Plant diversity in the diet of Costa Rican primates in contrasting habitats: a meta-analysis
Bibliographic record
Abstract
Abstract In human-modified tropical landscapes, the survival of arboreal vertebrates, particularly primates, depends on their plant dietary diversity. Here, we assessed diversity of plants included in the diet of Costa Rican non-human primates, CR-NHP (i.e. Alouatta palliata palliata , Ateles geoffroyi , Cebus imitator , and Saimiri oerstedii ) inhabiting different habitat types across the country. Specifically, we assessed by analyzing 37 published and unpublished datasets: (i) richness and dietary α-plant diversity, (ii) the β-diversity of dietary plant species and the relative importance of plant species turnover and nestedness contributing to these patterns, and (iii) the main ecological drivers of the observed patterns in dietary plant . Diet data were available for 34 Alouatta , 16 Cebus , 8 Ateles , and 5 Saimiri groups. Overall dietary plant species richness was higher in Alouatta (476 spp.), followed by Ateles (329 spp.), Cebus (236 spp.), and Saimiri (183 spp.). However, rarefaction curves showed that α-diversity of plant species was higher in Ateles than in the other three primate species. The γ-diversity of plants was 868 species (95% C.I.=829-907 species). The three most frequently reported food species for all CR-NHP were Spondias mombin , Bursera simaruba , and Samanea saman . In general, plant species turnover, rather than nestedness, explained the dissimilarity in plant diet diversity (β sim > 0.60) of CR_NHP. Finally, primate species, habitat type (life zone and disturbance level) and, to a lesser degree, sampling effort were the best predictors of the dietary plant assemblages. Our findings suggest that CR-NHP diets were diverse, even in severely-disturbed habitats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".