Female guppies can recognize kin but only avoid incest when previously mated
Bibliographic record
Abstract
Kin recognition plays a key role in inbreeding avoidance for taxa from all major animal groups; however, perplexingly, a number of species, for which the risks and costs of inbreeding are high, do not discriminate against relatives in mate choice experiments. We tested a possible explanation for this paradox: most tests of inbreeding avoidance have used virgin females, but theory predicts that virgin females should first mate indiscriminately to gain reproductive assurance and, only subsequently, become choosy. To test this idea, we used the Trinidadian guppy, which suffers inbreeding depression but for which evidence for kin recognition is equivocal. We manipulated rearing environment to disentangle 2 forms of kin recognition and tested the preferences of both virgin and nonvirgin females. As predicted, virgins were indiscriminate in their mate choice. Nonvirgins, in contrast, expressed strong preferences for unfamiliar and unrelated males. Therefore, female guppies recognize kin in a mate choice context but become selective only once reproductively assured. This dynamic likely increases population fitness in newly colonized or small, at-risk populations by ensuring that virgins mate even when only unfamiliar or unrelated males are available. Our findings suggest that inbreeding avoidance may be more common than previously thought and that the use of virgin females can sometimes prevent the detection of powerful mate preferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".