Human–Hyena (Crocuta crocuta) Conflict in the Tarangire Ecosystem, Tanzania
Bibliographic record
Abstract
Interactions between people and large carnivores on shared landscapes can have harmful social and ecological consequences. Human–carnivore coexistence depends on an assemblage of sociological factors including effective management institutions that address the social costs of carnivore conservation and promote tolerance toward wildlife. In East Africa, large carnivores are particularly troublesome for herders who depend on livestock for subsistence and wellbeing. This paper provides an overview of human–hyena conflict in the Tarangire ecosystem of northern Tanzania. It presents descriptive results from a questionnaire survey (n = 1076) administered as part of an anthropological study (2019–2020; 2022; 2023) of human–wildlife interactions across twelve villages inhabited by Maasai agropastoralists. The survey instrument was designed through community-based participatory research methods to convey herder concerns about the impacts of spotted hyenas (Crocuta crocuta) on the livestock economy. Based on the perceptions and local ecological knowledge of Maasai interlocutors, the paper provides an overview of the spatial and temporal patterns of human–hyena interactions. Perceived frequencies of hyena attacks on kraaled livestock were unevenly distributed geographically, with those homesteads surrounding Manyara Ranch most heavily affected. Based on herder-reported livestock losses, the costs of depredation by spotted hyenas across the study area were estimated at approximately USD 904.84 per household per year. Most homesteads lacked fortified bomas and would benefit from the provision of lights and fencing materials to improve kraal structures. The paper’s central finding is that spotted hyenas represent a pressing, everyday concern for local pastoralists. Unsurprisingly, herders despise hyenas and are intolerant of sharing landscapes with them. For carnivore conservation outside protected areas to thrive in Tanzania, conservationists and policy makers must engage more meaningfully with the lived experiences of local herders who bear the brunt of conservation costs on their livelihoods.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".