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Introduction

In fisheries management, the selection of appropriate indicators or model-based trends for use in Harvest Control Rules (HCR) is vital. These indicators should accurately classify the stock’s status and identify trends in stock status. This vignette discusses choosing and fine-tuning indicators for HCRs, focusing on Receiver Operator Characteristics (ROC) analysis.

The Need for Effective Indicators

For HCRs, indicators or model-based trends must correctly classify a stock as overfished or experiencing overfishing. They should also identify trends in stock status. We follow a structured approach to assess these criteria.

The Process

  1. Operating Model Without Feedback: Start by running an Operating Model without feedback, incorporating critical life history information. This model reflects the fishery system’s dynamic nature, including fishing mortality factors.
  2. Simulating an Index: An empirical index, created using an observation error model, gauges the stock’s abundance as a measurable proxy.
  3. Receiver Operator Characteristics (ROC): ROC analysis evaluates the empirical indicators’ effectiveness in classifying stock status and detecting trends.

The process involves using an operating model with life history information, generating an empirical index of abundance, and fine-tuning selected indicators for HCRs using ROC analysis. This structured approach ensures effective decision-making tools for stock management. The steps when conducting a Management Strategy are i) Identify and prioritise objectives, and trade-offs ii) Selection of Hypotheses for Conditioning the Operating Model iii) Conditioning the Operating Models based on data and knowledge iv) Identifying candidate management strategies v) Running the Management Procedure as a feedback controller to simulate the long-term impact of management; and then vi) Identifying the Management Procedures that robustly meet management objectives

Jump to Packages

Jump to Objectives Jump to Scenarios Jump to Conditioning the Operating Model Jump to Generating Data Jump to Management Procedure Jump to Running Jump to Selection Jump to More Information Jump to References

Objectives

Scenarios

Conditioning the Operating Model

Generating Data

Management Procedure

Running

Selection

More Information

References

Simulation

A detailed discussion of the simulation process and the associated R packages follows.

Empirical Rules

Explanation of X-Over-Y Rule and Linear Trend in the context of fisheries management.

Operating Model Conditioning

Details on using life history parameters to condition a FLBRP equilibrium object.

Classification Skill & Receiver Operating Characteristics

Discussion on the use of ROC for evaluating classification skill and reference levels.

More Information

Guidance on submitting bug reports, pull requests, and additional resources for FLife and FLR Project.

Software Versions and Author Information

Details about software versions used and author contact information.

Acknowledgements and References

Appreciation for contributions and a comprehensive list of references.

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