United States

Department

of Agriculture

Forest Service

Pacific Southwest

Research Station

http://www.psw.fs.fed.us/

General Technical Report

PSW-GTR-174

A Compendium of Forest Growth

and Yield Simulators for the Pacific

Coast States

Martin W. Ritchie

FPS FVS

DFSIM

SWO-ORGANON

RVMM

WWV-ORGANON

SYSTUM-1

CRYPTOS

CACTOS

LPSIM

SORNEC

SPS

WESSIN
Publisher:

Albany, California

Mailing address:

PO Box 245, Berkeley, CA

94701-0245

(510) 559-6300

http://www.psw.fs.fed.us

January 1999

Pacific Southwest Research Station

Forest Service

U.S. Department of Agriculture

Abstract

Ritchie, Martin W. 1999. A compendium of forest growth and yield simulators for the Pacific

Coast states. Gen. Tech. Rep. PSW-GTR-174. Albany CA: Pacific Southwest Research Station,

Forest Service, U.S. Department of Agriculture; 59 p.

The capabilities of 31 existing growth and yield simulators for California, Oregon, Washington,

and Alaska were analyzed to determine their relevance in forest management and planning.

Most of the simulators are available for the PC environment. Some are available at no charge,

but others may require users to purchase a license. Simulators are classified in five groups:

individual-tree/distance-independent, individual-tree/distance-dependent, whole stand,

disaggregative, and gap. Simulators are briefly described in terms of the range of data,

appropriate species, data requirements, and hardware requirements for execution.

Retrieval Terms: computer models, simulation.

The Author

Martin W. Ritchie is a biometrician assigned to the Station’s Western Forest Management

Research Unit, 2400 Washington Avenue, Redding, CA 96001.
PSW-GTR-174

General Technical ReportResearch StationPacific SouthwestForest Serviceof AgricultureDepartmentUnited States

Coast Statesand Yield Simulators for the PacificA Compendium of Forest Growth
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The Pacific Southwest Research Station

• Represents the research branch of the Forest Service in California, Hawaii, American

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marital or familial status. (Not

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Pacific Southwest

Research Station

USDA Forest Service

General Technical Report

PSW-GTR-174

January 1999

A Compendium of Forest Growth and Yield

Simulators for the Pacific Coast States

Martin W. Ritchie

Contents

In Brief . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Form vs. Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Individual-Tree/Distance-Independent Simulators: FVS, CRYPTOS,

CACTOS, SPS, ORGANON, SYSTUM-1,

RVMM-Individual Tree, CONIFERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

FVS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Southeast Alaska (1)1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Blue Mountain (2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

East Cascades (3). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Inland Empire (4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Klamath (5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Pacific Northwest Coast (6) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

SORNEC (7) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

West Cascades (8). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

WESSIN (9) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

CRYPTOS (10) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

CACTOS (11) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

SPS (12) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

SWO-ORGANON (13) and WWV-ORGANON (14) . . . . . . . . . . . . . . . . . . 26

SYSTUM-1 (15) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

RVMM: Individual-Tree (16) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

CONIFERS (17) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Individual-Tree/Distance-Dependent Simulators: FPS, G-SPACE . . . . 30

FPS (18) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

G-SPACE (19) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

GAP Simulators: CLIMACS, SILVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

CLIMACS (20). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

SILVA (21) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 1 Numbers in parentheses refer to

the simulator index for tree species

in appendix A.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174.1999. ii

Whole-Stand Simulators: DFIT, PPYMOD, DFSIM, PPSIM, PSME,

DFETAL, SOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

DFIT (22) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

PPYMOD (23) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

DFSIM (24) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

PPSIM (25). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

PSME (26) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

DFETAL (27) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

SOS (28) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Disaggregative Simulators: LPSIM, STIM, RVMM . . . . . . . . . . . . . . . . . . 42

LPSIM (29) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

STIM (30) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

RVMM: Stand (31) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Appendix A: Referenced Tree Species List . . . . . . . . . . . . . . . . . . . . . . . . . 51

Appendix B: FVS Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Appendix C: CRYPTOS Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Appendix D: CACTOS Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Appendix E: ORGANON Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Appendix F: DFSIM Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174.1999. iii

In Brief. . .

Ritchie, Martin W. 1999. A compendium of forest growth and yield simulators for the

Pacific Coast states. Gen. Tech. Rep. PSW-GTR-174. Albany CA: Pacific Southwest

Research Station, Forest Service, U.S. Department of Agriculture; 59 p.

Retrieval Terms: computer models, simulation

Growth and yield simulators are available for many of the forested regions of the western

United States. A forest simulator is a computer program which, through a system of equations,

produces forecasts of forest stand development. Historically, growth and yield information

was published in the form of tables indexed to discreet ages and levels of site productivity.

With time the transition has been made to computer generated output. Currently, users

generate yield information geared to their exact specifications on the computer.

This information is useful in evaluating the potential for various treatment regimes to

produce some desired future outcome. Such information is also needed to develop forest

plans. The planning process depends on knowledge of forest conditions over time. While

forest managers can easily measure the current conditions, the simulator is a tool which

provides a look into the future so as to catch a glimpse of the likely or potential conditions.

No simulators are perfect. All simulators have danger zones: those combinations of

species, stand and site conditions that produce shaky results. A given simulator may be

totally incapable of completing some tasks and may handle others poorly. However, output

from a simulator rarely comes with any warnings when the user is operating in a danger

zone. The prevailing philosophy is one of caveat emptor. Users must not rely on a simulator

to review its own output. Determining legitimacy of output is the purview of the user.

Simulators vary in architecture. Some of the simulators require stand level statistics as

input; these are typically referred to as whole stand simulators. Others require individualtree information as input; most of these are classified as individual-tree simulators. There

are also a couple of hybrid disaggregative simulators that have been developed for this

region.

Simulators may also vary with respect to input, output and management options. Some

simulators are designed to be run in a “batch” mode. That is, process of multiple stands is

easily facilitated. Others are designed to be most efficiently run interactively on a stand-bystand basis. Management options may include various thinning options, fertilization, pruning,

and final harvest.

Simulators are categorized herein as belonging to one of five distinct types: individualtree/distance-independent, individual-tree/distance-dependent, whole stand, disaggregative,

and gap (otherwise referred to as successional models).

This manuscript provides information needed for the user to access current information

about forest growth and yield simulators. Ultimately, the best source of information for any

simulator is the user’s guide and the sage advice of those who built the simulator. In some

instances, these people are easy to find and are willing to provide all the support for the

program. Other simulators have, unfortunately, been abandoned due to retirements and other

career moves of researchers. Users should take advantage of the opportunity to make direct

contact with developers whenever possible. Much which is useful is not published, and

much which is published is not useful.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174.1999. iv

Glossary

ASCII: the American Standard Code for Information Interchange (ASCII) is a standard

code for representing numbers, letters, and symbols.

BAF: basal area factor.

BAL: basal area per unit area of trees larger than the subject tree.

Basal area: cross-sectional area of a tree at 4.5 feet above the ground.

Basal area factor: amount of basal area per unit area represented by a single tree in a

sample drawn with probability proportional to size (basal area) of the tree.

Basal diameter: diameter of the stem at 0.5 feet.

CCF: crown competition factor (Krajicek and others 1961).

Compile: a process whereby code for a given language, such as FORTRAN, is converted

to instructions which may be executed by the computer.

Cover: vertical projection of crown area expressed as a percent.

CPU: central processing unit.

Crown ratio: live crown ratio, the ratio between crown length (numerator) and total tree

height (denominator) wherein both are measured in the same units.

DBH: diameter at breast height (4.5 feet), outside bark unless otherwise specified.

d/D ratio: ratio of quadratic mean diameter of trees cut divided by the quadratic mean

diameter of all trees prior to cutting.

DOS: acronym for disk operating system, the operating system for most IBM compatible

computers.

Deterministic: used to describe a process that does not contain a random element.

Driving function: a function responsible for directly forecasting a dynamic element of any

particular simulator.

Expansion factor: number of stems per unit area for each sampled individual. For a 10th

acre plot, the expansion factor is 10. For a variable-radius plot, expansion factor is a

function of tree diameter and basal area factor (BAF). For English units expansion

factor.

FVS: Forest Vegetation Simulator, formerly Prognosis Model for Stand Development.

FVS refers to all those simulators (variants) derived from the original Prognosis architecture.

Growth interval: the length of time associated with the dynamic functions embedded in a

simulator. A growth interval of 10 indicates driving functions that predict 10-year

change in the response variable.

Height: total tree height.

Housekeeping function: a species-specific, and/or simulator-specific function for producing output or quantifying certain parameters within a simulator (Bruce 1990).

HT40: Height of the 40 largest (diameter) trees on a unit area (typically an acre), also

maybe referred to as dominant height.

K: kilobytes; one kilobyte is 1024 bytes.

MAI: mean annual increment.

MB: megabyte; one megabyte is 1000 kilobytes.

Primary driver: that driving function which occurs first in the progression of growth

estimates and often has the greatest impact on overall simulator behavior because the

results of this function permeate the forecasts throughout the simulator.

QMD: quadratic mean diameter; breast height diameter of the tree of mean basal area in

a stand. Quadratic mean diameter is always larger than mean tree diameter.

RAM: random access memory.

SDI: stand density index (Reineke 1933); and index relating any given stand to a reference stand with a quadratic mean diameter of 10 inches as expressed by number of

trees per acre.

SDI maximum: a specified maximum value that SDI may attain for any given species. By

convention, forest stands will exist in log(N), log(D) space where SDI is greater than

zero but less than SDI maximum.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174.1999. v

Self-extracting file: an executable file that contains one or more compressed files.

Execution of this type of file results in the decompression of the specified files.

Site index: height achieved by free-to-grow dominant or dominant and codominant

trees at some specified base age.

Stochastic: random; a stochastic process is one that incorporates a random variable; hence,

forecasts from a stochastic simulations will vary even when all input parameters are

fixed.

Structural function: in growth and yield simulators, a standard, or generic function for

producing some quantity in a common manner, e.g., stand basal area or stand growth

rate (Bruce 1990).
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 1

Introduction

Effective management of more than 70 million acres of commercially productive forest

land in the Pacific Coast states (herein defined as California, Oregon, Washington, and

Alaska) requires not only knowledge of the current status of the resource, but the ability to

forecast future conditions and effects of management practices as well.

To this end, many growth and yield simulators have been developed for forests of this

region, as no single tool can satisfy all the needs of such a large and diverse landscape and

potential user group. Yet, documentation of these simulators is often inadequate. Information

is lacking on appropriate scenarios for application and on the scope of many existing

simulators. Published resources on growth and yield models are often presented in journals

or series, which are not readily available to those outside the research community.

Potential users need information on what simulators are available, their sources and

capabilities. This report contains information useful for deciding which simulators (if any)

are appropriate, for any given application. Information on data requirements and management

options are included where appropriate.

This document is not intended to be a replacement for a user's guide, nor is it intended

to answer all the questions pertaining to the detail of simulator functions. It is not possible

to provide a complete technical support reference for so many different simulators in a

single manuscript. Users are encouraged to go to the source for more complete and detailed

information on any particular simulator. Typically, those who know a simulator best are

those who built it, or those who work with it frequently. To this end, references for individual

simulators are included in appendices.

For this presentation, a growth and yield simulator is a forecasting tool, which is executed

on a computer. It is composed of a suite of mathematical relationships (models) which are

integrated under some unifying concepts to form a system which can mimic the dynamics

of selected stand parameters (e.g., volume, basal area, stems per acre) of forest ecosystems.

These parameters may be expressed for the whole stand, or for some cohort of trees within

the stand. That is, a simulator may produce only a forecast of volume per unit area over time

for all trees in the stand, or the simulator may be capable of higher resolution output, say,

volume for trees of a given species and/or range of diameters.

Growth and yield information presented in the form of equations or traditional yield

tables is not included in this manuscript. Hann and Riitters (1982) have compiled an

exhaustive reference to these for the Pacific Northwest.

It is important to note the close parallel between today’s computer simulators and the

yield tables of the past. Computer simulators represent a logical extension of pioneering

growth and yield research (Dunning and Reineke 1933, McArdle and others 1961, Meyer

1934, Schumacher 1926, Schumacher 1930). One primary difference between a computer

growth simulator and a yield table is the form of the presentation. Traditional yield tables

present information in a rigid form for discrete increments of input data. Yields are typically

presented for fixed levels of site index and age at 10-year intervals. A computer simulator

may present information for many combinations of input variables, may operate at a higher

resolution, and offers a greater variety of output. In order to provide users with a more

powerful descriptive tool, these simulators have become increasingly complex.

The inclusion of a simulator here should not be interpreted as an endorsement for any

particular application. Rather, the intent is to provide users with information for selecting

decision support systems for application or, at the very least, to direct users toward sources

which may provide more detailed information on simulators.

Proprietary simulators are not included in this manuscript unless they are marketed to the

public. Some simulators may be viewed as freeware and are distributed as such. For some

simulators, new versions are released frequently, most notably CACTOS, FVS, and ORGANON.

Thus, with time, information on distribution and current version numbers may become obsolete.

Because of ongoing modifications to many of these programs, it is highly recommended that

users go to the source in order to obtain the most recent version and proper documentation.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 2

The strengths and limitations of simulators are generally not well documented and may

change as simulators are modified and updated. Few studies have evaluated simulator

forecasts. Some notable exceptions are Curtis (1994), Curtis and Marshall (1993), Korpela

and others (1992), and Zhang and others (1993). Unless reported in the literature, model

reliability or lack thereof, is not presented here.

Because there is no standard for documentation of these simulators, the information

presented for each varies. In all cases, I have endeavored to provide the reader with a source

from which more information may be obtained. Where possible, Internet access to software

and documentation are provided. All internet addresses are shown in bold. Users should be

aware that these may change over time.

Form vs. Function

All questions pertaining to simulator utility and behavior relate to either the form or function

of the simulator. Users typically have these concerns relating to form:

1. How to execute the simulator.

2. Management alternatives allowed by the simulator.

3. What kind of data input is required by the simulator.

4. Output options provided by the simulator.

Yet, users should also be concerned with what happens between input and output. This

relates to function, the primary concern of modelers. Users who ignore this question must

accept simulator output unquestioningly and run the risk of ignoring some of the faults of

all simulators. In this manuscript I will attempt to provide answers to the primary questions

of form and application of each simulator.

Users should be aware that the simulators are, of necessity, simplifications of complex

systems. Whole-stand simulators ignore much of the information typically required for

individual-tree simulators. Modelers understand that individual-tree information may be

useful in forecasting stand development, but assume that this loss of information is not

critical to the execution of whole-stand simulators. This assumption is most likely to be met

when dealing with even-aged, single-species stands.

When forecasts are made for conditions beyond the range of the data, or when forecasts

are made for long time frames, simulators may exhibit unreasonable behavior. Users should

exercise caution by not regarding output from any simulator as “correct.” Simulator output

should be presented as a likely scenario for stand development.

Five distinctly different types of simulators are presented: individual-tree/distance

independent, individual-tree/distance-dependent, whole-stand, disaggregative, and gap

models. The first three of these are described by Munro (1974). Using a strict interpretation

of Munro’s classification scheme, both gap simulators and disaggregative simulators may

be classified as individual-tree/distance-independent simulators. Both accept input of treelevel data and do not require a stem map. However, gap simulators function very differently

and have very different input requirements. Disaggregative simulators are constrained in

the allocation of growth to individual sample trees.

Terminology

To fully understand the computer simulators presented here, it is imperative that one

understand some basic concepts and terminology. Terminology varies somewhat by author,

so I will attempt here to define some terms that will be used extensively.

It is important to understand what variables are used to derive forecasts of tree growth

and how they enter into the process of any particular simulation. Bruce (1990) used a

convenient classification of functions in growth and yield simulators which I will employ.

In this classification, there are three distinct types of functions: driving functions,

housekeeping functions, and structural functions. Driving functions are those functions which

actually characterize the dynamic nature of stand and/or tree development. Examples are

height growth, diameter growth, or basal area growth. The primary driver is that function
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 3

which comes first in the progression of forecasts. Other drivers will be considered secondary.

Housekeeping functions are those functions which may vary between simulators and even

between species within a simulator but which do not directly impact the dynamic nature of

the simulator itself. Examples are volume equations or some species-dependent indices of

stand density such as crown competition factor (Krajicek and others 1961). Structural

functions are common functions invariant across species and simulator. Examples are

calculations for stand basal area or stems per acre.

Individual-Tree/Distance-Independent Simulators:

FVS, CRYPTOS, CACTOS, SPS, ORGANON,

SYSTUM-1, RVMM-Individual Tree, CONIFERS

Individual-tree/distance-independent simulators function with the tree as the basic modeling

unit. All individual-tree simulators require a list of sampled trees to make forecasts of stand

development over time. Some are capable of generating a list from some stand-level

parameters. The tree list variables usually include diameter at breast height (DBH), height,

crown ratio, and expansion factor. A standard format for tree lists does not exist, and users

must ensure that the data are in a format consistent with the requirements of a particular

simulator. Trees may be sampled on fixed- or variable-radius plots, or even on a combination

of the two for most simulators. It is important that all trees be measured on a plot.

Measurement of only crop trees, or completing partial measurements in such a way that

favors the largest or most healthy trees will result in biased forecasts. Spatial coordinates

(stem maps) are not required for execution of these simulators.

