Risks of ignoring fish population spatial structure in fisheries management
Bibliographic record
Abstract
Ignorance of spatial structures in fisheries management may lead to unexpected risks of overexploitation. Based on the information about small yellow croaker ( Larimichthys polyactis ) off the coast of China, we simulated a fish population consisting of three subpopulations mixing at intermediate levels, which was considered in the “true” spatial structure of the population in this study. Three scenarios of population spatial structure were assumed in assessing and managing this simulated fishery: (i) metapopulation, which has the same structure as the “true” population; (ii) three independent subpopulations, which overlook the exchanges among the subpopulations; and (iii) unit population, which completely ignores the population spatial structure. Corresponding approaches were applied to assess and manage each of these assumed fish populations. The management time period was assumed to be 10 years with two harvesting levels (i.e., maximum sustainable yield (MSY) and f0.1). Assessing and managing the metapopulation as several independent populations could lead to a high probability of overexploitation. Managing the metapopulation as a unit population could lead to local depletion. Use of MSY as a management target may be risk prone in the existence of a metapopulation, and use of a fishing mortality lower than f0.1as a management target is more desirable.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".