Quiet violence: Savannah Sparrows respond to playback‐simulated rivals using low‐amplitude songs as aggressive signals
Bibliographic record
Abstract
Abstract When animals compete over resources such as breeding territories, they often use signals to communicate their aggressive intentions. By studying which signals are associated with aggressive interactions, we gain a deeper appreciation of animal behaviour. We studied aggressive signalling in male Savannah Sparrows, Passerculus sandwichensis, focusing on signals that precede physical attack against territorial intruders. We simulated intruders using song playback and taxidermic models, and we determined which behaviours were associated with physical attack. Previous studies that have used this approach suggest that many species produce songs of dramatically lower amplitude, or “soft songs,” as signals of aggressive intention. Savannah Sparrows, however, are not known to produce soft songs, and therefore, they provided an interesting system for testing signals that predict attack. Of 93 playback subjects, 23 males attacked the simulated intruder and 70 did not. To our surprise, Savannah Sparrows produced soft songs, and the number of soft songs was a significant predictor of attack on the simulated intruder. Birds also showed a nonsignificant tendency to produce more “chip” calls prior to attack on the simulated intruder, whereas three other measured behaviours (aggressive calls, wing waving and passes over the model) did not predict attack. Our study contributes to the growing body of research on aggressive territorial signals and reveals that soft song is an even more widespread signal of aggression in songbirds than previously recognized.
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 | 0.000 |
| 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".