From the point of view of the simulator, the subject stand is characterized by a list of

sampled individual trees. Each tree in the sampled tree list is representative of some number

of trees per unit area. The larger the number of sampled plots for any given stand, the fewer

trees represented by each sampled tree and the better the description of the candidate stand.

It is imperative that trees be sampled in such a way that they may be related to an expansion

factor. The expansion factor is the number of trees per acre (or hectare) represented by each

individual in the list. Expansion factor is a constant for trees sampled on a fixed-radius plot;

the expansion factor varies by tree size (diameter) for those trees sampled using point sampling.

For example, table 1 contains a tree list with plot number, tree number, DBH and

expansion factor. Expansion factors for this particular example are derived assuming a

Table 1–Example of a tree list showing a sample

from three plots with expansion factors

determined assuming a 1/100th-acre fixed-area

plot for trees with DBH less than or equal to 6

inches in diameter and a 20 BAF variable-radius

plot for all trees over 6 inches DBH.

P e lot T H re DB Expansion

Factor

11 21 52

12 5 0 10

133 0 10

14 7 57

2 14 0 10

2250 10

239 54

249 54

31 11 03

326 0 10

334 0 10

344 0 10

3550 10
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 4

fixed-area, 0.01-acre plot for trees less than or equal to 6 inches DBH and a 20 basal area

factor (BAF) variable-radius plot for larger trees. The individual-tree expansion factor is

then the inverse of the plot area (100 for the smaller trees on the fixed-area plot and 3666.929/

DBH2 for the trees larger than 6 inches). A stand density measure such as trees per acre may

be calculated, but expansion factors must be adjusted to account for the number of plots; in

this case all expansion factors must be divided by 3. Thus, the stand-level estimate of trees

per acre from the example of table 1 is 340. For stand-level summaries, then, the number of

trees represented by any given sampled individual is a function of both plot size and sampling

intensity.

Growth increments may be annual or periodic (5- or 10-year increments are common).

If one wishes to predict growth for 10 years using a simulator with a 5-year forecast interval,

the simulator will make two consecutive growth forecasts. The tree list is updated twice in

this process.

The individual-tree/distance-independent simulator will forecast changes in diameter, height

and usually live crown ratio for each growth interval, as well as mortality. The tree list data are

updated and represent conditions forecast for some future period. The driving functions are

typically height growth, diameter or diameter squared increment, change in crown ratio and

mortality. Functions of tree growth will typically characterize growth as a function of four

factors: size (or in some cases age), tree vigor, competitive stress, and site quality.

Tree size may be presented in the form of transformations of DBH or total tree height.

Growth as a function of tree size should be a peaking function, that is, growth increases

with increasing size, eventually reaching a peak, followed by decreasing growth rates as

size further increases, tending toward zero for very large trees. This results in an effective

upper limit for tree size.

Tree vigor is often quantified by crown ratio of the subject tree. Longer crowns typically

result in greater predicted growth rates, all other factors being held constant. Crown ratio may

be a very important factor in determining response to thinning. Models containing this variable

will tend to continue to predict slower growth rates for trees with smaller live crown ratio.

Competitive stress affecting any particular tree may be quantified by such standard measures

of density as number of stems or basal area per unit of area. There are many other quantities

which may be interpreted as indices of competition: Crown competition factor (CCF) (Krajicek

and others 1961), Curtis’ relative density index (Curtis 1982), and Reineke’s stand density

index (Reineke 1933), to name a few. Competitive stress is most often averaged across all

plots in a stand. However, in some simulators it will be explicitly defined at the plot level.

Stand-level variables are derived using all individuals in a sampled stand, while the plot-level

variables are calculated using only individuals from a sampled plot (or point) in a stand.

Some individual-tree simulators use an index, which is tied to tree size or position in

relation to its competitors. Examples include basal area in trees larger than the subject tree and

crown closure at 66 percent of tree height (CC66). Basal area in larger trees is a value which

varies from zero (for the largest tree) to a value near total stand basal area for the smallest tree

in the list. CC66 is a value which could vary from zero, or some value close to zero, for the

largest tree, to a value close to total crown closure of the stand for the smallest tree.

Finally, site quality is present to affect changes in response due to variations in

productivity of the site. Some simulators depend solely on site index, while others use

combinations of factors. Many Forest Vegetation Simulator (FVS) variants, for example,

use a combination of location, elevation, slope, aspect, and site index.

Mortality is simulated by predicting a probability of death for each tree in the tree list.

Once this probability of death for a given period is estimated for each tree, the unit area

expansion factor is reduced accordingly. Trees are not removed from the tree list; rather, the

number of trees represented by each individual in the sample is reduced with each successive

forecast by the amount indicated by the mortality function(s). Thinning is easily facilitated

in a similar manner. Expansion factors are reduced according to the thinning regime specified

by the user. It is quite possible that all trees of a given cohort will be removed, resulting in

expansion factors of zero for some trees.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 5

Individual-tree/distance-independent simulators may have a pseudo-stochastic feature.

Typically this feature allows the simulator to better characterize the differentiation in size

classes which becomes accentuated over time with stand development. This may be exhibited

as a record duplication scheme such as that featured in CACTOS and ORGANON, or it

may be a random error term attributed to each growth forecast for each tree as in SYSTUM-1.

FVS variants feature both a record duplication scheme and a random growth component

which may be added to each tree’s growth prediction.

The power of individual-tree simulators is the capability to produce output summarized

for the entire stand, or for higher levels of resolution (e.g., by species, or by size-classes).

Also, individual-tree simulators are generally better suited to mixed-species or uneven-aged

stands than are whole-stand simulators.

FVS

FVS (Forest Vegetation Simulator, also referred to as Prognosis) was originally developed

for northern Idaho and western Montana (Stage 1973, Wykoff and others 1982). Numerous

variants have been developed for different geographic regions throughout the United States,

with most of these being in the western United States. There are currently nine variants for

the Pacific Coast states (table 2). Default growth intervals are either 5 or 10 years, depending

on the variant. However, interval length may be changed at runtime.

The FVS system has had numerous extensions developed which allow users to integrate

such factors as pest outbreaks, into forecasts. Not all of these extensions are currently available

for all of the Pacific Coast states FVS variants. Users need to check periodically on the

availability of extensions for individual variants.

Each variant comes with documentation on aspects of execution unique to that particular

variant. More general documentation on execution may be found in Wykoff and others

(1982) and Wykoff (1986). Wykoff and others (1991) documents execution of version 6 for

all existing variants.

The data needed for execution are generally the same for all of the FVS variants. Tree

list variables include:

1. plot identifier (integer)

2. tree count (number of trees represented by the sample tree)

3. species (two letter code)

4. DBH (inches)

5. DBH increment (inches), period of this increment should correspond to the growth

increment of the variant

6. height (feet)

7. height to topkill (feet)

8. height increment (feet), period of this increment should correspond to the growth

increment of the variant

9. crown ratio (integer code from 1-9)

10. damage code(s)

Crown ratio, DBH increment, height increment, damage code, height to topkill, and

height are not required. Crown ratio and height may be filled in by the simulator.

All FVS variants have a separate set of routines for juvenile stand development. Small

trees (typically those less than 3 inches DBH) are modeled using a different set of functions

than that used for large trees. Generally, FVS has a primary driver of diameter increment for

trees greater than 3 inches, whereas height growth is the primary driver of small tree growth.

The diameter growth function for large trees is derived from a linear least squares fit of a

squared diameter increment model (Wykoff and others 1982) of the following form:

dds = exp(b0

+ b1x1 +...+bpxp)

where:

dds = predicted increment of diameter squared

xi

 = variable.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 6

Table 2—Current Forest Vegetation Simulator (FVS) variants for the Pacific Coast states and self-extracting file

names for IBM-PC executable code associated with each version and documentation file.

V e ariant name F e ile nam Documentation fil

1. Southeast Alaska/Coastal British Columbia akvar.txt

F e VS with Dwarf Mistletoe Extension akdos.ex

2. Blue Mountains bmvar.txt

F e VS with Western Spruce Budworm Model bmddos.ex

F e VS with Dwarf Mistletoe Model bmdos.ex

F e VS with Douglas-fir Beetle Model bmfdos.ex

F e VS with Lodgepole Mountain Pine Beetle Model bmldos.ex

F e VS with Western Root Disease Model bmrdos.ex

F e VS with Combined Pest Extension bmxdos.ex

3. East Cascades ecvar.txt

F e VS with Dwarf Mistletoe Model ecdos.ex

F e VS with Western Root Disease Model ecrdos.ex

4. Inland Empire nivar.txt

F e VS with Annosus/Bark Beetle Model niados.ex

F e VS with Western Spruce Budworm Damage Model niddos.ex

F e VS with Dwarf Mistletoe Model nidos.ex

F e VS with Douglas-fir Beetle Model nifdos.ex

F e VS with Lodgepole Mountain Pine Beetle Model nildos.ex

F e VS with Spatial Dwarf Mistletoe Model nimdos.ex

F e VS with Western Root Disease Model nirdos.ex

F e VS with Douglas-fir Tussock Moth Model nitdos.ex

F e VS with White Pine Blister Rust Model niudos.ex

F e VS with Fire Model nifmdos.ex

F e VS with Parallel Processor ppnidos.ex

5. Klamath Mountains ncvar.txt

F e VS with Dwarf Mistletoe Model ncdos.ex

6. Pacific Northwest Coast pnvar.w51

F e VS with Dwarf Mistletoe Model pndos.ex

F e VS with Douglas-fir Beetle Model pnfdos.ex

F e VS with Western Root Disease Model pnrdos.ex

7. South Central Oregon/Northeast California sovar.txt

F e VS with Annosus/Bark Beetle Model soados.ex

F e VS with Western Spruce Budworm Damage Model soddos.ex

F e VS with Dwarf Mistletoe Model sodos.ex

F e VS with Lodgepole Mountain Pine Beetle Model soldos.ex

F e VS with Western Root Disease Model sordos.ex

F e VS with Douglas-fir Tussock Moth Model sotdos.ex

FVS with Lodgepole Mountain Pine Beetle and

Western Root Disease Model sorldos.exe

8. West Cascades wcvar.w51

F e VS with Dwarf Mistletoe Model wcdos.ex

F e VS with Western Root Disease Model wcrdos.ex

9. Western Sierras wsvar.txt

F e VS with Annosus/Bark Beetle Model wsados.ex

F e VS with Dwarf Mistletoe Model wsdos.ex
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 7

This function is appealing because it is strictly positive and intrinsically linear. That is,

the model may be transformed by taking the natural logarithm of both sides of the equation

such that the resulting model is linear with respect to the parameters. This transformation

often results in an error term which is approximately normally distributed. Since this

transformation results in a linear model, the parameters are easily estimated using ordinary

linear least squares regression. Although the model is not explicitly defined as a potential

times modifier, the model may be interpreted as such (Hann and Larsen 1991) by merely

partitioning the explanatory (predictor) variables into those which are related to potential

growth (site productivity and tree size) and those which are related to tree vigor or competitive

stress (crown ratio, tree position, stand density):

dds = exp(b0 + b1x1 +... +brxr ) • exp(br+1xr+1 +...+bpxp )

FVS offers a number of different output tables. The input summary table summarizes the

keyword directives and calibration statistics for the run. The stand composition table includes

a description of the stand DBH by percentiles (10th, 30th, 50th, 70th, 90th, 100th) of trees per acre

and volume. The DBH associated with the percentiles of trees per acre is displayed in one

line, the DBH associated with the percentiles of total stem cubic foot volume on another line,

and so on. For example, in table 3, 90 percent of the 590 trees per acre are below 8.5 inches in

diameter. However, 90 percent of total cubic foot volume is found in trees above 5.8 inches.

Tree and stand attributes are presented in a similar fashion, by trees per acre percentile. Tree

list output produces a tree list for each time step in the forecast. Finally, the stand summary

table produces stand summaries at each growth interval in the forecast.

The nine variants applicable to the Pacific Coast states region are all very similar in

execution. The primary difference between variants is in the parameterization of individual

growth and mortality functions as well as static functions in the model, such as crown ratio

and height-diameter relationships. Housekeeping functions such as volume equations also

vary between simulators. The means by which site productivity is quantified also varies. In

most variants, site productivity will be expressed as a function of some combination of

slope, aspect, elevation, site index, and location. Site index reference depends on the variant

and species prevalent in the region.

The form of driving functions (what variables are included) also may vary somewhat

between simulators and within simulators between species. Users must also be aware of the

individual species designators associated with each variant. This information is provided in

the documentation file associated with each variant (table 2).

One key feature of the FVS system is the Event Monitor (Crookston 1990). The Event

Monitor, while documented separately, is integrated into the code. It provides users with an

expanded group of keywords which allow conditional execution of the simulator. Users

may specify that thinnings or output of data will take place when certain stand conditions

are reached. The target stand condition for execution of a thinning can be defined by the

user. A powerful suite of functions in the Event Monitor give the user great latitude in

defining new variables and specifying conditions for executing management options.

It is possible for the user to define a condition in the stand using the SPMCDBH function.

This function will allow the user to specify that the simulator calculate trees per acre; basal area

per acre; cubic volume per acre; board-foot volume per acre; quadratic mean diameter, or average

height, by species; and tree value class within any specified range of diameters and or heights.

These values may be calculated for either surviving trees or mortality. Values returned by the

SPMCDBH function may be written as output or used to define timing and intensity of thinnings.

The system requirements for all variants: IBM compatible DOS 3.3 or higher, 4 MB

RAM memory, hard disk, and 386DX or better with a math coprocessor (recommended).

Additional references are listed in appendix B. Current information on variants and the

latest version may be downloaded off the World Wide Web at:

http://www.fs.fed.us/fmsc/fvs.htm

A general FVS overview may be found at:

http://forest.moscowfsl.wsu.edu/4154/fvsoview.html
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 8

1

Numbers in parentheses refer to the

simulator index for tree species in

appendix A.

Southeast Alaska (1)1

Primary Species: Geographic Range: Site Index/Productivity:

• Alaska yellow-cedar Southeastern Alaska and Western hemlock

• Lodgepole pine coastal British Columbia and Sitka spruce

• Mountain hemlock site index (Farr 1984)

• Pacific silver fir

• Sitka spruce

• Subalpine fir

The southeast Alaska variant (Dixon and others 1992) was developed for the western

hemlock-Sitka spruce type in southeast Alaska (fig. 1). There are four areas or localities

within this range: the Chatham, Ketchikan, and Sitkine areas of the Tongass National Forest

and the British Columbia/Makah Indian reservation. The user must specify the area within

which the sampled stand resides.

The southeast Alaska variant uses the site index of Farr (1984). The user must input the

site index and the species associated with that site index value (Sitka spruce or western

hemlock). The simulator will calculate the site index for other species as a function of the

value provided by the user.

The strength of the data base for this modeling effort was in western hemlock and Sitka

spruce (table 4). Ten species, or species groups, are noted in the simulator documentation.

Species other than those shown in the table are grown as Sitka spruce. White spruce, although

listed in the species table, had no representation in the data base. It is possible that this

distinction is due to the fact that users wish to categorize white spruce separately, or there

may be housekeeping equations which justify the presence of white spruce as an acceptable

input species.

As with other FVS variants, three separate procedures are associated with tree growth:

establishment (for seedlings), small tree functions (for trees with small diameters), and

large tree growth functions for trees with larger diameters. The establishment submodel

allows users to simulate natural regeneration and plantings (Dixon and others 1992, pages

22-25). The primary driving function for small trees is height growth. There is a generic

height growth function for small trees in which height growth is forecast as a function of

crown ratio, basal area in larger trees, and site index. Small trees are nominally defined as

those with breast height diameters less than 5 inches. However, for all trees between 2 and

5 inches DBH, the forecast height growth is actually a weighted average of the small and

large tree height growth functions, such that there is a smooth transition between small and

large tree height growth predictions. The large tree model employs a diameter growth model

Table 3—Example FVS output table displaying percentile points of the diameter distribution.

PERCENTILE POINTS IN THE

DISTRIBUTION OF STAND

TOTAL/ACRE

ATTRIBUTES BY DBH

YEAR

STAND

ATTRIBUTES 1 0 0 3 05 07 09 0 10

OF STAND

ATTRIBUTES

DISTRIBUTION OF STAND ATTRIBUTES BY

SPECIES AND 3 USER-DEFINED SUBCLASSES

(DBH IN INCHES)

1990 TREES 0 1 .1 0 2 . 3 1 . 6 5 . 8 7 . 1 S 2. 5 , 90. TREE 2 , 7.% DF2 1 1 5.% WF2 1 1 5.% WF 12.% RF

VOLUME:

TOTAL 5 9 .8 7 4 . 9 0 . 1 5 0. 1 7 1. 1 T 2. 1 , 290. CUF 3 , 0.% SP1 2 , 2.% WF1 2 2 1.% DF1 12.% SP

MERCH 8 4 .0 9 6 . 9 4 . 1 5 0. 1 7 1. 1 T 2. 8 , 51. CUF 3 , 9.% SP1 2 , 8.% DF1 1 1 5.% SP2 10.% WF

MERCH 1 4 0.0 1 9 0. 1 5 0. 1 7 1. 1 7 2. 1 T 2. 1 , 469. BDF 2 , 1.% DF1 2 , 1.% WF1 1 2 9.% SP1 18.% DF
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 9

as the primary driver. The growth interval is 10 years for the large tree functions. Diameter

growth for large trees is forecast as a function of stand basal area per acre, DBH, basal area

in larger trees, crown ratio, tree height divided by height of the 40 largest trees (HT40),

elevation, slope, aspect, and site index.

