Is vessel hull fouling an invasion threat to the Great Lakes?
Bibliographic record
Abstract
Abstract Aim Hull fouling is a leading vector for the introduction of marine, non‐indigenous species (NIS) worldwide, yet its importance to freshwater habitats is poorly understood. We aimed to establish the complement of NIS transported via this vector to the Great Lakes and to determine if they pose an invasion risk. Location Laurentian Great Lakes. Methods During 2007 and 2008, we collected scrapings from exterior surfaces as well as underwater video‐transects from 20 vessels shortly after their arrival in Great Lakes’ ports. Invertebrates present were sorted and identified in the laboratory. Results Total estimated abundance averaged > 170,000 invertebrates per ship belonging to 109 taxa. Most (72%) of these taxa were freshwater species already present in the Great Lakes, whereas 11 and 31% were native to estuarine and marine habitats respectively, and would not be expected to survive in this habitat. Abundance was dominated by barnacles (51%), cladocerans (19%), bivalves (12%) and amphipods (11%). Sea‐chest grating and the rudder were hot‐spots for biofouling. Invertebrate diversity and total abundance were positively associated with total time spent in port during the last year and time in Pacific South American ports and negatively related to time in high latitudes and sailing speed. Although we found some live, established invaders such asGammarus tigrinusandDreissena rostriformis bugensis, only one individual of a freshwater NIS (Alexandrovia onegensis, Oligochaeta) not yet reported in the Great Lakes was detected. The animal’s poor condition and seemingly low population abundance indicated the risk of live introduction by this vector was likely quite low. Main conclusion Our results indicate that hull fouling appears to pose a low risk of introductions of new species capable of surviving in the Great Lakes, unlike foreign‐sourced freshwater ballast water that historically was discharged by these transoceanic vessels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".