Ice-age endurance: DNA evidence of a white spruce refugium in Alaska
Bibliographic record
Abstract
Paleorecords offer key information for evaluating model simulations of species migration in response to forecast climatic change. However, their utility can be greatly compromised by the existence of glacial refugia that are undetectable in fossil records (cryptic refugia). Despite several decades of investigation, it remains controversial whether Beringia, the largely unglaciated area extending from northeastern Siberia to the Yukon Territory, harbored small populations of certain boreal tree species during the last glaciation. Here, we present genetic evidence for the existence of a glacial refuge in Alaska that helps to resolve this long-standing controversy. We sequenced chloroplast DNA (cpDNA) of white spruce (Picea glauca), a dominant boreal tree species, in 24 forest stands across northwestern North America. The majority of cpDNA haplotypes are unique, and haplotype diversity is relatively high in Alaska, arguing against the possibility that this species migrated into the region from areas south of the Laurentide Ice Sheet after the end of the last glaciation. Thus, white spruce apparently survived long glacial episodes under climatic extremes in a heterogeneous landscape matrix. These results suggest that estimated rates of tree migration from fossil records may be too high and that the ability of trees to track anthropogenic warming may be more limited than previously thought.
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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 | 0.000 |
| 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".