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.,
,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
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LD $655 V855' ;'174 C57
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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 W1ldÂ
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
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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
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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
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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 anglerÂ
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 susÂ
tained by a fishery in a given period........... 54
8. A feedbaok loop wh1ch will show simulator preÂ
d1ctions are aoourate or show simulator structure
is invalid...................................... 62
v
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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 hypotheÂ
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 reÂ
sulting from information and education efforts 1n
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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 hypotheÂ
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 hypoÂ
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 anglerÂ
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
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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
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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
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Table
1.
LIST OF APPENDIX TABLES
Results of oontrol runs for sensitivity experiÂ
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
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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 manageÂ
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
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F1gure 1. Graphical model of a generalized freshwater
recreational fishery showing major system
components
2
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\-\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
SWIMMERSÂWATER SKIERS
TACKLE MANUFACTURERS
: I , I ANGLERS I ., \ 1 \ I \ I I GAME FISHESI \ 'II \ I
~------~~ / TACKLE , /
DISTRIBUTORS " /
MAN
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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 comÂ
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, conÂ
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 acquiÂ
sition, dam construction, pollut1on control, f1sh stocking,
and access development are common eXB.mples.
Planning in fisheries management 1s largely involved with
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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 fishÂ
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 indiÂ
vidual management activities and angler consumption might also
be effeotive.
Present day angling regulations, information distribuÂ
tion, and education programs address human components in fishÂ
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,
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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 repreÂ
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 effeotiveÂ
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 proÂ
gramming model 1s a set of symbols which represent the decision
variables of a system (Taha 1971). The solution to most mathÂ
ematical programming models defines the values of decision
variables which maximize or minimize a given objective funct1on.
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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. fishÂ
eries) makes operations research techniques potentially useÂ
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) miniÂ
mize management cost, subject to production requirements; and
(2) maximize the value of management, subjeot to capital
constraint.
The deer hunter participation simulator (DEPHAS) developÂ
ed by Bell and Thompson (1973) is an example of a oomputer simÂ
ulator for predioting outputs resulting from state wildlife
agency activities. DEPHAS 1s designed to allow state wildÂ
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,
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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 strucÂ
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 mathematiÂ
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.
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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 interÂ
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 anglerÂ
days to deorease, or cause angler-days to be displaoed from
one location to another. One exemple of an 8Fency activ1ty
9
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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 disÂ
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 anglerÂ
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 manageÂ
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
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F1gure 2. Classif1oat1on of angler-days used in PISCES
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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
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Figure ,. Recommended sequence of steps for use of PISCES
1n identifying the best management policy
1;
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APPLY PISCES
PREDICTION
1
ARE
OBJECTIVES
ACHIEVED?
YES
" IS COST
ACCEPTABLE?
YES
IMPLEMENT POLICY
-
NO -
NO
START
,r
MANAGEMENT POLICY
MODlFY POLICY
4~
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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
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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 oonsumpÂ
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.
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statistios
Present day statistios may be defined as decisionÂ
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 calcuÂ
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 iteratÂ
ing a number of times in a simulator containin~ random
variables produces frequency distributions on the output
variables.
17
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18
output for PISCES oontains predictions of angler-day
change. Produc1ng frequency distr1butions for these preÂ
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 conÂ
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.
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PROGRAM SEGMENTS
The aotivities of inland fisheries management agenoies
1n most states can be encompassed in the following cateÂ
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 cateÂ
gory influences fisheries resource consumption and 1s considÂ
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
Page 31
Figure 4. Flow chart of caloulations done within PISCES
20
Page 32
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--Â
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
Page 33
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). SubÂ
routine HATCH contains instructions for caloulating the change
in angler-days resulting from catchable trout program; subÂ
routine ACCESS from the access area development program; subÂ
routine INED from the information and education program; subÂ
routine RESEAR from the research program; and subroutine REGULA
22
Page 34
23
from regulation changes. Subroutine WATER accounts for manageÂ
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 anglerÂ
day change calculated by these subprograms 1s the third comÂ
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 sigÂ
nificant only between adjacent management areas, and more anglerÂ
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 mathemaÂ
tical relationships throughout the program: (1) subroutine
Page 35
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 variÂ
ables; (3) subroutine RANDOM contains a Weibull process
generator; (4) subroutine MODEX defines a modified exponential
relationship between a given management activity and anglerÂ
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 subÂ
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.
