Evolutionary responses to increased opportunity for sexual selection in yeast
Bibliographic record
Abstract
Abstract Sexual selection contributes to biodiversity and the costs and benefits of sexual reproduction. In organisms where sex is infrequent, these impacts of sexual selection are likely to be limited. An increased frequency of obligate sex would increase the opportunity for sexual selection, which could promote the evolution of sexual traits and sexual differentiation. To study these dynamics, we conducted experimental evolution in the yeast Saccharomyces cerevisiae , which is predominantly asexual, with two isogamous mating types. We used selectable markers to impose frequent obligate sex in 96 populations. We manipulated the opportunity for sexual selection by imposing skewed mating-type ratios and by altering the degree to which mating occurred by selfing versus outcrossing. After just ten sexual cycles, we observed evolution in growth, cell size, pheromone production, and mating, with the mating types responding asymmetrically, but little evolutionary change in sporulation rate. Mating type dimorphism increased, with evident trade-offs between growth, attractiveness, and cell size. Genome sequences from a subset of populations revealed many mutations affecting sex-related genes. Unexpectedly, the selfing populations evolved to become sporulation-competent haploids, unlinking meiosis from ploidy change. Our results illustrate that sexual differentiation can evolve rapidly in response to an increased opportunity for sexual selection.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".