.,

,PISCES: A COMPUTER SIMULATOR TO AID PLANNING

IN STATE FISHERIES MANAGEMENT AGENCIES/

by

Richard D. \\Clark# Jr.

Thesis submitted to the Graduate Faculty of the

Virg1nia Polytechnic Institute and state University

1n partial fulf1llment of the requirements for the degree of

APFROVED:

Warren A.

MASTER OF SCIENCE

in

Fisheries and Wildlife Sciences

(Fisheries Science Option)

R~l.d~ Robert T. Lackey, Chairman

V(~~). Robert H. Giles, Jr.

October 1974

Blacksburg, Virg1nia 24061

LD $655 V855' ;'174 C57

ACKNOWLEDGMENTS

This researoh was funded by the Division of Federal Aid,

Fish and W1ld11fe Serv1ce, Un1ted States Department of the

Interior. My appreciat10n 1s extended to the Tennessee W1ld­

life Resources Agency for their oooperation.

I offer my deepest gratitude to my w1fe, Donna, who was

the reason for starting and the 1mpetus for f1nishing my

graduate work.

11

TABLE Q! CONTENTS

ACffi~OWLEDGMENTS •••••••••••••••••••••••••••••••••••••••••

Page

11

LIST OF FIGURES......................................... v

LIST OF TABLES.......................................... vi

LIST OF APPENDICES •••••••••••••••••••••••••••••••••••••• vii1

LIST OF APPENDIX FIGURES................................ 1x

LIST OF APPENDIX TABLES................................. x

INTRODUCTION............................................ 1

Regulating Angler Consumption........................ 4

Operations Researoh.................................. 6

PROCEDURES. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 9

General Desoription of PISCES........................ 9

Components of Angler-day Prediotion.................. 15

Angler-day Estimates from Previous year.......... 15

Popularity Trends................................ 15

Change ln Angler-days Resulting from Management.. 16

Mathematioal Techniques.............................. 16

statistios....................................... 17

Monte Carlo Simulation........................... 17

PROGRAM SEGMENTS........................................ 19

Maln Program and Subroutine TALLy.................... 19

Subroutines INPUT. ENVIRO, and OUTPUT................ 22

Computing Instructions............................... 22

Subroutine DRAW...................................... 23

i1i

Mathematioal Subroutines............................. 23

HYPO'l'HETICAL APPLICATION IN TENNESSEE................... 2.5

Definition of Variables.............................. 25

Definition of Objectives............................. 26

Baokground Data...................................... 27

Management Policy.................................... 30

OBJECTIVES FOR FISHERIES MANAGEMENT..................... .50

}1axlml zing Angler-days............................... .56

Minimizing Crowding.................................. 56

EVALUATION OF SIMULATOR UTILITy......................... 59

VALIDATION OF PISCES.................................... 61

DISCUSSION.............................................. 64

LITERATURE CITED........................................ 66

APPENDIX A....................... . . . . . . . . . . . . . . . . . . . . . . . 69

APPENDIX B.............................................. 7.5

APPENDIX C.............................................. 78

P;PP ENDIX D.............................................. 88

APPENDIX E.............................................. 9.5

APPENDIX F.............................................. 128

APPENDIX G •••••••••••••••••••••••••••••••••••••••••••••• 158

VITA. • • • •• • • • • • • • • •• • •• • • • • • • •• • • • • • • • • • • • • • • • • • • • • •• • •• 20S

1v

LIST OF FIGURES

FIGURE ~

1. Graphical model of a genera11zed freshwater

recreat10nal fishery showing major system

components...................................... 2

2. Classification of angler-days used in PISCES.... 11

3. Recommended sequence of steps for use of PISCES

in ident1fy1ng the best management policy....... 13

4. Flow chart of calculations done within PISCES... 20

5. Fisheries management areas in Tennessee used by

the Tennessee Wildlife Resouroes Agenoy......... 26

6. General relationship between number of angler­

days sustained by a f1shery and the qua11ty per

angler-day...................................... 52

7. Two possible relationships between total angling

benef1ts der1ved and number of angler-days sus­

tained by a fishery in a given period........... 54

8. A feedbaok loop wh1ch will show simulator pre­

d1ctions are aoourate or show simulator structure

is invalid...................................... 62

v

Table

1.

2.

LIST OF TABLES

Estimates of angler-days which occurred 1n

year previous to the year of hypothetical appli-

cation of PISCES ••••••••••••••••••••••••••••••••

Projeoted trend 1n popular1ty of angling in the

year of hypothetical applioation of PISCES ••••••

;. Standard deviat10ns of popularity trend projec-

31

;2

tions of PISCES................................. 33

4. Predicted changes 1n angler-days resulting from

access area development in the year of hypothe­

tioal application of PISCES..................... 34

5. Standard deviations of changes 1n angler-days

resulting from aocess area development 1n the

year of hypothetical applioation of PISCES...... ;5 6. Predicted changes in angler-days resulting from

water gain or loss in the year of hypothetioal

application of PISCES........................... 36

7. standard deviations of ohan[7,es 1n angler-days

resulting from water gain or loss 1n the year of

hypothetical application of PISCES ••••••••••••••

8. Pred1cted changes in angler-days resulting from

37

information and education efforts in the year of

hypothetical 8nplication of PISCES.............. 38

9. standard deviations of changes 1n angler-days re­

sulting from information and education efforts 1n

vi

the year of hypothetical application of PISCES.. 39

10. Predicted change in angler-days resulting from

research efforts in the year of hypothetical

application of PISCES........................... 40

11. standard deviations of changes in angler-days

resulting from research efforts in the year of

hypothetical applicat10n of PISCES.............. 41

12. Predicted changes in angler-days resulting from

catchable trout stocking in the year of hypothe­

tical application of PISCES..................... 42

standard deviations of changes in an~ler-days

resulting from catchable trout stocking in the

year of hypothetical application of PISCES ••••••

14. Predicted changes in angler-days resulting from

4;

shortening the trout season in the year of hypo­

thetical application of PISCES.................. 44

15. standard deviations of changes in angler-days

resulting from shortening the trout season in

the year of hypothetical application of PISCES.. 45

16. Total changes in angler-days predicted for the

year of hypothetical application of PISCES...... 46

17. standard deviations of total changes in angler­

days predicted for the year of hypothetical

application of PISCES........................... 47

18. Prediction of total number of angler-days which

will occur in the year of hypothetical applica-

tlon of PISCES.................................. 48

vii

Appendix

A.

B.

C.

D.

LIS~ OF APPENDICES

Development of Subroutine WEIBUL •••••••••••••

Development of Subroutine RANDOM •••••••••••••

Development of Subroutine MODEX ••••••••••••••

Sens1tivity Experiments on MODEX •••••••••••••

Page

69

75

78

88

Control Curve........................... 89

Experiment 1............................ e9 Experiment 2............................ 92

Part A............................. 92

Part B............................. 92

E. Variable Defin1t1ons......................... 95

Input Variables......................... 96

Internal Var1ables...................... 111

F.

G.

Input for Hypothet1cal Applioation ••••••••••• 128

Background Data......................... 129

Management Po11oy Deoisions •••••••••••••

Souroe Deok List1ng of PISCES ••••••••••••••••

viii

150

158

LIST OF APPENDIX FIGURES

FIGURE Page

1. Modif1ed exponent1al relat1onsh1p used 1n

PISCES ••••••••••••••••••••••••••••••••••••••••••

General form of mod1fied exponent1al curve ••••••

Form of mod1f1ed exponent1al ourve used 1n

80

83

PISCES.......................................... 85

lx

Table

1.

LIST OF APPENDIX TABLES

Results of oontrol runs for sensitivity experi­

ments using 50 iterations of subrout1ne MODEX... 90

2. Results of sens1t1v1ty experiment 1 using 50

iterations of subroutine MODEX.................. 91

J. Results of Part A of sensitivity experiment 2

4.

using 50 1terations of subroutine MODEX •••••••••

Results of Part B of sensit1v1ty experiment 2

93

~s1n$ SO iterat10ns ot ~y'prout1ne MODEX......... 94

x

INTRODUCTION

Fisheries are systems consisting of aquatic populations,

aquatio habitat, and man, interacting through t1me and space

(Fig. 1). Fisheries management is the art and sc1ence of making

and implementing decisions to maintain or alter the structure,

dynamics, and interactions of fisheries to achieve specif10

human objectives. Throughout the United States, agencies in

state government have fisheries management as their legal

mandate. These agencies make decisions about where and when

to stock fish, how long to hold seasons, how muoh to charge

for licenses, and how to allocate mill10ns of dollars in publio

funds.

Choosing among deoision alternatives is a difficult

task for fisheries agencies. A state fisheries management

system contains many different fisheries types (i.e. lakes,

ponds, streams, etc.), each complex and interacting. The

impact of implementing different decisions in the management

system may not be clear.

Methods for predicting the impact of alternative manage­

ment decisions include rules of thumb, past experience,

standard population models, experimentation, trial and error,

and pure guess (Lackey 1974). Each has a place in fisheries

management, but the complexity of fisheries may reduce the

reliability of such predictions to unacceptable levels.

Therefore, fisheries agencies may be inconsistent in their

1

F1gure 1. Graphical model of a generalized freshwater

recreational fishery showing major system

components

2

\-\ABITAT

I WATER LEVEL I

I WATER QUALITY I

HYDROGRAPH IC CHARACTERISTICS

ICOVER I

.".,_---_....... AQUATIC \ ............ PLANTS ----..

/f-""-~\ _---, " /', I INDUSTRIAL ~ I WATER USES / /' " I PHYTOPLAN KTON I

I \ /7 I \ L."I'

.f. __ ".". \, ,. ·r-I B-EN-T-H-OS---"j '------~ I ----- \

/ \ I MICROBESI

SWIMMERS­WATER SKIERS

TACKLE MANUFACTURERS

: I , I ANGLERS I ., \ 1 \ I \ I I GAME FISHESI \ 'II \ I

~------~~ / TACKLE , /

DISTRIBUTORS " /

MAN

4

ability to ohoose the best deoision alternat1ves.

Recognition of the problem of choosing among deoision

alternatives prompted the Division of Federal Aid, Fish and

Wildlife Service, United states Department of Interior, to

sponser a research project at Virginia Polytechnic Institute

and State University. The objective of the research was to

develop a methodology for predicting the consequences of

fisheries management agency activities and expenditures on

angler consumption of fisheries (an~ler-days). PISCES, a com­

puter simulator, was developed in partial fulfilment of the

project objective. The purpose of PISCES is to aid in p'lanning

fisheries management decision policies at the macro level in

state agencies.

Regulating Angler Consumption

Angler consumption of fisheries is one of the major

1nteractions of man with aquatic populations and habitat (Fig. 1).

Thus, consumption should be a major concern of management

agencies. Consumption trends of recreational fisheries are

generally out of control (MoFadden 1968). Functionally, con­

sumption trends are nearly always viewed as phenomena extrinsio

to fisheries management, but they are only nartially extrinsic.

Virtually all agency programs a.nd activities have an effect on

the location and intenSity of an~ler oonsumption. Land acqui­

sition, dam construction, pollut1on control, f1sh stocking,

and access development are common eXB.mples.

Planning in fisheries management 1s largely involved with

5

forecasting the demand and providing an adequate supply of

fisheries for the future. Producing or mainta.ining the

necessary supply of fisheries may be difficult. All agenc1es

have political, technical, and b1olog1cal constraints, and

limited financial resources. Angler consumption of fisheries

resources threatens to exceed managerst_: ability to supply

fisheries of the desired qua.lity.

Management polioies are usually designed to respond to

angler consumption trends, but rarely to shape them. If fish­

eries management polioies were designed to regulate angler

oonsumption, greater benefits might be achieved from the

fisheries resource. Regulation of angler consumption could

be achieved by limit1ng lioenses, but suoh a tactic is not

politically or culturally acceptable in most cases. A less

dictatorial approach based on the relationships between indi­

vidual management activities and angler consumption might also

be effeotive.

Present day angling regulations, information distribu­

tion, and education programs address human components in fish­

eries management, but such efforts alone cannot be relied upon

to direct angler consumption in a des1rable direction. One or

two aotions in a complex management system are probabl~

inadequate to achieve the desired change. For example, while

information and education efforts are working to direct angler

consumption along a partioular oourse, other agency activities

may be working subtly against that course. Multiple aotions,

6

each moving in the same direction with correct timing and

proper force, are needed to regulate angler oonsumption

successfully.

Operations Researoh

Operations research can be used effect1vely to 1dent1fy

optimal decision policies for complex systems. The performance

of a policy is evaluated through a mathematical model repre­

senting the system under study. The effectiveness of operations

research techniques for evaluating complex systems in many

disciplines was well-established in the past several decades.

Impressive progress in operations researoh was due, largely,

to the parallel development of the digital computer. Computer

oalculating speed and information storage oapacity have

enabled workers to address the large-scale oomputational

problems typical of operations research. The ourrent wide

application of operations research techniques in industry, the

military, and government serves to emphasize their effeotive­

ness (Schmidt and Taylor 1970).

Operations research is identified primarily with the use

of mathematical models which can be divided into two categories:

mathematical programming and simulation. A mathematical pro­

gramming model 1s a set of symbols which represent the decision

variables of a system (Taha 1971). The solution to most math­

ematical programming models defines the values of decision

variables which maximize or minimize a given objective funct1on.

7

A simulation model is a digital representation which

imitates the behavior of a system. Statistios describing

measures of change in the state of the system are accumulated

as the simulator advanoes (Tahs 1971). The performanoe of

alternative deoision po11cies are evaluated based on their

effect upon the system statistics.

The oomplexity of natural resouroe systems (i.e. fish­

eries) makes operations research techniques potentially use­

ful for formulating and evaluating management polioies. MAST

is one example of a mathematical programming model developed

for wildlife management (Lobdell 1972). In MAST, linear

programming techniques are used to define optimal budget

allooation for two common managerial objectives: (1) mini­

mize management cost, subject to production requirements; and

(2) maximize the value of management, subjeot to capital

constraint.

The deer hunter participation simulator (DEPHAS) develop­

ed by Bell and Thompson (1973) is an example of a oomputer sim­

ulator for predioting outputs resulting from state wildlife

agency activities. DEPHAS 1s designed to allow state wild­

life administrators to analyze interaction between input and

output of their proposed management policies. Many other

examples of operations research models for use in natural

resources management are given by Titlow (1973), Mills (1974),

and Bare (1971).

Operations researoh, in general, and oomputer simulat1on,

8

in partioular, were ohosen to fulfill the objeotive of this

study becausea

(1) Operations researoh foroes formal problem definition,

and formulating the exaot problem under review is

a step towards the solution.

(2) Operations research methods include ways of struc­

turing and measuring unoertainty which enhance

decision making.

(3) A computer simulator allows experimentation with

various deoision alternatives without endangering

the resource.

(4) S1mulation models are more flexible than mathemati­

oal programming models, and hence, may be used more

easily to represent a system as complex as a fishery.

(5) Simulation methods oan be applied in systems, such

as fisheries, where much information 1s lacking or

inoomplete.

(6) Once construoted, a mathematical programming model

optimizes a system for a single set of objeotives

and constraints, but a simulator oan be used to

evaluate the system using different objectives.

PROCEDURES

Tennessee's state fisheries management system was used

as a case study for simulator development. The Tennessee

Wildlife Resources Agency literature, planning reports, and

budget allocation reoords were analyzed to gain an understand~

ing of the management system. Personal oommunicat1on with

Agenoy personnel was an integral part of the study.

A simulator, PISCES, was developed in four phases: (1)

system components were identified; (2) important interactions

between the components were identified; (3) mechanisms for

the interactions were quantified; (4) components and inter­

aotions were arranged in a logical order.

General Description of PISCES

In its present form, PISCES is a model of the inland

fisheries management system of Tennessee, but it can be

modified for use in any state. Decisions which constitute the

fisheries agency's management policy for a fiscal year are

treated as input. Simulator output includes a prediction of

the number of angler-days (man-days of angling) which will

occur within the year and predictions of how resource consumption

(measured in angler-days) will be affeoted by the management

policy.

Management activities may produce angler-days, oause angler­

days to deorease, or cause angler-days to be displaoed from

one location to another. One exemple of an 8Fency activ1ty

9

10

which produces angler-days 1s stocking oatohable trout. A

decrease in angler-days will result from decisions such as

increasing the lioense fee. Access area development is an

example of an activity which causes angler-days to be dis­

placed from one location to another.

Angler-da.ys are olassified by their physical location

(i.e. management area) and. fisheries type (Fig. 2). The

fisheries types considered in PISCES are typical of many statesz

(1) warmwater streams; (2) ma.rginal trout streams; (3)

natural trout streams; (4) ponds and small lakes; and (5)

reservoirs and large lakes. In the simulator output, an angler­

day in one part of the state can be distinguished from one in

another part, and an angler-day on a na.tural trout stream can

be distinguished from one on a reservoir.

In planning, evaluations of management policies should

be based on their performance towards rea.ching objectives and

their cost of implementation. Angler-day predictions from

PISCES are designed to serve as performance measures for

fisheries management policies, and the cost of implementing

the policies ca.n be ascertained from simulator input. The

planning sequence recommended for identifying the best manage­

ment policy with PISCES is similar to other modes of decision

analysis (Fig. J).

PISCES was developed and tested on an IBM/J70 computer.

The program is written in FORTRAN IV, has an execution and

compilation time of less than 5 min. on a level "GI' FORTRAN

F1gure 2. Classif1oat1on of angler-days used in PISCES

11

WARMWATER STREAMS

I MARGINAL

TROUT STREAMS

MANAGEMENT AREA #1

NATURAL TROUT

STREAMS

ANGLER-DAYS

RESERVOIRS AND

LARGE LAKES

I PONDS AND

SMALL LAKES

1

WARMWATER STREAMS

I MARGINAL

TROUT STREAMS

MANAGEMENT

AREA #2

NATURAL TROUT

STREAMS

RESERVOIRS AND

LARGE LAKES

I PONDS AND

SMALL LAKES

Figure ,. Recommended sequence of steps for use of PISCES

1n identifying the best management policy

1;

APPLY PISCES

PREDICTION

1

ARE

OBJECTIVES

ACHIEVED?

YES

" IS COST

ACCEPTABLE?

YES

IMPLEMENT POLICY

-

NO -

NO

START

,r

MANAGEMENT POLICY

MODlFY POLICY

4~

lS

compiler, and requires 120 K bytes of storage.

Components of Ano:ler-dal Prediction

The major components of predictions of the number of

angler-days which will occur in the planning year are: (1)

estimates of the number of angler-days which occurred in the

previous year; (2) the projected trend in angling popularity

in the state; and (3) the change in angler-days resulting from

the proposed management policy. The f1rst two components are

part of the input and the third 1s calculated by the simulator.

All three components are added to obtain the final prediot1on.

Angler-dal ~~tlmates from Prev10us Year

Estimates of the number of angler-days which occurred 1n

the year previous to the planning year must be prov1ded as

input. Angler-days realized on each fisheries type in all

management areas must be estimated. Methods suitable for

making the estimates 1nclude making surveys, extrapolating from

eXisting data, direct counting, and random sampling. The

method used should be the one which will obtain the most aocurate

estimates at a cost which the agency can afford.

Popularity Trends

Many factors extrinsic to fisheries management, such as

human population growth, influence resource consumption. The

sum of many extrinsic factors may be expressed as a popularity

16

trend, data wh1ch most state fisheries agenc1es possess.

High, low, and most probable estimates of the statew1de ohange

in popularity for each fishery are part of the simulator

input.

Change 1B Angler-dars Resulting ~ Management

The largest part of PISCES is used to caloulate changes

in angler-days resulting from management policy decisions.

Knowledge of how management decisions affect resouroe oonsump­

tion should give agency planners insight into whioh decisions

are best for aohiev1ng their obJeotives.

Mathematical Techniques

Fisheries management systems contain many complex variables

which are poorly understood. Some of these variables, such

as weather, appear to act randomly from year to year. In years

with good weather, more anglers take to the field than in years

with poor weather. Thus, the accuraoy of prediotions of the

number of angler-days in a year depend, in part, upon the

weather. It is difficult to obtain acourate predictions under

such uncertain conditions, but management decisions must be

made, regardless. Two techniques which can be app11ed to

account for random variation in making predictions are the use

of statistics and Monte Carlo s1mulation. PISCES is designed

to use both techniques.

statistios

Present day statistios may be defined as decision­

making under uncertainty. A range or pattern of possible

values of a random variable oan be determined based on past

experience or experimental data (Hicks 1964). A measure of

uncertainty associated with a given prediotion can be calcu­

lated and the risks ascertained for each management deoision.

Many of the ra.ndom variables in a fisheries management

system desoribe events which have never occurred in the past

or for which very little or no data exist. Traditional

statistioal techniques for aSSigning objeotive probability

distributions cannot be applied to such variables. PISCES

uses a teohnique of subjeotive probability assignment developed

by Lamb (1967). It is a. method of fitting Weibull probability

funotions by utilizing best available subjective and

objeotive information about variables. Low, most probable,

and high estimates of the variables are used to develop the

Weibull probability distributions (Appendix A).

Monte Carlo Simulation

Monte Carlo simUlation is the process of produoing

frequency distributions for simulator outputs. One iteration

of a simulator produces one set of output values, but iterat­

ing a number of times in a simulator containin~ random

variables produces frequency distributions on the output

variables.

17

18

output for PISCES oontains predictions of angler-day

change. Produc1ng frequency distr1butions for these pre­

dictions allows calculation of an expected value (mean) and

standard deviat10n for each. Expected values are considered

as· the actual predictions and standard deviations are con­

sidered as a measure of risk associated with basing decisions

on the prediotions. A measure of risk in decision-making 1s

an important statistic. For example, two different decision

alternatives may produce the same predicted result, but the

result may be much more certain for one one alternative than

for the other. If the costs of implementing each of the two

alternatives are equal, then the alternative with the lesser

risk is the best choice.

PISCES employs 50 1terations to produce an 'expected

value and standard dev1ation for each predict1on.

PROGRAM SEGMENTS

The aotivities of inland fisheries management agenoies

1n most states can be encompassed in the following cate­

gories: (1) trout hatcheries; (2) aocess area development;

(3) information and education; (4) land acquisition; (5)

regulations; (6) research; (7) warmwater hatcheries; (8)

pollution control; and (9) water development. Each cate­

gory influences fisheries resource consumption and 1s consid­

ered in PISCES (Fig. 4).

Many factors influenc1ng resource consumption are totally

or partially independent of fisheries agencies. PISCES

directly accounts for several of these factors such as

reservoir construct1on, federal trout stocking programs, and

popularity trends. Other independent factors, such as

weather, are not directly addressed in the Simulator, but are

considered to be the cause of the random variation 1n the

probability distributions.

~ Program ~ Subroutine TALLY

The main program has two functions: the subprograms

are called 1n their proper order; and projected popularity

trend changes are assigned to the appropriate fisheries type.

The three components (i.e. last year's estimate, popularity

trend projection, and change resulting from management policy)

of the angler-day prediction are accumulated by subroutine

TALLY.

19

Figure 4. Flow chart of caloulations done within PISCES

20

ESTIMATES OF LAST YEAR1S

ANGLER-DAYS

POPULARITY

TREND

PROJECTIONS

MANAGEMENT CHARACTERISTICS ~ POLICY OF MANAGEMENT P

DECISIONS SYSTEM U ~--~--~ ~--~I----~ ~--~~--- ~----~----~T

----, --~---------------- ----------"'-----

!

SUBROUTINE ~ ! SUBROUTINE! INPUT ENVIRO

I ... I V

EFFECT OF EFFECT OF

TROUT HATCHERIES

~ __________________ ~h. ACCESS AREA C DEVELOPMENT e

EFFECT OF

INFORMATION

AND

EDUCATION

PROGRAM

EFFECT OF

REGULATION 1--­

CHANGES SUBROUTINE

OUTPUT -

'------.--------'

,...--------'~L.._______. 8 EFFECT OF L RESEARCH A PROGRAMS T

'---_-.-__ --J I ....-----,bL-------. ~

EFFECT OF

WATER GAINS

OR LOSSES

SUBROUTINE

TALLY

S

----------------_1 I ______________ ~

PREDICTED

CHANGE IN

ANGLER-DAYS

FOR EACH

MANAGEMENT

ACTIVITY

PREDICTED

TOTAL CHANGE

IN ANGLER - DAYS

DUE TO

MANAGEMENT

POLICY

PREDICTED

NUMBER AND

LOCATlON OF

ANGLER-DAYS

FOR YEAR

o U T P U T

Subroutines INPUT, ENVIRO, and OUTPUT

The ~urpose of subroutines INPUT and ENVIRO is to read

the simulator input (Fig. 4). INPUT reads values describing

the management policy for the yea.r such as budget expend1tures,

locat1ons of new access areas, and regulation chan~es. ENVIRO

reads values which characterize the state fisheries management

system such as costs of different management activities,

inflation rate, regression coeffiCients, and high, low, and

most probable estimates for Weibull d1stributions. Once the

data for ENVIRO is obtained, it becomes a semi-permanent part

of the simulator and needs only slight maintenance in aocordanoe

to feedback. A complete list of simulator input in proper

order and format is in source deck list of PISCES (APpendix G).

Subroutine OUTPUT instructs the computer to print the

simulator output in an appropriate format.

