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. Author manuscript; available in PMC: 2026 Jun 19.
Published in final edited form as: Limnol Oceanogr Lett. 2026 Apr 30;11(3):lol2.70129. doi: 10.1002/lol2.70129

Seasonal differences and potential biological drivers of the methane paradox in two peri-Alpine lakes

Niharika Sharma 1, Manuela Felsberger 1, Zeynep Kurt 1, Markus Möst 2, Barbara Bayer 1,*
PMCID: PMC7619199  EMSID: EMS214247  PMID: 42328308

Abstract

Seasonal variations and the biological drivers underlying the methane paradox in freshwater lakes are poorly understood. Here, we investigated the relationship between subsurface methane inventories and phytoplankton in two peri-Alpine lakes across different seasons. Surface waters of both lakes were consistently saturated in methane, with maxima reaching 570 and 205 nmol L−1 during summer in the metalimnion of lakes Mondsee and Attersee, respectively. Methane concentrations were positively correlated with phytoplankton abundance in meso-oligotrophic lake Mondsee but not in ultra-oligotrophic lake Attersee. However, although phytoplankton peaked in abundance at the methane maxima in Mondsee, incubation experiments with 13C-labeled bicarbonate revealed negligible methane production from primary productivity. Instead, our results suggest that phytoplankton might only be indirectly involved in methane production through alternative pathways, or by providing precursor compounds to other members of the microbial community in these oligotrophic lakes.

Methane (CH4) is the second most important greenhouse gas after carbon dioxide, contributing approximately 25% to global warming since the industrial revolution (Ganesan et al. 2019; IPCC 2023). Freshwater ecosystems emit the largest amount of CH4, with lakes alone estimated to emit 151 ± 73 Tg CH4 per year (Rosentreter et al. 2021). Traditionally, CH4 production in aquatic ecosystems has been attributed to methanogenesis in anoxic sediments (Bastviken 2009). However, this view has been challenged by increasing evidence for CH4 production in oxygenated waters of both oceans and freshwater lakes (Karl et al. 2008; Grossart et al. 2011; Schroll et al. 2023; Mao et al. 2024). The occurrence of elevated CH4 concentrations in oxic surface waters is referred to as the methane paradox and might account for 18–90% of total CH4 emissions from freshwater lakes to the atmosphere (Donis et al. 2017; Günthel et al. 2020). Growing evidence suggests a link between elevated CH4 concentrations ([CH4]) and subsurface chlorophyll (Chl) maxima (Bogard et al. 2014; Tang et al. 2014; Donis et al. 2017; Günthel et al. 2020; Thottathil et al. 2022; Ordóñez et al. 2023), indicating a contribution of phytoplankton activity to the methane paradox. A relationship between phytoplankton and CH4 production has also been demonstrated in both culture experiments (Lenhart et al. 2016; Klintzsch et al. 2019; Bižić et al. 2020) and field-based studies using stable isotope tracing approaches (Tang et al. 2014; Günthel et al. 2020; Morana et al. 2020). However, while a direct link to photosynthesis has been proposed previously (Bižić et al. 2020; Günthel et al. 2020), the exact mechanisms involved in CH4 production by phytoplankton remain unresolved.

Phytoplankton blooms are expected to become more frequent during future climate scenarios (Jones and Brett 2014), which could lead to higher CH4 emissions from lake ecosystems (Beaulieu et al. 2019). Consequently, understanding the role of phytoplankton in seasonal CH4 dynamics is essential to better understand and predict future CH4 emission scenarios. Furthermore, despite its widespread occurrence, the methane paradox is not a universal phenomenon and may be absent in some lakes (Khatun et al. 2019; Ordóñez et al. 2023). This highlights the importance of understanding the influence of environmental and seasonal dynamics on the occurrence of the methane paradox in lake ecosystems.

In this study, we investigated seasonal CH4 dynamics in oxic surface waters of the two Austrian lakes Mondsee and Attersee, which are located in the Alpine foothills and formed by tectonic-glacial processes (= peri-Alpine; Tolotti et al. 2018). Both lakes lie within the same catchment but differ in trophic state, offering a natural contrast for comparison. Our primary objective was to understand the biological drivers, particularly the role of phytoplankton, in the formation of the methane paradox in these lakes.

Methods

Study site and sampling procedures

The study was conducted in a twin lake system, Mondsee and Attersee, located in the Salzkammergut region of Austria (Fig. 1). Attersee is a deep ultra-oligotrophic lake while Mondsee is an oligo-mesotrophic lake. Water samples were collected from both lakes at their deepest points during 17–18 July 2024 (summer), 5–6 November 2024 (autumn), 1–2 April 2025 (spring), and 24–25 June 2025 (summer). Water column profiles of temperature, dissolved oxygen (DO), Chl, phycoerythrin (PE), and conductivity were recorded with a multiparameter probe (YSI 6600 V2, Ohio, USA). PE is an accessory pigment present in some cyanobacteria and eukaryotic phytoplankton (Bryant 1982). Despite prior calibration of fluorescence probes with Milli-Q, some profiles in lake Attersee showed continuously negative values, possibly as a result of the low pigment abundance in this ultra-oligotrophic lake. Consequently, we additionally collected discrete water samples for Chl a analysis from both lakes in the latter two sampling events. In situ light measurements were taken using a LI-COR light sensor (LI-193, LiCOR, Lincoln, USA) whenever sampling was performed during midday and cloud coverage was minimal. Water samples were collected with a 10 L Niskin bottle, targeting discrete depths within and below the euphotic zone, ranging from 0 to 40 m in Mondsee and from 0 to 60 m depth in Attersee.

