Group Title: Technical paper / Florida Sea Grant College Program ; no. 47-A.
Title: FINMAN : a fisheries institution management-training simulation model : model description and operations manual
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Permanent Link: http://ufdc.ufl.edu/UF00075997/00001
 Material Information
Title: FINMAN : a fisheries institution management-training simulation model : model description and operations manual
Series Title: Technical paper Florida Sea Grant College
Physical Description: 127 p. : ill. ; 28 cm.
Language: English
Creator: Ault, Jerald S
Fox, William W
Publisher: Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School of Marine and Atmospheric Science
Place of Publication: Miami Fla
Publication Date: 1986
 Subjects
Subject: Fishery management -- Computer programs   ( lcsh )
Fishery management -- Simulation methods   ( lcsh )
Genre: non-fiction   ( marcgt )
 Notes
Statement of Responsibility: Jerald S. Ault and William W. Fox, Jr.
General Note: "August 1986."
Funding: This collection includes items related to Florida’s environments, ecosystems, and species. It includes the subcollections of Florida Cooperative Fish and Wildlife Research Unit project documents, the Florida Sea Grant technical series, the Florida Geological Survey series, the Howard T. Odum Center for Wetland technical reports, and other entities devoted to the study and preservation of Florida's natural resources.
 Record Information
Bibliographic ID: UF00075997
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved, Board of Trustees of the University of Florida
Resource Identifier: oclc - 15496909

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FINMAN

A Fisheries Institution Management-Training Simulation Model





MODEL DESCRIPTION AND OPERATIONS MANUAL





Jerald S. Ault and William W. Fox, Jr.



Cooperative Institute for Marine and Atmospheric Studies
Rosenstiel School of Marine and Atmospheric Science
University of Miami
4600 Rickenbacker Causeway
Miami, Florida 33149




Project No. E/C-8
Grant No. NA80AA-D-00038





Technical Papers are duplicated in limited quantities for specialized audiences
requiring rapid access to information. They are published with limited editing
and without formal review by the Florida Sea Grant College Program. Content is
the sole responsibility of the author. This paper was developed by the Florida
Sea Grant College Program with support from NOAA Office of Sea Grant, U.S.
Department of Commerce, grant number NA85AA-D-SG059. It was published by the
Sea Grant Extension Program which functions as a component of the Florida
Cooperative Extension Service, John T. Woeste, Dean, in conducting Cooperative
Extension work in Agriculture, Home Economics, and marine Sciences, State of
Florida, U.S. Department of Commerce, and Boards of County Commissioners,
cooperating. Printed and distributed in furtherance of the Acts of Congress of
May 8 and June 14, 1914. The Florida Sea Grant College is an Equal
Employment-Affirmative Action employer authorized to provide research,
educational information and other services only to individuals and institutions
that function without regard to race, color, sex, or national origin.



TECHNICAL PAPER NO. 47-A (Appendix)
August 1986
Price $8.00









Table of Contents
Page

Abstract 7

1.0 Identification 8

2.0 Introduction 9

3.0 Model Flow Diagram and General Description 10

4.0 Species-Type Modules for Management Strategy Simulation 12
4.1 Grouper
4.2 Tuna
4.3 Anchovy
4.4 Shrimp
4.5 Seatrout
4.6 Snapper

5.0 FINMAN Initial Conditions 16
5.1 Starting Conditions and Option Selections for Management Scenario
5.2 Options Sequence
5.2.1 Management System Type 16
5.2.1.1 Autocratic
5.2.1.2 Commission
5.2.1.3 Legislature
5.2.2 Scope of Management Authority Over Stock 18
5.2.2.1 100 Percent
5.2.2.2 67 Percent
5.2.2.3 33 Percent
5.2.3 Fishery Exploitation Type 19
5.2.3.1 Commercial, 1 Gear Type
5.2.3.2 Commercial, 2 Gear Types in Sequence
5.2.3.2.1 Gear Types in Sequence
5.2.3.2.2 Overlapping Age Structure Effects of Gear Types
5.2.3.3 Recreational and Commercial
5.2.4 Current Fishery Status 20
5.2.4.1 Developing
5.2.4.2 Fully Exploited
5.2.4.3 Recruitment Overfished
5.2.4.4 Unknown
5.2.5 Historical Data Availability 21
5.2.5.1 Complete
5.2.5.2 Sketchy
5.2.5.3 Very Sketchy
5.2.5.4 None
5.3.0 Decisions Sequence 21
5.3.0.1 Management Measures to Be Implemented
5.3.1 Fishing Effort Strategy 23
5.3.2 Size and Age of Capture
5.3.3 Seasonal Closures 24
5.3.4 Catch Limits and Allocations
5.3.4.1 Overall Catch Limit










5.3.4.2 Catch Limit Subdivided by Fleets 25
5.3.4.3 Boat Quotas and/or Bag Limits
5.4 Overall Budget Allocations Among Enforcement, Research, 26
Assessment & Monitoring and "Influence" with Constituency
5.4.1 Assesment and Monitoring, Research
5.4.2 Enforcement
5.4.3 Development
5.5 Research Budget Allocations Among Data Collection and Analysis 28
Projects
5.5.1 Basic Fishery Statistics
5.5.2 Catch Analysis
5.5.3 Resource Surveys
5.5.4 Economics
5.5.5 Environmental Trends & Effects of Fishing
5.6 Review of Input Parameterization 28
5.7 Calculation of a Simulation Sequence 29
5.8 Timing and Accounting
5.9 Destination for FINMAN Output 30

6.0 Biological Model Structure 30
6.1 Mortality 32
6.1.1 Stochastic Natural Mortality
6.1.2 Average Number Alive
6.2 Growth 33
6.2.1 Von Bertalanffy Growth Formulation
6.2.2 Linear Segmental Growth Formulation
6.3 Maturation 33
6.3.1 Average Number of Males
6.3.2 Average Number of Females
6.3.3 Sex Ratio
6.4 Recruitment 34
6.4.1 Beverton & Holt Dynamics
6.4.2 Ricker Dynamics
6.4.3 Stochastic Recruitment Variability
6.4.3.1 Random Uniform Distributions 35
6.4.3.2 Autocorrelated Sine Wave Periodicity 36
6.4.4 Spawning Stock
6.4.5 Number of Larvae
6.5 Yield 37
6.5.1 Fishable Average Population 38
6.5.2 Numbers
6.5.3 Weight
6.6 Other Characteristics of the Catch and Population 38
6.6.1 Mean Length and Weight in the Catch
6.6.2 Age Distribution 39
6.6.3 Mean Age of the Fish in the Population and Catch
6.6.4 Equilibrium Yield Per Recruit 40
6.7 Stock Size 41
6.8 Selection 41
6.8.1 Non-stochastic Availability
6.8.1.1 Stochastic Availability 42
6.8.2 Season Closures










7.0 Catch Quotas
7.1 Overall Catch Quota
7.1.1 Single Fleet or Two Fleets in Sequence
7.2 Catch Limit Subdivided by Fleets
7.3 Bag Limit 44
7.4 Seasonal Closures
7.5 Management Systems 45
7.5.1 Autocratic Body
7.5.2 Commission Body
7.5.3 Legislative Body

8.0 Economics (Costs and Returns) 46
8.1 Fishing Effort (Units)
8.1.1 Single Fleet 47
8.1.2 Multiple Fleets
8.2 Unit Costs 47
8.2.1 Single Fleet
8.2.2 Multiple Fleets 48
8.3 Gross Revenue 48
8.3.1 Single Fleet
8.3.2 Multiple Fleets
8.4 Net Returns 49
8.4.1 Single Fleet
8.4.2 Multiple Fleets
8.5 Rate of Profit 49
8.6 Budget Factors 50
8.6.1 Budget Activities
8.6.2 Profit Activities 52
8.6.3 Population Activity
8.6.4 Budget Appropriation

9.0 Enforcement Budget Decision Filters 52
9.1 Fishing Mortality Exceeding Recommended 53
9.2 Age of 100% Vulnerability Below Recommended
9.3 Season Closures Less than Recommended 54
9.4 Catch Quota Exceeding Recommended

10.0 Effort Development Decision Filters 54
10.1 Commercial, 1 Gear Type 56
10.2 Commercial, 2 Gear Types
10.3 Commercial, 1 Gear Type and Recreational Fishery
10.3.1 Commercial Sector
10.3.2 Recreational Sector 57
10.3.2.1 Virgin Population Criterion
10.3.2.2 Human Population Density Effect
10.3.2.3 No Growth 58
10.3.2.4:Availability
10.3.2.5 Factor of Recreational Effort Increase

11.0 Assessment & Monitoring Filters 58
11.1 Decision Variables
11.2 Standard Decision Loop 59










11.3 Sampling Curve 59
11.4 Effect on Simulation Output 60
11.4.1 Single Gear-Type Output Modification 61
11.4.1.1 Quota and Catch
11.4.1.2 Costs and Returns
11.4.1.3 Catch Analyses
11.4.1.4 Stock and Recruitment Trends 62
11.4.1.5 Yield Per Recruit Estimate
11.4.2 Multiple Gear-Type Output Modification 63
11.4.2.1 Quota & Catch
11.4.2.2 Average Size in the Catch 64
11.4.2.3 Costs and Returns
11.4.2.4 Catch Analyses 65
11.4.2.5 Stock and Recruitment Trends 66
11.4.2.6 Yield Per Recruit Estimation

12.0 Point Allocations and Development of Utility Function Parameters 69
12.1 Newness Allocation
12.2 Conservation Ethic 70
12.3 Commercial Ethic 71
12.4 Budget Management 72
12.5 Perceptions
12.6 Multiattribute Function Development and Parameterization 73

13.0 FINMAN Computer Program Modules Descriptions 78
13.0.1 Generalized Program Flow
13.1 FINMAN.MAIN (FINMAN)
13.2 "SPECIES"
13.3 READ.DATA (RDAT)
13.4 DATA.INIT (DTIN)
13.5 BHYIELD (BHYLD) 80
13.6 CATCHIST (CTHS)
13.7 DECISIONS (DEC)
13.8 BUDGET (BDGT)
13.9 ASSESSMENTS (ASMT)
13.10 ENFORCE (ENFOR)
13.11 REVIEW.DATA (RVDT)
13.12 GXFIN (GXFIN)
13.13 QUOTA(QUOTA) 81
13.14 ASSESS.FILTER (ASFL)
13.15 GXOUT(GXO)
13.16 GXOUT2 (GX02)
13.17 RECRUITMENT (RECT)
13.18 YPRA (YPRA)
13.19 ECON (ECON)
13.20 POINTS (POINT) 82
13.21 CEFF2 & CEFF2.1 (CFF & CFF2)
13.22 FISHECON2 (ECON2)
13.23 BUDGET.HIST (BDGH)
13.24 ASSESS.HIST (ASHT)









13.25 FDEV (FDEV) 82
13.26 BUDFAC (BFAC)
13.27 MST (MST)
13.28 MAN.AUTH (MNAT)
13.29 REG.QUOTA (RGQUO)

14.0 Loading and Creating Random Access Memory Text Data Modules (TXT) 83
14.1 Editing the RAM Data Text Files (FINFILE)
14.1.0.1 Apple Computers (Applesoft BASIC)
14.1.0.2 IBM Personal Computers (Microsoft BASIC) 84
14.1.1 INPUT 85
14.1.2 POPN "X 86
14.1.3 PARAPOPN
14.1.4 NATMORT 87
14.1.5 AVAIL."FILENAME" 88
14.1.6 FISHMORT."X "
14.1.7 FECUND
14.1.8 FMULT
14.1.9 MATUR
14.1.10 VONBERT "X," 89
14.1.11 ECONOMICS
14.1.12 CHIST 91

15.0 Life History Modular Contents 94
15.0.1 Mortality Parameters
15.0.2 Growth Parameters
15.0.3 Maturation Parameters 95
15.0.4 Reproduction Parameters
15.0.5 Recruitment Parameters
15.0.6 Economic Characteristics 96
15.0.7 Yield Per Recruit Surface
15.1 Gag Grouper, Mycteroperca microlepis 96
15.2 Yellowfin Tuna, Thunnus albacares 101
15.3 Northern Anchovy, Engraulis mordax 106
15.4 Pandalid Shrimp, Pandalus borealis 111
15.5 Speckled Seatrout, Cynoscion nebulosus 116
15.6 Vermillion Snapper, Rhomboplites aurorubens 121

16.0 Computer Code Variable Lists 126
16.1 Alphabetized Composite Variable List 126

















ABSTRACT

FINMAN (Fishery INstitutional MANagement-Training Simulation Model) is a

microcomputer-based program which simulates decision-making responses at

three levels within the fishery management institution: (1) fishery management

rules, (2) fishery agency general budget allocations, and (3) research budget

allocations. The program also allows for a variety of fishery types, rule

development structures, and levels of authority over the fishery. FINMAN

serves as (1) an analysis program for investigating system responses, and (2) an

educational program for demonstrating system responses under a variety of

situations. The program is written in BASIC with versions available for the

Apple lie, Apple IIc and IBM-PC microcomputers.











1.0 Identification:


Program Name: FINMAN Verson 1.0*

Language: BASIC



Model description and operations manual for the University of Miami

Fishery Institution Management-Training and Research Simulation Project:

William W. Fox, 3r., Principal Investigator. By herald S. Ault and William W.

Fox, 3r., CIMAS, Rosenstiel School of Marine and Atmospheric Science,

University of Miami, 4600 Rickenbacker Causeway, Miami, Florida. This work is

a result of research sponsored by NOAA Office of Sea Grant, Department of

Commerce, Florida Sea Grant College Program, under Grant No. E/C-8.

Designed for the Apple IIe, IIc, and the IBM/PC microcomputer systems.

Program storage requirements are 128K in RAM (64K in ROM) and one disk

drive; output is arranged for an 80-column display. Available versions are

written in Applesoft BASIC (Apple computers) and Microsoft BASIC (IBM/PC's),

and are user-interactive with specialized data base file manipulation features.












*To obtain a copy of the program send one (1) blank two-sided 5 1/4"
diskette or two (2) blank single-sided 5 1/4" diskettes, specifying the
type of microcomputer you have, along with a prepaid return mailer, to
the authors at Rosenstiel School of Marine and Atmospheric Science,
University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149.











