United States 

Department of 

Agriculture 

Forest Service 

Southeastern Forest 

Experiment Stat ion 

General Technical Report SE-35 

Stephen G. Boyce 

GOAL FOR 

MANAGEMENT 

/ 

1 Management Actions 2- 1 

ENVIRONMENT 

\ 0' 

\- 

1 Inventories _ 4' ---.---,-- 

(Sensors) 
December 1985 

Southeastern Forest Experiment Station 

200 Weaver Boulevard 

Asheville, North Carolina 28804 
Forestry Decisions 

Stephen G. Boyce 

Chief Forest Ecologist, Retired 

U.S. Department of Agriculture, Forest Service 

and 

Adjunct Professor of Natural Resources 

School of Forestry ar;d Environmental Studies 

Duke University 

Durham, North Carolina 

Chapter 

Contents 

Preface ...................... vii 

Acknowled~roent .................. i x 

1 Introduction 

The Homan Use of Forests ............ 1 

The New Direction ............... 7 

Decision and Control .............. 9 

Complexity ................... 21 

2 Structure of the Decision and Control System 

Overview .................... 25 

Control Loop .................. 25 

Decision Loop ................. 26 

Cybernetic Foundation ............. 30 

Negative Feedhack Loop ............. 33 

Feed-Forwa rd Loop ............... 31 

Positi ve Feedback Loop ............. 39 

Cybernetic Structure for Directed Systems ... 42 

Concept of Dynamic Plans ............ 48 

3 Ecosystem Dynami cs 

Overview .................... 51 

An Operational Def~nitio~ of Ecosystems .... 53 

Three Elements of Ecosystem Dynam~cs ...... 56 

Continuity ................. 56 

I rreversi bi 1 i ty ............... 57 

Structure .................. 57 

Four Btonornic Theor~cs ............. 59 

Ind~ v~ dual i st1 c Systems ........... 59 

Or-yanlzation of Forest Commu~lt7es ..... 68 

Flows of Energy and Materials ........ 72 

Mu3tipleBenefits .............. 7G 

Validat~on of the Blon~mic Th~or~cs ...... 78 

Hypothesis for Falsifrcatson of Theor~es .... 79 

Xndlv~dualistic Theory ........... 79 

Organization Theory ............. 79 

Flow Theory ................ €3:) 

Vu'tlple ReneF~ ts Theory .......... 8G 

4 Silvicultural Applications 

OVPTVIPW .................... 81 

Rir, qomc Theories That Underlie S~lv;c~i'it.ure . . 81 

Steady-State and a R~qulated Forest ..... 84 

Sustained Benefrts ............. 85 

Selective Mortal~ty ............. 87 

Ei 01 oqi ca 1 Potent 3 a l ............ 89 

Rate of Tlrnher Harvest ............. 91 

S~ngle Rotatloq Perrod ........... 92 

Superimposed Yotat 1 or Per1 ods ........ 94 

Size of Openiqgs .............. 96 

!P-Place Eqhancerne~ts ............. 97 

Tl?~rln~ngs .................. 9,Q 

Changes IR Genotypes and Type Ccrvcrsions . . 9lt: 

iii 
Natural Regeneration .............. 99 

Relation of DYNAST Model to Regeneration 

Systems ................. 101 

Selection Regeneration System ........ 102 

Group Selection Regeneratron System ..... 103 

Shelt~rwnod Regeneration System ....... 104 

Seed-Tree Regeneration System ........ 104 

Clearcut Regeneration System ........ 105 

Translating Information into Management PI ans . 106 

Complexity of Planning ........... 107 

Co~straint of Complexity .......... 10Y 

Translation Process ............. 109 

States of Forest Organization 

Ov~rview .................... 113 

Organization and Structure Re1 ated to 

Benefits and Impacts ........... 113 

An Algorithm for Timber ............ Ilh 

Potential Timber Index (PTI) ........ 121 

Meaningfi~!ness of PTI ............ 1'24 

Limiting Complexity ............. 124 

Small Openings Affect the PTI ........ 127 

PTI Projections ............... 129 

Water Yield Algorithm ............. 132 

Sediment Movement Algorithm .......... 138 

Habitat Algorithm for a Game Species ...... 142 

Habitat Algorithm for a Nongarne Spec1t.s .... 149 

Use of Forest Organization in Management Goals . 153 

Cash Flows From SI 1 vi cul ture 

Overview .................... 155 

Prescriptions for Si lviculture ......... 157 

Displays for Decisions ............. 159 

White Boxes for Silviculture .......... 165 

White Boxes for Cash Flows ........... 172 

Dynamic Form of Economic Functions ....... 174 

Net Present Value .............. 180 

Profitability Index ............. 181 

Equivalent Annual Rent ........... 181 

Realizable Rate of Return .......... 183 

Applications to Other Forests ......... 1n7 

Dynamic Analytic Si lvf cul ture Technique (DY RAST ) 

Overview .................... 185 

System Dynamics Method ............. 186 

Definition of the Question ........... 188 

System Dynamics Solution ............ 192 

White Boxes ................. i94 

Interdisciplinary Teams ........... 198 

A Simple Model ................. 200 
Structure of the DYNASf Model 

Ov~rvi~w .................... 2G3 

Frat crr-es .................... 204 

Inventory .................. 205 

The Big Ivy For~st ............. 205 

Travsformatinn of a Hahitat .......... 707 

Symbols and Equatrons ............. 210 

Trapsformation of a Forest Type ........ 216 

DYWAC>T-FAM ................. 270 

Familiar Rotat~on Res~rlts .......... 216 

lls~ of the Negative Feedback Loop ...... 227 

Successi on Rates .............. 231) 

Supplementary lnformat~on .......... 230 

Timber Potentla1 ................ 231 

Yotentla1 Tlrnber index ........... 231 

Timber and R~omass Volumes ......... 234 

Variable Rates of T~mber Harvest ...... 235 

Cash Flows ................... 236 

Net Present Value. NPV ........... 236 

Inflow Equations. IN ............ 238 

Outflow Equatlovs. OT ............ 239 

Exercises .................. 233 

Opening Size ................ 240 

Values for Initial Inflows and Outflows I . . 241 

Plantations. Genetically Improved Trees. 

and Th~nnlngs .............. 241 

WBTOB. the Hab~tat MACRO ............ 243 

OBTAH. the Forest Type MACRO .......... 247 

PTISO. the Tinher and Opecings MACRO ...... 251 

Forest Type Sectors .............. 251 

Supplementary Information .......... 258 

Renef i t and Impact lnf ormat i on ....... 258 

Cash Flow Algorithm ............. 250 

Program Control s Sector ........... 260 

Analytic Silv?culture Controls ....... 260 

Sequence of the Sectors ........... 262 

Exercises Without Type Convers~on ...... 203 

Forest Type Revers~ons and Conversions ..... 264 

A Checklist for Silvic"1ture Controls ..... 267 

Conservation of Bi 01 ogical Oi versi ty 

Overview .................... 209 

Defining Biolog~cal Diversity ......... 270 

Relation of Divers~ty to Benefits ....... 271 

Diversity in Land Management Planning ..... 277 

Diversity in tbe Nat iocal Forest Ma~ageme~t 

Act (NFMA) ................ 281 

The Important Points .............. 282 
Page 

10 The Use of Biologicat Diversity 

Overview .................... 285 

Forest Eudemonjcm ............... 255 

Diversity. Eudemonism. and Management ..... 287 

The Highlards Example ............. 288 

Predatcry Arthropods ............ 213 

Discussion of Findings ........... 293 

Translation of the Highlands Data ....... 295 

Use of Divers~ty to Evaluate Effects ...... 301 

Literature Cited ...................... 303 

Index ........................... 313 

Checklist for Figures and Table .............. 318 
Preface 

Conservation rather than liquidation is the 

theme of forestry. Scheduled removals of game 

and timber are for the purpose of having food, 

shelter, heat, and other benefits in perpetuity, 

From this theme came the rallying phrases for 

forestry: "sustained yield,'"%he greatest good 

for the greatest number ," "mu1 tiple benefits," 

"even flow," "regulated forests," and "1 iving in 

harmony with natrlre." The ideals expressed by 

these phrases are simple and appeal ing; implementation is very difficult* 

This book describes a new di rection for 

implementing the theme of forestry. The scheduling of harvest and culture found so useful is 

preserved, What is new is the question that leads 

to decisions and to control of the forests. Instead of asking, "'How much game, timber, this, and 

that do we want to produce?," we ask, "'If we do 

this, what will we have?" Decisions are answers 

to the questions: Wow will this or that schedule 

of culture change the forest over time? What 

monetary returns wif l we have? What will be the 

stream of benefits? What wi 11 be the potential 

produeivity of the forest? 

This simple change in questioning the choice 

of decisions and the use sf controls changes the 

way we view a forest* Rather than viewing a forest as a manufacturing plant putting together 

timber, game, and other benefits at the will of 

managers, a forest is viewed as a biological system that sel f-organizes in response to culture. 

Control becomes a process of using culture to di - 

rect the sel f-organizing forces to transform the 

forest from the present state through a stream of 

desired future states, Each state of forest organi zation provides some combi nati on of materi al s 

for harvest and some physical structure for nonharvestahle benefits. 
This new direction is the basis for providing 

game and nongame animals; trees for timber; shrubs 

and herbaceous plants; and, possibly most important, conservation of a wide variety of germ plasm 

for the livelihood of man. This new direction 

depends on the concept that behavior of complex 

ecosystems, such as forests, is pri nci pal ly caused 

by structure--how the component parts are connected. These connections di rect the flows of 

information, energy, and materials through feedback loops to integrate the behavior of the systems' elements. The result is a decision and 

control procedure that crosses forestry di sci - 

pl i nes without conf 1 i ct . 

Optimal strategy is derived by subjective 

decisions determined by insi ghts, vnl ue judgments, 

experience, and acumen of interested parties. The 

responsi bi 1 it i es of admi ni st rators are not usurped 

in mathematical expressions; mental models and 

scientifical ly derived relationships are communi - 

cated expl ici tly; people make the decisions. 

Many people contributed to my ideas and 

concepts, which evolved over five decades of 

observing, posing questions, and searching for 

answers. The thoughts, perceptions, and insights 

of Professor Bertram W. We1 1 s, North Carolina 

State University, are worthy of special note. It 

was Professor Wells who suggested in 1951 that the 

dynamics of coastal vegetation under the inf 1 uence 

of salt spray may be interpreted within the framework of cybernetics (Wiener 1961). From these 

discussions the four bionomic theories were 

drafted in 1951, and the falsification hypotheses 

for the theories guided my studies of plant responses to salt spray (Boyce 1954) and many later 

studies. The theories were formal ly pub1 i shed 26 

years later and are the foundation for this book 

(Boyce 1977, 1978b). 
My sincere appreciation is extended to my 

students and to mny people who posed questions, 

chal lenges, and suggestions ; tolerated ny questions; and helped me to structure the information 

I present in this book. 

Stephen G. Royce 

August 1985 

Acknowl edgment 

The information included here was published 

as technical and professional papers by the United 

States Department of Agriculture Forest Service. 

This book began as a set of lectures when I held 

an appointment at the School of Forestry and 

Envi ronmental Studies, Ya1 e Uni versi ty , from 1979 

to 1981. The course is now taught at the School 

of Forestry and Envi ronmental Studies, Duke 

University. 

Chapter 1 

Int roduct i on 

The Human Use of Forests 

This book tells how to use forests. Its 

focus is on providing multiple benefits within the 

biologi cal limits for sustaining producti vi ty of 

the forest and land. The chore is to use cultural 

practices, called silviculture, to direct the 

behavior of biological systems and bring about a 

desired state of organization of the forest. This 

process is the si lvicultural application of ecosystem dynami cs . The purpose of the process described here is 

to help parties identify alternative combinations 

of uses and help them choose a combination of uses 

acceptable to their best interest. The method is 

to di splay the dynami cs of selected combi nat i ons 

of benefits, impacts, and costs in simple charts 

and tables and to allow people to use judgment, 

insight, and experience to make subjecti ve deci - 

sions. Decisions are for a decade or less; 

control is through si 1 vicul ture. Cybernetic techniques keep plans dynamic and congruent with the 

real forest and with the perceptions of interested 

parties. The simulations incorporate the results 

of inventories, research, and moni tori ngs ; limit 

the di splays to hi 01 ogi cal ly possi bl e comhi nations 

of benefits; and relate each combination of benefits to the silvicultural cost. 

The question asked is: To what dynamic state 

of organization do the interested parties want to 

bring this forest? The answer, derived from each 

party's perceptions of best interest, is a state 

of forest organization toward which a1 1 si lvi - 

cultwe is directed* Benefits, costs, and impacts 

are related, independently of each other, to 
orderly changes in states of forest organization, 

Cultural actions are harmonized to modify the 

natural ecosystem dynamics and thus direct the 

forest toward the chosen state. 

Near the end of each planning period, the 

ent i re deci si on and control process is repeated, 

New information is added to the simulations; different perceptions and possi bly different interested parties are used in the subjective choice of 

an adjusted goal . In this manner, a new structure 

for managing the forest is derived. 

Forests are viewed as nonequi 1 i bri um, dynami c 

systems constantly transforming from one state of 

organization to another (Boyce 1978b). These aimless transformations, modified by s i l v i c u l t u r e and 

use, bring about the communities of plants and 

animals we call stands. They are driven by 

natural sources of energy and material and have 

provided food, shelter, clothing, and other benefits to man for thousands of years (fig. 1). The 

benefits, produced without si 1 viculture, are 

essentially free except for the costs involved in 

owning, harvesting, and protecting the forests. 

It is likely that most of the benefits from the 

world's forests result from little or no investment in silviculture. 

Many forests are converted to homes and highways--uses that are considered to be more valuable 

than the benefits derived from natural ecosystems. 

I have not included conversion of forests to nonforest uses in this book. My primary concern is 

the purposeful and systematic mdification of 

forests without destruction of the natural ecosystem dynamics that have been going on for 

mi 1 lions of years. 

Silviculture is viewed as the culture of 

forests for any and a1 1 benefits, singly and in 

different combinations. Si f vicul tare is used to 

di rect ecosystem dynamics for increased amounts 
ail4 irqproved quality of benefits (fig, 2). It 

requi res deci sions to invest management resources 

such as labor, capital, and knowledge. Although 

these investments are expected to improve human 

henef i t s, the most important const rai nt on si 1 vi - cr~lture is biology, It is apparent that decision 

and control techniques a r e r e q u i r e d to provide 

benefits within the biological limits for stistai ni ng long-term producti vi ty of the forest and 

1 and. 

This book describes a cybernetic decision and 

control system. The system is guided toward a 

goal by feedback processes, and it is based on a 

simulation of the managed forest. The timber harvest is regulated to guide the forest toward a 

steady-state distribution of habitats yielding a 

constant annual flow of harvested timber and other 

benefits. The cybernetic method is used to 

periodical ly ad just the goal , within the bi 01 ogi - 

ca7 limits, in relation to changes in social, economi c, and pol it i cal forces. 

Benefits are defined by people in relation to 

personal interest. The definition includes the 

satisfaction of viewing a forested scene from a 

roadside overlook; making a livelihood by harvesting, processing, and marketing timber; using 

wooden furniture and a newspaper; and enjoying a 

paneled wall and a pleasant hike. Perceptions of 

benefits change faster than forests can transform 

from state to state. In other words, technology 

for processing tangible benefits changes faster 

than forest managers can genetical ly and physiologically modify the physical properties of hiomass. Economic cycles and social attitudes change 

faster than forest managers can transform forest 

states. Thus, the use of silviculture to match 

combi nations of benefits with perceptions of personal interests requi res dynamic plans that are 

designed with cybernetic principles to integrate 

persona1 perceptions of interested parties with 

ecosystem dynami cs . 
Figure 1.--Human benefits derived from forest 

ecosystems without silviculture. 
NATURAL INPUTS 

radiant energy 

organic matter 

NA TURAL OUTPUTS DISSIPA TED . 

Figure 2.--The use of si 1 viculture to direct ecosystem 

dynamics for more and better benefits. 
Ecosystem dynamics includes transformi ng communities of organisms from one state of organization to another and the behavior, evolution, and 

extincti on of large numbers of plants and animal s. 

A1 though these transformations are aimless, observations of repeated changes from state to state 

provide information for projecting transformations 

for short time periods. For example, we may observe that most stands whose-trees now average 4 

inches in diameter will transform to stands whose 

trees average 6 inches in diameter during the next 

10 years. It is unlikely that the 4-inch stands 

will transform during the next 10 years to seedlings or sap1 ing~ or to stands whose trees average 

20 inches in diameter. Observing the most frequent transformations of plant and animal cammunities from one state to another led ecologists 

to the concept of succession. 

Succession is viewed here as a special case 

of transformation of plant and animal communities 

from one state to another. The important point is 

that many forest transformations can be predicted 

with an acceptabf e accuracy for one or more decades. The validity sf these predictions is used 

to di rect ecosystem dynamics. The approach presented here is to use knowledge of ecosystem dynamics to predict transformations after biomass 

is removed. Biomass is removed, usually by timber 

harvesting, to bring the forest to a certain state 

of organization, which is defined as the distri bution of stands by stand condition classes, Stand 

condition classes are defined by age and area 

classes and by forest types. 

Managers and management teams, however organized, are provided a simple and effective tool to 

systemati cal ly provide benefits singly and in di f - 

ferent combinations. The new di recti on uses conventional si 1 vicultural techniques, some of which 

were developed more than a hundred years ago, The 

method is not radically new; it is a restructuring 

of knowledge and technology* 
The Mew Dl rection 

The new direction is to ask and to answer the 

question: What state of organization do we want 

for this forest? 

Order in forests, also called structure, is 

the way plants and animals are arranged and connected to each other and to the environment. The 

concept of organizational state includes these 

arrangements, distributions, and connections. 

A certain state of organization is described 

by a specified distribution of component parts 

that are maintained as a dynamic system. For 

example, a forest may be kept in a state that is 

defined by the proportion of stands in certain 

type, age, and area classes. The stands are dynamic in that they are constantly changing from one 

condi ti on cl ass to another* 

Consider a steam engine operating at a constant speed. The state of organization is defined 

by the proportion of fuel, water, and steam in the 

engine. This state of the engine may he retained 

for a long. time only by the constant transformation cf the fuel, water, and steam from state to 

state. 

The benefits, possibly power, derived from 

the steam engine are determined by the state of 

organi zation. Changing the oryani zational state-- 

the proportions of fuel, water, and steam--changes 

the availability of the benefits* Unpredictable 

or uncontrol 1 able organizational states of the 

steam engi ne--and of forest systems--resul t in 

unpredictable kinds and amounts of benefits 

The new direction far forestry is to control 

organizational states of a forest to provide human 

benefits* The state of organization determines 

the biological potenti a1 for harvesting tl'mber; 

for hunting, hiking, fishing, camping, enjoying 
wi 1 derness condi ti ons ; and for conservi ng endemic 

genotypes. The new direction uses a knowledge of 

the dynamics of Forest organization to direct organi sm behavior and provide human benefits . For management purposes, operational cri teri a 

are used to describe the organization of forests. 

"Operati onal " mans that the criteria can be readi 1y defined, measured, and related to management 

decisions. Criteria that have been used for many 

years for both natural and artificial stands are 

forests types, the age classes of stands, and the 

area classes of stands. The distribution of 

stands by these stand conditions describes, for 

practical purposes, the forest's organizational 

state. 

Structure or organization may be viewed as 

the way parts of a system are connected. The 

wall s of a bui Iding are connected to form spaces 

for working, cooking, eating, playing, resting, 

and sleeping. The values of the benefits deri ved 

from a bui ldi ng are determined by the arrangement, 

the interrelation, and the linkage of the walls, 

floors, doors, windows, and other parts. Different hui l di ngs provide d i f f e r e n t cornbi nations of 

benefits, primari ly because different kinds of 

parts are structured in different ways. The combinations of benefits available from both 

buildings and forests are determined by structure. 

Different forests provide di fferent combi - 

nations of benefits because different kinds of 

plants and animals are arranged and connected in 

different ways to form different kinds of stands. 

Furthermore, forests are dynamic rather than 

static. The dynamic structure is driven by mortality, which results primarily from the mechanisms of natural selection. Physical mortality 

associated with natural selection is caused hy the 

inability of individuals to maintain some essential variable within the limits for life. 
Essenti a1 variabt es include water content, photosynthesis rate, nutrient absorption, food capture, blood sugar content, and the body's physical 

integrity. Genetic mortality occurs when survivors fail to transmit genes to the next generations* Both physical and genetic mortality are 

consequences of the physical and chemi ca 1 behavior 

of each organism as control led by a specific 

genetic code being translated in a specific environment. Physical and genetic mortality not only 

influence the kinds and proportions of genes 

transmitted to the next generation but also organize the structure of communities. Once the 

genetic code is fixed by asexual or sexual reproduction, physical mrtal i ty structures the forests 

and determines the avai f abi l i ty of human benefits 

for some period of time. 

Thus, the bi 01 ogi cal structure of unmanaged 

forests and the dynamics of this structure are 

determi ned by the processes of genetic mutation, 

recombi nation of the genetic code, and natural 

selection of phenotypes. Both the structure of 

unmanaged forests and organic evolution are dri ven 

by the same biological mechanisms. For the same 

reasons that organic evolution is aimless, the 

natural, unmanaged forest is also aimless, In 

unmanaged forests, Future states of organization 

are uncontrol 1 ed; productions of benefits are not 

di recled. This relationship is important for 

making decisions and taking actions to direct the 

organizational states of a forest toward a desired 

state and for producing biological ly possible and 

desi red combinations of benefits. 

Decision and Control 

The typical question forest managers attempt 

to answer is, "How much timber, water, wildlife, 

recreation, and wi 1 derness shoul d be made avai l - 

able to users?" This question is difficult to 

answer because of the complexity of integrating 
and projecting more than three or four benefits 

with an equal number of management actions and 

because sf the difficulty of predicting social , economic , and pol it i cat changes. 

The new di rection poses different kinds of 

questions: To what dynamic state of organization 

do the interested parties want the manager to 

bring a forest? What states are biological ly 

possible and desi rable? What combinations of 

benefits are possible? The answer is a single 

goal toward which all cultural actions are 

di rected. Complexi ty is constrained. Now the 

manager can schedule an orderly change in the 

forest's organizational states, and the biologi - 

cally possible combinations of benefits can be 

projected for many decades* 

Decision and control uses si 1 viculture as 

part of a larger management system. Regardless of 

the interested parties--a large corporate st ructure, a landowner with less than 100 acres of 

forest, or a pub1 ic agency d i r e c t i n g the use of 

millions of acres of public land--the question is 

always, "To what state do I want to bring this 

forest?" Implementing the answer requi res investments and di recti on of management resources. The 

resources used by silviculture are only a portion 

of the larger management system. 

The di st i nct i on is not a1 ways cl ear between 

directing to achieve a silvicultural response and 

directing to achieve goals of the larger management system. In practice, silviculture must be 

congruent with the larger management system. The 

new di rection achieves this by integrating deci - 

sion and control loops (fig* 3). The particular 

management projections selected become the dynamic 

pl an. 

States of a forest are projected by the 

DYNAST simulator from the present state of the 

forest, through many transformations, toward a 
Silvicultural State Ecosystem of Forest Dynamics Applications Organization 

t 

Implement 

Silviculture 

t 

(YES) 

Are 

Projections 

Desirable 

3 

i 

(NO) 

CONTROL inventories 

LOOP Monitorings 

Research 

-C 

Make DYNAST 

Congruent 

With Forest 

DYYAST 

Change 

DECISION Model 

Parameters 

LOOP 4 

Consider 

Different 

Alternatives 

Land, 

Labor, Energy, 

Capital, 

Materials, 

Markets 

I 

Social 

Economic, 

Political 

Forces 

Figure 3.--Structure of the decision and control system for 

integrating biological and managerial information. 
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social, economic, and political forces whi le 

sustai ni ng the basic resource values. DYNAST simulates annual changes and long-term effects for 

the alternatives being considered. 

DYNAST simulates transformations from state 

to state for any forest that can be described by 

spatial and temporal changes in stands by age 

class and forest type. The simulation model 

transforms a forest to a future state in relation 

to a mode of silviculture. The model is an analytic technique that enhances the decision and 

control process. The simul atisn uses informati on 

about ecosystem dynamics and si 1 vicul ture to 

transform the present state of the forest to the 

next most probable slate. 

This analytic technique interrelates quantifiable variables of silviculture, inventories, 

moni torings, and research with the subjective 

variables for decisions* Consciously or unconsciously all interested parties are engaged in 

subjecti vely percei vi ng some future benefits from 

the forest. It is on the basis of these perceptions that commitments are made to invest labor, 

forest land, capital, energy, and materi dl s to 

implement si 1 vicul ture, The actions are designed 

to transform the forest toward a certain state of 

organization. 

When the management team is given responsi - 

bility to implement si lviculture for the management mode selected, then land, labor, capital, 

energy, and materials are committed for a short 

time. 7he progress rate in applying silviculture 

is monitored against the rate used in the DYNAST 

simulation. Transformations in the forest's organizational state are inventoried. Research adds 

new knowledge, data, and insights about ecosystem 

dynamics and about the forest's responses to 

silviculture, This information is used to keep 

the model, DYNAST, congruent with the real forest. 
A -Seedlings 

B -Saplings 

C - Pole 6 

D - Pole 8 

E - Pole 10 

0 5 10 "' 100 

YEARS FROM PRESENT 

I W- Water 

0 1 1 1 

0 5 10 

YEARS FROM PRESENT 

100 
B -Profitability 

E - Equivalent 

Annual Rent 

N - Net Present 

Value 

R - Realizable Rate 

of Return 

T -Timber 

0 1 I I j j-l-l.o 

0 5 10 100 

YEARS FROM PRESENT 

Figure 4.--Consequences of regulating the 

Big Ivy forest (described in ch. 8) with 

an 80-year rotation period. 
B -Bear 

D - her 

F - Flicker 

P - Pileated 

S - Spiders 

W- water 

YEARS FROM PRESENT 

A -Seedlings 

B - Saplings 

C - Pole 6 

D - Pole 8 

E - Pole 10 

F - Mature 

T~mber 

G - Old 

Growth 

0 1 I 1 

0 5 lo 

YEARS FROM PRESENT 

100 

01 I I 

0 5 lo 

YEARS FROM PRESENT 

100 
2.0 - B - Profl!abillty - 1.0 

E - Equivalent 

Annual Rent 

N -Net Present 

2;" Value 

3 R - Realizable Rate 

I I j J-1-1.0 

0 5 10 100 

YEARS FROM PRESENT 

Fi gure 6.--Consequences of regulating the 

Big Ivy forest (described in ch. 8) with 

superimposed rotations; 80 percent is 

rotated through an 80-year period and 20 

percent through a 200-year period. 
Also, the management mode may he changed at the 

end of each planning period, or before if a major 

change has taken place. For such a change, the 

simulations provide information about expectations 

and consequences before a1 teri'ng the real forest. 

Management percept i ons change in re1 at i on to 

social and economic forces, the avai labi lity of 

management resources, and forest transformations. 

The situation is continual ly evaluated by management and interested parties. The combinations of 

benefits provided and the kinds and rates of 

investments are adjusted. The two closed loops 

(Control and Decision) are coordinated and integrated by a dynamic plan that is selected from the 

simulation alternatives (figs. 4, 5, 6). Adjustments of the dynamic plan keep the loops responsive to new information about social and economic 

forces and ecosystem dynamics. 

The participation of a1 1 interested parties 

depends on communication of expl ici t information 

about the decision and control process. The simulations of alternatives are presented in explicit 

charts and tables. The availabilities of benefits 

and amounts of impacts are plotted on a common 

scale of 0 to 1. Differences are apparent from 

visual examinations. The DYNAST programs are 

documented. The equations are easy to understand. 

The relations for benefits are expressed verbally 

and in simple tables and charts. These relations 

can easily be changed as new information becomes 

available. There are no complex matrices and no 

arbitrary interaction coefficients. The process 

can be made explicit to a1 1 interested parties and 

all parties can participate in using and choosing 

a1 ternati ves. 

The choice of a management mode, one of the 

simulated plans, is subjectively determined by the 

knowledge, insights, value judgments, and experi - 

ence of each interested party. Answers to the 

question, "Are projections acceptable?" (figs. 4, 

5, 6) are derived in the same way that we choose 
food or a new pair of shoes* Gmpromise is an 

essential part of the decision process. Strong 

differences of opinion may not be easily compromised, but the possibility For compromise is 

enhanced by a1 1 interested parties participating 

i n posi ng and choosi ng a1 ternati ves. The bi 01 ogi - 

cal constraints in the control loop keep the considerations within the limits of biological 

possibility. 

By using this method, cultural practices can 

be scheduled to provide forest benefits for many 

decades. The new di rection provides for compromi se of strong di f f erences of opi ni on ; the 

integration of data and opinion; the use of interdisciplinary knowledge and technology; the use of 

a dynamic plan that is responsive do changes in 

social , economic, and political forces; and constraint of complexity in the decision and control 

process. 

The decision and control process is a cycle 

of three events: (1) assembling information about 

the state of the forest; (2) using mental models 

to make decisions; and (3) applying cultural 

actions (fig. 3). An important difficulty is that 

i nformatien can pro1 i ferate to unmanageable proportions in a1 7 three events. 

Fi rst event. Assembling information about 

forest types, stand condition classes, age 

classes, trophic classes, biomass classes, soil 

classes, product i vi ty cl asses, and si mi l ar el ements increases the complexity for decision and 

control. The amount of time, effort, and cost 

r e q u i r e d to systematical ly assemble an enormous 

variety of information on the states of a forest 

and then to consider the ecosystem dynamics in 

relation do alternative cultural actions is an 

appaf fjng challenge, The way to reduce complexity 

is to limit the number of inventory and rnonitoriwj 
variables admitted to the decision and control 

cycle. The criterion is to admit only variables 

that ref 1 ect the transformations in states of 

forest organization. Research techniques are 

used to identify the essential variables that 

describe the ecosystem dynamics, develop new 

cultural practices, and provide knowledge to 

explain relationships. 

No single rule or guide exists for limiting 

the amount and kind of information admitted to the 

decision and control cycle. In general, the 

shorter the cycle, the less data required to keep 

the dynamic plan congruent with the forest a 

Second event . The second event is troublesome because of the difficulty of keeping mental 

models for the behavior of ecological systems 

congruent with the real forest and with responses 

to cultural actions. Mental models are built from 

experience, insights, persona1 perceptions, and 

princi p1 es--re1 ations typical ly derived from a 

systematic assembly of knowledgee Mental models 

are communicated in various ways among managers, 

interdisciplinary staffs, and other interested 

parties. Communication modifies mental models of 

participants. Major difficulties among participants are to know what variables to include in 

explicit models, how to structure the variables to 

be congruent with the real forest, how to maintain 

dynamic plans, and how do identify and overcome 

limitations resulting from the absence of information. 

Research, imaginatively conceived to answer 

fundamental questions about ecosystem dynamics, is 

apparently the most effective way to limit 

complexity in the second event Relations that 

can be operationally structured and hasic principles that apply to an array of l ife forms can 

contribute to mental models and lead to simple, 

explicit models that limit the number of control 

variables, 
DYNAST is an aid for the second event. Large 

amounts of information are reduced to explicit , simple charts and tables. These charts and 

tables, which represent projections in relation to 

actions, help each participant 1 imi t the complexity ~f building mental models and communicating 

these models in some explicit form. The active 

participation of a1 1 interested parties in 

changing the control variables permits indi v i d u a l s 

to communicate with others (fig. 3). Manipulating 

the DYNAST controls is a good way to aid the 

structuring of mental models. 

Third event. The thi rd event is troublesome 

because the complexity of si 1 viculture is increased by constant changes in cultural actions 

caused by soci a1 , economic, and pol i tical forces. 

New knowledge from research, moni tori ngs, and 

inventories must be integrated with silviculture. 

Sorting and using new knowledge and technology are 

di ffi cul t chores. Attempts to match responses 

one-to-one with cultural actions can proliferate 

compl exi ty to unmanageabl e proportions. A cultural scheme is used here to limit both complexity 

and the number of cultural actions that must be 

harmonized in the appl ications. 

The DYNAST technique is to systematically 

use three control variables--rates of timber harvest, sizes of openings formed by timber harvest, 

and the conversions of forest types. These are 

the primary controls used to order the ecosystem 

dynamics of forests. This orderly way to transform the forest from one state of organization to 

another provides predictable variables--the temporal and spatial dispersion of habi tals--that can 

be used to project the potential for all forest 

benefits singly and in combinations (figs. 4, 5, 

6). 

The DYNAST method is useful because values 

for only a few variables are needed to predict 

multiple benefits for alternative cultural actions. 

Chapter 2 

Structure sf the Decision and Control System 

Overview 

Structure, the way the component parts are 

connected, determines the dynamics of the decision 

and control system. For s i l v i c u l t u r a l applications, the managers in the decision and control 

system must he able to anticipate organizational 

states of a forest for a year up to many decades. 

This is achieved with a feed-forward loop linked 

with a number of negative and positive feedback 

1 oops * These connections orient and schedule 

i nforrnation flows that operate through delays, 

amp1 i f i ers, and transducers to si mu1 ate states of 

organi nation of the managed forest. Benefits are 

projected by relating each benefit to an organizational state. The si 1 vicultural applications, 

i nventori es, research, monitoring, management, and 

biological constraints are all forms of delays, 

amp1 i f i ers , and transducers (Mi l sum 1966). A1 1 of 

these elements are components of the decision and 

control process. 

Control loo^ 

The control loop is structured to give preference to the biological constraints (fig. 3). 

This structure is essential if the focus is to be 

on uses within the biological limits for sustai ni ng basic resources. For example, investments 

of money and labor can only partially control photosynthetic rates, mortality, conversion of carbohydrates to marketable forms of cellulose and 

game, delays in growth and reproduction rates, and 

many other elements of ecosystem dynamics. In 

manmade manufacturing systems, constraints such as 

marginal costs, marketing, and supply and demand 

functions can be controlled more than in biological Systems. When the production functions are 
based primari ly on ecosystem dynamics, the bi 01 ogical constraints must be given preference in the 

control loop, 

The control loop must be kept simple. It is 

not possible to incorporate the behavior of every 

organism into controls. It is possible to incorporate elements, such as delays and inventory 

information, that c o n s t r a i n the simul ations to 

reflect the behavior of the real forest. Once 

these constraints are put into DYNAST, all of the 

simulations are considered to be biological ly 

possible and within the biological limits for sustaining basic resource values. 

Biological theories, hypotheses, and a1 gorithms are adjusted with results from research, 

monitoring, and inventories. This kind of new 

information must continually flow into the control 

1 oop. 

Deci si on LOOD 

A management decision is a subjective choice 

of one plan for some period of time from two or 

more alternative plans (figs. 4, 5, 6). We do not 

know how to quantify the choosing process because 

it invol ves integration of data with experience, 

insights, and value judgments. We can quantify 

and manipulate the data components of the process. 

Our inability to quantify the subjective components of the decision process is the primary 

reason for the "Yes" or "No" question following 

the DYNAST projections (fig. 3). These kinds of 

decisions are taken by mental processes similar to 

those used for purchasing a pair of shoes, a coat, 

and food. These mental processes cannot be 

modeled and predicted by mathematical techniques 

such as cost -benef i t a n a l y s i s and 1 i near programing. The decision loop must provide for the 

i ntegrat i on of these sub jecti ve deci si ons based on 

an individual 's mental perceptions of personal 

interest. The integration results from di recti ng 
the decision toward the choice of on€ dynamic 

plan, making the cost and benefits of each option 

explicit, and giving each party an opportunity to 

participate in developing plans. 

Decisions are often conceived as limited by 

flows of money, labor, materials, equipment, and 

capital. These variables are important, but the 

process of choosing uses insights, value judgments, acumen, experience, and flai re Information 

that may pass as fact and marketplace values 

almost invariably are integrations of data and 

specifications resulting from value judgments. 

The data are derived from research, inventories, 

and moni tori ngs. The value judgments are deri ved 

from informati on that originates in social , economi c, special interest, and governmental acti vi - 

ties. These activities provide the basis for 

opinion, fashion, reputation, attitudes, and percepti ons of self-interest, 

The interested parties, one or more people, 

shi ft thei r value judgments about forestry goods 

and services* As different social and economic 

forces interact in the larger social system, 

information from outside the boundary of forestry 

changes perceptions of sel f -1 nterest . For 

example, changes in transportation cost change the 

values that hikers, lumbermen, and hunters place 

on a particular forest. Criteria for decisions 

received by forest managers change because new 

speci al -i nterest groups ari se and others decl i nee 

Legislative activities change directives for the 

managers, change pub1 i c attitudes, constrai n 

public expenditures, and directly and indirectly 

change the "Yes" or "No" answers for both private 

and public forests* 

Choosing a forestry goal , a dynamic plan to 

move the forest toward a state of organization, is 

more like solving a problem in social dynamics 

than solving a problem in silviculture. The decision taken subjectively integrates the constrai nts, expressed as vaf ue judgments, of the 
socicleconomic situation at that moment. In a few 

years, even months, a different decision may be 

taken, not because states of the forest changed 

rapidly but because the state of the external, 

social, economic, and legislative systems changed 

rapidly. 

It has been argued that the dispassionate 

analysis of data, the hallmark of scientists, 

should elevate management deci si ons above the 

uncertai nties of untried mental perceptions and 

value judgments (Ei lon 1980). Attempts to derive 

"Yes" or "No" answers with analytic techniques 

have been unsuccessful for specific segments of 

some management systems. For example, maximizing 

or minimizing a single variable such as present 

value is appropriately determined with 1i near 

programming techniques and is appropriate for 

decisions about investments in land, labor, 

energy, and materi a1 s. 

Linear programming can be used to locate roads 

for harvesting timber, to minimize cost for a 

technical strategy, and to maximize values from a 

thinning and harvest schedule. In practical ly a1 1 

organizations, these ki nds of analyses concentrate 

on technical strategy (Ei 1 on 1980). This strategy 

is based on the use of technical knowledge tb 

achieve defined goals 

Science makes si gni ficant and essential 

contributions to the decision and control system 

(fig. 3). The choice "Yes" or "No" results from 

mental integration of scientifically and subjecti vely deri ved know1 edge. The sci ent i f i c 

variables aid the choice by reducing cornplexi ty, 

explicitly communicating a1 ternati ves for consideration, and keeping the alternatives congruent 

with the real forest. 

A basic concept of the decision and control 

loop (fig. 3) is to recognize the subjectively 

chosen plan as a reflection of the aspirations of 
the interested parties for forest goods and services. This approach compl i cates the si 1 vi - 

cultural chore because these aspi rations requi re 

culture for multiple benefits. Yet, culture for 

multiple benefits appears essential for most 

industrial forests to produce profits, for noni ndustri a1 private owners to achieve personal 

gains, and for publicly owned forests to provide 

diverse goods and services. For example, a private corporation seeks short-term profit, but 

rarely at the expense of long-term disadvantage. 

A compromise by the corporation is to provide 

multiple forest benefits in response to the value 

judgments, opinions, and fashions of interested 

parties but with due regard to the interest of the 

shareholders. A forest may be managed for raw 

materi a1 s, industri a1 jobs, and woody products, 

but legislative concerns may include constraints 

for the interest of hunters, recreationists, and 

other special-interest groups. 

Another basic concept of the decision and 

control loop is: for every statement of a single 

desire there is a counter desire or biological 

constraint that prevents the first from being 

maximized or minimized. For example, a manager 

can increase total water yield by removing a1 1 of 

the forest, but the cost of erosion, floods, and 

water during droughts can be greater than accepting a forest cover and a more moderate amount 

of water in the management scheme. Accumulated 

volumes of timber can be rapidly harvested without 

concern for sustai ned producti vi ty, but the depreciated cost for the long restoration period can be 

greater than the gain from rapid timber harvest. 

The situation is this. Biological constraints are uscd in the decision and control loop 

to assure that biological ly possible alternatives 

are considered; basic resources are sustained; 

multiple benefits and impacts are integrated in 

biological ly possible combinations ; the choice of 

a goal is congruent with the real forest; and 
variables of the DYNAST model, especial ly delays 

and algorithms for benefits, are constantly updated as new research and inventory information 

becomes avai 1 able. 

Cvberneti c Foundation 

Control and communication in hi 01 ogi cal 

systems are possible because of structure--the way 

the component parts are connected. There are many 

kinds of structure; one type forms feedback loops 

that direct the behavior of the system to achieve 

or maintain some goal, such as keeping the level 

of blood sugar within the limits for life. These 

loops are negative feedback loops with goals. A 

negative feedback process is one in which a 

decision-maki ng process regulates the system by 

comparing the immediate past conditions with a 

standard or goal and making adjustments to achieve 

a steady state (Wiener 1961). 

Negative feedback loops make the system aware 

f its own performance by using outputs to reguate inputs. Algebraic signs can reverse in the 

decision process. If there is too much, a quantity will be reduced; if there is too little, it 

will be increased. When this kind of system is 

disturbed, it typically reacts with a series of 

dimini shing osci 1 lations. A hand reaching for an 

object moves through such oscillations on a nearly 

invisible scale. These oscillations illustrate 

reversals of algebraic sign by which a decision 

process alters states to approach the goal 

(Forrester 1968; Milsum 1966). 

Other kinds of structure may have no feedback 

loops or the loops may be positive rather than 

negative. Positive feedback loops, such as interest rate, tend only in one direction and have no 

mechanism for reversing an a1 gebraic sign. Systems with only positive feedback loops cannot take 

corrective action in pursuit of a goal or to maintain a standard. In such systems counteractions 
may achieve a sort of balance. For example, the 

interaction of death rate and birth rate may tend 

to maintain a population within some limits. 

However, a system without a goal does not move 

toward a predictable steady state. 

Some concepts for control with negative and 

positive feedback loops. have been known for hundreds of years (Wiener 1961). In the 1940's, 

the word "cybernetics" was applied to these kinds 

of decision and control mechanisms in both organic 

and inorganic systems. During the 1950's the cybernetic principles, primari 1y from examples in 

electrical engineeri ng, were broadened to form 

a system dynamics phi 1 osophy for management 

(Forrester 1961). This philosophy provides the 

method01 ogy needed for the new di recti on in forest 

management because of its cybernetic orientation 

to a system's behavior and its use of dynamic 

models to search for flaws in alternatives before 

implementation. 

The most effective decision and control 

systems include one or more negative feedback 

loops, positive feedback loops, and feed-forward 

loops (Ashby 1973; Beer 1966; Mi lsum 1966). These 

loops may he viewed as information networks that 

1 ink the components of a system to form a certain 

organizational structure. According to the system 

dynamics philosophy, this structure is the principal cause of the system's behavior. The structure includes the physical components, such as 

those in the control loop (fig. 3), and the intangible policies, desires, perceptions, insights, 

and acumen included in the decision loop. These 

structures contain sources of delays, amp1 i ficati ons, and transformations that generate complex, 

nonlinear responses to simple input changes. The 

analysis of these kinds of large nonlinear systems 

and the design of decision and control methods is 

the primary use of the system dynamics phi losophy. 

The analytic approach in system dynamics 

views organizational behavior in terms of the 
flows of information, energy, and material s rather 

than in terms of functional units. The flows of 

stands through age classes in a forest, the flows 

of timber volumes, the flows of wildlife habitats, 

and the flows of water and nutrients are known to 

forest managers. The flows of labor, money, materials, energy, capital investments, and markets 

can be identified in a1 1 management organizations. 

In the system dynamics model, DYNAST, the integrating flows of information link these components 

to form a time-varying flow structure. This flow 

structure crosses functional , academic, and managerial disciplines without conflict. This 

approach di spel s the funct i on const rai nt s to 

decisions and promotes interdiscipl inary harmony. 

A1 1 areas contribute equal ly to the decision and 

control process . 

Recent developments have increased the application of cybernetic concepts for decision and 

control in management and have improved the quantitative analysis of the flows of information and 

management resources in organi zati ons . Fl aws in 

the plans may be identified by system dynamics 

models, such as DYNAST, and by the research, 

inventory, and moni tori ngs that keep DYNAST 

congruent with the forest. Simulations may be 

constrained by 1 imited information and by 

incompletely val idated data. These constraints 

are inherent in a1 1 decision and control procedures, especial ly for biological systems. They 

cause inefficiences that are reduced in DYNAST by 

recursive examination of the data, suppositions, 

and decisions. What is important is the general 

usefulness of the interactive, cybernetic approach 

to policy design* This is the conceptual framework for the new directionThe initial assumptions and the initial data 

used in the first DYNAST projections are derived 

from the literature, personal experience of forest 

managers, and discussions with persons in many 

di sci pl i nes. After imp1 ementi ng the plan, repeated inventories, monitorings, and research 
efforts are used to make DYNAST congruent with the 

forest and to make the management pl an descri pti ve 

of past experi ences and probl ems. 

Negat i ve Feedback Loop 

By definition,^ control is evident in both 

biological and inanimate systems. The mechanism 

i n dynamic control systems always includes information networks structured to form one or more 

negative feedback loops. These kinds of information loops direct the behavior of the system 

toward a goal that originates outside the information loops (fig. 7). 

Effe 

IENERGY AND 

MATERIAL 

SOURCES 

STATE OF ORGANIZATION I OF THE SYSTEM I 

ZI--CSensors ENERGY AND 

DISSIPATION 

Figure 7.--A negative feedback loop illustrating the structure and 

flow of information (dotted lines) and materials (solid lines). 
Information received by the decision mechanism is compared with a standard or goal that 

originates from outside the information loop, 

Once the decision mechanism makes the compari son 

between the incoming information and informat ion 

from the goal, the mechanism generates new information that is transmitted as signals to effectors, The decision operating through the 

effectors may continue or reverse the behavior of 

the system, For example, if a driver finds the 

automobi ie is headed off the hi ghway, the decision 

may he to change the di rection of the front 

wheels, change the speed of the car, or revert to 

a number of other forms of behavior, The end 

result of decisions is to direct the organizational state of the system relative to its preceding state. The flow of information may 

continue or reverse the behavior of the system to 

achieve a goal. These kinds of closed loops that 

continue or reverse behavior are called negative 

feedback control systems, 

Information directs effectors of the system 

to do something, This is achieved most often by 

ampl i fiers that increase the force, the flow of 

energy, and the amount and duration of change in 

relation to the very small amount of energy 

carried by the flow of information. The amplifiers raise the amount of energy used in the 

system by using another energy source. Muscles 

are examples of biological ampl i fiers, The automobi le dri ver implements deci sions to change the 

car's direction by using information flows to the 

muscles to amplify the force on the steering 

wheel. Mechanical ampl i fiers on the car, such as 

levers and power steering, further increase the 

forces exerted on the wheels, 

Transducers, which convert energy from one 

form to another, are important parts of the informati on networks. An ampl i fyi ng transducer converts energy and raises it to a higher level by 
uti 1 izi ng another energy source, The photosynthetic mechanism is an amp1 ifying transducer that 

converts and raises diffuse radiant energy to a 

higher level of chemical energy. In organisms, 

cells in sense organs are usually amplifying 

transducers. For example, in olfactory organs a 

few molecules of a foreign chemical are signals 

for a cell to use larger amounts of energy to 

ampl i fy and transform the information for transmi ssi on through the central nervous system. 

Energy enters the control process as signals 

and as the capacity to do work. Small amounts of 

energy carry signals throughout the information 

network. The amount of work done by these signals 

is negligible. Although signals may flow with 

large forces, it is the effectors that amplify the 

signals with large amounts of energy from outside 

the information network to change the state of 

organization of the system. The total amount of 

energy present is not related to control in the 

system, Yet, some minimum amount of energy is 

necessary for the effectors to ampl i fy the si gnal s 

and change the state of the system. It is the 

presence and the structure of the information network that directs the system's behavior toward 

achi evement of goal s . 

In the automobile, control is attained by 

shaping and connecting the component parts to 

develop a certain structure. The particular 

structure is designed to convey information and 

energy from the driver to the wheels and thus 

maintain the automobile on the highway, From a 

control point of view, however, energy in the fuel 

is recognized simply as another component such as 

brake linings, axles, ball bearings, nuts, and 

bolts. Control is always determined by the structure of information channels that direct the 

flows of information and energy within the system 

to achieve a goal. 
The flow of information in a negative feedback 

system is through a closed loop. The signals may 

be forms of radiation, electric put ses, force 

exerted on a steering wheel, and other forms of 

communication and work. Regardless of the kind of 

signal, an essential requi rement is the exi stence 

of physical communication channels to transmit the 

s-r'gnals* 

A driver must have access to physical channels of communication that transmi t signals about 

the location and speed of the automobile, the condition of the highways, and the location of other 

automobi 1 es. The sensi ng mechani sms in the human 

body constantly feed information to the central 

nervous system, which is the decision mechanism. 

The central nervous system compares the incomi ng 

information with a number of standards and goals 

such as the delay far stopping the car and the 

speeds for negotiating curves. Many negative 

feedback loops are involved in this process. 

Decisions produce information that is channeled to 

the effectors such as muscles in the driver's 

body, the steering wheel, the foot pedals, and 

other components of the automobile. Control of 

the automobile is brought about by the flow of 

information through structured networks within 

the dri ver-automobi le system. A goal, such as to 

travel from one place to another, originates outside of the control systemo 

The sensing mechanisms are not a part of the 

control system, The informati on sensed and 

transmitted always describes the immediate past, 

not the future organizational state of the system. 

The future state is perceived in the decision 

mechanism, The automobile driver knows about a 

hole in the hi ghway because of current informat i on 

received by the eye. It is very important for the 

design of monitoring and inventory methods to have 

current information used in control systems. 

Delays in informing the decision mechanism of the 

system's present organizational state may cause 

the system to fail. 
The dynami c behavi or of negati ve feedback 

systems compensates, within periods of del ay, for 

deviations of the system from the desired goal. 

The current state of the system is compared with 

the desired state and the effectors are instructed 

to bring performance closer to the goal in the 

next time interval. The available information 

about the system's present state is the basis for 

the current decision that controls the system's 

behavior during the next moment of time. These 

kinds of systems actively compensate to maintain a 

partictrlar state of organization, a goal, in relation to disrupting forces from the external 

envi ronrnent . 

