Microsatellite Variation in Yukon River Coho Salmon: Population Structure and Application to Mixed-Stock Analysis
Bibliographic record
Abstract
Abstract Knowledge of population structure facilitates the effective management of species that are harvested in mixed-stock fisheries. In this study, we analyzed genetic variation at 19 microsatellite loci for 14 populations of Coho Salmon Oncorhynchus kisutch in the Yukon River. We then used this data to estimate the stock composition of adults that were harvested in a lower river test fishery. The Coho Salmon populations in the Yukon River exhibited a high degree of geographically based genetic structure (GST = 0.078), with a strong genetic disjunction between the lower and upper river populations. Further substructure was observed among the upper river populations, which allowed for the estimation of stock composition to areas within this region. Analyses involving simulated and real mixtures indicated that this level of divergence can be used to apportion Coho Salmon to regions and areas accurately (95–100%). The stock composition estimates for the test fishery revealed that the spawning migration was evenly divided (50:50) between the lower and upper river populations. The upper river populations generally had earlier migration timing, comprising 66% of the mixture in the beginning and 33% at the end. The largest component of the upper river region was Tanana at 44%, followed by Nenana at 5%, and Porcupine at 1%. While escapement monitoring is essential for the management of Coho Salmon, it is limited by funding shortfalls. However, mixed-stock analysis in conjunction with sonar enumeration can provide information on stock-specific proportions, abundances, and migration timings that can increase our knowledge and ability to manage Coho Salmon populations in the Yukon River.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".