Manipulation of Bee Behavior by Inflorescence Architecture and Its Consequences for Plant Mating
Bibliographic record
Abstract
Angiosperms display flowers in many three-dimensional arrangements, but the functional significance of this diversity is largely unknown. We examined influences of inflorescence architecture on pollination and mating by quantifying the responses of bumblebees to three architectures and then using these observations as the basis of a model that simulated pollen dispersal. On artificial panicles, racemes, and umbels, each with 12 identical flowers, bees visited one more flower, on average, on umbels than on panicles (with racemes being intermediate). In contrast to this weak response, the consistency of foraging paths among flowers differed strongly among architectures (raceme > panicle > umbel). The simulation model revealed limited differences in self-pollination and pollen export among the three inflorescence designs when all flowers presented and received pollen, because mating differences depended on only the number of flowers visited. In contrast, in simulations of inflorescences on which pollen receipt and presentation were segregated so as to minimize interference among flowers, the consistency of movement paths governed mating. In this case, racemes self-pollinated much less than umbels (with panicles being intermediate), and racemes exported much more pollen than umbels and panicles. These effects have diverse consequences for the evolution of inflorescence architecture, flower design, and sexual segregation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 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".