Mortality is driven by an approach to the maximum stand density index (SDI) (Reineke

1933). The functional maximum SDI is defined as 85 percent of maximum SDI. That is,

stands will tend to approach this functional maximum over time. The maximum SDI value

varies by species (table 5).

Table 4—Distribution of tree records with observed growth

across species for the southeast Alaska/coastal British

Columbia variant of the Forest Vegetation Simulator

(Dixon and others 1992).

Table 5—Stand Density Index (SDI) maximum (Reineke 1933) and functional

maximum, expressed in trees per acre, for the mortality functions of the southeast

Alaska/coastal British Columbia variant of the Forest Vegetation Simulator (Dixon

and others 1992).

Figure 1—Geographic range of the Southeast Alaska variant

of the Forest Vegetation Simulator (Dixon and others 1992).

S e pecies Cod SDI maximum SDI functional maximum

(85 percent of SDI)

W S hite spruce W 0 4 0 7 40

W C estern redcedar R 5 5 0 6 48

P F acific silver fir S 0 5 2 9 50

M H ountain hemlock M 0 6 0 0 51

W H estern hemlock W 3 6 0 2 53

A C laska yellow-cedar Y 5 5 0 6 48

L P odgepole pine L 0 4 0 7 40

S S itka spruce S 0 6 0 0 51

S F ubalpine fir A 0 4 0 7 40

H D ardwood H 0 4 0 7 40

S e pecies C s od Number of record

W S hite spruce W 0

W C estern redcedar R 2 40

P F acific silver fir S 89

M H ountain hemlock M 8 90

W H estern hemlock W 1 5,58

A C laska yellow-cedar Y 0 88

L P odgepole pine L 87

S S itka spruce S 7 3,01

S F ubalpine fir A 1

H D ardwood H 96
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 10

Blue Mountain (2)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Northeast Oregon and Site index reference

• Englemann spruce southeast Washington varies by species

• Grand fir

• Lodgepole pine

• Mountain hemlock

• Ponderosa pine

• Subalpine fir

• Western larch

• Western white pine

The Blue Mountains FVS variant (Johnson 1993) was developed for the Malheur, Ochoco,

Umatilla, and Wallowa-Whitman National Forests in northeast Oregon. The geographic range

is displayed in figure 2. Primary species in the modeling data base were ponderosa pine,

Douglas-fir, and grand fir. There were fewer numbers of white pine, western larch, mountain

hemlock, lodgepole pine, Englemann spruce, and subalpine fir (table 6). Western juniper

was not represented in the data base; the simulator employs the functions from the SORNEC

variant (see 7) for this species. Site productivity is characterized by site index (table 7).

The small-tree model’s primary driver is height growth. Height growth in this submodel

is forecast as a function of site index, height, CCF, and crown ratio. Diameter growth is a

secondary driver. Diameter growth is derived indirectly from a height-diameter relationship.

The simulator operates on a 10-year growth cycle for the large tree models (DBH>3

inches). Site index is used in both the height growth and diameter growth equations of the

large tree component. In addition, the diameter growth model requires location code (unique

for each National Forest), slope, aspect, and elevation of the sampled site as well as DBH,

crown ratio, basal area per acre, plot-level CCF, basal area in larger trees, and a relative

density function that is not defined in the documentation.

Mortality functions are driven by an approach to 85 percent of the maximum SDI.

Maximum SDI values for each species are shown in table 8.

 Table 6—Distribution of observations in the

modeling data base for the Blue Mountains

variant of the Forest Vegetation Simulator.

S s pecies Number of tree

W 6 estern white pine 25

W 9 estern larch 1,20

D 8 ouglas-fir 3,47

G 3 rand fir 2,96

M 0 ountain hemlock 1,00

L 7 odgepole pine 1,11

E 6 nglemann spruce 59

S 9 ubalpine fir 59

P 7 onderosa pine 6,57

Figure 2—Geographic range of the Blue Mountains variant

of the Forest Vegetation Simulator (Johnson 1993).
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 11

Table 7—Site index curve references for the Blue Mountains variant of the

Forest Vegetation Simulator (Johnson 1993).

S e pecies R e eferenc Base ag

W 0 estern white pine B 0 rickell 197 Total age 5

W 5 estern larch C 0 ochran 198 Breast height age 5

D a ouglas-fir C 0 ochran 1979 Breast height age 5

G b rand fir C 0 ochran 1979 Breast height age 5

M e ountain hemlock N 0 ot availabl Breast height age 10

E 7 nglemann spruce A 0 lexander 196 Breast height age 10

L 4 odgepole pine D 0 ahms 196 Breast height age 5

P 8 onderosa pine B 0 arrett 197 Breast height age 10

S 0 ubalpine fir D 0 emars and others 197 Breast height age 10

Table 8–Maximum Stand Denisty Index (SDI)

values, by species, used by default in the Blue

Mountains variant of the Forest Vegetation

Simulator (Cochran and others 1994).

Species Maximum

SDI

P 5 onderosa pine 36

L 6 odgepole pine 27

W 0 estern larch 41

W 0 hite fir and grand fir 56

D 0 ouglas-fir 38

S 6 ubalpine fir 41

E 9 nglemann spruce 46

Figure 3—Geographic range of the East Cascades

variant of the Forest Vegetation Simulator (Johnson 1990).

East Cascades (3)

Primary Species: Geographic Range: Site Index/ Productivity:

• Douglas-fir East side of the Cascades Site index reference

• Engelmann spruce in Washington and varies by species

• Grand fir northern Oregon

• Lodgepole pine

• Mountain hemlock

• Pacific silver fir

• Ponderosa pine

• Subalpine fir

• Western larch

The east Cascades variant was developed using data from the Gifford Pinchot, Mount

Hood, Okanogan, and Wenatchee National Forests (Johnson 1990). As with other FVS

variants, the data came from a variety of sources. The species most well represented were

Douglas-fir, grand fir, and ponderosa pine (table 9). The geographic range of the simulator

is shown in figure 3.

S e pecies C s od Number of Record

W P estern white pine W 0

W L estern larch W 2 65

D F ouglas-fir D 9 6,24

W C estern redcedar R 0

G F rand fir G 0 1,95

P P onderosa pine P 0 4,04

L P odgepole pine L 9 1,47

E S nglemann spruce E 3 62

balpine fir

silver fir

S F u A 9 72

M H ountain hemlock M 3 1,44

P F acific S 0 1,21

Table 9—Number of observations by species used in

development of the east Cascades variant of the Forest

Vegetation Simulator (Johnson 1990).
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 12

This simulator features a 10-year growth interval for large trees (those greater than 3

inches DBH). In the large tree submodel, diameter increment is forecast as a function of location,

slope, aspect, elevation, site index, DBH, crown ratio, BAL, CCF, and stand basal area.

A variety of site index functions are used, depending on species (table 10).

Inland Empire (4)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Eastern Montana, central Habitat type designation

• Engelmann spruce Idaho and northeastern (Wykoff and others 1982)

• Grand fir Washington

• Lodgepole pine

• Mountain hemlock

• Ponderosa pine

• Subalpine fir

• Western hemlock

• Western larch

• Western redcedar

• Western white pine

The Inland Empire variant is the original version of the simulator. Operation of the

simulator is described by Wykoff and others (1982) and Wykoff (1986). Models were

parameterized for: western white pine, western larch, Douglas-fir, grand fir, western hemlock,

western redcedar, lodgepole pine, Engelmann spruce, subalpine fir, ponderosa pine and

mountain hemlock. The relevant national forests are Bitterroot, Clearwater, Coeur d’Alene,

Colville, Flathead, Kaniksu, Kootenai, Lolo, Nezperce, and St. Joe (fig. 4).

Large tree height growth is a function of height, DBH, site factors, and predicted diameter

growth. The static crown ratio model is a function of basal area, CCF, DBH, height and the

tree’s basal area percentile.

As with other variants, small tree growth is driven by a separate height growth function

for trees less than 2 inches DBH. For trees between 2 and 10 inches, the small tree predicted

height growth is combined with the large tree estimate.

Large tree growth is driven by a squared diameter-inside-bark 10-year growth equation.

One important characteristic of this function, and the variant as a whole, is that site index is

not a factor used to forecast growth. Rather, site productivity is reflected in variables such

as slope, aspect, elevation, location, and habitat type. Habitat type is input into the simulator

by a three-digit code which is used in diameter growth, crown ratio, and height growth

equations (table 11).

Table 10—Site index curves employed by the east Cascades

variant of the Forest Vegetation Simulator (Johnson 1990).

S e pecies C e od Referenc

Western white

pine W ) P Brickell (1970

W L estern larch W ) Cochran (1985

D F ouglas-fir D ) Cochran (1979a

P F acific silver fir S ) Cochran (1979b

G F rand fir G ) Cochran 1979b

L P odgepole pine L ) Alexander and others (1967

E S nglemann spruce E ) Alexander (1967

S F ubalpine fir A ) DeMars and others (1970

P P onderosa pine P ) Barrett (1978

M H ountain hemlock M e Not availabl
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 13

Figure 4—Geographic range of the Inland-Empire

variant of the Forest Vegetation Simulator.

Table 11–Habitat types and associated codes for the Inland Empire variant of the

Forest Vegetation Simulator (Wykoff and others 1982).

C n ode A e bbreviatio Habitat type nam

1 P 30 PIPO/AGS Pinus ponderosa/Agropyron spicatum

1 L 70 PIPO/SYA Pinus ponderosa/Symphorocarpus albus

2 A 50 PSME/VAC Pseudotsuga menziesii/Vaccinium caespitosum

2 A 60 PSME/PHM Pseudotsuga menziesii/Physocarpus malvaceus

2 L 80 PSME/VAG Pseudotsuga menziesii/Vaccinium globulare

2 O 90 PSME/LIB Pseudotsuga menziesii/Linnaea borealis

3 L 10 PSME/SYA Pseudotsuga menziesii/Symphorcarpus albus

3 U 20 PSME/CAR Pseudotsuga menziesii/Calamagrostis

rubescens

3 E 30 PSME/CAG Pseudotsuga menziesii/Carex geryeri

4 N 20 PICEA/CLU Picea/Clintonia uniflora

4 O 70 PICEA/LIB Picea/Linnaea borealis

5 E 10 ABGR/XET Abies grandis/Xerophyllum tenax

5 N 20 ABGR/CLU Abies grandis/Clintonia uniflora

5 N 30 THPL/CLU Thuja plicata/Clintonia uniflora

5 I 40 THPL/ATF Thuja plicata/Athyrium filiz-femina

5 O 50 THPL/OPH Thuja plicata/Oplopanax horridum

5 N 70 TSHE/CLU Tsuga heterophylla/Clintonia uniflora

6 O 10 ABLA/OPH Abies lasiocarpa/Oplopanax horridum

6 N 20 ABLA/CLU Abies lasiocarpa/Clintonia uniflora

6 A 40 ABLA/VAC Abies lasiocarpa/Vaccinum caespitosum

6 O 60 ABLA/LIB Abies lasiocarpa/Linnaea borealis

6 E 70 ABLA/MEF Abies lasiocarpa/Menziesia ferruginea

6 E 80 TSME/MEF Tsuga mertensiana/Menziesia ferruginea

6 E 90 ABLA/XET Abies lasiocarpa/Xerophyllum tenax

7 E 10 TSME/XET Tsuga mertensiana/Xerophyllum tenax

7 L 20 ABLA/VAG Abies lasiocarpa/Vaccinium globulare

7 C 30 ABLA/VAS Abies lasiocarpa/Vaccinium scoparium

8 I 30 ABLA/LUH Abies lasiocarpa/Luzula hitchcockii

8 A 50 PIAL/ABL Pinus albicaulis/Abies lasiocarpa

9 R 99 OTHE
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 14

Klamath (5)

Primary Species: Geographic Range: Site Index/Productivity

• California black oak Southwestern Oregon and site index reference

• Douglas-fir northwestern California varies by species

• Pacific madrone

• Ponderosa pine

• Tanoak

• White fir

The Klamath variant of FVS (Dixon and Johnson 1993) was developed for the Klamath,

Six Rivers, Siskiyou, and western Shasta-Trinity National Forests (fig. 5). Data were also

provided by Hoopa Indian Reservation and Simpson Timber Company. The simulator

incorporates some of the models presented by Dolph (1988a) for the WESSIN variant. The

simulator operates on a 5-year forecasting interval. Height growth and diameter growth

models use site index as one of the input variables (table 12). In addition, the diameter

growth model requires elevation and slope. As with other FVS variants, there are separate

models for small and large trees. The cutoff between large and small trees models is 3

inches DBH.

The data used in model development were heavily weighted to the smaller diameter

classes. Primary tree species in the modeling data base are Douglas-fir and tanoak. Other

hardwoods and conifers were not as well represented (table 13). For Douglas-fir, two thirds

of the trees were less than 15 inches DBH. For ponderosa pine, 92 percent of the trees were

less than 15 inches DBH. There was a very even representation of Douglas-fir for stands

below 4,000 feet in elevation. However, ponderosa pine was limited primarily to stands

above 2,000 feet in elevation.

Figure 5—Geographic range of the Klamath variant of the

Forest Vegetation Simulator (Dixon and Johnson 1993).

Table 12–Site index references for the Klamath variant of the

Forest Vegetation Simulator (Dixon and Johnson 1993).

S e pecies Referenc

D ) ouglas-fir, other conifers King (1966

White fir, incense cedar, red fir Dolph (1987)

P ) acific madrone Porter and Wiant (1965

C ) alifornia black oak Powers (1972

Porter and Wiant (1965)

S ) ugar pine, ponderosa pine Powers and Oliver (1978

Tanoak

Table 13—Distribution of number of trees by

species for the Klamath variant of the Forest

Vegetation Simulator modeling effort (Dixon

and Johnson 1993).

S s pecies Number of tree

D 2 ouglas-fir 6,50

W 7 hite fir 29

P 7 acific madrone 46

C 8 alifornia black oak 19

T 6 anoak 2,37

P 9 onderosa pine 38
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 15

Pacific Northwest Coast (6)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Coast range of Oregon Unknown

• Western hemlock and Washington

At this time, the Pacific Northwest Coast variant is an early release and is still being

tested. The geographic range is displayed in figure 6.

Large tree diameter increment is forecast as a function of site index, elevation, slope,

aspect, location (National Forest indicator), DBH, crown ratio, stand basal area, point CCF,

and relative height. Height growth is a function of potential and a modifier. The potential

height growth is determined by height and site index. The modifier is expressed as a function

of crown ratio and relative height.

Figure 6—Geographic range of the Pacific Northwest Coast

variant of the Forest Vegetation Simulator.

SORNEC (7)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Southern Oregon and Site index

• Incense-cedar northeastern California varies by species

• Lodgepole pine

• Mountain hemlock

• Red fir

• Sugar pine

• Western white pine

The south-central Oregon/northeastern California (SORNEC) variant of FVS was

developed for a region dominated by interior ponderosa pine type and lodgepole pine type

forests. Data for the SORNEC variant came primarily from National Forest inventories

ranging from the Lassen National Forest in the south to the Deschutes National Forest in the
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 16

north (Dixon 1992). Additional data from research plots were provided by the Pacific

Northwest and Pacific Southwest Research Stations. The geographic range of the simulator

is illustrated in figure 7.

Ponderosa pine, lodgepole pine, and white fir are well represented in the modeling

data base (table 14). Seven other species were also present on far fewer plots. Table 15

shows the number of plots in the modeling data base by National Forest. A total of 23,143

tree records from 1,500 sample plots were used. The actual ranges of heights and diameters

of these trees have not been published, but given the number of plots and the number of

tree records observed, these should reflect a broad range of tree sizes and stand conditions.

The establishment model (Ferguson and Crookston 1991) guides establishment of trees

up to the time of a tally. From this point until a tree reaches 3 inches DBH, the small tree

submodel is responsible for forecasting tree growth. Growth forecasts of trees greater than

3 inches DBH employ the large tree submodel.

Height growth is the primary driving function for growth of small trees. As with some

other FVS variants there is actually an averaging of the forecasts from the small-tree and

large-tree height growth models falling within a transition range. The average is weighted

in such a way that trees near the low end of this diameter range have a growth prediction

that is weighted more heavily toward the estimate from the small tree model. For trees near

the high end of the range, the opposite is true. For SORNEC, this transition range is 2 to 4

inches DBH for most species. Exceptions are ponderosa pine and red fir (2 to 6 inches) and

lodgepole pine (1 to 5 inches). A diameter-height relationship is used to predict diameter

growth as a function of predicted height growth for trees less than 3 inches DBH.

The growth interval for the large tree submodel is 10 years, and logarithm of diameter

squared increment is the primary driving function. In the large tree model, diameter growth

is forecast as a function of location, slope, aspect, site index, elevation, DBH, crown ratio,

basal area in larger trees, point-level CCF, stand-level CCF, relative height (H/HT40) and

stand potential MAI (derived from yield table values). Height growth is a potential-modifier

type of function. The potential growth is derived from site-age-height relationships (table

16). The modifier function adjusts potential growth as a function of relative height and

crown ratio.

Mortality is guided by an approach to a maximum size-density relationship (85 percent

of the maximum SDI). The default maximum SDI values in SORNEC are shown in table 17.

As with other FVS variants these values may be changed by the user in order to modify

rates of mortality.

