Group Size Dynamics over 15+ Years in an African Forest Primate Community
Bibliographic record
Abstract
Abstract Group size affects many aspects of the ecology and social organization of animals. We investigated group size stability for five primate species in Kibale National Park, Uganda from 1996 to 2011 at three nested spatial scales. Survey data indicated that group sizes did not change for most species, with the exception of red colobus monkeys (Procolobus rufomitratus), in which group size increased at all spatial scales. Mangabey (Lophocebus albigena) group size increased in old‐growth forest, but the sample size and increase were small. To augment this survey data, we collected several years of demographic data on three habituated groups of redtail monkeys (Cercopithecus ascanius), eight groups of black‐and‐white colobus (Colobus guereza), and one red colobus group. The red colobus group increased from 59 to 104 individuals, while redtail monkey and black‐and‐white colobus group sizes were stable, mirroring our survey results. To understand mechanisms behind group size changes in red colobus versus stability in other primates, we monitored forest dynamics at two spatial scales between 1990 and 2013, considered changes in predator population, and explored evidence of disease dynamics. The cumulative size of all trees and red colobus food trees increased over 24 yr, suggesting that changing food availability was driving group size changes for red colobus, while predation and disease played lesser roles. Overall, our results and evidence of changing primate densities suggest that the Kibale primate community is in a non‐equilibrium state. We suggest future conservation and management efforts take this into consideration.
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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 | 0.001 |
| 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".