EVOLUTION OF FLORAL DISPLAY IN EICHHORNIA PANICULATA (PONTEDERIACEAE): DIRECT AND CORRELATED RESPONSES TO SELECTION ON FLOWER SIZE AND NUMBER
Bibliographic record
Abstract
Trade-offs between flower size and number seem likely to influence the evolution of floral display and are an important assumption of several theoretical models. We assessed floral trade-offs by imposing two generations of selection on flower size and number in a greenhouse population of bee-pollinated Eichhornia paniculata. We established a control line and two replicate selection lines of 100 plants each for large flowers (S+), small flowers (S-), and many flowers per inflorescence (N+). We compared realized heritabilities and genetic correlations with estimates based on restricted-maximum-likelihood (REML) analysis of pedigrees. Responses to selection confirmed REML heritability estimates (flower size, h2 = 0.48; daily flower number, h2 = 0.10; total flower number, h2 = 0.23). Differences in nectar, pollen, and ovule production between S+ and S- lines supported an overall divergence in investment per flower. Both realized and REML estimates of the genetic correlation between daily and total flower number were r = 1.0. However, correlated responses to selection were inconsistent in their support of a trade-off. In both S- lines, correlated increases in flower number indicated a genetic correlation of r = -0.6 between flower size and number. In contrast, correlated responses in N+ and S+ lines were not significant, although flower size decreased in one N+ line. In addition, REML estimates of genetic correlations between flower size and number were positive, and did not differ from zero when variation in leaf area and age at first flowering were taken into account. These results likely reflect the combined effects of variation in genes controlling the resources available for flowering and genes with opposing effects on flower size and number. Our results suggest that the short-term evolution of floral display is not necessarily constrained by trade-offs between flower size and number, as is often assumed.
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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 | 0.000 | 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) | 0.000 | 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".