Constrained Performance in a Communication Network: Implications for the Function of Song‐Type Matching and for the Evolution of Multiple Ornaments
Bibliographic record
Abstract
Many species of territorial songbirds exhibit a behavior known as song-type matching, in which a male sings the same song type that his neighbor is singing. Song-type matching is associated with increased aggression, but researchers have not come to a consensus on its adaptive function. Building on studies that identify singing performance as a variable relevant to sexual selection, we hypothesize that higher-performance singers benefit from matching their opponent's song type because matching improves eavesdroppers' ability to compare the two males' performances. We present a model of song-type choice that predicts that males that can outperform their rivals benefit by matching. In contrast, lower-performance males should avoid both matching and being matched. Our hypothesis is compatible with some existing hypotheses of song-matching function, but it is not compatible with the hypothesis that song matching is a conventional signal of aggression. We offer unique predictions that could be used to test our idea. We speculate that lower-performance individuals might have driven the evolution of repertoire complexity because they stand to benefit from novel, unmatchable songs. The phenomenon that dissimilar signals are less accurately compared than similar signals may favor the evolution of multiple ornaments and of plastic signal development (e.g., song learning) in general.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".