Environmental and anthropogenic effects on the nesting patterns of Nigeria–Cameroon chimpanzees in North‐West Cameroon
Bibliographic record
Abstract
Environmental conditions and human activity influence the selection of nest sites by chimpanzees and may have serious conservation implications. We examined the characteristics of nesting trees preferred by chimpanzees, investigated the effect of vegetation composition and topography on nest site locations and seasonality on nesting heights of chimpanzees, and verified the effect of predator occurrence and human activity on the nesting behavior of the Nigeria-Cameroon chimpanzee (Pan troglodytes ellioti) in Kom-Wum Forest Reserve (KWFR) and surrounding unprotected forest in Cameroon. We recorded 923 nests, 502 signs of human activity, and 646 nesting trees along line transects and recces (reconnaissance) for two seasons. We found that chimpanzees constructed more arboreal nests on tall primary trees with high lowest branch height and large diameter at breast height. Moreover, they oriented their nests within trees in the slope direction when the nesting trees were located on slopes. Additionally, the occurrence of chimpanzee nests was positively related to increasing elevation and slope and decreased with distance to primary forest. In contrast, the number of nests increased with distance to secondary forest, open land, and villages, and nesting height was not influenced by seasons. While we recorded no signs of large nocturnal chimpanzee predators at nesting trees, we found signs of hunting activity at nesting locations. Nesting high in trees is likely a way of avoiding hunting, while nest orientation within trees in slope direction shortens escape routes from human hunters. Our findings suggest that chimpanzees select safe trees (tall trees with high lowest branch height) located in nesting areas (primary forest, high elevation, and steep slopes) that are not easily accessible by humans. Therefore, conservation efforts should focus on protecting primary forests at high elevation and steep slopes and reducing human impact.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".