Elevated atmospheric CO_2 decreases methylmercury production in freshwater lakes

Domain: earth_science
Published: 2025-12-26
Editorial outcome: major_revision

Abstract
Elevated atmospheric carbon dioxide (CO2) levels reshape microbial communities in nature, yet its consequences for neurotoxic methylmercury (MeHg) production in waters remain unclear. Here, we show that elevated CO2 levels (650 and 1000 ppm) consistently reduced net MeHg production across 45 freshwater lakes spanning 1200 longitudinal kilometers, particularly in eutrophic conditions (54–96%). Elevated CO2-driven shifts in carbon substrates favored hydrogenotrophic methanogens (e.g., Methanobacterium) lacking the hgcA methylation gene over hgcA-harboring acetoclastic strains (e.g., Methanosarcina), decreasing methanogen abundance (18–98% in hgcA copies) and activity (13–53% in net CH4 production) and suppressing Hg methylation. Model simulations predict a 33%–74% global decline in lake MeHg production under future CO2 scenarios, partially counteract MeHg increases associated with intensified algal blooms under warming. This overlooked pathway highlights the need to integrate interacting climate drivers to improve predictions of MeHg risks in a climate-changing future.

Introduction
The biogeochemical cycling of the global pollutant mercury (Hg) is closely linked with methanogenesis1–3, a primary process for organic matter decomposition that produces the potent greenhouse gas (GHG) of methane (CH4)4. This connection is mainly due to methanogens, which are pivotal in methanogenesis and meanwhile act as essential microbes for Hg methylation5, converting inorganic mercury (IHg) into neurotoxic methylmercury (MeHg). Consequently, changes in methanogens and methanogenesis significantly impact the MeHg production in natural waters and its subsequent accumulation in aquatic food chains, where MeHg can biomagnify up to ten million times6 and lead to a demonstrable decrease in newborn intelligence quotient worldwide7. Particularly in eutrophic lakes, the decomposition of algal organic matter (AOM) increased the availability of labile carbon and CH4 production by 16–44%, which was accompanied by a sharp spike in MeHg levels (1–2 orders of magnitude), primarily driven by methanogens8. Understanding the shifts in methanogen-mediated MeHg production in lakes is thus essential for predicting future risks of this potent neurotoxin in aquatic systems and its adverse impacts on wildlife and human health.

The composition and functions of methanogenic communities are impacted by the ambient levels of carbon dioxide (CO2), a primary GHG with increasing atmospheric concentrations9,10. Crucially, this influence potentially extends to microbial MeHg production in water, which is a major source of MeHg in human food chains11. However, the cascading effects of elevated CO2 on methanogenic communities in natural waters and hence on microbial MeHg production remain unclear. Evidence shows that elevated CO2 levels increase the abundance of methanogens, with a 31–156% rise in rice paddies and 63–98% in marshes, leading to a 28–120% increase in CH4 emissions12,13. Nevertheless, these changes may not directly apply to other natural waters, including freshwater lakes, due to differences in methanogen communities and their metabolic types across aquatic systems4, with distinct capacities for Hg methylation14. For example, the acetoclastic methanogens, which use acetate as an energy substrate, predominate in agricultural wetlands15, whereas hydrogenotrophic methanogens use H2/CO2, are more prevalent in freshwater lakes16, yet their Hg methylation capacities differ17. Understanding how microbial MeHg production responds to elevated CO2 is further complicated by the presence of a wide array of microbial groups, apart from methanogens, that co-contribute to MeHg formation, including sulfate-reducing bacteria (SRB) and iron-reducing bacteria (FeRB)18. In consequence of this complexity, existing knowledge of changes in methanogens under elevated CO2 levels may not reliably predict concomitant changes in microbial MeHg production and its bioaccumulation in human food chains amid climate change.

Here, we aim to elucidate the effects of elevated CO2 on Hg methylation in freshwaters, which are essential to aquaculture production, currently accounting for 77% of edible aquaculture production globally19. Particularly in Asia, human MeHg exposure was dominated by freshwater aquatic food intake, in addition to seafood20. Meanwhile, freshwater lakes are well-recognized hotspots for microbial MeHg production, a process significantly enhanced by eutrophication, which generates AOM upon algal decomposition21 and thus provides carbon sources that boost the growth of Hg-methylating methanogens8. We thus quantified the net changes of MeHg in response to elevated atmospheric CO2 levels (650 ppm and 1000 ppm) across 45 freshwater lakes spanning 1200 longitudinal kilometers along the Yangtze River in China. Results indicate that rising CO2 levels consistently lead to a decline in both the abundance of Hg-methylating methanogens and net MeHg production. Global simulations, facilitated by our newly compiled dataset, reveal a crucial pathway through which the interplay of climatic factors helps reduce uncertainties in predicting MeHg risks in natural waters amid climate change. These discoveries deepen our understanding of global changes in distribution and risks of this potent neurotoxin under future scenarios.

Results
Reduced MeHg production in freshwater lakes under elevated CO2
We demonstrate that elevated CO2 concentrations inhibit MeHg production in freshwater lakes. This is achieved by quantifying net MeHg production in 45 freshwater lakes under various CO2 and AOM levels (see Supplementary Table 1 for lake characteristics). As algal proliferation increases in response to nutrient enrichment, their biomass commonly serves as an indicator of nutrient abundance and eutrophication22,23. Therefore, microcosm experiments involving the addition of algal biomass to simulate eutrophic conditions provide a practical approach to studying MeHg production in eutrophic waters. Under ambient CO2 levels, AOM markedly enhanced MeHg production, yielding 1–3 orders of magnitude higher levels across 45 lakes (Supplementary Fig. 1), likely driven by stimulated methanogenesis-mediated MeHg production8,24. Under the projected CO2 level of 1000 ppm in the year 2100, corresponding to the high GHG emissions scenario (SSP5-8.5) outlined in the Sixth Assessment Report from Intergovernmental Panel on Climate Change25, however, we observed a substantial and prevalent decrease in net MeHg production across the surveyed freshwater lakes. The reduction ranged from 14 to 96%, with an average decrease of 63% compared to ambient 420 ppm CO2 conditions (p < 0.05, Fig. 1 and Supplementary Fig. 1). Similar trends were also evident for other projected CO2 levels in 2100 (i.e., 650 ppm, corresponding to the SSP2-4.5 scenario with intermediate GHG emissions), where net MeHg production showed reductions ranging from 45 to 61% (Fig. 2a and Supplementary Fig. 2a).

Particularly, decline of MeHg production under elevated CO2 levels was most pronounced under eutrophic conditions, simulated as high algal biomass additions. The most obvious decrease in net MeHg production (by 54–96%) was observed in microcosms incubated at 1000 ppm CO2 and with added algal biomass (Fig. 1b). Furthermore, the CO2-induced effect remained consistent across different amounts of AOM (i.e., “+200 Algae” to “+1000 Algae”), resulting in a reduction of MeHg from 26 to 93% (p < 0.05, Fig. 2b and Supplementary Fig. 2b). During the long-term (60-day) of algal decomposition in lake water with added algal biomass, the inhibition ranged from 60 to 93% with averaging 80% (Fig. 2c and Supplementary Fig. 2c).

We further extended the environmental relevance of elevated CO2-impacted MeHg dynamics (see Supplementary Text 1). The CO2-driven suppression of microbial Hg methylation is independent of background Hg levels, as shown by spiking experiments across a broad inorganic Hg (IHg) gradient (2–500 ng L−1). Elevated CO2 consistently reduced net MeHg production relative to ambient CO2 levels, with 21–33% inhibition in unamended lake water and 66–83% in algal biomass-added treatments (Supplementary Fig. 3). We also established DOM gradients reflecting different autochthonous: allochthonous ratios (100:0 to 0:100; AOM vs. Soil organic matter, SOM). Results confirmed that AOM was a stronger driver of methylation than SOM, consistent with previous studies8,26, and that elevated CO2 significantly inhibited net MeHg production only when AOM comprised ≥50% of the DOM pool (p < 0.001; Supplementary Fig. 4), underscoring the need to clarify how DOM composition modulates CO2-driven MeHg inhibition. Moreover, under combined climate forcing (elevated CO2 and/or warming, ΔT = 4.4 °C, SSP5-8.5 by 2100), MeHg production inhibition under “Elevated CO2” and “Warming × Elevated CO2” treatments (26–76% reduction) did not differ significantly (p > 0.05; Supplementary Fig. 5), indicating that the inhibitory effect of elevated CO2 outweighs the stimulatory impact of warming, thus supporting the robustness of our model in projecting global MeHg trends. Importantly, such inhibition was also observed in other freshwater systems (e.g., 79% decrease in pond water; 75% in river water, Supplementary Fig. 6), suggesting that elevated CO2 may mitigate MeHg risks across global inland waters. In summary, elevated CO2 exerts a robust suppression of microbial MeHg production that is independent of background Hg levels, dependent on DOM sources, overrides warming effects, and extends broadly across freshwater systems.

Our results expand understanding of how elevated CO2 reshapes Hg biogeochemistry and the associated risks of MeHg production, going beyond previous considerations of the impacts of CO2 elevation on Hg toxicity27, Hg bioaccumulation28, and global Hg budgets29. To date, there has been relatively little focus on understanding the effect of elevated CO2 on microbial MeHg production30,31, the primary source of MeHg in human food chains. This knowledge gap is particularly critical in freshwater lakes, known as hotspots for MeHg formation8,21 and sensitive to climate changes32. While recent studies indicate that AOM derived from algal decomposition in eutrophic lakes boosts microbial MeHg formation and inhibits its photodegradation8,33, our study shows that such increases of MeHg levels in eutrophic waters would potentially be counteracted by future elevation in atmospheric CO2 levels and thus inhibited Hg methylation. Implications of these processes are further discussed in the later sections.

The underlying mechanism by which elevated CO2 levels inhibit Hg methylation in eutrophic waters
Given the more pronounced decreases in net MeHg production in eutrophic waters under elevated CO2, we then focus on elucidating the mechanisms of such decreases under eutrophic conditions. Theoretically, the decrease in net MeHg production could be caused by decreased Hg methylation and/or increased MeHg demethylation18. Using Hg stable isotope tracing technique, we found that Hg methylation rates (km) were 83–89% lower under elevated CO2 than under ambient conditions in both natural or eutrophic waters (p < 0.001), whereas MeHg demethylation rates (kd) did not differ significantly between treatments (Supplementary Fig. 7). These complementary lines of evidence demonstrate that the observed decrease in net MeHg production lake water under elevated CO2 was primarily attributed to inhibited Hg methylation, rather than the increases in MeHg demethylation. Consequently, we focused on how the dominant methylating microbes respond to elevated CO2.

In eutrophic lakes, methanogens have been recognized as the key microbes for Hg methylation due to their higher relative abundances and increased activities facilitated by low-molecular weight AOM derived from algal decomposition8. Their enhanced activity significantly contributes to Hg methylation34. Therefore, changes in methanogens under elevated CO2 offer a compelling explanation for the corresponding shifts in MeHg production in eutrophic waters. Our investigations demonstrate a substantial impact of elevated CO2 on methanogenic activity, evidenced by the net daily CH4 production rates. In eutrophic waters, these rates were considerably suppressed, averaging a 30% decrease with a range between 13 and 53% in comparison to the control under ambient CO2 (Fig. 3a). Furthermore, the qPCR assays demonstrate that elevated CO2 results in a decrease in copies of hgcA—as the Hg methylation gene—of Archaea by 18–98% (Fig. 3b), which are strongly correlated with the CO2-inhibited MeHg production (R2 = 0.866, p < 0.001, Supplementary Fig. 8). These various pieces of evidence imply that methanogenetic activity and abundance are lowered under elevated CO2, which provides an explanation for the inhibited microbial Hg methylation in eutrophic waters, where methanogens play a dominant role in MeHg production8.

We further demonstrate that the observed MeHg decrease in eutrophic waters under elevated CO2 could be explained by the shifting abundance of different metabolic types of methanogens, with an increase in weak or non-Hg methylating types and a decrease in strong Hg methylating counterparts. Methanobacterium spp., the most dominant group of methanogens in this study, is generally known as hydrogenotrophic35,36, while Methanosarcina is primarily known as acetoclastic methanogen37. High-throughput sequencing and bioinformatic analysis of mcrA genes revealed that elevated CO2 leads to a notable shift in the methanogenic community: a decrease in the relative abundance of aceticlastic methanogens (e.g., Methanosarcina reduced from 29% in ambient CO2 to 10% under elevated CO2 on Day 24) and an increase in hydrogenotrophic methanogens (e.g., Methanobacterium increased from 22% in ambient CO2 to 35% under elevated CO2 on Day 24, Fig. 3c). Additional BLAST searches of HgcAB orthologs, indicates that among the 51 HgcAB orthologs identified in Euryarchaeota, 88% of potential Hg-methylating methanogens are associated with Methanomicrobia38. Within this taxon, primarily as the acetoclastic methanogen, Methanosarcina was observed in our samples. In contrast, hydrogenotrophic Methanobacterium spp., are much less likely to carry hgcA genes necessary for Hg methylation38,39. Consequently, we propose that the concomitant increase in hydrogenotrophic non-Hg methylating methanogens and the decrease in aceticlastic Hg methylating methanogens contributed to the reduced net MeHg production under elevated CO2. This hypothesis requires further confirmation through a more quantitative comparisons of Hg methylation abilities across different methanogenetic taxa. Additionally, the interaction between methanogens and syntrophs, which has not been examined in this study, might play a role in modulating Hg methylation activities40.

Such a transition from aceticlastic to hydrogenotrophic methanogenesis is ascribed to the changes in the predominant carbon sources under elevated CO2 levels. In eutrophic lakes, the decomposition of planktonic biomass produces low-molecular-weight organic matter, e.g., aromatic proteins, stimulating aceticlastic methanogens8. These methanogens are possibly highly effective at utilizing bioavailable carbon and thrive across a broad range of environmental niches, including eutrophic lakes41,42, which make them key contributors in microbial MeHg production under eutrophic conditions8. However, elevated CO2 levels, known as the preferred carbon source of hydrogenotrophic methanogens43, might facilitate the growth of this metabolic type of methanogens, leading to a compositional shift from acetoclastic methanogens to hydrogenotrophic methanogens in eutrophic waters, a shift likely attributable to CO2-stimulated fermentative H2 production that relieves H2 limitation and promotes hydrogenotrophic dominance (see detailed discussion in Supplementary Text 2). Similarly, elevated CO2 increased the relative abundance of hydrogenotrophic methanogens (e.g., Methanobacterium and Methanocella) while significantly reducing the relative abundance of acetoclastic methanogens (e.g., Methanosaeta) in rice paddies12. An increase in the relative abundance of hydrogenotrophic methanogens was also reported in temperate marsh microcosms when exposed to elevated CO2 levels43.

