Divergence in mating signals correlates with genetic distance and behavioural responses to playback
Bibliographic record
Abstract
Animals use acoustic signals to defend resources against rivals and attract breeding partners. As with many biological traits, acoustic signals may reflect ancestry; closely related species often produce more similar signals than do distantly related species. Whether this similarity in acoustic signals is biologically relevant to animals is poorly understood. We conducted a playback experiment to measure the physical and vocal responses of male songbirds to the songs of both conspecific and allopatric-congeneric animals that varied in their acoustic and genetic similarity. Our subjects were territorial males of four species of neotropical Troglodytes wrens: Brown-throated Wrens (Troglodytes brunneicollis), Cozumel Wrens (T. beani), Clarion Wrens (T. tanneri) and Socorro Wrens (T. sissonii). Our results indicate that birds respond to playback of both conspecific and allopatric-congeneric animals; that acoustic differences increase with genetic distance; and that genetic divergence predicts the strength of behavioural responses to playback, after removing the effects of acoustic similarity between subjects' songs and playback stimuli. Collectively, these results demonstrate that the most distantly related species have the most divergent songs; that male wrens perceive divergence in fine structural characteristics of songs; and that perceptual differences between species reflect evolutionary history. This study offers novel insight into the importance of acoustic divergence of learned signals and receiver responses in species recognition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".