Genetic Variation and Climatic Impacts on Survival and Growth of White Spruce in Alberta, Canada
Bibliographic record
Abstract
Abstract Because climate has the greatest effect in determining the genetic structure of forest tree species, climatic variables with large effects on growth and survival need to be identified. This would enable proper matching of tree populations to planting sites in the present and future climates. We analysed 24-year survival (S24), height (H24) and diameter (D24) from a series of white spruce provenance trials with 46 populations and 8 test sites in Alberta, Canada. We determined: (1) the amount and pattern of genetic variation, (2) the response of populations to climatic transfer and (3) the potential effects of climate change (2030-2039) on H24 and S24 of the species in Alberta. We found that: (1) using the intraclass correlation, the between-population genetic variance was 10.6% (H24) and 6.6% (D24) of the betweenpopulation phenotypic variance across sites, (2) three climatic white spruce regions exist in Alberta within which variation in growth potential is strongly clinal, (3) the annual moisture index (AMI) expressed as a ratio of degree days above 5°C (GDD) and mean annual precipitation (MAP) was the major determinant of survival and growth at the test sites, (4) we found that at the level of AMI predicted for the 2030-2039 period, survival and growth would decline substantially in the continental part (northern and central) of Alberta where drought already exists. However, during the same period, survival and growth would increase substantially in the foothills and Rocky Mountains region where growth is currently limited by low GDD due to a short growing season.
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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 | 0.000 | 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".