Genetic variability and phylogeographical patterns of a nonindigenous species invasion: a comparison of exotic vs. native zebra and quagga mussel populations
Bibliographic record
Abstract
Abstract There have been few investigations of the number of founding sources and amount of genetic variability that lead to a successful nonindigenous species invasion, although genetic diversity is believed to play a central role. In the present study, population genetic structure, diversity and divergence patterns were analysed for the zebra mussel Dreissena polymorpha [n=280 samples and 63 putative randomly amplified polymorphic DNA (RAPDs) gene loci] and the quagga mussel D. bugensis (n=136 and 52 loci) from 10 nonindigenous North American and six Eurasian sampling sites, representing their present-day ranges. Results showed that exotic populations of zebra and quagga mussels had surprisingly high genetic variability, similar to those in the Eurasian populations, suggesting large numbers of founding individuals and consistent with the hypothesis of multiple colonizations. Patterns of genetic relationships indicate that the North American populations of D. polymorpha likely were founded by multiple source populations from north-western and northcentral Europe, but not from southcentral or eastern Europe. Sampling areas within North America also were significantly divergent, having levels of gene flow and migration about twice those separating long-established Eurasian populations. Samples of D. bugensis in Lakes Erie and Ontario were significantly different, with the former being more closely related to a native population from the Dnieper River, Ukraine. No evidence for a founder effect was discerned for either 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 | 0.001 |
| 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) | 0.001 | 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".