Dynamic feeding habits: efficiency of frugivory in a nectarivorous bat
Bibliographic record
Abstract
Fluctuations in food availability may result in drastic changes of main dietary habits in some animals despite the lack of adaptations to alternative food types. We compared feeding efficiency between the nectarivorous bat Glossophaga commissarisi Gardner, 1962 (Phyllostomidae) that switches during nectar shortages to frugivory and the specialized frugivore Carollia brevicauda (Schinz, 1821) in a combination of behavioural experiments and HPLC (high performance liquid chromatography) analysis of fruit and faecal samples. We assessed feeding duration and employment of different bite types while animals were feeding on fruits from their natural diet. Although both bat species employed predominantly mechanically more efficient bite types, feeding efficiency (mg of fruit ingested/s) was significantly lower in G. commissarisi. Our study showed that the two bat species employed distinctly different handling and feeding strategies when feeding on the same fruits. “Frugivorous” G. commissarisi consume mostly fruit juices, but fruit anatomy seems to influence feeding efficiency. In an ecological context, the trophic shift of G. commissarisi from nectar to fruits may have implications for plant species that coevolved with bats, as well as for niche partitioning of coexisting bat species. In a more general context, our study highlights a dynamic nature of feeding niches, which might be rather common for animals living in habitats with changes in resource availability.
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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 | 0.001 | 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) | 0.001 | 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".