Expanding the horizon: the Red Queen and potential alternatives
Bibliographic record
Abstract
The Red Queen hypothesis (RQH) is one of the most widely accepted hypotheses explaining the persistence of sexual reproduction despite its costs. It posits that sexual species, compared with asexuals, are more adept at countering parasites, because their per-generation recombination rate is higher. Despite theoretical support, current empirical studies have failed to provide unanimous support. Here, we suggest that future tests of the RQH should more thoroughly elucidate its underlying assumptions and potential alternative hypotheses. While the RQH predicts that negative frequency-dependent selection shapes host–parasite interactions, differences between sexuals and asexuals are potentially important. Key assumptions about asexual species and their sexual close relatives include (i) ecological and behavioral traits are similar, (ii) among-individual genetic diversity is greater in sexuals than in asexuals, and (iii) within-individual genetic diversity is similar in asexuals and sexuals. We review current evidence for the RQH, highlight differences between asexual and sexual species and how those differences might translate into differential responses to parasite infections, and discuss how they can influence the results and interpretation of empirical studies. Considering differences between asexual and sexual species in future tests of the RQH will help to refine predictions and eliminate alternative hypotheses.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.004 | 0.017 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".