Risk of predation increases susceptibility to parasitism via trait-mediated indirect effects
Bibliographic record
Abstract
The presence of natural enemies can cause organisms to change habitat use, foraging behaviour and/or resource allocation in response to a perceived risk; responses that may come at the cost of other fitness-related traits. Since most species encounter multiple natural enemies, defensive behaviours against one attacker may make the focal organism more vulnerable to attack by a different natural enemy. Anti-predator behaviours can lead to trait-mediated indirect effects such as an increased risk of attack by parasites, and vice versa. Few empirical studies have examined the response of a single focal species to the risk of attack by multiple species. Our experiments provided Drosophila nigrospiracula with opportunities to prioritise either anti-predator or anti-parasite behaviour at the cost of increased infection or predation, respectively. When exposed to parasites in the presence of predator cues, D. nigrospiracula experienced increased infection compared to flies without predator cues, but the presence/absence of parasite cues had no analogous effect on predation rates. We suggest that flies perceived parasitic infection to be a lesser threat and responded more strongly to predation risk at the cost of increased infection. In an ecological context, we shows how trait-mediated indirect effects could regulate community structure by increasing susceptibility to infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".