Elevational Differentiation Increases Rates of Trait Evolution but not Diversification in Neotropical Passerine Birds
Bibliographic record
Abstract
The importance of ecologically-mediated divergent selection in elevating rates of trait evolution has been poorly studied in the most species-rich biome of the planet, the continental tropics. I performed a macroevolutionary analysis of trait divergence and diversification rates across closely-related pairs of passerine birds, belonging to the Amazon basin and adjacent Andean slopes, to assess whether the difference in elevational range separating species pairs influences the speed of trait evolution and diversification rates. Difference in elevation was used as a proxy for the degree of ecological divergence. I found that the amount of elevational separation is associated with faster differentiation of song frequency, a trait important for premating isolation, and several morphological traits, which may contribute to extrinsic postmating isolation. However, ecological differentiation does not primarily drive bird diversification and, thus, may have limited influence on patterns of species richness along the eastern slope of the tropical Andes.
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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) | 0.002 | 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 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".