Ground warming releases inorganic mercury and increases net methylmercury production in two boreal peatland types
Bibliographic record
Abstract
Boreal peatlands are considered sinks for atmospheric mercury (Hg) and are important sources of methylmercury (MeHg) to downstream ecosystems. Climate change-driven increases in average annual temperature in coming decades will be amplified at higher latitudes and will modify many biogeochemical processes in high boreal and subarctic peatlands that are important landscape features in these regions. Changes in water quality are an important issue for Northern ecosystems and fish consumers, and the directionality of changes in mercury levels due to climate warming presents considerable uncertainty. Peatlands are key landscape hotspots for MeHg production, however, the impact of climate warming on Hg cycling in boreal peatlands is not well studied. We use a multi-year field-based warming experiment (2 years passive, 1 year active ground warming) across two boreal peatland types (moss and sedge dominated) to explore the effects of ground warming on inorganic Hg (IHg) release, net MeHg production, and biogeochemical controls on both of these processes including the availability of sulfate (SO 4 2− ) and dissolved organic matter (DOM) quality and concentration. There were higher porewater IHg and MeHg concentrations under active ground warming conditions in both peatlands, likely related to both increased microbial metabolism, and changes in biogeochemical conditions that favor Hg methylation. Both SO 4 2− (electron acceptor) and bioaccessible DOM (electron donor) are nutrients for sulfate-reducing bacteria which are dominant Hg methylators in freshwater environments, and increases in SO 4 2− and/or bioaccessible DOM concentrations under warming played an important role in the observed changes in net MeHg production. Warming increased SO 4 2− concentrations in the sedge-dominated but not in the moss-dominated fen likely because of a larger pool of groundwater derived SO 4 2− in the sedge-dominated site. Warming increased DOM concentration in both peatland sites through enhanced decomposition of peat and increased release of root exudates from vascular plants, and the balance of these processes varied by peatland type and degree of warming. Experimentally increased ground temperatures increased microbial metabolism, organic matter turnover, and the availability of IHg all of which resulted in increases in porewater MeHg, indicating that climate-driven ground warming will increase MeHg production in northern peatlands in the future.
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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 | 0.001 |
| 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".