Origins of the Greenland shark (<i>Somniosus microcephalus</i>): Impacts of ice‐olation and introgression
Bibliographic record
Abstract
Abstract Herein, we use genetic data from 277 sleeper sharks to perform coalescent‐based modeling to test the hypothesis of early Quaternary emergence of the Greenland shark (Somniosus microcephalus) from ancestral sleeper sharks in the Canadian Arctic‐Subarctic region. Our results show that morphologically cryptic somniosids S. microcephalus and Somniosus pacificus can be genetically distinguished using combined mitochondrial and nuclear DNA markers. Our data confirm the presence of genetically admixed individuals in the Canadian Arctic and sub‐Arctic, and temperate Eastern Atlantic regions, suggesting introgressive hybridization upon secondary contact following the initial species divergence. Conservative substitution rates fitted to an Isolation with Migration (IM) model indicate a likely species divergence time of 2.34 Ma, using the mitochondrial sequence DNA, which in conjunction with the geographic distribution of admixtures and Pacific signatures likely indicates speciation associated with processes other than the closing of the Isthmus of Panama. This time span coincides with further planetary cooling in the early Quaternary period followed by the onset of oscillating glacial‐interglacial cycles. We propose that the initial S. microcephalus–S. pacificus split, and subsequent hybridization events, were likely associated with the onset of Pleistocene glacial oscillations, whereby fluctuating sea levels constrained connectivity among Arctic oceanic basins, Arctic marginal seas, and the North Atlantic Ocean. Our data demonstrates support for the evolutionary consequences of oscillatory vicariance via transient oceanic isolation with subsequent secondary contact associated with fluctuating sea levels throughout the Quaternary period—which may serve as a model for the origins of Arctic marine fauna on a broad taxonomic scale.
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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 | 0.000 | 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) | 0.001 | 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".