Differential effects of plant ontogeny and damage type on phloem and foliage monoterpenes in jack pine (Pinus banksiana)
Bibliographic record
Abstract
Coniferous trees have both constitutive and inducible defences that deter or kill herbivores and pathogens. We investigated constitutive and induced monoterpene responses of jack pine (Pinus banksiana Lamb.) to a number of damage types: a fungal associate of the mountain pine beetle (Dendroctonus ponderosae Hopkins), Grosmannia clavigera (Robinson-Jeffrey & R.W. Davidson); two phytohormones, methyl jasmonate (MJ) and methyl salicylate (MS); simulated herbivory; and mechanical wounding. We only included the fungal, MJ and mechanical wounding treatments in the field experiments while all treatments were part of the greenhouse studies. We focused on both constitutive and induced responses between juvenile and mature jack pine trees and differences in defences between phloem and needles. We found that phytohormone applications and fungal inoculation resulted in the greatest increase in monoterpenes in both juvenile and mature trees. Additionally, damage types differentially affected the proportions of individual monoterpenes: MJ-treated mature trees had higher myrcene and β-pinene than fungal-inoculated mature trees, while needles of juveniles inoculated with the fungus contained higher limonene than MJ- or MS-treated juveniles. Although the constitutive monoterpenes were higher in the phloem of juveniles than mature jack pine trees, the phloem of mature trees had a much higher magnitude of induction. Further, induced monoterpene concentrations in juveniles were higher in phloem than in needles. There was no difference in monoterpene concentration between phytohormone applications and G. clavigera inoculation in mature trees, while in juvenile trees MJ was different from both G. clavigera and simulated herbivory in needle monoterpenes, but there was no difference between phytohormone applications and simulated herbivory in the phloem.
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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 | 0.001 |
| 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".