1. Introduction to Stock Synthesis

2. Outline Websites Why we need a general model AD Model Builder
Stock Synthesis Specifications
Using Stock Synthesis

3. Websites AD Model Builder information
Stock Synthesis II/III information
Stock assessment course
All can be accessed from here

4. Why we need a new general model
Too many populations to assesses
Not enough qualified analysts
Common language
Current models are reaching their limitations
Fit to data

5. Common language Facilitates discussions Easier to review
use of SS2 in west coast STAR panel process and Pacific cod assessment
Comprehensive analysis and testing to develop best practices
Focuses development
Reduces duplication

6. Advantages of a general model
Less development time
Tested code
Familiarity
Diagnostics and output

7. AD Model Builder Tool for developing nonlinear models
Efficient estimation of model parameters
C++ libraries
Template

8. Simplifying the development of models
Removes the need to manage the interface between the model parameters and function minimizer.
The template makes it easy to input and output data from the model, set up the parameters to estimate, and set up objective function to optimize (minimize).
adding additional estimable parameters or converting fixed parameters into estimable parameters is a simple process.
ADMB is also very flexible as model code is in C++
Experienced C++ programmers to create their own libraries

9. Efficient and stable function minimizer
Analytical derivatives
Adjoint code
Chain rule
More efficient and stable than other packages that use finite difference approximation.
Stepwise process to sequentially estimate the parameters
Bounds on all estimated parameters that restrict the range of possible parameter values.

10. Other features MCMC for Bayesian integration
Automated profile likelihoods
Random effects

11. What its good for: Highly parameterize nonlinear models
Large data sets
Hundreds of thousands of data points
Complex models
Thousands of parameters
Numerous optimizations of the objective function
Combining many data sets or analyses
General Models

12. Richard D. Methot NOAA Fisheries Seattle, WA
Stock Synthesis
Richard D. Methot
NOAA Fisheries
Seattle, WA

13. What is SS2/SS3
A general statistical age-structured model programmed in AD Model Builder
Includes many types of data
Includes prior information
MLE or Bayesian context
Calculates uncertainty
Performs forward projections and MSY calculations

14. Main specifications One or two sexes
One or more areas (movement and tagging data in SS3)
One or more seasons per year
Growth morphs
Environmental covariates for parameters
Popes approximation or Baranov catch equation

15. Initial conditions: (see spread sheet)

16. Initial conditions: two approaches
Fit to initial catch
Set initial equal to the catch in the first few years or to a guess of the catch in the years preceding the start of the model
Reduce parameters
Rather than use one parameter for each age
Don’t fit to initial catch
Use one fishery that catches small fish and one that catches large fish
Often the two approaches give the same results after the first few years

17. Recruitment S-R relationship: Beverton-Holt Ricker
Environmental index: h’ is environmental index linkage parameter
Vy is value of environmental index

18. Dynamics Seasons, proportion natural mortality by length of season
Length based and age based selectivities
Retention curves to model discards

19. Natural mortality (has been modified to include more options)

20. Growth (has been modified to include more options)
Von Bertalanffy
Variation of length-at-age is normally distributed

21. Selectivity Many different functional forms Some non/semi parametric
Double normal most commonly used
See excel spread sheet

22. Data types Abundance index Catch-at-length Catch-at-age
Aging error
Mean length-at-age
Mean body weight
Discards
Now tagging

23. Index of abundance Allows for a nonlinear (power) relationship
Log-normal likelihood
Can’t estimate standard deviation

24. Composition data (C@L and C@A)
Uses the multinomial distribution
Can be compressed at the tails
Can include aging error
Can be one sex, combined sex, or both sexes

25. Priors – normal (or beta see page 34)

26. Using SS2

27. Main files Starter file (starter.ss) Data file (user defined name)
Names of data and control files
Options for running the model
Data file (user defined name)
Dimensions (years, ages, fisheries, areas, seasons,…)
Data (catch, discards, indices, environmental indices, ….)
Control file (user defined name)
Parameter definitions
Likelihood control
Forecast file (forecast.ss)
Forecast definitions (years, harvest rates, MSY calculations, …)

28. User interface
See website

29. Results Excel workbook (R code also available, what is available for SS3)
Provides results
Graphs
Diagnostics
Management quantities and Projections

30. 14 values for parameter estimation control
Smaller parameter definitions
Exponential offsets
Parameter modifications
Temporal deviates
Environmental variables
Time blocks
Growth morphs

31. Parameter estimation controls
Determines
Which parameters are estimated
Bounds and priors
Temporal variation
Covariates

34. Exponential offsets
Many of the parameters are based on exponential offsets from other parameters
P = Base*exp(offset)
if offset = 0 they are the same
P can’t go negative (assuming Base is not negative)

35. Temporal deviates Like annual recruitment deviates
Log-normal with penalty applied

36. Environmental variables
Pt = base*exp(β*Xt)

37. Time blocks
Sets groups of years that have the same values for a parameter
The ways of defining it
0: base * exp(blockparm)
1: base + blockparm
2: blockparm

38. Show EPO BET files

39. Initial conditions: data file
#_init_equil_catch_for_each_fishery

40. Initial conditions: Control file
#_Spawner-Recruitment …
# SR_sigmaR
# SR_R1_offset
1977 # first year of main recr_devs; early devs can preceed this era
#_initial_F_parms
#_LO HI INIT PRIOR PR_type SD PHASE
# InitF_1_Jan-May_Trawl_Fishery_
1 #_init_equ_catch lambda
Note new controls
in SS3
(more options)
Note new controls
in SS3
(turn on/off defaults)

41. Recruitment controls (see advanced options in SS3)
#_Spawner-Recruitment
1 #_SR_function
#_LO HI INIT PRIOR PR_type SD PHASE
# SR_R0
# SR_steep
# SR_sigmaR
# SR_envlink
# SR_R1_offset
# SR_autocorr
1 #_SR_env_link
2 #_SR_env_target_0=none;1=devs;_2=R0;_3=steepness
1 #do_recdev: 0=none; 1=devvector; 2=simple deviations
1977 # first year of main recr_devs; early devs can preceed this era
2006 # last year of main recr_devs; forecast devs start in following year
2 #_recdev phase