Computing Instruotions

Six subroutines whioh serve as computer instructions

for calculating angler-day changes are HATCH, ACCESS, INED,

RESEAR, REGULA, and WATER. Each subroutine calculates the

increment of angler-day change expected from their respective

management activities and outputs the result (Fig. 4). Sub­

routine HATCH contains instructions for caloulating the change

in angler-days resulting from catchable trout program; sub­

routine ACCESS from the access area development program; sub­

routine INED from the information and education program; sub­

routine RESEAR from the research program; and subroutine REGULA

22

23

from regulation changes. Subroutine WATER accounts for manage­

ment activities (i.e. pollution abatement and land acquisition)

and other activities (i.e. posting by landowners and reservoir

construction) which cause changes in the amount of water

available for an~ling. The sum of the increments of angler­

day change calculated by these subprograms 1s the third com­

ponent of the angler-day prediction.

Subroutine ~

A management activity, such as prov1ding a new access area

to a reservoir, usually increases the number of angler-days

on the reservoir. Many of the increased angler-days are newly

generated, but many may be redirected to the reservoir from

nearby fisheries. Subroutine DRAW uses subjective probability

to calculate the number and source of redirected angler-days.

Distance between fisheries and similarity in fisheries

type are two factors in DRAW which are oonsidered to influence

angler-day displacement. Displacement is assumed to be sig­

nificant only between adjacent management areas, and more angler­

days are assumed dis~laced between fisheries types which are

similar (i.e. reservoirs and ponds) than between those whioh

are not similar (i.e. reservoirs and natural trout streams).

Mathematioal Subroutines

PISCES contains five subroutines which describe mathema­

tical relationships throughout the program: (1) subroutine

24

WEIBUL calculates parameters for a Weibull distribution given

high, low, and most probable estimates of a variable; (2)

subroutine RANDU generates uniformly distributed random vari­

ables; (3) subroutine RANDOM contains a Weibull process

generator; (4) subroutine MODEX defines a modified exponential

relationship between a given management activity and angler­

days; and (5) subroutine STAT calculates the mean and standard

deviation for the output distributions.

Subroutine RANDU was developed by International Business

Machines Corporation. The mathematical development of sub­

routines WEIBUL, RANDOM, and MODEX are given in Appendices A,

Band C, respectively. The results of sensitiv1ty experiments

on the important variables in subroutine MODEX are given in

Appendix D.

HYPOTHETICAL APPLICATION IN TENNESSEE

Utilization of PISCES will be illustrated by a hypotheti­

cal application in Tennessee. Input data for the application

are realistic hypothetical values derived from histor1cal

data provided by the Tennessee Wildlife Resources Agency (here­

after called ttAgencyd). A complete list of input values used

for the application is given in Appendix F.

Definition of Variables

The first step in applying PISCES is to define the follow­

ing variables: (1) management areas; (2) fisheries types;

(J) water closed to fisheries; (4) angler-days; (5) developed

and undeveloped access areas; (6) trout hatcheries; (7)

research activities; and (8) information and education

activities. Definitions of these variables are only restricted

to what seems reasonable for a partioular state. Definitions

chosen for the hypothetioal applioation are given below.

Tennessee is divided into four management regions by

the Agenoy. Eaoh region is divided into three management

areas. The areas were numbered 1 through 12 for the applioa­

t10n (Fig. S).

Fisheries types were defined as follows: (l) warmwater

streams include streams too warm for trout stocking; (2)

marginal trout streams include streams stocked with trout;

(3) natural trout streams include streams not stocked with

2S

Figure S. F1sher1es management areas in Tennessee used

by the Tennessee W1ldlife Resouroes Agenoy

26

REGION I REGlON 2 REGION 3 REGION 4

AREA 4 AREA II

AREA 2

28

trout but which support reproducing trout populations; (4)

ponds and small lakes inolude all man-made impoundments

(except Tennessee Valley Authority and United states Army

Corps of Engineers reservoirs) and natural lakes under 1000

acres; and (5) reservoirs and large lakes inolude Tennessee

Valley Authority and United States Army Corps of Engineers

reservoirs (including those under 1000 aores) and natural

lakes over 1000 acres.

The types of water closed to f1sheries were defined as:

(1) polluted water where fishk11ls recently oocurred; (2)

continuously polluted water in which fish are few or non­

existent; (3) water posted by pr1vate land owners; (4)

water inundated by reservoirs or ponds; and (5) streams

severly damaged by channelization.

Angler-days were defined as any part of a day a person

spends angling.

A developed access area consists of a parking lot, boat

ramp, access road, and picnic area 1n a single location. An

undeveloped access area is e1ther a parking lot, boat ramp,

access road, or a combination of two or three of these

facilities.

The Agency operates four trout hatcheries, Erwin, Flint­

v111e, Tellico, and Buffalo Springs. In the BUDGET and COST

arrays in PISCES, these hatoheries correspond to array members

1 through 4 respectively.

Research activities were defined as all research projects

29

oonduoted tn the planning year and the stooking of exotic

wermwater fish speoies (l.e. striped bass and muskellunge).

Information and eduoation actlvities were defined as

popular publications and brochures, talks given by personnel,

advertisements, and news releases.

Definition of Objeotives

Most fisheries management agencies have objectives such

as t'to protect and reclaim fisheries habitats'· or "to provide

the best possible angling". PISCES requires definition of

objectives pertaining to resource consumption whioh are

consistent with other agenoy objeotives. In the hypothetical

applioation, the objeotive is "to reduce angler-days on

trout streams". A oonstraint in achieving the objeotive will

be "the number of catchable trout stocked cannot be reduoed tI.

Other obJeotives whioh can be used ln PISCES are discussed in

the OBJECTIVES FOR FISHERIES ~mNAGEMENT seotion.

Background ~

Background data needed for PISCES include estimates of

angler-days occurring in the prevlous year, estimates of random

variables, and other information characterizing Tennessee. A

list of baokground data used for the hypothetical appli-

cation is given in Appendix F.

Estimates of angler-days occurr1ng in the previous year

were extrapolated from. angling demand inventories taken in

30

1970 by Tennessee Wildlife Resources Agenoy. High, low, and

most probable estimates of random variables are hypothetical.

Data classified as information characteriz1ng the State were

taken from Agenoy planning reports (1971).

Management Po11cy

A detailed list of deo1sions constituting the manage­

ment policy for the hypothetical application is given in

Appendix F. The policy was formulated by testing several

e,lternat1ve decision schemes in the planning system (Fig. 3)

recommended for PISCES.

The following is a summary of the decisions which allowed

satisfactory achievement of the objective of reducing an~ler­

days on trout streams: (1) no access areas were developed

on trout streams; (2) information and education and research

efforts were increased on warmwater streams while decreased

to minimal levels on trout streams; (3) catchable trout

stocking was maintained at levels nearly equivalent to those

of the previous year to satisfy the constraint; and (4) the

trout season was closed in November, December, January,

February, and March.

Tables 1 through 18 show the angler-day predictions for

the chosen policy as given in the output of PISCES. A

summary of the important events which occurred in the year of

the application and resulted in a ohange in angler-days is as

follows:

Table 1.-- Estimates of angler-days whioh occurred in year previous to year of

hypothetical application of PISCES

Area Warmwater Marginal Trout Natural Trout Ponds & Reservoirs & streams streams streams Small Lakes Large Lakes

1 94,110 0 0 665,296 0

2 60,630 0 0 509,464 0

:3 49,456 24,210 0 65,640 2,944,690 w ......

4 165,311 26,166 0 )36,840 1,514,975

5 140,8)) 1),950 0 236,814 27,160

6 87,736 12,740 0 177.336 583,500

7 135,815 25,620 0 544,19.5 193,120

8 61,642 32,890 0 92,166 293,420

9 37,458 4),395 4,288 52,371 772,240

10 31,160 1,823 7,648 35,025 666,842

11 81,088 130,140 8,544 38,265 724,090

12 106,200 130,226 19,)80 58,174 433,410

Table 2.-- Projected trend in popularity of ang11ng 1n year of hypothetical

applicat10n of PISCES

Area. Warmvlater Marginal Trout Natural Trout Ponds & Reservoirs & Strea.ms streams streams Small Lakes Large Lakes

1 -1,790 0 0 12,287 0

2 -1,153 0 0 9,410 0

3 -1,269 45 0 ),576 24,551 \.N N

4 -1,209 33 0 4,268 8,300

5 -1,271 12 0 ),172 186

6 .. 792 12 0 2,552 3,730

7 - 699 13 0 2,426 4,629

8 - 328 31 0 1,502 5,627

9 195 39 19 812 14,809

10 365 1 37 709 11,625

11 - 509 47 38 1,216 7,308

12 - 431 47 91 947 8,311

Table 3.-- Standard deviations of popularity trend projeotions of PISCES

Area Warmwater Marginal Trout Natura.l Trout Ponds & Reservoirs & streams Streams streams Small Lakes Large Lakes

1 52 0 0 217 0

2 34 0 0 166 0

J 31 3 0 63 266 \.A.) \.A.)

4 35 2 0 76 90

5 37 1 0 56 2

6 23 1 0 45 40

7 20 1 0 43 50

8 10 2 0 27 61

9 6 3 2 14 160

10 11 0 4 13 126

11 15 J 4 22 79

12 13 J 9 17 90

Table 4.-- Predioted changes in angler-days resulting from access area development

in year of hypothetical application of PISCES

Area Warmwater Marginal Trout Natural Trout Ponds & Reservoirs & Streams streams Strea.ms Small Lakes Large Lakes

1 - 82 0 0 -110 0

2 -632 0 0 6,355 0 \..)

:3 - 82 - 47 0 -110 - 81 ~

4 -123 - 73 0 -124 -161

5 - 66 - 39 0 - 61 - 89

6 -'778 -461 0 -780 7,799

1 -886 -526 0 -898 8,966

8 -123 - 13 0 -124 -167

9 - 57 - 34 -4 - 58 - 17

10 - 51 - 34 -4 - 58 - 77

11 0 0 0 0 0

12 0 0 0 0 0

Table 5.-- standard deviations of changes in angler-days resulting from access area

development in year of hypothetioal application of PISCES

Area Warmwater Marginal Trout Natural Trout Ponds & Reservoirs & streams streams Streams Small Lakes Large Lakes

1 20 0 0 27 0

2 111 0 0 991 0

3 20 12 0 27 19 \.A.) \J\

4 2:3 14 0 22 27

S 13 9 0 13 17

6 120 83 0 107 822

7 111 79 0 22 27

8 23 14 0 22 27

9 11 1 1 11 41

10 11 7 1 11 14

11 0 0 0 0 0

12 0 0 0 0 0

Table 6.-- Predicted changes in a.ngler-days resulting from water gain or loss in

year of hypothetical application of PISCES

Area \Varml'1B. t er Marginal Trout Natural Trout Ponds & Reservoirs & streams streams streams Small Lakes Large La.kes

1 59 0 0 2 0

2 -3,248 0 0 5,733 0

:3 0 0 0 2 2 'vJ 0\

4 - 217 10 0 - 418 25

.5 13 8 0 12 16

6 - 924 2,396 0 - 148 36,600

7 -4,658 -1,2'74 0 - 133 34,637

8 10 -2,148 0 14 26

9 831 :3 1 -1,109 9

10 441 .5 .5 0 - 1,623

11 121 -8,974 38 23 J

12 91 5 .5 631 1

Table 7.-- standard deviations of changes in angler-days resulting from water gain

or loss in year of hypothetical application of PISCES

Area Warmwater Marginal Trout Natural Trout Ponds & Reservoirs & Streams Streams streams Small Lakes La.rge Lakes

1 13 0 0 1 0

2 618 0 0 408 0 \.»

3 1 1 0 1 1 ~

4 37 4 0 36 9

5 4 3 0 4 5 6 104 211 0 4 3,.516

7 422 66 0 39 4,748

8 " 164 0 7 9

9 100 2 1- 115 4

10 66 3 2 2 187

11 17 577 11 7 1

12 7 2 2 62 1

Table 8.-- Predicted changes in angler-days resulting from information and education

efforts in year of hypothetical application of PISCES

Area Warm11ater MargInal Trout Natural Trout Ponds & Reservoirs & streams streams streams Small Lakes Large Lakes

1 270 0 0 370 0

2 150 0 0 967 0 \..lJ

J 145 61 0 350 359 co

4 162 71 0 265 170

5 158 68 0 567 - 38

6 166 74 0 269 27.5

7 172 76 0 - 30 282

8 181 82 0 - 22 86

9 163 71 - 9 268 274

10 167 71 89 - 31 284

11 266 269 83 - 33 185

12 261 265 82 - 37 181

Table 9.-- standard deviations of cha.nges 1n angler-days resulting from information

and education efforts in year of hypothetical applicat10n of PISCES

Area Warm~i'a ter Marg1nal Trout Natural Trout Ponds & Reservoirs & streams streams streams Small Lakes Large Lakes

1 15 0 0 21 0

2 9 0 0 55 0

3 9 4 0 20 19

4 9 4 0 15 9

5 9 5 0 32 6

6 9 5 0 16 15

7 10 5 0 :3 16

8 10 5 0 2 5

9 9 ,5 1 16 15

10 10 ,5 ,5 3 11

11 11 16 ,5 :3 11

12 16 16 S 3 10

\..> \()

Table 10.-- Predicted changes in angler-days resulting from research efforts in

year of hypothetical application of PISCES

Area Warm.'rater f.larginal Trout Natural Trout Ponds & Reservoirs & streams streams streams Small Lakes Large Lakes

1 389 0 0 886 0

2 327 0 0 2,339 0 -'="

:3 569 -284 0 2,075 4,120 0

4 240 -171 0 741 2,264

5 265 -156 0 1,269 1,279

6 262 339 0 1,278 1,805

7 224 327 0 760 1,788

8 790 -154 0 789 1,313

9 745 -181 -21 240 2,787

10 730 -194 -28 228 2,773

11 755 320 -44 766 1,805

12 724 300 -46 736 1,775

Table 11.-- Standard deviations of changes in angler-days resulting from researoh

efforts in year of hypothetical application of PISCES

Area Warm1iTater ft!arginal Trout Natural Trout Ponds & Reservoirs & Streams streams streams Small Lakes Large Lakes

1 51 0 0 114 0

2 48 0 0 302 0

82 44 269 .(:'"

) 0 53.5 ...,.

4 39 26 0 269 293

5 42 24 0 164 167

6 40 47 0 166 133

7 39 45 0 99 233

8 10.5 23 0 103 170

9 101 28 4 38 360

10 95 33 4 37 358

11 100 43 6 100 234

12 97 44 6 97 231

Table 12.-- Pred1cted changes in angler-days resulting from catchable trout stocking

in year of hypothetical application of PISCES

Area Warmwater Marginal Trout Natural Trout Ponds & Reservoirs & streams Streams Streams Small Lakes Large Lakes

1 0 0 0 0 0

2 0 0 0 0 0

3 0 86 0 0 18 ~ N

4 1 164 0 0 -31

5 -1 158 0 0 2

6 0 19 0 0 3

7 0 88 0 0 0

8 -1 280 0 -1 4

9 0 45 0 0 - 3 10 -2 76 -1 -1 82

11 -8 1,967 -8 -.5 8

12 -1 15 -1 -1 1

Table 13.-- standard deviations of changes in angler-days resulting from catchable

trout stocking in year of hypothetical application of PISCES

Area Warm1'1a ter Marginal Trout Natural Trout Ponds & Reservoirs & streams streams streams Small Lakes Large Lakes

1 0 0 0 0 0

2 0 0 0 0 0

0 +:-

3 3 0 0 1 w

4 0 4 0 0 1

5 0 ? 0 0 1

6 0 1 0 0 0

7 0 2 0 0 0

8 0 5 0 0 0

9 0 1 0 0 0

10 0 3 0 0 5

11 2 59 2 1 1

12 0 3 0 0 0

Table 14.-- Predicted changes in angler-days resulting from shorteing length of

trout season in year of hypothetical application of PISCES

Area Warmwater Marginal Trout Natural Trout Ponds & Reservoirs & streams streams streams Small Lakes Large Lakes

1 5 0 0 3 0

2 5 0 0 3 0

3 80 - 3,599 0 48 6

4 94 - 3,855 0 55 '7

5 59 - 2,0'74 ° 34 .5

6 58 - 2,250 0 34 5

'7 99 - 3,621 0 59 8

8 111 - 4,608 0 67 9

9 192 - 6,405 - 459 114 15

10 124 113 -1,058 74 10

11 434 -18,486 - 886 255 34

12 513 -19,491 -2,469 306 40

~ -"="

Table 15.-- standard deviations of changes in angler-days resulting from shortening

length of trout season in year of hypothetical application of PISCES

Area Warmwater Marginal Trout Natura.l Trout Ponds & Reservoirs & Streams Streams streams Small Lakes Large Lakes

1 1 0 0 0 0

2 1 0 0 0 0

:3 1 2 0 5 1

4 1 :3 0 4 1

.5 5 :3 0 4 1

6 .5 :3 0 3 1

1 6 .5 0 .5 1

8 8 4 0 1 1

9 11 9 11 10 2

10 12 16 13 9 1

11 28 ? 32 23 .5

12 :37 14 :34 26 .5

+=" \..n

Table 16.-- Total ohanges in angler-days pred1cted for year of hypothetical

application of PISCES

Area Warmwater Marg1nal Trout Natural Trout Ponds & Reservo1rs & Streams streams streams Small Lakes Large Lakes

1 -1,149 0 0 13,435 0

2 -4,552 0 0 24,806 ° j 558 - 3,737 0 5,936 28,973

4 -1,053 - 4,169 0 4,787 10,520

5 - 869 - 2,040 ° 4,963 1,328

6 -2,008 130 ° 3,205 50,217

7 -5,700 - 4,907 0 2,202 50,310

8 619 - 6,590 0 2,197 6,846

9 1,679 - 6,559 - 476 268 17,793

10 1,040 189 - 961 921 13,073

11 1,059 -24,859 - 779 2,222 9,343

12 975 -18,799 -2,340 2,.582 10,308

~ 0'\

Table 17.-- standard deviations of tot&l changes in angler-days predicted for

year of hypothetioal application of PISCES

Area Warm\lT8 t er Ma.rginal Trout Natura.l Trout Ponds & Reservoirs & streams Streams Streams Small Lakes Large Lakes

1 151 0 0 382 0

2 820 0 0 1,922 0

3 156 68 0 386 842

4 149 59 0 252 431

5 110 51 0 273 200

6 301 349 0 379 4,628

7 608 203 0 309 6,028

8 163 218 0 168 273

9 239 55 19 204 556

10 206 66 29 75 707

11 177 705 61 156 331

12 170 81 57 205 337

+--""'"

Table 18.-- Pred1ction of total number of angler-days wh1ch will occur in year of

hypothet1cal application of PISCES

Area Warmwe.ter Marginal Trout Natural Trout Ponds & Reservoirs & streams streams streams Small Lakes Large Lakes

1 92,961 0 0 678,731 0

2 56,078 0 0 534,270 0

3 48,898 20,473 71,576 2,973,662 .&::-

0 OJ

4 164,258 21,997 0 341,627 1,525,495

5 139,964 11,910 0 241,777 28,488

6 85,728 12,870 0 180,541 633,717

? 130,115 20,713 ° 546,397 243,430

8 62,261 26,300 ° 94,363 300,266

9 39,137 36,836 ),812 52,639 790,033

10 32,200 1,634 6,687 )5,946 679,915

11 82,147 105,283 7,765 40,487 733,433

12 107,175 111,427 17,040 60,756 44),718

49

(1) A public pond was constructed in management area

2 (Table 6). As a result, access areas were.devel­

oped (Table 4) and substantial information and

education efforts were allooated (Table 8) to area

2 on pond and small lake fisheries.

(2) The Tennessee Valley Authority opened a new reservoir

to angling on the border of management areas 6 and

7 (Table 6). Consequently, access areas were

developed (Table 4) and information and education

efforts were allocated (Table 8) to areas 6 and 7

on reservoir and large lake fisheries.

(J) The new pond and reservoir 1nundated warmwater

streams in areas 2, 6, and 7 and marginal trout

streams in area 7 (Table 6).

(4) The tailwaters of the new reservoir increased the

acreage of marginal trout streams in area 6 (Table 6).

(5) lvarm~later streams in area 4, marginal trout streams

in areas 8 and 11, ponds and small lakes in areas 4

and 9, and reservoirs and large lakes in area 10

were lost to the State's fisher1es system.

Input values corresponding to these events are found in

Appendix F.

OBJECTIVES FOR STATE FISHERIES MANAGEMENT AGENCIES

A reasonable objeotive for a state fisheries management

agency is to maximize total angling benefits derived from

the state's recreational fisheries within the limits of its

fixed budget. Total angling benefits are a function of a number

of factors, but the most significant and encompassing faotors

are quantlty and quality of a.ngler-days.

[QUANTITY OF

TOTAL ANGLING BENEFITS = f ANGLER-DAYS,

That ls:

QUALITY PER ] ANGLER-DAY, •••

lJIaximlzing angling benefits in an operations reses,rch

model requires definition of the above function and preoise

measurement of both quantity and quality of angler-days.

Unfortunately, no totally aoceptable definition of the exact

functional relationship or method for mea,suring the quality

aspect of angler-days has been developed. Research efforts

should be direoted towards developing aoceptable formulas and

methods, but managers oannot stop and wait for the answers.

One immediate solution for management would be to use other,

more quantifiable objeotives to approximate maximizing angling

benefits. Two suoh objectives B.re maximizing quantity of

angler-days and minimizing orowding of occurring angler-days.

Before discussing the objectives of maximizing Quantity

and min1mizing crowding of angler-days, it 1s desira.ble that

a functional relationship. however 1mpreoise, between quantity

50

51

and quality of angler-days be defined. Many natural resource

managers accept the premise that crowding reduces the quality

of an outdoor experience (Stankey 1973, Moeller and Engelken

1972, Shafer and Moeller 1971, and Lime and Stankey 1971).

If this premise is correct, crowding or quantity of angler­

days occurring simultaneously on a fishery has some form of

inverse relationship with quality. Thus, the quality of each

individual angler-day would decrease as the quantity of

angler-days increases (Fig. 6).

If the relationship in Fig. 6 is correct, two general

shapes for the curve relating total angling benefits derived

and number of angler-days sustained by a fishery in a given

time period are possible (Fig. 7). Which relationship, I or

II, 1s true depends upon the slope of the curve in Fig. 6.

Present inability to measure the quality aspeot of angler-days

prevents exact determination of that slope, so the true

relationship between total benefits and number of angler-

days cannot be specified.

F1gure 6. General relationsh1p between number of angler-days

sustained by a fishery and the quality per angler­

day

S2

>­<{ o I

a::: w ..J (.!)

Z « a::: w a..

>­r-..J « :::l o

NUMBER OF ANGLER-DAYS

Figure 7. Two possible relationships between total angling

benefits derived and number of 9.ngler-days

sustained by a fishery in a given period

54

(f) l-LL

Z W m (!) z ..J (!) Z « ...J

~ g

I

NUMBER OF ANGLER-DAYS

IJIaximizlng Angler-days

Maximizing an~ler-days has one important advantage as an

objective: the performance of management policies is relatively

easy to measure and evaluate. Intu1tively, maximizing angler­

days appears to approximate maximizing total angling benefits,

but close inspection reveals that maximizing an~ler-days has

undesirable ramifications. First, if the true relationship

between total benefits and number of an~ler-days follows curve

II in Fig. 7, maximizing angler-days may produce angler-day

numbers greater than the number corresponding to maximum benefits.

Second, the relationship between quality per angler-day and

number of angler-days (Fig. 6) shows that maximizing the

number of angler-days is equivalent to minimizing the quality

per angler-day. And finally, maximizing number of angler-days

may accelerate deterioration of fisheries resources through

excessive use.

Minimizing Crowding

Minimizing crowd1ng consists of dispersing existing

resource consumption as evenly as possible among fisheries

in a state or in proportion to the available resource in a

particular part of a state. If the inverse re1atlonship between

quantity and quality of angler-days (Fig. 6) 1s acoepted, then

by minimizing crowding of a specifio number of angler-days (l.e.

the number expeoted to occur in a planning year), the quality

per individual angler-day is maximized.

Maximizing quality per angler-day of a number of

56

57

angler-days, say X, is equivalent to maximizing the total

angling Quality derived from the X angler-days. Total angling

quality defined as:

TOTAL ANGLING QUALITY

FOR X ANGLER-DAYS = [ X ANGLER-DAYS] •

[QUALITY PER]

ANGLER-DAY·

If quantity of angler-days and quality per angler-day

are accepted as the major components of total angling benefits,

it seems reasonable to assume that total angling quality

approximates total angling benefits. Thus, minimizing crowding

approximates maximizing total angling benefits for a given

number of angler-days. That ls:

MAX TOTAL ANGLING

BENEFITS FOR X ANGLER-DAYS

MAX TOTAL ANGLING

QUALITY FOR X ANGLER-DAYS

MAX QUALITY PER

ANGLER-DAY

=

=

MAX TOTAL ANGLING

QUALITY FOR X ANGLER-DAYS

MAX QUALITY PER

ANGLER-DAY

MIN CROWDING OF THE

X ANGLER-DAYS

Minimizing crowding as an objective is not without com-

plexities. For example, one fishery may accrue more benefits

per a.ngler-day than another fishery, regardless of crowding.

58

Thus, if angler-days are displaoed from the former fishery to

the latter fishery, total benefits may be reduced. Despite

complexities, minimizing orowd1ng wa.rrents serious considera­

tion as an objective for fisheries management agenoies for

two reasons: (1) 1t 1s a quantif1able objective dealing with

the human component of management; and (2) it approximates

maximizat10n of angl1ng benef1ts.

EVALUATION OF SIMULATOR UTILITY

The utility of PISCES was evaluated by test runs using

hypothetical data. and discussion with fisheries agency per­

sonnel, but evaluations should only be considered preliminary.

'I'he best method for thoroughly evaluating the utility of

PISCES is an application study where actual ma.nap:ement problems

can be addressed.