Fig. 1.

Fig. 1

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Study area. Morphometric data is taken from the annual report on ecological state of the lakes in the province of Upper Austria (Krisa and Hainz 2024).

Methane concentration analysis

Samples for dissolved CH4 concentrations were collected directly from the Niskin bottle using silicon tubing (Masterflex, Germany). Duplicate water samples were collected in 120 mL glass serum vials filling from bottom and overflowing 2 to 3 times to remove air, and bottles were closed bubble-free with gray butyl rubber septa (#27232, Merck) and aluminum crimps. The rubber septa were pretreated by autoclaving twice with Milli-Q to prevent leaching from the septa. After preserving with 150 μL saturated ZnCl2 solution, samples were stored in the dark at 14°C prior to analysis on a Picarro G2201-i analyzer (Picarro Inc., CA, USA). Information on instrument calibration and measurement procedures can be found in Supporting Information Text S1.

Nutrient and chlorophyll analysis

Samples for ammonium (NH4+) and nitrite (NO2−) concentrations were collected directly from the Niskin bottle and analyzed on the same day. Samples for nitrate (NO3−) and phosphate (PO43−) concentrations were collected after syringe-filtration (0.22 μm, Ministar, Sartorius) and frozen at −20°C for later analysis. Samples for Chl a were collected in 1 L amber bottles and stored at 4°C in the dark until sample processing on the same day. Concentrations of NO2−, NO3−, and PO43− were measured colorimetrically, while that of NH4+ and Chl a were measured fluorometrically. Detailed methodological procedures for nutrient analysis are provided in Supporting Information Text S2.

Cell abundance measurements

Samples for flow cytometry analysis were collected in 1.8 mL cryotubes (Carl Roth, Germany), fixed with glutaraldehyde (final concentration 0.5%) for 10 min, flash frozen in liquid nitrogen, and stored at −80°C (Marie et al. 1997) until analysis on a CytoFLEX S flow cytometer (Beckman Coulter, CA, USA). Prior to sample analysis, internal fluorescence calibration was performed with 3 μm fluorescent microsphere beads (Beckman Coulter, CA, USA). A threshold of 750 was set to side scatter to optimize event and abort rate of the measurement, and the samples were analyzed at a flow rate of 30 μL min−1. All data were collected on a log scale and analyzed using the inbuilt CytExpert Software (Beckman Coulter, CA, USA). Total cell abundance was quantified after staining with SYBR green I dye (1:10,000 dilution; Lonza Biosciences, Switzerland) for 10 min in the dark (Marie et al. 1997). Phyto-plankton cells were quantified by their autofluorescence (red fluorescence from Chl a and yellow fluorescence from PE) and scattering properties (side scatter and forward scatter) in unstained samples. Non-photosynthetic cells were identified by the difference between SYBR green positive cells and phytoplankton cells.

Microbial community composition

Water was collected in acid-washed 2 L polycarbonate bottles and the biomass was sequentially filtered onto 3 μm (25 mm, Isopore, Millipore) and 0.22 μm (25 mm, Durapore, Millipore) poresize membrane filters, which were subsequently frozen in 2 mL gasketed bead-beating tubes (Lysis Matrix E, Qiagen) at −80°C until extraction. DNA was extracted using the Qiagen PowerSoil Pro kit as described in Supporting Information Text S3. The V4 hypervariable region of the bacterial and archaeal 16S rRNA gene was amplified using the V4-EXT primer pair (forward/reverse: GTGYCAGMMGBNKCGGTVA/RGACTAMNVRGGTHTCTAAT) (Hu et al. 2025). DNA extraction, sequencing, and raw data processing was performed at the Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna following a standardized 2-step PCR protocol described previously (Pjevac et al. 2021), and sequenced on an Illumina MiSeq (2 × 300 bp) sequencer. Information on sequence data processing is provided in Supporting Information Text S3.

