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

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Presentation on theme: "Introduction to Stock Synthesis. Outline Websites Why we need a general model AD Model Builder Stock Synthesis Specifications Using Stock Synthesis."— Presentation transcript:

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 – taught.htm taught.htm 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 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 –Dont 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 Environmental index:h is environmental index linkage parameter V y is value of environmental index S-R relationship:Beverton-Holt Ricker

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 Cant 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 ( –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, C@A, C@L, environmental indices, ….) Control file (user defined name) –Parameter definitions –Likelihood control Forecast file ( –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 cant go negative (assuming Base is not negative)

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

36 Environmental variables P t = base*exp(β*X t )

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 4250000000000 #_init_equil_catch_for_each_fishery

40 Initial conditions: Control file #_Spawner-Recruitment … -99 99 0.65 0 0 0 -1 # SR_sigmaR … -5 5 -1.41416 0 -1 0 1 # 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 0 2 0.138176 0 -1 0 1 # 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 12 16 13.245 0 -1 0 1 # SR_R0 -99 99 1 0 0 0 -1 # SR_steep -99 99 0.65 0 0 0 -1 # SR_sigmaR -99 99 0 0 0 0 -1 # SR_envlink -5 5 -1.41416 0 -1 0 1 # SR_R1_offset -99 99 0 0 0 0 -1 # 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

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