Transcript PowerPoint

An exploration of alternative methods
to deal with time-varying selectivity in
the stock assessment of YFT in the
eastern Pacific Ocean
Alexandre Aires-da-Silva and Mark Maunder
CAPAM – Selectivity Workshop
La Jolla, USA, 11-14 March, 2013
Outline
• Background on YFT assessment

Stock Synthesis (SS3) model

Selectivity issues: time-varying process

Retrospective pattern in recent recruitments
• Explore SS3 approaches to deal with timevarying selectivity

Ignore time-varying selectivity (base case model)

Full time-varying selectivity (deviates)

Time-varying for terminal years only
YFT fishery definitions
Baitboat
Unassociated
Longline
40
40
40
30
30
30
20
20
20
10
10
0
0
11
5
10
12
10
10
0
10
10
6
20
20
20
30
30
30
40
40
150
140
130
120
110
100
90
80
70
40
150
140
130
120
110
100
90
80
Floating Objects
40
30
30
20
20
4, 16
7
10
2, 14
0
140
130
120
80
70
8
0
3, 15
10
150
Dolphin
40
10
70
9
10
1, 13
20
20
30
30
40
40
150
140
130
120
110
100
90
80
70
150
140
130
120
110
100
90
110
100
90
80
70
YFT Stock Synthesis model
•
•
•
Quarterly time-step model
Fishery definitions: 16 fisheries
Data weighting: the CV of the southern LL fishery
was fixed (0.2), others estimated (NOA, DEL)
Growth modeling: Richards curve, L2 and variance
of length-at-age are fixed
•
•
Modeling of catchability and selectivity:



Catchability coefficients for 5 CPUE time series are estimated
(NOA-N, NOA-S, DEL-N, DEL-I, LL-S)
Size-based selectivity curves for 11 of the 16 fisheries are
estimated (fit to size composition data)
Logistic selectivity for LL-S and DEL-S, and dome-shape for
other fisheries
YFT size selectivity
OBJ time-varying selectivity?
F1-OBJ_S
F2-OBJ_C
F3-OBJ_I
F4-OBJ_N
OBJ LF residual pattern
F1-OBJ_S
F2-OBJ_C
F3-OBJ_I
F4-OBJ_N
Retrospective pattern
Projections
CATCHES
SPAWNING BIOMASS
Purse seine
Longline
Numerical and convergence issues
•
Unstable selectivites (OBJ)



Sensitive to initial parameter values and phases
Long run times (> 4 hours)
Issues inverting hessian matrix (steepness run)
Objectives of study
• Test approaches available in SS to deal timevarying selectivity

Improve selectivity process (time-varying)

Minimize retrospective pattern

Shortcoming: more parameters, longer run times
• Simplify model

Less data, collapse fisheries (OBJ)
• Some considerations

We assume that retrospective pattern is mainly driven by
model misfit to recent OBJ LF data caused by misspecified
selectivity

We recognize that other sources of bias and
misspecifcation may exist
A single “lumped” OBJ fishery
F1-OBJ_S
F2-OBJ_C
F3-OBJ_I
F4-OBJ_N
Model 0: Constant selectivity
• Selectivity: Estimate “average” constant selectivity
• Data: Fit to OBJ length-frequency data for all historic
period
• Base case model configuration
1.2
1.0
Selectivity
0.8
OBJ-F1 - SAC3
OBJ-F2 - SAC3
0.6
OBJ-F3 - SAC3
OBJ-F4 - SAC3
0.4
OBJ - lumped
0.2
0.0
0
25
50
75
100
125
Length (cm)
150
175
200
Model 0: Constant selectivity
Model 1 - Full time-varying selectivity
• Selectivity: Quarterly time-varying selectivity
• Estimate quarterly deviates on base selex parameters
of double normal OBJ selectivity curve
• Data: Fit to OBJ LF data for all historic period
• SD of quarterly deviates need to be defined:

First run: freely estimate devs with high flexibility (SD=1)

Second run: Use SD of estimated devs from first run in penalized
likelihood approach
Model 1 - Full time-varying selectivity
OBJ Fishery
1.2
1.0
Selectivity
0.8
0.6
0.4
0.2
0.0
0
25
50
75
100
125
150
175
200
Length (cm)
Paramter
P1 - peak
P2 - top
P3 - ascending
P4 - descending
M1-P2fixed
0.13
fixed at -15
0.55
1.03
M1-P2est
0.14
1.08
0.51
0.41
Model 1 - Full time-varying selectivity
Model 1 - Full time-varying selectivity
Model 1 - Full time-varying selectivity
Constant selectivity model 0
Time-variant model (M1-P2fix)
Model 1 - Full time-varying selectivity
400,000
Mod 0 - cons. Selex (BC)
300,000
Mod 1 - Full tvar selex
250,000
200,000
150,000
100,000
50,000
0
2006
2007
2008
2009
2010
0.40
2011
2012
2013
2014
2015
Year
0.35
Spawning biomass ratio
Recruitment (x1000 fish)
350,000
0.30
0.25
0.20
0.15
Mod 0 - cons. Selex (BC)
0.10
Mod 1 - Full tvar selex
0.05
0.00
2006
2007
2008
2009
2010
2011
Year
2012
2013
2014
2015
2016
Model 2 – “hybrid” approach
• Recent period is the most influential on management
quantities (recent recruitments, Fs)
• Time-varying selectivity process in recent period only
• Estimate quarterly deviates on base selex parameters
of double normal OBJ selectivity curve
• Fit to OBJ LF data for recent period only

