Method evaluation and risk assessment: A framework for evaluating management strategies for data‐limited fisheries
Bibliographic record
Abstract
Abstract Fisheries managers are in need of quantitative tools to inform decisions regarding selection of robust management practices, prioritising research gaps and stocks to focus on, particularly where there are limited resources or data. To support these decisions, the use of Management Strategy Evaluation (MSE), that is, closed loop simulation‐testing of management procedures, is widely regarded as best practice. However, applying MSE is time‐ and computationally intensive, and requires highly skilled expertise and processes for stakeholder input and peer review. For data‐ and capacity‐limited fisheries, MSE may be particularly challenging to implement. Yet, these are the contexts where it is most critical to test assumptions, evaluate the implications of all sources of uncertainty and identify the most informative data sources. To facilitate wider use of MSE, the Method Evaluation and Risk Assessment (MERA) framework was developed as an accessible online interface, with quick processing time, focused on generic data‐limited management procedures, but allowing progression to tailored and more data‐rich methods. The framework links a quantitative questionnaire and data input standard to a flexible operating model with optional customisation via command line access to the back‐end open‐source R libraries. Here, we illustrate a case study application of MERA for the bocinegro (Pagrus pagrus, Sparidae) fishery in the Gulf of Cadiz, where in conjunction with fishery stakeholders, a custom management procedure was developed and tested and key research gaps and data collection priorities were identified. We discuss implications for wider use of MSE in various contexts, including eco‐certification and fishery improvement projects.
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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 | 0.333 | 0.349 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.025 | 0.008 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".