Climate, habitat, and geographic range overlap drive plumage evolution
Bibliographic record
Abstract
Organismal appearances are shaped by selection from both abiotic and biotic drivers 1–5 . For example, Gloger’s rule describes the pervasive pattern that more pigmented populations are found in more humid areas 1,6,7 , and substrate matching as a form of camouflage to reduce predation is widespread across the tree of life 8–10 . Sexual selection is a potent driver of plumage elaboration 5,11 , and species may also converge on nearly identical colours and patterns in sympatry, often to avoid predation by mimicking noxious species 3,4 To date, no study has taken an integrative approach to understand how these factors determine the evolution of colour and pattern across a large clade of organisms. Here we show that both habitat and climate profoundly shape avian plumage. However, we also find a strong signal that many species exhibit remarkable convergence not explained by these factors nor by shared ancestry. Instead, this convergence is associated with geographic overlap between species, suggesting strong, albeit occasional, selection for interspecific mimicry. Consequently, both abiotic and biotic factors, including interspecific interactions, are potent drivers of phenotypic evolution.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".