American Dippers Indicate Contaminant Biotransport by Pacific Salmon
Bibliographic record
Abstract
Migrating salmon can increase productivity in Pacific Northwestern streams and lakes through the deposition of nutrients from their decomposing carcasses after spawning. Several studies also report simultaneous biotransport of persistent organic pollutants that have contaminated lake food webs, although no similar effect has been shown conclusively in rivers. We tested the prediction that salmon enhance contaminants in river food webs using the American dipper (Cinclus mexicanus), an aquatic songbird and a recognized indicator of stream quality. Over 3 years, we analyzed 29 dipper eggs and aquatic invertebrate samples from 14 different rivers in 10 catchments in southern British Columbia, Canada to assess whether variations in autumn spawning density of Pacific salmon were reflected in dipper egg contamination or stable carbon and nitrogen isotopes. δ(13)C isotope signatures, but not δ(15)N, in aquatic invertebrates and dipper eggs increased among catchments in proportion to the average density of spawning salmon. Concentrations of brominated flame retardants (PBDEs), dichlorodiphenyltrichloroethane metabolites (DDTs), and chlordane compounds were related in part to the δ(13)C measure of salmon density, but mercury, chlorobenzenes, and polychlorinated biphenyls (PCBs) were explained better by dipper trophic level. We conclude that spawning Pacific salmon result in the increased availability of salmon fry as dipper prey and salmon are a significant source of PBDEs, DDTs, and chlordanes to river ecosystems. However, contrary to lake studies, postspawn concentrations of legacy PCBs in river birds, even in salmon-rich rivers, were not significantly higher than would be expected from atmospheric deposition alone. We recommend using δ(13)C isotopes to trace salmon-derived lipids which may persist over winter particularly in rivers, and are potentially a better reflection of lipophilic contaminant transfer.
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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) | 0.002 | 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".