On the predictability of phenotypic divergence in geographic isolation
Bibliographic record
Abstract
Do related populations that are separated by barriers predictably evolve differences from one another over time, or is such divergence idiosyncratic and unpredictable? We test these alternatives by investigating patterns of trait evolution for 54 sister pairs of Andean forest birds that live in similar environments on either side of the arid Marañón Gap, a strong dispersal barrier for humid montane species. We measured divergence in both sexual (song and plumage) and ecological (beak size and beak shape) traits. Sexual traits evolve in a clock-like fashion, with trait divergence positively correlated with genetic distance (r = 0.6-0.7). In contrast, divergence in ecological traits is uncorrelated or only loosely correlated with genetic distance (r = 0.0-0.3). Thus, for geographically isolated Andean montane forest birds that live in similar environments, divergence is predictable in sexual traits, but not for ecological traits. This means that sexual trait divergence occurs independently of adaptive ecological divergence within the mega-diverse tropical Andean avifauna. Last, we show that variation in genetic divergence across a biogeographic barrier is associated with traits that are proxies for species' opportunities for dispersal (low elevation limit and elevational niche breadth), but not with traits that are proxies for species' dispersal abilities (hand-wing index and foraging strata).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".