Animal Calling Behaviours and What This Can Tell Us about the Effects of Changing Soundscapes
Bibliographic record
Abstract
The behavioural, physiological, and energetic repercussions for wildlife that result from changes in their soundscapes are increasingly being realized. To understand the effects of changing acoustic landscapes, we first must establish the importance of the acoustic sense for species to transfer information between the environment, con- and heterospecifics, and a receiver, and the functional role of calling in behaviours such as foraging, navigation, mate attraction, and weaning. This review begins with a discussion of the use of calling and the acquisition of the vocal repertoire, before providing examples from multiple taxa on the functional applications of signals and communication. The acoustic sensory mode adds to, if not being inherent in, many critical life history stages over a range of species. The potential effects on an animal resulting from a change in its perceived soundscape and disturbance on its acoustics use is outlined. This can then be used to consider the implications of an altered acoustic niche or active space in the success and survival of an individual or species. Furthermore, we discuss briefly metrics that could be used to understand the implications of these changes, or could be used to guide mitigation action to lessen the impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".