Genomics-based Mixed-stock Analysis of Brook Trout Reveals Cryptic Population Structure and Complex Lake Migrations
Bibliographic record
Abstract
Effective fishery management relies on knowing the contributions of genetically distinct populations to mixed-stock harvests. We investigated population genomic structure and harvest contributions of lake-migratory brook trout inhabiting three large Quebec lakes (Mistassini, Mistasiniishish, Waconichi). These brook trout support fisheries important to the Cree Nation of Mistissini and their tourism outfitting industry. Together with local partners we collected 1063 samples from spawning sites and feeding areas between 2020-2022. We then used a GTseq (Genotyping-in-Thousands by sequencing) panel of 393 single nucleotide polymorphisms to: i) infer population genetic structure and test for unknown populations; ii) assign individuals to their population of origin, and iii) determine harvest contributions of genetically distinct populations. Our results revealed population structure in two of three study lakes and extensive movements of brook trout, with some individuals travelling over 100km away from spawning rivers. In the largest lake (Mistassini), two of three populations contributed over 90% of the lake’s harvest and exhibited distinct spatial distributions that were stable across years. In Mistasiniishish Lake, over 80% of harvested trout originated from a single, previously known population; the remaining trout originated from a cryptic, unsampled population with a strongly overlapping spatial distribution. No population structure was detected in Waconichi Lake. We also detected low levels of migration from Mistasiniishish Lake into Mistassini Lake through a waterfall historically reported to be a dispersal barrier. Our results illustrate the precision afforded by GTseq to inform insights into the ecology and genetics of lake-migratory salmonids, thereby facilitating local management 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".