Test runs under realistic, hypothetical situations show

that PISCES may help fisheries agencies in several ways.

First, PISCES should improve budget allocation decisions. Many

of the decisions which constitute the total state management

policy (input for PISCES) are budgetary in nature, and most

others can be traced to a budgetary base. PISCES allows

experimenting with alternative allocation polic1es, and there­

by, identif1es the best policy.

Second, PISCES oan be used to formulate multiple-action

decision polioies for re~ulatlng resource consumption. Fish­

eries resource consumption might be manipulated in amounts

Significant enough to achieve objectives, such as minimizing

crowding.

And third, regional fisheries development (i.e. construc­

tion of ponds or stocking fish) may be enhanced through PISCES.

Fisheries resource consumption predictions may clarify how

fisheries development in one area affects resource consumption

in other areas. This information can be used in deciding where

to looate access areas and state ponds •

.59

60

A meeting was held with personnel from the Fish Division

and Planning Section of the Agency to discuss PISCES. Some

skepticism was expressed concerning the effectiveness of PISCES

in regulating trends in angler consumption. Fish Division

personnel doubted that the Agency significantly influenced

resource consumption. Aside from these crit1c1sms, plann1ng sect10n

personnel were very receptive to PISCES. Evaluations concern1ng

simulator uti11ty are inconclusive and concrete results can

only be obta1ned through further investigation.

VALIDATION OF PISCES

Va11d1ty is a vague term which can be def1ned in many

ways. Mills (1974) discussed different types of validity, and

accepted the general definition that validity is a measure

of the extent to which a model satisfies its design objectives.

PISCES is designed as a pragmatic approach for predicting the

effect of fisheries management u~on resource consumption.

Therefore, the validity of PISCES should be judged by its

predictions.

PISCES simulates a system where actual data are very

limited, so validation through statist10al tests of f1t to

real deta is not praotical. One method of evaluating the

predictions is through application. A feedback loop can be

created (Fig. 8) which would evaluate the predictions by

oomparing them with estimates of angler-days made as the

planning year progresses (i.e. estimates of the values which

were predicted). A consistent bias between predictions and

estimates would be a favorable comparison and would validate

PISCES. A bias which 1s inconsistent would indicate input values

were faulty or PISCES 1s invalid.

61

Figure 8. A feedback loop whioh will show simulator

prediotions are aoourate or show simulator .

struoture is inva11d

62

START

'7

APPLY PISCES

TO PREDICT ESTIMATE

NUMBER OF ANGLER-DAYS

- ANGLER - DAYS -- OCCURING - -WHICH WILL AS YEAR

OCCUR IN PROGRESSES

NEXT YEAR

~1

AT END

OF YEAR

COMPARE

ESTIMATE USE ANGLER-DAY WITH

ESTIMATE PREDICTION AS PART OF

INPUT FOR

NEXT YEAR'S H - PISCES

APPLICATION -- IS YES ARE THEY --- CONSISTENT? • ,1 VALID

NO

CORRECT n

FAULTY INPUT _ YES ARE INPUTS PISCES - IS NOT VALVES -- FAULTY? -.

WORKING

NO n

MODIFY OR PISCES

ABANDON ......... IS --PISCES INVALID

DISCUSSION

Some fisheries management activities have clearer relation­

ships with angler consumption than others, e,nd the clarity 1s

usually reflected by the amount of historieal data available

upon which to base the relationship. Adequate historical data

exists to derive the relationships between angler-days and

access development, water development, regulation chan~es, and

catchable trout stocking, so· these relationships are probably

the most reliable in PISCES. Little historical data exists to

assess the effects of research and information and education

activities upon angler-days. Therefore, the segments of PISCES

accounting for research and information and education are

probably the least reliable pa.rts of the model.

PISCES can be improved before it is utilized in decision

analysis. First, the efficiency of the computer program

could be improved. PISCES is functional, but computer time

and storage space might be saved by altering the program.

Second, sensitivity analysis of input variables would provide

important information to future users of PISCES. And finally,

an application study would reveal any unforeseen problems

which might arise in using PISCES.

If PISCES is never used to formulate management decision

policies, it is hoped that some of the modeling techniques

employed will prove useful in future efforts to model natural

resource systems. For example. the technique of subjective

64

probability assignment used in PISCES has potential for

improving d,ecision analysis in resource management. r40deling

natural resource systems is often hampered for two reasons,

(1) system data may be inoomplete and (2) experimental

e.nalysis of system variables may be impra.ctical. Subjective

probability assignment helps overcome these two problems by

utilizing the best information about the system that is

attainable.

Operations research models such as PISCES can be power­

ful fisheries management tools, but management expertise must

include modeling skills in order to use them effectively.

While understanding how to use a particular model 1s fairly

simple, obtaining the best results with it usually requires

a degree of skill. Some models may be "one man dogs" with

regard to producing desirable results, and the man such a

model i'obeys" is usually its creator. The conclusion is

that operations research models can be utilized to best

advantage in fisheries management only if the manager and

model builder is the same person.

LITERATURE CITED

Bare, Bruoe. 1971. App11cations ot o~eratlons researoh in

forest management: a survey_ Presented August 24, 1971

at Amer. Stat. Assoc. Meeting, Fort Collins, Colorado.

51 pp.

Bell, Enoch F. and Emmett F. Thompson. 1973. Planning resouroe

allocat1on in state fish and game agenc1es. Trans.

Thirty-Eighth N. A. Wildl. and Nat. Res. Conf. 369-377.

Butler, R. L. and David P. Borgeson. 1965. California

"catohable" trout fisheries. Cal. Dept. Fish and Game.

Fish. Bullet1n 127. 47 pp.

Croxton, F. E., D. J. Cowden, and S. Kle1n. 1967. Applied

general statistios, 3rd ed. Prentioe-Hall, Englewood

Cliffs, N. J. 754 pp.

Hicks, Cha.s. R. 1964. Fundamental ooncepts in the design

of experiments. Holt, Rhinehart, and Winston. New ·York.

293 pp.

Laokey, Robert T. 1974. Introductory fisheries science. Sea

Grant, V.P.l. & S.U.; Blacksburg, Va. 275 pp.

Lamb, W.D. 1967. A technique for probability ass1gnment in

decision analysis. General Electr1c Tech. Inf. Sere Mal

Library, Appliance Park, Louisv1l1e, KY. 20pp.

Lime, David W. and George H. Stankey. 1971. Carrying capaoitYI

maintaining outdoor reoreation qua11ty. Recreation

Sympos1um Preceed1ngs. N. E. For. Expt. Sta. U.S.D.A.

Upper Darby, Pa. 174-184.

66

Lobdell, Charles H. 1972. MAST: A budget allocation system

for w1ldlife management. PhD Thesis, V.P.I. & S.U.

Blacksburg, Va. 227 pp.

McFadden, J. T. 1968. Trends in freshwater sport fisheries

of North America. Trans. Am. Fish. Soc. 98(1)1136-150.

M1lls, P.J. 1974. An appl1oation of simulation to deer

management deo1sions. MS Thesis, V.P.I. & S.U.

Blaoksburg, Va. 119 pp.

Moeller, George H. and John H. Engelken. 1972. What fisher­

men look for in a fish1ng experienoe. J. Wildl. Manag.

36(4)11253-1257.

Sohmidt. J. W. and R. E. Taylor. 1970. S1mulation and analysis

of 1ndustrial systems. R1chard D. Irwin, Inc. Homewood,

Illinois. 644 pp.

Shafer, E. L. and H. Moeller. 1971. Predioting quantitative

and qualitat1ve values of reoreation participation.

Recreation Sympos1um Prooeedings. N. E. For. Expt. Sta.

U.S.D.A. Upper Darby, Fa. 5-22.

Stankey, Georse H. 1973. Visitor perception of wilderness

recreation carrying oapao1ty. U.S.D.A. For. Serve Res.

Pap. INT-142. 61 pp.

Tabs, Hamdy A. 1971. Operat1ons research: an introduct1on.

The MacMillan Co. New York. 703 pp.

Tennessee Game and Fish Commission. 1970. Inventory of

hunting and fish1ng acoess on pr1vate land and water.

Plann1ngReport No.3. SO pp.

67

Tennessee F1sh and Game Commission. 1910. Hunt1ng and fish­

ing demand and harvest inventory. Planning Report No.4.

46 pp.

Tennessee Fish and Game Commission. 1971. Hunting and fish­

ing demand, habitat, and partiCipation standards. Plan­

ning Report No.7. 51 pp.

Tennessee Fish and Game Commission. 1971.

reservoir and stream acreage analys1s.

No.8. 26 pp.

68

Pond and lake,

Plann1ng Report

APPENDIX A. MATHEMATICAL DEVELOPMENT OF SUBROUTINE WEIBUL

(see Lamb 1967 for greater deta1l)

70

Let:

X = ra.ndom variable

p(X) = probability density funotion for X

Xo = mode of the probability density funotion

Xl = lower estimate of X

Pi = probability that X is less than Xl

X2 = high estimate of X

.P2 = probability that X is greater than X2

The above estimates desoribe properties of the probabi11ty

distr1bution for X and form the basis for the following oon-

straints:

Xo = d~D~X~l 0 dX =

Xo (1 )

Pi = S:lp(XldX (2 )

P2 = SCO p(X)dX X2

(3)

Theoretioally, there are an infinite number of ~robability

density funotions whioh would satisfy the above oonstraints.

Sinoe the true distribution form is not knotm, the Weibull

distr1bution was assumed for convenienoe and for its versat1lity.

The Weibull distribution takes on a wide variety of shapes,

skews left and right, and is relatively easily solved mathe-

matically.

The three-parameter Wei bull probabilIty density funotion

71

oan be written in the form:

p(X) (4)

where,

X~ K

m ~ 0

and,

m = shape 'parameter

L = scale paramete~

K= constant

The objective is to determine the values for m, K, and L

which satisfy equations (1), (2), and (3). First, it 1s

convenient to determine the oumulative and oomplementary

oumulative functions for the Weibull density function. The

cumulative funotion is defined aSI

X' F(X') = S p(X)dX

o = 1 - exp [-(X~K)mJ

The complementary oumula,tlve is:

R{X') = 1 - F(X')

= exp [_(X~-K)j

(5)

(6)

72

Applying the modal constraint in equation (1) to the Weibull

density function in equat10n (4)&

(7)

Equation (7) gives the scale parameter, L, in terms of the

shape parameter, m, the constant, X, and the modal value, Xo.

Because the complementary cumulat1ve funct10n represents

the probability that X is greater than X', equations (2) and

(J) can be substituted 1nto the expression for R(X') to obtain:

= exp [_ (m;l) (X1-X)ml Xo-X J

= up ~ (m;l )(ig:~)~

(8)

The results are two equat10ns with two unknowns, m and K. How-

ever, these equations cannot be solved direotly for these two

parameters. An iterat1ve scheme was employed to solve for m

and K.

Select1ng an initial value of ml, equation (8) 1s solved

for Kl- That iSl

or,

=

73

Let A equal the r1ght s1de of the eq.U8.t1on to arr1ve at:

Subst1tut1ng Xl and ml into equat10n (9), the value of the

complementary oumulat1ve funct1on, call 1t Pi, can be calculated.

The process 1s repeated for another value of mt say m2 t and a

value P2 1s obtained. Unless by cha.nce the correct value of

m was selected, Pi and Pi are different from P2. Def1ne:

f - P _ pt. 2 - 2 2

The correct value of m 1s the one which makes £i equal zero.

The Newton-Raphson technique was employed to select progress­

ively improved values of m as in equation (10).

(10)

Iteration 1s continued until £1 is essentially zero. In this

study, 1teration was oeased when fi~ 0.00001.

The Weibull density function is nearly symmetric (approx­

imating the normal distr1bution) when the shape parameter. m,

is approximately 3.5. For greater values of mt the distr1but1on

assumes a skewed shape "tal1ing" to the left, and for values

less than 3.5, the- distribution tails to the rlght. It should

74

be noted, under the assumption of an unimodal distribution,

m must be greater than unity, since the Weibull density is

monotonioally deoreasing for X > K when m ~ 1.

APPENDIX B. MATHEMATICAL DEVELOPMENT OF SUBROUTINE RANDOM

75

76

The three-parameter We1bull probab1lity density function

can be written as (La.mb 1967):

p(X) = ~ (X_K)m-l exp [-(¥)~

where,

x ~ K

m 2 0

and,

x = random variable

m = shape parameter

L = scale parameter

K = constant

The cumulative distribution function ls:

(XI r X'-K ml F(X') = . .too p(X)dX = 1 - exp L- (--z;-) J

To produoe a prooess generator, set R = F(X'), where R 1s a

uniformly distributed random variable such that 0<&<1. Then:

(1)

Solve (1) for the random var1able XI.

XI = K + L <_In(l_R))l/m (2)

71

S1nce Rand l-R have the same uniform distr1but1on, equation

(2) can be written as:

1~ X' = K + L (-In(R))

Equat10n () can be used to generate Weibully d1str1buted

random variables.

(3)

APPENDIX C. MATHEMATICAL DEVELOPMENT OF SUBROUTINE MODEX

78

79

Subroutine MODEX defines modified exponential relation­

ships between:

(I) X = information and eduoation expenditures

Y = number of angler-days produoed by information

and eduoation

(II) X = researoh expend1tures

Y = number of angler-days produoed by research

(III) X = pounds of catchable trout stocked

Y = number of angler-days produced by catchable

trout stocking

where X 1s the absoissa and Y 1s the ordinate (Fig. 1).

Relationship I and II were derived empirioally. Relation­

ship III was based on the findings of Butler and Borgeson

(1965). They showed that as oatchable trout stocking inoreased,

angling pressure increased. Crowding effects, such as space

limitations, make the asymptote to relationship III seem

reasonable.

The general form of the modified exponential relationship

is:

where,

Y = K +as X

a. < 0

13 < 1

X = absc1ssa

Y = ordinate

K = asymptote

CJ., 13 = parameters

(1)

Figure 1. Mod1f1ed exponent1al relationsh1ps 1n PISCES

80

(/)

>­c::{ o I

0::: W ..J (.!) Z «

(/)

~ o I

0::: W ..J (.!) Z «

(/)

~ o I

0::: W ..J (.!) Z «

I AND E EXPENDITURES.

RESEARCH EXPENDITURES

POUNDS OF TROUT STOCKED -

82

Equation (1) describes relat1onsh1ps where the amount of change

in Y dec11nes by a constant percentage as X inoreases (Fig. 2).

Parameter a represents the d1stance between Y and K when X = o. Parameter 8 1s the ratio between suocessive increments of Y.

A oomplete disoussion of the modified exponential relationsh1p'

is given by Croxton, Cowden, and Klein (1967).

The asymptote, K, and the parameters, a and S , must be

estimated for eaoh of the three relationships in Fig. 1 to

solve equation (1). K is est1mated direotly and 1s part of

the input of PISCES. The asymptotes for these relationships

are diffioult to determine, but sensitivity experiments con­

ducted during this study (Appendix D) show that great changes

in the value of K have little effect upon the f1nal MODEX

prediction. The parameter a 1s, by def1n1t1on, equal to

negative K when the curve goes through the origin (Fig. 3).

In each of the three relationships, a value for X, say E,

is known from last year (e.g. last year's budgets for rela­

tionships I and II and the pounds of trout stocked last year

for relationsh1p III). An estimate of the Y value, call 1t P,

can be given a We1bull distr1but1on as in Append1x A (Fig. J).

Then the following relationship oan be formed:

P = K +aS E (2)

Sinoe 8 1s the only unknown value 1t can be calculated as:

Figure 2. General form of modified exponential ourve

83

y

o

----------------------K

Y = K +a,8x a<O ,8<0

x

Figure 3. Form of modified exponential ourve used 1n PISCES

85

a

y

-- ------------------------ K

P

E x

P = K +aJ3E

K = ASYMPTOTE P = LAST YEAR'S Y VALUE (WEIBULL DISTRIBUTION) E = LAST YEAR'S X VALUE a= PARAMETER {3= PARAMETER

I> = [(P:K~ liE

Now that K, at and sare known, th1s year's X value, say M,

oan be app11ed to equat10n (3) to g1ve th1s year's prediotion.

Prediotion = K+aS M (3)

APPENDIX D. SENSITIVITY EXPERIMENTS ON MODEX

88

Control Curve

A oontrol ourve was oonstruoted for sensitiv1ty experiments

on MODEX with the following variable values (see Append1x Ct

Fig. 3 for variable definit1ons).

K = 500,000

E = 75,000

p = 30,000 'E-'(-~) 40,000 ~(----.) ,0,000

Where P varies from 30,000 to 50,000 with a most probable

value of 40,000. Three d1fferent values were given to M

(Appendix Ot Equation (3») in three different experimental

runs. Table 1 gives the results of the oontrol runs. It should

be noted (Table 1) that in Run 2 where M = E, the prediotion

is very olose to the most probable value of P <:39,89'~40tOOO).

EXneriment .1

Experiment 1 was conduoted to test the effeot of vary1ng

the width of We1bull probabil1ty distr1but1on ranges for P

upon the MODEX prediotion. The low and high values of the

distr1but1on for P were var1ed, while other variables rema1ned

consistent with those of control runs. Table 2 g1ves the

results of experiment 1.

Results of experiment 1 show two important oharacteristics

of MODEX: (1) the MODEX ~rediotlons are not sensitlv~ to

variations in range of Weibull distribution for P; (2) the

standard deviations associated with the predictions show that

89

Table 1.-- Results of control runs for sensitivity experiments using 50 iterat10ns

of subroutine MODEX

RUN

1 2 :3

M

.500 75,000

500,000

PREDICTION (Mean)

277 39,895

212,.577

STANDARD DEVIATION

20 2,824

11,820

\0 o

Table 2.-- Results of sensitivity experiment 1 using 50 iterations of subroutine

MODEX

RANGE OF P DIS':rRIBUTION PREDICTION S'I'ANDARD

LOW MOST HIGH M (MEAN) DEVIATION PROBABLE

Control 30,000 40,000 50,000 500 277 20 ~ Narrow Range 35,000 40,000 45,000 ;00 277 10 ~

Wide Range 20,000 40,000 60,000 500 277 41

Control :30,000 40,000 50,000 75,000 39,895 2,824 Narrow Range :35,000 40,000 45,000 75,000 39,947 1,412 Wide Range 20,000 40,000 60,000 75,000 39,790 5,647

Control 30,000 40,000 50,000 500,000 212,577 11,820 Narro~'1 Bange 35,000 40,000 45,000 500,000 212,947 5,888 Wide Range 20,000 40,000 60,000 500,000 212,534 2),832

--

92

the ranges of the output (pred1ot1on) d1str1butions vary 1n

w1dth as the ranges of the P d1stributions vary in w1dth.

Exper1ment £ Exper1ment 2 was conducted 1n two parts. Part A tests

the effect of vary1ng the K value upon the MODEX predictions.

Part B tests the effect of varying the looation of the Weibull

probab1lity distribution for P.

~A

The value of K was var1ed, while other variables remained

oonsistent with those of control runs. Table) g1ves the

results.

The results of Part A show that MODEX is not very sensitive

to variations in the K value. These results are important

because of the diff1culty of precisely est1mating K.

~12

The location of the We1bull distribution for P was va~ied

by changing the h1gh, low, and most probable est1mates for P.

The values of all other variables remained consistent with

those of control runs. Table 4 gives the results.

Results of Part B indicate that the MODEX prediotion is

sensitive to ohanges in locat1on of the Welbull proba.bility

distribution for P.

Table J.-- Results of Part A of sensitivity experiment 2 using 50 1terations of

subrout1ne MODEX

K M PREDICTION srANDARD (MEAN) DEVIATION

200,000 500 297 23 500,000 (control) 500 277 20 750,000 500 273 20

1,000,000 500 271 20 10,000,000 500 267 19

200,000 75,000 39,895 2,824 500,000 (control) 75,000 39,895 2,824 750,000 75,000 39,895 2,824

1,000,000 75,000 39,895 2,824 10,000,000 75,000 39,895 2,824

200,000 500,000 154,356 5,440 500,000 (control) 500,000 212,577 11,820 750,000 500,000 228,888 13,861

1,000,000 500,000 237,579 14.986 10,000,000 500,000 262,968 18,407

\,() ~

Table 4.-- Results of Part B of sensitivity experiment 2 using 50 iterations of

subroutine MODEX

LOCATION OF P DISTRIBUTION PREDICTION

LOW MOST HIGH M (rmAN ) PROBABLE

\.0 1,900 2,000 2,100 500 13 .(:

30,000 40,000 .50,000 500 277 59,000 60,000 61,000 500 426 74,000 75,000 76,000 500 .541

300,000 450,000 400,000 500 4,000

1,900 2,000 2,100 75,000 1,999 30,000 40,000 50,000 75,000 39,895 59,000 60,000 61,000 75,000 .59,989 74,000 75,000 76,000 75,000 74,989

300,000 350,000 400,000 75,000 349,374

1,900 2,000 2,100 500,000 13,177 30,000 40,000 50,000 .500,000 212,577 59,000 60,000 61,000 500,000 286,731 74,000 75,000 76,000 500,000 330,759

300,000 350,000 400,000 500,000 499,801

APPENDIX E. VARIABLE DEFINITIONS

95

IInput Var1ables

96

Variable

AFEE

BA(I)

BFEE

BUDGET(l)

BUDGET(2)

BUDGET(:)

BUDGET (4)

BUDGET(S)

BUOOET(6)

CM(I)

C!'t1LOST( I)

Location

REGULA

HATCH

REGULA

HATCH

HATCH

HATCH

HATCH

INED

BESEAR

REGULA

WATER

Definition

regression coefficient relating angler-

days to license fee inoreases

peroent of oatohable trout produced at

Buffalo Springs hatchery going to each

area (I = 1 through 12 areas)

regression coefficient relating angler-days

to license fee increases

funds for Erwin hatchery

funds for Flintville hatohery

funds for Tellico hatohery

funds for Buffalo Springs hatohery

Information and Education budget

Research budget

peroent of angler-days occurring in eaoh

month on marginal trout streams (I = 1

through 12 months)

acres of ma.rginal streams lost to

fishery (I = 1 through 12 areas)

'-0 ~

Var1able Locat1on

CN(I) REGULA

CNLOST(I) WATER

COST ( 1) HATCH

COST(2) HATCH

COST(J) - HATCH

COST(4) HATCH

DH, DL,DH 1NED

EA(I) HATCH

Def1nition

percent of angler-days ocourr1ng in each

month on natural trout streams (I = 1

through 12 months)

acres of natural trout streams lost to

fishery (I = 1 through 12 areas)

estimated cost of running Erwin hatchery

at full capacity

estimated cost of running Flintville

hatchery at full capacity

estimated oost of rQ~ing Tellioo

hatchery at full capaoity

estimated cost of running Buffalo Springs

hatohery at full capacity

high, low, and most probable estimates

of effect upon angler-days of the information

and education program from last year

percent of catchable trout produoed at

Erwin hatchery going to each area (I = 1 through 12 areas)

'" CD

Variable

EK

ERN(I)

EX

FA(I)

FED

FEDA(I)

FEE

HCH(I),HCL(I),HCM(I) -

Looation

INED

INED

INED

HATCH

HATCH

HATCH

REGULA

WATER

Definition

maximum effect that an information and educa-

tion program can have on angler-days in state

peroent effort of I & E program spent on

eaoh region and fishery (I = 1 through 60,

1 through 5 = fisheries in area 1, etc.)

last year's I & E budget

peroent of catchable trout produced at

Flintville hatchery going to each area

(I = 1 through 12 areas)

estimate of pounds of federal catchable

trout produced for stocking.

percent of catchable trout produced at

federal hatcheries going to each area

(I = 1 through 12 areas)

amount of license fee inorease

high, low and most probable estimates of

'!) \0

the ohange 1n angler-days per acre of marginal

trout ntreams if gained or lost (I = 1

through 12 areas)

Variable Location

HNH(I),HNL(I),HNM(I) - WATER

HPH(I),HPL(I),HPM(I) - WATER

HRH(I),HRL(I),HBM(I) - WATER

H1VH ( I ) ,H\'IL ( I) ,H\1M ( I ) - WATER

IX RANDU

Definition

high, low, and most probable estimates

of the change in angler-days per acre of

natural trout streams if gained or lost

(I = 1 through 12 areas)

high, low, and most probable estimates

of the change 1n angler-days per acre of

ponds and small lakes 1f gained or lost

(I = 1 through 12 areas)

high, low, and most probable estimates

of the change in angler-days per acre of

reservoirs and large lakes if gained or

lost (I = 1 through 12 areas)

high, low, and most probable estimates

of the change in angler-days per acre of

warm streams if gained or lost (I = 1

throu'gh 12 areas)

random number seed - an odd integer with

1 to 9 digits

t­o o

· Variable Looat1on

LDE(I,J) ACCESS

LUD(I,J) ACCESS

MAXB HATCH

MAXE HATCH

MA.XF HATCH

MAXT HATCH

MOCM(I) REGULA

Defin1t1on

number and location of new developed access

areas which will become functional this

year (I = 1 through 5 fisheries and J = 1 through 12 areas)

number and location of new undeveloped

acoess areas which will become functional

th1s year (I = 1 through 5 fisheries and

J = 1 through 12 areas)

maximum catchable trout production capa-

city of Buffalo Springs ~atchery

maximum catchable trout production capa-

city of Erwin hatchery

maximum catchable trout production capa-

oity of Flintville hatchery

maximum catchable trout production capa-

city of Tellico hatchery

months to be closed to angling on marginal

trout streams (I = 1 through 12 months)

~

o .....

