Variation in natural plant products and the attraction of bodyguards involved in indirect plant defenseThe present review is one in the special series of reviews on animal–plant interactions.
Bibliographic record
Abstract
Plants can respond to feeding or egg deposition by herbivorous arthropods by changing the volatile blend that they emit. These herbivore-induced plant volatiles (HIPVs) can attract carnivorous natural enemies of the herbivores, such as parasitoids and predators, a phenomenon that is called indirect plant defense. The volatile blends of infested plants can be very complex, sometimes consisting of hundreds of compounds. Most HIPVs can be classified as terpenoids (e.g., (E)-β-ocimene, (E,E)-α-farnesene, (E)-4,8-dimethyl-1,3,7-nonatriene), green leaf volatiles (e.g., hexanal, (Z)-3-hexen-1-ol, (Z)-3-hexenyl acetate), phenylpropanoids (e.g., methyl salicylate, indole), and sulphur- or nitrogen-containing compounds (e.g., isothiocyanates or nitriles, respectively). One highly intriguing question has been which volatiles out of the complex blend are the most important ones for the carnivorous natural enemies to locate "suitable host plants. Here, we review the methods and techniques that have been used to elucidate the carnivore-attracting compounds. Electrophysiological methods such as electroantennography have been used with parasitoids to elucidate which compounds can be perceived by the antennae. Different types of elicitors and inhibitors have widely been applied to manipulate plant volatile blends. Furthermore, transgenic plants that were genetically modified in specific steps in one of the signal transduction pathways or biosynthetic routes have been used to find steps in HIPV emission crucial for indirect plant defense. Furthermore, we provide an overview on biotic and abiotic factors that influence the emission of HIPVs and how this can affect the interactions between members of different trophic levels. Consequently, we review the progress that has been made in this exciting research field during the past 30 years since the first studies on HIPVs emerged and we highlight important issues to be addressed in the future.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".