Dissolved CH4 coupled to Photosynthetic Picoeukaryotes in Oxic Waters and Cumulative Chlorophyll-a in Anoxia

CH4 emissions from reservoirs are responsible for the majority of the atmospheric climatic forcing of these aquatic 10 ecosystems, comparable to emissions from paddies or biomass burning. Primarily, CH4 is produced during the anaerobic mineralization of organic carbon in the anoxic sediments by methanogenic archaea. However, the origin of the recurrent and ubiquitous CH4 supersaturation in oxic waters (i.e., methane paradox) is still controversial. Here, we determined the dissolved CH4 concentration in the water column of twelve reservoirs during the summer stratification and the winter mixing. We obtained that the dissolved CH4 concentration varied up to four orders of magnitude (0.02-213.64 μM), and all 15 depths were consistently supersaturated (710-7082234 %) in both periods. Phytoplanktonic sources of carbon appear to determine the concentration of CH4 in the reservoirs. In the anoxic waters, the depth-cumulative chlorophyll-a concentration, a proxy for the total phytoplanktonic biomass exported to sediments, determined the CH4 concentration. In the oxic waters, the photosynthetic picoeukaryotes abundance significantly determined the dissolved CH4 concentration both during the stratification and the mixing. The mean depth of the reservoirs, as a surrogate of the CH4 transport from sediment to the oxic 20 waters, also contributed in shallow systems. Our findings suggest that photosynthetic picoeukaryotes can have a significant role in determining the CH4 concentration in oxic waters and, in comparison to cyanobacteria, have been poorly explored as CH4 sources.

as an end product of organic matter degradation in anoxic conditions, and to methanotrophs, which consume it in oxic 30 conditions (Schubert and Wehrli, 2018). In freshwater ecosystems, the anoxic sediments are a primary source of CH4 (Segers, 1998), where methanogens are very sensitive to temperature and quantity and quality of the organic matter used as substrate (Marotta et al., 2014;Rasilo et al., 2015;Sepulveda-Jauregui et al., 2018;Thanh-Duc et al., 2010;West et al., 2012;Yvon-Durocher et al., 2014). They are also affected by the extent of anoxia in the sediments, as far as they are obligate anaerobes and will not survive and produce CH4 under aerobic conditions (Chistoserdova et al., 1998;Schubert and Wehrli, 35 2018). However, many observations from freshwaters and marine waters have detected CH4 supersaturation in the oxic layers. A widespread phenomenon called the "methane paradox" (Bogard et al., 2014;Damm et al., 2010;Donis et al., 2017;Grossart et al., 2011;Kiene, 1991;Murase et al., 2003;Owens et al., 1991;Schmidt and Conrad, 1993;Schulz et al., 2001;Tang et al., 2014).
This persistent CH4 supersaturation in oxic layers of marine and freshwater ecosystems requires extra inputs to compensate 40 for the CH4 losses by methanotrophy and the emissions toward the atmosphere. CH4 inputs may become from anoxic sediments or from in situ sources in the oxic waters. The transport of CH4 from the bottom and littoral sediments in shallow zones has been proposed to explain the supersaturation in the surface waters of some lakes (Bastviken et al., 2004;Encinas Fernández et al., 2016;Michmerhuizen et al., 1996;Murase et al., 2003;Peeters et al., 2019;Rudd and Hamilton, 1978). The vertical transport may be relevant in small lakes, but in deep and thermally stratified systems, the vertical diffusion rates of 45 dissolved gases across the thermocline are too low, and there is not apparent CH4 upward movements from the hypolimnion (Peeters et al., 1996;Rudd and Hamilton, 1978). CH4 diffusion from shallow sediments in littoral zones may be a significant source in the open surface of some lakes and reservoirs. However, lateral transport does not fully explain CH4 supersaturation in the open ocean and other freshwater ecosystems, hence, other alternative in situ CH4 sources likely occur (Damm et al., 2010;DelSontro et al., 2018;Grossart et al., 2011;Owens et al., 1991;Schmidt and Conrad, 1993;Schulz et 50 al., 2001;Scranton and Brewer, 1977;Tang et al., 2014;Tilbrook and Karl, 1995).
Previous works demonstrated the CH4 production in oxic waters using stable isotope techniques in experiments and field samples (Bogard et al., 2014;DelSontro et al., 2018) and using molecular approaches (Grossart et al., 2011). There are different alternatives proposed as CH4 sources in the literature. On the one hand, the occurrence of methanogenesis in microanoxic zones in the guts of zooplankton, and within sinking particles (Angelis and Lee, 1994;Karl and Tilbrook, 1994). In 55 both micro-niches, the CH4 production was too low to sustain the total CH4 supersaturation of the oxic waters (Schmale et al., 2018;Tang et al., 2014). On the other hand, there is a consistent link between dissolved CH4 concentration and autotrophic organisms, primary production, and chlorophyll-a concentration (Bogard et al., 2014;Grossart et al., 2011;Owens et al., 1991;Schmidt and Conrad, 1993;Tang et al., 2014). Grossart et al., (2011) detected potential methanogenic Archaea attached to photoautotrophs as Chlorophyta (Eukarya) and Cyanobacteria (Bacteria) in the epilimnion of an 60 oligotrophic lake and confirmed the production of CH4 in the presence of oxygen in laboratory incubations. If occurring, that symbiosis would require that the methanogens tolerate the oxygen exposure, contrary to general belief (Angel et al., 2011; diverse metabolic pathways in Bacteria and Eukarya. These metabolic pathways contribute to the dissolved CH4 in oxic waters due to the degradation of methylated compounds. In the open ocean, bacteria decompose the abundant algal osmolyte 65 dimethylsulfoniopropionate producing methane as a by-product (Damm et al., 2008(Damm et al., , 2010(Damm et al., , 2015Zindler et al., 2013).
Common methyl-containing substances as methionine produce methane in algae, saprotrophic fungi, and plants (Lenhart et al., 2012(Lenhart et al., , 2015(Lenhart et al., , 2016. Another reported pathway is the degradation of methyl-phosphonates (MPn) as an alternative source of phosphorus (P) in phosphate-starved bacterioplankton. The hydrolysis of these compounds, using the enzyme C-P lyase, also releases methane as a by-product. This pathway appears in environments chronically P starved, as the ocean gyres, 70 oligotrophic lakes, and microbial mats (Beversdorf et al., 2010;Carini et al., 2014;Gomez-Garcia et al., 2011;Karl et al., 2008;Repeta et al., 2016;Teikari et al., 2018;del Valle and Karl, 2014;Wang et al., 2017;Yao et al., 2016a). Recent studies using phytoplankton cultures and stable isotope techniques propose that the production of CH4 may rely directly on the photoautotrophic carbon fixation of algae and Cyanobacteria (Bižić et al., 2020;Lenhart et al., 2016). All these alternative sources of CH4 in oxic waters, however, still have not been widely tested in reservoirs, despite the known high impact of 75 these freshwater ecosystems in the global CH4 emissions.
In this study, we measured the dissolved CH4 concentration in the water column of twelve reservoirs covering a wide diversity (León-Palmero et al. in review) during the summer stratification and the winter mixing. We explored the potential sources of the dissolved CH4 in the anoxic zone, where the classical methanogenesis occurs, and particularly in the oxic zone. In the oxic zone, we considered the next potential CH4 sources: 1) vertical and lateral transport; 2) in situ production by 80 Archaea; 3) in situ production by methylphosphonates degradation; 4) in situ production by direct relationship with photoautotrophic carbon fixation using chlorophyll-a and the abundance of photosynthetic picoeukaryotes and cyanobacteria as surrogate.

