Colour polymorphism is associated with lower extinction risk in birds
Bibliographic record
Abstract
Colour polymorphisms have played a major role in enhancing current understanding of how selection and demography can impact phenotypes. Because different morphs often display alternative strategies and exploit alternative ecological niches, colour polymorphism can be expected to promote adaptability to environmental changes. However, whether and how it could influence populations' and species' response to global changes remains debated. To address this question, we built an up-to-date and complete database on avian colour polymorphism based on the examination of available data from all 10,394 extant bird species. We distinguished between true polymorphism (where different genetically determined morphs co-occur in sympatry within the same population) and geographic variation (parapatric or allopatric colour variation), because these two patterns of variation are expected to have different consequences on populations' persistence. Using the IUCN red list, we then showed that polymorphic bird species are at lesser risk of extinction than nonpolymorphic ones, after controlling for a range of factors such as geographic range size, habitat breadth, life history, and phylogeny. This appears consistent with the idea that high genetic diversity and/or the existence of alternative strategies in polymorphic species promotes the ability to adaptively respond to changing environmental conditions. In contrast, polymorphic species were not less vulnerable than nonpolymorphic ones to specific drivers of extinction such as habitat alteration, direct exploitation, climate change, and invasive species. Thus, our results suggest that colour polymorphism acts as a buffer against environmental changes, although further studies are now needed to understand the underlying mechanisms. Developing accurate quantitative indices of sensitivity to specific threats is likely a key step towards a better understanding of species response to environmental changes.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".