Forest Elephants in a Human-Dominated Landscape: Are They Risk-Takers?
Bibliographic record
Abstract
Habitat loss from forest conversion to agriculture threatens tropical biodiversity. Despite documented risk-avoidance behaviors, some species may adopt riskier strategies to gain access to food. Recent conversion of a protected area in southern Cameroon to an agro-industrial plantation coincides with increased sightings of forest elephants near human settlements, which is unusual and suggests a drastic change in their habitat use. This study aims to examine the influence of human activity on forest elephant habitat use and evaluate the effectiveness of two survey methods in documenting elephant and human occurrence. Twenty-one camera traps were deployed along the border between the declassified protected area and the community land, and reconnaissance walks were conducted between camera trap stations. Results from both methods were compared. Elephant occurrence tended to be negatively affected by human activity, and elephants were inactive during peak human activity. However, their presence near human settlements suggests a general risk-taking behavior in habitat use. Moreover, reconnaissance walks proved more effective than camera traps in providing a greater amount of data. This risky proximity to humans points to a complex trade-off between risk and access to food resources, where the nutritional benefits and easy access of crops and secondary forest resources may outweigh the perceived human-mediated risk. At the same time, elephants may adopt strategies to minimize direct interactions with humans. Further habitat fragmentation and human encroachment on wild areas are expected in the near future. As elephant presence near human settlements often lead to increased conflict, continued monitoring of elephant habitat use in human-dominated landscapes using efficient survey methods is crucial to design up-to-date and effective management and conservation strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".