Tangled and drowned: a global review of penguin bycatch in fisheries
Bibliographic record
Abstract
Penguins are the most threatened group of seabirds after albatrosses. Although penguins are regularly captured in fishing gear, the threat to penguins as a group has not yet been assessed. We reviewed both published and grey literature to identify the fishing gear types that penguins are most frequently recorded in, the most impacted species and, for these susceptible species, the relative importance of bycatch compared to other threats. While quantitative estimates of overall bycatch levels are difficult to obtain, this review highlights that, of the world's 18 species of penguins, 14 have been recorded as bycatch in fishing gear and that gillnets, and to a lesser extent trawls, are the gear types that pose the greatest threats to penguins. Bycatch is currently of greatest concern for yellow-eyed Megadyptes antipodes (Endangered), Humboldt Spheniscus humboldti (Vulnerable) and Magellanic Spheniscus magellanicus penguins (Near Threatened). Penguins face many threats; reducing bycatch mortality in fishing gear will greatly enhance the resilience of penguin populations to threats from habitat loss and climate change that are more difficult to address in the short term. Additional data are required to quantify the true extent of penguin bycatch, particularly for the most susceptible species. In the meantime, it is crucially important to manage the fisheries operating within known penguin foraging areas to reduce the risks to this already threatened group of seabirds.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".