Consequences of pollen defense compounds for pollinators and antagonists in a pollen‐rewarding plant
Bibliographic record
Abstract
Plants produce an array of defensive compounds with toxic or deterrent effects on insect herbivores. Pollen can contain relatively high concentrations of such defense compounds, but the causes and consequences of this enigmatic phenomenon remain mostly unknown. These compounds could potentially protect pollen against antagonists but could also reduce flower attractiveness to pollinators. We combined field observations of the pollen-rewarding Lupinus argenteus with chemical analysis and laboratory assays to test three hypotheses for the presence of pollen defense compounds: (1) these compounds are the result of spillover from adjacent tissues, (2) they protect against pollen thieves, and (3) they act as antimicrobial compounds. We also tested whether pollen defense compounds affect pollinator behavior. We found a positive relationship between alkaloid concentrations in pollen and petals, supporting the idea that pollen defense compounds partly originate from spillover. However, pollen and petals exhibited quantitatively (but not qualitatively) distinct alkaloid profiles, suggesting that plants can adjust pollen alkaloid composition independently from that of adjacent tissues. We found no relationship between pollen alkaloid concentration and the abundance of pollen thieves in Lupinus flowers. However, pollen alkaloids were negatively associated with bacterial abundance. Finally, plants with more alkaloids in their pollen received more pollinator visits, but these visits were shorter, resulting in no change in the overall number of flowers visited. We propose that pollen defense compounds are partly the result of spillover from other tissues, while they also play an antimicrobial role. The absence of negative effects of these compounds on pollinator visitation likely allows their maintenance in pollen at relatively high concentrations. Taken together, our results suggest that pollen alkaloids affect and are mediated by the interplay of multiple interactions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".