An angiosperm-wide perspective on reproductive strategies and floral traits
Bibliographic record
Abstract
Summary Flowering plants have many modes of sexual reproduction, notably varying from selfing to outcrossing, and from bisexual flowers to individuals with separate sexes (dioecy). These reproductive modes are associated with floral and life-history traits that have evolved together, making it difficult to interpret correlations between traits. We analysed variation in 21 traits related to flowers, pollination, mating, sexual systems and life history from 361 species representative of flowering plant diversity. Outcrossing was mainly found among long-lived, large-stature plants, but hermaphroditic outcrossers and dioecious species appeared as largely non-overlapping strategies in the trait space. Level of floral investment was the main difference between these strategies, with dioecious species having generally smaller, less rewarding flowers, a pattern that also occurred in biotically pollinated species. This multi-trait study shows that pollination can be achieved in many, often contrasting, ways. Despite extensive variation in reproductive traits, dioecy stands out as being linked to floral traits primarily, while correlations with lifespan and dispersal traits appear spurious. We provide a conceptual framework based on lifespan, floral investment, and sexual separation that can be used to integrate pollination, reproduction and plant growth in future research on plant evolution and ecology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".