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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".