Judge it by its shape: a pollinator‐blind approach reveals convergence in petal shape and infers pollination modes in the genus <i>Erythrina</i>
Bibliographic record
Abstract
PREMISE: Pollinators are thought to exert selective pressures on plants, mediating the evolution of convergent floral shape often recognized as pollination syndromes. However, little is known about the accuracy of using petal shape for inferring convergence in pollination mode without a priori pollination information. Here we studied the genus Erythrina L. as a test case to assess whether ornithophyllous pollination modes (hummingbirds, passerines, sunbirds, or mixed pollination) can be inferred based on the evolutionary analysis of petal shape. METHODS: We characterized the two-dimensional dissected shape of standard, keel, and wing petals from 106 Erythrina species using geometric morphometrics and reconstructed a phylogenetic tree of 83 Erythrina species based on plastid trnL-F and nuclear ribosomal ITS sequences. We then used two phylogenetic comparative methods based on Ornstein-Uhlenbeck models, SURFACE and l1OU, to infer distinct morphological groups using petal shape and identify instances of convergent evolution. The effectiveness of these methods was evaluated by comparing the groups inferred to known pollinators. RESULTS: We found significant petal shape differences between hummingbird- and passerine-pollinated Erythrina species. Our analyses also revealed that petal combinations generally provided better inferences of pollinator types than individual petals and that the method and optimization criterion can affect the results. CONCLUSIONS: We show that model-based approaches using petal shape can detect convergent evolution of floral shape and relatively accurately infer pollination modes in Erythrina. The inference power of the keel petals argues for a deeper investigation of their role in the pollination biology of Erythrina and other bird-pollinated legumes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".