Socioeconomic predictors of pastoralist tolerance towards large carnivores in northern Tanzania
Bibliographic record
Abstract
Human-carnivore interactions can negatively affect wildlife and people, particularly in pastoral areas where herding communities depend on livestock for livelihood and well-being. The success of large carnivore conservation initiatives in such regions largely hinges on the extent to which people are willing to share landscapes with carnivores. Tolerance for predatory wildlife, and the array of tangible and intangible factors that shape it, is thus of central importance for promoting human-carnivore coexistence. Based on questionnaire surveys ( n = 424), this paper identifies socioeconomic predictors of pastoralist tolerance towards two species of large carnivores in the Tarangire ecosystem of northern Tanzania. Informed in part by theory on the Hazard Acceptance Model and Wildlife Tolerance Model, we used mixed effect binomial regression models to assess the effects of perceived monetary costs, conservation importance, hidden impacts on well-being, and attitudes about species on people’s tolerance levels for sharing landscapes with leopards ( Panthera pardus ) and spotted hyenas ( Crocuta crocuta ). Pastoralist tolerance for hyenas and leopards was influenced by the magnitude of livestock losses experienced, frequency of carnivore-induced sleep disturbances, individually held attitudes, and the perceived importance of carnivore conservation. Notably, tolerance for hyenas and leopards declined only when perceived impacts rose from moderate to severe levels suggesting that herders are willing to absorb some of the minor costs of coexistence. Based on these findings, we conclude that regional conservation initiatives and wildlife governance institutions should address herder concerns about the risks engendered by large carnivores to ensure that the hazards posed by dangerous wildlife remain lower than people’s thresholds of tolerability.
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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 | 0.000 | 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) | 0.001 | 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".