Stepping up to genome scan allows stock differentiation in the worldwide distributed blue shark <i>Prionace glauca</i>
Bibliographic record
Abstract
Abstract The blue shark Prionace glauca is a top predator with one of the widest geographical distributions of any shark species. It is classified as Critically Endangered in the Mediterranean Sea, and Near Threatened globally. Previous genetic studies did not reject the null hypothesis of a single global population. The blue shark was proposed as a possible archetype of the “grey zone of population differentiation,” coined to designate cases where population structure may be too recent or too faint to be detected using a limited set of markers. Here, blue shark samples collected throughout its global range were sequenced using a specific RAD method (DArTseq), which recovered 37,655 genome‐wide single nucleotide polymorphisms (SNPs). Two main groups emerged, with Mediterranean Sea and northern Atlantic samples (Northern population) differentiated significantly from the Indo‐west Pacific samples (Southern population). Significant pairwise FST values indicated further genetic differentiation within the Atlantic Ocean, and between the Atlantic Ocean and the Mediterranean Sea. Reconstruction of recent demographic history suggested divergence between Northern and Southern populations occurred about 500 generations ago and revealed a drastic reduction in effective population size from a large ancestral population. Our results illustrate the power of genome scans to detect population structure and reconstruct demographic history in highly migratory marine species. Given that the management plans of the blue shark (targeted or bycatch) fisheries currently assume panmictic regional stocks, we strongly recommend that the results presented here be considered in future stock assessments and conservation strategies.
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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) | 0.002 | 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".