Hull fouling as an invasion vector: can simple models explain a complex problem?
Bibliographic record
Abstract
Summary 1. The most effective way to manage nonindigenous species and their impacts is to prevent their introduction via vector regulation. While ships’ ballast water is very well studied and this vector is actively managed, hull fouling has received far less attention and regulations are only now being considered despite its importance for introductions to coastal, marine systems. 2. We conducted comprehensive in situ sampling and video recording of hulls of 40 transoceanic vessels to assess propagule and colonization pressure in Vancouver and Halifax, dominant coastal ports in Canada. Concomitant sampling was conducted of harbour fouling communities to compare hull and port communities as part of a vector risk assessment. 3. Although this vector has been operational for a long time, hull and harbour communities were highly divergent, with mean Sørensen’s similarity values of 0·03 in Halifax and 0·01 in Vancouver, suggesting invasion risk is high. Propagule pressure (up to 600 000 ind. ship−1) and colonization pressure (up to 156 species ship−1) were high and varied significantly between ports, with Vancouver receiving much higher abundances and diversity of potential invaders. The higher risk of fouling introductions in Vancouver is consistent with historical patterns of successful hull fouling invasions. 4. The extent of hull fouling was modelled using ship history predictors. Propagule pressure increased with time spent in previous ports‐of‐call and time since last application of antifouling paint, whereas colonization pressure increased with time since last painting and with the number of regions visited by the ship. Both propagule and colonization pressure were negatively related to the time spent at sea and the latitude of ports visited. 5. Synthesis and applications. A major challenge for applied invasion ecology is the effective management of introduction vectors. We found that hull fouling has a strong potential for introduction of many species to coastal marine habitats and that management should be considered. Simple variables related to the vessels’ hull husbandry, voyage, and sailing patterns may be used to predict and manage hull fouling intensity. The results presented here should interest policy makers and environmental managers who seek to reduce invasion risk, and ship owners seeking to optimize fuel efficiency.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".