Primary production associated methane production

CH4 production associated with primary production (= dissolved inorganic carbon [DIC] fixation) was assessed during summer 2024 and autumn 2024 to test the role/contribution of phytoplankton primary production to oxic CH4 formation. We collected water samples from the depths corresponding to Chl and PE fluorescence maxima as phytoplankton biomass is expected to be high at these depths. Water samples were collected as described above (see section on Methane concentrations) and 13C-labeled bicarbonate (98 atom%, Merck KGaA, Darmstadt, Germany) was added to incubation bottles to achieve a labeling of 10–20%. Samples were incubated for a maximum of 96 h at close to in situ temperature and light conditions (Supporting Information Table S1), and incubations were terminated by adding 150 μL saturated ZnCl2 solution. CH4 concentrations and isotopic enrichment were measured with a Picarro G2201-i analyzer (Picarro Inc., CA, USA), following the methodological details described in Supporting Information Text S1. Separate water samples were collected to quantify in situ DIC concentrations (Supporting Information Text S1). Methane production rates were calculated from the increase of 13CH4 concentrations over time (Hama et al. 1983). Rates were considered significant when the slope of the linear regression was statistically different from zero (one-sided Student’s t-test, p < 0.05).

Statistical analyses

Pearson correlation coefficients were calculated to quantify the strength and direction of relationships between [CH4] and environmental parameters for lakes and within different lake layers (epilimnion, metalimnion and hypolimnion) in seasons where a distinct metalimnion was identified. Subsequently, linear regression analysis was performed to describe the linear relationships of some of the observed correlations.

Metalimnion depth was calculated using a temperature gradient threshold of ≥ 1°C m−1. The shallowest and deepest temperature change exceeding this threshold was used to define the depth range of the metalimnion. For duplicate CH4 samples, the arithmetic mean was used for calculations. Statistical analysis and data visualization was conducted using R software (version 4.3.3) and Sigmaplot (version 14.0).

Results

Environmental context

Average surface water temperature of lakes Mondsee and Attersee ranged from 5 to 23°C during different sampling events. Both lakes were thermally stratified with a distinct formation of epilimnion, metalimnion, and hypolimnion during summer and autumn (Fig. 2). In contrast, the water column was completely mixed in spring, indicating the persistent effects of winter turn-over. Irrespective of the season, the entire water column of both lakes remained oxic, ranging from 49 to 363 μmol L−1 in Mondsee and from 268 to 353 μmol L−1 in Attersee (Supporting Information Fig. S1, Sharma et al. 2026). The euphotic zone (defined as 0.5% of surface light levels; Wu et al. 2021), extended to approximately 16 m in Mondsee and 37 m in Attersee. Chl and PE fluorescence was consistently higher in Mondsee compared to Attersee (Fig. 2), corresponding to differences in trophic state between the two lakes. NH4+ and NO2− reached maximum concentrations of 899 and 408 nmol L−1 in lake Mondsee and 640 and 333 nmol L−1 in lake Attersee, respectively, while NO3− concentrations ranged from 15 to 47 μmol L−1 in both lakes throughout the year (Supporting Information Fig. S1). Both lakes were phosphorus-limited, with PO43− concentrations below the detection limit (≤ 0.25 μmol L−1) in most depth layers, corresponding to nitrogen: phosphorus (N : P) ratios of ≥ 52 : 1 throughout the year (Supporting Information Fig. S1).

Fig. 2.

Fig. 2

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Vertical profiles of dissolved methane (CH4), temperature, chlorophyll, phycoerythrin, and light intensity in lakes Mondsee (MS, a–d) and Attersee (AS, e–h) during different seasons. Sampling time corresponding to each plot is denoted by month-year. Chlorophyll and phycoerythrin multiprobe profiles are not reported in some seasons due to continuously negative values (see Methods section). Sporadic negative fluorescence values were set to zero for the purpose of visualization. Light intensity was only measured during midday on clear, sunny days. The metalimnion is indicated by gray shaded boxes.

Phytoplankton abundance ranged from 1.47 × 104 to 2.46 × 105 cells mL−1 in Mondsee and from 1.56 × 104 to 2.52 × 105 cells mL−1 in Attersee, whereas abundances of non-photosynthetic cells ranged from 8.58 × 105 to 3.15 × 106 cells mL−1 in Mondsee and from 6.87 × 105 to 1.71 × 106 cells mL−1 in Attersee (Supporting Information Fig. 2). Phytoplankton abundance peaked at the lower boundary of the metalimnion in Mondsee and below the metalimnion in Attersee, while non-photosynthetic cells were rather uniformly distributed in the epi- and metalimnion of both lakes (Supporting Information Fig. S2). Phytoplankton made up 1–7% and 2–15% of microbial cells in lakes Mondsee and Attersee, respectively, and mostly consisted of cells less than 3 μm in size when compared to standard size beads (Supporting Information Fig. S3). Cyanobacteria made up the majority of phytoplankton during the summer season in both lakes in the 0.2–3 μm size fraction, and almost exclusively consisted of members of the order Synechococcales (Supporting Information Fig. S4). In contrast, eukaryotic phytoplankton became relatively more abundant in autumn and spring in both lakes. Diatoms (Bacillariophyta) were the most abundant eukaryotic phytoplankton classified by chloroplast 16S rRNA gene sequences, with the exception of lake Mondsee in autumn, where Cryptomonadaceae made up most of the phytoplankton community (Supporting Information Fig. S4).

