Primates adjust movement strategies due to changing food availability
Bibliographic record
Abstract
Animals are hypothesized to search their environments in predictable ways depending on the distribution of resources. Evenly distributed foods are thought to be best exploited with random Brownian movements; while foods that are patchy or unevenly distributed require non-Brownian strategies, such as Lévy walks. Thus, when food distribution changes due to seasonal variation, animals should show concomitant changes in their search strategies. We examined this issue in 6 monkey species from Africa and Mexico: 3 frugivores and 3 folivores. We hypothesized that the more patchily distributed fruit would result in frugivores showing more levy-like patterns of motion, while folivores, with their more homogenous food supply, would show Brownian patterns of motion. At least 3 and up to 5 of 6 species conformed to the overall movement pattern predicted by their primary dietary item. For folivorous black howler monkeys (Alouatta pigra), ursine colobus (Colobus vellerosus), and red colobus (Procolobus rufomitratus), Brownian movement was supported or could not be ruled-out. Two frugivores (spider monkeys, Ateles geoffroyi yucatanensis, and gray-cheeked mangabeys, Lophocebus albigena) showed Lévy walks, as predicted, but frugivorous vervet monkeys (Chlorocebus pygerythrus) showed a Brownian walk. Additionally, we test whether seasonal variation in the spatial availability of food support environmentally driven changes in movement patterns. Four of 5 species tested for seasonal variation showed adjustments in their search strategies between the rainy and dry seasons. This study provides support for the notion that food distribution determines search strategies and that animal movement patterns are flexible, mirroring changes in the environment.
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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 | 0.001 | 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".