Exploring potential establishment of marine rafting species after transoceanic long-distance dispersal
Bibliographic record
Abstract
Aim On 11 March 2011, the Great East Japan Earthquake triggered a massive tsunami that resulted in the largest known rafting event in recorded history. By spring 2012, marine debris began washing ashore along the Pacific coast of the United States and Canada with a wide range of Asian coastal species attached. We used this unique dataset, where the source region, date of dislodgment and landing location are known, to assess the potential for species invasions by transoceanic rafting on marine debris. Location Northeast Pacific from 20 to 60°N. Time period Current. Major taxa studied Forty-eight invertebrate and algal species recorded on Japanese tsunami marine debris (JTMD). Methods We developed maximum entropy (MaxEnt) species distribution models for 48 species recorded on JTMD to predict establishment potential along the Pacific coast from 20 to 60°N. Models were compared within the context of historical marine introductions from Japan to this region to validate the emergence of marine debris as a novel vector for species transfer. Results Overall, 27% (13 species) landed with debris at locations with suitable environmental conditions for establishment and survival, indicating that these species may be able to establish new populations or introduce greater genetic diversity to already established non-native populations. A further 21 species have an environmental match to areas where tsunami debris likely landed, but was not extensively sampled. Nearly 100 Japanese marine species previously invaded the northeastern Pacific, demonstrating this region s environmental suitability for rafting Japanese biota. Historical invasions from Japan are highest in California and largely known from bays and harbours. Main conclusions Marine debris is a novel and growing vector for non-native species introduction. By utilizing a unique dataset of JTMD species, our predictive models show capacity for new transoceanic invasions and can focus monitoring priorities to detect successful long-distance dispersal across the world s oceans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".