Scared of the dark? Phototaxis as behavioural immunity in a host–parasite system
Bibliographic record
Abstract
Behavioural immunity describes suites of behaviours hosts use to minimize the risks of infection by parasites/pathogens. Research has focused primarily on the evasion and physical removal of infectious stages, as well as behavioural fever. However, other behaviours affect infection risk while carrying ecologically significant trade-offs. Phototaxis, in particular, has host fitness implications (e.g. altering feeding and thermoregulation) that also impact infection outcomes. In this study, we hypothesized that a fly host, Drosophila nigrospiracula , employs phototaxis as a form of behavioural immunity to reduce the risk of infection. First, we determined that the risk of infection is lower for flies exposed in the light relative to the dark using micro-arena experiments. Because Drosophila vary in ectoparasite resistance based on mating status we examined parasite-mediated phototaxis in mated and unmated females. We found that female flies spent more time in the light side of phototaxis chambers when mites were present than in the absence of mites. Mating marginally decreased female photophobia independently of mite exposure. Female flies moved to lighter, i.e. less infectious, environments when threatened with mites, suggesting phototaxis is a mechanism of behavioural immunity. We discuss how parasite-mediated phototaxis potentially trades-off with host nutrition and thermoregulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".