Linking genetic and ecological differentiation in an ungulate with a circumpolar distribution
Bibliographic record
Abstract
Genetic differentiation among populations may arise from the disruption of gene flow due to local adaptation to distinct environments and/or neutral accumulation of mutations and genetic drift resulted from geographical isolation. Quantifying the role of these processes in determining the genetic structure of natural populations remains challenging. Here, we analyze the relative contribution of isolation‐by‐resistance (IBR), isolation‐by‐environment (IBE), genetic drift and historical isolation in allopatry during Pleistocene glacial cycles on shaping patterns of genetic differentiation in caribou/reindeer populations Rangifer tarandus across the entire distribution range of the species. Our study integrates analyses at range‐wide and regional scales to partial out the effects of historical and contemporary isolation mechanisms. At the circumpolar scale, our results indicate that genetic differentiation is predominantly explained by IBR and historical isolation. At a regional scale, we found that IBR, IBE and population size significantly explained the spatial distribution of genetic variation among populations belonging to the Euro‐Beringian lineage within North America. In contrast, genetic differentiation among populations within the North American lineage was predominantly explained by IBR and population size, but not IBE. We also found discrepancies between genetic and ecotype designation across the Holarctic species distribution range. Overall, these results indicate that multiple isolating mechanisms have played roles in shaping the spatial distribution of genetic variation across the distribution range of a large mammal with high potential for gene flow. Considering multiple spatial scales and simultaneously testing a comprehensive suite of potential isolating mechanisms, our study contributes to understand the ecological and evolutionary processes underlying organism–landscape interactions.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".