Searching in heterogeneous and limiting environments: foraging strategies of white-lipped peccaries (<i>Tayassu pecari</i>)
Bibliographic record
Abstract
Searching for patchily distributed, highly localized, and seasonally variable resources in heterogeneous environments poses significant challenges for social species living in cohesive groups. Here, we studied the searching strategies of a highly social mammal, the white-lipped peccary (Tayassu pecari), in Calakmul Biosphere Reserve, Mexico. Calakmul Biosphere Reserve is a seasonal tropical forest where important resources, such as water and food, are patchy distributed and temporarily scarce. We attempted to determine what theoretical searching model best explained the movement patterns of groups of white-lipped peccaries, including short-tailed, long-tailed, and scale-free distributions. We found that the only distribution that was well supported by the data was a zero-inflated lognormal distribution; this implies a general pattern of normally short-range intensive searching with occasional long-distance directed movements taking the animals away from previously searched areas. We also found that groups concentrated foraging activities around sources of water during the dry season, behaving as central-place foragers while occasionally searching distant areas. We discuss the potential adaptive values of such behavioral strategies for social species living in highly heterogeneous environments.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".