Hunting and the conservation of a social ungulate: the white-lipped peccary <i>Tayassu pecari</i> in Calakmul, Mexico
Bibliographic record
Abstract
Abstract The white-lipped peccary Tayassu pecari is a social ungulate that forms the largest groups documented for any tropical forest ungulate species. Since the 1950s the species has become increasingly rare in Mesoamerica, and the more frequent reporting of smaller groups may be related to increased hunting pressure. Here we address the conservation status of this species in terms of its group size and structure, breeding season, population density, and relationship with hunting patterns in the Calakmul region of southern Mexico. Group sizes, age structure and breeding season were recorded in one large non-hunted site (Calakmul Biosphere Reserve) and four adjacent hunted sites. Population density was estimated in the Reserve and hunting patterns were recorded from three adjacent villages. Results indicate that hunting pressure affects peccary populations by reducing group size. White-lipped peccary groups were larger in the Reserve (median = 25) than in the hunted areas (median = 16) but groups were generally smaller than those reported in other forests. These smaller group sizes indicate conservation concern for this species in the Calakmul region. In addition, the estimated population (0.43 km2) is one of the lowest reported for this species. Hunting occurs mainly in the dry season, which is the peak of the breeding season and when peccary groups visit water bodies, where they are more easily hunted.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".