Highly nested diets in intrapopulation monkey–resource food webs
Bibliographic record
Abstract
Recently several studies have focused on the structure of ecological networks to provide insights into ecological and coevolutionary dynamics of interacting species. However, rarely have the tools of ecological networks been used to understand how feeding relationships vary among individuals of the same population. Here we use 7 years of data and network analyzed to examine the intrapopulation diet variation in a group of howler monkeys (Alouatta palliata). We show that individual monkey-resource food webs are nested, but not modular and the stability of these patterns is independent of time of day or season. Our findings indicated that individuals do not forage randomly when compared to null models and that the diets of more selective monkeys represent subsets of the diets of other individuals. Moreover, there are no subgroups that eat a particular set of available plant species more frequently than other sets, suggesting that the spatial strategy of group foraging plays an important role in the feeding ecology of each group given that individuals of the same group tend to share similar resources while the group remains at a feeding site. Since the diets of more selective individuals are a subset of other monkeys, we suggest that more selective monkeys are able to outcompete others for preferred foods. Additionally, we did not observe differences in nutritional content or spatial abundance of more frequently eaten plant species when compared with less frequently eaten species, but in most cases, the more frequently eaten plant species were Ficus (Moraceae). This reinforces the important role that Ficus trees play in howler monkey feeding ecology, likely due to its year-round availability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".