Food supply and poaching limit giraffe abundance in the Serengeti
Bibliographic record
Abstract
Abstract The iconic giraffe, an ecologically important browser, has shown a substantial decline in numbers across Africa since the 1990s. In Serengeti National Park, Tanzania, giraffes reached densities of 1.5–2.6 individuals km −2 in the 1970s coincident with a pulse of Acacia tree recruitment. However, despite continued increases in woody cover between the 1980s and the 2000s, giraffe recruitment and survival rates have declined and density has dropped to only 0.3–0.4 giraffes km −2 . We used a decision table to investigate how four extrinsic factors may have contributed to these declines: food supply, predation, parasites, and poaching, which have all been previously shown to limit Serengeti ungulate populations. Lower recruitment likely resulted from a reduction in diet quality, owing to the replacement of preferred trees with unpalatable species, while decreased adult survival resulted from illegal harvesting, which appears to have had a greater impact on giraffe populations bordering the western and northern Serengeti. The Serengeti giraffe population will likely persist at low‐to‐moderate densities until palatable tree species regain their former abundance. Leslie matrix models suggest that park managers should meanwhile redouble their efforts to reduce poaching, thereby improving adult survival.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".