Local predation pressure predicts the strength of mobbing responses in tropical birds
Bibliographic record
Abstract
Abstract Many birds join cooperative mobbing aggregations and collectively harass predators. Individuals participating in these ephemeral associations benefit by deterring the predator, but also incur energetic costs and increased risk of predation. Explaining the evolution of mobbing is challenging because individuals could prevail by selfishly seeking safety while allowing others to mob. An important step in understanding the evolution of mobbing is to identify factors affecting its expression. The ecological constraints model suggests that animals are more likely to cooperate under adverse environmental conditions, such as when local predation pressure is high. We tested this prediction by comparing the mobbing responses of several species of birds to the local abundance of their primary predator, the ferruginous pygmy-owl Glaucidium brasilianum. We used acoustic playback to elicit mobbing responses in environments where owls were common, uncommon, or rare. Stimuli were either the song of a ferruginous pygmy-owl or the mobbing calls of three of the owl’s common prey species. During each playback, we characterized mobbing responses by noting the number of species and individuals that approached the loudspeaker, as well as the closest approach by any bird. Mobbing responses to both stimuli were strong in locations where Ferruginous Pygmy-owls were common, intermediate where owls were uncommon, and weak where they were rare. This pattern persisted even after controlling for differences in species richness and composition among the three environments. Results support the ecological constraints model and provide strong evidence that intense predation pressure increases the expression of cooperative mobbing in tropical birds.
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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 | 0.002 |
| 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) | 0.002 | 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".