Acoustic Monitoring Reveals Congruent Patterns of Territorial Singing Behaviour in Male and Female Tropical Wrens
Bibliographic record
Abstract
Many animals defend territories against conspecific individuals using acoustic signals. In birds, male vocalizations are known to play a critical role in territory defence. Territorial acoustic signals in females have been poorly studied, perhaps because female song is uncommon in north-temperate ecosystems. In this study, we compare male vs. female territorial singing behaviour in Neotropical rufous-and-white wrens Thryothorus rufalbus, a species where both sexes produce solo songs and often coordinate their songs in vocal duets. We recorded free-living birds in Costa Rica using an eight-microphone Acoustic Location System capable of passively triangulating the position of animals based on their vocalizations. We recorded 17 pairs of birds for 2–4 consecutive mornings and calculated the territory of each individual as a 95% fixed kernel estimate around their song posts. We compared territories calculated around male vs. female song posts, including separate analyses of solo vs. duet song posts. These spatial analyses of singing behaviour reveal that males and females use similarly sized territories with more than 60% overlap between breeding partners. Territories calculated based on solo vs. duet song posts were of similar size and similar degrees of overlap. Solos and duets were performed at similar distances from the nest for both sexes. Overall, male and female rufous-and-white wrens exhibit very similar spatial territorial singing behaviour, demonstrating congruent patterns of male and female territoriality.
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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 | 0.001 | 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".