Developing tree improvement strategies for challenging environmental stresses under global climate change: a review from traditional tree breeding to genomics of adaptive traits for the quaking aspen
Bibliographic record
Abstract
Abstract Quaking or trembling aspen in North America and Euro-Asia (Populus tremuloides and P. tremula, respectively) are both widely distributed species with a long history of scientific research and tree improvement work in areas such as carbon sequestration, biomass, bioenergy, wood, and fiber, as well as studies evaluating the social, economic, and ecological benefits of the species. This chapter reviews the ecological genetics and genomics of quaking aspen’s adaptive traits with a broad perspective of the relationship between phenotypic variation and genetic (G) and environmental (E) effects as well as their interactions (GxE). Based on recent studies, several adaptive traits are discussed, including spring and fall phenology and stress tolerance to environmental factors such as frost, salinity, drought, heat, UV radiation, etc. We also conducted a meta-analysis of empirical studies on adaptive traits of P. tremuloides and its sister species, as research using P. tremuloides as a true “model species” is currently limited. However, molecular tools and experimental designs in the form of different common gardens constitute an integrated pathway for the development of traits and varieties/populations to promote reforestation under changing climatic conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".