Seed dispersers shape the pulp nutrients of fleshy-fruited plants
Bibliographic record
Abstract
The dispersal-syndrome hypothesis posits that fruit traits are a product of selection by frugivores. Although criticized as adaptationist, recent studies have suggested that traits such as fruit or seed size, colour and odour exhibit signatures that imply selection by animal mutualists. These traits imply nutritional rewards (e.g. lipid, carbohydrate), attracting frugivores; however, this remains incompletely resolved. Here, we investigated whether fruit nutrients (lipid, sugar, protein, vitamin C, water content) moderate the co-adaptation of key disperser-group mutualisms. Multivariate techniques revealed that fruit nutrients assembled non-randomly and grouped according to key dispersal modes. Bird-dispersed fruits were richer in lipids than mammal-dispersed fruits. Mixed-dispersed fruits had significantly higher vitamin C than did mammal- or bird-dispersed fruits separately. Sugar and water content were consistently high irrespective of dispersal modes, suggesting that these traits appeal to both avian and mammalian frugivores to match high-energy requirements. Similarly, protein content was low irrespective of dispersal modes, corroborating that birds and mammals avoid protein-rich fruits, which are often associated with toxic levels of nitrogenous secondary compounds. Our results provide substantial over-arching evidence that seed disperser assemblages co-exert fundamental selection pressures on fruit nutrient trait adaptation, with broad implications for structuring fruit-frugivore mutualism and maintaining fruit trait diversity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".