Policy Evaluation II (Feedback strategies) Lecture 8

Why Evaluate Feedback Strategies? The strategies we examined in lecture 6 (constant catch, constant effort, etc.) were based on calculations that assumed that the strategies would be applied for the next 20 years irrespective of any data that might be collected. This isn’t very realistic.

The Management “Cycle” The Fishery (actual removals) Data Collection Management Decisions Data Analysis We wish to capture this entire system within our decision analysis.

Evaluating a Feedback Approach Operating Model The Fishery (actual removals) Data Generation Management Strategy Management Model Assessment Model Management Strategy = “Decision rule”, “Management Procedure”, “Operational Management Procedure”, etc.

The operating model The management strategy The fishery Assessment data Stock assessment The stocks Model outputs: abundance, MSY The catch control law The catch limit The environment

The Operating Model Includes the biology of the system, the fishery, and how any data are generated. Represents the “real world” for the analyses Several operating models need to be considered: each is an alternative state of nature. Several types of uncertainties (model, process, estimation, etc.) can be represented as states of nature.

A Management Strategy Specifies: which data are to be collected; how those data are to be analyzed (incl. CPUE standardization; population modelling…); and how the results of the analysis of the data determine management measures.

Example 1 – Cape Hake (The Management Strategy) Data collection: Catches and catch-rates. Data analysis: Fit a biomass dynamics model to estimate the time-trajectory of biomass. Management decision: Quota is r/2 multiplied by current biomass. The management strategy is trying to mimic a fixed proportion strategy, except that the estimates of biomass are obtained using a method of stock assessment (instead of being assumed to be known exactly).

Example 1 – Cape Hake (Equations for the Management Strategy)

Example 1 – Cape Hake (Generating Future CPUE Data) Future catch-rate data are assumed to be log-normally distributed, i.e.:

Example 1 – Cape Hake (Computational Aspects) A separate program has been written to fit the biomass dynamics model and determine the quota. For each future year, the projection software: creates an input file for the assessment program; calls the program; and reads the results back into the projection program.

Example 1 – Cape Hake (Results) q=0.15 113 75 0.37 0.01 q=0.25 74 0.40 C=113 97 0.17 Comparing the results for different values of  permits an evaluation of the value of collecting additional data. Comparing the results for constant catch against those for the feedback strategy allows the value of being reactive to data to be evaluated.

Example 1 – Cape Hake (Issues Arising-I) This example considered several sources of uncertainty: Model uncertainty: The structure of the biomass dynamics model differs from that of the (age-structured) operating model. Process uncertainty: Recruitment in the operating model is log-normal. Observation uncertainty: The data used by the management strategy are subject to (log-normal) observation error.

Example 1 – Cape Hake (Issues Arising-II) There are many ways to extend this analysis: Management strategy: Use additional data (e.g. survey estimates of biomass). Use a different rule for determining the quota based on the results of assessments (e.g. reduce quotas by the extent of uncertainty, i.e. ABCs). Operating model: Consider additional sources of uncertainty. Outputs: We could see how well the estimates of biomass from the biomass dynamics model correspond to the biomass in the operating model.

Example 2 (Anchovy and Sardine) Catches (by mass) off southern Africa are dominated by those of the pelagic species (anchovy, sardine and round herring). The pelagic fishery is affected substantially by technological interactions: Adult anchovy are targeted in January- March, but the fishery is primarily a recruit fishery. Adult sardine are targeted throughout the year. Mixed shoaling of juvenile anchovy and sardine means that anchovy fishing causes a by-catch of juvenile sardine. Fishing for round herring leads to a by-catch of adult sardine.

Example 2 (Overview) Sardine Fishery Anchovy Fishery Human Consumption Fish Meal

Example 2 (Biomass trends)

Example 2 (Structure of the analysis) The operating model: Includes two species (anchovy and sardine). Includes technical interactions between the two species (catching anchovy leads to catches of juvenile sardine). Includes process uncertainty in recruitment (and bycatch rates). Generates survey indices of abundance for the two species. The management strategies: Include separate rules for the two species, but makes allowance for technical interactions. Modify catch limits during the year based on the results of surveys and the amount of bycatch.

Example 2 (Management Objectives) 1. Maintain biomass of stocks at biologically “safe” levels 2. Minimise year to year variability in TACs 3. Allow for growth in pilchard biomass (recovery) 4. Within the constraints of 1. to 3., maximise average annual TAC 5. In order to achieve 4., and complying with 2. and 3., provide adequate bycatch TAC of juvenile sardine

Example 2 (Performance measures) Risk-related performance measures: Anchovy – no more than an 30% probability of dropping below 15% of the virgin biomass over the next 20 years. Sardine - no more than an 10% probability of dropping below 20% of the virgin biomass over the next 20 years. Reward-related performance measure: Average annual catch of sardine and anchovy.

Example 2 (Results) Sardine only fishery So what is this? Anchovy only fishery

Example 2 (Impact of additional data collection)

Example 3 (Northern Prawn Fishery) Value ~ A$150 million Over 17.5 million pounds landed. Premium product (Banana, Tiger, Endeavour prawns) Over 90% exported 500 species of bycatch Australia

Example 3 (The operating model) Uncertainties: “Effort creep” Implementation error Dealing with banana prawns Effort allocation Recruitment variation 2 tiger species 5 stocks of each species

Example 3 (Modeling the Management Controls) Allow for Implementation Error Advice on effort and season length for tiger prawns Split effort to week Generate a banana prawn season Split weekly effort to week & area Allocate weekly effort by area to species

Example 3 (Implementation Error) This is an “input control” fishery so the actual effort in the fishery (fishing days) may differ from the number of days of fishing expected by the managers. There is also a 1 in 3 chance that scientific management advice will not be followed at all.

Example 3 (Management Strategies-I) Three assessment types: Biomass dynamics model. Empirical. Age-structured model. Various control rules: Conservative Aggressive Spawning stock in current year Intended target effort 1.2Smsy Emsy Smsy 1.0Smsy

Example 3 (Management Strategies-II) The season (the weeks that the fishery is open for) depends on how depleted the stocks are assessed to be.

Example 3 (Strategies and Operating Models)

Example 4 (Multispecies considerations)

Management Model Structures and Management Systems Age-; sex- Cull scenarios Age-; sex- structured Hake Management Procedure Fixed F Multi-species models are being developed to examine the implications of changes in the biomass of top predators on system behavior (including fishery catches). This particular chart shows the structure of a model used to examine the implications of culling Cape fur seals on the catches of Cape hake off South Africa. Species-; age- structured Delay-difference

Developing a Management Strategy (A Final Overview) Agree on Objectives Implement Strategy No Yes Agree? Select Performance Measures No Develop Testing Framework Discuss Results Test a Range of Management Strategies

References (some) Butterworth. 2007. ICES J. mar. Sci. 64: 613-617. Butterworth & Punt. 1999. ICES J. mar. Sci. 56: 985-998. De Oliveira & Butterworth. 2004. ICES J. mar. Sci. 61: 1432-1442. Dichmont et al. 2006. Fish. Res. 82: 204-245. Dichmont et al. 2008. Fish. Res. 94: 238-250. Dichmont et al. 2013. J. Appl. Ecol. 50: 1060-1070. Punt & Butterworth. 1995. S. Afr. J. Mar. Sci. 15: 255-285. Punt & Donovan. 2007. ICES J. mar. Sci. 64: 603-612. Punt et al. 2016. Fish and Fish. 17: 303-334.