Genome-wide shifts in climate-related variation underpin responses to selective breeding in a widespread conifer
Bibliographic record
Abstract
Abstract Locally adapted temperate tree populations exhibit genetic trade-offs among climate-related traits that can be exacerbated by selective breeding, and are challenging to manage under climate change. To inform climatically adaptive forest management, we investigated the genetic architecture and impacts of selective breeding on four climate-related traits in 105 natural and 20 selectively bred lodgepole pine populations from western Canada. Growth, cold injury and growth initiation and growth cessation phenotypes were tested for associations with 18,600 SNPs in natural populations to identify ‘positive effect alleles’ (PEAs). The effects of artificial selection for faster growth on PEA frequencies for each trait were quantified in breeding populations from different climates. Substantial shifts in PEA proportions and frequencies were observed across many loci after two generations of selective breeding for height, and responses of phenology-associated PEAs differed strongly among climatic regions. Extensive genetic overlap was evident among traits. Alleles most strongly associated with greater height were often also associated with greater cold injury and delayed phenology, although it is unclear whether potential trade-offs arose directly from pleiotropy or indirectly via genetic linkage. Modest variation among populations in PEA frequencies across many loci was associated with large phenotypic differences and strong climatic gradients, providing support for assisted gene flow polices. Relationships among genotypes, phenotypes and climate in natural populations were maintained or strengthened by selective breeding. However, future adaptive phenotypes and assisted gene flow may be compromised if selective breeding further increases the PEA frequencies of SNPs involved in adaptive trade-offs among climate-related traits.
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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 | 0.001 |
| 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".