Spruce Budworm (Lepidoptera: Tortricidae) Oral Secretions II: Chemistry
Bibliographic record
Abstract
As sessile organisms, plants have evolved different methods to defend against attacks and have adapted their defense measures to discriminate between mechanical damage and herbivory by insects. One of the ways that plant defenses are triggered is via elicitors from insect oral secretions (OS). In this study, we investigated the ability of second-instar (L2) spruce budworm [SBW; Choristoneura fumiferana (Clemens)] to alter the volatile organic compounds (VOCs) of four conifer species [Abies balsamea (L.) Mill., Picea mariana (Miller) B.S.P., Picea glauca (Moench) Voss, Picea rubens (Sargent)] and found that the emission profiles from all host trees were drastically changed after herbivory. We then investigated whether some of the main elicitors (fatty acid conjugates [FACs], β-glucosidase, and glucose oxidase) studied were present in SBW OS. FACs (glutamine and glutamic acid) based on linolenic, linoleic, oleic, and stearic acids were all observed in varying relative quantities. Hydroxylated FACs, such as volicitin, were not observed. Enzyme activity for β-glucosidase was also measured and found present in SBW OS, whereas glucose oxidase activity was not found in the SBW labial glands. These results demonstrate that SBW L2 larvae have the ability to induce VOC emissions upon herbivory and that SBW OS contain potential elicitors to induce these defensive responses. These data will be useful to further evaluate whether these elicitors can separately induce the production of specific VOCs and to investigate whether and how these emissions benefit the plant.
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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 | 0.000 | 0.000 |
| 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".