Where do we draw the line? A simulation approach for evaluating management of marine fish stocks with isolation-by-distance stock structure
Bibliographic record
Abstract
There is no established management protocol for stocks subject to isolation-by-distance (IBD) stock structure. This study examines several management strategies for two marine fish species subject to IBD using simulation: Pacific cod (Gadus macrocephalus) in the Aleutian Islands (AI) and northern rockfish (Sebastes polyspinis) in the Eastern Bering Sea (EBS) and AI. A one-dimensional stepping stone model was used to model IBD and was intended to mimic regions where marine species are exploited along a continental shelf. The performance of spatial assessment and management methods depended on how the range was split. Splitting anywhere within the managed area led to fewer demes falling below target and threshold biomass levels and higher yield than managing the entire area as a single unit. Equilibrium yield was maximized when each deme was assessed and managed separately and under catch cascading, in which harvest quotas within a management unit are spatially allocated based upon the distribution of survey biomass. The longer-lived rockfish declined more slowly than Pacific cod and experienced greater depletion in biomass under disproportionate fishing effort because of lower productivity. Overall, splitting a management area of the size simulated in the model improved performance measures, and the optimal management strategy grouped management units by demes with similar relative fishing effort.
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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 | 0.007 | 0.029 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".