Group social conditions and environment predict foraging behavior in wild baboons
Bibliographic record
Abstract
For group-living animals, conditions in the physical and social environments are closely linked to foraging outcomes, but the nature and causal direction of many aspects of these relationships remain unclear. Here, we use long-term data from a well-studied population of wild baboons in Amboseli, Kenya, to examine how group-level social traits (group size and social network density) and climate variables (rainfall and temperature) are linked to two types of foraging outcomes for adult female baboons: (i) foraging-related time budgets and (ii) diet composition (time spent on fallback foods, such as grass corms, versus high-energy foods). We find that rainfall and temperature interact to predict multiple foraging outcomes: more rainfall is associated with more time spent feeding, less time spent walking without feeding, and more feeding time spent on grass corms, but this influence is more pronounced in hotter years than in cooler years. Females in intermediate-sized groups spend more time walking without feeding than those in other groups, but group size does not significantly predict other foraging-related outcomes (i.e., time spent feeding or diet composition). We also find that females in groups with denser social networks spend less time feeding, and less time feeding on grass corms, than those in sparser networks. However, in a preliminary causal analysis meant to further explore this result, we find support for the hypothesis that this relationship is driven by effects of foraging on social behavior, as opposed to effects of network density on foraging: more time spent eating grass corms leads to lower social network density and more time spent feeding. Our results show how the social and the physical environments are linked to foraging outcomes, and highlight the importance of future studies of their pattern and mechanism.
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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 | 0.000 | 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".