Empirical Control Rule: Diver survey, catch, effort and length data

Implementation Review: Periodically assess stock relative to reference points; requires time series and priors

Currently data is limited but will get better, i.e. field sampling, tagging, lab based studies

Permits a formal treatment of all sources of uncertainty, such that objectives-based management decisions can be maintained within acceptable risk levels and deliver outcomes consistent with expectations (Roux et al. 2022).

Fisheries management needs to ensure that resources are exploited sustainably, and the risk of depletion is at an acceptable level. However, often uncertainty about resource dynamics exists, and data availability may differ substantially between fish stocks (Fischer, José, and Kell 2020)

Therefore a correct evaluation of this uncertainty may result in considerable and quantifiable biological, economical and social benefits.

• Risk equivalence means that management objectives should be met, despite their level of uncertainty, i.e.

  • Acceptable risk of not achieving targets and breaching limits
  • Priotisation of data (collection, scientific study and monitoring, control and surveillance) that may results in higher uncertenty reduction and higher quantifiable benefits.
  • Targeting investments on these prioritized data.
  • Utility, e.g. as used by Fischer, José, and Kell (2020), but can also include a happiness, …

• Predict and Act: Walters and Hilborn (1976) pointed out there was, and still is, a tendency to observe disturbed systems, synthesise elaborate models, and conduct optimisation exercises which pretend that our actions will not affect the way we observe and learn in the future.

• Learn and Adapt: In natural resource management the importance of adaptive management to reduce the risk of failing to achieve management objectives due to uncertainty has long been recognised (Walters and Hilborn 1978). Adaptive management learns by doing, so that policies evolve as new observations and information become available (Walters and Holling 1990)

Six steps

1. Problem Assessment
2. Design
3. Implementation
4. Monitoring
5. Evaluation, and
6. Adjustment.

Six steps

1. Identify and priorities objectives, and trade-offs
2. Selection of hypotheses for the Operating Models;
3. Conditioning the OMs based on data and knowledge
4. Identifying candidate management strategies
5. Running the Management Procedure as a feedback controller to simulate the long-term impact of management; and then
6. Identifying the Management Procedures that robustly meet management objectives

Adaptive management and MSE both consider feedback

MSE helps in the design of robust management strategies that can still meet ecological, social and economic objectives despite uncertainty (Sharma et al. 2020).

Once implemented an MP requires less effort than conducting a stock assessment each time advice is required. This should allow time to gain a better understanding of resource dynamics

After an MP has been implemented a review should be conducted to evaluate whether objectives have been achieved, has the MP performed as designed, and how can improvements be made.

• Problem Assessment: define objectives and potential data and knoweldge
• Design: and evaluate Management Procedures (MPs) with components representing the collection and analysis of data, assessment methods or empirical rules, and the feedback harvest control rules (HCRs) used to set catch limits.
  • Test uisng Operating Models that represent the main uncertainties about stock and fleet dynamics
• Implementation of the MP that best meets objectives

1. Develop Operating Model
2. Run OM without feedback to generate empirical indicators using Observation Error Model
3. ROC curves to identify best indicator
4. Evaluate MP based on best empirical indicator(s)
5. Implement Management
6. Perform implementation review, based on a stock assessment

Define objectives e.g.

• High continuing catches
• Low risk of stock collapse
• Low inter-annual variability in catches
• …

Condition Operating Model, for historical scenarios

• Reference Case
• Robustness Set

Run Operating Model for historical scenarios

• Generate empirical indicators

Use ROC curves as a filter for indicator & reference levels

Empirical MP with best indicators, e.g. set catches using a relative harvest rate

After implementation check have

• Have objectives been achieved?
• Were the OM dynamics plausible?

Adaptitive Management

• Monitoring
  
• Evaluation
  
• Adjustment

Review

• Historical status of stocks
• Current status relative to reference points
• Advice on response to management

Data

• Type
• Lengths of time series
• Quality

Time series, multiple runs

Knowledge, i.e. 

• quality of priors

• Sealifebase
• Stock specific
• Tagging studies
• Lab based experiments

Population Growth Rate \(r\), single assessment

0.6

0.3

Estimates of \(MSY\) reference points, single assessments

Prior CV=0.6

Estimates of \(MSY\) reference points, single assessments

Prior CV=0.3

1. Develop Operating Model
2. Run OM without feedback to generate empirical indicators using OEM,
3. ROC curves to identify best indicator
4. Run MSE to evaluate MP based on best empirical indicator
5. Implement Management
6. Perform implementation review, e.g. based on a stock assessment
7. Evaluate risk equivalence and value-of-information

• The aim of this approach is to implement Adaptive Management using Management Strategy Evaluation. An objective is to show how a correct evaluation of uncertainty may result in considerable and quantifiable biological, economical and social benefits applied in a our real world in constant and quick change.