Elevational differentiation accelerates trait evolution but not speciation rates in Amazonian birds
Bibliographic record
Abstract
The importance of ecologically mediated divergent selection in accelerating trait evolution has been poorly studied in the most species-rich biome of the planet, the continental Neotropics. We performed macroevolutionary analyses of trait divergence and diversification rates across closely related pairs of Andean and Amazonian passerine birds, to assess whether the difference in elevational range separating species pairs - a proxy for the degree of ecological divergence - influences the speed of trait evolution and diversification rates. We found that elevational differentiation is associated with faster divergence of song frequency, a trait important for pre-mating isolation, and several morphological traits, which may contribute to extrinsic post-mating isolation. However, elevational differentiation did not increase recent speciation rates, possibly due to early bursts of diversification during the uplift of the eastern Andes followed by a slow-down in speciation rate. Our results suggest that ecological differentiation may speed up trait evolution, but not diversification of Neotropical birds.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".