3 terminal years (3-year average used for management quantities)

5 terminal periods (a longer period)
• As for early period, fix to “average” constant
selectivity from terminal years (base parameters)
Model 2 – “hybrid” approach
Tvar selex- 3 years
Tvar selex - 5 years
Model 2 – “hybrid” approach
Tvar selex- 3 years
Tvar selex - 5 years
Model 2 – “hybrid” approach
350,000
Mod 0 - cons. Selex (BC)
Mod 1 - Full tvar selex
250,000
Mod 2 - Hybrid 3 yrs
200,000
Mod 2 - Hybrid 5 yrs
150,000
100,000
50,000
0
2006
2007
2008
2009
2010
2011
2012
2013
2014
Year
0.40
0.35
Spawning biomass ratio
Recruitment (x1000 fish)
300,000
0.30
0.25
0.20
Mod 0 - cons. Selex (BC)
0.15
Mod 1 - Full tvar selex
0.10
Mod 2 - Hybrid 3 yrs
0.05
Mod 2 - Hybrid 5 yrs
0.00
2006
2007
2008
2009
2010
2011
Year
2012
2013
2014
2015
2016
2015
Conclusions
•
Allowing for OBJ time-varying selectivity helped to minimize
retrospective pattern in recent YFT recruitment estimates
•
Balance between the amount of selectivity process (# of
params.) needed in the model and the OBJ LF data to include
in model fit (whole series or few recent years only?)
•
Allowing for time-varying selectivity (quarterly deviates) in
terminal years of the assessment only while fitting to LF data
for this period seems a reasonable compromise
•
An “average” constant selectivity curve is applied to the early
period while not fitting to the LF data for that period
•
A simulation study is needed to more rigorously investigate
selectivity issues and associated bias in the
YFT assessment
QUESTIONS?
Total catches
500 000
450 000
LL
400 000
LP
350 000
DEL
NOA
Catch (t)
300 000
OBJ
250 000
200 000
150 000
100 000
50 000
0
1975
1980
1985
1990
1995
Year
2000
2005
2010
Models
Fix selectivity
• Assume “average” stationary OBJ selectivity
• “Drop” (not fit) all OBJ LF data
• Fix to base selectivity parameters estimated in full
time-varying runs (models 1)
Models
Fix selectivity
M2-P2fixed
M2-P2est
Models
Fix selectivity
M2-P2fixed
M2-P2est
Recruitment – all models
700,000
SAC3
600,000
M1-P2fix
Recrutiment (x 1000 fish)
M2-P2fix
500,000
M3-P2fix_3YRS
M3-Pfix_5YRS
400,000
M4-Pfix_Tblocks_5YRS
300,000
200,000
100,000
0
1975
1980
1985
1990
1995
Year
2000
2005
2010
2015
SBR – all models
0.8
SAC3
0.7
M1-P2fix
M2-P2fix
0.6
0.5
M3-P2fix_3YRS
M3-Pfix_5YRS
SBR
M4-Pfix_Tblocks_5YRS
0.4
0.3
0.2
0.1
0
1970
1980
1990
2000
Year
2010
2020
Model type 1
a) MODELS 0 and 1
Model 0
Fit to OBJ LF
Base sel params
Devs
MANAG QUANT
msy
Bmsy
Smsy
Bmsy/Bzero
Smsy/Szero
Crecent/msy
Brecent/Bmsy
Srecent/Smsy
Fmultiplier
SAC3
Yes
Estimated
No
262,642
356,682
3,334
0.31
0.26
0.79
1.00
1.00
1.15
Yes, all period
Estimated
No
262,852
348,836
3,208
0.31
0.25
0.78
1.04
1.07
1.20
MODEL 1 CONFIGURATION
M1-P2fixed
M1-P2est
Yes, all period
Yes, all period
Estimated
Estimated
Yes, all qrts
Yes, all qrts
255,597
353,123
3,304
0.31
0.25
0.81
0.87
0.90
1.07
260,027
348,560
3,203
0.30
0.25
0.79
0.91
0.91
1.05
Model type 3
c) MODELS 3
Fit to OBJ LF
Base sel params
Devs
MANAG QUANT
msy
Bmsy
Smsy
Bmsy/Bzero
Smsy/Szero
Crecent/msy
Brecent/Bmsy
Srecent/Smsy
Fmultiplier
MODEL 3 CONFIGURATION
M3-P2fixed-3yrs M3-P2fixed-5yrs
Yes, last 3 yrs
Yes, last 5 yrs
Estimated
Estimated
Yes, last 3 yrs
Yes, last 5 yrs
261,728
350,789
3,278
0.32
0.26
0.79
0.99
0.99
1.14
257,126
351,377
3,273
0.31
0.25
0.8
0.84
0.86
1.03