Phylogeography of Arctic plants: where are we after 35 years, and where to go?
Bibliographic record
Abstract
Background The Arctic provides an excellent system to study climate change effects on geographic ranges and genetic diversity. We re-examine the large number of phylogeographic studies of Arctic plants to assess general patterns and identify knowledge gaps.Aims To synthesise advances and address century-old controversies, e.g. is it necessary to invoke separate glacial refugia to explain Arctic disjunctions?Methods We undertook a literature survey of the phylogeography of Arctic vascular plants.Results We provide a list of 88 taxa studied, representing a striking diversity of phylogeographic histories. In many widespread species, recent trans-oceanic long-distance dispersal (LDD) is sufficient to explain disjunctions, but three rare species show clear signals of separate Scandinavian and American glacial refugia. The extreme bipolar disjunctions are apparently caused by Plio-Pleistocene LDDs. Beringia and western Siberia have served as long-standing northern refugia; in contrast, North Atlantic areas harbour much less genetic diversity and distinctiveness. The genomic era is now providing evidence for multiple refugia from modern and ancient DNA and demonstrating that selfing leads to high biological species diversity within taxonomically recognised species.Conclusions More extensive sampling, reference genomes, and population genomic studies are required for in-depth understanding of past distributions, dispersal routes, and ability to track ongoing climate change.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".