Conservation lessons from large‐mammal manipulations in East African savannas: the KLEE, UHURU, and GLADE experiments
Bibliographic record
Abstract
African savannas support an iconic fauna, but they are undergoing large-scale population declines and extinctions of large (>5 kg) mammals. Long-term, controlled, replicated experiments that explore the consequences of this defaunation (and its replacement with livestock) are rare. The Mpala Research Centre in Laikipia County, Kenya, hosts three such experiments, spanning two adjacent ecosystems and environmental gradients within them: the Kenya Long-Term Exclosure Experiment (KLEE; since 1995), the Glade Legacies and Defaunation Experiment (GLADE; since 1999), and the Ungulate Herbivory Under Rainfall Uncertainty experiment (UHURU; since 2008). Common themes unifying these experiments are (1) evidence of profound effects of large mammalian herbivores on herbaceous and woody plant communities; (2) competition and compensation across herbivore guilds, including rodents; and (3) trophic cascades and other indirect effects. We synthesize findings from the past two decades to highlight generalities and idiosyncrasies among these experiments, and highlight six lessons that we believe are pertinent for conservation. The removal of large mammalian herbivores has dramatic effects on the ecology of these ecosystems; their ability to rebound from these changes (after possible refaunation) remains unexplored.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".