robert.kope@noaa.gov 206-860-3374 Jim Scott (NMFS) james.b.scott@noaa.gov 360-783-5827 Ken Newman (UI) newman@uidaho.edu 208-885-6861 Jim Norris (UW) jnorris@olympus.net 360-385-4486 Dell Simmons (USFWS) dell_simmons@mail.fws.gov 260-695-7605 Jennifer Gutmann (UWIFC) jgutmann@nwifc.wa.gov 360-438-1180 Din Chen (CDFO) chend@pbs.dfo.ca 250-756-7341 Cara Campbell (NMFS) cara.campbell@noaa.gov 206-860-6780 Jim Anderson (UW) jim@fish.washington.edu 206-543-4772 Christine Muongchanh (UW) cket@fish.washington.edu 206-543-7848 Carrie Cook-Tabor (USFWS) cooktckc@dfw.wa.gov 360-902-2838 Norma Jean Sands (ADFG) normas@fishgame.state.ak.us 907-465-4254 Gary Morishima (QINC) morikog@aol.com 206-236-1406 David Caccia (UW) davec@oak.cbr.washington.edu Peter Lawson and Tom Wainwright joined the meeting via speaker phone from Newport, OR. 2. Update on code development (Jim Norris): Jim Norris began by describing the fundamental parameter specification dilemma: How to maintain complete parameter specification flexibility without restricting the process equations? For example, most existing models assume the natural mortality rate is age specific, but is constant for all years and stocks. In this case, the parameter is specified by a single age index--NatMortRt(age). It is conceivable that future models also may want the natural mortality rate to vary by year, time step, region, and cohort. To get this type of flexibility would require a five dimensional array -- NatMortRt(age,year,step,region,cohort). It is probably impossible to have arrays this big, so some sort of variable array specifications will be required. Jim gave a summary of the potential specification levels for different processes: Non-salmon processes (e.g., physical environment and non-salmon biological environments) may have parameters that vary by: -- year -- time step -- region Salmon processes (e.g., natural mortality, growth, spawning, migration) may have parameters that vary by: -- year -- time step -- region -- cohort Fishing processes (e.g., legal and incidental fishing mortalities) may have parameters that vary by: -- year -- time step -- region -- cohort -- fishery To fully specify all parameters with maximum flexibility using a traditional multidimensional array approach would require a huge number of parameters. Jim distributed a summary of a Generic Parameter System that Troy developed that allows complete flexibility in specifying parameters (a copy of the description is available at /harvest/gen_param.html). The goal of the system is to provide sparse arrays of arbitrary dimension and size at run time, without requiring changes in the underlying source code. The primary component of the system is a GenericParamArray object that holds pointers to either another GenericParamArray or a GenericParamFinal value. This provides for the arbitrary number of dimensions for each parameter. One requirement of this system is to have some dimensions specified at the model level: -- Number of geographic areas -- Number of species -- Number of stocks for each species -- Number of age classes for each species The following dimensions can be specified at the year level: -- Number of time steps for each year -- Number of cohorts for each stock for each year -- Number of fisheries for each year Jim Scott questioned why the number age classes was under the model level, and not the stock level. Jim explained it was necessary to specify the number of age classes at the highest possible level to make the GenericParamArrays work properly. Stocks that typically had fewer age classes (e.g, 4 instead of 6) would simply have zero abundance in the higher age classes. For example, the migration matrices for those stocks would migrate all the age 4 fish into the spawning grounds. Gary noted that the number of years was missing from the model level. Jim agreed. Carrie pointed out that fixing the number of geographical areas at the model level could cause a problem because the Proportional Migration (PM) model sometimes has different areas in different years and time steps. She noted that the PM model uses CWT recoveries from statistical areas to determine the "donor" areas, and these can change from time step to time step. Jim N suggested that this may not be a problem if the areas are specified at the highest possible resolution in the model, and then defining migration matrices in terms of groups of areas. Jim N said he would discuss this issue with Troy. Gary noted that the generic system is to set up pointers to different parameters. He also questioned about the data types and was concerned that small typos could mess up the configuration. He stressed the need to make sure that pointers point to the right location. Jim Norris will pass on this concern to Troy. Robert commented that this configuration will allow us to set up separate data types. The configuration file will be set up without having to access the source code. Jim Norris added that the Generic Parameter System was not implemented yet and might not be the final model. Jim Scott encouraged that we try this configuration to see how it works. There was general concern about the added access time the generic system might require. Jim said he and Troy had discussed this and had concluded it would not be significantly slower. The group recommended that the system be tested against a