Do flower-colonizing microbes influence floral evolution? A test with fast-cycling Brassica
Bibliographic record
Abstract
Pollinators are thought to be the main drivers of floral evolution. Flowers are also colonized by abundant communities of microbes that can affect the interaction between plants and their pollinators. Very little is known, however, about how flower-colonizing microbes influence floral evolution. Here we performed a six-generation experimental evolution study using fast-cycling Brassica rapa, in which we factorially manipulated the presence of pollinators and flower microbes to determine how pollinators and microbes interact in driving floral evolution. We measured the evolution of six morphological traits, as well as plant mating system and flower attractiveness. Only one of the six traits (flower number) evolved in response to pollinators, while microbes did not drive the evolution of any trait, nor did they interact with pollinators in driving evolution of morphological traits. Moreover, we did not find evidence that pollinators or microbes affected the evolution of flower attractiveness to pollinators. However, we found an interactive effect of pollinators and microbes on the evolution of autonomous selfing, a trait that is expected to evolve in response to pollinator limitation. Overall, we found only weak evidence that microbes mediate floral evolution. However, our ability to detect an interactive effect of pollinators and microbes might have been limited by weak pollinator-mediated selection in our experimental setting. Our results contrast with previous (similar) experimental evolution studies, highlighting the susceptibility of such experiments to drift and to experimental artefacts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".