Towards standardization of MSE algorithms

Is there a minimum MSE ?

Ernesto Jardim*Colin Millar

Iago MosqueiraFinlay ScottChato Osio

JRC Unit of Maritime Affairs

Fishreg* ernesto.jardim@jrc.ec.europa.eu

Why ?

➔ Growing demand to set up long term management plans (LTMP) for commercial stocks.

➔ Growing number of data moderate stocks that require LTMP type of analysis.

➔ Management Strategy Evaluation (MSE) is a powerful tool to test how uncertainty, decision making and implementation of management actions impact LTMP objectives.

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However ...

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MSEs are extremely complex and require:

➔ Long time to develop

➔ Human resources with strong statistical and coding background

Furthermore, MSE results are difficult to communicate

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The Joint Research Center “assessment for all” initiative (a4a) started in 2012, with the aim of:

➔ Develop, test and distribute the necessary methods to assess a large numbers of stocks in an operational time frame.

➔ Build the necessary capacity on stock assessment and advice provision.

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➔ Core team: group of fisheries scientists from the EC JRC responsible for development.

➔ Members: small but global network of scientists from different fisheries research and advice institutions.

➔ Supporting group: network of scientists

➔ Stock assessment: [N], [F]➔ Short term forecast: [Ny+] | [F], [R]➔ Medium term forecast: [Ny++] | [F], [S/R], error(s)

➔ MSE: [Ny++] | [F], [S/R], error(s) +

decision making, implementation error, obs. error

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From stock assessment to MSE

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MSE

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Standardization

1. Identify the most important processes within the MSE framework e.g. OM (partially done).

2. Implement methods in R/FLR to deal with uncertainty in each process in a transparent way (partially done).

3. Identify a limited number of models for each process based on groups of species or sea basins (to be done).

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Components of a standard MSE

➔ Operating modelnatural mortality, growth, S/R, exploitation pattern

➔ Management procedureindicator to inform HCR, HCR shape

➔ Observation error modelerror in catch, error in the abundance index

➔ Implementation error modelerror in effective F, overcatch

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Example standard management procedure

➔ Three HCR➔ Model-free (no assessment)➔ Relative (based on relative stock status)➔ Absolute (ICES MSY rule)

➔ Four indicators➔ Commercial catch➔ Survey biomass➔ Biomass dynamic SSB/Bmsy & F/Fmsy➔ SC@A SSB & F

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(loosely based on S.aurita in Northwest Africa)

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Disadvantages

Wrapping up

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➔ Important advantages in making MSE widely used:➔ Engage stakeholders in designing management

plans/actions.➔ Makes available sophisticated algorithms.➔ Improves awareness of uncertainty and impacts

on management goals.

➔ Standard MSE for moderate data stocks has to include the most important processes.