Mixed-stock analysis reveals long-distance movements and few populations with large harvest contributions in lake-migratory brook trout
Bibliographic record
Abstract
Effective fishery management relies on knowing the relative contributions of distinct populations to mixed-stock harvests. Mixed-stock analyses increasingly adopt single-nucleotide polymorphism (SNP) panels but could make better use of precise spatial information to improve understanding of population distributions, movements, and structure. Together with local partners, we collected and georeferenced 1051 samples of lake-migratory brook trout between 2020-2022 from three large lakes in Quebec (Mistassini, Mistasiniishish, Waconichi). We then used a GT-seq (Genotyping-in-Thousands by sequencing) SNP panel to infer population genetic structure and determine spatial harvest contributions of genetically distinct populations. Our results revealed population structure in two of three study lakes, with few populations (n = 1-2) contributing the majority (> 80%) of mixed-stock harvest in each lake. We also detected extensive movements of brook trout within and between lakes, spatial segregation of populations in one lake, and an unknown (unsampled) population in another lake. Our results illustrate the precision afforded by combining GT-seq and georeferencing of samples to generate insights into the ecology and genetics of migratory fishes, thereby facilitating local decision-making for sustainable fisheries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".