Body Size Predicts Echolocation Call Peak Frequency Better than Gape Height in Vespertilionid Bats
Bibliographic record
Abstract
In most vocalizing vertebrates, lighter animals tend to produce acoustic signals of higher frequency than heavier animals. Two hypotheses propose to explain this negative relationship in vespertilionid bats: (i) mass-signal frequency allometry and (ii) emitter-limited (maximum gape) signal directionality. The first hypothesis, that lighter bats with smaller larynges are constrained to calls with higher frequencies, is supported at the species level. The second hypothesis proposes that in open space, small bats use higher frequencies to achieve narrow sonar beams, as beam directionality increases with both emitter size (maximum gape) and signal frequency. This hypothesis is supported within a comparative context but remains untested beyond a few species. We analyzed gape, body mass, and echolocation data under a phylogenetic comparative framework to test these hypotheses, and considered forearm length as both a proxy for wing design and an alternative measure of bat size. Controlling for mass, we found no support for the directionality hypothesis. Body mass and relative forearm length were negatively related to open space echolocation call peak frequency, reflecting species-specific size differences, but also the influence of wing design and preferred foraging habitat on size-independent species-specific differences in echolocation call design.
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 | 0.002 |
| 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".