Dividing up the bill: Interactions between how parasitoids manipulate host behaviour and who pays the cost
Bibliographic record
Abstract
Abstract Controlling host behaviour can be costly for parasites. In some parasitic systems, such as insect parasitoids, this physiological cost can be paid for by the mother parasite, her offspring or both. Parasitoid wasps provide examples of how individual parasites in a host–parasite system can vary in their opportunity, means and motive (i.e. fitness benefits) for manipulating host behaviour. Changes in host behaviour that occur very soon after infection are typically paid for by the mother parasitoid. She has the greatest opportunity (offspring are often still eggs), the means (neuroactive venoms) and benefits by promoting her offspring's success. Changes in host behaviour that occur late in the development of the offspring (e.g. host bodyguard behaviour) often hinge on some behaviour of the offspring (e.g. the exiting of the host). In these cases, the cost is paid largely by the offspring. The offspring have the greater opportunity, the means (e.g. secreting compounds into the host) and directly benefit by their increased survival. Gene delivery agents, such as symbiotic viruses, allow a reduction in the direct cost of parasitic manipulation to the parasite because the host is induced to use its own resources to produce the compounds needed to alter its behaviour. However, this method leads to indirect costs that are paid for by the offspring, due to a reduction in the host's resources that are available for their own growth. In gregarious systems, the possibility of cheating among the offspring (i.e. some individuals paying less than others to alter host behaviour) may select for modes of control that make cheating difficult. Determining who pays the physiological cost of manipulating host behaviour, and why, promises exciting insights into the evolution of parasitic manipulation in these systems. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".