Selective breeding of lodgepole pine and interior spruce generates growth gains but maintains phenotypic and genomic adaptation to climate
Bibliographic record
Abstract
Climate change is disrupting local adaptation in temperate and boreal tree species. As climates shift, tree breeding zones are becoming dissociated from their historical climatic optima and no longer represent optimal seed deployment zones. Assisted gene flow (AGF) policies that match reforestation seedlots with future climates require accurate knowledge of genetic variation in climatically adaptive traits in breeding populations. In this thesis I evaluate the effects of selective breeding on climatic adaptation in the two most planted species in western Canada, lodgepole pine (Pinus contorta) and interior spruce (Picea glauca, P. engelmanii and their hybrids), to inform provincial AGF prescriptions. I compared natural stand seedlots (n = 105 pine, 154 spruce) with selectively bred seedlots (n = 20 pine, 18 spruce) from across Alberta and British Columbia in common garden experiments. Phenotypic variation among breeding zones was assessed for growth, phenology and cold hardiness in relation to climate. For both species, phenotypic differences between natural and selected seedlings in growth traits were substantial. Height gains resulted from increased growth rate and delayed growth cessation, but autumn cold hardiness was not substantially reduced. Seedlings were also genotyped for ~30,000 candidate single nucleotide polymorphisms for growth and adaptive traits. Selection for growth has shifted interior spruce hybrid ancestry in some breeding populations, but these effects are not consistent across zones. A genome-wide association study of pine identified many trait-associated SNPs. Positive effect allele frequencies among pine breeding zones were strongly associated with climatic variation. Selection has resulted in small increases in the frequency of positive effect alleles in breeding populations. Associations among cold hardiness phenotypes, genotypes and climate dominated signals of local adaptation were preserved in breeding populations. Selection, breeding and progeny testing combined have produced taller pine and spruce seedlings without compromising climatic adaptation. Strong phenotype-genotype-climate associations suggest AGF will be necessary to match breeding populations with future climates, but selectively bred and natural seedlots can be safely redeployed using the same AGF prescriptions. Multi-locus genomic profiles of adaptive traits associated with climate provide an accurate, rapid method to assess climatic adaptation that is independent from long-term provenance trials.
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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".