Data from: Evolution and plasticity: divergence of song discrimination is faster in birds with innate song than in song learners in Neotropical passerine birds
Bibliographic record
Abstract
Plasticity is often thought to accelerate trait evolution and speciation. For example, plasticity in birdsong may partially explain why clades of song learners are more diverse than related clades with innate song. This “song learning” hypothesis predicts that 1) differences in song traits evolve faster in song learners, and 2) behavioral discrimination against allopatric song (a proxy for premating reproductive isolation) evolves faster in song learners. We tested these predictions by analyzing acoustic traits and conducting playback experiments in allopatric Central American sister pairs of song learning oscines (N = 42) and non-learning suboscines (N = 27). We found that non-learners evolved mean acoustic differences slightly faster than did leaners, and that the mean evolutionary rate of song discrimination was 4.3 times faster in non-learners than in learners. This unexpected result may be a consequence of significantly greater variability in song traits in song learners (by 54–79%) that requires song-learning oscines to evolve greater absolute differences in song before achieving the same level of behavioral song discrimination as non-learning suboscines. This points to “a downside of learning” for the evolution of species discrimination, and represents an important example of plasticity reducing the rate of evolution and diversification by increasing variability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.021 |
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".