Using landscape genomics to delineate seed and breeding zones and project genetic offset for lodgepole pine
Bibliographic record
Abstract
Well-defined seed and breeding zones are critical for developing adaptive forest resource management strategies. These zones are traditionally delineated based on local adaptation of phenotypic traits associated with climate variables, determination of which requires long-term field experiments. In this thesis, I applied a landscape genomics approach to delineate seed and breeding zones for lodgepole pine (Pinus contorta) in British Columbia and Alberta, Canada, based on genomic evidence of local adaptation of this widespread forest tree species across western North America. A gradient forest (GF) model was built by aggregating relationships between spatial variation in 28,954 single-nucleotide polymorphism (SNPs) and 20 climate variables across 281 lodgepole pine populations. The fitted GF model confirmed winter-related climate variables are the major climatic factors associated with genomic patterns of variation among lodgepole pine populations. I used the GF model to delineate the lodgepole pine distribution range in British Columbia and Alberta into six seed and breeding zones. Genomic-based zones delineated by the GF model are comparable to existing common garden-based zones, suggesting that this landscape genomic approach could provide an effective alternative for delineating seed and breeding zones. This approach has the potential to provide a novel and effective alternative over traditional approaches for delineating seed and breeding zone, and offers an innovative means for guiding assisted gene flow in tree species lacking data from provenance trials or common garden experiments. Additionally, using the GF model, I predicted the spatial pattern of genetic offsets associated with seed and breeding zones to identify zones that are susceptible to genotype-environment mismatches under two future climate scenarios for the 2050s.
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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 | 0.001 |
| 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 | 0.000 |
| 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".