Mercury Transformation in Greenland’s Thermokarst Lakes: A Combined Geochemical and Microbial Perspective
Bibliographic record
Abstract
Arctic warming accelerates permafrost thaw, driving the formation of thermokarst lakes. These ecosystems alter the mercury (Hg) biogeochemical cycle, possibly leading to the formation of highly toxic methylmercury (MMHg). This process is mainly conducted by microorganisms possessing hgcA and hgcB genes. To investigate the Hg dynamics and identify Hg-related microbiome, two thermokarst lakes (ZAC1 and ZAC2) were sampled in the Zackenberg Valley, Northeast Greenland. Total Hg (THg) and MMHg concentrations were measured in the water column and sediments, where methylation and demethylation rates were also calculated along with the potential impact of known Hg-methylating microorganisms. Sediment DNA was isolated to assess the presence and abundance of marker genes associated with Hg-related processes. While no differences were found in water column Hg concentrations, ZAC2 THg and MMHg sediment concentrations were 2-fold greater than ZAC1. The demethylation potential was similar in the sediments of both lakes. However, ZAC2 exhibited higher methylation rates, supported by a greater availability of organic substrate and higher abundance of hgcA and hgcB sequences. The microbial community also outlines potential for methane (CH4) production, sulfate (SO42-) reduction, and conversion of divalent Hg (Hg2+) in elemental Hg (Hg0). No evidence of merB-mediated MMHg degradation was found. This study presents the first characterization of Hg-related microbial communities in Greenland’s thermokarst lakes. Moreover, it emphasizes the lake development stage as an important determinant of sediment methylation potential and the critical role of sulfate-reducing bacteria (SRB) in Hg methylation, integrating evidence from Hg stable isotope incubation experiments and gene-centric metagenomic analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".