Postcopulatory consequences of female mate choice in a fish with alternative reproductive tactics
Bibliographic record
Abstract
Mate choice plays a well-known role in the evolution of secondary sexual traits important in precopulatory competition. However, few studies have linked mate choice with the evolution of postcopulatory competitive traits. Here, we explore how variation in male mating behaviors and female mate choice influences male investment in reproductive traits that enhance sperm competition, a form of postcopulatory male–male competition. By combining ecological and physiological data from wild plainfin midshipman ( Porichthys notatus ), a marine fish species with 2 alternative reproductive tactics (guarder and sneaker males), we show that female mate choice is associated with uneven sperm competition risk between male reproductive tactics as well as among males using the same reproductive tactic. Larger guarder males attracted more females and experienced higher rates of attempted cuckoldry compared with smaller guarder males. In turn, larger guarder males appear adapted to this increased sperm competition risk, producing faster sperm than smaller guarder males. Sneaker males (the smallest males of all) had faster swimming sperm, with larger sperm midpieces and smaller sperm heads than did guarder males. These results suggest that female choice can amplify the selection gradient acting on males both between and within reproductive tactics.
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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 | 0.000 |
| 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".