When should a trophically transmitted parasite exploit host compensatory responses?
Bibliographic record
Abstract
Abstract Parasites are known to manipulate the behavior of their hosts in ways that increase their probability of transmission. Theoretically, different evolutionary routes can lead to host manipulation, but much research has concentrated on the ‘manipulation hypothesis’ sensu stricto. Among the arsenal of host compensatory responses, however, some seem to be compatible with the parasite objectives. Another way for parasites to achieve transmission, therefore, would be to trigger specific host compensatory responses. In order to explore the conditions favoring this manipulative strategy, we developed a simulation model in which parasites may affect their hosts' behavior by using two nonmutually exclusive strategies: a manipulation sensu stricto strategy and a strategy based on the exploitation of host compensatory responses. Our model predicts that the exploitation of host compensatory responses can be evolutionary stable when the alteration improves the susceptibility to predation by final hosts without compromising host survival during parasite development. Inversely, when the behavioral modification resulting from a compensatory response conflicts with the host's interest we expect parasites to use both strategies. From this result, we conclude that the strategy based on the exploitation of host compensatory responses should be more common among nontrophically transmitted parasites. Furthermore, our findings indicate that the transmission rate of parasites in a definitive host is highest when each of the two strategies affects different traits, which supports the hypothesis that host manipulation is a multidimensional phenomenon in which each altered trait contributes independently to increase parasite transmission efficiency.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".