Drought effects on root and needle terpenoid content of a coastal and an interior Douglas fir provenance
Bibliographic record
Abstract
Douglas fir (Pseudotsuga menziesii) is a conifer species that stores large amounts of terpenoids, mainly monoterpenoids in resin ducts of various tissues. The effects of drought on stored leaf terpenoid concentrations in trees are scarcely studied and published data are partially controversial, since reduced, unaffected or elevated terpenoid contents due to drought have been reported. Even less is known on the effect of drought on root terpenoids. In the present work, we investigated the effect of reduced water availability on the terpenoid content in roots and needles of Douglas fir seedlings. Two contrasting Douglas fir provenances were studied: an interior provenance (var. glauca) with assumed higher drought resistance, and a coastal provenance (var. menziesii) with assumed lower drought resistance. We tested the hypothesis that both provenances show specific patterns of stored terpenoids and that the patterns will change in response to drought in both, needles and roots. We further expected stronger changes in the less drought tolerant coastal provenance. For this purpose, we performed an experiment under controlled conditions, in which the trees were exposed to moderate and severe drought stress. According to our expectations, the study revealed clear provenance-specific terpenoid patterns in needles. However, such patterns were not detected in the roots. Drought slightly increased the needle terpenoid contents of the coastal but not of the interior provenance. We also observed increased terpenoid abundance mainly in roots of the moderately stressed coastal provenance. Overall, from the observed provenance-specific reactions with increased terpenoid levels in trees of the coastal origin in response to drought, we conclude on functions of terpenoids for abiotic stress tolerance that might be fulfilled by other, constitutively expressed mechanisms in drought-adapted interior provenances.
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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".