Effects of Turtle Excluder Devices (TEDs) on the Bycatch of Three Small Coastal Sharks in the Gulf of Mexico Penaeid Shrimp Fishery
Bibliographic record
Abstract
Abstract The stock of blacknose sharks Carcharhinus acronotus in the U.S. South Atlantic and the Gulf of Mexico is overfished, and according to the 2007 stock assessment conducted by the National Marine Fisheries Service overfishing continues to occur. Penaeid shrimp trawl bycatch rates in the Gulf of Mexico were modeled for this species as well as for the Atlantic sharpnose shark Rhizoprionodon terraenovae and bonnethead shark Sphyrna tiburo using a combination of research trawl and observer data. Research trawls have never used turtle excluder devices (TEDs), which are expected to exclude larger specimens of blacknose sharks. Most of the observer data that contain blacknose shark occurrences were collected during the pre-TED era when the two data sets tracked one another. Minimum observer data were available for the post-TED period (1990–present). As a consequence, the pre-TED (1972–1989) relationship between observer and research trawl catch per unit effort (CPUE) is driving the observer CPUE estimates from 1990 to the present, a period characterized by increased blacknose shark abundance. We suspected that the increase in predicted observer CPUE in the post-TED era is an artifact of application of the pre-TED observer and research trawl relationship to the post-TED era. This suspicion led us to question whether the bycatch of these species was altered due to the use of TEDs. We used negative binomial regression in a before-after-control-impact setting to test the effects of TEDs on the bycatch rates of these small coastal sharks. The TED effect was found to substantially reduce the bycatch of blacknose sharks (by 94%) and to do so moderately for bonnethead sharks (31%); the results were inconclusive for Atlantic sharpnose sharks. The management implication of our findings is that the existing small coastal shark–penaeid shrimp fishery bycatch model needs to be modified or replaced with a model that explicitly incorporates the potential for a TED effect. Received March 23, 2011; accepted December 15, 2011
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".