Can color vision variation explain sex differences in invertebrate foraging by capuchin monkeys?
Bibliographic record
Abstract
Abstract Invertebrates are the main source of protein for many small-to-medium sized monkeys. Prey vary in size, mobility, degree of protective covering, and use of the forest, i.e. canopy height, and whether they are exposed or embed themselves in substrates. Sex-differentiation in foraging patterns is well documented for some monkey species and recent studies find that color vision phenotype can also affect invertebrate foraging. Since vision phenotype is polymorphic and sex-linked in most New World monkeys - males have dichromatic vision and females have either dichromatic or trichromatic vision - this raises the possibility that sex differences are linked to visual ecology. We tested predicted sex differences for invertebrate foraging in white-faced capuchins Cebus capucinus and conducted 12 months of study on four free-ranging groups between January 2007 and September 2008. We found both sex and color vision effects. Sex: Males spent more time foraging for invertebrates on the ground. Females spent more time consuming embedded, colonial invertebrates, ate relatively more “soft” sedentary invertebrates, and devoted more of their activity budget to invertebrate foraging. Color Vision: Dichromatic monkeys had a higher capture efficiency of exposed invertebrates and spent less time visually foraging. Trichromats ate relatively more “hard” sedentary invertebrates. We conclude that some variation in invertebrate foraging reflects differences between the sexes that may be due to disparities in size, strength, reproductive demands or niche preferences. However, other intraspecific variation in invertebrate foraging that might be mistakenly attributed to sex differences actually reflects differences in color vision.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".