Dispersers shape fruit diversity in <i>Ficus</i> (Moraceae)
Bibliographic record
Abstract
Seed dispersal by vertebrates is one of the most common and important plant-animal mutualisms, involving an enormous diversity of fruiting plants and frugivorous animals. Even though plant reproduction depends largely on seed dispersal, evolutionary ecologists have been unable to link co-occurring traits in fruits with differences in behavior, physiology, and morphology of fruit-eating vertebrates. Hence, the origin and maintenance of fruit diversity remains largely unexplained. Using a multivariate phylogenetic comparative test with unbiased estimates of odor and color in figs, we demonstrate that fruit traits evolve in concert and as predicted by differences in the behavior, physiology (perceptive ability) and morphology of their frugivorous seed dispersers. The correlated evolution of traits results in the convergence of general appearance of fruits in species that share disperser types. Observations at fruiting trees independently confirmed that differences in fig traits predict differences in dispersers. Taken together, these results demonstrate that differences among frugivores have shaped the evolution of fruit traits. More broadly, our results underscore the importance of mutualisms in both generating and maintaining biodiversity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".