Freshwater discharges drive high levels of methylmercury in Arctic marine biota
Bibliographic record
Abstract
Elevated levels of neurotoxic methylmercury in Arctic food-webs pose health risks for indigenous populations that consume large quantities of marine mammals and fish. Estuaries provide critical hunting and fishing territory for these populations, and, until recently, benthic sediment was thought to be the main methylmercury source for coastal fish. New hydroelectric developments are being proposed in many northern ecosystems, and the ecological impacts of this industry relative to accelerating climate changes are poorly characterized. Here we evaluate the competing impacts of climate-driven changes in northern ecosystems and reservoir flooding on methylmercury production and bioaccumulation through a case study of a stratified sub-Arctic estuarine fjord in Labrador, Canada. Methylmercury bioaccumulation in zooplankton is higher than in midlatitude ecosystems. Direct measurements and modeling show that currently the largest methylmercury source is production in oxic surface seawater. Water-column methylation is highest in stratified surface waters near the river mouth because of the stimulating effects of terrestrial organic matter on methylating microbes. We attribute enhanced biomagnification in plankton to a thin layer of marine snow widely observed in stratified systems that concentrates microbial methylation and multiple trophic levels of zooplankton in a vertically restricted zone. Large freshwater inputs and the extensive Arctic Ocean continental shelf mean these processes are likely widespread and will be enhanced by future increases in water-column stratification, exacerbating high biological methylmercury concentrations. Soil flooding experiments indicate that near-term changes expected from reservoir creation will increase methylmercury inputs to the estuary by 25-200%, overwhelming climate-driven changes over the next decade.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".