Study Reservoirs, Morphometry, and Vertical Profiles 85
We sampled twice 12 reservoirs between July 2016 and August 2017 in southern Spain during the summer stratification and the winter mixing. The reservoirs were built between 1932 and 2003, and they differ in morphometry, chemical, trophic, and watershed characteristics (more details in León-Palmero et al., 2019, in review The shoreline development ratio (DL) (Aronow, 1982) is a comparative index relating the shoreline length (i.e., the perimeter of the reservoir) to the circumference of a circle that has the same area. The closer this ratio is to 1, the more circular the 95 lake. A large ratio (>>1) indicates the shoreline is more scalloped than a low ratio. The equation is as follows (Eq. 2): The shallowness index (m -1 ) was obtained by dividing the shoreline development index (DL) by the mean depth (m), as follows in eq. 3: We performed the vertical geochemical profiles of the reservoirs using a Seabird 19plus CTD profiler, coupled to Spherical Underwater Quantum Sensor (LI-193R), and a fluorimeter Turner® SCUFA (model CYCLOPS-7). We obtained continuous measurements of temperature, dissolved oxygen, conductivity, turbidity, density, PAR/Irradiance, fluorescence, specific conductance, and salinity. We designed, based on the temperature and oxygen profile obtained, a discrete sampling with 6 to 9 depths along the water column. We took the water samples using a UWITEC sampling bottle. We also measured 105 barometric pressure using a multi-parameter probe (HANNA HI 9828) for the gas saturation calculations. We calculated the saturation values (%) for dissolved oxygen as the ratio of the dissolved gas measured and the gas concentration expected in equilibrium. We calculated the gas concentration in equilibrium, taking into account the differences in temperature, salinity, and barometric pressure (Mortimer, 1956).

Dissolved CH4 in the water column 110
We collected samples for dissolved CH4 analysis in air-tight Winkler bottles by duplicate, preserved with a solution of HgCl2 (final concentration 1mM) to inhibit biological activity and sealed with Apiezon® grease to prevent gas exchange. We stored the samples in the dark at room temperature until analysis in the laboratory. We measured dissolved CH4 using headspace equilibration in a 50 ml air-tight glass syringe by duplicate or triplicate from each sample (Sierra et al., 2017). Then, we analyzed the CH4 concentration using a gas chromatograph (GC; Bruker® GC-450) equipped with Hydrogen Flame 115 Ionization Detector (FID). We daily calibrated the detectors using three standard gas mixtures with CH4 concentrations of 1952, 10064, 103829 ppbv, made and certified by Air Liquide (France). We calculated the saturation values (%) as the ratio between the concentration of the dissolved gas measured and the gas concentration expected in equilibrium considering the temperature, salinity, and barometric pressure of each reservoir. We calculated the gas concentration in equilibrium using the Bunsen functions for CH4 (Wiesenburg and Guinasso, 1979;Yamamoto et al., 1976). We used the atmospheric gas 120 the local atmospheric concentrations are assumed to be small compared with the high dissolved concentrations obtained in 125 the study reservoirs.

Chemical analysis in the water column
From the discrete sampling, we selected three or four relevant depths for C, N, and P analysis. We chose representative depths, covering the epilimnion, metalimnion (oxycline), and hypolimnion/bottom layers during the stratification period. We determined total nutrients using unfiltered water, while we filtered the samples through 0.7 μm pore-size Whatman GF/F 130 glass-fiber filters for the dissolved nutrients. We acidified the samples for dissolved organic carbon (DOC), total dissolved nitrogen (TDN), and total nitrogen (TN) samples with phosphoric acid (final pH<2). We measured DOC, TN, and TDN by high-temperature catalytic oxidation using a Shimadzu total organic carbon (TOC) analyzer (Model TOC-V CSH) coupled to nitrogen analyzer (TNM-1) (Álvarez-Salgado and Miller, 1998). We calibrated the instrument using a four-point standard curve of dried potassium hydrogen phthalate for DOC, and dried potassium nitrate for TN and TDN. We analyzed two 135 replicates and three to five injections per replicate for each sample. We purged the DOC samples with phosphoric acid for 20 min to eliminate all the dissolved inorganic carbon.
We measured the NO3concentration using the ultraviolet spectrophotometric method, using a Perkin Elmer UV-Lambda 40 spectrophotometer at wavelengths of 220 nm and correcting for DOC absorbance at 275 nm (Baird et al., 2012). We measured NH4 + and NO2concentrations by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). 140 Dissolved inorganic nitrogen (DIN) is the addition of the NO3 -, NH4 + , and NO2concentrations. We measured total phosphorus (TP) concentration by triplicate using the molybdenum blue method (Murphy and Riley, 1962) after digestion with a mixture of potassium persulphate and boric acid at 120 °C for 30 min (Baird et al., 2012).

