Global population structure in an arctic-breeding bird: Diversifying and stabilizing selection consistent with local adaptation
Bibliographic record
Abstract
Environmental variation among isolated populations can drive genetic differentiation by selection, while isolation alone results primarily in genetic drift. Genetic analyses can aid in identifying genetically isolated populations and population structure of a species across its range. Additionally, such analyses can provide indirect evidence of local adaptation through the comparison of allele frequencies at neutral and functional genetic markers, with the aim of identifying outlier loci consistent with the effects of selection. Here, we examine the genetic divergence and patterns of functional divergence among six breeding populations of arctic-breeding snow buntings (Plectrophenax nivalis). We genotyped 221 birds at 9 microsatellite markers and at 101 single nucleotide polymorphisms (SNPs) located within known-function genes. We identified substantial population differentiation using both marker types with relatively greater divergence and hence finer population structure using the microsatellite markers. While population structures resulting from the two marker types were in general agreement, functional SNPs showed evidence of stabilizing selection at both global and population pairwise levels, with a few key SNPs showing signatures of pairwise divergent selection, consistent with expectations of local adaptation. The observed complex and inconsistent pattern of pairwise divergence (selection) at key candidate-gene loci may reflect rapid environmental change decoupling locally adapted genotypes from actual local environmental conditions. Our work highlights microevolutionary changes that are likely to be very important not only in arctic-breeding songbirds, but in Arctic and Sub-Arctic vertebrates in general, which are experiencing strong environmental effects from accelerated climate change and human-induced stressors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".