Seasonal methane dynamics

The epilimnion of both lakes was consistently oversaturated with CH4 compared to atmospheric equilibrium (3 nM; Mao et al. 2024) (Fig. 2), with slightly higher concentrations in Mondsee (100–290 nmol L−1) than Attersee (96–149 nmol L−1). Depth-integrated CH4 concentrations were similar between both lakes and varied between 1.7–6.4 μmol m−2 in Mondsee (0–30 m depth) and 2.5–4.8 μmol m−2 in Attersee (0–40 m depth) throughout the year. [CH4] peaked within the metalimnion in summer in both lakes, with maximum values in Mondsee in June 2025 (570 nmol L−1) (Fig. 2). In autumn, deepening of the surface mixed layer led to uniform concentration within the upper 15 m of both lakes, while [CH4] peaked just below the metalimnion to 155 nmol L−1 in Attersee. No clear vertical pattern in [CH4] distribution was observed in spring in either lake as a result of winter mixing.

We observed a statistically significant linear relationship between [CH4] and phytoplankton abundance in lake Mondsee (R2 = 0.66, Fig. 3a), which was consistent across lake layers (epilimnion: R2 = 0.82, p = 0.0051; metalimnion: R2 = 0.67, p = 0.0241; hypolimnion: R2 = 0.95, p < 0.0001).

Fig. 3.

Fig. 3

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Relationship between methane concentrations and phytoplankton abundance in lakes (a) Mondsee and (b) Attersee. Samples corresponding to different lake layers (epilimnion, metalimnion and hypolimnion) are indicated by different colors. Samples from April, where no distinct metalimnion could be identified (indicative for a mixed water column), are shown by square shapes. Linear regression analysis is shown for all samples from lake Mondsee (n = 39) and lake Attersee (n = 42).

In contrast, no statistically significant relationship between phytoplankton and [CH4] was observed in lake Attersee (R2 = 0.07, Fig. 3b). Lake level correlation analysis further indicated a positive association between non-photosynthetic cells and [CH4] in the meta- and hypolimnion of lake Mondsee (Supporting Information Fig. S5), which was absent in lake Attersee (Supporting Information Fig. S6). No consistent positive correlation between [CH4] and nutrient concentrations was observed (Supporting Information Figs. S5 and S6), yet, [CH4] profiles closely resembled NH4+ concentration profiles in both lakes in autumn and in Mondsee in summer 2024. To further explore links between primary production and CH4 production, we performed stable isotope tracing experiments with 13C-bicarbonate in summer and autumn 2024 at the depths of the Chl and PE maxima. However, no increase in 13C-CH4 concentrations was observed during the incubation period (24–96 h) across all tested depths and seasons in both lakes (Table 1).

Table 1.

Primary productivity-associated methane production determined via 13C-bicarbonate stable isotope tracing incubation experiments in lakes Mondsee and Attersee. The change in 13CH4 concentration (average ± standard deviation) during the incubation period from the start of the incubation time (T0) until the end of the incubation time after 24 to 96 h (T2) are shown. 13CH4 concentrations were calculated from total CH4 concentrations and isotopic values (d13C-CH4) (Sharma et al. 2026). Incubation times varied between experiments (details are provided in Supporting Information Table S1). Sampling depth denotes the lake depth where water for incubation experiments was taken from, roughly corresponding to pigment maxima (see Methods section).

Lake Season Sampling depth (m) 13CH4 (nmol L−1)
T 0 T1 T2
Mondsee Summer 7.5 2.36 ± 0.18 2.41 ± 0.03 2.25 ± 0.29
Mondsee Summer 15 0.20 ± 0.01 0.16 ± 0.00 0.18 ± 0.01
Mondsee Autumn   4 0.67 ± 0.11 0.76 ± 0.02 0.72 ± 0.02
Mondsee Autumn 10 0.84 ± 0.01 0.76 ± 0.01 0.68 ± 0.03
Attersee Summer 10 n.a* 0.97 ± 0.01 0.98 ± 0.01
Attersee Summer 25 0.52 ± 0.01 0.52 ± 0.01 0.53 ± 0.02
Attersee Autumn 10 0.78 ± 0.00 0.77 ± 0.00 0.74 ± 0.04
Attersee Autumn 20 1.09 ± 0.01 1.09 ± 0.02 0.99 ± 0.01
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*

Samples are not available (n.a.) due to human error.

