200 
Fishery Bulletin 109(2) 
Table 1 
Biological, fishery, and modeling parameters used in the simulation model to evaluate interactions between mortality and selec- 
tivity. See Appendix for equations, definitions of parameters, and symbols. Parameters are the same for both sexes. True values 
are the base parameter values used in the simulation models. Lower and upper bounds are boundary limits used in the stock 
assessment program. NA=not applicable. 
Parameter 
Symbol 
True value 
Estimated in 
assessment model 
Lower and 
upper bounds 
Unit and note 
Minimum age 
a min 
0 
No 
NA 
Year 
Maximum age 
a max 
30 
No 
NA 
Age plus group 
Virgin recruitment 
R 0 
10 
Yes 
0.1, 30 
Log scale 
Recruitment steepness 
h 
0.6 
Yes 
0.2, 1.0 
Annual recruit deviation 
R \ 
0 
Yes 
-5, 5 
Log scale, 76 years 
Growth 
K 
0.14 
No 
NA 
Per year 
Growth 
50.54 
No 
NA 
cm 
Growth 
*0 
-2.68 
No 
NA 
Year 
Length-weight 
h 
5.45e-6 
No 
NA 
Length-weight 
h 
3.2878 
No 
NA 
Kg/cm 
Natural mortality 
M 
0.15 
Yes or no 
0.01, 1 
Per year, varied, see text 
Logistic selectivity 
'll 
8 
Yes 
0, 50 
50% selectivity at age 8 
Logistic selectivity 
42 
5 
Yes 
0, 50 
Width for 95% selection 
Double normal selectivity 
'li 
13 
Yes 
-507, 533 
See Appendix 
Double normal selectivity 
n 2 
-2 
Yes 
-82, 80 
See Appendix 
Double normal selectivity 
n 3 
3.5 
Yes 
-136, 143 
See Appendix 
Double normal selectivity 
n 4 
2.6 
Yes 
-101,106 
See Appendix 
Double normal selectivity 
n 5 
-5 
Yes 
-205, 195 
See Appendix 
Double normal selectivity 
46 
0.65 
Yes 
-25, 26 
See Appendix 
Catchability — survey of juveniles 
<h 
0 
Yes 
-5, 5 
Log scale 
Catchability — survey of adults 
<?2 
0 
Yes 
-5, 5 
Log scale 
Recruitment variability 
°R 
0.6 
No 
NA 
Catch variability 
0.05 
No 
NA 
Variability — survey of adults 
a M 
0.25 
No 
NA 
Variability — survey of adults 
°),2 
0.25 
No 
NA 
Annual age sample size 
n 
500 
No 
NA 
Stock assessment model 
The simulation data were fitted to the stock assess- 
ment model by using stock synthesis (SS3, vers. 3.04b) 
software (Methot, 2009a, 2009b). Other than patterns 
in natural mortality and selectivity, a correct popula- 
tion structure was assumed in the assessment model, 
and likewise for the growth, fecundity, and the length- 
weight relationship. There were three ways in which 
natural mortality (M) was treated in the assessment 
models. First, M was assumed to be constant and was 
fixed at the same value of M=0.15/yr as in the simula- 
tion model (Fig. 1; Table 2, runs 1-12). Second, a single 
M was estimated (runs 13 and 15). Third, four values of 
M were estimated (runs 14 and 16). In the third case, 
we used the breakpoint method in the SS3 program, 
and the four breakpoints (M v M. ? , M 3 , and M 4 ) were 
defined for ages 2, 3, 24, and 25. In this case, was 
used for ages 0 to 2, M 2 was used for age 3, M 3 was 
used for age 24, M 4 for ages 25 to 30, and M values 
for ages between 4 and 23 were linearly interpolated 
between M 3 and M 4 . 
Fishery data from the simulation model consisted 
of annual catches, annual age composition data, and 
survey indices. Fishing mortality was estimated by us- 
ing the hybrid method in the SS3 program. The hybrid 
method in the SS3 program is a simplified parameter- 
ization method (see Methot, 2009a). Because of rela- 
tively small variations of catch data generated in the 
simulation models (coefficient of variation [CV] = 0.05), 
this method produces nearly identical fishing mortality 
estimates as in fully parameterized fishing mortality 
(see Methot, 2009a). Other estimated parameters in the 
assessment model included the stock-recruit relation- 
ship, selectivity, catchability coefficients, and annual 
recruitment deviations from the stock-recruit curve 
(Table 1). Initial values for all estimated parameters 
were set to be the same as those in the simulation 
models. Noninformative priors were used in parameter 
