Comparing microsatellites and single nucleotide polymorphisms to evaluate genetic structure and diversity in wolverines (<i>Gulo gulo</i>) across Alaska and western Canada
Bibliographic record
Abstract
Abstract The Wolverine (Gulo gulo) is a cold-adapted species of conservation interest because it is sensitive to human development, disturbance, exploitation, and climate warming. Wolverine populations have been studied across much of their distributional range to evaluate patterns of genetic diversity, genetic structure, and gene flow. Little population structure has been detected in northwestern North America with microsatellite loci, but low genomic diversity in wolverines may limit detection of genetic differences in this highly vagile species. Here, we genotyped a relatively large sample of wolverines from across Alaska (US) and adjacent Yukon (Canada) with 12 microsatellite loci (n = 501) and 4,222 single nucleotide polymorphisms (SNPs; n = 201) identified using restriction-site associated DNA sequencing. We compared the relative ability of our microsatellite and SNP datasets to evaluate population genetic structure, genetic diversity, differentiation, and isolation by distance (IBD). We predicted that the SNP dataset would detect a higher degree of genetic structure and provide more significant support for IBD. We found evidence for multiple genetic clusters, including genetic distinctiveness of wolverines in southeast Alaska and on the Kenai Peninsula. The SNP dataset detected additional genetic clusters that align largely with ecoregions, and the SNP dataset showed stronger evidence of IBD, while the 2 datasets were generally consistent in estimates of genetic diversity and differentiation among regional groups. Our results highlight the importance of genomic methods to assess gene flow in wolverines. Identifying population genetic structure allows an assessment of the potential impacts of conservation threats and is an important precursor for designing population monitoring programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".