Population genetic structure and demographic history of Pacific blue sharks (Prionace glauca) inferred from mitochondrial DNA analysis
Bibliographic record
Abstract
Cosmopolitan pelagic species often show shallow genetic divergence and weak, or no, genetic structure across a species’ range. However, there have been few such genetic studies for pelagic sharks. The pelagic blue shark (Prionace glauca) has a broad circumglobal distribution in tropical and temperate oceans. To investigate the population genetic structure and demographic history of this species, we analysed variation in the mitochondrial cytochrome b sequence for a total of 404 specimens collected from 10 locations across the Indo-Pacific region. The observed genetic diversities were comparable among sampling locations (h = 0.77–0.87; p = 0.17–0.23%). Spatial analysis of molecular variance (SAMOVA), pairwise FST and conventional FST estimates, and analysis of isolation with migration indicated weak or no genetic differentiation of this species across the Indo-Pacific region. The results of three phylogeographic analyses (i.e. mismatch distribution and parsimony haplotype network analyses and a neutrality test) suggested that the Pacific blue shark had historically experienced a sudden population expansion. These results, coupled with the biological properties of this species, imply that historical climate fluctuation has had only a minor effect on the genetic structuring of the blue shark.
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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 | 0.000 |
| 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".