Phytoplankton, Chlorophyll-a and Primary Production in the water column
We determined chlorophyll-a concentration by filtering the particulate material of 500 to 2000 ml of water through pre-145 combusted Whatman GF/F glass-fiber filters. Then, we extracted the pigments from the filters with 95% methanol in the dark at 4 °C for 24 h (Baird et al., 2012). We measured chlorophyll-a (Chl-a) absorption using a Perkin Elmer UV-Lambda 40 spectrophotometer at the wavelength of 665 nm and for scattering correction at 750 nm. To obtain the integrated mean of chlorophyll-a (μg Chl-a L -1 ), from the discrete points along the water column, we used the trapezoidal rule (León-Palmero et al., 2019). To obtain the cumulative chlorophyll-a concentration in the whole water column (mg Chl-a m -2 ), we summed the 150 concentration of Chl-a from each stratum using the trapezoidal rule, as we did for the integrated chlorophyll-a before, but we omitted the division between the maximum depth.
We determined the abundances of cyanobacteria and photosynthetic picoeukaryotes using flow cytometry using unfiltered water. We collected and fixed the samples with a mixture of 1% paraformaldehyde and 0.05% glutaraldehyde for 30 min in the dark at 4 °C. Then, we froze the samples in liquid nitrogen and stored them at −80 °C until analysis. We analyzed the 155 samples in the FACScalibur flow cytometer equipped with the BD CellQuest Pro software for data analysis. We used https://doi.org/10.5194/bg-2020-21 Preprint. Discussion started: 4 February 2020 c Author(s) 2020. CC BY 4.0 License. yellow-green 0.92 μm latex beads (Polysciences) as an internal standard to control the cytometer performance every day. We used different signals for groups determination: the side scatter (SSC), chlorophyll-a (red fluorescence, FL3), phycoerythrin (the orange fluorescence, FL2), and phycocyanin (the blue fluorescence, FL4); following the protocols and indications for data previously published (Cellamare et al., 2010;Collier, 2000;Corzo et al., 1999;Gasol and Giorgio, 2000;Liu et al., 160 2014). In figure S13, we show a cytogram of the populations of cyanobacteria and photosynthetic picoeukaryotes.
We estimated gross primary production (GPP), net ecosystem production (NEP), and ecosystem respiration (R) by measuring temporal changes in dissolved oxygen concentration and temperature using a miniDOT (PME) submersible water logger during the stratification period. We recorded measurements every 10 minutes for 24-48 hours. We established the start and ended time for photosynthesis as 30 minutes before sunrise and 30 minutes after dawn (Schlesinger and Bernhardt, 165 2013). We calculated the respiration rate during the night (the period between 60 minutes after dawn and 60 minutes before sunrise) (Staehr et al., 2010), and we assumed that the respiration rate overnight was similar to the respiration rate over the day. We used the equations proposed by Staehr et al. (2010) to calculate GPP, NEP, and R.

DNA analysis
We pre-filtered the water through 3.0 μm pore-size filters and extracted DNA following the procedure developed by 170 Boström et al., (2004) for environmental samples. During the DNA extraction protocol, we combined a cell recovery step by centrifugation of 12 -20 mL of the pre-filtered water, a cell lysis step with enzyme treatment (lysozyme and proteinase K), and finally, the DNA recovery step with a co-precipitant (yeast tRNA) to improve the precipitation of low-concentration DNA. DNA was quantified using a DNA quantitation kit (Sigma-Aldrich) based on the fluorescent dye bisBenzimide (Hoechst 33258). Extracted DNA served as the template for PCR and quantitative PCR (qPCR) analysis to test the presence 175 and abundance of the mcrA gene and the phnJ gene. For PCR analysis, we used the recombinant Taq DNA Polymerase (Thermo Fisher Scientific) using the Mastercycler X50 thermal cycler (Eppendorf). We ran the qPCR plates using SYBR Green as the reporter dye (PowerUp™ SYBR™ Green Master Mix, Thermo Fisher Scientific) in the Applied Biosystems 7500 Real-Time PCR System and the 7500 Software. In both cases, PCR and qPCR, we designed the standard reaction mix recipes and the thermocycling conditions using the provider specifications and primer requirements. We chose specific 180 primers from similar studies in freshwaters and pure cultures as positive controls. We targeted the alpha subunit of methylcoenzyme reductase (mcrA) as a genetic marker to determine the existence and abundance of methanogenic Archaea in our samples. This gene appears to be an excellent marker since all known methanogens have the methyl coenzyme-M reductase, which is the enzyme responsible for the conversion of a methyl group to CH4 (Grabarse et al., 2001). We used specific primers from West et al. (2012) adapting their procedure. The forward primer was mcrAqF (5'-185 AYGGTATGGARCAGTACGA-3'), and the reverse primer was mcrAqF (5'-TGVAGRTCGTABCCGWAGAA -3'), and the annealing temperature was 54 ºC. We used a culture of Methanosarcina acetivorans (ATCC 35395) as a positive control.
We also tested the presence of the phnJ gene, which encodes a subunit of the C-P lyase complex (Seweryn et al., 2015;White and Metcalf, 2007). This enzyme cleaves C-P bonds in phosphonate compounds releasing methane, and changes in https://doi.org/10.5194/bg-2020-21 Preprint. Discussion started: 4 February 2020 c Author(s) 2020. CC BY 4.0 License. response to phosphate availability (Yao et al., 2016a). We ran the amplification with a pair of primers previously used by 190 Fox et al., (2014); Karl (2008); and Yao et al., (2016). The forward primer was PhnJoc1 (5'-AARGTRATMGAYCARGG-3') and the reverse PhnJoc2 (5'-CATYTTYGGATTRTCRAA-3') adapting the PCR procedure from Yao et al., (2016). The annealing temperature was 52.5 ºC, and the positive controls were ran using a pure culture of Rhodopseudomonas palustris (ATCC 33872). We checked the result of the amplification by running 1.5 % (w/v) agarose gel electrophoresis. If we did not detect amplification in the PCR or qPCR samples, we changed the standard procedure by increasing the DNA amount and 195 the primers concentration to corroborate the negative results.

