Population Structure Of The Hard Clam, Mercenaria Mercenaria, Along The East Coast Of North America
Bibliographic record
Abstract
Virginia leads the nation in production of aquacultured hard clams, Mercenaria mercenaria (Linnaeus, 1758), with an estimated farm gate value of $38.8 million in 2018. Despite the high economic value, there are few genomic resources available to support the hard clam aquaculture industry.To develop effective genetic tools for industry, it is important to first understand population structure. Hard clams have a pelagic larval phase that allows for dispersal, but the level of genetic connectivity among populations is not well understood. This study used genotyping-by-sequencing to delineate the genetic stock structure of wild clams sampled along the East Coast of North America and identify a subset of informative loci for population discrimination. Samples were collected from 15 locations from Prince Edward Island, Canada, to South Carolina, USA. Following DNA isolation, 452 individuals were sequenced and 153,842 single nucleotide polymorphisms (SNPs) were identified. The SNP loci were filtered for quality control, resulting in a final dataset of 4,960 SNPs from 448 individuals that was analyzed to delineate population structure and quantify levels of genetic divergence among populations and levels of diversity within populations. Data provided evidence of five genetic breaks separating six genetically distinct populations; Canada, Maine, Massachusetts, Mid-Atlantic, Chesapeake Bay and the Carolinas. The use of next-generation sequencing markers in this study enabled identification of finer scale population structure than was previously recognized. Data were used to identify a subset of SNP markers capable of geographic discrimination and population assignment with 75–93% accuracy. This is the first study to assess population genetic structure of the economically important hard clam along a large portion of their native range with high resolution genomic markers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".