Feed-forward loops are characteristic of 

anticipatory control systems (fig. 8). Automobile and truck drivers use feed-forward deci - 

sions in attempts to maintain a desired highway 

speed. Consider a heavily loaded truck going down 

a hi 1 1 on an open hi ghway . The dri ver can see in 

the distance, or remembers from past travel, that 

soon the grade will change and the loaded truck 

will be climbing a steep hill, The driver presses 

the accelerator and feeds forward information do 

the engine before the truck begins to climb the 

hill. The driver anticipates the need to make a 

control decision before the truck is slowed on the 

upgrade* With feed-forward deei sions, a more 

desirable speed is maintained than with the negative feedback hop only, Thus, decisions in feedforward systems are based on predictions of future 

states rather than on information sensed from 

present states as in negative feedback loops. 

Feed-forward loops require humanlike intelligence, They have mechanisms and processes to 

project future states and to adjust behavior 

aceordi ngly. Simulations are often useful both 

for projecting and for determining appropriate 

response. 
GOAL ENERGY AND 

-=----- 

SWATE OF ORGANIZATION 

OF THE SYSTEM 

DISSIPATION 

Figure 8.--A feed-forward loop illustrating the strucrure and 

flow of informati00 (dotted lines) and materials (solid lines). 

Feed-forward loops are limited by human abi 1 - 

ities to project the dynamics of systems into the 

future. Projected future states of the system are 

used to make decisions and to take actions for 

control several years into the future. Feedforward loops are a part of the structure for the 

decision and control process (fig. 3). 

The DYNAST model is formulated to simulate 

future states of forest organization in relation 

to silviculture. This model includes nonlinear 

time-varying functions, control functions, and 
vectors for age and area classes of stands by 

forest tyaesAlgorithms for benefits and impacts are 

linked to the simulated organizational states, 

which are the core of the model, As the simulated 

forest transforms from state to state, the future 

availability of benefits and the potential for 

impacts are computed* A display of these potential benefits and impacts is used to choose the 

goal, which is a particuf ar organizational state. 

The DYNASP model is the mechanism that aids 

interested parties to perceive future states in 

the feed-forward loop (fig. 8). 

When the predicted benefits and impacts are 

undesi rabt e, a different organizational state is 

considered, This is done by changing the rate of 

harvest, the size of openings formed by harvest, 

the species, or a1 7 of these. 

A prohl en of feed-forward loops results from 

errors introduced into the deci sisn mechani srn by 

inaccurate perceptions of future states (fig. 8). 

These errors are kept to a minimum hy frequent 

moni tori ngs, periodic inventories, and constant 

research. This interac'ti ve procedure is an important part of the decision and control sLructure 

(fig. 3). 

Control systems may include informati on networks structured to form one or more positive 

feedhack loops, These loops d-iffer from negative 

and feed-forward loops in that change is always in 

oqe direction, Positive feedback loops are often 

called density dependent because the loop has no 

goal and the response rate is related to an 

existing amoiint of something (fig. 9). 
Effectors 

I 

/ 

/' 

/ 

I 

I STATE OF ORGANIZATION 

\ OF THE SYSTEM 

\ 

\ 

\ 

\ 

\ .--- 

ENERGY AND 

DISSIPATION 

Figure 9.--A positive feedback loop illustrating the structure and 

flow of information (dotted lines) and materials (sol id lines)* 

Compound interest on funds in a savings 

account is an example of a positive feedback 

system. Assume a constant interest rate; the 

larger the amount of the fund, the more interest 

is paid at succeeding time intervals. The positive feedback loop does not change the interest 

rate or the organizational state of the system to 

achieve some goal. 

Positive feedback loops have no goal. Such 

systems continue to increase or decrease something, always in one direction, until some force 
a 4-J mat 

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future states, even though the states (positions 

of the hands) are repetitive. The clock becomes a 

part of a negative feedback process when a person 

adjusts the speed control device. A person compares the clock's performance with a standard and 

decides to take corrective action by retarding the 

speed. Later, the person evaluates the performance again and takes further action, probably 

making small er corrections. The system--composed 

of a cl ock and a person--osci 11 ates and gradual ly 

reaches the goal, which is the display of time 

within acceptable limits of error. 

Cybernetic Structure for Di rected Systems 

An automobile and a driver form a directed 

system that responds to changes in the environment 

(fig. 10). The mechanical parts, energy source, 

and driver are structured to form a physically 

identifiable and functioning system directed 

toward a specific goal. The generalized goal is 

survival in moving from place to place. The 

system transforms from state to state in relation 

to the external environment. For example, the 

speed of the automobile changes in relation to 

curves, holes, and other cars on the road. The 

thickness of the tires, the frictioq of the moving 

parts, the attitude of the driver, and the environment are constantly changing. 

The system's environment consists of the 

forms of energy, materials, and information that 

influence transformations in its states. Radiant 

energy from the sun may change the temperature of 

the car and the driver; a nail may deflate a tire; 

and water in the cooling system and water as rain 

may influence the temperature of the engine, the 

driver, and the car. An internal and external 

environment may be defined for convenience. In 

reality the environment is an inseparable part of 

the system. Operational definitions are used to 

classify situations such as determining when a 
GOAL 

/ 

/ 

STATE STATE I OFTHE OF THE 

\ 

\ / /I Sensors 

/// 0' / 

'L 

-'-----.- 

Figure 10.--The structure of the information network far a directed 

system for a driver, car, and highway. (Dashed lines represent the 

information loops.) 

molecule of carbon dioxide is part of an organism, 

part of the internal environment, or part of the 

external envi ronment . Energy, materi a1 s, and 

i nformat i on not known to i nf 1 uence the dynamics of 

the system are often not considered parts of the 

environment of the system, 

A goal is part of the cybernetic structure of 

a directed system* Goals are adjusted with 

changes in the environment of the directed system, 

Thus, the dynamics of systems cannst be precisely projected into the future. For example, 

the driver can identify a destination and even the 
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PLAN 

NFOR MANAGEMENT 

/ 

/ 

SiI viculture ----- --c \ --. \\ 

ENVIRONMENT 

'.F------ 

Inventories, Monitorings, Research 

Figure 11.--The structure of the information network for a managed 

forest system for forest managers, the forest, and the environment. 

i nventories and moni tori ngs for control should 

include only the elements required to descri he 

important transformations of the system. A 

limited variety of information should be admitted 

to the communication channels for decisions and 

control. In this way the control variable for 

the forest can he limited to a small number of 

silvicultural actions, such as harvesting timber, 

cont roll i ng the si ze of openi ngs, regenerating 

openi ngs, and changing forest types. 

Many si 1 vicul tural actions to enhance the 

structltre of speci fic stands are most effectively 
planned and applied as heuristic decisions. Because of many uncertai nt ies, automobi l e dri vers 

cannot plan the times to apply the brakes and the 

accelerator for al 1 stoplights expected on a trip. 

Likewise, uncertainties limit the planning for 

many cultural actions such as thinning, weeding, 

pruning, and fertilizing. These kinds of cultural 

actions are heuri sti cal ly adapted to speci f i c 

stands and are incorporated into the cybernetic 

structure for in-pl ace decision and control (fig. 

3) * 

Direct controls for the car are the steering 

wheel, brakes, and accelerator. Activities such 

as changing the oil, adjusting the carburetor, and 

inflating the tires are secondary controls. Direct controls for a forest influence the differential mortality rate by species, size, and age 

classes. The most important direct controls are 

harvesting some form of biomass, regulating 

openi ng size, and changi ng species composition * 

Secondary controls are modifications of the envi ronment, such as adding ferti 1 i zers and drai ni ng 

wet areas. 

Interactions between the automobile and the 

environment and between the forest and the 

envi ronment are important consi berat i ons. The 

temperature of the environment affects starting 

and cooling rates of the automobile, which in turn 

affect the envi ronment by radiating heat and 

emitting compounds of carbon and nitrogen. 

Similarly the environment affects the physiology 

of forest organisms. These organisms, through 

their behavior, change the environment by producing dead organic matter and emitting compounds 

such as water, oxygen, organics, and carbon 

dioxide. 

Interactions between the environment and the 

forest are outside the dynamics of the system. 

They are not controlled by flows of information, 

decision mechanisms, and goals. There are no 
known physical, information channels structured as 

negative feedhack loops that direct the environment to provide radiant energy, nutrients, rain, 

and other materials in relation to the state of 

forest organization. No information networks 

exist that regulate emissions of pl ant and animal 

compounds through negative feedback loops to bring 

the environment to a certain state. 

The human element plays another important 

part in cybernetic structure for the di rected 

forest--the development of a plan (fig. 11). The 

dynamic plan describes the goal and the purpose of 

management. It originates outside the system as 

do goals for a1 1 negative feedback loops. 

External forces, especi a1 ly those re1 ated to economic, social, and political situations, determine 

and modify the goals for forest management. 

Concept of Dynamic Pl ans , 

The word "dynamic" is used in the sense of 

changing. This definition is not far removed from 

the original meaning of power because systems 

typical ly change only as a result of forces acting 

upon them, All biological systems are dynamic 

because of the noneyui 1 i bri urn, energy-sorti ng, and 

energy-dissipating activities that maintain life. 

Biological systems, whether of forests or organisms, do not change to a constant level instantaneously after a sudden "step" change such as the 

death or harvest of dominant trees. After a sudden "step" change, the characteristic dynamics of 

living systems is a perceptible delay followed by 

transitions to a new state. 

Biological systems of both individuals and 

communities have irreversi hle dynamics. Step 

changes, such as harvesting a stand, change the 

plant and animal community to a different stand 

condi tioq class. After a relati vely short period 

of time, a natur3l succession through a series of 
stand condition classes transforms the area. 

These transient changes create classes that may be 

cal led successional stages. Such changes may 

bring a stand now classed as "mature" to a 

seedling srrccessional stage after the stand is 

harvested. This new stand wi 11 differ from the 

original in the proportional distribution of individuals by species, in the location and size of 

individuals by species, and in the genetic composition of genotypes. The new stand may approxi - 

mate the harvested stand in many superficial ways, 

but the original stand is not repeated. Repetition does not occur because there are no negative 

feedback loops, at least there is no evidence for 

them, with goal s for hri ngi ng ecosystem dynamics 

and natural evolution to some steady state, 

Exact projections of structure and behavior 

are not possible. Nevertheless, repeated observations of the transformations from one state to 

another can lead to some likely suppositions about 

the future. Me know what kinds of transformations 

have the highest probabi 1 ity of occurrence and can 

be most easi 1y directed. For example, when a 

stand of hardwoods is harvested, the area will 

normal ly regenerate in hardwoods that wi 1 1 transform through size classes at a rate that can be 

estimated on the basis of experience. 

Biological systems typical ly di splay compl i - 

cated response patterns to simple management 

actions such as timber harvesting. If we are to 

change a forest from some original state to some 

desired state at a future time, it is necessary to 

develop dynamic plans in which simple management 

actions can di rect the dynamics of the system. 

The dynamic plan is examined at least every 

decade. Harvest rates, expressed as rotati on 

periods, are changed in relation to the organizational state of the forest, the uses desired, and 

the social, economic, and political situation. The 

goal is a desired state of forest organization, 

which may or may not be achieved. 
Dynamic control requi res that timber harvest 

he scheduled each year for the planning period, 

usually one decade. Each year's narvest is 

related to the rates of dynamic change of the 

habitats as determined by delays for biological 

changes. As the forest approaches a desired state 

of organization, the annual harvest rates may 

approach a steady state. 

With dynamic planning, decisions are used 

to guide the forest toward a goal. For example, 

we can identify a distribution of habitats as a 

goal many decades into the future. We cannot 

specify where each habitat wi 1 l be or the number 

of each kind of plant and animal in each habitat. 

We can identify an amount of timber expected to be 

harvested in the next decade, but we cannot specify how much timber wi 11 be harvested. The method 

for dynamic plans is to make decisions, i .e., 

change the goal (fig. 3) at 5- to 10-year intervals in relation to changes in social, economic, 

and pol itical situations. Behavior-correcting 

feedback loops keep the manager and forest system 

oriented toward the goal whi l e currently produci ng 

benefits. 
Chapter 3 

Ecosystem Dynamics 

Overview 

A knowledge of ecosystem dynamics is important for predicting forest transformations from 

state to state in relation to silviculture (figs. 

4, 5, 6). In this chapter, operational criteria 

(Benjamin 1955; Bridgman 1927) are used to develop 

four bi onomi c theories (Boyce 1978b ) , The 

theories are used to frame the mental and physical 

models, which are the bases For decision and 

control The models are derived from flows of 

information sensed from the real world, Application and validity of the theories and the models 

are fottnd hy an analysis of the operations perfarmed in concrew ss'i;tua"iions, For example, the 

application and validity of a measure of demperatrrrs is determined by the sequence of operations 

that manipulate the thermometer. With operational 

criteria and theory from physics, the flows of 

information sensed with the thermometer may he 

~rf;;ed to form a model for explaining temperature 

changese The hionomic theories reduce the dynamics nf ecosystems to a description of human perceptions of the actual happenings, give the mental 

perceptions of one person a frame for comparison 

with experience hy others, and help to reduce 

mysticism in both experiences and mental models. 

The theories are a frame of reference for mntal 

model s . 

Mentai models are dynamic perceptions of the 

immediate past states of a system and mental projections of future states in relation to informatioo flows. Mental models are validated by a 

frarne of reference to what is accepted to he 

"true," to past experiences, to information 

derived by others, and to imp1 ied and explicit 

theories for behavior of the system in question. 

The hascs for decisions are one's mental models, 

which are dynamical ly adjiisited to the informatiorl 
flows. Thus, most wntaf models, c;ilch as "ham 

hungry," are transient bases for decisons, such as 

"purchase this food." Rational decisions, those 

that maintain essential variables within the 

bounds of a goal, are guided by a frsrne of reference for validating the mental model (fig. 7). 

Mental models are communicated in physical forms 

such as diagrams, equations, words, maps, and 

other signals, It is this frame of reference in 

the cybernetic structure (see ch. 2) that determines the behavior of di rected systems. The bionomic theories are the frames of reference that 

(1) aid our understanding of ecosystem dynamics 

a~d (2) guide the use of information for decisions. 

The operat ional approach provides for theories that are constantly challenged by applying 

the operations and by hypotheses for falsification. Theories are modified by each additional 

element of knowledge that meets the operational 

criteria of being physical ly identifiable, measurable, and meaningful for decision and control. 

The theories are restated from Boyce (1977): 

1. Each 'living organism and its envi ror~ment 

form an individual istic system with the 

goal of survival and the dynamics of 

systems with negative feedback loops. 

2. The mortal i ty of incli vidiral i stic systems 

organizes survi vors into communities 

without goals and with the dynamics of 

systems without negative feedkack loops. 

3. The flows of energy, nutrients, carbon 

dioxide, water, and organic m a t e r i a l s are 

unidirectional and have the dynamics of 

systems without goals and without negati ve feedback loops. 

4, The states of forest organization determine the kinds and proportions of mu1 - 

tiple benefits available from a forest. 
The practical use of the theories is to 

desi gn mnagenent schemes that wi 11 di rect sel f - 

organizing, i~dividualistic systems to bring about 

desi red states of forest orgaqi zation, 

The word "ecosystem" was defined by i4.G. 

Tans1 ey (1935). The concept "ecosystem" is 

appeal iny because it is holistic and includes a1 1 

of the physical, chemical , and biol ogi cal elements 

of the universe. Tansley said that ecosystems ". . . are of the most various kinds and sizes. 

They form one category of the multitudinous physical systems of the universe, which range from the 

universe as a whole down to the atome'' 

Tansley's operational method was ". . . to 

isolate systems mentally for the purposes of 

study, . . . whether the isolate be a solar 

system, a planet, a climatic region, a plant or 

animal community, an individual organism, an organic molecule, or an atom. Actually the systems 

we isolate mentally are not only included as 

parts of larger ones, but they also overlap, 

interlock and interact with one another." Thus, 

the words "organism," "community," "forest," and 

'%and" are operational definitions of ecosystems 

as used by Tansley. A specific community, organism, forest, and stand are isolates that we can 

identify to study and manage. These units include 

all of the organisms and the organic and inorganic 

components of the communities and the individual 

and its environment. According to Tansley, the 

ecosystem includes "constant interchange of the 

most various kinds within each system, not only 

between the organisms, but between the organic and 

inorganic," These interchanges and flows of 

energy, materi a1 s , and organi sms are the dynamics 

of the ecosystem* 
Tansley 's (1935) operational analysis reduces 

ecosystem dynamics to a description of human observations, measurements, and operations. This approach avoids giving ecosystems metaphysical and 

idealized properties, such as indicated by the term 

"complex organi sin." Terms such as "species," "succc?ssion," "habitat ," "climax," "stability," "bi - 

ome,'"Yorest," and "stand" are defined by the use 

to which the terms can be put. For example, Tansley (1929) defined succession relative to use: ". . . the actual value . . . of the concept of 

succession . . . is measured by the rrse to which it 

can he put, to its success in enabling us to focus 

on phenomena more successfully, and thus to discover new starting points for fresh investigation." 

Another advantage of the operational approach 

is that it reduces ecosystem dynamics to hrninan 

perceptions of actual happeni ngs and provides for 

validating actual experiences by repeating the 

operations used to define a term or concept. 

Organisms were observed by Tansley (1914) to live 

as individuals and as part of a system consisting 

of a1 1 those elements, physical and biological , that could concei vably affect the organisms. 

Cowles (1901) took this same view when he 

described succession on sand dunes near Lake 

Michigan. Succession is the change Cowles 

measured (Cowles 1901:108). 

Adams (1908) used Cowles' operational 

approach to def i ne ecol ogi cal successi on of bi rds 

and other animals. Cooper (1926) also used this 

method to document observations of ecological succession for long periods of continuous change. 

Cooper (19261, Gleason (19391, and others (May 

1976) have also used this approach to describe the 

individualistic concept of plant and animal communities. And this operational approach 1~d to 

rapid advances in quantitative methods (Southwood 

1978), 
En science, the changes from usi ng propert i es 

to using operations for defining terms and concepts was apparently brought about by a change 

from a Newtonian do an Einsteinian approach 

(Benjamin 1955), The convention is that terms and 

concepts be defined hy physical nperations rather 

than by properties. This viewpoint is accepted 

by most scientists- For example, according to 

Newton, simrll tanei ty was an ahsof ute property 

of time, which he defined as that which has the 

property of flowing uniformly independent of material happenings. Ei nstei n introduced the idea 

that simultaneity in a system, and time, is relative to the operations of the observer rather than 

a property of the system (Bridgman 1927). 

For ecosystem dynamics the change is from 

studying plant and animal communities as defined 

hy ideal i zed properties, such as the organi smal 

concept described by Phil lips (1934, 1935), to 

studying operati onaf ly the dynamics of structure 

and the functioning of individuals in their 

natural surroundi ngs as described by Tansley 

(1935) and Evans (1976). The change is from 

defining organisms and communities in terms of 

properties, such as a kind of strategy that 

di rects behavior toward a predetermined state or 

structure, to defining organisms, communities, and 

behavior in terms of the operations of the 

observer. 

Ecosystem dynamics are the ti me-changi ng 

f 1 ows of organi sms, materi a1 s, and energy that 

form the units He operationally isolate to study 

and manage. Organisms, thei r inseparable envi rannents, and aggregates of these systems are operationally cal led ecosystems as defined by Tansley 

(1935). These individualistic systems, populations, stands, and communities are the units most 

frequent 1 y de1 i mi ted as ecosystems for management. 
Three ki nds of observations based on experi - ences of many peopf e are the primary sogrces of 

information for developing th~ faur bionsmic 

theories used in DYNASP. Thew are observations 

of the continuity of biof ogica9 change, the irreversi bi 1 i ty of biological changes, and the re1 ation of these changes to the structw-3 of the 

organi 5ms and the ~nvi ronment , 

Continuity 

The continuity of kiological dynamics is so 

much a part of our everyday operadioos it is difficult to believe we are experiencing anything 

significanr, From experience we accept the transformation sf babies into adults and acorns into 

oak trees rather than into chickenss Iqportant 

imp1 ications are in the observation: a biologicaf 

system cannot pass from one state to another 

without passing through 31 1 the intermed-late 

states that are subject to the same processes of 

growth and rnortal i ty, 

This ohservat i on of bi 01 ogi cal dynami cs known 

as the law of continuity is restated in mathematics, An impneatant s%a,racteris"e;ie of continuity 

in both matheriiatics and b-ialsgical systerris is that 

each succeeding state is dependent or: the preceding state- Forest seands and organisms do not 

randomly jump from state to slate; each succeeding 

state is dependent on the preceding state, 

Through the use of operational definitions 

and measurements, we can project the directioq and 

often the rate of transformation, For example, if 

a tree has a diameter at breast height (d.b.n.) of 

6 inches, the projected transformations are for 

the tree to either die or increase in diameter, 

The tree is not expected to shrink, 
Projecti cn i series of successi onai ststes 

from rets'ri.4 e~.oplaad to forest depends on "Lhe 

continuity of ~osystern dynam-i cs. The measured 

species cornonsition of one state of 2 community 

car) he used to suggest and possibly project the 

next state of species composition. Observations 

of the continuity of biological change are used to 

project futukc states of ecosystemss 

Few people expect to find Ponce de ie6n's 

Fouccain of Youth, Feople do not transform from 

adlrlts te children "c babies. The phst~synthetie 

pracess in green plants is not reversed by turning 

off the lights or limiting the amounts 0% carbon 

dioxide and water, Respiration is not the reverse 

of photosynthesis. Reproduction is not the 

reverse of death, Regenerating a forest stand is 

not the reverse of harvesting* Ecosystem dynamics 

are irreversible, 

The natural succession of seti red cropla~d 

from grass to Forest js irreversible, Should the 

land be converted again from forest to cultivated 

land for food crops and be retired again from 

agricul turep the biological systeq would not 

reverse but would again transform from grass to 

foresc. The second series of transformations 

would not be ide~tical to those of the first* 

The specks compos-ition, gene frczijtiencfes, and 

components sf the envi ronment are di fferent for 

each state in both serics sf transformations. 

Structure 

Athletes change their bodies from state to 

state to outplay others because sf the way bones, 

muscles, nerves, and other components are con- 

~ected, Structure sf human hands rJermi ts behavior 

different from the feet of monkeys, Injuries and 

diseases, especially those that change linkages in 

the central nervous system, limit behavior, 

Structure determi nes ecosystem dynamics, 
Organisms are typi saf ly defined by disconLjnuities, Genetic discontinuities, such as those 

within and between interbreed3 ng popui ations, may 

be operationally measured by differences in the 

chemical and physical structure of genes and 

chromosomes, Morphological disconGinuities are 

criteria For assigning a plant or animal to a species, The taxonomic decisions result from the 

operations one performs ts masure mokphol agi cal 

and genetic differences. Assigning two individuals to the same species is relative ta the 

observing system, a person trained in systematics, 

Operational criteria are also used do define 

and measure forest types, communities, sides, 

stands, and other aggregates sf plants and animals, The di scontinuities For these classifications are determined by structural differences, 

Cl assi Fications based on structural di fferences 

are meani ngful because structure determines ecosystem dynamics, For example, the behavior of a 

food chain depends on structure that is defined by 

the prspor"cion sf individuals by species, their 

environments, and how these component parts are 

linked, 

Structural changes in an ecosystem wi ll 

change behavior: thinning a forest usual ly 

changes growth rates of residual trees ; changes in 

the species composition of a stand change the 

habitat for some plants and animals; and changes 

in the age classes of stands change the potential 

livelihood for some plants and animal s, Changing 

gene frequencies hy arti f i ci al sel ec&i on changes 

plant and animal structure for agriculture, pets, 

and sports , Changi ng the ecosystem structure 

di rects ecosystem dynami cs for human benefits . 

If Forests are do proviide sustained mtrltipf e 

benefits, they must be managed as negative feedback systems with a goal and with the potential 

for one or more steady states. To attain this, 

forest management must be compatible with the 
dynamics of natural forests. Theories, validated 

by potential fa1 si fication (Becht 1974; Popper 

1962), can describe the structure of the biological system and thus provide the standards, the 

pri nci pf es, and the guides for analyzing si 1 vi - 

cultural options. Such theories are the frameworks for rational decisions. Following is a 

di scuss i on of four bi onomi c theories . 

Four Bi onomic Theories 

Individualistic System 

Each living organism and its envi - 

ronment form an indi vidual i stic 

system wFth the goal of survival and 

the dynamics of systems with negative feedback loops. 

The goal of survival is described by 

Slobodkin (1975) as "permitting the organism to 

continue in the game." The analogy is to the 

gambler's "ruin game" in which the payoff is to 

continue in the game as Tong as possible. The 

players self-organize in relation to the state of 

the game; each player is an individualistic 

system. 

Behavior is directed by a decision mechanism. 

Thi s mechani sm is genetical ly and envi ronmental ly 

determined and is part of the physi 01 ogical , anatomical , and morphological structure of the indi - 

vidual i stic system. Each indi viduaf istic system 

senses and reacts to its own state and uses past 

or present states to influence actions to adapt to 

the envi ronment . 

Conrad (1979) defines adaptabi l i ty as 

'"bit ity to continue to function in the face of 

uncertainty e'Y~onrad proposes that a reasonably 

objecti ve measure sf maximum, a1 lowable, envi ronmental uncertainty for indi vidual organi sms is 
death. This is another way to say that death 

occurs when a feedback loop fails to maintain an 

essential variable within the limits for life. 

The epic of adaption plays itself out, 

according to Swartzman (1975), in a multi - 

di mensi oned envi ronment , The frame of reference 

For individuals must he the set of envi ronmentaf 

variables, the everyday variations, and the uncertainty of perturbations, Self -orga,lization in 

unpredictable, multidimensioned environments is 

the way organisms attempt to maintain essential 

variables within the limits for life through all 

stages of development, Those indi vidual istic 

systems that survive through the reproductive 

stage may transmit genes to succeeding generations* The actual rate of gene transmission may 

be modified by fecundity and uncertain events 

duri ng the reproduct i ve stage* 

For al l individualistic systems, including 

the large numbers that die before the reproductive 

stage, the negative feedback loops selr-organi ze 

the system in accordance with the goal of survi val . This is not a new theosy but a very much 

condensed restatement of the model of organisms 

developed by Darwin and modified by others (Becht 

1974; Gleason 1939; Machin 1964; Tansley 1914, 

1935; Waddington 1970). This theory is the foundation of the other three biononic theories. The 

dynamic behavior of individual istic, goal -seeking 

organisms of di verse structure determines the 

organi ration of the forest communities and the 

transformations of these communities from state to 

state, The individuality of these systems and 

thei r goal -seeking behavior cause every indi vidualistic system to develop differently from every 

other individual i stic system. Thus, in forestry, 

tree mortal i ty cannot be predicted except as a 

statistical probability for a popufation in some 

specified state or community organization. It is 

difficult to predict which individual seed1 i ngs 

wi 11 li ve in succeeding states* 
Mortality, like other forms of behavior, 

results from the dynamics of individualistic 

systemse Death occurs when a feedback loop fails 

to maintain one or more essential functions, such 

as respi ration, ci rculation, food consumption, 

hydration, or physical integrity, within the 

limits for survival. Failure in a feedback loop 

may result from changes in the environment or 

failure in the structure of the individualistic 

system. This mortality, resulting from the dynamics of individualistic systems, organizes the 

forest community. In the words of Tansley (1935), 

'Though the organisms may claim our primary 

interest, . . . we cannot separate them from their 

special environment, with which they form one 

physical system." Each organism and its inseparable and special envi ronment is viewed as an 

individual istic system uniquely different from a1 1 

other individualistic systems. And the state of 

each system changes with migration, age of the 

organi sm, and changes in the organi sm's envi ronment . 

Each tree and its environment is an individual i sti c system that is self -organi zi ng. Self - 

organization results from negative feedback loops 

functioning to maintain essential variables within 

the limits for life. However, the properites of 

the old and new environment may be used with 

operational measurements to he1 p explain why the 

trees behaved as they did (Evans 1976). Numerous 

examples of unexpected behavior of re1 ocated trees 

are given by Dorman (1976) and Wright (1976). The 

experience is that behavior and responses of indi - 

vidual istic systems are not properties flowing 

independently of material happenings. Thus, tree 

ri ngs may ref 1 ect past envi ronment . 

The operational definition of individualistic 

systems is the measurement used to identify the 

discontinuities, Systematists may use these 

measurements to place the individual istic system 
in a species, Geneticists may use the measurements to place the individual istic system in an 

interbreeding population, These two types of 

cl assi f i cat ions are based on the observed bi ol agi - 

cal characteristics of continuity, i rreversi - 

hility, and structure of the measured elements. 

Individualistic systems arp structured to he 

self-organizing. They do not respond in exactly 

the same way to the same environmental change 

every time the change is encountered. For 

example, people react differently to a sudden, 

loud noise if it is expected than if it is unexpected, Plants respond to a rapid decrease in 

temperature in relation to the preceding temperature regime, Organi sms have parts connected 

to physical channels that transmit information in 

the form of energy and matter to direct the 

responses of the system in relation to the state 

of the system, 

In contrast to self-organization, other 

systems are deterministic. For example, a watch 

responds repeti ti vely to the same envi ronmental 

change, such as winding and setting, every time 

the change occurs. The parts of the watch are 

connected to transmit information about time as 

recorded deterministically. Watches do not selforgani ze re1 at i ve to uncertai n envi ronments. A 

watch cannot sense when it is keeping incorrect 

time and sel f-organize itself to make corrections, 

The watch system, therefore, is deterministic 

because the structure does not include decision 

mechanisms for sel f -organi zat i on ; the watch cannot 

change the information flows across the wheels as 

the external envi ronment changes , 

Decision mechanisms in organisms include not 

only the central nervous system in higher animals 

hut a1 so the anatomic, physiological, and genetic 

mechanisms. Decision mechanisms are observed in 

both single (Koshland 1977) and inulticel led organisms (Horridge 1977). The decision procedures 
are mechanical or chemical . The evidence is that 

organisms are able to self-organize, to change 

behavior in relation to the goal for survival, to 

change information flows internally, and thus 

change states of organization in an uncertain 

envi ronment . These deci si on mechanisms are 

explained by cybernetic concepts (Am. Soc. Cybern. 

1972; Ashhy 1973; Becht 1974; Forrester 1961; 

George 1977; Klir 1969; Kogan 1975; Milsurn 1966; 

Wiener 1961) as discussed in chapter 2. 

Deci si on mechani sms , sensors, and physi cal 

information carriers in organisms include mernbranes in single cells, chemical compounds, and 

physical structure. Effectors (fi g. 7) include 

mt~scl es , membranes, biochemical reactions, water 

movement, ion transfer, and cell movement. 

The decision mechanism decides what is to 

follow in relation to a goal built into the organism by genetics, physiology, and anatomy. The 

overall goal (see fig. 7) built into a1 1 organisms 

is survival. Of course, this goal is composed of 

many di fferent subgoal s and feedback loops. 

Examples are the negative feedback loops that 

maintain levels of blood sugar, balance in animals, and openings and closings of stomata in 

plants. Organisms without the overall goal of 

stnrvi val do not live long errough to play signifi - 

cant roles in the structure of communities, in 

reproduction, and in evolution (Dobzhansky and 

others 1977; Simpson 1969; Stebbins 1974). 

Fai lure of only one of the essential feedback 

loops results in death. "Correct" decisions are 

those that change the state of the organism to 

maintain life. Decisions result in many kinds of 

act ion, such as movement from i rri tati ng substances, feeding, and a change in el ectropotenti a1 

of a membrane. Most survival decisions are determined not hy crises, such as escaping from a 

predator, hut by goals to maintain essential variables, such as the amount of water in leaves, 

within the limits for life. 
kcision and control hetween twn closely 

arranged organi sms both respondi ng to approxi - 

matoly the same environment can now he examined 

(fig. 12). The envi ronment di rectly affects the 

state of each organism and indirectly affects the 

kinds of decisions. Changes in environmental factors--light inteqsity, day length, ions around a 

plant root, and temperature--change the state of 

the organisms. When the information about the 

GOAL OF 

- SURVIVAL 

i 

GOAL OF 

SURVIVAL 

ENVIRONMENT 

Figure 12, --Decision and control between two cf osely arranged 

organisms both responding to approximately the same environment. 

(The lines connecting the organism and the environment are wavy 

to indicate the unplanned effects between the two.) 
stcttr;. of the oryanis:il tloii~s to the dc~cis~on I~ICC~?- 

nism, another change usually occrirs in the stdte 

of the organism* The organism chaqges itself 

in relation to the environment. Ksmoving one's 

finger from a hot stove irflri~eiiiately rather than 

waiting until the state of the environment is 

changed from "hot" to "cool" is an example of 

how envi ron~rlent affects the organisin's selforganization. The environrljent is not a part of 

the negative feedback loop, hut the envi ronrnent 

may change the behavior of the organism by indirectly affecting the kinds of decisions. 

The envi ronrnent is chdngcllrl incidental ly to 

the behavior of the organisms. We have no evidence that trees lose their leaves in order to 

maintain some amount of organic matter and nutrients in the soil * Stomata open and close in 

relation to the stat^ of tho plant and not to 

maintain some amount of water, oxygen, and carbon 

dioxide in the surrounding ai re Conversely, the 

amounts of organic matter and nutrients in the 

soil are unplanned consequences of the states of 

organization. Ri rd nests, termite nests, and 

other st ructrrres bui 1 t by organi sms change the 

environment. This kind of dynamic change by organisms is self-organization; that is, a change is 

made in the chemical and physical state of the 

organi sm in relation to envi ronmental changes to 

maintain essential variables within the limits of 

life. 

Some components of environrnents such as heat, 

water, and oxygen seem to influence all organisms. 

Other elements in the environment affect only 

certain organisms. Whether or not the element is 

an envi roninental factor depends on the physical 

and chemical state of individual organisms. For 

example, col orb1 i nd people respond to wave1 engths 

of light differently from normal -sighted people. 

Sharks and related animals respond to lowfrequency, feehle, voltage gradients that are not 

detected by other organi srns (Kalmi jn 1977). Certai n wavelengths and periods of radiation are 
envi ronmental factors for some organi sms but not 

for others. Each organism and its special environment is di f ferent from every other organi sm 

and its speci a1 envi ronment . 

It is the individual istic system that selforganizes and dies or survives, contributes to 

community structure, and transmits genes to the 

next generation. Sel f-organization is for the 

present state of the organism. The physiological 

mechanisms are negative feedback loops and not 

feed-forward loops (see ch. 2). Sel f-organization 

is directed toward keeping essential variables 

within the limits for life now and not toward 

organizing for some optimum strategy (Lewonti n 

1979). When i ndi vi dual i st i c systems sel f - 

organize, thei r communities transform aimlessly, 

Mi thout a structure that contains negati ve feedback loops to direct members toward a common goal, 

communities do not have a strategy to partition 

envi ronmental resources, to mai ntai n a certai n 

community structure, to occupy a certain habitat, 

or to form a certain gene pool. 

The individual istic systems that se1 forganize through all stages of development and in 

relation to a1 1 envi ronmental changes are the ones 

most 1 i kely to transmit genes to succeeding 

generations. Those individual s that transmit 

genes to the next generati on are "natural ly 

selected"; by definition those individuals have 

the highest Darwinian fitness in the population* 

Natural selection is viewed as self-organization 

of individual istic systems directed by genetic and 

devel opmental processes. 

A clear understanding of this definition of 

"natural selection" is important for di rectiny the 

states of forest organization to provide desi red 

human benefits. No external control mechani sms 

naturally select which organisms are to die and 
which are to survive. Rarwinian fitness 

(Dobzhansky and others 1977) and adaptabi l i ty 

(Conrad 1979) result from self-organization of 

individual istic systems* 

Because of self-organizing capabilities, 

the individual istic system functions in uncertain 

environments, Conrad? ((1979) definition of 

adaptability is: The ability to function in the 

face sf environmental uncertainty. This defini - 

tion implies negative feedhack loops that selforganize the system For the present environment 

and adjust the organism's structi~re as the environment changes, 

Conrad" ((1979) definition can be expressed 

mathematically, Communities of individualistic 

sy%tems consist of ensembles of di stinguishable 

states with the behavior of the former represented 

by a set of transition probabilitiess These Crawsition probabi l ities are recognized as stages of 

succession. Conrad (1979) expresses this mental 

model in mathematical symbol s and demonstrates a 

simul a&i on of envi ronmental uncestai nty a DYNAST 

is a modified system dynamics version of this mentat model. 

The concept sf the indi vi dual i sdi c system 

places the dynamics of all ecosystems trn a physical basis. It requires that interchanges and 

interactions within ecosystems be physical ly 

described in terms of the structure for transfer 

of energy, information, and materials. For an 

ecosystem to exhi bit goal -st ri vi ng behavior, there 

must be physical information networks that iml ude 

sensors, effectors, and decision mechanisms that 

function by comparing the stage of the ecosystem 

to a goal. Also, corrective actions must direct 

behavior of the ecosystem toward the goal. 
Organization oh Forest Cornunities 

The mortality of individualistic 

systems organizes survivors into 

communities without goals and with 

the dynami cs of systems wi thout 

negative feedback loops, 

barge numbers of seeds and other propagules, which 

are individualistic systems, die hy their own 

dynamics This mortal i ty organizes the survi vors 

into successive states, 

These successi ve sf ates have dynamics si mi l ar 

to those of the watch previously described, Rut 

biological communities have ;-9, major difference, 

Watch hands exactly repeat former states, whereas 

biological communities do not, If the genotypes 

and environments are similar to combinations in 

preceding forests, the kinds of states and individualistic systems will be similar but not identical to those of the preceding forest. The 

successi ve organi zational states are i rseversi bie 

and may appear to be repetiti ve, These apparently 

repetitive organi zat i onal states are known as 

stages of succession, 

Forest csmmuni ties are organizational Sy 

unstable because the surviving individual istic 

systems are joi ned without community goal s 

directing negative feedback loops. In the absence 

of such feedback loops, the community cannot sense 

ids own organizational state and cannot use past 

states to influence future actions do achieve a 

goal for the community, The community has no 

decision mechanisms to di rect the li fe, death, 

reproduction, and rep? acement of indi vidual i stic 

systems to achieve a community goal* The forest 

community is an aggregation of srrrvi vors, and ~o 

species is essential for community organization 

because there is no common goal toward which a1 7 

direct thei r behavior, For example, the loss of 
the Ameri can chestnut (Castanea dentata (Marsh. ) 

Borkh.) did not destroyeastern forests. 

The chestnut trees were replaced by other individualistic systems. 

Successi ve generations of i ndi vi dual i st i c 

systems are genetical ly and envi ronmental ly di f - 

ferent from preceding generations and wi 11 never 

again he exactly repeated, In the absence of communi ty sensing and deci si on mechani sms, forests 

continue without goals. 

Thi s theory for community organization means 

that unmanaged forests never come to a steady 

state. In the words of Gaylord Simpson (1969), ". . . if indeed the earth's ecosystems are 

tending toward lung-range stabilization or static 

equilibricrm, 3.5 billion years has been too short 

a time to reach that condition." 

During the 100-year life of dominant trees in 

a forest, mi 11 ions of seeds and other propagules 

die. This organizing mechanism for communities is 

different from that of indi vidual istic systems. 

Communities have no physical communication channels that link the individualistic systems to a 

decision mchanism. Therefore, the forest has no 

centralized decision mechanism that decides which 

species are to survive and which are to die. 

There is no goal for a certain association or for 

a certain distribution of species. Thus, there 

cannot be natural steady states, equilibria, or 

cl imax forests. 

Individual istic systems in a community are 

1 i nked only through the envi ronment (fi g. 12) 

and not through a communication network to a 

decision mechani srn that coordinates behavior, 

survi va1 , and mortd i ty. Coordi nated behavior 

results from simi 1 ar genotypes responding to 

similar environments. 
The theory for organization of forest communities states that order in natural communities 

results from the mortality of individual istic 

systems, Mortality occurs when the individuali stic system fat 1s to maintain some essential 

variable within the limits of life, The dynamics 

of thi s organi zi ng mechanism for cornmuni t ies are 

similar to those of systems without negative feedback loops. 

Aim1 ess systems are operational 1 y def i ned as 

those without information networks that link sensors, effectors, decision mechanisms, and goals. 

A watch is such an aimless system. Information, 

energy, and materials may flow through the system, 

and transformations from state to state may be 

predictable, However, no decision and control 

structure can be physical ly identified, measured, 

and related to a goal for the transformations. 

Forest communities are aimless systems 

because no information networks have been st ructured for decision and control. Communities are 

aggregations of individual istic systems that we 

identify and measure in terms of the distribution 

of species, The structure of communities is 

def i ned by the morphol ogi c, chemi cal , and genet i c 

measures that are meaningful for our purposes, 

These measures describe the community in terms of 

human percept i ons of actual experi ence ; the 

simultaneity of communities is described relative 

to the operations for taking the measurements. It 

is the actual experiences of different people that 

make the concept of communities useful for 

forestry. 

The terms "natural selection," "succession," 

and "competition," imply optimization, di rection, 

purpose, decision, and control in the transformations of communities. Tansley (1935) presented 

evidence for rejecting the "organi smal concept," 

which proposes self-organizing qua1 ities for cornmunities, The idea that ecosystem dynamics is an 
ordered, directed process that leads to predetermined community process characteristics, such as 

stabi l i ty, is an appealing human perception that 

is not supported by operational definitions, 

Succession is sometimes described as an 

orderly process, reasonably di rectional , community 

controlled, and culminating in a stabilized ecosystem (Odum 1975). Both communities and organi sms are gi ven strategies. The succession 

strategy is increased control of the physical 

envi ronment to achi eve maxi mum protect i on from 

envi ronmentaf perturbations, Another view 

i nterprets behavi or of i ndi vi dual s as mechani sms 

that increase the representation sf thei r genes in 

subsequent generations (Wilson 1975). The concept 

of strategy is defined as groupings of similar 

genetic characteri stics that recur widely among 

species and cause them to eexhi bit simi lar behavior 

(Grime 1979), Medawar (1982) proposed that many 

people have a deep-seated sense of purpose of 

things, especially of natural communities* This 

sense 06 purpose, strategy, and stabif ity in ecosystems seems to be supported hy beliefs that an 

organi sm "s behavior should contribute to the 

i nheri tance of progeny. 

"Optimization" is isth jargon for describing 

the organi smal concept. Lewonti n (1979) suggests 

that optimization is 2 convenient way to explain 

ecosystem dynamics in terms of problem solving and 

popul at i on st rateyy Some arguments postuf ate 

that organisms, populations, or communities transfer genes to the next generation through evolved 

phenotypic mechanisms* These arguments explain 

evolution in terms of idealized properties, which 

are not described by operations performed in 

concrete situations to measure the actual happenings. In this approach, evolution is described 

as an aimless process in ecosystem dynamics 

(Dohzhansky and others 1977), 
The aimless dynamics of the forest cornrr~unlty 

result from the aggregate behavior of individual - i stic systems that, as populations, are aimlessly 

evolving. The phrases "natural selection," 

8t~cce~i~n "and ""competition" ddescsi be measured 

changes in ecosystem dynamics, 

Flows of Energy and Haterials 

The flows oF energy, nutrients, 

carbon di oxi de, water, and srgani G 

materi at s are uni di rect i anal and 

have the dynamics of systems without 

goals and without negatlve feedback 

1 oaps 0 

The flows of energy and materials through the 

f andscape are delayed by the icdivibl_ial i stic 

systems that conpose the forests. F1ol.r rates 

are modified by changes in the states of community 

organization. Gycl ing is viewed as an add? tional 

delay ultimately determined hy the dynamics of 

i ndi vi dual s, 

The significance of this theory is that the 

delays in flows of energy and materials are 

brought to a steady state when the state of forest 

organization is brought to a steady state (see 

ch. 1). There are many such states and an equal 

number of flow combinations for energy and materials, Perhaps an inventory of the organizational 

states of the forest, such as that supplied by the 

model (see figs. 4, 5, 6), can eventually he used 

to indicate the delays in the energy and material 

flows. One appficatiov would be to estimate the 

nutrient flow into streams. This information 

could contribute to an index for biological product i vi ty of st reams, 

This theory is supported by information from 

many sources. Consider, for example, the flow of 

radiavt energy to the earth and its eventual dissipation to the universe (fig. 1). The forest 
ecosystem traps small amounts of this energy and 

delays the rates of flows for varying amounts of 

time- Regardless of the delay per-iods and the 

hierarchy of organisms in the communication channel, the direction of flow is always in one direction--4a'ssi patisn ts the universe, 

Cycles, such as the "nitrogen cycle," can be 

concei ved for materials that are not dissipated to 

the universe. However, the flow of nitrogen 

throilgh the forest ecosystem is always in one 

di rect i 00. The hierarchy of organisms through 

which nitrogen flows delays the flow relative to 

the sel fv-organizing capabi 1 .itips of the indi vf dual istic systems. 

The delay and the dissipatson of energy and 

materials by organisms is not easily explajnee' by 

theories or equi l i kri urn thermodynami cs, The di f - 

f i cirl ty is we1 1 stated by Spanner (1964). In an 

cpi log to his book he notes that life as we know 

it is always assocSaled with the delay and the 

dissipation of matter and energy, the two entities 

with which the second law of thermodynamics is 

concerned at equi l i brm urn conditions * Then he 

points out that eveky aspect of fife turns on the 

existence of a. Tack of equilibrium; for instance, 

the ahi l i ty to see presupposes nonequi l i hrium between the radiation the eye is emitting and the 

radiation it is receiving from the outside, He 

observed that if the uni verse is moving inevitably 

toward thermodynamic equilibrium, then as far as 

the biologist is concerned, a1 1 organisms wi lf 

diea 

Ri ol ogi cd processes lead to evol uti on, 

migration, and extinction of life forms with no 

genetic and envi ronmental stahi 1 i ty (Dobzhansky 

and others 1977; Simpson 1969; Spurr and Barnes 

f973), A new stand of trees is not usually the 

repet i t i on of a precedi ng stand, a1 though repet i - 

tive stands may be physically classified as 

seedl i ngs, sap1 -ings, poletimber, mature Limber, 
and old growth. It is a remote probability that 

envi ronments and species wi 11 be repetitive for 

two consecutive 100- to 300-year periods, The 

probabi 1 i ty that newly establ i shed seed1 i rigs wi 11 

have the same yenotype distribution changes with 

mortality as the stands transform from seedling to 

old-growth states (Boyce and Cost 1978). 

Phenomena that do not pass through successive 

equdli brium states are considered in studies of 

nonequi 1 i bri urn thermodynami cs. Thi s area of 

investigation is also ca1 led the thermodynamics of 

i rreversi ble processes (Wi2niewski and others 

1976). Nonequi 1 i brium phenomena are descri bed by 

modern nonequilihrium thermodynamics, This section re1 ies on the monographs by Glansdorff and 

Pri gogi ne (1971) and Nicol i s and Pri gogi ne (1977) 

for most oF the ideas applied to nonequilibrium 

forest states* For additional information, examine the works of Groot and Mazur (1962), 

If the amount of living biomass comes to 

some steady state, the outputs of energy and 

materi a1 s equal the inputs (Vi tousek and Rei ners 

1975). Natural mortality and tree harvest temporari ly increase flow rates of water, nutrients, 

and energy. As stands succeed to the old-age 

organizational state, increased mortal i ty rates 

cause increased energy and materials flow. 

Various forms of perturbation, such as storms, 

insects, diseases, fire, silviculture, and harvest, increase the mortality rates* The result 

is to speed the natural, aimless transformation 

of the forest through a series of successive new 

states (Smith 1977). But, the states are in 

nonequilibrium as are the flows of energy and 

materi a1 s . 

Tf a small opening is formed in a forest ky 

the death of a single, dominant tree, seedling 

growth may increase and new propagules may he 

established, However, an understory seedling or 
sapling may not fill the space left by the tree 

that died; more often, surrounding trees grow 

until their crowns and roots fill the vacated 

space (Roach and Gi ngrich 1968). The forest has 

nod returned do the previous state but has 

shifted to a new state, 

The simultaneous death of conti guous, domi - 

nant trees creates large openings that may he 

fi7 led by ingrowth from the understory, In this 

case, the sprouts and seedlings in the understory may begin to grow rapidly but eventually 

die, New propagules may he established but many 

of them diea Mortality accentuates the fluctuations in numbers of survivors, Survivors in 

large openings continue to grow, often at an 

ivcreasing rate, 2nd may fi I1 the space vacated 

by the dominant trees that died, The result is 

the transformation of the stands from one orgaqizational state to another. Species composition is transforming through nonequilibrium 

states; species composition and flows of energy 

and material s do not reverse, 

Noneqri l i briun conditions resulting from 

mortality lead to unidirectional energy and 

materials flows, The flows through the forest 

conmuni ty are da'ssi padi ve rather than conservati ve and are toward increased entrophy, The 

dynamics of these flows thror~gh the forest are 

wi thsut goals or negative feedback loops, 

Conservati ve PI ows occur iq the negati ve feedback loops of the individualistic systems. For 

example, ions in many plants move from the leaves 

to the stems before the leaves fall, But this 

movement of ions within the plant is not determined by the plant behaving to achieve a god 

for the forest. The negative feedback loops 

function within the plant to maintain certain 

variables, such as ion concentrations, within 

c~rtain limits, We have no evidence that plants 

store and refease ions to achieve an ionconcentration goal for the forest s Organisms 
delay and dissipate hut do not regulate flows of 

energy and materials to achieve a goat for the 

forest , 

Some authors (Dobzhansky and others 3977; 

Simpson 1969) estimate that 99 perceqt of all 

species that ever existed are extinct* As orga- 

 isms evolve, migrate, and hecome extinct, the 

coexistent species that form a state of forest 

organization changee Changes in both envi ronnlents and in genotypes cause each succeeding 

forest stand to differ from the preceding one, 

Thus, the dynamics of forests may he readi l y 

understood and explained by the principles of 

nonequilibrium phenomena. 

