Ship Biofouling as a Vector for Non-indigenous Aquatic Species to Canadian Arctic Coastal Ecosystems: A Survey and Modeling-Based Assessment
Bibliographic record
Abstract
Ship biofouling is a major vector for the introduction and spread of harmful marine species globally; however, its importance in Arctic coastal ecosystems is understudied. The objective of this study was to provide insight regarding the extent of biofouling (i.e., percent cover, abundance, and species richness) on commercial ships operating in the Canadian Arctic. A questionnaire was used to collect information on travel history, antifouling practices, and self-reported estimates of biofouling extent from ships operating in the region during 2015–2016. Twenty-five percent of ships operating in the region during the study period completed the questionnaire (n = 50). Regression trees were developed to infer the percent cover of biofouling, total abundance of fouling invertebrates, and fouling species richness on respondent ships based on previous underwater wetted surface assessments of commercial ships in Canada. Age of antifouling coating system was the only significant predictor of percent cover and total abundance of biofouling invertebrates, while the number of biogeographic realms previously visited and port residence time were significant predictors for fouling species richness. Comparison of relevant travel history features reported through the questionnaire to the regression tree models revealed that 41.9% of 43 respondent ships had antifouling coating systems older than 630 days and are therefore inferred to have relatively high (> 9.3%) biofouling percent cover. More than half of respondent ships (62.8%) had antifouling coating systems older than 354 days and are therefore inferred to have a relatively high total abundance (over 6,500 individuals) of fouling invertebrates. Nearly half of respondent ships (45.9% of 37 ships) had visited at least three biogeographic realms during their last 10 ports-of-call and are therefore inferred to have relatively high fouling species richness (mean 42 taxa). Self-reported estimates of biofouling cover were unreliable, being much lower than model inferences. Although the regression tree models have relatively low predictive power, explaining only 15–33% of the variance in biofouling extent, this study indicates that commercial ships are an active pathway for the transportation of non-indigenous aquatic species to Canadian Arctic coastal ecosystems via biofouling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".