Whole‐genome analyses disentangle reticulate evolution of primroses in a biodiversity hotspot
Bibliographic record
Abstract
Biodiversity hotspots, such as the Caucasus mountains, provide unprecedented opportunities for understanding the evolutionary processes that shape species diversity and richness. Therefore, we investigated the evolution of Primula sect. Primula, a clade with a high degree of endemism in the Caucasus. We performed phylogenetic and network analyses of whole-genome resequencing data from the entire nuclear genome, the entire chloroplast genome, and the entire heterostyly supergene. The different characteristics of the genomic partitions and the resulting phylogenetic incongruences enabled us to disentangle evolutionary histories resulting from tokogenetic vs cladogenetic processes. We provide the first phylogeny inferred from the heterostyly supergene that includes all species of Primula sect. Primula. Our results identified recurrent admixture at deep nodes between lineages in the Caucasus as the cause of non-monophyly in Primula. Biogeographic analyses support the 'out-of-the-Caucasus' hypothesis, emphasizing the importance of this hotspot as a cradle for biodiversity. Our findings provide novel insights into causal processes of phylogenetic discordance, demonstrating that genome-wide analyses from partitions with contrasting genetic characteristics and broad geographic sampling are crucial for disentangling the diversification of species-rich clades in biodiversity hotspots.
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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 | 0.000 |
| 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 | 0.000 | 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".