Effects of introgression on the genetic population structure of two ecologically and economically important conifer species: lodgepole pine (<i>Pinus contorta</i>var.<i>latifolia</i>) and jack pine (<i>Pinus banksiana</i>)
Bibliographic record
Abstract
Forest trees exhibit a remarkable range of adaptations to their environment, but as a result of frequent and long-distance gene flow, populations are often only weakly differentiated. Lodgepole and jack pine hybridize in western Canada, which adds the opportunity for introgression through hybridization to contribute to population structure and (or) adaptive variation. Access to large sample size, high density SNP datasets for these species would improve our ability to resolve population structure, parameterize introgression, and separate the influence of demography from adaptation. To accomplish this, 454 transcriptome reads for lodgepole and jack pine were assembled using Newbler and MIRA, the assemblies mined for SNPs, and 1536 SNPs were selected for typing on lodgepole pine, jack pine, and their hybrids (N = 536). We identified population structure using both Bayesian clustering and discriminate analysis of principle components. Introgressed SNP loci were identified and their influence on observed population structure was assessed. We found that introgressed loci resulted in increased differentiation both within lodgepole and jack pine populations. These findings are timely given the recent mountain pine beetle population expansion in the hybrid zone, and will facilitate future studies of adaptive traits in these ecologically important species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".