Primate population dynamics over 32.9 years at Ngogo, Kibale National Park, Uganda
Bibliographic record
Abstract
We present census data for eight primate species spanning 32.9 years along the same transect at Ngogo, Kibale National Park, Uganda, demonstrating major changes in the composition of the primate community. Correlated with an estimated decline of ∼89% in the red colobus population was an increase in encounter rates with chimpanzee parties. Our data, along with the unusually high rates of predation by chimpanzees on red colobus at Ngogo and the fact that the chimpanzee community at Ngogo is the largest ever recorded, support the conclusion that the red colobus decline was caused primarily by chimpanzee predation. This seems to be the first documented case of predation by one nonhuman primate causing the population decline in another. We evaluated disease and interspecific competition as other possible causes of the red colobus decline, but judged them to be relatively insignificant compared with predation by chimpanzees. Notable changes in encounter rates with other primate species may have resulted from forest expansion. Those for mangabeys, redtails, and black and white colobus increased significantly. Encounter rates increased for l'Hoest's monkeys too, but the increased sightings may have been an artifact of increased habituation. Sightings of blue monkey and baboon groups declined. There was no significant change in encounter rates for all species combined. The Ngogo primate community seemed to be in a nonequilibrium state, changing from one dominated by two species, a folivore (red colobus) and a frugivorous omnivore (redtails), to one dominated by three species of frugivorous omnivores (redtails, mangabeys, and chimpanzees). This study demonstrates the importance of long-term monitoring in understanding population dynamics and the role of intrinsic variables in shaping the species composition of a community.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".