The Potential of Aspen Clonal Forestry in Alberta: Breeding Regions and Estimates of Genetic Gain from Selection
Bibliographic record
Abstract
BACKGROUND: Aspen naturally grows in large, single-species, even-aged stands that regenerate clonally after fire disturbance. This offers an opportunity for an intensive clonal forestry system that closely emulates the natural life history of the species. In this paper, we assess the potential of genetic tree improvement and clonal deployment to enhance the productivity of aspen forests in Alberta. We further investigate geographic patterns of genetic variation in aspen and infer forest management strategies under uncertain future climates. METHODOLOGY/PRINCIPAL FINDINGS: Genetic variation among 242 clones from Alberta was evaluated in 13 common garden trials after 5-8 growing seasons in the field. Broad-sense heritabilities for height and diameter at breast height (DBH) ranged from 0.36 to 0.64, allowing 5-15% genetic gains in height and 9-34% genetic gains in DBH. Geographic partitioning of genetic variance revealed predominant latitudinal genetic differentiation. We further observed that northward movement of clones almost always resulted in increased growth relative to local planting material, while southward movement had a strong opposite effect. CONCLUSION/SIGNIFICANCE: Aspen forests are an important natural resource in western Canada that is used for pulp and oriented strandboard production, accounting for ~40% of the total forest harvest. Moderate to high broad-sense heritabilities in growth traits suggest good potential for a genetic tree improvement program with aspen. Significant productivity gains appear possible through clonal selection from existing trials. We propose two breeding regions for Alberta, and suggest that well-tested southern clones may be used in the northern breeding region, accounting for a general warming trend observed over the last several decades in Alberta.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".