Progress of Operating Model development and preliminary MSE results for SKJ & YFTEriko Hoshino, Richard Hillary, Campbell Davies, Craig Proctor Stakeholder Workshop, Bogor, 22-23 November, 2018

• Operating Models (OMs): provide a mathematical representation of the “true” system

• More than single OM to cover range of uncertainty

What is an Operating model?

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Biological Fishery

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Case study area

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Indonesian AW WCPO statistical regions

• Used information from the WCPO regional stock assessment outputs and Indonesian port-based monitoring data.

• Single region: 4 (skipjack), 7 (yellowfin) with no migration in/out the areas.

• Harvest strategies applied only to Indonesian fisheries.

• Catch/effort levels of other fishing nations/miscellaneous gear fisheries assumed constant (past 5 year average).

• Our ability to “observe” fish abundance from fishery monitoring data is assumed reasonably accurate.

• Made number of other assumptions

Key assumptions in prototype OMs

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SKJ and YFT catch estimates (000’t)

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0

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0' T

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Skipjack in region 4

Dom

PS_UNA

PS_A

PL

PS_PHID

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Yellowfin in region 7

Dom

LL

PS_UNA

PS_A

PL

PS_PHID

HL_PHID

Source: Adapted from MULTIFAN-CL output files

Current WCPFC advice on stock status - SKJ

Region 4

SSB2015/SBBF=0

= 0.25

Overall

SSB2015/SBBF=0

= 0.58

Current WCPFC advice on stock status - YFT|

Region 7 SSB2015/SBBF=0

= 0.27

OverallSSB2015/SBBF=0

= 0.33

• Disaggregated catch and effort data, size distribution in catch provide us important information about the stock

• CPUE –key indicator of stock abundance if we can remove the other factors that influence CPUE (standardization) • Skipper

• Location

• Time/month/season

• FAD use

• Unit of effort

• Size distribution in catch

Key fishery data to be monitored

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Skipjack preliminary HS

AnalysisTrend in mean size, standardized CPUE

HCRs2 example of

empirical HCRs

Management measure• Effort Limit (same rules

applied to ID PL & PS)• no changes - misc. gears

Implementation(TBD)

Monitoring1. PL catch & Effort2. Size dist of PL catch

Snapshot of performance statistics (SKJ)

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1

HSnames

Scenarios Performance statistics

Meeting target?

Risk tostock ?

Ave.catch% C1

C1 Status quo (maintain E/C2010-2015) 100

HS1 Adjust effort moderately based on mean length index

96

HS2 Adjust effort aggressively based on mean length index

85

HS3 Adjust effort moderately based on CPUE index

110

HS4 Adjust effort aggressively based on CPUE index

99

Effort trajectory for HS3 vs HS4 for SKJ

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2

HS3

HS4

Yellowfin preliminary HS

AnalysisTrend in mean size, standardized CPUE

HCRs2 example of

empirical HCRs

Management measure• Effort Limit (same rules

applied to HL/LL/PL/PS)• no changes - misc. gears

Implementation(TBD)

Monitoring1. HL catch & Effort2. Size dist of Catch

Snapshot of performance statistics (YFT)

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4

HS names

Scenarios Performance statistics

Meeting target?

Risk tostock ?

Ave. catch % C1

C1 Status quo (maintain E/C2010-2015) 100

HS1 Adjust effort moderately based on mean length index

90

HS2 Adjust effort aggressively based on mean length index

79

HS3 Adjust effort moderately based on CPUE index

75

HS4 Adjust effort aggressively based on CPUE index

58

Future work

• Extend the OMs to include highly plausible and important hypothesis and uncertainties.

• How do we change effort/catch? Applied to industrial fleet only? Applied equally to all gears?

• What management measures to be used in reality? How to implement them and to ensure the compliance?

• Inclusion of social and economic data