Intraspecific complexity in mercury contamination of two harvested fishes revealed by genetics: Food security and conservation implications
Bibliographic record
Abstract
Contaminants in harvested species can pose serious concerns for health and food security. However, the risks of contaminant exposure can be challenging to track as many species migrate extensively between breeding and feeding environments and usually form genetically distinct populations. Such intraspecific complexity may translate into variation in exposure and bioaccumulation. We firstly investigated the genetic structure and the mixed-stock fishery origin of migratory Walleye (Sander vitreus) and Lake Whitefish (Coregonus clupeaformis) samples harvested from western Lake Athabasca and the Peace-Athabasca Delta (Alberta, Canada), using species-specific panels of single nucleotide polymorphisms (SNPs; n = 211-357 loci). We then explored which variables impacted mercury concentration in fish muscle tissue, including breeding (distinct populations) and feeding environments (fishery capture location). We identified two genetically distinct populations in each species whose harvest proportions differed between the lake and delta. In both species, the population spawning in the river upstream of, and migrating through the Alberta Oil Sands was exposed to higher mercury levels. In Walleye, this translated into 65 % more mercury than in the second population, with 43 % of individuals exceeding Health Canada recommended levels for human consumption. In Whitefish, river spawners, which were much younger and contributed more the harvest, had higher mercury concentrations than lake spawners when controlling for age. We also found different relationships between mercury and individual heterozygosity or body condition among populations. Collectively, our results reveal varying mercury loads at the population level in two fishes with widespread importance for fisheries, highlighting the utility of genetic-based monitoring to better understand contaminants.
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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 | 0.001 |
| 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 | 0.000 | 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".