Mitey Costly: Energetic Costs of Parasite Avoidance and Infection
Bibliographic record
Abstract
Parasites reduce host fitness via perturbations to host energy allocation, growth, survival, and reproduction. Here, we investigate the independent effects of parasite exposure and infection on host metabolic rate. Our study focuses on Drosophila hydei and a naturally occurring ectoparasitic mite, Macrocheles muscaedomesticae. We use flow-through respirometry to measure the metabolic rate of flies during the period of exposure (preinfection) and during mite attachment. Flies were exposed to mites either indirectly (through a mesh screen) or directly, allowing for physical contact between the fly and the mite. We predicted that fly metabolic rate would increase with the level of parasite exposure: unexposed flies < flies with indirect exposure to mites < flies with direct contact with mites < flies actively infected with mites. As expected, flies indirectly exposed to but not in direct contact with mites produced 70% more CO2 than unexposed flies. Flies in direct contact with mites produced 35% more CO2 than flies with indirect contact, and this was more than double the amount of CO2 produced by unexposed flies. However, infected flies—those actually carrying mites—did not produce significantly more CO2 than uninfected flies. Our results show that simply being exposed to mites, either indirectly or directly, was sufficient to elicit a response from the host in terms of elevated CO2 production. Our results show that the costs of parasitism can potentially extend beyond the physiological costs of infection per se to include the energetic costs associated with parasite avoidance. Although studies have shown energetic costs associate with predator-avoidance behaviors, no study to our knowledge has measured the metabolic cost of parasite avoidance.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".