Understanding long‐term primate community dynamics: implications of forest change
Bibliographic record
Abstract
Understanding the causes of population declines often involves comprehending a complex set of interactions linking environmental and biotic changes, which in combination overwhelm a population's ability to persist. To understand these relationships, especially for long-lived large mammals, long-term data are required, but rarely available. Here we use 26-36 years of population and habitat data to determine the potential causes of group density changes for five species of primates in Kibale National Park, Uganda, in areas that were disturbed to varying intensities in the late 1960s. We calculated group density from line transect data and quantified changes in habitat structure (cumulative diameter at breast height [dbh] and food availability [cumulative dbh of food trees]) for each primate species, and for one species, we evaluated change in food nutritional quality. We found that mangabeys and black-and-white colobus group density increased, blue monkeys declined, and redtails and red colobus were stable in all areas. For blue monkeys and mangabeys, there were no significant changes in food availability over time, yet their group density changed. For redtails, neither group density measures nor food availability changed over time. For black-and-white colobus, a decrease in food availability over time in the unlogged forest surprisingly coincided with an increase in group density. Finally, while red colobus food availability and quality increased over time in the heavily logged area, their group density was stable in all areas. We suggest that these populations are in nonequilibrium states. If such states occur frequently, it suggests that large protected areas will be required to protect species so that declines in some areas can be compensated for by increases in adjacent areas with different histories.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".