Contaminants and nutrients in fish from the Wolastoq | Saint John River (New Brunswick): Spatial and species variability
Bibliographic record
Abstract
Fish provide a rich source of nutrients, like the omega-3 fatty acids (FAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), but can also accumulate harmful contaminants such as mercury (Hg), organochlorine contaminants and trace elements. In rivers, dams can alter the distribution and biogeochemical cycling of contaminants and nutrients, which can sometimes increase their availability to and uptake in fish. However, it is unclear whether FAs covary with contaminants within and among species along dammed systems. This study examined the spatial and species differences in contaminants and nutrients in fish from the Wolastoq | Saint John River (New Brunswick), which has a large hydroelectric dam and supports the subsistence fishing of six First Nation communities. In 2020 and 2021, Smallmouth Bass, Yellow Perch, American Eel and Striped Bass were collected from locations upstream and downstream of the dam and analyzed for Hg, FAs, organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), trace elements and stable isotope ratios of nitrogen (δ15N) and carbon (δ13C), to determine the trophic position and basal carbon source of the fish, respectively. Contaminants varied by species where the lipid-rich American Eel were highest in lipophilic contaminants (OCPs and PCBs) and the top predator Striped Bass was highest in biomagnifying elements like Hg and selenium. Furthermore, EPA was highest in Yellow Perch while greater DHA concentrations were observed in higher-trophic-level fish, both within and among species. Fish from the dam’s reservoir were highest in the elements sulfur and phosphorus, and Hg for Yellow Perch. The dam also appeared to alter food web dynamics as fish from the reservoir and just below the dam had higher trophic positions, and reservoir fish were depleted in δ13C. Preliminary risk-benefit analyses indicated that the fish do not provide optimal EPA + DHA intake if they are consumed at levels that are considered safe for contaminants like Hg. Overall, this study suggests that dams can alter food web dynamics and the uptake of contaminants and nutrients by fish, and that location and species are important factors to include in risk-benefit analyses for fish consumption.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".