Vocal behavior of New World migratory warblers in wintering grounds (Colombia)
Bibliographic record
Abstract
Migratory birds display distinct ecological behaviors between their breeding and wintering grounds. On breeding grounds, individuals must establish territories, build nests, attract mates, lay eggs, protect the nests, feed the chicks, and raise young. In contrast, wintering grounds are primarily used for foraging and survival, often involving temporary territory use. These ecological differences are thought to result in reduced vocal behavior, and it is widely believed that birds vocalize little or not at all during winter. In this study, I used both Active Acoustic Monitoring (AAM) and Passive Acoustic Monitoring (PAM), coupled with BirdNET automatic detection, to examine the vocal behavior of New World warblers in wintering grounds in Colombia. I assessed which species vocalize, the types and characteristics of vocalizations, their timing, frequency, habitat use, and ecological contexts. I detected five species vocalizing: Blackpoll Warbler, Canada Warbler, Mourning Warbler, Northern Waterthrush, and Tennessee Warbler. These results provide clear evidence that vocal activity persists in tropical wintering areas. These findings highlight the complexity and ecological relevance of winter vocal behavior in migratory birds and underscore the importance of conserving wintering habitats such as those found in Colombia, where vocalizations may support territory defense, social cohesion, or song development.
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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) | 0.002 | 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".