In particular, the inhibited MeHg production under elevated CO2 is unlikely due to other major Hg methylating microbes, such as SRB or FeRB, or possible changes in Hg bioavailability under elevated CO2 due to acidification. Further discussion is presented in Supplementary Text 3.

Methanogens are widely distributed in natural environments where MeHg production occurs, including wetland soils, peatlands, freshwater, or marine sediments44–46. The key role of methanogens in Hg methylation, as well as their sensitivity to climatic factors, including elevated CO2, global warming, and water acidification10,47,48, highlight the urgency to elucidate and consider methanogen-mediated MeHg production in future climate change scenarios. Our results provide the initial evidence that the effects of climate change on methanogens would in turn affect microbial MeHg production, particularly in eutrophic waters subject to algal blooms, which are increasing under global warming49,50. Global implications of these findings are estimated and discussed in the following section.

Estimating how elevated CO2 reduces MeHg levels in global lakes
To estimate the potential impact of the projected CO2 elevation in 2100 (i.e., 1000 ppm25) on MeHg levels in global lakes, we coupled a relationship between elevated CO2 and inhibited net MeHg production in lakes with different ambient DOC contents, as established in this study (see Methods for details), and existing global datasets on (1) predicated51,52 or (2) observational DOC and MeHg levels (summarized in Supplementary Data 1) in global lakes. Our simulations, based on global distribution of predicted DOC and MeHg, suggest that elevated CO2 could lead to decreases in MeHg levels by 33–74% (average 47%) in lakes worldwide (Fig. 4). More specifically, in lakes characterized by high ambient DOC levels (averagely 18.7 mg L−1) and high MeHg contents (averagely 0.61 ng L−1), accounting for approximately 2.5% of the lakes examined, MeHg levels are projected to drop by 60–74% as CO2 levels rise. Approximately one-third of the global lakes are anticipated to experience a reduction in MeHg levels between 30 and 45%, another third between 45 and 50%, whilst the remaining third will witness a decrease exceeding 50%. Similarly, inputting observed ambient DOC and MeHg concentrations—based on literature data (Supplementary Data 1)—into the same model predicts that elevated CO2 could lead to a decrease in MeHg concentrations in lakes across the globe, ranging from 34 to 79%, with an average of 47%. Uncertainty analysis by Monte Carlo simulation shows that elevated CO2 inhibits MeHg production in lakes worldwide, with an uncertainty range of 36–68% (Supplementary Text 4).

While the global reduction in MeHg levels under elevated CO2 is estimated based on our modeling and found to be evident in lakes, it is important to note that there are numerous other factors that may influence the Hg methylation process in real-world scenarios, adding uncertainty to our estimates. More field studies quantifying the responses of MeHg production to changes in multiple climatic factors, including but not limited to CO2 levels, are needed to further improve the reliability, and reduce the uncertainty of predicting MeHg levels in lakes in the context of climate change.

Environmental impacts: the interplay of climatic factors helps reduce uncertainties in MeHg risk prediction
Excessive nutrient inputs to lakes have led to a globally accelerated eutrophication, which in turn affects the magnitude, frequency, and duration of algal blooms53. Recent laboratory tests and field observations indicate that AOM in eutrophic lake waters promotes MeHg formation while reducing its photodegradation8,33. This contributes to MeHg accumulation in eutrophic lakes and increases the associated risks to wildlife and humans. Nevertheless, our findings demonstrate a counterbalancing effect from future climate change conditions: elevated atmospheric CO2 mitigates methanogen-mediated Hg methylation in eutrophic waters, potentially offsetting the risks of global warming-amplified algal blooms54 and hence enhanced Hg methylation8.

Currently, predictions concerning Hg biogeochemistry and associated risks in the context of climate change commonly concentrate on the influence of individual climatic factors, such as rising CO255 or global warming56. However, the interactive effects of multiple drivers are rarely assessed, partly because of the limited understanding of how the Hg cycle reacts to multiple simultaneous controlling processes. Our research highlights the potential for biased predictions concerning future Hg risks when considering a single climatic factor. This is because various climatic factors can exert conflicting influences on Hg biogeochemical cycles, as exemplified by the counteracting effects of CO2 elevation and global warming—and hence algal blooms—on microbial MeHg production. Such a previously overlooked interplay of climatic factors can play a crucial role in stabilizing MeHg levels in natural settings amidst climate change. These combined effects should be incorporated into regional or global models of Hg biogeochemical cycles. Failing to acknowledge this unrecognized self-stabilization mechanism could lead to an overestimation of the impacts of climate change on MeHg risks (further detailed in Supplementary Text 5).

Interestingly, our experimental results indicate that the decrease in MeHg production under elevated CO2 levels may extend from lakes to other freshwater systems, e.g., 79% decrease for pond water and 75% decrease for river water (see Supplementary Fig. 6). This implies that elevated CO2 in the future could potentially mitigate MeHg formation on a broader scale, possibly affecting global inland freshwater systems that supply food to local populations and provide other critical ecosystem services57. Consequently, it is essential to address the crucial knowledge gaps related to the distinct and combined impacts of diverse climate facets on the production, accumulation, and transfer of MeHg in aquatic ecosystems. These insights serve as the foundation for developing more nuanced models of the Hg biogeochemical cycle, aiding in the prediction of the regional and global risk of MeHg to wildlife and humans.

More importantly, incorporating the combined effects of multiple climatic factors on the Hg cycle is key to reducing uncertainty in evaluating the effectiveness of our ongoing global Hg mitigation efforts. Currently, evaluating the effectiveness of Hg emission reductions under the Minamata Convention on Mercury is hindered by a significant disparity: while Hg emissions have been reduced, human exposure to MeHg has decreased at a much slower rate7. Per unit reduction in Hg emissions, human exposure has been estimated to decline by only one-third to one-half, reflecting the influence of complex processes such as legacy Hg in environmental reservoirs and bioaccumulation lags in food chains. This observed “mismatch”—where exposure decreases less than expected from emission reductions—likely arises from such multi-faceted drivers, including remobilization of legacy Hg by natural and anthropogenic factors58.

Our study identifies a distinct climatic pathway that further complicates this emission–exposure relationship: elevated CO2 suppresses microbial MeHg production in lakes, leading to reduced aquatic MeHg levels regardless of emission changes. This pathway would amplify the decline in human exposure beyond direct emission reductions, and should be accounted for when evaluating the effectiveness of global Hg mitigation efforts and interpreting temporal trends in biotic MeHg concentrations. Thus, our findings do not resolve the current mismatch, but rather reveal a previously unrecognized climate–microbe–MeHg interaction that may shape future exposure risks. Specifically, future increases in atmospheric CO2 may counteract warming-driven enhancements of algal blooms, thereby reducing uncertainties in predicting aquatic MeHg risks under climate change. Incorporating this climate–microbe–Hg nexus into regional and global models of the biogeochemical cycling of Hg is crucial for reducing the uncertainty in the prediction of environmental MeHg concentrations under climate change scenarios. Such an improvement will substantially improve our ability to assess the effectiveness of the Minamata Convention on Mercury in mitigating MeHg exposure under the complex influence of multiple climatic factors.

Methods
Field sample collection
To examine the impacts of elevated CO2 on MeHg production in lakes, we selected 45 freshwater lakes (No. 1–45; 48 sampling sites, Supplementary Fig. 9 and Supplementary Table 1) spanning 1200 longitudinal kilometers in China. We also conducted laboratory microcosm experiments to characterize the kinetics of MeHg levels and the shifts in Hg methylating microbes under both ambient and elevated CO2 conditions. These water samples were collected exclusively from the lakes along the middle and lower reaches of the Yangtze River, which constitute 28.6% of China’s population and contribute 20.9% to the national Gross Domestic Product in 201859. Many of these lakes experience anthropogenic eutrophication60. Lake water samples were gathered during the period from August to September in 2022.

At each sampling site, approximately 500 mL surface water samples were collected in triplicate at 0.5 m below the lake surface. Samples for Hg and nutrient analyses were preserved with 0.1% HCl, and the fresh unfiltered water was used for all microcosm experiments. All water samples were stored in a dark, cold room (4 °C) until processing/analysis. Algal biomass samples were collected during the algal bloom period in August of 2022 from Chaohu Lake (31°42′48.94″N, 117°21′48.37″E), the fifth largest freshwater lake in China8. Briefly, at the lake, algal biomass was collected using a plankton net and stored at 4 °C until processing. Before experiments, algal biomass was washed, freeze-dried, and mortar-homogenized, and then stored at −20 °C prior to use. Samples for total mercury (THg), MeHg, and major nutrients were analyzed following established protocols (see Supplementary Fig. 10 for results).

Experimental microcosm setup
A series of microcosm experiments were designed to elucidate the effects and mechanisms of elevated CO2 on microbial MeHg production in lake water. Specially, Experiments A to C aimed to reveal the general phenomenon of elevated CO2 on microbial MeHg production under different conditions, such as different eutrophication scenarios or elevated CO2 levels, by using diverse lake waters. Moreover, Experiment D was designed to elucidate the mechanisms of elevated CO2-impacted MeHg production, and Experiment E was designed to explore whether elevated CO2 affects the MeHg demethylation process. At the same time, MeHg levels were also determined for all processed incubation vials. The abundance of the hgcA genes in the mixture was quantified using the clade-specific degenerate primer pairs ORNL-Delta-HgcA and ORNL-Archaea-HgcA for Deltaproteobacterial methylators (e.g., SRB or FeRB) and Archaeal methylators (e.g., methanogens)61, respectively (Supplementary Text 6). The quantification method for the relative abundances of acetoclastic to hydrogenotrophic methanogens was adopted from previous work (see Supplementary Text 7). Details of all treatments are summarized in Supplementary Table 2 and Text 8.

Determination of MeHg levels in water
The levels of dissolved MeHg were determined following the GEOTRACES protocol handbook62. The process involves adding 2 mL of the filtered sample to a 40 mL brown-glass bottle and adjusting the pH to 4 using a 2 M citrate buffer. A 2 mL sample volume was selected because, after the 7-day incubation, MeHg concentrations exceeded 0.2 ng L−1 in all treatments. Within the calibration curve range of 0.5–50 pg, this volume ensured accurate quantification. Subsequently, 50 μL of NaBEt4 is added to the mixture. The solutions are then diluted with deionized water and measured using an automatic MeHg analyzer (CVAFS, Brooks Rand Model III, Brooks Rand Laboratories, USA) following Method 1630. Concentrations of MeHg were obtained using external calibration curves containing no less than 5 points (r2 > 0.999). The detection limit for MeHg determinations was calculated as three times the standard deviation, the derived value being 0.01 ng L−1. During the analyses of MeHg samples, quality assurance and quality control (QA/QC) measures were implemented, including the use of blanks, blank spikes, sample duplicates, and matrix spikes. Levels of MeHg were measured following daily calibration with a MeHg stock solution (MeHgCl source, 1 μg mL−1 in 0.5% HOAc, 0.2% HCl, 30 mL, Brooks Rand Laboratories, USA). Blank samples of ultrapure water and lake water from CHL without or with algal biomass addition were spiked with MeHg at an initial concentration of 10 ng L−1 (n = 5 for all treatments). The recovery percentages of MeHg standard analyses were 93% ± 7.3% and 96% ± 9.8% for the blank (ultrapure water) and lake water samples, respectively. For lake water added with AOM, the MeHg standards showed a recovery of 99% ± 10.9%. The recoveries of all spikes (ranging from 79 to 122%) fell within the acceptable range as outlined in the EPA Method 1630 (i.e., between 65 and 135% for MeHg, Supplementary Fig. 11).

Estimation of global MeHg reduction in lake water under elevated CO2
To predict MeHg changes in response to elevated CO2-induced MeHg production in water, we developed a global model for MeHg in lake water. This model relies on the observed drop in the MeHg production under elevated CO2, as quantified in this study. Firstly, using data from Experiment A, we established that the relative reduction in MeHg production under elevated CO2 in 45 lakes increases linearly with the rise of ambient DOC and MeHg contents (see Supplementary Table 3). Given that DOM in lake water has multiple sources (autochthonous and allochthonous), and that DOM from different sources exerts variable inhibitory effects on Hg methylation under elevated CO2 (Supplementary Fig. 4), we incorporated this effect into our model predictions: for eutrophic waters, a mixture of autochthonous and allochthonous organic matter (AOM:SOM = 80%:20%) was adopted in the model. Indeed, the decline in MeHg production rate (%) under elevated CO2 can be calculated as: R (Inhibited rate of MeHg (%) by elevated CO2) = (0.015 ± 0.003) × [DOC] + (0.01 ± 0.001) × [MeHg] + (0.333 ± 0.027), r2 adjust = 0.701, p < 0.001, [DOC] < 30 mg L−1, where ± SE is the standard error of the fit and is considered one of the uncertainties in the model and r2 adjust is the correlation coefficient of the relationship. The above relationships are statistically significant (p < 0.001). The DOC in “Lakewater + Algae” treatments can be estimated as: [DOC]Lake water+algal biomass = (1.389 ± 0.075) × [DOC]lake water + (6.4 ± 0.638) (r2 adjust = 0.991, p = 0.003, see Supplementary Fig. 12a).

Subsequently, we estimated global MeHg reduction under elevated CO2 using two independent databases: (1) global data on the predicated DOC51 and MeHg levels52 or observational ambient DOC and MeHg levels in global lakes. For the first one, predicted DOC concentrations in global lakes with a surface area larger than 0.1 km2 were used (1,427,496 lakes in total, ranging from 0.0002 to 27.0 mg L−1, with an average of 9.24 mg L−1)51. Using the relationship between DOC and MeHg from Lavoie et al.52, we calculated the MeHg concentrations of 1,427,496 lakes globally: [MeHg]Asia = 0.004 × [DOC] − 0.022; [MeHg]Europe = 0.015 × [DOC] + 0.156; [MeHg]North America = 0.045 × [DOC] − 0.158. The global average slope and intercept were used to represent the relationship between MeHg and DOC concentrations in lakes located in other continents (including South America, Oceania, and Africa), i.e., [MeHg]others = 0.029 × [DOC] − 0.019. The average concentration of calculated MeHg was 0.27 ng L−1. This dataset covers sites spanning six continents and a broad latitudinal range. Eventually, MeHg concentrations in global lake water bodies were predicted at the anticipated CO2 concentration of 1000 ppm in 2100, utilizing the formula and data.