Page 36
HYPOTHETICAL APPLICATION IN TENNESSEE
Utilization of PISCES will be illustrated by a hypothetiÂ
cal application in Tennessee. Input data for the application
are realistic hypothetical values derived from histor1cal
data provided by the Tennessee Wildlife Resources Agency (hereÂ
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 followÂ
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 applioaÂ
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
Page 37
Figure S. F1sher1es management areas in Tennessee used
by the Tennessee W1ldlife Resouroes Agenoy
26
Page 38
REGION I REGlON 2 REGION 3 REGION 4
AREA 4 AREA II
AREA 2
Page 39
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 nonÂ
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, FlintÂ
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
Page 40
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
Page 41
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 manageÂ
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~lerÂ
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:
Page 42
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
Page 43
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
Page 44
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
Page 45
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
Page 46
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
Page 47
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
Page 48
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
Page 49
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
Page 50
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
\..> \()
Page 51
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
Page 52
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
Page 53
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
Page 54
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
Page 55
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
~ -"="
Page 56
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
Page 57
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'\
Page 58
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
+--""'"
Page 59
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
Page 60
49
(1) A public pond was constructed in management area
2 (Table 6). As a result, access areas were.develÂ
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.
Page 61
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
Page 62
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 anglerÂ
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.
Page 63
F1gure 6. General relationsh1p between number of angler-days
sustained by a fishery and the quality per anglerÂ
day
S2
Page 64
>Â<{ o I
a::: w ..J (.!)
Z « a::: w a..
>Âr-..J « :::l o
NUMBER OF ANGLER-DAYS
Page 65
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
Page 66
(f) l-LL
Z W m (!) z ..J (!) Z « ...J
~ g
I
NUMBER OF ANGLER-DAYS
Page 67
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 anglerÂ
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
Page 68
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.
Page 69
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 consideraÂ
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.
Page 70
EVALUATION OF SIMULATOR UTILITY
The utility of PISCES was evaluated by test runs using
hypothetical data. and discussion with fisheries agency perÂ
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 thereÂ
by, identif1es the best policy.
Second, PISCES oan be used to formulate multiple-action
decision polioies for re~ulatlng resource consumption. FishÂ
eries resource consumption might be manipulated in amounts
Significant enough to achieve objectives, such as minimizing
crowding.
And third, regional fisheries development (i.e. construcÂ
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
Page 71
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.
Page 72
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
Page 73
Figure 8. A feedback loop whioh will show simulator
prediotions are aoourate or show simulator .
struoture is inva11d
62
Page 74
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
Page 75
DISCUSSION
Some fisheries management activities have clearer relationÂ
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
Page 76
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 powerÂ
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.
Page 77
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
Page 78
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 fisherÂ
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
Page 79
Tennessee F1sh and Game Commission. 1910. Hunt1ng and fishÂ
ing demand and harvest inventory. Planning Report No.4.
46 pp.
Tennessee Fish and Game Commission. 1971. Hunting and fishÂ
ing demand, habitat, and partiCipation standards. PlanÂ
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
Page 80
APPENDIX A. MATHEMATICAL DEVELOPMENT OF SUBROUTINE WEIBUL
(see Lamb 1967 for greater deta1l)
Page 81
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
Page 82
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)
Page 83
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,
=
Page 84
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 progressÂ
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 (approxÂ
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
Page 85
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.
Page 86
APPENDIX B. MATHEMATICAL DEVELOPMENT OF SUBROUTINE RANDOM
75
Page 87
76
The three-parameter We1bull probab1lity density function
can be written as (La.mb 1967):
p(X) = ~ (X_K)m-l exp [-(Â¥)~
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)
Page 88
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)
Page 89
APPENDIX C. MATHEMATICAL DEVELOPMENT OF SUBROUTINE MODEX
78
Page 90
79
Subroutine MODEX defines modified exponential relationÂ
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. RelationÂ
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)
Page 91
Figure 1. Mod1f1ed exponent1al relationsh1ps 1n PISCES
80
Page 92
(/)
>Âc::{ 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 -
Page 93
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 conÂ
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 relaÂ
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:
Page 94
Figure 2. General form of modified exponential ourve
83
Page 95
y
o
----------------------K
Y = K +a,8x a<O ,8<0
x
Page 96
Figure 3. Form of modified exponential ourve used 1n PISCES
85
Page 97
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
Page 98
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)
Page 99
APPENDIX D. SENSITIVITY EXPERIMENTS ON MODEX
88
Page 100
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
Page 101
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
Page 102
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
--
Page 103
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.
