Trophy Hunting Versus Ecotourism as a Conservation Model? Assessing the Impacts on Ungulate Behaviour and Demographics in the Ruaha-Rungwa Ecosystem, Central Tanzania
Bibliographic record
Abstract
Trophy hunting may influence wildlife populations in many ways, but these effects have received little consideration in many of Africa’s protected areas. We assessed the effects of trophy hunting on group size, behaviour, flight initiation distance, sex ratio and calf recruitment rate in two model species, impala (Aepyceros melampus) and greater kudu (Tragelaphus strepsiceros), in Rungwa Game Reserve (RGR), Tanzania. The adjoining Ruaha National Park (RNP) served as a control site, since only ecotourism is permitted. Road transects were driven and data recorded immediately upon sighting animals. Both impala and greater kudu had higher flight initiation distances, smaller group size, lower calf recruitment rates and higher levels of vigilance behaviour in RGR compared to those in RNP. Sex ratios did not differ between the two areas. The observed differences are ascribed to the direct and indirect effects of trophy hunting in RGR. Low calf recruitment rates in RGR are of concern, as this may directly compromise population growth rates. Long-term studies may therefore be required to assess how hunted populations are affected by different hunting intensities and at what point this may threaten population persistence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".