Figure 7—Geographic range of the SORNEC variant

of the Forest Vegetation Simulator.

Table 14—Number of trees with growth

measurements used in the SORNEC

modeling effort (Dixon 1992).

S s pecies Number of tree

Western white

pine 260

S 6 ugar pine 32

D 7 ouglas-fir 71

W 7 hite fir 5,79

M 3 ountain hemlock 78

I 1 ncense-cedar 51

L 6 odgepole pine 3,41

E 1 nglemann spruce 3

R 6 ed fir 1,21

P 8 onderosa pine 10,03
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 17

R 6 egion 5 Region

Species Max.

SDI

85 percent of

max.

Max.

SDI

W e estern white p 4 in 6 0 2 5 7 3 4 0 4 38

6 0 47 5 7 5 4 0 4 38

5 5 47 4 7 6 4 0 4 38

7 5 59 6 9 4 6 0 5 56

6 0 24 5 8 3 7 4 5 64

7 0 06 6 7 0 4 0 4 38

4 5 06 3 1 4 5 0 4 46

6 0 71 5 9 7 6 0 5 56

8 0 00 6 9 8 6 0 5 56

5 5 71 4 9 8 4 5 2 36

Sugar pine

Douglas-fir

White fir

Mountain hemlock

Incense-cedar

Lodgepole pine

Englemann spruce

Red fir

Ponderosa pine

85 percent of

max.

Table 16—Site index sources for the southern Oregon/

northeastern California variant of the Forest Vegetation

Simulator (Dixon 1992).

Table 15—Number of plots with given species present displayed by source for the southern Oregon/northeastern

California variant of the Forest Vegetation Simulator (Dixon 1992).

Species Deschutes

NF

Winema

NF

Fremont

NF

Klamath

NF

Modoc

NF

Lassen

NF Other

w 7 hite pine 3 23 31062 11 0

S 35 ugar pine 3 31 0 244 1

Douglas-fir 5 6 1 7 12 0 52 4

White fir 8 7 8 1 1 6 1 9 8 1 0 1 5 7 1 9 6

Mountain hemlock 7 6 9 1 2 0 5 10

Incense-cedar 1 3 0 2 04042 25 4

Lodgepole pine 3 2 60 1 9 0 7 593 43 2

Englemann spruce 9 3 00 0 00

Red fir 3 4 7 7 24 05 52 0

Ponderosa pine 3 3 77 2 1 0 2 9 6 2 2 2 7 9 1 8 7 6

Western

Table 17–Maximum Stand Density Index (SDI) (Reineke 1933) values for the southern Oregon/

northeastern California variant of the Forest Vegetation Simulator.

West Cascades (8)

Primary Species: Geographic Range: Site Index/Productivity:

• Alaska yellow-cedar Western Cascades of Site index reference

• Douglas-fir Oregon and Washington varies by species

• Engelmann spruce

• Grand fir

• Incense-cedar

• Lodgepole pine

• Noble fir

• Pacific silver fir

• Ponderosa pine

• Red alder

• Shasta red fir

• Subalpine fir

• Sugar pine

• Western hemlock

• Western redcedar

• Western white pine

• White fir

S e pecies Referenc

W ) estern white pine Brickell (1970

S 8 ugar pine Barrett (197

D ) ouglas-fir Cochran (1979a

W b hite fir Cochran (1979

M e ountain hemlock Not availabl

I ) ncense-cedar Cochran (1979b

L ) odgepole pine Dahms (1964

E ) ngelmann spruce Alexander (1967

R ) ed fir DeMars and others (1970

P ) onderosa pine Barrett (1978

O ) ther Cochran (1979a

)

)
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 18

The West Cascades FVS variant was developed for forests of the western slope of the

Cascade range throughout Oregon and Washington (fig. 8). Data were obtained from six

National Forests: Gifford Pinchot, Mt. Baker/Snoqualmie, Mt. Hood, Rogue River, Umpqua,

and Willamette. The primary species in the data base were Douglas-fir, western hemlock,

mountain hemlock, and Pacific silver fir; however, a number of other species were

represented (table 18).

As with other variants, there are separate submodels for small trees and large trees.

Presumably there is also an establishment submodel for simulating plantings and natural

regeneration, but this is not documented by Johnson (1992). The small-tree model, for trees

less than 3 inches DBH, has height growth as its primary driver. Diameter growth is derived

from the predicted height growth model. Small-tree height growth is forecast as a function

of point-level CCF, basal area per acre, and crown ratio. The large-tree submodel has diameter

Table 18—Percent distribution of sample trees by National Forest used in model development for the West Cascades variant of the

Forest Vegetation Simulator (Johnson 1992).

S e pecies Cod

Gifford

Pinchot

NF

Mt. Baker

Snoqualmie

NF

Mt.

Hood

NF

Rogue

River

NF

Umpqua

NF

Willamette

NF

Number of

observations

P F acific silver fir S 21 42 33 04 82 8 3,87

W F hite fir W4 0 53 82 12 11 4 1,04

G F rand fir G4 0 53 82 12 11 4 50

S F ubalpine fir A 82 21 81 132 91 7 22

C F alifornia red fir R 57 520 44

S 5 hasta red fir 7 520 5 51

N F oble fir N4 3 23 271 34 5 1,55

A C laska yellow-cedar Y7 64 91 82 2 11

W L estern larch 74

I C ncense-cedar I 296

E S nglemann spruce E4 1 31 71 63 92 9 20

S S itka spruce S 2

L P odgepole pine L5 03 844 31 8 89

S P ugar pine S 240

W P estern white pine W2 0 9 11 64 33 4 41

P P onderosa pine P2 76 41 512 2 43

D F ouglas-fir D9 5 02 432 04 0 17,25

W C estern redcedar R8 42 82 373 4 1,35

W H estern hemlock W8 42 13 16 03 8 5,00

M H ountain hemlock M3 7 61 322 94 9 3,01

B M igleaf maple B 89

R A ed alder R 11 32 25 67 5 12

W A hite alder W 2

P M acific madrone P 70

G C iant chinkapin G 62

T O anoak T 1

B O lack cottonwood C 8

O O regon white oak W 12

C B alifornia black oak C 4

W B hitebark pine W 2

P Y acific yew P 5
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 19

Figure 8—Geographic range of the West Cascades

variant of the Forest Vegetation Simulator (Johnson 1992).

Table 19—Site index references, by species, for the West

Cascades variant of the Forest Vegetation Simulator

(Johnson 1992).

growth as a primary driver. The predictor variables for the diameter growth function are

location, site index, elevation, aspect, slope, DBH, crown ratio, and basal area in larger

trees. Site index references are shown in table 19. Large tree height growth is obtained from

potential-modifier type of function with height, crown ratio and relative height (height divided

by HT40) in the modifier function.

Mortality is derived from an approach to maximum SDI (Reineke 1933); however,

the default maximum values are not presented in the documentation. As with other

FVS variants, the user may modify mortality rates by specifying the maximum SDI

value to be used for any given species in the stand. Raising the default SDI value will

delay the onset of mortality and result in denser stands forecast over long periods of

time, and lowering it will have the opposite effect. The zone of imminent mortality is

at 55 percent of maximum SDI.

S e pecies Referenc

P ) acific silver fir Hoyer and Herman (1989

G ) rand fir Cochran (1979

S ) ubalpine fir Alexander (1967

R ) ed fir Dolph (1991

N ) oble fir Herman and others (1978

E ) ngelmann spruce Alexander (1967

A ) laska yellow-cedar Curtis (1974

L ) odgepole pine Dahms (1964

P ) onderosa pine Barrett (1978

W ) hite pine Curtis (1990

D ) ouglas-fir Curtis (1974

W ) estern hemlock Wiley (1978

M e ountain hemlock Not availabl

Red alder Worthington and others (1960)
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 20

WESSIN (9)

Primary Species: Geographic range: Site Index/Productivity:

• Douglas-fir Western Sierra Nevada Dunning (1942) for

• Incense-cedar of California most species

• Jeffrey pine

• Lodgepole pine

• Ponderosa pine

• Sugar pine

• White fir

WESSIN is the western Sierra Nevada variant of FVS. Data for the modeling effort

were obtained from National Forest inventory and the growth analysis data of Dolph (1988a,

1988b). The original models developed by Dolph have undergone some revision with

additional data. The geographic range for the WESSIN variant is the west side of the Sierra

Nevada (Dixon 1994) as shown in figure 9.

The distribution of growth sample trees by species for the FVS WESSIN variant is

shown in table 20 (Dixon 1994). The WESSIN documentation presents models with

parameter estimates but does not go into any detail on ranges of data. For greater detail,

users may refer to Dolph (1988a, 1988b). Table 21 shows the diameter distribution, by

species, of trees used by Dolph (1988a) in fitting the basal area increment model.

The establishment submodel handles trees from establishment (from planting or natural

regeneration) until a tally is made. From that point on, tree growth is guided by the functions

in the small-tree submodel. In this submodel, height growth is the primary driving function.

The small-tree height growth function forecasts tree growth as a function of basal area in

larger trees, site index, and crown ratio for most conifers. The function for Douglas-fir,

giant sequoia, red and white fir is similar but does not have site index. The black oak and

tanoak functions forecast height increment as a function of basal area in larger trees only for

trees from 2 to 3.5 inches DBH, the small tree height growth prediction is averaged with the

Figure 9—Geographic range of the WESSIN variant of the

Forest Vegetation Simulator (Dixon 1994).

Table 20—Number of diameter growth and height growth

measurements available for the western Sierra variant of the

Forest Vegetation Simulator (Dixon 1994).

S h pecies D h iameter growt Height growt

W 1 hite fir 3 7 ,30 40

I 9 ncense-cedar 1 8 ,33 20

D 0 ouglas-fir 4 6 8 4

P 8 onderosa pine 1 8 ,52 15

S 0 ugar pine 6 8 5 6

J 4 effrey pine 1 1 ,14 3

L 9 odgepole pine 4 0 1

T 1 otal 8 8 ,86 91
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 21

large-tree model to produce a smooth transition between the two. Diameter increment for

trees less than 3 inches DBH is derived from a simple linear DBH-height relationship.

For trees larger than 3 inches DBH, growth forecasts are made on a 5-year interval,

with diameter increment as the primary driver. The diameter increment function employs

DBH, point-level basal area in larger trees, stand basal area, point-level CCF, crown ratio,

elevation, slope, aspect, site index, and a location code as predictor variables. The height

growth function uses predicted diameter increment, stand basal area, basal area increment,

crown ratio, elevation, slope, site index, and a location code as predictors.

Mortality is derived from an approach to maximum stand density index. The zone of

imminent mortality threshold is at 55 percent of maximum stand density index, and stands

tend to track toward a level of 85 percent of maximum stand density index. Users may

change default maximum values for SDI (table 22).

Table 21–Distribution of trees by species and DBH (inches), used in development of

the basal area increment model for the western Sierra variant of the Forest Vegetation

Simulator (Dolph 1988a).

Number of Trees

DBH White

fir

IncensecedarPonderosa

pine

Sugar

pine

Jeffrey

pine

Douglasfir

0 0 -4.9 1 4 6 1 8 4 3 71 822

5 5 .0-9.9 4 8 9 2 8 7 1 4 4 5 52 55

1 5 0.0-14.9 4 5 5 1 3 6 1 8 3 5 23 54

1 3 5.0-19.9 2 6 5 4 2 1 9 2 3 72 9

2 9 0.0-24.9 1 6 0 3 15 71 21 6

2 5 5.0-29.9 3942 86 3

3 9 0.0+ 1 42 32 3143

T 6 otal 1 2 ,52 7 9 0 5 6 3 2 4 0 1 3 1 14

Table 22—Default maximum Stand Density Index (SDI) (Reineke 1933) values for mortality

functions in the western Sierra variant of the Forest Vegetation Simulator.

S I pecies M I aximum SD 5 I 5 pct of max. SD 85 pct of max. SD

O 4 ther conifers 6 3 2 3 0 4 53

S 7 ugar pine 6 6 4 3 0 5 55

D 7 ouglas-fir 5 1 4 3 6 0 45

W 9 hite fir 7 7 5 4 5 1 64

G 8 iant sequoia 5 3 8 3 0 2 50

I 6 ncense-cedar 7 8 0 3 0 8 60

C 2 alifornia black oak 3 0 8 2 5 1 32

J 1 effrey pine 5 4 7 3 5 1 48

R 0 ed fir 8 0 0 4 0 4 68

P 1 onderosa pine 5 4 7 3 5 1 48

T 9 anoak 7 7 5 4 5 1 64
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 22

CRYPTOS (10)

Primary Species: Geographic Range: Site Index/Productivity:

• Redwood Coast range of northern Krumland and

• Douglas-fir California Wensel (1977)

CRYPTOS (California Redwood Yield Project’s Timber Output Simulator) is similar

to CACTOS in overall design and model forms. The design and philosophical underpinnings

of the model are described by Krumland and Wensel (1980). Execution of the model is

described by Krumland and Wensel (1982) and Wensel and others (1987).

The CRYPTOS simulator was developed for the north coast of California. All of the

stands were within the redwood/Douglas-fir type. Species-specific models were developed

for redwood, Douglas-fir and tanoak. Other conifers (e.g., grand fir) are assumed to have

the growth form of Douglas-fir.

The modeling data were obtained from permanent and temporary growth plots in Mendocino,

Humboldt and Del Norte counties. Trees sampled for growth model development were Douglas-fir

(8,168), coast redwood (2,280), alder (26), and tanoak (626) (Krumland 1982).

Data were obtained from 412 plots, and 70 percent of these were from the coastal zone

that is subject to fog influence. These stands were predominantly composed of young growth

timber, but 15 percent had at least one residual old-growth tree. Approximately 25 percent

had been subjected to some form of partial harvest prior to growth measurements (Krumland

1982). Model form and results of analysis for redwood, Douglas-fir, and tanoak are presented

by Krumland and Wensel (1981).

Execution of CRYPTOS is very similar to that of CACTOS. One notable difference is

that CRYPTOS is capable of accepting input from fewer species (Krumland and Wensel 1982).

CRYPTOS requires a tree list with species, DBH, total height, live crown ratio, and expansion

factor (trees per acre) for each tree, as well as site index, in an ASCII file with format similar

to that of CACTOS. As with CACTOS, a utility is provided to fill in missing values and, if

necessary, develop a tree list from stand basal area and number of trees per acre.

Requirements for the simulator are DOS version 2.1 or later, at least 320K of RAM,

math coprocessor, and hard disk sufficient in size to handle the CRYPTOS files.

 Additional references for CRYPTOS are listed in appendix C. The most recent version

is CRYPTOS v7.8. Purchasing information on the University of California, Berkeley’s

Web site: http://www.cnr.berkeley.edu/~wensel/cryptos/crypt.htm

CACTOS (11)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Mixed conifer region of Biging (1985)

• Incense-cedar northern California

• Ponderosa pine

• Red fir

• Sugar pine

• White fir

CACTOS (CAlifornia Conifer Timber Output Simulator) is a mixed-conifer growth and

yield simulator for northern California (Wensel and others 1986). This simulator is the result

of a cooperative research effort. CACTOS is actually a suite of programs produced by

researchers at the University of California. CACTOS itself is an interactive simulator that

allows users to simulate the growth of sampled mixed conifer stands. Provided along with

CACTOS are a number of related utility programs: STAG (a stand generation routine which

allows users to fill in missing data), COMPARE (a routine which allows for comparison of

actual and predicted yield for either CACTOS or CRYPTOS), YDAVG (produces average

yields or summaries of average stocking condition for strata or management units), and SDAVG

(a program for obtaining diameter distributions, stand and stock tables of sampled stands).

Data were obtained from permanent sample plots distributed throughout the northern

Sierra Nevada, southern Cascades, and southern Klamath Mountains of California (fig. 10).
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 23

Data were provided by private landowners; there were no Forest Service data used in

development of the CACTOS growth equations. However, development of the taper equations

did include Forest Service data.

The primary trees species are ponderosa pine, sugar pine, Douglas-fir, white fir, red fir,

and incense-cedar. In addition, the simulator will accept input of lodgepole pine, western

white pine, Jeffrey pine, miscellaneous conifers, chinkapin, California black oak, tanoak,

and miscellaneous hardwoods.

Tables 23 and 24 show a range of tree sizes used in the growth modeling effort. A

broader range of tree heights and diameters was reflected in the diameter growth modeling

effort than in height growth. However, since the height growth model is bounded by the

potential height growth reflected in dominant height curves, these height growth models

should be fairly robust to extrapolation beyond the range of heights shown in table 23.

To execute the simulator you will need to provide a stand description input file. This

file contains a stand identifier, the number of tree records sampled, site index for each

species present (up to 14), breast-height ages associated with each species sampled, and a

list of sampled trees with the following data for each tree: species code, DBH (in inches),

total height (in feet), live crown ratio (decimal fraction) and per-acre expansion factor. The

user may also provide the simulator with information on ingrowth, user-defined calibrations,

and species grouping for output. Execution is described in the user’s guide (Wensel and

others 1986) provided with the simulator.

Figure 10—Geographic range of permanent plots used in the

development of CACTOS (Wensel and others 1987).

Table 23—The ranges of heights (feet) and site index values (feet) for the CACTOS

height growth models presented in Wensel and others (1987).

Table 24— Ranges of height (feet) and breast height diameter (inches) for trees used in modeling

diameter growth for CACTOS (Wensel and others 1987).

