Genomics‐Enabled Mixed‐Stock Analysis Uncovers Intraspecific Migratory Complexity and Detects Unsampled Populations in a Harvested Fish
Bibliographic record
Abstract
The contributions of distinct populations to annual harvests provide key insights to conservation, especially in migratory species that return to specific reproductive areas. In this context, genetic stock identification (GSI) requires reference samples from source populations to assign harvested individuals, yet sampling might be challenging as reproductive areas could be remote and/or unknown. To investigate intraspecific variation in walleye (Sander vitreus) populations harvested in two large lakes in northern Quebec, we used genotyping-by-sequencing data to develop a panel of 303 filtered single-nucleotide polymorphisms. We then genotyped 1465 fish and assessed individual migration distances from GPS coordinates of capture locations. Samples were assigned to a source population using two methods, one requiring allele frequencies of known populations (RUBIAS) and the other without prior knowledge (STRUCTURE). Individual assignments to a known population reached 93% consistency between both methods in the main lake where we identified all five major source populations. However, the analyses also revealed up to three small unsampled populations. Furthermore, populations were characterised by large differences in average migration distance. In contrast, assignment consistency reached 99% in the neighbouring lake and walleye were assigned with high confidence to two populations having a similar distribution throughout the lake. The complex population structure and migration patterns in the main lake suggest a more heterogeneous habitat and thus, greater potential for local adaptation. This study highlights how combining analytical approaches can inform the robustness of GSI results in a given system and detect intraspecific diversity and complexity relevant for conservation.
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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 | 0.001 | 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".