Hybridization between pollination syndromes as an ecological and evolutionary resource
Bibliographic record
Abstract
In plants, pollination syndromes (the correlated presence of many features of relevance to pollination mode, for instance pollination by a particular animal clade) are a striking feature of plant biodiversity, providing great floral phenotypic diversity (Fenster et al. ). Adaptation to a particular animal pollinator provides an explanation for why recently diverged plants can have such extreme differentiation in floral form. One might expect such elaborate adaptations to provide a high degree of pollinator specificity and hence reproductive isolation, but there are many cases where substantial gene flow exists between extreme floral morphs (see Table 1), and the resulting hybrids may be highly fertile. This gene flow provides tremendous opportunities to study the genetics and biology of the pollination syndromes by providing intermediate forms and segregating genotypes. If it is true that pollination syndromes result from adaptation under strong selection, we will expect such flowers to be crucibles of natural selection. If strong selection for particular floral phenotypes can be shown, then this, when coupled with hybridization, will give us one of the most valuable of all experimental systems for evolutionary research: gene flow and selection in balance. In this issue of Molecular Ecology, the paper of Milano et al. () delivers this. It shows that in populations of the Ipomopsis aggregata complex, gene flow between pollination morphs is high and selection to stabilize those morphs is also high: a probable case of gene flow-selection balance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".