Using reproductive values to define optimal harvesting for multisite density-dependent populations: example with a marine reserve
Bibliographic record
Abstract
A new method for determining optimal harvest from age-structured populations with a density-dependent stock-recruit relationship is presented. The theoretical optimal harvest comes from removing the age-class with the smallest ratio of reproductive value to weight. The method is derived from considering the sensitivity of equilibrium egg production to harvest using results for density-dependent Leslie matrices. The method holds in both single- and multi-site contexts and is derived for both Ricker and BevertonHolt recruitment functions. I illustrate the method for a one-site model of Arcto-Norwegian cod (Gadus morhua) and obtain the same optimal strategy as previous methods, namely that age-class 6 should be harvested 45%. Including age-specific selectivities, the best constrained yields occur at a harvest rate of 11% on ages 512. This yield is 73% of the theoretical optimum. I considered the same model when a reserve is established and found that high transfer rates out of the reserve (where spawners attain a higher fecundity) produced greater yields that were 86% of the one-site (no reserve) yield. Also, if overfishing occurs at 1.5 and 2.0 times the optimal level in the one-site case, then most yields from the reserve model are greater than those from the one-site model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".