Trichromatic perception of flower colour improves resource detection among New World monkeys
Bibliographic record
Abstract
Many plants use colour to attract pollinators, which often possess colour vision systems well-suited for detecting flowers. Yet, to isolate the role of colour is difficult, as flowers also produce other cues. The study of florivory by Neotropical primates possessing polymorphic colour vision provides an opportunity to investigate the importance of colour directly. Here we determine whether differences in colour vision within a mixed population of wild dichromatic and trichromatic white-faced capuchins (Cebus capucinus imitator) affect flower foraging behaviours. We collected reflectance data for flower foods and modelled their chromatic properties to capuchin colour vision phenotypes. We collected behavioural data over 22 months spanning four years, determined the colour vision phenotype of each monkey based on amino acid variation of the L/M opsin gene from fecal DNA, and compared foraging behaviours of dichromats and trichromats. Most flowers were more conspicuous to trichromats, and trichromats foraged in small flower patches significantly more often. These data demonstrate a difference in wild primate foraging patterns based on colour vision differences, supporting the hypothesis that trichromacy enhances detection of small, ephemeral resources. This advantage, which may also extend to other foods, likely contributes to the maintenance of colour vision polymorphism in Neotropical monkeys.
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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 | 0.000 | 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".