Elevated Mercury Concentrations and Isotope Signatures (N, C, Hg) in Yellowfin Tuna (<i>Thunnus albacares</i>) from the Galápagos Marine Reserve and Waters off Ecuador
Bibliographic record
Abstract
Abstract We examined how dietary factors recorded by C and N influence Hg uptake in 347 individuals of yellowfin tuna (Thunnus albacares), an important subsistence resource from the Galápagos Marine Reserve (Ecuador) and the Ecuadorian mainland coast in 2015–2016. We found no differences in total Hg (THg) measured in red muscle between the two regions and no seasonal differences, likely due to the age of the fish and slow elimination rates of Hg. Our THg concentrations are comparable to those of other studies in the Pacific (0.20-9.60 mg/kg wet wt), but a subset of individuals exhibited the highest Hg concentrations yet reported in yellowfin tuna. Mercury isotope values differed between Δ199Hg and δ202Hg in both regions (Δ199Hg = 2.86 ± 0.04‰ vs. Δ199Hg = 2.33 ± 0.07‰), likely related to shifting food webs and differing photochemical processing of Hg prior to entry into the food web. There were significantly lower values of both δ15N and δ13C in tuna from Galápagos Marine Reserve (δ15N: 8.5–14.2‰, δ13C: −18.5 to −16.1‰) compared with those from the Ecuadorian mainland coast (δ15N: 8.3–14.4‰, δ13C: −19.4 to −11.9‰), of which δ13C values suggest spatially constrained movements of tuna. Results from the pooled analysis, without considering region, indicated that variations in δ13C and δ15N values tracked changes of Hg stable isotopes. Our data indicate that the individual tuna we used were resident fish of each region and were heavily influenced by upwellings related to the eastern Pacific oxygen minimum zone and the Humboldt Current System. The isotopes C, N, and Hg reflect foraging behavior mainly on epipelagic prey in shallow waters and that food web shifts drive Hg variations between these populations of tuna. Environ Toxicol Chem 2022;41:2732–2744. © 2022 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 | 0.000 |
| 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".