Genetic structure of sugar kelp in the St. Lawrence Estuary and Gulf (Québec, Canada) Genetic structure of sugar kelp
Bibliographic record
Abstract
Abstract The sugar kelp, Saccharina latissima is cultivated at low scale in Quebec, Canada and current practice involve seeding meiospores or gametophyte stocks onto spools carrying twine and transferring these to a seaweed farm site. As the stocks can originate from locations spanning several hundreds of kilometers from the farm sites, such practice could involve genetic contamination and disrupt local adaptations. Assessing genetic structure can inform of the potential risks associated with this practice. Here we characterized the genetic diversity and structure of S. latissima from locations in the St. Lawrence Estuary and Gulf at both microsatellite loci (308 sporophytes at 22 loci in 16 sites) and genomic markers (228 sporophytes at 6578 single nucleotide polymorphisms in 13 sites). Several populations had low heterozygosity values and significant FIS values at microsatellite loci. No genetic structure was found among populations with microsatellite loci but strong genetic structuring was found with the genomic data. Population structure followed a geographic pattern and was congruent with major currents. Individuals from the wild population in the vicinity of the farm site were genetically distinct from the sporophytes on the growing lines that belong to a genetically distinct group. There was no significant genetic differentiation between wild individuals living in proximity of the farm site and another wild population of the same area. Hence aquaculture practices have not resulted in changes in the genetic composition of the wild population at large scale. Our results are important to guide future conservation efforts and for the seaweed farming industry.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".