Sexual conflict inhibits female mate choice for major histocompatibility complex dissimilarity in Chinook salmon
Bibliographic record
Abstract
In many species females prefer major histocompatibility complex (MHC) dissimilar mates, which may improve offspring resistance to pathogens. However, sexual conflict may interfere with female preference when males attempt to mate with all females, regardless of compatibility. Here we used semi-natural spawning channels to examine how mating behaviour and genetic similarity at the MHC class II peptide binding region affected parentage patterns in Chinook salmon (Oncorhynchus tshawytscha). We found that females directed aggression at more MHC-similar males than expected by chance, providing a possible mechanism of female MHC choice in salmon. Males also directed aggression towards MHC-similar females, which was consistent with males harassing unreceptive mates. Males' aggression was positively correlated with their reproductive success, and it appeared to overcome female aversion to mating with MHC-similar males, as females who were the target of high levels of male aggression had lower than expected MHC divergence in their offspring. Indeed, offspring MHC divergence was highest when the sex ratio was female-biased and male harassment was likely to be less intense. These data suggest that male harassment can reduce female effectiveness in selecting MHC-compatible mates, and sexual conflict can thus have an indirect cost to females.
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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".