Defence compounds in pollen: why do they occur and how do they affect the ecology and evolution of bees?
Bibliographic record
Abstract
Pollen plays two important roles in angiosperm reproduction, serving as a vehicle for the plant's male gametes, but also, in many species, as a lure for pollen-feeding animals. Despite being an important food source for many pollinators, pollen often contains compounds with known deterrent or toxic properties, as documented in a growing number of studies. Here we review these studies and discuss the role of pollen defensive compounds in the coevolutionary relationship between plants and bees, the preeminent consumers of pollen. Next, we evaluate three hypotheses that may explain the existence of defensive compounds in pollen. The pleiotropy hypothesis, which proposes that defensive compounds in pollen merely reflect physiological spillover from other plant tissues, is contradicted by evidence from several species. Although plants may experience selection to defend pollen against poor-quality pollinators, we also find only partial support for the protection-against-pollen-collection-hypothesis. Finally, pollen defences might protect pollen from colonisation by antagonistic microorganisms (antimicrobial hypothesis), although data to evaluate this idea are scarce. Further research on the effects of pollen defensive compounds on pollinators, pollen thieves, and pollen-colonising microbes will be needed to understand why many plants have chemically defended pollen, and the consequences of those defences for pollen consumers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".