Characterizing the elusive Vancouver Island wolverine, <i>Gulo gulo vancouverensis</i>, using historical DNA
Bibliographic record
Abstract
Abstract The wolverine (Gulo gulo) is a Holarctic species found in North America primarily across the boreal forest, the subarctic, and along the Pacific coast, including Vancouver Island (VI), British Columbia. While wolverines on VI are rare and possibly extirpated, they have been previously described as a unique subspecies, G. g. vancouverensis, distinct from G. g. luscus from the mainland of North America. However, the validity of the VI subspecies is contentious, with conflicting results from studies of skull morphology. Here, we used molecular analyses to characterize the genetic diversity of the VI population and resolve this taxonomic debate to assist with conservation priorities. Historical DNA of VI wolverines was obtained from museum specimens, amplified at 16 nuclear microsatellite loci, and sequenced at the mitochondrial D-loop control region to compare with wolverines from mainland British Columbia. The VI population had lower allelic richness and was fixed for a single common mtDNA haplotype. Bayesian and non-Bayesian assignments using microsatellites generally revealed admixture across populations, implying allele frequencies between the VI and mainland populations were not significantly different. Hence, both types of genetic markers showed little evolutionary divergence between VI and the mainland population. Combined, these results do not provide evidence of significant genetic distinction for VI wolverines, nor support the subspecific classification. Immediate conservation efforts should focus on estimating population size, while future conservation planning can assume VI wolverines likely are not a unique genetic population and there remains the potential for natural recolonization of wolverines to VI.
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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 | 0.001 |
| 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".