Beware of bats, beware of birds: the auditory responses of eared moths to bat and bird predation
Bibliographic record
Abstract
The allotonic frequency hypothesis (AFH) proposes that the preponderance of moths in the diets of some bats (e.g., Rhinolophidae) is the result of these bats echolocating at allotonic frequencies, that is, outside of the typical hearing range of most moths (ca., 20–60 kHz). The broader hearing range of African moths (5–110 kHz) suggests that their ears may function at frequencies usually considered allotonic. We investigated 1) whether moth ears were functionally audible to the Cape horseshoe bat, Rhinolophus capensis (Rhinolophidae), which forages in dense vegetation and echolocates at 84 kHz, and 2) whether moth auditory sensitivity below 10 kHz allows them to detect the rustling noises made by bird predators as they pursued moths through vegetation. The calls of R. capensis were audible to moths albeit over shorter distances relative to syntonic bats. Shorter detection distances combined with the constrained spaces in the cluttered habitat in which rhinolophids forage give moths both less time and less space within which to react to an attacking bat. Thus, the AFH in combination with habitat offers a better explanation for the preponderance of moths in the diets of rhinolophids than either of them on their own. Moths also responded both neurologically and behaviorally to the rustling sounds made by birds (Cape Bulbul, Pycnonotus capensis) as they pursued moths. We suggest that the high sensitivity of moths to frequencies from 5 to 10 kHz allows them to avoid these avian attacks by using responses that have traditionally been considered solely anti-bat behavior.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".