Discussion

We investigated seasonal CH4 dynamics in the water columns of the two Austrian peri-Alpine lakes Mondsee and Attersee. In agreement with previous observations in temperate lakes (Bastviken 2009; Tang et al. 2016; Bartosiewicz et al. 2023), we identified a pronounced methane paradox in both lakes, with highest [CH4] within the metalimnion, coinciding with seasonal stratification during summer (Fig. 2). Though concentrations were highest in summer, [CH4] was elevated throughout the water columns of both lakes during all sampling events, indicating year-round CH4 over-saturation. These findings are in stark contrast to previous studies which reported clear seasonal differences, with CH4 often disappearing in winter (Schulz et al. 2001; Khatun et al. 2019). Our results together with previous observations suggest a high variability in seasonal CH4 dynamics between different lakes (Tang et al. 2014; Khatun et al. 2019; Günthel et al. 2020; Liu et al. 2024), yet distinctions between continuous and episodic CH4 production cannot be made with our current dataset.

Both lakes remained oxic throughout the year, limiting the possibility for the formation of low-oxygen niches to support canonical methanogenesis pathways (Bastviken et al. 2008). In support of this observation, 16S rRNA gene sequences of methanogenic archaea were not detected in the water columns of both lakes across all seasons (Sharma et al. 2026). The transport of CH4 from the littoral sediment zone as a potential contributor to sub-surface CH4 accumulation (Murase et al. 2005) also seems unlikely in these deep, oxic lakes, where CH4 oversaturation in surface waters is largely decoupled from sedimentary sources (Günthel et al. 2019).

We further explored the role of phytoplankton in the formation of the methane paradox in lakes Mondsee and Attersee. Despite similar phytoplankton composition in both lakes, a significant positive linear relationship between [CH4] and phytoplankton abundance was only observed in meso-oligotrophic lake Mondsee and not in ultra-oligotrophic lake Attersee (Fig. 3), suggesting that lake trophic state might influence phytoplankton-associated CH4 production. Our study does not take into account potential CH4 consumption, which might also shape [CH4] profiles. Methanotrophic bacteria were present throughout the water columns of both lakes in every season, making up between 0.1–2.1% and 0.4–2.3% of the microbial community in Mondsee and Attersee, respectively (Sharma et al. 2026). However, while CH4 consumption could potentially explain the lack of a relationship between [CH4] and phytoplankton abundance in lake Attersee, in situ CH4 oxidation rate measurements are required to derive any conclusions.

Cyanobacterial Synechococcales and diatoms were particularly abundant in both lakes throughout most of the year (Supporting Information Fig. S4) and have previously been suggested to be associated with the seasonal formation of CH4 maxima in freshwater lakes (Khatun et al. 2019; Günthel et al. 2020). However, contrary to previous studies which reported substantial CH4 production from primary production (0.2–11 nM d−1; Günthel et al. 2020; Morana et al. 2020), we observed no significant production of 13CH4 from 13C-bicarbonate in lakes Mondsee and Attersee (Table 1). Consequently, DIC fixation likely only plays a very minor role in the formation of the methane paradox in both lakes. However, phytoplankton could be involved in the production of CH4 through pathways other than DIC fixation. CH4 might be released during the breakdown of methylated substrates including methylphosphonate (MPn) and methylamine (Karl et al. 2008; Bižić-Ionescu et al. 2019; Wang et al. 2021). Both lakes were strongly P-limited, indicating that microbes might satisfy their cellular P demand by demethylating organic phosphonate compounds such as MPn (Karl et al. 2008; von Arx et al. 2023). Some members of freshwater Synechococcales encode the phosphonate degradation gene cluster (phn operon) (Zhao et al. 2022), suggesting the potential to use MPn as alternative P source. Alternatively, phytoplankton might also release organic compounds, which could be further broken down by heterotrophic microbes thereby releasing CH4 (Repeta et al. 2016; Wang et al. 2021). The dependence of CH4 production on in situ algal dissolved organic carbon release has been demonstrated previously (Bogard et al. 2014), and a positive correlation between non-photosynthetic cell abundance and dissolved CH4 in the meta- and hypolimnion of lake Mondsee (Supporting Information Fig. S5) could be indicative of such a coupling.

Taken together, our results suggest that despite differences in their trophic state, both lakes Mondsee and Attersee are a source of CH4 to the atmosphere throughout the year. While phytoplankton might play a role in regulating CH4 dynamics in oxic surface water of meso-oligotrophic lake Mondsee, CH4 production does not seem to be directly associated with primary production in these peri-Alpine lakes. Our results emphasize the need for more in-depth studies to resolve oxic CH4 production pathways and sub-seasonal differences in CH4 dynamics to predict CH4 emissions from freshwater lakes, particularly in lakes at higher elevation, where climatic changes occur at faster rates compared to the global average (Thompson et al. 2005; Råman Vinnå et al. 2021).

Supplementary Material

Additional Supporting Information may be found in the online version of this article.

Supporting_Information

Scientific Significance Statement.

Methane oversaturation in oxygenated surface waters (= “methane paradox”) is a commonly observed phenomenon in fresh-water lakes and might contribute substantially to methane emissions due to its proximity to the atmosphere. Understanding the underlying mechanisms involved in the formation of the methane paradox is therefore essential to better predict future climate scenarios. In this study, we investigated seasonal methane dynamics in oxic surface waters of two Austrian peri-Alpine lakes which differ in their trophic state. We show that primary production is not directly linked to the formation of the methane paradox in both lakes. Our results further indicate that irrespective of their trophic state, peri-Alpine lakes might be a significant source of methane throughout the year.

