Effects of Temperature on Mercury Methylation and Demethylation in Boreal Wetland Soils
Bibliographic record
Abstract
Abstract Wetlands are critical sites for methylmercury (MeHg) production, a neurotoxin that bioaccumulates in organisms and biomagnifies in aquatic food webs. Net MeHg production in wetland soil depends on the balance between microbially mediated MeHg production and the degradation of MeHg through both microbial and abiotic pathways. Given that microbial activity is temperature sensitive, climate warming is expected to alter MeHg production in wetland soil. The temperature dependence of both mercury methylation and demethylation processes in boreal wetland soil remains unclear, thus inhibiting our ability to predict future changes. To address this research gap, we conducted a controlled, growth chamber‐based experiment in a closed flow‐through system incubating wetland soils across ecologically relevant temperatures (5°–25°C). Using additions of enriched mercury isotopes, we simultaneously measured first order rate constants for methylation (k m ) and demethylation (k d ). We found that temperature had a stronger effect on k m than k d , with no significant response from k d to warming temperatures. Methylation peaked at 20°C (∼5x higher than the lowest temperature (5°C)), with changes in k m being significantly related to wetland soil respiration, as measured by CO 2 and CH 4 production. Our study shows that mercury methylation becomes sulfate limited at higher temperatures (25°C), revealing a possible substrate availability negative feedback to mercury methylation. Thus, climate driven MeHg risks in boreal wetlands will depend on both warming and substrate supply interactions, which can feed back into methylation processes.
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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 | 0.000 | 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) | 0.001 | 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".