For the second database, we systematically reviewed literature spanning the period from 2000 to 2023 that documented MeHg and DOC levels in lakes (Web of ScienceTM search on June 6, 2023). From the search results of 201 scientific papers, we obtained 49 articles containing 229 samples for analyses (see Supplementary Data 1). These data were selected according to specific criteria: (i) inclusion of only field sampling data; (ii) acquisition of both unfiltered and filtered water samples; (iii) utilization of data from the most recent year collected at the same sampling point, and (iv) restriction of DOC concentrations to no more than 30 mg L−1. More specifically, this dataset comprises 74 water samples that exclusively display unfiltered total MeHg values, 88 water samples exclusively exhibiting filtered MeHg values, and 67 water samples providing both unfiltered and filtered MeHg values. Notably, linear regression analyses revealed a significant relationship between filtered and unfiltered total MeHg levels present in water: [MeHg]filtered = (0.708 ± 0.015) × [MeHg]unfiltered − (0.001 ± 0.004), r2 adjust = 0.972, p < 0.001 (see Supplementary Fig. 12b). The fitting errors mentioned above were considered as the uncertainty of the model. Accordingly, filtered MeHg concentrations in the 74 water samples lacking such data were calculated using the formula above. The average concentrations of DOC and MeHg in 229 lake water samples were 9.51 mg L−1 and 0.14 ng L−1, respectively. This dataset covering lakes spanning five continents was used to predict MeHg concentrations under the future CO2 concentration (1000 ppm).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Lei et al. present a detailed study on the relationship between atmospheric CO2 and methylmercury production in freshwater\nlakes. It is a novel and interesting work that shows a possible pathway of reduced methylmercury under global warming,\nespecially related to the decrease in hgcA genes. However, we have some concerns about its methods and arguments\nregarding their key findings on inhibited MeHg production and projected reduced MeHg levels in global lakes.\nMajor concerns:\nThe implications of the experimental design with no-light and constant temperature conditions need to be considered in the\nmodel extrapolation the results to lakes globally. “All vials were incubated in the dark at 25C to mimic the prevalent\ndegradation conditions in lakes and preclude MeHg photodegradation” (supplementary information L326-328). To project\nMeHg dynamics in global lakes, the influence of light, but especially temperature changes need to be mentioned. A\nsignificant rise in CO₂ concentration will drive a marked increase in temperature. How do you exclude the potential\nsignificant temperature influence on methylation and demethylation activities in global lakes when estimating the reduction\nin MeHg levels caused by elevated CO2? High temperature can likely stimulate microbial activities, including Hg\nmethylation. The paper could still be published with the existing data and model, but the implications of not considering light\nand temperature need mentioned.\nFurthermore, the global model on MeHg in lake water only uses DOC and MeHg as the parameters. The experiment,\nhowever, focuses on Algal Organic Matter (AOM), but DOC is used by the authors use to model the global trend of MeHg\nlevels. The fact that AOM is only a fraction of DOC, and DOC is more complex regarding methylation and demethylation of\nHg also needs to be acknowledged as a limitation of the model. .\nFinally, the concluding lines of the paper state that its findings “offer a potential explanation for this mismatch“. I think that\nstatement is incorrect. The mismatch, as the authors present it, is that the reductions in emissions have only resulted in a\ndecrease in human exposure that is proportionally smaller that then emission reductions. The findings of the present paper\nsuggest that even without any change in emissions, the MeHg in water would be going down as CO2 levels rise, thus\nreducing human exposure even if Hg emissions were not reduced. This would tend to make human exposure decline faster\nthe Hg reductions. The authors should reconsider what they claim for this paper in regards to explaining the current\nmismatch between Hg emission reductions and human exposure.\nMinor comments:\n1. The abstract section on hydrogenotrophic and acetoclastic methanogens does not talk about differences in the hgcA\ngenes between the two types of methanogens. It would be clearer if it did.\n2. The North arrow needs to be indicated on the map Figure 1A.\n3. Only data from Chaohu and Yangshan Lakes are presented in Figure 2. In supplementary Figure 2, Xuanwu Lake data is\nadded to demonstrate the negligible impact of elevated CO2 levels on demethylation. How about other lake data? Are they\navailable in the supplementary information?\n4. In Figure 2, only %MeHg data is shown. How about the MeHg concentrations?", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "I am very impressed by this extensive research study and the thorough effort to address potentially confounding factors in the\nsupplemental material (demethylation, FeRB/SRB, etc). There was a lot of great research performed and a lot of novel\nfindings. However, I believe that the context of the publication needs to be reconsidered – this is water column\nmethylmercury production and not sediment MeHg production or a total system MeHg production (which would need a\nsediment-water incubation). By overstating the implications, you detract from the value of the truly novel findings – in my\nopinion that is the connection between methylation and CO2 in the water column and the methanogen types\n(acetoclastic vs hyrogenic).\nMy recommendation is to reduce the claims of how this research shows worldwide reductions in lake methylation by 30 –\n90% to something more grounded and reasonable: it is applicable to Hg-impacted, hypereutrophic lake water column\nmethylation. These conditions are not very common, but understanding the mechanisms that you have elucidated is very\nhelpful for the scientific community and will lead to follow-up research that will cite your paper and look more closely at\nscenarios that mimic real-world conditions. You can comment on how it might be applicable to non-Hg impacted and\nhypereutrophic lake water column methylation and if it is applicable to sediment methylation, but don’t assume that it\nautomatically is and be grounded in the theories of why it may be. You can also comment on the analytical challenges of\nrunning this type of experiment without adding Hg or AOM and how it could be accomplished in the future.\nSediment vs Water Column Methylation\nSignificantly more (> 10x) MeHg is produced in the sediment where mercury, microbial activity, and anoxia are all\nsignificantly higher. So even if water column methylation decreases by 80%, this does not necessarily mean a significant\ndecrease in the total water column MeHg concentration. You briefly mentioned this in the supplemental material, but\ngenerally avoided the topic. I think it is a very significant stretch to say that increasing CO2 decreases MeHg production in\nlakes as a whole. It is more powerful to be grounded in the implications of your data.\nHowever, the compelling results are meaningful for water column methylation, which has been of increasing interest within\nthe scientific community and understudied compared to sediment MeHg production. If masses are equal, water column\nmethylmercury production has a much greater impact on human health due to its closer proximity to the pelagic food web.\nYour research is extremely helpful from this standpoint.\nGrounding the results of the Mesocosm Experiment\nIn the main paper, it appears as if this is a natural mesocosm test and you have to read the supplemental material to find that\nyou added 200 ng/L as HgCl2. This is not a natural system – most eutrophic lakes have < 2 ng/L and > 20 ng/L is extremely\nrare. 200 ng/L in the water column only occurs in the most impacted lakes (this is 0.2 ug/L!). Your citation lists sediment and\nanerobic digestion studies for the range of (20 – 1000 ng/L). The sediment study is a contaminated site and not natural.\nNatural lake water generally has ~0.1 – 2 ng/L. If you were trying to extrapolate on natural conditions then you would have\njust used normal lake water, which already has some Hg in it and adding 1 ng/L might be reasonable.\nHowever, I do understand the analytical difficulties of measuring MeHg when it is below 0.5 ng/L (which would be the\noutcome when water column Hg is generally < 1 ng/L) and I understand that it is just not feasible.\nSo it’s not a deal breaker and it is still informative, but it’s important not to overstate the implications of this if you didn’t test\nthe methylation changes without adding Hg. So it is important to be upfront that the mesocosms where run in absolute worst\ncase scenario conditions and be very blunt about stating that these results are applicable to lakes that have water column\nmercury concentrations > 100 ng/L – this is almost exclusively contaminated sites and not natural sites.\nNeed to demonstrate that you did not enhance anaerobic conditions by adding CO2\nIn the experimental design, it appears as if you may have introduced more oxygen into the controls than the treatments by\naerating the incubator with air - this keeps the incubator atmosphere at 20% oxygen saturation. For your treatments, only\nadding pure CO2 would purge the oxygen and not replenish it, allowing it to get lower in the incubator and ultimately lower\nin the tubes. Please make the methodology section more robust to explain how you overcame the potential change in the\nincubator oxygen concentrations - if you measured oxygen in the incubator or oxygen/redox in the tubes, this would suffice.\nModel equation\nThe equation below results in 31.1% decrease in methylation if no DOC or MeHg are present. I think this multiple linear\nregression needs to be forced through the origin (set b = 0). This model is also biased towards a reduction, you don’t have to\ncalculate anything to know that it will result in a significant decrease in methylation under all circumstances. This\nexaggerates the true impact of your research, which does provide prominent evidence of decreasing water column\nmethylation.\nThe data that was used to produce the multiple linear regression also needs to be shown, it is more helpful than\nsupplemental table 1, which is helpful, but just lit review data. The only way in which a model should always produce a\ndecrease in MeHg is if every single mesocosm experiment resulted in a\ndecrease. If even one out of the 45 experiments\nshowed a decrease, then this would need to be incorporated into the model. I can’t see the data so I don’t know.\nAnother issue I have is that your results have 5-30 ng/L MeHg as the outcome in the non-elevated CO2 scenarios, but the\ndata set you used in your model has < 1 ng/L for more than 200 lakes from you lit review, with the average being ~0.1 ng/L.\nSo I don't think it is appropriate to use a model where the MeHg is > 50x more than the systems you are modeling. I would\nstrongly reconsider the parameters that affect the model - DOC seems appropriate, but are there other factors like pH,\nconductivity, redox, sulfate, etc?\nR (Rate of inhibited MeHg (%) by elevated CO2)= (0.020 ± 0.003) × [DOC] + (0.013 ± 0.002) × [MeHg] + (0.311 ± 0.03),\nr2adjust = 0.753, p < 0.001\nNovelty of your research\nYour findings that increasing CO2 shifted the methanogen community from acetoclastic (more significant methylators) to\nhydrogenotrophic (less methylation capabilities) was very compelling and helpful for the scientific community. I think you\nshould comment on the energetics and biochemistry of hydrogenotrophic vs acetoclastic metabolism more.\nHydrogenotrophic metabolism produces more energy per mole of methane produced and is preferred if all substrates are\nequal. However, the law of the limiting factor suggests that it is much more complex than simply carbon dioxide\nconcentration – which should not often be limited.\nCO2 + 4 H2 → CH4 + 2 H2O\nCH3COOH → CH4 + CO2\nNote that 4 hydrogen molecules are needed per molecule of carbon dioxide. In anaerobic respiration, approximately 2 moles\nof H2 are produced per mole of acetate. So as AOM is broken down, hydrogen will be limited compared to acetate and\ncarbon dioxide and hydrogen production would need to be increased in order to stimulate hydrogenotrophic metabolism. I\nsuspect fermenting organisms that produce more hydrogen than acetate were involved, since the incubations were\nperformed in the dark and because you aerated the control (which introduces oxygen), but only added CO2 to the treatments\n– they may not have had enough oxygen for redox compound cycling (Fe3+ -> Fe2+ -> Fe3+; SO4 -> H2S -> SO4, etc).\nThere are also algae, cyanobacteria, and green/purple sulfur bacteria that produce hydrogen during photosynthesis. Since\nthey would be stimulated by carbon dioxide (especially in 2 month incubation), they could increase the hydrogen\nconcentration needed to encourage hydrogenotrophic growth and although your experiment didn't look at this, it is a\nreasonable connection to your results - increasing CO2 would encourage them and increase hydrogen and carbon dioxide,\nultimately selecting for hydrogenotrphic metabolism.\nMinor Comments\nFigure 2 – amazing graph, but it’s difficult to interpret that it is ~90% reduction due to the way it is shown and the label. I\nwould suggest having the Y axis be 0% at the top and -100% at the bottom, with the larger bars further downwards. This\nemphasized that CO2 had a negative impact on methylation. The Y axis label is kind of confusing, I would suggest “MeHg\nproduction compared to the control” and then the negative numbers will highlight that increased CO2 led to decreased MeHg\nproduction.\nLine 340 – change to “collected using a plankton net and stored at 4 C”\nLine 347 – change experiment to experiments\nLine 351 – add “of” inbetween “the mechanisms of elevated Co2-impacted”\nLine 363 – is this supposed to be 20 ml?\nLine 386 – “the” is typed twice before observed, remove one.\nSupplemental Material\nFig T3-2 – adjust the X-axis so that it starts at 0.0. It is slightly off and starts < 0.0."}, "author_response": "Response to reviewers\nFor clarity, we present reviewer comments in italics, our response to comments\nin regular text, and a description of the changes to the manuscript in underlined\ntext. Revisions in the revised manuscript and supplementary information are\nhighlighted in green.