Why is Female Choice not Unanimous? Insights from Costly Mate Sampling in Marine Iguanas
Bibliographic record
Abstract
Females do not unanimously choose the single ‘best’ male, even when female choice is strong, such as in leks, or in polygynous mating situations. A possible explanation is that females base their choices on limited information, perhaps because gathering information is costly. We tested this hypothesis by continuously observing individual female marine iguanas throughout the mating period in order to document the information they gathered about each potential mate. Females actively visited approximately five additional males during the 3 d prior to copulation, compared to the males seen on their normal foraging routes. Females were more likely to visit large‐bodied males, but preferentially copulated with the male that had the highest display rate of all males they visited. Females that mated on a dense territory cluster mated with more active males than did those that mated on dispersed territories. However, females on a dense cluster also lost more body mass, potentially as a consequence of high rates of interaction with males. This mass loss may represent an important cost and result from postural changes in response to male attention. Such costs may explain why females only gather a certain amount of information and why females on dispersed territories choose less active mates. Lack of complete information introduces subjectivity into female choice: what is perceived as best by one female may not be perceived as best by another. Thus, lack of complete information may prevent unanimity of female choice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".