When do host–parasite interactions drive the evolution of non‐random mating?
Bibliographic record
Abstract
Interactions with parasites may promote the evolution of disassortative mating in host populations as a mechanism through which genetically diverse offspring can be produced. This possibility has been confirmed through simulation studies and suggested for some empirical systems in which disassortative mating by disease resistance genotype has been documented. The generality of this phenomenon is unclear, however, because existing theory has considered only a subset of possible genetic and mating scenarios. Here we present results from analytical models that consider a broader range of genetic and mating scenarios and allow the evolution of non-random mating in the parasite as well. Our results confirm results of previous simulation studies, demonstrating that coevolutionary interactions with parasites can indeed lead to the evolution of host disassortative mating. However, our results also show that the conditions under which this occurs are significantly more fickle than previously thought, requiring specific forms of infection genetics and modes of non-random mating that do not generate substantial sexual selection. In cases where such conditions are not met, hosts may evolve random or assortative mating. Our analyses also reveal that coevolutionary interactions with hosts cause the evolution of non-random mating in parasites as well. In some cases, particularly those where mating occurs within groups, we find that assortative mating evolves sufficiently to catalyze sympatric speciation in the interacting species.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".