It is better to be choosy in small populations: drift promotes the evolution of weak female preference for rare phenotypes
Bibliographic record
Abstract
Evidence from lab and field studies suggests that females sometimes prefer males bearing rare phenotypes. Such a finding poses a theoretical challenge, because preference for a rare phenotype makes that phenotype less rare, thereby lowering the fitness of sons bearing it. Further, a preference for rarity creates negative frequency-dependent selection, which leads to equal representation of male types. This then eliminates any benefit to a preference for rarity. It seems paradoxical, then, that preference for rarity has been observed across so many taxa. Genetic drift, by promoting stochastic fixation or loss of alleles, is a source of constant rarity. Here, we ask whether finite population sizes might provide the necessary conditions that favor a preference for rarity. Indeed, we find that drift, by constantly perturbing male display allele frequencies, provides the fuel required to favor a choosy female preference allele. Once this preference allele spreads, resultant negative frequency-dependent selection at the male display locus can act to maintain diversity in male display ornaments. Thus, drift plays an atypical role by helping maintain diversity. Further, we show that this finding is stronger in multi-patch landscapes. This work provides a novel potential explanation for the repeated evolution of female preference for rarity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".