\nReviewer #1:\nLei et al. present a detailed study on the relationship between atmospheric CO\n2\nand methylmercury production in freshwater lakes. It is a novel and interesting\nwork that shows a possible pathway of reduced methylmercury under global\nwarming, especially related to the decrease in hgcA genes. However, we have\nsome concerns about its methods and arguments regarding their key findings\non inhibited MeHg production and projected reduced MeHg levels in global\nlakes.\nResponse：We sincerely appreciate your thorough and insightful feedback,\nwhich has greatly improved scientific rigor and clarity of our study. In response\nto your concerns on methods and result interpretation, we have conducted\nNEW experiments and implemented corresponding revisions in the revised\nmanuscript. Our detailed responses to each comment are provided below.\nMajor concerns:\nThe implications of the experimental design with no-light and constant\ntemperature conditions need to be considered in the model extrapolation the\nresults to lakes globally. “All vials were incubated in the dark at 25°C to mimic\nthe prevalent degradation conditions in lakes and preclude MeHg\nphotodegradation” (supplementary information L326-328). To project MeHg\ndynamics in global lakes, the influence of light, but especially temperature\nchanges need to be mentioned. A significant rise in CO concentration will drive\n2\na marked increase in temperature. How do you exclude the potential significant\ntemperature influence on methylation and demethylation activities in global\nlakes when estimating the reduction in MeHg levels caused by elevated CO ?\nmethylation. The paper could still be published with the existing data and model,\nbut the implications of not considering light and temperature need mentioned.\nResponse: We appreciate your insightful comments regarding the implications\nof light and temperature for extrapolating our results to global lakes. These\nfactors could be important in affecting MeHg dynamics in aquatic systems, and\nR1\nwe address their relevance as follows:\n(1) Temperature: We agree with you that elevated atmospheric CO , often\n2\ncoupled with global warming, may jointly regulate microbial activity, thereby\ninfluencing both Hg methylation and demethylation processes 1. To examine\nthis interplay, we conducted NEW experiments simulating future climate\nscenarios with elevated CO AND/OR increased temperature:\n2\n1) “Control”: 25°C incubation under ambient CO (420 ppm).\n2\n2) “Warming”: 29.4°C incubation (ΔT=4.4°C), consistent with the SSP5-8.5\nprojections for 2100 from IPCC AR6 2 under ambient CO (420 ppm).\n2\n3) “Elevated CO ”: 25°C incubation under elevated CO (1000 ppm).\n2 2\n4) “Warming × Elevated CO ”: 29.4°C incubation under elevated CO (1000\n2 2\nppm).\nResults showed that warming alone increased net MeHg production by 17–\n37% (p < 0.01) in both control lake water and algal biomass–amended systems,\nlikely due to enhanced microbial activity 3. In contrast, elevated CO , either\n2\nalone or in combination with warming, significantly suppressed net MeHg\nproduction by 26–74% relative to the control (p < 0.001). Importantly, the\ninhibition rates under the “Elevated CO ” and “Warming × Elevated CO ”\n2 2\ntreatments (33–76% reduction) were not significantly different (p > 0.05;\nSupplementary Fig. 5). Thus, under elevated CO conditions, temperature\n2\nincreases had no significant additional effect on microbial MeHg production—\nregardless of whether natural lake water or eutrophic (algae-amended)\nconditions were considered. This demonstrates that the inhibitory effect of\nelevated CO on microbial Hg methylation outweighs the stimulatory\n2\neffect of warming. Consequently, our model remains valid for projecting\nglobal-scale trends in MeHg production under climate change, with CO\n2\nelevation as the dominant driver rather than temperature rise.\nR2\nSupplementary Fig. 5 Effects of warming and elevated CO on MeHg production.\n2\n“Lake water”: unfiltered lake water from Yangshan Lake (Nanjing City, China); “+ 1000\nAlgae”: unfiltered lake water (40 mL) amended with 0.005 g of algal biomass. “Control”:\nincubated at 25°C under ambient CO (420 ppm) for 7 days. “Warming”: incubated at\n2\n29.4°C (ΔT=4.4°C) and ambient CO (420 ppm). “Elevated CO ”: incubated at 25°C under\n2 2\nelevated CO (1000 ppm); “Warming × Elevated CO ”: incubated at 29.4°C under elevated\n2 2\nCO (1000 ppm). Prior to incubation, 200 ng·L-1 of HgCl was added into the lake water.\n2 2\nDifferent lowercase letters above the bars indicate significant differences among\ntreatments (p < 0.05).\nFor clarity, we have added the following discussion in the main text and\nsupplementary text.\nMain text (Page 10, Line 179-184): “Moreover, under combined climate\nforcing (elevated CO and/or warming, ΔT=4.4°C, SSP5-8.5 by 2100), MeHg\n2\nproduction inhibition under “Elevated CO ” and “Warming × Elevated CO ”\n2 2\ntreatments (33–76% reduction) did not differ significantly (p > 0.05;\nSupplementary Fig. 5), indicating that the inhibitory effect of elevated CO\n2\noutweighs the stimulatory impact of warming, thus supporting the robustness\nof our model in projecting global MeHg trends.”\nSupplementary Text 1 (Page S6, Line 107-121): “Furthermore, elevated\natmospheric CO typically co-occurs with global warming, which may alter\n2\nmicrobial processes involved in Hg methylation and demethylation 1. To clarify\nthis interplay, we conducted supplementary experiments simulating different\nclimate scenarios: “Control” (25°C, ambient 420 ppm CO ), “Warming” (29.4°C,\nΔT=4.4°C, consistent with SSP5-8.5 projections for 2100, ambient 420 ppm\nCO ), “Elevated CO ” (25°C, elevated CO of 1000 ppm), and “Warming ×\n2 2 2\nElevated CO ” (29.4°C, elevated CO of 1000 ppm). Results showed that\n2 2\nwarming alone increased net MeHg production by 17–37% (p < 0.01), while\nelevated CO , either alone or combined with warming, significantly suppressed\nrates (33–76%, p > 0.05, Supplementary Fig. 5). These findings indicate that\nthe inhibitory effect of elevated CO on microbial Hg methylation outweighs the\n2\nstimulatory effect of rising temperature, supporting the robustness of our model\nin projecting global MeHg trends despite concurrent warming.”\n(2) Light: Light is a key regulator of MeHg dynamics—particularly via\nphotodegradation 4, but its effect is strongly modulated by eutrophication status,\nwhich was central to our experimental design. Previous studies have shown\nthat algal organic matter (AOM) from eutrophic conditions significantly inhibits\nMeHg photodegradation through light-shielding effects 5. To specifically isolate\nthe impact of elevated CO on microbial Hg methylation—the focus of this\n2\nstudy—we incubated all samples under dark conditions at 25 °C to eliminate\nphotodegradation interference. This setup is particularly relevant to eutrophic\nlake systems, where high AOM concentrations substantially inhibited light\npenetration and photodegradation.\nImportantly, during incubation, DOM concentrations showed no significant\ndifferences (except at day 60) between ambient and elevated CO treatments\n2\n(p > 0.05, Supplementary Fig. T1-1). Thus, while MeHg concentrations would\ntheoretically be lower under light exposure than in dark incubation, the relative\nreduction rates are expected to be similar under ambient (420 ppm) or\nelevated CO (1000 ppm) CO levels, because DOC levels were not\n2 2\nsignificantly altered by elevated CO . Consequently, the observed inhibitory\n2\neffect of elevated CO on net MeHg production remains valid even when\n2\nphotodegradation is considered, supporting the robustness of our model\nextrapolation. Therefore, light effects were NOT considered in our global\nextrapolation. Future studies should integrate in-situ light regimes and the long-\nterm effect of elevated CO on water DOC level to capture broader lake types.\nSupplementary Fig. T1-1 Dynamic changes of DOC levels during incubation under\nambient CO (420 ppm) or elevated CO (1000 ppm) levels.\n2 2\nTo address your concerns, we have incorporated these points in our model\nuncertainty analysis (See Supplementary Text 1, Page S3-S4, Line 51-65):\n“First, we quantified the responses of microbial Hg methylation to elevated CO\n2\nlevels over a two-month incubation under dark conditions. This design\nintentionally excluded photodegradation, which is strongly suppressed in\neutrophic lakes due to the light-shading effect of AOM 5, but remains important\nin oligotrophic or clear-water systems. While MeHg concentrations would be\nlower under light exposure compared to dark incubation, the relative reduction\nrates are expected to remain similar under both ambient (420 ppm) and\nelevated (1000 ppm) CO , because DOC concentrations were not significantly\n2\naltered by CO elevation (p > 0.05, Supplementary Fig. T1-1). Thus, the\n2\nobserved inhibitory effect of elevated CO on net MeHg production is robust\n2\neven when photodegradation is considered. Future work should incorporate in-\nsitu light regimes to extend applicability to a broader range of lake types. In\naddition, further research is needed to assess long-term responses, as Hg-\nmethylating microbes such as methanogens may gradually acclimate to\nelevated CO and other environmental changes during prolonged incubation 6,\n2\n7.”\nFurthermore, the global model on MeHg in lake water only uses DOC and\nMeHg as the parameters. The experiment, however, focuses on Algal Organic\nMatter (AOM), but DOC is used by the authors use to model the global trend of\nMeHg levels. The fact that AOM is only a fraction of DOC, and DOC is more\ncomplex regarding methylation and demethylation of Hg also needs to be\nacknowledged as a limitation of the model.\nResponse: We thank you for highlighting the distinction between dissolved\norganic carbon (DOC) and algal organic matter (AOM), as well as its\nR5\nimplications for our global model. This distinction is indeed critical, since\nalthough AOM can constitute a major fraction of DOC in eutrophic lakes, it\ncoexists with diverse autochthonous and allochthonous sources 8. Such\ncompositional heterogeneity exerts differential controls on Hg methylation and\ndemethylation processes 9.\nOur model employs DOC as the parameter primarily because global-scale\nDOC datasets are available, enabling broad extrapolation. However, we\nacknowledge that DOC is heterogeneous, with AOM (algae-derived,\nautochthonous) representing a functionally distinct fraction—particularly in\neutrophic lakes, where it dominates and strongly stimulates methanogen-\nmediated Hg methylation 10. In contrast, allochthonous DOC (e.g., soil-derived\norganic matter, SOM) generally exerts weaker or even different effects on Hg\ntransformation processes 9.\nTo directly address your concern, we performed NEW experiments using\nmixtures of autochthonous and allochthonous DOM, to clarify how varying\nAOM contributions (within the broader DOC pool) affect CO -driven MeHg\n2\ninhibition. We established a gradient of DOM mixtures representing different\nautochthonous: allochthonous ratios (100:0, 80:20, 50:50, 20:80, and 0:100),\ncorresponding to AOM (autochthonous), M-1, M-2, M-3, and SOM\n(allochthonous), respectively. All treatments were buffered to neutral pH (≈7)\nto avoid pH effects. The results confirmed that AOM is a much stronger driver\nof methylation than SOM, consistent with previous studies 9, 10. Importantly,\nelevated CO (1000 ppm) significantly inhibited net MeHg production only when\nprogressively from 75% (pure AOM) to 68% (80:20, M-1), 52% (50:50, M-2),\nand became statistically insignificant (<10%) when AOM constituted <50% of\nthe DOM pool (20:80 of M-3 and pure SOM; Supplementary Fig. 4).\nThese results indicate that our global model—which implicitly equates\n“high DOC” with “AOM dominance”—may overestimate CO -driven MeHg\n2\nsuppression in lakes where terrestrial DOM dominates. Nevertheless, the\nprediction error for eutrophic lakes (where AOM is typically dominant and where\nour study is primarily focused as recognized MeHg hotspots 10) remains\nmoderate, with an overestimation of 10–44% (mean 28% ± 24%). To reduce\nthis bias, we normalized the inhibition rates of net MeHg production in the\n“Algae” treatments using the result from a mixture of autochthonous and\nallochthonous organic matter (AOM: SOM =80%: 20%). This refinement\nslightly narrowed the global projected inhibition range: from 31–86% (mean\n50%) before normalization to 33–74% (mean 47%).\nR6\nSupplementary Fig. 4 Effects of elevated CO (1000 ppm) on MeHg production\n2\ncompared to ambient CO (420 ppm) under different DOM sources and mixing ratios.\nfrom soil and algal biomass using water (soil: water = 1:2, algae: water = 1:100, w/w) in the\ndark at 25 °C for 12 h, centrifuged (4000 rpm, 10 min), filtered (0.45 µm PES), and diluted\nto 20 mg·L-1 DOC. To generate mixing gradients, algae- (AOM) and soil-derived (SOM)\nDOM stock solutions were combined at fixed ratios: 100:0 (AOM), 80:20 (M-1), 50:50 (M-\n2), 20:80 (M-3), and 0:100 (SOM). For the “Lake water”: 40 mL of unfiltered lake water; for\neach “+DOM”: 20 mL lake water + 20 mL DOM solution (final DOC ≈ 12.5 mg·L-1,\ncomparable to the “+1000 Algae” treatment ≈ 13.3 mg·L-1). All treatments were incubated\nfor 7 days under either ambient (420 ppm) or elevated (1000 ppm) CO . The asterisk above\n2\nthe bars indicates a significant difference between “420 ppm CO ” and “1000 ppm CO ” (*:\n2 2\np < 0.05; **: p < 0.01; ***: p < 0.001; without *: p > 0.05).\nFor clarity, we have added the following discussion in the main text and\nsupplementary text to explicitly acknowledge this limitation and to incorporate\nour new DOM-mixing results.\nMain text (Page 10, Line 173-179): “We also established DOM gradients\nreflecting different autochthonous: allochthonous ratios (100:0 to 0:100; AOM\nvs. Soil organic matter, SOM). Results confirmed that AOM was a stronger\ndriver of methylation than SOM, consistent with previous studies 9, 10, and that\nelevated CO significantly inhibited net MeHg production only when AOM\n2\ncomprised ≥50% of the DOM pool (p < 0.001; Supplementary Fig. 4),\nunderscoring the need to clarify how DOM composition modulates CO -driven\nMeHg inhibition.”\nSupplementary Text 1 (Page S5-S6, Line 89-106): “Our model incorporates\nDOC as a parameter primarily due to the availability of global DOC datasets,\nwhich enable broad-scale extrapolation. However, DOC represents a\nheterogeneous pool encompassing both autochthonous and allochthonous\nfractions that may differentially regulate Hg methylation and demethylation 9.