Modeling co-occurring species: a simulation study on the effects of spatial scale for setting management targets
Bibliographic record
Abstract
Many species of marine fish are found in similar habitats and display similar vulnerability to fishing pressure, although their sustainable exploitation rates may differ considerably. Managing and setting harvest limits is challenging for such co-occurring species because the management targets for the less productive species may affect the fishing opportunities for the more productive species. We used simulation modeling to explore the effects of setting multi-species management targets and harvest regulations at local area or coast-wide levels. Setting management targets over the entire coast, and identifying optimal harvest rates within each local area, consistently led to the same or higher yields than setting management targets for each area. Essentially, the global conservation goal can be achieved by protecting areas in which the less productive species is abundant and by taking most of the harvest from other areas. The increases in yield do not increase the coast-wide probabilities of either species being overfished or severely depleted, but do increase these probabilities at the local area level for the less productive species. These results are magnified with increased spatial variation in the ratio of abundance and differences in intrinsic rates of growth among the fished species.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".