Testing Pollination Syndromes in <i>Oenothera</i> (Onagraceae)
Bibliographic record
Abstract
Pollinators are considered a major selective force in shaping the diversification of angiosperms. It has been hypothesized that convergent evolution of floral form has resulted in “pollination syndromes” - i.e. suites of floral traits that correspond to attraction of particular pollinator functional groups. Across the literature, the pollination syndrome concept has received mixed support. This may be due to studies using different methods to describe floral traits and/or the pollination syndrome concept being supported more often in species highly reliant on pollinators for reproduction. Here, we assess the predictive ability of pollination syndromes in Oenothera, a species rich clade with pollination systems existing on a gradient of specialization, and in which species are either self-compatible or self-incompatible. We ask the following questions: Do Oenothera species follow the pollination syndrome concept using traditional, categorical floral trait descriptions and/or quantitative floral trait measurements? And, are floral traits more predictive of primary pollinators in species with specialized pollination systems and/or species that are self-incompatible? Mapping floral traits of 54 Oenothera species into morphospace, we do not find support for the pollination syndrome concept using either categorical or quantitative floral trait descriptions. We do not find support for specialization or breeding system influencing the prediction of primary pollinators. However, we find pollination syndromes were more predictive in Oenothera species with moth pollination systems. Collectively, these results suggest that the pollination syndrome concept cannot be generally applied across taxa and that evolutionary history is important to consider when evaluating the relationship between floral form and contemporary pollinators.
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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 | 0.001 | 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) | 0.001 | 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".