Jasmonate‐deficient plants have reduced direct and indirect defences against herbivores
Bibliographic record
Abstract
Abstract Plants employ a variety of defence mechanisms, some of which act directly by having a negative effect on herbivores and others that act indirectly by attracting natural enemies of herbivores. In this study we asked if a common jasmonate‐signalling pathway links the regulation of direct and indirect defences in plants. We examined the performance of herbivores (direct defence) and the attraction of natural enemies of herbivores (indirect defence) to wild‐type tomato plants and mutant plants that are deficient in the production of the signalling hormone jasmonic acid. Wild‐type plants supported lower survivorship of caterpillars compared with jasmonic acid‐deficient plants. Damaged wild‐type plants were more attractive to predaceous mites compared with undamaged wild‐type plants, whereas damaged jasmonate‐deficient plants were not more attractive to predators. Damaged wild‐type plants induced a greater production of volatile compounds (primarily the sesquiterpene β‐caryophyllene and the monoterpenes α‐pinene, β‐pinene, 2‐carene and β‐phellandrene) compared with damaged jasmonate‐deficient plants. Treating jasmonate‐deficient plants with exogenous jasmonic acid restored both the direct and indirect defence capabilities, demonstrating that jasmonic acid is an essential regulatory component for the expression of direct and indirect plant defence.
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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) | 0.001 | 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) | 0.002 | 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".