The latitudinal gradient in rates of evolution for bird beaks, a species interaction trait
Bibliographic record
Abstract
Where is evolution fastest? The biotic interactions hypothesis proposes that greater species richness creates more ecological opportunity, driving faster evolution at low latitudes, whereas the 'empty niches' hypothesis proposes that ecological opportunity is greater where diversity is low, spurring faster evolution at high latitudes. We tested these contrasting predictions by analysing rates of beak evolution for a global dataset of 1141 avian sister species. Rates of beak size evolution are similar across latitudes, with some evidence that beak shape evolves faster in the temperate zone, consistent with the empty niches hypothesis. The empty niches hypothesis is further supported by a meta-analysis showing that rates of trait evolution and recent speciation are generally faster in the temperate zone, whereas rates of molecular evolution are slightly faster in the tropics. Our results suggest that drivers of evolutionary diversification are either similar across latitudes or more potent in the temperate zone, thus calling into question multiple hypotheses that invoke faster tropical evolution to explain the latitudinal diversity gradient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".