Evaluating the Sustainability of a Cisco Fishery in Thunder Bay, Ontario, under Alternative Harvest Policies
Bibliographic record
Abstract
Abstract Sustainable management of fish stocks is promoted through the application of management strategy evaluations (MSEs), providing information to managers on the relative performance of alternative management approaches (strategies) while accounting for uncertainty. In this study, we developed a simplified MSE of a stock of Cisco Coregonus artedi in Thunder Bay, Ontario, to determine both the sustainability of the current harvest control rule (i.e., a constant exploitation rate [U] of 10%) and the performance of alternative harvest control rules in meeting fishery objectives. Success in meeting fishery objectives was evaluated through attained yields, interannual variation in yields, magnitude of spawning stock biomass (SB), and the risk of reaching low SB—performance metrics established based on consultation with an advisory group to Lake Superior fishery managers. Our simulations explicitly accounted for uncertainty in the frequency of strong year-classes being produced by Cisco, the stock–recruit relationship, stock abundance, and the sex-specific nature of roe harvest. Assuming that future productivity is similar to the productivity observed over the period from 1985 to 2015, results suggest that the current U of 10% is sustainable in terms of maintaining SB above 20% of the unfished level. Variants of constant U control rules that included thresholds defining when U is to decrease as a function of SB increased yield, decreased risk, and increased the magnitude of SB at the end of the simulation period. However, these advantages came at the expense of greater interannual variation in yield. Constant catch control rules greatly underperformed constant U control rules in terms of magnitude in yield; however, they did reduce interannual variation in yield compared to constant U control rules. Furthermore, conditional versions of constant catch control rules (i.e., threshold stock sizes below which the catch limit was reduced) mitigated risks of staying at low stock size.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".