Correction: Patterns of Polymorphism and Demographic History in Natural Populations of Arabidopsis lyrata
Bibliographic record
Abstract
Background: Many of the processes affecting genetic diversity act on local populations.However, studies of plant nucleotide diversity have largely ignored local sampling, making it difficult to infer the demographic history of populations and to assess the importance of local adaptation.Arabidopsis lyrata, a self-incompatible, perennial species with a circumpolar distribution, is an excellent model system in which to study the roles of demographic history and local adaptation in patterning genetic variation.Principal Findings: We studied nucleotide diversity in six natural populations of Arabidopsis lyrata, using 77 loci sampled from 140 chromosomes.The six populations were highly differentiated, with a median FST of 0.52, and STRUCTURE analysis revealed no evidence of admixed individuals.Average within-population diversity varied among populations, with the highest diversity found in a German population; this population harbors 3-fold higher levels of silent diversity than worldwide samples of A. thaliana.All A. lyrata populations also yielded positive values of Tajima's D. We estimated a demographic model for these populations, finding evidence of population divergence over the past 19,000 to 47,000 years involving non-equilibrium demographic events that reduced the effective size of most populations.Finally, we used the inferred demographic model to perform an initial test for local adaptation and identified several genes, including the flowering time gene FCA and a disease resistance locus, as candidates for local adaptation events. Conclusions:Our results underscore the importance of population-specific, non-equilibrium demographic processes in patterning diversity within A. lyrata.Moreover, our extensive dataset provides an important resource for future molecular population genetic studies of local adaptation in A. lyrata.
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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 | 0.005 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.176 | 0.033 |
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".