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

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Year

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Existing (2012) assessment MSE Simulations

Animation

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.

Average Annual Variability in Catch (illustration)

Medians vs Means

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.

Perfect information (con’t)

Annual Survey Case

Biennial Survey Case

Summary