Variable Looation Definition

MOCN(I) REGULA months to be closed to ang:ling on natura.l

trout streams (I = 1 through 12 months)

MONTHS REGULA indioator of season length ohange (0 = no change, 1 = change)

MOPL(I) REGULA months to be closed to angling on ponds

and small lakes (I = 1 through 12 months)

MORL(I) REGULA months to be closed to angling on reser-

voirs and large lakes (I = 1 through 12 t-:> 0

months) N

NO\iS(I) REGULA months to be olosed to angling on warm

streams (I = 1 through 12 months)

PCN(I) WATER aores of marginal trout streams recruited

to fishery this year (I = 1 through 12

areas)

PCN(I) WATER acres of natu.ral trout streams reoruited

to fishery this year (I = 1 through 12

areas)

Variable

PL(I)

PLLOST(I)

PPL(I)

PRL(I)

PWS(I)

RATE

RHP,RLP,RMP

BL(I)

-

Location

REGULA

WATER

WATER

WATER

WATER

RESEAR,INED

HE BEAR

REGULA

Definition

percent of angler-days ooourring in each

month on ~onds and small lakes (I = 1

through 12 months)

acres of ponds and small lakes lost to

fishery (I = 1 through 12 areas)

acres of ponds and small lakes recruited

to fishery this year (I = 1 through 12 areas)

acres of reservoirs and large lakes re-

cruited to fishery this year (I = 1 through

12 areas)

aores of warm streams recruited to fishery

this year (I = 1 through 12 areas)

estimate of inflation ra,te

high, 1011 t and most probable estimates of

effect upon angler-days of the researoh

program from la,st year

percent of angler-days occurring in each

month on reservo1rs and large lakes (I = 1 through 12 months)

..... o

\.",)

Variable

RLLOST(1)

BK

RN(1)

RX

SHP(I),SLP(I),SMP(1) -

SK(I)

SX(1)

Location

WATER

RESEAR

RESEAR

RESEAR

HATCH

HATCH

HATCH

Definition

aores of reservoirs and large lakes lost

to fishery (I = 1 through 12 areas)

maximum effect that a research program can

have on angler-days in the state

percent effort of research program spent

on each region and fishery (I = 1 through

60, 1 through 5 = fisheries in area 1. ets.)

last year's research budget

high, low, and most probable estimates

of last year's angler-day produotion from

oatohable trout stocked in marginal trout

stres.ms (I = 1 through 12 areas)

maximum angler-days which can be pI'oduced

in each area from catchable trout stocked

in marginal trout streams (I = 1 through

12 areas)

pounds of catchable trout stocked in ea.ch

area in marginal trout streams last year

(I = 1 through 12 area.s)

f-1 o +:"

Variable Locat1on

TA(I) HATCH

TDCM(1) MAIN ,DRAW

TDCN(I) MAIN ,DRAW

TDPL(I) MAIN.DRAW

TDRL(I) MAIN ,DRAW

TDWS(I) ~1AIN t DBAltJ'

THP(1) ,TLP(I) ,TMP(I) HATCH

TK(I) HATCH

Definition

percent of catchable trout produced at

Tellico hatchery going to each area

(I = 1 through 12 areas)

aeres of fishable marginal trout streams

1n each area (I = 1 through 12 areas)

acres of fishable natural trout streams

1n each area (I = 1 through 12 areas)

acres of fishable ponds and small lakes

1n each area (I = 1 through 12 areas)

acres of fishable reservoirs and large

lakes in each area (I = 1 through 12 areas)

acres of fishable warm streams in each

area (I = 1 through 12 areas)

same as SHP(I),SLP(I),SMP(I) but for

reservo1rs and large lakes

maximum angler-days which can be produced

in eaoh area from catchable trout stocked

in reservoirs and large lakes (I = 1 through

12 areas)

J-.::. o \.1\

Variable Location

TL(I) MAIN.REGULA

TN(I) MAIN ,REGULA

TN(I) MAIN ,REGULA

TP(I) MAIN ,REGULA

TW(I) MAIN ,REGULA

TX(I} HATCH

Definit10n

estimate of last year's angler-days on

reservoirs and large lakes in each area

(I = 1 through 12 areas)

estimate of ls.st year's angler-days on

marg1nal trout streams in eaoh area

(I = 1 through 12 areas)

estimate of last yearts angler-days on

natural trout streams 1n eaoh area

(I = 1 through 12 areas)

est1mate of last year's angler-days on

ponds and small lakes in each area

(I = 1 through 12 areas)

est1mate of last year's angler-days on

warm streams 1n each area (I = 1 through

12 areas)

pounds of oatohable trout stocked in each

area in reservoirs and large lakes last

year (I = 1 through 12 areas)

....... o 0'\

Variable Location

WS(I) REGULA

WSLOST(I) WATER

XDH(1.J) ACCESS

XDL( I, J) ACCESS

XDM(I,J) ACCESS

XUH(I,J) ACCESS

Definition

percent of angler-days occurring in each

month on ~larm streams (I = 1 through 12

months)

acres of warm streams lost to fishery

(I = 1 through 12 areas)

high estimate for angler-day inorease

due to new developed aocess area (I = 1

through 5 fisheries, J = 1 through 12 areas) ~

o low est1mate for angler-day increase due ~

to new developed access area (I = 1 through

5 fisheries, J = 1 through 12 areas)

most probable estimate for angler-day

inorease due to new developed aocess

area (I = 1 through 5 fisheries, J = 1

through 12 areas)

h1gh estimate for angler-day inorease due

to new undeveloped access area (I = 1

through 5 fisheries, J = 1 through 12

areas)

Variable Location

XUL(I,J) ACCESS

XUM(I,J) ACCESS

YR(I) HATCH

YS(I) HATCH

ZAH,ZAL,ZAH ACCESS

ZEH,ZEL,ZEM 1NED

Definition

low estimate for angler-day increase due

to new undeveloped access area (I = 1

through 5 fisher1es, J = 1 through 12 areas)

most probable esti~ate for angler-day

increase due to new undeveloped access

area (I = 1 through 5 f1sheries, J = 1

through 12 areas)

percent of oatchable trout stocked in

reservoirs and large lakes for each area

(I = 1 through 12 areas)

peroent of oatohable trout stocked in

marginal trout streams for each area

(I = 1 through 12 areas)

high, low, and most probable esti~ates

of percent of angler-day 1ncreases from

acoess area development which are new

high, ION, and most probable est1mates of

percent of angler-day increases from I &

E program which are new

t-:. o (»

Variable Location Definition

ZH(I),ZL(I),ZM(I) DRA~f high, ION, and most proba.ble estimates

of pe-:"cent of angler-days migrating 'tIlhich

migrate from fisheries in the same area

ZH ( 2) J ZL ( 2 ) ,Z£'l ( 2 ) DRA~I high, 10 itT , and most probable estimates

of angler-de,ys migrating from second

ra.nked fishery

ZH ( .3) ,ZL ( ;3 ) ,ZH ( .3 ) DRA!:! high, low, and most probccble estimates

of a.ngler-days migr8.tlng from third ;-." 0

ranked fishery \0

ZH ( 4 ) , ZL ( 4) ,zr'l ( 4 ) DRAW high, lO~'lJ 8.ncl mo probable estimates

of 8,ngler-days migrating from fourth

ranked fishery

ZH ( 5) ,ZL ( 5) t ZH ( 5 ) DRArl high, low, and most probable estimates

of angler-days migrating from fifth

ranked fishery

ZRH.ZRL,ZRM REGULA high, low, and most probable estimates

of percent of engler-days lost when season

is closed whioh do not migrate

Variable Location

ZWH,ZWL,ZWM WATER

ZXH t ZXL, ZX1~ RESEAR

ZZH(I),ZZL(I),ZZM(I) !-IAIN

Definition

high t low, and most probable estimates

of percent of angler-day inorease from

gain of a wB.ter type which are ne\1 (or

percent angler-days lost which do not

migrate when water is lost)

high, low, and most probable estimates

of percent of angler-da.y inoreases from

research ~rogram whioh are new

high, low, and most probable estimates

of statewide popularity trend ohanges

tor eaoh fishery (I = 1 thro~gh 5 fisheries)

t--~

...... o

Internal Variables

111

Variable Location

A(I) WATER

AC(I) ACCESS

Ace(r) ACCESS

ADe~l( I) MAIN,TALLY

ADCN{I) MAIN ,TALLY

ADPL(I) MAIN ,TALLY

ADR(I) HATCH

Definition

change in an~ler-days per acre of warm

strea.ms gained or lost (I = 1 through

12 areas)

change in angler-days due to new unde­

veloped access areas (I = 1 through 12 areas)

ohange in angler-days due to new devel­

oped aocess areas (I = 1 through 12 areas)

acoumulated angler-day changes on mar-

ginal trout streams (I = 1 through 12

areas)

accumulated B.ngler-day changes on natural

trout streams (I = 1 through 12 areas)

acoumulated angler-day changes on ponds

and small lakes (I = 1 through 12 areas)

angler-days produced on reservoirs and

large lakes from oatchable trout stook-

ing (I = 1 through 12 areas)

....... f-'. l\)

Variable Looation

ADS(I) HATCH

ADHL(I) MAIN ,TALLY

ADWS(1) MAIN ,TALLY

AN(I) WATER

ANG(1) WATER

ANGL(1) WATER

Definition

angler-days produced on marginal trout

streams from oatchable trout stocking

(I = 1 through 12 areas)

aocumulated angler-day changes on

reservoirs and large lakes (I = 1 through

12 areas)

aooQ~ulated angler-day changes on warm

streams (I = 1 through 12 areas)

change in angler-days per acre of mar-

ginal trout streams gained or lost (I = 1 through 12 areas)

change in angler-days per acre of natural

trout streams gained or lost (I = 1

through 12 areas)

ohange in angler-days per acre of ponds and

small lakes gained or lost (I = 1 through

12 areas)

......

...... V>

Variable Looation

ANGLE(I) WATER

AREA(I) HATCH

CHCltI( I) TALLY

CHCN(I) TALLY

CHED(I) INED

CHPL(I) TALLY

CHRE(I) RESEAR

CHRL(l) TALLY

Definition

change in angler-days per aore of

reservoirs and large lakes gained or

lost (I = 1 through 12 areas)

pounds of trout stocked in each manage­

ment area (I = 1 through 1e areas)

angler-day changes on natural trout streams

(I = 1 through 12 areas)

angler-day changes on marginal trout

streams (I = 1 through 12 areas)

change in an~ler-days from information

and education program (I = 1 through 60)

angler-day changes on ponds and small

lakes (I = 1 through 12 areas)

ohange in angler-days from research

program (I = 1 through 60)

angler-day changes on reservoirs and

large lakes (1 = 1 through 12 areas)

a­t--'> .(::"

Varie,ble Location

CRT,IS (I) TALLY

D(I) DRA~v

DHClVI(I) INED,RESEAR,

HATCH,ACCESS,

WATER.REGULA

DHCN(I) .1

DHPL(I) "

DNRL(I) fI

DN~'lS (I) ..

Definition

angler-day change s on warm stree.ms (I = 1 through 12 areas

number of 8ngler-days which "N'ill migrate

(I = 1 through 12 areas)

:migrations from m2crginal trout stresLms

to other ms.rginal trout streams (I = 1

through 12 areas)

migrations from natural tliout stree..ms to

marginal trout streams (I = 1 through 12

areas)

migrations from ponds and small lakes to

marginal trout streams (I = 1 through 12

areas)

migrations from reservoirs and large lakes

to marginal trout streams (I = 1 through

12 areas)

!nigra tions from t'larm streams to marg1nal

trout stre~"Jms (I = 1 through 12 areas)

~ t-' \J\

Variable

DNCH(I)

DNCN(I)

DNPL(I)

DNRL{I)

DN~fS (I)

DPCI1(I)

Loc8.tion

INED,RESEAH,

HATCH,ACCESS,

WATER.REGULA

INED,REGULA,

ACCESS, vIATER,

RESEAR

"

tt

If

tt

Definition

migre.tions from mcrglnal trout streams

to natural trout streams (I = 1 through

12 a.reas)

migrations from natural trout streams to

other ne..tural trout streams (I :; 1 through

12 areas)

migrations from ponds end small lakes to

natural trout streB.ms (I :; 1 through 12

a.reas)

migrations from reservoirs and large lakes

to natural trout streams (I = 1 through

12 areas)

m1gra.tlons from \'.raI'm streams to natural

trout streams (I :; 1 through 12 e.ree.s)

migrations from marginal trout streams

to ponds and small l~kes (I = 1 through

12 areas)

1-=­..... ~

Variable

DPCN(I)

DPPL(I)

DPRL(I)

DPWS(I)

DRCM(I)

DRCN(I)

Location

INED , REGULA ,

ACCESS , WATER,

HE SEAR

"

"

"

INED,REGULA,

HATCH ,ACCESS,

WATER,RESEAR

It

Definition

m1grat1on from natural trout streams

to ponds and small lakes (I = 1 through

12 areas)

migrations from ponds and small lakes to

other ponds and small lakes (I = 1 through

12 areas)

m1grations from reservoirs and large lakes

to ponds and small lakes (I = 1 through

12 areas)

migrations from warm streams to ponds

and small lakes (I = 1 through 12 areas)

migrations from marginal trout streams to

reservoirs and large lakes (I = 1 through

12 areas)

migrations from natural trout streams to

reservoirs and large lakes (I = 1 through

12 areas)

t-' ~ .....;)

Variable

DRPL(I)

DHHL(I)

DRWS(1)

DWCM(I)

DWCN(I)

DvlPL( I)

D\afRL( I)

Looation

INED, REGULA ,

HATCH,ACCESS,

WATER,RESEAR

..

"

1NED,RESEAR,

ACCESS ,WATER,

REGULA

II

"

n

Definition

migrations from ponds and small lakes to

reservoirs and large lakes (I = 1 through

12 areas)

migrations from reservoirs and large lakes

to other reservoirs and large lakes (I = 1 through 12 s.reas)

migrations from warm streams to reservoirs

and large lakes (I = 1 through 12 areas)

migrations from marginal trout streams

to warm streams (I = 1 through 12 areas)

migrations from natural trout streams to

warm streams (I = 1 through 12 areas)

migrations from ponds and small lakes to

warm streams (I = 1 through 12 areas)

migrations from reservoirs and large

lakes to warm streams (I = 1 through 12

areas)

...... ~

co

Variable

DW\-1S( I)

EAD

EP

GYPED

IY

J(I)

N

Pl(I),P2(I),P3(I),

P4(I),P.5{I)

peTB

Location

INED,RESEAR,

ACCESS ,WATER ,

REGULA

IN ED

INED

REGULA

RANDU

DRAW

DRAW

DRAW

HlJoTCH

Definition

migrations from warm streams to other

warm streams (I = 1 through 12 areas)

total change in angler-days from I & E

program

last yearts total effect of I & E program

on angler-days

percent of original angler-days left after

an increase in license fee

random number seed transfer variable

the 1dentit1es of adjacent areas (I = 1 through 4)

number of areas adjacent to the area in

question.

angler-day migrations (I = 1 through 12

areas)

percent of maximum production capac1ty

at which BUffalo Springs hatchery 1s

operating

..... t-' '\0

V0ri8~'ble

PCirE

PCTF

PCTT

Q'

R

R

HAD

ReM

Loc8tion

HATCH

HATCH

HATCH

DR AU

DRAW

RANDOH

RESEAR

REGULA

Definition

percent of' ID9ximum production cap8c1 ty

at which Er:1in hat chery is opers.ting

~ercent of m~7imum produotion capacity

at which Flintville hcttchery is operating

perc.ent of nlflXimum production c9,p9,ol ty

at wh1ch Tel~lco hatchery 1s operating

the percent of migrating angler-days

which will come from fisheries in the

same area as the fishery in question

percent of new ~ngler-deys which will

be created from program in question

ra,nclom number (uniformly distributed)

total effect of research program on

angler-des s

sum of monthly angler-de,y percentages

for the closed months on marginal trout

streams

1-', N o

Var1sble LoOcttion

HeN REGULA

HP RESEAR

RPL REGULA

RRL REGUL.I\

SDCr.I(I) STAT ,TALLY

SDCN(I) STAT ,TALLY

Definition

sum of monthly angler-day percentages

for the closed months on nCltural trout

streams

last year's total effect of research

program on engler-days

sum of the monthly angler-day percentages

for the closed months on ponds and small

lakes

sum of monthly 8,ngler-day percentages

for the closed months on reservoirs and

large lakes

standard deviation of change in angler-days

on marginal trout streams (I = 1 through

12 areas)

standard deviation of change in angler-days

on natural trout stre8.ills (I = 1 through

12 arcas)

...... N .......

Variable

SDPL(I)

SDRL(I)

SDWS(1)

Sl?( I)

SSCM(I),SSCN(I),

SSPL(I),SSRL(I),SSWS(I)

SXCM(I),SXCN(I),

SXPL(I),SXRL(I),SXWS(I)

Location

STAT ,TALLY

STAT t TALI,Y

STAT ,TALLY

HATCH

STAT

STAT

Definition

standard deviations of change in angler-

days on ponds and small lakes (I = 1

through 12 areas)

standard d.eviations of ohange in angler­

days on reservoirs and large lakes (I = 1 through 12 areas)

standard deviations of change in angler­

days on warm strea.ms (I = 1 through 12

areas)

estimate of last year's angler-day pro­

duction from stocking catchable trout in

marginal trout streams (I = 1 through 12

areas)

sums of squares (I = 1 through 12)

transfer variables (I = 1 through 12)

)-«:1.

N N

Variable Location

'l'D1(I) DRAW

TD2(I) DRAW

TD3(I) DRAW

'1'04(1) DRAW

TD.5(I) DRAW

TR(I) HATCH

TROUTB HATCH

TROUTE HATCH

Definition

aoreage in each area of first ranked

fishery (I ; 1 through 12 areas)

acreage 1n each area of second. ranked

fishery (I = 1 through 12 areas)

a.oreage in each area of third ranked

fishery (I = 1 through 12 areas)

aoreage in each area of fourth ranked.

fishery (I = 1 through 12 areas)

acreage in each area of fifth ranked

f1shery (I = 1 through 12 areas)

pounds of oatchable trout stooked in

reservoirs and large lakes in each area

(I = 1 through 12 areas)

pounds of oatohable trout produced at

Buffalo Springs hatchery

pounds of oa.tchable trout produced. at

Erwin hatohery

...... N VJ

Variable

TROUTF

TROUTT

TS(I)

TaCH

TSCN

TSPL

TSRL

TS\1S

VCIr1(I),VCN(I),

VEL ( I) J VRL ( I) , V\~ S ( I)

location

HATCH

HATCH

HATCH

MAIN

r·1AIN

YLAIN

MAIN

HAIN,TALLY

Definition

pounds of c8"tchable trout produced at

Flintville hatchery

pounds of catch8.ble trout produced at

Tellico hatchery

pounds of catchable trout stocked in

marginal trout streams in each area

(I = 1 through 12 areas)

total acrec .. ge of margi.nal trout stre<::l.Qs

in state

total acreage of natural trout stren.ms

in state

total of ponds and small lakes

in ste.te

total of reservoirs and l~rge

lakes in state

total of ~Tarm stre[mlS in stHte

standard aevl£ttlon acoumulators (I = 1

through 12 tlre&s)

~ l\)

+:-

Variable

x

XB(I)

XBcr~( I) ,XBCN( I) ,

XBPL(I) ,XBRL( I) ,XBt-/S( I)

XCM(I),XCN(I),

XPL(I) ,XRL(I),

XWS(1)

XH

XHIGH

XK

XL

XLAt-iDA

XLOW

XM

XM

XMODE

Looation

RANDOM

DRAW

STAT ,TALLY

INED,RESEAR,

HATCH,ACCESS,

WATER ,REGULA

WEIBUL

RAN DO !1

RANDOlt!

WEIBUL

RAN DO 11

RANDOM

RANDOM

WEIBUL

RAMOOItt

Definition

random variable (Welbull distribution)

angler-day changes before migrations (I = 1 through 12)

average angler-day changes (I = 1 through

12 areas)

variables storing estimates of an~ler-

day chenges for each iteration (I = 1

through 50)

highest poss1ble value for variable

highest possible value for variable

location parameter of Weibull distribution

lot1est possible parameter for variable

soale parameter of Welbull d1stribution

lowest possible value for variable

shape parameter of Weibull distribution

most probable value of variable

most probable value of variable

....... N \J\

Variable

1(1)

YFL

YH.YL,YM

YN1,YN2,YN3,

YN4,YN5,

ZA

ZAD

ZB

ZE

Location

DRAW

RANDU

DRAt.J'

DRAW

MODEX

MODEX

MODEX

MODEX

Definition

percent of angler-days migratIng from

fisheries ranked 1 through 5 (I = 1

through 5)

uniformly d1stributed random variable

high, low, and most probable estimates

of the peroent of new angler-days whioh

will be created from a management program

number of an~ler-days migrating from

fisheries ranked 1 through 5

parameter of modified exponential rela­

tionship (distance between Y and K when

x = 0)

angler-day prediction

parameter of modified exponential rela­

tionship (ratio between successive inore-

ments of Y)

last year's budget for program in question

~ l\)

0'\

Variable

ZHP ,ZLP , zrtlP

ZHR , ZLR ,Z~IR

ZIP

ZK

ZK

ZM

ZM

ZP

ZZ(I)

Location

MODEX

MODEX

MODEX

MODEX

WEIBUL

MODEX

WEIBUL

MODEX

MAIN

Definition

high, 10"''1, and most probable estimates

of last year's effect on angler-days of

program in question

high, low, and most probable estimates of

minimum program budget which will

affect angler-days

double precision exponent

asymptote of modified exponential rela-

t1onsh1p

location parameter for Welbull distribution

budget for program 1n question

shape parameter of Welbull distribution

last year's effect on angler-days of

program in question

statewide popularity trend estimates

for eaoh fishery (I = 1 through 5 fisheries)

..... N

.....::l

APPENDIX F. INPUT FOR HYPO'l'HETICAL APPLICATION

128

Baokground ~

The follow1ng is baokground data for the hypothet1oal

app11oation listed aooord1ng to the formats required by

PISCES (APPENDIX G). Var1able names are in the right margin,

and variable definitions are given in APPENDIX E.

129

94·11~) • b0630.

135315. 61642.

J.O 0.0

2~62G. 32890.

0.0 0.0

0.0 0.0

665296. 50946 /t.

5441 q 5. 92166.

O.D 0.0

1931LJ. 2 934Z( •

.~ N G L E F - [: /\ Y S R t: A LIZ ED Ii,; P Fd: VIC U S YEA R

49456. 165311. 1 i t0f333. 87136.

37453. 31160 .. 810BS. 106200.

24210. 26166. 13950. 12740.

1t3395. 1823. 130140. 130226.

O.G D.C 0.0 0.0

4288. 7648. 8544. 19380.

65640. 336840. 236F14. 177336.

52371. 35025. 38265. 58174.

2Y4469J. 1514975. 27160. 58350J.

772Z40. 6~ ~Z. 724090. 433/t10.

TW-l

T\'j-2

TH-l

T~1-2

TN-l

TN-2

TP-1

TP-2

Tl-l

Tl-2

..... V.> o

H I G H , L 0 1" A N !J tlO S T PRO B A Sl E V A L U E S F I] R 0 R A ~~

.96 .sa 1.0 Z Tl. r.1, H

.40 .50 .60 ZAL,M,H

.96 .98 1.0 lWLf~~,H

.88 .90 .92 lRl,M,H

t-Jo

.10 .80 .90 lELtM,H \.N t-Jo

.65 .7':> .85 ZXL,'-1,H

.70 .20 .20 .10 .01 ZL

.80 .25 .25 .15 .02 1M

.90 .30 .30 .20 .03 ZH

9000.

10000.

110;JO.

2. 2.

6. 6.

10. 10.

0 .. o.

HIGH, LOh, AND MOST PROBABLE VALUES FOR PJPULARITY TREND

200. 100. 40000. 85000. ZZl

284. • 43G64. 8920C. ZZf;rl

350. 250. 45000. 93000. ZZH

HIGH, LOW, AND ~GST PROBABLE VALUES FOR WATER

1. 5. 5. 5. 12 .. 8. 10. 10. 8. 15. HWL

it. 13. 10. 10. 23. 17. 20. 18. 14. 24. HWN

7. 21. 15. 15. 30. 25. 28. 26. 20. 30. HWH

12. 30. 40. 40. 9 ,'~ v. 35. 35. 110. 100. 100. HCl

...... 'vJ l\)

u. o. 30. 42. 62. 60. 122. 55. 55. 140. 135. 149. HCt"1

o. G. 40. 5C. 75. 75. 145. 70. 70. 160. 155. 170. HCH

u. c. J. '\ o. o. o. o. 10. lu. 10. 10. HNL u.

J. o. o. u. o. o. o. o. 16. 16. 16. 17. HNM

o. o. o. o. o. ;). o. o. 25. 25. 25. 25. HNH

35. 35. 4. 30. 45. 45. 100. 2~. 25. 15. 10. 25. HPL ..... \v.)

\JJ

43. 43. 3. 40. 58. 54. 127. 33. 33. 25. 15. 34. HPN

55. 55. 12. 55. 75. 70. 145. 50. 50. 35. 20. 50. HPH

0. v. 15. 20. 20. 20. 5. 5. 5. 10. 12. 5. HRl

J. o. 23. 35. 28. 30. 8. 10. 10. 11. 19. 11.1. HRr.1

o. r', . , :"\ 50. 35 • 40. 12. 18. 18. 20. 25. 15. HRH v. .,:;)u.

