Ecological Drivers of Mercury Bioaccumulation in Fish of a Subarctic Watercourse
Bibliographic record
Abstract
Abstract Mercury (Hg) is a serious concern for aquatic ecosystems because it may biomagnify to harmful concentrations within food webs and consequently end up in humans that eat fish. However, the trophic transfer of mercury through the aquatic food web may be impacted by several factors related to network complexity and the ecology of the species present. The present study addresses the interplay between trophic ecology and mercury contamination in the fish communities of two lakes in a pollution-impacted subarctic watercourse, exploring the role of both horizontal (feeding habitat) and vertical (trophic position) food web characteristics as drivers for the Hg contamination in fish. The lakes are located in the upper and lower parts of the watercourse, with the lower site located closer to, and downstream from, the main pollution source. The lakes have complex fish communities dominated by coregonids (polymorphic whitefish and invasive vendace) and several piscivorous species. Analyses of habitat use, stomach contents, and stable isotope signatures (δ15N, δ13C) revealed similar food web structures in the two lakes except for a few differences chiefly related to ecological effects of the invasive vendace. The piscivores had higher Hg concentrations than invertebrate-feeding fish. Concentrations increased with size and age for the piscivores and vendace, whereas habitat differences were of minor importance. Most fish species showed significant differences in Hg concentrations between the lakes, the highest values typically found in the downstream site where the biomagnification rate also was higher. Mercury levels in piscivorous fish included concentrations that exceed health authorization limits, with possible negative implications for fishing and human consumption. Our findings accentuate the importance of acquiring detailed knowledge of the drivers that can magnify Hg concentrations in fish and how these may vary within and among aquatic systems, to provide a scientific basis for adequate management strategies. Environ Toxicol Chem 2023;42:873–887. © 2023 SETAC
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".