Species min. Ht m . ax Site m . in. max Site No. of observations

P 2 onderosa pine 2 3 1 7 4 5 3 1 1 2 15

S 8 ugar pine 3 1 1 4 2 5 9 1 7 1 4

D 0 ouglas-fir 3 1 1 5 4 5 6 1 5 1 14

W 3 hite fir 1 2 1 1 2 5 5 1 9 1 27

R 0 ed fir 4 79 16 37 73

I 6 ncense-cedar 1 97 07 07 17

Ht

Species Ht

m . in

Ht

max.

DBH

min . . max

DBH No. of

observations

P 04 onderosa pine 10 1 8 4 4 2,06

S 01 ugar pine 10 0 7 5 5 90

D 00 ouglas-fir 10 0 7 4 5 1,46

W 08 hite fir 11 1 6 4 3 3,12

R 54 ed fir 11 1 5 5 9 57

I 42 ncense-cedar 11 7 8 6 8 1,13
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 24

If height and live crown ratio are not measured, these missing values may be filled in

using the STAG program provided with the simulator. There are two modes, one for filling

in missing tree data for which, as minimum, the species code, DBH and expansion factor

must be given for all trees. In the second mode, stand descriptions can be created from

summary information: basal area per acre and number of stems per acre for trees greater

than 6 inches DBH for each species separately.

The simulator requires that simulations be made for trees on a plot-by-plot basis. If the

user has a stand with 10 plots, these are run separately and a yield stream for the stand is

generated by averaging the individual plot projections with a post processor called YDAVG.

The growth increment is estimated for a 5-year period.

CACTOS allows the user to specify thinnings from the very simple to the very complex.

The user may choose from four alternative cutting algorithms: (1) DBH control, (2) basal

area control, (3) sanitation cut, and (4) a free cut. The sanitation cut allows the user to

specify trees to be removed on the basis of live crown ratio. The free cut allows the user to

specify any combination of variable-width diameter classes and harvest from any of these

classes according to live crown ratio or DBH. Basal area control allows the user to specify

a target basal area (to leave or to cut) and then to thin from above or below to this target. The

DBH control allows the user to thin to any proportion of trees by diameter class.

The simulator is provided at no charge to members of the CACTOS cooperative. Others

may purchase the simulator, copies of the user’s guide and other CACTOS research notes

for a nominal fee.

The most recent version is CACTOS v6.3. CACTOS comes with the stand generator

(STAG v4.3) and utility programs. DOS version 2.1 or later and 320K RAM, a math

coprocessor and hard disk space sufficient for all files, are required for installation. Additional

references on CACTOS are listed in appendix D. For more information contact:

Professor Lee Wensel, Department of Environmental Science Policy & Management,

145 Mulford Hall, University of California, Berkeley CA 94720-3114

Web site: http://www.cnr.berkeley.edu/~wensel/cactos/cactoss.htm

SPS (12)

Primary Species: Geographic Range: Site Index/Productivity:

• Dependent on variant Dependent on variant Zeide (1978)

SPS is an individual-tree simulator originally developed for Douglas-fir in Oregon and

Washington (Arney 1985). Although classed as an individual-tree simulator, SPS will allow

users to input stand data at a number of levels of resolution: stand average values, stand table

values, or a tree list. If stand averages are input, the simulator will generate a tree list internally.

Unlike most other individual-tree simulators, SPS is age-dependent through the top

height function which drives all projections in the simulator. If age is not known, SPS will

calculate an effective age from top height and the top height growth form (site index). This

effectively makes SPS an even-aged stand simulator, since age for uneven-aged stands is a

meaningless number.

SPS will allow users to evaluate both thinning and fertilizer response. Thinnings maybe

done from above, from below, or to a target d/D ratio. Fertilizer applications may be simulated

at rates between 0 and 400 pounds elemental nitrogen per acre.

SPS is adapted to other species by modifying the form of the top-height function. The

simulator does not operate on a fixed forecasting time step; growth increments are presented

in approximately equal steps of height increment (12 to 15 feet).

Five functions are employed for any given forecast:

1. Top height growth as a function of breast height age and site index, where top height

is defined as the height of the 40 largest diameter trees per acre (HT40).

2. Breast height diameter growth as a function of top height, relative tree size, crown

ratio, and CCF.

3. Height growth as a function of top height, relative height, crown ratio, and CCF.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 25

4. Survival as a function of relative tree size, crown ratio and CCF.

5. Tree volume derived from taper functions.

The mortality function forecasts mortality rates for each diameter class as a function of

CCF, crown ratio, and diameter.

Site productivity enters the model in the form of a site index value as derived using

Zeide’s (1978) principle of two points. Zeide’s method uses two user-supplied height-age

pairs separated by at least 20 years to index to a specific top height curve form.

The simulator comes with a comprehensive list of acceptable species and their associated

coefficients (table 25), although the source of these coefficients is not presented. The species

codes are indexed by geographic region. It should also be noted that not all species codes are

presented for all geographic regions of the simulator because not all species occur in all regions.

The primary reference for the simulator is the SPS User’s Guide (most recent version is

2.4). The current version of the simulator is Version 4.0. SPS requires an IBM compatible

with DOS (version not specified) and will emulate a math coprocessor if one is not available.

The SPS simulator is distributed and maintained by Mason Bruce and Girard. For more

information see their web site: http://www.masonbruce.com

1 Regions appropriate for this simulator: Pacific northwest

(1), inland northwest (2), western Montana (3), southwest

Oregon/northern California (4), western British

Columbia (5), interior British Columbia (6), Alberta,

Saskatchewan, Manitoba (7).

Table 25—Acceptable species codes for SPS by

geographic region. Source: SPS User’s Guide, Mason

Bruce & Girard and coefficient library file SPS.LIB.

Region numbers (1-7) are those employed by SPS to index

each geographic region.

S s pecies Code S s pecie Region 1

D r F D 6 ouglas-fi 1,2,3,4,5,

W k H W 6 estern hemloc 1,2,3,4,5,

R r C W 6 estern redceda 1,2,3,4,5,

N r F N 4 oble fi 1,

S r F P 5 acific silver fi 1,

G r F G 6 rand fi 1,2,3,4,

A r F S 6 ubalpine fi 1,2,3,

S e S S 5 itka spruc 1,4,

W e P W 6 estern white pin 1,2,3,4,5,

L e P L 6 odgepole pin 1,2,3,4,5,

R r A R 5 ed alde 1,4,

W h L W 7 estern larc 1,2,3,6,

P e P P 6 onderosa pin 1,2,3,4,

E e S E 6 ngelmann spruc 1,2,3,

M k H M 5 ountain hemloc 1,2,

B e M B 5 igleaf mapl 1,

Q n A Q 7 uaking aspe 3,

I r C I 4 ncense-ceda

S e P S 4 ugar pin

A r C A 5 laska yellow-ceda

W r F W 5 hite fi

C d W B 6 lack cottonwoo

W e S W 7 hite spruc

B e S B 7 lack spruc

J e P J 7 effrey pin
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 26

SWO-ORGANON (13) and WWV-ORGANON (14)

Primary Species: Geographic Range: Site Index/Productivity:

• California black oak (SWO) Southwest Oregon Hann and Scrivani

• Douglas-fir (SWO&WWV) mixed-conifer forests (SWO) (1987) for SWO

• Giant chinkapin (SWO) and Western Willamette King (1966)

• Grand fir (SWO&WWV) Valley (WWV) for WWV

• Incense-cedar (SWO)

• Pacific madrone (SWO)

• Ponderosa pine (SWO)

• Sugar pine (SWO)

• Western hemlock (SWO)

• White fir (SWO)

ORGANON is a simulator for mixed conifer stands developed at Oregon State University

(OSU). There are two versions of the simulator embedded in the code: SWO and WWV

(southwest Oregon and western Willamette valley respectively). Users select at run time

which area is desired. SWO-ORGANON was developed with the support of a research

cooperative. Data were obtained from the USDI Bureau of Land Management, USDA Forest

Service, and private firms operating in southern Oregon.

The WWV-ORGANON models were developed using data from the OSU Forest

Properties. The stands used to develop the WWV models are all located just west of Corvallis,

Oregon, in the mid-Willamette Valley.

The diameter growth equations for the southwest Oregon version were developed using

data from some 19,060 trees on which growth measurements were taken. The majority of

these (11,974) were Douglas-fir (table 26).

Users need to provide the simulator with tree-level and stand-level data (Hann and

others 1995). At the tree level, the simulator requires a sampled tree list with:

1. plot number,

2. species code (Forest Service three digit codes) (Curtis 1983)

3. DBH (in inches),

4. height (in feet),

5. live crown ratio (decimal fraction),

6. expansion factor (trees per acre) may be calculated by the simulator, given plot

configuration, and

7. 5-year radial growth inside bark (inches).

At the stand level, the user must provide site index (Hann and Scrivani 1987) for

Douglas-fir and ponderosa pine for SWO-ORGANON, or Douglas-fir alone (King 1966)

for WWV-ORGANON. Optionally, the user may specify treatments of thinning, fertilization,

and pruning.

Pruning to a given height may be targeted to trees of a given species and in a given

diameter range. Pruning effects are reflected in the resulting change in crown ratio. Crown

ratio is a key input variable in the driving functions for diameter growth, height growth, and

mortality.

There are several harvest options. Among these are a diameter thin, basal area thin, SDI

thin, and user code thin. The diameter thin allows thinnings specified for species based on

an upper and lower DBH limit and percent removal (in terms of trees per acre). The basal

area thin allows a thin to targeted basal area, with removal from above, below, or proportional

to the diameter distribution. The SDI thin is similar to the basal area thin but the target is

specified as SDI. The user code thinning allows removal of trees based on a user defined

code associated with each tree.

Fertilizer application may be simulated for 200 pounds per acre. This management

activity is restricted to even-aged stands less than 70 years old with at least 80 percent

basal area in Douglas-fir. Fertilizer application may not be reapplied until three 5-year

growth cycles have elapsed.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 27

Model form and parameter estimates for the southwest Oregon height growth and

diameter growth functions are presented by Ritchie and Hann (1990) and Hann and Larsen

(1991). The western Willamette Valley version uses the same model forms, but different

parameterizations (Zumrawi and Hann 1993).

There were six targeted species in southwest Oregon: Douglas-fir, white fir, grand fir,

ponderosa pine, sugar pine, and incense-cedar. Diameter growth equations were also

developed for western hemlock, bigleaf maple, California black oak, canyon live oak, giant

chinkapin, tanoak, Pacific madrone, and Oregon white oak. There were no height growth

equations for these secondary species. For these species, height growth is obtained from

static height-diameter relationships. Target stands should have 80 percent basal area in

Douglas-fir, grand fir, white fir, ponderosa pine, sugar pine, and incense-cedar. Stands

sampled in southern Oregon (fig. 11) for SWO-ORGANON ranged from 900 to 5,100 feet

in elevation and were dominated by the six targeted conifer species. Data ranges for trees

used in diameter growth modeling are shown in table 26 (Hann and Larsen 1991).

The WWV-ORGANON data base had 9,526 Douglas-fir trees and 595 grand fir trees

in 136 stands sampled from the McDonald-Dunn Forest in northwest Oregon. DBH ranged

from 4.1 to 87.1 inches for Douglas-fir, and 4.1 to 48.1 for grand fir. The diameter growth

function forecasts diameter growth as a function of DBH, crown ratio, site index, basal area

in larger trees, and stand basal area.

Height growth is the primary driving function in ORGANON, although predicted height

growth is not a predictor variable in diameter growth. Height growth is forecast using a

potential-modifier function The height growth modifier is a function of crown ratio and

crown closure at tree height.

A PC with 386, or higher, CPU is required with a minimum of 2 MB of RAM and a

hard disk. DOS 5.0 or higher is required.

ORGANON is available by mail or through the internet. The user’s guide comes in the

form of a WordPerfect file which can also be downloaded. Included on the ORGANON

home page are answers to frequently asked questions and sample data files. All information

on ORGANON, including instructions on downloading the current version, may be obtained

through their World Wide Web home page:

http://www.cof.orst.edu/cof/fr/research/organon

Additional references are listed in appendix E.

Table 26–Range of number of observations, DBH, crown ratio (CR), and site index in SWOORGANON data base (Hann and Larsen 1991).

Figure 11—Geographic range of ORGANON.

Species No. of

o s bservation

DBH

min.

DBH

max.

CR

min.

CR

max.

Site

min.

Site

max.

D 4 ouglas-fir 1 4 1,97 5 1 14

G 2 rand fir 9 9 4 5 4 12

I 8 ncense-cedar 1 5 ,00 5 4 12

P 4 onderosa pine 1 5 ,59 5 1 14

S 0 ugar pine 3 4 5 5 7 12

W 5 estern hemlock 1 3 0 6 4 12

W 3 hite fir 1 2 ,37 6 1 14

B 1 igleaf maple 4 67 1 14

C 0 alifornia black oak 3 5 0 5 1 12

C 9 anyon live oak 8 45 8 10

G 9 iant chinkapin 3 4 9 5 6 12

P 3 acific madrone 7 4 9 5 1 14

O 9 regon white oak 2 55 08

T 3 anoak 6 37 7 11

0.3

0.8

0.2

0.1

1.1

1.4

0.6

1.9

2.0

2.4

1.1

1.5

2.9

1.3

83.8

48.9

66.3

59.6

59.9

21.4

51.1

19.8

48.4

9.2

26.5

44.5

24.4

11.8

0.02

0.01

0.05

0.05

0.10

0.02

0.05

0.13

0.05

0.08

0.07

0.01

0.23

0.18

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

.96

1.00

1.00

1.00

.64

.97
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 28

SYSTUM-1 (15)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Mixed-conifer stands of Powers and Oliver

• Incense-cedar southwestern Oregon and (1978)

• Ponderosa pine northwestern California

• Sugar pine

• White fir

SYSTUM-1 is a simulator designed to simulate growth of young plantations in northern

California and southern Oregon (Ritchie and Powers 1993). This is the result of a cooperative

effort to produce a simulator geared towards young stand forecasts and the particular problems

presented in simulating systems with both trees and shrubs. Data for parameter estimation

were provided by private landowners, as well as the Forest Service and Bureau of Land

Management.

The simulator is a prototype, developed using existing research data (permanent plots)

and some plantation survey data. It was intended to illustrate the feasibility of modeling

both trees and shrubs growing in competition with one another. The feature that separates

SYSTUM-1 from many other simulators is the inclusion of competition from shrubs and

grasses into the forecasts. The simulator requires input of a tree list and plot-by-plot

descriptions of competing vegetation (species, percent cover and height).

Users are required to provide a sampled-tree list from the candidate plantation. This

tree list may be in CACTOS, ORGANON, or free format (comma or space delimited). In

addition to the tree list file, users are required to provide plot-level data on percent cover

and height, by species, for competing vegetation. Site index (base age 50) is also required

for the stand.

The model was originally intended for sampled stands from age 3 to about age 20.

There are no age restrictions built in, but execution beyond age 25 or 30 is not recommended.

Users can export a file from SYSTUM-1 in a form that may be read into either CACTOS,

ORGANON or FVS (WESSIN, SORNEC and Klamath Variants) for simulation beyond

this age limit.

Validation runs of the simulator (Powers and others 1989) indicated that the height

growth models functioned well for stands less than 45 years of age. Diameter growth

projections were found to be best for those stands where site index was known with some

precision and where inter-tree competition is not a limiting factor for tree growth.

Users may specify management in the form of thinnings and reduction in the level of

competing vegetation. Thinnings may be allocated by one of three different algorithms.

SYSTUM-1 does not have a record duplication scheme. Optimally, a random error

term may be added to all tree height growth predictions. This is an important feature because

of the very uniform nature of young plantations. The random error term facilitates forecasts

with variation in tree size developing over time. Without this option, the simulator tends to

produce forecasts of stand structure that are far too uniform.

The range of the of data used in development of this simulator is the northern Sierra

Nevada, extending into the Cascades of southern Oregon, and the Klamath Mountains of

northwestern California and southwestern Oregon.

The primary tree species in this simulator are ponderosa pine and Douglas-fir. Growth

equations were also developed for sugar pine, incense-cedar, and white fir. Sample sizes for

these three species were much smaller than those for ponderosa pine and Douglas-fir.

Execution of the simulator is interactive. SYSTUM-1 requires a tree list, but users may

opt for a list generated from stand averages. Tree-level data input for SYSTUM-1 include:

1. plot number (integer), required

2. species (two letter code), required

3. DBH (inches), optional, may be filled in by the simulator

4. height (feet), required

5. crown ratio (decimal), optional

6. expansion factor (trees per acre), required, and

7. periodic annual height increment (feet), optional.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 29

In addition, the user must input percent cover and height, by species, of competing

vegetation for each plot. These values may be entered from the keyboard at runtime, or read

from an ASCII file. The simulator will accept up to six species per plot.

The data ranges varied greatly across species and varied for the specific models being

fit. For stands exceeding the recommended age range, established stand simulators such as

CACTOS, ORGANON, or FVS should be considered.

Required hardware is a PC with DOS 3.0 or later version. A math coprocessor is not

required but will speed execution of the simulator substantially. At least 400 K of RAM is

required for execution. The simulator will operate under Microsoft Windows as well, although

it does not take advantage of the graphical user interface or other features of Windows.

