Effects of cryptic mortality and the hidden costs of using length limits in fishery management
Bibliographic record
Abstract
Abstract Fishery collapses cause substantial economic and ecological harm, but common management actions often fail to prevent overfishing. Minimum length limits are perhaps the most common fishing regulation used in both commercial and recreational fisheries, but their conservation benefits can be influenced by discard mortality of fish caught and released below the legal length. We constructed a computer model to evaluate how discard mortality could influence the conservation utility of minimum length regulations. We evaluated policy performance across two disparate fish life‐history types: short‐lived high‐productivity (SLHP) and long‐lived low‐productivity (LLLP) species. For the life‐history types, fishing mortality rates and minimum length limits that we examined, length limits alone generally failed to achieve sustainability when discard mortality rate exceeded about 0.2 for SLHP species and 0.05 for LLLP species. At these levels of discard mortality, reductions in overall fishing mortality (e.g. lower fishing effort) were required to prevent recruitment overfishing if fishing mortality was high. Similarly, relatively low discard mortality rates (>0.05) rendered maximum yield unobtainable and caused a substantial shift in the shape of the yield response surfaces. An analysis of fishery efficiency showed that length limits caused the simulated fisheries to be much less efficient, potentially exposing the target species and ecosystem to increased negative effects of the fishing process. Our findings suggest that for overexploited fisheries with moderate‐to‐high discard mortality rates, reductions in fishing mortality will be required to meet management goals. Resource managers should carefully consider impacts of cryptic mortality sources (e.g. discard mortality) on fishery sustainability, especially in recreational fisheries where release rates are high and effort is increasing in many areas of the world.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".