Phenotypic and genomic insights into population differentiation, introgression, and selection in Quercus rubra across a narrow but steep environmental gradient
Bibliographic record
Abstract
Adaptive differentiation in functional traits and their underlying loci can occur across a small geographic area if natural selection is stronger than the countervailing effects of gene flow and drift. We investigated this hypothesis in a long-lived, wind-pollinated species, Quercus rubra, across a fine spatial scale with a steep climate gradient. We examined phenotypic differentiation in a common garden study with eight populations sampled 0-160 km from the coast of Lake Superior. We estimated genomic differentiation for these and 22 additional populations from the same region, along with two populations of a congener, Quercus ellipsoidalis , using RAD-seq. We found a strong signal of population differentiation associated with climate in the common garden study, and differentiation was significantly associated with at least one climate factor for nine of ten measured traits. At the genomic level, we discovered widespread introgression from Q. ellipsoidalis into Q. rubra that increased with distance from the lake. Pairwise F ST among Q. rubra populations was low, but both distance-based and environmental association analyses identified loci under selection, with one locus in common across all analyses (CalS10/GSL8) . This locus was associated with the precipitation of the driest month, a climate factor that was also significant in the common garden analyses. In sum, this study reveals signatures of selection at the phenotypic and genomic level consistent with climate adaptation, a pattern that is usually seen across a much broader 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".