The most current version of the model is 2.14. It is provided at no charge to members of

the cooperative. Nonmembers may make payment to:

Gary Nakamura, U. C. Cooperative Extension,

1851 Hartnell Avenue, Redding, CA 96002.

Additional information may be found on the World Wide Web site:

http://redding.psw.fs.fed.us/systum.html

RVMM: Individual-Tree (16)

Primary Species: Geographic Range: Site Index/ Productivity:

• Bitter cherry Coast range of Oregon Shula 1998

• California hazel and Washington,

• Douglas-fir Cascades of Oregon

• Red alder and Washington

• Western hemlock

• Vine maple

RVMM (Regional Vegetation Management Model) is a simulator for young stands in

western Oregon and Washington. There are two distinct segments to this simulator: an

individual-tree simulator and disaggregative whole-stand simulator. The user may choose

between the two at runtime.

Individual-tree RVMM maintains a list of trees for simulation. The acceptable species

are bigleaf maple, bitter cherry, California hazel, cascara, Douglas-fir, red alder, western

hemlock, vine maple, and willow. Breast height diameter is required input. Other variables

(basal diameter, total height, crown width, height to crown base) are optional and, if missing,

will be filled in by the simulator. Height to crown base and crown width apply only to

Douglas-fir. Shrubs and herbaceous vegetation input is in the form of percent cover and

height. Other input for execution includes site index (Shula 1998), plantation age, and years

to breast height. Input may be either in metric or English units.

Management options include precommercial thinnings, simulated on a plant-by-plant

basis. Users may also simulate release from competing vegetation by specifying either

treatment efficacy or post-treatment percent cover and shrub height.

Files may be exported in the format of ORGANON, SPS, or FVS. The relevant variant

of FVS has not been specified.

Data for model development include 3,455 tree growth records from 98 stands, 92 of

these stands were less than 16 years old. Stands ranged in age from 1 to 20. Tree growth

equations were developed for Douglas-fir, western hemlock, red alder, vine maple, California

hazel, bitter cherry, and cascara.

The simulator requires Microsoft Windows 3.0 or Windows 95, a 486 or Pentium

processor, and 7 megabytes of RAM.

Web site: http://www.cof.orst.edu/cof/fr/research/organon/rvmm/rvmm_idx.htm
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 30

CONIFERS (17)

Primary Species: Geographic Range: Site Index/Productivity:

• Deerbrush Klamath Province Soil depth, slope,

• Douglas-fir aspect, and soil water

• Incense-cedar holding capacity

• Greenleaf manzanita

• Pacific madrone

• Ponderosa pine

• Snowbrush

• Sugar pine

• Tanoak

• White fir

This model is a result of a continuation of the young stand modeling effort that produced

SYSTUM-1. The first version of this model is being parameterized for the Klamath Province

of southwest Oregon and northern California.

The primary differences between this simulator and SYSTUM-1, besides the geographic

range, are a more sophisticated front end and a more comprehensive data base for model

development. Equations are being developed to simulate the growth of individual shrubs

and hardwood trees as well as common species of conifers. It is expected that this will

provide more reliable predictions of stand growth in response to changes in the levels of

competing vegetation, particularly hardwood trees and shrubs.

More than 100 stands have been sampled in the Klamath Province of southwest Oregon

and northwest California (fig. 12). The modeling data set includes more than 5,000 tree and

shrub growth records. The geographic range of the data at this time is very similar to that of

the Klamath version of FVS and overlaps most of the range of SWO- ORGANON.

Primary tree species include ponderosa pine, sugar pine, Douglas-fir, white fir, and

incense-cedar. Other tree species present, although not as well represented in the data base,

are Oregon white oak, California black oak, tanoak, and Pacific madrone.

The stands used in model development ranged from 3 to 26 years of age. Elevational

range is approximately 2,000 to 5,000 feet. All stands are conifer plantations. A few of the

selected stands have residual overstory trees.

A plant list is required to execute this simulator. Users will need to provide the simulator

with a tree/shrub list for all woody vegetation on each plot in the sample. Shrubs that are not

measured on a plant by plant basis may be input as percent cover and height by species for

each plot. From this, the simulator generates a plant list for simulation.

The simulator currently operates on IBM compatibles with Windows 95. A Math

coprocessor is required.

Access to documentation and the model is available through the World Wide Web site:

http://redding.psw.fs.fed.us/conifers.html

Individual-Tree/Distance-Dependent Simulators:

FPS, G-SPACE

Distance-dependent simulators require spatial coordinates of trees to execute growth

forecasts; otherwise, they maintain a tree list in much the same manner as individual-tree/

distance-independent simulators. The spatial coordinates are used to quantify inter-tree

competition. Thus, growth forecasts are inter-tree distance-dependent. These models offer

the potential for better thinning response and may better characterize inter-tree competition.

However, they require more information to process and are generally more demanding of

computer resources than distance-independent simulators.

Often these simulators will offer an option to generate spatial coordinates of trees. Under

such a scenario, the user does not benefit from the information contained in actual spatial

coordinates because none has been provided. The benefit then is in the structure of the simulator

itself and the ability to derive, in theory, more reliable predictions from the tree list information.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 31

FPS (18)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Western Oregon and May be specified by

• Red alder western Washington the user

• Sitka spruce

• Western redcedar

• Western hemlock

The Forest Projection System (FPS version 5.3) is an individual-tree/distance-dependent

growth simulator. FPS allows users flexibility in data input, in much the same fashion as

SPS. Internally, the simulator grows a 0.1-hectare square plot. The simulator will accept

observed x and y coordinates, but it will also generate coordinates for trees if coordinates

are not supplied. The simulator will also accept stand summary information and is capable

of disaggregating these data to a tree list internally. The usual application is to provide a

stand table summary by species and 1-inch diameter classes.

The model is a Microsoft Windows application and maintains a link to a number of

commercial data base packages which allows the simulator to pull data directly from an existing

user data base. Users are not required to manipulate files directly for execution of the simulator.

FPS can import either tree list or stand summary information directly from a commercial data

base and generate forecasts accordingly. Another feature is the ability of this simulator to

update data bases directly, facilitating easier simulations for large tracts of land.

There are two versions of this simulator (western Oregon and western Washington).

The western Oregon version data base was dominated by Douglas-fir and western hemlock

with lesser representation of western redcedar, red alder, and Sitka spruce. The composition

of the western Washington data base was not available at the time of this writing.

The simulator will handle stands up to age 100.

Required hardware is a PC with 386, or higher CPU, and Microsoft Windows 3.1. For

more information contact: Jim Arney at jdarney@forestbiometrics.com, or refer to the

world wide web site: http://www.forestbiometrics.com

Figure 12—Geographic range of CONIFERS.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 32

G-SPACE (19)

Primary Species: Geographic Range: Site Index/Productivity:

• White fir Northern Sierra Nevada Biging and Wensel (1985)

This is an individual-tree/distance-dependent model for white fir. Funding for this project

was a result of a cooperative effort. The current version supports true fir. Future versions may

be made available for ponderosa pine. As with all distance-dependent simulators, the model

requires spatial coordinates of trees, although the model is capable of generating hypothetical

spatial coordinates and thus facilitates predictions where spatial coordinates are not known.

Early work on the conceptual framework for this simulator is reported on by Cavallaro

(1990). Tree volume growth equations are driven by a function for net assimilation rate

which is a function of estimated leaf surface area and height (Cavallaro 1990).

Height growth is driven entirely by Biging and Wensel (1985) curves. These are dominant

height curves developed for mixed conifer stands ranging in age from 10 to 100 years at

breast height and site index values ranging from 35 to 131 feet. They do not reflect changes

in height growth due to competition. That is, in this simulator, all trees assume height growth

rates of dominant trees.

Data were collected from even-aged, pure, white fir stands in the northern Sierra Nevada.

Sampled trees ranged from 65 to 120 feet in height; site index (Biging and Wensel 1985)

ranged from 55 to 90 feet (at breast height age 50).

An IBM compatible PC with DOS 5.0 or higher, is required. Contact:

Dr. Janet Cavallaro, U.C. Department of Environmental Science,

Policy, and Management, 145 Mulford Hall, Berkeley, CA 94720.

Gap Simulators: CLIMACS, SILVA

Gap simulators are individual-tree based programs designed to simulate successional changes

in forested ecosystems. As such, they are geared to producing descriptions of stand

composition, in terms of number of trees by species, over long time horizons. They generally

feature both mortality and ingrowth components. Gap simulators follow, to some degree,

the concepts described by Botkin and others (1972). A distinguishing feature of this type of

simulator is that trees are modeled on a specific fixed-area location in the stand, known as a

gap. This gap typically is defined as an area corresponding to the area of the forest occupied

by a fully mature tree. The assumed gap size is equal to the size of the sampled plot.

Gap simulators differ greatly in the foundational assumptions from most examples of

individual-tree modeling found in the literature. Trees are treated as being located in or

related to a gap in the forest canopy. Typically, gap simulators are driven by diameter growth.

Height and crown ratio are not needed for input.

The gap simulator does not treat the tree as a representative of similar trees in the stand.

Rather, in the gap simulator the forest is viewed as consisting of a number of gaps, or

potential gaps, each of which is described by the tree or trees in that gap. Thus, each “gap”

is described by a tree list associated with a plot. Thinning is generally not an option presented

in these simulators because when trees die (or are harvested), they are, in effect, removed

from the tree list altogether.

Another limitation of gap simulators is that they are not capable of accepting input in

the form of trees sampled from nested and/or variable-radius plots. Some research also

suggests that these models are sensitive to plot (gap) size selected (Shugart and West 1979).

Since many forest survey sampling designs involve either variable-radius plots, nested

fixed-area plots, or plots of a size which do not meet the definition of a “gap,” these simulators

are poorly suited to forest growth and yield forecasts. They offer very little, if any, output of

tree volume, and harvesting options are very limited.

Disturbance events, such as fire, may be integrated into some of these simulators and

the growth models are more physiologically based than those driving many other types of

growth and yield simulators. Ingrowth trees are brought into the simulation through a stochastic

process. Mortality is also affected by removing trees from the tree list stochastically. There
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 33

are no expansion factors. The expansion factor associated with any tree is an implied constant,

based on the plot size. Output is related to per-unit area summaries by simply multiplying

appropriate parameters by the expansion factor indicated by plot size, which remains static.

CLIMACS (20)

Primary Species: Geographic Range: Site Index/Productivity:

• N/A Western Oregon and None

Washington

CLIMACS was derived from FORET (Shugart and West 1979). This simulator is like

many other gap models in that it is intended to simulate successional patterns over long

time horizons. It has no capability for growth and yield output such as volume by size class

or species, and it is incapable of simulating the effects of any management activity other

than clearcut harvest of trees. The model has been parameterized for four geographic regions

of western Oregon and Washington (fig. 13).

Although a comprehensive list of species will be accepted by the simulator, the only

five for which any data are reported are Douglas-fir, western hemlock, mountain hemlock,

western redcedar, noble fir, and Pacific silver fir. Quadratic height-diameter functions are

presented for these six species.

The simulator forecasts by annual steps and characterizes stands on the basis of individual

trees greater than 3.9 inches (10 cm) breast height diameter. Trees are assumed to be sampled

from a 0.5-acre (0.2-hectare) plot.

Regeneration is a stochastic event driven by species and leaf area index. Tree growth is

essentially diameter-driven. All trees approach a chosen maximum diameter and height for

the given species. Height growth is obtained from a density-independent quadratic heightdiameter function. Diameter growth is affected by a position variable describing leaf area in

trees taller than the subject tree.

The mortality function is diameter-driven and varies depending on the seral

characteristics of a given species. Mortality may also be affected by disturbance events.

Disturbance events recognized by the simulator are fire, windthrow, and clearcutting. No

provision is made for partial cutting or other silvicultural treatments.

The code is maintained in the Forest Science Data Bank at: Forest Science Department,

Oregon State University, Corvallis, OR 97331.

Figure 13—Four regions of the CLIMACS simulator.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 34

SILVA (21)

Primary Species: Geographic Range: Site Index/Productivity:

• N/A Sierra Nevada forests Synthesis of climatic

and edaphic factors

Silva (Kercher and Axelrod 1981, 1982, 1984) is a descendant of JABOWA (Botkin

and others 1972). Silva was developed to investigate the possible effects of sulfur dioxide

emissions on the composition of Sierra Nevada forests. Also integrated into the Silva model

is a stochastic fire frequency model. This simulator was not built as a decision support tool

and has evidently received little support or continued development by the authors.

Silva is stochastic with respect to certain discrete events such as mortality and birth of

trees. Simulations must be repeated many times to obtain a forecast that represents some

“central tendency.” The model is deterministic with respect to growth of trees.

Growth is functionally linked to climatic and edaphic factors. Silva is driven directly

by moisture and day length. This may facilitate evaluation of such factors as climate change

and periodic drought effects. However, the edaphic factors component is very weak. Edaphic

factors enter the model through a discrete carrying capacity classification which identifies

all stands as belonging to one of two classes: true fir and ponderosa pine. This seems to be

an oversimplification of the variation in site productivity to be found in the Sierra Nevada.

Ostensibly, the range of Silva includes Sierra Nevada forests; however, some of the

component models were either hypothetical or derived from data outside the range of Sierra

Nevada forests.

The acceptable species for this model are ponderosa pine, sugar pine, incense-cedar,

white fir, Douglas-fir, Jeffrey pine, and California black oak. However, the equations in the

model are not derived empirically; there really is no data range to report. Pieces of the

model have been derived from published relationships from a variety of sources (e.g., Botkin

and others 1972, Jarvis and others 1976, Waring and others 1978).

Whole-Stand Simulators: DFIT, PPYMOD,

DFSIM, PPSIM, PSME, DFETAL, SOS

A whole-stand simulator is one that accepts stand-level parameters as input. Typically, input

includes such parameters as age, site index, number of stems per unit area, and quadratic

mean diameter. They are best suited to simulation of even-aged, single-species stands (Curtis

and Hyink 1985), although examples of application in uneven-aged stands have also been

presented (Ek 1974, Moser 1972, Moser 1974). Stands are grown as aggregates. Whole

stand simulators are usually driven by a gross growth or yield model. The gross growth may

be for volume or basal area. Mortality also enters the simulation at the aggregate level.

Whole-stand simulators are computationally efficient and much less demanding of

computer resources than individual-tree simulators. However, they do not provide users with

as many options for output. Whole-stand simulators are limited in output to aggregate parameters

such as stand basal area, total stem volume, merchantable volume, and trees per acre.

Some whole-stand simulators are more complex, in that they forecast diameter distributions

in addition to stand-level yields. These simulators characterize the distribution of stems by

means of a probability density function. While the distribution function provides for higher

resolution, its use is limited in that these functions are not capable of describing the diameter

distribution resulting from many silvicultural alternatives (Ek and Monserud 1981).

DFIT (22)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Coast range of Oregon and McArdle and others

Washington (1961)

DFIT (Douglas-fir Interim Tables) is a whole-stand simulator for Douglas-fir in the

Pacific Northwest. DFIT (Bruce and others 1977) is an outgrowth of the development of
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 35

managed stand yield tables for Douglas-fir. Results and implications of forecasts derived

from this simulator were described by Reukema and Bruce (1977). Site index has a base-age

of 100 (McArdle and others 1961).

DFIT is not widely used; DFSIM has supplanted it as the stand-level simulator of choice

for coastal Douglas-fir forests.

PPYMOD (23)

Primary Species: Geographic Range: Site Index/Productivity:

• Ponderosa pine Westside ponderosa pine Powers and Oliver

stands of California (1978)

This variable density yield table (Oliver and Powers 1978) was not originally adapted

for computer use but was presented as a set of tables for plantations with square spacing at

four levels (6, 8, 10 and 12 feet). Some years later Scott Holmen developed a spreadsheet

version of this for Lotus 1-2-3. The most recent version of this is version 1.2.

Users should be aware that this spreadsheet is an approximation to the values presented

in Oliver and Powers (1978). It follows the predicted values of Oliver and Powers (1978)

closely in some areas, but in others, most notably cubic foot volume, the differences can be

quite substantial. Best agreement appears to be when age is between 15 and 40 and survival

is set at 85 percent. This is the planting survival rate assumed by Oliver and Powers (1978).

There appears to be better agreement with basal area than with volume in the runs I have

made. Height follows Oliver and Powers (1978) very closely.

The user must input a site index value (Powers and Oliver 1978), a spacing value, and

planting mortality. The yield table is then automatically updated. Although this was developed

for Lotus 1-2-3 version 1 (a DOS product), it actually works well using any spreadsheet

program that will import a Lotus .WKS file.

Data for the Oliver and Powers yield tables were obtained from twelve plantations in the

southern Cascades and northern Sierra Nevada (fig. 14). Sampled plantations ranged from 3 x

3 foot spacing to 12 x 12 foot spacing on sites from 35 to 120 feet (at a base age of 50).

The original yield tables run from age 10 to 50. The tables presented in the PC version

adaptation run to age 60. Required hardware is an IBM compatible PC with DOS or Windows.

Figure 14—Geographic range of Oliver and Powers (1978).

Numbers indicate locations of sampled stands.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 36

Also required is a spreadsheet program which will import a version 1 Lotus file.

The spreadsheet file may be downloaded from the Pacific Southwest Research Station

simulator web site: http://redding.psw.fs.fed.us/sim.html

DFSIM (24)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Coastal Oregon and King (1996)

Washington

DFSIM is a simulator for managed stands dominated by Douglas-fir (Curtis and others

1981, Curtis and others 1982). Data came from a variety of sources; all were remeasured

research plots. The model reflects the development of homogeneous, even-aged stands,

and may be used to estimate probable future development of existing stands within the

range of the data used to develop the model.

