Population genetic structure of the cyclic snowshoe hare (<i>Lepus americanus</i>) in southwestern Yukon, Canada
Bibliographic record
Abstract
Spatial population structure has important ecological and evolutionary consequences. Little is known about the population structure of snowshoe hares (Lepus americanus), despite their ecological importance in North American boreal forests. We used seven variable microsatellite DNA loci to determine the spatial genetic structure of snowshoe hares near Kluane Lake, Yukon during a cyclic population peak. We sampled 317 hares at 12 sites separated by distances ranging from 3 to 140 km, and used 46 additional samples from Alaska and Montana. The level of genetic variation was high (13.4 alleles/locus, 0.67 expected heterozygosity) and the distribution of alleles and genotypes was not homogeneous across the sites. The degree of differentiation was low among Yukon sites (FST = 0.015) and between Yukon and Alaska (FST = 0.012), but the Montana site was highly differentiated (FST = 0.20). A weak pattern of isolation by distance was found over the Yukon study area, with an indication that local genetic drift may be important in shaping the regional genetic structure. Landscape barriers expected to influence gene flow did not consistently affect genetic structure, although there was evidence for a partial barrier effect of Kluane Lake. The high level of inferred gene flow confirms that snowshoe hare dispersal is widespread, successful and equal between the sexes. A stepping-stone model of gene flow, potentially influenced by the synchronous density cycle, appears to best explain the observed genetic structure. Our results suggest that despite their dramatic fluctuations in density, snowshoe hares in the northern boreal forest have a large evolutionary effective population size and are not strongly subdivided by either physical or social barriers to gene flow.
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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 | 0.001 | 0.001 |
| 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".