Mating success of alternative male phenotypes and evidence for frequency-dependent selection in Chinook salmon, Oncorhynchus tshawytscha
Bibliographic record
Abstract
As with other species, frequency-dependent selection during reproduction has long been proposed as an important mechanism in maintaining alternative male reproductive phenotypes in Pacific salmon ( Oncorhynchus spp.). Jack salmon mature one year earlier than the youngest females in a population and are much smaller than older “adult” males. We tested the hypothesis that mating success of both phenotypes is consistent with the frequency-dependent selection model. By holding male density constant and varying the frequency of adults and jacks in eight separate breeding groups, we found that adult male access to females, participation in spawning events, and adult-to-fry reproductive success increased with their decreasing frequency in a breeding group. Jacks exhibited the same pattern (increasing success with decreasing frequency), although the relationships were not as strong as for adults. Overall, jack and adult males mated with a similar number of females, but jacks sired only 20% of all offspring. Observational data suggested that adult males benefited from sperm precedence associated with their ability to court females and enter the nest first at the time of spawning. Our work provides the first experimental evidence of frequency-dependent selection during mating in the family Salmonidae.
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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 | 0.000 |
| 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".