Genetic Structure of Natural Northern Range-Margin Mainland, Peninsular, and Island Populations of Northern Red Oak (Quercus rubra L.)
Bibliographic record
Abstract
Plant populations at the leading edge of the species’ native range often exhibit genetic structure as a result of genetic drift and adaptation to harsh environmental conditions. Hence, they are likely to harbour rare genetic adaptations to local environmental conditions and therefore are of particular interest to understand climate adaptation. We examined genetic structure of nine northern marginal mainland, peninsular and isolated island natural populations of northern red oak (Quercus rubraL.), a valuable long-lived North American hardwood tree species, covering a wide climatic range, using 17 nuclear microsatellites. We found pronounced genetic differentiation of a disjunct isolated island population from all mainland and peninsular populations. Furthermore, we observed remarkably strong fine-scale spatial genetic structure (SGS) in all investigated populations. Such high SGS values are uncommon and were previously solely observed in extreme range-edge marginal oak populations in one other study. We found a significant correlation between major climate parameters and SGS formation in northern range-edge red oak populations, with more pronounced SGS in colder and drier regions. Most likely, the harsh environment in leading edge populations influences the density of reproducing trees within the populations and therefore leads to restricted overlapping of seed shadows when compared to more central populations. Accordingly, SGS was negatively correlated with effective population size and increased with latitude of the population locations. The significant positive association between genetic distances and precipitation differences between populations may be indicative of isolation by adaptation in the observed range-edge populations. However, this association was not confirmed by a multiple regression analysis including geographic distances and precipitation distances, simultaneously. Our study provides new insights in the genetic structure of long-lived tree species at their leading distribution edge.
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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 | 0.000 |
| 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) | 0.000 | 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".