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 stocking on wild fish populations has long been of interest as negative consequences such as reduced fitness and loss of genetic diversity are commonly reported outcomes. In an attempt to sustain a fishery, managers implemented nearly five decades of extensive stocking of over a million Muskellunge ( Esox masquinongy ), a native species in the Lower St. Lawrence River (Québec, Canada). We investigated the effect of this stocking on population genetic structure and allelic diversity in the St. Lawrence River in addition to tributaries and several stocked inland lakes. Using genotype by sequencing, we genotyped 643 individuals representing 22 locations and combined this information with forward simulations to investigate the genetic consequences of long‐term stocking. Individuals native to the St. Lawrence watershed were genetically differentiated from stocking sources and tributaries, and inland lakes were naturally differentiated from the main river. Empirical data and simulations within the St. Lawrence River revealed weak stocking effects on admixture patterns. Our data suggest that the genetic structure associated with stocked fish was diluted into its relatively large effective population size. This interpretation is also consistent with a hypothesis that selection against introgression was in operation and relatively efficient within the large St. Lawrence River system. In contrast, smaller populations from adjacent tributaries and lakes displayed greater stocking‐related admixture that resulted in comparatively higher heterozygosity than the St. Lawrence. Finally, individuals from inland lakes that were established by stocking maintained a close affinity with their source populations. This study illustrated a benefit of combining extensive genomic data with forward simulations for improved inference regarding population‐level genetic effects of long‐term stocking, and its relevance for fishery management decision making.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".