The phenotypic and genetic effects of drought-induced stress on apical growth, ring width, wood density and biomass in white spruce seedlings
Bibliographic record
Abstract
Abstract Forest plantations play an important role in maintaining a supply of high-quality timber from managed forest. With an expected increase in the prevalence of drought in some forested areas, climate change increases concerns about future seedling growth. A promising approach to promote the suitability of plantation seedlings to current and future climate would be to use variation in growth and wood traits of trees under drought as selection criteria in tree breeding programs, especially at a young stage when they are most vulnerable to drought. We evaluated the genetic control of the growth and wood density response of white spruce clonal seedlings submitted to various drought conditions in a greenhouse experiment. By varying the watering treatment of 600 two year-old seedlings from 25 clones, we simulated three levels of drought-induced stress during two growing seasons. Apical and radial growth decreased markedly as the intensity of drought increased, whereas wood density tended to increase. We also developed a woody biomass index composed of wood density and ring area, which was observed to decrease slightly with increasing drought. There was important variation in all traits among clones and heritability tended to decrease with the intensity and duration of drought-induced stress, mainly for wood density and radial growth. However, the heritability of apical growth tended to increase under drought conditions. Our results show that the response of young white spruce clones to drought is highly variable, and together with the significant levels of heritability noted, the results indicate that multi-trait genetic selection for drought stress response at a young age could represent a promising approach to increase resilience to drought.
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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) | 0.000 | 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".