This bionornic theory for flows of energy 

and materials is simply a statement of oh5erved 

phenomena that relates the behavior of oryani sms 

and communities to the thermodynamic pri nci pl e 

of increasing entropy of the universe, 

Multiple Benefits 

The states of fokest organ1 zat i on 

determine the kinds and proportions sf multiple benefits available from a foresto 

Many variables define the physical organization of 

a forest area: plant and animal spec-ies, aye and 

size of the dominant plants, amount of shrubs and 

herbaceous plants, depth of the li tter, volume of 

accumulated wood, amount and proportional di stri - 

bution sf nutrients, physl'cal struchure of the 

soil, biomass, and the flow sf nutrients and water 

through the forest. Different combinations produce a large variety of habitats For plaots and 

animals and a mrrespondingly large variety of 

potential human henefi"i iinclud-ing scenic values, 

hartting, hiking, timber, and strearnffow. It is 

the proportional distribution of habitat type that 

provides a particuf ar cornhi nat ion of benefits, 
The sigqifica~ce of this theory is: changes 

in organizational states of a forest determine the 

partictlf ar combinations of sustained henefi ts produced, There arp as many patterns of benefits as 

there are patterns of physical organi zation 

States of forest organization are operationally described in terms of stand type, size, 

and age class, We can recognize and measure 

stands of seedlings, saplings, poletimber, mature 

timber, and old growth by forest types, And we 

can measure the areas and the ages sf the stands, 

which are dependent on the time and the size of 

openi ngs formed by natural mortal i ty and timber 

harvest. For convenience, we call these stales 

habitats (Elton 1949), 

Seedl i ng stands provide a potential li vel i - 

hood for plants and animals that do nat occur in 

older stands- Old stands provide for the livelihood sf plants and animals that do not exist in 

younger stands. Yet, some organisms exist in a1 1 

states of forest org2nizatione Furthermore, the 

area and the dispersion of stand types influence 

the quality of the habitats for plants and animals, For a given forest area, the distribution 

and the areas of seedling, sapling, pole, mature, 

and ol d-growth stands deterrni ne the potential for 

a live1 i hood of most endemic plants and animals. 

Nonequilibrium environmental conditions 

appear conducive to a diversity of genotypes and 

to the continued natural evolution of the largest 

number of endemic species. A highly constrained 

and uniform environment limits the number of species, limits the range of variation for selforganization, and may speed the natural rates of 

extinction, 

The multiple benefits theory describes 

natural rates of evolution, migration, and 

extinction in relation to the organization of 
habitats. Each habitat combi nation provides particular human benefits, such as timber, water, sr 

hunting opportunities, 

Validation of the Bionomic Theories 

One way to validate the bionomic theories 

is to put them to practical uses that wi 11 either 

confirm or deny them, In forestry, this method 

requires a large number of periodic observations. 

It is incorporated into the management system 

through monitoring, research, and inventories 

(fig. 3). Statistical methods and empirical 

research are used to evaluate ref at i onshi ps 

described by the theories. Thus, the value of the 

theosi es and the management structure is exami ned 

by day-ts-day experience as we1 l as by specialized 

sci ent i f i c and techno1 ogi cal methods , Constant 

iteration of the control loop (fig. 3) helps to 

apply technology, val idate theories, and improve 

deci si ons, 

The di rect method for mai ntai ni ng congruence 

of the mental models with the real forest requires 

ref at i ng the theori es to changes in macroscopi c 

phenomena such as pl ant and ani ma1 reproduction, 

growth, and mortal ity. Model predictions are compared with changes in the real forest. Following 

the comparison, the theories are accepted, 

rejected, or modified, The direct method is traditional for practically a1 1 methods of forest 

management* 

This approach reduces the dynamics of forest 

ecosystems to a description of human perceptions 

of the actual happenings, gives the mental models 

the validity of actual experience hy different 

people, and reduces mysticism in both the physical 

and mental models, 
Another way theories are validated is to 

state a critical hypothesis that would falsify the 

theory if found to be true (Becht 1974; Popper 

1959, 1952), Examples of such k~ypotheses are 

stated for each of the bisnomic theories, Until a 

critical hypothesis is found to be true, the four 

theories are not Falsified and can be used to 

construct forest management systems (fig. 3). 

Individualistic Theory 

The theory Psr indi vidua l istic systems wauld 

be fa1 si f ied by bi scovery that any individual i sti c 

system? existence, mortality, or behavior is for 

the exclusive achievement sf a goal for the forest 

community * Proof of "ti s hypothesis requi res evi - 

deuce Par a central i zed, comuni ty deci si an mchani srn and communication channels to control and 

4i rect the behavior of each individual to benefit 

the enti re community, 

Organization Theory 

The theory for organization of forests would 

bc fa1 si Pied by di scoverj~ of a control and cnmmunication mechanism that senses the states of 

conimurrity organization, makes deci sians, and di rects the mortal 5ly and behavio~ sf organisms to 

achieve organizational goals for forest corn- 

%unity. This cri"lca1 i~ypatijesis is "Ltle same as 

for falsificatisn of tke individualistic theoryD 

This hypolkesis, however, emphasizes an infori~ation network that senses the organizational 

state sf the forest, whereas the preceding !;ypothesis emphasizes an informatino network that 

senses the behavior of individualsl Proof of both 

hypotheses depends on di scovery of physical cornmuni cati on networks and deci si on mechani sms that 

direct the behavior sf each individual to benefit 

the enti re forest, 
Flotrrs Theory 

The theory would be falsified by discovery sf 

sensing and decision mechanisms in the community 

that regulate the inflows, internal status, and 

outflows of energy and mterials to achieve organizational goals for the forest community. This 

hypothesis depends on the discovery of an information network that monitors the energy and 

materials flows and a decision mechanism that 

directs behavior of the individualistic systems to 

regulate the flows to achieve a goal * 

Hultiple Benefits Theory 

The theory for multiple benefits would be 

falsified by discovery that a1 1 potential benefits 

are simultaneously available from a single state 

of community organization, This hypothesis requi res proof that any si ngf e hahi tat coal d provide 

all potential benefits, 
Silviculturaf Applications 

Overview 

Forests are cultured to provide from one to 

many henefi ts singly and in different combination~~ Silviculture, based on scientific infarmatioa and experience, is the use of cut turd 

practices to enhance the benefits perceived to be 

in the sel f-interest of the interested parties, 

"Interested parties" incl udes a1 l people who are 

interested in how a forest is cultured--from joint 

l andowners, a landowner and a timber buyer, a 

management team for a forest industry, a forest 

supervisor and the staff, Forest users, pub1 ic 

forest managers, a board sf directors, to a 

forestry consultant and a c7ient. Bencfi ts 

include commodity items used in commerce, such as 

timber, and noncommodity items not used in cornrnerce, sucll as Iriking and bi rd watching, The 

kinds sf benefits expected may incf ude wi lderness 

experiences only, timber only, habitat for a 

single game animal, or any number sf combinatio~s~ 

Silviculture is the use of knowledge and 

technology to direct the behavior of the selforganizing, indi vidual istic systems that make up a 

forest, The directing actions or cultural practices are designed to guide the ecosystem and 

order the forest to produce biological f y possi b? e 

and desi red benefits, 

In this chapter f examine some concepts oF 

Lhe si l vicutural appl icalion of ecosystem 

dy nami cs a 

Bionomic Theories That Under1 ie Si l viculture 

Forest ecosystems are enormously cornplex because large numbers of variables change 
simultaneously Yet because of sel f -organi zat i on 

of individual istic systems, only three control 

variables are required to systematically order 

ecoqystem dynamics of forests--conversi on of 

forest types, rates of timber harvest, and sizes 

of openings formed hy harvesting, Ibis orderly 

way to traosform the forest From ow state of 

organization to another creates predictable, timechanging states. The states are temporal and spatial dispersion of habitats that can be used to 

project the potential For any and a1 l forest benefits, singly and in combination. 

The theory of multiple benefits (see ch* 3) 

relates benefits to states of forest organization. 

The theory of organization of forest corilmuni t i es 

relates states of forest organization and stand 

structure to mortality* Mortality is related to 

sel f -organization of the indi vidual i stic systems 

theory. Control l ing timber harvests, openi ng 

sizes, and conversions of types are effective ways 

to di rect ecosystem dynami cs and achi eve desi red 

states of forest organization* This approach is a 

modificatispm of many si lviciaftural practices used 

for hundreds of years- The new approach is more 

responsive to social and economic changes and is 

simpler do use than past practices, It provides a 

way to plan the future avai labi f i ty of benefits 

singly and in different combinations, a way to 

integrate the knowledge of ecosystem dynamics 

and the technology for silviculture, and a way 

to project biol ogical fy possi hle combinations of 

benef i ts 

The uniqueness of the bionomic theories is 

that they are operational, The theories are 

stated in terms of control and communication mechani sms for organi sms and comnuni t i es of nrgani sms 

The theories descri he cybernetic relations within 

and among organisms and provide the basis for 

1 sgical si l vicul tural actions. Management deci - 

sions founded on this approach to silviculture 

convert "ce ai in1 ess dynamics of unmanaged forests 

to a goal-oriented management system, 
GOAL FOR 

MANAGEMENT 

UNCERTAIN 

ENVIRONMENT 

\ / 

\ / Pa / Research / 

--,' Monitorings .-" \ 0" L Inventories , I 

=---. .--. 4 -.-- 

Figure ~~.--HOW a state of forest organization is made a single 

goal for silvicultural applications (Boyce 1978b)- 

The management goal (fig. 13) is determined 

by consensus of a1 1 interested parties (see also 

fig. 3). The particular combination of benefits, 

those projected by the DYNAST model and chosen by 

the interested parties, determines the organizational state toward which the forest is to be 

moved. This particular state is the management 

goal for the next planning period, about 10 years. 

Once the interested parties reach a consensus, the 

control loop is activated (fig. 3). Then management of a specific forest becomes the responsibility of persons trained in the various forestry 

disciplines such as hydrology, wi Id1 i Fe biology, 

si? viculture, soils, engineering, pathof ogy, entomol ogy, recreation, and landscape architecture. 

These specialists are included under the word 

"managers. 'I 
The control loop is a cybernetic system that 

functions for a planning period or until some unusual event, such as wi ldfi re or storm, requires a 

new decision, Managers use their professional 

knowledge, experience, and insight to choose specific stands for harvest, to shape openings, to 

identify stand areas that do not exceed the range 

of opening sizes used in the selected DYNAST simulation, and to implement in-place enhancements. 

The harvest rates and opening sizes projected by 

DYNAST are parts of a dynamic plan that guide the 

decisions of managers to apply the si 1 vicul tural 

practices needed to bring the forest to a single 

goal, whi ck is the desired organizational state, 

Si lvicultural applications modify both the 

state of the forest and the environinent (fig. 13). 

Sef f -organiza"cisn of the indi vidua! i stic systems 

in relation to direct and inciirect effects of 

silviculture contribute to changes in the forest Is 

organizational state. Research, moni torings, and 

i nventori es inform managers how the forest is 

transforming. This information is used to adjust 

the silvicultural applications that keep the 

forest transforming toward the goal. 

Steady-State and a Regulated Forest 

A forest is in a steady-state whenever the 

distribution of stands by forest type, age, and 

area classes is constant* The forest is dynamic 

in that each age class transforms to an older 

class and the oldest class transforms to the 

youngest class, Such a condition is maintained 

by a constant timber harvest rate and an equally 

constant regeneration rate, As time approaches 

infinity, oscillations in the distribution of 

stands by age and area classes are insignificant. 

The steady-state condition does not occur in real 

forests because of di fferi ng harvest and regeneration rates and unpl anned disturbances such as fire 

and insect attacks. 
A typical silvicrlltural problem is to bring 

the distribution of age classes toward a desi red 

steady-state. When at the steady-state, a forest 

is said to be regulated (Ford-Robertson 1971). 

Most forests deviate from the desi red steady-state 

because of inadequate time under management, 

catastrophic events, land use changes, revised 

management objectives, or cha~ges in social and 

economic forces. The steady-state is a goal for 

planning purposes. It is a goal toward which 

silvicultural actions are directed but one that is 

rarely achieved in a real forest (fig. 13). 

Sustained Benefits 

The quantity of timber that can be removed 

year after year from a forest is determined operationally by the distribution of stands by forest 

type, age, and area. An enormous number of 

possi hle stand distributions wi 11 provide a sustained, annual removal of a quantity of timber. 

Achieving the sustained harvest goal is determined by success: (1) in bringing the stands to 

the reqt~i red physical distribution of age classes 

and (2) in bringing about harvest and regeneration 

rates that mai ntai n the age classes within acceptable limits of the required distribution. The 

latter condition is a steady-state distribution of 

age classes that is rarely achieved and difficult 

to maintain, In reality the age class distribut ion osci l lates as management decisions respond 

to the effects of wildfire, insects, diseases, 

weather, and social and economic changes. 

The value of the sustained benefits concept is to set a single goal for the next planning period toward which cultural actions are 

harmonized. 

When the harvest rate is set, the physical 

distribution sf stands by age classes is also set. 
In order to have harvestable stands available for 

a sustained timher yield, a minimum amount of area 

must be kept in younger aged stands. This minimum 

distribution of age classes for annually harvesting a quantity of timber is determined biologically by the tree species, the soi 1 productivity, 

the geographic area, the cultural practices, and 

the climate. 

The minimum distribution of age classes 

required and the quantity of timber that can be 

removed annual ly determi nes the rate of transformation from the present state of forest organization toward the desired state, It is this "near 

future" transhrmation period that determines the 

availability of timber and other benefits, And it 

is this next decade of transformations, directed 

toward a distant goal, that is of special interest 

for choosing cultural actions and for determining 

the availability of benefits. 

Transformations toward the steady-state 

determine not only the quantity of timber but also 

the quantities of a11 other forest henefits that 

can be enjoyed and continuously removed from a 

forest. The combinations of habitats for a1 1 

plants and animal s, changes in st reamf l ow, movements of sediment and nutrients, and a1 1 other 

benefits including the quantity and kinds of 

timber are functions of the harvest and regeneration rates (arealyear) and the sizes of openings 

(area) formed by harvest, 

This relation is the common denominator for 

producing any desi red combination of benefi ts. 

The state of organization is the objective identified in a land management plan, The objective 

i s a goal , a real i sti c way, to plan for continued 

benefits, But the combination of benefits actually available in the near future is determined by 

the initial state and the transformations through 

which the forest must pass in the next Few years 

to achieve the goal. 
The choice of a management mode for the next 

planning period is influenced by the benefits 

expected in the distant future, the goal, and the 

benefits expected in the near future, The latter 

combinations of benefits, which may dominate the 

decision, are determined hy the essential transfor~nations from the present state (ch. 3). The 

explicit model must and should display these 

transformations and the associated benefits 

Selective Msrtal it y 

Mortal i ty is the primary mechani sm that 

brings about the transformation of genetic and 

ecological structure, Selective mortality is used 

in si 1 viculture to direct orderly changes in the 

states of forest organization, 

Stand dynamics is driven by mortality, which 

results primari ly from the mechanisms of selforganization. Mortal ity may result from human 

plans, natural destructive forces, or the inabi 1 - 

ity of the organisms to maintain some essential 

variable within the limits for life, Mortality 

not only influences the kinds and proportions of 

genes transmitted to the next generation but also 

organizes the stands and the forests. 

The most effective cultural technique is to 

selectively change mortal i ty rates of forest organisms, Thi s practice modifies mortal i ty associated with sel f-organization. Regulating wi id1 ife harvest, timber harvest, stand thinning, 

removal of undesi rable insects, and use of herbi - 

cides and fungicides are a1 1 examples of practices 

for selecti vefy changing mortality rates. These 

cultural techniques speed the transformation from 

one stand structure to another, initiate the 

beginning of new stands, and change the species 

composition of stands, The operational signi fi - 

cance of these relationships is that transformations can he ordered, future organizational 

states can be planned, and the future availability 

of benefits can be projected, 
Many hi stori cal concepts of succession, the 

transformation of stands frorn state to state, were 

hased on some preconceived state of stdkiiity 

toward which transformations in such things as 

species, hi onlass, soi l development , energy ef f i - 

ciency, and diversity were inevitably moving the 

plant and animal communities. These hypotheses 

are considered improbable kecarlse we have no pvidence for the existence of n corrtcrrunity infornlation 

network that 1 inks sensors, ef fect,ors, and a goal 

to a decision mechanism. The transfortnations that 

are measured by variables such as timber volumes, 

hi omass, speci es di versi ty , energy cf f i ci ency , and 

soi l devel opment are quant i tati vely expl ai ned by 

the behavior of individualistic systems. Individual istic systems, sel f-organizing and dying in an 

uncertain environment, drive the transformations 

that we call succession. Silviculture can speed 

these transforrnation rates. 

Once we interpret succession as a visible 

result of organic evolution (ch. 3), we can beyin 

to explain observed transformations in terms of 

speci f i c phenotypes sel f -organi zi ng in response to 

uncertain environments. This is not totally new 

to forestry. Most silvicultural plans are hased 

on projecting the performance of phenotypes, species, or improved genotypes in relation to harvesting, site preparation, ferti 1 ization, stand 

density, or some other variable. Gonnell and 

Sl atyer (1977), for example, describe succession 

in terms of the behavior of individualistic 

systems--phenotypes develop and die in an undirected sequence of envi ronrnental changes. Thei r 

models are made operational by projecting succession in terms of species facilitation, tolerance, and inhibitive properties. These phenotypic characteristics are further subdi vided into 

attributes useful for projecting transformations 

in relation to past observations (Cattelino and 

others 1979). This operational approach leads to 

useful projections of both single and mu1 tiple 

succession pathways. 
Once a new stand is heyun in either unmanaged 

or managed forests, differential mortal ity rates 

among the individualistic systems determine the 

transformation rates of the stand from one condition class to another. The differential mortal i ty rates may result from planned si 1 vicultural 

actions, self-organization that fai 1s to maintain 

essential variables within the limits for life, 

and envi ron~nental uncertai n i t i e s such as storms 

and droughts. Once observed for some historic 

period, many transformations can be predicted 

within limits of error acceptable for decision 

and control . Such predictions are based on the 

expected sel f -organi zation of individualistic 

systems in an uncertain environment; thus, the 

predi ct i ons are not determi ni sti c Peri odi c 

i nventories and rnoni tori ngs, possi bly at 5- to 

10-year intervals, are required to adjust the predictions in relation to the real transformations. 

Selective mortality is the most useful and 

most effective si 1 vicul tural practice. Almost 

every si 1 vicul tural action involves selective mortal ity to favor or eliminate a phenotype. This 

changes the rates and the directions of natural 

transfortnations and changes competition and 

natural selection (ch. 3). The specific techniques include thinning stands, preparing 

pl anti ng sites, addi ng fert i 1 i zers, drai ni ng 

soi 1 s, ki 11 i ng certain insects and disease organi sms, rernovi ng and ki 11 i ng animals , harvesting 

game animals, and harvesting timber. 

Timber harvest is the cultural practice that 

most effectively and efficiently changes mortality 

rates and directs ecosystem dynamics. 

Biological Potential 

The b'ological potential is defined as the 

amount of dne or more benefits that are expected 

as a result of certain cultural practices (Royce 

1975). The upper limits of the biological potential cannot be estimated accurately. Manipulating 
genotypes and cu1 tural practices has increased the 

biological potential for specific kinds of benefits such as timber, water, recreation, and wild1 ife habitat. Knowledge ahout photosynthesis 

rates, water availability, incoming radiation, 

nutrient suppl i es, and respi rat i on rates tend to 

confirm our intuitive feeling that the biological 

potential has an upper limit. However, it is more 

important for the practical decision and control 

process to understand the biological potential in 

relation to investment and cultural practices than 

to attempt to establish a hypothetical upper 

limit, 

In practice bi ol ogi cal production of benefits is limited more often by the cost of investments for manipulating genetic mechani sms and 

cultural practices than by the biological constraints. Most si 1 vicultural research and development is carried out with the expectation that 

findings, when applied, wi 11 produce a lower cost 

benefit, an improved benefit, a new benefit, or 

some economically favorable combination of benefits. Basic research di rected toward increasing 

the biological potential in relation to cultural 

practices leads the way for research and development to enhance the economic effectiveness of 

silviculture, The result is an increase in the 

biological potential resulting from step-by-step 

improvement in the environment of the forest or an 

improvement in the plant and animal genotypes. 

A primary concern in forestry is not to 

reduce the resources that determine biological 

potenti a1 . For example, when cultural practices 

increase soil erosion and nutrient losses, the 

biological potential may decl i ne. The biological 

potential may be mai ntai ned by increased invest - 

ments in mechanical actions, ferti 1 izers, and 

water, For some resources such as genetic diversity of plants and animals, cultural practices are 

designed to create habitat diversity in hope of 

maintaining the potential livelihood of al 1 or 

most endeni c speci es , 
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the gaps or openings formed in the canopy. The 

land area harvested for thinnings may he estimated 

or indicated by the crown area or the basal area 

sf the stems removed, 

Areas expected to be harvested are easily 

projected for a few decades. These projections 

are used to indicate the stand distribution by 

forest type, age, and area classes. These projected organizational states deterni ne the avai lability of benefits at each moment. Scheduling the 

harvest rates is the primary control variable for 

projecting the future avai labi 1 i ty of di fferent 

eombi nati ons of benef i t s . 

Rotation periods for a fixed area are a convenient way to express harvest rates, The rotation period is the approximate age when the stands 

of trees are scheduled for harvest, For a given 

forest the scheduled harvest rate is the total 

forest area di vi ded by the rotation peri od 

Single Rotation Period 

Conventional ly , si ngl e rotation peri ods have 

been used in forestry to produce different farest 

types and different site classesl The concept was 

to plan the successive tree harvest froin the same 

l and at approximately the same age. The criterion 

for a single rotation period is to produce a maximum amount of timber for a given forest type and 

site class. 

Consider a forest of one type and with a 

narrow range of site Classes. From experience and 

research, 80 years is the harvest age known to 

provide the largest amount of a given kind of 

timber (fig. 14). That single rotation age at 

which a11 stands are harvested determines the 

future organizational state of the forest if the 

purpose is to produce a certain kind of timber. 

Maturg stands older than 80 years, old-growth 
stands, and natural removals would not be permitted. The forest is transformed toward a state 

of organizatioo in which each age class occupies 

an area equal to the total area allocated to 

timber harvest divided by the rotation age. 

The single rotation method produces a certain 

kind of timber in the shortest possible time. No 

consideration is given to bringing about a dispersion of age and area classes of stands for producing a desired combination of benefits. Each 

stand area is scheduled to flow through a single 

Years I 

I 

Harvested 

Stands 

Figure t4,--A single rotation period is used when stands are harvested Prom the saw laod at approximately the same agel Diversity 

of habitats and benefits are limited ts thsse provided by stands 

younger than the rotation age. 
cycle of transformations such as from seedling 

stands to 10-inch pole stands and be harvested at 

the single rotation age. 

The controls for a single rotation period are 

the single harvest age, the opening sizes, and the 

forest types formed by regeneration. The single 

rotation period can provide for a 1 imited number 

of combined benefits (Boyce 1978a). The number 

and kind of benefits available are incident to the 

primary benefit, usually timber, that is the basis 

for choosing the rotation period, 

Superimposed rotations, a1 so cal led dual 

rotations, are mu1 tiple rotation periods for 

the same piece of land. For example, a stand 

harvested at age 80 will pass through seedling, 

sapling, and ather stages until id reLurns to 

the 80-year age class (fig. 15). At this time 

the stand nay be harvested or permitted to transform to a 100- to 200-year-old stand. After the 

harvest of old-growth stands, the next harvest 

from the same area may be at 80 years or even 

younger. The rotation periods are superimposed 

on the same piece of land for succeeding generations* The stand ages at harvest may vary for 

each generation. 

Superimposed rotations are the only way to 

create a diversity of age and area classes for 

any and a1 1 possible benefit combinations. Every 

biologically possible stand age and area can be 

scheduled, and combinations of benefits can be 

projected in relation to the transformation of the 

forest Prom the present to a future organi zat i anal 

state. 

The controls are: the number of superimposed rotations, the harvest age for each rotation, the proportion of area cycl i ng through each 

rotation, and the sizes of openings. For example, 

consider three rotations of 80, 100, and 200 years 
(fig. 15). The harvest rate for these rotations 

can he changed hy changing tho propcrtion of area 

cycling through each rotation. When 80 percent of 

the area is cycled through 80 years, 10 percent 

through 100, and 10 percent through 200 years, 

most of the habitats will be in age classes less 

than 80 years. Changing the proportions of the 

forest cycl i ng through the superimposed rotations, 

the size of areas harvested and the rotation 

periods permits an enormous number of different 

states of forest organization. 

Harvested 

Stands 

Figure 15.--Superimposed rotation periods are used when stands are cycled through different ages for harvest on the same piece 

of taod. Diversity of habitats and benefits are not limited by 

stand age. 
In-Pl ace Enhancements 

Many cultl~ral actions reqtii re decisions made 

in-place by a person trai~ed in one of the forestry disciplines. These cultural actions are 

opportunistic in that they depend upon the state 

of the forest, the avai 1 abi 1 ity of investment 

funds, local markets, or demands for certain benefits. Some examples are thinning, pruning, 

leaving den trees, making water holes, locating 

trai 1 s, applying ferti l izers, and weeding. 

Hecai~se such measures are planned by someone on 

the scene, they are termed "in-place." These 

actions cannot be easily predicted and are not 

i ncorporated into the pl anni rig model . 

One important in-pl ace deci si on for game 

animals is to schedule annual timber removals 

within the range of the animal's mobility. The 

di spersi on of openi ngs can be somewhat control l ed 

by changing the harvest rates and opening sizes, 

hut someone who knows the area's topography must 

review the specific sites with this principle in 

mi nd. 

The results of any in-place enhancements 

should become readi ly apparent in the inventories 

of habitats and in the assessments of the algorithms. For example, if thinnings in pole stands 

speed the transition to mature-timber habitats, 

this change should be apparent in the inventories. 

It is not realistic to plan a thinning schedule 

far into the future in hopes of achieving some 

specific goal. Such plans can rarely be implemented because of changes in social and economic 

forces, techno1 ogy , and benefi t preferences. A 

better approach is to view those cultural actions 

other than harvest rates and opening sizes as 

opportunistic because in-place actions are taken 

to enhance benefits already made available by 

these, 
Thinning is the process of making openings 

small enough to limit the formation of a new age 

class yet large enough to increase the diameter 

growth of the residual trees. Practically a1 1 

research and controversies about thinning revolve 

around the size and number of openings needed to 

i ncrease benefits and timber val ues . For exarnpl e, 

the questions of whether to thin from below or 

above the average canopy level, how much basal 

area to leave, which species to keep, and when to 

thin are concerned with the monetary value of 

timber and the change in other benefits. 

Thinning may change benefits other than 

timber. For example, if openings are large in 

thinned southern pi ne stands, understory hardwoods 

will increase thei r growth and provide more food 

and shelter for deer and other animals. These 

values may he provided in ways other than by thinning. For example, hardwood pole stands interspersed with pine stands could provide adequate 

feeding areas for some kinds of birds, and hardwood seedling habitats may provide bro~se for 

deer. 

A7 1 of these enhancements depend on a large 

number of vari abl es that are di f f i cul t to project . Thus, the rational approach to thinning for single 

or multiple values is to use DYNAST to simulate 

thinnings and to update the dynamic plan whenever 

moni tori ngs and inventories indicate an enhancement in henefi ts. 

Changes in Genotypes and Type Conversions 

In some areas increased production of timber 

and other benefits can be achieved by changing 

genotypes and converting forest types. A type 

change can result from practices to favor diff erent species or genetical ly developed genotypes. 

These practices depend on the manager's ability to 

control the kinds of genotypes that regenerate 
after harvest, to increase the mortality of competing species, and to protect the desired genotypes from fire, insects, and diseases. Opportuni ties for making these kinds of investments 

typically occur on a small portion of natural 

forest l and that has conditions suitabl e for 

cul turi ng improved genotypes. 

Forest types are changed primarily to enhance 

timber values, but type changes also may enhance 

other benefits. For example, by using silviculture to organi ze natural , intensively cultured, 

and other artificial stands by type, age, and area 

classes can increase the diversity of habitats and 

the potential livelihood for many plants and animals. Artificial ly establishing stands among 

natural stands is a way to maintain borders with 

di sti nct ly contrasting age and type classes, 

Natural Reaeneratian 

Most of the world's forests result from 

natural regeneration, which is defined as stands 

ori gi nat i ng from propagul es formed by precedi ng 

stands and dispersed by natural events. Cultural 

practices can modi fy natural regeneration processes to favor desirable species. The most important cultural practice is to schedule the opening 

size formed by harvesting. This practice and the 

many associ ated modi f i cations are often termed 

"si 1 vicul tural systems" or "regeneration systems." 

The opening size formed by harvesting timber 

i nf 1 uences but does not control the species cornposition of the next stand, The opening size 

affects the potential livelihood of different species by changing mortality rates for the propagules. For example, opening size in coastal 

Doug1 as-fi r (Pseudotsuga menzi esi i (Mi rb. ) Franco) 

forests influences soil surface temperatures, 

1 i ght , soi 1 moi sture, and frost (Hi 1 1 i amson 1973). 

A1 1 of these variables influence the mortality 

rates for the propagules of Douglas-fir and all 
other plants. The rates cannot be predicted 

within acceptable limits of error; thus, it is 

di f f i cult to predi ct the species composi ti on of 

future stands of Douglas-fir in relation to 

openi ng sizes. Some general i zati ons about expectations are useful when determined in-pl ace. 

In some forests the opening size may be chosen based on anirnaf populations, For example, in 

the northern hardwoods the availability of browse 

for a given deer population influences the choice 

of the opening size (Marquis 1972). 

The most obvious generalization from reports 

for each of 37 forests types is that scheduling 

opening size and harvest rates cannot assure a 

preconcei ved composi ti on of natural regenerat i on 

(USDA FS 1983h). A second general ization is that 

harvested forest stands almost always naturally 

regenerate to another forest stand rather than to 

a grass or shrub stand. The regeneration delays 

and the species composition of the succeeding 

stands may be influenced by the opening sizes and 

the harvest rates. Additional practices such as 

disturbing the surface litter and soil during harvest, prescribed burning, di sking, removing the 

slash after harvest, and di rect seeding of desi red 

species can contribute to achieving a desirable 

kind of successi ve stand, but these enhancements 

are not deterministic because individualistic 

systems are sel f-organi zi ng. 

It is likely that most of the world's forests 

will continue to be naturally regenerated for many 

decades. This situation coupled with the uncertainty of precisely predicting the species cornposition of natural ly regenerated stands prohi bits 

the use of dynamic plans dependent on predicting 

species composition for successive generations. 

For this reason the OYNAST planning model projects 

the distribution of stands by type, age, and area 

classes in relation to opening sizes and harvest 

rates. 
If regeneration naturally or artificial ly 

changes species composition sufficiently to change 

the forest types, the model is appropriately 

adjusted. Monitorings and inventories at 5- to 

10-year intervals are the sources of information 

for adjustments. Most of the conventional 

"regeneration systems" and si 1 vicul tural systems 

can he expressed in the WNAST model by harvest 

rate, openi ng si zes , and del ays. These simul at i ons 

of change are not viewed as models of the 

"regeneration systems." 

Relation of DYNAST Model to 

Regeneration Systems 

The DYNAST model simulates the classical regenerati on systems of si 1 vicul ture (Assrnann 1970; 

Davis 1954; Hiley 1954; Smith 1962; Troup 1952; 

USDA FS 1983b). DYNAST includes and perhaps supersedes these systems. It explores the consequences 

of manipulating the rotation period and opening 

size. Actual ly, the classical regeneration systems 

can be described by these two variables as modified 

by in-place enhancements. Properly considered, 

such apparently antithetical methods as "clearcutting" and "selection" appear as points on a continuum. Removing a selected large tree leaves a 

small opening in the forest; clearcutting a tract 

leaves a large one. Likewise, the "shelterwood" 

si 1 vicul tural system can be analyzed into harvest 

rates, opening size, and removal delays. 

Other features by which the classical regeneration systems are classified seem to be based on 

erroneous assumptions. We speak of " h i g h - f o r e s t " 

methods, which are supposed to produce stands 

grown from seed, and "coppice-forest" methods, 

which are supposed to produce stands from sprouts. 

But in the real forest, naturally regenerated 

stands contain trees originating from both seeds 

and sprouts. Sprouts can grow into tall, dominant 

trees. Likewise, we speak of "even-age" versus 

"uneven-age" management, but except for plantati ons, there are no even-aged stands. 
The dynamically analytic silviculture technique enhances the classical systems. DYNAST is 

not limited by arbitrary categories but can exami ne thousands of possi b1 e combi nations of rotation periods and opening sizes, assess the wider 

range of benefits produced under a multiple rotation system, and direct attention to the biologi - 

cat ly possible combinations of sustained benefits 

that can be provided by harmonized management 

act i ons 

Selection Regeneration System 

Mature and immature trees are selected and 

harvested singly and in groups to form openings of 

small and indeterminate size. The openings are 

small for "group selection" and often are 

arbitrarily limited to 5, 3, or 2 acres. In 

si ngl6-tree selection the openings are approxi - 

mately equal to the crown area, which is related 

to basal area sf the harvested tree. The harvest 

area is the enti re forest or some area that includes the nonharvested trees. Harvest rates are 

control led and measured by the timber volumes 

removed as related to the residual volumes, by a 

limit on the diameter of trees harvested, or by a 

formula, such as "Q values" (Wenger 1984), for 

removing trees from a1 1 diameter classes to bring 

the diameter distribution of an area to a preconcei ved standard. 

The mental model presumes a forest structure 

in which every tree, except the largest and 

ta1 lest, is a candidate to replace the next 

largest tree if it is removed. The dynamics of 

the real forest cannot function in this way 

because trees are rooted in place. They do not 

move to fill space vacated by harvesting; rather, 

each i ndi vi dual i sti c system sel f -organi zes re1 ative to whatever environmental changes are wrought 

by harvesting. There are no known information 

networks and deci si on wchani sms to structure 

diameter and age distributions for successively 
replac1 ng harvested trees, Expectations of successi ve rep1 acemend by ecosystem dynamics are di f-. 

ficvlt to fulfill when harvesting is controlled by 

vol ume, diameter 1 i mi t , or a preconcei ved standard 

for diameter di st ri buti on . 

The selection system favors understory trees, 

which usually have distorted stems and crowns, as 

the next dominants. These understory trees and 

the seedlings and sprouts that have survived in 

small openings are represented by a high proportion of tolerant species. These tolerant species, 

such as American beech (Fagus randi fol i a ~hrh. ) , - 

sugar map1 e (Acer saccharum Miheastern 

hemlock (Tsuga canadensi s (L. ) Carr. ) , can sel forganize to survive periods of low light intensity, low soil moisture, low nutrients, and 

allelopathic chemicals formed by plants or by 

organic decomposi tion . Some species, classed as 

intolerant to the conditions of closed canopy and 

small openings, may survive to become dominant 

trees . 

Over several generations small openi ngs wi 11 

usually favor the tolerant trees, shrubs, and herbaceous plants. Note that it is not the method 

for control 1 i ng harvest rates but the opening si ze 

that influences the establishment, survival, 

growth, and eventually the kind and the shape of 

trees that occupy the overstory. Opening size is 

an effective variable for projecting, as in 

DYNAST, future states brought about by the selection system. 

Group Selection Regeneration System 

Group sel ect ion is appl i ed to the set ecti on 

system when openings are larger than whatever 

arbitrary limit is assigned to the "single-tree" 

selection definition. Typical ly, group-selection 

openings are less than 10 acres and may be as 

small as half an acre. 
Because of the relatively small openings 

formed, these slection systems are often named 

"uneven-age management." The mental model is a 

forest brought to a state in which all annual age 

classes are represented in small stands. The 

smal ler the stands, the more difficult it is to 

achieve the mental model in a real forest* In 

practice the selection systems represent the 

small-opening cornponent of a continuum of regeneration systems. 10 DYNAST the selection systems 

are sirnuf ate4 as the small openings in a continuum 

of opening sizes. 

Shelterwood Regeneration System 

This system is characterized hy tirned rernoval 

of the overstory as a way to provide a few years 

of 'khhet ter" for seedlings and sprouts, The overstory nay be removed in two or mare successive 

harvests at 1- to several -year intervals, The 

mental model is a stand of desirable species in 

which the rapid growth of seed1 ings and sprouts 

begins at about the same time. The term "evenaged" is applied to the new stand, although the 

root systeins may he from 1 year to more than 10 

years old. 

The opening size after tfte last refnova1 

may he small but is usually larger than 5 acres, 

Harvest rate for the forest is controlled by the 

area and the number of openings. The opening 

sizes created by shelterwood systems overlap the 

larger openings formed by selection systems. 

DYNAST simulates the forest state by inserting 

opening sizes (area), harvesting delays (years), 

and harvest rates (acreslyear) into the model. 

Seed-Tree Regeneration System 

This system invol ves leaving the minimum 

nunher of seed-producing trees that wi 11 resuf t in 

a new stand* This rnethod is used for intolerant 

species-elhose in~apable of self-organizing when 
under a canopy to survive periods of low light 

intensity, low soil moisture, and low nutrients. 

The mental model is similar to that for the 

shelterwood system. 

Clearcut Regenerat ion System 

This system identifies an area from which all 

trees larger than about 1 or 2 inches d.h.h. are 

removed or felled. The opening size is the area 

harvested. Harvest rate is controlled by the size 

and number of openings. The mental model of the 

new stand is essentially the same as for the seedtree and the she1 terwood systems. 

The new stand originates from seed stored in 

the litter of the preceding stand, sprouts, seedlings established under the preceding stand, and 

seeds deposited on the area after harvesting. 

Intolerant and fast-growing seedl ings and sprouts 

are usual ly favored. Tolerant species may survive 

under the faster growing intolerants and some of 

the tolerants may eventually become dominant 

trees. 

Opening size may range from I to 100 acres or 

more. Classifying an opening by which regneration 

system was used is based on arbitrary definitions 

of the systems. The kinds, growth, and amounts of 

seedlings and sprouts after harvest are related to 

the opening sizes that form a continuum from the 

1 argest sizes formed by she1 terwood and clearcutting systems to the smal lest openings formed by 

removing a single tree. The DYNAST model simulates the transformations of organizational states 

based on a continuum of opening sizes in conjunction with classical regeneration systems. 

Both large and snal 1 openings may be scheduled to create a mosaic of stands by type, age, 

and area classes, Even the smaf lest openings 

formed by remrav-r'ng a single large tree typically 
result in a new small stand. These small openings are often called "gaps," especially if formed 

by natural mortality. Differences in species composition and growth rates are continuums across 

the largest to the smallest openings. Criteria 

for measuring harvest openings can he based on the 

contiguous basal area removed, the contiguous 

crown area removed, or the contiguous land area 

exposed. Meani ngful di f ferences among the cl asses 

of openings are the correlations for species composition, growth rates, hahi tat potential for 

various plants and animals, aqd the timber harvesting cost. This operational definiti on is used 

i n the DYNAST model . 

PI anni ng invol yes predicting the dynamics of 

a system in relation to actions. Assumptions 

about the future state of a system constrain both 

the number of variables and the rates and directions of changeo The conventional variables of 

forest management are marketing, production, 

i nvestment, and si 1 vicul ture* Addi ng vari ah1 es 

that describe the potential livelihsod of individual game animal s, nongame wi 1 dl i fe, endangered 

species, and recreation opportirni t i es increases 

the complexity of decision making. The decision 

may not be "yesi\r ""no"Yor different amounts of 

present net value but could be a confusing comhi nation of matrices and interaction coefficients, 

The solution is to use simulation techniques 

that incorporate in-place decisions in the tactical processes and provide for a1 7 interested 

parties to use judgment as we1 1 as scientific data 

to make decisions, The judgment decisions are 

delayed until after the mathematical analyses are 

completed, fhis approach limits complexity in the 

c~mmunication of alternatives; aids the subjective 

use of insights; speeds the discarding of unacceptable a1 ternati ves ; provides for periodic 

adjustment of decisions as social , economic, and 
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Opti tnal analyses based on li near programming 

concepts are limihd din management science by the 

tact i cal p! anni ng processes (Ei 1 on 1980). What is 

desired is an integration of quantitative and subjecti ve techniques to ! imi t complexity and aid 

deci si on and control in the strategic management 

processes I 

Constraint CP6 Complexity 

Complexity is constrained in the DYNAST approach by the subjective choice of a single goal, 

namely a forest's organizational state. The enormous variety of large numbers of benefits and 

cultural practices is absorbed hy the equal vari - 

ety of hahitats that are dispersed spatially and 

temporally by the harvest rate and the opening 

size. FsanagemenL actions do not; attempt to match 

benefits on a one-to-one basis. Rather, they are 

used ts direct the natural dynamics of the ecosystem to bring about desired organizational 

states . 

One important constraint sf complexity is 

to relate the availabi lity of each benefit in a 

management unit to the distribution of various 

"habitats." A habitat is an area that possesses 

uniform physiography and stand condi tians (El ton 

1949), where plants and animals live (Odum 1971); 

some organi sms reqd re several habitats. The 

stages in a stand" development are habitats; 

a simple classification of them might inclt~de 

seedling, sapling, pole, mature timber, and old 

growth* Each such habitat is more Favorable to 

certain species than to others (USDA FS 1979, USDI 

Fish and Wi ldlife Service 1982). The opening size 

Formed by timber harvest determines the size of 

the habitat at all stages of the stand" development and also the quality of the habitat For certain plants and animals* 

limher harvest plays a dual role in the 

system, The timber rernoved -is a her7efi t ; the 

seedling habitat that follows is the beginning of 
a sequence of new habitats* Because new stands 

transform at a predictable rate, the distribution 

and size of habitats at any time in the future can 

be estimated if the timber harvesting rate and the 

opening size are known. 

Compl exi ty is const rai ned. The avai 1 abi 1 i ty 

of a combination of benefits, including timber, is 

determined by the distribution of stands by age, 

area, and type classes. The goal of silviculture 

is a desired distrihution of habitats on the management unit . Recause habitats transform autonomously from 

state to state, only certain distributions can be 

maintained by s i 1 vicul ture. Ecosystems are never 

static, It is impossible, for example, to have 

only sap1 ing and mature-timber habitats for a long 

period of time. The saplings eventually become 

poles and the mature timber rnay reach sufficient 

age to be considered old growth. Changes in the 

age for harvesting and the use of dual rotations 

coupled with control of the opening size provide 

many pessi ble habitat combinations , For example, 

delays in harvesting mature timber increase the 

amounts of tree seeds, hard mast, and dens. Dual 

rotations provide fast rates of timber harvest 

from some areas, large trees for high-value timber 

from other areas, and habitats for many plants and 

animals that live in both old and young stands, 

The opening area determines the future stand area 

and thus influences the quality of the habitat for 

both plants and animals, The transformation rates 

in age and area classes are determined from hiological knowiedge, Thus, the displays of habitat 

di stri hutions are those that are biological ly 

possible, 

Tramlat ion Precess 

What we want is to translate, in mathematical 

symbols, biological information into a new structure for forest management. The translation is to 

retain the original biological sense and limit the 
complexity for using the information in decision 

and cont rsl . For example, the organi zat i onal 

state of a forest and the associated benefits can 

be expressed in mathematical terms, 

Consider the time to be t and the time before 

it to be t-1. The difference is a small designated DT interval such as 1 day. A quantity of 

timber and other benefits, a, could accumulate in 

this interval and a quantity of timber and other 

benefits, r, could be removed or used in the same 

interval , Each increment or decrement is defined 

as an addition or subtraction to the cumulative 

quantity of timber and other benefits as stands 

flow through the number, n, of DT intervals, and 

thus through age cf asses . Mi thout management and 

use there i s s t i l l an increment and decrement 

(chs. 2, 3). For each quantity of increment there 

is a j age class and an i benefit and each addition must relate to a certain time interval DT. 

The whde range of ossibte accumulations is 

represented by aj j ( . Similarly, removing a 

quantity of timber or other benefit is specified 

in the same way ri j(t-')e The range in age class 

distribution is hj for benefits including timber. 

The fol I owi ng expression descri bes the forest at a 

steady-state distribution of age classes. The 

accumulations equal removal s for a1 1 benefits and 

are independent of time when DT is small. 

The accumulation sign indicates the range for the 

system of equations represented by this expression 

for all possible benefits from the forest, 

The definition of these equations is the subject of subsequent chapters. The unknowns to be 
calculated are: (1) the transformations of the 

forest's organizational state from time to time, 

and (2) the benefits available at each state of 

transformation. 

Bi 01 ogical informati on about the potential 

livelihood of plants and animals is translated 

into structures, such as yield tables, and then 

used in the simulation process. These kinds of 

transl ati ons provide informati on about di fferences 

in benefits and the potential livelihood of plants 

and animals. The future number of plants and animal s, timber volumes, and present net values are 

projected for several decades. A1 1 of these vari - 

ables are related as indices to changing states of 

forest organization in relation to silviculture. 

The organizational state of the forest-- 

the proportional distribution of stands by age, 

area, and type cl asses--and the transformations 

from various states determine the avai 1 abi 1 i ty of 

benefits. The state of forest organization is 

the common denominator for transl ations and for 

deci si on and control . 

Chapter 5 

States of Forest Organization 

Overview 

A forest is viewed as a dispersion of stands 

that can be classified by age, area, and forest 

types. The stands transform from state to state 

naturally and as directed by silviculture. These 

transformations change the proportions of the 

stand classes and the way the stands are connected. For example, the transition areas between 

stands change as stands age. The dispersion of 

stands by type, age, and area classes defines the 

organizational state of a forest at a given time. 

The state of organization is the common denominator for comparing the amounts of different 

benefits. It is the cornmon connection among benefits. This connection can be defined in terms of 

the kinds and proportions of stands that provide 

more or less of one benefit in relation to another 

benefit. Benefits in DYNAST are not directly 

dependent on each other. The model requires no 

i nteraction coefficients among benefits, Benefits 

are projected independently of each other in re1 ation to transformations in the organizational 

states of a forest. 

This chapter describes how organization and 

structure are used in DYNAST to project benefits 

i n re1 at i on to a1 ternat i ve management modes. 

Structure is the way parts of a system are 

connected. The walls of a building are connected 

to form spaces for working, cooking, eating, 

playing, resting, and sleeping, The value of the 

benefits derived from a building are determined by 
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For example, a potential timber index may be 

developed from existing yield tables. Monitoring 

the yields from harvested stands and new research 

provide information for making the a1 gori thm more 

congruent with a speci f i c forest. Moni tori ng and 

research in the control loop are important sources 

of information for improving the a1 gori thms 

(fig. 3). 

An important concern for the decision loop is 

to compare different benefits and impacts in relation to a common denominator (fig. 3). When a1 l 

benefits of interest are commodities, then dollar 

values are established and money can be the common 

denomi nator for deci si ons . For commodities , the 

amount of each benefit multiplied by the market 

value establishes a basis for choosing the organizational state. If money is the basis for choice, 

the interested parties are expected to choose the 

DYNAST simulation that projects the largest net 

present value, highest realizable return rate, 

largest profitability index, or other measure of 

the monetary value. 

However, many benefits are noncommodi ties-- 

benefits are used or enjoyed without having an 

established dollar value. These kinds of benefits cannot be equated with the market price of 

1 umber. Noncommodi t i es may be pri ced in arbitrary 

ways such as estimati ng "money foregone"; esti - 

mating the cost for enjoying the benefit such 

as the money paid for binoculars, clothing, and 

travel ; assi gning the hi ghest do1 lar values that 

interested parties will accept; and by finding out 

what some people think a noncomodity is worth. 

These kinds of evaluations are arbitrary and subject to disagreement among the interested parties. 

The dollar values assigned to the different benefits predetermine the choice of silviculture when 

the decision is controlled by the money values 

of the benefits (fig. 3). A lack of consensus 

occurs because the il lusive values assigned to 

noncommodities cannot be proved by research and 

monitoring. 
An approach based on what is biot ogical ly 

possi ble provides a way to compare amounts of 

benefits in relation to the costs for ail benefits. The bases for decision are the optional 

benefit conrbi nati ons that are bi o? sgieal ly possible relative to aggregate costs, For this 

approach, each benefit --i ncl udi ng monetary 

returns--is re1 ated to the organizational state 

of the forest, The DYNAST simulations display 

these amounts of benefits as indices ranging from 

0 to 1, This kind of value function is computed 

by the appropriate a1 gorithm. 

One advantage to this approach is that 

algorithms are based on experience and research 

information. The mathematical re1 ations are 

displayed as simple charts easily interpreted by 

people in di f ferent di sci pl i nes. And, each a1 gorithm is d i r e c t l y dependent on an organizational 

state of the forest that can be defined, measured, 

and manipulated. Disagreements among interested 

parties can be assessed, but not necessarily 

resolved, by information from mclinitori ng and 

research. 

Another advantage is that all benefits, 

i ncl udi ng cash flows, are operational ly re1 ated 

to a common denominator. Benefits change due 

to transformations in organizational states and 

are only incident to changes in other benefits, 

The state of forest organization is the common 

denominator for both commodity and noncommodi ty 

benefits. 

The next sections describe how information 

from research and experience is used to develop 

algorithms that relate amounts of benefits and 

impacts to states of forest organi zalion. 

The purpose of the algorithm for timber yield 

index is to provide information for decisions and 
for directing the use of control variables. 

AI gori thms are not suitable for accurately predicting timber volumes (Avery and Burkhart 1983; 

Clutter and others 1983; Husch and others 1972). 

What is desired is a simulation of differences in 

the amounts of timber expected under different 

management modes. Yield tables based on age, 

site, and stand density are as useful for this 

purpose as any other prediction technique. A 

basic assumption for the timber yield algorithm is 

that errors associated with unexpected weather 

conditions, tree survival in regenerated stands, 

mortality rates, and changes in species composition will have approximately the same future 

effect for a1 1 management modes* This assumption 

is not always true becatlse an old stand may he 

damaged by some agent more than or less than a 

young stand. Projections are useful primarily for 

assessi ng a1 ternati ve management modes, 

Once a new stand is initiated in either 

unmanaged or managed forests, relative rates 

of mortality among the species present determine 

the transformation rate of the stand from one 

condition class to another* Because of the similarity of genetic codes from generation to generation, the transformat ion rates can be projected 

within acceptable limits of error. 

