The effectiveness of hazing African lions as a conflict mitigation tool: implications for carnivore management
Bibliographic record
Abstract
Abstract Human–carnivore conflict (HCC) represents one of the greatest threats to rural livelihoods and the persistence of large carnivores. The application of aversive conditioning, the association of unpleasant stimuli with the occurrence of unwanted behaviors, to mitigate HCC has achieved mixed results within and across species, making a better understanding of the factors driving intervention success critical to inform management practices. We explored the degree to which the chasing of African lions (Panthera leo) out of no tolerance zones conditions lion behavior to reduce their rate of return into community lands or rate of repeated livestock killing, providing evidence‐based understanding of program outcomes. We used data from 15 global positioning system (GPS)‐collared lions adjacent to Hwange National Park, Zimbabwe, with each lion receiving 0–17 conditioning treatments, and analyzed the data using recurrent event survival analysis and logistic regression. Chases were most successful (i.e., lion pushed out of the no tolerance zone by sunrise of the following day) in the dry season (i.e., when wild prey were more predictable), in areas closer to the park, and for individuals from smaller and more stable prides (i.e., had not lost a pride male within six months). Adult females and subadult males were more likely than adult males to reenter community lands, and subadult males were most likely to repeatedly depredate livestock. While livestock depredation has decreased since program initiation, the individuals in this study were overall not less likely to enter community lands or depredate livestock in response to chases when chases were considered isolated events. Rather, it was the consistency of deterrence events that proved most important in reducing livestock depredations, likely because of a stronger reinforcement between the undesired behavior and the negative stimulus. However, lions that had previously habitually killed livestock had greater depredation rates even after several conditioning treatments. Aversive conditioning holds promise in the management of carnivores that depredate livestock, but intervention must be consistent, ideally early in the development of problem behaviors, to maximize intervention effectiveness. Methods that separate wildlife from people (i.e., fencing, livestock enclosure fortification), in combination with aversive conditioning, may be needed to provide a sustainable, long‐term solution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".