Darwin's beautiful contrivances: evolutionary and functional evidence for floral adaptation
Bibliographic record
Abstract
Although not 'a professed botanist', Charles Darwin made seminal contributions to understanding of floral and inflorescence function while seeking evidence of adaptation by natural selection. This review considers the legacy of Darwin's ideas from three perspectives. First, we examine the process of floral and inflorescence adaptation by surveying studies of phenotypic selection, heritability and selection responses. Despite widespread phenotypic and genetic capacity for natural selection, only one-third of estimates indicate phenotypic selection. Second, we evaluate experimental studies of floral and inflorescence function and find that they usually demonstrate that reproductive traits represent adaptations. Finally, we consider the role of adaptation in floral diversification. Despite different diversification modes (coevolution, divergent use of the same pollen vector, pollinator shifts), evidence of pollination ecotypes and phylogenetic patterns suggests that adaptation commonly contributes to floral diversity. Thus, this review reveals a contrast between the inconsistent occurrence of phenotypic selection and convincing experimental and comparative evidence that floral traits are adaptations. Rather than rejecting Darwin's hypotheses about floral evolution, this contrast suggests that the tempo of creative selection varies, with strong, consistent selection during episodes of diversification, but relatively weak and inconsistent selection during longer, 'normal' periods of relative phenotypic stasis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".