2.0 Introduction

Fisheries worldwide are under increasing pressure for rigorous management

policy due to competing uses for available oceanic resources from both

commerical and recreational interests. Presently, with escalating resource

usage, managers are increasingly pressured to make immediate decisions on the

regulation and allocation of marine resources that have significant biological,

economic and social impacts. Fishery management decisions are based on these

complex variable systems. A successful fishery management institution builds

resource-user confidence. Such "success" depends on the decisions made by

fishery managers, fishery agency administrators and fishery research supervisors.

Thus, managers, agency administrators, as well as research supervisors need to

understand the complex relationships among these aforementioned impacts, and

to anticipate situations which may arise.

An educational tool has been needed to assist students, professionals, and

fishery management appointees in gaining experience making management and

research program decisions. The objective of this work is to fill that need by

presenting a numerical simulation model which allows examination of system

responses to exploitation for a wide variety of aquatic life-history patterns while

incorporating different management, social, and economic schemes. The

simulation model examines the mechanisms involved in the evolution of a fishery

system and can also be used to evaluate the expected transitional states from an

annual fishery, as well as the expected equilibrium. It is also useful in that it

demonstrates large-scale and often, counter-intuitive interactions.

Throughout the simulation sequence the user works interactively with the

computer, selecting and modifying management plans, budgetary allocations and

enforcement efforts in order to optimize the objective function, satisfy

9











constituency expectations, and achieve appropriate biological and economic

goals. These exercises provide the user: (1) clearer understanding of the inter-

relationships between factors impinging on fisheries institutions, and (2) the

impetus for considering the implications of effort strategies coupled with

budgetary allocations in evaluating management alternatives. Thus, one can

utilize the FINMAN model for planning, evaluating alternatives, and identifying

sensitive areas of the system.



3.0 Model Flow Diagram and General Description

A flow chart of the age-structured simulation model is shown in Figure 3.0.

The user's goal is to maximize the objective function through a utility function

that allows comparisons of different manager's orientations and varying human

attitudes, by preference orderings, towards management objectives. Survival of

the user through all the iterations is based on probabilities influenced by the

performance of the fishery and constituency "contentment". FINMAN allows the

user to select among a series of options: (1) the species-type for management

strategy simulation; (2) the management system type, (3) management scope or

competence, (4) the fishing pressure pattern, (5) the level of recruitment

variability, (6) the stocks) existing condition, and (7) the extent of the fishery

and economic information available. The program then generates the initial data

and information set, including the situation the user is confronted with in terms

of his continued employment, and queries the user on a series of budget and

management decisions. To establish the constraints placed on management

authority and abilities, a series of options are shown so that an initial conditions

information base, used to make basic decisions regarding management measures

and institutional budget allocations, can be generated. These decisions include (1)

10























































Flow Diagram of FINMAN


Figure 3.0:


Flow Diagram of the Fisheries Institution
Management Training Computer Simulation
Model FINMAN.










management measures to be implemented, (2) overall budget allocations among

research, enforcement, and development, and (3) research budget allocations

among data collection and analysis projects. The program then updates the

history of the fishery management network. The precision and/or accuracy of

the overall view of the state of the fishery is controlled in part by the budget

allocations and particular management decisions. Since most processes have a

stochastic element, the program uses chance variables in formulating each

"annual" update. When the user's employment is terminated due to poor

performance, or after a selected number of iterations is attained, FINMAN

provides a summary of the user's performance.



4.0 Species-Type Modules for Management Strategy Simulation

The present version of FINMAN has six "Fish stock" life history types, each

parameterized with economic, sociological and biological variable settings that

typify fishery institution management frameworks for their respective fisheries.

The available species-types for management strategy simulation strategy are (by

section): Grouper (4.1), Tuna (4.2), Anchovy (4.3), Shrimp (4.4), Sciaenid (4.5),

and Snapper (4.6).

The following narratives encapsulate the background information on these

five species-types and set the stage for the management strategy simulation

scenarios. The user has the ability to modify the life history and fishery specific

information as necessary to gain a wider picture of the possible avenues of

fishery response to exploitation and various management schemes.

One of the useful features of the FINMAN simulator is that it allows one to

visualize approximate system response of a particular exploited stock, even when

the fine details of the fishery system are not known. This situation is, of course,

12











prevalent in developing countries and developing fisheries. For such situations,

the user may approximate the unknown variables by "best guesses", transferring

information from other similar but better understood fish stocks. In that way

the investigator may study system responses to various parameter scalings to

gain insight concerning system sensitivity; and to determine the envelope in

which system responses are guaranteed to occur, and further disregard infeasible

parameter and/or system space. In this regard, intuition may be gained with

respect to developing or initiating prudent management alternatives for

successful system evolution.



4.1 Gag Grouper, Mycteroperca microlepis

Groupers (Serranidae) form an ecologically and economically important

component of reef fish communities. The term "reef fish" applies to a diverse

category of co-occurring demersal and semi-pelagic fish often exploited as a

multispecies complex. The gag grouper is an Atlantic province grouper which is

recognized as an apex predator. It is a reef and continental slope dweller and is

slightly migratory. In the United States Fisheries Conservation Zone both

commercial and recreational fisheries are involved in their capture, and species

associated with the continent suggest that these populations behave essentially

as a unit stock. Gag Grouper have a relatively low natural mortality rate and

are slow growers which reach a relatively large size (M/K = 1.639), with

approximately 13 yearclasses in the fishable life span. Gag groupers are

protogynous hermaphrodites, i.e., individuals mature as females but later

transform to function as males. The FINMAN model explicitly considers the

effects of either interspecific interaction or hermaphroditism and can be used to

investigate the potential effect of protogyny on responses to exploitation and the

population dynamics of groupers. 13











4.2 Yellowfin Tuna, Thunnus albacares

Yellowfin tuna (Scombridae) support extensive commercial fisheries in

almost all the world's tropical and subtropical seas. The sex ratio of yellowfin is

biased towards males at the older ages. The yellowfin tuna is considered an apex

predator which inhabits the open ocean to nearshore pelagic waters. Yellowfin

tuna have an intermediate natural mortality rate and are considered fast growers

which reach a relatively large size (M/K = 1.333), with approximately 10

yearclasses involved in the fishable life span. The tunas are highly migratory,

are esteemed for their flesh, and are pursued by many nation's fishing fleets thus

making management of this species a complex task. Development of specialized

fishing craft equipped with purse seines and large refrigeration systems for

freezing and holding tuna aboard for long periods has permitted development of

several distant-water fisheries. Both commercial and recreational fisheries

involved in their capture.



4.3 Northern Anchovy, Engraulis mordax

Clupeoids, in general, are pelagic species which are known to concentrate

in frontal and upwelling regions, areas which support a relatively large biomass

of small pelagic fishes. These species often support major commercial

reduction fisheries where the fishes are processed to provide fish meal and oil

for other ancillary industries. Stock sizes fluctuate widely due to environmental

factors, fishing activity, and competition among the species themselves during

their early life history. Anchovies have a very high rate of natural mortality and

can be considered intermediate growers which reach a relatively small size (M/K

= 3.549). The sex ratio is biased slightly towards females. Seven year-classes

are involved in the fishable life span. The anchovy is a primary predator, feeding

14











extensively off dinoflagellate and micro-zooplankton blooms. The fishery is

limited to the commercial sector.



4.4 Pandalid Shrimp

Shrimp have an intermediate natural mortality rate and are considered to

have a low to intermediate growth rate, reaching a relatively small size (M/K

1.997), with approximately 6 yearclasses involved in the fishable life span.

Pandalid shrimps are protandric hermaphrodites i.e., individuals mature as

males but later transform to function as females. Fertilization is accomplished

through copulation, the females carry fertilized eggs 3-9 months until hatching,

and they exhibit pronounced stepwise growth. The fishery for pandalid shrimps is

limited to the commercial sector.



4.5 Sciaenid

Sciaenids form an ecologically important component of nearshore and

estuarine fish communities throughout tropical and subtropical waters. The

seatrout is a tertiary level predator feeding extensively on shrimps and small

fishes. The seatrout is a coastal Atlantic and Gulf of Mexico coast fish that, in

conjunction with others in the Sciaenid complex, comprises and important

component of the commercial and recreational catch in the U.S. FCZ. The

seatrout has a low to intermediate rate for both natural mortality and growth

(M/K = 1.163), reaching a intermediate size spread over 12 year-classes in the

fishable life span.



4.6 Snapper

Snappers form an important component of shallow reef, slopes, and

nearshore communities in tropical to subtropical waters worldwide. Snappers are
15










upper level carnivores consuming small fishes, shrimps, crabs and various

molluscs. The snapper studied here has an intermediate natural mortality rate

and an attendant low growth rate (M/K = 2.485), reaching a relatively small size

in the 8 years of the fishable life span. Snappers have a sex ratio biased towards

males at young ages, and then biased towards females at the older ages. Snapper

fisheries typically support recreational and small commercial fisheries.



5.0 FINMAN Initial Conditions

5.1 Starting Conditons and Option Selections for Management Scenario

FINMAN is characterized by several levels of complexity or difficulty for

each of six different program elements. As the director of this management

system you are able to select the degree of your ability to exert control over the

actual implementation of management measures as you deem appropriate, both

in terms of the political system through which management measures are

enacted, and the degree of control exerted over the stock. To establish the

constraints to be placed on your management authority and abilities within a

particular fishery, you will be shown a series of options so that an initial

conditions information base can be generated. The information base is used to

guide basic decision processes regarding management measures and institutional

budget allocations (Figure 5.2).



5.2 Options Sequence

5.2.1 Management System Type

This option allows you to choose 1 of among 3 types of fishery management

structures. The management system types are ordered in ascending levels of

complexity and difficulty for achieving optimum fishery management policy.

16



















Management SystemType


FINMAN Initial Conditions Setup


Figure 5.2:


Flow Diagram of FINMAN Model Initial
Conditions Selection Setup.
GR = Grouper, T = Tuna, A = Anchovy,
SH = Shrimp, ST = Seatrout, SN = Snapper,
DEV = Developing, FE = Fully Exploited,
RO = Recruitment Overfished, C = Complete,
S = Sketchy, VS = Very Sketchy, N = None.











5.2.1.1 Autocratic

In this category your decisions for managing the fishery are 100%

implemented. You hold independent and unlimited powers of government

decision-making. However, these decisions are additionally filtered by other

budget and policy decisions made by the user and their relationship to optimum.



5.2.1.2. Commission

You are a member of a body of persons authoritatively charged with

stewardship of a fishery. You make recommendations on fishery institution

management decisions. Your policy implementing power is limited to having one

vote on a commission member board of seven individuals. Again, your

recommendations are filtered by other policy and budget decisions.



5.2.1.3. Legislature

You are empowered to serve on a managing body who exercise the function

of making or enacting laws; however, under this selection you strictly make

recommendations, and implementation of your recommendations) is based on the

strength of your constituency support, which dictates the controlling factor on

the amount of policy recommendations that actually are implemented.



5.2.2 Scope of Management Authority Over Stock

This option allows you to set the level of your control of the unit stock of

interest.



5.2.2.1. 100% Authority

This fishery is essentially a unilateral fishery management institution, i.e.

one policy body for the stock management.











5.2.2.2. 67% Authority

This unit stock is shared with one other management entity where you have

complete control of 67% of the stock. This is the case of the bilateral policy on

a stock with overlapping distributional boundaries. The remaining 33% control is

computed as a stochastic variable each iteration to determine your overall level

of control.



5.2.2.3. 33% Authority

This unit stock is shared with two other management entities and you have

control of only 33% of the stock. This is the multilateral policy case.



5.2.3 Fishery Exploitation Type

Your control over the type of fishery to be managed includes an array of

choices ranging from:



5.2.3.1 Commercial (1 Gear Type)

One-gear fishery with constant selectivity properties for all ages past t .



5.2.3.2 Commercial (2 Gear Types)

Sequential competition (i.e., one segment of the fishery operates on a

younger portion of the stock than another).



5.2.3.3 Commercial and Recreational In Sequence

The most difficult level where nonconsumptive (recreational) interests also

compete with consumptive (commercial) interests or values.

If the user selects either of the two-gear functions (i.e. Fishery Type = 2 or

3), then following either of these selections in the major prompt, the user must











decide whether he would like to select the targeted ages exploited by the two

gear types. Otherwise default conditions will be applied. The choice allows for

simulating the effects of overlapping gear selectivity patterns. Default

conditions cause the effect of (1) discrete non-overlapping selection of gears for

Fishery Type 2 (2-commercial gears), and (2) slightly overlapping selection of

gears for Fishery Type 3 (recreational and commercial). If the user chooses to

set ages of selection then he must (1) enter the maximum age to be fished by

Fleet 1 which then has the gear fishing from ages 1 to the 1st choice (=

maximum age) and (2) enter the minimum age fishes by Fleet 2 which then runs

from the 2nd choice (= minimum age) to the oldest age. Ages of overlap receive

summed effects.



5.2.4 Current Fishery Status

5.2.4.1 Developing

A developing fishery where the fish stock is in essentially the virgin state.



5.2.4.2 Fully Exploited

A fully exploited condition that has the fishery at roughly maximum

sustainable yield (MSY).



5.2.4.3 Recruitment Overfished

The fishery is well past MSY and the spawning stock has been severely

reduced.



5.2.4.4 Unknown

The program generates a prompt which asks the user to enter an integer

number of any size. Upon entry of the integer, the program selects randomly

20











either a developing, fully exploited, or recruitment overfished condition of the

fishery.



5.2.5. Historical Data Availability

5.2.5.1 Complete

Program provides all data and great analyses.



5.2.5.2 Sketchy

Program provides catch and effort statistics, some economic data, and a

production model estimate.



5.2.5.3 Very Sketchy

Program provides imprecise catch and effort statistics and a production

model estimate.



5.2.5.4 None

No data available.



5.3.0 Decisions Sequence

5.3.0.1 Management Measures To Be Implemented

The program displays the scope of your management authority over the

stock and then begins the management measures sequence. Four major groupings

of fishery management measures are available, (1) effort limits, (2) size limits,

(3) season limits, and (4) catch limits and allocations (Figure 5.3.0).














































FINMAN Management Decisions Flow Diagram


Figure 5.3.0:


Flow Diagram of Generalized FINMAN Model
Management Decisions Process.











5.31 Fishing Effort Strategy

Presently the user defines the fishing mortality rate, and this is modified

to units of gear effort by the appropriate operation of the catchability

coefficient. All mortality rates are age-specific and constant across ages in the

single gear fishery, although age-specific mortality rates can be made variable if

so desired. It will be remembered that for a given relative distribution of fish

and fishing in a specified area the value of instantaneous fishing mortality, F, is

proportional to the total fishing effort, measured in standardized units.

