In-flight social calls: a primer for biologists and managers studying echolocation
Bibliographic record
Abstract
Recent technological advances have permitted collection of immense data sets through automated recordings that are primarily aimed at capturing bat echolocation. Analyses of echolocation calls are used to identify species, relative abundance, and some aspects of behaviour, such as foraging or commuting. Here we propose that social calls recorded in flight are also valuable tools for understanding bat ecology and behaviour. First, we examine how and why the acoustic structure of social calls differ from echolocation. Differences in form make social calls often, but not always, easy to identify. We then use a case study on in-flight song in Brazilian free-tailed bat (Tadarida brasiliensis (I. Geoffroy, 1824)) to show that what may appear as echolocation may instead be predominantly used for social communication. Next, we review three basic functions of in-flight social calls, including examples of each, and develop a framework for testing these alternative functions using automated recordings. In a second case study, we use automated recordings of the endangered Florida bonneted bat (Eumops floridanus (G.M. Allen, 1932)) to illustrate how behavioural information can be gleaned by examining patterns of social call production. Finally, we discuss why and how social calls provide novel information that can be crucial for conservation and management efforts.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.022 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".