Trends in the exploitation of South Atlantic shark populations
Bibliographic record
Abstract
Approximately 25% of globally reported shark catches occur in Atlantic pelagic longline fisheries. Strong declines in shark populations have been detected in the North Atlantic, whereas in the South Atlantic the situation is less clear, although fishing effort has been increasing in this region since the late 1970s. We synthesized information on shark catch rates (based on 871,177 sharks caught on 86,492 longline sets) for the major species caught by multiple fleets in the South Atlantic between 1979 and 2011. We complied records from fishing logbooks of fishing companies, fishers, and onboard observers that were supplied to Brazilian institutions. By using exploratory data analysis and literature sources, we identified 3 phases of exploitation in these data (Supporting Information). From 1979 to 1997 (phase A), 5 fleets (40 vessels) fished mainly for tunas. From 1998 to 2008 (phase B), 20 fleets (100 vessels) fished for tunas, swordfishes, and sharks. From 2008 to 2011 (phase C), 3 fleets (30 vessels) fished for multiple species, but restrictive measures were implemented. We used generalized linear models to standardize catch rates and identify trends in each of these phases. Shark catch rates increased from 1979 to 1997, when fishing effort was low, decreased from 1998 to 2008, when fishing effort increased substantially, and remained stable or increased from 2008 to 2011, when fishing effort was again low. Our results indicate that most shark populations affected by longlines in the South Atlantic are currently depleted, but these populations may recover if fishing effort is reduced accordingly. In this context, it is problematic that comprehensive data collection, monitoring, and management of these fisheries ceased after 2012. Concurrently with the fact that Brazil is newly identified by FAO among the largest (and in fastest expansion) shark sub-products consumer market worldwide.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".