DFSIM requires input in the form of control cards that specify the run parameters.

However, DFSIM comes with a companion program called SIMIN2.EXE that will query

the user and set up a run file called TAPE3 with all the parameters in the appropriate columns.

TAPE3 is the default input file for the simulator. The simulator sends output to the screen by

default, but users may output to a file using the DOS redirection operators.

Users may specify a broad range of commercial thinning options. Among the information

users may specify with regard to thinning are d/D ratio, residual basal area, and/or residual

trees per acre (any two of these three). Timing of thinnings may be specified with respect to

age, height, height increment, or thinning interval in years.

The model provides a yield stream with age, breast height age, dominant height (feet),

height of tree of mean volume (feet), quadratic mean diameter (inches), basal area (square

feet per acre), trees per acre (total stem cubic foot volume per acre), current annual increment

(total stem cubic foot volume per acre), gross and net mean annual increment (cubic foot

volume total stem, cubic foot volume to a 4-inch top), and board foot volume to a 6-inch top.

Board foot volumes are presented in both Scribner and International 1/4.

The authors have clearly documented the limitations of the simulator:

1. Stands with less than 300 stems per acre at establishment (or those pre-commercially

thinned to this level) are outside the range of data.

2. The simulator should not be used to simulate more than one precommercial thinning

and fertilization during the juvenile stage of stand development.

3. Users should not begin simulations for natural unthinned stands at dominant height

less than 30 feet.

4. Users should avoid use of the simulator for very young plantations or

precommercially thinned stands.

5. Users should avoid using data from very small plots or including understory stems

of associated species, data which are otherwise inconsistent with the basic data

used to develop the simulator.

There are also a number of published studies in which DFSIM has been validated.

Curtis (1987) employed data from nine levels-of-growing-stock (LOGS) study sites in

western Oregon and Washington to compare DFSIM forecasts with observed growth over

time. Curtis (1987) found that DFSIM predicted gross stand volume and basal area growth

well. However, the two most notable problems were mortality estimation and the lack of an

ingrowth component in the simulator. While ingrowth had little effect on stand volume and

basal area increment, inclusion of ingrowth can cause significant differences in quadratic

mean diameter and number of stems.

The original version of DFSIM had a limiting relative density (Curtis 1982) of 70.

Since many of the LOGS plots have exceeded this limit it appears that the relative density

limit of 70 is somewhat low and produces unreasonably high rates of mortality in dense

stands (Curtis 1987). The most recent version of the model has a modified upper relative

density limit in response to these findings.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 37

Remeasured plot data were provided by private landowners and public agencies. The

authors do not display the specific locations of installations used in the modeling effort.

However, the final data set used for analysis came from 203 installations in western Oregon,

Washington, and British Columbia.

It is recommended that stands have 80 percent or greater basal area in Douglas-fir, as

this restriction was used in the process of selecting stands for analysis.

Equations were developed using a total of 2,654 growth period measurements on 1,434

plots at 203 installations. The age range was roughly 10 to 100 years (total age). King’s (1966)

site index ranged from roughly 50 to 170 feet (at breast height base age 50). The authors report

that there were no data from plantations greater than 40 years old and very little data from

stands greater than 80 years of age. Stands with and without commercial and precommercial

thinning were used in the analysis, as well as with and without fertilizer treatments.

The simulator requires site index (King 1966), starting age, and cutting and fertilization

parameters for a generic yield stream. Then, if the user wishes to forecast growth of a

specific observed stand, quadratic mean diameter, basal area, and number of trees per acre

1.6 inches and greater DBH at the start of the forecast are also required. Data are input as

formatted lines in a text file (Curtis and others 1981, p 43-56); however, the input utility

(SIMIN2.EXE) will facilitate creation of this file.

The most recent version is 1.4 and requires a PC with DOS 3.0 or higher. Additional

references are listed in appendix F. Contact:

• Gary Clendenen, Mensurationist, Forestry Sciences Laboratory, 3625 93rd Ave. SW

Olympia, WA 98512-9193, (360)753-7674.

• Robert O. Curtis, Mensurationist (retired), Forestry Sciences Laboratory, 3625 93rd

Ave. SW, Olympia, WA 98512-9193, (360) 753-7669.

Web site: http://forsys.cfr.washington.edu/~dfsim/

PPSIM (25)

Primary Species: Geographic Range: Site Index/Productivity:

• Ponderosa pine Ponderosa pine region Barrett (1978)

of California, Oregon, and

Washington

PPSIM (DeMars and Barrett 1987) is a simulator for even-aged ponderosa pine stands.

Data were derived from a variety of sources: permanent and temporary plots in Oregon and

Washington and natural stand data from Meyer (1938). The model is structured in much the

same manner as DFIT (Bruce and others 1977). An input file contains a description of the

run and, optionally, a description of the stand as well.

The required input is a master control record. A master control record is required for

each yield stream to be produced. Other records are optional. PPSIM requires input of site

index (Barrett 1978), unless the user specifies the starting stand statistics, in which case the

simulator will calculate site index given dominant height and stand breast height age.

The simulator is capable of generating generic yield streams, or yield streams for specific

sampled stands. For a sampled stand, the simulator assumes one of three types of stands:

1. Understocked stands.

2. Recently thinned stands.

3. Fully stocked stands scheduled for immediate thinning prior to any growth forecasts.

The sampled stand record format is described in table 27. The sampled stand record

must follow a master control record for each. Control records may be stacked in one file to

provide for a number of forecasts in one run of the simulator.

Yearly volume growth may be adjusted to account for local growth conditions,

fertilization, or genetic improvement. There is also a dwarf mistletoe option for managed
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 38

stand simulations. The user may specify the number of trees infected and the age of the

onset of dwarf mistletoe. Fertilization assumes:

1. An application of 200 pounds elemental nitrogen per acre, resulting in a 25 percent

increase in growth at the minimum site index value of 40,

2. Fertilization response decreases linearly to zero at a site index of 200, and

3. The fertilizer effect lasts 5 years by default.

Output includes mean diameter, basal area, and cubic foot volume per tree. The single

table of stand output contains trees per acre, basal area per acre, cubic foot volume per acre,

mean tree diameter, and mean tree basal area for each report age. These statistics are output

for the total stand, cut and leave. A sample output table is shown in table 28.

Data for natural stands were obtained from Meyer’s (1938) yield study (fig. 15), using

only data from Washington, Oregon, and California (DeMars, personal communication).

Ages range from 25 to 182 years. Barrett’s (1978) site index ranges from 46 to 184 feet

(base age 100). Basal area ranged from 77 to 369 square feet per acre. Stems per acre

ranged from 58 to 9,470.

Managed stand data consisted of 151 plots in Oregon and Washington. Site index among

these stands ranged from 33 to 139 feet. Ages range from 15 to 93 at the start of the growth period.

Basal area ranges from 1.5 to 194 square feet per acre. Stems per acre ranged from 36 to 1,000.

The simulator requires, as minimum input, a formatted file containing site index, total

stand age, breast height age, age at mistletoe infestation (if any), and timing of thinnings (if

any). This produces a generic stand table. If the user desires to forecast the growth of an

existing forest stand under various management treatments, additional input required is

height of the five largest trees in the stand, number of trees per acre at the start of the growth

simulation, basal area per acre of the stand at the start of the growth simulation, and quadratic

mean diameter at the start of the simulation. For thinnings, trees per acre, or d/D ratio, or

SDI may be used to control thinning levels.

Table 27—Sampled stand input record for PPSIM (DeMars and Barrett 1987, page 29) with read format.

Input

Columns

Variable

Name Variable Description ReadFormat

1 E - 4 STSIT Site index of the stand (Barrett 1978). If blank or zero, then this variable

is set to the site index from the master control record. If both are zero,

then site index is calculated from STTAGE and STHT using Barrett's

curves.

F4.0

5 T - 8 STH Dominant height (height of the five tallest trees in the stand). If blank or

zero, STHT is calculated from STSITE and STTAGE. F4.0

9 G -12 STTA E Total age of the stand at the start of the forecast. If blank or zero,

STTAGE is calculated from STSITE and STHT. F4.0

1 E 3-16 STAG Breast height age of the stand at the start of the forecast. If blank or

zero, STAGE is calculated from STTAGE and STSITE. F4.0

1 A 7-22 STT Number of trees per acre at the start of the forecast. If blank or zero,

then STTA is calculated from STGA and STDBH. If provided, STTA

must be mathematically consistant with STGA and STDBH.

F6.0

2 A 3- 28 STG Basal area per acre at the start of the forecast. If blank or zero, then

STGA is calculated from STTA and STDBH. If provided, STGA must

be mathematically consistent with STTA and STDBH

F6.2

2 H 9-34 STDB Quadratic mean diameter at breast height at the start of the forecast. If

blank or zero, then STDBH is calculated from STTA and STGA. If

provided, STDBH must be mathematically consistent with STTA and

STGA.

F6.2

35- 78 BLANK

79- 80 O 2 ptional input record identification. I

.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 39

PONDEROSA PINE

(PINUS PONDEROSA LAWS.)

MANAGED STAND TABLE

BARRETT SITE INDEX=160

Variables on master control record specify that:

Minimum diameter of a merchantable tree= 8.00

Number of thinning or output ages= 2

Number of years between thinning or output ages= 10

Harvest age= 10

Variables from optional input records:

REPORT OR

THINNING

NUMBER

REPORT

OR

THINNING

AGE

THINNING

HEIGHT

SQ.FT.

BASAL

AREA

BEFORE

CUT

SQ.FT.

BASAL

AREA

AFTER

CUT

STAND

DENSITY

INDEX

BEFORE

CUT

STAND

DENSITY

INDEX

AFTER

CUT

TREES

PER

ACRE

AFTER

CUT

RATIO

DIAMETE

CUT TO

R

DIAMETE

MERCH.

R

PART OF

STAND

NUMBER

OF DWARF

MISTLETO

TREES

E

AFTER CUT

1 . 0 . 0 . 0 . 0 . 0 . 0 . 1 . 80 0 . 0

Barrett Site Index= 160.

The starting stand is specified

TOTAL AGE

YEARS

STAND

HEIGHT

FEET

STAND

COMPONENT

MEAN

TREE DBH

INCHES

MEAN

TREE

G

SQ FT

MEAN

TREE

VOL. CU

FT

TREES

PER

ACRE

G

PER

ACRE

SQ FT

CU FT

VOLUME

PER ACRE

4 . 0. 7 d 9 S 0 tart stan 1 5 0. . 8 54 1 0 2. 3 9 50. 1 . 90. 4465

C 0 ut 1 5 0. . 8 54 1 0 2. 1 7 70. 9 . 2. 2169

T 0 otal leave 1 5 0. . 8 54 1 0 2. 1 2 80. 9 . 8. 2296

5 1 0. 9 d 7. T 2 otal stan 1 9 1. . 4 68 1 0 9. 1 1 80. 1 . 24. 3483

C 2 ut 1 9 1. . 4 68 1 0 9. 8 1 0. 5 . 5. 1548

T 2 otal leave 1 9 1. . 4 68 1 0 9. 1 9 00. 6 . 8. 1935

1 2 00. 1 t 55. H 1 arvest cu 1 9 6. 1 9 ,41 6 0 0. 1 9 00. 1 . 41. 6094

Table 28—Sample output table from PPSIM run (De Mars and Barrett 1987).
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 40

The PC version requires DOS 3.0 or later. Unlike DFSIM, there is no preprocessor

routine to set up runs. The user is required to set up an ASCII file with input records containing

instructions in a fixed format.

The simulator is available on the World Wide Web at:

http://redding.psw.fs.fed.us/ppsim.html

PSME (26)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Coast range of southern None

Oregon

PSME is a computer simulator for plantation-grown Douglas-fir stands with mixed

evergreens (tanoak, Pacific madrone, giant chinkapin). It simulates the development of stands

up to age 10, beginning at age 3 (Harrington, and others 1991b). The simulator will not

allow projections beyond age 10. Forecasts are produced for stand development of Douglas-fir

with only one of the three named hardwood species. Users must determine which of these

three species occupies more than 66 percent of the hardwood basal area in the subject stand.

Equations were actually developed only for Douglas-fir trees growing in competition

with tanoak or Pacific madrone. The simulator cannot distinguish between tanoak and

chinkapin, the model reflects an assumption that chinkapin and tanoak will behave similarly

and have the same effect on Douglas-fir growth.

Primary output of the simulator is a stand summary table of height and cover by species

for Douglas-fir, the primary hardwood competition and herbaceous vegetation, as well as

mean basal diameter of Douglas-fir. Optional output is a diameter distribution of Douglas-fir

at age 10 derived from a Weibull distribution function. The simulator does not produce any

volume or biomass estimates.

Figure 15— Managed stand plot locations of Meyer (1938) used in PPSIM.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 41

This is not a compiled program, rather it is written in BASIC. Users must have a BASIC

interpreter installed to execute PSME

The data were obtained from three studies on the effect of varying levels of competing

vegetation on young stand growth in southwest Oregon. These studies were conducted under

the auspices of the FIR (Forestry Intensified Research) Cooperative. The southernmost site

was located near Cave Junction, and the two northern sites were between Glendale and

Riddle (Harrington and others 199la). Users should be aware that tanoak was dominant on

two of the three plantations. The third plantation featured competition from Pacific madrone.

Plantations with initial age of 3 were observed up to age 10. Elevation on all three sites

was between 700 and 900 feet. Site index ranged from 82 to 121 (McArdle and others 1961).

Pre-harvest basal area of hardwood trees, pre-harvest density of selected species, post

harvest hardwood cover, Douglas-fir plantation age, herb and shrub cover, Douglas-fir mean

height, Douglas-fir stem basal diameter, Douglas-fir crown width, and Douglas-fir density

are required input for this simulator. The simulator is interactive and will prompt the user

for each of these values.

An IBM compatible PC with DOS 3.0 or higher is required. The user’s guide and the

program may be obtained from: Forestry Publications Office, Oregon State University, Forest

Research Laboratory, Corvallis, OR 97331.

DFETAL (27)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Coast range of Oregon and None

Washington

The DFETAL simulator is a result of a cooperative research project entitled “A Diagnostic

Tool for Predicting the Effects of Interspecific Competition on Growth and Yield of Young

Douglas-fir Stands.” A preliminary version of this simulator was released to cooperators in

1990 (Opalach and others 1990). There was no subsequent release of the simulator.

Equations for this simulator were developed using data from a CRAFTS competition

release study (Harrington and Wagner 1986).

This model predicts the growth of homogeneous young Douglas-fir plantations (those

less than 20 years old). The species allowed are Douglas-fir, red alder, vine maple,

salmonberry, and thimbleberry. The model grows the average tree in the stand. It assumes

that there are some known number of identical Douglas-fir trees per acre. Since it grows

only one average tree in the stand, this is, in effect, a whole stand simulator.

Primary (required) input for Douglas-fir is starting plantation age, crown ratio, trees

per acre, height, and cover. For other species, percent cover and height are required as input.

The model will grow stands up to a specified age, not to exceed 20. The vegetation

management option allows the user to simulate the effect of complete removal or application

of triclopyr or glyphosate to plots containing thimbleberry and Douglas-fir.

DFETAL will produce a table showing percent cover and height of each species

throughout the forecast, and more detailed statistics of Douglas-fir: height, crown ratio,

crown radius, and trees per acre.

There is no ongoing technical support for this simulator, and no system for distribution.

SOS (28)

Primary Species: Geographic Range: Site Index/Productivity:

• Douglas-fir Idaho, western Montana, User specified site

northeast Oregon, central index Vander Ploeg and

and northeast Washington Moore (1989), or

Thrower and Goudie

(1992), or Milner

(1992), or Monserud

(1984)
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 42

The SOS simulator is a whole stand simulator for interior Douglas-fir stands in northern

Idaho, Montana, central Idaho, northeast Oregon, central and northeast Washington reported

on by Zhang and others (1993). The authors do not provide a specific description of the

geographic range of the data. The data used in development of this simulator are from 229

plots in 94 thinning trials. Site index (Vander Ploeg and Moore 1989) ranged from 39 to 105

feet at 50 years.

There is some uncertainty as to the range of the data used in development of the model.

Zhang and others (1993) show a range in age from 0 to 100, yet in reporting on the same data

Moore and others (1991) and Shafii and others (1990) show a range from 27 to 100. Moore

and others (1991) report percent basal area in Douglas-fir ranging from 27 to 100 with a

mean of 87.

Both the survival and basal area prediction equations are constrained by defined

minimum and maximum values.

The simulator has a primary driving function of top height growth, using the model

presented by Vander Ploeg and Moore (1989). Predicted height is factored into the survival

model and the basal area prediction equation. Finally, the model predicts total stem cubic

foot volume per acre as a function of stand basal area per acre and top height.

Disaggregative Simulators: LPSIM, STIM, RVMM

Disaggregative models feature a two-stage process for growth simulation. The first step is

to predict growth of the stand using a whole-stand growth or yield equation. In the second

step, the resulting predicted stand growth or yield is allocated (disaggregated) among trees

in a list. The simulator functions as a whole-stand model with respect to stand growth

(Ritchie and Hann 1997a). The tree list is essentially maintained to describe the distribution

of stand parameters.

