Hydrogeographic Vicariance Determines the Genetic Structure of Northwestern Walleye Populations
Bibliographic record
Abstract
Abstract Walleye Sander vitreus is one of the most important freshwater commercial and sport fishes in western Canada. It is an intensively managed species that is under considerable harvest pressure, yet little is known about the patterns of genetic variation and diversity of walleyes between connected water bodies and among river basins in this region. We examined the genetic variation of walleyes from 12 lakes in five different river basins of northern Alberta. Each lake contained a genetically distinct walleye subpopulation nested within a larger population of the river basin in which the lake was situated. Differentiation between subpopulations varied (FST = 0.05–0.29) and exhibited a broad‐scale isolation‐by‐distance pattern. Patterns of genetic divergence aligned closely with the current hydrogeographical landscape, as subpopulations in the same river basin were more similar than those in different river basins. The clear and distinct pattern of genetic structure is likely to have been generated and maintained by historical vicariance and natal philopatry. Because walleye populations are so clearly genetically structured by hydrogeography in western Canada, these data can be used to monitor population status, assess stocking programs, delineate management units, and enable forensic enforcement of harvest restrictions in this region.
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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 | 0.001 |
| 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 | 0.001 |
| 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".