The Birds, the Bees, and the Virtual Flowers: Can Pollinator Behavior Drive Ecological Speciation in Flowering Plants?
Bibliographic record
Abstract
Biologists have long assumed that pollinator behavior is an important force in angiosperm speciation, yet there is surprisingly little direct evidence that floral preferences in pollinators can drive floral divergence and the evolution of reproductive (ethological) isolation between incipient plant species. In this study, we expose computer-generated plant populations with a wide variation in flower color to selection by live and virtual hummingbirds and bumblebees and track evolutionary changes in flower color over multiple generations. Flower color, which was derived from the known genetic architecture and phenotypic variance of naturally occurring plant species pollinated by both groups, evolved in simulations through a genetic algorithm in which pollinator preference determined changes in flower color between generations. The observed preferences of live hummingbirds and bumblebees were strong enough to cause adaptive divergence in flower color between plant populations but did not lead to ethological isolation. However, stronger preferences assigned to virtual pollinators in sympatric and allopatric scenarios rapidly produced ethological isolation. Pollinators can thus drive ecological speciation in flowering plants, but more rigorous and comprehensive behavioral studies are required to specify conditions that produce sufficient preference levels in pollinators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".