Page 104
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
\,() ~
Page 105
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
Page 106
APPENDIX E. VARIABLE DEFINITIONS
95
Page 107
IInput Var1ables
96
Page 108
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 ~
Page 109
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
Page 110
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)
Page 111
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
tÂo o
Page 112
· 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 .....
Page 113
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)
Page 114
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
\.",)
Page 115
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 +:"
Page 116
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\
Page 117
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'\
Page 118
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)
Page 119
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 (»
Page 120
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
Page 121
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
Page 122
Internal Variables
111
Page 123
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 undeÂ
veloped access areas (I = 1 through 12 areas)
ohange in angler-days due to new develÂ
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\)
Page 124
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>
Page 125
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 manageÂ
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)
aÂt--'> .(::"
Page 126
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\
Page 127
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-=Â..... ~
Page 128
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-' ~ .....;)
Page 129
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
Page 130
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
Page 131
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
Page 132
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 .......
Page 133
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 anglerÂ
days on reservoirs and large lakes (I = 1 through 12 areas)
standard deviations of change in anglerÂ
days on warm strea.ms (I = 1 through 12
areas)
estimate of last year's angler-day proÂ
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
Page 134
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
Page 135
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\)
+:-
Page 136
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\
Page 137
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 relaÂ
tionship (distance between Y and K when
x = 0)
angler-day prediction
parameter of modified exponential relaÂ
tionship (ratio between successive inore-
ments of Y)
last year's budget for program in question
~ l\)
0'\
Page 138
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
Page 139
APPENDIX F. INPUT FOR HYPO'l'HETICAL APPLICATION
128
Page 140
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
Page 141
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
Page 142
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
Page 143
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\)
Page 144
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.
Page 145
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 .... ,
~r
) r) I;, ,'" ~ .-I ,~
) ,~)
....J ('J ('1') .) .' ) " ! • ,_F C, . .) l- I' I )
:' ~. i ·,t .. .,1- e',1 .j'- ,';') ..... r
~')
...,.-
,...., ( )
: . .;'\ "') . • ') -:J r-:· c,'· ~ \: I r ;~, c:' ..:1-
I :-::,) , "') C-', (' } ,.. .:.~
• • "\., ';-'.1 .', 1"'1 .. -i .-I
Page 146
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
Page 147
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
Page 148
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
Page 149
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
Page 150
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
Page 151
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
Page 152
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
Page 153
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)
0 ~-:
a Z ...:l: .. ~<: 0 -.J
.. :J: (.,:) -::c
142
.:::::: .. :i:
0.. -J 0::
• (:) (')
0 IJ"\ ,.....
• 0 ::) ;::)
0 t..n
• o ::> a o f'O
Page 154
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
Page 155
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
Page 156
>-l-I-«
U <r. 0.. '-::1: U
....J
....J :,) u..
t-<!
V')
..i..i -c::: 'JJ :=: U I-~;l
X
<.!)
z """" :.:: z ::> 0::
u.. 0
(,/)
t-V)
a w
I-V) r"""t ~.J
U
• 0 0 0 0 ('<')
• 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
Page 157
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
Page 158
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"
Page 159
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
Page 160
149
rJ .....-J i'.'
I I I x )0< ~~ x.;, VI (,;'1 1-. f--
)-
~ .. (.,'{
..J -:: (""') l!'" " I
,. < IJ' ,-I .. ..4 r',
" (".j ~
.:.":
-'-t, ) • <:., • ~~t • , ) .~"'\ If')
rc; \'~I r"'1 ,,,t ,....j Ci r!~
0' lr'. ·.T cr,
t. ,J
~
~-'
• .. !- if, 1.[1 1.(\ L/) ...... ~
t~'\ "" I-
,,, r·"! ,,:t- ,..oj
t~J ~(
\---
U .• :." ) ... - l"---
1"1"-
. iI', .... t 1"' ... .1 ,.,'\ ...-!-- ,. . .,.
