Temporal Population Genetic Structure of Yellow Perch Spawning Groups in the Lower Great Lakes
Bibliographic record
Abstract
Abstract This study tested the hypothesis that the genetic composition of Yellow Perch Perca flavescens spawning groups at specific sites remained consistent among years or age‐cohorts; this likely would influence spatial population structure and be important for delineating management units. Previous studies identified that spawning groups genetically differed among locations across fine geographic scales, but it was unknown whether these patterns persisted from year to year. We analyzed 15 nuclear DNA microsatellite loci from Yellow Perch spawning at six Lake Erie locations in 2009 in reference to two out‐groups spawning in Lakes St. Clair and Ontario. Results were compared with a prior study of samples from the same locations that had been collected in various years, ranging from 2001 to 2005. We evaluated consistency for two of the spawning groups across multiple birth‐year‐cohorts. Results indicated that the levels of genetic diversity were similar across all spawning groups and years. All eight spawning groups genetically differed from one other, with their allelic compositions varying between the two sampling periods. Some variation occurred among individual sampling years and birth‐cohorts, with the 2003 cohort being the most distinctive. Sampling groups contained relatively high proportions of full siblings (mean = 18.5%, ranging to 75% for the 2001 birth‐cohort spawning at the eastern Lake Erie site), yet inbreeding appeared relatively low. Differences at sampling sites over time did not appear to reflect genetic drift but may instead suggest that spawning groups reproduce in slightly different locations from year to year or perhaps within a given season; this merits examination. Spatial and temporal patterns may reflect kin‐group structuring and differential reproductive success, in which strong year‐classes dominate spawning groups and impact genetic structure. Received October 28, 2013; accepted October 22, 2014
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".