Foraging behaviour data for sympatric Ateles geoffroyi, Alouatta palliata, and Cebus imitator
Bibliographic record
Abstract
Senses form the interface between animals and environments, and their form and function provide a window into the ecology of past and present species. However, research on the senses used during foraging (e.g. smell, vision, touch, taste) by wild terrestrial frugivores is sparse. Here, we combine 26,094 fruit foraging sequences recorded from three wild, sympatric primates (Cebus imitator, Ateles geoffroyi, Alouatta palliata) with data on within- and between-species variation in colour vision, olfaction, taste, and hand anatomy. We hypothesize that dietary and sensory specialization shape foraging behaviours. We find that frugivorous spider monkeys (Ateles geoffroyi) sniff fruits most often, that omnivorous capuchins (Cebus imitator), the species with the highest measure of manual dexterity, uses manual touch most often, and that main olfactory bulb volume is a better predictor of sniffing behaviour than nasal turbinate surface area. We also identify an interaction between colour vision phenotype and use of other senses. Controlling for species, dichromats sniff and bite fruits more often than trichromats, and trichromats use manual touch to evaluate cryptic fruits more often than dichromats. Our findings help reveal how dietary specialization and sensory variation shape foraging behaviours, and inform methods for investigating relationships between behaviour and anatomy.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".