Livelihoods and large carnivores: Identifying social-ecological drivers of interaction dynamics in northern Tanzania to foster coexistence
Bibliographic record
Abstract
Across shared landscapes, negative human-carnivore interactions can bring about serious consequences for both wildlife and human livelihoods. Identifying factors that promote coexistence is thereby one of the most pressing and complex environmental issues facing wildlife managers and local communities globally. The nature and intensity of human-carnivore interaction dynamics are contingent on both ecological and human social factors. Carnivore behavioural ecology is influenced by external environment and distributions of available resources, whereas human interactions with predatory wildlife are shaped by an array of social, economic, and cultural factors. This paper explores carnivore-pastoralist interactions in Maasai communities within the Tarangire ecosystem of northern Tanzania, employing a mixed-methods framework to analyze the ecological and social dimensions of coexistence. Based on anthropological data from household surveys (n = 424), we assess reported levels of livestock predation by leopards (Panthera pardus) and spotted hyenas (Crocuta crocuta) across a savanna landscape adjacent to a mountain forest. We examine the effects of environmental factors (vegetative structure and proximity to protected area) and livestock husbandry practices (presence of fencing and predator deterrent lighting) on the perceived frequency of carnivore homestead visits using cumulative link mixed models. We found that leopards and hyenas mainly attacked corralled livestock at night. Variations in predator visits to homesteads were better explained by the presence of preventative infrastructure than environmental factors. Though predator deterrent lighting had negligible effects, and interior livestock corral fencing had minor effects, robust perimeter fencing was associated with major reductions in carnivore visitation frequency. We conclude that targeted investments in fortified homestead fencing may offer the most effective strategy for reducing negative human-carnivore interactions at the household scale, particularly in areas of the Tarangire ecosystem where spatial overlap between pastoralist communities and predators is consistently high.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".