Data from: Spatial patterns of immunogenetic and neutral variation underscore the conservation value of small, isolated American badger populations
Bibliographic record
Abstract
Small and isolated populations often exhibit low genetic diversity due to drift and inbreeding, but may simultaneously harbour adaptive variation. We investigate spatial distributions of immunogenetic variation in American badger subspecies (Taxidea taxus), as a proxy for evaluating their evolutionary potential across the northern extent of the species’ range. We compared genetic structure of 20 microsatellites and the Major histocompatibility complex (MHC) to evaluate if small isolated populations show low adaptive polymorphism relative to large and well-connected populations. Our results suggest that gene flow plays a prominent role in shaping MHC polymorphism across large spatial scales, while the interplay between gene flow and selection was stronger towards the northern peripheries. The similarity of MHC alleles within subspecies relative to their neutral genetic differentiation suggests that adaptive divergence among subspecies can be maintained despite ongoing gene flow along subspecies boundaries. Neutral genetic diversity were low in small relative to large populations, but MHC diversity within individuals was high in small populations. Despite reduced neutral genetic variation, small isolated populations harbour functional variation that likely contribute to the species evolutionary potential at the northern range. Our findings suggest that conservation approaches should focus on managing adaptive variation across the species range rather than protecting subspecies per se.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.028 |
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".