Positive association between foliar silicon and extrafloral nectar in Vicia faba with application of methyl jasmonate
Bibliographic record
Abstract
Plants have evolved direct and indirect defences against herbivores, which may come at a cost to other plant functions. Many plants can uptake and deposit large amounts of silicon within plant tissue, creating structures which can reduce herbivore performance. Silicon uptake can increase plant defenses against herbivores, but it has also been shown to trade-off with defensive phenolic compounds due to interference with jasmonic acid (JA) signaling. Additionally, plants can recruit predacious insects with extrafloral nectar (EFN), a sugar secretion not involved in pollination. It is currently unclear whether silicon uptake reduces other putative defences associated with the JA pathway, like EFN production. We used faba bean ( Vicia faba) to identify potential trade-offs between silicon accumulation, phenolic content, and EFN production. We grew four genotypes of faba bean that varied in tannin content in control soil, or soil supplemented with silicon. After five weeks of growth, we exposed plants to either a buffer or methyl jasmonate (MeJA) solution to simulate an herbivory response. We measured EFN production at 24 and 48 hours after treatment, and harvested leaves to quantify silicon and phenolic content. We found silicon supplementation, but not MeJA treatment, increased foliar silicon concentration. Silicon supplementation did not affect foliar phenolic content or EFN sugar content. Silicon concentration (ppm) and MeJA treatment did not decrease foliar phenolic content or EFN sugar content. However, we found an interaction between silicon concentration (ppm) and MeJA treatment with EFN sugar content: across MeJA-treated plants, we detected a positive association between foliar silicon concentration and the amount of sugar (mg) in EFN. This study is the first to show MeJA can interact with leaf silicon concentration to alter EFN response, with the potential for cascading effects on other trophic levels.
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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 | 0.001 |
| 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) | 0.001 | 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".