ATTENDING LIST

Jim Anderson	UW
Jim Berkson	CRITFC
Rich Comstock	USFWS
Carrie Cook-Tabor	USFWS/NMFS
Judy Cress	UW
Peter Dygert	NMFS
Robert Kope	NMFS
Pete Lawson	ODFW/NMFS
Richard Methot	NMFS
Marianne McClure	CRITFC
Rick Moore	WDFW
Gary Morishima	QIN
Ken Newman	U of Idaho
Jim Norris	UW
Jim Scott	NWIFC
Tom Wainwright	NMFS

NMFS EXPECTATIONS

Robert Kope (NMFS - NWFSC) outlined the rationale and expectation of NMFS for this project. The National Marine Fisheries Service (NMFS) has responsibility for administering the US Endangered Species Act (ESA) for anadromous fish and shared responsibility for managing ocean salmon fisheries. Because of this dual responsibility, NMFS has a need to evaluate the constraints of ESA considerations on harvest management, and the impacts of harvest and habitat management on the recovery of depleted stocks. These evaluations need to be made on a coastwide basis and should be internally consistent and consistent with models used in other management arenas.

The models necessary to make these evaluations will have many similarities. They will utilize the same catch and escapement data, and incorporate the same stocks and fisheries. We would like to be able to accomplish these objectives within a single broad modeling framework to ensure as much consistency between harvest and conservation models, and reduce redundancy as much as possible. We believe the most rational way to approach this is to link harvest models for chinook and coho salmon with life-cycle models for these species. This coupling will allow evaluation of harvest impacts within a single year, as well as evaluation of management strategies over the long run. We would also like to explicitly incorporate migration as a means of allowing greater flexibility in modeling fishing patterns that depart from historic conditions and in calibrating models to recent years with zero harvests in some areas.

The goals of this modeling project are to develop a modeling framework to:

1. Provide a common framework for both conservation risk assessment and harvest management analysis.
2. Expand the geographic scope of current harvest models
3. link coho and chinook salmon harvest models.
4. incorporate life cycle (production) models for both species to evaluate harvest and conservation strategies.
5. allow flexibility to accommodate new methods and model designs.
6. provide an interface with a large subset of ocean and freshwater databases maintained by PSMFC

Ideally the harvest models should be capable for running of a single year to evaluate regulations or run in a simulation mode to evaluate harvest and restoration management strategies over multiyear horizons. The model that presently comes closest to meeting these needs is the CRiSP harvest model developed by UW - CQS, with a C++ implementation of the PSC chinook model as its engine and a GUI.

To begin this effort of model consolidation, the NMFS NWFSC has initiated a modeling project in cooperation with the University of Washington, Center for Quantitative Science. We have convened this committee to provide advice on the specification and design of the model framework in order to maximize the compatibility of this modeling effort with the needs and modeling efforts of the co-managers.

STATE-TRIBAL EXPECTATIONS

Jim Scott (NWIFC), Jim Berkson (CRITFC), and Gary Morishima (QIN) all expressed concerns that: 1) the role of this advisory committee needs to be clearly defined in terms of the responsibility of the committee and the scope of their input into the model development process, 2) The committee needs to have more than an advisory role or the model will not be accepted and used by the co-managers.

Gary Morishima added that the end product needs to be clearly defined in order for the co-managers to decide whether it is more worth while to invest their time in participating in this process, or to invest their time and energy in other modeling enterprises. The most urgent need, at the present time, is to develop a consensus model to evaluate selective fisheries for mass marked hatchery coho salmon for use in the management process for 1998 fisheries. He reiterated that there needs to be a clear understanding of the role of this committee in the designing and implementing any model in order for the participants to commit to the project.

Editors notes

[ Jim Scott, suggested in an earlier email to Ken Newman, that the role of the committee should be to "oversee development and implementation of the model". The purposes of the committee are twofold:

1) With the assistance of the team, develop specifications for model processes, resolution, input screens, and output reports.

2) Assist the team in the collection and analysis of data, testing of computer code, and implementation of the model.

Scott also indicated:

"I want to assure that the model is used in the future by the states, tribes, and NMFS. The best way to assure this is to have representatives of each of these agencies develop the specifications, assists in data analysis, etc. This may initially slow model development, but any other course will result in the development of an alternative model by the tribes and states."

