Influence of time-varying productivity on fishery reference points and implications for conservation objectives and management advice
Bibliographic record
Abstract
Providing science advice for fisheries management generally involves estimating reference points, commonly defined in terms of a proportion of the biomass at maximum sustainable yield or unfished biomass. These reference points assume a population in equilibrium, a premise frequently challenged by the time-varying productivity observed in many fish stocks. Reference points can serve as control points in harvest control rules (HCRs) and as indicators of stock status that can trigger a rebuilding plan. The guidance for addressing time-varying productivity varies among jurisdictions (e.g., using mean productivity over a time series or recent productivity only). Fisheries and Oceans Canada (DFO) has recently identified a need for further research on time-varying reference points before providing policy guidance for use in fisheries management. In this study, we describe how individual components of productivity influence reference points using three generalized fish life-histories. We also assess the impact of alternative approaches (i.e., static vs. time-varying) to defining reference points on implied stock status (using the DFO status categories of critical, cautious, and healthy) and management advice using reference points as control points in HCRs. Using a static limit reference point (LRP) to operationalize DFO’s objective to avoid serious harm to stock productivity, we evaluate the performance of various HCRs under time-varying productivity, with control points defined via different productivity scenarios. We identify an HCR with a static biomass lower control point and a dynamic fishing mortality upper control point that has relatively high yields while maintaining a high probability of keeping the stock above the LRP. This HCR performs well across both increasing and decreasing productivity scenarios. An HCR with control points based only on recent productivity performed well under decreasing productivity only when stock biomass didn’t fall far below the LRP. We show that perceived stock status can vary from critical to healthy in a given year, depending on choice of productivity period used to define stock status reference points, implying that careful selection of such reference points is needed. There can be risks to using policy default approaches based solely on recent productivity when productivity is decreasing over time.
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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 | 0.020 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".