Short‐ and long‐term benefits and detriments to recombination under antagonistic coevolution
Bibliographic record
Abstract
We explored the evolution of recombination under antagonistic coevolution, concentrating on the equilibrium frequencies of modifier alleles causing recombination in initially nonrecombining populations. We found that the equilibrium level of recombination in the host depended not only on parasite virulence, but also on the strength of the modifier allele, and on whether or not the modifier was physically linked to the parasite interaction loci. Nonetheless, the maximum level of recombination for linked loci at equilibrium was about 0.3 (60% of free recombination) for interactions with highly virulent parasites; the level decreased for unlinked modifiers, and for lower levels of parasite virulence. We conclude that recombination spreads because it provides a combination of an immediate (next-generation) fitness benefit and a delayed (two or more generations) increase in the rate of response to directional selection. The relative impact of these two mechanisms depends on the virulence of parasites early in the spread of the modifier, but a trade-off between the two dictates the equilibrium modifier frequency for all nonzero virulences that we examined. In addition, population mean fitness was higher in populations at intermediate equilibria than populations fixed for free recombination or no recombination. The difference, however, was not enough on its own to overcome the two-fold cost of producing males.
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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 | 0.003 |
| 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 | 0.001 |
| 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".