The utility of genetics in marine fisheries management: a simulation study based on Pacific cod off Alaska
Bibliographic record
Abstract
Information on genetic population structure has been documented in many marine fish species, but it is not always incorporated into management plans. This study examines how conservation status and yield change when management units are established using genetic data versus treating the entire area as a single management unit. Simulations use a spatially structured, individual-based model that combines multilocus microsatellite genotypes and a traditional fish population dynamics model that establishes abundance-at-age by cohort. Results are considered in terms of marine fish species in general, and parameters in the model are based on Pacific cod (Gadus macrocephalus) in the Bering Sea and Aleutian Islands region of Alaska. Population dynamics are projected under several management strategies, some of which establish management units based on the results of genetic testing and some that do not. Simulations incorporate annual stock assessments and fishing for 100 years. Results show that managed fishing can result in a reduction in stock sizes below target reference points when distinct populations are not managed based on the results of genetic testing. However, stock size is maintained at target levels and catches may increase when stocks identified using genetics are managed separately, even given error rates inherent to genetic testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".