Advice in spite of great uncertainty: assessing and addressing bycatch of small fishes with limited data using Stellifer illecebrosus as a case study
Bibliographic record
Abstract
ABSTRACT Tropical shrimp trawlers mostly catch small fish species as bycatch, and commitments to ecosystem‐based management and food supply demand adjustment of such bycatch to sustainable levels. Pragmatic ways to assess and address the impact of nonselective fishing practices on small fishes need to be found, especially given the limited knowledge of their life histories, population dynamics, and ecology. The use of matrix models for understanding and mitigating such impacts is explored using a small fish,Stellifer illecebrosus(silver stardrum) in the southern Gulf of California, Mexico, as a case study. A deterministic matrix model, populated with vital rates generated through six months of fisheries‐dependent sampling, was used to estimate the rate of population growth for the species, conduct elasticity analyses to see which vital rates have greatest relative effects on population growth, test the sensitivity of model outcomes to uncertainties in life history parameters, mortality rates and the number of age classes modelled, and explore potential mitigation tools. Despite great uncertainty regarding the impact of industrial shrimp trawling onS. illecebrosus, principally due to uncertainty in mortality rates but also in other life history parameters, the matrix model was still useful in indicating that any precautionary management should focus on increasing the survival of younger age classes. This could be achieved with trawl closures where smaller fish live. The study demonstrates the importance of understanding age‐based changes in vital rates to the predictions of matrix models; estimated population growth rates were most uncertain when all mature individuals were assumed to contribute equally to recruitment. But nevertheless, the study supports claims that deterministic matrix models may be useful for conservation and management where life history parameters can only be estimated crudely. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".