Circumpolar Population Structure, Diversity and Recent Evolutionary History of the Bearded Seal in Relation to Past and Present Icescapes
Bibliographic record
Abstract
The Arctic environment plays a critical role in the global climate system and marine biodiversity. The region's ice-covered expanses provide essential breeding and feeding grounds for a diverse assemblage of marine species, who have adapted to thrive in these harsh conditions and consequently are under threat from global warming. The bearded seal (Erignathus barbatus), including two subspecies (E. barbatus nauticus-Pacific and E. barbatus barbatus-Atlantic), is an ice-obligate Arctic species using sea ice for many aspects of its life history, rendering it particularly vulnerable to sea ice loss. It is one of the least studied and hence enigmatic of the Arctic marine mammals, with little knowledge regarding genetic structure, diversity, adaptations, and demographic history, consequently hampering management and conservation efforts. Here, we sequenced 70 whole nuclear genomes from across most of the species' circumpolar range, finding significant genetic structure between the Pacific and the Atlantic subspecies, which diverged during the Penultimate Glacial Period (~200 KYA). Remarkably, we found fine-scale genetic structure within both subspecies, with at least two distinct populations in the Pacific and three in the Atlantic. We hypothesise sea-ice dynamics and bathymetry had a prominent role in shaping bearded seal genetic structure and diversity. Our analyses of highly differentiated genomic regions can be used to complement the health, physiological, and behavioural research needed to conserve this species. In addition, we provide recommendations for management units that can be used to more specifically assess climatic and anthropogenic impacts on bearded seal populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".