\nSpecifically, algae-derived AOM, particularly in eutrophic lakes, is a functionally\ndistinct driver of microbial Hg methylation 10, while soil-derived SOM typically\nexerts weaker effects 9. To clarify how AOM contributions modulate CO -driven\n2\ninhibition of MeHg production, we conducted additional experiments with mixed\nDOM sources: pure AOM, pure SOM, and AOM:SOM ratios of 80:20, 50:50,\nand 20:80. Elevated CO significantly suppressed net MeHg production only\n2\nwhen AOM constituted ≥50% of the DOM pool (p < 0.001). The inhibition rate\ndeclined from 75% (pure AOM) to 68% (80:20), 52% (50:50), and became\nnegligible (<10%) when AOM was <50% (Supplementary Fig. 4). These\nfindings demonstrate that while our model may overestimate MeHg declines in\nlakes dominated by terrestrial DOM, the deviation for eutrophic lakes (the\nprincipal focus of this study) is moderate. After normalizing by the AOM:SOM\nratio, projected global inhibition ranges were refined from 31–86% (mean 50%)\nto 33–74% (mean 47%).”\nFinally, the concluding lines of the paper state that its findings “offer a potential\nexplanation for this mismatch. I think that statement is incorrect. The mismatch,\nas the authors present it, is that the reductions in emissions have only resulted\nin a decrease in human exposure that is proportionally smaller that then\nemission reductions. The findings of the present paper suggest that even\nwithout any change in emissions, the MeHg in water would be going down as\nCO levels rise, thus reducing human exposure even if Hg emissions were not\n2\nreduced. This would tend to make human exposure decline faster the Hg\nreductions. The authors should reconsider what they claim for this paper in\nregards to explaining the current mismatch between Hg emission reductions\nand human exposure.\nResponse：We sincerely thank you for your insightful comment, which has\nhelped us identify an inaccuracy in our original conclusion. We fully agree that\nour findings do not directly explain the observed mismatch between reductions\nin Hg emissions and the proportionally smaller declines in human MeHg\nexposure. In response, we have revised the conclusion to provide a more\naccurate and nuanced interpretation that better reflects our experimental\nevidence (Page 20-21, Line 377-394):\nR8\n“Per unit reduction in Hg emissions, human exposure has been estimated\nto decline by only one-third to one-half, reflecting the influence of complex\nprocesses such as legacy Hg in environmental reservoirs and bioaccumulation\nlags in food chains. This observed “mismatch”—where exposure decreases\nless than expected from emission reductions—likely arises from such multi-\nfaceted drivers, including remobilization of legacy Hg by natural and\nanthropogenic factors 11.\nOur study identifies a distinct climatic pathway that further complicates this\nemission–exposure relationship: elevated CO suppresses microbial MeHg\n2\nproduction in lakes, leading to reduced aquatic MeHg levels regardless of\nemission changes. This pathway would amplify the decline in human exposure\nbeyond direct emission reductions, and should be accounted for when\nevaluating the effectiveness of global Hg mitigation efforts and interpreting\ntemporal trends in biotic MeHg concentrations. Thus, our findings do not\nresolve the current mismatch, but rather reveal a previously unrecognized\nclimate–microbe–MeHg interaction that may shape future exposure risks.\nSpecifically, future increases in atmospheric CO may counteract warming-\n2\ndriven enhancements of algal blooms, helping to stabilize aquatic MeHg levels\nunder climate change.”\nMinor comments:\n1. The abstract section on hydrogenotrophic and acetoclastic methanogens\ndoes not talk about differences in the hgcA genes between the two types of\nmethanogens. It would be clearer if it did.\nResponse: We appreciate this valuable suggestion, which prompts us to clarify\nthe difference in hgcA gene distribution between hydrogenotrophic and\nacetoclastic methanogens. Based on our findings, we have revised the relevant\ndescription to explicitly link metabolic types to their Hg methylation potential via\nhgcA (Page 3, Line 51-55): “These shifts result from CO -induced changes in\n2\ncarbon substrates, promoting hydrogenotrophic methanogens (e.g.,\nMethanobacterium)—which typically lack the hgcA methylation gene—over\nacetoclastic strain (e.g., Methanosarcina) that harbor hgcA and mediate Hg\nmethylation. This competitive shift thus reduces Hg methylation by\nmethanogens.”\n2. The North arrow needs to be indicated on the map Figure 1A.\nResponse：Added as suggested in Figure 1A.\nR9\nFig. 1 Elevated CO inhibited MeHg production in lakes along the middle and lower\n2\nreaches of the Yangtze River in China. (a) “Lake water”: unfiltered lake water. (b) “+1000\nAlgae”: unfiltered lake water amended with fresh algal biomass to simulate bloom (resulting\nin approximately 1000 µg·L-1 chlorophyll a) in Experiment A. Inhibited MeHg production\nrates by elevated CO are denoted as the percentage changes of MeHg levels between\n2\n“Ambient CO ” and “Elevated CO ”.\n2 2\n3. Only data from Chaohu and Yangshan Lakes are presented in Figure 2. In\nsupplementary Figure 2, Xuanwu Lake data is added to demonstrate the\nnegligible impact of elevated CO levels on demethylation. How about other\n2\nlake data? Are they available in the supplementary information?\nResponse：We thank you for your question regarding data presentation. To\nclarify, our full dataset includes results from 45 freshwater lakes spanning\n~1,200 km along the Yangtze River. In the main text, we highlighted three\nrepresentative lakes—Chaohu, Yangshan, and Xuanwu—to illustrate key\ntrends under contrasting ecological contexts. ◎ Chaohu Lake, a large\nR10\neutrophic system with frequent algal blooms, represents conditions of high\nAOM where methanogen-mediated methylation is most active 10. ◎\nYangshan Lake (suburban, relatively undisturbed) and Xuanwu Lake (urban,\nanthropogenically impacted) capture contrasting trophic states and land-use\nsettings 12. This selection allows us to demonstrate the consistency of CO -\n2\ninduced effects across diverse lake types.\nThe core finding—a ubiquitous reduction in net MeHg production under\nelevated CO (14–96% across all 45 lakes)—is supported by the full dataset.\npatterns seen in the three highlighted lakes are representative of the broader\nset. Regarding demethylation, Supplementary Fig. 7 presents results from\nChaohu Lake showed Hg methylation rates (k ) were 83%–89% lower under\nm\nelevated CO than ambient conditions in both natural or eutrophic waters (p <\n2\n0.001), whereas MeHg demethylation rates (k ) did not differ significantly\nd\nbetween treatments, reinforcing our conclusion that the observed MeHg\ndeclines are primarily driven by inhibited methylation rather than altered\ndemethylation.\nTo make this clearer, we have added explanatory text in Supplementary\nText 9 (Page S30-S31, Lines 517–524):\n“In Experiments B–H, water from Chaohu and Yangshan Lakes was\nselected to represent a range of ecological contexts along the Yangtze River.\nChaohu Lake, a large eutrophic system characterized by frequent algal blooms,\nexemplifies high AOM conditions and active methanogen-mediated methylation.\nYangshan Lake, a suburban lake with relatively low human disturbance, was\nchosen to capture contrasting trophic states. While the two lakes are highlighted\nfor clarity, the conclusions are supported by data from 45 freshwater lakes\nacross the Yangtze Basin, ensuring broad applicability.”\n4. In Figure 2, only %MeHg data is shown. How about the MeHg concentrations?\nResponse：As suggested, we have supplemented the MeHg concentrations in\ndifferent scenarios under ambient (420 ppm) or elevated CO (1000 ppm)\n2\nconditions (Supplementary Fig. 2).\nR11\nSupplementary Fig. 2 Net MeHg production in different scenarios under ambient\n(420 ppm) or elevated CO (1000 ppm) conditions. (a) Different CO levels; (b) Different\n2 2\ntrophic statuses; (c) Different decomposition stages. “Lake water”: unfiltered lake water.\n“+200 Algae”, “+1000 Algae”, and “+2000 Algae”: unfiltered lake water (40 mL) amended\nwith 0.001, 0.005, and 0.01 g of algal biomass, respectively. Before sampling on “Day 0”,\nall lake water was equilibrated for 4 h with Hg(II). Different lowercase letters above the bars\nindicate significant differences among different treatments (p < 0.05). The asterisk above\nthe bars indicates a significant difference between “420 ppm CO ” and “1000 ppm CO ” (*:\n2 2\np < 0.05; **: p < 0.01; ***: p < 0.001; without *: p > 0.05).\nR12\nReviewer #2:\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training\nin peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\nResponse：We sincerely appreciate your thorough and insightful feedback,\nwhich has significantly strengthened both the scientific rigor and clarity of our\nmanuscript.\nR13\nReviewer #3:\nI am very impressed by this extensive research study and the thorough effort\nto address potentially confounding factors in the supplemental material\n(demethylation, FeRB/SRB, etc). There was a lot of great research performed\nand a lot of novel findings. However, I believe that the context of the publication\nneeds to be reconsidered – this is water column methylmercury production and\nnot sediment MeHg production or a total system MeHg production (which would\nneed a sediment-water incubation). By overstating the implications, you detract\nfrom the value of the truly novel findings – in my opinion that is the connection\nbetween methylation and CO in the water column and the methanogen types\n2\n(acetoclastic vs hyrogenic).\nMy recommendation is to reduce the claims of how this research shows\nworldwide reductions in lake methylation by 30 – 90% to something more\ngrounded and reasonable: it is applicable to Hg-impacted, hypereutrophic lake\nwater column methylation. These conditions are not very common, but\nunderstanding the mechanisms that you have elucidated is very helpful for the\nscientific community and will lead to follow-up research that will cite your paper\nand look more closely at scenarios that mimic real-world conditions. You can\ncomment on how it might be applicable to non-Hg impacted and\nhypereutrophic lake water column methylation and if it is applicable to sediment\nmethylation, but don’t assume that it automatically is and be grounded in the\ntheories of why it may be. You can also comment on the analytical challenges of\nrunning this type of experiment without adding Hg or AOM and how it could be\naccomplished in the future.\nResponse：We sincerely appreciate your time and insightful comments.\nParticularly, your actionable suggestions are valuable in enhancing our study,\nwhich have prompted additional experiments to further enhance the scientific\nrigor and overall quality of our manuscript. We have addressed all your\nconcerns—both the general comments above and specific points below—in the\nfollowing detailed responses.\nSediment vs Water Column Methylation\nSignificantly more (> 10x) MeHg is produced in the sediment where mercury,\nmicrobial activity, and anoxia are all significantly higher. So even if water column\nmethylation decreases by 80%, this does not necessarily mean a significant\ndecrease in the total water column MeHg concentration. You briefly mentioned\nthis in the supplemental material, but generally avoided the topic. I think it is a\nvery significant stretch to say that increasing CO decreases MeHg production\n2\nin lakes as a whole. It is more powerful to be grounded in the implications of\nR14\nyour data.\nHowever, the compelling results are meaningful for water column methylation,\nwhich has been of increasing interest within the scientific community and\nunderstudied compared to sediment MeHg production. If masses are equal,\nwater column methylmercury production has a much greater impact on human\nhealth due to its closer proximity to the pelagic food web. Your research is\nextremely helpful from this standpoint.\nResponse: We appreciate your insightful comments highlighting the\nsignificance of sedimentary MeHg production and its implications for our\nconclusions. Your suggestions underscore the importance of clarifying the\nscope of our study while carefully contextualizing it within the broader\nframework of aquatic Hg cycling.\nOur study specifically focuses on water column MeHg production, because\nthis pool directly interfaces with pelagic food webs—where MeHg enters\nphytoplankton, zooplankton, fish, and ultimately human diets 13. Water column\nMeHg is therefore more tightly linked to pelagic trophic transfer, making its\nregulation particularly critical for understanding human exposure risks 14. For\ninstance, across a latitudinal field survey of 12 subtidal sites within three\nestuaries, Taylor et al. (2019) found that MeHg concentrations in resident fish\nwere linearly predicted by dissolved water-column MeHg (Atlantic silverside r²\n= 0.73–0.82, p < 0.01), while NO significant relationship was observed with bulk\nsediment MeHg (p > 0.05) 15. This finding illustrates why the study of\nmethylation processes in water column, rather than sedimentary pathways,\nremains important for predicting MeHg bioaccumulation in edible aquatic\norganisms.\nTo further address your concern regarding sediments, our recent parallel\nstudy on coastal marine sediments (East China Sea) reveals that elevated CO\n2\n(1000 ppm) suppressed sedimentary MeHg production by 54–77% relative to\nambient conditions (420 ppm) 16. This inhibition was associated with a\nsubstantial decrease (67–99%) in the abundance of the Deltaproteobacteria-\nhgcA gene, the key functional gene for Hg-methylating microbes in marine\nsediments. The consistency of this suppression across both water column and\nsediment systems suggests that elevated CO -induced inhibition of microbial\nthereby reinforcing the broader significance of our findings.