Predators can influence the host‐parasite dynamics of their prey via nonconsumptive effects
Bibliographic record
Abstract
Abstract Ecological communities are partly structured by indirect interactions, where one species can indirectly affect another by altering its interactions with a third species. In the absence of direct predation, nonconsumptive effects of predators on prey have important implications for subsequent community interactions. To better understand these interactions, we used a Daphnia‐parasite‐predator cue system to evaluate if predation risk affects Daphnia responses to a parasite. We investigated the effects of predator cues on two aspects of host–parasite interactions (susceptibility to infection and infection intensity), and whether or not these effects differed between sexes. Our results show that changes in response to predator cues caused an increase in the prevalence and intensity of parasite infections in female predator‐exposed Daphnia. Importantly, the magnitude of infection risk depended on how long Daphnia were exposed to the cues. Additionally, heavily infected Daphnia that were constantly exposed to cues produced relatively more offspring. While males were ~5× less likely to become infected compared to females, we were unable to detect effects of predator cues on male Daphnia–parasite interactions. In sum, predators, prey, and their parasites can form complex subnetworks in food webs, necessitating a nuanced understanding of how nonconsumptive effects may mediate these interactions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".