Postglacial population genetic differentiation potentially facilitated by a flexible migratory strategy in Golden-crowned Kinglets (<i>Regulus</i><i>satrapa</i>)
Bibliographic record
Abstract
Relatively recently, temperate regions in North America were covered by extensive ice sheets, making them inhospitable to contemporary flora and fauna. Since the retreat of the ice sheets, these regions have been recolonized by a diversity of taxa, some of which have undergone rapid postglacial divergence. Evidence supports the hypothesis that some taxa persisted in unglaciated refugia during the Last Glacial Maximum, such as on Haida Gwaii (formerly the Queen Charlotte Islands). Many taxa on Haida Gwaii are genetically distinct from mainland populations at neutral molecular markers possibly as the result of isolation in refugia or postglacial colonization. The Golden-crowned Kinglet (Regulus satrapa Lichtenstein, 1823) is a continentally distributed, short-distance migratory passerine inhabiting mature conifer forests including those on Haida Gwaii. We used five microsatellite markers and a 568 base-pair fragment of the mitochondrial control region to determine the likelihood that Haida Gwaii region acted as a refugium for this species during the last ice age. We report significant gene flow between Haida Gwaii and the western North American mainland from mitochondrial markers, but significant population genetic differentiation at nuclear markers. We also report genetic divergence between eastern and western Golden-crowned Kinglets, as well as higher genetic diversity and population substructuring within the western population than within the eastern population. The east–west differentiation probably arose due to isolation in separate Pleistocene refugia south of the ice sheets. However, population differences within the west are likely caused by more recent processes; contemporary differentiation of Haida Gwaii Golden-crowned Kinglets most likely occurred postglacially.
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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 | 0.000 |
| 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) | 0.000 | 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".