Using camera trap bycatch data to assess habitat use and the influence of human activity on African elephants (Loxodonta africana) in Kasungu National Park, Malawi
Bibliographic record
Abstract
Abstract African elephants (Loxodonta africana) are increasingly exposed to high levels of human disturbance and are threatened by poaching and human–elephant conflict. As anthropogenic pressures continue to increase, both inside and outside protected areas, understanding elephant behavioural responses to human activity is required for future conservation management. Here, we use bycatch data from camera trap surveys to provide inferences on elephant habitat use and temporal activity in Kasungu National Park (KNP), Malawi. The KNP elephant population has declined by ~ 95% since the late 1970s, primarily because of intensive poaching, and information on elephant ecology and behaviour can assist in the species’ recovery. Using occupancy modelling, we show that proximity to water is the primary driver of elephant habitat use in KNP, with sites closer to water having a positive effect on elephant site use. Our occupancy results suggest that elephants do not avoid sites of higher human activity, while results from temporal activity models show that elephants avoid peak times of human activity and exhibit primarily nocturnal behaviour when using the KNP road network. As key park infrastructure is located near permanent water sources, elephant spatiotemporal behaviour may represent a trade-off between resource utilisation and anthropogenic-risk factors, with temporal partitioning used to reduce encounter rates. Increased law enforcement activity around permanent water sources could help to protect the KNP elephant population during the dry season. Our findings highlight that camera trap bycatch data can be a useful tool for the conservation management of threatened species beyond the initial scope of research.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".