Acknowledgments

We would like to thank Sebastian Mayer for his support with fieldwork and boat operations, Marc Mußmann for support with flow cytometry, Thomas Reinthaler for support with the Picarro instrument, and Kira Lange for assistance with fieldwork and sample processing in June 2025. We also acknowledge the Joint Microbiome Facility for their support with DNA extraction, 16S rRNA gene amplicon preparation, sequence data processing and data submission to SRA. The raw processing of sequencing data has been achieved using the Life Science Compute Cluster (LiSC) of the University of Vienna. This research was funded in part by the Austrian Science Fund (FWF) [10.55776/STA101 and 10.55776/Y1442].

This work was supported by the European Research Council (ERC) Starting Grant METHANIAQ (101116021), to Barbara Bayer. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Open Access funding provided by Universitat Wien/KEMÖ.

Footnotes

Author Contributions

Barbara Bayer designed research; Niharika Sharma, Manuela Felsberger, Zeynep Kurt, and Barbara Bayer performed research; Niharika Sharma and Barbara Bayer analyzed data; Markus Möst provided laboratory equipment and support with experiments; and Niharika Sharma and Barbara Bayer wrote the paper, with contributions from all co-authors.

Conflicts of Interest

None declared.

Data Availability Statement

Data supporting the findings of this study are available in the Zenodo repository (https://doi.org/10.5281/zenodo.19394022). Raw 16S rRNA gene amplicon sequencing data is available at the Sequence Read Archive under the Bioproject Accession PRJNA1422683.

