Diet variation driven by color vision phenotype in wild capuchin monkeys
Bibliographic record
Abstract
The polymorphic color vision of platyrrhine monkeys is a fascinating example of balancing selection acting on multiple genetic and phenotypic morphs. Yet, the mechanism of natural selection maintaining this variation remains elusive. Past research has demonstrated task-specific foraging advantages to dichromatic (two-opsin vision, red-green colorblind) and trichromatic (three-opsin vision, human “normal”) monkeys, raising the potential for dietary niche differentiation. We ask whether color vision type influences diet variation in a population of wild, white-faced capuchins (Cebus imitator) in Costa Rica. To assess this, we record food intake during simultaneous focal follows of adult female dichromats and trichromats, and classify the conspicuity of diet items in capuchin visual space. We assess the degree of dietary and nutritional niche overlap during periods of high and low fruit abundance using nutritional geometry and Pianka’s index. We find that in months of high fruit abundance, trichromats have higher intake of conspicuously-colored fruits, while dichromats have higher intake of camouflaged invertebrates, resulting in lower niche overlap. These differences disappear in months of low habitat-wide fruit abundance, a time when overall dietary breadth collapses. Our results support the hypothesis that niche differentiation in diet contributes to maintaining color vision variation without compromising species-specific nutritional intake.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".