Gene flow, linked selection, and divergent sorting of ancient polymorphism shape genomic divergence landscape in a group of edaphic specialists
Bibliographic record
Abstract
Interpreting the formation of genomic variation landscape, especially genomic regions with elevated differentiation (i.e. islands), is fundamental to a better understanding of the genomic consequences of adaptation and speciation. Edaphic islands provide excellent systems for understanding the interplay of gene flow and selection in driving population divergence and speciation. However, discerning the relative contribution of these factors that modify patterns of genomic variation remains difficult. We analysed 132 genomes from five recently divergent species in Primulina genus, with four species distributed in Karst limestone habitats and the fifth one growing in Danxia habitats. We demonstrated that both gene flow and linked selection have contributed to genome-wide variation landscape, where genomic regions with elevated differentiation (i.e., islands) were largely derived by divergent sorting of ancient polymorphism. Specifically, we identified several lineage-specific genomic islands that might have facilitated adaptation of P. suichuanensis to Danxia habitats. Our study is amongst the first cases disentangling evolutionary processes that shape genomic variation of plant specialists, and demonstrates the important role of ancient polymorphism in the formation of genomic islands that potentially mediate adaptation and speciation of endemic plants in special soil habitats.
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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 | 0.001 |
| 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".