The user must decide whether he would like to alter the fishing effort

strategy for the present year of simulation. If the decision is YES, the input

constant value represents potential fishing mortality which is the instantaneous

fishing mortality coefficient of a fully available age group in a given year.

If two fleets have previously been selected for by the user (i.e., Fishery

Type = 2 or 3), then the user must decide whether he wants to regulate the

fishing effort strategy in the present year for the two fleet groups separately.

Your potential choices are in the present year (iteration) are: (1) no regulated

change in F for both fleets, (2) change in regulated F for fleet I and no change

for fleet 2, (3) no change in F for fleet I and change in regulated F for fleet 2,

and (4) change in regulated F for both fleets.

If the decision is YES for either or both fleet 1 and fleet 2, then a prompt

will appear asking you to input the value of F recommended for the respective

fleet. The input value of F will then be applied against the segment of the age

distribution choose in the initial options sequence.



5.3.2 Size and Age of Capture

The user selects the age at first capture (= age and/or size of 100%

vulnerability) to be used by the particular gear-type operating. Like fishing
23











effort strategy, this decision is obviously tempered by what the manager feels is

optimum, and can also be affected by the various management filters in place.

The age of capture is set as a minimum size available to the fleet(s) as a whole.

This decision then sets the minimum age (size) at vulnerability which may be

altered depending on the filters operating during the present decision loop.



5.3.3 Seasonal Closures

Closed seasons (specific months of the year) can be selected to

achieve specific mortality rates, and to protect the stock during critical events

like spawning and recruitment. Closures are set for the fleet(s) as a whole.

Closures are operable from day one of the starting month to day one of the

ending month. That is, a closure that runs from March (=3) to June (=6) would

run from March 1 to June 1, a total of 3 months.



5.3.4 Catch Limits and Allocations

Catch quotas, by individual fleets, can be instituted in order to adjust the

age-specific instantaneous fishing mortality rates upwards or downwards to

acheive yields or catch rates within a particular tolerance.

The tolerance for the catch quota set by default is 0.5% over the

recommended upper limit for catch in the present year of simulation. The type

of catch quota available to the user is dependent upon the selection of the

fishery type. Specific options are:



5.3.4.1. Overall Catch Limit (Fishery Type 1)

Catch quota set for the single gear-type fishery. The user sets the catch

limit for the single fleet in weight, which is entered in terms of the units of W,.

(i.e., ultimate weight from the von Bertalanffy formulation).











5.3.4.2. Catch Limit Subdivided by Fleets (Fishery Type 2)

Catch quota sequence set for the two commercial gear-types fishery. The

catch limits can be set for either or both fleets in the same year, the program

will solve the exact solution of the catch equation, even when the gears have

overlapping selective properties. Quotas for both fleets are set in weight (in

units of W,) allowable for the respective fleets with a 0.5% tolerance at solution

considered acceptable.



5.3.4.3. Boat Quotas and/or Bag Limits (Fishery Type 3)

This is the sequence of catch restrictions for commercial and recreational

fleets operating jointly. Catch limits for the commercial fleet are set in terms

of weight of the catch, while catch limits for the recreational fleet are set in

terms of maximum number of fish per angler allowable (i.e., bag limit). If the

recommended bag limit is not exceeded the program provides a solution to the

catch equation on the first iteration; however, if the bag limit is exceeded by

virtue of the present level of fishing effort, then the program minimizes the

catch equation, within the specified constraints, to achieve the recommended

number of fish per angler for the entire recreational segment available to

capture.

Reduction of catch by setting a limit to the total catch that may be taken

in a given year is an indirect method of controlling fishing effort. It is indirect

because the catch obtained in a particular year by the expenditure of a given

effort is influenced to a greater or lesser extent by biological factors, of which

fluctuations in recruitment and especially for migratory fish, the distribution of

fish, are the most important.











5.4.0 Overall Budget Allocations Among Enforcement, Research and Influence

with Constituents

You will have an initial total resource management budget of one million

dollars for the first year of management simulation. This budget can increase or

decrease in subsequent years based on the level of constituency satisfaction.

Each simulation year the general budget must be distributed as allocations

among the following three (3) agencies and subgroups (Figure 5.4.1).



5.4J Assessment and Monitoring

Sets the overall budget available to five (5) component research and

monitoring groups.



5.4.2 Enforcement

Generates the dollars for the necessary police action to maintain imposed

regulations within their recommended levels.



5.4.3 Development

Allows the development of the fishery from an economic and effort

prospective, and also feeds into the constituency function.

The dollar amounts allocated to each of the above agencies dictates the

level of accuracy and precision you will observe in the external output the

degree of compliance with your recommended management measures, the rate of

fishery development and the level of constituency satisfaction. After responding

to the general budget prompts with allocations, you will be shown a screen with a

summary of your decisions. The amount in the "unallocated" cell is considered

fiscal surplus which feeds back in the "Influence with Constituency" function,

and proportionalizes potential budget increases for the next program iteration.

26





















































FINMAN Budget Decisions Flow Diagram


Figure 5.4.1:


Flow Diagram of FINMAN Budget Decisions.
AM = Assessment and Monitoring, EN = En-
forcement, DEV = Development, BFS = Basic
Fishery Statistic, RS = Resource Surveys,
CA = Catch Analyses, EC = Economics, EE =
Environmental Effects.










5.5 Research Budget Allocations Among Data Collection and Analysis Projects

From the total dollar amount allocated to Assessment and Monitoring

activities you must then allocate funds to the following component research

endeavors. Once again, the dollar amount allocated to each of the following

component data collection and research activities dictates the level of accuracy

and precision you will observe on the external output, tempered by the initial

conditions selections and the internal modifying functions. The specific

component variables for each of these five assessment and monitoring group

allocations are completely delineated in sections 11.4.1 and 11.4.2 of this manual

for one- and two-gear fisheries, respectively.

5.5.1 Compilation of Basic Fishery Statistics

5.5.2 Catch Analysis

5.5.3 Resource Surveys

5.5.4 Economic Analysis

5.5.5 Environmental Trends and Effects on Fishing Activities

After you have allocated the assessment and monitoring budget, a screen

with a summary of your allocations will appear. The amount in the unallocated

cell is considered misallocatedd" and as such has a negative impact on your

"influence" on the constituency.



5.6 Review of Input Parameterization

The review consists of the input parameters for the biological 'portion of

the model. This screen aids the model builder and is not available in the final

"General User" version. It constitutes the module REVIEW.DATA (RVDT)

explained in Section 13.11 of this manual.











5.7 Calculation of a Simulation Sequence

Upon completion of all management, budget, and decisions input and

bypassing the review sequence, FINMAN will then compute the present year's

simulation. The program first passes through a decisions modification loop which

adjusts the input decision values according to the present budget decisions; this

influences the precision and/or accuracy of the output. After adjustments are

made, if any, the program then calculates statistics for the present loop. This

entire calculation process takes approximately fifteen seconds.

If the user has chosen to implement a catch quota, the above calculations

will undoubtedly take longer than fifteen seconds. Computation time for the

catch quota loop is a function of: (1) the complexity of the fishing pattern (i.e.

two gears more complex than a single gear), and (2) the segment of the yield

curve on which the desired quota value lies. When the desired quota value is

attained, the prompt "Quota within tolerance" will appear on the screen.



5.8 Timing and Accounting

The ordinal numbering system is used throughout FINMAN. This means

that Yearclass 1 are the young of the year (0's), and Yearclass 2 are individuals

in their second year of life (l's), etc. The unit length of time is the reproductive

cycle, commonly a year in subtropical and temperate species. Computations are

conducted once during each time unit, thus it represents a year for the species

considered. Time unit (1,1) is either (1) the time of spawning for viable

fertilized ova which hatch in less than one-half month or for specific life

histories where the ova are cast freely into the environment (i.e., most marine

fishes) or (2) the time of hatching for eggs which hatch after periods greater

than one-half months incubation and during this period are carried by one of the

29










parents (i.e., most crustaceans). All accumulated statistics are carried on a

fiscal basis. P(1,1) is either the number of viable fertilized ova cast under case

(1), or the number of larvae hatched under case (2). P(4,1) is the number of

individuals in their fourth year of life at the start of the first month.



5.9 Destination for FINMAN Output

After completion of a simulation sequence, a prompt is shown which asks:

(1) Do you want the results sent to the printer?, or (2) Do you want the results to

be displayed on the monitor screen? If the results are to be sent to the printer,

then the output contains all the present loops' statistics, plus complete time

series information for the years of simulation to date, including the number of

years of historical data originally provided. If the results are displayed on the

monitor screen, then the information presented includes all data available from

the present loop, plus time series data equivalent to the last t years in the

fishery.



6.0 Biological Model Structure

A flow chart of FINMAN indicating the optional fishery types and life

history sectors of the model are presented in Section 5.0 (see Figure 6.0). Each

sector is described by the equations below. The basic time period for

calculations in FINMAN is annual with all processes summed on an annual basis.

Output is age-specific and summed for all ages and fishery types operating. In

the following sections continuous time expressions are denoted as C(x,y) while

their discrete time analog formulations are represented as Cx,y.

















































Figure 6.0:


A ^ ^------^ 3-i0}
I. Q !r-j- -!------ 3
SICF







-------1----------
RECRUITS MALES PEVALES






FF O
CATCH OUOTA NO
i SURVIVAL N
YE VARIABILITY
YES
NO IN CATCH
WINDOW
NATURAL
-YES DEATHS


CATCH.


FINMAN Fishery Sector Flow Diagram




Flow Diagram of FINMAN Biological and Fishery
Sector. MF = Sex-specific Maturation Function,
CF = Copulation Function, FF = Fishing Mortality
Function, SF = Stochastic Function, RF = Recruit-
ment Function.










6.1 Mortality

Mortality is age-specific and is assumed to be representable by an

exponential decline:
N(x,t+l) = N(x,t)e-Z(x't) (6.1.01)

with Z(x,t) = M(x,t) + A(x,t)*F(x,t) (6.1.02)

where,

N(x,t) = number of animals belonging to the xth yearclass at the

beginning of year t.

M(x,t) = instantaneous rate of natural mortality for yearclass x in

year t.

F(x,t) = instantaneous rate of fishing mortality of a fully available

yearclass, x = 1,...,tX, in year t.

A(x,t) = availability multiplier for yearclass x in year t.



The generalized stating of natural and fishing mortality allows the model

to simulate catch limits and allocations, effort limits, size limits, season limits

and the effects of seasonal mortality patterns.



6.1.1 Stochastic Natural Mortality

The stochastic version of the simulation model allows natural mortality (M)

to become a random variable with a known mean and variance. This procedure is

explained more fully in Section 6.4.3.1.



6.1.2 Average Number Alive

The average number of yearclass x during year t is given by

R(x,t) = Nx,t(1 eZxt)/Zx,t (6.1.2)
x.t (6..2











6.2 Growth

The growth in weight of the fish can be represented by at least two

alternative formulations:



6.2.1 von Bertalanffy Formulation

W(x,t) =W (1 e-Kx,t(x-t0))3 (6.2.1)

where,

W(x,t) = average weight of an individual in yearclass x at the

beginning of the interval t.

Wo,K,t0 are parameters of the von Bertalanffy growth equation.



6.2.2 Linear Segmental Formulation

Wij = a + bAt (6.2.2)

where, a = wi, j-1

b = (Wij Wi, j-1)

At =


6.3 Maturation

A maturity schedule of the sexes is computed. This is accomplished in the

simulator with two vectors of age-specific values, one denoting the average

fraction of males in each yearclass curing the breeding season, Omx, and the

other denoting the female fractions, 0fx'



6.3.1 Average Number of Males

The mean number of males, N during year t at spawning is:

Nm(t)= ~l mxNx,t(1 ex,t)/Zx,t (6.3.1)
33











6.3.2 Average Number of Females

The mean number of mature females, Nf, during year t at spawning is:

tX
Nf (t) = xl f xNx,t(I e-x,t)/Zx,t (6.3.2)


6.3.3. Sex Ratio
S(t) = Nm(t)/Nf(t) (6.3.3)

where S(t) = mean sex ratio for time interval t.



6.4 Recruitment

The computation of the number of recruits entering the population takes

the following forms:


6.4.1 Beverton and Holt Dynamics 1
R(t+) = a+P/SX,t (6.4.1)



6.4.2 Ricker Dynamics

R(t+) = Sx, taSx,t (6.4.2)

where, a and p are parameters of the models, and

Sx,t = total production of gametes by the stock in year t.

Note that recruitment is defined as the joining of the progeny to the population

and not necessarily to the fishable population (i.e., that portion vulnerable to the

fishing gear).


6.4.3 Stochastic Recruitment Variability

This life history option allows the user to set the level of recruitment

variability he would like to have operating in the population of interest. If
34











recruitment variability is choose, then the desired percentage variability around

present mean recruitment level must be entered. The +/- percentage is

computed as a stochastic variable around the mean of the density-dependent

stock-recruitment relationship.



6.4.3.1 Random Uniform Distributions (R.U.D.)

The R.U.D. is simulated by introducing the parameters of the cumulative

density function, i.e.,
U
F(x)=/f(x)dx Pr{ X x

E[x] = xf(x)dx = X (6.4.3.1.01)

Var(x) = f(x)(x E[x])2dx = 2 (6.4.3.1.02)
L
U = Upper Bound

L = Lower Bound

Passing these parameters to the uniform distribution where there is equal

probability of selection anywhere along the density function:

a = E[x] (3 Var(x))0'5 (6.4.3.1.03)

b = 2E[x]- a (6.4.3.1.04)

r = uniform random variable between 0 and 1

r = F(x) = (x a)/(b a)

x = a + (b a) r (6.4.3.1.05)

The stochastic version of the simulation model allows annual recruitment,

R; natural mortality, M: availability, A; and several other budget and

management decision variables to become random variables, each with a known

mean and variance.











6.4.3.2 Autocorrelated Sine Wave Periodicy for Recruitment

To simulate the autocorrelated time trend of environmental variation with

that of recruitment to the fishery, an autocorrelated sine wave with variable

period is incorporated into the recruitment function. This function can be

activated with a preselected period. For example, the southern oscillation (i.e.,

El Nino event) is suggested to have a phase of seven (7) years. The input value is

then the cycle phase (period) in years.

