Inbreeding, outbreeding and environmental effects on genetic diversity in 46 walleye (Sander vitreus) populations
Bibliographic record
Abstract
Genetic diversity is recognized as an important population attribute for both conservation and evolutionary purposes; however, the functional relationships between the environment, genetic diversity, and fitness-related traits are poorly understood. We examined relationships between selected lake parameters and population genetic diversity measures in 46 walleye ( Sander vitreus ) populations across the province of Ontario, Canada, and then tested for relationships between six life history traits (in three categories: growth, reproductive investment, and mortality) that are closely related to fitness, and genetic diversity measures (heterozygosity, d 2 , and Wright’s inbreeding coefficient). Positive relationships were observed between lake surface area, growing degree days, number of species, and hatchery supplementation versus genetic diversity. Walleye early growth rate was the only life history trait significantly correlated with population heterozygosity in both males and females. The relationship between F IS and male early growth rate was negative and significant ( P < 0.01) and marginally nonsignificant for females ( P = 0.06), consistent with inbreeding depression effects. Only one significant relationship was observed for d 2 : female early growth rate ( P < 0.05). Stepwise regression models showed that surface area and heterozygosity had a significant effect on female early growth rate, while hatchery supplementation, surface area and heterozygosity had a significant effect on male early growth rate. The strong relationship between lake parameters, such as surface area, and hatchery supplementation, versus genetic diversity suggests inbreeding and outbreeding in some of the populations; however, the weak relationships between genetic diversity and life history traits indicate that inbreeding and outbreeding depression are not yet seriously impacting Ontario walleye populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".