Age Composition, Growth, and Density-Dependent Mortality in Juvenile Red Snapper Estimated from Observer Data from the Gulf of Mexico Penaeid Shrimp Fishery
Bibliographic record
Abstract
Abstract Beginning in May 1998, the National Marine Fisheries Service has required that bycatch reduction devices be installed in penaeid shrimp trawl gear in the Gulf of Mexico. Changes in observer protocols were introduced, one of which was that all of the red snapper Lutjanus campechanus collected would be enumerated and measured when possible. This change has yielded catch and length information from the Gulf of Mexico shrimp fishery. To date, however, an integrated approach to determining the age composition, growth, and mortality of juvenile red snapper has not been attempted. To address this problem, we constructed a length-based, age-structured model to objectively estimate the growth and mortality parameters and age composition of the shrimp trawl bycatch of red snapper in the western Gulf of Mexico from 81 monthly length frequency data sets (a total of 239,521 fish were measured) from July 1999 to February 2007. Our modeling strategy was to make simple and straightforward representations of the deviations in growth from the mean trend, partial recruitment, density-dependent mortality, and population dynamics. Bayesian parameter estimation was accomplished by calculating the mode of the posterior distribution. Important findings of this paper include (1) the shrimp trawl bycatch during the first and third trimesters is dominated (86–87%) by age-0 fish, (2) the growth patterns of age-0 and age-1 red snapper suggest that the fish form an opaque annulus in winter months, (3) the natural mortality of age-0 fish appears to be about double (a minimum 70% increase) the value used in the last stock assessment, and (4) the evidence for density-dependent juvenile mortality is overwhelming. Inclusion of these findings in the red snapper stock assessment has the potential to substantially alter management practices.
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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 | 0.001 | 0.000 |
| 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".