134

,...-' f'J .....t ~ ,-l (, I ,.....,. (,J ..-! ~

I I I I t I I C, ~1.. C 0 (:. c.. 0 c.,

-1 ....J -"1-' ......i -l "- -'-' u V1 V': V) L0 v") l- t- ~- I-

" ~

1-'

• ,-) t, ,,,,, ..... 1 (', ! ("") ~·~r (,1 ) '''"'l

. f"') r .....

1.-.... -1,1 Co. I~' (' !~"") "')

(') .. -' f\" ~ - · ...... 1 t(' 1::",,\ 1,<1 "; (./" .-I ..... .-I ,..-1 ..-l .-I ,....j ,..-l {'v N ! :

_J

< • > , (; ('

( .,J '.j" C! I lr, t'''' ;, , ..J ' ... ) 0,' C' ( 1 C)

( .... (','I t- It', .' ..i \

<1 .......l .....t ,....j -I ... .., .....t ..... 1 r ,,1 .....t rt"

C,

~- C: • ',/') ) )

r r) I:l ,...l .'.j C ~- -1

,- r''l (") ,.')

(,\,1 .....t :--~. . ..... 1'\1 rt"j -j" ....:t L .... ,

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) ,~)

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• • "\., ';-'.1 .', 1"'1 .. -i .-I

135

...... ~ (,.J (·'1 ',:1' lj,"'1 ["-.

I I I I I I I I w,. e:.,. ~ _J -I -J ...J

I (:..1 c' L C' !=~ l- I-. X X X "< >- ,... X

V U·.l

I

( )

t

'1'

( ..... r, ~) L

lC r:"'l r·1 =1'.

l ::')

...... ..t: ~,

• U.' c' c., \:::0 ( I

(~) ...J C) t:.., r .. "; () < .. ) j:~~ t(, If', U"" ~'"'\I U'\ Lr, l(',

(':"', ',r'I <', 0.1 r-,; ('.J r·J ,,, (',I ,'J C ~:l ,

c.

i- • t.,/'.: (: 0 J

C: ... :'''\ ) ".> 0 , ') (.:> , ..... " C"t t ...... ·, CJ (-~\ Cl 'r, • .. L) r~'1 ~I r<'1 C') f'l \"1

'·1

~ • ~ ) -1 ", r ", ..) .,.J

;'::0 I c: .. "."J I~: (' "

,n ..... r, ,!\ 1;'\ IC lC , ;",

.I ,',,D

" :-:'> ,....,. r" .~

1_.J '-" '" II L'

, " -I ,....; .....-4 ,......, .-I M

• ( , " r',

" ,~ ...... C":, (~ .... (

, ~ "',,", l,\.j " \,I " , ',''.I " "-,1 ,.......t r· ... ,....~ ..... 1 ....... i ,.......t --4

1200. 150iJ. ?GO. 30JO. 2500. XDl-8

1200. 15·jO. SOO. 3000. 2500. XDl-9

1200. 1500. 500. 3000. 2500. XDl-lO

12JO. 1500. 500. 3000. 2500. XDL-ll

...... \..A) .

120). 1500. 500. 3uGO. 2500. XDL-12 0\

3CO'). 3000. 2000. 5000. 4500. XDM-l

3l00. 3000. 2000. 5000. 4500. XDM-2

3000. 30G0. 2000. 5000. 4500. XDM-3

30J:]. 3000. 2000. 5000. 4500. XDM-4

300). 3000. 2000. 5JOO. 4500. XDM-5

3L0"';. 30:':0. 2000. 5000. 4500. XDM-6

3()Ou. 3000. 2000. 5000. 4500. XDM-7

3000. 3000. 2000. 50JO. 4500. XDM-8

3000. 3000. 2000. 5000. 4500. XD1'4-9

3uO'J. 300U. 2000. 50)0. 4S0C. XDM-IO

3000. 3000. 2000. 5JOJ. 450u. XDM-ll

3000. 3000. 2000. 5000. 4500. XOt·1-12 ..... \..t.) -....J

5v00. 5000. 3500. 9OCO. 8500. XDH-l

5CJO. 5000. 3500. 9000. 8500. XDH-2

5('00. 5000. 3500. 4000. 8SJ,J. XDH-3

:.. '..i..J...; • 5000. 3500. 9000. 8500. XDH-4

3GJJ. 5000. 3.5UO. 9000. 8500. XDH-5

50Cu. SevG. 35-)0. 9000. 8500. XOH-6

.:>OJU. 5000. 3S00. 9000. 3500. XDH-1

5uOG. 5000. 3:)00. gOOO. 8500. XDH-8

5000. 5000. 3:;00. 9000. 8500. XDH-9

5CJJ. 5000. 3500. 9000. 8500. XDH-IO

.... 5000. 5000. 3500. 9000. 8500. XDH-ll

\..rJ OJ

5000. 5000. 35JO. 9000. 8500. XDH-12

Ie J. 308. 100. 500. 500. XUl-l

l~J. 300. luO. 50(; • 500. XUl-2

10·). 300. 100. 500. 500. XUl-3

1(0. 3C0. 100. 500. soo. XUl-4

100. 300. 100. 500. 500. XUl-5

100. 300. 100. JOO. 500. XUl-6

100. 300. 100. 500. 500. XUl-7

lCO. 300. 100. 500. 500. XUl-8

1(0. 300. 100. 50J. 500. XUl-9

luJ. 300. 100. SOD. 500. XUl-10 ~ \.A)

-...0

1(:0. 300. 100. 50J. 500. XUL-ll

100. 300. 100. 500. 500. XUl-12

8LU. 900. 500. 1500. 1500. XUM-l

800. 900. 500. 1500. 1500. XUIVl-2

8(:J. 90G. 500. 1500. 1500. XUM-3

dLJ. 900. 500. 1500. 150C. XUM-4

8G0. 90';]. 500. 1 u. 1500. XUM-5

800. 900. 500. 1500. 1500. XUM-6

800. 900. 500. 1500. 1500. XUM-7

800. 900. 500. 1500. 1500. XUf'-1-8

......

.f:" 8 O. 900. 500. 15UO. 1500. XUM-9 0

800. 9CO. 500. 1500. 1500. XUM-IO

B00. 900. 500. 1500. 1500. XUM-ll

aGo. 900. 5(,0. 1500. 1500. XUM-12

l~OO. 150C. 1000. 2000. 2000. XUH-l

14(-J. 1500. 1000. 2000. 2000. XUH-2

140J. 1.;;0 C. 1~00. 2000. 2000. XUH-3

1400. 1500. 1000. 2000. 2000. XUH-4

1400. 1500. 100\.;. 2000. 2000. XUH-5

1400. 1500. 1000. 2000. 2000. XUH-6

140J. 15CC. 1000. 2000. 2COO. XUH-7

litO). 1500. 10CG. 2000. 2000. XUH-8 ~

-+:-~

1 'tOO. 15CO. lOGO. 2000. 2000. XUH-9

1400. 1500. 1000. 2COO. 2000. XUH-10

140J. 15,]0. 1000. 2000. 2000. XUH-11

1400. 1500. lOOu. 20uu. 20Ge. XUH-12

0 I.ll Z t--I

fY.: 0 u.. V')

1.1.1

::> -J <: > L:..J -J ~Q

<t c.:J U 0::. t'i

l-V') I" '~, ''''';

L

Q ...,... 6 . .,.

<:t

.. .:s: 0 -J

:r: l!) H

:c

:t: Cl .. :a: 0 .. -J 0

• C) -,)

<) N ......

• ':) 0 () .-, f'-4

• (')

o o 7.0

cc <t I..tJ V> UJ cC

r.(.; r..J u.. V')

I..lJ :::;; ..J <t > L.U -.J OJ < cD i..J cL: :l..

l-V)

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142

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LAST YF:AR' S eUD(~ TS

95008. 5eooo. EX,RX

ACRES Of WATER RECRUITED TU fISHERIES

10. o. o. o. o. o. o. o. 42. 2 ,-:J. 6. o. p~-;s ...... +:"

'"'" o. 00. o. o. o. 42. 16. ,J. o. J. o. o. PCiJl

o. o. o. o. o. o. o. 0. o. o. .. l) • peN v •

o. 131. o. o. o. o. c. o. o. o. o. 13. PPL

o. o. o. o. G. 1201. 449d. J. v. o. J. o. PF;,l

ACRES i]F v.1ATER LOST 10 FISHEi.zIES

o. 526. o. 15. o. 76. 199. u. o. o. O. 4. toJ S l CIS T

o. o. o. o. o. D. 26. .39. o. o. 61. o • C(,>'ILCST

o. o. o. o. o. o. o. o. o. o. o. J. CNlClS T

0. o. o. 10. o. 1"'\ o. o. 32. o. J. o. PlLnST 'oJ • ~

+:-.(::"

o. c. o. o. c. o. o. o. "" 135. . "'1 o • RllUST u. ...,.

RANDOM NUMBER SEED

1973 IX

>-l-I-«

U <r. 0.. '-::1: U

....J

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t-<!

V')

..i..i -c::: 'JJ :=: U I-~;l

X

<.!)

z """" :.:: z ::> 0::

u.. 0

(,/)

t-V)

a w

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U

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• 0 0 O a \.('\

• 0 0 .::')

U'\ U'\

• a (")

o t.n 1..(\

U.l ;-<t et:..

Z ,--. '-' t-4

l-~ ....J u.. z ......

' . .IJ I-<.J; ct

t.n o •

145

ASYMPTOTES FOR HATCH

0.0 0.0 15000. 7500G. 5,JJOO. 50CCi.). SK-1

15000. 100000. 125000. 8000. SODoeo. 5CJOJJ. 5K-2

0.0 0.0 300000. 150000. 3000. 6QQ(tJ. TK-1

20000. 30000. 80000. 10000. 70000. 45)00. TI<.-2 J-A +-0\

ASYMPTOTES FOR RESEAR

1000000. 200GOO. EK,RK

147

I X 0 <! '..1.;

~ u..

>-,.:.: tl1 :J: (.)

l-e:::(

:.T

_1: U <t '.1.1

.:::;, I- LU <- U

:,::)

0 Ct i.LJ C) u rt: - a. --' ~ r- I-....... ~ ='" a.. CJ

c.,; ill I-0.)

...J Z 0 « ~ 0 0.: W r.) UJ

11"\ a N UJ

u.. ,..... :r:: u.. . ..,..

0

:r: (:i tI)

V) ..::; 0 - (1) '2 U. 0 ::>

0' 0 u.. a. 0

V)

0 z 0 ::> 0 0 '-0 0.. r-

oo :E: :J ::E ..... )( <t ()

'lC 0 ...0 r-.... " • c

0 0

"" <4"

ACR~S OF FISHABLE WATER IN EACY A~EA

15293. 9352. 1~843. IJ)326. 10857. 6764. TDWS-l

5710. 2JJJ. 1665. 3116. 4350. 3682. TO~'!S-2

0.0 0.0 708. 506. 191. 194. TDCM-l

20 • 478. 610. 1 \). 724. 721. TOCI'-1-2 ....,. ..f:"

J.J 0.,) D.C 0.0 0.0 0.0 TDCN-l ():)

0.0 0.0 199. 382. LtOl. 949. TDCN-2

11413. 8744. 3323. 3966. 29.:.8. 2371. TOPL-1

225/" • 1396. 1;:r;:, :;.-'. 659. 1130. 880. TDPL-2

0.0 0.0 128030. 43285. 970. 1 tg i f-5J. TDRL-l

. 2414J. 2: 9.3 f t 2. 772;'4 • 6'J622. 3811(;. 43341. TOttL-2

149

rJ .....-J i'.'

I I I x )0< ~~ x.;, VI (,;'1 1-. f--

)-

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..J -:: (""') l!'" " I

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rc; \'~I r"'1 ,,,t ,....j Ci r!~

0' lr'. ·.T cr,

t. ,J

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t~'\ "" I-

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t~J ~(

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1; ) ,..~' 1 r-- ~ ., 'J"\

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C,,! ..... 4 ('''', ...-I

r·_

...j- ("', ""'j

• j'-'" ""!

Management Policy Decisions

The following list of data represents the management

policy decisions for the hypothetical applioation. Da.ta are

listed according to the formats required by PISCES (APPENDIX G).

Variable names are in right margin, and variable definitions

are given in APPENDIX E.

150

BUDGET EXPENDITURES

4300(J. 40000. 3H000. 23000. 100000. 50000. BUDGET

LOCAT[ONS UF EW ACCESS AREAS

l) 0 G r 0 ...... ~ a 0 1 0 lOE-l v v

~

\J\ u 0 0 0 G a {) 0 0 0 lDE-2 t-.a.

"" (} c· C ,"'I 0 0 0 1 LDE-3 ~,.J \.1

0 0 0 0 1 0 0 0 0 0 LDE-4

0 J 0 f" 0 0 0 0 0 0 LDE-5 v

G 0 0 "",' u 0 0 0 c 0 LDE-6

152

(/)

1"""'1 N ('("\ -.T Lf\ \Q J.:

• I I , , , I-a a 0 0 a Q z. w :::::> :::> :;:, :;:I ::> :::> 0 w -J ...J -J ...J ...J -J :'i: u..

'') a N 0 0 0

VI llJ

L' ...... i,) 0 0 <:.) ';:) ~ < J: U

a 0 0 0 0 0 z 0 ...... I-<t

(.] Q 0 0 0 0 .....J ::> 0 l-U 0::

() 0 a 0 0

0 () 0 (1) (? c>

'.J '.J :> 0 ,...,) (.)

("') D r..) 0 0 0

(-:") () ? () 0 0

,') '~') 0 :.:) () :.:J

c\ C)

:l53

V') :£ Z -J -J ;.~ U W a.. ex 0 f..:J 0 0 0 :r.: z ;£ T- ~

0 ('\J N 0 f-f ......

fJ ..... ...... {) 0 ~!) ...... ...... ;;; -..J t.:J 0 0 0 ,:J 0 Z. < I.:)

I- 0 ~) a 0 0

..::J L.U (.;)

C 0 0 0 c) CJ ..J l.)

LlJ :.0 0 0 0 0 Q

:,) l-

I/) ('J 0 0 r.) a I I-Z ~J Z I::> C,) 0 0 C'.>

:; <.J 0 0

0 r<"\ 0 0

,::') N N 0 <,

;~) c'> 0

PERC:I\T EfFCHT IN EACrl AREA CF If.[ A~,U RESEArZCH PRL:GRAHS

~ ~:.

• ';; ,J (\ ,"', • :)/-t .0,) 0"" ., ~

• !Ju .10 .CO ERN-l • 1:10.'-' . '- '-' · ~ • ~-..: \.J

.02 .01 .00 .J4 • 4 • (2,2 .01 ("'" ["" • '-,.J\.- .03 .02 ERN-2

.02 .01 .00 .06 .DO .02 .01 .00 .03 .03 ERN-3

.02 • 01 .00 .00 .03 .(;2 .01 .00 .00 .01 ERN-i •

~

\J\ .02 .Cl • 00 .03 • ()3 .G2 .el .01 .co .03 ERN-5 -'="

.C3 .Cd .01 .00 .02 .03 .03 .01 .00 .02 ERN-6

.01 .00 .OJ O? • L.

.00 .01 .c.J .00 .05 .OJ RN-l

.02 .vu .0:,) .u 5 .J9 • .)1 • ~:1 C,; .00 .C2 • (i5 Rl'l-2

.01 .OJ .00 j-::t, .~-' .03 .01 .01 .oe .03 .C4 RN-3

.01 .01 ,-, :"'"": aU'.) • L.,2 .C4 .02 .JC .00 .02 .03 t{N-4

·C2 .OJ .GO .01 .vb .02 .00 .OC .01 .06 RN-S

.02 .Gl .00 .J2 .04 .02 .J1 .00 .02 .04 RN-6

~'j HER E T R GUT F H T'i f f;I C H H t, T C HE: p, Y \JI L L BE S T ;J eKE D

0. c.) J.~ ,', c- ..) ...... () ("~ 0.0 0.0 0.) 0.0 1. ') EA ;,.; • \.1 ...... 1 .........

..... \J\

"\ : ~" C.J 0.0 ""I ,", .e2 .. 1 f t .16 ,. 23 .'t2 .02 .Cl J. 'J F t\ Vt v.'.) .J.J

O.G 0.0 0.0 \.I.>J' c.o n ,..,. ",.'.1 C.Q lJ .0 .26 0.0 .70 .04 TA

0.0 0.) .38 .46 .16 0.0 0.0 O.G 0.0 0.0 0.0 J.O BA

.OJ

.00

" "\ '".}.:..J

p r~~C~NT LF T CJUr STCCKE Ij\) R St-P.VCJIKS ANO STF<[J\:·jS Ii\! EI~CH APE"

j-' ~. .V.l

• OJ

0.0

.63

.37

0.

.45

.5~

.C4

.05 .20 .02 .10 .30 .93 .06 .03

• S5 .3 .98 .90 .70 .07 .94 .97

WHERE FEDERAL 1 CUT WILL B STGCKED

.12 .'J4 o. ;..J 0.0 .11 J.G .34 .35

VR

YS

FEDA

..... \..n 0\

PERCE~T OF ANGLEG-DAYS UCCURRI~G IN EACH ~CNTH FUR EACH FISHERY

.~:, 3 • C13 .(;3 • <jb .13 .15 .15 .13 1~ . ~ .08 .03 .03 WS

.(3 .03 .03 .15 le • ..,I • 1j .13 .08 .13 .08 .03 .03 eM

.C3 .03 .03 It:: . -' .15 .13 .13 .08 .13 .08 .03 .03 eN

.03 .03 .03 .J8 .OS .15 .15 • 1 .13 .08 .03 .03 Pl

t-""

.03 .03 .03 .08 .oa .15 • 15 .1..::;;. .13 .08 .03 .03 RL \Jl -...J

APPENDIX G. SOURCE IECK LISTING OF PISCES

158

c c c c c c c c c c

**********~:* ************ ** PISCES ** **:;-:********* ****~*:.;c*****

COMMON ADCM(12),ADCN(12),AOPl{12),ADRl(12),ADWSI12),AFEE,AHIGH(S), lALOW(5)~AMOOE(5),BA(12)fBFEE,BUDGET(6)9CM(12),CMlOST(lZ),CN(12}, 2CNLOST(lZ),COST(4),DH,Dl,OM,EA(12),EK ,ERN(60),EX 3tFA(12)tFEO~FEDA'12),FEE~HCH(lZ),HCL(12),HCM(12)tHNH{12),HNL(lZ), 4H~M(12)tHPH(12},HPl(12),HPM(12),HRH(12)fHRL(12},HRM(12),HWH(12),

5HWL(12),HWH(12),IX,IY,lDE(S,12),LUD(S,lZ),MAXB,MAXE,MAXF,MAXT, 6MOCMCIZJ,MOCN(121,MCNTHS,MOPL(12),MGRL(lZ},MDAS(12),PCM(12),PCN(12 7l.Pl(lZ),PLLOST(lZ),PPl(12),PRL{12),PWS(12),RATE, RHP,RK, RL(12 e), RLLOST(12),RlP, RMP, RN(6u),RX, SHPtlZ), 9SK(12) ,SlP(12J, SMP(12}, SX(121, *TA(12J,TDCM(lZ),TDCN(12),TOPl{12),TDRL(IZ),TDWS(12), THP(12 1)~TK{12) ,TLtI2}, TLP{lZ1, TM{lZ), TMP(lZ), 2 TN(12J,TP(12),TW(121,TX(121,VCM(12),VCN(12),V PL(lZ),VRl{12 3),VWS(12),WS(12),WSlOST(12),XCM(600),XCN(6CO),XDH(S,l2),XDl(S,lZ), 4XOM(S,12),XP{600),XPlt600),XRL(600),XUH{S,lZl,XULIS,12),XUM(5,12), 5XW{600},XWS(600),YR(12),YS(12J,ZAH,IAL,ZAM,ZEH,ZEl,ZEM,ZH(S),Zl(S) 6,IM(S),IRH,ZRl,ZRM,lSH,lSL,ISM,lTH,lTl,ITN,lWH,ZWL,ZWM,lXH,ZXl,lXM 1,ZZ(S},ZZH(S),ZZl(S},llM(5)

DIMENSION CHWS(12),CHCM(12),CHCN(12),CHOL(12),CHRL(12),XB~S(12), *XBCM(12),XBCN(12),XBPl(12),XBRl(12),SDWS{12),SDCM(lZ),SDCN(12), *SOPl(ll),SDRL(12)

DATA TSwS,TSCN,T$CN,TSPL,TSRL,CHWS,CHC~,CHCNtCHPL,CHRL/65*O .01

~

\J\ \0

CALL INPUT CALL ENVIRO DO 93 1=1.12 VWS(I'=O.O VCM(Il=O.O VCNtI)=O.O VPL(Il=O.O VRl(I)=O.O ADWS(l)=O.O ADCM(I)=O.O ADCN(I)=O.O ADPL(I)=O.O

93 ADRL(I)=O.O WRITE(6,7001

700 FORMAT(1Hl,T50,tANGLER-DAYS FROM LAST YEAR'} CALL OUTPUT(TW,TM,TN,TP,TLI

C TREND IN POPULARITY OF ANGLING DO 20 1=1,12 TSWS=TSWS+TDAS(I) TSCM=TSCM+rOCM(I) TSCN=TSCN+TDCN(I) TSPL=TSPL+TDPL(I)

20 TSRl=TSRl+TDRl(l) DO 150 ~-1= 1,50 DO 15 1=1,5 CALL RANDOM(ZlL(I),ZZM(I),llH({),IX,IY,lZ(!})

15 CQNTINUE DO 18 1=1,12 CHWS(I)=CHWS(Il-(ZZ(I)/TSWSJ*TOWS(I) CHCM{IJ=CHCMIIl+(ZZ(Z)/TSCM)*TDCM(I) CHCN(I)=CHCNlI)+(ZZI3)/TSCN)*TOCNtI) CHPL(1)=CHPl(I)+(ZZI4)/TSPL)*TDPLlI)

~ 0\ o

18 CHRL{I)=CHRL(I)+(ZZ{S}/TSRLl*TDRL(I) DO 71 1=1 t 12 J= 1+( 12*j'.i-12) XW S (J) =CH ~~ S ( I ) XCi", (J ) =CHCt-1( I ) XCN(J)==CHCN(I) XPL(J)=CHPL(I)

71 XRL(J)=CHRL{I) DO 123 1=1,12 CHWS(I)=O.O CHC'vU I )=0.0 CHCN(IJ=O.O CHPlII)=O.O

123 CHRLtI)=O.O 150 CONTINUE

CALL STAT{XWSJXCM,XCN,XPL,XRl,XBWS,XBCM~XBCN,XBPL,XBRl,SDWS,SDCM,S *DCN,SDPl,SDRl)

CALL TALlY(AD~S,ADCM,ADCNtADPl,ADRL,VWS,VCM,VCN,VPL,VRltSDWS,SDCM, *SDCN,SOPl,SDRl,XBWS,XBCM,XBCN,XBPl,XBRL)

wRITE(6,701 70 FORMAT(lH1,T52,'POPUlARITY TREND')

CALL CUTPUT{XBWS,XBCM,XBCN,X8Pl,XBRLJ WRITE(6,80}

80 FORMAT(lH1,T52,'POPULARITY TREND - STANDARD DEVIATIONS') CALL OUTPUT(SDWS,SDCM,SDCN,SDPl,SDRl) CALL ACCESS CALL w!~ TER CALL INEO CALL RESEAR CALL HATCH IF(MONTHS.EQ.J.AND.FEE.EQ.O.O)GOT08 CALL REGULA

~ Q"\ ~

8 CONTINUE WRITE(6,601)

601 fORMATtlHl,T53,'ANGLER-DAY CHANGE FOR YEAR') CALL OUTPUT(ADWS,ADCM,ADCN,AOPl,ADRl) ~JRITE(6,705}

705 FORMAT{lHl,TSO,'CHANGE FOR YEAR - STANDARD DEVIATIONS') CALL OUTPUT(VWS,VCM,VCN,VPL,VRl) 00 810 1=1,12 ADWS(ll=AOWS(I)+TW(I) ADCM(I)=ADCM(IJ+TM(I) ADCN(IJ=AOCN(I)+TN(I) ADPl(IJ=ADPL{I)+TP(Il

810 ADRl(I}=ADRL(I)+Tl(I) WRITE(6,930}

930 FORMAT(lHl,T55,'ANGlER-DAY PREDICTION') CAll OUTPUT(ADWS,ADCM,ADCN,ADPLyADRl)

4 STOP END

..... Q"\ f\l

SUBROUTINE INPUT COMMON ADCM(12),ADCNI12},ADPL(12),AORlflZ),AOWS(lZ),AFEE,AHIGH(S),

lALOW(S),AMODE(S},BA(lZ),BFEE,BUDGET(6).CMClZ),CMlOST(12),CN(lZ), 2CNlOST(12},COST(4),DH,Dl,DM,EA(lZ),EK ,ERN(60),EX 3,FA(12),FED,FEDA(lZ),FEE,HCH(12),HClI12),HCM(lZ),HNH(12),HNl(lZ), 4HNM(12),HPH(lZ),HPL(12J,HPMC12),HRH{12),HRl(12),HRM(12),HWH(12), SHWL(12),HWMtlZ),IX,Iy,lOEt5,lZ),lUDf5,12),MAXB,MAXE,MAXF,MAXT, 6MOCM(lZ),MOCN(lZ),MONTHS,MOPL(lZ),MORl(lZ),MOWS(12),PCM(lZ),PCN(12 1),Pl(12J,PlLOST{12),PPl(12),PRl(lZ),PWS(lZ),RATE, RHP,RK, Rl(12 Ol, RllOST(lZ),RlP, RMP, RN(60),RX, SHP(IZ), 9SK(12) ,SlP(lZ), SMP(lZ), SX(12), *TA(12),TDCMi12),TDCNt121,TDPLf12),TDRl(12),TDWS{lZ), THP{lZ l),TK(lZ) ,TL(12), TlP(lZ), TM(12), TMP(lZ}, 2 TN(12),TP(lZ},TW(lZ},TX(12),VCM{lZ),VCN(lZ),VPL(12),VRl(12 3),VWS(lZ),WSIIZ1,WSLOSTIIZ},XCM(600),XCN(600),XDH(S,lZ),XDL(S,lZ}, 4XDM{S,12),XP{60C),XPl(600),XRl(600),XUH(5,lZ),XUl(S,12),XUM(S,lZ), 5XW(600),XWS(600),YR(12),YS{12),ZAH,ZAl,lAM,ZEH,ZEl,ZEM,ZHtS),ll{5) 6,ZM(5),IRH,ZRl,ZRM,ZSH,ZSl,ZSM,ZTH,ZTL,ZTM,ZWH,ZWl,l~H,ZXH,lXl,ZXM