\nNevertheless, it is important to note that although sediments are the\ndominant zones of MeHg production, the partitioning of MeHg between\nsediment solids and overlying waters strongly constrains the flux of sediment-\nderived MeHg to the water column. This partitioning is described by a partition\nR15\ncoefficient (K ), which ranges from 103–105 L·kg-1 17. Using an average K value\nd d\nof 104 L·kg-1 and typical sedimentary MeHg concentrations of 0.1–0.5 ng·g-1 in\nfreshwater lakes 18, we estimate that elevated CO -inhibited sedimentary MeHg\n2\n(54–77% reduction) would contribute only 0.006–0.03 ng·L-1 (average 0.018\nng·L-1) to the overlying water. By comparison, our observed background MeHg\nconcentrations in lake water were 0.04–0.47 ng·L-1 (average 0.15 ± 0.10 ng·L-\n1), indicating that CO -impacted sediment-derived MeHg represents <12%\n2\nof the total aqueous MeHg pool. This aligns with previous findings (Li and Cai,\n2013) 19, which showed that only a small proportion of sedimentary MeHg is\ntransported to the water column. Thus, while sedimentary processes remain\ncritical for whole-lake budgets, the influence of reduced sedimentary\nmethylation under elevated CO on dissolved MeHg concentrations in the water\n2\ncolumn is limited. For this reason, our primary emphasis remains on in situ\nwater-column MeHg production.\nFurthermore, our water-column focus is justified by the fact that\natmospheric CO directly influences surface waters, where rapid gas exchange\n2\nequilibrates CO concentrations 20. In contrast, sedimentary CO dynamics are\n2 2\nmoderated by porewater diffusion and in situ microbial processes, which buffer\nagainst direct atmospheric forcing. Thus, constraining our analysis to the water\ncolumn enables a clearer isolation of atmospheric CO effects on methylation—\n2\na process that most directly impacts human exposure through freshwater\nfisheries.\nWe acknowledge, however, that integrating sedimentary pathways is\nessential for a holistic understanding of lake-wide MeHg cycling. Future studies\nshould therefore investigate how rising atmospheric CO indirectly affects\n2\nsedimentary methylation—for instance, through altered organic matter export\nfrom surface waters or porewater geochemical changes—to provide a more\ncomplete assessment of climate-driven impacts on lake MeHg budgets.\nTo directly address your suggestions and strengthen the rigor of our study,\nwe have expanded our discussion (Supplementary Text 1, Page S4–S5, Line\n70–88) to include: (1) the rationale for focusing on water-column processes,\nand (2) the limitations and uncertainties of not incorporating sedimentary\nprocesses. Specifically, we state:\n“Another key factor that may interfere with the CO -MeHg nexus in lake\n2\nwaters is sediments, which represent the largest pool of Hg in aquatic\necosystems. Predicting climate-driven changes in freshwater MeHg levels also\nrequires understanding how elevated CO affects sedimentary MeHg\n2\nproduction and water-sediment MeHg fluxes. Our recent parallel study on\ncoastal marine sediments (East China Sea) reveals that elevated CO (1000\nppm) also suppresses MeHg production in sediments, with reductions ranging\nfrom 54% to 77% compared to ambient conditions (420 ppm) 16. However,\nconsidering the partitioning of MeHg between sedimentary solid and water\nphases (e.g., with partition coefficient K of 103–105 L·kg-1 17), the CO -impacted\nd 2\nMeHg from sediment represents only a small fraction (e.g., <12%) of the total\ndissolved MeHg observed in lake water 19. Nevertheless, this consistency\nacross water column and sediment systems suggests that elevated CO -\n2\ninduced inhibition of microbial Hg methylation may represent a generalizable\npattern across aquatic environments, reinforcing the broader relevance of our\nfindings. Future studies will explore how elevated atmospheric CO indirectly\n2\naffects sedimentary methylation (e.g., via altered organic matter export from the\nwater column or porewater chemistry) to better quantify total lake MeHg\nbudgets under climate change.”\nGrounding the results of the Mesocosm Experiment\nIn the main paper, it appears as if this is a natural mesocosm test and you have\nto read the supplemental material to find that you added 200 ng/L as HgCl .\nis extremely rare. 200 ng/L in the water column only occurs in the most\nimpacted lakes (this is 0.2 ug/L). Your citation lists sediment and anerobic\ndigestion studies for the range of (20 – 1000 ng/L). The sediment study is a\ncontaminated site and not natural. Natural lake water generally has ~0.1 – 2\nng/L. If you were trying to extrapolate on natural conditions then you would have\njust used normal lake water, which already has some Hg in it and adding 1 ng/L\nmight be reasonable.\nHowever, I do understand the analytical difficulties of measuring MeHg when it\nis below 0.5 ng/L (which would be the outcome when water column Hg is\ngenerally < 1 ng/L) and I understand that it is just not feasible.\nSo it’s not a deal breaker and it is still informative, but it’s important not to\noverstate the implications of this if you didn’t test the methylation changes\nwithout adding Hg. So it is important to be upfront that the mesocosms where\nrun in absolute worst case scenario conditions and be very blunt about stating\nthat these results are applicable to lakes that have water column mercury\nconcentrations > 100 ng/L – this is almost exclusively contaminated sites and\nnot natural sites.\nResponse: We thank you for highlighting that our spiked inorganic mercury\n(IHg) concentration (200 ng·L-1) exceeds typical lake levels. We acknowledge\nthat this concentration does not represent pristine systems, where IHg is usually\n~0.1–2 ng·L-1, and instead reflects conditions characteristic of highly\nR17\ncontaminated sites. Here, we would like to clarify the rationale for this design\nand its implications for interpreting our results.\nThe 200 ng·L-1 IHg spike was applied primarily to overcome analytical\nconstraints. As you noted, quantifying net MeHg production in natural lake\nwaters (where IHg is often <1 ng·L-1 and MeHg <0.5 ng·L-1 21) is hampered by\ndetection limits, making it difficult to capture subtle shifts induced by elevated\nCO . This design therefore prioritized detecting the microbial response to CO ,\n2 2\nwhich is the central focus of our study.\nTo address the concern about environmental relevance, we conducted\nNEW experiments across a broad IHg gradient (2–500 ng·L-1),\nencompassing both slightly contaminated and heavily impacted conditions 20,\n22. Although net MeHg production increased with higher IHg inputs, the\ninhibitory effect of elevated CO (1000 ppm) was consistent across all\n2\ntreatments. Compared to ambient CO , net MeHg production was reduced by\n2\n21–32% in unamended lake water (mimicking low-trophic conditions) and by\n66–84% in algal biomass-amended treatments (mimicking eutrophic conditions)\n(Supplementary Fig. 3). While a 200 ng·L-1 spike better represents heavily\ncontaminated rather than pristine systems, our gradient experiments confirm\nthat the key mechanism—CO reshaped methanogenic communities,\n2\nparticularly a decreased abundance of hgcA-containing acetoclastic\nmethanogens—persists across environmentally relevant Hg gradients. Thus,\nalthough our experiments simulate lakes with elevated Hg levels (e.g.,\nanthropogenically impacted systems), the identified CO -mediated pathway\n2\nprovides critical insights into methylation regulation even in low-Hg\nenvironments.\nR18\nSupplementary Fig. 3 Effect of elevated CO on MeHg production across different\n2\nspiked into the lake water, respectively. “Lake water”: unfiltered lake water from Yangshan\nLake (located in Nanjing City, China); “+ 1000 Algae”: unfiltered lake water (40 mL)\namended with 0.005 g of algal biomass from Chaohu Lake. The asterisk above the bars\nindicates a significant difference between “420 ppm CO ” and “1000 ppm CO ” (*: p < 0.05;\n2 2\n**: p < 0.01; ***: p < 0.001; without *: p > 0.05).\nTo address your concerns, we have revised our discussion and included\nresults of these NEW experiments to explicitly contextualize the results within\nthis scope, avoiding potential overgeneralization and misleading (Page 10, Line\n168-173): “The CO\n2\n-driven suppression of microbial Hg methylation is\nindependent of background Hg levels, as shown by spiking experiments across\na broad inorganic Hg (IHg) gradient (2–500 ng L-1). Elevated CO consistently\n2\nreduced net MeHg production relative to ambient CO levels, with 21–32%\n2\ninhibition in unamended lake water and 66–84% in algal biomass-added\ntreatments (Supplementary Fig. 3).”\nNeed to demonstrate that you did not enhance anaerobic conditions by adding\nCO in the experimental design, it appears as if you may have introduced more\n2\noxygen into the controls than the treatments by aerating the incubator with air -\nthis keeps the incubator atmosphere at 20% oxygen saturation. For your\nR19\ntreatments, only adding pure CO would purge the oxygen and not replenish it,\n2\nallowing it to get lower in the incubator and ultimately lower in the tubes. Please\nmake the methodology section more robust to explain how you overcame the\npotential change in the incubator oxygen concentrations - if you measured\noxygen in the incubator or oxygen/redox in the tubes, this would suffice.\nResponse: We appreciate this critical concern regarding potential oxygen\nchanges in our experiments, as maintaining consistent redox conditions is\nessential for examining and interpreting microbial Hg methylation responses to\nelevated CO . Here, we clarify the methodology and supplement relevant data\n2\nto address this concern:\nOur experimental design maintained consistent oxygen concentrations\nacross all treatments to isolate CO effects from potential anaerobiosis changes.\n2\npartial pressure (~20%). The gas mixtures were continuously bubbled into\nincubators at a constant flow rate (200 mL·min-1), ensuring both CO levels and\n2\noxygen concentrations were stable across control (420 ppm CO in air) and\n2\ntreatments with elevated CO throughout the incubation period. To verify this,\n2\nwe measured dissolved oxygen (DO) in triplicate culture tubes. The results\ndemonstrated NO significant differences in dissolved oxygen (DO) levels\nbetween ambient (420 ppm) and elevated (1000 ppm) CO treatments across\n2\nmost time points, consistently observed in both lake water controls and algal\nbiomass-amended treatments (p > 0.05, Supplementary Fig. T8-1). Particularly,\nthe measured DO levels in our experimental systems reflect the microaerophilic\nconditions characteristic of eutrophic lake water columns—under conditions\nwhere methanogens and other Hg-methylating microbes remain metabolically\nactive.\nSupplementary Fig. T8-1 Dissolved oxygen in “Lake water” (a) and “+1000 Algae” (b)\nduring incubation under ambient CO (420 ppm) or elevated CO (1000 ppm) levels.\n2 2\nR20\nTo avoid potential confusion, we have updated the Materials and Methods\nsection to include: 1) explicit descriptions of the gas mixture protocols, and 2)\nDO monitoring data (Supplementary Fig. T8-1), thereby ensuring transparency\nabout redox stability across treatments (Page S25, Line 402-409): “Meanwhile,\nelevated CO levels used calibrated gas mixtures of air and commercially\n2\nprepared CO gas ensuring target CO levels (approximately 650 or 1000 ppm)\n2 2\nand atmospheric O partial pressure (~20%). This design explicitly prevented\n2\nconfirmed no significant differences (p > 0.05) between ambient and elevated\nCO₂ treatments (1000 ppm) across lake water controls and algal-amended\ntreatments at most time points (Supplementary Fig. T8-1), consistent with\nmicroaerophilic conditions in eutrophic lakes where methylators thrive.”\nModel equation\nThe equation below results in 31.1% decrease in methylation if no DOC or\nMeHg are present. I think this multiple linear regression needs to be forced\nthrough the origin (set b = 0). This model is also biased towards a reduction,\nyou don’t have to calculate anything to know that it will result in a significant\ndecrease in methylation under all circumstances. This exaggerates the true\nimpact of your research, which does provide prominent evidence of decreasing\nwater column methylation.\nThe data that was used to produce the multiple linear regression also needs to\nbe shown, it is more helpful than supplemental table 1, which is helpful, but just\nlit review data. The only way in which a model should always produce a\ndecrease in MeHg is if every single mesocosm experiment resulted in a\ndecrease. If even one out of the 45 experiments showed a decrease, then this\nwould need to be incorporated into the model. I can’t see the data so I don’t\nknow.\nAnother issue I have is that your results have 5-30 ng/L MeHg as the outcome\nin the non-elevated CO scenarios, but the data set you used in your model has\n2\n< 1 ng/L for more than 200 lakes from you lit review, with the average being\n~0.1 ng/L. So I don't think it is appropriate to use a model where the MeHg is >\n50x more than the systems you are modeling. I would strongly reconsider the\nparameters that affect the model - DOC seems appropriate, but are there other\nfactors like pH, conductivity, redox, sulfate, etc?\nR (Rate of inhibited MeHg (%) by elevated CO )= (0.020 ± 0.003) × [DOC] +\n2\n(0.013 ± 0.002) × [MeHg] + (0.311 ± 0.03), r2 = 0.753, p < 0.001\nadjust\nResponse: Your constructive feedback on the linear regression model is\ngreatly appreciated. We fully agree with you that limitations and uncertainties\nR21\nexist in our model construction and extrapolation, and these should be clearly\nindicated in the manuscript to avoid potential misleading or overstating. To\naddress these concerns, we have expanded our discussion of the\nmethodology, dataset, and model limitations, with corresponding revisions\nincorporated throughout the manuscript.\n(1) Linear regression: The regression equation R (Rate of inhibited MeHg\n(%) by elevated CO )= (0.020 ± 0.003) × [DOC] + (0.013 ± 0.002) × [MeHg] +\n2\n(0.311 ± 0.03), (adjusted r2 = 0.753, p < 0.001) was derived from data spanning\n45 lakes, where all samples exhibited variable concentrations of DOC (3.2–\n28.7 mg·L⁻¹) and MeHg (0.23–39.3 ng·L⁻¹) (Supplementary Table 4). As noted,\nnatural waters seldom, if ever, exhibit zero DOC or MeHg concentrations—DOC\nis ubiquitous in lakes due to organic matter inputs, and MeHg is consistently\ndetected in lakes. Forcing the model through the origin (intercept = 0) leads to\nimplausible results: at high DOC (>20 mg·L⁻¹), the predicted reduction exceeds\n100% (e.g., 20 mg·L⁻¹ DOC would yield 100.0% reduction under the forced\nmodel), which contradicts our experimental observations (maximum 96%\nreduction). Thus, retaining the intercept better reflects ecological realism, where\nbaseline reductions (the intercept) may arise from CO -induced microbial shifts\n2\nindependent of DOC/MeHg concentrations (e.g., direct effects on methanogen\nmetabolism). Additionally, as requested by Reviewer #1, we found that the\ninhibitory effect of elevated CO on MeHg production became more pronounced\n2\nwith increasing proportions of algal-derived organic matter (AOM) in the\ndissolved organic carbon (DOC) pool. To account for this effect, the inhibition\nrates of net MeHg production in the “Algae” treatments were normalized using\nthe M1/AOM ratio (where M1 represents a mixture of 20% terrestrial organic\nmatter and 80% AOM), thereby establishing a refined predictive model. This\nadjusted model was subsequently applied to extrapolate projected changes in\nMeHg concentrations across global lake systems. Before normalization, the\nestimated decreases in global lake MeHg levels ranged from 31% to 86%\n(mean: 50%). After normalization, the inferred inhibition range narrowed to 33–\n74% (mean: 47%).