Social environment influences the temporal dynamics of sneak-spawning in a fish with alternative reproductive tactics
Bibliographic record
Abstract
Several predictions of sperm competition theory are not well supported empirically. One potential reason is that most current theory and empirical research ignore how the social environment influences the temporal dynamics of mating. We propose that understanding these dynamics is key to understanding sexual selection and improving the predictive power of theory. To demonstrate the importance of these dynamics, we quantify how males’ social roles, interactions among males, and current social environment influence the timing of mating in Symphodus ocellatus, a species with three alternative male reproductive tactics. Nesting males spawn synchronously with females; sneakers and satellites sneak-spawn with some time-delay. Satellites also cooperate with nesting males. We found that satellites have shorter sneak-spawning delays than sneakers, a benefit of their cooperation with nesting males. Sneak-spawning delays decreased with increasing nest activity for sneakers but not satellites, suggesting sneakers may benefit from increased sperm competition intensity. Current sperm competition models ignore this potential benefit which may be why the prediction that males should decrease investment when sperm competition involves more than two males is not well supported. Our study provides insight into mechanisms that drive variation in the timing of spawning, which could explain mismatches between theoretical and empirical results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".