Investigating effects of climate-induced changes in water temperature and diet on mercury concentrations in an Arctic freshwater forage fish
Bibliographic record
Abstract
The amount of mercury (Hg) in Arctic lake food webs is, and will continue to be, affected by rapid, ongoing climate change. At warmer temperatures, fish require more energy to sustain growth; changes in their metabolic rates and consuming prey with potentially higher Hg concentrations could result in increased Hg accumulation. To examine the potential implications of climate warming on forage fish Hg accumulation in Arctic lakes, we quantified growth and Hg accumulation in Ninespine Stickleback Pungitius pungitius under different temperature and diet scenarios using bioenergetics models. Four scenarios were considered that examined the role of climate, diet, climate × diet, and climate × diet × elevated prey Hg. As expected, annual fish growth increased with warmer temperatures, but growth rates and Hg accumulation were largely diet dependent. Compared to current growth rates of 0.3 g⋅y−1, fish growth increased at least 200% for fish consuming energy-dense benthic prey and decreased at least 40% for fish consuming pelagic prey. Compared to baseline levels, the Hg burden per kilocalorie of Ninespine Stickleback declined up to 43% with benthic consumption – indicating strong somatic growth dilution – but no more than 4% with pelagic consumption; elevated prey Hg concentrations led to moderate Hg declines in benthic-foraging fish and Hg increases in pelagic-foraging fish. Bioenergetics models demonstrated the complex interaction of water temperature, growth, prey proportions, and prey Hg concentrations that respond to climate change. Further work is needed to resolve mechanisms and rates linking climate change to Hg availability and uptake in Arctic freshwater systems.
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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".