Data from: Signals of selection in outlier loci in a widely dispersing species across an environmental mosaic
Bibliographic record
Abstract
Local adaptation reflects a balance between natural selection and gene flow and is classically thought to require the retention of locally adapted alleles. However, organisms with high dispersal potential across a spatially or temporally heterogeneous landscape pose an interesting challenge to this view requiring local selection every generation or when environmental conditions change to generate adaptation in adults. Here, we test for geographical and sequence-based signals of selection in five putatively adaptive and two putatively neutral genes identified in a previous genome scan of the highly dispersing purple sea urchin, Strongylocentrotus purpuratus. Comparing six populations spanning the species’ wide latitudinal range from Canada to Baja California, Mexico, we find positive tests for selection in the putative adaptive genes and not in the putative neutral genes. Specifically, we find an excess of low-frequency and nonsynonymous polymorphisms in two transcription factors and a transporter protein, and an excess of common amino acid polymorphisms in the two transcription factors, suggestive of spatially balancing selection. We test for a genetic correlation with temperature, a dominant environmental variable in this coastal ecosystem. We find mild clines and a stronger association of genetic variation with temperature than latitude in four of the five putative adaptive loci and a signal of local adaptation in the Southern California Bight. Overall, patterns of genetic variation match predictions based on spatially or temporally balancing selection in a heterogeneous landscape and illustrate the value of geographical and coalescent tests on candidate loci identified in a genome-wide scan for selection.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.032 |
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".