Accounting for transient dynamics could improve the use of marine protected areas as a reference point for fisheries management
Bibliographic record
Abstract
Biological reference points for fishery management depend on estimates of current stock status relative to unfished biomass (depletion). The ratio of fish density outside to inside a marine reserve, the density ratio, could serve as a proxy for depletion for data-poor management. However, transient dynamics associated with time lags in returning to the unfished state following reserve implementation make that proxy inaccurate on short time scales. We assessed density ratio management rules using an age-structured, spatially explicit model of four US west coast nearshore fishes following reserve implementation, with scenarios encompassing sampling error, recruitment variability, and uncertainty in natural mortality. In deterministic simulations, management incorporating time lags generally resulted in a higher mean and lower variability in biomass over 20 years, but lower mean yield compared to management that did not. However, when stochastic recruitment was included, differences among simulations due to stochasticity were much greater than any difference in performance between management strategies. Nonetheless, in certain cases, accounting for time lags could help avoid unwarranted increases in harvest effort after reserve implementation.
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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 | 0.004 | 0.017 |
| 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 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".