6. Statistical tests
We conducted all the statistical analysis in R (R Core Team, 2014) using the packages car (Fox and Weisberg, 2011), nortest (Gross and Ligges, 2015), and mgcv (Wood, 2011). We performed the Shapiro-Wilk test of normality analysis and Levene's test for homogeneity of variance across groups. We performed a one-way analysis of variance test (ANOVA) when the data 200 were normally distributed. In case the data did not meet the assumptions of normality, we used the Kruskal-Wallis rank-sum test (K-W) or the Wilcoxon test. We analyzed the potential sources of dissolved CH4 using simple regression analysis and generalized additive models (GAMs) (Wood, 2006). GAM is a generalized model with a linear predictor involving a sum of smooth functions of covariates Tibshirani, 1986, 1990). The model structure is shown in Eq. (4): Where the [ are the smooth functions, and the ∈ B are independent identically distributed (0, C ) random variables. We fit smoothing functions by penalized cubic regression splines. The cross-validation method (Generalized Cross Validation criterion, GCV) estimates the smoothness of the functions. We fitted the models to minimize the Akaike Information Criterion (AIC) and the GCV values. We provide details on these GAMs in Supplementary Table 5. We calculated the percentage of variance explained by the model (adj R 2 ) and the quality of the fit (deviance explained). We also fixed the 210 effect of each predictor to assess the contribution of the other predictor on the total deviance explained. Then, the sum of the deviance explained by two predictors can be different from the deviance explained by the model due to interactive effects.

1. Profiles description
We found pronounced differences in the concentration of dissolved CH4 of the study reservoirs among depths and seasonal 215 periods (Figs 1-3, Figs S1-9). The concentration of dissolved CH4 varied up to four orders of magnitude from 0.06 to 213.64 µM during the summer stratification (n = 96), and it was less variable during the winter mixing (n = 84) ranging only from 0.02 to 0.69 µM. All depths were consistently supersaturated in CH4, which ranged from 2224 % to 7082234 % during the stratification period, and from 710 % to 20006 % during the mixing period. The dissolved CH4 concentration and the https://doi.org/10.5194/bg-2020-21 Preprint. Discussion started: 4 February 2020 c Author(s) 2020. CC BY 4.0 License. saturation values were significantly higher during the stratification period than during the mixing period (V = 78, p-value < 220 0.001; V = 78, p-value < 0.001). These differences in the concentration of dissolved CH4 are coherent with the differences found in the CH4 emissions from these reservoirs in the stratification and mixing periods (León-Palmero et al. in review).
The wide range in CH4 concentrations found in this study covers from values found in boreal lakes (Donis et al., 2017;Grossart et al., 2011;Murase et al., 2003;Tang et al., 2014), temperate lakes (West et al., 2016), to those found in tropical reservoirs (Naqvi et al., 2018;Okuku et al., 2019). In surface waters, we found values from 0.06 to 8.18 µM, which is about 225 three times the minimum values and eighty times the maximum values found in the surface waters of Lake Kivu (Africa) by Roland et al., (2017). We found similar values to the concentrations reported in subtropical and tropical reservoirs (Musenze et al. 2014, and references therein).
The dissolved CH4 profiles were uniform during the winter mixing in all the reservoirs (Figs. 1b-3b, Figs S1b-9b), whereas during the summer stratification, we found considerable differences in the concentration of dissolved CH4 in the water 230 column (Figs. 1a -3a, figs S1a-9a). Based on the differences found during the stratification in the dissolved CH4 profiles, we sorted the reservoirs in three types. The first type included six reservoirs, in which the dissolved CH4 profile increased from the oxycline to the anoxic bottom, just above the sediments, where reached its maximum. When the oxycline and the thermocline were spatially coupled, the dissolved CH4 concentration increased exponentially from the thermocline along the anoxic hypolimnion to the sediments. The reservoirs Béznar, San Clemente, and Iznájar showed this profile ( Fig. 1a and figs. 235 S1a and 2a). The existence of a sizeable almost anoxic hypolimnion led to a massive accumulation of CH4 in this layer. The differences in the CH4 concentration between the surface and bottom waters were up to three orders of magnitude, as we found in Béznar (from the 0.25 to 56.17 µM; Fig. 1a), San Clemente (from the 0.23 to 45.15 µM; Fig S1a), and Iznájar (from the 0.82 µM to 213.64 µM; Fig. S2a). When the oxycline and the thermocline were not spatially coupled, the dissolved CH4 concentration increased just above the sediments where the anoxic-oxic interface was closed to the bottom. The reservoirs 240 Cubillas, La Bolera, and Francisco Abellán showed this profile type (Figs. S3a,S4a,and S5a). This accumulation of CH4 in the hypolimnion and above sediments might be related to the high rates of methanogenesis in the sediments and its subsequent diffusion to the water column. Dissolved CH4 concentration declines at the oxycline level, where the highest rates of CH4 oxidation are usually measured (Oswald et al., 2015(Oswald et al., , 2016. The CH4 profiles in this group were similar to the ones found in tropical eutrophic and temperate reservoirs (Naqvi et al., 2018;West et al., 2016). The second profile type 245 presents a small peak of metalimnetic CH4, concomitant with peaks of dissolved oxygen, chlorophyll-a, photosynthetic picoeukaryotes, and cyanobacteria (Fig. 2a). In the Negratín reservoir, we found the maximum concentration of CH4 in the oxic hypolimnion. Unlike several previous works (Blees et al., 2015;Grossart et al., 2011;Murase et al., 2003), we did not find a metalimnetic CH4 maximum. Donis et al. (2017) augmented that the observed metalimnetic CH4 maximum represented only an accumulation physically driven. The third profile type included five reservoirs, in which the dissolved 250 CH4 profile presented a CH4 accumulation more significant in the epilimnion than in the hypolimnion. The reservoirs Jándula, Bermejales, Rules, El Portillo, and Colomera showed this profile type (Fig. 3a, Figs. S6a-9a). These reservoirs had https://doi.org/10.5194/bg-2020-21 Preprint. Discussion started: 4 February 2020 c Author(s) 2020. CC BY 4.0 License. a mean CH4 concentration in the water column lower than the reservoirs from the first type. Similar profiles have been reported in boreal lakes (Murase et al., 2003;Tang et al., 2014).

