Proximity to parasites reduces host fitness independent of infection in a <i>Drosophila</i>–<i>Macrocheles</i> system
Bibliographic record
Abstract
Parasites are known to have direct negative effects on host fitness; however, the indirect effects of parasitism on host fitness sans infection are less well understood. Hosts undergo behavioural and physiological changes when in proximity to parasites. Yet, there is little experimental evidence showing that these changes lead to long-term decreases in host fitness. We aimed to determine if parasite exposure affects host fitness independent of contact, because current approaches to parasite ecology may underestimate the effect of parasites on host populations. We assayed the longevity and reproductive output of Drosophila nigrospiracula exposed or not exposed to ectoparasitic Macrocheles subbadius. In order to preclude contact and infection, mites and flies were permanently separated with a mesh screen. Exposed flies had shorter lives and lower fecundity relative to unexposed flies. Recent work in parasite ecology has argued that parasite-host systems show similar processes as predator-prey systems. Our findings mirror the non-consumptive effects observed in predator-prey systems, in which prey species suffer reduced fitness even if they never come into direct contact with predators. Our results support the perspective that there are analogous effects in parasite-host systems, and suggest new directions for research in both parasite ecology and the ecology of fear.
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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 | 0.000 |
| 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".