Postglacial history of a widespread conifer produces inverse clines in selective neutrality tests
Bibliographic record
Abstract
Deviations of the site frequency spectrum of mutations (SFS) from neutral expectations may be caused by natural selection or by demographic processes such as population subdivision or temporal changes in population size. As most widespread temperate and boreal tree species have expanded from glacial refugia in the past 13,000 years, colonization bottlenecks associated with this migration may have left variable demographic signatures among geographic populations corresponding to distance from the refugia. To determine whether the signature of postglacial re-colonization has skewed the SFS in the widely distributed conifer Sitka spruce (Picea sitchensis (Bong.) Carr.), we re-sequenced 153 nuclear genes in six populations from across the species range. We found that while the SFS for the pooled sample produced negative values for Tajima's D and Fay and Wu's H, these statistics exhibited strong clinal variation when populations were analysed separately (R(2) = 0.84, P = 0.007 for Tajima's D and R(2) = 0.65, P = 0.033 for Fay and Wu's H). When historical bottlenecks of varying age were simulated using approximate Bayesian computation, distance of populations from the southern range limit explained most of the variation in bottleneck timing among populations (R(2) = 0.89, P = 0.003). These data suggest that sequential population bottlenecks during postglacial re-colonization have resulted in diverse among-population signatures within the contemporary SFS in Sitka spruce, with rare variants more common in the south, and medium-frequency variants more common in the north. Our results also emphasize the need to consider sampling strategy and to explore population-specific null demographic models in surveys of nucleotide variation in widely distributed species.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".