Data from: Rethinking long-term vegetation dynamics: multiple glacial refugia and local expansion of a species complex
Bibliographic record
Abstract
Evidence is accumulating that some arcto-boreal plant taxa persisted through the Last Glacial Maximum (LGM) in Alaska and adjacent Canada. However, the spatial patterns of glacial persistence and associated postglacial colonization remain largely unknown. In this study, we investigated the LGM refugia of an alder (Alnus) species complex (n = 3 taxa) and assess the spatiotemporal dynamics of Alnus in this vast region. Specifically, we conducted high-throughput DNA sequencing (ddRADseq) on Alnus foliar samples collected from a dense population network to investigate patterns of genetic structure and infer the presence of glacial lineages. Species distribution modeling (SDM) was used to investigate the probability and possible locations of glacial persistence. These analyses were integrated and then compared with fossil pollen data to identify the locations of refugial populations and spatial patterns of postglacial colonization. Our genetic analyses revealed two glacial lineages with separate geographic origins for each Alnus taxon, suggesting that the genus persisted in multiple LGM refugia. Non-overlapping hindcast distributions based on SDMs further support the presence of multiple, spatially distinct refugia. These ddRADseq and SDM results, in conjunction with reassessment of fossil pollen records, suggest that Alnus expanded from several population nuclei that existed during the LGM and coalesced during the Holocene to form its present range. These results challenge the unidirectional model for postglacial vegetation expansion, implying that climate buffering associated with landscape heterogeneity and adaptation to millennial-scale environmental variability played important roles in driving late-Quaternary population dynamics.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".