2. CH4 sources in the water column 255
We found two well-differentiated groups of CH4 data based on the dissolved oxygen concentration (D.O.) (Fig. S10). The

2. 1. CH4 sources in anoxic waters
Archaeal methanogens are obligate anaerobes that decompose the organic matter and produce CH4 in anoxic environments, as freshwater sediments. We analyzed the presence of the methanogenic Archaea in the water column of the anoxic samples, 265 by targeting the typical genetic marker of this group: the alpha subunit of methyl-coenzyme reductase, determined by the gene mcrA. We did not detect the amplification of the mcrA gene in the PCR or the qPCR analysis. Therefore, we assumed that the methanogenic Archaea are not present as free-living microorganisms in a significant number in the water column of the anoxic samples. We did not find a significant relationship between the water temperature and the dissolved CH4 concentration in the anoxic samples from the water column (n=17, p-value = 0.66), even though methanogenesis is a 270 microbial process very susceptible to temperature (Marotta et al., 2014;Sepulveda-Jauregui et al., 2018;Yvon-Durocher et al., 2014). These two results (the no detection of the mcrA gene and the absent of relationship of dissolved CH4 with water temperature) suggest that CH4 production is not happening in the water column of the study reservoirs. Segers (1998) reported that in freshwaters the organic matter decomposition in the anoxic sediments produces dissolved CH4. We presumed that methanogenic Archaea must be present in the sediments, where they produce CH4 that diffuses up to the water column 275 developing vast accumulations of CH4 in the hypolimnion.
Methanogenesis in the sediments may be affected by organic matter quantity and quality (West et al., 2012). In the study reservoirs, the dissolved organic carbon concentration did not show a significant relationship with the dissolved CH4 concentration (n=12, p-value = 0.10). We examined the importance of the autochthonous organic matter produced by primary producers using the total cumulative chlorophyll-a (Chl-a, mg m -2 ). The cumulative Chl-a is a surrogate for the 280 vertical exportation of the phytoplankton biomass and their by-products for the whole water column. We obtained that the CH4 concentrations in anoxic samples depended on the cumulative Chl-a following a power function (CH4 = 3.0 10 -4 Cumulative Chl-a 2.28 ; n=17, adj R 2 =0.40, p-value <0.01) (Fig. 4). The autochthonous organic matter was a better predictor https://doi.org/10.5194/bg-2020-21 Preprint. Discussion started: 4 February 2020 c Author(s) 2020. CC BY 4.0 License.
for the concentration of CH4 in anoxic waters than the dissolved organic matter likely because methanogenesis is mainly affected by the origin of the organic matter. Previous experimental studies have demonstrated that the addition of algal 285 biomass on sediment cores increase the CH4 production more than the addition of terrestrial organic matter West et al., 2012West et al., , 2015. The stimulation of the methanogenesis rates appears to be related to the lipid content in phytoplankton biomass (West et al. 2015). West et al. (2016) found a significant relationship between the chlorophyll-a concentration in the epilimnion and the potential methanogenesis rates from sediment incubations. In this study, we corroborate the importance of the autochthonous-derived organic matter on the CH4 concentrations, considering the 290 phytoplankton biomass of the whole water column.

2. 2. CH4 sources in oxic waters
In this study, we observed CH4 supersaturation in all the samples of the oxic waters ranging from 827 % to 363131 %, and the dissolved CH4 concentration ranged from 0.02 µM to 8.18 µM. To explain the origin of this CH4 supersaturation we tested different hypotheses: (1) the lateral and vertical CH4 transport from littoral and deep layers, (2) the in-situ CH4 295 production by methanogenic Archaea, or methyl-phosphonate degradation under extreme P-limitation, and 3) the CH4 production by other processes linked to phytoplankton.

Vertical and lateral CH4-transport from anoxic environments
Several previous works pointed out that CH4 supersaturation in oxic waters can be explained by the vertical transport from the bottom sediments, and the lateral inputs from the littoral zones that are in contact with shallow sediments where 300 methanogenesis occurs (Bastviken et al., 2004;Encinas Fernández et al., 2016;Michmerhuizen et al., 1996). To test the importance of the lateral and vertical transport explaining the concentration of CH4 in oxic waters, we used two morphometric parameters: the mean depth (m) as a proxy for the vertical transport and the shallowness index as a proxy for the lateral transport. We also studied the influence of wind speed on surface waters. The dissolved CH4 concentration was an exponential decay function of the reservoir mean depth (Fig. 5a) both during the stratification period (CH4 = 4.0 10 -2 e (50.0/ 305 mean depth) , adj R 2 = 0.95) and during the mixing period (CH4 = 3.7 10 -2 e (22.9/ mean depth) , adj R 2 = 0.54) (Fig. 5a). We observed that at mean depths shallower than 16 meters, the dissolved CH4 concentration increased exponentially (Fig. 5). Several studies have proposed that the vertical transport of CH4 from bottom sediments explains the supersaturation in surface waters (Rudd & Hamilton 1978, Michmerhuizen et al. 1996, Murase et al. 2003, Bastviken et al. 2004). However, the vertical diffusion rates of dissolved gases across the thermocline are too low in deep and thermally stratified systems and no 310 movements of methane upwards from the hypolimnion have been detected (Rudd and Hamilton, 1978). The shallowness index increases in elongated and dendritic lakes with more impact of the littoral zone and decreases in near-circular lakes, with low shoreline length per area. In this study, we did not find a significant relationship between the shallowness index and the dissolved CH4 concentration (Fig. 5b). Surface winds can cause wave mixing, promoting the CH4 transport from littoral zones. We studied the relationship between the wind speed and the CH4 concentration in surface waters, but we did not find 315 a significant relationship during the stratification period (p-value = 0.43) or the mixing period (p-value = 0.40). In the study reservoirs, wind speeds were relatively low ranging from 0 to 4.11 m s -1 . The diffusion from the shallow water zones may be an important process in some areas closed to the littoral zone, but not in the open waters. Then, these lateral inputs may not be enough to explain extreme CH4 supersaturations (DelSontro et al., 2018).