\n(2) Dataset: The regression draws on 45 lake microcosm experiments. In\nall these experiments, elevated CO led to decreased net MeHg production,\n2\nwith reductions ranging from 14% to 96%. No lake experiment showed\nincreased or unchanged MeHg production under elevated CO , confirming the\n2\nuniform inhibitory effect of CO on net MeHg production. This consistency\n2\nsupports the model’s trend projection: The inhibition rates of net MeHg\nproduction varied across lakes, and these differences were mainly controlled\nby dissolved organic carbon (DOC) and MeHg concentrations (adjusted r2 =\nR22\n0.701). Specifically, higher ratios of DOC to MeHg correlated with stronger\ninhibition. This aligns with the role of AOM (a major DOC component)—AOM\ncan enhance methanogen activity, thereby heightening the system’s sensitivity\nto CO .\n2\n(3) Methylmercury concentration: We recognize that the MeHg\nconcentrations in our microcosms (0.23–39.3 ng·L-1) exceed typical lakewater\nlevels reported in the literature (averagely 0.14 ng·L-1, Supplementary Table 4).\nThis discrepancy arises from experimental design: IHg amendments and algal\nadditions (simulating eutrophication) were employed to enhance MeHg\nproduction, ensuring detectable MeHg levels in water. This approach was\nnecessary because ambient lakewater MeHg levels (typically < 0.5 ng·L-1 23)\nare insufficient for quantify CO ’s effects on MeHg production. To validate our\n2\nmodel, we compared simulated outputs with experimental data from 45 lakes.\nThe model, when applied to 229 natural lake samples (DOC: 1.2–27.0 mg·L-1;\nMeHg: 0.01–1.2 ng·L-1), predicted MeHg concentration reductions of 34–79%\n(average 47%) under elevated CO . These predictions align well with our\n2\nexperimental observations (14–96%, mean = 63%), demonstrating strong\nmodel consistency.\n(4) Additional parameters: Regarding additional parameters (pH, sulfate,\netc.), our analysis focused on DOC and MeHg because: (1) DOC is a key\ndeterminant of methanogen activity in eutrophic lakes as AOM serves of carbon\nsources for methanogens 10; (2) Elevated CO -decreased abundance of\nsuch as sulfate may influence Hg methylation in certain environments, our\nresults in the examined lakes revealed no significant associations between\nthese variables and CO -induced inhibition (Supplementary Text 3). This lack\n2\nof correlation supports their exclusion from our model.\n(5) Limitations: We acknowledge the inherent limitations and\nuncertainties associated with our model development and subsequent\nextrapolations: 1) photodegradation impact: our experiment excluded\nphotodegradation, which is a key factor in regulating MeHg dynamics in\noligotrophic or clear-water lakes. This omission may affect the accuracy of\nabsolute MeHg level predictions for these lake types; 2) microbial acclimation:\nwe quantified the responses of microbial Hg methylation to elevated CO levels\n2\nover a time span of two months. Further investigation is needed to quantify the\nlong-term responses, as microbes including methanogens, could gradually\nacclimated to environmental changes with prolonged incubation time; 3) Joint\neffects of multiple climatic factors: We focused solely on the effects of elevated\nR23\nCO concentrations and rising temperature on MeHg production. However,\n2\nclimate change encompasses a range of factors, including precipitation,\nultraviolet radiation, and other variables, which may interact in complex ways.\nThe influence of these additional climatic factors, as well as potential multi-\nfactor coupling, could lead to significant variations in Hg cycling pathways within\nlakes; 4) DOC Heterogeneity: In our experiment, the inhibitory effect of elevated\nCO on MeHg production was more pronounced as the proportion of AOM in\n2\nthe DOC pool increased. This suggests that our model may overestimate MeHg\nreductions in lakes with high DOC concentrations dominated by terrestrial DOM.\nTo address your concerns and to ensure scientific rigor, we have revised\nthe Methods to include Supplementary Table 4 (raw regression data) and\nclarified the potential limitations, emphasizing the model’s robustness for\neutrophic systems where DOC/MeHg levels align with our experimental range\n(Page S5-S6, Line 89-106).\n“Our model incorporates DOC as a parameter primarily due to the\navailability of global DOC datasets, which enable broad-scale extrapolation.\nHowever, DOC represents a heterogeneous pool encompassing both\nautochthonous and allochthonous fractions that may differentially regulate Hg\nmethylation and demethylation 9. Specifically, algae-derived AOM, particularly\nin eutrophic lakes, is a functionally distinct driver of microbial Hg methylation 10,\nwhile soil-derived SOM typically exerts weaker effects 9. To clarify how AOM\ncontributions modulate CO -driven inhibition of MeHg production, we\n2\nconducted additional experiments with mixed DOM sources: pure AOM, pure\nSOM, and AOM:SOM ratios of 80:20, 50:50, and 20:80. Elevated CO\n2\nsignificantly suppressed net MeHg production only when AOM constituted ≥\n50% of the DOM pool (p < 0.001). The inhibition rate declined from 75% (pure\nAOM) to 68% (80:20), 52% (50:50), and became negligible (<10%) when AOM\nwas <50% (Supplementary Fig. 4). These findings demonstrate that while our\nmodel may overestimate MeHg declines in lakes dominated by terrestrial DOM,\nthe deviation for eutrophic lakes (the principal focus of this study) is moderate.\nAfter normalizing by the AOM:SOM ratio, projected global inhibition ranges\nwere refined from 31–86% (mean 50%) to 33–74% (mean 47%).”\nSupplementary Table 4 Data used in the multiple linear regression analysis\nNo.a DOC/mg·L-1 Dissolved MeHg/ng·L-1 Reduction rate N-Reduction rate b\n1 2.055 0.939 0.402 0.402\n2 4.716 0.862 0.474 0.474\n3 2.774 0.834 0.434 0.434\n4 4.115 1.073 0.363 0.363\n5 3.649 0.889 0.323 0.323\nR24\n6 6.301 0.902 0.267 0.267\n7 3.395 0.922 0.386 0.386\n8 4.600 0.849 0.402 0.402\n9 3.483 0.784 0.398 0.398\n10 4.039 0.813 0.389 0.389\n11 7.788 0.846 0.487 0.487\n12 5.549 0.852 0.379 0.379\n13 8.149 0.836 0.437 0.437\n14 8.254 0.856 0.496 0.496\n15 5.886 0.808 0.514 0.514\n16 5.753 1.019 0.572 0.572\n17 4.741 0.479 0.528 0.528\n18 4.462 0.347 0.485 0.485\n19 4.522 0.452 0.452 0.452\n20 6.297 1.070 0.525 0.525\n21 5.102 0.947 0.504 0.504\n22 4.448 0.934 0.448 0.448\n23 8.975 0.756 0.441 0.441\n24 2.799 0.703 0.554 0.554\n25 5.892 0.611 0.550 0.550\n26 5.215 0.643 0.526 0.526\n27 4.371 0.538 0.578 0.578\n28 4.578 0.920 0.386 0.386\n29 8.897 1.099 0.295 0.295\n30 6.406 0.988 0.415 0.415\n31 4.949 1.149 0.465 0.465\n32 8.852 1.085 0.395 0.395\n33 7.587 0.744 0.284 0.284\n34 5.546 0.894 0.286 0.286\n35 8.337 0.854 0.168 0.168\n36 9.420 0.788 0.253 0.253\n37 6.182 0.616 0.603 0.603\n38 5.248 0.328 0.282 0.282\n39 7.483 0.229 0.433 0.433\n40 8.611 0.225 0.247 0.247\n41 6.010 0.295 0.225 0.225\n42 6.918 0.471 0.267 0.267\n43 7.711 0.805 0.238 0.238\n44 4.450 0.738 0.419 0.419\n45 6.368 0.796 0.141 0.141\n46 4.934 0.888 0.356 0.356\n47 5.233 0.894 0.416 0.416\n48 3.843 0.943 0.318 0.318\n49 9.254 35.865 0.936 0.838\nR25\n50 12.949 39.297 0.892 0.798\n51 10.253 30.367 0.935 0.837\n52 12.115 13.161 0.944 0.845\n53 11.468 21.810 0.880 0.788\n54 15.151 10.903 0.730 0.653\n55 11.115 12.714 0.843 0.755\n56 12.788 39.011 0.948 0.848\n57 11.238 18.636 0.931 0.833\n58 12.009 23.330 0.927 0.830\n59 17.216 18.891 0.917 0.821\n60 14.107 22.180 0.934 0.836\n61 17.718 24.576 0.905 0.810\n62 17.864 23.027 0.914 0.817\n63 14.575 2.877 0.544 0.486\n64 14.390 1.904 0.656 0.587\n65 12.985 7.425 0.851 0.761\n66 12.597 2.424 0.798 0.714\n67 12.681 11.245 0.915 0.819\n68 15.146 23.711 0.912 0.816\n69 13.486 18.137 0.873 0.782\n70 12.578 15.161 0.827 0.740\n71 18.865 18.462 0.819 0.733\n72 10.288 26.538 0.814 0.729\n73 14.583 13.358 0.817 0.731\n74 13.643 9.541 0.699 0.625\n75 12.471 21.715 0.919 0.822\n76 12.758 18.811 0.923 0.826\n77 18.757 14.620 0.829 0.741\n78 15.297 21.647 0.913 0.817\n79 13.274 15.906 0.894 0.800\n80 18.694 16.423 0.858 0.768\n81 16.937 24.433 0.958 0.858\n82 14.102 24.044 0.915 0.819\n83 17.978 24.323 0.899 0.804\n84 19.483 21.409 0.847 0.758\n85 14.986 18.101 0.868 0.777\n86 13.689 5.844 0.865 0.774\n87 16.792 6.841 0.820 0.733\n88 18.359 7.910 0.838 0.750\n89 14.747 16.998 0.789 0.706\n90 16.008 8.317 0.687 0.614\n91 17.109 16.862 0.850 0.761\n92 12.580 26.090 0.694 0.621\n93 15.244 28.929 0.932 0.834\nR26\n94 13.253 19.332 0.876 0.784\n95 13.668 24.322 0.887 0.794\n96 11.737 7.472 0.833 0.746\n97 13.844 4.493 0.888 0.888\n98 25.354 28.929 0.932 0.834\na 1-48 and 49-96 represented the results for the lake water control group and “+Algae” treatment group\nalong the middle and lower reaches of the Yangtze River. 97-98 represented the results for Chaohu Lake\nwater and its “+ algae” treatment group from the mechanism investigation experiment. The DOC in “+\nAlgae” treatments can be estimated as: [DOC]Lake water + algal biomass = (1.389 ± 0.075) × [DOC]lake water + (6.4\n± 0.638) (r2 adjust = 0.991, p = 0.003, see Supplementary, Fig. 12).\nb 49-96 and 98 represented the values of the inhibition rates of MeHg net production in the “+Algae”\ntreatments used in the model after normalization according to the “M1/AOM” ratio (i.e., 89.5%, where M1\nrepresents the mixture of 20% terrestrial organic matter and 80% AOM)\nNovelty of your research\nYour findings that increasing CO shifted the methanogen community from\n2\nacetoclastic (more significant methylators) to hydrogenotrophic (less\nmethylation capabilities) was very compelling and helpful for the scientific\ncommunity. I think you should comment on the energetics and biochemistry of\nhydrogenotrophic vs acetoclastic metabolism more. Hydrogenotrophic\nmetabolism produces more energy per mole of methane produced and is\npreferred if all substrates are equal. However, the law of the limiting factor\nsuggests that it is much more complex than simply carbon dioxide\nconcentration – which should not often be limited.\nCO + 4 H → CH + 2 H O\n2 2 4 2\nCH COOH → CH + CO\n3 4 2\nNote that 4 hydrogen molecules are needed per molecule of carbon dioxide. In\nanaerobic respiration, approximately 2 moles of H are produced per mole of\n2\nacetate. So as AOM is broken down, hydrogen will be limited compared to\nacetate and carbon dioxide and hydrogen production would need to be\nincreased in order to stimulate hydrogenotrophic metabolism. I suspect\nfermenting organisms that produce more hydrogen than acetate were involved,\nsince the incubations were performed in the dark and because you aerated the\ncontrol (which introduces oxygen), but only added CO to the treatments – they\n2\nmay not have had enough oxygen for redox compound cycling (Fe3+ -> Fe2+ ->\nFe3+; SO -> H S -> SO , etc). There are also algae, cyanobacteria, and\n4 2 4\ngreen/purple sulfur bacteria that produce hydrogen during photosynthesis.\nSince they would be stimulated by carbon dioxide (especially in 2 month\nincubation), they could increase the hydrogen concentration needed to\nR27\nencourage hydrogenotrophic growth and although your experiment didn't look\nat this, it is a reasonable connection to your results - increasing CO would\n2\nencourage them and increase hydrogen and carbon dioxide, ultimately\nselecting for hydrogenotrphic metabolism.\nResponse: We sincerely appreciate your theoretical analysis of CO -mediated\n2\nshifts in methanogen communities, particularly your insights from energy and\nhydrogen perspectives. These insights have significantly advanced our\nmechanistic understanding of these processes at molecular scales. Particularly,\nyour conceptual framework addressing substrate limitations and metabolic\ntrade-offs has provided a critical foundation for interpreting our experimental\nfindings.\nThe core distinction between hydrogenotrophic and acetoclastic\nmethanogenesis lies in their energy yields and substrate requirements.\nHydrogenotrophic methanogens generate methane via CO + 4 H → CH +\n2 2 4\n2\n1.5–2.5 ATP) compared to acetoclastic metabolism CH COOH → CH + CO ,\n3 4 2\nwhich yields only ~1 ATP per mole 24, 25. This energetic advantage explains why\nhydrogenotrophy is favored when substrates (especially H and CO ) are\n2 2\nabundant. However, as noted, H availability is often limiting: acetate\n2\nfermentation typically produces only 2 moles of H per mole of acetate, creating\n2\na bottleneck for hydrogenotrophic growth in natural systems.\nIn our experiments, the observed shift toward hydrogenotrophic\nmethanogens (e.g., Methanobacterium) under elevated CO likely reflects a\n2\nreorganization of substrate fluxes. Algal organic matter (AOM) degradation by\nfermentative bacteria—such as Clostridia and Enterobacter—would have\ngenerated both acetate and H via dark fermentation (e.g., glucose → 2\n2\nacetate + 2 CO + 4 H ). Elevated CO may have stimulated these fermenters\n2 2 2\nto prioritize H production over acetate, alleviating H limitation and enabling\n2 2\nhydrogenotrophs to outcompete acetoclastic populations (e.g., Methanosarcina)\ndespite their lower energy yield per substrate. This aligns with your hypothesis\nthat fermenting organisms play a pivotal role in mediating this shift in\nmethanogen communities.\nWe acknowledge your emphasis on H as a key regulator and regret that\n2\nour attempts to quantify headspace H were unsuccessful due to technical\n2\nconstraints: the combination of hydrogen’s low solubility and rapid microbial\nconsumption by methanogens created substantial methodological challenges\nfor accurate H quantification in our microcosm experiments. However, the\n2\nconsistent shift toward hydrogenotrophs across all treatments may indirectly\nsuggest that H availability remained sufficient to sustain methanogenic activity\nunder elevated CO conditions.\n2\nexplicitly excluded this pathway, as algae and cyanobacteria cannot perform\nphotosynthesis in the absence of light. Thus, H in our system must have\n2\noriginated from dark fermentation of AOM, reinforcing the role of fermentative\nbacteria as the primary H source.