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MSE Performance Metrics and Tentative Results Summary
Joint Technical CommitteeNorthwest Fisheries Science Center, NOAA
Pacific Biological Station, DFOSchool of Resource and Environmental Management, SFU
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Outline
• Review of MSE• Graphics of preliminary results
– Omniscient case– Annual case– Biennial case
• Key performance statistics– discussion
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Objectives of the MSE
• Use the 2012 base case as the operating model.
• As defined in May 2120– Evaluate the performance of the harvest control
rule– Evaluate the performance of annual, relative to
biennial survey frequency.
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Organization of MSE Simulations
Operating Model* Stock dynamics* Fishery dynamics* True population
Management Strategy* Data choices* Stock Assessment* Harvest control rule
CatchData
Performance Statistics* Conservation
objectives* Yield objectives* Stability objectives
Feedback
Loop
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1960 1970 1980 1990 2000 2010 2020 2030
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0.5
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2.0
2.5
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Year
SS
Bt
Existing (2012) assessment MSE Simulations
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Animation
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Performance Measures• Choose metrics that capture the tradeoffs between conservation,
variability in catch and total yield for specific time periods.• Define short, medium and long time periods as Short=2013-2015,
Medium=2016-2020, Long=2021-2030.• The main conservation metric is the proportion of years depletion
is below 10%• The main variability in catch metric is the Average Annual
Variability in catch for a given time period.• For yield we used the median average catch• We’ve chosen what we think are the top six. We’d like to discuss
if others are needed.
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Average Annual Variability in Catch (illustration)
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Medians vs Means
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Perfect Information Case
• We created a reference, perfect information case where we simulated data with no error
• The purpose of the perfect information case was to provide:– Separate observation vs process error i.e. variable
data don’t affect management procedure performance
– a reference to compare the annual/biennial survey cases to.
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Perfect information (con’t)
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Annual Survey Case
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Biennial Survey Case
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Summary