full array system. Jim agreed to do the test. 3. Update on migration algorithm report (Jim Norris): Jim Norris stated that many people had trouble with the migration matrix notation in the State Space Model (SSM). Currently, the rows index the "to" regions and the columns index the "from" regions. Thus, each element of the matrix, say a(i,j), represents the fraction moving from region j to region i. Most people would like each element to represent the fraction moving from region i to region j. Jim asked if anyone would object to changing the definition of the matrix. This would result in the SSM formula using the transpose of the matrix. No strong objections were voiced. Jim asked if anyone had come up with a better intuitive definition for the migration matrix elements when applied to the Proportional Migration model. No one had given this any thought. Jim would like to add the "Box Car" model to the report. Robert thought it was a good idea and agreed to give Jim some references on it. 4. Report on FRAM development for 1998 season (Carrie Cook-Tabor, Jennifer Gutmann). Jennifer briefly reported on the July 15 meeting on FRAM development. Jim Packer is doing most of the coding to allow the FRAM model to evaluate selective fisheries. This modification is required by the letter of agreement between the Tribes and Washington Department of Fish and Wildlife regarding mass marking. Minutes of the July 15 meeting were distributed (these are available at /harvest/fram.html). Basically, the FRAM model is being modified to double the number of stocks, thus allowing each stock to have marked and unmarked components. Gary asked how the new FRAM model would be evaluated and tested. He suggested using the same datasets used by the PSC Selective Fishery Model a few years ago to see how different the results are from the two models. It was left uncertain who, when, or how the new FRAM model would be tested. 5. Update on State Space Model development, parameter estimates, and utilization (Ken Newman). Ken began by outlining general project objectives: -- Protection/Adequate Escapements -- Set Catches -- Season "stability" Management actions to meet these objectives include time/area closures, quotas, ceilings, selective fisheries, etc. Tools for evaluating the actions must have the following characteristics: -- Reflective of actions -- Structurally meaningful (causal, biological) -- Must reflect uncertainties Ken showed some examples of how the State Space Model (SSM) can be used. He showed results of Monte Carlo simulations (box plots of total catches from 100 games). The sources of variation in these simulations were: annual and stock to stock variation in SSM parameters (i.e., initial survival, initial distribution, catchability coefficients, migration parameters) or whatever the deviation is between what is said will be done management-wise and what actually occurs. There are 5 specific parameter estimates from the current version of the SSM: 1. Initial survival rate 2. Initial location (location parameter of a beta distribution; the shape parameter is fixed at 2.0) 3. US Fishing Effectiveness 4. Canadian Fishing Effectiveness 5. Movement parameter (location parameter of a beta distribution; the shape parameter is fixed at 3.0) The natural mortality parameter is set to zero during the time steps of the fishing period. The latest results show a more consistent pattern for the fishing effectiveness estimates (i.e., the US and Canadian values are in the same relative position from year to year). The previous results were inconsistent. Ken has found that the estimated migration rates range from 0 to 86 km/day, but are generally within observed values. Ken described his latest work using both an inner model and an outer, or hypermodel. There are 5 parameters in the inner or SSM. These are generated from a multivariate hyperdistribution, a so-called "outer" model. This is currently formulated as simply the joint distribution of 5 independent random variables. Four of the distributions are gamma(alpha[i], beta[i]) i=1,..,4 and one is beta(alpha,beta). The parameters of this hyperdistribution are called hyperparameters, of which there are 10. Five of the hyperparameters are fixed and 5 are estimated from historical data. Ken noted that his biggest headache now is getting data. Specifically, for a given year and a given stock he needs two tables (matrices) of data. The first has areas for rows, statistical weeks for columns, and all CWT recoveries (by gear type if possible) for each element. The second matrix has the same row and column indices, but has some consistent measure of effort for each element. Ken asked for comments on whether his model system has anything missing (leakage). For future work, Ken suggested the following: -- Use non-normal, non-linear SSM (e.g., log-normal, Poisson) -- More complex spatial framework (e.g., ocean and Puget Sound migration path) -- More complex fisheries interaction (e.g., competing gear types and different temporal scales) -- Partitioning sources of variation (i.e., isolate and quantify each of the sources of variation, trying to determine the more serious or influential sources. Potentially, perhaps, data collection methods might be changed. The meeting was adjourned at 12:30 pm. Next meeting was scheduled for Tuesday, October 28 at NMFS Montlake Lab.