Terrestrial carbon inputs drive methylmercury accumulation in zooplankton of boreal and subarctic lakes
Bibliographic record
Abstract
Abstract Boreal and subarctic lakes are subject to the climate‐sensitive process of browning, whereby transport of terrestrial dissolved organic matter (tDOM) to lakes results in greater dissolved organic carbon (DOC) concentrations and associated darker water color. Increasing tDOM will increase mercury (Hg) transport to these lakes, but whether this leads to greater methylHg (MeHg) bioaccumulation in food webs remains unclear. We determined whether increasing DOC increased MeHg bioaccumulation in the lower food web (i.e., zooplankton) by measuring a suite of water chemistry characteristics (including aqueous MeHg and DOC) along with stable isotopes of C (δ13C) and N (δ15N), fatty acid (FA) profiles, and MeHg content of zooplankton from 16 Scandinavian boreal and subarctic lakes along a DOC gradient in the Fall of 2016. We found that both aqueous and zooplankton MeHg were positively correlated with DOC concentration, and that DOC and zooplankton MeHg both increased with the bacterial FA marker 18:1n‐7 and decreased with docosahexaenoic acid (DHA) : arachidonic acid and DHA : eicosapentaenoic acid ratios in zooplankton, which are indicators of diet or taxonomic composition. Zooplankton MeHg content was best predicted by δ13C and the FA 18:1n‐7, indicating that zooplankton MeHg bioaccumulation in zooplankton was associated with changes in their resource use along a DOC gradient. Our results suggest that lake browning will likely lead to an increase in MeHg bioaccumulation in zooplankton by affecting aqueous MeHg exposure and lower food web dynamics. In turn, this may lead to increased MeHg contamination in fish and other wildlife.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".