Experimental evolution of infectious behaviour in a facultative ectoparasite
Bibliographic record
Abstract
Parasitic lifestyles have evolved many times in animals, but how such life-history strategies evolved from free-living ancestors remains a great puzzle. Transitional symbiotic strategies, such as facultative parasitism, are hypothesized evolutionary stepping stones towards obligate parasitism. However, to consider this hypothesis, heritable genetic variation in infectious behaviour of transitional symbiotic strategies must exist. In this study, we experimentally evolved infectivity and estimated the additive genetic variation in a facultative parasite. We performed artificial selection experiments in which we selected for either increased or decreased propensity to infect in a facultatively parasitic mite (Macrocheles muscaedomesticae). Here, infectiousness was expressed in terms of mite attachment to a host (Drosophila hydei) and modelled as a threshold trait. Mites responded positively to selection for increased infectivity; realized heritability of infectious behaviour was significantly different from zero and estimated to be 16.6% (±4.4% SE). Further, infection prevalence was monitored for 20 generations post-selection. Selected lines continued to display relatively high levels of infection, demonstrating a degree of genetic stability in infectiousness. Our study is the first to provide an estimate of heritability and additive genetic variation for infectious behaviour in a facultative parasite, which suggests natural selection can act upon facultative strategies with important implications for the evolution of parasitism.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".