Drifting Fish Aggregating Devices in the Indian Ocean: Impacts, Management, and Policy Implications
Bibliographic record
Abstract
Abstract The Indian Ocean has seen a significant increase in drifting fish aggregating devices (dFADs) used in purse seine fisheries, resulting in an exponential rise in tropical tuna catches. However, the negative impacts such as catches of juvenile tunas, increase in catches of non-targeted species, ghost fishing, and abandoned and lost fishing gear remain a significant concern of developing coastal States. The study examines the abundance and ecosystem consequences of abandoned, lost, and discarded fishing gear (ALDFG) dFADs in the Indian Ocean, focusing on their impact on the marine ecosystem, risks to marine ecosystems and the legality of these unauthorized ALDFG dFADs posing IUU fishing on the Somali coast. The study also critically evaluates the effectiveness of existing regulatory frameworks and governance mechanisms in addressing these issues. Investigating the prevalence of ALDFG dFADs in Somalia's waters, the paper underscores the failure of current Indian Ocean Tuna Commission (IOTC) dFAD management and governance frameworks to mitigate these impacts effectively. Over a six-month period, 63 dFADs were opportunistically recovered along the sample coastline, projecting an annual influx of approximately 160 dFADs, not one was fully compliant with IOTC regulations. The research further calculated a proportional number of dFADs per km per annum over the entire Somali shelf, estimating a total of approximately 1,439 dFADs recovered annually. The study's findings reveal explicit non-compliance with existing regulations, emphasizing the urgent need for enhanced monitoring, regulatory measures, and international cooperation to address the challenges posed by dFADs to marine ecosystems and the livelihoods of coastal communities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".