Geographic comparison of plant genera used in frugivory among the pitheciids<i>Cacajao</i>,<i>Callicebus</i>,<i>Chiropotes</i>, and<i>Pithecia</i>
Bibliographic record
Abstract
Pitheciids are known for their frugivorous diets, but there has been no broad-scale comparison of fruit genera used by these primates that range across five geographic regions in South America. We compiled 31 fruit lists from data collected from 18 species (three Cacajao, six Callicebus, five Chiropotes, and four Pithecia) at 26 study sites in six countries. Together, these lists contained 455 plant genera from 96 families. We predicted that 1) closely related Chiropotes and Cacajao would demonstrate the greatest similarity in fruit lists; 2) pitheciids living in closer geographic proximity would have greater similarities in fruit lists; and 3) fruit genus richness would be lower in lists from forest fragments than continuous forests. Fruit genus richness was greatest for the composite Chiropotes list, even though Pithecia had the greatest overall sampling effort. We also found that the Callicebus composite fruit list had lower similarity scores in comparison with the composite food lists of the other three genera (both within and between geographic areas). Chiropotes and Pithecia showed strongest similarities in fruit lists, followed by sister taxa Chiropotes and Cacajao. Overall, pitheciids in closer proximity had more similarities in their fruit list, and this pattern was evident in the fruit lists for both Callicebus and Chiropotes. There was no difference in the number of fruit genera used by pitheciids in habitat fragments and continuous forest. Our findings demonstrate that pitheciids use a variety of fruit genera, but phylogenetic and geographic patterns in fruit use are not consistent across all pitheciid genera. This study represents the most extensive examination of pitheciid fruit consumption to date, but future research is needed to investigate the extent to which the trends in fruit genus richness noted here are attributable to habitat differences among study sites, differences in feeding ecology, or a combination of both.
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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 | 0.001 |
| 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".