CH4-production by methanogenic Archaea or methyl-phosphonate degradation 320
The ubiquitous CH4 supersaturation found in oxic waters may not be fully explained, in many cases, by the vertical and lateral transport and it seems that there is an in situ production of CH4 (Bogard et al., 2014;DelSontro et al., 2018;Grossart et al., 2011). However, the ultimate mechanisms involved in this production are not clear enough. We studied the presence of the methanogenic Archaea in the oxic samples by targeting the gene mcrA by PCR and qPCR, but we were unable to detect this gene (Fig. S11). This result indicates that methanogenic Archaea are not present in a significant number in the water 325 column of the oxic samples in the study reservoirs. The classical methanogens (i.e., Archaea with the mcrA gene) are obligate anaerobes without the capacity to survive and produce CH4 under aerobic conditions (Chistoserdova et al., 1998).
Previous studies showed that methanogens may tolerate oxygen exposure in soils (Angel et al., 2011;Angle et al., 2017) and detected potential methanogenic Archaea attached to photoautotrophs in lake oxic waters (Grossart et al., 2011).
We also considered the possibility of methylphosphonates (MPn) degradation as an in situ CH4 source. This metabolic 330 pathway appears in the bacterioplankton under chronic starvation for phosphorus . Diverse pieces of evidence have shown that marine bacterioplankton can degrade the MPn and produce CH4 through the C-P lyase activity in typically phosphorus starved environments, as the ocean gyres (Beversdorf et al., 2010;Carini et al., 2014;Repeta et al., 2016;Teikari et al., 2018;del Valle and Karl, 2014). Freshwater bacteria can also degrade the MPn and produce CH4, as it has been demonstrated in Lake Mantano (Yao et al., 2016b(Yao et al., , 2016a. Lake Mantano is an ultra-oligotrophic lake with a severe 335 P deficiency (below 0.050 µmol P L -1 ) due to the permanent stratification, iron content, and extremely low nutrient inputs (Crowe et al., 2008;Sabo et al., 2008). The ratio of dissolved inorganic nitrogen (DIN) to total phosphorus (TP) (µmol N: µmol P) is widely used to evaluate P-limitation (Morris and Lewis, 1988). DIN:TP ratios greater than 4 are indicative of phosphorus limitation (Axler et al., 1994). In the study reservoirs, the TP concentration ranged from 0.13 to 1.85 µmol P L -1 during the stratification period, and from 0.10 to 2.17 µmol P L -1 during the mixing period. The DIN:TP ratio ranged from 340 15 to 985 during the stratification period, and from 28 to 690 during the mixing period. The more extreme P-limitation conditions, the higher the CH4 production by methylphosphonates (MPn) degradation is. However, we did not find a significant relationship between the DIN:TP ratio and the CH4 concentration (Fig. 6). We also analyzed the presence and abundance of the gene phnJ, which encodes the enzyme complex C-P lyase that hydrolyzes the MPn and changes in response to phosphate availability. We did not detect the phnJ gene in the PCR or the qPCR analysis in any of the study 345 samples (Fig. S12). These results indicate that the MPn degradation was not a quantitatively relevant source of CH4 in the oxic waters of the study reservoirs. Our results are in concordance with Grossart et al. (2011), who did not detect CH4 https://doi.org/10.5194/bg-2020-21 Preprint. Discussion started: 4 February 2020 c Author(s) 2020. CC BY 4.0 License. production by adding inorganic phosphate or methylphosphonates to lake samples in laboratory experiments. Although we used different methodologies, both studies may indicate that MPn degradation is only an important source of CH4 in ultraoligotrophic systems, as Lake Mantano or ocean gyres. 350

CH4-production coupled to photosynthetic organisms
Previous studies have consistently reported CH4 production in oxic waters associated to phytoplankton in the field, in situ incubations, and in floating mesocosms (Bogard et al., 2014;Grossart et al., 2011;Owens et al., 1991;Schmidt and Conrad, 1993;Tang et al., 2014). In the study reservoirs, we analyzed the relationship between phytoplankton and the dissolved CH4 concentration using the gross primary production (GPP, g O2 m -3 d -1 ), the concentration of chlorophyll-a (Chl-a, µg L -1 ), and 355 the abundance of photosynthetic picoeukaryotes (PPEs, cell mL -1 ) and cyanobacteria (CYA, cell mL -1 ). The PPEs are marine and freshwater microorganisms of ca. 3.0 µm or less in size. In the freshwaters, the PPEs include species from different phyla, as non-colonial Chlorophyta (green algae), and Haptophyta. We show the vertical profiles of the Chl-a concentration and the abundance of PPEs and CYA profiles of each reservoir in figs. 1, 2, 3, and figs. S1-9. The abundance of cyanobacteria ranged from 1513 to 204201 cells mL -1 and was more than one order of magnitude higher than the abundance 360 of PPEs that ranged from 32 to 7450 cells mL -1 . Using optical microscopy, we determined that the main groups of photosynthetic picoeukaryotes in the study reservoirs. PPEs were non-colonial green algae from the order Chlorococcales (class Chlorophyceae, phylum Chlorophyta), and the genus Chrysochromulina spp., (class Coccolithophyceae, phylum