\nthese perspectives in Supplementary Text 2, emphasizing the interplay\nbetween fermentative H production and methanogenic pathways in driving the\n2\nobserved shifts (Page S7-S8, Line 124-156).\n“In the process of methanogenesis, H plays a crucial role, and its\n2\navailability is a key factor regulating the community structure of\nhydrogenotrophic and acetoclastic methanogens 26. Hydrogenotrophic\nmethanogenesis occurs through the reaction CO + 4 H → CH + 2 H O,\n2 2 4 2\nproducing approximately 1.5–2.5 ATP per mole of methane. In contrast,\nacetoclastic methanogenesis (CH COOH → CH + CO ) yields only about 1\n3 4 2\nATP per mole 24, 25. Therefore, hydrogenotrophic methanogenesis exhibits\nadvantages in energy output and is more favored when substrates such as H\n2\nand CO are abundant. However, the supply of H is often limited because\n2 2\nacetate fermentation typically produces only 2 moles of H per mole, which\n2\nbecomes a bottleneck for the growth of hydrogenotrophic methanogens in\nnatural systems 27.\nIn this study, the observed shift of the methanogen community towards\nhydrogenotrophic types (e.g., Methanobacterium) under high CO conditions\n2\nmay be since high CO stimulates fermentative bacteria such as Clostridia and\nglucose → 2 acetate + 2 CO + 4 H ), the substrate flux is adjusted to favor H\n2 2 2\nproduction over acetate, thereby alleviating H limitation and enabling\n2\nhydrogenotrophic methanogens to outcompete acetoclastic populations (e.g.,\nMethanosarcina). Nevertheless, the combination of hydrogen’s low solubility\nand rapid microbial consumption by methanogens created substantial\nmethodological challenges for accurate H quantification in our microcosm\n2\nexperiments. The consistent shift towards hydrogenotrophic methanogens\nacross all treatments suggests that H availability was sufficient to support their\n2\ngrowth under high CO conditions. Given the critical role of H , future research\n2 2\nshould focus on the following aspects: using more sensitive detection methods\nto track H dynamics in real-time to quantify its coupling with changes in the\n2\nmethanogen community; identifying key fermentative taxa responsible for H\n2\nproduction under high CO conditions; and measuring ATP yields in pure\ncultures of hydrogenotrophic and acetoclastic methanogens to directly verify\nenergy trade-offs, thereby further clarifying the role and mechanism of H in\n2\nmethanogenesis and community structure regulation under climate change.”\nMinor Comments\nFigure 2 – amazing graph, but it’s difficult to interpret that it is ~90% reduction\ndue to the way it is shown and the label. I would suggest having the Y axis be\n0% at the top and -100% at the bottom, with the larger bars further downwards.\nThis emphasized that CO had a negative impact on methylation. The Y axis\n2\nlabel is kind of confusing, I would suggest “MeHg production compared to the\ncontrol” and then the negative numbers will highlight that increased CO led to\n2\ndecreased MeHg production.\nResponse: We appreciate your constructive feedback on improving the clarity\nof Figure 2, which aims to communicate the inhibitory effect of elevated CO2\nmore effectively on MeHg production. To maintain consistency with the\nmanuscript’s use of “Inhibited MeHg production by elevated CO ”\n2\nterminology, we presented the Y-axis scale as 0%-100% (rather than 0%-[-\n100%]) throughout all figures. To ensure clarity, we have added explanatory\ninformation to clarify the axis labeling as follows: “Inhibited MeHg production by\nelevated CO are denoted as the percentage changes of MeHg levels between\n2\n“Ambient CO ” and “Elevated CO ”. For enhanced clarity, we present both\n2 2\ninhibition rates (Fig. 2) and MeHg concentrations (Supplementary Fig. 2).\nR30\nFig. 2 Elevated CO inhibited MeHg production under different scenarios. (a)\n2\ncompared to ambient 420 ppm CO . (b) Inhibition rate of MeHg production by elevated\n2 2\nµg·L-1 chlorophyll a). (c) Inhibition rate of MeHg production by elevated CO (1000 ppm)\n2\ncompared to ambient CO (420 ppm) under different stages of algal decomposition. “Lake\n2\nwater”: unfiltered lake water. “+200 Algae”, “+1000 Algae”, and “+2000 Algae”: unfiltered\nlake water (40 mL) amended with 0.001, 0.005, and 0.01 g of algal biomass, respectively.\nBefore sampling on “Day 0”, all lake water was equilibrated for 4 h with Hg(II). Inhibited\nMeHg production by elevated CO are denoted as the percentage changes of MeHg levels\n2\nbetween “Ambient CO ” and “Elevated CO ”. Error bars show standard errors of the mean\n2 2\n(n=3 or 4).\nR31\nSupplementary Fig. 2 Net MeHg production in different scenarios under ambient\n(420 ppm) or elevated CO (1000 ppm) conditions. (a) Different CO levels; (b) Different\n2 2\ntrophic statuses; (c) Different decomposition stages. “Lake water”: unfiltered lake water.\n“+200 Algae”, “+1000 Algae”, and “+2000 Algae”: unfiltered lake water (40 mL) amended\nwith 0.001, 0.005, and 0.01 g of algal biomass, respectively. Before sampling on “Day 0”,\nall lake water was equilibrated for 4 h with Hg(II). Different lowercase letters above the bars\nindicate significant differences among different treatments (p < 0.05). The asterisk above\nthe bars indicates a significant difference between “420 ppm CO ” and “1000 ppm CO ” (*:\n2 2\np < 0.05; **: p < 0.01; ***: p < 0.001; without *: p > 0.05).\nR32\nLine 340 – change to “collected using a plankton net and stored at 4℃”\nResponse: Revised as suggested.\nLine 347 – change experiment to experiments\nResponse: Revised as suggested.\nR33\nLine 351 – add “of” inbetween “the mechanisms of elevated CO -impacted”\nLine 363 – is this supposed to be 20 ml?\nResponse: A 2 ml sample volume was used because post-7-day-incubation\nMeHg concentrations exceeded 0.2 ng·L-1 across all treatment groups. Given\nthe calibration curve range of 0.5-50 pg, this loading volume ensures accurate\nquantification. To ensure clarity, we have added an explanation (Page 23, Line\n442-444): “A 2 mL sample volume was selected because, after the 7-day\nincubation, MeHg concentrations exceeded 0.2 ng·L⁻¹ in all treatments. Within\nthe calibration curve range of 0.5–50 pg, this volume ensured accurate\nquantification.”\nLine 386 – “the” is typed twice before observed, remove one.\nResponse: Thanks for identifying this typo. Revised as suggested.\nSupplemental Material\nFig T3-2 – adjust the X-axis so that it starts at 0.0. It is slightly off and starts <\n0.0.\nResponse: Revised as suggested.\nR34\nSupplementary Fig. T4-2 The levels of dissolved MeHg in the water from global lakes.\nR35\nReferences\n1. Zhang, Y.; Dutkiewicz, S.; Sunderland, E. M., Impacts of climate change on\nmethylmercury formation and bioaccumulation in the 21st century ocean. One Earth\n2021, 4, (2), 279-288.\n2. IPCC Climate Change 2023: Sixth Assessment Report (AR6). Intergovernmental\nPanel on Climate Change; 2023; p https://www.ipcc.ch/report/ar6/syr/.\n3. Yang, Z.; Fang, W.; Lu, X.; Sheng, G.-P.; Graham, D. 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M.; Liu, Y.-R.; Zhong, H.; Pan, K., Mechanisms of algal biomass\ninput enhanced microbial Hg methylation in lake sediments. Environment International\n2019, 126, 279-288.\n19. Li, Y.; Cai, Y., Progress in the study of mercury methylation and demethylation in\naquatic environments. Chinese Science Bulletin 2013, 58, (2), 177-185.\n20. Shi, X.; Zhao, X.; Zhang, M.; Yang, Z.; Xu, P.; Kong, F., The responses of\nphytoplankton communities to elevated CO show seasonal variations in the highly\n2\neutrophic Lake Taihu. Canadian Journal of Fisheries and Aquatic Sciences 2015, 73,\n(5), 727-736.\n21. Beckers, F.; Rinklebe, J., Cycling of mercury in the environment: Sources, fate, and\nhuman health implications: A review. Critical Reviews in Environmental Science and\nTechnology 2017, 47, (9), 693-794.\n22. Qiu, G.; Feng, X.; Wang, S.; Shang, L., Environmental contamination of mercury from\nHg-mining areas in Wuchuan, northeastern Guizhou, China. Environmental Pollution\n2006, 142, (3), 549-558.\n23. Wu, Z.; Li, Z.; Shao, B.; Zhang, Y.; He, W.; Lu, Y.; Gusvitskii, K.; Zhao, Y.; Liu, Y.; Wang,\nX.; Tong, Y., Impact of dissolved organic matter and environmental factors on\nmethylmercury concentrations across aquatic ecosystems inferred from a global\ndataset. Chemosphere 2022, 294, 133713.\n24. Conrad, R., Importance of hydrogenotrophic, aceticlastic and methylotrophic\nmethanogenesis for methane production in terrestrial, aquatic and other anoxic\nenvironments: A mini review. Pedosphere 2020, 30, (1), 25-39.\nR37\n25. Demirel, B.; Scherer, P., The roles of acetotrophic and hydrogenotrophic methanogens\nduring anaerobic conversion of biomass to methane: a review. Reviews in\nEnvironmental Science and Bio/Technology 2008, 7, (2), 173-190.\n26. Kakuk, B.; Wirth, R.; Maróti, G.; Szuhaj, M.; Rakhely, G.; Laczi, K.; Kovács, K. L.; Bagi,\nZ., Early response of methanogenic archaea to H as evaluated by metagenomics and\n2\nmetatranscriptomics. Microbial Cell Factories 2021, 20, (1), PMC8254922.\n27. Szuhaj, M.; Acs, N.; Tengölics, R.; Bodor, A.; Rákhely, G.; Kovács, K. L.; Bagi, Z.,\nConversion of H and CO to CH and acetate in fed-batch biogas reactors by mixed\n2 2 4\nbiogas community: a novel route for the power-to-gas concept. Biotechnology for\nBiofuels 2016, 9, 102.\nR37\nResponse to reviewers\nFor clarity, we present reviewer comments in italics, our response to comments\nin regular text, and a description of the changes to the manuscript in underlined\ntext. Revisions in the revised manuscript and supplementary information are\nhighlighted in yellow.\nReviewer #1:\nThis paper considers the question of how a warmer climate with higher\natmospheric CO concentrations and algal blooms will affect the concentrations\n2\nand net production of neurotoxic methylmercury in lakes. This is a relevant\nquestion, and the paper presents an ambitious, well-executed set of\nexperiments to answer that question. The authors have responded very well to\nan earlier round of reviews with new experiments and revisions to address the\nfirst set of reviews. I think this manuscript could be a valuable addition to the\nliterature that is appropriate to the readership of Nature Communications.\nResponse: We sincerely appreciate your positive evaluation and constructive\ncomments. Your feedback has helped us further improve the clarity, balance,\nand scientific accuracy of the manuscript.\nI have just two minor concerns that I suggest the authors’ address.\nThe first concern is that the paper claims that the findings show that elevated\nCO will counterbalance the effect of algal blooms on MeHg concentrations in\n2\nlake, and will help stabilize MeHg levels. I think this suggestion of stability in\nfuture MeHg levels is claiming somewhat more than is possible given the\ncomplexity of mercury methylations. Claiming a little less would be more\nconvincing in my ears. For instance, in the abstract (line 57), to write “counteract”\nrather than “counterbalance” would be more appropriate. I also don’t think it is\nappropriate to speak of ‘helping to stabilize future MeHg concentrations’\n(paraphrasing lines 59-60). To speak about counteracting is quite important\nenough.\nResponse: We sincerely thank you for this thoughtful suggestion. We fully\nagree that, given the complex and nonlinear nature of Hg methylation and\ndemethylation processes, future trends in MeHg levels should be described\nwith more caution. Following your recommendation, we have revised the\nabstract and all relevant sections to replace “counterbalance” (Abstract line 53)\nand change the statement of “stabilize” by “reduce uncertainties in predicting\nMeHg risks.” (Results and discussion line 108-109; 399-400). This wording\nmore accurately reflects our findings: elevated CO substantially attenuated the\nstabilization of MeHg concentrations. We believe this revised phrasing avoids\noverinterpretation while preserving the key scientific message.\nThe other minor concern is that I tried to look for what the effect of adding 1000\nug/L of algal organic matter without changing the CO levels was in the\n2\nexperiments. That information may be there, but I think this effect deserves to\nbe more clearly stated in the text (apologies if I missed that). I could locate in\ndiagrams the experimental effect of increasing CO without increasing algal\nthe focus on the effect of combining increases in algal organic matter and CO .\nof algal organic matter (AOM) addition under ambient CO conditions should\n2\nbe presented more explicitly in the main text to complement the combined\ntreatment results. In our experiments, AOM addition alone led to a substantial\nincrease in MeHg concentrations, with increases ranging from 86% to 4592%\nacross 45 eutrophic lakes in the middle and lower Yangtze River basin, yielding\nan average increase of 2345% (Supplementary Fig. 1a&c). This strong\nenhancement is consistent with our previous findings, e.g., Lei, P. et al.,\nEnvironmental Science & Technology, 2021, 55(15), 10811-10820, which\ndemonstrated that algal-derived organic matter stimulates methanogen\nabundance and activity, thereby promoting MeHg production in eutrophic lake\nwaters. In the original submission, we summarized this outcome briefly to\nmaintain focus on the combined effects of climate drivers. Following your\nsuggestion, we have explicitly stated the effect of AOM addition alone in the\nResults and discussion (lines 121–124): “Under ambient CO levels, AOM\n2\nmarkedly enhanced MeHg production, yielding 1–3 orders of magnitude higher\nlevels across 45 lakes (Supplementary Fig. 1), likely driven by stimulated\nmethanogenesis-mediated MeHg production”. These revisions make the\nindependent role of AOM clearer and strengthen the logical progression of our\nargument regarding interactive climate effects.\nSupplementary Fig. 1 Elevated CO (1000 ppm) decreased the levels of MeHg in water\n2\nfrom lakes along the middle and lower reaches of the Yangtze River. (a) the levels of MeHg\nin “Lake water” groups under 420 ppm CO . (b) the levels of MeHg in “Lake water” groups\n2\nunder 1000 ppm CO . (c) the levels of MeHg in “+ 1000 Algae” treatments under 420 ppm\n2 2.\nsampling time was set to Day 7. “+ 1000 Algae”: unfiltered lake water (40 mL) amended\nwith 0.005 g of algal biomass from Chaohu Lake. “Lake water”: unfiltered lake water with\nno algal addition.The map of the middle and lower reaches of the Yangtze River was\ngenerated using 1:1,000,000 National Fundamental Geographic Information Data of China\n(https://www.webmap.cn/commres.do?method=result100W)."}]

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