Does recent experience affect large male advantage in a sequentially-mating fish?
Bibliographic record
Abstract
Abstract In many mating systems, large male body size is associated with dominance in direct contests with rivals and females may exhibit preference for larger males. As such, body size is often positively associated with mating success. However, mating experience can influence the potential advantage of large body size through alterations in behaviour and depletion of sperm reserves. In Japanese medaka ( Oryzias latipes ), males mate with many females each day, and larger males mate more frequently than smaller males. In an observational experiment, we tested the following alternate predictions: (1) recent mating experience may enhance mating success through a carry-over effect of prior mating, whereby small experienced males gain an advantage over large inexperienced rivals in mating contests; or (2) recent mating experience decreases mating success through a reduction in fertilization due to sperm limitation, effectively dampening the large-male advantage against a small inexperienced rival. We examined the interactive effect of size and recent experience on mating behaviour and success. While mating contests were monopolized by large males, recent experience enhanced mating success, especially in small male winners. Experienced males courted more readily than those without recent experience, suggesting that recent prior mating enhances this behaviour. Furthermore, males who had copulated recently did not exhibit sperm depletion when in the presence of a competitor, nor did female behaviour indicate a preference for inexperienced males. This suggests that males can use sexual experience to increase their reproductive success in future mating situations, which may influence the action of sexual selection and alternative tactics in shaping mating systems.
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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) | 0.003 | 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".