The general formulations utilized are:

Y(t) = e sin (at.+b) (6.4.3.2.01)

where t = year of simulation

6 = amplitude = fixed % of maximum recruitment.

Period = 2 r/a
1
Frequency Period = a/2r

To maintain recruitment in the positive domain therefore,

recruitment in year t is:

R(t) = P(1,33%) + Y(t) (6.4.3.2.02)



6.4.4 Spawning Stock

MY = year of life during which reproductive maturity begins.

SS(t) = spawning stock in numbers at the beginning of year t.

SS(t) x= Y N (6.4.4)
x=MY x't


6.4.5 Number of Larvae

Ext = number of viable ova per individual female age x.

NL(t) = number of larvae in year t.
tx
NL(t) = (I e-Zxt)/Z (6.4.5)
x=my fxxt xt e x,t
36











Where, Nxt = population abundance at the beginning of the year t for fish

aged x reference years old.



6.5 Yield

Yield from the population is computed both in numbers and weight for each

yearclass x under the von Bertalanffy growth model. The total annual yield from

population is the sum of the yields from each of its constituent year-classes

during one year of life.

Yn(x,t) = Ax,tFx,tNx,t(1 eZx,t)/Zxt (6.5.01)

where, Nxt = population abundance at the beginning of the year t for fish

aged x reference years old.

and Yn(x,t) = the yield of fish in numbers and which is complementary in

form to Baranov's catch equation.

Thus, the yield in numbers from the entire population in year t is equivalent to:

Yn(t) = x1 Ynx,t (6.5.02)

Under the von Bertalanffy growth model, the yield in weight, Yw(x,t), is

computed as: U eKxt(Xto)
Yw(x,t) A ,tF ,tN W C. -( n P
Yw(xt) AtFt t n=o Zx,t+nKx, 1 e-(x,t + nKx,t
where Un = 1,-3,3,-1, respectively. (6.5.03)

Therefore the yield obtained in weight from the population throughout its

fishable life span, i.e. between ages tp' and tX is obtained as:
Yw(t) = Yw
w(t) tp' xt (6.5.04)

It has been assumed that the recruitment of a year-class to the exploited area

and its entry to the exploited phase takes place instantaneously at ages tp and

tp', respectively.










6.5.1 Fishable Average Population

R(xt)fishable = Nx,t(l e-(Mx,t +A Fx, (Mx, + Axt Fx,t
(6.5.1)

where, Ax,t) 0



6.6 Other Characteristics of the Catch and Population

In a study of an exploited fish population there are several quantities,

besides the annual yield in weight, that will require assessment. From an

economic perspective it is necessary to know the catch per unit effort and the

mean weight of fish in the catch, while the analysis of situations in which factors

such as natural mortality and growth vary with the population density requires

expressions giving numbers and biomass of the population. Finally, when the

behavior of population models comes to be tested by observation or experiment,

or they are used as an adjunct to fishery management, it is of help to obtain

from the models predicted values of certain quantities such as the mean length

and mean age of fish in the catch, which are directly estimated from samples.



6.6.1 Mean Length and Weight of Fish in the Catch

TL(t) = Fx,t Nx,tLx,t (6.6.1.01)
x=tp'
where L ,t mean length of a fish aged x in year t.

tp = age at 100% vulnerability to the gear.

tX = oldest age of fish in the catch.

TL(t) = total length of all fish caught in year t.

Yn(x,t) = total number of fish caught aged x in year t.

= F Nxdx (6.6.1.02)
tp


x,t x= tp" Nx,t (6.6.1.03)











L(t) = mean length of fish in the annual catch in year t.
tA tx
L(t)=F t x tN t/FXt* N (6.6.1.04)
xt x=tp xt xt xt x=tp' xt
W(t) = mean weight of fish in the annual catch in year t.
t 1
W(t) Ywt/YN = F x,t xt Y /[Fx,t XStp' Nx,t] (6.6.1.05)



6.6.2 Age Distribution

Ux,t = age-specific mortality rate.

Bxt = birth rate per individual as a function of aged x in year t.
x,t
Cx,t = fraction of population in age class x.

IC(x,t)dx = 1 (6.6.2.01)
b = C(x,t)B(x,t)dx and u = C(x,t)u(x,t)dx
0
The relationship between survivorship and mortality schedules are:

l(x,t) = e-Yu(y)dy (6.6.2.02)

The fraction of the population in age class x at time t for a stable population can

be calculated as:

C(x,t) = N(x,t)/N(t) = e-rx(x)/e-ryl(y)dy (6.6.2.03)
0
in discrete form:

C ,t (X)1x/ (k)lk

= (er)-x l/ (er)=k 1k (6.6.2.04)
k= tp,
where, X = er

and r = b- u



6.6.3 Mean Age of Fish in the Population and Catch

The mean age of fish in the population is;

t Ntx N (6.6.3.1)
X~tp X't Xt P X't










Similarly, the mean age of the fish in the annual catch is:


1 AA'- t y
T N (6.6.3.2)
y F+ e-(F+M) X= x x x,t (6632)


It can be shown that the latter statement remains true irrespective of the

occurrence of fluctuations in the annual number of recruits.



6.6.4 Equilibrium Yield Per Recruit

The concept of equilibrium (or steady-state) is intimately tied to the yield

per recruit approach. Yield per recruit models examine the balance of growth

and mortality assuming a constant level of reproduction. The yield per

recruitment is simply the total annual yield from a given number of recruits

computed from the sum of the yield from all age classes available to the fishery

- or the yield from one yearclass throughout its life in the fishery, since the

calculations are for a population at equilibrium.

Yw(t) =/ F(t)N(t)W(t)dt (6.6.4.1)

where, Yw(t) = the total yield per recruitment in weight.

F(t) = instantaneous coefficient of fishing mortality.

N(t) = numbers of recruits, R, surviving to time t.

W(t) = mean weight of an individual at time t.

tt = age of 100% vulnerability to the fishing gear.

tA = maximum age attained in the fishery.

A simple and reasonable expression of equation 6.6.4.1 due to Beverton and

Holt is: l -)

Yw/Rc = FW., E F + M + nK (1 e(F+M+nK)(tX-tC')) (6.6.4.2)
n=o










where, Un = 1,-3,3,-1 respectively.

Rc = number of recruits surviving to te'.

M = instantaneous coefficient of natural mortality.

Wo,K,to = parameters of the von Bertalanffy growth equation.
The result of these calculations gives the yield per recruit as a function of

two independent variables, which are in principle controllable by suitable

management action the fishing mortality, F, and the age at first capture tc.

These results are best presented in a two-dimensional diagram (or three-

dimensional graphic representation, see Sections 15.1 15.6), in which the yield

from any combination of F and tc can be easily read off.



6.7.0 Stock Size

Nx,t = number of animals aged x at the beginning of year t.

S(t) = t tal fish stock size at the beginning of year t.

S(t) = xl Nx,t (6.7.0)



6.8.0 Selection

The selection curves can have any desired shape. The default settings of

the present model assumes knife-edged selection (100%) begins at the age of tc

(=t ') and remains constant for all older age groups.



6.8.1 Non-stochastic Availability

Selection is accomplished by the modifying the coefficient Axt in

Z(x,t) = M(x,t) + A(x,t)* F(x,t) (6.8.1)

so,

A(x,t) 0.0 < tc

1.0 > tc










An infinite number of selection patterns are potentially simulated by this

generalization.



6.8.1.1 Stochastic Availability

The stochastic version of the simulation model allows availability to

become a random variable with a known mean and variance (see Section 6.4.3.1.).



6.8.2 Season Closures

The entire fishery may be closed for any (or every) month of the year. In

this case no vessels may land anything until the closure is lifted.

Seasonal closures are accomplished in the annual simulation by modifying

the availability coefficient through a proportionality constant that indicates the

span of the year in which fishing shall take place.

MW(t) = month of year in which closure begins.

ME(t) = month of year in which closure ends.

The closure is instituted and ends on the first days of the months selected.

That is, a closure beginning in March (MS=3) and running through June (MS=6)

will be of three months total duration and is calculated as:

MC(t) = months of closure in year t.

MC(t) = ME(t) MW(t)

The availability coefficient, Ax,t, is modified accordingly as:

A(x,t) = (12-MC(t)/12) A(x,t) For x = 1 to1A











7.0 Catch Quota

Y1W(t) = total catch in weight in year t.

Y W(t) = E Yw (7.0)
1 x=6 4 x,t
CL(t) = proposed catch quota in year t.

TOL(t) = tolerance for catch quota in year t.

CBL(t) = lower bound for catch limit in year t.

CUB(t) = upper bound for catch limit in year t.



7.1 Overall Catch Quota

Quotas, as a tool of management, rank highly in terms of flexibility and

implementation. To the extent that the state of the biological stock can be

determined before the season, pertubations in the level of population can be

allowed for in the quota. Quotas may be a satisfactory tool to maximize yield

from the fishery, but in of themselves are not sufficient to obtain the maximum

economic yield.


7.1.1 Single Fleet or Two Fleets in Sequence

CBL(t) = CL(t) (CL(t) TOL(t)) (7.1.1.01)

CUB(t) = CL(t) + (CL(t) TOL(t)) (7.1.1.02)

if Y.W(t) > CUB(t) then Fx,t downwards

if Y1W(t) < CLB(t) then Fx,tupwards



if CLB(t) < Y1W(t) < CUB(t) then Y W(t) =,Y
II L x,t


7.2 Catch Limit Subdivided by Fleets

04CQ(t) = proposed catch quota for fleet I in year t.

05CQ(t) = proposed catch quota for fleet 2 in year t.
43










QL(t) = lower bound for catch limit in year t for fleet 1.

QH(t) = upper bound for catch limit in year t for fleet 1.

KL(t) = lower bound for catch limit for fleet 2 in year t.

KH(t) = upper bound for catch limit for fleet 2 in year t.

Y5W(t) = yield for fleet 1 segment (1 to SE1) of age structure in year t.

Y6W(t) = yield for fleet 2 segment (S2 to NIYC) of age structure in year t.

Q = Q1 = I when catch quota on; Q = QI = 0 when catch quota not on for

both fleets.



if Y5W(t) QH(t) Fxt downwards

Fleet 1 if Y5W(t) < QL(t) F,t upwards x = 1 to SEl
xt SEO
if QL(t) 5 Y5W(t) Li-x
if Y6W(t) > KH(t) F xt downwards
x,t
Fleet 2 if Y6W(t)(KL(t) F,t upwards x.= S2 to NIYC
x,t rNIYC
if KL(t) < Y6W(t) < KH(t) then Y6W(t) = Yw
-. x,t


7.3 Bag Limit

Purpose is to set an upper limit restriction on the number of fish taken per

angler or the recreational fishing effort unit. Program will solve a catch vector

equivalent to the recommended level per angler for every angler if the initial

bag per angler exceeds the recommended value.



7.4 Seasonal Closures

In principal the quota on catch is a simple and direct regulation. It

operates through limitation of fishing time in any one year. The quota then is

equivalent to the closed season. The date when the season is to be opened is

determined in advance together with the total allowable catch (TAC the
44











quota); when this catch has been taken the season is closed until the following

year.



7.5 Management Systems

Three types of management system structures for designing fishery

management rules are available in the FINMAN model. System types are

ordered in ascending levels of difficulty in achieving the 100% probability for

incorporating management decisions in the present loop.



7.5.1 Autocratic Body

Your decisions for managing are fully implemented.



7.5.2 Commission Body

CD(t) = proportion of total budget allocated to research in year t.

CR(t) = random number generated from CD(t) allocated in year t.

p = probability of measures being implemented.

p(t) = probability of compliance = C +p[(AM/t)/BVD(PZ)] (7.5.2)

a=0, p=1

1.0 p= CD(t)

if CD(t) 0-.999... p = CD(t)

l-p = probability of vetoing policy



7.5.3 Legislative Body

LD(t) = proportion of total budget allocated to research in year t.

LF(t) = proportion of total budget allocated to development in year t.

CD(t) = probability of measures being implemented in year t.

WTi(t) = weighting function for proportion i in year t.
45










CD(t) = (WT (t) + LD(t)) + (WTi(t) LF(t)) (7.5.3)

LD(t) = (AM/t)/BUD(t))1'5

LF(t) = DEV(t)/BUD(t)

If policies vetoed in year t, then regulations in effect in year t-1 will supercede

the present policy recommendations.


8.0 Economics (Costs & Returns)

The economic aspects of the FINMAN model have intimate interaction

with the other major components of the model. In general, profits realized by

each fishing effort unit and the distribution of fishing effort determines catch,

which determines profits. We are implicitly assuming that the distribution of

effort has been determined, and this effort is applied to the respective fishery

for a simulated year to generate the yields by cohorts of the species by each

gear type. Vessel profits for the year are then calculated. Fishing effort can be

readjusted to better exploit profit potential. At year's end, vessels may enter or

leave the fishing fleet based upon historical profits or losses and other factors

(see Section 10.0).


8.1 Fishing Effort (Units)

q(t) = catchability coefficient in year t.

F(x,t) = instantaneous rate of fishing mortality of a fully available

(vulnerable) year-class x in year t.

f(t) = effective fishing effort in year t where we assume that the fishing

intensity is unity in all areas. The gross stock of vessels of class K

targeting on species S can be calculated by:











8.1.1 Single Fleet

NVK(t) = total effective effort units of class K in year t

if, F(x,t) = q(t) f(t)

then f(t)= F(x,t)/q(t) = NVK(t)


8.1.2 Multiple Fleets

ql(t) = catchability coefficient for Fleet 1 in year t.

q2(t) = catchability coefficient for Fleet 2 in year.
Ul(t) = number of fishing effort units in Fleet 1.

U2(t) = number of fishing effort units in Fleet 2.
so,
U2(t) = F(x,t)/q2(t) and
U2(t) = F(x,t)/q2(t)
where x is an aged x member of the population under a knife-edge selectivity

pattern for fleet K (K= 1,2).


8.2 Unit Costs

Vessel costs can be either fixed costs (slip fees, insurance, etc.) or variable

costs (costs that may vary with effort, i.e. fuel, gear repair, etc.). The

conditions in a specific fishery are simulated by the appropriate assignment of

cost coefficients.


8.2.1 Single Fleet

OCi(t) = operating costs for vessel i in year t.

VC(t) = total vessel costs in year t.
NV,.
VC(t) = f(t) OCi(t) K=l (8.2.1)
i=l











8.2.2 Two Fleets

OC1(t) = operating costs for a fleet I vessel in year t.

