Elevated temperature and browning increase dietary methylmercury, but decrease essential fatty acids at the base of lake food webs
Bibliographic record
Abstract
Climate change scenarios predict increases in temperature and organic matter supply from land to water, which affect trophic transfer of nutrients and contaminants in aquatic food webs. How essential nutrients, such as polyunsaturated fatty acids (PUFA), and potentially toxic contaminants, such as methylmercury (MeHg), at the base of aquatic food webs will be affected under climate change scenarios, remains unclear. The objective of this outdoor mesocosm study was to examine how increased water temperature and terrestrially-derived dissolved organic matter supply (tDOM; i.e., lake browning), and the interaction of both, will influence MeHg and PUFA in organisms at the base of food webs (i.e. seston; the most edible plankton size for zooplankton) in subalpine lake ecosystems. The interaction of higher temperature and tDOM increased the burden of MeHg in seston (< 40 μm) and larger sized plankton (microplankton; 40-200 μm), while the MeHg content per unit biomass remained stable. However, PUFA decreased in seston, but increased in microplankton, consisting mainly of filamentous algae, which are less readily bioavailable to zooplankton. We revealed elevated dietary exposure to MeHg, yet decreased supply of dietary PUFA to aquatic consumers with increasing temperature and tDOM supply. This experimental study provides evidence that the overall food quality at the base of aquatic food webs deteriorates during ongoing climate change scenarios by increasing the supply of toxic MeHg and lowering the dietary access to essential nutrients of consumers at higher trophic levels.
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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".