Impact of flow diversion by run-of-river dams on American dipper diet and mercury exposure
Bibliographic record
Abstract
Abstract Run-of-river dams produce lower greenhouse gas emissions than large hydropower projects, but there is a paucity of research on their potential ecotoxicological impacts through disruption of natural flow regimes. We used stable isotopes (δ13C, δ15N, δ34S) to reconstruct diet and trace methylmercury in a predatory river-resident passerine, the American dipper (Cinclus mexicanus), at 7 regulated and 6 free-flowing mountain streams in coastal British Columbia, Canada. Assimilated diets were comparable among regulated and unregulated streams, dominated by benthic macroinvertebrates and resident freshwater fish, with negligible contributions from anadromous Pacific salmon. Although invertebrates at unregulated streams were isotopically similar along their gradient, dippers and invertebrates sampled below dams on regulated streams had 34S-depleted tissues, suggesting increased activity of sulfate-reducing bacteria and more Hg methylation below the dams. Mercury concentrations in dipper blood (417.6 ± 74.1 standard error [SE] ng/g wet wt at regulated streams, 340.7 ± 42.7 SE ng/g wet wt at unregulated streams) and feathers (1564 .6 ± 367.2 SE ng/g dry wt regulated, 1149.0 ± 152.1 SE ng/g dry wt unregulated), however, were not significantly different between stream types. Relative to other passerines across western North America, dippers in these densely forested mountain streams experienced high mercury exposure; and one recently regulated stream supported dippers with mercury concentrations of potential toxicity concern (up to 8459.5 ng/g dry wt in feathers and 1824.6 ng/g wet wt in whole blood). Elevated mercury in dippers is likely attributable to the birds’ relatively high trophic position and high regional inorganic mercury deposition; however, biogeochemical conditions in reservoirs of some regulated streams may be contributing to methylmercury production. Environ Toxicol Chem 2018;37:411–426. © 2017 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 | 0.000 | 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".