Already at the bottom? Demographic declines are unlikely further to undermine genetic diversity of a large Arctic ungulate: muskox, Ovibos moschatus (Artiodactyla: Bovidae)
Bibliographic record
Abstract
Abstract Low genetic diversity is associated with low fitness and evolutionary potential, yet the demographic and life-history traits of some species contribute to low genetic diversity, without empirical evidence of negative impacts on fitness. Modelling past and future trajectories of genetic diversity under different demographic scenarios can provide insight into how genetic variation might impact population fitness. The muskox is an Arctic species that has undergone multiple population bottlenecks and, although populations have rebounded repeatedly, two large populations have recently declined by > 50%. It is unclear how these demographic patterns influence muskox genetic diversity and fitness. We compared the genetic diversity of Canadian muskox populations undergoing opposing population trends. Genotyping 84 mainland and 244 Victoria Island individuals at ten microsatellite loci revealed low genetic variation (Victoria Island, mean allelic richness 1.66, expected heterozygosity 0.16; mainland, mean allelic richness 2.58, expected heterozygosity 0.41), with no evidence of further reductions in diversity subsequent to recent demographic declines. Bayesian modelling showed that a 1900s bottleneck contributed to the lack of diversity in contemporary populations, and forward-in-time simulations suggested little effect on genetic diversity over the next 100 years. Muskoxen might have reached a genetic diversity minimum, and additional research will be needed to determine their capacity to adapt to rapid changes in selective pressures in a rapidly changing Arctic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".