Relationships between roost preferences, ectoparasite density, and grooming behaviour of neotropical bats
Bibliographic record
Abstract
Abstract Evidence suggests that behavioural defences, such as habitat selection and grooming behaviour, have evolved in animals in response to the costs associated with ectoparasites. Bat fly and mite densities were compared among wild‐caught bats in Belize with different roosting preferences (cavity, foliage, or both), and grooming behaviour was analysed for bat species with high and low ectoparasite density. Ectoparasites of bats were removed using forceps, and bat grooming behaviour was recorded with a camcorder. Because bat flies pupate on the surface of host roosts, bats that use cavity roosts (a sheltered environment for the pupae) were predicted to have higher densities of bat flies than those that use foliage (exposed environment). Cavity‐roosting species generally had higher densities of bat flies and mites, although the relationship was more evident for bat flies. The grooming behaviour of bats was predicted to differ among species with high or low ectoparasite densities. Although there was no difference in the frequency of grooming behaviours for individuals with and without bat flies, there were differences in grooming behaviour at the species level. Bat species with high ectoparasite densities scratched more than those with low ectoparasite densities. These results suggest that ectoparasite densities and grooming behaviour are related to roosting preferences in bats.
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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 | 0.001 | 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) | 0.002 | 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".