For the committee to serve this purpose, "periodic meetings (once every 6 months, say)" will probably not be sufficient. Perhaps you already anticipated this, but key (perhaps all) members of the committee will require at least weekly contact. Monthly progress reports may be sufficient for the remainder of the committee. ]

RELATIONSHIP BETWEEN RESEARCH AND MANAGEMENT MODELS

Tom Wainwright and Jim Anderson discussed the distinction between the research/development phase of modeling and production of a particular model for direct management analysis. Jim Anderson noted the need for a formal process for pulling a specific model version out of the research stream into the management context.

To elaborate this two stream process consider that research model development occurs in small incremental steps. Changes in the model are typically designated by subnumbers (example 1.1, 1.2) and the models remain in the beta test phase and are not formally released. Ideally research model should encompass the best available ecological and statistical approaches and serve as a neutral analytical tool. The research models can explore ecological interactions through sensitivity analysis, identify needed research and assess calibration approaches.

The Management models are formally released for a specific management purpose and they evolve from a particular version from the research model stream. Typically releases coincide with the yearly need to set harvest policy. Management models are part of a negotiation process and thus require some type of certification. This may involve model calibration, validation, documentation and finally presentation to the management agencies. Management model should serve as a quantitative expectation of a negotiated agreement between management agencies.

Editors Notes

[ The advisory committee should take an active role in the communication between these two streams of model development. Two distinct activities are envisioned in the figure below.

A) Specifications: the committee specifies model requirements and helps identify research priorities and schedules to meet the needs of management, Specification activities occur as needed.

B) Certification: the committee certifies a version of the research model for use in management. Certification activities take place within specified dates.

 Time Research Model Management Model
 versions versions and actions

 | | 1.0 <------A-------- manager specification
 | | 1.1
 | | 1.2
 year1 | 1.3 -------B------- |Version 1.3 certification
 | |
 | | 2.1 <------A-------- manager specification
 | |
 year2 | 2.2 -------B------- |Version 2.2 certification
 | |
 v v

Proposed tasks and responsibilities of the committee for specification and certification are outlined below.

A. Specification activities:

- committee indicate delivery dates of management models

- identify functionality for ESA and co-management

- assist in developing conceptual framework

- identify input and output requirements

- work on a regular, but informal basis, with development team

B. Certification activities

- identify and provide data for calibration

- evaluate sensitivity of model to define critical assumptions and implications

- communicate results and characteristics of model to management community

- use model in negotiation process

What is not resolved is how different management goals, ESA and co-management, would interact within this framework. As was proposed, two research model streams could be implemented with each one attached to a different management stream. An alternative is to merge the modeling streams or set up a semi-formal comparison of the model. Our current tack seems to be to compare the models and at a later data (soon though) determine if we can merge the modeling efforts. In any case these issues are worth articulating further. ]

WORK BY CQS/UI TO DATE

Jim Norris (UW - CQS) said that he thinks we can have a coho selective fisheries model operational in this framework within 2 years.

Jim Norris outlined the model specification and design process and the flow of software development. He stressed that the common approach to software development used to be for an initial analysis of the problem to produce a functional specification, then the design phase to produce a design document, programmers to develop the code, and a test phase to result in a deliverable product. If the deliverable product failed to meet original need, the entire process was reiterated. Current model design follows the same general path with feedback at each level in order to avoid the need to iterate over more than one level in the development process. See figure below.

 Software Development Process

 Analysis ----- Functional Specification
 | ^
 | |
 v |
 Design ----- Design Document
 | ^
 | |
 v |
 Code ----- Alpha Application
 | ^
 | |
 v |
 Test ----- Deliverable

 Goal: Avoid iterating over more than one level.

He also pointed out the need to decouple the research aspect of modeling from the management aspect. Through research, models are constantly developing and changing. For management purposes, everyone needs to be working from the same page, so a model version must be spun off from the research phase and held fixed for a management cycle.

Jim Norris then presented a demonstration of the current capabilities of the CRiSP Harvest model. The most recent version of the PC model for Windows NT or Windows 95 can be downloaded from the CQS Website.

CRiSP.2 is a user-friendly computer model that simulates the harvest of 30 chinook salmon stocks by 25 fisheries over an extended time horizon. The geographic range covered by the model extends from Southeast Alaska to the Oregon coast. Ten stocks and two fisheries from the Columbia River basin are included in the model. The computational engine of CRiSP.2 is based upon the forecasting portion of the Pacific Salmon Commission (PSC) Chinook Model.

