Dispersal modes affect tropical forest assembly across trophic levels
Bibliographic record
Abstract
We examined assemblages of trees and two major groups of vertebrate seed dispersers, birds and primates, in Ugandan protected areas to evaluate the roles of dispersal limitation and species sorting in community assembly. We conducted partial Mantel tests to investigate relationships between community similarity, environmental distance and geographic distance. Results showed that environmental factors, specifically temperature and rainfall, significantly and more strongly structured tree assemblages than geographic distance. Analysis of tree dispersal modes revealed wind‐dispersed tree guilds were significantly dispersal limited but trees dispersed by animals were not. For assemblages of vertebrate seed dispersers, dispersal limitation significantly and more strongly structured assemblages of primates than species sorting whereas environmental factors significantly and more strongly structured assemblages of birds than dispersal limitation. We therefore examined whether trees dispersed by primates were more dispersal limited than trees dispersed by birds. We found consistent trends that primate fruit trees were more dispersal limited than bird fruit trees using three definitions of dispersal syndromes based on fruit color. Our results suggest that the dispersal abilities of primary consumers may affect the distribution of primary producers at large spatial scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".