<scp>RAD</scp> genotyping reveals fine‐scale genetic structuring and provides powerful population assignment in a widely distributed marine species, the <scp>A</scp>merican lobster (<i><scp>H</scp>omarus americanus</i>)
Bibliographic record
Abstract
Deciphering genetic structure and inferring connectivity in marine species have been challenging due to weak genetic differentiation and limited resolution offered by traditional genotypic methods. The main goal of this study was to assess how a population genomics framework could help delineate the genetic structure of the American lobster (Homarus americanus) throughout much of the species' range and increase the assignment success of individuals to their location of origin. We genotyped 10 156 filtered SNPs using RAD sequencing to delineate genetic structure and perform population assignment for 586 American lobsters collected in 17 locations distributed across a large portion of the species' natural distribution range. Our results revealed the existence of a hierarchical genetic structure, first separating lobsters from the northern and southern part of the range (FCT = 0.0011; P-value = 0.0002) and then revealing a total of 11 genetically distinguishable populations (mean FST = 0.00185; CI: 0.0007-0.0021, P-value < 0.0002), providing strong evidence for weak, albeit fine-scale population structuring within each region. A resampling procedure showed that assignment success was highest with a subset of 3000 SNPs having the highest FST . Applying Anderson's (Molecular Ecology Resources, 2010, 10, 701) method to avoid 'high-grading bias', 94.2% and 80.8% of individuals were correctly assigned to their region and location of origin, respectively. Lastly, we showed that assignment success was positively associated with sample size. These results demonstrate that using a large number of SNPs improves fine-scale population structure delineation and population assignment success in a context of weak genetic structure. We discuss the implications of these findings for the conservation and management of highly connected marine species, particularly regarding the geographic scale of demographic independence.
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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".