Low genomic divergence and high gene flow between locally adapted populations of the swamp sparrow
Bibliographic record
Abstract
Abstract Populations that have recently diverged across sharp environmental gradients provide an opportunity to study the mechanisms by which natural selection drives adaptive divergence. Inland and coastal populations of the North American swamp sparrow have become an emerging model system for studies of natural selection because they are morphologically and behaviourally distinct despite a very recent divergence time (<15,000 years), yet common garden experiments have demonstrated a genetic basis for their phenotypic differences. We characterized genomic patterns of variation within and between inland and coastal swamp sparrows via reduced representation sequencing in order to reconstruct the contributions of demography, gene flow and selection to this case of recent adaptive divergence. Compared to inland swamp sparrows, coastal swamp sparrows exhibited fewer polymorphic sites and reduced nucleotide diversity at those sites, indicating that a bottleneck and/or recent selective sweeps occurred in that population during coastal colonization and local adaptation. Estimates of genome-wide differentiation (F ST =0.02) and sequence divergence ( Φ ST =0.05) between inland and coastal populations were very low, consistent with postglacial divergence. A small number of SNPs were strongly differentiated (max F ST =0.8) suggesting selection at linked sites. Swamp sparrows sampled from breeding sites at the habitat transition between freshwater and brackish marshes exhibited high levels of genetic admixture. Such evidence of active contemporary gene flow makes the evolution and maintenance of local adaptation in these two populations even more notable. We summarize several features of the swamp sparrow system that may facilitate the maintenance of adaptive diversity despite gene flow, including the presence of a magic trait.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".