7.ZZ(5),ZIH(S),ZZl(S),ZZM(S) C BUDGET EXPENDITURES

REAO(S,l)BUOGET 1 FORMAT{6FIO.2}

C LAST YEAR'S BUDGETS READ{S,21EX,RX

2 FORMAT(2FIO.2) C ACRES OF ~ATER RECRUITED TO FISHERIES

REAO(S,3)PWS,PCM,PCN,PPL,PRL 3 FORMAT(12F6.0/12F6.0/12F6.0/12F6.0/1ZF6.0)

C ACRES OF WATER lOST REAO(S,3)WSLOST,CMLOST,CNLOST,PLLOST,RLLOST

C ACCESS AREAS REAO(S,3)lOE

f--o\ 0'\ \.JJ

REAO(S,B)lUD 8 FORMAT(lOIS/IGI5/1015/101S/IOISJIOIS)

C REGULATIONS REAO(S,lllMONTHS

11 FORMAT(ll) READ(5,l2)FEE

12 FORMAT(FS.2) IF(NONTHS.EQ.O)GOTCIO READ(S, 13)r~Oi~S READ(S,13)MOCM READ( 5,13 )t·l0CN READ L5,13) NOPl READ(S,13)MORL

13 FORMAT(l215J 10 CONTINUE

C PERCENT EFFORT IN EACH AREA OF I&E AND RESEARCH PROGRAMS READ(S,4)ERN,RN

4 FORMAT(lOF5.2/10F5.2JIOF5.2/10F5.2/10FS.2/10F5.2/10F5.2/10F5.2/10F *5.2/10F5.2/10F5.2/10F5.2)

C ANGLER-DAYS REALIZED IN PREVIOUS YEAR READ{S,lS)TW,TM,TN,TP,TL

15 FORMAT(6FIO.O/6FIO.O/6FIO.O/6FIO.O/6FI0.0/6FIO.O/6FIO.O/6FIO.O/6Fl *O.O/6FIO.O)

C RANDOM NUMBER SEED READ(S,16)IX

16 FORt-iAT(I9) RETURN END

..... ~ +='

C

SUB DUTI == 'C.nVIF<U C U i'1\1 eN A lJ C :;:j ( 1 2 ) , A DC i\l ( 12 ) , A [J P L ( 12 ) ,'\ D ? L ( 12 ) , !\;) \,-, S ( 1 2 ) , L\ FEe , A H I G r H 5 } ,

11:\ L 0 ~J , S ) , 11 i' J Ij ': ( 5 ) , 6:'" ( 1 2), f [C , B IJ U G =: T { (; ) ,C i': ( 1 2) ,C LOS T ( 12) ,(. f\ ( 1 2 ) , 2C LGST(12) ,COST(4),DH,LL,O~tFA{12),EK ,ERN(6U),EX 3 , F A ( 12 ) , FeU, f f () A ( 12 ) ,F E E , He rJ ( 1 2 ) , He L ( 1 2 ) , H ( {12), l-f\ll-j( 1 2 ) ,H N L ( 1 2 ) , 4 h ;\j ;'-1 ( 12) ,Ii F H { 1 ~ ) ,H P L ( 1 2) ,H ( 1 2 ) , t1 }<. H ( 1 2 ) , H F~ L ( 1 2 ) , H R i'; ( 1 2 ) ., H, '.; H ( 1 2 ) , 5h~l(12).,H~ (12),Ix,ly,Ln[,~,12),lU)(5J12},MAX ,MAXE,MAXF, AXT, 6/·; l.J C (1 2 ) , ,\ U Ct\H 12 ) ,t< [; i'~ T H S , f;JLj P L ( 12 ) ,:"\ L R L { 12 ) ,;.~ C) '.,i S { 1 2 ) , P c;\; ( 12 ) , peN ( 1 Z 7 J , P L ( 12 ) , P L L CST ( 12) ,P P L ( 12 ) , P R L ( 12) ,P I, S ( 12) ,K L\ T E., RH P , R K, R L ( 12 8), RLLGST(IZ),RLP, RMP, RN(6:>,RX, SHP(12), 9SK(12) ,SLP(lZ), Snp(12), SX(12), ;~ T A ( 12) , T DC (12), T DC j\ ( 12 ) , T L) P L ( 12 ) , T DR l ( 12 ) , T Lhl S ( 12 ) , T H P ( 12 1),TK(12) ,TL(12), TLP(12), TM(IZ), ThP(lZ), 2 TN ( 1 L) , T P ( 12 ) ,·T (1·), T X ( 12 ) , VCr.'; ( 12 ) , V eN ( 12) , V P L ( 12 ) , V F\ L ( 12 3 ) ,V S { 12 1, S ( 1 2), S l (. S T ( 1 2 ) , XC !\i { 6 C·) ) , XC:J ( 60 C ) , X:J H ( 5 , 1 2) , Xu L ( 5 , 12 ) , 4 X D ; ,j( 5 , 1 L ) , X? ( 6 j ~ ) ,X P L { (: CO) , X ;-< L ( 0 0 C) ,X U H ( 5 , 1 2 ) , XU L ( 5 t 1 2 ) , XU rH 5 , 12 } , 5X~{uOC),X~:S(60J},YR(12}tYS{12),ZAH,ZAl,ZA ,ZEH,ZEL,ZE ,lH(S),lL(S) 6 , l'vi ( 5 ) , Z R Ii , Z ;.: l , Z t:~ l~1 , l S H , Z S L , Z S 1'.1 , Z T H , 7 T l , Z T I.~, Z H, Z ~.j l , l'1'1 r,j , Z X h , Z XL, l X t-i 7 , Z l ( 5 ) , Z l F. { ') 1 , l Z l ( 5 ) , Z Z 'VI ( ::> )

COST PER fA AGEMENT UNIT REAU(:"l)CJSr

1 C

fOR ;-,1 A T ( I" FlO. 2 j Ii'4FlATrOI~ RATt REAO(S,ll)PATc

4 FJ~~AT(F5.2J

C R~GRESSIO~ C [FFICIENTS IF(F~~. G.C.J)GO TG 3 KEAO(5,2])AFff,aFE~

~j FORMAT{2flO.2) 3 CUNT Ii'!UE

CHI G H, L , j\ !'J D ;\i; U S T L IKE L Y V i\ L U E S C FOR DRA~J

,..t-0\. \J\

hcAJ{5,7~}lTl,ITM,lTH

~[A (5,7.)llAL,L~ ,1 V c A (5 t -I J ) L L, Z ~,; j:! ,L H F, ~_;(::.,7IJ}Z\,L,l;", ,Zt<!l F [:.. [.., ( :; , -( ..; ) I r: L t Z L , Z i~ H REA ;.J ( S , 7 :..: ) Z XL, l X \, , l X H

70 FCJ/\'j;i,T(.jF1J • .2)

71 C FU

C fLJ-Z

P.EA') ( ,71}ZL,l' ,ZH FOR~AT{5~lJ.~/5Fl~. 15f1J.2) POPUL:\RITY TREt;D ~ AO(S,71)ZlL,ZZN,lIH ~'.. r ~:,

F::':Ad(S, )hiL,i- ,h:!H 8,ci\U(:t,S)HCL,t-Li ,HCH 1< E A J { .5 , :; j t~ i'; L , H f'l " f-1 H R t AD ( :; ,:; ) tIL , ~. p ;", , H P

R tH)(S,5)h l,r:K ,HR.h :i f 1-; R :': t\ T ( 1 L F (; • J I 1,2 F 6 • C 11.2. F • 0 )

C Fun H~TCH

F,cl\U(S,O)SL?, t-1P,$HP REA J ( ? ,')~ ) T l F , T .. ; P , T H P REJ.\0(5,':lSK,lK

Su FU~MAT(0F10.J/0~1~.l/6~10.0/6F10.0)

C f'-::~R ACe c$ S PEA (5, )XDL. R C AD ( 5 , 6 ) X D l'i RcL\C( 5, C) XJt RtAC(~,6}XUL

Fe AD ( 5, ("') XU R An(~),()XUj--,

6 fUR AT()FIJ. 15 10.l/5FLv.~/5Fl .0/5fl:.0/5FlJ.0/5F10.OI5~lO.O/5Fl

~J.J/~flu.O/~rlJ.O/5FlJ.OJ

.... 0\ 0\

C. FUR I&E RE~D(5,7JDLtu~,DH

C fOR P t: S E ,{\ R, REA J ( 5 , 7 ) f: l P , S, '(1 P f R H P RcAD(:;,2J)EK,FK

7 FORMAT(3F18.UJ C FLJ P,EGUL A

IF(t1CNTHS.E .C;}GO TO 25 R[AD(5,c)~lJ;'/f~ DE,ArlICH

8 FURNAT(5FIG.OI Flv.D/5FlG.J) 25 COf,lT I hUE: 9 F~RMAT{6FIJ.O/oFlO.O/6FlO.O/6FlG.O/6FlC.O/6F10.J)

C AC cS Ji- F I SHl,3Li:: 'L~\ T [R lEACH AREA K c A;J ( 5 , 1 J ) T L; :'; S t Toe (:1 , T J C I'J , T LJ P L , T D P l

10 FORMAT{6FIO.J/6F10.0/oFlu.OlbFlO.O/6FlO.O/&FlO.O/bF10.O/6FIO.O/6Fl ;,;' 0 • iJ I 6 F 1 .i • (; )

c j'·1,~xrf';Uj'1 i'~U>lGER IJF FISH trJHICH CI,H'l BE PROGU({;O AT Ci\Cri HATCHERY R [ A D ( 5, 1 2 )i A XC, A X F, 1\ X T ,'1.b, X B

12 FORMAT(4IIO) C ~~Jd ERE T ~\IJUT :~ I lL DE S TCC KEG

~EAD(5,2)fA,FA,TA,3A

2 F~J~1'lAT( 12f6.2/12F6.2/12F6.2/12f6.2} CPr.: R C L\~ T 0 F T R (J U T S T (; eKE D I ~; t: IX C H A R f: t\

P A;)(j,15}YR,YS 15 FOR ATtI2F6.2/12fL.2)

C F '- iJ j, l\ L T R lJ tJ T P :'. oJ cue f D RE.L\f)(S,lolFCD

1 () F J K piA 1 { r 1 (; • j )

C WHERE F~O[RAL T~OUT GO R~AO(5,17)FE)A

17 FURMAT(12f6.2) C T GUT ST C< 0 LAST Y~A

~

0'\ -...J

READ(5,3.;)SX,TX 3 J FJ j-{ ;' 1 J\ T ( 6 r: 1 v • u I ;) f 1 J • I] I 6 F- 1 J • J I 6 FlO • C )

C PE;,Ct:~T Of l\i\lGLcl;'-GAYS IN E;\CH ;'·~UNTH

IF(,':i THS.=W.~}GOTC 13 REAU(~,14)~S,C~,C~,PL,kL

14 F;:;RciAT( L2F5.2./12F5.2/12F5.2/12FS.Z/12f5.2} 13 R~ TURi\j

t:l~L)

.... !

0'­a>

SUBROUTINE HATCH COMMON ADCM(lZ),ADCN(lZl,ADPLC12),ADRl(12),ADWS{12),AFEE,AHIGH(S),

lAlOW(S),AMOOE(SJ,BA(lZ),BFEE,BUDGET(6),CM(lZl,CMlOST(lZ),CN(lZ), 2CNLOSTtI2),COST(4),DH,Ol,DM,EA{12),EK .ERN(60},EX 3,FACI2),FED,FEDA(lZ),fEE,HCH(12),HCl(12),HCM(lZ),HNH(lZ),HNl(12', 4H N M ( 12 ) ,H P H ( 1 i) ., H P l ( 12 ) ,H P ~,1{ 1 2 ) , H R H ( 12 ) ,H R l ( 1 2 ) ,H R N ( 1 Z ) , H ~j H ( 12 ) t 5H~L(12),HWM(12),IX,Iy,lOE(5,12),lUO(5,12),MAXB,MAXE,MAXF,MAXT,

6MOCM(12),MOCNt12),MONTHS,MOPL(12),MORl(12),MOWS(12),PCM(12),PCN(12 1 ) , P L ( 12 ) , P L lOS T ( 12 ) ,P P l ( 12 ) , P R L { 12 ) • P liJ S ( 12 ) , R AT E , R H P , R K , R l ( 12 al, RllOSTtI2),RlP, RMP, RN(60),RX, SHP(12J, 9SK(12) ,SlP(12l, SMP(lZl, SX(12), *TA(12),TOCM(12),TDCNI12),TDPL{121,TDRl(IZ},TDWS(IZ), THP(12 1).TK(12) ,Tl(12}, TlP(12), TM(12), TMP(12), 2 TN{lZ),TP(12),TW(12),TX(lZ),VCM(lZ),VCNCIZ),VPl(12},VRl(12 3),VWS(lZ)yWS(12),WSlOSTt12),XCM(600),XCN(600},XDH(S,12),XDL(S,lZ), 4XDM(S.12},XP(600),X P L(600),XRL(600J,XUHtS,12),XUL(S,1Z),XUM(S,12J, 5XW(600),XWS{600),YR(12),YS(lZ),ZAH,ZAL,ZAM,ZEH,ZEl,IEM,ZH(5),Zl(S) 6,ZM(S),ZRH,ZRL,ZRM,ZSH,lSl,ZSM,lTH,ZTL,lTM,ZWH,ZWL,lWM,ZXH,lXl,IXM 1,lZ(S',ZZH(Sl,lll{Sl,ZZM(S)

DIM ENS I ON ARE A ( 12 ) , C H Ii S ( 12 ) ,C HC l·H 12 ) 1 C HeN ( 1 2) ,C H P l ( 12 ) ,C H R L ( 12 ) , *ADS(12),ADR(12),TR(12),TS(121,SP(12),RP(lZ),XBWS{lZ),XBCM(l *2J,XBCN(12),XBPl(12),XBRL(12),SDWS{12),SDCM(12),SOCN(12),$DPl(lZ),S *SDRl(12),DRWS{12),DRCM(lZ),DRCN(12),DRPL(lZ),DRRl(lZ},DMWS(12), *DMCM(lZ},DMCNl12),DMPl(12),DMRl(12)

DATA AREA,CH~S,CHCM,CHCNtCHPl,CHRl/7Z*O.OI C CALCULATE THE POUNDS OF TROUT PRODUCED AT EACH HATCHERY

PCTF=BUOGET(Zl/COST(2) PCTT=BUDGET(3}/COSTt3) PCTE=8UDGET(11/COST(11 PCTB=BUOGET(4}/COST(4) TROUTE=MAXE*PCTE TROUTF=MAXF*PCTF

.... ~ '\0

TROUTT=MAXT*PCTT TROUTB=MAXB*PCTB

C WHERE FISH GO DO 1 1=1,12

1 AREA(I)=AREA(I)+TROUTE*EA(I)+TROUTF*FA(I}+TRDUTT*TA{I)+TROUTB*BA{I *)+FED*FEDA(I)

C DETERMINE THE POUNDS OF TROUT STOCKED IN EACH FISHERY IN A GIVEN AREA DO 2 1=1,12 TR(IJ=AREA(Il*YR(I)

2 TS(I)=AREA(I)*VS(I) C DETERMINE ANGLER-DAYS GENERATED ON RESERVOIRS

DO 12 ""'=1 t 50 DO 4 1=1,12 IF{TR(I).lT.10.01GOT03 CALL MOOEX(TLP(I',TMP{I},THP(II,TK(I),TX(II,TR{I),ADR(I),RP(I),

*IX,IY) GOTD 4

3 ADR(Il=O.O 4 CONTINUE

C DETERMINE ANGLER-DAYS GENERATED ON STREAMS DO 6 1=1,12 IF(TStI).lT.IO.O)GOT05 CALL MOOEX{SLP(I),SMP(IlfSHP{I),SK{I),SX{I),TS(I),ADS(Il,SP(I),

*IX,IY) GO TO 6

5 ADS(I)=O.O 6 CONTINUE

C DETERMINE NET ANGLER-DAY CHANGE C ON RESERVOIRS

DO 8 1=1,12 IF(ADR(I}.EQ.O.OIGOT07 CHRLll)=ADR(I}-RP(I}

..... .....;J o

7 8

C ON

9 10

500

13

GOTO 8 CHRl(I)=O.O CONTINUE

STREAMS DO 10 1=1,12 IF(ADS(Il.EQ.O.O)GOT09 CHCM(IJ=ADS(I)-$PfIl GO TO 10 CHCN(I)=O.O CONTINUE CALL DRAW(CHRL,ZTL,ZTM,ITH,ll,ZM,ZH,TDRl,TDPL,TOWS,TDCM,TOCNtDRRl,

*ORPL,ORWS,DRCM,DRCN,IX,IY) CALL DRAW(CHCM,lTl,ZTM,ZTH,ZL,ZM,ZH,TOCM,TDCN,TDWS,TDPl,TDRl,DMCM, *DMCN,DM~S,OMPltOMRL,IX,IY}

00 500 I=1,12 CHWSCI1=CHWSCI1-DRWS(I)-DMWS{I) CHCM(I)=CHCM(IJ-DRCM(I)-DMCM(IJ CHCN(ll=CHCN{I)-DRCN(I)-DMCN(I) CHPl(I)=CHPL(I)-DRPl(I)-DMPLII) CHRl(I)=CHRL(I}-DRRl(Il-DMRlCI) DO 13 1=1,12 J=1+(12*N-12) X~~ S ( J ) =CHH S ( I ) XC N ( J ) = C He t·~ ( I ) XCN (J) =CHC N ( I) XPL(J}=CHPltI) XRL(J)=CHRl{I) DO 123 1=1,12 CHWS(I)=O.O CHeFt( 1)=0.0 CHCN(I)=O.O CHPL(I)=O.O

..,lI. -....J .....

123 CHRlII1=O.O 12 CONTINUE

CALL STAT(XWS,XCM,XCN,XPL,XRL,XBWS,XBCM,XBCN,XBPl,XBRl,SDWS,soeM,S *OCN,SOPl,SDRl)

CALL TALlV(AOWS,ADCM,AOCN,ADPl,AORl,VWS,VCM,VCN,VPL,VRL,SDWS,SDCM, *SDCN,SDPL,SDRL,XBWS,XBCM,XBCN,XBPl,X8RL)

WRITE(6,100) 700 FORMAT(lHl,T47,'ANGLER-OAY CHANGE DUE TO TROUT STOCKED'}

CALL OUTPUT(XBWS,XBCM,XBCN,XBPl,X8RL) WRITE(6,800)

800 FORMAT(lHl,T50,'TROUT STOCKED - STANDARD DEVIATIONS') CALL OUTPUT(SDWS,SOCM,SDCN,SDPL,SDRL) RETURN END

~ .....;J I\)

SUBROUTINE ACCESS COMMON AOCMC12l,ADCN(12),ADPl(lZ),ADRLC12),ADWS(12),AFEE,AHIGH(S),

lAlOW(S),AMODE(51,BA(lZ),BFEE,BUDGET(6),CM(lZ),CMlOST(12),CN(12), 2CNLOST(lZ)~COST(4),DH,Dl.DM,EA(12),EK ,ERN(60),EX 3,FA(12),FED,FEDA(lZ),FEE,HCH(12),HCL(lZ),HCM(12),HNH(12),HNl(12), 4HNM(12),HPH(lZJ,HPl(12),HPM(lZ),HRH(12"HRl(lZ},HRM(12)tHWHlIZ), 5HWL(12),H~M{12)tIX,Iy,lDE{5,12),LUD(S,12),MAXB,MAXE,MAXF,MAXT, 6MOCM(12),MOCN(12),MONTHS,MOPl{lZ),MQRL(12),MDWS(12),PCM(lZl,PCN(12 7),PLt12"PlLOST(lZ),PPl(12),PRl{lZ),PWS(lZ),RATE, RHP,RK, RL{lZ 8), RLLOST(12),RlP, RMP, RN(60),RX, SHP(12), 9SK(lZ) ,SlP(12', SMP(lZ), SX(12), *TA(12),TDCM(12},TOCN(12},TDPL(12},TORL(12),TDHS(12l, THP(12 11.TK(12) ,TL(IZ}, TLP(12), THII2), TMP(12), 2 TN(lZ),TP(12l,TW(lZ),TX(12),VCM(12),VCNflZJ,VPL(IZ),VRll12 3),VWS(12),HStlZ),WSlOST(12),XCM(600),XCN(600),XDH(5,l2l,XDl(S,12), 4XDM(S,12),XP(600}yXPL(6001,XRl(600J,XUH(S,12},XUL(5,l2),XUM(S,12}, 5XW(6001,XWSl600),Yk(lZ),YS(lZ),IAH,lAl,lAN,ZEH,ZEl,IEM,lH(S),Zl{SJ 6,ZM(SJ,ZRH,lRL,IRM,ZSH,ZSL,ZSM,ZTH,ZTl,ITM,ZWH,ZWL,lWMtZX~i,ZXL,ZXM 1,lZ(51,ZZHtS),ZZl(SJ,ZZM{Sl

DIMENSIGN ACC(S,lZl,AC(S,lZ},CHWS(121,CHCM(lZ),CHCNIIZ),CHPL(IZ),C *HRl(121,XBWS(12),DWWS(121,OWCM(12),DWCN{12),DWPl{12',DWRL(12) *,XBCMt121,XBCN(12),X8Pl(12),XBRL(12),SDWS(lZ),SDCM(12),SDCN(lZ),SD *PL(lZ),SDRl(lZ),DMWS(12),DMCM(lZ),DMCN(12),DMPL(12),DMRl(lZ), *DNWS(lZ),DNCM(12),DNCN(12),DNPLt12),DNRl(12),DPWS(12),OPCM(lZ), *DPCN(lZ),DPPL{12),DPRl(12),DRWS(12J,DRCM(lZJ,DRCN(lZ),DRPl(lZ), *ORRL(12)

DATA ACC,AC,CH~S,CHCMtCHCNtCHPL,CHRL/180*O.OI C CALCULATE THE EFFECT OF ACCESS AREAS ON ANGLER-DAYS

00 22 M=lt50 1=0

9 J=O 10 I=I+1

....... -...J v)

IF(I.EQ.6)GOT012 11 J=J+l

IF(J.EQ.13)GO TO 9 IF(lDE(I,J).lT.l)GOTO 11 CALL RANOOM(XDl(I,J),XDM(I,J),XDH(I,J},IX,IV,ACC(I,J)} IF(l.EQ.l)CHWS(J)=CHWS(J)+ACCII,Jl*LOE(I,J) IF(I.EQ.2)CHCM(J'=CHCM(J)+ACC(I,J)*LDE(I,J) IFCI.EQ.3)CHCNtJ)=CHCN{J)+ACC(I,J)*lOE{t,J) IF(I.EO.41CHPL(Jl=CHPL(J)+ACC{I,Jl*LDE(I,J) IFtI.EQ.5)CHRl(J}=CHRltJ)+ACC(I,J)*LDE(I,J) IF(J.EQ.12)GOTO 9 GOTO 11

12 CONTINUE 1=0

13 J=O 14 1=1+1

IF(I.EQ.6)GOTO 16 15 J=J+l

IF(J.EQ.13)GOTO 13 IF(lUD(I,J).LT.l)GOTO 15 CALL RANOOM{XUL(I.J),XUM{),J),XUH(I,J),IX,IY,AC(I,J) IF(I.EQ.l}CHWS(J)=CHWS(J)+ACfI,J)*LUDtl,J) IF(I.EQ.2)CHCM{J)=CHCM(J)+AC(I,J'*LUDCI,J) IF(I.EQ.3)CHCN(Jl=CHCN(J)+AC(I,J)*LUD(I,Jl IF(I.EQ.4)CH Pl(J)=CHPL(J)+AC(I,Jl*LUO(I,J) IFCI.EQ.S)CHRL(J)=CHRL[J)+ACtI,Jl*LUDII,J) IF(J.EQ.IZ1GOTO 13 GOTO 15

16 CONTINUE CALL DRAW(CHWS,ZAL,ZAM,ZAH,ZL,ZM,ZH,TDWS,TDPl,TDCM,TORL,TDCN,DWWS, *DWPL,DWCM,O~RL,DWCN,IXtIY)

CALL DRAW(CHCM,lAL,ZAM,ZAH,ll,ZM,ZH,TDCM,TDCN,TDWS,TOPL,TDRl,DMCM, .

