Genetic differentiation across a steep and narrow environmental gradient: Quantitative genetic and genomic insights into Lake Superior populations of Quercus rubra
Bibliographic record
Abstract
Adaptive differentiation of traits and underlying loci can occur at a small geographic scale if natural selection is stronger than gene flow and drift. We investigated this hypothesis using coupled quantitative genetic and genomic approaches for a wind-pollinated tree species, Quercus rubra , along the steep, narrow gradient of the Lake Superior coast that encompasses four USDA Hardiness Zones within 100 km. For the quantitative genetic component, we examined phenotypic differentiation among eight populations in a common garden. For the genomic component, we quantified genetic differentiation for 26 populations using RAD-seq. Because hybridization with the congener Quercus ellipsoidalis has been documented elsewhere, we also included two populations of this species for comparison. In the common garden study, we found a strong signal of population differentiation that was significantly associated with at least one climate factor for nine of ten measured traits. In contrast, we found no evidence of genomic differentiation among populations based on FST or other measures. However, distance-based, and genotype-environment association analyses identified loci showing the signature of selection, with one locus in common across five analyses. This locus was associated with the minimum temperature of the coldest month, a climate factor was also significant in the common garden analyses. In addition, we documented introgression from Q. ellipsoidalis into Q. rubra , with rates of introgression correlated with the climate gradient. In sum, this study reveals signatures of selection at the quantitative trait and genomic level consistent with climate adaptation occurring at a small geographic scale.
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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 | 0.001 | 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".