Phylogeographic History of White Spruce During the Last Glacial Maximum: Uncovering Cryptic Refugia
Bibliographic record
Abstract
Although a recent study of white spruce using chloroplast DNA uncovered the presence of a glacial refuge in Alaska, chloroplast failed to provide information on the number or specific localities of refugia. Recent studies have demonstrated the utility of nuclear microsatellites to refine insights into postglacial histories. The greater relative rate of mutation may allow finer scale resolution of historic dynamics, including the number, location, and sizes of refugia. Genetic data were acquired from screening 6 microsatellite loci on approximately 14 trees from each of 22 populations located across the central and western boreal forests of Canada and Alaska. Our studies combining microsatellites with Bayesian analyses of population structure in white spruce support the phylogeographic patterns uncovered using chloroplast, separating Alaskan from non-Alaskan regions. Results also support the idea that north-central Alaska served as a glacial refugium during the last glacial maximum. Additionally, the relationship between the degree of genetic differentiation and geographic distance indicated that gene flow played a more important role in structuring non-Alaskan populations, whereas drift played a more important role in structuring Alaskan populations (R(ST)'s for non-Alaskan populations 0.029 ± 0.007 and 0.083 ± 0.012 for Alaskan populations). Microsatellites also substantiate the bidirectional patterns of gene flow previously uncovered using chloroplast DNA but indicate much greater movement and mixing. Results from our Bayesian analyses also suggest the existence of additional cryptic refugia. However, the locations have been obscured by high gene flow (R(ST) averaging 0.057 ± 0.004).
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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 | 0.001 | 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 | 0.001 | 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".