Glacial History and Landscape Features Shape the Hierarchical Population Genetic Structure of Woodland Caribou ( <i>Rangifer tarandus caribou</i> ) in Western Canada
Bibliographic record
Abstract
ABSTRACT Characterising hierarchical population structure is crucial to understanding a species' evolutionary history and informing effective conservation and management strategies. Many terrestrial species in North America have experienced a wide range of evolutionary pressures at multiple scales, ranging from large‐scale range shifts and recolonisations driven by glacial cycles to more localized contemporary habitat degradation and fragmentation. Hence in this region, given the multi‐level evolutionary forces at play, genetic variation and diversity are often hierarchically structured. We analysed genomic diversity and variation in woodland caribou ( Rangifer tarandus caribou ) across western Canada using genotypes from ~33,000 Single Nucleotide Polymorphism (SNP) loci from 759 geo‐referenced individuals spanning 45 pre‐defined subpopulations. We employed genetic clustering methods and measures of genetic differentiation to characterise hierarchical population structure in the region and tested for latitudinal changes in heterozygosity resulting from post‐glacial recolonisation and hybridisation. Our results confirm that woodland caribou genetic diversity and differentiation occur at multiple hierarchical levels, reflecting post‐glacial recolonisation patterns and landscape heterogeneity. Notably, the major genetic clusters identified in our study do not align with current recognised units for the species in this region. We also observe elevated heterozygosity in the mid‐latitudes of the sampled range, indicative of hybridisation following secondary contact during post‐glacial recolonisation. These findings underscore the need to consider and include genetic diversity at all hierarchical levels in conservation planning, as wide‐ranging species often experience diverse and complex evolutionary histories and pressures.
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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 | 0.001 | 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".