When sexual selection meets genetic drift: the coevolution of male traits and female preferences in finite populations
Bibliographic record
Abstract
Abstract Fisher’s mechanism is central to sexual selection theories, where mate choice generates a genetic correlation between male trait and female preference alleles, driving the coevolution of the trait and preference with positive feedback. However, how Fisher’s mechanism operates in finite populations remains unclear, as sexual selection can interact with genetic drift, influencing both trait-preference correlation and allele frequencies. By using population genetic models, this study addresses the gap in our understanding of interactions between fundamental evolutionary forces. We show that more frequent recombination increases trait-preference correlations in infinitely large populations, unless a positive correlation initially exists. In finite populations, interactions between sexual selection and drift elevate trait-preference correlation when the male trait is rare but reduce the correlation when the trait is common, potentially making it negative when recombination is rare or population size is small. Also, these interactions tend to slow the spread of the male trait while promoting the evolution of preferences. These results differ from the Hill-Robertson effect under natural selection due to two key factors: mate choice generates positive linkage disequilibrium, and the strength of indirect selection on preferences increases with linkage disequilibrium. The fixation of trait and preference alleles is positively correlated. This correlation peaks at intermediate recombination rates and is often stronger in small populations than in large ones, so large population sizes tend to reduce the likelihood that both trait and preference alleles fix. We discuss how the results provide insights into the progression of sexual selection in nature.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".