(see Jim Norris' Outline for a Harvest Model at end of text)

A key feature of the model is the interaction between stocks through annual catch ceilings, or quotas, imposed upon fisheries that harvest multiple stocks. Catch ceilings are the primary PSC management tool. As stocks rebuild or decline at different rates over time, relative harvest rates in fisheries with catch ceilings also change. Single stock models cannot simulate this type of interaction.

Gary Morishima suggested that it is difficult to keep track of the changes you have made to parameters when you are running the CRiSP harvest model in its interactive mode. It would be helpful to be able to save a listing of the parameter values used in a particular run along with the output from that run.

Jim Norris responded that he is aware of this problem and has been exploring two ways of addressing it. The first is have a "save" utility that saves the current model parameters in folder that can be named by the operator. The second is to create a "log file" that keeps a continuous record of all parameter changes during a work session. He intends for the new model to have one or both of these capabilities.

Jim also responded that the input files are all text files and the model can be run in a batch mode with multiple sets of input files without using the GUI. This allows you to make multiple runs faster and to associate the output with the input files.

Jim also expressed the view that the most difficult part of the model to code will be the ability to simulate complex management scenarios with multiple objectives over multiple time steps and fisheries.

After lunch, Ken Newman talked about the state space approach he is investigating to explicitly model migration of coho salmon. He explained the basic structure of the state space model and the algorithms he is using to describe the initial distribution of fish and to move them around. Details are available at the CQS Website. He is currently using maximum likelihood criteria to fit the model parameters with a Kalman Filter algorithm to calculate the likelihood function. He is currently working with coded wire tag data for 3 cohorts of coho from Grays Harbor and using a 1 dimensional migration model with 13 area cells for migration and fishing.

Jim Scott pointed out the problems of scaling models for single populations to account for total catch. When single populations are modeled individually, and catches for individual stocks are added to get total catch in a port area, there will be discrepancies between the data and the model results. We should consider ways to fit the modeled catches from individual stocks to the total catch data simultaneously.

MODEL TIMELINE AND COMMITTEE STRUCTURE

This discussion focused on two fundamental questions: the goals of the project and the time-frame for completing goals. The project has a long-term goal of developing a coastwide salmon model framework that can be used for both conservation risk assessment and harvest management. The first year focus is on developing software infrastructure (data structures, user interface, database interface, etc.) and to develop a prototype coho harvest model as a first application.

Jim Norris said that he believes we can have a mechanistic model to move fish around in multiple timesteps by September of 1997. This would not be a complete working model, and would probably not include all stocks of interest.

Gary Morishima pointed out that the immediate need is to have a model for selective coho fisheries ready for the management process for the 1998 season. In order to be useful this would need to be thoroughly reviewed by October or November of 1997.

This time frame is not consistent with the current project scope, but it may be possible to have something ready for the 1999 season.

The committee recommended the following goals:

1. The short-term focus of the model framework should be selective fisheries evaluation.
2. The framework may be useful to compare the FRAM and PM models with the State Space model.

Jim Scott sees the framework as a useful platform to compare the 3 models as 3 alternative methods within the same model framework.

Gary Morishima pointed out that there is another selective fisheries model developed in the PSC process. Jim Scott characterized the PSC model as analogous to FRAM with different parameterization and estimation algorithms. He sees the comparison with the state space approach as problematic because it is unclear how the SS will mesh with the spatial complexity of Puget Sound.

Rich Comstock recommended the coho cohort reconstruction model (MSM) output as a useful test data set for the comparison of the different model algorithms.

MODEL EVALUATION FRAME WORK

For evaluation five (4?) models are identified:

PM: proportional migration (PM) selective fishery model. Authors Moore, Lawson and Comstock June 29 1996

SS: state space approach model, Author Newman

FRAM: fishery Regulation Assessment Model, Authors Washington and Oregon

SFM: PSC Selective Fishery Model. Author Moore May 26 1995.

MSM: Mixed Stock Model,

CRiSP: Author Norris (?)

