Designing marine reserves to reduce bycatch of mobile species: a case study using juvenile red snapper (Lutjanus campechanus)
Bibliographic record
Abstract
Marine reserves have not been widely used to conserve mobile species because species abundance levels can be highly variable over space and time. Here we explore the potential for marine reserves to reduce bycatch of mobile species using red snapper ( Lutjanus campechanus ) as a case study. Bycatch in Gulf of Mexico shrimp trawls is a major source of juvenile red snapper mortality, and marine reserves may be mandated if bycatch reduction targets are not met. Using geographic information system (GIS) analyses of fishery-independent data, we investigated whether red snapper juveniles concentrate in “hot spots” and examined the trade-offs between abundance within hot spots (intensity) and predictability over time (persistence). These trade-offs allow fishery managers to tailor marine reserves to meet specific conservation goals. For red snapper, hot spots were primarily located around the 30 m isobath, with hot spots spread along the Texas coast in fall and clustered around the Texas–Louisiana border in summer. Increased intensity of hot spots led to lower persistence due to the smaller spatial area of higher intensity hot spots. Hot spots moved annually but generally persisted in the same locations over time, indicating that marine reserves could reduce red snapper bycatch. This approach provides a foundation for making informed decisions about design and placement of reserves for mobile species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".