1; ) ,..~' 1 r-- ~ ., 'J"\
'"',
C,,! ..... 4 ('''', ...-I
r·_
...j- ("', ""'j
• j'-'" ""!
Page 161
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
Page 162
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
Page 163
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)
Page 164
: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
Page 165
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
Page 166
·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
Page 167
.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\
Page 168
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
Page 169
APPENDIX G. SOURCE IECK LISTING OF PISCES
158
Page 170
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
Page 171
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
Page 172
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"\ ~
Page 173
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
Page 174
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
Page 175
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
..... ~ +='
Page 176
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\
Page 177
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\
Page 178
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
Page 179
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'Âa>
Page 180
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),$DPl(lZ),S *SDRl(12),DRWS{12),DRCM(lZ),DRCN(12),DRPL(lZ),DRRl(lZ},DMWS(12), *DMCM(lZ},DMCNl12),DMPl(12),DMRl(12)
DATA AREA,CH~S,CHCM,CHCNtCHPl,CHRl/7Z*O.OI C CALCULATE THE POUNDS OF TROUT PRODUCED AT EACH HATCHERY
PCTF=BUOGET(Zl/COST(2) PCTT=BUDGET(3}/COSTt3) PCTE=8UDGET(11/COST(11 PCTB=BUOGET(4}/COST(4) TROUTE=MAXE*PCTE TROUTF=MAXF*PCTF
.... ~ '\0
Page 181
TROUTT=MAXT*PCTT TROUTB=MAXB*PCTB
C WHERE FISH GO DO 1 1=1,12
1 AREA(I)=AREA(I)+TROUTE*EA(I)+TROUTF*FA(I}+TRDUTT*TA{I)+TROUTB*BA{I *)+FED*FEDA(I)
C DETERMINE THE POUNDS OF TROUT STOCKED IN EACH FISHERY IN A GIVEN AREA DO 2 1=1,12 TR(IJ=AREA(Il*YR(I)
2 TS(I)=AREA(I)*VS(I) C DETERMINE ANGLER-DAYS GENERATED ON RESERVOIRS
DO 12 ""'=1 t 50 DO 4 1=1,12 IF{TR(I).lT.10.01GOT03 CALL MOOEX(TLP(I',TMP{I},THP(II,TK(I),TX(II,TR{I),ADR(I),RP(I),
*IX,IY) GOTD 4
3 ADR(Il=O.O 4 CONTINUE
C DETERMINE ANGLER-DAYS GENERATED ON STREAMS DO 6 1=1,12 IF(TStI).lT.IO.O)GOT05 CALL MOOEX{SLP(I),SMP(IlfSHP{I),SK{I),SX{I),TS(I),ADS(Il,SP(I),
*IX,IY) GO TO 6
5 ADS(I)=O.O 6 CONTINUE
C DETERMINE NET ANGLER-DAY CHANGE C ON RESERVOIRS
DO 8 1=1,12 IF(ADR(I}.EQ.O.OIGOT07 CHRLll)=ADR(I}-RP(I}
..... .....;J o
Page 182
7 8
C ON
9 10
500
13
GOTO 8 CHRl(I)=O.O CONTINUE
STREAMS DO 10 1=1,12 IF(ADS(Il.EQ.O.O)GOT09 CHCM(IJ=ADS(I)-$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 .....
Page 183
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\)
Page 184
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)
Page 185
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 ~
Page 186
*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
Page 187
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\
Page 188
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
Page 189
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 (»
Page 190
*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-~
......;]
'"
Page 191
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
Page 192
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 ~
Page 193
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\)
Page 194
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
Page 195
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::;. ~
Page 196
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
Page 197
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\
Page 198
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
Page 199
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)
Page 200
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
Page 201
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
Page 202
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 ......
Page 203
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
Page 204
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
Page 205
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::"
Page 206
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\
Page 207
>--... 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:
Page 208
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
Page 209
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:>
Page 210
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
'"
Page 211
-~ N , :E: X
*
* *
..... ~:r.
NZ O-oc t--::>
IIt-C Q...,IUJZ (.!)Na:W
200
Page 212
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 .....
Page 213
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\)
Page 214
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>
Page 215
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 +:'"
Page 216
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
Page 217
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 techÂ
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
Page 218
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 appliÂ
cation of PISCES in Tennessee is given.