Landscape genomics of the American lobster (<i>Homarus americanus</i>)
Bibliographic record
Abstract
In marine species experiencing intense fishing pressures, knowledge of genetic structure and local adaptation represent a critical information to assist sustainable management. In this study, we performed a landscape genomics analysis in the American lobster to investigate the issues pertaining to the consequences of making use of putative adaptive loci to reliably infer population structure and thus more rigorously delineating biological management units in marine exploited species. Toward this end, we genotyped 14,893 single nucleotide polymorphism (SNPs) in 4190 lobsters sampled across 96 sampling sites distributed along 1000 km in the northwest Atlantic in both Canada and the USA. As typical for most marine species, we observed a weak, albeit highly significant genetic structure. We also found that adaptive genetic variation allows detecting fine-scale population structure not resolved by neutral genetic variation alone. Using the recent genome assembly of the American lobster, we were able to map and annotate several SNPs located in functional genes potentially implicated in adaptive processes such as thermal stress response, salinity tolerance and growth metabolism pathways. Taken together, our study indicates that weak population structure in high gene flow systems can be resolved at various spatial scales, and that putatively adaptive genetic variation can substantially enhance the delineation of biological management units of marine exploited 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".