Algal Organic Matter Drives Methanogen-Mediated Methylmercury Production in Water from Eutrophic Shallow Lakes
Bibliographic record
Abstract
Algal blooms bring massive amounts of algal organic matter (AOM) into eutrophic lakes, which influences microbial methylmercury (MeHg) production. However, because of the complexity of AOM and its dynamic changes during algal decomposition, the relationship between AOM and microbial Hg methylators remains poorly understood, which hinders predicting MeHg production and its bioaccumulation in eutrophic shallow lakes. To address that, we explored the impacts of AOM on microbial Hg methylators and MeHg production by characterizing dissolved organic matter with Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) and three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy and quantifying the microbial Hg methylation gene hgcA . We first reveal that the predominance of methanogens, facilitated by eutrophication-induced carbon input, could drive MeHg production in lake water. Specifically, bioavailable components of AOM ( i.e., CHONs such as aromatic proteins and soluble microbial byproduct-like materials) increased the abundances (Archaea- hgcA gene: 438–2240% higher) and activities (net CH 4 production: 16.0–44.4% higher) of Archaea ( e.g., methanogens). These in turn led to enhanced dissolved MeHg levels (24.3–15,918% higher) for three major eutrophic shallow lakes in China. Nevertheless, our model results indicate that AOM-facilitated MeHg production could be offset by AOM-induced MeHg biodilution under eutrophication. Our study would help reduce uncertainties in predicting MeHg production, providing a basis for mitigating the MeHg risk in eutrophic lakes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".