Invasion genetics of the Eurasian round goby in North America: tracing sources and spread patterns
Bibliographic record
Abstract
The Eurasian round goby Neogobius melanostomus (Apollonia melanostoma) invaded the North American Great Lakes in 1990 through ballast water, spread rapidly, and now is widely distributed and moving through adjacent tributaries. We analyse its genetic diversity and divergence patterns among 25 North American (N = 744) and 22 Eurasian (N = 414) locations using mitochondrial DNA cytochrome b gene sequences and seven nuclear microsatellite loci in order to: (i) identify the invasion's founding source(s), (ii) test for founder effects, (iii) evaluate whether the invasive range is genetically heterogeneous, and (iv) determine whether fringe and central areas differ in genetic diversity. Tests include F(ST) analogues, neighbour-joining trees, haplotype networks, Bayesian assignment, Monmonier barrier analysis, and three-dimensional factorial correspondence analysis. We recovered 13 cytochrome b haplotypes and 232 microsatellite alleles in North America and compared these to variation we previously described across Eurasia. Results show: (i) the southern Dnieper River population was the primary Eurasian donor source for the round goby's invasion of North America, likely supplemented by some alleles from the Dniester and Southern Bug rivers, (ii) the overall invasion has high genetic diversity and experienced no founder effect, (iii) there is significant genetic structuring across North America, and (iv) some expansion areas show reduced numbers of alleles, whereas others appear to reflect secondary colonization. Sampling sites in Lake Huron's Saginaw Bay and Lake Ontario significantly differ from all others, having unique alleles that apparently originated from separate introductions. Substantial genetic variation, multiple founding sources, large number of propagules, and population structure thus likely aided the goby's ecological success.
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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 | 0.001 |
| 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".