For example, a yield table for timber relates 

an expected amount of timber to a future organizational state--the area and age class of stands. 

The yield table is used to estimate changes in 

timber volumes nhen states of forest organization 

change. Yield tables and their use are described 

i n numerous pub1 ications (Avery and Burkhart 1983; 

Clutter and others 1983; Husch and others 1972). 

A timber yield index if lustrates how algorithms 

for timber can be developed. 

For example, growth culminates at 50 years of 

age on 211 sites for mixed hardwood stands in the 

Big Ivy area near Barnardsvil le, NC (Schnur 1937). 

Age 50, therefore. is used as a reference age. 
The index is referenced also to 100 percent stocking and to an average site index of 70, Yields 

for different management modes are compi-rted and 

compared with the yield of 100 percent stocked, 

50-year-old stands on areas nith a site index of 

70, Of course, other ages, stocking, and site 

indices co~ld he us~d as a reference point for 

maximum timber production, 

The reference yield selected is 2,680 cubic 

feet for all Lrees 0.6 inch d,h,h. and larger, 

excluding bark, age 50, density class 100, site 

index 70 (Schnur 1937, Table 34)- This 2,680 is 

divided into the values for all ages, density 100, 

and site index 70- This procedure produces index 

values, called vol ume factors, that are plotted 

over age (fig. 16). 

0 10 20 30 48 50 60 70 BC 90 I06 110 i20 133 140 I50 160 

AGE (Years) 

Figure 16.--Volume factors extrapolated t~ 160 years for site 

index class 70, density class 100 (Schnur 1937, Table 341, 
At 50 years of age the volume factor is 1, 

As stands age, both the yield and the index 

increase but at a decreasing rate. We do not have 

enough information to accurately predict increments beyond 100 years. Thus, the yield index is 

extended at a conservative rate to 160 years. 

Site index variations are related to timber 

yields, The reference value, 2,680, is divided 

into values for site indices 40 to 80, for age 50 

and density 100 (Schnur 1937, Table 34). The 

results are plotted as a site index factor over 

site index (fig. 17). Few areas on the Big Ivy 

area have a site index of 90. The curve for site 

iqdex factor is extended to 1.4 although no 

research data are available to support this value. 

0 

30 40 50 60 70 80 90 

SITE iNDEX 

Figure 17.--Site index factors plotted over site index classes 

for site index 70 and density class 100 as the base (Schnur 1937, 

Table 34). 
Figure 18.--Stand density factors plotted over stand density 

index referenced to site index 70 and density class 100 (Schnur 

1937, Table 34). 

Stand density variations are related to 

timber yields. The reference value, 2,680, is 

divided into values for stand densities 50 to 130, 

for age 50, site index 70 (Schnur 1937, Table 34). 

The results are plotted as a stand density factor 

over stand density (fig. 18). Most stands in the 

Big Ivy area are in the 90 to 100 density classes 

although some thinned stands may have a density 

class between 70 and 90, 

If we know the age, site index, and density 

of a stand, the timber volume can be estimated by 

interpolating the factors from the appropriate 

curves and multiplying the factors by the reference volume, 2,680. For example, the volume estimated by this method for a stand age 80, site 

index 60, and stand density 90 is about 3,000 

cubic feet (1.47 x 0.81 x 0.94 x 2,680). Schnur's 

table value is 3,045, Differences between the 
interpolated and the table values are due to 

rounding and because the tables are only approximately harmonic. 

The interpolated values are usually within 5 

percent of the table values and are well within 

acceptable limits of error, An error of 20 percent is acceptable because of the uncertain future 

environment. It is likely that the real error for 

projecting volumes into the future is as high as 

40 percent of the volumes in the yield table 

(Schnur 1337, Table 341, 

The advantage of translating the yield tables 

into the indices is that these relations can be 

used in the continual simulation compiler DYNAMO 

(Pugh 1983). Values are interpolated between 

points an the charts. The reference volume, 

2,680, can be changed to a different value without changing the indices because the tables are 

approximately harmonic. The indices can be easi ly 

changed to make them congruent with a specific 

forest, Most important, a potential tirnber index 

can be derived and related to the simulated organizational states of the forest. 

Potential Tiaer Index (BTI) 

The potential timber index is the ratio of 

the timber volume projected for harvest hy the 

DYNAST simulation and the volume that would be 

expected for maximum, sustai ned timber production , 

The equation is: 

where: 

PTI = potential timber index 

(dimensionless) 

VT = timber removed from all habitats 

(cubic volume) 

TIM = timber maximum for sustained 

annual harvest (cubic volume) 

t = time 
The projected timber volumes, VT, are derived I 

from the DVNAST simul ation and are determined by I 

harvest rates used in the management modes being 

considered. The general equation for each habitat 

harvested is: 

where: 

VTW = volume of timber removed from a 

habitat (cubic volume) 

RML = area of habitat harvested (area) 

YST = yield standard being used, i .e. 

2,680 (cubic volume) 

VF = volume factor, (fig. 16) 

(dimensi on1 ess) 

SIF = site index factor (fig. 17) 

(dimensionless) 

SDF = stand density factor (fig. 18) 

(dimensionless) 

t = time 

The timber volume expected for maximum, 

sustained timber production, TIM, is determined by 

the forest type area and harvest rate. The harvest rate, measured by the rotation period, determines the yield standard, YST. For example, if 

the goal is to produce the largest, sustained 

cubic vdune and site index averages 70 and stand 

density averages 100, 50 years is the rotation 

period and the yield standard is 2,680 cubic feet. 
Vhe general equation for TIN is: 

TIM = (TAH/TMR)*YST*VF*SIF*SDF 

where: 

TIM = timber maximum (cubic volume) 

TAH = total area of habitats for a forest 

type (area) 

TMR = timber rotation for kind of 

timber desi red (years ) 

YST = yield standard being used 

(cubic volume) 

VF = volume factor (fig. 16) 

(dimensionless) 

SIF = site index factor (fig. 17) 

(dimensionless) 

SDF = standard density factor (fig. 18) 

(dirnensi on1 ess ) 

If small saw logs are desired, the timber 

rotation, TMR, is changed to 80 years. The yield 

standard then becomes 3,950 cubic feet far average 

site index 70 and average stand density index 100 

(Schnur 1937, Table 34), 

It is very difficult to predict which stands 

will be harvested (see ch. 4). The dynamic plan 

projects areas for annual harvest from each habitat, but in-place decisions are made to choose 

stands for harvest in relation to the social, economic, and envi ronmental situations, For pl anni ng 

purposes values can be randomly assigned to harvested stands for the site index and stand density 

factors. Such stochastic approximations are not 

expected to coincide with the real values. For 

example, the randomly chosen site index factor 
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SITE INDEX CLASS 

Figure 19.--The frequency of plots for five site classes for the 

yield tables (Schnur 1937). 

Monitoring and periodic inventories, however, 

provide information ahout average stand densities 

for the stands being harvested, The variable SDF 

is omitted from the algorithm* Average stand density is included in the calculations by changing 

the value of the yield standard, YST, The yield 

standard is 2680 for a forest with average 

stocking in the 100 percent class, site index 70, 

and timber maximum rotation of 50years6 If this 

forest is consistently thinned to keep the average 
stand density near the 70 percent class, the yield 

standard would be 2135 (Schnur 1937, Table 34). 

The yield standard, VST, can be used to reduce complexity in the PTI algorithm* The yield 

standard selected for a particular forest is determined by the (1) rotation period, TMR, for the 

kind of timber desired, (2) the average site index 

for the specific forest being simulated, and (3) 

the average density of stands being harvested, 

Additional 'Tine tuning" of the model to the forest results from using information from moni - 

torings, research, and inventories to change the 

yield table. 

Small Openings Affect the PTI 

The potenti at timber index is reduced whenever the average opening size is less than 3 

acres. This reduction in PTI compensates for 

reduced seedling growth; for injuries to residual, border trees ; and for the higher costs for 

harvesting small areas, 

Timber harvest from small openings injures 

the crowns, roots, and trunks of surrounding 

trees, The losses may not be apparent at harvest, 

but in time the total yield is reduced (Biltonen 

and others 1976; bland and others 1976). 

It is difficult to remove a large tree 

without creating an opening of at least 0.2 acre. 

The natural felling of trees in old-growth stands 

forms openings of this size (Williamson 1973). 

Growth is slow for seedlings, sprouts, and 

residual trees in small openings (Marks 1974; 

Roach and Gingrich 1968; Trimble 1970, 1973). 

Growth is also retarded near the border of an 

opening. As the opening size decreases, the 

ratio of area to perimeter decreases. In a 

3-acre, Gi rcui ar openi ng, there are 100 square 

feet of area for each foot of perimeter, In a 
0 0.5 1.0 1.5 2.0 2.5 3.0 

SIZE OF OPENING (SO), (Acres) 

Figure 20,--The small-opening factor, SOF, adjusts timber 

volumes for losses when opening size is less than 3 acres. 

0a5-acre opening, the area per perimeter foot is 

only 40 square feet. The limitation on growing 

space caused by this border effect is insignificant for openings larger than 3 acres, but it 

rapidly becomes more important for smal fer openings. In the timber algorithm, the size of 

opening factor (SOF) adjusts for losses on a curve 

that approxi mates the decreases in perimeter when 

opening size declines (fig. 20). 

The SOF factor is used as a multiplier in the 

equation for computing the timber volume removed, 

VTH, The opening-size factor has a value of 1 for 

opening sizes of 3 acres and larger, For these 

lhger openings the timber volumes are not adjusted by the opening-size factor. For openiqgs 

less than 3 acres the opening-size factor reduces 

the estimated volumes relative to the ratio of 

area to perimeter, 

Timber volume losses associated with openings 

less than 3 acres are only partially measured at 

harvest time. The largest losses are in the adjacent stands. The injuries increase in size due to 

growth of wood-decaying micro-organisms and stain 

formationw* The algorithm could include these 

delays, but they would probably not affect the 

dec i s i on and cont rsl process. 
c------ 

---- J 

Example 2 

0 10 50 90 1 30 

TIME FROM PRESENT (Years) 

Figure 21,--Potential timber indices projected from time zero for 

two si lvicultural modes. (Chart is copied from a plot made by the 

DYNAMO cornpi ler.) 

PTI Pro jeet ions 

PTI is simple to calculate. Values can he 

computed by using appropriate values from the 

diagrams (figs. 16, 17, 18). Calculating the PTI 

values by hand is laborious when projected for 

many years and when several different harvest 

rates are considered. A computer can speed the 

cal cul ati ons. 

Two exampf es of PHI projections are i l lustrated (fig. 21). The DYNAST model is used; the 
displays are modified from those typically pro- 1 

ducod by the DYNAMO compiler (Pugh 1983). B 

I 

The first example for projecting PTI envisions a silvicultural mode to harvest the oldgrowth stands and eventually harvest timber only 

from mature stands at 80 years of age. The timber 

vol une removed approaches the maximum yield before 

time 50 years and then decli~es to a potential 

timber index of about 0.9 of the maximum yield 

that would be obtained at a rotation period of 50 

years. The reason for the high index between time 

5 and 50 is that the old-growth stands have higher 

volumes per acre than the 80-year-old, mature- 

.timber stands. The old-growth stands are harvested during the first 50 years at a rate that 

will rapidly bring the forest to the desired organizational state, After 5n years the sale of oldgrowth timber declines. At time 130 years, the 

harvest is from RO-year-old, mature-timber stands. 

The PTI is ahout 90 percent of the timber volumes 

that could be obtained by harvesting when the mean 

annl~al increment culminates, age 50, and the 

forest is at a steady state. After about 130 

years the index approaches steady state at about 

0. 9. 

The next exanpfe for projecting PTI envisions 

the organizational state of a forest to maintain 

ahout 25 percent of the area in old-growth stands 

and to harvest timber from old-growth stands at 

age 300 and from mature-timber stands at age YO. 

For the first 30years timber sales are made only 

from malure-timber stands. During this period the 

potential timber index increases to 0.6 of the 

maximum. From time 30 to 75, safes of old-growth 

stands are increased, The total volumes removed 

increaw and the potential timber index is maintained at about 0.7 of the volumes that could be 

ohdained if the rotation period were 50 years. 

The timber volumes expected for the 10-year 

planning period are much greater for the first 
than for the second example (table 1). In the 

second example more large-di ameter trees are harvested after time 30, The lower volumes harvested 

during the first decade in the second alternative 

are the result of old-growth stands being accumulated. These of d-growth stands may be desired for 

some reason as a part of the future organizational 

state of the forest* 

What is important is not the predicted volumes but the differences in the volumes. These 

differences are the bases for decisions, The 

calculations and displays for timber alone would 

Table 1.--Volumes of timber expected to be harvested during the first 10 years for two alternati ve management modes 

Years from Fi rst Second 

i nventory a1 ternati ve a1 ternati ve 

Total 

Thousands of cubic feeta 

'Cubic feet to a 4-inch top for ful ly stocked 

stands, site index 70 (Schnur 1337). 
likely result in decisions equivalent to those 

derived from other analytic mthods, What is 

unique about the dynamic analytic mthod is that 

any benefit that can be quantitatively related to 

organizational states can be plotted and directly 

compared with other benefits, including timber. 

Calculations of indices for benefits other 

than timber are based on the same principles as 

those used to project timber volumes. Data origi - 

nate from research and mnitoring. The data are 

translated into a usable format, such as a yield 

table. For wildlife habitats the format could be 

a habitat evaluation table, Comparisons are based 

on using a common denominator--the forest's organizational state--and displaying dl Pferences on a 

common scale from 0 to 1. The next sections examine this translation process for selected 

variables 

Water Yield Al aorithm 

Predictions of water yield in relation to 

future states of forest organization have uncertainties similar to those for predicting any 

future event, However, information from experimental watersheds can be used to project differences in streamf l ow expected for di fferent organi zat ional states. These projections are not 

expected to be precise for a specific future year 

because unpredictable variables such as r a i n f a l 1 

and radiant energy reception due to variations in 

cloud cover affect the estimates, What we are 

concerned with is making a choice between alternati ve management modes in re? at i on to differences 

i n streamfl ow and differences in other benefits , 

Experience and research leads to the mental 

model that watersheds with maximum forest cover, 

which have hi gh potenti a1 s for evaporation and 

transpi ration, have minimum water yields relative 

to precipitation. Maximum water yield is expected 
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Dobglass and Swank (1975) equation to estimate the 

annual yield in inches of water, The first-year 

increase is estimated by the equation: 

Q = ~(B/I)' 

where : 

= increase in water yield (acre-inches) 

a = constant with the value 0.00224 

x = constant with the value 1.4462 

€3 = proportion of basal area removed by 

harvesting (percent) 

I = insolation index with the value 0.27 

for the Big Ivy area 

Water yield for a given stand area increases 

during the first growing season after timber is 

harvested. The increase in water yield over that 

of undisturbed stands declines to approximately 

TIME SINCE HARVEST (Years) 

Figure 22, --Accumulated increases in streamfl ows estintated for the 

Big Ivy forest, by time since harvest of 100 percent of the basal 

area of a stand; average slope is about 14 percent and faces west. 
Table 2.--Increases in streamflow expected on the 

Big Ivy forest, North Carolina, after harvest of 

100 percent of the basal area 

Years since 

harvest Annual Accumul ated 

zero about 18 years after harvest (tab1 e 2). For 

a stand area in Big Ivy, if we plot the accumulated increase per acre in water yield for 100 

percent basal area removed by years since harvest, 

we have a relation that can be translated into an 

algorithm in relation to the forest's organizational state (fig. 22). 

No information is available about how opening 

size affects water yield. The algorithm is based 
on the assumption that all opening sizes contri bute equally to water yield and that the area harvested is the sum of the opening areas formed. 

The change in yield follows the curve for a Bvalue of 100 perce~t (fig. 23). The equations 

are: 

MY1 = NV/ (Typl x MSWI) 

WY = (SEWI x Al) + (SAWI x Bl) 

SEWI = SEW/DA1 

sawr = (SAWN - SEW)/DBI 

where : 

MY1 = water yield index (dimensionless) 

WV = annual increase in water yield 

from enti re forest type (acrefeetlyear) 

Typl = the forest area of Type 1 (acres) 

NSWT = maximum sustainabf e increased 

water yield (acre-feet/year) ; 

this constant for the Big Tvy 

area is 0.417 

SEW1 = water from seedling habitats 

(acre-feet /year) 

A1 = area of seedling habitats (acres) 

SAWI = water from sapling habitats 

(acre-feet /year) 

B1= area of sapling habitats (acres) 

SEW = water increase for seed1 ing habi - 

tats, read from the chart (fig. 

22) (acre-feet /year) 
DA1 = delay for seedling habitats (years) 

SAWM = maximum accumulated increase for 

harvested areas, read from the 

chart (fig. 22) for age 18 years 

(acre-feet/year) 

DB1 = delay for sapling habitats 

(years 

1 .o 

0.75 

- 3 s? r 

.P 3 0.50 g 

L 

s 

3 p--.------ 

---d 

- - -# 

0.25 Example 2 

0 

0 10 50 90 I 30 

TIME FROM PRESENT (Years) 

Figure 23.--Water yield indices projected from time zero for 

two si lvicultural modes. (Chart is copied from a plot made 

by the DYNAHO compiler.) 
Over time, monitoring and research data can 

fit the algorithm more closely to the forest's 

behavior . 

The water yield index is simple to calculate 

and can he done quickly by using the DYNAST model 

to project organizational states and to relate the 

water yield index to these states. Two examples 

are illustrated. The organizational states are 

those used to illustrate the potential timber 

index (fig. 21). 

The increases in water yield are related pri - 

marily to the increased areas in seedling habitat 

as'the timber harvest rates increase. Both the 

potential timber index and the water yield index 

are based on transformations in the forest's organizational states. Timber harvests may be used to 

direct the transformations, but the transformations and the indices change in relation to the 

dynamics of the ecosystem. 

Sediment Movement A1 gori thm 

Particles of sediment flow through the landscape at varying rates, which are affected by 

s 1 ope steepness, wi nd, water movement above and 

be1 ow ground, and geologic substrate. Forests 

retard sediment flows. The flow rates of both 

organic and inorganic particles are delayed by 

the matrix of roots and soi 1 and by the protective 

litter cover. Trees can be removed from forests 

with small increases in the sediment flow if the 

surface layers of litter and root matrix are not 

destroyed. Roadbuilding breaks up the litter 

cover and root matrix and increases sediment 

flows. 

A practical index of sediment flow can he 

based on the number of openings formed per square 

mile of forest per year. The number of openings 
is related to the numher of miles of roads and 

skid trails required to remove the timber (Nyland 

and others 1976). As the size of openings decreases, the number of openings for equal harvest 

areas increases. However, when many small openi ngs are Formed, s~h as when single trees are 

removed, the miles of roads and skid trials 

required per opening may he reduced by clustering 

the openings* Thus, the relation between the 

openings per syuare mi le and the silt production 

is not a straight line; sediment flow increases at 

a declining rate as the number of openings 

i ncreases 

The sediment index has two components: a 

sediment increase based on the number of openings 

per square mile per year and a sediment decrease 

based on average opening size whenever the size is 

less than 3 acres. The sediment decreases whenever opening sizes are less than 3 acres because 

small openings are clustered to reduce the total 

miles of road and skid trails, 

The rate of sediment decline for opening 

sizes less than 3 acres is determined by a tahle 

function (fig. 24). As the average opening sirs 

approaches zero, the amount of roadi ng reqrii red 

for clustering the skid trails is assumed to he 

one-third of that for 3-acre openings. When the 

openings are larger than 3 acres, the ;a:nount of 

sediment is not reduced because it is difficult to 

reduce the amount of roading for these opening 

sizes. 

The most important reldtinn for sediment 

production is the number of openings created per 

square mi le of forest per year. As this number 

increases, the rniles of roads and skid trai I s 

that increase sediment rnovcment inus t increase 

(fig. 25). An 80-year rotation at steady state 

requires a harvest of one 8-acre opening per year 

per square mile if harvest is restricted to a 

squar-e-tni le area and opening sires are limit4 to 
SIZE OF OPENINGS (Acres) 

Figure 24.--Sediment decrease as a function of clustering openings 

less than 3 acres. 

OPENINGS / SQUARE MLLE / YEAR 

Figure 25.--Sediment increase resulting from increased road and 

skid trail construction as the number of openings per square mile 

per year increases, 

8 acres* This is the maximum dispersion of 

openings for rnost management modes on the Big Ivy. 

Thus, the chart relates one opening per square 

mile per year to the maximum amount of sediment 

f rorn road construction. The more typical case 

would be opening sizes of 25 to 30 acres with less 
than one opening per square mile. Large openings 

greatly 1 imi t road construction and sediment flow, 

The organizational states used to i 1 lustrate 

the potential timber index are used to illustrate 

the sediment flow index. In the first example, 

25-acre openings with a random variation are used 

because it is not possible to rnake a1 1 openings 

exactly 25 acres. The random variation is based 

on a standard error of 2 acres; most openings are 

expected to be 25 + (2 x 2.4) acres. In the 

second example, 10-acre openi ngs with a standard 

error of 1 acre are used; most openings are 

expected to be 10 i- (1 x 2.4) acres. The smallest 

openings are larger than 3 acres, so the sediment 

decrease factor is not used. 

By projecting the harvest rates for each 

example, we can project the number of openings 

formed per square mile per year. A random number 

table can be used to vary the opening sizes 

appropri ate for the assigned standard errors. The 

DYNAST model is used to compute and plot the sedi - 

ment indices for the two examples (fig. 26). 

Example 2 has the higher sediment flow although 

less timber is harvested than for Example 1. 

Example 1 has openings about 2.5 times larger than 

those for Example 2. Although more timber is harvested for Example 1, fewer roads and skid trails 

are required to connect the 25-acre openings than 

the 10-acre openings in the second example. The 

differences in sediment flows between Examples 1 

and 2 are determined both by the harvest rates and 

the opening sizes. 

stat 

amou 

Harvest rates and sizes of openi ngs determine 

es of forest organization and are related to 

nt of road construction, which is related to 

sediment flows. A high sediment flow rate, relative to undisturbed forests, is an undesirable 

impact, The objective is to limit the sediment 

flow index to less than 1, which is the maximum 

acceptable limit. At an index of 1, one opening 
.= -r h rg '4 Example 2 

Example 

0 I 1 I I 

0 I0 50 90 1 30 

TIME FROM PRESENT (Years) 

Figure 26.--Sediment flow indices projected from time zero for 

two silvicultural modes. (Chart is copied from a plot made by 

the DYNAMO compiler.) 

is formed per year for each 640 acres of forest. 

In most forest situations more sediment is moved 

by constructing access roads to an opening than by 

harvesting the opening. For a given harvest rate 

(acreslyear), sediment flow can best be reduced by 

the opening size, thereby reducing the miles of 

access roads and trai Is. 

The white-tailed deer uses many habitats in 

the Sauthern Appalachian Mountains. The habitat 
for this animal can he enhanced by bringing about 

an appropriate di st ri buti on of seedl i ng, sap1 i ng, 

and mast-produei ng stands. Seed1 i ng habitats 

provide browse, soft mast, and minimum cover, 

Sap1 i ng habitats provide escape cover and beddi ng 

areas. If harvest rates maintain seedling habi - 

tats adequate for browse, adequate sapling habitats will be available. The 10-inch pole, mature 

timber, and 01 d-growth habitats provide hard mast 

and cover. 

Opening size is important for browse in the 

seedling stage, for cover and bedding in the 

sapling stage, and for the frequency of ecotone 

areas. About 1- to 6-acre openings provide large 

amounts of browse and a high frequency of mi xed 

vegetation. Openings larger than about 1 acre 

provide escape and bedding areas. Openings that 

are 50 acres or more limit the utilization of 

browse in the seedling stage. Mature timber provides hard mast and cover. 

The effect of opening size on deer habitat 

can be expressed by using one chart for speni ng 

size (Boyce 1977) and one for frequency in relation to harvest rate. Another way is to relate 

the deer habitat index to the number of openings 

per square mile (fig. 27). 

The assumption is that one opening formed per 

year per 1,280 acres, 2 square miles, provides the 

sizes and the frequencies, both spatially and temporal ly, for maximum use of soft mast and browse 

by deer in the Big Ivy area. As the number of 

openings formed per year per square mi le declines, 

the use of browse declines; this number declines 

as opening size increases and as tiniher harvest 

rates decline. Deer index due to openings does 

not go to zero when no openings are formed, In 

old growth and undisturbed forest some hrowse is 

avai 1 able, 

The total amount of soft mast and hrowse is 

related to the proportion of th~ forest in seedling hahitat (fig. 28). When no seedling hahitat 
0 

0 0. I 0.2 0.3 0.4 0.5 

OPENINGS / SQUARE MILE I YEAR 

Figure 27.--Index of soft mast and browse potential for deer as a 

function of the number of openings formed per square sile per year, 

FRACTION OF FOREST IN 

SEEDLING HABITATS 

Figure 28.--Index of soft mast and browse potential for deer as a 

function of the proportion of area in seedling habitats. 

is available, the amount of browse and soft mast 

is abut 0.1 of the maximum. As the proportion of 

the forest in seedling habitat increases, the contribution of this hahitat to the deer index also 

i ncreases The maximum amount of seedl i ng habitat 

that could be acceptable for the Big Ivy area is 

about 5 to 6 percent of the forest. At steady 
state, 6 percent of the area in seedling habitat 

1 to 5 years old requires a rotation period of 83 

years; for this rotation period, the deer index 

for seed1 ings is set at 1. Shorter rotations to 

increase the amo~~nt of browse and soft mast would 

not improve conditions for deer in the Big Ivy. 

In different forests the relation between the 

deer browse index and seedlings may have a different shape from that il lustrated in figure 28, 

From research monitoring and experience, different 

amounts of seedling habitats may be found to provide maximum browse. For example, the index may 

be found to have a value of 1 when 4 percent of 

the area is in seedling habitat. However, the 

direction of the relation is not likely to change. 

An increase in amount of seed1 ing habitat from 

zero will increase the amount of deer browse up to 

a limit. With no information one could draw a 

straight line from 0.1 for zero seedlings to 1 for 

4 percent of the area in seedlings. Even this 

limited assumption is useful for the decision and 

control process. Detai led research and monitoring 

would not change this relation but would provide 

evidence for modifying the shape and the limit of 

the relation. 

First estimates for algorithms can be produced from experi ence and 1 i brary sources. But 

new information is often needed to adjust the 

shape and the limit of the relationship to make 

the indices more congruent with a particular 

forest. 

Hard mast may not be essential for deer in 

some forests, but an increase enhances the habitat 

for deer in many areas, When no habitats produce 

hard mast, the deer index is about 0.1 for the Big 

Ivy (fig. 29). When more than 70 percent of the 

forest is hard mast habitat, increasing the proportion does not improve the livelihood for deer* 
The habitats producing hard mast are the 

areas in 10-inch pole, mature timber, and old 

growth. Old-growth habitats, especi a1 ly some species of oaks (USDA FS 1980), produce less hard 

mast per unit of forest than the younger age 

FRACTION OF FOREST IN 

HARD MAST HABITATS 

Figure 29.--Increase in availability of hard mast with an increase 

in the proportion of forest area in hard mst-producing habitats, 

0 

8 0.1 0.2 0.3 0.4 0.5 0.6 0.7 

FRACTION OF FOREST IN OLD GROWTH 

Figure 30.--Decrease in hard mast production as a portion of the 

stand advances into the old-growth age class. 
classes. When the proportion of hard mast habitat 

consists of more than 10 percent old growth, the 

hard mast production index is decreased (fig. 30). 

The decrease is not rapid and is 75 percent of the 

maximum potential when a1 1 of the hard mast habitat is old growth, The hard mast production index 

may be used to estimate the potential livelihood 

for other animals such as squirrels and turkeys. 

Because the indices are all on the scale of 0 

to 1, values for the three relations can be reduced to a single number by mu1 tip1 ication. The 

deer habitat index is mathematically expressed: 

DEER = HTD x OTD x HM 

where: 

DEER = deer habitat index 

HTD = deer habitat due to seedlings 

(fig. 28) 

OTD = deer habitat due to opening size 

and frequency (fig. 27) 

HM = hard mast production index 

(figs. 29, 30) 

Information for the calculations may be 

derived from the projection of the forest's 

organizational states. Projections are plotted 

(fig. 31) for the deer habitat index for the two 

exarnpl es previously described. 

For the first decade, possibly a planning 

period, the deer habitat index is approximately 

the same for both examples (fig. 31). After tirne 

10 the habitat index for deer continues to increase for Example 2 but declines for Example 1. 

After 50 years the index for Example 1 is sti 11 

twice the initial value at time zero. For Example 

2, the index is about three times the initial 
/- Example 2 L---- 

Example 1 \ 

0 

0 10 50 90 1 30 

TIME FROM PRESENT (Years) 

Figure 31.--Deer habitat indices projected from time zero for 

two si lvicultural modes. (Chart is copied from a plat made by 

the DYNAMO compiler. f 

value at time zero, For the first 10-year planning period, either example is acceptable. And, 

at the end of the decade, a new managenenL mode 

may be chosen that is different from either 

Example 1 or 2. Simulating the two examples for 

130 years provides some assurance that either one 

can maintain the deer habitat well above the initial value for many years. 
The decline in the deer habitat index for 

Example 1 after time 10 is due to a reduction in 

the amount of habitat producing hard mast as the 

forest is transformed toward the steady state for 

a one-rotation period of 80 years. Deer habitat 

index for Example 2 continues to increase after 

t imp 10 hecairse the superimposed rotati on periods 

with rotations of 90 and 300 years increase both 

the amotrnt of browse and hard mast. Also, the 10- 

acre openings used for Example 2 provide for 

better dispersion and use of the browse than the 

25-acre openings used for Example 1 (fig. 27). 

The number of openings per square mile per year 

increases as the opening size changes from 25 to 

10 acres. This change increases the dispersion 

of the openings, and the deer are better able 

to use browse in 10-acre openings than in 25-acre 

openings. 

The habitat for pileated woodpeckers 

increases with an increase in the proportion of 

the area in old-growth stands, These old stands 

provide trees larger than 20 inches in diameter 

for nesting cavities; the dead trees, dead 

branches, and logs in old stands are a source of 

insects for the bird. Carpenter ants are an 

important source of food for this bi rd, especial ly 

during the winter when other insects and fruits 

are not available. The most frequent nesting 

sites are in dense, old-growth stands with largediameter trees and many standing and fa1 len dead 

trees. If no old-growth stands are present, the 

pi leated woodpeckers wi 11 survive in 1 imi ted numbers in mature and younger timber stands* The 

woodpecker has been known to nest in wooded suburban areas, in developed recreation areas, along 

busy highways, and in other areas inhabited by 

people* However, the potential habitat of this 
bird is greatly increased as the proportion of 

forest area in old-growth stands increases 

(fig. 32). 

The absence of old-growth stands does not 

necessarily eliminate the potential live1 i hood for 

pileated woodpeckers. Consequently, the index is 

0.1 at the zero fraction of old growth. The index 

increases rapidly to a maximum when 40 percent of 

the area is in old-growth habitat. Although the 

number of pileated woodpeckers may increase if the 

proportion of old-growth area increases beyond 40 

percent, no data are available to support this 

assumpt i on. 

The habitat for pi 1 eated woodpeckers is 

assumed to decrease if more than 20 percent of the 

area is in seedling and sapling habitat (fig. 33). 

However, pileated woodpeckers may be found on 

snags in clearcut openings and some sapling habitats* As the proportion of seedling and sapling 

habitats increases to about 70 percent of the 

area, the pi l eated woodpecker hahi tat decl i nes to 

about 10 percent of the maximum. This situation 

would occur for specialized management purposes 

such as increasing water yield by harvesting mst 

of the area at about 20 years of age while still 

permitting some of the stands to survive to oldgrowth ages, 

The waveform of the relations may differ from 

one geographic area to another. The important 

point is that specialized habitat relations can be 

expressed in simple white boxes (See ch. 6) and 

can be adjusted when new information is gathered 

from research and experi ence. 

The al gori thm for pi leated woodpecker habitat 

is the product of the contributions of old growth, 

reed1 ing, and sap1 ing habitats. 

PIL = PLI x PLD 

where: 

PIL = index for the potential habitat of 

pileated woodpecker 
0 0. I 0.2 0.3 0.4 

FRACTION OF FOREST IN OLD GROWTH 

Figure 32.--The habitat for pileated woodpecker increases with 

an increase in the proportion of old growth. 

FRACTION OF FOREST IN 

SEEDLINGS AND SAPLINGS 

Figure 33.--The habitat for pileated woodpecker decreases as the 

proportion of forest in seedling and sapling habitat increases 

above 10 percent. 

PLI = habitat increase due to old growth 

(fig. 32) 

PLD = habitat decrease due to seedling and 

sapling habitats (fig. 33) 
Of the two si I vicuftura% modes discussed 

earlier, Example 2 shows increases in the habitat 

for pi feated woodpeckers (fig. 34). This increase 

i s due to the increase in the proporti on of of d 

growth--30 percent of the area is rotated through 

300 years. The single rotation of 80 years, 

Example 1, decreases the habitat for pi leated 

woodpecker because the accumulated old-growth 

habitats that were found in the initial inventory 

8 

0 10 50 90 I 30 

TIME FROM PRESENT (Years) 

Figure 34.--Piteated woodpecker indices projected from time zero 

for two silvicultural modes. (Chart is copied from a computer 

plot made by the DYNMO cowiler.) 
are harvested. Also, the 80-year rotation increases the proportion of seedl i ng and sap1 i ng 

hahitats to about 40 percent by the year 90. This 

state contributes to the decline in the potential 

for pileated woodpeckers. 

Use of Forest Organization in Management Goals 

The bionomic theories reveal a logical management structure. The new management structure 

is to convert the positive feedback systern for 

organization of forest communities to a systern 

with negative feedback loops and with a goal of 

bri ngi ng about a certain di st ri buti on of stands by 

forest types, ages, and areas. The goal is determined by selecting a biologically possible organi - 

zational state of a forest on the basis of the 

benefits it is likely to produce. The selected 

henef i t cornbi nati ons determi ne the proporti ona 1 

habitat distribution, a single goal, toward which 

management actions are do be directed. With a 

single goal, the complexities generated by 

attempts to match benefits and actions on a oneto-one basis are harmonized. 

The way to achieve a habitat distridution is 

to organize the stands b-y age classes, If two or 

more rotation periods are used, habitats can be 

proportionately di stri buted by age classes to provide various combinations of sustained benefits. 

If two or more rotation periods are superimposed 

so that no habitat is a1 located to a rotation 

period until the time for harvest of stands from 

the shorter rotation, a large number of different 

combi nat i ons of age cl asses and benefits are 

available. 

The decision and control system links the 

forest yanager and a1 1 interested parties to the 

biological sjtstems of the forest as negative feedback loops (fig. 3). Inventories and moni torings 

are used to compare the forest's performance with 

a standard, which is the organizational state of 

the forest and the associated combinations of 
henef its. Periodic adjustments are mde in si 1 vi - 

cultural applications, primari ly harvest rates and 

opening sizes. The biological systems of the 

forest osci l 1 ate and gradual ly approach the chosen 

organizational state, However, the more likely 

situation is that the forest can never be brought 

to a steady state because frequent changes in 

soci a1 , economic, and pol i tical forces requi re 

frequent changes in the goal. 

The decision and control system uses simulation techniques to relate the algorithms for benefits to si 1 vicultural applications. The process 

allows the manager and other interested parties 

to use jrrdgment, insight, and experience for 

subjective deci sions in the strategic planning 

processes. The simulations provide for limited 

complexity in the communication of a1 ternati ves ; 

for subject i vely and rapi dly di scardi ng unaccept - 

able a1 ternatives; for periodical ly adjusting 

deci5ions as changes occur in social, economic, 

and pol i tical forces ; for independent partici - 

pation of a1 1 forestry disciplines; and for rnai ntai ni ng the projected a1 ternati ves congruent with 

the real forest. 

Complexity is limited by displays of projected benefits in simple charts. Up to nine 

benefits can be displayed on one chart and as many 

charts as desired can be printed for each silvicul turaf mode. 

A1 1 of the indices for benefits are independent of each other. There are no interaction 

coeffi cents among the different benefits. Fach 

di sci pl i nary area independently develops a1 gorithrns for the benefits of interests. For examplo, 

the pot~ntial timber index is developed independently of thp game manager's index for deer and the 

hydrologist's index for water yield. 

The mathemat ical techniques for projecting 

henefi t comhi nations for different si 1 vicultural 

modes are described in succeeding chapters. 
Chapter 6 

Cash Flows From Silviculture 

Overview 

Silvicultural choices include cash flows from 

the cultural options. Cultural options "depend on 

the characteristics of the tree species that make 

up the kind of forest type, the features of the 

site on which the trees are grown and the objectives and resources of the owner" (Soc. Am, For. 

1981 ). Di rect observations, simple inventories, 

and routine compilations of benefits and costs are 

the basis for identifying cultural opportunities. 

These simple kinds of integrations tell us most of 

what we need to know to apply silviculture to each 

stand. 

Complex situations arise when si 1 vi cul ture is 

to direct the ecosystem dynamics of a forest for 

combinations of benefits such as timber, game and 

nongame habitats, water, and cash flows. Complexity, measured by the number of pairs of variables 

changing simultaneously, increases exponenti a1 ly 

with each addition of a benefit to the desi red combination. As complexity grows, the task of communicating relevant information among the interested parties becomes increasingly difficult: 

All variables can no longer be measured, coordination of silviculture among stands is almost 

impossible, and confusion arises among managers and 

other interested parties. The complexity of computing and displaying cash flows jointly with other 

benfits is described in "An Assessment of the 

Forest and Range Land Situation in the United 

States" (USDA FS 1980). 

Thi s chapter presents simp1 e ways to compute 

and display si 1 vicultural cash flows. A technique 

is described for reducing complexity and for providi ng cl ear channel s of communi cat i on when di rect - 

ing forests to provide cash flows with other 

benefits. 
The cash flow a1 gorithm provides a way to analyze alternaGive silvicultural modes in terms of 

inflows and outflows of money. It also provides 

simpl e ways to project percept1 ons, assumptions, 

and insight for future changes in economic relations, These projections may be linear or curvi - 

1 inear, reqrai re no cornplex equations, and nay be 

adjusted daily, weekly, or at any time interval 

required to keep them current with new information. 

Common costs need not be al located among benefits, 

benefits do not have to be ranked in order of their 

ref ative worth to society, and monetary val ues need 

not be justi f i ed for noncommodi ty benefits 

The choice of a silvicultural option is 

greatly simplified by choosing a flow of organizational states of a forest on the basis sf the benefits likely ta be produced. The simplicity of the 

method has several inherent advantagesl All interested parties are creatively invol ved in eval uati ng 

si l vicul ture options by integrating persona1 insights, experiences, and subjective values with 

quantitative data. Relations and assumptions are 

expl icitly displayed far scrutiny in white boxes 

(Boyce 1982b). Information is communicated with 

simple chasts and tabl@s; values for decisions can 

be gercei ved quickay and explicitly, 

The result is a basis for choices when the 

goal is for maximum monetary return or for "the 

relative values of the various resources, and not 

necessarily the combination of uses that wi 7 1 give 

the greater dollar return or the greatest unit output" (Mu1 tiple-Use Sustai ned-Yi eld Act of 1960) 

j USOA FS l983a), 

The chore is simpl i f i ed for "speci Pi c identi - 

fication of program outputs, results anticipated, 

and benefits associated with investments in such a 

manner that the anticipated costs can be directly 

compared with the total related benefits.. ." 

(Forest and Rangel and Renewable Resources Pl anni ng 

Act of 1974) (USDA FS 1983a). 
Likely the most important result of a1 1 is 

that administrators and other interested parties 

can inject personal experiences and insights into 

the simulations. Optimal strategy is derived by 

subjective decisions determined by insights, value 

judgments, experience, and acumen of interested 

parties. The responsi bi 1 ities of administrators 

are not usurped in mathematical expressions, mental 

models and scientifical ly deri ved relations are 

communicated expl i ci t ly, and people make the 

decisions. 

Stand prescriptions are used by si 1 vicul turists to direct ecosystem dynamics and achieve a 

desired organizational state for a stand. The 

organizational state of a stand, often called 

stand structure, is defined by species composition, 

age, stand area, basal area, timber volume, number 

of trees, aqd many other variables. The myriad 

of interactions hetween staods for sustained flows 

of timber, cash, water, wildlife habitats, and 

other benefits precludes a consi stent choice of 

stand prescri pti ons without di recti ons to achieve a 

single forest goal. When the goal for the forest 

is a single benefit, such as a kind of wildlife 

habitat, other benefits are viewed as constraints 

i n the choice of si 1 vi cul tore prescriptions. Even 

if a single benefit is used as the guide For stand 

prescriptions, variable results are achieved for 

the forest. For example, the scheduling of stands 

for harvesting to achieve a maximum or a constrained cash flow leads to underperformance because resource a1 1 ocati ons and pol i ci es for each 

stand are designed in is01 ation. Stand prescri ptions are isolated by the perceptions for each 

stand of a "best practice" by si lviculturists, 

economists, wi ldl i fe biologists, hydrologists, and 

other specialists* Yet the composite performance 

of the stands determines the flows of cash, timber, 

wildlife habitats, water, and other benefits from 

the forest. 
The new di recti on is to integrate stand prescriptions and benefits with a single goal: to 

achieve and maintain a particular organizational 

state. Organizational states are operational ly 

defined as the proportional di stri bution of stands I 

by age, area, and type classes. Stand prescrip- I 

tions under the new direction become in-place deci - 1 

sions directed toward a common, single goal for a1 1 1 specialists. The goal is achieved by controlling i j 

timber harvest rates, openi ng sizes, and forest I 

type conversions. All silviculture is directed 

toward this goal. 

The new di recti on expands the earl i er concept 

of a regulated forest (Wenger 1984), which is 

1 inked to the desire for sustained yield of forest 

benefits. Original ly conceived for timber production, the concept is to bring about a uniform distribution of age classes for a sustained yield of 

a certai n timber type (Wackerman 1934). Recently, 

concern for multiple benefits has changed the goal 

from regulating age classes for timber alone to 

regulating age classes for the sustained yield of 

joi ntly produced benefits. 

Single rotation periods are used to transform 

a forest from the present state toward a steady 

state in which stands are equally distributed among 

age classes. Superimposed rotations are used to 

transform a forest from the present state toward a 

steady state in which stands are unequally distributed among age classes. Because regulated states 

can rarely be achieved in practice, they are used 

only as a basis for setting timber harvest rates 

for about a decade. 

The choice of a goal --a desired state of forest organization--is based on the benefits that 

accrue from the state. The only thing needed to 

make this choice is a good view of the expected 

benefits and thei r price. The system dynamics 

model cal 1 ed DYNAST provides such a vi en. The 

displays answer the general question: If we do 

this, what will we have? Decisions are based on 
ansz.itzrs to the specific questions: How will this 

or that schedule cf rultiwe change The s'ates of 

forest osgani zation? Which si 1 vicu! tural modes 

for the forest in question produce desirab'e cornbi nations of benefits that exceed the common costs 

in the aggregate? Which silvicultural modes can 

produce desirable combinations of both marketable 

and nonmarketable benefits at the smal lest equivalent annual costs? What monetary returns will we 

have? What will be the stream of benefits from the 

present? 

Simple charts display biological ly possi hle 

combinations of benefits, including cash flows, for 

three silvicultural options (figs. 4, 5, 6). These 

di splays are the medi urn for expl i ci t communi cat i on 

of information among the interested parties. Each 

plot is an integration of scienti fical ly derived 

refatdons and perceptions of mntal models about 

responses of the forest to a silvicultural mode, 

For each individual the choice of a goal is 

based on subjectively made decisions rather than on 

objectively made decisions for a mathematical ly 

identified optimal strategy. The approach is not 

greatly different from the process used to buy a 

pair of shoes. Most people do not enter a store 

with a fixed notion about the most desirable shoe, 

They look at the alternatives and make mental 

t radeof f s among sty1 e, comfort, ut i 1 i ty , and pri ce, 

and after a time of indecision they select a pair. 

Their choice is based on a good view of the merchandise and its price. This is the information 

conveyed in the DYNAST charts. 

For the illustrations, I use an upland hardwood forest on the Pisgah National Forest in North 

Carolina. The forest area is called Big Ivy and 

contains 6,396 acres. Biological and economic consequences are illustrated for 80- and 200-year 

si ngl e rotations. The 80-year rotation was 

sel ected because it provides a 1 arger sustai ned 
I 

i 

yiel d of sawtimber by current uti 1 ization standards 

than any other rotation period. Because of the 

high sawtimber yield, a larger sustained cash flow 

is expected than for other rotation periods. 

Therefore, a forestry agency is likely to choose 

an 80-year rotation for similar upland forests. A I 

200-year rotation was also selected for il lustration because after several decades this harvest i 

rate provides old-growth stands that enhance the I 

habitat for animals such as black bears and for ! 

pi 1 eated woodpeckers. Such a 1 ong rotation reduces 

timber harvest and cash flow rates. 

I use the system dynami cs model , DYNAST (Boyce 

1977, 1979), to plot the habitat distribution, 

potential timber index, a selected combination of 

benefits, and several economic variables (figs. 

4, 5, 6). Plots are made at half-year intervals 

for the first 10 years because this is the period 

of primary concern. The plots are then extended at 

9-year intervals to 100 years so the long-term 

consquences of this kind of regulation can be 

exanti ned . 

As the forest is transformed toward the goal, 

the common denominator for a1 1 benefits, i ncl udi ng 

the timber removed and the economic benefits, is 

the organizational state at each moment. Organizational states are operational ly defined as the 

the distribution of stands by forest type, age, and 

area classes (Boyce 1978b). These stand classes, 

ca1 led habitats, are places to live for a1 1 endemic 

plants and animals. Habitats for a forest type are 

related to the plant and animal habitat, timber 

product i on, and st reamf 1 ow. 

The changes in the organizational state during the first 10 years for the 80-year r~tation 

are: harvest of 01 d-growth habitat (G) ; increase 

in seedling (A), 8-inch pole (D), and mture-timber 

(F) habitats; and decrease in 6-inch pole (C) and 

10-inch pole (E) habitats (fig. 4). Also during 
this time the habitat increases for eastern whitetailed deer (D), flicker woodpecker (F), and spiders (S); is stable at a low level for black bear 

(B); and declines for pi leated woodpecker (P). 

Streamfl ow (W) a1 so increases. 

Timber production (T) is the ratio of the timber volume harvested to the volume expected if the 

forest were in a regulated state at the age when 

mean annual increment culminates. This age for the 

Big Ivy forest is 50 years (Schnur 1937). During 

the first 10 years, timber harvest (1) increases to 

approach the index value of 1. 

Economic benefits depend on the costs and 

returns from the sale of timber. Computations of 

these benefits include stumpage values, annual 

overhead and timber marketing costs, capital costs, 

and rei nvestment rates. 

Figure 4 shows estimated economic benefits for 

the 80-year rotation in index form for four economic variables: net present value (N), benefitcost ratio (B), equivalent annual rent (E), and 

realizable rate of return (R). Computation is as 

described by Clark and others (1979), hut the values are divided by a constant for the purpose sf 

scaling as an index for plotting. Adual values 

a1 so can be plotted or printed in tahles (Boyce 

19771, but for this discussion we are nore eoncerned with the general trends in the indices than 

with the actual values. All the economic indices 

begin at a low level but increase rapidly during 

the next 10 years. During the next 90 years some 

increase and some remain constant. The initial 

values are low because approximately $64,000 is 

reinvested to increase timber sales to the extent 

needed for an 80-year rotation. The economic benefits shown for the 80-year rotation are near the 

sptimum achievable from timber production on a 

sustained-yield basis in the forest, 
Let us imagine, however, that we are concerfled 

about the welfare of black bears as well as the 

economic returns from timber. Let us a1 so assume 

that we know that a 200-year rotation is near optimum for the bears. Ry year 10 the proportions of 

old-growth (G) and mature (F) habitats are greater 

and the proportion of seedling habitats (A) is less 

for the 200-year rotation than for the 80-year 

rotation (figs. 4, 5). The habitat for pi leated 

woodpeckers (P) increases for the 200-year schedule 

(fig. 5) and decreases for the 80-year rotation. 

In year 10 all other benefits are less for the 

200-year than for the 80-year rotation. After 

about 30 years the habitat increases for bears, 

deer, and fl i cker woodpeckers. 

The economic benefits for the 200-year rotation are discouraging, Timber harvesting is low, 

timber production (T) is about half of the potential in the long run, and the economic indices are 

we? 1 below those for the 80-year rotation. Based 

on these projections, it is easy to conclude that 

the joint production of timber and habitat for 

black bears is too costly. This conclusion is 

based on regulating the forest with single rotation 

periods. Economi c benefits rapidly decl i ne to unacceptable levels as single rotation periods are 

moved away from the harvest ages that maximize the 

production of marketable benefits. The biological 

reason for this economic constraint on the joint 

production of henefi ts is that single rotation 

periods greatly limit the diversity of habitats 

by age, area, and forest type. 

When the rotation period is at the age for 

the maxi mum economi c benefits, a1 1 ol der classes 

are harvested and the amount of area in seedling 

and sapling habitats increases (fig. 4). The long 

periods such as 200 years provide old-growth habi - 

tats for animals such as black bears by reducing 

the amount of area in seedling habitat and limiting 

the habitat of species dependent on these conditions (fig. 5). Changing the harvest age from 
the rotati on period for maximum economic benefits 

often decreases marketable benefits faster than 

nonmarketabl e benefits are increased (Boyce 1982b). 

Thi s economic const rai nt on the joi nt production of 

benefits can be relaxed by using superimposed rotation periods (Boyce 1977). 

Two or mre rotation periods superimposed on 

the same area transform the forest toward a steady 

state in which stands are unequally distributed 

among age, area, and forest type classes. To 

achieve this unequal distribution a stand is harvested at the age for one of the desi red rotations. 

Then the succeeding stand on the same area of land 

is harvested at the age of an alternative rotation. 

