<scp>RAD</scp> sequencing reveals genomewide divergence between independent invasions of the European green crab (<i>Carcinus maenas</i>) in the Northwest Atlantic
Bibliographic record
Abstract
Abstract Genomic studies of invasive species can reveal both invasive pathways and functional differences underpinning patterns of colonization success. The European green crab (Carcinus maenas) was initially introduced to eastern North America nearly 200 years ago where it expanded northwards to eastern Nova Scotia. A subsequent invasion to Nova Scotia from a northern European source allowed further range expansion, providing a unique opportunity to study the invasion genomics of a species with multiple invasions. Here, we use restriction‐site‐associated DNA sequencing‐derived SNPs to explore fine‐scale genomewide differentiation between these two invasions. We identified 9137 loci from green crab sampled from 11 locations along eastern North America and compared spatial variation to mitochondrial COI sequence variation used previously to characterize these invasions. Overall spatial divergence among invasions was high (pairwise FST ~0.001 to 0.15) and spread across many loci, with a mean FST ~0.052 and 52% of loci examined characterized by FST values >0.05. The majority of the most divergent loci (i.e., outliers, ~1.2%) displayed latitudinal clines in allele frequency highlighting extensive genomic divergence among the invasions. Discriminant analysis of principal components (both neutral and outlier loci) clearly resolved the two invasions spatially and was highly correlated with mitochondrial divergence. Our results reveal extensive cryptic intraspecific genomic diversity associated with differing patterns of colonization success and demonstrates clear utility for genomic approaches to delineating the distribution and colonization success of aquatic invasive species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".