Hatchery Strain Contributions to Emerging Wild Lake Trout Populations in Lake Huron
Bibliographic record
Abstract
Recent assessments indicate the emergence of naturally produced lake trout (Salvelinus namaycush) recruitment throughout Lake Huron in the North American Laurentian Great Lakes (>50% of fish <7 years). Because naturally produced fish derived from different stocked hatchery strains are unmarked, managers cannot distinguish strains contributing to natural recruitment. We used 15 microsatellite loci to identify strains of naturally produced lake trout (N = 1567) collected in assessment fisheries during early (2002-2004) and late (2009-2012) sampling periods. Individuals from 13 American and Canadian hatchery strains (N = 1143) were genotyped to develop standardized baseline information. Strain contributions were estimated using a Bayesian inferential approach. Deviance information criteria were used to compare models evaluating strain contributions at different spatial and temporal scales. The best performing models were the most complex models, suggesting that hatchery strain contributions to naturally produced lake trout varied spatially among management districts and temporally between time periods. Contributions of Seneca strain lake trout were consistently high across most management districts, with contributions increasing from early to late time periods (estimates ranged from 52% to 94% for the late period across 8 of 9 districts). Strain contributions deviated from expectations based on historical stocking levels, indicating strains differed with respect to survival, reproductive success, and/or dispersal. Knowledge of recruitment levels of strains stocked in different management districts, and how strain-specific recruitment varies temporally, spatially, and as a function of local or regional stocking is important to prioritize strains for future stocking and management of the transition process from primarily hatchery to naturally produced stocks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".