~ .....:J ~

*DMCN,DMWS,DMPL,DMRL,IX,IY) CALL DRAW{CHCN,ZAl,ZAM,ZAH,IL,ZM,ZH,TDCN,TOCM,TDWS,TDPl,TDRL,DNCN,

*DNCM,DNWS,DNPL,ONRl,IX,IY) CALL ORAW(CHPL,lAL,ZAM,ZAH,Zl,ZM,lH,TDPL,TDRl,TDWS,TDCM,TDCN,DPPL,

*DPRL,DPWS,DPCN,DPCN,IX,IY) CALL DRAW(CHRl,ZAL,ZAM,IAH,ll,ZM,ZH,TDRL,TDPL,TDWS,TDCM,TOCN,DRRL,

*DRPL,DRWS,DRCM,DRCN,IX,IY) 00 500 1=1,12 CHWS(IJ=CHWS(I)-DwwStI)-DMwStI)-DNWS[I)-OPWS(Il-DRWS(I) CHCM(I)=CHCM(I)-DWCMCI1-DMCM(IJ-DNCM(I)-DPCM(I)-DRCMII) CHCN(Il=CHCN{I)-DWCN(I)-DMCN(Il-DNCN(IJ-DPCN(I)-DRCN(I) CHPL(I)=CHPL(I)-OWPL(Il-DMPL(IJ-DNPL(I)-DPPlCI1-ORPltI)

500 CHRllIJ=CHRL(I)-DWRl(Il-OMRL(It-DNRl(Il-DPRl(I)-DRRL(I) DO 23 1=1,12 J=I+(12*M-12) XWS(JJ=CHWS(l) XCM(J)=CHCMtl) XCN{J)=CHCN(I} XPLtJ)=CHPl(I)

23 XRL(JJ=CHRL(I} DO 123 I=1,12 CHWS(Il=O.O CHCM(I)=O.O CHCN(I)=O.O CHPl(Il=O.O

123 CHRL(Il=O.O 22 CONTINUE

CAll STAT(XWS,XCM,XCN,XPl,XRL,XBWS,XBCM,XBCN,XBPL,XBRL,SDWS,SDeM,S *OCN,SOPl,SDRL)

CALL TALLY(AOWS,AOCM,ADCN,AOPL,ADRL,VWS,VCM,VCN,VPL,VRl,SDWS,SDCM, *SOCN,SOPL,SDRL,XBWS,XBCM,XBC~,X8PL,XBRl)

WRITE(6,100)

..... -...J \..n

70J FORMAT(lHl,T47,'ANGLER-OAY CHANGE DUE TO ACCESS CHANGE') CALL OUTPUT(XBWS,XBCM,XSCN,XBPL,X6Rl) WRITE(6,800}

800 FORMAT(lHl,T50,'ACCESS CHANGE - STANDARD DEVIATIONS') CALL OUTPUT(SDWS,SDCM,SDCN,SDPL,SDRL) RETURN END

..,.a. -...J 0\

1

SUBROUTINE WATER COMMON ADCM(lZ},ADCN(12},AOPl(12),ADRl(12),ADWS(lZ),AFEE,AHIGH(5), lAlOW(5)tAMOOE(5)tBA(12),BFEE,BUDGET(6}~CM(12),CMLOST(12),CNCIZ),

ZCNlOST{lZ),COST(4),DH,DL,DM,EA(12),EK ,ERN(60),EX 3,FACIZ},FED,fEOA(12),FEE,HCH(12J,HCltlZ),HCM{lZ),HNH(lZ1,HNL(12}, 4HNM(12),HPH(lZ},HPL(12),HPM(12),HRH(12},HRl(lZ),HRM(1Z),HWH(lZ), 5HWL(12),HWM(12),[X,IY,lDE(5,12),LUD{5,12},MAXB,MAXE,MAXF,MAXT, 6MOCM{lZ),MOCN(12),MONTHS,MOPLt12},MURl(12),MOWS(12),PCM(12),PCN(12 7),PL(12),PLlOST(lZ),PPLCIZ),PRl(lZ),PWS{lZl,RATE, RHP,RK, RL(12 8), RLLOST(lZ),RLP, RMP, RN(60),RX, SHP(IZ), 9SKlIZ) ,SLP(12), SMP(12), SX(lZ), *TA(lZ),TDCM(lZ),TDCN(12),TOPLtlZ),TDRl(12),TDWS(lZ), THP(12 l),TK(12) ,Tl(IZ), TLP(lZ), TH{12), TMP(12), 2 TN(lZ),TPf12),TW(12),TX(12),VCM(12),VCN(lZ),VPLflZ),VRl(12 3),VWS{12),WS(121,WSLOST(12),XCM(600),XCN{6CO),XDH(S,12),XDL(S,121, 4XDM(S,12a,XP(600),XPl(600),XRL(600),XUH(S,lZ),XUl(S,12),XUMfS,lZ), 5XW(60C),XWS{600),YR(12},YS(12),lAH,IAl,ZAM,ZEH,ZEL,IEM,ZH(Sl,ll(5) 6,ZM(5),ZRH,lRL,ZRM,lSH,ISL,ISM,LTH,ZTL,ZTM,ZWH,lWL,ZHM,IXH,ZXL,lXM 1,ZZ(S),ZZHlS),lll(S),ZZM(5)

DIMENSION CHWS(lZ),CHCM(12),CHCN(12),CHPlI12),CHRl(12),A(lZ),AN(12 *},ANGflZ),ANGL(12),ANGLE(12), * XBWS(12),XDCM(12),XBCN(lZ),X6PL(12},XBRL(lZ),SDWS(lZ) *,SOCM(lZl,SDCN(12),SDPl(12),SDRL(12), *DWWS(IZ),OWCM(12),OWCN(12),DWPL(12),DWRL(12',OMWS(12),DMCMCIZ), *DMCN(12),D~PL(12)tDMRL(lZ), *ONWS(12),DNCM(12),DNCN(12l f DNPl(12),ONRl(12),OPWS(12),OPCM(12), *DPCN(12),DPPl(lZ),DPRLf12},DRWS(12),DRCM(121,DRCN(lZ),DRPl(12J, *DRRL{lZ)

DATA CHWS,CHCM,CHCN,CHPl,CHRL/60*O.OI DO 1 1=1.12 IF(PRL(I).GT.O.O.AND.WSlOST(I)+CMLOST(I)+CNlOSTlI)+PllOST(Il.LT.Z5

*.O)WRITE(6,2)

,..... -....J ......:J

2 FORMAT(lHl,II/IIIIIII/llllllllllX,'ERROR: IF RESERVOIR ACREAGE IS *GAINED THERE MUST BE A lOSS OF SONE OTHER TYPE OF WATER.'}

C DETERMINE THE CHANGE IN ANGLER-DAYS PER ACRE 00 81 M=l, 50 DO 1 1=1,12 IF(PWS(I)-WSlOST(Il.EQ.O.O)GOT03 CAll RANDOM(HWl(I),HWM(I),HWH(Il,IX,IY,A(I»

3 IFIPCM(I)-CMlnST{I}.EQ.D.01GOT04 CALL RANDOH(HCL(I),HCM(I),HCHtIl,IX,IY,AN(I»

4 IF(PCN(I)-CNlOSTtI).EQ.O.O)GOT05 CALL RANDOH(HNL(I),HNMII),HNHtI),IX,IY,ANGfl»

5 IF(PPl(I)-PLlOSTf Il.EQ.O.OlGOT06 CALL RANDOM(HPl(Il,HPM(I),HPH{I),IX,IY,ANGl(I»

6 IF(PRl(I)-RLlOST(I).EQ.O.O)GOT01 CALL RANOOM(HRl(I),HRMtI),HRH{I),IX,IY,ANGLElI»)

1 CONTINUE C DETERMINE TOTAL CHANGE IN ANGLER-DAYS FOR EACH FISHERY IN EACH AREA

DO 70 1=1,12 IF(PWS(I)-WSlCST(I).EQ.O.O)GOT030 CHWS(IJ=CHWSCI)+{PWS(I)-WSLOST(IJ1*A(I)

30 IF(PCN(I)-CMLOST(I}.EQ.O.O)GOT04Q CHCM(I)=CHCM(I)+(PCM{I)-CMlOSTII)}*AN(l)

40 IF{PCN(I)-CNlOSTCI).EQ.O.O)GOT050 CHCN(IJ=CHCN{I)+(PCNfI)-CNlOST(I»*ANG(I)

50 IF(PPl(I)-PLLOST(I).EQ.O.O)GOT060 CHPL(I}=CHPL(I)+(PPLlIl-PllOST(I»*ANGlII)

60 IF(PRL(I)-RLLOST(Il.EQ.O.O)GOT070 CHRL(Il=CHRl(I)+(PRL(I)-RLlOST(I»*ANGlE(I)

10 CONTINUE CAll DRAW(CHWS,ZWL,ZWM,ZWH,ZL,ZM,lH,TDWS,TDPl,TDCM,TDRL,TDCN,DWWS,

*OWPl,DWCM,DWRL,DWCN,IX,IYl CALL ORAW(CHCMtIWL,IWM,ZWH,Zl,ZM,lH,TDCM,TDCN,TDWS,TDPL,TDRl,DMCM,

....... 'l (»

*CMCN,DMWS,DMPL,DMRL,IX,IY) CALL DRAW(CHCN,ZWl,IWM,I~H,ZLtIMtZH,TDCN,TDCMJTDWS,TDPL,TDRL,DNCN,

*DNCM,DNWS,DNPl,DNRl,IX,IY) CAll DRAW(CHPl,ZWL,ZWM,ZWH,Zl,ZM,ZH,TDPl,TDRL,TDWS,TDCM,TDCN,DPPL, *DPRl,DP~StDPCM,DPCN,IX,IY)

CALL DRAW(CHRL,ZWL,ZWM,ZWH,Zl,ZM,ZH,TDRL,TOPL,TOWS,TDCM,TDCN,DRRL, *DRPL,DRWS,DRCM,DRCN,IX,lY)

00 500 1=1,12 CHWSfI)=CHWS(l)-DW~S(I)-DNWS{I)-DNWS(I)-DPWS(I)-DRWS(l) CHeN (I) =CHCN (I) -DHeN ( I) -O;'1Cl\i{ I l-DNCH ( I )-DPcr~ ( 1) -ORCM ( I ) CHCNt I }=CHCNt 1 )-D~JCN( I )-DNCN( I )-ONCN{ I )-DPCN( I )-DRCN( I) C H P l ( I 1 = C H P L ( I ) - 0 ~J P L ( I ) - 0 f.1 P L ( I ) - 0 N P L ( I ) - 0 P P L ( I ) - 0 R P l ( I )

500 CHRl(I}=CHRL(I)-DWRL(I)-D~RL(I)-DNRL{I)-DPRl(I)-DRRL(I) DO 11 1=1,12 J= I +, 12*l'-1-12) XWS(J)=CHt~S( IJ XCN(Jl=CHCNtI} XCN(JJ=CHCN(I} XPL(J)=CHPl(I)

71 XRLtJ}=CHRL(Il DO 123 1=1,12 CHJ'fS(I}=O.O CHCM(I)=G.O CHCN(I)=O.O CHPL(I)=O.O

123 CHRLtI)=O.O 81 CdiJTINUE

CALL STAT(XWS,XCM,XCN,XPL,XRl,XBWS,XBCM,XBCN,XBPL,XBRL,SDWS,SOCM,S *DCN,SDPL.,SDRL)

CALL TALlY(ADWS,ADCM,ADCN,ADPL,ADRl,VWS,VCM,VCN,VPl,VRL,SDWS,SDCM, *SDCN,SDPL,SDRL,XBWS,XBCM,XBCN,XBPL,XBRL)

WRIIE(6,700)

f-~

......;]

'"

700 FORMAT(lH1,T44,'ANGLER-OAY CHANGE DUE TO WATER GAIN OR lOSS') CALL OUTPUTtXBWS,XBCM,XBCN,XBPl,XBRl) WRITE(6.800)

BOO FORMAT(lH1,T50,'WATER GAIN OR LOSS - STANDARD DEVIATIONS') CALL OUTPUT(SDwS,SDCM.SDCN,SDPl,SDRl)

10 RETURN END

..... CO o

c

SUBROUTINE REGULA COMMON ADCM(lZ),AOCNt12),AOPlt12),ADRl(lZ),ADWS(12),AFEE,AHIGH(S),

lAlOW{S),AMODE(S),BA(lZ),BFEE,BUDGET(6},CM(12J,CMlOST(12),CN(121, 2CNlOST(12),COST(4),DH,DL,DM,EA(12},EK ~ERN(60),EX

3fFA(12),FEO,FEDA(12),FEE,HCH(12),HCLI12),HCM{lZ),HNH(12),HNl(lZJ, 4H~M(12)tHPH(12),HPl(lZ),HPM(12),HRH(12}tHRl(12),HRM(12),HWH(12),

5H~L(12},HWM'12)tIX,IY,lDE(5,12),lUD{5,12)JMAXBtMAXE,MAXF,MAXT, 6MOCM(12),~OCN(12),MONTHS,MOPL(12J,MORl(12),MOWS(12)tPCM(12J,PCN(12

7),PL(12),PllOST(12),PPL{lZ),PRl(12),PWS(12),RATE, RHP,RK, RL(12 81, RLlOST(12),RlP, RMP, RN(60),RX, SHPflZ), 9SK(12) ,SLP(12), SMP(12), 5X(lZ}, *TA(12),TOCM(lZ},TOCN(12),TDPLCIZ),TDRl(12),TDHS(12), THP{12 11,TKt12) ,Tl(12}, TlP(lZ), TM(12), TMP(IZ), 2 TN(12),TP(12},TW(12},TX(12),VCM{12),VCNl12),VPL(121,VRL(12 3),VWS(lZ),WS(12),WSlOST(121,XCMI6001,XCNt600>,XDH(S,12),XDL(S,lZl, 4XDM(5,12),XP(600},XPl{600),XRl(60C),XUH(5,lZ),XUl(S,12),XUM(5,lZ), 5XW(600),XWS(600J,YR(lZl,YS(12),ZAH,ZAl,ZAM,ZEH,ZEl,ZEM,ZH(S},IL(5) 6,ZM(5),ZRH,ZRL,ZRM,ZSH,ZSl,ZSM,ITH,ZTL,ZTM,lWH,ZWl,ZWM,ZXH,IXl,IXM 1,lZ{SJ,IZH{S),IZl(5),ZZM{S)

DIMENSION CHWS(12),CHCM(12},CHCN(lZl,CHPll12J,CHRl(12),XBWSlIZ),XBCM(12) *CM(lZ),XBCN(lZ),XBPLtlZ),XBRL(lZ},SDWS(121,SDCM(lZ),SDCN(lZ),SDPl( *12),SDRl(12), *DWWS(lZ),DWCM{121,DWCNtlZ),DWPL(12},DWRL(12J,DMWS(lZ),DMCM(lZ), *DMCN(lZ),DMPL(121,DMRLC121, *DNWS(12},ONCM(12),DNCN(lZ),DNPL(12),DNRLCIZ),OPWS(12),OPC~(12),

*DPCN(lZ),DPPl{12),DPRl(lZ),DRWS(lZ),ORCM(12),DRCNC12),DRPL(lZ), *ORRL(12)

DATA RWS,RCM,RCN,RPL,RRl,CHWS,CHCM,CHCN,CHPL,CHRl/65*O.GI IF(MONTHS.EQ.O.ANO.FEE.GT.O)GOT05 REDUCTION IN SEASON LE~GTH DO 1 1=1,12 IF(MGWS(I).EQ.I)~WS=RWS+WS'I}

....,. co ~

1

c

c

2

IF(MOCM(I).EQ.I)RCM=RCM+CMtIl IF(MOCN(Il.EQ.I1RCN=RCN+CN(IJ IF(MOPL(I).EQ.I)RPL=RPL+PL(I) IFtMORL(I).EQ.I)RRL=RRL+RLII) CONTINUE 00 10 1-1=1,50 ADJUST REDUCTION ESTIMATE IF(RWS.GT.O.O)CALL RANDOM(ALOW(l),AMOOE(l),AHIGH(l),IX,IY,REOW) IF{RCM.GT.O.O)CALL RANDOMIALOW(2),AMODE(2),AHIGH(2),IX,IY,REDMJ IF(RCN.GT.O.O)CALL RANDOM(ALOW(3),AMOOE(3),AHIGH{3J,IX,IY,REDN) IF(RPL.GT.O.O)CALl RANDOMCAlCW(4),AMODE{41,AHIGH(4),IX,IY,REDP) IF(RRl.GT.O.O)CAll RANDOM(ALOW(S),AMODE(S),AHIGH(S),IX,IY,REDR) IF(RWS.GT.O.O)RWS=PWS*REDW IF(RCM.GT.O.O)RCM=RCM*REDM IF(RCN.GT.O.O)RCN=RCN*REDN IF(RPL.GT.O.01RPL;RPL*REDP IF(RRL.GT.O.O)RRL=RRL*REDR ADJUST ANGLER-DAYS DO 2 1=1,12 C H~Y S ( I ) = C t-Hl S ( I ) - t T \-:t I ) + A 0 H S ( I ) ) * R H S CHCM(Il=CHCM{I)-(TM(I)+ADCM(I»*RCM CHCN(Il=CHCN(IJ-{TN(I}+AOCN(I»*RCN CHPl(I)=CHPL(I)-(TP(I)+ADPltIJ1*RPl CHRL(I}=CHRl(Il-(TLCI)+ADRL(I»*RRL CALL DRA~{CHWS,ZRL,ZRM~ZRH,Zl,ZM,ZH,TDWS,TDPL,TDCM,TDRLfTDCN,DWWS,

* 0 ~'4 P L , 0 \'1 C r~, D I:! p, L , 0 1,; eN, I X t I Y ) CALL DRAW(CHCM,ZRl,ZRM,ZRH,ZL,ZM,ZH,TDCM,TDCN,TDWS,TDPl,TDRl,DMCM,

*OMCN,OMWS,DMPL,OMRL,IX,IY) CALL ORAW(CHCN,lRL,ZRM,ZRH,Zl,ZM,ZH,TOCN,TDCM,TDWS,TDPl,TDRl,DNCN,

*DNCM,DNWS,DNPL,DNRl,IX,IY) CALL DRAW{CHPl,ZRl,ZRM,ZRH,Zl,ZM,ZH,TOPL,TORL,TDWS,TDCM,TDCN,DPPl,

*DPRL,DPWS,OPCM,OPCN,IX,IY}

~ co l\)

CAll ORAW(CHRl,ZRL,ZRM,ZRH,lL,ZM,ZH,TDRl,TOPl,TOWS,TDCM,TDCN,DRRL, *DRPl,ORWS,DRCM,DRCN,IX,IY)

00 500 1=1,12 CHWS(I)=CHWSCI)-OWWSII)-DMWS(I)-DNWS(Il-OpwStI)-DRWSII) CHGM(I)=CHCM(I}-OWCM(Il-DMCM(I)-DNCM(I)-DPCH(I)-DRCM(I) CHCN(I)=CHCN(I)-DWCN(I)-D~CN(I)-DNCN'I)-OPCN(I)-DRCN(I} CHPl(IJ=CHPl(I)-DWPl(I)-DMPL(I)-DNPL(I)-DPPl(I)-DRPL(I)

50J CHRl(I)=CHRl(Il-DWRL(Il-DMRl(Il-ONRL{I)-DPRl(I)-DRRL(I) DO 9 1=1,12 J=I+(12*M-12) XWS(J)=CHJS(l) XCMIJ)=CHCM(I) XCN{J)=CHCNtl) XPL(J'=CHPL{I)

9 XRL(J)=CHRltl} DO 123 1=1,12 CHWS(I)=O.O CHCM(IJ=O.O CHCN(ll=O.O CHPl(I)=O.O

123 CHRLtl}=O.O 10 CONTINUE

CALL STATfXWS,XCM,XCN,XPl,XRl,X8WS,XBCM,XBCN,XBPl,XBRL,SDWS,SOCM,S *DCN,SDPl,SDRl)

CALL TALlY(AO~S,AOCM,ADCN,ADPl,ADRl,V~S,VCM,VCN,VPl,VRl,SOWS,SDCM, *SDCN,SDPL,SDRL,X8WS,XBCM,XBCN,XBPl,XBRl)

WRITE(6,669) 669 FORMAT(lHl,T45,'ANGlER-DAY CHANGE FROM SHORTENING SEASON')

CALL OUTPUT(XB~StXBCMfXBCN,XBPl,XBRl) WRITE(6,800)

800 FORMAT(IHl,T45,'SHORTENING SEASON - STANDARD DEVIATIONS') CALL GUTPUT(SDWS,SDCM,SOCN,SDPl,SORLJ

~

ex> VJ

C INCREASE IN LICENSE FEE IF(FEE.EQ.O.01GOTO 30

5 GYPED=AFEE+8FEE*FEE C ADJUST ANGLER-DAYS

DO 3 1=1,12 CHWS(IJ=CHHS(I)-(TW(I)+ADWS(I)J*(l.O-GVPEO) CHCM(I)=CHCM(IJ-tTM(I)+ADCM(I)*(l.O-GVPEO) C He N ( I ) = C He N t I ) - ( TN ( I ) ... A DC N ( I ») llq 1. 0-GYP ED) CHPl(I)=CHPL(I)-(TP(I)+ADPl(Ill*(l.O-GYPED) CHRL(I)=CHRl(I)-(TL(I)+ADRl(I»*tl.O-GVPEO) ADWS(Il=ADWS(I)+CHWS(I} ADCMtI)=ADCMCI)*CHCMCI) ADCN(I)=ADCN(I)+CHCN(I) AD?l(Il=ADPl(I)+CHPL(I)

3 AORL(I)=ADRL(I)+CHRltI) 30 CONTINUE

WRITE(6,700) 700 FORMAT(lHl,T45,'ANGLER-DAY CHANGE DUE TO LICENSE FEE INCREASE')

CALL OUTPUT(CHW$,CHCM,CHCN,CHPL,CHRl) RETURN END

t-''\, 0::;. ~

c

SUBROUTINE INED COMMON ADCM(12),ADCNCI2),ADPL{lZ),ADRL(12J,AOWS(lZ),AFEE,AHIGH{S), lALOW(5),A~ODE(S).BA(12)tBFEE~BUDGET(6),CM(12),CMLOST(121,CN(lZl,

2CNLOST(lZ),COST(4),DH,DL,OM,EA(lZ),EK ,ERN(60),EX 3,FA(lZ),FEO,FEOAtlZ},FEE,HCH{12),HCl(12),HCM(lZ),HNH(lZ),HNL(12), 4HNM(lZ),HPH(lZ),HPL(12),HPM(lZ),HRHl12),HRL(lZ),HRM(12),HWH{12), 5HWL(12),HWMCIZ1,IX,IY,LDE(S,12),LUD(S,12),MAXB,MAXE,MAXF,MAXT, 6MOCM(12),MOCNt12),MONTHS,MOPL(lZ),MORl(lZI,MDWSflZ),PCM(12),PCN(12 7),Pll12),PLLOST(12},PPL(lZ),PRl(12),PWS{lZl,RATE, RHP,RK, RL{lZ al f RlLOST(12),RLP, RMP, RN{601,RX, SHP(lZ), 9SKI12} ,SLP(lZ), SMP(12J, SX(12), *TA(lZ),TDCM(12),TDCN(12),TDPlCIZ),TORL(12),TDWS(lZ), THP(12 1),TK(12) ,TL(IZ), TLP(12), TH(12), TMP(12), 2 TN(12),TP(lZ),TWt12),TX(lZJ,VCM{lZ),VCN(12),VPL{lZ),VRL(12 3),VWS{12),WS(12),WSlOST(12l,XCM(600),XCN(600),XDH(S,l2),XDL(5,12), 4XDM(5,12),XP(600),XPl(600),XRL(600),XUH(S,lZ),XUl(S,12),XUM(S,lZ), 5XW(600),XWS(600),YRlIZ),YSC12),IAH,ZAL,ZAM,ZEH,ZEL,ZEM,ZH(S),lL(S} 6,lM(S),ZRH,ZRL,ZRM,ZSH,ZSl,ZSM,ZTH,ITL,ZTM,ZWH,IWL,ZWM,ZXH,IXl,lXM 1.ZZ(S),ZZH(S.,ZZl(5),ZZM(S)

DIMENSION CHWS(12),CHCMC12),CHCN(12),CHPL(12),CHRL(lZ),CHED(60}, *XBWS(12),XBCM(12)~XBCN(lZJ, * XBPL(121,XBRL(121,SOWS(12),SDCM(12),SOCN(lZ),SDPL(12J,S *DRL(lZ), *DWWS(12),DWCM(12),OWCNI12),DWPL(12)yDWRl(lZ),DMWS(12),OMCM(lZ), *DMCN(lZ},DMPl(12),DMRlIIZ), *DNWS(12),DNCM(lZ),DNCNCIZ),DNPl(12),ONRl(12),DPWS(lZ),OPCM(12), *DPCN(lZ),DPPL(12),DPRl{12),DRWS(12J,ORCM(12),DRCNf12),DRPL(12), *ORRl(lZl

DATA CHWS,CHCM,CHCN,CHPl,CHRl/60*O.OI INFLATION BUDGET(S)=BUDGET(Sl*{l.O-RATE) DO 4 M=1,50

I-" co \.n

CALL MODEX(OL,DM,DH,EK,EX,BUDGET(S),EAD,EP,IX,IY) C ADJUST ANGLER DAYS

00 1 1=1,60 1 CHEO(I)=EAD*ERNlI)

DO 2 1=1,12 J= I *5- 1•

CH~~S { I )=CHHS ( I J +CHED (J)

CHCM(Il=CHCM(IJ+CHED(J+l) CHCN(Il=CHCN(I)+CHEO(J+2) CHPl(I'=CHPl(Il*CHEO(J+3)

2 CHRl(Il=CHRl(I}+CHED(J+4) CALL DRAW(CHWS,ZEl,ZEM,ZEH,ZL,ZM,ZH,TDWS,TDPL,TDCM,TDRL,TDCN,DWWS, *D~PL,DWCM,DWRL,DWCN,IX,IY)

