Latitudinal gradients in climatic‐niche evolution accelerate trait evolution at high latitudes
Bibliographic record
Abstract
Despite the importance of divergent selection to the speed of evolution, it remains poorly understood if divergent selection is more prevalent in the tropics (where species richness is highest), or at high latitudes (where paleoclimate change has been most intense). We tested whether the rate of climatic-niche evolution - one proxy for divergent selection - varies with latitude for 111 pairs of bird species. Using Brownian motion and Ornsetin-Ulhenbeck models, we show that evolutionary rates along two important axes of the climatic-niche - temperature and seasonality - have been faster at higher latitudes. We then tested whether divergence of the climatic-niche was associated with evolution in traits important in ecological differentiation (body mass) and reproductive isolation (song), and found that climatic divergence is associated with faster rates in both measures. These results highlight the importance of climate-mediated divergent selection pressures in driving evolutionary divergence and reproductive isolation at high latitudes.
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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) | 0.000 | 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".