2 CO 3 ; CO 2 þ OH À ¼ HCO 3 À ), augmenting the flux of atmospheric CO 2 into the lake (chemical enhanced diffusion). The chemical enhancement factor β was computed according to the model proposed by Hoover and Berkshire 75 and Wanninkhof and Knox 76 , as follows (Eq. (8)): where: (i) D was the molecular diffusivity (in cm 2 /s), calculated according to Zeebe 77 , i.e., D ¼ 14:6836 10 À5 ½ð273:15 þ tð CÞÞ =217:2056 À 1 1:997 ; (ii) r (in s −1 ) was the combined rate constant for the hydration of CO 2 either directly or via carbonic acid, calculated as r ¼ r 1 þ r 2 K * w a À1 H , where r 1 (in s −1 ) and r 2 (in L mol −1 s −1 ) were the CO 2 hydration rate constant and the CO 2 hydroxylation rate constant, respectively 78 , K w * was the equilibrium constant for water, and a H was the activity coefficient for the hydrogen ion; , where K' 1 and K' 2 were the first and second equilibrium constants for carbonic acid, respectively 79 .
High-throughput 16S rRNA amplicon sequencing and bioinformatics. Extracted DNA was amplified in a first PCR with the primer pairs 27F (5′-AGAGTTTGAT CCTGGCTCAG-3′) and 534R (5′-ATTACCGCGGCTGCTGG-3′) and 340F (5′-CCCTAHGGGGYGCASCA-3) and 915R (5′-GWGCYCCCCCGYCAATTC-3′) targeting the regions V1-V3 and V3-V5 of bacterial and archaeal 16S rRNA genes, respectively. PCR reactions were performed following the protocol described elsewhere 80  After checking read quality with fastqc, the sequences were processed and analyzed using QIIME2 v. 2018.2. The reads were demultiplexed using demux plugin (https://github.com/qiime2/q2-demux) and the primer sequences were removed by using cutadapt plugin (https://github.com/qiime2/q2-cutadapt). The demultiplexed reads were denoised, dereplicated and chimera-filtered using DADA2 algorithm. Additionally, DADA2 resolved amplicon sequence variants (ASVs), which infer the biological sequences in the samples prior to the introduction of amplification and sequencing errors and distinguish sequence variants differing by as little as one nucleotide 81 . The reads were subsampled and rarefied at the same number for each sample by using the feature-table rarefy plugin. Taxonomy was assigned to ASVs using a pre-trained naïve-Bayes classifier based on the 16S rRNA gene database at 99% similarity of the Silva132 release.
Real-time quantification of mcrA genes. The quantification of functional genes involved in the methane production pathway (mcrA gene) was performed by qPCR using Sso Advanced Universal SYBR Green Supermix (BIO-RAD, United States) on a CFX96 Touch Real-time PCR detection system. The primer pair mlas (5′-G GTGGTGTMGGDTTCACMCARTA -3′) and mcrA-rev (5′-CGTTCATBGCGT AGTTVGGRTAGT -3′) was used for the detection of mcrA gene. Standard curves for the absolute quantification were constructed by using the long amplicons method. Melting curves were performed for each reaction to confirm the purity of amplified products 82 .
Chlorophyll-a signals. Chlorophyll-a (Chl-a) was assessed after overnight cold 90% acetone-methanol (5:1, by volume) extraction 83 of plankton retained on a Whatman GFC glass fiber filter after filtering 100 ml of a freshly collected water sample, stored not longer than 3 h, transported in a portable cool box. After boiling (2 min at 65°C), the extracts were centrifuged and readings of the clear supernatant were obtained using a HACH DREL 2900 spectrophotometer set in wavelength scan mode (320-882 nm). The value retained corresponded to the highest peak recorded in the region 663-665 nm. Absorbance conversion to μg L −1 was carried out considering a specific absorption coefficient of 84.1 ml μg −1 cm −1 .
Epifluorescence and confocal microscopy. Total prokaryotic abundance was estimated by DAPI staining. Bacteria and Archaea abundances were determined by Catalyzed Reported Deposition-Fluorescence in situ Hybridization (CARD-FISH) 84 . Specific rRNA-target Horseradish peroxidase labeled oligonucleotidic probes (Biomers, Ulm, Germany) targeted Bacteria (EUB338 I-III), and Archaea (ARCH915). Stained filter sections were inspected on a Leica DM LB30 epifluorescence microscope (Leica Microsystems GmbH, Wetzlar, Germany) at ×1000 magnification. At least 300 cells were counted in >10 microscopic fields randomly selected across the filter sections. The relative abundance of hybridized cells was estimated as the ratio of hybridized cells to total DAPI-stained cells. Among the total EUB-positive cells, Cyanobacteria were discriminated by their red autofluorescence (excitation wavelength 550 nm). According to dominant cell morphologies, it was possible to distinguish between Cyanobium-like and Synechocystis-like cells. In order to visualize specific cells within the 3D structure of the aggregates, CARD-FISH was combined with confocal laser scanning microscopy (CSLM; Olympus FV1000). The hybridized Archaea cells were excited with the 488 nm line of an Ar laser (excitation) and observed in the green channel from 500 to 530 nm (emission). Cyanobacteria were excited with the 543-nm line of a He −Ne laser and observed in the red channel from 550 to 660 nm. The threedimensional reconstruction of CSLM images was elaborated by IMARIS 7.6 (Bitplane, Switzerland).
Flow cytometry. The abundance of microbial free-living cells and aggregates was assessed by an A50-micro flow cytometer, equipped with a 488-nm solidstate laser (Apogee Flow System, Hertfordshire, England). Absolute volumetric counts were performed by staining with SYBR Green I (1:10,000 dilution; Molecular Probes, Invitrogen). A threshold was set to the green channel and samples were run at low flow rate (<1000 events per s −1 ). Light scattering signals (i.e., forward and side scatter), and green fluorescence (530/30 nm) were registered for the characterization of each single cytometric event. Photomultiplier voltages and gating strategy were set using control water samples containing mainly single cells, and performed either by epifluorescence microscopy or flow cytometry 85 . Fixed gates were designed to discriminate between free-living cells and aggregates according to their signatures in a side scatter vs. green fluorescence plot 86 . Total microbial aggregates were backgated on a forward scatter histogram plot and divided into submicrometric and micrometric particles, respectively showing forward scatter intensities lower and higher than those of 1-µm size calibration beads used as internal standard.
Data visualization and extraction were computed with Apogee Histogram (v89.0-Apogee Flow System).
Statistics and reproducibility. Non-parametric multivariate analysis of variance (one-way PERMANOVA) was used to test differences between water layers and sediments (OSW vs ASW vs BW vs Sed) in all major physical, chemical, and microbial parameters. Spearman's rank correlation after inter sample rank ordination of SPE-DOM molecule was calculated to explore the relationship between DOM chemodiversity with the methane concentrations. PCoA, based on a Bray-Curtis similarity matrix, was applied to visualize all identified microbial taxa in an ordination plot, along with the percentage variance accounted for by the first two components. Data elaborations were computed using PAST (version 4.0) 87 .
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability
Sequencing dataset is available through the Sequence Read Archive (SRA) under accession PRJNA731062. Flow cytometry.fcs files are available at the Flow Repository identifier: https://flowrepository.org/id/FR-FCM-Z3T7. All other data (geochemical variables, abundance of microbial cells and genes) are available from the corresponding author on reasonable request.
Received: 8 October 2020; Accepted: 14 June 2021; Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.