Fifty+ years of primate research illustrates complex drivers of abundance and increasing primate numbers
Bibliographic record
Abstract
Many primate populations are threatened by human actions and a central tool used for their protection is establishing protected areas. However, even if populations in such areas are protected from hunting and deforestation, they still may be threatened by factors such as climate change and its cascading impacts on habitat quality and disease dynamics. Here we provide a long-term and geographically wide-spread population assessment of the five common diurnal primates of Kibale National Park, Uganda. Over 7 year-long or longer census efforts that spanned 52 years, our team walked 1466 km, and recorded 480 monkey groups. Populations were generally relatively stable with a few exceptions, for which no apparent causative factors could be identified. This stability is unexpected as many ecological changes documented over the last 34+ years (e.g., decreasing food abundance and quality) were predicted to have negative impacts. Populations of some species declined at some sites but increased at others. This highlights the need for large, protected areas so that declines in particular areas are countered by gains in others. Kibale has large areas of regenerating forest and this most recent survey revealed that after 20+ years, forest regeneration in many of these areas appears sufficient to sustain sizeable primate populations, except for blue monkeys that have not colonized these areas. Indeed, the average primate abundance in the regenerating forest was only 8.1% lower than in neighboring old-growth forest. Thus, park-wide primate abundance has likely increased, despite many pressures on the park having risen; however, some areas in the park remain to be assessed. Our study suggests that the restoration, patrolling, and community outreach efforts of the Uganda Wildlife Authority and their partners have contributed significantly to protecting the park and its animals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".