OC2(t) = operating costs for a fleet 2 vessel in year t.

VIC(t) = vessel costs for fleet 1 in year t.

V2C(t) = vessel costs for fleet 2 in year t.
UI
VIC(t)= OCli(t) (8.2.2.01)

V2C(t) = OC2i(t) (8.2.2.02)



8.3 Gross Revenue

The ex-vessel price being established for a particular species by unit

weight, we are now in a position to calculate the revenues collected by each

vessel. All vessels of a given fleet are assumed to behave similarly, hence each

will generate the same catch and profits and calculations based on a per-boat

basis can be easily extrapolated to fleet totals.



8.3.1 Single Fleet

V(x,t) = value per unit of catch aged x in year t.

NU(t) = gross revenue for fishery in for fishery year t.

NU(t) = E (Yw(x,t) V(x,t)) (8.3.1)
x=l


8.3.2 Two Fleets

V3G(t) = gross revenue for fleet 1 in year t.

V4G(t) = gross revenue for fleet 2 in year t.
A
V3G(t) = E Yw(x,t) V(x,t) (8.3.2.01)
x=A
V4G(t) = I Yw(x,t) V(x,t) (8.3.2.02)
x=-A
3
NU(t) = V3G(t) + V4G(t) (8.3.2.03)

48











8.4 Net Returns

The vessel profits are calculated for each vessel type under each species

scenario. In addition to their obvious role in determining the economic survival

of the fishery, profits can influence vessel and constituency behavior in that; (1)

vessels may choose to target on a more profitable species, and (2) vessels may

enter or exit the fishery as a result of potential profits or real losses.


8.4.1 Single Fleet

RG(t) = net returns to the fishery in year t.

RG(t) = NU(t) VC(t) (8.4.1)



8.4.2 Two Fleets

R2G(t) = net returns to fleet 1 in year t.

R3G(t) = net returns to fleet 2 in year t.

R2G(t) = V3G(t) V C(t) (8.4.2.01)

R3G(t) = V4G(t) V2C(t) (8.4.2.02)

RG(t) = R2G(t) + R3G(t) (8.4.2.03)


8.5 Rate of Profit

RP(t) = rate of profit in year t.

RP(t) = RG(t)/VC(t) (8.5.01)

While vessels may fish or lay idle, the actual fleet size does not change

throughout a simulated year. At year's end, however, vessels may enter or exit

the fishery based upon the expected return available in the fishery relative to

the rest of the economy. To facilitate the computations, a profit stack is kept

for each vessel class, wherein the annual profits realized for each of the four

49










previous years are stored. The expected profits for the coming year are

calculated as a weighted sum of the previous profits, i.e.
4
EPK(t) = PWL RPKL (8.5.02)
L=1
EPK(t) = expected annual profits for one vessel of class K in year t.

RPKL = rate of profit realized by vessel of class K in Lth previous
year (L=l the year just completed).

PWL = weights dependent on time.



8.6 Budget Factors

The budget appropriation for the t+l iteration is a function of three factors

computed in iteration t. These appropriations can be increasing, decreasing, or

stable as outlined in Table 8.6.



8.6.1 Budget Activities

PD(t) = development fraction in year t.

PX(t) = research fraction in year t.

PP(t) = enforcement fraction in year t.

BUDFAC(t) = budget activities factor in year t.

WTi(t) = weighting factor for activity in year t.

PD(t) = (DEV(t)/BUD(t))1.5 (8.6.1.01)

PX(t) = (AM(t)/BUD(t))1.5 (8.6.1.02)

PP(t) = -((EN(t)/BUD(t)1.5) (8.6.1.03)

BUDFAC(t) = (WTi(t) PD(t)) + (WTit) PX(t)) + (WTi(t) PP(t)) (8.6.1.04)





















Summary of Budget Factors in Iteration t and their

Effect on Budget Appropriations in Iteration t+1.


Budget Increments


Positive


Stable


Negative


Budget

Activities



Profit

Activities



Population

Activity


1. $ to Development

2. $ to Research


Decreasing

Profits



Decreasing

Population


Stable Profits





Steady Pop'n


1. $ to Enforcement





Increasing Profits





Increasing Pop'n


Table 8.6:


BUDGET

FACTORS












8.6.2 Profit Activities
RP(t) = Rate of profit for fleets in year t.
PROFAC(t) = Profit factor in year t.


<(
If RP(t) 0

>0


PROFAC (t) = -(RP(t)1.5)
PROFAC (t) = 0
PROFAC (t) = -(RP(t)1.5)


8.6.3 Population Activity
P3(t) = Population size in year t.

DEL(t) = Percentage change in population size in year t.
POPFAC(t) = Population Factor in year t.

DEL(t) = (P3(t) P3(t-1))/P3(t-1)


<0
If DEL(t) 0

>0


PROFAC (t) = -(DEL(t)1'5)
PROFAC (t) = 0

PROFAC (t) = -(DEL(t)1.5)


8.6.4 Budget Appropriation

BUDAPRO(t) = (WTi(t) BUDFAC(t)) + (WTi(t) PROFAC(t)) + (WTi(t) *POPFAC(t))
BUDGET(t+l) = BUDGET(t) = (BUDAPRO(t) BUDGET(t))


If BUDGET(t+l) <0

>0


BUDGET(t+l) = $100K
BUDGET (t+l)


9.0 Enforcement Budget Decision Filters
When the user under-allocates with respect to the optimal enforcement

allocation required, the program then reverts to the enforcement decision
filters.










These filters deleteriously affect the major components of the management

regulation framework as follows:

BUD(t) = total budget in year t.

EF(t) = optimal enforcement budget in year t.

EN(t) = amount allocated to enforcement budget in year t.

OA(t) = percent allocation of optimal enforcement budget in year t.

OA(t) = EN(t)/EF(t)

if, OA(t) < 1.0 then go to filters (9.1 -9.4)

OA(t) > 1 then bypass filters


9.1 Fishing Mortality Exceeding Recommended

FIL(t),= OA(t)1.5 fo

if, FIL(t) > 3 th


and,


r FIL (t) ( 3

en FIL(t) = 3


F(x,t) = F(x,t) + (F(x,t) FIL(t))


9.2 Age of 100% Vulnerability Below Recommended

TC(t) = age of 100% vulnerability to gear in year t.

FIL(t) = -(OA(t)1.5)

if, FIL(t) > 3 then FIL(t) = 3 for TC(t) = 1,2,3,...,tX

TC(t) = TC(t) + FIL(t)

if, TC(t) < 2 then TC(t) = 2


0.0 for
1.0 for >_ TC(t)


x = 1,..., (TC(t)-l)
x = (TC(t)-l) to tX


(9.1)


(9.2)


.,Ax,t










9.3 Season Closures Less than Recommended

A(x,t) =[(12-MC(t)/12) A(x,t) FIL(t)

which modifies the availability coefficient accordingly.


9.4 Catch Quota Exceeding Recommended

CL(t) = recommended catch quota in year t.

FIL(t) = OA(t)2.0 (9.4.01)

if, FIL(t) ) 1.0 then FIL(t) = 1.0

CL(t) = CL(t) + (CL(t) FIL(t)) (9.4.02)


10.0 Effort Development Decision Filters

This set of filters allows for the potential maximum rate of development of

fishing effort (without regulation) with an optimal choice or allocation to

development. In this regard, a weighted evaluation by the utility function is

made at each iteration to determine if the manager is to be removed from his

position. The more money allocated to development increases the probability

that the manager will stay in position by adding positive weighting to the utility

function, and thus an overall favorable view of this action by the constituency.

RGK(t) = net profits from fleet type K in year t.

VCK(t) = total vessel costs of fleet type K in year t.

RPK(t) = rate of profit for fleet type K in year t.

FDIK = factor of effort increase for fleet type k in year t+1.

DEV (t) = development budget allocation in year t.

OPTDEV(t) = optimal budget allocation to development in year t.

DEVFAC (t) = ratio of development budget to optimal developmental.

budget in year t.

WX(t) = development budget factor in year t+1.
54










F2MULTK = fishing mortality multiplier for year t+1.

DEV(t)/OPTDEV(t) when DEVFAC(t) < 1.0 and RP > 0

WX(t) = 1.0 DEVFAC(t) > 1.0 and RP(t) > 0

0.5 DEVFAC(t) > 1.0 and RP(t) < 0.0

1.0 DEVFAC(t) < 1.0 and RP(t) < 0.0



The flow of effort units into and out of the fishery follows approximately

the pattern outlined in Table 10.0.


Table 10.0:


Rates of effort flow into and out of fishery based on rate

of profit and budget levels


RATE OF PROFIT (RP)


Negative


Low


Development Budget

(DEV)


High


Positive


High F Low F

Departure Entry




Low F High F

Departure Entry









10.1 Commercial, 1 Gear Type (K=1)
The factor of potential effort increase is calculated as:
FDI(t+1) = (RGK(t)/VCK(t))05 = (RPK(t))0.5 when RP 2 0.0

FDIK(t+l) = -((ABS(RP))1.5 0.0 > RP(t) > -1.0

FDIK(t+l) = -1.0 -1.0>RP


So, F2MULTK(t) = FDIK(t) *WX(t) (10.0.01)
then the fishing mortality development calculation without regulation is:
FK(t+l) = FK(t) = FK(t) F2MULTK(t) K = 1 (10.1.02)


10.2 Commercial, 2 Gear Type in Sequence
Factor of potential effort increase for 2-gear t

FDIK(t+l) = (R2GK(t)/VICK(t))0.5 K=l

FDIK(t+l) = (R3GK(t)/V2CK(t))0.5 K=2

FDIK(t+l) = ((ABS(RPK))1.5) K=1,2

FDIK(t+l) = -1.0

F2MULTK(t+1) = FIK(t+l) WX(t)


ypes (K=2) is calculated as:
. when RPK 0.0


0.0> RP,(t) -1.0


-1.o > RPK
K=1,2


(10.2)


10.3 Commercial 1-Gear Type and Recreational Fishery

10.3.1 Commercial Sector

The factor of potential effort increase for the commercial gear sector of


the fishery is calculated as:

FDIC(t+1) = (RGc(t)/VCC(t)) when RPC 2 0.0

FDIC(t+1) = ((ABS(RPC))1'5) 0.0> RPC(t) > -1.0
FDIC(t+l) = -1.0 -1.0 > RPC(t)

F2MULTc(t+l) = FIC(t+l) WX(t)
56


(10.3)











10.3.2 Recreational Sector

The factor of potential effort increase for the recreational fishery sector

is the product of three multipliers:


10.3.2.1 Virgin Population Criterion

MX(t) = virgin population size in numbers in year t.

P3(t) = population size in numbers in year t.

VPC(t) = % of virgin population present in year t.

PMSY = population size atmaximum sustainable yield.

VPC(t) = P3(t)/MX(t) (10.3.2.1)

if, P3(t)> PMSY then FIP (t+l) = (VPC(t))1.5 > 1.0

P3(t) < PMSY then FIV (t+l) = 1.0
VPC


10.3.2.2 Human Population Density Effect

Longterm growth of recreational fishing intensity is proportional to the

growth of the human population density in the urban area near the fishery. Thus,

if the quality of the stock is high (i.e., VPC(t) = 1) then this fact indicates good

recreational fishery action and a high potential change in recreational fishing

effort.

HP(t) = human population density increase in year t.

R = intrinsic rate of human population growth.

HP(t+l) = factor of effort increase due to human population growth in

year t+1.

HP(t+l) = HP(t) EXP(R) (10.3.2.2.01)

HI(t+l) = (1+R) (10.3.2.2.02)











10.3.2.3 No Growth

If P3(t) PMSY population then there will be no direct growth in fishing

intensity from a recreational fishery standpoint, thus FIR(t+1) = 1 and

DEVFAC(t) = 1.



10.3.2.4 Availability

Recreational fishing intensity is ultimately controlled by pure availability.



10.3.2.5 Factor of Recreational Effort Increase

FIR(t+l) = FIVC(t+I) HI(t+l) (10.3.2.5.01)

and F2MULT(t+l) = FIR(t+l) WX(t) (10.3.2.5.02)



11.0 Assessment and Monitoring Filters

Of the dollars for research and analysis projects allocated for Assessment

and Monitoring purposes, there are 5 destination groups for these funds.

Suboptimal budget decisions for the various groups causes variations in the

reported values of particular levels. Deviations are group specific.



11.1 Decision Variables

FINMAN utilizes decision variables to set stochastic variation tolerance

for output in the following respective categories.

OMA = Optimum Management Allocations in Dollars.

OLMA = 50% Level of OMA.

BFS = Basic Fishery Statistics Allocation.

RS = Resources Survey Allocation.

CT = Catch Analysis Allocation.

EC = Economic Analysis Allocation.
58











EE = Environmental Trends Allocation.

R = Random Variable.

VAR = Variable.



11.2 Standard Decision Loop

A standard decision loop for the respective output categories is computed

as follows:

Ml = BFS/OMA = % of Optimum Allocation.

IF = (BFS 2 OMA) then Bypass Filter.

IF = (OLMA < BFS) and (BFS < OMA) then 1

IF = (BFS < OLMA) then 2

1. AO = 0.6 + (0.4 Ml): Go to 3 (11.2.01)

2. AO = 1.6 MI : Go to 3 (11.2.02)

3. AR = VAR (VAR (1-AO)) (11.2.03)

BR = VAR + (VAR (1-AO)) (11.2.04)

VAR = AR + (BR-AR) R (11.2.05)


11.3 Sampling Curve

The sampling curves for the Assessment filters for one and two fleet

output, computed as the +/- stochastic oscillation around the mean response are

formulated by the following linear equations.

Sampling Segment A:

Y = 0.6 + 0.4x (Budget Allocation 2 50% of Optimum) (11.3.01)

Sampling Segment B:

Y = 1.6x (Budget Allocation < 50% of Optimum) (11.3.02)












11.4 Effect on Simulation Output

The following four modular segments listed are functional transformation

routines in the module ASSESS.FILTER. These modular routines alter the

original variables according to the segment of the sampling curve the value lies.

Relative variation in sampling survey precision is due to allocations in the major

headings of the assessment and monitoring budgetings.

BFS = Basic Fishery Statistics.

CT = Catch Analysis.

EC = Economic Analysis.

RS = Resource Surveys.

