Molecular ecology of the sleeper shark subgenus<i>Somniosus (Somniosus)</i>reveals genetic homogeneity within species and lack of support for<i>S. antarcticus</i>
Bibliographic record
Abstract
Inferences made from molecular data support regional stock assessment goals by providing insights into the genetic population dynamics of enigmatic species. Population genomics metrics, such as genetic diversity and population connectivity, serve as useful proxies for species health and stability. Sleeper sharks (genus Somniosus) are ecologically important deep-sea predators, estimated to reach ages of 250 to 300 yr and taking decades to reach sexual maturity. The subgenus Somniosus (Somniosus) is comprised of 3 species: S. pacificus, S. microcephalus, and S. antarcticus. Given the life history strategy of somniosids, they are vulnerable to overfishing and population declines. Further, data to assess the stocks of these species are limited. To address this deficiency, we used the reduced representation library method Restriction-site Associated DNA sequencing (RADseq) to conduct phylogenomic and population genomics analyses, providing novel information for use in stock assessments. Our results strongly support the species status of S. microcephalus (N = 79), but recover S. antarcticus (N = 2) intermixed within the S. pacificus (N = 170) clade. Population genomics analyses reveal genetic homogeneity within S. pacificus and S. microcephalus, and estimates of effective population size were in the hundreds for both species. Kinship analysis identified 2 first-degree relative pairs within our dataset (1 within each species). Our results contribute new information for stock assessments of these uniquely long-lived species by providing the strongest molecular evidence to date for the synonymization of S. antarcticus and S. pacificus, as well as estimating population genomic metrics for each supported species within the Somniosus (Somniosus) subgenus.
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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) | 0.001 | 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".