Cannibalism facilitated by parasite infection induces dispersal in a semi-aquatic insect
Bibliographic record
Abstract
Parasites are known to have direct effects on host dispersal ability and motivation. Yet, parasites have a variety of impacts on host populations, including shaping predation and cannibalism rates, and therefore may also have indirect effects on host dispersal; these indirect pathways have not been studied. We tested the hypothesis that parasites influence host dispersal through effects on cannibalism using backswimmers ( Notonecta undulata ) and Hydrachnidia freshwater mites. Mite parasitism impedes swimming in backswimmers, which we found increased their vulnerability to cannibalism. We imposed a manipulation that varied cannibalism rates across experimental populations consisting of a mix of backswimmers with and without simulated parasites. Using simulated parasites allowed us to examine the effects of cannibalism without introducing infection risk. We found that the odds of dispersal for infected backswimmers increased by 2.25 × with every 10% increase in the risk of being cannibalized, and the odds of dispersal for healthy backswimmers increased by 2.34× for every additional infected backswimmer they consumed. Our results suggest that cannibalism was used as an energy source for dispersal for healthy individuals, while the risk of being eaten motivated dispersal in infected individuals. These results elucidate the complex ways that parasites impact host populations and strengthen our understanding of host–parasite interactions, including host and parasite population stability and spread. This article is part of the theme issue ‘Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics’.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".