Selective breeding for growth does not compromise drought resistance in western larch seedlings
Bibliographic record
Abstract
As the impacts of drought intensify with climate change, adaptation to drought is becoming an increasingly important consideration for tree breeding and forest management. This elevates the need to understand how breeding for faster growth affects drought tolerance and if breeding to improve drought tolerance could help mitigate the impacts of drought on forest health and productivity. To understand the implications for tree breeding in future climates, we studied how selective breeding of western larch ( Larix occidentalis ) for faster growth affects drought resistance (a component of drought tolerance); the quantitative genetics of drought resistance traits; and the effects of drought on genetic variation in growth and phenology. We established a seedling common garden experiment in a warm climate outside the natural range with 23 natural populations and 29 selectively-bred families from two breeding zones in British Columbia. We imposed two drought treatments of varying duration and severity with a control treatment in the third growing season. Height growth, bud phenology and drought resistance traits were phenotyped. We found that selective breeding for faster growth has not compromised drought resistance in seedlings. Weak correlations between drought resistance and height growth or bud phenology among family phenotypes suggest that breeding for improved drought resistance may be possible without affecting other traits relevant to adaptation to local climates. However, we detected limited genetic variation and low heritability for drought resistance, as well as diminished genetic variation in growth and phenology traits under drought, indicating potential barriers to breeding for improved drought resistance in seedlings.
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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".