Does trophy hunting matter to long‐term population trends in<scp>A</scp>frican herbivores of different dietary guilds?
Bibliographic record
Abstract
Abstract The persistence of largeAfrican herbivores in trophy hunting areas is still unclear because of a lack of data from long‐term wildlife monitoring outside national parks. We compared population trends over the last 30 years inHwangeNationalPark,Zimbabwe, and the neighbouringMatetsiSafariArea where large herbivores were harvested at an average yearly rate of 2%. We investigated whether trophy hunting altered densities and the proportion of adult males in several large herbivore species. Large herbivores generally thrived as well, or even better, in the hunting areas than in the national park. The proportion of adult males did not differ between the two zones, except for species with higher harvest rates and proportionally more males harvested. Densities were not lower in the hunting areas than in the national park, except for elephant and impala. Large herbivores generally declined throughout the 30‐year period in both zones, particularly selective grazers. This is probably because of their greater sensitivity to variation in rainfall compared with other herbivores. Rainfall indeed declined during the study period with droughts being particularly frequent during the 1990s. Browsers, mixed feeders and non‐selective grazers generally declined less in the hunting areas than in the national park, possibly because of lower densities of natural predators and elephants outside the park. Our study highlighted that large herbivores may persist in trophy hunting areas as well as in national parks. When rigorously managed, trophy hunting areas may be relevant conservation areas for large herbivores, particularly under the current global decline of wildlife abundance acrossAfrica.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".