Hidden colour signals as key drivers in the evolution of anti-predator coloration and defensive behaviours in snakes
Bibliographic record
Abstract
The initial evolution of warning coloration ("aposematism") within a cryptic population of defended prey presents an evolutionary paradox. A recent phylogenetic analysis of amphibia suggests a new solution: prey that combine cryptic colours with conspicuous patches on concealed body parts ("hidden signallers"), may have mediated the transition of species from camouflage to aposematism. Here, we focus on the colour-diverse snake family Elapidae and test whether species with hidden colours could also serve as an intermediate stage in the evolution of aposematism in this group. Phylogenetic comparative analysis reveals several key patterns in their anti-predator colour evolution: (i) a few major transitions influenced the overall distribution of hidden-colours, camouflage, and aposematism in the group, and (ii) aposematism evolved multiple times, with hidden coloration a common precursory state, while direct transitions from camouflage to aposematism are also observed. We also quantify associations between colour patterns and defensive behaviours that reveal ventral surfaces (i.e. hidden signals). We find that venter-revealing defensive behaviours frequently co-occur with hidden colour signals. Our results suggest that aposematism can evolve through multiple routes and highlight the prevalence of co-evolution between venter-revealing defensive behaviour and anti-predator coloration in snakes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".