In this way both the diversity and the dispersion 

of habitats by age, area, and forest type classes 

are di rected at a minimum cost. To compare the results with those for single rotations, I use the 

fol 1 owi ng exampl e: Stand areas pass through seed1 ing, sap1 ing, and other habitats until they reach 

the 80-year age class. At this time the manager 

decides to either harvest or to delay harvest until 

age 200. Eighty percent of the forest area is 

rotated through 80 years and 20 percent through 200 

years. Many other combinations are possible. 

The superimposed rotations change the st ream 

of transformations. By year 10 both seedling and 

old-growth habitat increase (fig. 6). A1 1 benefits increase during the first 10 years, but the 

increases are not maximum for any one benefit. The 

biological consequence is increased habitat diversity and increased habitat for a1 1 endemic species 

(Boyce 1981 ). 

The superimposed rotations favor the harmoni ous and coordi nated production of j o i n t benefits 

with less anticipated costs than with a single 

rotation period (figs. 4, 5, 6). The differences 

may be observed by compari ng opportunity costs. - 

Opportunity costs for benefits in the aggregate can be compared by using the equivalent annual 
rent as a measure. This measure ranks different 

cash flows for different time periods and ranks 

nonprofit producing options. For the illustration, the largest equivalent annual rents are 

obtai ned by regulating the forest for maximsm 

timber production (table 3). When the regulation 

is with a 200-year rotation period, which increases 

the habitats for black bears after year 10, there 

is an equivalent annual cost. The superimposed 

rotations produce an equivalent annual rent in year 

7 and for later years. However, the rent is less 

than when the forest is regulated for maximum 

timber producti on. 

Economic variables for several rotation 

lengths and combinations can be viewed as returns 

for all forest benefits produced in the aggregate. 

Aggregated benefits include nonmarketable values 

such as the habitat for most endemic species. An 

a n a l y s i s of the biological consequences need not 

allocate costs to each species or to each benefit. 

Instead, speci es are selected to represent cl asses 

of habitat requi rements. 

Displayed here are some biological and economic consequences for di fferent si l vicul tural 

modes. The values of the various resources are 

considered without a1 locating costs to each benefit 

or species, and anticipated benefits are associated 

with investments in such a manner that the antici - 

pated costs are directly compared with the total 

benefits. The di spl ays provide i nf ormat i on for 

all interested parties to select the option percei ved as best. Experiences of satisfaction or 

displeasure are embodied in each person's perception of the option that answers the question: What 

benefits wi 11 I have then? 

A1 1 interested parties participate in deri ving 

the displays by injecting information, insights, 

value judgments, and personal experiences into the 

simulations. This is done with simple charts of 
Table 3.--Opportunity costs for three kinds of 

forest regulation for the Big Ivy area, Pisgah 

National Forest, North Carolina, measured as an 

estimate of the equivalent annual rent 

Years Single Single Superimposed 

from 80-year 200-yea r 80- and 200- 

present rotati on rotation year rotations 

- 

- - Thousands of dot lars - - 

Negative values are equivalent annual costs for 

the time indicated. 

how two variables change simultaneously or how the 

changes are perceived for the near future. The 

relations are called white boxes because the information is conveyed explicitly and in a form that 

can be scrutinized by all interested parties. 

White Boxes for Silviculture 

In the DYNAST model some benefits accruing 

from a particular organizational state of a 

forest are computed from simple relations cal led 
white boxes, which are graphs and explained in 

chapter 7. For example, the current stumpage price 

varies with stand age class. The graph for this 

relation is one source of information used in the 

model (fig. 35). It is an explicit communication 

to all interested parties of ref ations and assumptions used to derive the displays of joint benefits. It is also a channel for resolving differences in data and opinion--it is a white box. 

The waveform of the white boxes need not be 

described or "fitted" with mathematical expressions. The DYNAST model follows whatever waveform 

is drawn on a piece of graph paper and inserted 

into the model as a table function. The waveform 

can be adjusted at any time to improve congruence 

0 20 40 60 80 100 I20 140 160 

HARVEST AGE (Years) 

figure 35,--Current stumpage price by harvest age of stands. 
between the simulations and information from 

soci a1 , pol i ti cal , economic, and research sources, 

The analytic procedure invol ves changi ng 

control variables in the DYNAST model to simulate 

desi rahle si 1 vicul tural options. The variables are 

rotation periods, opening sizes, and forest type 

conversi ons. The DYNAST model uses feedback loops 

to simulate transformations of the forest from the 

present state through a stream of states toward the 

steady state identified by the particular option. 

The information required is a stand inventory 

by forest type, age, and area classes (table 4); 

Table 4.--The relation of diameter, age, delay, 

and the inventory value to habitats for the Rig 

Ivy area, Pisgah National Forest, North Carolina 

Median 

~abi tat a diameter Age range Delay Inventory 

range 

Inches - - - Years - - - Acres 

Seedling 0-1.0 0-5 5 200 

Pol e-6 5-6-9 36-50 15 1,843 

Pol e-8 7-8.9 51-65 15 740 

Mature 11-15-9 86-120 40 366 

Old 

growth 16+ 121-300 180 185 

%efined in Boyce 1977, 1980. 
a1 gori thms for various benefits such as wi ldl i fe 

habitats, water yi el d, recreation, and timber vol - 

umes ; and economi c assumpti ons and parameters. 

Uses of inventories and noneconomic algorithms are 

described in chapters 5 and 8. Here I describe the 

use of economic assumptions and parameters that 

link the silvicultural options to net present 

value, profitabi 1 i ty index, equivalent annual rent, 

and realizable rate of return. 

The cash inflows and outflows depend on assumptions about the cost of cultural actions, the 

cost of marketing the products, and the cash recei ved for goods and services sold. 4 stylized 

diagram illustrates how the equations are structured to compute both the amounts and the time of 

cash inflows and outflows as the forest is transformed (fig. 36). These auxiliary equations can he 

modi fi ed to fit any specific forest. 

s 

MONEY FROM TIMBER SALES (fig. 35) .......... NH . , I 

\ MULTIPLIER FOR PRICES (fig. 37) .............. VYR - --,* INFLOW (Dollars / Year) rC ADJUSTMENT FOR OPENINGS (fig. 38) .......... MOS " 

MARKETING COSTS (fig. 39) .................. MRK - -- , 

OUTFLOW (Dollars / Year) OVERHEAD COSTS (fig. 40). ............... OVH +- - - 

Figure 36.--Examples of silvicultural information used 

to calculate the cash inflows and outflows. The cash 

flow rates are determined by the suppositions in the 

white boxes and the silvicultural mode. 

The inflow of money (IN) is the money received from timber sales (NH). IN is adjusted for 

the increased cost of harvesting small openings 

(NOS) and for expected changes in future stumpage 

prices (VYR). The money coming in from timber 

sales (NH) is the stumpage price at age of harvest 

multiplied by the timber volumes harvested. This 

information is from the DYNAST simulation. The 

current price for stumpage by age is obtained from 

a white box (fig. 35). 
A white box is used to calculate a multiplier for stumpage price in future years (fig. 37). 

This multiplier (VYR) can be changed daily, weekly, 

yearly, or as needed to adjust the tnoclel to economi c changes. 

0 5 10 I5 20 

TIME FROM NOW (Years] 

Figure 37.--The perception sf future changes in stumpage prices. 

The amount of money received from the timber 

sales is reduced when the opening size is less than 

5.5 acres. Such small openings require more skid 

roads and logging trails and cost more to harvest 

than larger ones. Stumpage prices are reduced as 

openings decrease helow 5.5 acres (MOS) (fig. 38). 

0 

0.1 1.0 1.9 2.8 3.7 4.6 5,5 

SIZE OF OPENINGS (Acres) 

Figure 38.--Stumpage prices are reduced when openings are less 

than 5.5 acres. 
The outflow of money is determined ky costs 

(OT) for carrying out a silvicultural mode. This 

outflow is calculated for each period and includes 

expected decreases or increases in management 

costs . 

One cost is for timber marketing (MRK). Market i ng i ncl udes estahl i shi ng houndari es, rnarki ng 

the trees, carrying out the sales, examining the 

harvest operation for compliance with the contract, and other associated costs. These costs are 

determined by the total area of timber sold each 

year (TAS), which is deri ved from the DYNAST model. 

The marketing costs (MRK) are from a graph in which 

the cost at time zero is determined by current 

experience (fig. 39). Projected costs are from 

insights , past experiences, and expectations for 

future economic changes. 

Overhead (OVH) includes property taxes and I maintenance costs of the enti re property whether I 

owned by forest industry, nonindustrial pri vate 

landowners, or public agencies. The overhead 

costs (OVH) are calculated from a table that projects expected increases or decreases (fig. 40). 

The cost at time zero is the current cost to the 

landowner. The incoming do1 lars are the actual 

amounts of money received for commodities sold from 

the forest . No imaginary values are assigned to 

noncommodity items such as an assumed value for the 

habitat for spiders and pi 1 eated woodpeckers. 

Outflowing do1 lars are the actual amounts of money 

invested to maintain the forest and to transform 

the organizational state from the present toward a 

desired state, Information from the stream of 

transformations is used in a cash flow algorithm to 

compute net present va1 ue, equivalent annual rent, I 

realizable rate of return, and profitability index. 
0 5 10 15 20 

TIME FROM NOW (Years) 

Figure 39.--Projection of marketing costs from insights, past 

experiences, and expectations for future economic changes. 

5 10 15 20 

TIME FRBM NOW (Years) 

Figure 40,--Projection of expected changes in overhead costs. 
White Boxes for Cash Flows 

It is difficuf t to know what reinvestment 

(Kf Nj and discount rate (DSR) to use for evaluating 

si 1 vi cul Lure modes. The cask i nfl ow reinvestment 

rates for the next several decades and the future 

discount rates for cash outflows cannot be accurate1 y predi cted. Because we cannot predi ct future 

interest rates, I suggest the use of white boxes 

and system dynamics methods as a simple way for a1 l 

interested parties to examine the effects of thei r 

perceptions of future interest rates on the aggregate costs of benefits (figs. 41, 42, 43). This 

a1 1 cws administrators and other interested parties 

to inject personal experiences and insights into 

the displays of benefits (figs. 4, 5, 6). Some 

0.20 

0.15 

w Reinvestment E? 

2 

t; 0.10 Discount 

ru 

tr 

ftt 

s 

0.05 

0 

0123456789IO 

TIME (Years) 

Figure 41.--Table for a constant discount rate of 10 percent for 10 

years and a constant reinvestment rate of 12 percent for 10 years. 
di fferenc~s i n perc~pti ons hy di PP~retilt, part i ~c; cart 

bc rcsot vpd by examining the qensi tivity of henefits pror-ft4cc.d by a silvic~rlttlral mode for different 

cii scot~nt and reinvestment rates. For a particular 

forestry enterpri se, percei ved di f ferences in 

di scount and reinvestment rates are incorporated 

into the analyses by simply drawing the perceptions 

on graph paper. The values are inserted into the 

DYNAST model as table functions. 

Some exampl es of interest rate project i ons 

viewed as perceptions of trends are: Interest 

rates wi 1 l be constant for the next decade (fig. 

411, interest rates will increase sharply in 2 or 

3 years and stabilize by the end of the decade 

(fig. 42), and interest rates will decline for the 

0.05 

0 

01234567E3950 

TlME (Years) 

Figure 42.--A table function for increasing interest rates for 

fO years, 
Figure 43. --A tab1 e function for decreasing interest rates for 

10 years. 

0.20 ,- 

. 

- 

L 

next 6 to 7 years and stabilize by the end of the 

decade (fig. 43). The perception of these kinds of 

trends change because new speci a1 -i nterest groups 

arise and others decline. Legislative activities 

change di recti ves for the managers, change pub1 i c 

attitudes, constrain public expenditures, and 

change interest rates. 

0.15 

u) k! 

B 

Dynamic Form of Economic Functions 

- 

- 

.m 

- 

.. 

- 

The indicators of economic benefits such as 

equivalent annual rent (fig. 4) are simulated continually as the forest is transformed from state 

to state. This is accomplished by translating the 

t; 0.10 

2 E! 

s 

- Reinvestment 

0.05 - 

- 

- 

- 

- 

0. I I I 1 I I I I I I 

012345678910 

TIME (Years) 
economic functions into dynamic farms. The procedure is illustrated by describing how the function 

for continual compounding is translated to a dynamic form. 

A principal (P) is to accumulate to a sum (S) 

at a certain time (n) and at a continual compounding rate (r). The complexity for calculating the 

sum is reduced by using natural logarithms, e 

(Clark and others 1979): 

The equation is translated into dynamic form 

and expressed in the DYNAMO language (Pugh 1983). 

A diagram suggests how the sum (SUM) increases as 

amounts of interest (INTR) are continual ly added 

with variable rates (RATE) (fig. 44). The level of 

Figure 44.--Structure of the dynamic algorithm for continual compounding with a variable interest rate, The solid arrow indicates 

a flow of money into a "box." The dotted arrows indicate the flaw 

of information. 
money in the box increas~s in relation to the rate 

of inflow determined by the value (INTR). The rate 

of inflow is calculated with information coming 

from the SUM and the RATE table. The RATE is an 

interest rate derived froin a ~hite box (figs. 41, 

42, 43). The waveforms of these white boxes ref lest insi ghts, experiences, and perceptions of 

interested parties ahout firture interest rates. 

In DYNAMO language the functioq is written: 

The level equation (L) acct~mulates the inflows 

of interest (INTR) at each differential time (DT) 

and adds these amounts to the SUM at the immediately preceding time (J) to calculate the SIJM at 

the present time (K). 

The equation designated by the letter N sets 

the value for SUM equal to the principal (PRNCP) at 

the beginning of the simulation. 

The rate equation (R) calculates the amount of 

inflow in dollars for the next interval of differential time (DT). The SUM at present time (K) is 

mu1 tip1 ied by the multiplier for the interest rate 

at this time. This multiplier is the base of the 

natural logarithms (e) raised to the power of the 

interest rate (RATE). EXP is a function built into 

DYNAMO that computes e to the Ath power. 
The auxil iary equation A is a table function 

(TABHL) that selects the interest rate for the 

present time (T1ME.K). 

The tahle equation T contains the interest 

rates perceived by the interested parties to be 

those expected in the future. The table values in 

this example are from the waveform for reinvestment 

rates illustrated by the white box in figure 47, 

All of the indicators of potential economic 

benefits are transformed to dynamic form in this 

way, The diagram in figure 44 is one example of 

the basic structure for continual compounding and 

discounting. This structure can be modified to fit 

a variety of complex situations, yet the structure 

and the suppositions are explicitly communicated to 

a1 1 interested parties, Some other advantages of 

the dynamic form are: The reinvestment and discount rates are easi ly separated and projected 

in whatever waveform the interested parties perceive (fig. 42); the white boxes make the suppositions explicit to a1 l interested parties; and the 

diagrams of structure are aids to understanding 

the dynamics of the model. Cash flow is a perception of money flowing from silviculture, 

Conventional ~quatiovs for evaluatinq cash 

flows (Clark and others 1979) are translated into 

DYNAM9 language (Pugh 1983) and inclu?~d in the 

OYNAST model. The equations are auxiliary to 

OYNAST and are not a part of the dynamic system 

that determines the transformation of the forest 

from one state to another, The controls for determining the direction and rate of forest transformation are harvest rates, opening sizes, 2nd forest 

type conversion rates , 
The sty1 ired diagram of part of the algorithm 

str~rctrrre (fig. 45) is used to illustrate the 

sourcps of information that determine the dynamic 

change in cash flows 

where: 

(1TA.K = the accumulated outflow of cash 

(do1 1 ars ) at the present time 

OTK.KL = the rate of increase in OTA in 

the immediately fol lowing peri od (KL) (do1 l arslyear ) 

DSC. K = cont i nual di scount di vi sor 

DSR.K = the discount rate at present 

time (K) 

0T.K = the outflow (dollars) at present 

time (K) (years); from DYNAST 

simulation 

1N.K = the inflow (do1 lars) at present 

time (K) (years); from DYNAST 

simulation 

INR-KL = the rate of increase in INA in 

the irnmedi ately fol 1 owi ng period 

(KL) (do1 lars/year) 

R1N.K = the reinvestment rate at present 

time (K) 

1NA.K = the accumulated inflow of cash 

(dollars) at present time (K) 

(years ) 
Fi gure 45. --The dynamic cat cut at ion of accumulated cash outflows 

(OTA) ; accumulated cash inflows (INA), which are compounded wi th 

variable reinvestment rates (RIN); and OTA's and INA's discounted 

to the present (OTAD and INAD) with variable discount rates (DSR). 
0.r-G a, a+-, V) 

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03 5,330v- - - t m+T; ~mmtaroE~o > >.rO Q1.1- 

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Profitability Index 

This index (PI ) is the ratio of the present 

value of the after-tax cash inflows (INAD) to the 

outflows (OTAD). PI ratios of 1 or greater indicate the silvicultural modes expected t o y i e l d 

rates of return equal to or greater than the 

assumed discount rate. The PI is a measure of a 

silvicultural mode's profitability per dollar of 

investment , In continual simulation, PI ranks 

si 1 vicultural modes in the order of profitabi 1 ity 

at each time interval . Continual simulation by 

the algorithm described here indicates both the 

direction and the rate of change in PI. 

Equivalent Annual Rent 

Net present va 1 ues and prof i t abi li ty i ndi ces 

are not valid ways to compare silvicultural options 

that have different cash flows over different 

periods. This problem is solved by computing a 

perpetual "equi v a l e n t annual rent " (EAR 1, Thi s 

value is the perpetual annual income or cost that 

would yield the same net present value as a perpetual repetition of the silvicultural mode for 

identical assumptions about interest rates, This 

value is often termed the "equivalent annual 

income" or when negative, the "equivalent annual 

charge" (Clark and others 1979). For si lviculture, 

we use the term "equivalent annual rent" because 

the values represent positive or negative annual 

rents for a silvicultural mode and do not include 

the values or the appreciation of the land and the 

residual forest, 

Equi valent annual rent (EAR) is calculated by 

determi ni ng the equal periodic payments needed to 

amortize the net present value over a certain time. 

The procedure is to multiply the net present value 

at a given time by the capital recovery factor at 

that time (Clark and others 1979). 
Mhen the EAR is positive, the si lvicultural 

mode yields a rate of return greater than the 

discount rate. The amount of return in dollars is 

indicated by the value of EAR. When EAR is zero, 

the rate of return equals the discount rate. When 

EAR is negative, the rate of return is less than 

the discount rate. The negative value of EAR, the 

equivalent annual rent, is the equivalent annual 

cost of benefits received for the period since the 

beginni ng of the simulation. Thus, EAR is a way to 

rank desirable combinations of benefits when sales 

do not equal or exceed the common costs in the 

aggregate and when cash flows are different for 

different periods. 

For example, an industry may be wi 11 ing to 

accept a cost for growing timber as a raw material 

when the primary source of profits is the value 

added by the manufacture and sale of forest products. The EAR values can be used as an index to 

identify the si 1 vicul tural mode that produces 

timber at the smal lest equivalent annual cost. 

Negative EAR values are useful for estimating 

public charges for multiple benefits that are not 

profit producing. The method can be used tb compare different combinations of mu1 tiple benefits 

when the si 1 vicultural options have unequal lives 

and different costs. It is essentially the same 

method used by many utilities for evaluating nondiscretionary expenditure a1 ternatives and for setting rate structures. When linked to benefits, EAR 

for forestry is a valid way to consider "the relative values of the various resources, and not necessari ly the combination of uses that wi 11 give 

the greatest dollar return or the greatest unit 

output" (Multiple-Use Sustained-Yield Act of 1960 

and National Forest Management Act of 1976) (USDA 

FS 1983a). 

For example, a government may be wi l ling to 

pay for certain nonmarketable benefits such as a 
wilderness, songbird habitats, dispersed recreational opportunities, and endangered species habitats. When these nonmarketable henefits are enhanced or reduced by the si 1 vicul tural mode that 

increases marketahle benefits, no va1 id method 

exists for a1 locating the common costs or the do1 - 

lar values of the common henefits. Calculating 

the EAR with the DYNAST model is a way to answer 

the question: "Which silviculture modes can produce the desired combination of both marketable 

and nonmarketable benefits at the 'srnal lest equi valent annual cost?" The bases for comparison are the 

EAR'S and the combi nations of benefits. A1 1 of 

these are linked by a common denominator, which is 

the forest's organizational state. The choice is 

the si 1 vicul tural mode that transforms the forest 

from the present state through a stream of states 

to produce the desired combination of benefits 

(Boyce 1978a, 1978b) . Realizable Rate of Return 

The realizable rate of return, RRR, is the interest rate that compounds the present value of the 

cash flows to equal the accumulated cash inflows 

for the investment period. RRR value is found by 

s o l v i n g for the compound interest rate, 

Rate of return is an appealing index for comparing si lvicultural options with other investment 

opportunities. Rate of return is a universal 

index and is widely understood in its simplest 

form. It is taken by many as a norm representative of investment opportunities in general . This situation makes some measure of return rate 

an important index when choosing a silvicultural 

schedule. 

Am1 ications to Other Forests 

The methods described here can be applied 

to other forests. The information required is: 
An inventory by age classes, diameter 

classes, and delays (table 4). (Separate 

inventories are needed for each kind of 

forest to be managed as a species type, 

productivity type, or other class that 

can be readily identified by forest workers--habitat classifications can be 

changed to fit the situation.) 

2. A timber yield table for each kind of 

forest to be managed. 

3. Algorithms for the benefits of interest. 

4. Identification of the elements for cash 

inflows and outflows as a basis for computing the inflow (IN) and outflow (OT) 

values. (The assumed changes are drawn 

on graph paper as white boxes. The values for the graphs are inserted into the 

DYMAST model without fitting the curves 

to mathematical equations. The white 

boxes may represent perceptions for interest rates, market values, and any other 

i nsi ghts, experiences, research f i ndi ngs, 

and information thought to influence the 

choice of a silvicultural option.) 

The model is manipulated with the analytic 

si 1 vi cul tural c o n t r o l s (Boyce 1980) to change harvest rates, size of opening harvested, and forest 

type conversion rate. If desi red, sensitivity 

tests can be made for different assumptions such as 

i nterest rates and market values, 
Chapter 7 

Dynamic Analytic Silviculture Technique 

(DY NAST ) 

Overview 

Mental models are the analytic technique most 

often used to make si t vicul ture prescriptions for 

stands, Direct observations, simple inventories, 

persona l experiences , and routi ne data compi 1 at i ons 

are adequate for choosing prescriptions for most 

stands, Stand prescriptions are most effecti ve 

when made in-place hy professionally trained specialists (See ch, 4). Yet, it is the interactions 

and the responses of the stands in aggregate that 

determine the flows of henefits from the forestc 

The forest may perform poorly if si 1 vicutture is 

applied to achieve an isolated goal for each stand 

rather than to achieve a single goal for the entire 

forest. To keep performance of the forest consistent with the cornhinations sf benefits desired, a 

single goal should serve as the cornwon denomi nator 

for stand prescri pt i ons When many ki nds of stands 

are changing simultaneously, the specialist has 

difficul ty fixing in-place decisions that di rect 

the dynamics of the forest toward a desired combination of benefits. A list of desirable benefits 

may be considered a single goal for the forest; 

yet, this is not the kind of goal that aids the 

specialist to make integrated, in-place stand 

prescriptions. With each benefit added to the list 

and with each action that must be considered in 

making the stand prescription, complexity increases 

exponent i a1 ly ; coordi nat i on of prescriptions among 

stands is almost impossible; and there is confusion 

among the specialists, managers, and other interested parties, 

Thp prirpose of this chapter is to descrihe an 

analytic technique for reduci ng complexity, identifying a single goal that links the choice of 

stand prescriptions with the choi c~ of forest benof ids, a~d provi di ~g clear channel s of cornmuni cation 
for a1 1 interested parties. The technique is 

cal led the mic - Analytic - Silviculture - Technique 

(DYNAST). 

Svstem Dvnami cs Method 

Complexity , caused by many vari abl es changi ng 

simultaneously, accounts for the uncertainties in 

most decision and control processes (Beer 1966; 

Ei 1 on 1980). Most managers can 1 imi t uncertai nty 

by mentally reducing to a minimum the number of 

variable pai rs. This procedure, cal led minimum 

account (Tversky and Kahneman 1981), is a function 

of one's mental model and is appropriate for many 

situations; it is the method used to make most 

decisions. When the numher of variable pairs 

changing simultaneously cannot be limited to about 

three by the mental model, the manager's decision 

and control process can be speeded up by using an 

aid that reduces complexity, System dynamics is 

such an aid (Forrester 9961). In this method the 

1 inkage of large numbers of simultaneously changing 

variables limits choices to a few variables that 

determine the dynamics of the system. Reducing 

the numher of variables reduces complexity for the 

mental model and enhances the decision and control 

procedure (Lynei s 1980; Tversky and Kahneman 1981 ). 

The biological basis for using system dynamics 

is formed by the four bionomic theories (ch. 3). 

These theories provide the rationale for taking 

actions to bring about desired organizational 

states. The dynamics of these changes are directed 

by the death of dominant and codominant trees and 

the resulting openings formed in the forest (fig. 

46). In these openings, which are formed hy timber harvest and by natural mortality, trees are regenerated to form new stands, These new stands 

successively transform from one kind of hahi tat to 

another and thus determine the dynamic organizational states of the forest. 
Regeneration / (Natural and 

Artificial) 

Natural 

ed) ( Succession 01 

Habitats fir 

of Forest \ Oraanization 

Figure 46.--Diagram of the transformation dynamics of a forest as 

directed by tree mortality, openings, and regeneration. 
The model projects transformations in organizational states in terms of integrated flows of 

information, labor, energy, and materials instead 

of in terms of separate functions such as economics, timber, and wildlife. This approach is appropriate for biological transformations in forest 

communities that exhibit integrated flows of energy 

and material s. The structure and flow orientation 

is the basis for forest managers to cross disciplines without complications. For example, flows 

of energy, water, cash, nutrients, animal habitats, 

and age and area classes of forest stands transcend 

the interests of individual disciplines and special - 

interest groups. Interorganizational conflicts are 

reduced; decisions are interdiscipl inary. 

Definition of the Question 

The ancient theme of forestry is conservation 

for the purpose of perpetually providi ng benefits. 

Included in the theme is the idea of multiple use, 

defined as the deli berate and planned integration 

of various forest uses so that uses interfere with 

each other as little as possible (McArdle 1962). 

These concepts have been conspicuous in writings 

and in legislation for at least 100 years (Cliff 

1962; Marsh 1964). Apparently, these concepts 

reinforce a deeply ingrained, conservation ethic. 

And the ideas support strong fezlings that people 

can live in harmony with nature. The mental models 

for these ideas are clearly evident in the following phrases from some legislation: 

. . . harmonious and coordinated management of the various resources, 

each with the other, without impai rrnent of the productivity of the land, 

with consideration heing given to 

the relative values of the various 

resources, and not necessari ly the 

combination of uses that wi 11 give 

the greatest do1 lar return or the 
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is the forest manager; the sensors are forest 

inventories, moni tori ngs, and research ; the effectors are the silviculture. The forestry system is 

more complex than the car system because: 

1. The goal for forestry has multiple elements such as "increase the flows of cash, 

timber, water, wildlife habitats, and 

recreation opportunities," whereas the goal 

for the car system has one element, "go 

from here to there," 

2. The forest managers function in response 

to the perceptions of many interested parties, whereas the car system functions in 

response to only one person, the driver. 

3. The forest ecosystem is a self-organizing 

bi 01 ogi cal system that responds cybernetically to silviculture (Boyce 1978b), 

whereas the wheels of the car respond 

determini stical ly to changes in the 

steering wheel. 

4, Delays are long in the forestry system-- 

years and decades; hut in the car system 

they are short--seconds and minutes. The 

longer delays of the forest system 

increase the uncertainty in the decision 

process about future envi ronments. 

Complexity in the forestry decision and control process limits actions for the joint production of benefits. One administrator (Thornton 

1980) commented to the lfSDA Forest Service: 

"My only real fear is that we will succumb to the 

siren songs of the data gatherers and analysts who 

are entranced themselves by the power and potential of the computers. --We1 1 thought-out and 

carefully directed planning can be the heart and 

strength of the Forest Service of tomorrow." 
'%el 1 thought -out and careful ly di rected'hcan 

be interpreted as a plea for reduced complexity of 

the mental model, Aids to simplify the mental 

model are needed when managers "are determined not 

to let the analytical calculus of decision formulation rule the decision making" (Leisz 1981). 

Other signals showing that complexity in the 

decision processes exceeds the capahi 1 i ty of the 

mental model are: (1) the di fficul ties encountered 

i n attempts to a1 1 ocate market values to nonmarketable products (Krutilla and Fisher 1975); (2) disenchantment with attempts to rank benefits in order 

of their worth to management or to society (Steuer 

and Schuler 1978); and (3) the frustrations of 

attempti ng to project complex matrices of resource 

outputs to cultural actions and costs (Alston 

1979; Dyer and others 1979). 

All of these analytic approaches center on the 

lack of quantitative coefficients for the complex 

i nteract i ons among resources. The questions bei ng 

asked of the forest management team are: "How much 

timber, cash flow, water, wi ldlife habitat, recreation opportunities, and other benefits do you want 

to produce? What are the monetary values of the 

henefi ts? What are the interaction coefficients 

and production functions for each si 1 vicultural 

mode?"" (USAFS 7980:322). 

My analyses guided me to ask a different 

question: "If the management team appl i es this or 

that silvicultural mode, what benefits will be produced?" This question is asked by and not of the 

management team, 

In this approach, attention of all interested 

parties is focused on the biologically possible 

combinations of benefits. Choosing this or that 

combination of benefits establishes a single si lvi - 

cultural goal . This goal, to transform the forest 

toward a certain dynamic organizational state, integrates clll tural actions in a1 1 functional areas 

such as timber, water, economics, wi ldl ife, and 
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The state of forest organization is sensed by 

frequent inventories of stands, timber volumes, 

animals, and plants; mnitorings are made of the 

ecosystem's responses to cultural actions ; and research is designed to explore perceptions that 

consequences are causal ly 1 i nked to si 1 vi c u l t u r e . 

This information is used to keep a system dynamics 

model called DYNAST congruent with the forest. 

The "projections, " represented by the di amondshaped form, are the answers to the "if" question. 

These answers are displayed as charts (figs. 4, 5, 

6) and tables that transmit combinations of benefits for alternative silvicultural modes to all interested parties. The question "If this, what 

then?" is modified by parties in the control loop. 

Control variables such as the timber harvest rates, 

sizes of openings formed, and forest type conversion rates are changed until a desired combination 

of benefits is projected. Any party can be 

i nvol ved by asking questions about the benefit 

displays. For the display in figure 4 a question 

could be: What would be the stream of benefits if 

harvest age were 120 years and opening size were 5 

acres? A question for figure 6 could be: What 

would we have if 50 percent of the area were harvested at 90 years of age? 

The goal for managing the forest comes from 

outside the system and is determined by social, 

economi c, and pol i ti cal forces. These forces operate through the availability of land, labor, 

energy, capital, materials, and markets. Within 

the decision loop all interested parties partici - 

pate in examining the projections of expected benefits. These projections are the basis for consideri ng di fferent a1 ternati ves or for d e r i v i n g a 

consensus. A consensus results in activation of 

the effectors, in this case the silvicultural mode 

used in the control variables to project the combination of benefits selected by the parties. 

The biological potential for each combi nation 

of benefits is determined by the ecosystem dynamics 
that oryinate through the si lvicultural applications. These applications transform the forest 

through a stream of organizational states (fig. 46) 

and produce the des i red cornbi nat i on of benef i ts 

(Boyce 1978b). I call this procedure the dynamic 

analytic si 1 viculture technique. It is dynamic 

because the si 1 vicul tural mode is adjusted at frequent intervals to keep the benefits produced relevant to changes in the social, economic, and 

political forces. It is an analytic process that 

integrates information from the goals of' a1 1 interested parties and from the ecosystem dynamics of 

the forest to aid the design policy by interested 

parties. Rather than arhitrari ly setting goals 

such as producing so much of this and that and then 

a1 locating resources, a1 1 interested parties evaluate biologically possible goals, policies, and 

ecosystem dynamics. A1 1 parties also choose an 

acceptable sequence of outcomes. 

White Boxes 

Graphs, called white boxes, are used as table 

functions in the DYNAST model. The white boxes 

have value (1) as media for improved communications 

among managers, specialists, and other interested 

parties; (2) as a way to link specialists in functi onal areas to form interdi sci pl i nary teams ; and 

(3) as a way to explicitly display relations and 

suppositions for scrutiny by a1 1 interested parties 

(ch. 5). 

The situation is this: Choices are derived 

from the values of society. These choices are 

mediated through the judgment and insight of managers and the institutions they represent. The 

white boxes are used to enhance these processes by 

integrating more information than would be used 

normally and by communicating the relations in 

exp1-i ci t forms. Managers want communications 

structured for explicit displays of relations. 

Professionals in functional areas such as si 1 vi - 

culture, economics, and wi ldli fe want to be 
involved in the decision and control process by 

i ntegrati ng quanti tati ve data with personal 

insights, experiences, and subjective values. 

Ei Ion (1980) and Tobin and others (1980) describe 

how admi ni strators avoid the complexity of deal i ng 

with specialists by keeping mathematical analyses 

away from the boardrooms. Administrators, however, 

are also concerned about making decisions based on 

printouts derived from "black box" equations (Amara 

1981), and they need participation from others in 

their decisions. 

The white boxes are used to enhance admini strators ' judgment, insi ght, and understandi ng by 

integrating information from the political, social, 

economic, and sci ent i f i c arenas. The procedure is 

to translate large amounts of both quantitative and 

subjective information into white boxes that serve 

as si gnal s for expl i ci t communication (Boyce 1981). 

Behavior in the decision and control process is improved. An example is the algorithm for projecting 

the habitat for white-tailed deer in the Appalachian Mountains of North Carolina (ch. 5). 

Information about the habitat for deer in the 

Southern Appalachians is collected from publications, wildlife biologists, hunters, and forest 

managers. Suppositions are developed for the most 

important variables: 

supposition 1,--An increase in the area 

of seed1 ing stands increases the avai 1 - 

ability of high-qua1 ity browse and soft 

mast. 

supposition 2,--An increase in the area 

of 10-inch pole and mature-timber stands 

of hardwoods increases the avai 1 abi 1 ity 

of hard mast. 

supposition 3,--The area of the stands 

i nf 1 uences the di spersi on of forage, 
mast, and cover, and influences the uti - 

1 ization of the forage and mast. An 

i ntermingl i ng of small, di verse stands 

i s benef i ci a1 . 

Other considerations could be included, but I 

wi 11 use these three suppositions for il lustration. 

The next step is to transform these suppositions 

into quantitati ve values that can be interrelated 

and cl osely connected to management actions . 

Seed1 ing stands are defined as stands from 1 

to 5 years old. If the forest is harvested on a 

100-year rotation, about 5 percent [(5 years/100) x 

1001 of the area would be kept in seed1 ing stands. 

Changing the rotation periods changes the size of 

the area in seedlings and the availability of 

browse and soft mast. The shorter the rotation 

period, the greater the browse; but short rotation 

periods reduce the 10-inch and mature stands that 

provide hard mast. We know that to produce quality 

timber, the rotation periods must be at least 70 

years. This establishes the maximum limit for the 

amount of area in seedlings at 7 percent [(5 years/ 

70) x 1001. This relation is expressed quantitatively with a white box (fig. 47). 

The amount of browse is maximum--has a value 

of 1--when 7 percent of the area is in seedlings; 

good-quality browse declines to a very low value-- 

0,l--when there are no seed1 ing stands. Some 

browse is always available in the older stands. 

The direction of the curve is not in contention; 

its exact shape is unknown. A straight line can be 

used to connect the established points, line A, but 

a sigmoid curve, line B, is more likely to reflect 

the behavior of biological systems (fig. 47). 

What is important is that the waveform integrate 

and communicate the sense of the original information, As new information becomes available, the 

waveform is adjusted to improve congruence between 

the white box and the forest. 
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 

PROPORTION OF AREA IN SEEDLING HABITAT 

Figure 47.--A white box that explicitly displays a relation for 

scrutiny and adjustments by a1 1 interested parties. Lines A, B, 

and C are examples of adjustments made as new information became 

available. 

Each white box is based on the best information available and is used to explicitly display 

re1 ations for scrutiny by scientists, managers, 

resource speci a1 i sts in a1 1 d i s c i pl i nes , and other 

interested parties. Any participant can ask how a 

change in the waveform mi yht affect the or~tcorne of 

a decision. For example, a resource manager may 

have evidence for a waveform similar to that of 

curve C (fig. 47). The effect of this change on 

the decision procedure can be examined with charts 

similar to those in figures 4, 5, and 6. In this 

way moni torings, inventories, research, and personal experience adapt the waveforrns of white hoxes 

to a speci fi c Torest. 

Similar thinking is used to transform the 

second and third suppositions into white boxes. 
Simple arithmetic is the computational procedure 

for linking white boxes. These computations as 

we1 1 as the information conveyed by the white boxes 

are important to admi ni strators. This structure 

changes the mediation of society 's values by 

integrating more information than normally would 

be used in making choices. The uncertainties presented by compl exi ty and poor communi cations are 

reduced. The decision and control process is 

improved; the mental model is aided (Boyce 1982a; 

Norman 1981), 

Interdisciplinary TeaOrganizations use interdisciplinary teams to 

coordinate the activities of di fferent functional 

areas such as the physical, biological, and social 

sciences. Performance of the teams is often slowed 

because of poor communications and because the different functional areas do not have a common denominator for relating changes in variables, For 

example, timber values are related to the market 

prices for lumber; hunting values are not related 

to the market values of game kil led. Choices, 

which are derived from the values of society, must 

be mediated with a relation common to benefits in 

all functional areas. Specialists in different 

functional areas must have a common denominator 

for relating timber volumes, water amounts, recreational experiences, and fishing opportunities. In 

forestry that common denomi nator is the dynamic 

organization of the forest (fig. 46). 

As a forest is transformed from the present 

toward a future state, the common denominator for 

all benefits, including the amount of timber 

removed and the economic benefits, is the 'forest's 

organizational state at each moment. Organizational states are defined as the distribution of 

stands by forest type, age, and area classes (Boyce 

1978a). Called habitats (fig. 48), these stand 

classes are places to live for a1 1 endemic plants 
Ff gure 48. --Structure of the core of the system dynamics nodel, 

DVNAST, This structure, which is repeated for mtltiple forest 

types, transforms a forest from state to state. 
and animals, Habitats for a forest type are related to the plant and animal habitaL timber production, streamflow, and cash flow (figs. 4, 5, 5). 

Because forests can h~ transformed from state 

to state, the silvicmltural goal is a certain dynarni c st and di stri buti on by vari ous classes e Thi s 

single goal is achieved by controlling timber harvest rates, opening sizes, arld forest type conversions. All silviculture is directed toward this 

goal . 

The goal focuses attention on the white boxes 

which are made specific for each area, Complexity 

and confusion are reduced to the use of simple re1 ations that cross di sci pl i nes without conf 1 i ct, I 

i 

I 

From the beginning of this work my intent was 1 

to develop a simple model to aid decision and con- I 

trol in a forest system strtlctured as in figure 11, I My objectives were to s~arch for an understanding 

of both the hi ol ogical and managerial structures 

underlying the flows of timber, wat~r, wildlife 

habitats, cash, and other forest benefits; to vali date the re1 ations that influence behavior in both 

the biological and managerial areas; and to search 

for structures and policies to increase the joint 

production of forest benefits. The first studies 

included investi yations in functional areas, such I 

as the biol ogical potential for producing tirnber, 

water, and wi l dl i fe hahi tat (Royce 1975) ; the 

appl icationof si l vicul trrre; the functioning of I 

i nterdi sci pl i nary teams to resol ve di f Ferences in 1 

demands by speci a1 -interest groups ; and the mana- i 

i 

geri 31 nppl i cation of information and communication 

n~tworks, These 5tudies led to the discovery that 

structure in the exi sting functi onal areas enhanced 

poor communication among the interested parties and 

prof i ferated compl exi ty for administrators, 
This finding was a clear signal that simulating the structure illustrated in figure 11 could 

achieve the first two objectives and may reveal 

vari ahles control 1 i ng the dynamics of the system, 

but it could not assure the third objective. The 

difficulty is that such an encompassing model would 

have to include suppositions about the behavior of 

mental models for a diverse group of interested 

parties. To discover and project the dynamics of 

someone else's mental model is difficult because 

the structure is not visible, and observable 

responses are often di fferent from the modeler 's 

way of perceiving the forest. My perception was 

that the most important negative feedback loops in 

the forestry system were in the communications networks that led to the choice of a series of 

cultural actions and in the communications networks 

that professionals in the functional areas used to 

apply cultural actions. These networks cannot be 

discerned and mathematically modeled to be 

congruent with the real forest because the behavior 

of the system is dependent on in-place decisions of 

mental model s . 

The study plan was changed to develop a simple 

model that would aid in-place decisions for administrators, professionals, and other interested parties, The constraints to be relaxed were poor 

communi cat ion and pro1 i ferat i ng compl e x i t y . To 

find a useful structure, the diagram in figure 11 

was madified many times until the one in figure 3 

was selected. This choice of structure for the 

model l i nks management processes to practices familiar to trained foresters (fig. 48). When a forest habitat is harvested, natural or artificial 

regeneration establ i shes a seedl i ng habi tat which 

will transform through size classes at a rate that 

can be accuraLely predicted on the basis of many 

years of experience 

The model is started with information from an 

inventory of the real forest* Feedback loops in 
the rnodel determine the harvest rates to he used in 

the real forest to transform it toward the rfes-ired 

nrgani zational state. 

This kind of transformation is easy to direct 

hy scbedt~l ing the timber harvest rate, the species 

regenerated, and the stand area. Recause of the 

uncertainty of d-ynainics in large ecosystems, we 

cannot go much frlrther than this in prediction and 

control. However, it is a degree of control that 

is highly useful to forest managers. The pattern 

06 harvest followed by predictable succession 

through age classes can be used to create a certain 

organizational state for the forest--a proport40nal 

distribution of stands by age, area, and type 

classes. And the orgar~izstional state largely 

determines the kind and proportion of benefits 

available from a forest, s11ch as timber, wildlife 

habitat, and cash flow (Boyce 1980). 
Chapter 8 

Structure of the DYNAST Model 

Overview 

Models are perceptions of the structure and 

functioning o$ some aspect of reality. Any decision or action can be modeled, The model may be 

mental, such as a plan for a day's activities; 

physical, such as a floor plan for a building; or 

mathematical, such as an equation or mathematical 

algorithm. Simple models, such as highway maps, 

can be used without extensi ve documentation. When 

models become large, computers can he used to link 

large numbers of equations and algorithms and to 

dl splay results as charts, di agrams, and tab1 es 

The computers and the equations are tools used to 

organize, structure, and dl splay information in a 

way similar to how pencils are used to display 

information in the form sf words. 

Simulation models are ways to use a1 gori thms 

in a computer to describe the dynamics of a system. 

The model eontai ns a1 gosi thms representing el ements of the real system, These elements are 

linked as in the real system to emulate the transformations in states of the system as the envi ronment changes ConLi nual simul at i on rrtadel s are 

appropriate for systems, such as a forest, that 

have continuous Flows of energy and materials and 

the system cannot be stopped to study discrete 

changes. Such simulations use fini te-bi fference 

equations that approach the di fferential equations 

of csnti nuous Plows, Probably the best-known cornpi ler for translating and running continual model s 

is DYNAMO (Pugh 1983). 

DYNAMO was developed by the system dynamics 

group at Massachusetts Institute of Technology for 

simulating behavior in business, economic, and 

social systems (Forrester 1961). It is used here 

for forest systems to aid the decision and control 
process. DYNAMO concentrates the user Is attention 

on understanding the causal relations that lead to 

behavior and requi res little understandi ng of the 

complex computer requi rements evident in most 

mathematical models. This makes the DYNAMO made1 

easy to t~nderstand and manipulate by specialists in 

functional areas such as soi 1 s, hydro1 ogy, economics, si 1 vici~lture, wildlife, recreation, and 

inventory. Error analysis is thorough and error 

messages are expressed in easily understood terms. 

OYNAST is written in the OYNAMO language. 

DYNAST simulates fami 1 i ar management and si 1 vi - 

cul tural processes by referenci ng outcomes to perceptions in the mental models of functional specialists and managers. The structure of the model 

includes not only the biological aspects of the 

forest but also the social, economic, and political 

forces that dominate the choice of decisions. 

Features 

DYNAST has several important features that aid 

managers to direct the behavior of forests. First, 

the theory of feedback systems is used to integrate information from quantitative sources and 

from mental models to form a cybernetic structure, 

Second, four bionomic theories are used to organize 

biological information to reflect changes in the 

forest being managed. Thi rd, information that 

would otherwise stay in verbal, descriptive form is 

converted to an expl i ci t di splay of interrelations 

that can be used to make management decisions. 

Thi s means that a1 1 the assr~mptions, translated 

from research and experience, hecome quantitative 

and are displayed in interactive relations in the 

same way that they are perceived to occur in the 

real forest, Fourth, only easily obtainable information typical ly avai 1 able from forest inventories 

is required to operate the DYNAST model, 
Inventory 

Inventories at periodic intervals provide information for starting the model, keeping the model 

congruent with the real forest, and changing si lviculture in relation to social, economic, and political changes, An accurate, up-to-date inventory is 

essential for any kind of a plan. Vet, the inventory should not be complex, tJnnecessary detail 

should be avoided, For example, certain minimum 

data are required to classify stands by age, area, 

diameter class, and forest type. 

The stands are grouped according to diameter 

class of the dominant and codominant trees. These 

groupings are chosen to divide the continuum af 

stands into categories related to the plant and 

ani ma1 habi tat. 

The Big Hvy Forest 

This forest is in the Big Ivy Creek Watershed, near Barnardsville, Buncombe County, NG. 

Timber sales have been limited for many decades 

because practically a18 of the area was harvested 

about 1900. Only a few of the original stands were 

not harvested. The initial harvest left many fowvalue trees, which were crooked and decayed because 

of long suppression in the understory. Occasional 

wi 1 df i res burned some areas. Harvests in recent 

decades have been scheduled to remove the aged, 

previ eusly suppressed, and poorly formed trees ; do 

salvage fire-injured trees; and to thin the young 

stands that were accessible from existing roads. 

Some timber sales were designed to increase ageclgss diversity. 

The predominant forest type is upland oakhi ckory, and al 1 stands contai n approximately the 

same species (Boyce and Cost 1978). Small areas 

can be classified as beech-maple, map1 e-buckeye, 

yel8ow-poplar, and other types, depending on defini tions and interpretations* Spatial changes in 
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thinni ngs, use of genetical ly different trees, 

drainage of the soils, and application of fertilizers. Yet the primary managerial controls are the 

rotation age, the opening size forrxed, and the conversions of forest types. 

Transformation of a Habitat 

The reserves in the mature-timber habitat on 

the Big Ivy forest are found to be 306 acres of unsold timber, Mature-timber habitat sold, sti 11 

standing, and scheduled for harvest is 60 acres. 

Di agramatical ly the mature-timber reserves (KSR) 

are represented by a rectangle (fig. 49). This 

habitat has an age range from 86 to 120 years and 

a delay for succession to old growth of 35 years. 

The mature-timber reserves consist of stands arrayed by age from the 86- to the 120-year age class 

(fig. 49). The average area for each l-year age 

class is about 8.7 acres (306 acres/35 l-year age 

classes), An inflow of stands (TIN) comes from 

natural succession sf 10-inch poles to mature tinber; an outflow of stands (SEL) occurs at a rate 

determined by the timber sales rate. The timber 

sales rate (SEL) is a variable that is determined 

by a negative feedback loop. This loop has the 

goal of maintaining an amount of mature-timber 

reserve to meet the sales rate desired by management. This sales rate is control led by management 

with the control constant termed "rotation age. '' 

This is the approximate age that management wants 

the timber sold, for example, if management wants 

the timber sold at age 100 years, the negative 

feedback loop increases or decreases the sales rate 

to bring the amount of mature-timber reserves to 

the level required to sustain the sales rate, 

The negative feedback loop is described in 

chapter 2, The state of the system is the amount 

of mture-timber habitat in reserves (RSR) and the 

goal is the equilibrium amount of reserves (EQ). 

This amount is determined by the rotation period 

set by management, For example, the Big Ivy has 
Figure 49.--Diagram of the OYNAST model structure used to transform 

a habitat by harvesting timber. 

6,396 acres (table 4). If the rotation period is 

100 years, the annual area harvested at steady 

state would be about 64 acres ((6,396 acres)/100 

1-year age classes). This is the flow rate (FL), 

the average annual flow of stands across the 1-year 

age classes for the harvest age. Within the 

reserves for mature timber there are fifteen 1-year 

age classes between 86 and 100 years. Therefore, 

the equilibrium reserves for a 100-year rotation is 

about 960 acres (64 acreslyear x 15 years). This 
960 acres is the equilibrium area (EQ) for maturetimber reserves (RSR) when the forest is in a 

steady state for a 100-year rotation period. The 

equilibrium area is the goal for the negative feedback loop, which is determined outside of the loop 

by management's choice of a rotation period. 

The decision mechanism in the negative feedback loop increases or decreases the timber sales 

to maintain mature-timber reserves (RSR) at the 

equilibrium area (EQ) for a given harvest rate. 

The coverage function (CV) compares the amount of 

mature-timber reserves (RSR) with the goal (EQ) and 

produces a ratio that expresses the difference. 

Mhen the ratio is 1 or greater, timber sales (SEL) 

are equal to the flow rate (FL) for the rotation 

selected by management. When the coverage ratio is 

less than 1, the timber sales are reduced proportional ly unti 1 the mature-timber reserves have returned to the equilibrium amount. This scaling of 

sales is done with a table function called the sell 

fraction (SELF), This table can be adjusted to increase or decrease sales at different rates relative to the amount of mature-timber reserves. 

