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
100
200
300
400
500
600
20
00
20
01
20
02
20
03
20
04
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15
00
0' T
ON
NES
Skipjack in region 4
Dom
PS_UNA
PS_A
PL
PS_PHID
0
50
100
150
200
250
300
20
00
20
01
20
02
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00
0't
on
nes
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