Vessel Strikes of Large Whales in the Eastern Tropical Pacific: A Case Study of Regional Underreporting
Bibliographic record
Abstract
Vessel strike is recognized as a major modern threat to the recovery of large whale populations globally, but the issue is notoriously difficult to assess. Vessel strikes by large ships frequently go unnoticed, and those involving smaller vessels are rarely reported. Interpreting global patterns of vessel strikes is further hindered by underlying reporting biases caused by differences in countries’ research efforts, legislation, reporting structures and enforcement. This leaves global strike data “patchy” and typically scarce outside of developed countries, where resources are more limited. To explore this we investigated vessel strikes with large whales in the Eastern Tropical Pacific (ETP), a coastal region of ten developing countries where heavy shipping and high cetacean densities overlap. Although this is characteristic of vessel strike “hotspots” worldwide, only 11 ETP strike reports from just four countries (∼2% of total reports) existed in the International Whaling Commission’s Global Ship Strike Database (2010). This contrasts greatly with abundant reports from the neighboring state of California (United States), and the greater United States/Canadian west coast, making it a compelling case study for investigating underreporting. By reviewing online media databases and articles, peer review publications and requesting information from government agencies, scientists, and tourism companies, we compiled a regional ETP vessel strike database. We found over three times as many strike reports (n= 40), from twice as many countries (n= 8), identifying the geographic extent and severity of the threat, although likely still underestimating the true number of strikes. Reports were found from 1905 until 2017, showing that strikes are a regional, historic, and present threat to large whales. The humpback whale (Megaptera novaeangliae) was the most commonly hit species, and whale-watch industries involving small vessels in areas of high whale densities were recognized as a conservation and management concern. Industrial fishing fleets and shipping were suggested to be underrepresented sectors in the database, and are likely high-risk vessels for strikes with whales. We demonstrate the implications of known vessel strike reporting biases and conclude a more rapid assessment of global vessel strikes would substantially benefit from prioritized research efforts in developing regions, with known vessel strike “hotspot” characteristics, but few strike reports.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".