The transformation described by the diagram in 

figure 49 is familiar to many people in forestry 

because the concept has been the basis for regulating forests for more than 200 years. The concept of a regulated forest is linked to the desire 

for a sustained timber yield, The method for 

achieving this sustained yield is to divide the 

forest area by the rotation age, which is selected 

on the basis of the value, kind, and amount of 

timber desired. The intent is to bring about a 

uniform distribution of age classes, In practice, 

regulated states have been of interest primarily as 

a basis for setting timber harvest rates for about 

a decade because the regulated state can rarely he 

achieved. The regulated state is difficult to 

achieve because of changes in the standards for use 

of the timber, the economic conditions, and the 

rotation peri ode 
An important innovation is the use of the negative feedback loop, which adjusts the sales rate 

relative to the amount of timber reserves available 

far sale. This adjustment can dampen the oscillations in the timber reserves and aid management in 

responding to changes in economics, utilization, 

and other external conditions, Fami 1 i ar procedures 

are structured to aid the decision and control process more effectively than the structures used in 

the past. 

Symbol s and Equat i ons 

The following information is based on the 

"DYNAMO User's Manual " (Pugh 1983) and the introductory text by Richardson and Pugh (1981). Concern here is to provide minimum information for 

understanding the structure of the DYNAST model . Before the model is formulated in mathematical 

expressions, it is important to analyze the st ructure of the system being studied. The structure 

of the simulation model should he controlled by the 

purpose of its use. For DYNAST, the purpose is to 

aid mental models for improving decision and control for forestry systems (ch. 1 ). Analyses (chs. 

3, 4, 5) are used to conceptualize a structure 

(ch. 7) for the model to do what is desired. 

Formulating component parts of the model 

is best done by first diagramming the flows of 

information, materials, and energy; and second, 

chal lengi ng these structures before equations are 

written, The challenging questions may include: 

Where are the feedback loops? Do the loops function on the basis of valid arguments about the real 

world or do they mimic an empirical history? Does 

the model convey the dynamics of variables essential to what is desired? Are the loops a complex 

of valid arguments but superfluous to what is desired? Figures 44 and 49 are diagrams that have 

been reduced to the simplest form to illustrate the 
dynamics of variables essential to the desi red use 

of the model . These diagrams may appear simple and 

easy to conceive; yet, they are the result of many 

stages of questioning, analyzing, and restructuri ng 

the linkages and the flows of information and 

materi a1 s. 

Each of the symbols has a special meaning, 

Accumulations of flows are represented by a rectangle (fig. 49). These rectangles are called 

boxes, stock, state variables, or levels. The term 

"level" invokes images of various ranks of materials or information as the flow rates change. For 

example, forest stands flow into the mature-timber 

reserves at a certain rate and flow out at some 

desired harvest rate. The initial inventory and 

the differences in the rates determine the "level" 

of mature-timber reserves. Equations for these 

rectangl es are call ed "1 eve1 equations , " are designated by the letter L, and are mathematical expressions for the integration process. In equation form this can be stated: A quantity now is 

equal to the earlier quantity plus elapsed time 

multiplied by the rate of change. In DYNAMO language the equation for mature-timber reserves is 

written: 

The . means a subscript fo1 lows the name of 

the variable. Mature-timber reserves for the present are subscripted with the letter K; for the 

earlier quantity the subscript is J. The computation interval is called DT, also designated the 

differential time. The inflow and outflow occur 

between the time JK; thus, it is the subscript for 

the rate variables. The difference between the 

inflow and the outflow is the rate of change during 

the time DT. The relation of the subscripts to 

the computation interval is il lustrated in figure 

50. 
Computation Intervals I I 

.J . K . L 

Past Present Future 

Figure 50.--Diagram illustrating the relation of the subscripts in 

DYNAMO equations to the computation interval (DT) and to the direction of charrge in time. 

The primary use of N eqt~ations is to assign 

initial values to a level variable such as RSR. 

For example, the initial inventory of the Big Ivy 

area found 306 acres in the mature-timber reserves. 

When the model is begun at time 0, the 306 acres 

are inserted into the level equation RSR with the 

initial equation written: 

The inflow of stands (TIN) from the 10-inch 

pole class of habitats (fig. 48) is a simple differential rate dependent on the amount of land area 

in this habitat. This amount of land area (SL) is 

divided by the delay in years (DSL), which is the 

average ppriod for succession through this habitat. 

The rate for transfer in (TIN, as shown in fig. 49) 
is diagrammed as a stylized valve to indicate a 

changing flow rate* The rate equation is designated by Re The equation is written: 

The inflow of stands (TIN) is subscripted KL 

because the computation of rates is for the next 

time interval (fig. 50). The circles in the diagrains indicate equations auxi l i ary to the rates 

and are designated A equations, This procedure 

separates the rate changes into parts that can be 

~asi ly envi si oned, understood, and changed to make 

the rates consistent with the real world, The 

coverage of reserves for mattrre timber (CV) is 

auxiliary to the rate equation for timher sales, 

The coverage at the present time is simply the 

reserves at the present divided by the equilibrium 

area for the rotation period desired, The equation 

is written: 

The equilibrium area is a constant that is 

symbolized in the diagram with an under1 ine and the 

equation is designated C. For a 100-year rotation 

the constant value for EQ is 960 acres; this is 

written: 

The sell fraction is an auxiliary equation 

based on a table designated T, The circular syrnbol s for table functions have one or two horizontal 

lines and often the name of the function and the 

word "Table" to distinguish these kinds of auxiliaries* The equation for the auxiliary SELF is 

written: 

The sell fraction at the present time is computed by a table function called TABHL* Certain 
COVERAGE OF MATURE TIMBER (CV) 

Figure 51.--A white box illustrating one policy for changing the 

sell fraction when mature timber coverage is less than I. 

arguments must be given in an exact sequence between the parentheses. TSLF is the name I gave the 

table that I wanted to use; CV.K is the independent 

variable that I wanted to use for reading a value 

from the table; the table contains values extending 

from 0.4 to 1 in units of 0.2. Next, the dependent 

variables in the table are given with a T equation. 

The tables should be drawn as white boxes 

(fig. 51 ) because the slope and the waveform of the 

curves reveal more information about how the function works than does the T equation. Also, drawing 

the white box provides a medium for communication, 

a display for scrutiny, and a way to reduce the 

chances for making a mistake in recording the table 

in the model. 

The auxiliary SELF functions this way. When 

the coverage ratio (CV) has a value 1, the value of 

SELF, taken from the table, is also 1. When the 

mature-timber reserves (RSR) are less than the 

equilibrium area (EQ), the coverage is less than 1. 
For example, assume the coverage had a value 

of 0.8. Then from the table, the value of SELF 

would be 0.6, If the an~ount of mature-timber 

reserves decl ined to 0.4 or less of the equi 1 i brium 

area, the sell fraction would be 0 and no timber 

would be sold until the coverage increased to mre 

than 0.4. The TABHL interpolates between values 

given in the table* The independent variable in 

this example, CLK, can exceed the limits of the 

table (TSLF) without causing an error message. 

Beyond the limits of the table, TABWL takes the 

last value in the table. For this example, the 

timber safes rate is limited to that specified by 

the rotation period desired when the coverage is 

greater than 1, 

The timber sales rate is determined at each 

computational time by the sell fraction and the 

flow rate, which is a constant determined by the 

desired rotation period (ROT). The flow rate (FL) 

is calculated by dividing the area of the forest 

(TAH) by the desired rotation period. This constant can be computed with an f4 equation if the 

values for TAH and ROT are inserted into the 

model as constants* The equations would be 

wrs'tten: 

The sell Fraction functions through the negative feedback loop to increase or decrease the timber sales rate according to the amounts of maturetimber reserves* Furthermore, the table in the 

auxiliary loops stops timber sales when the reserves are low but does not increase sales above 

the desired flow rate when reserves are high. One 

can direct this negative feedback loop to do many 
different things by changing the limits of the Lable and the waveform of the curve. An important 

consideration is that all interested parties have 

access to the white boxes and thus are able to interpret the displays and the tables produced by the 

model . 

Natural succession and timber harvest transform forest types from state to stale. When the 

same species is regenerated, the transformations 

change the distribution of age and area classes* 

If silviculture or natural succession changes the 

species regenerated, the result can be an increase 

in habitat diversity and an important change in the 

avai 1 abi l i ty of benefits, These transformations 

can be tracked ~ith the DYNAST model, 

Seven hahitat classes are used in the model 

(Fig. 48). This number is selected on the basis of 

the relations of each habitat class to one or mre 

benefits. For example, mature-timber reserves 

(fig. 49) was selected because the amount of this 

habitat determi nes the amount of mature timber that 

can be harvested. The seedling habitat is important as a source of food for many animal so The 

grouping of age classes to define habitats for use 

in the model is based on the usefulness of each 

class for projecting benefits. For example, if a 

single rotation period is used to harvest a kind of 

mature timber, a rather simple structure with six 

habitat classes is followed (fig. 52). I use th~ 

illustration of harvesting natural lob101 ly pine 

stands and rep1 anti ng with genetical ly improved 

pine seedl i ngs (Boyce 1975, 1977). 

Excluding roads, devel opments, and nonpi ne 

stands, the inventory of the Big Pine forest on 

the Coastal Plains of North Carolina reveals an 

unequal distribution of age classes (table 5). 

The median diameters for the range of ages are 
Figure 52.--Diagram of the core model structure with six habitat 

classes for a pine forest (cf. with figs, 48 and 491, 

based on lscal yield tables and on natural stand 

succession, This information is used to choose 

the habitat classes, which then determi ne the 

delays. Because of unequal habitat distribution, 

the flow rate for a 35-year rotation would require 

about 4 decades to reduce the reserve volumes of 

mature timber to the equi l i brium amount (EQ). 
Table 5.--The relation of diameter, age, delay, and 

the inventory value to habitats for the Big Pine 

forest, Coastal Plains, North Carolina 

Nedi an 

diameter Age 

Habitat range range Delay Inventbry 

Inches Years Acres 

Seedl i ng 0-1.9 

Sap1 i ng 2-3.9 

Small pulp 4-6.9 

Large pulp 7-9. 9 

Small logs 10-12.9 

Mature 13-15.9 

Total 

Also, harvest of the mature stands in about 2 years 

woul d rapidly increase seed1 i ng and sap1 i ng stand 

area, thereby increasing the oscillation of age 

class and timber volume. The model is structured 

to moderate oscillations in the amounts of mature 

and seedling habitats by regulating the harvest 

rates according to the rotation age set by forest 

managers , Within the biological constraints--the 

i ni ti a1 inventory and the growth rates--the negative feedback loop achieves a uniform flow of 

timher to be harvested at about the desired rotation age. 

The feedback 1 oop, the mature-timber harvest 

(table 5), has already been described, A positive 

feedback loop is now added to the model to track 

eha~ges in the areas by habitat classes. 

Timber is not instantaneously harvested at the 

moment of the sale. There is a delay occasioned by 

the sale contract, desires of the buyer, time to 
operate equipment, etce Phis delay can be predicted from past experience or the limit for delay 

can Re specified in the contract. This delay 

creates an accumulation of timber standi ng, sold, 

and awaiting harvestc This accumulation is modeled 

with a level equation designated AS (fig. 52). The 

removal rate from AS is determined by the delay, 

called BRT, which is determined by the sates contract or by experience. The rate equation, call ed 

RML, is simply the amount of timber sold (AS) 

divided by the delay in removal (DRT) for each cornputation time interval (DT). 

The amount of seedling habitat, called SE, is 

the level equation integrated with the amount of 

timber harvested and the succession rate to the 

sap1 ing habitat class, cal led SA, The succession 

rate from the seedling (SE) to the sapling habitat 

(SA) is determined by the biological growth rate of 

the regenerated stand, If plantations of genetical lp improved seedl i ngs grow faster than natural 

stands (table 6), the delay is a variable that is 

Table 6.--The relation of diameter, age, and expected delay for genetical ly improved pine plantations established after natural pine stand harvest, 

Big Pine area, Coastal Plains, North Carolina 

Medi an 

diameter Age 

Habitat range range Del ay 

Inches - - - Years - - - 

Seedl i ng 0-1.9 0-5 5 

Sap1 i ng 2-3.9 6-11 6 

Small pulp 4-6.9 12-16 5 

Large put p 7-9,9 f 7-22 6 

Small fogs 10-12.9 23-30 8 

Mature 13-15.9 31-56 26 
changed according to the rate that the natural 

stands are replaced with plantations of genetically 

improved seedlings, To keep the first illustration 

simple, I use constants for the delays. Therefore, 

the stand succession from one habitat to aqother is 

the accumulation for each habitat divided by the 

delay for each computation time interval (DT), 

We now have an operating n~odel consisting of 

two closed loops--one negative feedback and the 

other posi"i;ive feedback, This simple model is 

familiar "L urnany people in forestry hecause the 

basic concept of' rerju'iat-ing age class distribution 

hy controlling harvest rate and regeneration is 

fundamental to forest manage~ent~ The innovation 

in this model is the negative feedback loop that 

provides much better control over the organizational state of the forest than the previous model, 

which divided the forest area hy the desired harvest age to obtain the fraction of area to be harvested each year, 

The equations for the FAM version of the model 

are listed for the Big Pine inventory ("cable 5, 

Pigs, 52, %3), 

A:E * ~"eatensent is "che Pi rst display line and 

is used to identify the model heading, such as the 

version of DYNASL The * appears in the first 

position followed by one or two spaces and then a 

heading of less than 50 characters including spaces, Sabsequent * sta%ements are treated as NOTE 

statements, 

NOTE statements, subsequent * statements, and 

statements with the first pSS4"Gion blank are used 

do 2dd information, define variables, identify sectors af the rncde!, and create spaces to mak~ the 

model li stiog easy to read, 

Each variable can be defined on the same line 

as tbe equation, The defr'nitinn shod 6i give the 
* DYNASI-FAM 

NOTE BIG PINE FOREST 

NOTE 

NOTE MAIb9E TIMBER LOOP 

2 RML,KL=F IFZE[AS,K, jilS.K/CRT; *SELtJK) REMOVALS (ACRES/VEAP) 

i kS,K=AS,J+DI"(SEL,Jk-RML-JK) ACCUPULATEJ SALES J4PEW) 

N AS=IAS INITIAL ACCLIW, SALES (AREA! 

R SEi,KL=SELF ,K*FL SELL RATE (ACRES/ YEAR) 

M FI=TAH/YAX(AMT,ROT) FLoW RATE {ACRESIYEAP) 

N AMT=DSE+DSA+BSP+DLP*DSL AGE MATURE TIMBER (YEA25') 

N fAH=ISE+ISA+ISP+ILPeISL+IMT+TPS TOTAL AREA HABITATS (4CREJ J 

A SELF ,K=TWEHL(ISELF ,CVVKI *is9 1, -2; SELL FRACTICN (DIM) 

t TSELF=Of,3/,6/1 TABLE SELL FRbCliON 

A CV.K=RSP ,K/EQ COVERAGE RATIO (DIM) 

N EQzFL"MAY(ROT-~MT),~) EQUILIBR1UV CONSTANT (AYES j 

NOTE 

NOTE STATES OF SUCCESSION 

L SE ,K=SE ,J+DTalRML ,3K-SSAii3K) SEEOLING HABITAT (ASEA) 

W SE=ISE INITIAL SEEDLING haBITAIIi4REAI 

R SSA*KL=SE ,K/0SE SiPXESSiOia; TO SAPLihGS IPPEAI'rR J 

L SA,K=fA,J+Dl*(SSA,JK-SSPP3i:) SAPLING HABITAT (AREA: 

N SA=ISA iNlTI41 SAPLING HAElTAT(4PEA) 

R SSP,KL=SA ,KIDS SUCCES5, TO SVPLL PYLF [AREA) 

L SP,X=SP,J+DB"tSSP,JK-SLP'JK) SMALL PULP (49CLi 

N SP=ISP 141TIAL SMFti PYLF (AREA) 

P SLP,KL=SP,KIDSP SUCCESS, TO I_P~GF PIYL PAPEAIvPI 

L LP.U=IF,J+DT"iSLP,dM-SSI IJV,) LXGE PqLP IAPEA) 

N IP=ILP rrirrrit~ LARGE PULP (AREA: 

R SSL , KL =LP ,K/DLP SUCC, T9 SPAALL iCGS [AREA) 

L fL,K=SL,J+DI"(SSL,JK-SRS -JK) iYAiL LOGS (AREA) 

Pr SL=ISL IIdITIPL SMALL LCbS IWPEAj 

R SRS ,<I=S% ,K/CS1 SLCC, 40 PESEPVEZ (4CYE5IYP ' 

L WSR,K=RS2,J+DT*:SRSdJK-SEL *JK) MJP'JPE RESERVES ';at& l 

N PSR=lMT INITIAL RESERVES I;A+E4) 

NOTE. 

NOTE CONTROLS AMC CONSTANTS 

SPEC DT=,25/LENGTH=IOO SPEC IF ICAr IONS 

A PRTPER ,K=I+STEP(79,2Ij P2Ihl bPEC3. 

PRINT SEL,RML,AS,RSF,SL,LF).S2PSPBSE 

A PLTFER,K=,S+SVEQf7,5s2Cc5) PLW SPECS, 

PLOT SE=S,Sd=A,SP=P,IP=L9SL=kiiiRSi?-ti:!Os~r!C)/5EL=*~9933~ 

C ISE=O/hSA=122/ISP=38/IiD=lO/ISi=261;IriiY=362~~AS-9 

NOTE INITIAL INVENIOqY F3R hAKIT4TS (AREA) 

C DSE=8/0SA=7/DSP=5/DIP=6/3SL=i?/ DELAYS FOR SUCCESS, (YEARS j 

C BRT=2 YR DELAY TIMGER REMOVAL 

C ROI=35 ROTATION DESIRED (YEARS) 

RUN QOTAIlON 35 YEARS 

Figure 53,--Equations for DYNAST-FAM. 

unit of masure or -indicate if the variable is dtmensionf ess* For the FAM versioo, definitions for 

the mature-timber loop begin in position 4 and for 

succession states in position 40* These positions 

are selected for neatness; however, one or more 

spaces must be Teft after the equation* 
It is convenient and orderly to begin the 

equa-e-ions in position 4. However, the equations 

may begin in positions from 3 to 3---one or more 

Hanks after the eqiiatio~ type that a'iwiiy~ appears 

in position 1, No spaces are permS"Lted within the 

equations For most parts of the model , the order 

of the equations is unimportant, The few excepti ons wi l t be noted as the mdel is devet oped, 

Control s and constants are conveni entf y located at 

the end of the model , after the fi rst * equation, 

or any other convenient place. 

The SPEC equation can specify Four parameters: DY, LENGTH, PRTPER, and PLTPER, FOP DVNASP 

it is conveujent to use th~ SPEC egilation to assign 

constants to Df, which is the computation interval , and to LENGTH, which is the number of yeass the 

model is to simulate, Because primary interest is 

in the first 5 to 10 years and then in the 100-year 

outcome, it is convenient to skip the printing and 

phtting of qrrtpiit in inbrmedid-te periods. This 

is don^ ny makinq PRTPFK and PLTFER variables with 

the rise of ailxi 1 i ary equati nns, 

In th~ WM version, PRTPER specifications are: 

Print results every year until time 21, then skip 

to year iOlie TMs specification can he changed as 

desired to produce tk~ most useful form of tables, 

The RTPEK specifications are: Plot at 0.5- 

year intervals until time 203 and then plot at 

R-year intervals. Note that the times used in the 

STEP functfon must be related to LENGTH and DT to 

achl'eve the desired resiilts, The FAM versl'on asks 

for plots through year 20, thus the change time is 

yeas 20 plus the original plotting interval of 0-5; 

the STEP constant is the anount desired, 8 years, 

minus the original plotting interval, The LENGT~ 

is set at 100 to coincide with the PKPTER and 

PLTPER speci f i cat i ons . For PRTPER a~d PLTPEK intervals of less than 1 year, Di should be between 

one-half and one-tenth of the shortest print or 

plat t?me. This rule is also useful when chnssing 

DT relative to time constants in the model such as 

DRT, 
The PRINT instructions are used to print only 

those variables that have value for decision and 

control of the forest. Any variable or constant 

may he printed and tables may he made very compl i - 

cated. When adapting the model to a new situation, 

it is often desirable to print variables such as GV 

and SELF to find out if the model is actually doing 

what is intended. It is not necessary to print 

these variables when the model is iterated for 

deci si on and control purposes. 

The PLOT instructions may be used to make any 

number of different plots that use the same or di fferent variables. Each plotted variable must be 

given a symbol. DYNAMO wi 11 scale each variable 

far plotting or the variables may be scaled as illustrated for the FAN version* Variables to be 

plotted on the same scale are separated with commas; a slash separates variables plotted on diff went scal es , 

Constants such as inventories and delays may 

be grouped on a card if separated with slashes. 

The variable for manipulating the model is 

the rotation period. A C statement is used to 

assign ROT, the rotation period desired. This 

statement may be located any place but is conveniently placed just before RUN, which is the last 

statement in the msdel. In the FAM version different rotation periods may be assigned to ROT, 

one for each run or rerun. However, an RUM 

statement must always fol low each C statement 

that gives an ROT, and an RUN statement must he 

the last in the model, 

RUM statements are also used to identify 

the run characteristics* Up to 50 characters, 

incl uding spaces, may be used one or mre spaces 

after RUN, RUN always begins in location 1, The 

name of the run is printed on the tables and the 

ps ots. 
e 

m 

LC, 

dl - vr 0) 

L) b 

+J rq 3 rn 

.#- 

r: due 

@ crO 

&I- 

@ 
As many reruns as desired for one iteration 

of the model may he made by alternating C statements for ROT with RUN. 

In printed outputs the columns are headed with 

E followed by the positive or negative power of 10 

to be multiplied by the number to reconstruct the 

true value (fig. 54). This scaling permits five 

significant figures to be printed. 

Plots may display one or more scales (fig. 

551, The variables associated with each scale are 

indicated for each plot. When variables coincide 

at a simulated time, one symbol is plotted and the 

coincident symbols are printed at the side of the 

plot. 

0 5 10 15 

TIME (YEARS) 

Fi gure 55. --A nodi f i ed plot produced by the DYNAST-FAM model . E = equivalent annual rent in thousands of dollars 

R = =Lure timber reserves, in thousands of acres 

T = YOIU~ af timber removed, in thousand cubic feetlyear 

EAR = E, RSR = R, VI = T 

Rotation 35 years 
Familiar Rotation Results 

The preceding version of the model permits 

harvest s~mrrlations only from ttrle mture-timber 

reserves--stands 35 years and ol der (table 5). 

Thus, the shortest rotation that can he simulated 

is 35 years, but the results of longer rotations 

can he examined. 

Set up a series of reruns that include rotation periods of 35, 45, 55, and 65 years. Examine 

the prints and plots for similarities and differences. Compare differences in the habitat areas 

for the ROT options. Consider the differences in 

timber sale rates for rotation periods. Why does 

the area of n~ature reserves increase for a number 

of years before declining toward the area for 

steady state? What is the effect of longer rotations on the rate of change in area of seedling 

habitat? sapling habitat? Why does the area in 

small logs decline rapidly for all rotation 

opt i ons? 

The initial inventory contains adequate areas 

in the mature and smal l-log habitats to support 

timber harvest for rotations of 35 years and 

1 onger. Different initial inventories would produce different results. Insert a C statement into 

the model as follows: 

The sum of acres in this inventory is 793, the 

same as for the first illustration (table 5). The 

difference is that the order of the values is 

reversed for the hahitats. Interpret the results 

in terms of your knowledge about forestry. 

Reruns can be made when both the inventory and 

the rotation period are changed simultaneously. 

Use the original inventory (table 5) and consider the result of planting harvested areas with 
genetically improved pines (table 6). For this exercise, change the delay for seedl ings, DSE, from 

8 to 5 years. Add a STEP equation to reduce the 

delay for sap1 ings, USA, from 7 to 6 years at time 

12 years. 

The above exercises suggest how the delays can 

he changed to examine the changes in states of 

organization that may he possible by using fertilizers, drainage controls, and weed controls in 

pl antati ons. 

The results of the preceding exercises have 

been familiar to foresters for about 200 years. 

The display of changes in the types by age classes 

was done years ago with skyline charts after laborious, hand calculations* The sell rates, when 

mature-timber reserves are adequate, are the 

amounts found by dividing the forest area by the 

rotation period (793 acres/35 years = 22.657 

acres). The negative feedback loop is permitted to 

function only when the equilibrium amount, EQ, of 

mature-timber reserves is less than 1 (fig. 51 ). 

This constraint on the negative feedback loop is 

intentional because I want to il lustrate how the 

DYNAST model is linked to familiar forestry concepts. From this link I will use one or more negative feedback loops to describe how to simulate 

states of forest organization and the associated 

benefits in ways that were not available to forest 

managers before DYNAST. 

Use of the Hegative Feedback Loop 

An important innovation in DYNAST is the use 

of negative feedback loops to make the development 

of dynamic plans (ch. 2) a self-organizing operation. Complexity for the planning process is 

reduced because the managers and other interested 

parties use only a fen control variables to simu1 ate the consequences of a? ternati ve strategies. 

The familiar procedures are structured to aid the 

deci si on and control process . 
In the model listed in figure 53, values for 

SELF are limited to a range of 0.4 to 1 by the 

function TABHL (fig. 51 ). When the independent 

variable, GV, exceeds 1, the TABHL function continues to have a valtre of 1. Thus, if there is a 

surplus in mature-timber reserves, the timher is 

not sold any faster than the constant rate determined by rii viding the forest area by the rotation 

period. It is this constraint that givs the 

familiar results (figs. 54, 55). A mar. trseful 

structure increases the timher sales when there is 

a surplus, CV exceeds 1, and reduces salcs when 

there is a shortage, CV is less than 1. 

The question is, what rates slzotnld be used in 

the table function for SELF? The range and the 

waveform for this table deterrni ne the behavior of 

the negative feedback loop and thus direct the timber sale in relation to ROT and the inventory. If 

the harvest rates are too fast, rlndesi rahle osci 1 - 

lations may be initiated in the habitats. If the 

rates are too slow, mature-timber sales will be unduly delayed, Also, the sell fraction, SELF, must 

be less than the coverage, CV, or more timber will 

be scheduled for sale than is available in the 

mature-timber reserves, 

The first step is to establish policies based 

on knowledge of the behavior of the forest and the 

desi re for achieving a conservative production 

rate. The range of potential oscillations in the 

habitats is limited by choosing the policy: Harvest rates will not exceed twice the flow rate for 

a selected rotation period. This policy sets the 

limits for the dependent values in the table (TSLF) 

from 0 to 2. The range of the independent values 

is limit~rl by choosing the policy: Harvest rates 

can equal twice the flow rate when the coverage is 

0.4 or less. These poli.cies are illustrated as a 

white box and are made available to all interested 

parties (fig. 56). 

The function TABHL is used because it limits 

the values for SELF when values for CV are less 
8.4 I .O 2 .O 3.0 

COVERAGE OF MATURE TIMBER (CV) 

Figure 56.--A white box illustrating policies for selling timber in 

relation to the coverage of mature timber reserves. CV and SELF are dimensionless. 

than 0.4 or greater than 3. When CV is 0,4 or 

less, SELF is always 0; when CV is 3 or greater, 

SELF is always 2. 

Change the equations for SELF and for TSLF to 

comply with the new policies (fig. 53). How do~s 

this change the behavior of the model? 

Consider the oscillations in the hahitats and 

the desirability of the new timber sale rates. 

Develop different policies based on your perceptions of ecosystem dynamics. How sensitive is the 

sell f ract i on, SELF? 

Explain how complexity for developing dynamic 

plans is reduced by establishing policies for the 

sell fraction and then manipulating only the rotation period, 
Succession Rates 

The rate equations for succession di vide the 

amount of a hahitat by the delay* This is correct 

if a1 1 stands pass through a habitat at the same 

rate and if there is an equal distribution of age 

classes in each habitat, If the delays for habi'- 

tats are short (about one-fifth of the rotation 

period) and if they are tho average time for a1 1 

stands passing through the habitat, then the errors 

are small and rarely influence the choice of a 

strat~gy ., 

Acctnracy in simulating succession rates can be 

improved by using a larger nunher of habitats and 

by using more complex rate equations* However, 

this increase in accuracy for the model should be 

relative to the accuracy that succession can be 

predicted in the real forest* If the forest is 

larger than about 200 acres and there are no annual 

records for a1 1 stands, the rate equations can be 

changed to reflect the more speci fie information, 

Such experimental ly deri ved rate equations should 

be appl i ed cautiously to d i f f e r e n t areas. 

Supplementary Informat ion 

Supplementary information is computed and 

used in prints, plots, and algorithms to estimate 

the flow of timber, water, wildlife habitat, 

cash, and other benefits* One of the most useful 

supplementary items is the proportion of the 

forest in each habitat by forest type* Changes 

in these proport ions result from timber harvest, 

srlccessi on, forest type conversion, genetical ly 

improved seed1 i ng use, thinning, ferti 1 izer 

appl icati on, drai nage, wi ldfi re, insect attack, 

disease, and storm damage, For the FAM version 

of DYNAST add the equations before the sector 

"controls and constants" (fig. 57). Add a PLOT 

card to plot these proportions. It is no longer 

necessary to plot the habitat area, 
NOTE 

NOTE SUPPLEMENTARY IYFORMAT ION 

A PSE.K=SE,KIIAH PERCENT SEEDL I NGS 

A PSA,K=SA,K/TAH PEPCENT SAPL l NGS 

A P'",K=SP,K/IAW PERCEVT SMALL PULP 

A PLP,K=LP,K/TAH PERCENT LARGE PULP 

A PSL ,K=SL ,Y/TAH PERCENT SMALL LilGS 

A Wf.K=RSR.K/TAM PERCENT MATURE TIMBER 

NOTE 

Figure 57,--Equations for calculating supplementary information, 

VOTE POTENTIAL TIMBER INDEX 

A PII,K=VT,K/IIM POTENTIAL TIMBER INDEX 

A VI,K=RML .JK*VU,K*YST VOLUME REMOVED (GI!, Ff .) 

P VU.K=TAPXP(TTVX,YWMT~K90~Zi)O~IO) VOLUME ilNKTSlACRE 

A HAYT,K=(RSW ,K/FL)+AMT HARVEST AGE (YEARS) 

Y TIM=jTAHf TMR)*lVI*vST TIMPER MAXIMUM /CY. Fl,) 

C TMR=30 90T FOR MAXIUM "IMBER (YEARS) 

Y TYI=TABXTfllYI,IMR,13910riIIi)~ VOL. IJl\i!TS/ACRE FOR MAX, TIWSER 

T ~TY~=O/.3/.68/l/l,22~113R/ii46/I.J9/l.51/l~53/l~55 

Figure 58,--Equations for calculating the potential timber index 

and the timber volum removed, 

Timber Potential 

The potential timber index (Pil) is the 

volume of timber projected for harvest by 

the DYNAST simulation divided by "ee volume 

that would be expected far maximum, sustained 

timber production (ch. 5). Insert the equations 

for PIE into DYNASI-FAM (fig, 58), 

The expected yields for natural pine 

stands on the Big Pine forest are given in 

table 7, Mean annual increment culminates at 

approximately age 30; the value for IMR is 30, 

-The yield at age 30 is 1,750 cubic feetlacre; 

YST=lf50, This constant is divided into ~01- 

umes for each class to produce the timber yield 

indices for the table TTYI, An important advantage to using the indices rather than the 
Table 7.--A local yield table and timber yield 

index for natural pine stands jn the Big Pine 

forest, Coastal Plaiw, North Carolina 

---- 

Stand 

age field" Vie7 d index 

(years ) jft3/acre) (vole un? tuacre) 

(3 Inside bark to a l-inch top diameter for a site 

index value of approximately 70 at age 50. 

volumes is the ease of changing the yields when 

the yield tables are harraonl'c. To change the 

yield table from site index class 70 to 80, it is 

snfy necessary to change the value of the constant for YSYe IF planting genetically improved 

seedlings is expected to increase harvested yields 

in year 25, a STEP function can be used to increase 

the value of VST at time 25, 

The volume that would be expected for maximum 

sustained timber production is inserted into the 

model with the N equation for TIM, which is called 

the timber maximum. The total Forest area, TAH, is 

divided by the age at which mean annual increment, 

TMR, culminates to give the area fQr harvest at 

steady state. This area is then multiplied by the 

yield standard, YST, and the timber yield index, 

TVZ, to ohtai n the maximum volume expected, 
TYI also is inserted wi th an N equation. By 

definition, TYI has a value of 1 when TMR is 30. 

However, someone may want to use a different TMR to 

compute PTI. For example, if concern is for saw 

logs from trees 60 years old rather than for pulpwood from trees 30 years old, the PTI can h~ adjusted by changing TMR, TTYI, and YST. The value 

of YST at age 60 is 2,555 (tahle 7); the C statements are changed to YST=2555, TTYI is chang~d, 

and TMR=6O. The new potential timber index is now 

based on producing saw logs rather than pulpwood. 

Yield tables can he used for any volume units 

available. It is only necessary to assure that the 

values for YST, TMR, and TTYI are appropriate for 

the scale, 

The harvest age is equal to the selected rotation period, ROT, when the forest is at steady 

state. However, forests are rarely at steady 

state. Depending on the policy for harvesting, 

harvest may occur at ages above or below that for 

ROT. The harvest age is estimated with the auxiliary equation HAMT. By definition, mature-timber 

reserves (RSR) equals the flow rate multiplied by 

the time the stands are in the mature-timber habitat. (See the N equation for EQ.) Therefore, 

dividing RSR by FL gives the number of years of 

timber supply in the reserves. This number of 

years is added to the beginning age for the maturetimber habitat to estimate the harvest age. The 

supposition is that the oldest stands in the reserves wi 11 be sold before the younger stands. 

This simple correction for harvest age is most 

important when a large area of older stands is in 

the reserves. 

The harvest age of mature timber, HAMT, is 

used as the independent variable to corr~pute the 

volume units/acre, VU, for each harvest (table 7). 

These units are multiplied by the area harvested, 

RML, and the yield standard, YST, to estimate the 

volume of timber harvested, VTo This harvested 
volume at time K is divided by the maximum potential volume, TIM, to give the potential timber 

i ndex, 

It is usefi~l to plot and to print VT. Change 

the PLOT statement to simultaneously plot PTI qnd 

changes in the hahitat proportions. Select a symbol for PTI and scale the plotting from 0 to 2. 

Idhen can PTI exceed l? 

Add VT to the PRINT statement. Five significant numbers are printed; however, the interpretation and use of these numhers is determined by the 

validity of the yield table used in the model. 

Yield tables are estimates of differences and are 

not intended to be predictions of exact timber 

volumes. Predictions of harvestable volume are 

made in-place by measuring merchantable trees 

before the sale, The PTI and the volume projections made by DYNAST are relative differences for 

alternative si l vicul turd modes; they are used to 

aid decision and control, not to make precise 

predi ct i ons. 

Run the version of FAM for some rotation 

periods from 35 to 100 years. Observe the relation of PTI to the timber volume harvested, the 

area in reserves, and the changes in the habitat 

distributions. How does changing the initial 

inventory affect PTI during the first decade? 

Timber and Biomass Volumes 

The equations are changed as fol lows to use 

yield estimates rather than the timber yield index 

(table 7): 
The timber potential can he changd by making 

YST the fractional increase or decrease e>pected 

for more or less productive sites or by using 

genetically improved trees. Any units of meastlre 

cao he used, such as hoard feet, wet weight, dry 

wejght, or Btu potential a 

frariabie Rates sf Timber Marvest 

For soine conditions it is desirable to change 

the timber harvest rates for a planning period of 

10 to 20 years, For example, if the initial inventory has a large proportion of accumulated mature 

timber, it may be desirable to have an initially 

fast harvest rate and scale the rate downward to 

smoothly approach the desired rotation period, If 

the desired rotation period for the Big Pine forest 

is 45 years, the mount of mature timber at steady 

state is 176 acres. The inventory of 362 acres of 

mature timber indicates a surplus of 186 acres that 

could be rapidly sold in the next few years, The 

safe of this surplus in 1 year is undesirable for a 

number of reasons, However, the DYNAST model can 

be used to scale a variable sales rate that would 

market the surplus timber during the next 5 years 

while rnuving the forest smoothly toward the desired 

rotation period of 45 years. 

Short rotations speed timber sales, and long 

rotattons reduce the sales ratee. Thus, a variable 

sales rate can be scheduled hy the DYNAST model by 

changing the constant ROT, rotation period, to a 

variable with the tahle function TARHL, Then for 

the next 10 or more years of planning, the timber 

sates rate will follow whatever waveform is put 

into the table, which I will call TROT, In the 

mature-timber loop (fig. 53), insert the equation: 

A ROT,K=PABH~TRBT,TIME.K90,t.091) 

Variable rotation (years) 
One variable rate of timber harvest could be: 

Tables far TROT are used to change the reruns 

rather than the constant ROT* However, all constants and initial equations dependent on ROT must 

be changed to auxiliary equations before the program is run, The fol lowing changer are made for 

the DYNAST-FAM version, 

Insert these changes into the model Draw 

several waveforms for TROT on graph paper, prepare 

T and the RUN statements, and examine the results* 

Wow do the timber sales rates vary from using the 

constant ROT=45? 

The variable rates are most useful when large 

areas of old-growth timber are to be converted to 

a forest with short rotations, 

Cash Flows 

Net Present Value, LV;PV 

Cash flows are simulated continually as the 

forest is transformed from state to state. This is 

accomplished by translating the economic functions 

into rlynamic forms (ch. 6). insert the eqtration 

for cash flow (fig. 59) into DYNAST-FAM. 

Net present value at time K is the difference 

between the present valijes 05- the inflows and 

outf 1 ows * Present val ues are deri ved by di vi di ng 

the accumulated inflows and outflows at time K 
NOTE CkS9 FtOk ALGSKITHY 

A NPV,K=:NAD,K-OTAD-K NET P;i'ESEKT "J"ILI:F [Bi 

W f NAD,K=TPiW ,K/nSC ,K INFkC)bi,SlW DISCOllNTE3 C$ j 

I INA,K=iNA,J+gT*{ lNR*JK) INFLOW SUWMED ($1 

N INA=IIN INITIAL INFLOla" SliFfl ($1 

R fM9,KL=(INA,K*(EXP(RIM~K)-1))+IhahaK INFLOW RATE ($/YR) 

A RIN,K=TABXT(TRIN,IIMEIK,OBIO,f) RElNVESTMENTQPTE(DEC1MALJ 

T =1//// REINVESTMENT TPBLE 

A fN.K=NH,K*VVR,K*MOS,K TOTAL INFLOW [ $: 

A NM ,K=VI ,K*MA ,K INFLOW FROM TIMBER j$j 

A MA,K=TABXPITVAG,HAMTti~~I09100sIO) STUWP PPICE AT HARVEST ($/Cue FT,) 

T TVAG=O/,12/,32/,42/m5~14$/I,1/I13/"i"i35/9.9. TASLE STUMP P2ICE/AGE 

A VYR.M=TkZXT(TVVR ,TIMEEK,0s20P5) SIUYPAGE PRICE INCREASE 

T TVYR=f/b,l/l,2/1,5/1.7 TABLE STUMPAGE INCPEASE 

A MOS,K=TWBHL(TMOS,ASQ~KB11s555s.9j MULTIPLIER FOR OQEhING SIZE 

T TM0S=,1/,45/,65/e8/Og/e95/1 MULIlPLiER/SIZE OF OPENIQG 

A ASO,K=NORMRN(ISO,ISDj AVERAGE SIZE OF OPENINGS 

A BSC,K=EXPf(MAX(l,TIMEEK))*OfRRK) CONTINUOUSDISZOUNTDIViSOR 

A DSR,K=IABXT(TDSR,~fPIE.M,O~1.OI1~ DISCOUNTRATE 

T T /I 1 1 / 1 DISCOUNT TPBLE 

A OTAD ,K=OIA ,K/DSC ,K OUTFLOW, SUM DISCOUNTED j !$ j 

i OTA,K=BTA,J+DT*(OIR*JK) OUTFLON, SUMMED ($1 

M OIW=IOI IrirrrAe C!:FLOI.I SU~S] 

R OTR,KL=(OTA,K*(EXP(DSRRK)-1) )+OTTK OUTFLOW RNE j$jYRj 

A 0T,M=(OWW,K*fAH)+(MRKKK*SEbb~?K)+(i;?4,<*TY2j TOTAL OUTFLOW j$l 

A OVH.K=TABXT(T0VH,TlKE .Y ,0,20,5j CVf R:9EAl: COSTS ($) 

T TOVV=3/3,5/5/6/7 TABLE OVERHEAD COSTS (R) 

A MRK,Y=IABXT:TMPK,TIMEEK,097095) MARKETING COSTS (9) 

T iMRK=8/ 20/12/15/17 TAPLF MARKETING COSTS ($1 

A PI ,K=lhAB,KfOTAR,K PROFiTPBIiiTV INDEX (DIMEN) 

A EAR ,K=NPL ,K'( (DSR ,K6DSC *K)/ (DSC -Y-I) ) EOI?IVALFNT AMNllAL RENT I$/YR) 

A RRR.K=EXP(RRT,KalOGFi~PRIIK) j-i REAL17blSLE RATF OF RETIJPN 

A RRf,K=FIFGE((INA,V/OTAP*K)31DliiP?i?iYPI) STEP I FOR RRP 

A RR2.K=P/JMAX;1,TIME,Kj) STEP TkO FOR kK9 

C TYR=.25 TAX RATE (TAX BRACKET FRAGTIOY) 

C ISO=SO/ISD=I IYITIAL SIZE OF OPEYfNG AND VARIANCE 

C IOT=1E5 INITIAL OlJiFiOW DOLLARS 

C XIN=O INITIAL INFLOW DOLLARS 

NOTE 

Figure 53.--Equations for the cash flow algorithm, 

by the discount divisor at time K (fig, 45), The 

inflow and outflow rates are the basic algsrithm 

for continual compounding (fig. 44) with the 

amounts of inflows (IN) and outflows (OT) added at 

time Ke The most important equations are those 

that calculate the inflows and the outflows for 

different silvicultural modes* It is these equations that link silvieultural strategies do cash 

flows for each forest. 
Ir;rflor*r Equations, IN 

Bi f f erent forests wi l 1 have di Pferent cornhinations of inflow equations. Those used here 

and in chapter 6 are for it lustration only. Cash 

inflow ad time K is the sum of a1 1 funds actually Flowing into the organization. Funds may 

come from the sale of timber and fuelwood, from 

rights-of-way, hunting rights, leases, water 

rights, or land sales. For example, the inflows 

from the mature-timber harvest (NH) are the 

volume of timber harvested (VT) mulLiplied by the 

stumpage value at the time of harvest (MA) (fig. 

59). The latter value is taken from a table 

(TVAG) that is dependent on the harvest age 

(HAMT) (fig. 58). Plot the table (TVAG) on a 

piece of graph paper. Change the table if you 

have information on current stumpage prices for 

pine in North Carolina. 

The perception of changes in stumpage prices 

(VYR) is from figure 37. 

A constraint (MOS) is placed on stumpage 

values when openings are less than 5.5 acres 

(fig. 38). 

The tax rate is illustrated as a constant 

(TXR). If tax rate changes, TXR can be written 

as a table function. 

The important consideration is to careful ly 

think through the source for cash inflows, how 

these may he affected by cultural practices, and 

how constraints, such as small openings, may affect 

inflows. The information, insights, and suppositions are expressed in white boxes for scrutiny and 

revision hy a1 1 interested parties before equatjons 

are written. Tahle functions are usually the most 

effective way to put the information into the 

model . 
Outflow Equatioaw, OT 

Cash outf1oi.i (OT) at time K is the sum of any 

paymenmy the organ-ization. These payments may se 

for overhead GGS~S, taxes, narketi ng costs, consulting fees, road costs, drainage, site preparaation, genetical ly improved seedlings, ferti l isers, 

sale preparation, supervision, equipment, and 

structures, Equations for these items are based an 

white boxes (figs. 39, 40), which are evaluated and 

challenged by a1 1 interested parties before the 

equations arp written, The number of equatiogs can 

be reduced by aqgregatirig costs that have sirrll'lar 

trends 

Exercises 

Hew do cash inflows differ for the perceptions 

of interest rates illustrated in figures 41, 42, 

4-33 

Make your assumptions about interest rates for 

the next 10 years visible in white boxes, Tqsert 

these assumptions into DYNASI-FAM and exanline cash 

flow for several si lvicultural options, 

How is cash flow affected by making the reinvestment and di scount rates equal ? When would 

these rates be equal? When would the reinvestment 

rate be less than the di seount rate? How do these 

differences affect cash flows? 

For purposes of choosing a silvicultt~ral made, 

which of the following changes increase EAR the 

most in the next decade: 

1, Reducing the rotation period From 50 to 35 

years or changing the iqterest rates from 

those in figure 41 to those in figure 42? 

2, Increasing the yield, YST, 10 percent by applying fertilizers to al l stands that enter the 

saw-log class, or reducing the rotation period 

from 50 to 35 years? The costs for applying 

the Fertilizers is ectimated to be SlOlacre. 
Keep interest rates constant (fig. 41) a04 

find the multipliers for stuqpage prices (fig, 37) 

that are required to make EAR positive in year 30. 

Assume reforestation is with improved seed1 -i ngs, 

planting begins at time I), the cost is $iOO/acre, 

and the cost increases at the rate of 5 percent pea 

yeas, 

If you have current l'nformat-ion on pine regeneration casts and expected stumpage valups, change 

the white noxes to fit your information and perceptisns, Examine the cash flows for different harvest rates, ROT, and regeneration costs. Find 

strategies &+at could be recommended to an investment cornpany for the next20 year?, 35 years, and 

70 years What unc~stai nti es about the future are 

most likely to change these strategies if the land 

is not soid and is kept in pine forests? 

Bpeniw Size 

The average opening size desired for timber 

harvest is a control variable. The shape and size 

of openf ngs are influenced by rock outcrops, soi 1 

erac'li bi 1 i ty, strearns, springs, bags, and other condi tions that cannot be included in the model e For 

these reasons the average opening size desired is 

specified on a G stateme~t as IS0 and a limit on 

the sizes is specified as the initial standard deviation, ISD, Th-i.: devSation nultiplied hy 2.4 

gives the rnaxjmi~m arid the m'inimtim range of opening 

sizes expected in the implementation. In practice, 

the in-place decisions are guided by the values 

assig~ed tn ZSi; and ISD, 

Opening sizes ar;d tho variances are modeled 

with the function NOKbtRP4, The result is an approximate normal distribution in the model, hut in the 

real forest the openiny sizes are likely to vary 

from a normal distributl'on, The stochastic vari - 

ance used in DYWST to model the opening size has 

value for the decisions primarily as a basis for 

deciding the limits, ISD, that should be impternented as rules For in-place decisions, 
Values far Initial Xrglows and Outfleaws 

At time 0, values are assigned on C cards to 

the initial inflow (IIN) and the initial outflow 

(IOT). These values may he 0. For the real forest, initial investments may have been or will 

likely be made to begin a silvicultural mode, 

These initial investments are inserted as IOT. 

Receipts at time 0 are less likely than costs, 

bud if there are inflows, they are inserted as 

IIN, 

When changing the si 1 vicul tural modes, the 

initial outflows and inflows should he carefully 

considered and the constants IOT and IfN estimated 

as accrtrately as possible, These initial values 

are for determining important cash flows, 

Modify DVNAST-FAM to consider paying for site 

preparation equipment, the salary of the operators, 

depreciation, and operating costs versus contracting for site preparation as needed, Use ctlrrent 

cost information, Is the choice different when 

ROP=35 than when ROT=70? 

Plantatiom, GenetieaLly Improved Trees, 

and Thinnings 

The gains in timber volumes expected from 

plantations and fron the use of genetically improved trees are based on the rapid, early growth 

of uniformly spaced seedl i ngs, The resuf t is to 

shorten the delays for transformations from the 

s~edling and sapling habitats and rarely from th~ 

older habitats (table 6)- \cfhen natural stands are 

hei ng rep1 aced with pl antat ions, the delays may be 

changed fron constants to vari ah1 ss that change 

according to the conversion rateo A tahl~ function 

is one way to incl~rde this change in the model, 

Another way is to use the STEP function, The 

equation could be written: 
The value of the def ay waul d be 8 years until 

time 6 when the delay w~ld be reduced to 5 years, 

For this particuf ar example the shorter delay, 5 

years, could be used initially because the inventory is U for natural seedling kakitats. For this 

example, it is only necessary to use a STEP function For the sapling habitat, The function would 

reduce the delay, DSA, from 7 to 6 years at time 

12 years. All other delays are the same for plantations as fir natural stands ("ible 6). 

Use the original inventory and consider the 

result of planting harvested areas with genetical fy 

improved pines (ta5les 5, 6), For this exercise, 

change the def ay for seedl ings, RSE, from 8 to 5 

yearsb Add a STEP function to reduce the delay for 

saplings, DSA, from 7 to 6 years at time 12 years 

(Pugh 19831. 

The above exercise suggests how the delays can 

be altered to examine the changes h the organizational states of a forest that may be possible by 

usi ng fert i 1 i zers, drai nage c~ntrol s,and weed 

controls in pla~tat~ons. 

Thinnings are simulated as changes in delays 

without regenerating the stand, The delays may he 

shortened for transforrnatisns to succeeding hahitats for two reasons: First, removing trees 

smaller than the average diameter mechanically 

increases the average diameter of the residual 

stand; and second, the residual trees may increase 

in diameter at a rate faster than the same size 

trees in unthinned stands, Thinnings may also 

requi re changes in the henef i t a1 gori thms, such as 

those for timber yields and wi Idlife browse 

(ch. 51, 

Simulating a complex forest--one with a numher of cover types, conversions, and thinnings--nay 

requi re a large numher of eq~~ations. The number of 

equations is greatly reduced when s series of simi - tar equations can be replaced with a single function. In the DYNAMO notation, such a function is a 
MACRO, Each MACRO has an abbreviation tnat is used 

in an equation whenever a particuiar series of compu"ctions, previously defined in the model, is to 

be made, The primary use of i"fWCROs in UYNAST -is tc 

provide f l @xi bi I i ly and ef f i ci ency i n ada7ti ng the 

model to multiple forest types and sther cgrnbinations. Three MACROS are defined: HBTCB for habi - tats, OBPAH for forest types, and PPISO far timber 

and openings; defini.tior?s for the dumtriy varGbles 

for each of these are given in tables 8, 9, and 33, 

The diagram in figure 49 is expanded in figure 60 to illusxrate the transformation of any 

hahi tat hy natural success-i ~n, timber rrarvest , or 

both. The series sf similar equations for different habitats is written as a MACRO called HRTOW, 

whi ck is the acronym for ""hahi tat transformation 

for optimal benefits." 

