Can parks protect migratory ungulates? The case of the Serengeti wildebeest
Bibliographic record
Abstract
Abstract The conservation of migratory species can be problematic because of their requirements for large protected areas. We investigated this issue by examining the annual movements of the migratory wildebeest, Connochaetes taurinus, in the 25000 km2 Serengeti‐Mara Ecosystem of Tanzania and Kenya. We used Global Positioning System telemetry to track eight wildebeest during 1999–2000 in relation to protected area status in different parts of the ecosystem. The collared wildebeest spent 90% of their time within well‐protected core areas. However, two sections of the wildebeest migration route – the Ikoma Open Area and the Mara Group Ranches – currently receive limited protection and are threatened by poaching or agriculture. Comparison of current wildebeest migration routes to those recorded during 1971–73 indicates that the western buffer zones appear to be used more extensively than in the past. This tentative conclusion has important repercussions for management and needs further study. The current development of community‐run Wildlife Management Areas as additional buffer zones around the Serengeti represents an important step in the conservation of this UNESCO World Heritage Site. This study demonstrates that detailed knowledge of movement of migratory species is required to plan effective conservation action.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".