Inferring the past from the present phylogeographic structure of North American forest trees: seeing the forest for the genes
Bibliographic record
Abstract
The study of past historical events that have led to ecological changes is a recurrent topic in many disciplines. Given that many of these events have left a large and long-lasting evolutionary imprint on the extant population genetic structure of species, phylogeographic studies on modern taxa have been largely used to infer the impacts of these events and to complement previous paleoecological and paleobotanical surveys. In spite of the geographical and geological complexity of North America, converging patterns can be observed when comparing the available genetic data for forest trees. Such patterns include the co-location of genetic discontinuities among species and their coincidence with mountain ranges (e.g., the Appalachians, the Rocky Mountains, the Sierra Nevada, or the Transverse Volcanic Belt) and with previously inferred glacial refugia. Using examples drawn from the available literature, we illustrate such shared features and present the contrasting phylogeographic patterns observed among the different regions of the continent. The various evolutionary consequences of historical events that can be deduced from these phylogeographic studies (e.g., past bottlenecks, founder effects, allopatric divergence, or introgressive hybridization) are additionally discussed. The present challenges and future research prospects that are likely to further advance this field are finally outlined.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".