The invasive bighead goby <i><scp>P</scp>onticola kessleri</i> displays large‐scale genetic similarities and small‐scale genetic differentiation in relation to shipping patterns
Bibliographic record
Abstract
Colonization events, range expansions and species invasions leave genetic signatures in the genomes of invasive organisms and produce intricate special patterns. Predictions have been made as to how those patterns arise, but only very rarely, genetic processes can be monitored in real time during range expansions. In an attempt to change that, we track a very recently established invasive population of a fish species, the bighead goby Ponticola kessleri, with high temporal and spatial resolution through 2 years to identify patterns over time. We then compare Swiss and German samples of bighead goby along the river Rhine using microsatellites, mitochondrial D-loop sequences and geometric morphometrics to investigate geographic patterns. We detect weak temporal and strong geographic patterns in the data, which are inconsistent with isolation by distance and indicate long range transport. In search of an explanation for our observations, we analyse the vector properties and travel patterns of commercial vessels on the river Rhine. We present evidence that freshwater cargo ships and tankers are plausible vectors for larvae of invasive goby species. We also present indications that cargo ships and tankers act as differential vectors for this species. In summary, we present genetic data at unique temporal resolution from a vertebrate invasion front and substantiate the paramount role of commercial shipping in freshwater fish translocations.
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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 | 0.001 |
| 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".