Crossing the pond: genetic assignment detects lobster hybridisation
Bibliographic record
Abstract
American lobsters (Homarus americanus) imported live into Europe as a seafood commodity have occasionally been released or escaped into the wild, within the range of an allopatric congener, the European lobster (H. gammarus). In addition to disease and competition, introduced lobsters threaten native populations through hybridisation, but morphological discriminants used for species identification are unable to discern hybrids, so molecular methods are required. We tested an array of 79 single nucleotide polymorphisms (SNPs) for their utility to distinguish 1,308 H. gammarus from 38 H. americanus and 30 hybrid offspring from an American female captured in Sweden. These loci provide powerful species assignment in Homarus, enabling the robust identification of hybrid and American individuals among a survey of European stock. Moreover, a subset panel of the 12 most powerful SNPs is sufficient to separate the two pure species, even when tissues have been cooked, and can detect the introduced component of hybrids. We conclude that these SNP loci can unambiguously identify hybrid lobsters that may be undetectable via basic morphology, and offer a valuable tool to investigate the prevalence of cryptic hybridisation in the wild. Such investigations are required to properly evaluate the potential for introgression of alien genes into European lobster populations.
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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 | 0.000 |
| 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) | 0.002 | 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".