Figure GO.--Diagram OF the WCRO HBTOE used to illustrate transfor- 

 ati ion of a habitat, by natural succession and t~rnber harvest 

(cf, with Pig. 491, 
For each hahitat kge, type, or size class) 

the MAC90 provides a set of feedback loops that 

control the harvest rate and the succession rate 

from this habitat. The harvest and succession 

rates are regulated hy the habitat avai 1 abi l i ty ; 

that is, if the area in a partiicrilar habitat is 

less than the amount required for the desired 

state nf organization, the harvest rate is appropriately delayed on a sliding scale. If the amount 

of the habitat is more than the desired proportion, 

its harvest rate is accelerated, Also, a portion 

of the habitat transforms to the next older class, 

The MACRO HBTOB computes changes in the habitat area. The area is the sum of the reserve accumulation of the habitat ($RSR) and the accumulated 

sales ($AS). The accumulated sales ($AS) are reduced by the amount of habitat removed hy harvest 

(RML) and increased by the safes rate (SEL), The 

harvest rate is the amount of accumulated sates 

($AS) divided by the delay for timber sale and 

removal (DRT), The latter value is a constant 

adjusted to reflect differences among contracts, 

The amount of hahitat to sell (SEL) is determined according to the management mode as specified 

by the analytic silviculture controls. The choice 

to he made by the feedback loops is whether ta liquidate the habitat ($LO) or to bring it to the 

steady state (SSDY) required by the management mode. 

A habitat is Uquidated when neither it nor 

any older habitat is to be retained at steady 

state. The equation for sales rate (SEL) chooses 

liquidation when no harvest is specified by the 

silvicuf ture controls for this or alder habitats. 

Thus, when the flow of area for harvest (FL) from 

the habitat is 0 and the flow of area for all older 

habitats (FOH) is 0, the function FIFZE chooses a 

harvest rate ($LO) that rapidly removes the particut ar habitat, 
The sales rate for liquidating a hahitat is 

limited by the reserve accumulation ($RSR) and the 

liarvest required frm the habitat (SHRN1. The 

minimum Punction (MINI results in the sale (SEL) 

el imi natt' ng the habitat if the reserve accumtrl at i ~n 

($RSR) is snial ler than the harvest required (SHRN). 

Until this final elimination, the sale (SEL) amount 

is limited by the amount sf previous sales from 

o! der habitats (PRS), 

No harvesting of a younger habitat ISHRN) is 

required if sales from older habitats /PRS) exceed 

the total sales permi$ted from a1 1 habitats (FDH), 

FBW is the amount of harvest needed to create the 

correct k~abitat balance in She future, as derivsd 

f r~m the controlse When FDI4 exceeds FRS, the harvest ($HRIC) is that i-cqui red to 'nakc ,i;~ the dif - 

ference betweer1 the3 sales fwm xne 57 der habitat5 

(PRS) and the total i*equii-cd to bring the habitat 

distribution to "che desired inanagement ~vsde~ 

When no sales are made from older hdkitats, either 

the harvest required is limited to WRN or the habitat is eliminated, 

The preceding set of equations is important 

because if allows older habitats to be smoothly avd 

efficiently liquidated without creating large oscillations in the habitat distribution, large and 

continual osci 1 lations, such as could occur wj thout the preceding feedback loops, could more than 

double the time required to bring the habitat bisLribution to the desired steady state, 

When the age for harvest indicated by the controls is equal to or greater than the age of the 

habiLat being considered, a different set of loops 

regr~l ates ar~ntmal sales to bring the habitaMto a 

steady state, The habitat wiit eventrrally occupy 

its appropriate fraction of the forest area, and 

stands of the designated age will be avzilable for 

harvest each yeas at a rate that can be s(~staine4 

indefinitely ($SRY), This node operates in cases 

where Ti+POH -is y-eater than 3, 
01 

7J 8 1, 

m ? cuEnSrrfctl 

On3 -2 I V)m+-' r--oatr,V)oa QI-CT sg .c-,OaJ@n, 

E:(OTUQ)UQ1=TiII--*P" m "II -&: 2 Em 

5- E= a+ w.c-,*r-7zr E cu 1, 

(210.c L". +J433=10 

W8FM rrf% V)v--r3 (4% 

If-, * C3a4- at 

CQSF m-V) 3.F QIFW 

a r*(-a a L. mws Qt cu 

I--.F U3 m at cr--W*Pmre-ac, QI~CTO) cc- 

=CS: cua .s= L.*w QJ*F 

kl 7JI-.mso~eF 03 @LC, 'f-- V)mc "P"atx+a,mata,rpt &t 

x -1,c?a~r-7Jm 

 an%.^ Ecsr- Z* ex a ao E ctl 

t-E-@=s 

+::aaa,o oro * 5-3ErmC)U GI - 

@dCL.r\..C-,E LU.r-QC3 

rclWGcj =3amx,C,s.1-,m 

*;, 3 CcE G-r- Cjt 

VI @O.l-,'P ecj C-WC-( 

m rCI.caz KSLT-at 

C3.Fac- Q.cIr:.c J=:Orc-~ 

Llrlre- Ocj*r+CJUC1- an CX u- a3 wm2 ec~ cu~)~ .P-ra, 2 warm *= a a at Lw 

wA31C) QI m-@-a-&: rCS w=rErcj.c-,w iC4"f-.0-, V1 CL=3IC1: u7 EfG ma +-, LCC: .~(21U*rr"Jtt:brm a,@-- E 3 L2113.*r-S=-s G*C-rcr;k: 3Lrn +-' .r 

rC- WV) 01& )1=1=c, 13 QIOVI O/Lm cuc--aro SZ a ti-- a Ccc-IF I;=*-&: + F *r- 0% o m 

a 3 .F- 0 U 

fiF m.2 2V)I-' Urn.rer 

EUaLE@mW@d+ 

23 LLT L. m 23-P-v- Qen &*vC"P" m >-a EP'" en 3-r- cs 

Q-. 

mu .A .4 "4- a. 

mi- * d 

w-8 rf: z3 h 
LC-- %*F 

Vt-F-, a I=: .I--. 

ceJL-6.J 

LCUU ws.3 0-a 

QJUO 

Q L. 

%-CS a- * Vt 

at" CULV) 

LOaJ 

C,%- vr 

Vt 
E I(- 3 0 .?-- 4-J L. m- e 

le- 3 v) 

t, 

a, +J 

leca 

+CUJ 

ale- ;61- Ucc- a -i- .r- V) 

%-W.a 

'r- 3 om m Q) lrr: CLa LS 
NOTE STATE OF A FOREST TYPE {OBTAHj *DEFINITI@M$ 

NOTE 

MACRO OBTAM f1A,IB,IC,ID,HE,IF,IG~CS,DS,ES,FS,GS,DA,DB~DCpDD,DE,DF9DG9 

X HC,HD,HE,HF,HG,FC,FDaFE,FF,FG,CR,DR,ER,FR+GR,CA9RL, 

X H,RV,SC,SD,SE,SF,SG,GXlOXaEX,FX,GX,GC,GDaGE,GF~GG,A,B~C~D~E~F,&) 

L OBTAH.K=OBTAH.J+DT*{$GON.JK-RV.JK) TYPE (AREA) 

N OBTAH=lA+IB+ICt1D+IEtIFtIGfCStDS+ES+FStGS INITIAL IMV, (AREA) 

R $CON.KL=CA,K COkVERSICN RATE (AREA/YR) 

R RV.KL=$RC .K+$RD,K+%RE *K+$RF .K~$RG.K REVERSION RATE (AREA/YR J 

A $RC .K=CX,JK*FIFGE(CR ,090BTAH.K,RL) REVERSION HABITAT C (AREA) 

A SRD.K=DX,JK*FIFGE (DR ,Q3i)i?TAH .K,RL) REVERSION HABITAT D (AREAJ 

A $RE,K=EX,JK*FJFGE(ER $O,ORTAH .K,RLf REVERSION HABITAT E (AREA) 

A bRF .K=FX.JK*FIFGE(FR ,O,OBTAH.K,RL) REVERSION HABITAT F (AREA) 

A SRG.K=GX ,JK*F IFGE (GR ,OPORTAH,K,RL) REVERSION YkBlTAT G (AREA) 

L A.K=A.J+DT*($TA.JK-$TOA.JK) HABITAT A (AREA) 

N A=IA INVENTORY WAB ITAT A 

R STA.KL=MAX( (H.K+$TOG.K+CA .K-RV .JKf ,O) TRANSFER TO HABITAT A(AREA) 

A H.K=CX.JK+DX.JK+EX.JKtFX.JKtGX.JK HARVEST TOTAL (AREA) 

R $TOA .KL=A .K/DA TRANSFER OUT OF A 

L B.K=B.J+DT*f$TOA.JK-$TOB.JK) HABITAT B (AREA) 

N B=IB INVENTORY HAB I TAT B 

R $TOB .KL=B .K/DB TRANSFER OUT OF B 

A C.K=HBTOB( $TOB.JK,$SDO.K,$FLC .K,$FD0.K,$FC0.KlIC,CSpDC~$AC9HC, 

X STOC.JK,SC.JK,CX.JK) HABITAT C (AREA) 

A D.K=HBTOB($TOC.JK,$SEO.K,$FLD.K,$FEO.K,$FCO,K,ID,DSsDD,$AD~HD~ 

X $TOD.JK,SD.JK,DX.JK) HABITAT D (AREA) 

A E.K=HBTOB($TOD.JK,$SFO.K,$FLE,K,$FFO.K,$FCOeK~IE,ES,DE~$AE~HE, 

X $TOE.JK,SE.JK,EX.JK) HABITAT E (AREA) 

A F.K=MBTOB($TOE.JK,SG.JK,$FLF.K,$FLG.K,$FCOeK,IF9FS9DF,$AF,MF, 

X $TOF .JK,SF .JK,FX.JK) HkB ITAT F (AREA) 

A G.K=HBTOB ( $TOF ,JK, NS , SFLG .K,NS , $FCO *K, IGsGS ,DG, $AG*MG, 

X $TOG.JK,SG.JK,GX.JK) HABITAT G (AREA) 

A $SDO,K=SD,JK+$SEO.K SALES HAB. D AND OLDER (AREA) 

A $SEO,K=SE .JK+%SFO,K SALES HAB. E AND OLDER (AREA) 

A $SF0 .K=SF .JK+SG.JK SALES HAB .F AND OLDER (AREA) 

A $FLC.K=(OBTAH.K*FC)/MAX($AC,HC)) FLOW HARITAT C fAREA/YR) 

N $AC=DA+DB+l AGE HABITAT C (YR) 

A $FLD,K=(OBTAH.K*FD)/MAX($AD,HD)) FLOW HABITAT D (AREA/YR) 

N $AD=DA+DR+DC+l AGE HARITAT D (YRf 

A $FLE.K=(OBTAH.K*FE)/(MAXf$AE,HE)) FLOW HABITAT C (AREA/YR) 

N $AE=DA+DB+DC+DD+l AGE HABITAT E (YR) 

A $FLF .K=(OBTAH.KfFF)/ (MAX($AF ,HF J ) FLOW HABITAT F (AREA/YR) 

N $AF=DA+DB+DC+DD+DEtl AGE HABITAT F (YR) 

A $FLG.K=(OBTAH .K*FG)/ (MAX ($AG,HG) ) FLOW HABITAT G (AREA/YR) 

N $AG=DA+DB+DC+DD+DE+DF tl AGE HABITAT G (YR ) 

A $FCO.K=$FLC .K+$FDO.K FLOW HAB. C AND OLDER (AREA/YR) 

A $FDO.K=$FLD.K+$FEO,K FLOW HAB. D AND OLDER (AREA/YR) 

A $FEO.K=$FLE .K+%FFO,K FLOW HAB. E AND OLDER (AREA/YR) 

A $FFO.K=$FLF,K+$FLG,K FLOW HAB. F AND OLDER (AREA/YR) 

A GC.K=(C.K/(FIFZE((,4TOC.JK+l),($FLC.K+l),HC)))+($AC-l) HAR AGE C 

A GD.K=(D.K/(FIFZE(($TOC.JK+l) ,f $FLD.K+l) lH $AD WAR AGE D 

A GE,K=(E,K/(FIFZE(~$TOC~JK+~) ,($FLE.I<+I) *HE A- WAR AGE E 

A GF .K=(F .K/(FIFZE(($TDC.JK+l) ,($FLF .Kt11 ,WF)))+($AF-1) HAR AGE f 

A GG.K=(G.K/fFIFZE(($TOC*JK+IJ, ($FLG.KtI], $-I HAR AGE G 

MEND 

C NS=O NULL STANDARD 

NOTE 

Figure 62. --Equations for the MCRQ OBTAH, opt1 ma1 bencf i ts from 

type and habitat transformtions, 
Tab1 e 9. --Definitions for dummy vari abf es used to 

define MACRO OBTAH 

Variable Definition 

IA--1G 

CS--CG 

DA--DG 

HC--HG 

FC--FG 

CR--GR 

C A 

fa L 

H 

RV 

SC--SG 

CX-GX 

GC --GG 

A--G 

INPUT 

Initial inventories (area) 

Initial accumulated sales (area) 

Delays (years) 

Harvest ages desi red (years ) 

Fractions rotating through habitats 

(decimal ) 

Reversion rates desi red (decimal ) 

Conversion to the type (arealyear) 

Reversion 1 imi t (area ) 

OUTPUT 

Total harvested rate (arealyear) 

Reversion from the type farealyear) 

Sell from habitats (arealyear) 

Removals from habitats (arealyear) 

Harvest ages (years ) 

Area of each habitat (area) 

The areas of seedling habitat, A, and sapling 

habitat, B, are calculated with level equations as 

in DYNAST-FAM. The transfer rate to seedling habitat, $TA, is the sum of the area harvested, ti, natural succession from the oldest habitat, $TOG, and 

the area converted to the type, CA, corrected for 

1 osses due to reversions, RV. 

MACRO HBTOB is used to compute the areas of 

habitats older than the saplings. The input and 

output variables for each habitat must be specified in the same number and in the order used in 

the definition (table 9). Some input variables 

to HRTOR are computed with equations in the MACRO 
OBTAH, These computations include the sales and 

flows ~f alder habitats and the harvest ages, The 

MACRO is ended with MEND. 

PTISO. The Timber and O~eninas MACRO 

The MACRO PflSO uses inrsrmatisn Prom OBVAH xo 

project the timber volumes har-vested from a type 

and Prom each habitat and the potential timber 

index for the type. PTISO is the potential timber 

index for the type and it 'is computed in the same 

way as described earlier. From initial values for 

opening sizes and variances, PTISO computes the 

annual number of openings and the average opening 

size. 

The first MACRO statement in PTISO (fig. 63) 

defines a number of inputs and outputs (table 10). 

Different yield tables are used for the differefit 

types. For this version, the format for TARXT is 

from 0 to 160 years in units of 10 years. If this 

format is changed, it should be uniform throughout 

the MACRO PTISO and should agree with the TTYI 

tables for a1 1 types. 

The MACROs are used to simultaneously simulate 

the transformations of a forest with multiple 

types, with different si 1 vi cultural modes, and with 

planned or natural type conversions. For each type 

the appropriate variables are substituted for the 

dummy variables used in the definitions. Variables 

are specified only for MACROs OBTAH and PTISQ because substitutions are automatically made for the 

MACRO HBTOB, which is inside the MACRO OBTAH, Appropriate constants and tab1 es are given for each 

type. The important control variables--such as desired harvest age, fraction rotating through the 

habitat, reversion rate, reversion 1 imit, and opening size--are located in a convenient place at the 

end of the model, In this version, this sector is 
NOTE 

NOTE POTENTIAL TIMBER INDEX, SIZE OF OPENINGS *DEFINITION* 

NOTE 

MACRO PTISO(CX,DX,EX,FX,GX,GC,GD~GE,GF,GG,H,TYPE,TTY~,YST,TMR,ISO,ISD, 

X VT, VC ,VD,VE,VF ,VG,NI),SO) 

A PTISO,K=VT.K/%TIN,K POTENTIAL TIMBER INDEX (DIM) 

A VT.K=VC,K+VD.K+VE,K+VF,K+VG,K TOTAL VOLUME HARVESTED 

A VC.K=CX.JK*bVUG,K*YST VOLUME HABITAT C HARVESTED 

A VD .K=DX ,JK*$VUD ,K*YST VOtilNE HABITAT D HARVESTED 

A VE.K=EX,JK*$VUE.K*YST VOLUME HABITAT E HARVESTED 

A VF .K=FX.JK*$VUF ,K*YST VOLUME HABITAT F HARVESTED 

A VG.K=GX.JK*$VUG.KXYST VOLUME HABITAT G HARVESTED 

A $VUG.K=TABXT(TTYI ,GC.K,OP160,1O) VOLUME UNITS HABITAT C 

A $VUD.K=TABXT(TTYI,GD.K,0,16O,f0) VOLUME UNITS HABITAT D 

A $VUE.K=TABXT(TTYI,GE.K,0~160,lO) VOLUMEUNITSHABITATE 

A $VUF.K=TABXT(TTYI,GF.K,Os160,1O) VOLUMEUNITSHABITATF 

A $YUG.K=fABXT~TTYI,GG,KyO~160,10f VOLUME UNITS HABITAT G 

A $TiH,K=(TYPE .K/TMR)*$TYI*YST TIMBER MAXIMUM (VOLUME) 

N $TYI=TABXT(TTYI ,TMR90, 160,O) TIMBER YIELD INDEX (VOLUME) 

A SO.K=FIFGE(H,K,(NDRMRN(ISOIISD))liISO,H,K SIZE OF OPENING(AREA) 

A NO.K=H,K/MAX(SO.K, .O2) OPENINGS HARVESTED (NUMBER) 

ME NO 

NOTE 

Figure 63.--Equations for the MACRO PTISO, potential timber index, 

and size of opening. 

Table 10.--Defini tions for dummy variables used to 

define MACRO PTISO 

Vari able Definition 

CX--GX 

GC--GG 

H 

TYPE 

TTY I 

Y ST 

TMR 

I SO 

I sn 

Kemova 

Harves 

Total 

TYPE f 

INPUT 

Is from habitats (area/year) 

t ages (years) 

harvest rate (area/year ) 

rom MACRO OBTAH (area) 

Table, timber volume (units, area) 

Yield standard (ft') 

Rotation for maximum timber (years) 

Initial opening size (area) 

Initial opening variance (area) 

OUTPUT 

Volume of timber removed, total (ft3) 

Timber removed from habitats (ft3) 

Openi ngs harvestedlyear (number ) 

Average openi ng si zelyear (area) 
caf led "Analytic Si 1 vicui ture Control s" because 

these are the controls used to examine the alternative silvicultural modes. 

Consider, for example, a forest in central 

Georgia (tables 11, 12, 13). The stands are classified into three types called pine, oak-pine, and 

hardwoods* The habitats and the types asre transformed by timber harvest, natural succession, 

timber stand improvements, and regeneration (Boyce 

and McCl ure 1975). Transformations may retai n the 

original forest type or convert the stand areas to 

a different type* The complexity of the transformations is illustrated with a diagram (fig. 64). 

To keep the illustrations simple, conversions are 

limited to seedling habitats and harvests are 

1 imited to mature habitats. 

PINE / TYPE I OAK-PINE / TYPE 2 t-tARDWOODS / TYPE 3 

Seedling 

Habitat 

Mature 

Habitat 

Old Growt 

Figure 64.--Diagram illustrating an example sf using the MACRO 

OBTAW Lo simulate the complex transformations of a forest with 

conversf ons among three types (cf, with Pig, 52). 
Table 11 .--TYPl, information for the pine type for 

a forest in central Georgia 

Symbol Median 

and diameter Age Invenhabitat range range Delay tory 

Inches Years Acres 

A1 seedling 0-1.9 0-8 8 106 

€31 sapling 2-3,9 9-1 5 7 409 

CI small pulp 4-6.9 16-20 5 807 

Dl large pulp 7-9.9 21-26 6 902 

El small logs 10-12.9 27-34 8 504 

FI mature 13-15.9 35-60 26 103 

G1 old growth 16+ 61-110 - 50 0 

Total 1111 2,831 

Table 12.--TYP2, information for the oak-pine type 

for a forest in central Georgia 

Symbol Median 

and di ameter Age Invenkabi tat range range Delay tory 

A2 seedlings 

B2 sap1 ing 

C2 pole-6 

D2 pole-8 

E2 pole-10 

F2 mature 

G2 old growth 

Total 

Inches 

0- 1 

1-4.9 

5-6.9 

7-8.9 

9-1 0.9 

11-15.9 

1Q+ 

Years Acres 

56 

238 

412 

210 

104 

96 

Cf 

1,116 
Table 13,--TVP3, information for the hardwood type 

for a forest in central Georgia 

- -- 

Symbol Medi an 

and di ameter Age Invenhabitat range range Delay tory 

Inches Years Acres 

A3 seed1 i ngs (3- 1 0-5 5 209 

83 saplings 1-4.9 6-35 30 396 

C3 pole-6 5-6,9 36-50 15 584 

03 pole-8 7-8.9 51-65 15 21 4 

E3 pole-10 9-70.9 66-85 15 105 

F3 mature 11-5 5.9 86-1 20 40 781 

G3 old growth 16+ 121-300 - 180 156 

Total 

When mature pine stands are harvested, some 

of the areas are regenerated back to pine and some 

to oak-pi ne. When oak-pi ne stands are harvested, 

some areas are regenerated to pine, some to oakpine, and some to hardwoods. Harvested hardwood 

areas tend to regenerate to hardwoods unless some 

cultural practice is used to regenerate them to 

pme, Many combinations of conversio~ rates are 

possible and each has a different flow of timber, 

cash, wi 1 dl i fe habitats, and other benefits. The 

MACRO OBTAH makes it possihfe to simulate these 

transformations for different si 1 vicul turd 

options. 

The structure of the MACRO OBPAH is essential ly the same as the core mde1 for DYNAST 

(fig. 52). Negative feedback loops have the goal 

of bringing about and maintaining the state of 

forest srgani zat i on that prsvi des the desi red opt i - 

ma1 combination of benefits, Harvest can he from 

any hahi tat and simultaneously from any comhi nation 

of habitats* 
The Georgia pine forest is used to il lustrate 

how "che inode1 is adapted to a forest. First, the 

essential information is obtained for the three 

forest types (tables 71, 72, 13). For the MACRO 

HBTOR, the pine type is designated TYP1, the oakpine type IYP2, and the hardwood type TYP3 (fig. 

65). Variables designated to be substituted into 

the MACROS are identified by appending a number, 

such as 1, 2, or 3, to agree with the type designation. Appropriate co~stants and tables are gjven 

values, 

Ten or more types could be used, but deci - 

sions and controls are less complex when the number 

of types is kept to five or less. For more than 

three types, the option jumbo, 3, for the DYNAMO 

compilsr should be used because the number of 

eq~jations can exceed 1,000 (Pugh 1983), 

NOTE ***********&OREST TYPE SECTOR^***********+* 

NOTE 

NOTE FOREST TYPE 1 **GEORGIA Pf NE TYPE*** 

A TYP1,K=OBTAH(IAl,IBlfIC1,ID1,IE1,IF1,IG1, 

X CSl,DS1,ES1,FS1,GSI9 

X DAi,DB1,DC1,DD1,3EIsDFlIDG1, 

X SC1,K,SDl.K,SE1.KySFl~K,SG1.K, 

X CX1.JK,DX1.$K,EX1.JK9Fxl1JK9GXl1JK, 

X GCI.K,GDi,K,GE1.K,GFZ.K,GG1.K9 

X A1.K,Bi,~,C1.K,DL.Y.rEi.K,F1.K,G1lK) 

NOTE 

NGTE TIMBER AND OPENINGS TYPE I **GEORGIA PINE TYPEk* 

NOTE 

A PTi .K~PTISO(CXl.JK,OXIIJK~EXIIJXPFXlLJK,GX~~JK~ 

X GCI.K,GDI.K,GE1.K,GF1,K,GGt~K,HI.K~T"r'PZZK,TTYi~YST~~TMR1~~SO1yISDl, 

X VTl,K,VCl .K,VDI.K,VEl.K~Vfl~iC~VG1lKPNOIIK~~Ol.K) 

C iA1=106/IB1=409/IC1~8Oi/IBI=9O2/TEi=5fl4/~F1=lfl3/~G~~O 

C CSI=O/DSf =O/ES1=O/FSl=O/GSl=O 

C DA1=8/CB1=7/DCl=S/DD1=6/DEi=8/DF3=26/DGt=50 

C YST1=175O/TMR1=30 

T rT~l=~~.3/.ss~1~l.27/1~38~1.1.4O~i.~(3~~.51~1.53~l *55/ 

X 1,5711.58/1.59/1.6/'I.6/I16 
Gont i nued 

NOTE 

MOTE FOREST TYPE 2 **GEORGIA OAY-DINE TYPE** 

NOTE 

A TYP?.K=OGTAHI IA?, IR2* IC2, iR2, iE2, iF2, IG2, 

X CS2,DSZSES2,FS2,GS2, 

X OAZ,DRZ,DC2,D82,DE2,OF2~, 

X WC2,HD2,HE2,HFZ,Y62, 

X FCZ,FDZ,FE2,FF2,FG2, 

X CR2,DR2,ER2,FR2,GR2, 

X CA2,KyRL2,H2,K,RV2.3K, 

X SC2,K,SD2,C,SE2.K,SF2,k:,SG2eK, 

X CX2.JK,DXZ.JK,EXZ.JK,FX2.JK,GX2.JK, 

X GC2.KYGD2,K,GE2.K,GF2.KfGG2*K, 

X A2.K,R2,KpC2.K,D2,K,E2,K,F2.V,62.K) 

NOTE 

NOTE TIMBER AND OPENINGS TYPE 2 **GEORGIA OAK-PINE TYPE** 

NOTE 

A P12.K=PTfSO(CXZ.JK,DX2.JK,EX2.JK,F)i22dK,GX2.JK, 

X GC2,K,GD2.K,GE2.K,GF2.K,EG2.K,H2.K,IYP2.K,TTY2,YST2,TMR2,iSOZsISD2, 

X VT~,K,VC~.K,VD~.K,VEZ~K~VF~~K,V~~.K,N~Z.K,S~Z.K) 

G IA2=56/~B2=238/IC2=412/ID2=2IOjIE2=1G4/IF2=96/IG2=0 

C CS2=O/DS2=O/ES2=0/FS2=O/GS2=0 

C ~A2=~/D~2=25/DC2=15/DD?=15/DE2=15/DF2=35/DG2=1~0 

C YST2=2680jTMR2=50 

T TTY2=~/.25/.45/.6/.81/1/1.IF;/l.33/1.47jI~6I/~~75/l,85/~e~~/l.~~/ 

X 1.94/1.46/1.97 

NOTE 

NOTE 

NOTE FOREST TYPE 3 **GEORGIA HARDWOOD TYPE** 

NOTE 

A TYP3.K=OBTAH(IA3,163, IC311D3,!E3,1F3,1G3, 

X CS3,DS3,ES3,FS3,GSJY 

X DA3,DB3,DC3,DD3,DE39DF3fOG3, 

X HC3,HD3,HE3,HF3,HG3, 

X FC3,F03,FE3,FF3,FG3, 

X CR3,DR3,ER3,FR3,GR3, 

X CA3.K,RL3,H3,K,RV3,jK, 

X SC3.K,SD3.K,SE3.K,SF3,K,SG3.Ky 

X CX3.JK,DX3.JK,EX3.J#,FX3.JK,GX3.JK, 

X GC3,K,GD3,K,GE3.K,GF3.K,GG3.K3 

X A3.K,B3.K,C3.K,D3.K,E3.K,F33KyG33K} 

NOTE 

NOTE TIMBER AND OPENINGS TYPE 3 **GEORGIA WRRDWOUU TYPE** 

hOTE 

A FT3.K=PTiSO(CX3.JK,3X33JKIEX33JK,FX33JK5GX3~JK, 

X GC3.K,GD3.K,GE3.K,GF3~K,GG33K,H3,K,iYD33K,TTY39YST39~BR3,lS~3,~S~3, 

X VT3.K,VC3.K,VB3.K,4E3,K,VF33K,VG3,Kfk03.K3S~3.K) 

C IA3=209/133=396iIC3=584/I 0321 iZ~IE3=105/1F3=781 /IG3=156 

C CS3=0/DS3=0/ES3=O/FS3=0/GS3=0 

C DA3=5/DB3=30/DC3=15/DD3='? 5iDE3=I5!DF3=4O/DG3=180 

C YST3=2680/TCR3=50 

T nY3=9i .23/ .41/.0/.81/1/1 118/if.33/II~7~~.61j~.~~/~e8~/~e8~/

X 1.94i:.96/1.37 

NOTE 

Figure 65.--Equations for the forest type sectors for a pFre forest 

in central Georgia. 
Supp2 ementary Informat ion 

This part of the model is expanded to calculate information n~edert far printing arrd plotting* 

The order of the equations is not important and 

equations can he added and rernovcri as the model is 

manipul at~d, If conversions are desired or if thky 

occlrr natirral iy (Royce and McCl lire 19751, eqvat ions 

f~r des i gnati ng these transformations may be locat~d in this sector. This sector s's a1 so a convenient place to calculate variables used in more 

than one algorithm for benefits and impacts, Examples of supplementary equations are given in 

figure 66. 

Benefit and larpace Idormtion 

7h-i s sector includes the a1 gori thms for whatever benefits and effects are to he examined, The 

techni yues for deveiopi og a1 gorithms are descri bed 

in chapter 5, The algorithms may be inserted and 

reiii,~ved at wi l l w-i thcut affecting "cansformations 

in the skate of forest organization. An example 

is the hahitat for deer (fig. 67). 

VOTE 

NOTE SIJPPLEMENTARY INFORMATION 

NOTE 

A CAI ,K=(RV2.JK*Ci2i)f(RV33JK*C?31~ CONVFRT TO TYPl (AREA) 

A CA2,K=(RLl.JK*CT12)+(RV33JK*CT32) CONVERTTOTYP2 (AREA) 

A CA3,K=jPVl .J~*CT13)+'FV2+JY*CT73) CONVERT TO T'fP3 (AREA) 

A PP1, K=TVPl .(/TAW .Y PEPCENT PINF TYPE 

A TAH.Y=TYPI,K+TYP7.Y+iYP33K TCTAL A2EA hAPITkTS 

P PP2.K=TYF7.K/TAH.K PERCERI OAF-PINE TYPE 

A Pp3,F=TYP3.K/TAM ,K PEPCENT HFPDWOOO TYPE 

A PSE .K=IA?.K+A2.Y+A3.V,/TAtitiK SEEDLING PARITAT i"? 

P 0PM.K=Th0.K/f'TAP.Kt6dOl OPENIYGS PEP 59. MILEIYR 

A 730. k=V01 .V*N02.K+NO3.K TOTAL OPEhl NGS (NUMBEP J 

A dSO.K=TH.K/YAX(TNO.Y,1; AVERAGE SiZE OF OPENINGS (AREA) 

A TH.K=Hl ,KcH2,K+H3,$ +OTAL HARVEST iARE4\ 

* >>>>>>,>>> HAPD VAST <<<< 

A YW.F=W; .K*HMD.L HADD MPST P3TE4TIPL 

A H~i.K=Tk9HLjTP~~,PuVC.Y,O~~i2.I~ HAQD MAST IYCREASE IYDEX 

A P&M0.Y=~F2.K+S2,K-FZ.K+G3.Y~/TiliYYK wO2G VAST "3B:TATS (9; 

TPMI=.l/.C"/.51.7/.F;/ .O,'.Q5, i VA9" YC ST INGPtPSE TAPLE 

k HM3.K=TA&HL"ThP3,POS3K1r)9e~,tf~ PbP? MAST DECPEASE IYGEY 

A POS.K=IG?.K+G~,KI/";+P.K 51 9 dAf?",k-' 4fBI'k;S '7' 

Figure 66.--Equations for supplementary information. 
NOTE ***BENEFIT AND IMPACT EQUATIONS**** 

NOTE 

* >>,>>>>>> DEER ec<c 

NOTE 

A DEER ,K=HTD .K*OTD ,KfHMDE .K DEER HABITAT POTE NTIAL 

A HTD.K=TABHL~THTD,PSE.KIC,0,~07~.01) BROWSE POTEN, FOR DEER 

T THTD=.1/.2/.4/.7/.9/~95/,98/I TABLE BROWSE POTEN. 

A OTD,K=TABXT(TOTD,OPM.KIII, ,5, *I) OPENING DISPERSION FOR DEEP 

T TOTD=,2/ .4/ .7/ .$/ .95/1 TABLE OF DEER OPENING DISPERSAL 

A HWDE.K=TABHL(THMDE,WF1.K90,.5,.1) HARD MAST FOR DEER POTEN. 

T THMDE=.3/ ,4/.6/.8/lfi TABLE HARD MAST FOR DEER 

NOTE 

Figure 67.--Equations for benefit and impact information. 

The benefit equations are structural ly independent of the core equations except that they use 

the output of the core model. The benefits depend 

di rectly on the organizational state, Thus, 

computing benefits is a matter of relating each 

benefit to the state of the forest or to the 

proportion of habitats. Once these relations or 

a1 gori t hms are determi ned by research and experience, benefits can be projected in relation to 

changes in the state of the forest. 

Fol lowing are several important poi nts about 

the benefit and impact equat i ons : 

1. The core equations are not dependent on 

any of the benefit equat i ons. 

2. Benefit equations can be added, modified, 

or removed from the DYNAST model without 

affecting any of the other benefit 

algorithms and without affecting the core 

equations* 

3. Algorithms can be written for any benefit 

and impact that can be related to the 

organizational state of the forest. The 

important variables are the distribution 

of forest stands within forest types by 
age and area classes, the timber harvest 

rates, and the size of opening formed by 

timber harvest. 

4. The management modes determine the organizational states, which, in turn, determine the combinations of benefits and 

i rnpact s 

Gash Flow Mgorithn 

The cash flow algorithm is included or 

excf uded at wi 1 l . When used, the cash inf 1 ow and 

outflow equations must be modified to reflect the 

mu1 tiple forest types, regeneration costs, conversion costs, timber sales, and sale of hunting and 

recreation ri ghts (fig. 68). 

Prqram Controls Sect~r 

This sector is used to give the specifications 

for DT, LENGTH, PRINT, and PLOT. Once these controfs are selected for a particular forest, few 

changes are needed when di fferent si l vicultural 

strategies are being evaluated (fig. 69). 

Aml y tic SiB vicud ture CantraJs 

This sector contains the controls used to 

change the silvicuftural mde; it is the set of 

control variables in the decision loop (fig. 3). 

The first run is called ZERO because a11 variables 

are set at zero or at natural transformation rates. 

In the reruns, concern is with the variables that 

ident-ify a sil vicul tural mode rather than with 

manipulating a1 1 of the constants. Both constants 

and tables my be changed in the reruns. When C 

and I statements are used in reruns, the constants 

and tables revert to the values in the ZERO run. 

RUN must be the last statement in each rerun 

(F-ig, 69). 
NOTE 

NOTE CASH FLOW ALGORITHM 

MOTE 

A NPV,K=I NAD .K-OTAD,K NET PRESENT VALUE (J) 

A INAD.K=INA.K/DSC .K INFLOW ,SUM DISCOUNTED f $1 

L INA,K=INA,J+DT*(INR.JK) INFLOW SUMMED ($) 

N lNA=IIN INITIAL INFLOW SUM (b) 

R INR.KL=(INA.K*(EXP(RIN*K)-1) )+IM.h: INFLOW RATE ($/YR) 

A RIM.K=TABXT(TRIN,TIME.K,O,lO,l) REINVESTMENT RATE (DECIMAL) 

T TRIN=.l/ .l/.l/.l/.l/ Il/.l/.l/.l/.l/.l/ REINVESTMENT TABLE 

A IN,K=NH,K+VYR.K*MOS.K TOTAL INFLOW ($1 

A NH.K=NCl.K+NDl.K+NEl.K+NFl.K+NGl.K+NC2.K+ND2.K+NE2~K+NF2.K+NGZ.K+ 

X NC3.K+ND3,K+NE3.K+NF3.KfNG3.K TOTAL DOLLARS FOR TIM. 

A NC1.K=VC1.K*(TABXT(TVAlfGCltK,lOylOO1lO)~ DOLLARS FOR TIM 

T TVAl=O/.l2/.32/.42/.5/ .9/1.1/1.3/1.35/1.4 PI NE STUMP PRICE/ AGE 

A NDl.K=VDl .K*(TABXT(TVAl,GDl.K,lO,lOO,lOf) DOLLARS FOR TIM 

A NE1.K=VE1,K*fTABXT(TVA1~GE1.K,lOylOOslO}) DOLLARS FOR TIM 

A NFl.K=VFl,K*(TABXT(TVAl ,GFILK,lO,lOO,lO)) DOLLARS FOR TIM 

A NGl.K=VGl .K*(TABXT(TVAl,GG1.K,10,100,10)) DOLLARS FOR TIM 

A NC2.K=VC2.K*(TABXf (TVA2*GC2*K,0,160,20)) DOLLARS FOR TIM 

T TVA2=0/ .04/.08/ .12/ .3/ -321 *34/ .44 f.5 HARDWOOD STUMP PRICE 

A ND2.K=VD2,K*(TABXT(TVA2~GD20K~Oy160~20)) DOLLARS FOR TIM 

A NE2.K=VE2.K*(TABXT(TVAZ,GE2*K,0,160,20)) DOLLARS FOR TIM 

A NF2,K=VF2,K*(TABXT(TVA2mGF22Kp0,160,20)) DOLLARS FOR TIM 

A NG2.K=VG2.K*(TABXT(TVA2,GG22K,0,160,20)) DOLLARS FOR TIM 

A NC3.K=VC3,K*(TABXT~T\rA22GC33KIOi 160f20) 1 DOLLARS FOR TIM 

A ND3.K=VD3,K*(TABXT(TVA2.GD3~KyO,160,2O)) DOLLARS FOR TIM 

A NE3.K=VE3.K*(TABXT(TVA2,GE3,K,0s160y20)) DOLLARS FOR TIM 

A NF3.K=VF3,K*(TABXT(TVA2.GF311K,0s 160,ZOf DOLLARS FOR TIM 

A NG3.K=VG3.K*{TABXT(TVA2.GG3.K,O,l60,20jj DOLLARS FOR TIM 

A VYR.K=TABXT(TVYR,TIME .K,O,20,5) STUMPAGE PRICE IkCREASE 

T TVYR=1/1,1/1.2/1.5/1.7 TABLE STUMPAGE INCREASE 

F, MOS .K=TABHL JTMOS ,ASO.K, .1,5.5, .9) MULTIPL IER FOR OPENING SIZE 

T TMOS=,l/ .45/.65/.8/.9/ *95/1 MULTIFLIERISIZE OF OPENING 

A DSC.K=EXP((MAX(l,TIMEEK))*DSR.K) CONTINUOUSDISCOUNTDIVISOR 

A DSR.K=TABXT(TDSR,TIME.K,O,ln,l) DISCOUNT RATE 

T TD§R=.1/.1/1/1/.1/1/.1/1//.1/.1 OISCOUNT TABLE 

A OTAD.K=OTA.K/DSC .K OUTFLOW, SUM DISCOUNTED ($) 

L OTA ,K=OTA .J+DT* (OTR ,JK) OIITFLOW, SIJMMED (Sf 

N OTA=IOT INITIAL OIITFLOW SUM ($) 

R OTR,KL=(OTA.K*(EXP(DSR.K)-l))+QT.K OIITFLOW RATE ($/YR) 

A OT.K=(OVH.K*TAH.K)+fMRK.K*TH.K)+(IN.K*TXR) TOTAL OUTFLOW ($) 

A OVH.K=TABXT(TOVK,TIME .Ky0920y 5) OVERHEAD COSTS ($1 

T TOVH=3/3.5/5/6/7 TABLE OVERHEAD COSTS ($) 

A MRK,K=TABXT(TMRK,TIME.K,0,20,5) MARKETING COSTS (bj 

T TMRK=8/10/12/15/17 TABLE MARKETING COSTS ($1 

A PI.K=INAD.K/MAX(OTAD.K,l) PROFITABILITY INDEX (DIM) 

A EAR.K=NPV.K*ffDSR.K*DSC.K)/(DSC.K-lf) EQUIVALENT ANNUAL RENT ($/YRf 

A RRR.K=EXP(RR2.K*LOGN(RRl.KJ)-1 REALIZABLE RATE OF RETURN 

A RR1.K=FIFGE((INA,K/MAX(OTAD.K~1)),lyNPVVK,l) STEP 1 FOR RRR 

A RR2.K=l/(MAX(l,TIME,K)) STEP TWO FOR RRR 

C TXR=.25 TAX RATE (TAX BRACKET FRACTION) 

NOTE 

Figure 68.--Equations for cash flow for three forest types. 
NOTE PROGRAM CONTROLS SECTOR 

NOTE 

SPEC DT=. 25/LENGTH=100 

A PRTPER.K=1+STEP(79,21) 

PRINT IfSCl ,SD1/2)SE1,SF1/3)SGlySC2/4)SD2,SE2/5)SF2,SG2/6)S&3,SD3/ 

X 7)SE3,SF3/8)S63,VT1/9)VT2,VT3/10~TW~0PM/11)TN0,ASO/12)HMyHTD/ 

X 13)NPV,PI/14)EAR,RRR 

A PLTPER.K=.S+STEP(7,5,20.5) 

PLOT PT1=1,PT2=2,PT3=3(0,2f/PPl=P,PP2=O,PP3=W,DEER=D{O,l) 

!VOTE 

NOTE **ANALYTIC SILVICULTURE CONTROL*** 

C DRT=O DELAY TIMBER REMOVAL (YR) 

C I IN=O/TOT=O INITIAL INFLOW AND OUTFLOM OF CASH($) 

C CTIZ=O/GTl3=0 TYPl TO TYP2 AND TYP3 (DECIMAL) 

C CT21=O/CT23=0 TYP2 TO TYPl AND TYP3 (DECIMAL) 

C CT31=O/CT32=0 TYP3 TO TYPl AND TYP2 (DECIMAL) 

C RLl=O/RL2=O/RL3=O REVERSION LIMITS (AREA) 

C ISDl=O/IS02=O/ISO3=0 DESIRED OPENING SIZE (AREA) 

C ISD1=O/ISDZ=O/ISDJ=O DESIRED OPENING DEVIATION (AREA) 

C HCl=O/HD1=0/HEl=O/HFl=O/HGl=O HARVEST AGE DESIRED (YR) 

C WC2=O/WDZ=O/HE2=O/HF2=0fH62=0 HARVEST AGE DESIRED (YR) 

C HC3=OfHD3=O/HE3=O/NF3=O/HG3=0 HARVEST AGE DESIRED (YR) 

C FC1=O/FDl=O/FEl=O/FFl=O/FGl=O FRACTIONS ROTATING (DECIMAL) 

C FC2=O/FD2=O/FE2=O/FF 2=O/FG2=0 FRACTIONS ROTATING (DECIMAL) 

C FC3=O/FD3=O/FE3=O/FF3=O/FG3=0 FRACTIONS ROTATING (DECIMAL) 

C CRl=O/DRI=O/ERl=O/FR1=O/GRI=O REVERSION RATES [DECIMAL) 

C CR2=O/DR2=O/ER2=O/FR2=O/GR2=0 REVERSION RATES (DECIMAL) 

C CR3=O/DR3=O/ER3=O/FR3=0/6R3=0 REVERSION RATES (DECIMAL) 

RUN ZERO 

C HEl=30/FE1=.3/HF1=45/FF1=.7/1SO1=30/ISD1=2 

C HD2=50/FD2=1/IS02=3O/ISD2=2 

C HE3=80/FE3=.8/ HG3=26O/FG3=.2/ISO3=1O/ISD3=1 

RUN OPTIOFd 1 

C XE1=30/FE1=,3/HFl=45/FF1=.7/IS01=3O/ISD1=2 

C HD2=50/FD2=1/ I S02=30/ ISM 

C HE3=80/FE3=,8/HG3=26O/FG3=.2/IS03=10/1SD3=1 

C CR1=.54/DRl=.54/ER1=.54/FRl=.~GR1=.54 

C CR2=.73/DR2=.73/ER2=.?3/FR2=,73/GR2=,73 

C CR3=.16/DR3=.16/ER3=.16/FR3=.16/GR3=,16 

C CT12=,68/CT13=.32/CT21=a26/CT23=.74/Cr31=.l2/CT32=.88 

RUN OPTION2 

Figure 69.--Equations for the program and the analytic 

silviculture controls. 

Sequence of the Sectors 

The constraint is that user-defined MACROS 

must be defined before being used. The important 

sequence is to define MACRO HBTOB, MACRO OBTAW, 

and MACRO PTfSO in this order. The other sectors 

may be in any desired sequence, The sectors are 

described in a convenient order* The first display line in the model should be an * statement to 
identify the version of OYNAST and the forest; the 

last should be an RUN statement, 

Exercises Without Type Convessio~l 

Thirty percent of the pine type is to be harvested at age 30; 70 percent is to be harvested at 

age 45. The desi red opening size is 30 + 5 acres. 

The cantrol s are: 

A1 1 of the oak-pine type is to be harvested at 

age 50 with openings 30 + 5 acres. The controls 

are: 

Eighty percent of the hardwood type is to be 

harvested at age 80 and 20 percent at age 260. 

Openings are to he 10 + 2.5 acres. The controls 

are: 

The desired variance for opening size must he 

divided by 2.4 because of the function NORMRN (Pugh 

1983). Run this option. What are the expected 

benefits for the next 10 years? 

What are the benefits expected when 90 acres 

of mture pine are initially sold, FSl=90; 200 

acres of oak-pine, pole 8, are initially sold, 

DS2=200; and 700 acres of mature hardwoods are 

initial ly sold, FS3=700? 

What are the benefits of delaying the contract . 

for removals, DRT, from 2 to 5 years? 

Develop three strategies to present to your 

organization; include a statement of organizational 

goals and exclude type conversions. 
Reversions from and conversions to forest 

types change the organizational state of the forest 

and the avail ahi lity of benefits, Roth natural and 

cultural forces change the forest type area. Retween 1961 and 1972, pine and hardwood forest land, 

cropland, and urhan land in Georgia were converted 

and reverted (Boyce and McClure 1975). How these 

kinds of transformations affect the avai labi 1 ity of 

forest benefits can he simulated with the DYNAST 

model, For illustration, I use the three forest 

types previously described. 

Fol lowing natural mortality and harvesting 

without cul tural practices to di rect regeneration, 

the reversion rate From pine is approximately 

0.54, that from oak-pine is 0e74, and that from 

hardwoods is 0.16. These proportions of the areas 

harvested are removed from the original type and 

converted to a different land use, The actual land 

area reverted at time K is the area harvested or 

cleared by natural mortality r~uttiplied by the 

reversion rate. Thus, the amounts of area reverted 

are a function of harvest and mortality which 

create openings for regeneration in mature and oldgrowth stands. 

The reversion rates are inserted into the 

model as values for CR--GR (fig. 69). The same 

rates are used for all age classes because there 

is no evidence for differences among the habitats, 

The analytic si 1 vicul ture control s for reversion 

rates are written: 

For this example, the areas reverted from one 

type may be converted to the other two types at 
Table 14,--Defini tions of quantity names for conversi ons, and quantity names and conversion rates 

for three forest types in central Georgia 

Definition of quantity Quantity name 

name and value 

TYPl converted to TYP2 

TYPl converted to TYP3 

TYP2 converted to TYPl 

TYP2 converted to TYP3 

TYP3 converted to TYPl 

TYP3 converted to TYP2 

different rates. By using the Georgia data (Boyce 

and NcClure 1975), these conversion rates are calculated and inserted into the model as values for 

CT12--CT32 (table 14). Note that the sum of the 

reversion rates from a type, CT13 plus CT12, should 

equal 1 unless the manager intends for some proportions of the reversions to be lost from the forest, 

Insert the reversion and conversion rates into 

the model. Changes in potential timber indices, 

deer habitat, and cash flows can be examined for 

di fferent si l vicul tural modes and di fferent reversion and conversion rates. For a given silvicultural mode, what is the affect of reducing the 

pine reversion rate to 0.1? 

Reductions in pine reversion rates incur costs 

for reducing the regeneration and the growth of 

hardwoods, In the economic a1 gori thm, write equations for the outflows of money to reduce pine 

reversion rates. The costs for different kinds of 

culture depend on the area of pine harvested at 

time K, H1.K. These costs for the initial pine 

regeneration are added costs to obtain the total 
costs for reducing the pine reversion rate* These 

costs are added to the total outflows (0T.K); any 

inflows that may result from regenerating the harvested land to pine, such as tax incentives, are 

added to the total inflows (1N.K) (fig. 68). 

Use some realistic costs and tax incentives to 

exarni ne potent i a1 returns from regenerat i ng pi nes 

after the pine harvest, When and how do such investments change NPV and EAR? HOW do discount and 

reinvestment rates interact with investments in 

pine regeneration to change the values of EAR? 

Analyses similar to the preceding one can be 

made ta evaluate the benefits expected from converting oak-pine and hardwood stands to pine. 

These kinds of analyses can be compared with silvicultural modes that are dependent on scheduling 

harvest rates, openi ng si zes, natural regeneration, and natural conversion rates. Differences 

in EAR are appropriate for examining economic 

differences in relation to other benefits. 