Haptophyta).
We did not find a significant relationship between the gross primary production (GPP) and the dissolved CH4 concentration 365 (p-value = 0.077, n = 12, Table 1). The Chl a concentration showed a significant relationship with the GPP (p-value < 0.01, n = 12, adj R 2 = 0.55), but the abundance of cyanobacteria or the abundance of the photosynthetic picoeukaryotes did not show a significant relationship with the GPP (p-value = 0.911, n = 12; p-value = 0.203, n = 12, respectively).
We found significant potential relationships between the Chl-a concentration, the abundances of the photosynthetic picoeukaryotes, and the abundance of cyanobacteria with the concentration of dissolved CH4 during the stratification period 370 (Fig. 7a, 7b, and 7c respectively, and Table 1). During the mixing period, the only predictor of the dissolved CH4 concentration was the abundance of photosynthetic picoeukaryotes (Fig. 7b). The variance explained, and the slope of the relationship (i.e. the exponent in the power relationship) between the dissolved CH4 and the abundance of photosynthetic picoeukaryotes was higher during the stratification than during the mixing (Table 1). By comparing the stratification slopes, the effect per cell of PPEs on CH4 concentration was slightly higher than the impact of cyanobacteria (Table 1). These results 375 agree with previous studies that showed a closed link between the CH4 concentration and the autotrophic organisms, primary production, or chlorophyll-a concentration (Bogard et al., 2014;Grossart et al., 2011;Schmidt and Conrad, 1993;Tang et al., 2014).
In this study, we show that the PPEs abundance was a better predictor of the CH4 concentration than the abundance of cyanobacteria. In the study reservoirs, the PPEs group included members from green algae and Haptophyta, which are 380 regular components of the marine plankton. Therefore, these results may be relevant also for marine waters. Cyanobacteria have received more attention as potential producers of CH4 in oxic conditions than photosynthetic picoeukaryotes (Berg et al., 2014;Bižić et al., 2020;Teikari et al., 2018). Grossart et al. (2011) also detected CH4 production in laboratory cultures of cyanobacteria and green algae. Overall, these results indicate a clear association between the CH4 production and the photoautotrophs from both Eukarya and Bacteria domains. The pathways involved in the CH4 production may be related to 385 the central photosynthetic metabolism or the release of methylated by-products different from methylphosphonates during the photosynthesis. Lenhart et al. (2016) demonstrated the CH4 production using 13 C-labeled bicarbonate in the eukaryotic microalgae Emiliania huxleyi. More recently, Bižić et al., (2020) also detected CH4 production from 13 C-labeled bicarbonate in marine, freshwater, and terrestrial cyanobacteria cultures. In both studies, the autotrophic organisms uptake bicarbonate in the reductive pentose phosphate cycle (Calvin-Benson cycle) (Berg, 2011). Carbon fixation is a crucial step of 390 photosynthesis in cyanobacteria, algae, and plants (Berg, 2011;Burns and Beardall, 1987). Therefore, CH4 production may be a common pathway in the central metabolism of photosynthesis of all the cyanobacteria and algae in freshwaters and marine environments.
On the other hand, the production of CH4 can also be related to the production of methylated compounds during photosynthesis. Lenhart et al. (2016) also detected the CH4 production in E. huxleyi cultures from the sulfur-bound methyl 395 group of the methionine. Common substances as methionine can act as a methyl-group donor during the CH4 production in plants and fungi (Lenhart et al. 2012(Lenhart et al. , 2015. Besides, algae use part of the methionine for the synthesis of dimethylsulfoniopropionate (DMSP), an abundant osmolyte, the precursor of dimethyl sulfide (DMS), and dimethylsulphoxide (DMSO). These methylated substances produce methane during their degradation (Damm et al., 2008(Damm et al., , 2010(Damm et al., , 2015Zindler et al., 2013). These substances, together with other organosulphur compounds, can also produce CH4 400 abiotically (Althoff et al., 2014). The production of DMSP, DMS, and other methylated substances as isoprene, has been extensively studied in marine phytoplankton, showing that taxa as photosynthetic picoeukaryotes and the cyanobacteria are relevant sources (Shaw et al., 2003;Yoch, 2002). Recent studies have reported that freshwater algae and cyanobacteria also produced DMS and isoprene (Steinke et al., 2018). Further studies are needed to quantify the potential role of the methylated by-products as CH4 sources in freshwaters. 405

