Range‐Wide Genomic Analysis Reveals Regional and Meta‐Population Dynamics of Decline and Recovery in the Grey Seal
Bibliographic record
Abstract
Wildlife populations globally have experienced widespread historical declines due to anthropogenic and environmental impacts, yet for some species, contemporary management and conservation programmes have enabled recent recovery. The impacts of decline and recovery on genomic diversity and, vice versa, the genetic factors that contribute to conservation success or failure are rich areas for inquiry, with implications for shaping how we manage species into the future. To comprehensively characterise these processes in natural systems requires range-wide sampling and international collaboration, particularly for species with wide dispersal capabilities, broad geographic distributions, and complex regional metapopulation dynamics. Here, we present the first range- and genome-wide population genomic analysis of grey seals based on 3812 nuclear SNPs genotyped in 188 samples from 17 localities. Our analyses support the existence of three main grey seal populations centred in the NW Atlantic, NE Atlantic and Baltic Sea, and point to the existence of previously unrecognised substructure within the NE Atlantic. We detected remarkably low levels of genetic diversity in the NW Atlantic population, and demographic analyses revealed a turbulent history of NE Atlantic and Baltic Sea grey seals, with bottlenecks in the Middle Ages and the 20th century due to hunting and habitat alterations. We found some localities deviated from isolation by distance patterns, likely reflecting wide-scale metapopulation dynamics associated with recolonisation and recovery in regions where they were historically extirpated. We identify at least six grey seal genetic populations and reveal marked genetic effects of past declines and recent recovery across the species' range.
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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 | 0.001 |
| 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".