References

  1. Bartosiewicz M, Venetz J, Läubli S, et al. Detritus-Hosted Methanogenesis Sustains the Methane Paradox in an Alpine Lake. Limnology and Oceanography. 2023;68:248–264. doi: 10.1002/lno.12263. [DOI] [Google Scholar]
  2. Bastviken D. In: Encyclopedia of Inland Waters. Likens GE, editor. Academic Press; 2009. Methane; pp. 783–805. [Google Scholar]
  3. Bastviken D, Cole JJ, Pace ML, Van de Bogert MC. Fates of Methane From Different Lake Habitats: Connecting Whole-Lake Budgets and CH4 Emissions. Journal of Geophysical Research—Biogeosciences. 2008;113:G02024. doi: 10.1029/2007JG000608. [DOI] [Google Scholar]
  4. Beaulieu JJ, DelSontro T, Downing JA. Eutrophication Will Increase Methane Emissions From Lakes and Impoundments during the 21st Century. Nature Communications. 2019;10:1375. doi: 10.1038/s41467-019-09100-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Biži’c M, Grossart H-P, Ionescu D. eLS. John Wiley & Sons, Ltd; 2020. Methane Paradox; pp. 1–11. [Google Scholar]
  6. Biži’c-Ionescu M, Ionescu D, Günthel M, Tang KW, Grossart H-P. Biogenesis of Hydrocarbons. Cham: Springer; 2019. Oxic Methane Cycling: New Evidence for Methane Formation in Oxic Lake Water; pp. 379–400. [DOI] [Google Scholar]
  7. Bogard MJ, del Giorgio PA, Boutet L, et al. Oxic Water Column Methanogenesis as a Major Component of Aquatic CH4 Fluxes. Nature Communications. 2014;5:5350. doi: 10.1038/ncomms6350. [DOI] [PubMed] [Google Scholar]
  8. Bryant DA. Phycoerythrocyanin and Phycoerythrin: Properties and Occurrence in Cyanobacteria. Microbiology. 1982;128:835–844. doi: 10.1099/00221287-128-4-835. [DOI] [Google Scholar]
  9. Donis D, Flury S, Stöckli A, Spangenberg JE, Vachon D, McGinnis DF. Full-Scale Evaluation of Methane Production Under Oxic Conditions in a Mesotrophic Lake. Nature Communications. 2017;8:1661. doi: 10.1038/s41467-017-01648-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ganesan AL, Schwietzke S, Poulter B, et al. Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement. Global Biogeochemical Cycles. 2019;33:1475–1512. doi: 10.1029/2018GB006065. [DOI] [Google Scholar]
  11. Grossart H-P, Frindte K, Dziallas C, Eckert W, Tang KW. Microbial Methane Production in Oxygenated Water Column of an Oligotrophic Lake. Proceedings of the National Academy of Sciences. 2011;108:19657–19661. doi: 10.1073/pnas.1110716108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Günthel M, Donis D, Kirillin G, et al. Contribution of Oxic Methane Production to Surface Methane Emission in Lakes and Its Global Importance. Nature Communications. 2019;10:5497. doi: 10.1038/s41467-019-13320-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Günthel M, Klawonn I, Woodhouse J, et al. Photosynthesis-Driven Methane Production in Oxic Lake Water as an Important Contributor to Methane Emission. Limnology and Oceanography. 2020;65:2853–2865. doi: 10.1002/lno.11557. [DOI] [Google Scholar]
  14. Hama T, Miyazaki T, Ogawa Y, et al. Measurement of Photosynthetic Production of a Marine Phytoplankton Population Using a Stable 13C Isotope. Marine Biology. 1983;73:31–36. doi: 10.1007/BF00396282. [DOI] [Google Scholar]
  15. Hu H, Karwautz C, Duszka K, et al. Revised 16S rRNA V4 Hypervariable Region Targeting Primers Enhance Detection of Patescibacteria and Other Lineages Across Diverse Environments. bioRxiv. 2025:2025.11.26.690684. doi: 10.1093/ismeco/ycag141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. IPCC. Climate Change 2021—The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. 1st ed. Cambridge University Press: 2023. [Google Scholar]
  17. Jones J, Brett MT. Global Environmental Change. Dordrecht: Springer; 2014. Lake Nutrients, Eutrophication, and Climate Change; pp. 273–279. [DOI] [Google Scholar]
  18. Karl DM, Beversdorf L, Björkman KM, Church MJ, Martinez A, Delong EF. Aerobic Production of Methane in the Sea. Nature Geoscience. 2008;1:473–478. doi: 10.1038/ngeo234. [DOI] [Google Scholar]
  19. Khatun S, Iwata T, Kojima H, et al. Aerobic Methane Production by Planktonic Microbes in Lakes. Science of the Total Environment. 2019;696:133916. doi: 10.1016/j.scitotenv.2019.133916. [DOI] [Google Scholar]
  20. Klintzsch T, Langer G, Nehrke G, Wieland A, Lenhart K, Keppler F. Methane Production by Three Widespread Marine Phytoplankton Species: Release Rates, Precursor Compounds, and Potential Relevance for the Environment. Biogeosciences. 2019;16:4129–4144. doi: 10.5194/bg-16-4129-2019. [DOI] [Google Scholar]
  21. Krisa H, Hainz R. Ökologischer Zustand der Seen im Land Oberösterreich im Jahr 2023 (Amtliches Seen-Messnetz ASM)—Bewertung anhand des Phytoplanktons. Amt der OÖ Landesregierung Direktion Umwelt und Wasserwirtschaft, Abt Wasserwirtschaft. 2024. 22/014-B02 [Google Scholar]
  22. Lenhart K, Klintzsch T, Langer G, et al. Evidence for Methane Production by the Marine Algae Emiliania huxleyi. Biogeosciences. 2016;13:3163–3174. doi: 10.5194/bg-13-3163-2016. [DOI] [Google Scholar]
  23. Liu L, Zhang X, Schorn S, et al. Strong Subseasonal Variability of Oxic Methane Production Challenges Methane Budgeting in Freshwater Lakes. Environmental Science & Technology. 2024;58:19690–19701. doi: 10.1021/acs.est.4c07413. [DOI] [PubMed] [Google Scholar]
  24. Mao Y, Lin T, Li H, et al. Aerobic Methane Production by Phytoplankton as an Important Methane Source of Aquatic Ecosystems: Reconsidering the Global Methane Budget. Science of the Total Environment. 2024;907:167864. doi: 10.1016/j.scitotenv.2023.167864. [DOI] [PubMed] [Google Scholar]