Some management pol i ci es requi re a certai n 

proportional di stri bution of di fferent forest 

types. The results of this kind of policy can be 

simulated with DYNASP by using the controls for 

reversion limits, RL (fig. 69). For example, the 

original inventory for the Georgia forest indicated 

44 percent of the area was in the pine type. I 

assume that no regeneration investments will be 

made unti 1 the pine type declines to 20 percent of 

the forest, The intent is to fulfill a policy that 

says diversity of habitats for some plants and ani - 

ma1 s requires 20 percent of the forest to be in 

pi nes 

The "Analytic Si 1 vicul ture Controls" are used 

as follows, The reversion limit for the pine type, 

RL1, is given the value equal to 20 percent of the 

total forest area, RL1=1278 acres. The pine type 

will revert after harvesting at the natural rates 

until the area of pine type, TYP1, equals 1,278 

acres (table 14). Mhen this limit is reached, a 

cost will be incurred to maintain the pine type* 
The cash otitf'tows for regenerating pine are O until 

the 20 percent limit for pine is reached, then 

sutflows are begun in the cash Plow algorithm, 

This simulation is achieved in the model with the 

limit function FIFGE. h the cash flow algorithm 

the costs for pine regeneration are multiplied by 

O when no regeneration costs are incurred and by 1 

when there are outflows for regeneration, The 

switch is controlled in the function FIFGE with the 

arguments: O,l,TYPl.K,RLl. The multiplier is 0 

until the pine type equals KLI, then the multiplier 

is 1. The reversion limits for oak-pine, RL2, and 

for hardwoods, RL3, remain 0. 

All of the reversion limits, RLl, RL2, and RL3, 

may be given values simultaneously if the sum of 

the values is less than or equal to the total area 

of the forest. 

A Checklist for Silviculture Controls 

DRT is in years and is limited in the DYNAST 

program to a minimum of 1 year for timber removals 

after sales. 

IIN and IOT are initial inflows and outflows 

in dollars. 

CT12 plus CT13 should equal 1, CT21 plus CT23 

should equal 1, and CT31 plus CT32 should equal 1. 

More area cannot be converted than is reverted and 

a11 reverted land should be converted to some use. 

4L1 plus RL2 plus RL3 may have values from 0 

up to the total forest area. 

IS0 , initial opening size is a guide for inplace deFisions. 

ISD , is the desired variance in opening size 

divided ry 2.4 (Pugh 1983: 28). 

HC --HG are harvest rates expressed as rotation pe'i;iodsor desi red ages of harvest and are 
measured in years. This c~ntrof is used in conjunction with: 

FC --FG , which are the fractions of a type 

rotating through the habitats. The sum of FC --FG - 

must equal 1 because it is not possible to ro'fBte 

more or less than is in the real forest* 

When a harvest age, H , is given in years, no 

area is diverted from the Fahitat to seedlings 

unless a fraction to be rotated, F - , is also given. 

When a fraction, F , is given and the harvest 

age, ti-, is 0, the appropriate area is diverted to 

seedlings with the minimum age for the habitat 

being used as the harvest age. 

CR - --GR are reversion rates with the ranae 0 

to 1. ~onve>ions by natural mortality occur only 

from the oldest age class. For example, when the 

age range for an old-growth h3hitat is from 121 to 

300 years (table 13) and a! 3 harvest controls, YG 

and FG , are 0, the natural mortality rate is bass 

on 300-years. If a reversion rate, GR , is given, 

the area reverted at tfme K will be the reversion 

rate mt~itiplied by the natural mortality rate for a 

300-year rotation period. 

The maximum reversion rate can he 1 for a71 

habitats simultaneously but no reversions occur, 

except as noted above, unless timber is harvested; 

"cat is, a value is given to F - for the habitatIA --IG are the initial inventory; CS---GSare the-areas of timber sold and stand7;na: TTY 

is the yield table; YST is the yield standardr and 

TMK is the rotation peF^iod for the type. Any of 

tile?% constants and the yield table may be changed 

in reruns. 

TSLF, the sell fraction table, can be changed 

i n rerum to exarni ne al ternati ve pol i ci es For harvesting accumulated timber such as for Forests composed mostly of ofd-growth stands. 
Chapter 9 

Censervat ion of Bio1 ogi cal Ci versi ty 

Overview 

Diversity is being different. Biological 

diversity means differences in elements such as 

genes, amino acids, flowers, species, and plant 

and animal communities. Information ahout diversity is usual ly expressed as the di stri hution 

of items, such as individuals, among different 

classes of an element, such as a species. This 

chapter is concerned with biological diversity 

and its conservation, 

The phrase "biological di vetrsi ty" and the 

word "'di versi ty" are used in si l vi cul lure, forest 

measurements, land management, planning, wi lcil i fe 

management, and forest insect and di sease management. In a managerial context, diversity is maintained or increased to provide human benefits. 

Many biologists use diversity in a functional rather than a managerial way, The Functional approach is to search for an understanding 

of the functioning of forest communities rather 

than directing the forests to provide human benefits. The functional approach leads to the computation of many multifaceted diversity indices 

(Hutchinsan 1978; May 1976; Pielou 1977; Southwood 

1378; Whittaker 1972). These indices are useful 

to some biologists but are not especially useful 

to forest land managers, si 1 vicul turi sts, and 

wi 1 dl i fe managers rn 

Pub1 i c concern for mai ntai ni ng certai n ki nds 

of di versi ty in renewable resources is expressed 

in the National Forest Management Act of 5976 

(USOA FS 1983a), The Act requires that diverse 

plant and animal communities be provided to meet 

multiple use objectives, The Act specifies that 
steps be taken to preserve the diversity of 

exi st i ng tree species To meet these provi si ons , a1 ternat i ve management pl ans ana def i nit i ons must 

be devel oped. 

The pur2ose of this chapter is to define 

"diversity" as it applies to forest management 

and to discuss concepts relative to maintaining 

a desired stability of forest plant and animal 

f ifss Data presented support the contention that 

the natural succession of forest stands be monitored for use in making management deci si ons, 

assuring diversity, and keeping multiple use objectives congruent with consumer attitudes toward 

soci al and economi c benefits, 

The kinds of animals, pl ants, and microorganisms differ within and among forest stands, 

As time progresses, these renewable resources 

change in kind and in proportion from place to 

pface. These variations are recognized as 

"clfversity," which is the condition of beins 

different. The cl assificalion, measurement, and 

control of the elements that make up forest and 

range diversity are activities associated with 

managing renewable resources* ft is the proportional distri butisn of diverse situations, such 

as different combinations of species and hahi tats, 

that determines the availability of timber, wild1 i fe, range production, recreation, streamf l ow, 

esthetics, and other benefits (Boyce 1977, 1978b; 

Flood and others 1977; Siderits and Radtke 1377). 

Classification, mensuration, and statistical 

sampling techniques have been developed to distinguish and measure differences in the elements 

of communities (Husch and others 1972; Wenger 

1984). These elements incl ude tree species, forest types, animal a~d plant populations, timber 

volumes, browse and hard mast, stand age classes, 

site index, and stand conditiov classes. Thus, 
these bi fferences , which are viewed as renewabf e 

resource diversity, are defined* Criteria are 

specified so that one animal species can he consistently distinguished from another; conti nuums, 

such as tree diameters and stand conditions, 

can be separated into classes; and differences 

in management decisions can be statistical ly 

determined. 

Briefly, a definition of renewable resource 

diversi ty is the @ti fferences in the elements of 

the biological communities, The criteria are: 

1, Identify elements of the community that 

are heing considered (i.e., tree species, 

bird species, forest types, stand conditions, age classes). 

2, Speei fy measurements or characteri stics 

that eval uate or di sti ngui sh elements 

(i .e., di fferences between species, 

measurements for cl asses of a cont i nuum) . 

3, Describe how the differences hetween elements are meaningful for management deci - 

sions (i ~e., stand condition cf asses, 

plant or animal habitat, tree sizes, and 

the wood product potential ). 

This definition of '"diversity" is essentially the same as in most dictionaries, The criteria provide the scientific basis for different 

people to repeat observations and measurements 

and, thus, provide sci enti f i c credi bi l i ty for 

management deci si ons (Bri dgman 1927). 

A wel l -known concept for providing a timber 

benefit helps to i l lustrate how resource diversity 

is related to a benefit. Timber can be harvested 

only from stands that have trees large enough to 

meet a definition for timber, I define "timber'" 
as trees I1 inches d.b*h. and larger and define a 

mature-timber stand as one with kal f of the domi - 

nant and codomi nant trees qual i fyi ny as timber, 

If a Forest has no timber, no timber benefits can 

be derived immediately. IF all the stands in the 

forest are mature, the maximum timber benefit can 

be achieved in the shortest time. However, if an 

annual timber benefit is desired for a long period 

of time, it is necessary to bring about a diversity of ages and areas for each forest type (Smith 

1962). If the age class difference is chosen to 

be I year, the maximum stand area for each age 

class equals the forest area divided by the time 

reqt~i recl for stands to nature. O"c&er diverse combinations are possible. 

The diversity of stand area and age classes 

provides benefits other than timber. For example, 

certain benefits for deer are provided by browse 

in the seedling years, by some hard mast as the 

stands approach maturity, and by cover in the 

sapl i;ng years (USDA FS 19791, During the seedling 

years the proportions of some kinds of spiders 

increase (Coyle 1981). If the annual age class 

diversity is changed, the timber benefit, the deer 

habitat, the species of spiders, and other benefits will change* Tt is the state sf forest srganization (in this example the annual age class 

di versi ty ) that determi nes the avai l abi l i ty of 

mu1 tiple benefits, 

This example illustrates one way diversity 

of speci f i ed el ements of renewable resources can 

be used to make management decisions about the 

availabi lity aF specified benefitse It also 

illustrates the theory for multiple benefits: 

""the kinds and proportions of states of organization determine the kinds of proportions of human 

benefits available from a forest" (Boyce 1977). 

We can now consider how reducing the proportion of habitats reduces the awailahility of 

benefits. Consider fi rst a random distribution 
of 88 age classes, each differing by 1 year, 

Such a distribution cao be maintained hy regulating the harvest rate and the opening size. 

Without a scheduled harvest and opening size, the 

larger trees increase in size and constrain both 

the opening size and the proportion of stands in 

the younger age classese In time the younger 

stands represent less of the forests, Correspondingly, benefits related to stands in the 

younger aye classes decl i ne, which affects the 

habitat diversity. 

If we take the appropriate action to increase 

the size and proportion of stands in the younger 

age classes, both the habitat diversity and the 

availability of certain benefits increase. Not 

only can timber be harvested periodically, but 

also habitat can be provided for an increased 

number of species. From Coyle's (1981) research, 

we can expect to provide habitat for the largest 

number of species when the l-year aye classes are 

present. This leads to another useful concept 

about diversity: An increase in the diversity of 

habitats increases the habitat for di verse kinds 

of organi smse 

Much of the evidence for this concept comes 

from studies of plant and animal evolution, rnigration, and extinction, As ill ustrated by numerous 

examples (Dobzhansky and others 1977; Harper 1977; 

Mayr 1970; Stehbins 19741, regions having many 

different habitats are more likely to have greater 

diversity of genotypes than regions with a few 

habitats. This fact is supported also by evidence 

that organisms with chromosomal variations are 

more likely to survive in the transition areas 

between habitats than in a large, uniform habitat, 

This concept is used as a basic principle when 

evaluating habitats for wildlife (Flood and others 

1977; Siderits and Radtke b977), When dealing 

with a certain species and with known habitat 

requi rements, the operati snal criteria for di versity can be specified, 
Southwood's (1978:429) guide to the analysis 

of diversity emphasizes the importance of examining 

the form and the meaningfulness of the data. He 

considers these steps an operational approach especi a1 ly important now that computeri zed data col lection and analysis tempt investigators to compute 

diversity indices without knowing the criteria. 

Indices without cri teria can become '"lack boxes" 

that are difficult to use in different situations, 

that are difficult For different people to reconstruct, and that may lack scientific credi bi 1 ity , 

Two features of diversity should be made 

explicit. The first feature is the items of 

diversity, The items of primary interest are 

tree species and plant and animal communities, 

Taxonomi c texts def i ne species , and ecol ogi c concepts can define communities, Speci f ications for 

classes of these items are taken or modified from 

taxonomi c treatments and from pub1 i shed descri ptions of forest types and habitatsa Any set of 

classes for any item, such as tree species, communities, soils, site index, and yield values, 

should have expl icit operational criteriaThe second feature of diversity is distribution. It is important to know how the items are 

distributed among the classes, For example, species are often ranked by the abundance of individual s; diameter cl asses are ranked from the small - 

est to the largest; habitat classes are ranked by 

kinds of dominant species, stand ages, and stand 

a reas. 

One of the most informative ways to communicate information about diversity is to use a table, 

Table 15 displays the distribution of items (forest 

types) for certain elements (forest areas) Diversi ty or di fference is apparent from observations 

of the table, If desired, quantitative values for 

difference can be computed from the data. Tables 

of this kind are simple ways to convey information 

about diversity* 
Table 15.--Diversity of forest types found on the 

Pisgah and Nantahala National Forests and the Mountain Region Survey in North Carolina (in percent) 

Pi sgah Nantahal a Mountai n 

National National Region 

Forest types Forest Forest Su rvey 

White pi ne-hem1 cck 

Spruce-f i r 

Lohlol ly pine 

Short l eaf pi ne 

Vi rgi ni a pi ne 

Pitch pine 

Oak-pi ne 

Oak-hickory 

Chestnut oak 

Elm-ash-cottonwood 

Map1 e-beech-bi rch 

From Boyce and Cost (1978). See Cost (1975) for 

definitions. 
A simple graphic method is often effective 

for communicating diversity. A common technique 

is to array the items, such as forest Qpes, in 

order of abundance, and plot the di stri kutions 

(fig. 70). The curves display the differences 

(di versi ty ) even though the forest types (classes ) 

may he ordered differently from the areas and elements. The graphic method has many variations 

(Play 1975; Pielou 1977; Southwood 1978). 

Region 

x--- x Pisgah 

0- -43 Nantahala 

RANI( OF FOREST TYPE 

Figure 78,--Example of a chart for displaying diversity, Items, 

the percent of forest types, are plotted by rank for three Forest 

areas, (Data are from table 15.) 
Tables communicate the most information ahout 

diversity; graphs and charts convey less information; and diversity indices, especial 1y the confotrnded form (Pielou 19771, display little meani ngful infortnation for si 1 vicul ttrre and management 

p1 anni ng. 

The intent of each table, chart, and graph 

should be to convey information about the distribution of items among defined classes of an element. 

The di spl ays should cornmuni cate the di fferences 

within and among the elements. It is important to 

i ntegrate information about di versi ty of forest 

cornmuni ties and tree species with mu1 tiple benefits. And there should be a way to integrate the 

information about diversity into land management 

plans, 

Diversity results from the state of forest 

organization, which is defined as the proportional 

stand bisdri bution by age classes, area classes, 

and forest types. Changes in the distribution of 

these stand classes affect timber, wi Id1 ife hahi - 

tat, scenic value, streamflow, and other benefits. 

Diversity of communities and tree species is 

determined by the di st si but i on of these stand 

classes. For each planning area, criteria are 

specified for stands by age, area, and type 

classes (Boyce 1977). Type classes are defined 

by tree species di versi tyrn 

The distribution of stand classes (figs. 4, 5, 

6) is the common denominator for multiple benefits, 

di versi ty re1 at ions, and management pl ans (Boyce 

1977). Projected di st ri huti ons are the bases for 

estimating potential timber yields, present net 

values, potential streamf low, scenic values, and 

plant and animal habitat in relation to organizational states, 
On the Nantahala National Forest in North 

Carolina, Coyle (1981) identified 134 kinds of spiders in four stands. One stand was a mature-timber 

habitat; the ather three were seedling habitats 

fro~n 1 to 5 years old. Some species of spiders 

lived in both kinds of habitats, some only in the 

mature-timber stand, and some only in one or more 

of the seedling stands (table 16). From Coyle's 

description, I trans1 ated the biological informati on into a forest management structure (Boyce 

1981). 

Table 16.--The number of species of spiders found in a rnatr~re stand 

and in three seedling stands in the Southern Appalachians 

Stand type 

P 

Mature and 

Ecol ogi cal one or more One or more 

category Mature seedl ing seedl i ng Tot a 1 

Sedentary litter 

spiders 4 16 18 38 

Hunting spiders 4 16 3 9 5 9 

Aerial web 

bui lders 12 10 15 3 7 

Total 2 0 4 2 7 2 134 
The trans1 ation (table 17) mathematical ly 

relates the biol ogical informati on to the proportional distribution of age classes in the stands. 

The spider hahitat is scaled from 0 to 1. A loss 

of either all seedlings or a11 mature timber would 

threaten the habitat for some species of spiders. 

This information integrates the spider habitat 

with other multiple benefits and makes it possible 

to project alternative land nanagement plans based 

on these integrated benefits. This technique can 

be used for any forest type or plant and animal 

group, guild, or community. 

Table 17,--Hahi tat index for spiders 1 imited to 

seedling or mature-timber stands as related to 

organizational states of the forest in the Southern 

Appalachian Mountai ns 

Habitat index Seed1 i ng Mature 

(dimensionless) habitat timber 

--- - Percent - - - - 

Source: Coyle (1981). 
One plot, mod1 fied from the DYNAST simulation 

program, illustrates how to project the potential 

timber production in relation to the potential 

habitat for spiders for one alternative plan (fig. 

71), ft also estimates net present values. These 

potentials and all other forest benefits change 

with changes in stand age classess This kind of 

information about di versi ty is important for deci - 

sions to control the organizational state of a 

forest. 

Figure 71.--.Example showing diversity of' animals integrated with 

other benefits by a OYNWST simlation for one silvicultural mode. 

W = spiders found in both mture and seedling stands 

S = spiders found only In seedling stands 

T = potential timber production 

M = spiders found only in mture stands 

N = estimate of net present value 
A section on diversity in the NFMA reflects 

a deep-seated ethic among many constituencies to 

live in harmony with nature, This ethic involves 

providi ng habitat for di verse pl ant and animal 

forms and maintaining diverse natural plant and 

animal communities, Fulfilling this concern requires that a forest consist of different forest 

types. Forest types, such as hardwood types and 

pine types, are defined by tree species diversity, 

The si gnal s from constituents to the Congress 

focused on perceptions of a decline in the forest 

type diversity and a decline in the potential habitat for some plants and animals that might result 

from converti ng eastern hardwood types to pine 

types, A summary of this information is given in 

the legislative history of the Act (U.S, Senate 

Committee on Agriculture 1978). Some of the items 

to Re noted from the Act's history are: 

1. In the dictionary meaning for the 

word "di versity," difference seems 

to be used. 

2. A primary concern of constituencies 

is to maintain different kinds of 

natural plant and animal communities 

and different tree species associated with different forest types. 

3. The forests are to be manipulated 

and di rected to provide biological ly 

possible combinations of benefits, 

including providing habitat Far a 

diversity of plants and animal s, 

4, Diversity is to he related to multiple use objectives. 
c a ozr: 

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When a combination of benefits--one of the 

DVNAST projecti ons--is chosen, it identifies a 

single silvicultural goal, This goal directs the 

forest transformations toward the desi red organizational state that provides the desired biologically 

possible combination of benefits, including diversity. Actions of all disciplinary areas of management can be integrated and directed toward this 

single goal 

Decisions are made by people responding with 

insights and experience to the use of management 

resources and to changes in social , economic, and 

political forces. The options for choice, the 

DYNAST simulations, are kept congruent with the 

real forest by information from inventories, moni - torings, and research. 

Chapter 10 

The Use Of Biological Diversity 

Overview 

Diversity, the distri hutisn of items among 

classes of elements, provides information ahout 

the availability of component parts for different 

kinds of systems, For exampte, the diversity of 

a randomly stacked pi le of ;;liitortlohi ie parts is the 

same as the diversity of parts in an autsmohile, 

80th the pile of parts and the automobile are 

systems ; each behaves di fferently because each has 

a di Fferend structure. Information about bi ologi - 

ca7 diversity is useful because it removes uncertainty ahout the availability of parts (such as 

organisms) for structuring a system (such as an 

ecosystem) .. 

Because nf tbi s kind of uncertainty, a section on diversity was included in the National 

Forest Management Act of 1976 (ch. 9). The desire 

for hi of ogi cal di versity comes from the deepseat~d feefinq of uncertainty about the Future. 

Thi s uncertai nty about man-caused extinctions of 

organisms is so universal that a widespread ethic 

of good conduct reyui res pub? ic and private 

i nvestment to preserve endangered and threatened 

species 

This chapter is ahnut the use of bialogical 

diversity; the widespread ethic of living in harmany with ~ature; and forest eudemonism, a system 

of ethics for evaluating forestry actions in terms 

of their capacity to provide a live1 ihoob, happi -. 

ness, and we1 1 -being For man. 
Forest Eudemoni sm 

Forests are an essential element for the happiness and well-being of man. The earth without 

forests would be a difficult place for man to live. 

The simple observation that is si gni Ficant 

for forestry is: The elements 06: eudeirlvnia are 

valued for their diversity and structure. Eudemonia for most people involves the amounts and 

availability of food, clothing, shelter, water, 

arid some other el ements of one @s snvi rclnment 

This is diversity as defined in chapter 9, and 

availability is determined by the system's structure, What is significant is how this diversity 

and structure contributes to the potential live1 i - 

hood of man and do man k 1i vi ng in harmony with 

nature* A desired diversity of errrfemoni stic items 

cannot be chosen From alternative sets by compar4 ng only di fferences in net present values, 

equivalent annual costs, or di versi ty indices. 

The monetary values of the alternatives as masured hy net present value are economical ly important, but economics, when considered outside of 

the context of diversity of organisms--such as 

spiders--can lead to poor decisions. Another 

example is water, which is important for life. 

But when one has enough water, it is a poor decision to procure more water regardless of the 

marginal price, 

The displays of cash flows are related to 

the potential habitat for no~comntodi ty spersi es , ts visual appeal, to wilderness appreciation, to 

streamflow, and to commodity iterris, The cash 

flows cannot be al located to the different itens. 

for example, limiting cash flews to the sales of 

commodities does not constrain the treatment 

effects to commodity itemsr The cash outflows for 

cultural practices change the forest's organirational state and thus change the diversity of most 

items, Cash flows are viewed as effects of a mode 

of silviculture (figs. 4, 5, 6). 
Some people have different perceptions of 

eudernonia. Some interested parties may view forests only as sources of cash flow, and others may 

have an interest only in opportunities for hunting 

and what those opportunities cost. The use of 

different a1 gorithm combinations and thei r associated white boxes provides opportunities for 

valuating forests in ~trdemonistic terms. 

Di versi ty , Eudemoni sm, and Management 

Biotic variety and the abundance and distribution of the elements are ultimately control led 

by variety in the physical environment. Variations 

occur in such factors as moisture regime, climate, 

physiography, geol oyy, land form, and soi 1. 

Beyond these, diversity is a consequence of continlied changes in species, stand ayes, stand 

areas, and patterns from one state of ecosystem 

organization to another (ch. 8). These dynamic 

transformations are hoth natural and mani nfl ~~enced through such activities as timber harvest, livestock grazing, water development, 

wild1 ife population management, prescribed fires, 

and so on. The multiple-resource products of a 

forest are also the results of these transformations. They are ultimately controlled by the 

productive capacity of land as determined by 

physical factors that control ecosystem productivity. Thus, diversity and the multiple-resource 

production hoth derive from the same set of physi - 

cal conditions, natural processes, awl man's 

cultural activities (ch, 3), The common denominator for di versi ty , eudemoni sm, and management is 

the organizational state of a forest and the 

transformations from state to state. 

Applying the concept of biological diversity 

requires determining how a si lvicul tural mode 

changes diversity in the elements of the ecosystem; 

determining ho~ these changes in diversity change 

linkages among the elements and thus the ecosystem 
structure ; and eval uat i ng the si gni f i cance of these 

changes in terms of the potential livelihood, happiness, and we1 1 -being of man. Many examples are 

found in the literature. The examptes given here 

evaluate the consequences of using clearcutting to 

harvest timber in the Southern Appalachian 

Moiintains near Highlands, NC (Bruce and Boyce 

1984), 

In 1974, the Highlands Biological Station, the 

Southern Region of the USDA Forest Service, and the 

Southeastern Forest Experiment Station began a 

series of cooperati ve research agreements desi gned 

to examine the effects of timber harvest on biotic 

diversity in Southern Appalachian hardwood forestsThe project was undertaken in the context of the 

DVNAST model . 

The initial studies were designed to utilize 

recent 1 y cl earcut stands near the Hi ghl ands 

Riological Station. All of the sites were within 

the Highlands Ranger District of the Nantahat a 

National Forest. The area lies near the southern 

terminus of the B1 ue Ridge Physiographic Province. 

In 1975 four sites were chosen that were similar in 

area, elevation, slope, exposure, and precut overstory vegetation. Two additional sites were added 

i~ 1976 and 1978, Based on timber cruise data and 

inspection of adjacent forests, the overstory 

before cutting was dominated by oaks (Ouercus alba 

L., O. prinus L., - 0. -- rubra Le) hickotmrr 

spp.7, and, at some sites, white pine (Pinus 

strobus L. ). Elevations ranqed from 2,806 to 

3,900 feet: A summary of the characteristics of 

the six sites is given in table 18. 

Rep1 icated sampl i ng areas were establ i shed 

in the clearcut stands and in adjacent uncut 

forests. Comparisons were made between clearcuts 

and adjoining forests and between clearcut stands 
Some people have different perceptions of 

eudemoni a. Some interested parties may view forests only as sources of cash flow, and others may 

have an interest only in opportunities for hunting 

and what those opportunities cost. The use of 

different a1 gori thm cornhi nat i ons and thei r associated white boxes provides opportunities for 

valuating forests in etrdemoni stic terms. 

Di versi tv. Eudemoni sm, and Manaaement 

Biotic variety and the abundance and distri - 

hution of the elements are ultimately controlled 

by variety in the physical envi ronment. Variations 

occur in such factors as moisture regime, climate, 

physiography, geology, land form, and soi 1. 

Beyond these, diversity is a consequence of continued changes in species, stand ayes, stand 

areas, and patterns from one state of ecosystem 

organization to another (ch. 8). These dynamic 

transformations are both natural and maninfluenced through such activities as timber harvest, livestock grazing, water development, 

wildlife population management, prescribed fires, 

and so on. The multiple-resource products of a 

forest are also the results of these transformations. They are ultimately controlled by the 

productive capacity of land as determined by 

physical factors that control ecosystem productivity. Thus, diversity and the multiple-resource 

production both derive from the same set of physi - 

cal conditions, natural processes, awl man's 

cultural activities (ch, 3), The common denominator for di versily, eucfemoni sm, and management is 

the organizational state of a forest and the 

transformations from state ts state. 

Applying the c~~tcept; of biological djversity 

requi res determining how a si 1 vieultural mode 

changes diversity in the elements of the ecosystem; 

determi ni ng how these changes in di versi ty change 

linkages among the elements and thus the ecosystem 
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Table 18, --Characterization of the study sites 

Site Yearcut Size Slope Aspect Elevation 

Ac re - Feee 

Horse Cove 1972 39 2-10 130 3,100 

Brush Creek 1973 39 5-10 150 3,900 

Rich mn. North 1974 35 10-15 130 3,900 

El licott Rock 1975 20 5-12 100 2,800 

Buck Creek 1976 25 20-30 20 3,190 

Rich Mtn. South 1978-79 27 15-25 234 3,190 

of di fferent ages* Intersite comparisons such as 

the latter are confounded by heterogeneity among 

sites. However, comparisons within sites in successive years will be possible eventual lye The 

study focused on vegetation and nongame animals, 

with particular attention to animal groups having 

important ecol ogi cal roles in the forest community. 

Sampling methods differed according to Laxon, 

One concern was to discover how forest managers i nfl uenced di versi ty when no experimental 

or research constraints were placed on managerial decisions and controls, Care was taken not 

to influence and not to be involved in the decisions and actions of the forest management 

organi zations, 

To date, the studies have included plants, 

bi rds, small mammal s, terrest ri a1 sal amanders, and 

predatory arthropods. Only the latter are used to 

i 1 lustrate this example. 

Predatory Arlhrop~ds 

Four groups of predatory arthropods have been 

investigated: spiders, harvestmen, centipedes, and 

ground beetlesl Because these. are generally large 

taxa with many species, the results have provided a 

rich body of data on diversity changes accompanying 
tirnber hdrvest. for three of the four grotrps the 

restrlts exhibit a cotfllnon trend; this suggests that 

a cotntnon ~ilechani sin is operating to regulate diversity among these animals. 

The first of the arthropod studies was conducted in 1976 on spiders (Coyle 1981). Three 

clearcuts (El 1 i cott Rock, Huck Creek, Horse Cove) 

and one uncut forest (El 1 i cott Rock ) were sampl ed. 

Recause of the ecol agi cal di versi ty of spiders, 

four sampling methods were used: pi tfall trapping, 

leaf litter sampling with Tullgren funnels, aerial 

sweep-net collecting, and hand-collecting. For 

each method, an attempt was made to equalize the 

sampl i ng effort among sites. A1 though total sampling efforts were approximately the same at the 

four sites, within a site there was no way to 

equal ize sampling efforts among methods because 

the efficiencies of the rnethods vary. Thus, the 

i ndi vi dual samples do not necessari ly reflect 

actual species abundances. Moreover, some habi - 

tats were not sampled (e.g., arboreal habitats in 

forest canopy). The col lecting schedule favored 

species active during the day in the summer. 

Coyle's collections yielded 1,729 individuals 

and 134 species. He sampled three clearcuts, hut 

sampled the adjacent forest at only one site 

(Ellicott Rock). Thus, the evaluation of the 

Ellicott Rock forest and clearcut samples represents the most reliable comparison from these data. 

Coyle classified spider species according to 

stratum (ground versus aerial) and prey capture 

mode (web bui lders versus cursorial hunters), 

Four gui 1 ds were recogni zed--ground web bui 1 ders, 

ground hunters, aerial web builders, and aerial 

hunters. Species composition differed markedly 

between the spider faunas of the Ellicott Rock 

forest and the clearcut sites--both species and 

i ndi viduals Mere reduced on the clearcut. Despite 

the lower species richness at the Ellicott Rock 

clearcut, the value of the Shannon index (Pielou 
1977) was greater in the clearcut sample. Further 

analysis showed that the decrease in species at 

El licott Rock was largely due to a loss of both 

ground and aerial web bui lders* Hunting spiders, 

particularly ground hunters, had increased. The 

decrease in dominance of web builders together 

with the influx of hunting spiders accounted for 

the increase in the evenness component of diversity. Coyle argued that the change in vegetation 

structure and microclimate brought about by clearcutting favors the more mobi le hunting spiders, 

many of which seem we1 1 adapted to open and cl i - 

matical ly variable envi ronments. Data from the 

other two cloarcuts, which were not paired with 

adjoi ni ng uncut forests, seemed to support hi s 

arguments. 

Two sther groups of predatory arthropods, 

harvestmen (or daddylongl egs) and centipedes, 

were studied in 1975 and 1979 by Summers. #2 

- 

1 ransects were established in an uncut forest 

adjacent to Ellicott Rock and in clearcuts at 

Ellicott Rock, Brush Creek, Worse Cove, and Rich 

Mountain North, In 1979 an additional study of 

harvestmen was made in a cove forest and forest 

ecotone, Line transects were established, and 

sampl i ng points were randomly selected for pi tfall 

trapping and litter sampl e col 1 ecting* Arthropods 

were extracted from the litter by using Berlese 

funnels and were hand-col lected for set time 

periods at each site. Thus, an attempt was made 

to equalize the sampling effort among sites. For 

centipedes, Summers used transpl anted 1 ogs, which 

had been fumigated beforehand, to supplement 

col 1 ect i on of l og-dwel 1 i ng species, Sets of logs 

having equi valent under-bark areas were placed at 

'summers, krald, The effects of clearcuttrng on opilionid populations in the Southern Appalachians, Unpublished report on file. 

Highlands, RG: Highlands Biological Station; 1979. 

2~umrs, Gerald. The effects of clearcutting on chi lopod populations in the Southern Appalachians. Unpublished report on file. 

Highlands, NC: Highlands Biological Stat~on; 1980. 
each site for an 8-week period in July 1978 and 

were then dissected for centipedes. In addition 

Summers masured structured features of the canopy, understory vegetation, 1 itter, and soi 1. 

Both species richness and abundance of harvestmen in the region were low in comparison with 

the other arthropod groups. Summers found fewer 

species on clearcuts (one to three) than in the 

uncut forest (five); he attributed the differences 

to the greater microcl imatic extremes resulting 

from clearcutting. 

The centipede faunas of the study sites were 

richer in both species and individuals, Summers 

recognized log and leaf litter species assemblages 

and noted a d i f f e r e n t i a l response to clearcutting 

beween them. Species richness and abundance were 

higher in all litter samples from clearcuts than 

in liGter samples from the uncut forest; an opposite trend was found for log samples. Overall, 

species richness and diversity as masured by the 

Shannon index were greater in the clearcuts than 

in the uncut forest. Summers related these findi ngs to several habitat variables, particularly 

habitat space in litter, 

A major study of ground beetles was conducted 

every spring and summer from 1979 to 1981 (Lenski 

1982), To determine the composition of the beetle 

fauna, Lenski sampled three cl earcuts--Ri ch 

Mountain South, Rich Mountai n North, and El 1 i cott 

Rock--each of which was pai red with an adjacent 

uncut forest stand. At each site four sample 

areas were established, two in the clearcut and 

two in the forest. Each area consisted of a 20- 

by 20-foot grid of pitfal l traps. A11 sample 

areas were located from 5% to 164 feet from the 

forest-clearcut edge. Traps were visited daily, 

and most ground beetles were identified to species 

in the field and then released, Sampling was conducted for about 50 days at each site in 1979 and 

for an additional 64 days at Rich Mountain South 

in 1980. 
Lenski's detailed and exacting experiments 

support the concept that species interactions, 

particularly competition, may represent factors 

that expl ai n some changes in speci es abundance 

fo1 lowing timber harvest. Yet his experimental 

work encompassed only two species and, 2s Lenski 

admitted, other beetle genera that show the same 

pattern may he regulated by other mechanisms. 

The one trend exhibited by the several 

arthropod studies is an increase in intrageneri c 

species di versi ty fol lowi ng harvest due to reduced 

abundance of dominant species. This trend is 

highly si gni ficant in ground beetles whwe the 

sampl es from a1 1 three cl earcuts have hi gher 

diversity than the correspondi ng uncut forest 

controls. For spiders and centipedes, only one 

clearcut (Ellicott Rock) was paired with an immediately adjoining forest, but in each case intrageneric diversity was greater in the clearcut than 

in the forest. The other two clearcuts studied 

also showed higher values of intrageneric diversity for spiders than did the El licott Rock 

forest. But for centipedes the results were more 

variable when three other clearcuts were sampled. 

The trend toward increased intrageneric diversity 

with timher harvest was not apparent for harvestmen, probably because there were few species and 

few individuals of each species. Although Lenski 

was able to relate this trend to competition in 

one beetle genus, it is by no means determined 

that this explanation applies to the other taxa. 

Additional experimental studies would be helpful 

to resolve this problem. 

Discussion sf Findings 

Generalizations from these studies are tentative because the oldest clearcuts are only 10 

years old and the rotation ages for these forests 

are more than 80 years. However, some relations 

are apparent 
O.t-.t'rt: .r-+JO 

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nonequilibrium, relaxes competition, and thereby 

allows greater diversity. What is needed to 

further test these ideas are (1) additional paired 

comparisons of flora and fauna in uncut and cut 

forest stands that include a1 1 major successi onal 

stages but pay special attention to intermediate 

stages, and (2) experimental manipulations within 

selected taxa designed to elucidate the role of 

biotic interactions in regulating diversity. 

The basic data generated Prom sampling 

programs of this type are lists of species and 

abundances. From a management viewpoint the best 

index of diversity available from such data is 

probably the number of species found or species 

richness. It is impractical, if not impossible, 

to sample the total flora and fauna. Therefore, 

sampling Prom species lists of selected components, representing important taxonomic and 

functional groups, probably represents a reasonable approach to evaluating the effects of a given 

management mode on total species diversity, 

In chapter 9, I illustrated the method of 

translating the data derived for spiders into a 

managerial format. This is an easy procedure 

because the spiders are found in discrete groups 

according to habitat type (table 16). Most of the 

ground beetles are found in both the seedling and 

mature-timber habitats (table 19). These two 

kinds of habitats have important differences, 

which are translated into white boxes for use in 

the DYNASI model . Eleven species of beetles, comprising 38 

percent sf the individuals trapped, seemed to be 

favored more by the presence of mature timber than 

by seedling habitats (table 20). Seven species, 

compri si ng 14 percent of the individuals trapped, 

seemed t0 be favored mre by the presence of" 
seedling than by mature-timber habitats (table 

21). FOP 27 species, about 48 percent of the 

i ndi vidual s, dl fferences in abundance were not 

consistent between the habitats, This latter 

group has l i tt 1 e si gni f i cance for management 

because these species of beetles can apparently 

survive in both habitat types (table 19). Our 

concern is to provide for species whose l i vel i hood 

is enhanced by a particular habitat. 

One supposition is: A silvicultural mode 

that maintains the most seedling habitats wi 11 

enhance, up to a limit, the habitat for seven 

Table 19, -44 stri bution of ground beet1 es trapped 

in malure-timber and seedling habitats, by habitat 

Q r QuP 

Habitat type 

Habitat 

group Mature timber Seedling Total 

Percent Number 

All individuals 

trapped 54 46 4,727 

No difference in 

abundance 53 47 2,2114 

Most abundant in 

mature Limber 69 31 1,837 

Most abundant i n 

seedl i ng hahi tat 13 87 676 

- 

Source: tenski (1982). 
species of ground beetles (table 211, The upper 

limit on the amount of seedling habitat is about 

6 to 7 percent in these hardwood forests because 

rotation periods are longer than 70 yearss 

Shorter rotations csufd increase the amount of 

seedling habitat, but the amount of habitat for 

other organisms would be decreased, Thus, the 

hahitat For these seven beetles is at the maximum 

Table 20.--Ground beetle spec?es f0bfId In consistently great~r 

numbers In mature-tim?er {YI than in seedlrnq (S) hab~tats on 

three sltes 

Rtch Moiinta~ n Rich Mountain 

South if l~cott Rack North 

Scaphi not~ls 

andrews~ 7 2 5 1 0 0 

c~uyot 1 13 5 3 0 5 1 

r rrequl ari s 10 -..A P 10 2 0 6 0 

viol aceous 14 3 3 3 5 0 

Spaeroderus 

canadens~s I8 0 19 I 22 7 

stenostomus 15 0 18 2 0 0 

Harpal us 

carol i nae 143 72 40 10 125 42 

Pi riacodera 

1 imhata 

pfaticolf is 

--- 

Total 857 456 188 38 226 62 
when 7 percent of the forest is in seedling hahitats (table 22). When no seedling habitats are 

created by timher harvesting, the beetles are 

assumed Lo survive in minimum ntrmbers. Thus, the 

white box value is set at 0.05 for the livelihood 

index. Retween these limits, the waveform of the 

relation is assumed to be a sigmoid curve. This 

last assumption is not based on experimental data. 

The sigmoid curve reflects the unproven ideas 

that: (1) the smaller the number of seedling 

habitats, the farther apart the habitats wit 1 be 

for travel by the beetles and the less the probability the habitats will be in a suitable location 

for other hahitat factors; and (2) the larger the 

Table 21 .--Ground beetle sp~ci es found in consistently greater 

oumbers in seedling (S) than in mature-timber (M) hahltats on 

three sites 

Rich Mountai n Rich Mountatn 

South El 1 i cott Rock North 

Speci es M S M S M S 

Harpat us 

pennsyl vani cus 3 120 6 103 0 2 6 

Anisodactylus 

interstitialis 0 1 I) 4 0 C1 

ovul ari s 0 5 0 0 0 1 

Pterostichus 

adoxus 7 10 11 2 8 2 1 33 

Galeri tula 

SPP * 0 3 0 14 0 2 9 

Tot a 1 23 205 21 240 4 7 134 

Source: Lenskl (1982). 

298 
C, 

rcj 

n 

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cr 

V) 

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3 

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c 

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at 

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.r 

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r 

3 V) 

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'4- 

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c3E 

I C, 

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number of habitats, the closer the habitats will 

be for beetle travel between them and the greater 

the probability of the habitats being in a location that satisfies other factors of the beetles1 

habitat. tenski" ddaa indicate differences in 

habitat related to factors other than the presence 

of seedling or mature-timber habitats, See, for 

example, the data for Carabus and 9 

(table 20). 

Another supposition is: A silviculturat mode 

that mi ntai ns the mst nature-timber and oldgrowth habitats will enhance, up to a limit, the 

habitat for 11 species of ground beetles (table 

20). No data are available for old-growth habitats, However, these older stands are not known 

to restrict the livelikssd of these beetles, The 

upper limit on the amount of alder stand habitat 

is about 58 percent in these hardwood stands, We 

do not have information on beetles for the age 

GI asses between seedl i ng and mature-eimber hahi - 

tats, We assumed that as the seedling habitats 

age, some of the beetle species wi l l decrease in 

abundance while others will inc~ease to approach 

the distributions shown in table 20 when the 

stands are mature-timber habitats a Al so, these 

data suggest that when there are no mature-timber 

kabilats and older stands, the beetles favored by 

these age classes survive in small numbers, Thus, 

the limits for the white box are set at 0,05 when 

there are no mature-timber and ol d-growth stands 

and at 1 when half of the forest is in these older 

age classes, For the same reasons given above, 

the waveform of the relation is assumed to be 

sigmoid (table 22). 

The data for white boxes may be plotted and 

adjusted as new information is received Prom 

research, experience, and monitoring. Most 

changes are likely ta be adjustments in the waveform of the relations and few changes are expected 

in the limits, It is unlikely that new data will 

change the general direction of the curves, 
The white boxes for the two habitat groups of 

beetles are put into an algorithm. The potential 

habitats for al ternate si 1 vi cul tural modes are 

displayed along with the potentials for spider 

habitats (fig. 71), timber production, cash flows, 

and other effects (figs. 4, 5, 6). The valuation 

of the alternative si lvicultural mdes is determined by subjectively choosing the diversity 

of those effects percei ved to provide optimal 

eudemony . 

The evaluation of a forest ecosystem for 

human benefits must be based on diversity. This 

is true because no single number, index of values, 

cultural practice, or monetary amount can serve 

as a common denominator for the diversity of 

elements such as timber, cash flow, pbtential 

1 i vel i hood or kinds of organi sms, visual appeal , and hunting opportunities. A cultural practice 

produces multiple effects inel uding cash flows 

and raw materials for paper, The costs of a 

cultural practice cannot be rational ly a1 located 

among the eudemonistic effects. The value of the 

effect of a cultural practice is not the monetary 

value of the mrketable elements but is the total 

contribution of the effects to the potential livelihood, happiness, and well-being of man, Information is in the displays of numbers and amounts 

of effects all linked to the common denominator, 

which is the transformations of the states of 

forest organization (fig. 4). 

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Index 

ASundance (see Diversity j 

A1 gori t hms 

Beet I es, 295-296 

Cash flow, 237 

Deer, 142 

Pi 1 eated woodpecker, 149 

Sed~rnent ,138 

Spiders, 278-279 

Timber, 116,235 

Water, 232 

Arthropods, 283, 290, 293 

Bear, 162 

Beeties, 292, 295-299 

Benefits, 113, 259, 271 

Aggregate, 159, 163, 164 

Nonmarketabte, 159, 182, 183 

Sustained, 85, 153 

B1g Ivy foreet, 159, 205, 207 

Big Pine forest, 216, 218, 219, 232, 235 

Biological limits, 1-2, 90 

Biolog~cal potential, 89, 90, 91 

Biomass, 88, -. 91. 234 

r ion omit theories, 51, 52, 59, 78, 81, 82, 

114. 153 

Browse, 98, 143, 144, 145, 149, 196 

Cash flow, 155, 236, 260, 261, 286 

Appl i cations, 156 

Equzt~ons, 260, 261 

Centipedes , 292 

Clearcutt? ng, 288, 291, 294 (E Regenerat? on 

systems) 

Communication (see I~formation) 

Complexity, 1, 107, 108, 109, 124 

Continuity , 56-57 

Conti qua1 tovpoundl ncj, 175, 177 

Control variables, 10, 23, 25, 84, 117, 267-268 

Conversions of forest, 98, 99, 248, 264, 265, 277 

Coppice forest, 101 

Cybernet~cs, 1, 30-32, 42-44 

Decision and control, 3, 9-12, 25-27, 64, 114, 

153, 154, 189, 190 

Deer hab~tat, 142-149, 161 

Delays, 72, 73, 190, 227, 230 

01 rected systems, 42-44 

Df scount rate (see Gash flow! 

Di stri but~oq (s2~versity 1 

Divers~ty, 269-277, 280, 261, 285, 287 
Dual rotations (see Rotation period) 

DYNAMO. 121, 130775, 204. 210, 256 

Ecoqomics, 3, 13, 162, 164, 174, 286 {see Cash flow) 

Ecosystem 

Aimless, 6, 70, 72 

Continuity, 56 

Dynamics, 1, 55 

Irreversibility, 56, 57 

Operational definition, 53 

Organization, 68 

Unstable, 70, 71 

Equations, 210-215 (see DYNASTf 

Equivalent annual reK 161, 181 

Eudenomism, 285, 280, 287, 301 

Even-age mnag~me~t, 101 

Falsification hypothesis, 79-80 

FAM, 220, 221, 225, 231, 241 

Feedback loops, 30 

Feed-forward, 31, 37, 38 

Negative, 30, 33, 227 

Positive, 31, 39, 40, 41, 218 

Flows 

Energy, 72, 74-76 

Information, 32, 34, 47 

Materials, 72, 74-76 

Theory, 72, 80 

Fctrest 

Aimless system, 2, 70 

Type sectors, 251, 256-257 

Uses, 2 

Forest and Rangeland Renewable Resources 

Planning Act, 156, 189 

Genetics, 9, 58, 70 

Georgia forest, 253, 254, 255, 256, 257 

Goal, 27, 59, 60, 67, 153, 160, 190, 193, 206 

Group selection (see Regeneration systems f 

Habitats, 3, 76, 77, 78, 108, 109, 206 

Hard mast, 145-147 

Harvestmen, 291, 293 

HBTOB, 253-247, 250 

High forest, 101 

High1ar)ds Eiolocji tal Station, 288, 291, 294 
Imparts, 85, 113, 258 

Individualistic systems, 59-62, 66, 67, 69 

Information, 21, 33, 34, 35, 159, 165, 189, 230, 

2 58 

Translation, 109, 111 

Initial values, 212, 226, 241, 251 

In-place decisions, 97, 106 

Insects (see Arthropeds, Beetles) 

Interdisciplinary teams, 193 

Inventory, 205 

Land mnagement planning, 106, 107, 277 

Linear programmi~g, 28, 107, * :8 

MACROS 

Definition, 242-243 (see HBTOB, OBTAH, PTISO) 

Mode of management, 13, 21-22, 87 

Model s 

Computer, 200-202, 210 

Mental, 22, 23, 51, 185, 189, 203 

Validity, 78, 79 

Mortality, 61, 66, 67, 75, 87, 89 

Multiple benefits, 1-2, 29, 76 

Mu1 tiple-Use S u s t a i n e d - Y i e l d Act, 156, 189 

National Forest Management Act, 269, 281-282 

Natural re gene ratio^ (see Regeneration systems) 

Natural selection, 66,- 

Net present value, 161, 180, 286 

New direction, 6, 7-8, 10, 158 

Noncommodrti~s, 7, 81, 170, 286 

OBTAH, 247-250, 253, 255 

Old-growth stands, 149, 150, 151, 16n 

Operational, 8, 52, 53, 54, 82, 271 

Ogt~rnsl strategy, 71, 10e, 159, 189 

Oryal~zation, 153 

Aimless, 7C, 72 

Theory, 69, 70 

Perpetuity (see Benefits 1 

Plans, 43-50, 106 

Potential timber index, 121, 124, 127, 129, 231 

Program co~trols, 26C, 262, 267-268 

Proflidb? lity, 181 

PTISC, 251-252 

Watershed, 133, 205 

Wh~te boxes, 165, 172, 194-196, 200, 214 

Wilderness, 183 

Wildlife (see Algorithms? 

Woodpecker 

Pi leated, 149-152, 160, 170 

Flicker, 162 

Yield, 89, 90, 116, 251 
Checkl ist for Figures 

Checkl ist for Tables 

Page 

131 

135 

165 

167 

218 

219 

232 

247 

250 

252 

254 

- No. Page 

12 254 

13 255 

14 265 

15 275 

16 278 

17 279 

18 289 

19 296 

20 297 

21 298 

22 299 
Boyce, Stephen G. 

Forestry decisions. Gen Tech. Rep. SE-35. Asheville, Nr: 

U.S. Departrne~t of Agriculture, Forest S~rvice, Sout"lecisterri 

Forest Experi mevt Station ; 1985. 318 pp. 

Viewing the forest as a system that self-organ~zes in response to 

a schedule of harvejt and culture provide2 a new Oasis for mklng 

forestry decisions. Cornpt:ter simulations of states of forest 

organizat~ on through tlme provide d~splays of tne product ~on of

forest benefits rangs'ng from timber and water to wlldl~fe and 

recreati on. From these di spl ays, tbp manager chooses a des r red 

schedule. Graphs are used to evaluate supposl ti on< about future 

events, The responsibt lities of managers are not usurped by computers; people make the decisions. 

KE YWORDS : DYNAST, ecosyste~ dynarni cs , forest managemeqt , harvest 

scheduling, multiple use, silviculttlre, system dynarmcs. 

Boyce, Stephen 6. 

Forestry decisions, C;en Tech. Rep. SE-35. Ashev.1 l le, NC: 

U.S. Department of Agriculture, Forest Service, Sotitheastern 

Forest Experiment Station; 1985. 328 pp 

Viewing the forest as a system that self-organ~zes in response to 

a schedule of harvest and culture prevides a new has1 s for nrakfng 

forestry decisions. Computer simulations of sta*~s of forest 

organization through time provide displays of tne proauctlop of 

forest benefits ranging from timber and water to w~ ldl ~fe and 

recreation. From these displays, the manager choo~ec a desrrea 

schedule. Graphs are used to evaluate sup posit^ ors about future 

events. The responsibilities of managers are not U~U~DP~ by iorputers; people make the decisions. 

KEYWORDS: DYNAST, ecosystem dynamics, forest mnagement, harvest 

scheduling, multiple use, silviculture, system dynav~cs.