Spring peepers Pseudacris crucifer modify their call structure in response to noise
Bibliographic record
Abstract
Abstract Acoustic interference can impede effective communication that is important for survival and reproduction of animals. In response to acoustic interference, some animals can improve signalling efficacy by altering the structure of their signals. In this study, we played artificial noise to 46 male spring peepers Pseudacris crucifer, on their breeding grounds, and tested whether the noise affected the duration, call rate, and peak frequency of their advertisement calls. We used two experimental noise treatments that masked either the high- or low-frequency components of an average advertisement call; this allowed us to evaluate whether frogs adaptively shift the peak frequency of their calls away from both types of interference. Our playback treatments caused spring peepers to produce shorter calls, and the high-frequency noise treatment caused them to lower the frequency of their calls immediately after the noise ceased. Call rate did not change in response to playback. Consistent with previous studies, ambient temperature was inversely related to call duration and positively related to call rate. We conclude that noise affects the structure of spring peeper advertisement calls, and that spring peepers therefore have a mechanism for altering signal structure in response to noise. Future studies should test if other types of noise, such as biotic or anthropogenic noise, have similar effects on call structure, and if the observed changes to call structure enhance or impair communication in noisy environments [Current Zoology 60 (4): 438–448, 2014].
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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 | 0.000 | 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".