Combining population genomics and forward simulations to investigate stocking impacts: A case study of Muskellunge ( <i>Esox masquinongy</i> ) from the St. Lawrence River basin
Bibliographic record
Abstract
Abstract Understanding the genetic and evolutionary impacts of fish stocking on wild populations has long been of interest as negative consequences such as reduced fitness and loss of genetic resources are commonly reported. Nearly five decades of extensive stocking of over a million Muskellunge ( Esox masquinongy ) in the Lower St. Lawrence River (Québec, Canada) was implemented by managers in an attempt to sustain a fishery. We investigated the effect of stocking on this native species’ genetic structure and allelic diversity in the St. Lawrence river and its tributaries as well as several stocked inland lakes. Using Genotype-By-Sequencing (GBS), we genotyped 643 individuals representing 22 sampling sites and combined this information with forward simulations to investigate the genetic consequences of stocking. Individuals native to the St. Lawrence watershed were genetically divergent from the sources used for stocking and both the St. Lawrence tributaries and inland lakes were also naturally divergent from the main stem. Empirical data and simulations revealed weak effects of stocking on admixture patterns within the St. Lawrence despite intense stocking in the past, whereas footprints of admixture were higher in the smaller stocked populations from tributaries and lakes. Altogether, our data suggests that selection against introgression has been relatively efficient within the large St. Lawrence River. In contrast, the smaller populations from adjacent tributaries and lakes still displayed stocking related admixture which apparently resulted in higher genetic diversity, suggesting that, while stocking stopped at the same time, its impact remained higher in these populations. Finally, the origin of populations from inland lakes that were established by stocking confirmed their close affinity with these source populations. This study illustrates the benefit of combining extensive genomic data with forward simulations for improved inferences regarding outcomes of population enhancement by stocking, as well as its relevance for fishery management decision making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".