Mechanisms of algal biomass input enhanced microbial Hg methylation in lake sediments
Bibliographic record
Abstract
Eutrophication is a major environmental concern in lake systems, impacting the ecological risks of contaminants and drinking water safety. It has long been believed that eutrophication and thus algal blooms would reduce methylmercury (MeHg) levels in water, as well as MeHg bioaccumulation and trophic transfer (e.g., by growth dilution). In this study, however, we demonstrated that algae settlement and decomposition after algal blooms increased MeHg levels in sediments (54-514% higher), as evidenced by the results from sediments in 10 major lakes in China. These could in turn raise concerns about enhanced trophic transfer of MeHg and deterioration of water quality after algal blooms, especially considering that 9 out of the 10 examined lakes also serve as drinking water sources. The enhanced microbial MeHg production in sediments could be explained by the algal organic matter (AOM)-enhanced abundances of microbial methylators as well as the input of algae-inhabited microbes into sediments, but not Hg speciation in sediments: (1) Several AOM components (e.g., aromatic proteins and soluble microbial by product-like material with generally low molecular weights), rather than the bulk AOM, played key roles in enhancing the abundances of microbial methylators. The copies of Archaea-hgcA methylation genes were 51-397% higher in algae-added sediments; thus, MeHg production was also higher. (2) Input of algal biomass-inhabited microbial methylators contributed to 2-21% of total Archaea-hgcA in the 10 lake sediments with added algal biomass. (3) However, AOM-induced changes in Hg speciation, with implications on Hg availability to microbial methylators, played a minor role in enhancing microbial Hg methylation in sediments as seen in X-ray absorption near edge structure (XANES) data. Our results suggest the need to better understand the biogeochemistry and risks of contaminants in eutrophic lakes, especially during the period of algae settlement and decomposition following algal blooms.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".