Floristic heterogeneity between forested sites in Kibale National Park, Uganda: insights into the fine‐scale determinants of density in a large‐bodied frugivorous primate
Bibliographic record
Abstract
1. Despite a long history of research on the influence of fruit availability on the population density of large-bodied vertebrate frugivores, operational understanding of the factors regulating density in these taxa remains elusive. We propose that fruit resources can be distinguished from one another on the basis of their functional role for the animals in question, and that such a classification system can aid in identifying the most influential determinants of frugivore density. 2. We compared the availability of several resource classes between two sites in Kibale National Park, Uganda separated by only 12 km yet differing threefold in density of chimpanzees (Pan troglodytes). 3. We categorized plant species used for fruit by chimpanzees according to their availability relative to habitat-wide fruit productivity, and by their tendency towards inter-individual fruiting synchrony. We predicted that the site of high chimpanzee density would support a higher density of food plant species tending to produce crops during periods of high habitat-wide productivity [high fruit abundance (HFA foods)] and of those tending to fruit synchronously among individuals during times of low habitat-wide availability (sLFA foods). The first food class should provide chimpanzees with a high nutrient density (and thus promote population growth), whereas the second should provide stable subsistence during lean periods and thus a temporally consistent resource base. 4. Counter to our prediction, only sLFA resources were more abundant at the site of high chimpanzee density than at the site of low density. We suggest that sLFA resources are most important in influencing density of large-bodied frugivores.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".