The influence of wing morphology and echolocation on the gleaning ability of the insectivorous bat <i>Myotis tricolor</i>
Bibliographic record
Abstract
On the basis of its external morphology, Myotis tricolor (Temminck, 1832) should be able to both aerial-feed and glean. Furthermore, this bat is known to use broadband calls of short duration, reinforcing the prediction that it gleans. However, results from this study indicate that M. tricolor does not commonly glean. This conclusion was reached after studying the foraging behaviour of M. tricolor in a flight room. We presented M. tricolor with mealworms, moths, mole crickets, beetles, and cicadas in a variety of ways that required either gleaning and (or) aerial feeding. Although M. tricolor readily took tethered prey, it did not take any of the variety of insects presented to it in a manner that required gleaning. We therefore compared its wing morphology and echolocation calls with those of several known gleaners, Nycteris thebaica E. Geoffroy, 1818, Myotis lucifugus (Le Conte, 1831), and Myotis septentrionalis (Trouessart, 1897), and an aerial forager, Neoromicia capensis (A. Smith, 1829). In a discriminant analysis wing-tip shape was the only variable to provide some degree of discrimination between species, with M. tricolor having more pointed wing tips than the known gleaners. Discriminant analysis of echolocation-call parameters grouped M. tricolor with the other Myotis species and separated it from N. capensis and N. thebaica. However, M. tricolor did not use harmonics as did the other Myotis species. The apparent failure of M. tricolor to glean might therefore be due to its relatively pointed wings and narrow-bandwidth echolocation calls, owing to the absence of harmonics in its calls.
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 | 0.000 |
| 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".