We require:

General descriptions of each model

Compare theory of models: can they be reduced to a single mathematical structure

Identify how models are aggregated across stocks for comparison (apples to apples)

Identify use common data for comparison

We agreed to:

1. get out a report of this meeting and an outline of a model evaluation framework by mid January for inclusion on the web site
2. circulate a draft statement of project scope and description of the role of this committee by mid-January for comment by attendees
3. compile and make available a set of documentation of the selective fisheries models and test dataset
   The selective fishery model is available on the web site and can be obtained as a WordPerfect file.
   (Comstock's test data will include coho production and expansion factors. This will be posted on the web site.)
4. Complete a comparison of the analytic structures of the FRAM, Proportional Migration, and Newman's migration models by mid-March.
5. use the CQS Website for information exchange
6. meet again at the Montlake Lab on Friday, February 21.
7. distribute an agenda and description of the role of this committee in development and implementation of the modeling framework prior to the next meeting.

JIM NORRIS' OUTLINE FOR A HARVEST MODEL

Draft 12/19/96 Draft

1. General Model Requirements
   1. Flexible time steps within a year.
   2. Ability to have multiple species (e.g., coho and chinook).
   3. Ability to have multiple stocks within species.
   4. Ability to have multiple cohorts within a stock (marked/unmarked).
   5. Ability to have multiple fisheries.
   6. Ability to have management constraints (e.g. catch ceilings, catch allocations between fisheries).
   7. For each stock, ability to define underlying migration pattern (in time and space) that is independent of simulated fishing pattern (in time and space).
   8. For each fishery, ability to define and simulate fishing patterns that are different from historical patterns.
   9. Ability to incorporate population genetics models.
2. Basic Idea to Handle Migration
   1. Divide the entire coast into high resolution (i.e. small) discrete geographic units (call these statistical areas).
      • Assign weighting factors to each stat area (e.g. based on relative size).
   2. Define stock migration areas as aggregations of statistical areas.
   3. Define fishery harvest areas as aggregations of statistical areas.
   4. When estimating migration patterns for individual stocks each year, there will be a one to one correspondence between stock migration areas and fishery harvest areas.
      • This is necessary because the migration patterns are estimated from CWT recovery data by fisheries.
   5. In order to simulate the effects of alternative fishing patterns, we need a mechanism that will decouple the stock migration areas from the fishery harvest areas.
      • To do this we allocate stock abundances and fishing efforts to individual statistical areas within stock migration areas and fishery harvest areas (using the weighting factors).
      • Harvests and abundances are always computed and tracked at the statistical area level.
      • Harvests and abundances can be reported by statistical area, migration area, or harvest area.
3. Each Simulation
   1. Define high resolution statistical (geographic) areas.
      • These will be the high resolution statistical areas used for CWT recoveries.
      • Each area will have a weighting factor (e.g., by relative size).
      • These areas can be combined to form stock migration areas and fishery harvest areas.
      • Stock abundances within a stock migration area will be distributed to individual statistical areas by the weighting factors.
   2. Define stocks.
      • Name.
      • Initial abundances.
      • Stock migration areas as collections of statistical areas.
      • Migration parameters.
      • Production parameters.
      • General info.
        • Spawning locations/descriptions
        • CWT groups
        • Photos
   3. Define fisheries.
      • Name.
      • Gear type.
      • Catchability coefficients.
      • Fishery harvest areas as collections of statistical areas.
      • General info.
        • Description.
        • Photos.
4. Each Time Step
   1. For each stock.
      • Determine abundance by age and migration area. Get the transition matrix for this year, stock, age, and time step. Transition matrices will be based on SSM results. Transition matrices may be computed a priori, at start-up, or at the start of each time step. Apply transition matrix to abundance vector.
      • Determine abundance by age and statistical area. Distribute abundance within each stock migration area to each stat area.
      • Determine abundance by age and fishery area. Re-aggregate abundances by fishery area. In most cases, the migration area and fishery areas will be the same each year. However, it may be desirable to hind cast the effects of a different fishing regime over an underlying migration pattern.
   2. For each fishery area.
      • Determine fishing effort by stat area. Distribute fishing effort from fishery area to stat areas.
      • For each stock. Determine legal harvests by age and stat area. Determine incidental mortalities by age and stat area.
   3. For each stock.
      • Determine final abundances by migration area. Aggregate harvest and incidental morts by migration area. Apply natural or other mortalities.