Following then are listings of the original variabiles names (OV), the name

of that when transformed (TV), the modification routines (AMR), and the storage

variable name of the time series variable (TSV). These are listed for (1) single

gear output (Section 11.4.1), and (2) multiple gear output (Section 11.4.2).










11.4.1 Single Gear Type Output Modification

Original
Variable
(OV)


Asses.Filter
Transformed Modification Time Series
Variable Routine Variable
(TV) (AMR) (TSV)


11.4.1.1

11.4.1.1.1

11.4.1.1.2

11.4.1.1.3

11.4.1.1.4

11.4.1.1.5

11.4.1.2

11.4.1.2.1

11.4.1.2.2

11.4.1.2.3

11.4.1.2.4

11.4.1.2.5

11.4.1.2.6

11.4.1.2.7

11.4.1.3

11.4.1.3.1

11.4.1.3.2

11.4.1.3.3

11.4.1.3.3.1

11.4.1.3.3.2

11.4.1.3.3.3

11.4.1.3.3.4

11.4.1.3.3


Quota and Catch

Proposed Catch Quota

Actual Total Catch

Total Number in Catch

Average Weight in the Catch

Average Length in the-Catch

Cost and Returns

Total Effort (# of Units)

Vessel (Unit) Cost

Gross Revenue from Fishery

Net Returns to Fishery

Rate of Profit

Yield in Weight by Age class

Yield in Numbers by Age class

Catch Analyses

Age-specific Fishing Mort.

Age-specific Natural Mort.

Von Bertalanffy Parameters

Ultimate Weight(W= )

Ultimate Length (L,)

Growth Coefficient

T-Zero (to)

Sex Ratio


EC

EC

BFS

BFS



BFS

EC

EC

EC

EC

EC

EC


OQC

Y1W

YZN

WA

AL



NV

VC

NU

RG

RP

YW(I,1)

YN(I,1)



F(I)

XM(I)



WINF

LINF

XAKV

TO

S1XRS


Y2W(T)



WBAR(T)

LBAR(T)



Z6(T)

EC(T)

HV(T)

NET(T)

EYPR(T)







F6(T)


CT

BFS


FC(I)

X0(I)



GI



G2

G3
It


CT



CT

CT

BFS







(TV) (AMR) (TSV)


11.4.1.3.4 Population Size @ Year's
Start by Age-class

11.4.1.3.5 Average Population Size
by Age-class

11.4.1.3.6 Fishable Average Population

11.4.1.4 Stock & Recruitment Trends

11.4.1.4.1 Stock Size @ Years' Start

11.4.1.4.2 Spawning Stock @ Year's
Start


11.4.1.4.3 Numbers of Larvae
RS


11.4.1.4.4

11.4.1.5

11.4.1.6

11.4.1.6.1

11.4.1.6.2

11.4.1.6.3

11.4.1.6.4

11.4.1.6.5

11.4.1.7

11.4.1.7.1

11.4.1.7.2

11.4.1.7.3

11.4.1.7.4

11.4.1.7.5

11.4.1.8

11.4.1.8.1

11.4.1.8.2


Recruitment

Yield Per Recruit Analysis

Fishery Economic History

Number of Effort Units

Gross Revenue

Effort Costs

Net Returns

Rate of Profit

Fishery Catch & Effort Record

Catch

Fishing Mortality Rate

Average Weight in the Catch

Average Length in the Catch

Age @ 100% Selection

Point Awards for Loop

Newness Allocation

Conservation Ethic


P(I,1)


RN(I,1)


FP(I,1)



P3

SP


P(1,1)
NL(T)

P(1,33%)

YA(I,3)



NV

NU

VC

RG

RP



Y1W

F(I)

WA

AL

TC



AN

CE

62


P0(I,1)


FS(T)

ST(T)


REC


BFS

EC

EC

EC

EC


RC(T)





Z6(T)

HV(T)

EC(T)

NET(T)

EYPR(T)



Y2W(T)

F6(T)

WBAR(T)

LBAR(T)

TC(T)


Is

FC(I)
it


BFS

BFS

BFS

CT


(OV)








(OV) (TV) (AMR) (TSV)

11.4.1.8.3 Commercial Ethic CM

11.4.1.8.4 Budget Management BM

11.4.1.8.5 Management Measures
Perception PAP

11.4.1.8.6 Total Award UT(PZ-1) PTS

11.4.1.9 Historical Budget Allocation Decisions

11.4.1.9.1 Assessment & Monitoring AM AM(T)

11.4.1.9.2 Enforcement EN EN(T)

11.4.1.9.3 Development DEV DEV(T)

11.4.1.9.4 Not Allocated LEFT NA(T)

11.4.1.9.5 Total Budget BUD(PZ) BDG(T)

11.4.1.10 Historical Assessment & Monitoring Decisions

11.4.1.10.1 Data Collection

11.4.1.10.1.1 Basic Fishery Statistics BFS BFS(T)

11.4.1.10.1.2 Resource Surveys RS RS(T)

11.4.1.10.2 Analysis

11.4.1.10.2.1 Catch Analysis CT CT(T)

11.4.1.10.2.2 Economics EC E3(T)

11.4.1.10.2.3 Environmental Trends EE EE(T)

11.4.1.10.5 Not Allocated ML ML(T)

11.4.2 Multiple Gear Type Output Modification

11.4.2.1 Quota and Catch

11.4.2.1.1 Proposed Quota

11.4.2.1.1.1 Numbers

11.4.2.1.1.1.1 Fleet 1

11.4.2.1.1.1.2 Fleet 2 QN







(TV) (AMR) (TSV)


11.4.2.1.1.1.3 Both Fleets Combined

11.4.2.1.1.2 Weight

11.4.2.1.1.2.1 Fleet 1

11.4.2.1.1.2.2 Fleet 2

11.4.2.1.1.2.3 Both Fleets Combined

11.4.2.1.2 Actual Catch

11.4.2.1.2.1 Numbers

11.4.2.1.2.1.1 Fleet 1

11.4.2.1.3.1.2 Fleet 2

11.4.2.1.2.1.3 Both Fleets Combined

11.4.2.1.2.2 Weight

11.4.2.1.2.2.1 Fleet 1

11.4.2.1.2.2.2 Fleet 2

11.4.2.1.2.2.3 Both Fleets Combined

11.4.2.2 Average Size in the Catch

11.4.2.2.1 Average Weight

11.4.2.2.1.1 Fleet 1

11.4.2.2.1.2 Fleet 2

11.4.2.2.1.3 Both Fleets Combined

11.4.2.2.2 Average Length

11.4.2.2.2.1 Fleet 1

11.4.2.2.2.2 Fleet 2

11.4.2.2.2.3 Both Fleets Combined

11.4.2.3 Economics (Costs & Returns)

11.4.2.3.1 Effort (# of Units)


0


04CQ

05CQ

(04CQ + 05CQ)


Y3N

Y4N

YZN



Y5W

Y6W

Y1W





A7W

A8W

WA



LIA

L2A

AL


EC

EC

EC



EC

EC

EC





BFS

BFS

BFS



BFS

BFS

BFS


(OV)








(TV) (AMR) (TSV)


11.4.2.3.1.1

11.4.2.3.1.2

11.4.2.3.1.3

11.4.2.3.2

11.4.2.3.2.1

11.4.2.3.2.2

11.4.2.3.2.3

11.4.2.3.3

11.4.2.3.3.1

11.4.2.3.3.2

11.4.2.3.3.3

11.4.2.3.4


Fleet 1

Fleet 2

Both Fleets Combined

Vessel Costs

Fleet 1

Fleet 2.

Both Fleets Combined

Gross Revenue

Fleet 1

Fleet 2

Both Fleets Combined

Net Returns


U1

U2

(Ul +U2)



VIC

V2C

VC



V3G

V4G

NU


11.4.2.3.4.1 Fleet 1 R2G

11.4.2.3.4.2 Fleet 2 R3G

11.4.2.3.4.3 Both Fleets Combined RG

11.4.2.3.5 Yield in Weight by Age-class YW(I,1)

11.4.2.3.6 Yield in Number by Age-class YN(I,1)

11.4.2.4 Catch Analysis

11.4.2.4.1 Age-specific Instantaneous
Fishing Mortality Rate F(I)

11.4.2.4.2 Age-specific Instantaneous
Natural Mortality Rate XM(I)

11.4.2.4.3 Von Bertalanffy Growth Equation Parameters

11.4.2.4.3.1 Ultimate Weight (W.) WINF

11.4.2.4.3.2 Ultimate Length (Lo) LINF

11.4.2.4.3.3 Growth Coefficient (K) XAKV

11.4.2.4.3.4 T-zero (t0) TO


FC(I)


xO(I)


Gl



G2

G3


BFS

BFS


BFS


(OV)







(TV) (AMR) (TSV)


11.4.2.5.4

11.4.2.6

11.4.2.7

11.4.2.7.1

11.4.2.7.1.1

11.4.2.7.1.2

11.4.2.7.2

11.4.2.7.2.1

11.4.2.7.2.2

11.4.2.7.3

11.4.2.7.3.1

11.4.2.7.3.1

11.4.2.8
S11.4.2.8.1

11.4.2.8.1.1

11.4.2.8.1.2


Recruitment

Yield-Per-Recruit

Fishery Economic History

Gross Revenue

Fleet 1

Fleet 2

Effort Costs

Fleet 1

Fleet 2

Net Returns

Fleet 1

Fleet 2

Fishery Catch & Effort Record

Number of Effort Units

Fleet 1

Fleet 2


11.4.2.4.4 Sex Ratio

11.4.2.4.5 Population Size @ Year's
by Age-class

11.4.2.4.6 Average Population Size
by Age-class

11.4.2.4.7 Fishable Average Population
by Age-class

11.4.2.5 Stock & Recruitment Trends

11.4.2.5.1 Stock Size @ Year's Start

11.4.2.5.2 Spawning Stock @ Year's Start

11.4.2.5.3 Number of Larvae


V3G

V4G


VIC

V2C



R2G

R3G





Ul

U2


BFS


P0(1,1)


SIXRS


P(I,1)


RN(I,1)


FP(I)



P3

SP

P(1,1)

P(1,33%)

YA(I,3)


FS(T)

ST(T)

NL(T)

RC(T)







Z7(T)

HV(T)



E4(T)

NET(T)



EYPR(T)

R3G(T)





U1(T)

U2(T)


(OV)


P0



LY

REC


CT

CT

RS

RS







EC

EC



EC

EC



EC

EC





BFS

BFS








(OV) (TV) (AMR) (TSV)


11.4.2.8.2

11.4.2.8.2.1

11.4.2.8.2.2

11.4.2.8.3

11.4.2.8.3.1

11.4.2.8.3.2

11.4.2.8.4

11.4.2.8.4.1

11.4.2.8.4.2

11.4.2.8.4.3

11.4.2.8.5

11.4.2.8.5.1

11.4.2.8.5.2

11.4.2.8.5.3

11.4.2.9

11.4.2.9.1

11.4.3.9.2

11.4.2.9.3

11.4.2.9.4

11.4.2.9.5

11.4.2.9.6

11.4.2.10

11.4.2.10.1

11.4.2.10.2

11.4.2.10.3


Catch in Weight

Fleet 1 Y5W

Fleet 2 Y6W

Catch Per Unit Effort

Fleet I II

Fleet 2 12

Average Weight in Catch

Fleet 1 A7W

Fleet 2 A8W

Both Fleets WA

Average Length in Catch

Fleet 1 L1A

Fleet 2 L2A

Both Fleets AL

Point Awards for Loop

Newness Allocation AN

Conservation Ethic CE

Commercial Ethic CM

Budget Management BM

Management Measures Perception PAP

Total Award UT(PZ-1)

Historical Budget Allocations

Assessment & Monitoring AM

Enforcement EN

Development DEV


U3(T)

U4(T)


U5(T)

U6(T)


BFS

BFS

BFS



BFS

BFS

BFS


C1(T)

C2(T)

C3(T)



C4(T)

C5(T)

C6(T)












PTS



AM(T)

EN(T)

DEV(T)








(OV) (TV) (AMR) (TSV)


11.4.2.10.4

11.4.2.10.5

11.4.2.11


11.4.2.11.1

11.4.2.11.1.2

11.4.2.11.2

11.4.2.11.2.1

11.4.2.11.2.2

11.4.2.11.2.3

11.4.2.11.3


Not Allocated

Total Budget

Historical Assessment &

Data Collection

Basic Fishery Statistics

Resource Surveys

Analysis

Catch Analysis

Economics

Environmental Trends

Not Allocated


LEFT

BUD(PZ)

Monitoring Allocations



BFS

RS



CT

EC

EE

ML'


NA(T)

BDG(T)


BFS(T)

RS(T)


CT(T)

E3(T)

EE(T)

ML(T)











12.0 Point Allocations and Development of Utility Equation Parameters

Rational management of a fishery by a public agency requires optimization

of an objective function which reflects benefits to the users. The decision maker

must specify certain measures of effectiveness, and then continues to develops a

utility function governing explicit measures or attributes. Given such a utility

function, the decision-maker would prefer the alternative with the greatest

expected utility. Such a utility function is an expression of preference. The

categories of utility function evaluation are defined by the following five

attributes for the management of the fishery:

xl = AN(t) = point award for relative newness of manager in year t.

x2 = CE(t) = point award by people interested in stock conservation and not

necessarily economics in year t.

x = CM(t) = point award by commercial sector based on economic input in

year t.

x = BM(t) = point award for relative amount of money to development

(buying favor) in year t.

x5 = PER(t) = point award relative to the number of management measures

enacted in year t.

The utility function describes the preference orderings of a hypothetical

decision maker, such as the Director of an International Fishery Management

Commission. The utility is used in the simulation-optimization model in which

(1) performance above a threshold level in a particular year, and (2)

maximization of the sum of utility over years, is the objective.



12.1 Newness Allocation

User allocated maximum points in year 1, with an exponential decay from

the period t to t + 1. We are assuming a discount rate here (bl) such that after 5
69











years the utility is about 10% of the original value.

PZ = year of management simulation.

AN(t) = award for newness in year t.

MAX = maximum initial point award for newness.

AN(t) = MAX e -0.45(PZ)= X (12.1.01)

u(x) = xlb1

U1 (x) = 0.5 [(xl*)] + 0.5 [Ul(Xl)] (12.1.02)
IA&.5s)
= In (xI)
x1 = 0.6666667

b = 1.7095113

Since I, is time as defined here, and j is multiplicative with the other

attributes, then the utility of this function represents an assumed discount rate

and the risk behavior associated with it.


