Loss of genetic integrity in wild lake trout populations following stocking: insights from an exhaustive study of 72 lakes from <scp>Q</scp>uébec, <scp>C</scp>anada
Bibliographic record
Abstract
Stocking represents the most important management tool worldwide to increase and sustain commercial and recreational fisheries in a context of overexploitation. Genetic impacts of this practice have been investigated in many studies, which examined population and individual admixture, but few have investigated determinants of these processes. Here, we addressed these questions from the genotyping at 19 microsatellite loci of 3341 adult lake trout (Salvelinus namaycush) from 72 unstocked and stocked lakes. Results showed an increase in genetic diversity and a twofold decrease in the extent of genetic differentiation among stocked populations when compared to unstocked. Stocked populations were characterized by significant admixture at both population and individual levels. Moreover, levels of admixture in stocked populations were strongly correlated with stocking intensity and a threshold value of total homogenization between source and stocked populations was identified. Our results also suggest that under certain scenarios, the genetic impacts of stocking could be of short duration. Overall, our study emphasizes the important alteration of the genetic integrity of stocked populations and the need to better understand determinants of admixture to optimize stocking strategies and to conserve the genetic integrity of wild populations.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".