Genotype by sequencing identifies natural selection as a driver of intraspecific divergence in Atlantic populations of the high dispersal marine invertebrate, <i>Macoma petalum</i>
Bibliographic record
Abstract
Abstract Mitochondrial DNA analyses indicate that the Bay of Fundy population of the intertidal tellinid bivalve Macoma petalum is genetically divergent from coastal populations in the Gulf of Maine and Nova Scotia. To further examine the evolutionary forces driving this genetic break, we performed double digest genotype by sequencing (GBS) to survey the nuclear genome for evidence of both neutral and selective processes shaping this pattern. The resulting reads were mapped to a partial transcriptome of its sister species, M. balthica, to identify single nucleotide polymorphisms (SNPs) in protein‐coding genes. Population assignment tests, principle components analyses, analysis of molecular variance, and outlier tests all support differentiation between the Bay of Fundy genotype and the genotypes of the Gulf of Maine, Gulf of St. Lawrence, and Nova Scotia. Although both neutral and non‐neutral patterns of genetic subdivision were significant, genetic structure among the regions was nearly 20 times higher for loci putatively under selection, suggesting a strong role for natural selection as a driver of genetic diversity in this species. Genetic differences were the greatest between the Bay of Fundy and all other population samples, and some outlier proteins were involved in immunity‐related processes. Our results suggest that in combination with limited gene flow across the mouth of the Bay of Fundy, local adaptation is an important driver of intraspecific genetic variation in this marine species with high dispersal potential.
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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 | 0.000 | 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) | 0.000 | 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 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".