Playing it safe? Solitary vervet monkeys (<i>Chlorocebus pygerythrus</i>) choose high‐quality foods more than those in competition
Bibliographic record
Abstract
An important goal in foraging ecology is to determine how biotic and abiotic variables impact the foraging decisions of wild animals and how they move throughout their multidimensional landscape. However, the interaction of food quality and feeding competition on foraging decisions is largely unknown. Here we examine the importance of food quality in a patch on the foraging decisions of wild vervet monkeys (Chlorocebus pygerythrus) at Lake Nabugabo, Uganda using a multidestination platform array. The overall nutritional composition of the vervet diet was assessed and found to be low in sodium and lipids, thus we conducted a series of experimental manipulations in which the array was varied in salt and oil content. Although vervets prioritized platforms containing key nutrients (i.e., sodium and lipids) overall, we found that solitary vervets prioritized nutrient-dense platforms more strongly than competing vervets. This finding was opposite to those in a similar experiment that manipulated food site quantity, suggesting that large, salient rewards may be worth competing over but slight differences in nutritional density may be only chosen when there are no potentially negative social consequences (i.e., aggression received). We also found that vervets chose platforms baited with oil-only, and oil combined with salt, but not salt-only, suggesting that energy was an important factor in food choice. Our findings demonstrate that when wild vervets detect differences in feeding patches that reflect nutritional composition, they factor these differences into their navigational and foraging decisions. In addition, our findings suggest that these nutritional differences may be considered alongside social variables, ultimately leading to the complex strategies we observed in this study.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".