Genome‐wide SNP assessment of contemporary European red deer genetic structure highlights the distinction of peripheral populations and the main admixture zones in Europe
Bibliographic record
Abstract
Genome-wide technologies open up new possibilities to clarify questions on genetic structure and phylogeographic history of taxa previously studied with microsatellite loci and mitochondrial sequences. Here, we used 736 individual red deer (Cervus elaphus) samples genotyped at 35,701 single nucleotide polymorphism loci (SNPs) to assess the population structure of the species throughout Europe. The results identified 28 populations, with higher degrees of genetic distinction in peripheral compared to mainland populations. Iberian red deer show high genetic differentiation, with lineages in Western and Central Iberia maintaining their distinctiveness, which supports separate refugial ranges within Iberia along with little recent connection between Iberian and the remaining Western European populations. The Norwegian population exhibited the lowest variability and the largest allele frequency differences from mainland European populations, compatible with a history of bottlenecks and drift during post-glacial colonization from southern refugia. Scottish populations showed high genetic distance from the mainland but high levels of diversity. Hybrid zones were found between Eastern and Western European lineages in Central Europe as well as in the Pyrenees, where red deer from France are in close contact with Iberian red deer. Anthropogenic restocking has promoted the Pyrenean contact zone, admixture events in populations on the Isle of Rum and in the Netherlands, and at least partly the admixture of the two main lineages in central-eastern Europe. Our analysis enabled detailed resolution of population structure of a large mammal widely distributed throughout Europe and contributes to resolving the evolutionary history, which can also inform conservation and management policies.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".