Divergent selection and heterogeneous migration rates across the range of Sitka spruce (<i>Picea sitchensis</i>)
Bibliographic record
Abstract
Gene flow and effective population size (N(e)) should depend on a population's position within its range: those near the edges are expected to have smaller N(e) and lower relative emigration rates, whereas those nearer the centre should have larger N(e) and higher relative emigration rates. In species with continuous ranges, this phenomenon may limit the ability of peripheral populations to respond to divergent selection. Here, we employ Sitka spruce as a model to test these predictions. We previously genotyped 339 single nucleotide polymorphisms (SNPs) in 410 individuals from 13 populations, and used these data to identify putative targets of divergent selection, as well as to explore the extent to which central-peripheral structure may impede adaptation. Fourteen SNPs had outlier F(ST) estimates suggestive of divergent selection, of which nine were previously associated with phenotypic variation in adaptive traits (timing of autumn budset and cold hardiness). Using coalescent simulations, we show that populations from near the centre of the range have higher effective populations sizes than those from the edges, and that central populations contribute more migrants to marginal populations than the reverse. Our results suggest that while divergent selection appears to have shaped allele frequencies among populations, asymmetrical movement of alleles from the centre to the edges of the species range may affect the adaptive capacity of peripheral populations. In southern peripheral populations, the movement of cold-adapted alleles from the north represents a significant impediment to adaptation under climate change, while in the north, movement of warm-adapted alleles from the south may enhance adaptation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".