Identifying patterns of dispersal, connectivity and selection in the sea scallop,<i>Placopecten magellanicus,</i>using<scp>RAD</scp>seq‐derived<scp>SNP</scp>s
Bibliographic record
Abstract
Abstract Understanding patterns of dispersal and connectivity among marine populations can directly inform fisheries conservation and management. Advances in high‐throughput sequencing offer new opportunities for estimating marine connectivity. We used restriction‐site‐associatedDNAsequencing to examine dispersal and realized connectivity in the sea scallopPlacopecten magellanicus, an economically important marine bivalve. Based on 245 individuals sampled rangewide at 12 locations from Newfoundland to the Mid‐Atlantic Bight, we identified and genotyped 7163 single nucleotide polymorphisms; 112 (1.6%) were identified as outliers potentially under directional selection. Bayesian clustering revealed a discontinuity between northern and southern samples, and latitudinal clines in allele frequencies were observed in 42.9% of the outlier loci and in 24.6% of neutral loci. Dispersal estimates derived using these clines and estimates of linkage disequilibrium imply limited dispersal; 373.1 ± 407.0 km (mean ± SD) for outlier loci and 641.0 ± 544.6 km (mean ± SD) for neutral loci. Our analysis suggests restricted dispersal compared to the species range (>2000 km) and that dispersal and effective connectivity differ. These observations support the hypothesis that limited effective dispersal structures scallop populations along eastern North America. These findings can help refine the appropriate scale of management and conservation in this commercially valuable 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 | 0.000 |
| 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".