  25. Marie D, Partensky F, Jacquet S, Vaulot D. Enumeration and Cell Cycle Analysis of Natural Populations of Marine Picoplankton by Flow Cytometry Using the Nucleic Acid Stain SYBR Green I. Applied and Environmental Microbiology. 1997;63:186–193. doi: 10.1128/aem.63.1.186-193.1997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Morana C, Bouillon S, Nolla-Ardevol V, et al. Methane Paradox in Tropical Lakes? Sedimentary Fluxes Rather Than Pelagic Production in Oxic Conditions Sustain Methanotrophy and Emissions to the Atmosphere. Biogeosciences. 2020;17:5209–5221. doi: 10.5194/bg-17-5209-2020. [DOI] [Google Scholar]
  27. Murase J, Sakai Y, Kametani A, Sugimoto A. Dynamics of Methane in Mesotrophic Lake Biwa, Japan. Ecological Research. 2005;20:377–385. doi: 10.1007/s11284-005-0053-x. [DOI] [Google Scholar]
  28. Ordo’ñez C, DelSontro T, Langenegger T, Donis D, Suarez EL, McGinnis DF. Evaluation of the Methane Paradox in Four Adjacent Pre-Alpine Lakes Across a Trophic Gradient. Nature Communications. 2023;14:2165. doi: 10.1038/s41467-023-37861-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Pjevac P, Hausmann B, Schwarz J, et al. An Economical and Flexible Dual Barcoding, Two-Step PCR Approach for Highly Multiplexed Amplicon Sequencing. Frontiers in Microbiology. 2021;12:669776. doi: 10.3389/fmicb.2021.669776. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Råman Vinnå L, Medhaug I, Schmid M, Bouffard D. The Vulnerability of Lakes to Climate Change Along an Altitudinal Gradient. Communications Earth Environment. 2021;2:35. doi: 10.1038/s43247-021-00106-w. [DOI] [Google Scholar]
  31. Repeta DJ, Ferro’n S, Sosa OA, et al. Marine Methane Paradox Explained by Bacterial Degradation of Dissolved Organic Matter. Nature Geoscience. 2016;9:884–887. doi: 10.1038/ngeo2837. [DOI] [Google Scholar]
  32. Rosentreter JA, Borges AV, Deemer BR, et al. Half of Global Methane Emissions Come From Highly Variable Aquatic Ecosystem Sources. Nature Geoscience. 2021;14:225–230. doi: 10.1038/s41561-021-00715-2. [DOI] [Google Scholar]
  33. Schroll M, Liu L, Einzmann T, Keppler F, Grossart H-P. Methane Accumulation and Its Potential Precursor Compounds in the Oxic Surface Water Layer of Two Contrasting Stratified Lakes. Science of the Total Environment. 2023;903:166205. doi: 10.1016/j.scitotenv.2023.166205. [DOI] [PubMed] [Google Scholar]
  34. Schulz M, Faber E, Hollerbach A, Schröder HG, Güde H. The Methane Cycle in the Epilimnion of Lake Constance. Archiv für Hydrobiologie. 2001;151:157–176. doi: 10.1127/archiv-hydrobiol/151/2001/157. [DOI] [Google Scholar]
  35. Sharma N, Bayer B, Felsberger M. Limnological Parameters Related to Seasonal Differences and Potential Biological Drivers of the Methane Paradox in Lake Attersee and Mondsee, Austria Version v3 [Data Set] Zenodo. 2026 doi: 10.5281/zenodo.19394022. [DOI] [Google Scholar]
  36. Tang KW, McGinnis DF, Frindte K, Brüchert V, Grossart H-P. Paradox Reconsidered: Methane Over-saturation in Well-Oxygenated Lake Waters. Limnology and Oceanography. 2014;59:275–284. doi: 10.4319/lo.2014.59.1.0275. [DOI] [Google Scholar]
  37. Tang KW, McGinnis DF, Ionescu D, Grossart H-P. Methane Production in Oxic Lake Waters Potentially Increases Aquatic Methane Flux to Air. Environmental Science & Technology Letters. 2016;3:227–233. doi: 10.1021/acs.estlett.6b00150. [DOI] [Google Scholar]
  38. Thompson R, Kamenik C, Schmidt R. Ultra-sensitive Alpine Lakes and Climate Change. Journal of Limnology. 2005;64:139–152. doi: 10.4081/jlimnol.2005.139. [DOI] [Google Scholar]
  39. Thottathil SD, Reis PCJ, Prairie YT. Magnitude and Drivers of Oxic Methane Production in Small Temperate Lakes. Environmental Science & Technology. 2022;56:11041–11050. doi: 10.1021/acs.est.2c01730. [DOI] [PubMed] [Google Scholar]
  40. Tolotti M, Dubois N, Milan M, Perga M-E, Straile D, Lami A. Large and Deep Perialpine Lakes: A Paleolimnological Perspective for the Advance of Ecosystem Science. Hydrobiologia. 2018;824:291–321. doi: 10.1007/s10750-018-3677-x. [DOI] [Google Scholar]
  41. von Arx JN, Kidane AT, Philippi M, et al. Methylphosphonate-Driven Methane Formation and Its Link to Primary Production in the Oligotrophic North Atlantic. Nature Communications. 2023;14:6529. doi: 10.1038/s41467-023-42304-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wang Q, Alowaifeer A, Kerner P, et al. Aerobic Bacterial Methane Synthesis. Proceedings of the National Academy of Sciences. 2021;118:e2019229118. doi: 10.1073/pnas.2019229118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Wu J, Lee Z, Xie Y, et al. Reconciling Between Optical and Biological Determinants of the Euphotic Zone Depth. Journal of Geophysical Research, Oceans. 2021;126:e2020JC016874. doi: 10.1029/2020JC016874. [DOI] [Google Scholar]
  44. Zhao L, Lin L-Z, Chen M-Y, et al. The Widespread Capability of Methylphosphonate Utilization in Filamentous Cyanobacteria and its Ecological Significance. Water Research. 2022;217:118385. doi: 10.1016/j.watres.2022.118385. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting_Information
EMS214247-supplement-Supporting_Information.pdf (4.4MB, pdf)

Data Availability Statement

Data supporting the findings of this study are available in the Zenodo repository (https://doi.org/10.5281/zenodo.19394022). Raw 16S rRNA gene amplicon sequencing data is available at the Sequence Read Archive under the Bioproject Accession PRJNA1422683.

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