Spatially explicit risk assessment of marine megafauna vulnerability to Indian Ocean tuna fisheries
Bibliographic record
Abstract
Abstract By‐catch is the most significant direct threat marine megafauna face at the global scale. However, the magnitude and spatial patterns of megafauna by‐catch are still poorly understood, especially in regions with very limited monitoring and expanding fisheries. The Indian Ocean is a globally important region for megafauna biodiversity and for tuna fisheries, but has limited by‐catch data. Anecdotal and scattered information indicates high by‐catch could be a major threat. Here, we adapt a Productivity Susceptibility Analysis tool designed for data‐poor contexts to present the first spatially explicit estimates of by‐catch risk of sea turtles, elasmobranchs, and cetaceans in the three major tuna fishing gears (purse seines, longlines, and drift gill nets). Our assessment highlights a potential opportunity for multi‐taxa conservation benefits by concentrating management efforts in particular coastal regions. Most coastal waters in the northern Indian Ocean, including countries that have had a minimal engagement with regional management bodies, stand out as high risk for fisheries interactions. In addition to species known to occur in tuna gears, we find high vulnerability to multiple gear types for many poorly known elasmobranchs that do not fall under any existing conservation and management measures. Our results indicate that current by‐catch mitigation measures, which focus on safe‐release practices, are unlikely to adequately reduce the substantial cumulative fishing impacts on vulnerable species. Preventative solutions that reduce interactions with non‐target species (such as closed areas or seasons, or modifications to gear and fishing tactics) are crucial for alleviating risks to megafauna from fisheries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".