Disentangling invasion processes in a dynamic shipping–boating network
Bibliographic record
Abstract
The relative importance of multiple vectors to the initial establishment, spread and population dynamics of invasive species remains poorly understood. This study used molecular methods to clarify the roles of commercial shipping and recreational boating in the invasion by the cosmopolitan tunicate, Botryllus schlosseri. We evaluated (i) single vs. multiple introduction scenarios, (ii) the relative importance of shipping and boating to primary introductions, (iii) the interaction between these vectors for spread (i.e. the presence of a shipping-boating network) and (iv) the role of boating in determining population similarity. Tunicates were sampled from 26 populations along the Nova Scotia, Canada, coast that were exposed to either shipping (i.e. ports) or boating (i.e. marinas) activities. A total of 874 individuals (c. 30 per population) from five ports and 21 marinas was collected and analysed using both mitochondrial cytochrome c oxidase subunit I gene (COI) and 10 nuclear microsatellite markers. The geographical location of multiple hotspot populations indicates that multiple invasions have occurred in Nova Scotia. A loss of genetic diversity from port to marina populations suggests a stronger influence of ships than recreational boats on primary coastal introductions. Population genetic similarity analysis reveals a dependence of marina populations on those that had been previously established in ports. Empirical data on marina connectivity because of boating better explains patterns in population similarities than does natural spread. We conclude that frequent primary introductions arise by ships and that secondary spread occurs gradually thereafter around individual ports, facilitated by recreational boating.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".