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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
Outline
• Review of MSE• Graphics of preliminary results
– Omniscient case– Annual case– Biennial case
• Key performance statistics– discussion
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.
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
1960 1970 1980 1990 2000 2010 2020 2030
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Year
SS
Bt
Existing (2012) assessment MSE Simulations
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.
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.