Effects of ovarian fluid and genetic differences on sperm performance and fertilization success of alternative reproductive tactics in Chinook salmon
Bibliographic record
Abstract
In many species, sperm velocity affects variation in the outcome of male competitive fertilization success. In fishes, ovarian fluid (OF) released with the eggs can increase male sperm velocity and potentially facilitate cryptic female choice for males of specific phenotypes and/or genotypes. Therefore, to investigate the effect of OF on fertilization success, we measured sperm velocity and conducted in vitro competitive fertilizations with paired Chinook salmon (Oncorhynchus tshawytscha) males representing two alternative reproductive tactics, jacks (small sneaker males) and hooknoses (large guarding males), in the presence of river water alone and OF mixed with river water. To determine the effect of genetic differences on fertilization success, we genotyped fish at neutral (microsatellites) and functional [major histocompatibility complex (MHC) II ß1] markers. We found that when sperm were competed in river water, jacks sired significantly more offspring than hooknoses; however, in OF, there was no difference in paternity between the tactics. Sperm velocity was significantly correlated with paternity success in river water, but not in ovarian fluid. Paternity success in OF, but not in river water alone, was correlated with genetic relatedness between male and female, where males that were less related to the female attained greater paternity. We found no relationship between MHC II ß1 divergence between mates and paternity success in water or OF. Our results indicate that OF can influence the outcome of sperm competition in Chinook salmon, where OF provides both male tactics with fertilization opportunities, which may in part explain what maintains both tactics in nature.
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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 | 0.000 |
| 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".