A multi-species mixed-stock analysis reveals latitudinal differences in stock overlap with implications for supporting local indigenous fisheries
Bibliographic record
Abstract
Abstract Indigenous subsistence fisheries are critical to food security and cultural continuity in northern communities but often lack tools to monitor stock contributions to mixed-stock fisheries. We used population-level genetic differentiation to quantify lake whitefish and brook charr relative stock contributions to Cree First Nations subsistence harvests along the eastern coast of James Bay, Canada, which spans >400 km. Using two Genotyping-in-Thousands by sequencing panels, we characterized the genetic composition of ~3800 harvested fish from multiple coastal sites over 5 years. Our analyses revealed a pronounced latitudinal gradient in stock mixing, with the two southernmost sites exhibiting higher levels of inter-population mixing and the northern sites displaying a more homogeneous stock composition for both species. Notably, up to 10% of individuals in southern harvests originated from northern stocks, suggesting extensive southward oceanic movements, particularly for lake whitefish, perhaps driven by ecological factors such as access to productive feeding areas and longer growing seasons. In contrast, we detected no southern fish in the north, indicating a predominantly southward, asymmetric movement pattern. This spatial stock structure supports localized management in most areas but highlights challenges for stock-specific monitoring and the need for integrated, coast-wide strategies in highly mixed southern fisheries. Our study underscores the ecological complexity of stock connectivity and its implications for the sustainability of culturally and economically vital northern fisheries. Keywords: indigenous fisheries; fisheries management; GT-seq, genetic stock identification; mixed-stock analysis; migration; anadromous salmonids
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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 | 0.001 | 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 | 0.000 |
| 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".