Modeling the CH4 production in oxic waters
The explanation of the CH4 oversaturation in oxic waters may relay on the interaction of several processes as the transport from anoxic environments and the biological activity (DelSontro et al., 2018). In this study, we found that vertical transport (mean depth as surrogate), water temperature, and the abundance of photosynthetic picoeukaryotes and cyanobacteria had a significant effect on the dissolved CH4 concentration. We combined these explanatory variables with significant effects 410 using generalized additive models (GAMs). The GAM model for the stratification period (n=78) had a fit deviance of 82.7% and an explained variance (adj R 2 ) of 81.4 % ( Table S1). The explanatory variables, in decreasing order, were: the photosynthetic picoeukaryotes abundance (log10 PPEs), the reservoir mean depth, the cyanobacteria abundance (log10 CYA), https://doi.org/10.5194/bg-2020-21 Preprint. Discussion started: 4 February 2020 c Author(s) 2020. CC BY 4.0 License. and the water temperature (Fig. 8a). The function obtained was: Log10 CH4 = -4.05 + 3.4 10 -1 Log10 PPEs + e (6.7/ mean depth) + 1.7 10 -1 Log10 CYA + 2.7 10 -2 Temperature. The abundance of PPEs was the variable explaining most of the variance of 415 dissolved CH4 concentration (Log10 CH4) during the stratification period, with an effect higher than the cyanobacteria abundance. Figure 8b-e shows the partial responses of each explanatory variable. The GAM model for the mixing period (n=82) only included two explanatory variables: the reservoir mean depth and the abundance of the photosynthetic picoeukaryotes. The reservoir mean depth was the variable explaining most of the variance of the dissolved CH4 concentration (Log10 CH4) during the mixing period, closely followed by the abundance of PPEs (Fig. 9a). The function for 420 the model was: Log10 CH4 = -2.07 + 1.5 e (-0.04 mean depth) + 1.8 10 -1 Log10 PPEs, with a fit deviance of 53.9 % and an explained variance (adj R 2 ) of 52.1 % (Table S1). In Figure 9b and 9c, we show the partial response plots for both variables.
These results show that the abundance of photosynthetic picoeukaryotes can be key for explaining the dissolved CH4 concentration in oxic waters, even though they have received less attention than cyanobacteria in previous studies (Berg et al., 2014;Bižić et al., 2020;Teikari et al., 2018). Finally, we have also included a simple model to explain the dissolved CH4 425 concentration (Log10 CH4) in both periods (n=160) using common explanatory variables like the water temperature (ºC) and chlorophyll-a concentration (Chl-a, µg L -1 ). The function for the model was: Log10 CH4 = -1.22 + 3.2 10 -2 e (0.13 Temperature) + 2.3 10 -1 Log10 Chl-a. The GAM model had a fit deviance of 49.7 % and an explained variance (adj R 2 ) of 48.8 % (Table S1).
Overall, during the stratification period, the in situ CH4 production was coupled to the abundance of photosynthetic picoeukaryotes in oxic waters (Fig. 8a). This CH4 source by photosynthetic picoeukaryotes can be crucial in large, deep lakes 430 and reservoirs, and the open ocean since the impact of the CH4 transport from sediments (i.e., mean depth) decreases with increasing depths. In deeper reservoirs, the thermal stratification during the summer produced that the vertical diffusion rates of CH4 from sediments is limited. Rudd and Hamilton, (1978) did not detect any movement of CH4 upwards from the hypolimnion during the stratification. Bogard et al., (2014) also suggested that CH4 produced in the oxic water column is a significant component of CH4 fluxes, especially in deep systems. This CH4 produced in the oxic layer can reach up to 90% of 435 total CH4 emissions during the stratification period (Donis et al., 2017).
In contrast, during the winter mixing, the mean depth had a more considerable influence than the in situ production by photosynthetic picoeukaryotes (Fig. 9a). The photosynthetic picoeukaryotes identified in the study reservoirs are considered indicators of eutrophic conditions and are bloom-forming genera (i.e., Chlorococcales and Chrysochromulina spp.) (Reynolds, 1984;Willén, 1987;Edvardsen and Paasche, 1998). Global future estimations suggest a rise in eutrophication and 440 algal bloom over the next century due to climate change and the growing human population (Beaulieu et al., 2019). In that situation, photosynthetic picoeukaryotes as Chlorococcales and Chrysochromulina spp., and cyanobacteria, would lead to an increment in CH4 production and emissions. Further studies are needed to better understand the role of the photosynthetic picoeukaryotes in the production of CH4 in oxic waters, and to quantify their influence in the methane oversaturation and CH4 fluxes from inland and oceanic waters. 445

Conclusions
The dissolved CH4 concentration in the study reservoirs showed a huge variability (i.e. up to four orders of magnitude), and presented a seasonal pattern. Surface waters were always supersaturated in CH4. The concentration of CH4 was closely linked to the phytoplankton dynamics. In the anoxic waters, the depth-cumulative chlorophyll-a concentration, a proxy for 450 the total phytoplanktonic biomass exported to sediments, determined the CH4 concentration. In the oxic waters, we considered different potential CH4 sources, including the vertical and lateral transportation of CH4 from anoxic zones and in situ production by different approaches. The mean depth of the reservoirs, as a surrogate of the CH4 transport from sediment to the oxic waters, contributed in shallow systems. We did not detect methanogenic Archaea or methylphosphonates degradation target genes (i.e. mcrA and phnJ genes, respectively), which suggests that these pathways are not responsible for 455 the in situ production of CH4 in the oxic waters in the study reservoirs. We found that dissolved CH4 was coupled to the abundance of photosynthetic picoeukaryotes (PPEs) during both periods, and to chlorophyll-a concentration and the abundance of and cyanobacteria during the stratification period. These PPEs were non-colonial green algae from the order Chlorococcales (class Chlorophyceae, phylum Chlorophyta), and the genus Chrysochromulina spp., (class Coccolithophyceae, phylum Haptophyta). Finally, we combined the explanatory variables with significant effects and tested 460 their relative contribution on the CH4 concentration using generalized additive models (GAMs). The abundance of PPEs was the variable explaining most of the variance of dissolved CH4 concentration during the stratification period, with an effect higher than the cyanobacteria abundance. During the mixing period the the reservoir mean depth and the abundance of the PPEs were the main drivers for CH4 concentration. In a simpler model, we can also predict the dissolved CH4 concentration in both periods using water temperature and chlorophyll-a concentration. Our findings show that the abundance of PPEs can 465 be key for explaining the dissolved CH4 concentration in oxic waters, and, in comparison to cyanobacteria, have been poorly explored as CH4 sources. These coupling of the abundance of the PPEs and the dissolved CH4 concentration is novel, and further studies should determine the ultimate mechanism involved in methane production.

Data availability
Additional figures and tables can be found in the supplementary information. The dataset associated with this manuscript is 470 available at Pangaea (XXXXXXXX).

Competing interests
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 480     picoeukaryotes abundance (log10 PPEs) (b), the mean depth (c), the cyanobacteria abundance (log10 CYA) (d), and water temperature (e). In partial response plots, the lines are the smoothing functions and the shaded areas represent 95% point-wise confidence intervals. Rugs on x-axis indicate the distribution of the data. More details are provided in Table S1. https://doi.org/10.5194/bg-2020-21 Preprint. Discussion started: 4 February 2020 c Author(s) 2020. CC BY 4.0 License.