12.2 Conservation Ethic

Point award based on constituency expectations by people interested in

conservation, and not strictly economic benefits. For certain, the value of

recreational fishing is a legitimate part of the economic yield from the fishery.

Most recreational fishermen see value in the fishing activity and not just the

catch. In this case the higher the percent present population of virgin population

(i.e. no fishery) the greater the reward because of the assumed higher catch

rates per angler for the recreational angler, and presumably more bigger fish

available.

MX = virgin fish population size in numbers = Nx,t t=0

P3(t) = population size in year t = x Nx,

x2 = P3(t)/MXo

=0.5 U2(x2*) +0.5 [2(x20) = 0.5 (12.2.01)
--------- -O










b2 = In (x2) (12.2.02)

x2 = 0.15

b2 = 0.3653681

CPP(t) = fraction of present population to virgin in year t = P3(t)/MX

CE(t) = conservation ethic award.

CE(t) = -100 + (200 x CPP(t)) = x2


12.3 Commercial Ethic

Point award based on the economics of the commercial sector, i.e., the

rate of growth of the within year investment (see section 8.0).

CO(t) = cost of I vessel operating in year t.

NV(t) = number of vessels operating in year t.

VC(t) = total vessel costs in year t.

NU(t) = gross revenue in year t.

RG(t) = NU(t) VC(t) = profit for year t.

PF(t) = profit per unit capital outlay in year t.

PF(t) = RG(t)/VC(t) (12.3.01)

CM(t) = commercial ethic point award in year t.

CM(t) = PF(t)/MZ = x3 (12.3.02)

MZ = maximum possible rate of profit = Cmax

u3(x3) = CM(t)/Cmax
u3(x3) = x3b3
b3 = In (0.5)

x3=.25

b3=0.5











12.4 Budget Management

Point award for the relative amount of money to development, i.e., buying

favor. The fisheries manager is allocated dollars to meet certain goals. If he

returns the money without achieving the goals, then he is doing a poor job.

BUD(t) = total research and management budget level in year t.

LEFT(t) = total dollars unspent in year t.

DEV(t) = total dollars allocated to development in year t.

BM(t) = DEV(t)/BUD(t) = x (12.4.01)

u (x4) = x

x = .40

b = 0.7774211

PM(t) = LEFT(t)/BUD(t) (12.4.02)

BM(t) = budget management point award in year t.

BM(t) = MAX x PM(t) 1.5 (12.4.03)


12.5 Perceptions

Constituencies view of the manager's use of potential regulation schemes.

In general, fewer regulations are better than more.

MAXREGS(t) = maximum number of possible regulatory measures in year t.

REGS(t) = number of management measures enacted in year t. (12.5)

PAP(t) = point award for perceptions in year t.

PAP(t) = (MAXREGS(t) REGS(t)) / MAXREGS(t) = x5 (12.5)

u5(x5) = x5b5
x5 = 0.95

b5 = 13.513407









12.6 Multiattribute Function Development and Parameterization
Given five attributes which contribute to our "quality of fishery
management" criterion, we desire to construct a utility function u(x1, x2, x3,

x4, x5) = u(x), reflecting all of these measures.
u(x) = f ui(x1),u2(x2),u3(x3),u (x ),u(x5) (12.6.1)
where, ui(xi) is a utility function over attribute xi.
Let, xiO = least preferred outcome of xi.
x* = most preferred outcome of x.
then, ui(xio) = 0 and ui(x*) = 1
so, U(XO1,X20,x30,x0o,x50) = 0
u(xl*,X2*,x3*,x*,x5*) = 1
As mentioned previously, ki = (xi*,x ). Therefore, each k. could be
evaluated by finding the probability p at which the decision-maker was
indifferent to u(xi*,xi_) for certain and a lottery offering u(x*) with a probability
p, or u(xo) with a probability I-p. This result yields
u(x*,xi-) = pu(x*) + (1-p) (xo)] (12.6.2)
since (x*) = 1 and (xo) = 0, then (12.6.2) reduces to
(xi*,xi) = p (12.6.3)
If Iki=l, and K=0 then the model is linear. If Ekil, then KA~ and must be
evaluated.
5
I+ Ku(x)= I1+Kk ui(xi) (12.6.4)


1+Ku(x) = (+KklUl(x1))(l+Kk2u2(x2)) (L+Kk3u3(x3))(1+Kku4(x4))(+Kk5u5(x5)) =


u(x) = [(+Kklul(x1))(1+Kk2u2(x2))(l+Kk3u3(x3)) (1+Kk4u4(x4))(l+Kk5u5(x5)) -11/K










where the k. are scaling factors (0 (-1< K). The scaling factor K may be found numerically by solving
5
1+K = "'(+Kk.) (12.6.5)
i=I
Equation (12.6.5) is derived from (12.6.4) when all attributes are offered at their

most preferred amounts; i.e x=x*. If 1ki> 1, then -1 K > 0. Thus (12.6.5) may be solved by trial and error (see Powers, 3.E. 1976.

Virginia 3. Sci. 27(4):191-198.). The scaling factor ki is equal to p, i.e.:
u[ul(new in position),u, (0)] = k = 0.8

u u2(maximum pop'n size),u2(0) = k2 = 0.45
u1u3(max.rate of profit),u3(0) = k3 = 0.4

uEu4(max.$ to development),u4 (0) = k = 0.6
u[u5(minimum regulations),u5(0) = k5 = 0.8
K = -0.995

A utility function represents the decision-maker's personal preference, and
there is no "right" or "wrong" associated with it. However, by the very nature of
his position a decision-maker employed by a public agency is required to make

value judgements about benefits to the public whom the agency serves. In all

likelihood, preference orderings by the users will not be consistent. Even if they
were, it is unlikely that the decision-maker's utility responses would completely
coincide with those of the public. Therefore, by definition the decision-maker
must decide on the utility responses himself. However, the decision-maker may
have no a prior judgements of marginal utilities and tradeoffs which will affect

public benefits. In such a case public input may be desirable.














Table 12.6.0 Utility Attributes and Their Ranges


Least Desirable Most Desirable
Attribute: Amount Amount


1) Newness (years)

x1:scaled



2) Conservation


x2:scaled


3) Economic Revenue to
Commercial Sector

x3:scaled



4) Dollars to fleet development

x4:scaled


5) Number of Management
Measures

x5:scaled


Zero and
Negative Profits


Max. Profit


1/3 Total Budget


All possible


None


Pmax











Table 12.6.1: Example Utility derived for outcomes of xl (newness),

x2(conservation), x3 (commercial), x4 (budget), and x5
(regulation) using parameter values given in Sections 12.1-12.5.


u5(x5) U4(x) u3(x3) u2(x2) ul(x1 x


3.0x10'14 .167 .316 .431 .020 .1

3.5x1010 .286 .447 .555 .064 .2

8.5x10-8 .392 .548 .644 .120 .3

4.2x10-6 .490 .632 .715 .209 .4

8.6x10-5 .583 .707 .776 .306 .5

1.0x103 .672 .755 .830 .418 .6

8x10-3 .758 .837 .878 .543 .7

.049 .840 .894 .922 .683 .8

.24 .921 .949 .962 .835 .9

1.0 1.0 1.0 1.0 1.0 1.0












u4 (x4)-


.4
'4


old nev
Time in Positon(xl)


0 l
low


Us5(X)


h I
1.0
high


Population Size(x2)
1.01
C


u3 (x3)


//
00000


fe I '
few


S I I
1.0
lots


Dollar Contributions(x4)


01.0
lots few

Management Measures(x5)

'f


I I I I I
0 1.0
low& high
neg of Profitx
Rate of Profit(x3)


Figure 12.6.0: Utility Derived Outcomes of A) xl(newness),
B) x2(conservation), C) x3(commercial),
D) x4(budget), and E) x5(regulation).


u (x1)


u2(x2)


.4
.4
I
I


']D


$ f
J











13.0 FINMAN Computer Program Module Descriptions

13.0.1 Generalized Program Flow

The FINMAN program flow for a typical single-gear type fishery

arrangement is diagrammed in Figure 13.0.1. Modules for both single- and

multiple-gear fisheries are executed in sequence to control the computational

and decisions flow network, variables are continuously entered and retained for

the entire simulation exercise. Modular routines and their general functions for

the Apple (and IBM) microcomputers are listed below:


Module Name


Function


13.1 FINMAN.MAIN

(FMAN)









13.2 "SPECIES"







13.3 READ.DATA

(RDAT)



13.4 DATA.INIT

(DTIN)


Allows selection of the species profile and sets controls

for the management system type, management

authority, species life history strategy, fishery

exploitation type, current fishery status, and historical

data availability.



Loads to random access memory (RAM) the 'SPECIES'

life history and management system profiles as RAM

text data modules.



Loads to active working memory the user selected

subsets of management and species profiles.



Initializes historical data with predetermined

precision bounds.

78







Figure 13.0.1: Simplified modular flow diagram for computer program
FINMAN module execution for a 1-gear type fishery.

FINMAN

SPECIES.DATA

READ!DATA

DATA.INIT

HISTORICAL.DATA'(CATCHIST/ECON)

BHYIELA/YPRA

DECISIONS 1 & 2

REG.QUOTA

MAN.AUTH

BUDGET

MST

ASSESSMENTS

ENFORCE

GXFI 1I & 2~QUOTA

ASSESS.FILTER 1 & 2

GXAUT

RECRUITMENT

ECON

CATCHIST

POINTS

BUDtAC

FDEV

(es-- CONTINUE?

NoSUMMARIZE

GXOUT

End











13.5 BHYIELD

(BHYLD)


13.6 CATCHIST

(CTHS)



13.7 DECISIONS

1&2

(DEC & DEC2)



13.8 BUDGET

(BDGT)



13.9 ASSESSMENTS

(ASMT)





13.10 ENFORCE

(ENFOR)



13.11 REVIEW.DATA

(RVDT)



13.12 GXFIN I & 2

(GXFIN & GXF2)


Calculates Equilibrium Yield per Recruit surface.


Calculates and displays the fishery specific historical

catch information for a single-gear type fishery.



Sets management decisions strategy for fishing

mortality and age of capture for the current iteration.





Sets research, enforcement, and development budgets

for current iteration.



Sets basic fishery statistics, resource survey, catch

analyses, economic, and environmental monitoring

budget decisions for the current iteration.



Modifies management decision strategy based on

enforcement allocations for the present iteration.



Allows review of the life history and management

settings prior to computation of the current iteration.



Calculates age- and sex-specific fishery effects for the

current loop including larvae, recruitment, growth,











yearclass sizes, spawning sex ratio, fertilized female

ratio, yields and associated economic conditions for the

current iteration.


13.13 QUOTA

(QUOTA)



13.14 ASSESS.FILTER

1 &2

(ASFL & ASFL2)



13.15 GXOUT

S(GXO)



13.16 GXOUT2

(GX02)



13.17 RECRUITMENT

(RECT)



13.18 YPRA

(YPRA)







13.19 ECON

(ECON)


Calculates quota-tolerances, catch quotas, and bag

limits for the current iteration.



Modifies all output categories based on assessment

and monitoring budget allocations for the current

iteration.



Single-gear type complete fishery and economic analyses

output for the current iteration.



Multiple-gear type complete fishery and economic

analyses output for the current iteration.



Displays historical stock and recruitment trends.





Displays in tabular form the equilibrium yield per recruit

analysis surface, and the fishing mortality and age of

capture estimations for year t-1, and the selections by

management in year t.



Displays historical fishery economic analyses for a

single gear type.
81










13.20 POINTS

(POINT)


Calculates and displays utility function solution and

evaluates user performance for the current iteration.


13.21 CEFF2 & CEFF2.1 Calculates and displays the fishery specific historical

(CFF & CEFF2) catch and effort information for a multiple-gear fishery.


13.22 FISHECON2

(ECON2)



13.23 BUDGET.HIST

(BDGH)



13.24 ASSESS.HIST

(ASHT)



13.25 FDEV

(FDEV)



13.26 BUDFAC

(BFAC)



13.27 MST

(MST)


Calculates historical fishery economic analyses for a

multiple-gear fishery.



Calculates and displays the historical, research,

enforcement and development budget decisions.



Calculates and displays the historical data collection and

analysis budget decisions.



Calculates the rate of fishing effort development

expected without regulation.



Calculates budget factor and expected level of budget

change in year t+1.



Calculates the decisions sequence implementation

probabilities for a commission or legislature-based policy

body.










13.28 MAN.AUTH Calculates the level of management decision compliance

(MNAT) under a multi-lateral fishery management structure.



13.29 REG.QUOTA Sets catch quota and bag limit policy for single- and

(RGQUO) multiple-gear fisheries for the current iteration.


14.0 Loading and Creating Random Access Text Modules

The control programs) GROUPER, TUNA, ANCHOVY, SHRIMP,

SEATROUT and SNAPPER each, when executed create a series of random access

text files which contain the starting condition input parameters necessary for

execution of the FINMAN module series. Upon execution of one of the six

aforementioned control programs, random access (Apple computers) or

sequential access (IBM computers) memory data modules are created.



14.1 Editing the Text Files

14.1.0.1 Apple Computers (Applesoft BASIC)

The general syntax for editing and subsequent interpretation of each field

variable takes the following form for the Apple IIe and IIc versions of Applesoft

BASIC:

D$ = CHR$(4): REM CONTROL-D

To create a file directory and the associated subdirectory to a RAM or Disk

location:

PRINT D$; "OPEN/RAM OR DISK/FINFILE/FILENAME, L"

To READ or WRITE to the subdirectory file of specified length L:

PRINT D$; "WRITE OR READ/RAM OR DISK/FINFILE/FILENAME, R"









Print statement for Value of Colon indicates
writing to a text file \ field variable end of field

[ ...PRINT (INPUT) VARIABLE:... ]


To CLOSE the specific directory and subdirectory:

PRINT D$; "CLOSE/RAM OR DISK/FINFILE/FILENAME"

L = File length in bytes

R = Text file record number



14.1.0.2 IBM Personal Computers (Microsoft BASIC)

The general syntax appropriate for creating, editing and subsequent

interpretation of each field variable takes the following form in Microsoft

BASIC:



To create a file directory and the associated subdirectory to Disk Drive A:

OPEN "A:/Directory/FILENAME" for output(input) as #1


To write or read to the subdirectory file:


Write statement for Value of field
Writing to a text file Variable #1


[ WRITE(INPUT) #1, VARIABLE(1), ...VARIABLE (N)


To close the directory and subdirectory:

CLOSE #1

The specific line numbers that require editing are referred to under each

data text file heading.











