Performance of management procedures for British Columbia Pacific Herring (Clupea pallasii) in the presence of model uncertainty : closing the gap between precautionary fisheries theory and practice
Bibliographic record
Abstract
The method of setting catch limits for Pacific Herring (Clupea pallasii) fisheries in British Columbia (BC) is similar to precautionary harvest policies found elsewhere in the world; however, 3 out of 5 herring fisheries have been closed in most years since 2006 due to persistent low spawning abundances and low productivity. Although the mechanisms underlying declines of these herring stocks remain unknown, temporal variation in natural mortality and stock assessment over-estimation of abundance are potential factors involved in these outcomes. We used closed-loop simulations to evaluate management procedure (MP) performance for West Coast Vancouver Island (WCVI) and Strait of Georgia (SOG) herring fisheries given uncertainties about past and future herring natural mortality and stock assessment estimation errors. This work represents the first phase of management strategy evaluation under Pacific Herring Renewal, where emphasis is on evaluating current MPs and modifications to these MPs, and not on identifying or selecting the most acceptable MP. We develop three operating models representing hypotheses for how stock-specific natural mortality changes over time. The first model (constant-M) assumes that natural mortality has remained constant over the 1951-2017 period, while the alternative model (time-varying-M) allows natural mortality to vary over that time. The time-varying-M operating model is further divided into two models for projecting future patterns in natural mortality. A density-independent-M model assumes that future natural mortality rates will fluctuate randomly around the recent 10-year average, while a density-dependent-M model allows random pulses of high natural mortality when spawning biomass is low. Increasing natural mortality rates are of concern and are relevant given increasing predator biomass. We simulated performance of nine feedback harvest control rules (HCRs) given by combinations of maximum harvest rate (20% vs 10%), HCR form (i.e., hockey-stick vs. minimum escapement), operational control points defining biomass cutoffs (25%, 30%, and 50% of B0) and thresholds below which harvest rates are reduced (none vs. 60% of B0), and absolute catch caps (0 vs 2,000 t for WCVI and 0 vs. 30,000 t for SOG). For WCVI, results show that the current MP would fail to meet spawning biomass objectives under most operating models. Reducing the maximum harvest rate from 20% to 10% and capping fishery quotas at a maximum 2,000 t would reduce the effective harvest rate and protect against over-estimates of abundance when they occur, thus providing acceptable performance against biomass objectives for two of three operating models. For SOG herring, the current MP was robust across almost all scenarios and objectives we examined (thus a more restrictive cap was not explored). For both WCVI and SOG herring, the maximum target harvest rate was the most important harvest control rule element controlling management performance compared to the shape and/or operational control points in harvest control rules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".