Simulation study located in sim_new
folder with R code sim_SS.R
.
- Life history scenarios (e.g.
short_slow
) - Fishing mortality scenario (e.g
F1
) - Recruitment variability scenario (e.g.
LowSigmaR
) - Files used to generate data in folder
files
- Simulation replicate, or iteration (e.g.
1
) - Output files for generated population -- true values found in
om
- True population values in
om/Report.sso
- Length composition in
om/data.ss_new
- Sample individuals per year to generate length composition
perfect.ss
usedSS_splitdat
to find perfect information on the length compositionss3.dat
sampled N number of individuals from the perfect length composition using multinomial- Number of years of length data for estimation models -- all use
ss3.dat
but will adjust the number of years of length to include (e.g.L100
uses 100 years,L75
uses 75 years,L1
uses last year only) - Recruitment estimation scenarios, with all files for model run within.
Includes some helper functions.
Operating model runs initial life history, F, and recruitment scenario files without hessian to generate true population.
- Four life history scenarios
- short_slow = shorter-lived (max age = 30 years), slower-to-Linf (expected to live at asymptotic length for final 10% of life)
- short_fast = shorter-lived, faster-to-Linf (expected to live at asymptotic length for final 50% of life)
- long_slow = longer-lived (max age = 60 years), slower-to-Linf
- long_fast = longer-lived, faster-to-Linf
- Each with variable M and k but sharing linf = 55 cm, length at 50% selectivity = 36.3 cm, h = 0.7, t0 = -1
- One fishing mortality time series, representative of U.S. West coast nearshore stocks.
- Two recruitment scenarios:
LowSigmaR
sigmaR = 0.4 andHighSigmaR
sigmaR = 0.8 (also explored deterministic) - 100 simulation replicates of each life history, F, and recruitment scenario.
- Used to create the "true population", with values in
Report.sso
and information on length structure indata.ss_new
Data generation -- multinomial to sample from length structure
- For each life history scenario and simulation replicate, generate length data from each year (e.g.
perfect
,N000
,N50
): - perfect information with 1000 length samples
- 100 samples (more representative)
- Changing from 100 to 50 samples over the data series
- Then choose the number of years to include in the model:
- All 100 years of length data with perfect information
- Final 75 years, 20 years, 10 years, and 1 year, all subset from the same sampling procedure so that the length data is the same, only the number of years varies.
Estimation model
- Stock Synthesis (and will be tested with LIME)
- Recruitment estimation scenarios:
- Unadjusted - using the default values for bias ramp from
ss3sim
- Bias adjusted - using
SS_fitbiasramp
to use estimated bias ramp parameters - No estimation - do no estimate recruitment deviates.
Performance
- Bias (median relative error) and precision (median absolute relative error)
- Interval coverage (proportion of iterations where true value lies within 50% confidence intervals; nominal coverage would be equal to 50%. Scenarios greater than 50% tend to over-estimate uncertainty, while scenarios less than 50% tend to under-estimate uncertainty.)