14.1.1 INPUT

o Line #180

Reads in the successive values of NY, LI, LF, ROG, NRE, NAFMT, NPCF,

N1YC, MY, MSB, MASE, MFR, ABREV, SE1, S2, ED, XH.

1. NY = Number of years that each F2MULT value is to be repeated.

Input controls the number of years of simulation duration. Must

equal 1.

2. LI = Numerical designation of initial yearclass to be printed on the

output. Suggest 1.

3. LF = Numerical designation of final yearclass to be printed on the

output. Suggest tX = N1YC.

4. ROG = Growth option. Growth in weight represented by one of the

options:

0) von Bertalanffy Formulation.

1) Linear Segmental Growth Formulation.

5. NRE = Stock and recruitment model option.

0) Beverton & Holt function.

1) Ricker function.

6. NAFMT = Number of F-multipliers. Must equal 1.

7. NPCF = Random mating model option (Suggest 1).

0) Retain.

1) Delete.

8. N1YC = Oldest age of the fish stock (tx).

9. MY = Year of life during which reproductive maturity beings.

Range: I to NIYC.











10. MSB = Month of year when spawning (or breeding) begins. Must

equal 1.

11. MASE = Month of year when spawning ends. Must equal 1.

12. MFR = Month of year during which recruitment occurrs. Must equal

2.

13. ABREV = Output control specification. Must equal 2.

14. SE1 = Upper bound of age selected by gear type 1 in two-gear type

fishery.

15. S2 = Lower bound of age selected by gear type 2 in two-gear type

fishery.

16. ED = Effort Development filter (0-OFF, 1-ON).

17. XH = Budget calculation for year t+l filter (0-OFF, 1-ON).



14.1.2 POPN"Xi"

o Line #'s 220, 260, 340.

Initial population age structure file (P(I,1)) where I = 1 to N1YC. The

initial age structure should be input as the numbers in each yearclass at the start

of month 1. Note: The input value for yearclass 1 should be zero; the program

will automatically compute recruitment in year 1, month 1 = P(1,l), based on the

selected stock-recruitment relationship.

X. = input selection for the current condition of the fishery.



14.1.3 PARAPOPN

o Line #380.

Reads in the successive values XKC, PCF, Al, A2, RVL, VP, GA, PER, FV,

THR.











The copulation coefficient may be thought of as consisting of two

multipliers:

1. XKC = The instantaneous coefficient of males contacting females at

random, i.e., rate of contact. Range: 0 to 1. (Suggest 1).

2. PCF = Fraction of the XKC encounters that result in copulations, i.e.,

copulated female fraction. Range: 0 to 1. (Suggest 1).

Input parameters of the mean stock-recruitment function relationship.

3. Al = magnitude of the relationship (=a).

4. A2 = asymptotic rate parameter of the relationship (= R).

5. RVL = Level of recruitment variability about mean (l-mean/2-variable

percentage).

6. VP = Percentage variability for recruitment (i.e., 90% variability = 0.9).

7. GA = Inclusion of autocorrelated recruitment waves (l-on/Ooff).

8. PER = Period for autocorrelated recruitment wave (entered in integer

years).

9. FV = Fraction of maximum recruitment used as scalar for

autocorrelated recruitment wave.

10. THR = Minimum threshold for recruitment if actual value is less than

zero.



14.1.4 NATMORT

o Line #420.

Reads in the instantaneous natural mortality coefficient (XM(I)). May be

input as constant or variable from yearclass to yearclass. One value for each

yearclass.











14.1.5 AVAIL. "Filename"

o Line #'s 455-470, 505-520, 555-570.

Starting values for the coefficients of availability or catchability (A(I)).

May be input as constant or variable from yearclass to yearclass to simulate

different selection patterns. One value per yearclass.



14.1.6 FISHMORT. "X"

o Line #'s 610, 650, 690.

Reads in the starting values for the instantaneous fishing mortality

coefficients (F(I)). May be input as constant or variable from yearclass to

yearclass. One value per yearclass.

X1 = selection for the current condition of the fishery (CCF).



14.1.7 FECUND

o Line #730.

Reads in the successive values of the number of viable ova produced per

individual female for the respective yearclasses (E(I)). One value per yearclass.



14.1.8 FMULT

o Line #770.

Inputs the year specific F2MULT values which range from 1 to NAFMT.

Suggest one F2MULT which is equal to 1.0.



14.1.9 MATUR

o Line #850.











Inputs two age specific vectors of percent maturity at age for males and

females.

1. Fraction of males reproductively mature for the respective

yearclasses (FM(I)). One value per yearclass. Range: 0 to 1.



o Line #860.

2. Fraction of females reproductively mature for the respective

yearclasses (FIMF(I)). One value per yearclass. Range: 0 to I.



14.1.10 VONBERT "X"

o Line #810.

Inputs the parameters of the Von Bertalanffy growth formulation.

WINF = ultimate weight.

XAKV = growth coefficient.

TO = time at which the weight/length of the species was equal to

zero.

LINF = ultimate length.



14.1.11 ECONOMICS

o Line #900.

Record 1: Inputs the optimal allocation amount (in dollars) for control of

the assessment filters:

OMA(1) = Basic fishery statistics allocation.

OMA(2) = Resource surveys allocation.

OMA(3) = Catch analyses allocation.

OMA(4) = Economics allocation.

89











OMA(5) = Environmental effects allocation.


o Line #920.

Record 2: Fishing effort unit (vessel) costs for the various gear types.

CO(1) = unit cost for single gear type.

CO(2) = unit cost for commercial gear 1.

CO(3) = unit cost for commercial gear 2.

CO(4) = unit cost for commercial gear competing with recreational

gear.

CO(5) = unit cost for recreational gear competing with commercial

gear.



o Line #940.

Record 3: Value of catch per unit weight by ageclass (VA(I)). One value

per yearclass dictated by unit weight in WINF.



o Line #960.

Record 4:" Catchability coefficients for the various gear types.

Q(1) = catchability for single gear type.

Q(2) = catchability for commercial gear 1.

Q(3) = catchability for commercial gear 2.

Q(4) = catchability for commercial gear competing with recreational

gear.

Q(5) = catchability for recreational gear competing with commercial

gear.











o Line #980.

Record 5: Scaling parameters.

MX = Virgin population size at start of year for utility function

calculation.

MZ = Optimum rate of profit for utility function calculation.

BUD(1) = Management budget in dollars for first year of simulation.

OPTP = Optimum population corresponding to MSY population

size.

RM = Number of management measures possibly enacted.

YMAX(I) = Estimated maximum equilibrium yield for fishery

condition type i.

MF(I) = Estimated optimum fishing effort for fishery condition type i.



14.1.12 CHIST

o Line # is 1000-1960.

These files produce catch and economic data histories corresponding to the

Current Fishery Status (CCF) and the Historical Data Availability (HDI)

requested by the user. In particular these create the files necessary when the

user selects CCF = 2 or 3, and HDI = 1 or 2, and the respective files based on

selection are outlined in Table 14.1.12.


















Table 14.1.12: Data files accessed upon user selection of the

subcategories of the Current Fishery Status (CCF)

and the Historical Data Availability (HDI).





CURRENT FISHERY STATUS


Fully Recruitment
Exploited Overfished


CHISTMSYF CHISTROF
Complete MEYF ECROF
HISTORICAL DATA
AVAILABILITY
CHISTMSYS CHISTROS
Sketchy MEYS ECROS











File 1: Catch History

Record 1: Y1W = Last year of fishery total catch in weight.

Y2W(T) = Fishery total catch in weight data for years t.

Record 2: FC(1) = Instantaneous fishing mortality for last year of fishery.

F6(T) = Instantaneous fishing mortality for years t.

Record 3: WA = Average weight of fish in the catch for last year of fishery

WBAR(T) = Average weight of fish in the catch for years t.

Record 4: AL = Average length of fish in the catch for last year of fishery.

LBAR(T) = Average length of fish in the catch for years t.

Record 5 TX = Age at 100% vulnerability for last year of fishery.

TC(T) = Age at 100% vulnerability for years t.



File 2: Economic History

Record 1: NV = Number of vessels participating in last year of fishery.

Z6(T) = Number of vessels in years t..

Record 2: NU = Gross revenue from last year of the fishery.

HV(T) = Gross revenue from fishery in years t.

Record 3: VC =Total vessel costs for participating fleet in last year of

fishery.

EC(T) = Total vessel costs in year t.

Record 4: RG = Net returns from fishery in the last year.

NET(T) = Net returns in year t.

Record 5: RP = Rate of profit from last year of the fishery.

EYPR(T) = Rate of profit in year t.










15.0 Life History Modular Contents

All subsequent life history descriptions follow the flow of this description

for life history modular contents. This initial section presents an overview, and

then each life history description presents the specific parameter values used for

the individual species simulations.



15.0.1 Mortality Parameters

All mortality rates are age specific. Natural mortality is estimated at

Mx,t. The user specifies the potential fishing mortality, Fx,t, which is the

instantaneous fishing mortality coefficient of a fully available age group x in a

given year t. Fishing mortality is the product of three multipliers; (1)

availability, Axt, represents the fraction of an age-class that is available to the

fishery in a given year, (2) potential fishing mortality, Fx,t' is the instantaneous

fishing mortality coefficient of a fully available age group in a given year, and

(3) the fishing mortality growth multiplier, F2MULT, is used to adjust F from

year to year, depending on development budget, economic, and constituency

input. The total instantaneous mortality coefficient, Zxt, for age group x in

year t of the simulation sequence is then:

Zxt = Ax,t Fx,t F2MULT + Mx,t


15.0.2 Growth Parameters

The growth in length and weight of the population of interest is

represented by the von Bertalanffy growth equation. The von Bertalanffy

formulation is:

W = W (I- e-k(x )3
x,t











where,Wxt is the average weight of an individual in yearclass x at the

beginning of year t,

and, Woo,k,t0 are parameters of the von Bertalanffy growth equation.



15.0.3 Maturation Parameters

Maturation coefficients are effected by sex- and age-specific multipliers

representing the fractions of males and females reproductively mature of each

age class at spawning time. This feature allows investigation of variable sex

ratios at age, and hermaphroditism or sex reversal in populations like Grouper.



15.0.4 Reproduction Parameters

The production of prodgeny (larvae) is calculated as the summation over all

reproductive age-classes of the numbers in each age class at spawning time

multiplied by (1) the age-specific female fraction mature, (2) the number of

vaible ova produced per individual female at age x, and (3) the fraction of

females (or eggs) being fertilized during the spawning season. The latter is

computed from the number of males present during spawning and selection of the

random mating model.



15.0.5 Recruitment Parameters

The computation of the number of recruits entering the population follows

the dynamics of the Beverton and Holt function, where Al = a, and A2 = P, are

parameters of the model. Note that recruitment is defined as the joining of the

young to the population and not necessarily the "fishable" population (that

portion vulnerable to the fishing gear).











15.0.6 Economic Characteristics

Costs and prices used in FINMAN have been adopted from among those

estimated for the various fisheries. While these values may not always be exact,

or may be dated; they still serve to illustrate the applications with respect to the

fishery of concern.



15.0.7. Yield per Recruit Surface

For each species profile three dimensional surface plots are shown utilizing

"best" life history data and the equations specified in Section 6.6.4.



15.1 Gag Grouper, Mycteroperca microlepis



15.1.1 Natural Mortality

Age specific natural mortality is estimated to be age-constant at M=0.2 in

the model.



15.1.2 Growth Parameters

Growth in length and weight of the Grouper is represented by the von

Bertalanffy equation with the following parameters:

W. = 25.032719

L. = 1290.0 mm

k = .122

t = -1.127



45.1.3 Maturation Parameters

The fractions of females and males reproductively mature by age-class at

96










spawning time are:


Maturity Fractions

Age Female Male

1 1.0 0.0

2 1.0 0.0

3 1.0 0.0

4 1.0 0.0

5 .98 .09

6 .85 .15

7 .70 .30

8 .50 .50

9 .40 .60

10 .25 .75

11 .10 .90

12 .05 .95

13 0.0 1.0



15.1.4 Reproduction Parameters

The fecundity coefficients for Grouper are as follows:

Age Fecundity

1-4 0.0

5 300,000

6 400,000

7 526,396

8 1,457,120

9 3,457,120

97










10 5,035,240

11 6,035,240

12 7,035,240

13 7,535,240



15.1.5 Recruitment Parameters

Number of Year Classes = 13

Age (in months) at First Recruitment = tr + 1 = 2

Month Spawning Begins = ts = 1

Month Spawning Ends = t's = 1

Year of First Maturity = tm = 5

Rate of Contact = k = 1

Recruitment Function Parameters: Al = 1.0E-06, A2 = 70,000



15.1.6. Economic Characteristics



15.1.6.1 Optimal Assessment Allocations

Basic fishery statistics = OMA(I) = 50,000

Resource surveys = OMA(2) = 50,000

Catch analyses = OMA(3) = 50,000

Economics = OMA(4) = 50,000

Environmental effects = OMA(5) = 50,000



15.1.6.2 Fishing Effort Unit Costs

Single gear = CO(1) = 1,000

Commercial gear 1 = C0(2) = 400










Commercial gear 2 = CO(2) = 500

Commercial gear competing with recreational = C0(4) = 500

Recreational = CO(5) = 25


15.1.6.3 Ex-Vessel Value of Catch per Unit Weight

Value of catch = VA(I) = 4.4 I = 1 TO NIYC


15.1.6.4 Catchability Coefficients

Single gear = Q(1) = .0001

Commercial gear 1 = Q(2) = .0001

Commercial gear 2 = Q(3) = .0001

Commercial gear competing with recreational = Q(4) = .0001

Recreational = Q(5) = .00001


15.1.6.5 Scaling Parameters

Virgin population size = MX = 3,925,147

Optimum rate of profit = MZ = 5.0

Starting management budget = BUD(1) = 1.OE+06

Optimum population size = OPTP = 3,145,635

Maximum equilibrium yield: YMAX(2) = 1,311,012 ; YMAX(3) = 1,418,503

Optimum fishing effort: MF(2) = 5,420 ; MF(3) = 3,050

Default selection settings for two fleets:

1. Maximum age fished by fleet-type 1 = SE1 = 7.

2. Minimum age fished by fleet-type 2 = S2 = 8.




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