Biological Factors Moderate Trace Element Accumulation in Fish along an Environmental Concentration Gradient
Bibliographic record
Abstract
Abstract Trace elements can accumulate in aquatic food webs, becoming potentially hazardous to wildlife and human health. Whereas many studies have examined mercury dynamics in freshwater environments, evidence for the bioaccumulative potential of other trace elements (e.g., arsenic) is conflicting. Trace element concentrations found in surface water of the Red Deer River, Alberta, Canada, have raised concern for potential accumulation in aquatic biota. We investigated fish from this river to better understand the influence of biological and environmental factors in trace element bioaccumulation. We analyzed 20 trace elements, and stable nitrogen (δ15N) and carbon (δ13C) isotopes, in the muscle tissue of 8 species. Zinc, selenium, arsenic, chromium, and nickel were detected in the majority of fish at low concentrations. However, mercury was detected in all fish and often exceeded criteria for the protection of consumers. Body size was often positively correlated with trace element concentrations. In addition, δ15N and δ13C were correlated to mercury and arsenic concentrations, indicating that mercury biomagnifies whereas arsenic biodiminishes. Spatial patterns of fish trace element concentrations did not reflect differences in surface water concentrations. These findings indicate that fish trace element concentrations are primarily moderated by biological factors, such as trophic position and body size, and are not locally restricted to areas of relatively high environmental concentrations in the Red Deer River. Environ Toxicol Chem 2021;40:422–434. © 2020 SETAC Abstract Biological characteristics, such as body size and trophic level, have a stronger influence on fish trace element concentrations (e.g., mercury) than an upstream to downstream environmental concentration gradient.
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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 | 0.001 | 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".