Neighbors affect vocal behavior of tropical wrens: a multispeaker density-manipulation experiment
Bibliographic record
Abstract
Abstract For territorial animals, the behavior of conspecific neighbors sets the social context of communication. Despite numerous investigations of vocalizations related to territory defense and mate attraction, the effect of neighbor density on animal vocal behavior has received little attention, particularly in tropical animals and animals where both sexes produce complex acoustic signals. In this study, we used an innovative multispeaker playback experiment to manipulate the apparent density of neighbors in rufous-and-white wrens, Thryophilus rufalbus, living in Costa Rica’s tropical dry forest. In this tropical songbird, both males and females defend year-round territories and sing complex, learned songs for territory defense. We recorded the singing behavior of 24 subjects (12 pairs), and then we used an array of 6 loudspeakers to simulate the presence of 6 new territorial neighbors (3 simulated pairs) outside each subject pair’s breeding territory. The stimuli persisted for 3 consecutive days, with both male and female songs broadcast at a natural rate from dawn to dusk. We found that the mean male song rate increased by almost 50% in response to the simulated increase in local density. Females showed less frequent song-type switching rates following the simulated increase in local density, although it was a marginal increase. These findings reveal that male and female songbirds’ vocal behavior varies with the local density of territorial neighbors. We conclude that birds are sensitive to acoustic signals of conspecific density arising from sounds beyond their territory boundaries, and that they use this public information to guide their vocal behavior.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".