Understanding Reversals in the Relative Strength of Sexual Selection on Males and Females: A Role for Sperm Competition?
Bibliographic record
Abstract
Sperm competition affects sexual selection intensity on males, but models suggest it cannot affect the relative intensity of sexual selection on males compared to females. However, if sperm competition depresses the payoff for male multiple mating, it could affect the relative intensity of sexual selection and even cause sexual selection to be more intense on females than males (reversal of typical pattern). To evaluate how sperm competition, energy availability, and parental investment affect the intensity of sexual selection on each sex, I constructed a simulation model using the relationship between fecundity and number of mates to estimate sexual selection gradients. Unlike earlier models, I include a trade-off between paternal investment and sperm competition ability. The amount of energy available for reproduction affects the sexual selection gradient for each sex. Reversals in the sex experiencing stronger sexual selection do occur when additional paternal investment reduces a male's ability to compete for fertilizations within females. The shape of the distribution of mates for each sex (determined by mate competition) is also important. Output from the model is qualitatively similar to empirical data from insects with paternal investment. This model challenges previous thinking about the role of sperm competition in sex-role reversal.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".