Human Impacts on Great Lakes Walleye <i>Sander vitreus</i> Structure, Diversity and Local Adaptation
Bibliographic record
Abstract
ABSTRACT Artificial propagation and wild release may influence the genetic integrity of wild populations. This practice has been prevalent in fisheries for centuries and is often termed ‘stocking’. In the Laurentian Great Lakes (Great Lakes here‐on), walleye populations faced declines from the 1950s to the 1970s, prompting extensive stocking efforts for restoration. By the mid‐2010s, walleye populations showed signs of recovery, but the genetic legacy of stocking on population structure at the genomic level remains unclear. Using a dataset of 45,600 genome‐aligned SNP loci genotyped in 1075 walleye individuals, we investigated the genetic impacts of over 50 years of stocking across the Great Lakes. Population structure was associated with both natural geographic barriers and stocking from non‐native sources. Admixture between Lake Erie walleye and walleye from the re‐populated Tittabawassee River indicate that stocking may have re‐distributed putatively adaptive alleles around the Great Lakes. Genome scans identified F ST outliers and evidence of selective sweeps, indicating local adaptation of spawning populations is likely. Notably, one genomic region showed strong differentiation between Muskegon River and walleye from the Tittabawassee River, which was re‐populated by Muskegon strain walleye, suggesting admixture and selection both impact the observed genetic diversity. Overall, our study underscores how artificial propagation and translocations can significantly alter the evolutionary trajectory of populations. The findings highlight the complex interplay between stocking practices and population genetic diversity, emphasising the need for careful management strategies to preserve the genetic integrity of wild populations amidst conservation efforts.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".