Post-glacial biogeography of trembling aspen inferred from habitat models and genetic variance in quantitative traits
Bibliographic record
Abstract
Using species distribution models and information on genetic structure and within-population variance observed in a series of common garden trials, we reconstructed a historical biogeography of trembling aspen in North America. We used an ensemble classifier modelling approach (RandomForest) to reconstruct palaeoclimatic habitat for the periods 21,000, 14,000, 11,000 and 6,000 years before present. Genetic structure and diversity in quantitative traits was evaluated in common garden trials with 43 aspen collections ranging from Minnesota to northern British Columbia. Our main goals were to examine potential recolonisation routes for aspen from southwestern, eastern and Beringian glacial refugia. We further examined if any refugium had stable habitat conditions where aspen clones may have survived multiple glaciations. Our palaeoclimatic habitat reconstructions indicate that aspen may have recolonised boreal Canada and Alaska from refugia in the eastern United States, with separate southwestern refugia for the Rocky Mountain regions. This is further supported by a southeast to northwest gradient of decreasing genetic variance in quantitative traits, a likely result of repeated founder effects. Stable habitat where aspen clones may have survived multiple glaciations was predicted in Mexico and the eastern United States, but not in the west where some of the largest aspen clones have been documented.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".