CALL DRAWfCHCM,IEL,ZEM,ZEH,ZL,ZM,ZH,TOCM,TDCN,TOWS,TDPL,TDRl,DMCM, *DMCN,DMWS,DMPl,DMRL,IX,IY)

CALL DRAW(CHCN,lEl,ZEM,ZEH,IL,ZM,IH,TDCN,TDCM,TDWS,TDPL,TDRl,DNCN, *DNCM,DNWS,ONPl,DNRl,IX,IY)

CALL DRAW(CHPl,ZEL,ZEM,ZEH,Zl,ZM,ZH,TDPL,TDRl,TOWS,TDCM,TDCN,DPPL, *DPRl,DPWS,DPCM,DPCN,IX,IY)

CALL DRAW(CHRL,ZEL,ZEM,IEH,ZL,IM,ZH,TORL,TDPL,TDWS,TDCM,TDCN,DRRL, *ORPl,DRWS,ORCM,DRCN,IX,IY)

00 500 1=1,12 CHWS(I)=CHWS(IJ-DWWS(I)-DMWS(Il-DNWS{I)-OPWS(Il-DRWStI} CHCM(I)=CHCM(I)-OWCM(l)-DMCM(Il-DNCMfI)-OPCMtI)-ORCMfI) C He N ( I ) = C H C N ( I } - 0 ~: C f\U I ) - 0: ,t eN ( I } - 0 ~~ C N { I ) - 0 P C N ( I ) - D R eN ( I ) C H P L ( I »= C H P l ( I ) - 0 \'J P L ( I ) - 0 ~·1 P L ( I ) - 0 ~; 9 L ( I ) - 0 P P l ( I ) - D R P l ( I )

500 CHRL(Il=CHRl(I)-DWRL(I)-DMRL(I)-DNRl(I)-DPRl(I)-DRRL(I) 00 5 1=1,12 J=I+(12*M-12) XWS (J }=CH~JS ( I) XCMfJ'=CHCM(I) XCN{J)=CHCN(I)

t-" OJ 0\

XPL(J)-:::CHPL(I) 5 XRl(J)-:::CHRl(I)

DO 123 1=1,12 C H ~~ S ( I ) == 0 • 0 CHCt-H I )=0.0 CHCN(Il=O.O CHPL(I)=O.O

123 CHRL(I}=O.O 4 CONTINUE

CALL STAT(XWS,XCM,XCN,XPl,XRl,XBWS,XBCM,XBCN,XBPL,XBRL,SOW$,50CH,S *OCN,SDPL,5DRL}

CALL TALLY(ADWS,ADCM,ADCN,ADPL,ADRL,VWS,VCM,VCN,VPl,VRL,SDWS,SOCM, *SOCN,SDPL,SORL,X8WS,XBCM,XBCN,XBPL,XBRl)

WRITE(6,3) 3 FORMAT(lHl.T35,'ANGlER-DAY INCREASE DUE TO INFORMATION AND EDUCATI

*ON PROGRAM') CALL OUTPUT(XBWS,XBCM,XBCN,XBPl,XBRl) WRITE(6,800}

800 FORMAT(lHl,T50,'INFORMATION AND EDUCATION - STANDARD DEVIATIONS') CALL CUTPUT(SDWS,SDCM,SOCN,SDPl,SDRL) RETURN END

~ co ""l

c

SUBROUTINE RESEAR COMMGN AOC~(12),ADC~(12),ADPl(12),ADRL(lZ),AD~S(12},AFEE,AHIGH(S},

lAlOW(S),AMODE(5),BA(lZ),BFEE,BUDGET(6),CM(lZ),CMlOST(lZ),CN(lZ), ZCNlOST(lZ),COST(4),DH,Dl,DM,EA(12),EK ,ERNt60),EX 3,FA(12),FED,FEDA(12),FEE,HCH(lZ),HCLC12),HCMCIZ),HNH(lZJ,HNl(lZ), 4HNM(lZ),HPH(lZ),HPL(lZ),HPM(lZ),HRH(12),HRLC12),HRM(lZ},HWH(lZ), 5h~L(12),HWM(12l,IX,Iy,LOE(5,12),LUD(5,12),MAXB,MAXE,MAXF,MAXT,

6MOCM(lZ),MOCN(lZ},MONTHS,MOPL{12},MORl(lZ),MOWS(12),PCM(lZ),PCN(12 7),PL(lZ),PllOST(lZ),PPL(12),PRL(lZ),PfiS(lZ},RATE, RHP,RK, Rl(lZ 8), RlLOST(lZ),RLP, RMP, RN(bO),RX, SHP(lZ), 9SK(12) ,SLP(12), SMP(12), SX(12}, *TA(lZ),TOCM(12},TDCN(lZ),TDPl(12),TDRl(12),TOWS(lZ), THP(12 IJ,TK(121 ,TL(lZ), TlP{lZ), TMIIZ}, TMP(12), 2 TN(lZ),TP(12),TW(lZ),TX(lZ),VCM(12),VCN(12),VPL{12),VRl(12 3),VWS(lZ),wS(lZ),WSLOST(12),XCM{600},XCN(600),XDH(5,l2),XDl(S,lZ). 4XDM(S,12),XP(6J01,XPL(600),XRl(600),XUH(S,12),XUL(S,1Z),XUM(5,lZ), 5XW(6QO),XWS(600),YRflZ),YS(lZ),ZAH,ZAl,lAM,ZEH,lEl,ZEM,ZH(5),Zl(S) 6,ZM(S),lRH,lRL,IRM,ISH,ISL,ISM,ZTH,ZTL,ZTM,IWH,IWl,IW~1,ZXH,ZXl,ZXM

7,lZ(5),IZH(S),ZIL{S),ZZM(S) DIMENSION CHRE(60),CHWS(lZ),CHCM{lZ),CHCNI12),CHPl(lZ),CHRLC12},

*XBWS(lZ),XSCM(lZ),X8CN(12), * XBPL(lZ),XBRl(12),SDWS(12),SOCM(lZ),SDCNlIZ},SDPL(12),S *DRL(lZ), *OWWS(12)yDhCM(lZ),DWCN(lZ),DWPl(121,OWRl(12),DMWS(lZ),O~CM(12),

*DMCN(12),DMPLr12) ,DMRl(lZ), *DNWS(12)tDNCM(12),DNCN(12),DNPl(12),DNRL(12),DP~S(lZ),DPCM(12),

*DPCN(lZ),OPPL(12),DPRL(12),DRWS{12J,DRCM(12),ORCN(lZ),ORPl(12), *DRRL(lZ)

DATA CHWS~CHC~,CHCN,CHPl,CHRL/60*O.OJ INFLATICN BUDGET(6)=8UDGET(6)*(1.O-RATE) DO 4 M=1,50

.... OJ 0)

CALL MOOEX(RLP,RMP y RHP,RK,RX,BUOGET{6J,RAD,RP,IX,IY) 00 1 1=1,60

1 CHRE(I)=RAD*RN(I) DO 2 1=1,12 J=I*5-4 CHWS{I)=CHWS(I)+CHRElJ) CHCMlI}=CHCM(I)+GHRE(J+ll CHCN(I)=CHCN(I)+CHREtJ+2) CHPL(11=CHPL(I)+CHRE(J+3)

2 CHRl(I)=CHRl(I)+CHRE(J+4) CALL ORAW(CH~SfZXL,lXM,ZXH,ZL,ZM,IH,TDWS.TOPL,TDCM,TDRL,TOCN,DHWS,

*OWPL,DWCM,DWRl,DWCN,IX,IY) CALL DRAW(CHCM,IXL,IXM,IXH,IL,IM,ZH,TDCM,TOCN,TDWS,TDPL,TDRL,DMCM,

*OMCN,OMWS,DMPL,DMRl,IX,IY} CALL DRAW(CHCN,IXL,IXM,lXH,ll,ZM,ZH,TOCN,TDCMtTDWS,TDPL,TORL,DNCN,

*DNCM,DNW$,DNPL,DNRL,IX,IY) CALL DRAW(CHPL,ZXL,ZXM,lXH,Zl,ZM,ZH,TDPl,TDRL,TDWS,TDCM,TDCN,DPPL,

*DPRL,DPWS,DPCM,DPCN,IX,IY) CALL DRA~{CHRltZXL,ZXM,ZXHtZL,ZM,ZHfTDRL,TDPl,TOWS,TOCM,TDCN,DRRl,

*DRPlfDRWS,ORCM,DRCN,IX,IY) DO 500 1=1,12 CHWS(I)=CHWS(I)-D~'WS(I)-DMWS(I)-DNWS(I}-DP~S(I)-ORWS(1) CHCM(I)=CHCM(I)-DWCM(I)-DMCM{I)-DNCM{I}-DPCM(IJ-DRCM(I) CHCN(Il=CHCN{l)-OWCN(Il-DMCN(Il-DNCN(IJ-DPCN{I)-DRCNtl) CHPl(I)=CHPl{I)-DWPL(l)-DMPL(I)-D~Pl(I)-DPPL(I)-DRPl(l)

50J CHRL(I}=CHRL{I)-OWRL(I)-DMRL(I)-D~Rl(I)-DPRL(I)-DRRL(I) DO 5 1=1,12 J=I+{12*M-12) XWS(J)=CHWSf!) XCM(J)=CHCM(I) XCN(J)=CHCN(Il XPL(J)=CHPL(l)

..... \ co '-D

5 XRL(J)=CHRL(IJ DO 123 1=1,12 CHWS(I)=O.O CHCM(Il=O.O CHCN(I)=O.O CHPL(I)=O.O

123 CHRL(I)=O.O 4 CONTINUE

CALL STAT(XWS,XCM,XCN,XPL,XRl,XBWS,XBCM,XBCN,XBPl,XBRL,SDWS,SDCM,S *DCN,SDPL,SDRl)

CALL TALLY{ADWS,ADCM,ADCN,ADPl,ADRL,VWS,VCM,VCN,VPL,VRL,SDWS,SDCM, *SDCN,SDPL,SDRL,XBWS,XBCM,X8CN,XBPL,XBRl)

WRITE(6,3) 3 FORMAT(1Hl,T43,'ANGLER-DAY INCREASE DUE TO RESEARCH PROGRAM')

CALL OUTPUT(XBWS,XBCM,XBCN,XBPL,XBRL) WRITE(6,800}

800 FORMAT(lHl,T50,'RESEARCH PROGRAM - STANDARD DEVIATIONS') CALL OUTPUTlSDWS,SDCM,SDCN,SDPl,SDRl) RETURN END

....... '-0 o

SUBROUTINE DRAW(XBJYL,YMtYHtZl,ZMyZHtTD1,T02,T03,TD4tTD5tPl~P2tP3, *P4,P5,IX,IY)

DIMENSION TDl(12),TD2(12J,TD3(12'JTD4(12)tTD5(lZ),JI4),Pl(12J, *P2(12),P3(lZ),P4(12),P5(lZ},XBt12),O(1Z),Y(S),ZL(S),ZM{S),ZH(S)

00 30 1=1,12 Pl(I'=O.O P2(I)=O.O P3tI)=O.O P4{IJ=O.O

30 PS(Il=O.O C DETERMINE THE PERCENT OF NEW ANGLER-DAYS FOR PROGRAM

CALL RANDOM(Yl,YM,YH,IX,IY,R) C DETERMINE THE NUMBER OF ANGLER-DAYS DRAWN

DO 15 1=1,12 15 O(11=XB(I)*(1.0-R)

C DETERMINE THE PERCENT GF ANGLER-DAYS DRAWN FROM FISHERIES IN THE SAME AREA CALL RA~DOM(Zl(l),ZM'l},ZH(I),IXtIYyQ) 00 1 1=1,12 IF(I.EQ.l)GOTOlOl If{I.EQ.2'GOTOI02 IF(I.EQ.3)GOT0103 IF(I.EQ.4}GOTOI04 IF{I.EQ.S)GOTOIOS If(I.EQ.6)GOTOI06 IF(I.EQ.7)GOTOI07 IF(I.EQ.8)GOTOI08 IF(I.EQ.9)GOTOI09 IF(I.EQ.I0)GOTOIIO IF(I.EQ.l1)GOTOlll IF{I.EQ.12)GOT0112

101 N=2 J(1)=2

...... '-0 ......

J(2)=3 J(3)=O j(4)=O GO TO 200

102 N=2 J(ll=l J(2J=3 J(3)=O J(4)=O GOTD 200

103 N='t J(l)=l J(2}=2 J(3)=4 J(4)=5 GOTD 200 f---l-

104 N=4 \0 l\)

J(1)=3 J(2)=5 J{31=6 J(4)=1 GOlD 200

105 N=3 J(1)=3 J{2)=4 J(3)=5 J(4)=O GOlD 200

106 N=3 J(l)=4 J(2)=5 J(3)=8

J(4l=O GOTD 200

107 N=4 J ( 1) =4 J(2)=8 J(31=9 J(4)=10 GOTD 200

108 N=3 J(l)=6 J(2)=7 J(3}=9 J(4)=O GOTO 200

109 N=3 .... J( 1)=7 ~

\.A)

J(2j=8 J(3)=12 J(4)=O GOTD 200

110 N=3 J(11=7 J(2)=ll J(3)=12 J(4)=O GOTO 200

111 N=2 J(ll=10 J(Z)=12 J(3)=O J(4)=O GOlD 200

lIZ N=3 J(1)=9 J{Z)=lO J(3J=11 J(4J=O

200 CONTINUE C DETERMINE HOW MANY ANGLER-DAYS ARE DRAWN FROM EACH FISHERY

00 59 18=2.,5 59 CALL RA~OOH(Zl{I8),ZM(IB},ZH(IB},IXtIy,Y(IB»)

Y(1)=1.0-(Y(2)+Y(3)+Y(4)+Y(S») C DIVIDE ACROSS

YN2=Y(2)*Q*O(I) YN3=Y(31*Q*O(!) YN4=Y(4'*Q*O{I) YN5=Y {5) :;<Q*O( I )

C DIVIDE DOlrJN DR1=(Y(1}*(1.O-Q)*O(I»/N OR2=(Y(Z)*(1.O-Q)*O(I»)/N DR3=(Y(3}*(1.O-Q)*O(I»/N OR4=(Y(4)*{1.O-Q)*O(I»/N DR (Y(S)*(l.O-Q)*O{I)}/N

C ANGLER-DAYS IN EACH FISHERY (DRAWN) C ACROSS

4 IF(TD2(I).lT.l.0)GOT05 PZ( I) P2( I )+Y~i2

5 IF(T03{IJ.lT.l.O}GOT06 P3(I) (I)+YN3

6 IF{T04{I).lT.l.01GOT07 P4tl)=P4(I)+YN4

7 IF(TD5(I).lT.l.O)GOT08 P5( I )=P5( I )+Y

8 CONTI

......

'" .t::"

C DOWN DO 1 M=I,4 IF(J(M).EQ.O)GOTOI IF(TDl(J(M».LT.l.0)GOTOlO Pl(J(M)J=Pl(JfM)}+ORl

10 IF(T02(J(M»).LT.l.O)GOT09 P2(J(M»=P2(J(M»+DR2

9 IFCTD3(J(M».LT.l.0lGOTOll P3(J(M)=P3(J(M»+DR3

11 IF(TD4(J(M».LT.l.O}GOT012 P4(J'M)=P4fJ{M)}+DR4

12 IF{T05(J(M».LT.l.O)GOTOl P5(J(M»=PS(J(Ml)+DR5

1 CONTINUE RETURN END ""'" '" \J\

>--... x ..... 0-N .. 0 <t N ... :t: N .. LU N -.. 0.. :x:: N N .. .. >-Cl. -J: .. N >< ....... Q.. ..

:i::c..o.. N_J: "'NN

0.. .... ..Jro~ NNZ

N X Z .. lUOo.. 0 ..... ....1 OU')N .::..~ ~~

UZ I.I"..! Ld I:) .~.;: ,''/ C' " ..... ~.b ~.

1-::.:; .. L

196

0.. ...... N

'* * :.!: ...... N « '* N .;} ...... co -N ~::: 'l~' r-...J <:

j :" .,~.,J ~ .. ~ + ......, ~~ c:

SUBROUTINE RANDCM{XlOW,XMODE,XHIGH,IX,IY,X) CAll WEIBUL(XLOW,XMODE,XHIGH,XK,XlAMDA,XM) CALL RANOUIIX,IY,R) IX=IY X=XK+XlAMDA*{-ALOG(R))**(l/XM) RETURN END

~

'-0 "'l

SU8ROUTINE RANDU(IX,IY,YFLJ IY=IX*65539

IF(IY)5,6,6 5 IY=IY+2147483647+1 6 YFL=IY

YFL=YFL*.4656613E-9 RETURN END

i-I' '-0 0:>

6

1

5

1 3 2

4

SUBROUTINE WEIBUL(Xl~XMtXH,IK,Zl7ZM) DIMENSION Z(30),F(301,Ht30) 8=(XH-Xl}/2+Xl IF(XM.LT.B}GO TO 6 GO TO 7 Z(1)=1.00001 ZM=1.00001 1(2)=l.l GO TO 5 Z(I)=4.0 1~'1=4. a Z(Z}=3.5 1=1 GO TO 2 ZM=Z(2) 1=1 +1 A=«(ZM/IZM-l»)*(-AlOG{.999991»)**(1/ZMl C=l.O-A IF(ABS{C).lT •• 00001)A=A-.OOOOl lK=(Xl-XM*A)/(l-A) G=EXP(-«(ZM-IJllM)*«XH-ZK)/(XM-IK)**ZM} H ( I )=G IFCI.EQ.I1GCTOl IF(I.EQ.2)J=1 J=J+l K=J-l l=J+l F(K1=.OOOOl-H(K) F(J)=.OOOOl-HlJ) IFtABS(F(J».lT •• OOOOl)GOTOll Z(L)=Z(J)-F(J)*(CZ(J)-Z(K»/(F(Jl-F(K)) Zlvl= Z ( L)

~

\0

'"

-~ N , :E: X

*

* *

..... ~:r.

NZ O-oc t--::>

IIt-C Q...,IUJZ (.!)Na:W

200

1

2

SUBROUTINE SrAT(XWS,XCM,XCNfXPl,XRl,XBWS~XBCMtXBCN,XBPltXBRL,SDWS, *SDCM,SDCN,SOPL,SDRL}

DIMENSION XWS(600},XCM(600),XCN(600),XPL(600),XRL(600),XBWS(12), * XBCM(12),X8CN(12l,XBPLCIZ),XBRl(12),SDWS(12),SDCM(12),SDCN(lZ), *SDPl(12),SORl(12),SSWS(12),SSCM{12),SSCNf12),SSPL(12),SSRL(12), *SXWS(12),SXCM{12),SXCN(lZl,SXPl(121,SXRl(121

00 2 1=1,12 SXrIS( I )=0.0 SXCN(I)=O.O 5XCN(I)=O.O SXPL ( I )=0.0 SXRL(I)=O.O SSWS(Il=O.O SSCM(I}=O.O SSCN{Il=O.O SSPL(11=O.O SSRL(I)=O.O DO 1 J=l,50 M=(J*12-11)+(1-1} SXWSII)=SXWS(I)+XWSIM) SXCMII)=SXCM(I}+XCM(M) SXCN(I)=5XGN{I)+XCN(Ml SXPl(I)=5XPLtI)+XPl(M) SXRl(Il=SXRL(I}+XRL(M) XBWS(I)=SXWS(Il/50 XBCM(I)=5XCM(I)/50 XBCN{I)=5XCN(I)/SO X8Pl(I)=5XPl(I)/50 XBRL(I)=SXRL(J)/50 DO 4 1=1,12 DO 3 J;1~50

M=(J*12-11)+(I-ll

N o .....

SSHS(IJ=SSWS(I}+(XWS(M)-XBWS(I)**2 SSCM(I)=SSCM(I)+(XCM(M)-XBCM(I})* SSCNlIl=SSCN(I)+(XCN(M)-XBCN(I)**2 SSPl(11=SSPL(I)+(XPl(M)-XBPL(I»**2

3 SSRL(I)=SSRl(I)+(XRl(M)-XBRLtI»**2 SDCM(I)=SQRT{SSCM(I)/(SO-l)} SDCN(Il=SQRT(SSCN(Il/(50-1») SOPL(I)=SQRT(SSPL(IJ/(SO-l» SD~S(I} T(SSWS(I)/(SO-l»

4 SORL(!)= RT(SSRL(I)/(50-1)} RETURN END

I\)

o I\)

SUBROUTINE TAllY(ADWS,ADCM,AOCN,ADPl,AORl,VWS,VCM,VCN,VPl,VRL,SDWS *,SDCM,SDCN,SDPl,SORl,XBWS,XeCM,XBCN,XBPl,XBRl)

DIMENSION ADWS(12),ADCM(12),ADCN(12),ADPl(12I,ADRl(12),VWSf12),VeM *(121,VCN(12),VPl(12),VRL(12),SD~S(12),SDCM(12),SDCN{12),SDPL(12),S

*DRL(lZl,XBWS(12).XBCMI12),XBCN(12),XBPLtI2),X8RL(121 DO 21 1=1,12 VWS(I)=VWS(I)+SDWS(I) VCM(I)=VCM(I)+SOCH(I) VGN(I)=VCNII)+SDCNtI) VPLCI};VPLtl)+SDPL(I) VRLII)=VRL(I)+SDRL(I} ADWS(I)=ADWS(I).XBWS(I} ADCM(I)=ADCMlI'+XBCM(I) ADCN(Il=AOCN{I)+XBCNCll ADPl(Il=ADPL(I)+XBPL(I)

21 ADRL(l)=ADRl(I)+XBRlfI) RETURN END

(\j

o U>

SUBROUTINE OUTPUT1AOWS,ADCM,ADCN,ADPL,ADRl) DIMENSION ADWS(12),AOC~i(12)tADCN(12),ADPl{12)tADRL(12) WRITE(6,701)

701 FORMAT(lHO/IX,T12,'.·,T36,'*·,T41,'MARGINAL TROUT',T60,'*',T66, 'N *ATURAL TROUT',T84,'*',T92,'PGNDS & ',TI08,'*',Tl13,'RESERVOIRS AND "/lX,T12,'*',TI8,'WARM STREAMS',T36,'*',T45,'STREAMS',T60,'.',T69, *'STREAMS'tT84,'~ltT91,'SMALl LAKES',TI08,'*',Tl15,'lARGE LAKES'/IX

705 702 103

704

*,'**************************************************************** ***************************************************************', *'****',lX,T3,'**AREA**',T12,·*',T36,···,T60,'.',T84,'*',TI08,'.')

DO 705 1=1,12 WRITE(6,702) WRITE(6,103)I,ADWS(Il,ADCM(I),AOCN(I),ADPL(I),ADRL(I) WRITE(6,104) fORMAT(lH ,T12,'*',T3b,'*',T60 J '*',T84,'*',TI08,'*') FORMAT(lH ,T7,12,T12,'*f,TI9,FIO.O,T36,'*ttT43.F10.0~T60,'*'tT67,F

*lD.O,T84,'*',T91,FI0.0,TI08,·*',TlI5,FI0.0) fORMAT(lH ,TI2,'*',T36,'*',T60,'*',T84,'*',TI08,'*'/IX,'**********

******************************************************************* **********************************************~********')

RETURN END

N o +:'"

VITA

The author was born in Altoona, Pennsylvania on March 17,

1950 and grew up in suburban Lancaster, Pennsylvania. He

graduated from Manheim Township High Sohool in June, 1968.

He reoeived a B.S. degree in Zoology in June, 1972 from the

Pennsylvania state University. He beoame a candidate for the

Master of Scienoe Degree in Wildlife Management at the Virginia

Polytechnic Institute and state University in Maroh, 1973.

The author is a member of Alpha Zeta Fraternity and the

American Fisheries Society. He served as president of Morrill

Chapter of Alpha Zeta June, 1971 through February, 1972 and

was selected to appear in 1971-72 edition of Who's Who Among

American Student Leaders in recognition of his leadership in

that position.

The author married the former Miss Donna Lee Jane Miller

of Shamokin, Pennsylvania on Deoember 16, 1972. He has one

son, Patrick Michael Clark.

.;~d 4 IYaJ~, R chard Dean Clark, Jr. yt

205

PISCES: A COMPUTER SIMULATOR TO AID PLANNING

IN STATE FISHERIES MANAGEMENT AGENCIES

by

Richard D. Clark, Jr.

(ABSTRACT)

A basic job of fisheries management agencies is to foroast

the demand and produce the necessary supply of fisheries.

Present d.ay angler consumntion rates often exoeed managers'

ability to supply fisheries of the desired quality. Therefore,

a primary means for improving modern fisheries management may

be to regulate angler consumption. Operations research tech­

niques are well suited for handling the complexities involved

with planning multiple action policies for regulating angler

consumption.

PISCES is a computer simulator of the inland fisheries

management system of Tennessee, but is adaptable for use in any

state. The purpose of PISCES is to aid in planning fisheries

management decision polioies at the macro level. PISCES

generates predictions of how fisheries mana~ement agency

activities will affect angler consumption for a fiscal year.

Subjective probability distributions for random variables and

Monte Carlo simulation techniques are employed to produce an

expected value and standard deviation for each prediction.

Test runs under realistic hypothetical situations and

discussions with personnel of Tennessee Wildlife Resources

Agenoy suggest tha.t PISCES may help fisheries management

agencies to improve budget allocation decisions, to formulate

multiple action policies for regulating angler consumption,

and to enhance fisheries development. A hypothetioa.l appli­

cation of PISCES in Tennessee is given.