Prevalence-Dependent Costs of Parasite Virulence
Bibliographic record
Abstract
Costs of parasitism are commonly measured by comparing the performance of infected groups of individuals to that of uninfected control groups. This measure potentially underestimates the cost of parasitism because it ignores indirect costs, which may result from the modification of the competitiveness of the hosts by the parasite. In this context, we used the host-parasite system consisting of the yellow fever mosquito Aedes aegypti and the microsporidian parasite Vavraia culicis to address this question: Do infected individuals exert a more or less intense intraspecific competition than uninfected individuals? Our experimental results show that, indeed, infected hosts incur a direct cost of parasitism: It takes them longer to become adults than uninfected individuals. They also incur an indirect cost, however, which is actually larger than the direct cost: When grown in competition with uninfected individuals they develop even slower. The consequence of this modification of competitiveness is that, in our system, the cost of parasitism is underestimated by the traditional measure. Moreover, because the indirect cost depends on the frequency of interactions between infected and uninfected individuals, our results suggest that the real cost of parasitism, i.e., virulence, is negatively correlated with the prevalence of the parasite. This link between prevalence and virulence may have dynamical consequences, such as reducing the invasion threshold of the parasite, and evolutionary consequences, such as creating a selection pressure maintaining the host's constitutive resistance to the parasite.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".