LPSIM and STIM both disaggregate growth predictions, not yield. There are two basic

methods for allocating growth: proportional allocation and additive allocation. With

proportional allocation, consistency between tree and stand growth predictions is ensured

by multiplying growth by some proportionality factor defined such that the sum of individual

tree growth predictions is equal to forecast stand growth. Additive allocation employs an

additive adjustment to each growth prediction such that the sum of individual tree growth

forecasts equals the forecast for stand growth. With either method, the dynamic component

of the stand-level forecast is driven by stand-level parameters, whereas individual-tree

simulators aggregate individual-tree forecasts to obtain an aggregate prediction. Individual

tree information does not have a first-order effect upon the stand-level growth prediction–

only how that stand growth is distributed among trees within the stand.

The disaggregative simulator is capable of producing the type of high-resolution output

common to individual-tree simulators, yet predictions are constrained by the forecasts derived

from stand-level model. Ritchie and Hann (1997b) found that this approach provided inferior

predictions when compared to the more traditional individual-tree approach for short term

forecasts of gross stand basal area growth. However, for longer term projections this approach

may provide some benefit.

LPSIM (29)

Primary Species: Geographic Range: Site Index/ Productivity:

• Lodgepole pine Central Oregon Dahms (1975)

LPSIM is an even-aged lodgepole pine simulator published by Dahms (1983). Recently,

the gross volume growth equation has been refit with the hope that the model will provide

more reliable forecasts for young stands (Pat Cochran, personal communication). The
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 43

original version (Dahms 1983) was best suited to stands greater than 30 years old. The

newer volume growth model and improved mortality functions may facilitate better

predictions for younger stands.

Stands for development of gross volume growth equations were developed from 94

temporary plots in natural stands on the eastern slope of the Cascade range between Crater

Lake National Park in the south, and Bend, Oregon in the north. Growth is allocated by

means of a proportional volume growth disaggregation function.

The lodgepole pine stands sampled for this model were even-aged stands less than 100

years old (Pat Cochran, personal communication), with quadratic mean diameter below 22

inches. Site index ranged from 60 to 110 feet (100 year total base age) using the curves

developed by Dahms (1975).

Required hardware is a PC with DOS 3.0 or later. Executable files take up about one

megabyte of hard disk space. The current version of LPSIM can be found on the World

Wide Web: http://redding.psw.fs.fed.us/lpsim.html

STIM (30)

Primary Species: Geographic Range: Site Index/ Productivity:

• Western hemlock Southwestern British User specified: Wiley

Columbia, western Oregon (1978), or Bonner and

and Washington others (1995)

The STIM simulator (Bonnor and others 1995) was developed for natural and planted

stands of western hemlock of Oregon, Washington, and British Columbia (fig. 16). Data

were collected from 1,339 permanent sample plots in pure, even-aged western hemlock

stands. Age range for data was from 4 to 251, and the mean age was 36 (table 29). Site index

ranged from 8.9 meters to 66 meters at a base age of 50. Either curves by Wiley (1978) or

Flewelling (Bonnor and others 1995) may be used to predict top height.

The simulator includes both stand and tree growth algorithms that are reconciled through

a proportional disaggregation process. The disaggregation process employed by STIM is

more sophisticated than that in LPSIM, being driven by an allocation of forecast individual

tree growth. The stand-level component forecasts top height increment, gross basal area

increment, and annual mortality (basal area and trees per acre). The simulator also includes

an ingrowth component that predicts the number of stems growing past the 5.08-cm (2-inch)

diameter threshold in a period. The simulator is designed to accept stand-level input or treelist input. Stand input can include site index, top height, basal area, quadratic mean diameter,

diameter at the 10th percentile, and coefficient of variation of tree basal area; alternatively,

default equations estimate any unspecified variables. Diameter at the 10th percentile is the

diameter of a tree at the 10th percentile in the stand diameter distribution; that is, the diameter

of a tree for which 90 percent of the trees in the stand would have a larger diameter.

Tree-list input requires site index, breast height age, plot size, and then for each sampled

tree: DBH (cm), tree height (in), and trees per plot. The simulator will also allow the user to

generate a sapling stand. The sapling stand input requires only site index. Top height, trees per

hectare, and quadratic mean diameter are optional when providing input for a sapling stand.

The primary driving function in the simulator is change in top height, which operates

internally on a 1-year growth increment. Basal area increment of an individual tree is forecast

as a function of DBH, top height increment, stand basal area, and thinning intensity as

defined by the ratio of residual basal area divided by the initial basal area. There are no

pseudo-stochastic features in this simulator; forecasts are deterministic.

Currently, the only management activity the simulator will allow is thinning. Users

may thin to basal area or trees per hectare by specifying either the amount to be removed or

left. The user may also define a d/D ratio for the thinning as well.

STIM will allow both graphical output (e.g., diameter histograms) and yield reports

by year.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 44

Table 29–Range of data used in development of STIM (Bonnor and others 1995).

STIM requires a PC with MS Windows 3.1 (or later). A 486 DX PC with 4 MB RAM is

recommended as a minimally qualified machine to run the simulator, although it will execute

on a 386 SX. Contact:

Steve Stearns-Smith, B.C. Ministry of Forests, Victoria. BC, Canada

World Wide Web: http//www.for.gov.bcca/resinv/G&Y/software/STIM/STIM.htm.

RVMM: Stand (31)

Primary Species: Geographic Range: Site Index/Productivity:

• Bitter cherry Coast range of Oregon and Shula (1998)

• California hazel Washington

• Douglas-fir

• Red alder

• Vine maple

• Western hemlock

V m ariable M m inimu M n aximu Mea

Basal area 0 0 .3 1 3 54. 49.

Trees (number per hectare ) 112.0 11,490.0 1,624.3

Top height (meters) 5 5 .0 5 0 0. 24.

Quadratic mean diameter (cm) 5 8 .0 6 1 4. 22.

Site index (meters at age 50) 8 0 .9 6 9 6. 32.

Breast height age 4 0 .0 2 0 51. 36.

Figure 16—Geographic range of the plots used in development of STIM (Bonnor and others 1995).
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 45

The stand-level version of RVMM was derived from the same data as the individual-tree

version. The stand-level version of RVMM requires dominant height, density, and plantation

age to effect a simulation. Optional input includes silvicultural treatments, slope, aspect,

elevation, basal area, and density of natural Douglas-fir, other conifers, and hardwoods as

well as cover of shrubs and herbs. The default site index system is King (1966), but Means

and Sabin (1989) or Hann and Scrivani (1987) may be specified by the user at run time.

Dynamics of the simulator include a dominant-height prediction model, basal area

prediction model for Douglas-fir (Knowe and Stein 1995), and a function for predicting the

diameter distribution of Douglas-fir trees (Knowe and others 1992), and survival prediction

functions.

The simulator will output a tree-list file derived from a Weibull-based diameter

distribution prediction system.

The simulator has not yet been released, and the user’s guide has not yet been published.

For more information access the web site:

http://www.cof.orst.edu/cof/fr/research/organon/rvmm/rvmm_idx.htm

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and grand fir in western Willamette Valley of Oregon. Res. Contribution 4. Corvallis:

Oregon State University, Forest Research Laboratory.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 51

Appendix A: Referenced Tree Species List

Common name Scientific name Simulator index1

Alaska yellow-cedar Chamaecyparis nootkatensis (D. Don) Spach 1, 8

Bigleaf maple Acer macrophyllum Pursh. 13

Bitter cherry Prunus emarginata Dougl. 15, 31

California black oak Quercus kelloggii Newb. 5, 9, 13

California hazel Corylus cornuta californica (A- DC.) Sharp 16

Canyon live oak Quercus chrysolepis Liebm. 13

Coast redwood Sequoia sempervirens (D. Don) Endl. 10

Douglas-fir Pseudotsuga menziesii (Mirb.) Franco 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,

12, 13, 14, 15, 16, 17, 18,

22, 24, 26, 28

Engelmann spruce Picea englemanii Parry ex Englem. 2, 3, 4, 8

Giant chinkapin Castanopsis chrysophylla (Dougl.) A- DC. 11, 13

Giant sequoia Sequoiadendron giganteum (Lindl.) Buchholz 9

Grand fir Abies grandis (Dougi. ex D. Don) Lindl. 2, 3, 4, 8, 13, 14

Incense-cedar Calocedrus decurrens Torr. 8, 13, 15, 17

Jeffrey pine Pinus jeffreyi Grev. & Balf 9, 11

Lodgepole pine Pinus contorta Dougl. ex Loud. 1, 2, 3, 4, 7, 8, 9, 29

Mountain hemlock Tsuga mertensiana (Bong.) Carr. 1, 2, 3, 4, 7

Noble fir Abies procera Rehd. 8

Oregon white oak Quercus garryana Dougl. ex Hook. 13

Pacific madrone Arbutus menziesii Pursh 5, 13, 17

Pacific silver fir Abies amabilis Dougl. ex Forbes 1, 3, 8

Ponderosa pine Pinus ponderosa Dougl. ex Laws. 2, 3, 4, 5, 8, 9, 11, 13

Red alder Alnus rubra Bong. 15, 16, 17, 18, 23, 25

Red fir Abies magnifica A. Murr. 7, 8, 9, 11

Sitka spruce Picea sitchensis (Bong.) Carr. 1, 18

Subalpine fir Abies lasiocarpa (Hook) Nutt. 1, 2, 3, 4, 8

Sugar pine Pinus lambertiana Dougl. 9, 11, 13, 15, 17

Tanoak Lithocarpus densiflorus (Hook. & Arn) Rehd. 5, 9, 13, 17

Vine maple Acer circinatum Pursh. 16

Western hemlock Tsuga heterophylla (Raf.) Sarg. 4, 6, 8, 13, 16, 18, 31

Western larch Larix occidentalis Nutt. 2, 3, 4

Western redcedar Thuja plicata Donn ex D. Don 18

Western white pine Pinus monticola Dougl. ex D. Don 1, 4, 7, 8, 11

White fir Abies concolor (Gord. & Glend.) Lindl. ex Hildebr. 5, 8, 9, 11, 13, 15, 17, 19

White spruce Picea glauca (Moench) Voss 1

1

Simulator index numbers correspond to the numbers in parentheses in the Table of Contents and throughout text

subheadings.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 52

Appendix B: FVS Bibliography

Amidon, Elliot L.; Dolph, K. Leroy. 1979. Two cores are better than one: Predicting

mixed-conifer growth in the Sierra Nevada. Res. Note PSW-340. Berkeley, CA: Pacific

Southwest Forest and Range Experiment Station, Forest Service, U.S. Department of

Agriculture; 3 p.

Cole, Dennis M.; Stage, Albert R. 1972. Estimating future diameters of lodgepole pine trees.

Res. Paper INT-131. Ogden, UT: Intermountain Forest and Range Experiment Station, Forest

Service, U.S. Department of Agriculture; 20 p.

Crookston, Nicholas L. 1985. User’s guide to the Event Monitor: An addition to the Prognosis

Model. Gen. Tech. Rep. INT-196. Ogden, UT: Intermountain Research Station, Forest Service,

U.S. Department of Agriculture; 36 p.

Crookston, Nicholas L. 1990. User’s guide to the event monitor: Part of Prognosis Model

Version 6. Gen. Tech. Rep. INT-275. Ogden, UT: Intermountain Research Station, Forest

Service, U.S. Department of Agriculture; 21 p.

Crookston, Nicholas L. 1997. Suppose: An interface to the forest vegetation simulator. In:

Teck, Richard; Moeur, Melinda; Adams, Judy, compilers. Proceedings: Forest Vegetation

Simulator conference; 1997 February 3-7; Fort Collins, CO. Gen. Tech. Rep. INT-GTR-373.

Ogden, UT: Intermountain Research Station, Forest Service, U.S. Department of

Agriculture; 7-14.

Crookston, Nicholas L.; Stage, Albert R. 1991. User’s guide to the Parallel Processing Extension

of the Prognosis Model. Gen. Tech Rep. INT-281. Ogden ,UT: Intermountain Research Station,

Forest Service, U.S. Department of Agriculture; 88 p.

Dixon, Gary E. 1992. South Central Oregon / northeast California Prognosis geographic

variant of the Forest Vegetation Simulator. Fort Collins, CO: Timber Management Service

Center.

Dixon, Gary E. 1994. Western Sierra Nevada Prognosis geographic variant of the Forest

Vegetation Simulator. Fort Collins, CO: Timber Management Service Center.

Dixon, Gary E.; Johnson, Ralph. 1993. Klamath mountains Prognosis geographic variant of

the Forest Vegetation Simulator. Fort Collins, CO: Timber Management Service Center.

Dixon, Gary E.; Johnson, Ralph; Shroeder, Daniel. 1992. Southeast Alaska / Coastal British

Columbia (SEAPROG) Prognosis variant of the Forest Vegetation Simulator. Fort Collins,

CO: Timber Management Service Center; 75 p.

Dolph, K. Leroy. 1983. Site index curves for young-growth incense-cedar of the westside

Sierra Nevada. Res. Note PSW-363. Albany, CA: Pacific Southwest Research Station, Forest

Service, U.S. Department of Agriculture; 8 p.

Dolph, K. Leroy. 1984. Relationships of inside and outside bark diameters for young-growth

mixed conifer species in the Sierra Nevada. Res. Note PSW-368. Albany, CA: Pacific

Southwest Research Station, Forest Service, U.S. Department of Agriculture; 4 p.

Dolph, K. Leroy. 1987. Site index curves for young-growth California white fir on the western

slopes of the Sierra Nevada. Res. Paper PSW-185. Albany, CA: Pacific Southwest Research

Station, Forest Service, U.S. Department of Agriculture; 9 p.

Dolph, K. Leroy. 1988a. Prediction of periodic basal area increment for young-growth mixed

conifers in the Sierra Nevada. Res. Paper PSW-190. Berkeley, CA: Pacific Southwest Forest

and Range Experiment Station, Forest Service, U.S. Department of Agriculture; 20 p.

Dolph, K. Leroy. 1988b. Predicting height increment of young growth mixed conifers in the

Sierra Nevada. Res. Paper PSW-191. Berkeley, CA: Pacific Southwest Forest and Range

Experiment Station, Forest Service, U.S. Department of Agriculture; 7 p.

Dolph, K. Leroy. 1989. Nonlinear equations for predicting diameter inside bark at breast

height for young-growth red fir in California and southern Oregon. Res. Note PSW-409.

Albany, CA: Pacific Southwest Research Station, Forest Service, U.S. Department of

Agriculture; 4 p.

Dolph, K. Leroy. 1991. Polymorphic site index curves for red fir in California and southern

Oregon. Res. Paper PSW-206. Albany, CA: Pacific Southwest Research Station, Forest Service,

U.S. Department of Agriculture; 18 p.

Dolph, K. Leroy. 1992. A diameter increment model for red fir in California and southern

Oregon. Res. Paper PSW-RP-210. Albany, CA: Pacific Southwest Research Station, Forest

Service, U.S. Department of Agriculture; 6 p.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 53

Dolph, K. Leroy. 1992. Predicting height increment of young growth red fir in California

and southern Oregon. Res. Paper PSW-RP-214. Albany, CA: Pacific Southwest Research

Station, Forest Service, U.S. Department of Agriculture; 4 p.

Dolph, K. Leroy; Amidon, Elliot L. 1979. Predicting growth of mixed-conifer species in the

Sierra Nevada: rationale and methods. Res. Note PSW-337. Berkeley, CA: Pacific Southwest

Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture; 7 p.

Ferguson, Dennis E. 1997. Regeneration models for FVS variants. In: Teck, Richard; Moeur,

Melinda; Adams, Judy, compilers. Proceedings: Forest Vegetation Simulator Conference; 1997

February 3-7; Fort Collins, CO. Gen. Tech. Rep. INT-GTR-373. Ogden, UT: Intermountain

Research Station, Forest Service, U.S. Department of Agriculture; 43-49.

Ferguson, Dennis E.; Carlson, Clinton E. 1993. Predicting regeneration with establishment

with the Prognosis Model. Res. Pap. INT-467. Ogden, UT: Intermountain Research Station,

Forest Service, U.S. Department of Agriculture; 54 p.

Ferguson, Dennis E.; Crookston, Nicholas L. 1984. User’s guide to the Regeneration

Establishment Model—a Prognosis Model Extension. Gen. Tech. Rep. INT-161. Ogden,

UT: Intermountain Forest and Range Experiment Station, Forest Service, U.S. Department of

Agriculture; 23 p.

Ferguson, Dennis E.; Crookston, Nicholas L. 1991. User’s guide to version 2 of the regeneration

establishment model: Part of the Prognosis model. Gen. Tech. Rep. INT-279. Ogden, UT:

Intermountain Research Station, Forest Service, U.S. Department of Agriculture; 34 p.

Ferguson, Dennis E.; Johnson, Ralph R. 1988. Developing variants for the regeneration

establishment model. In: Ek, Alan R.; Shifley, Stephen R.; Burk, Thomas E., Editors. Forest

growth modeling and prediction, Volume 2. Proceedings of the IUFRO Conference, 1987

August 23-27. Gen. Tech. Rep. NC-120. St. Paul, MN: North Central Forest Experiment Station,

Forest Service, U.S. Department of Agriculture; 2:369-376.

Hamilton, David A., Jr. 1991. Implications of random variations in the stand Prognosis model.

Res. Note INT-394. Ogden, UT: Intermountain Research Station, Forest Service, U.S.

Department of Agriculture; 11 p.

Hamilton, David A., Jr. 1994. Uses and abuses of multipliers in the Stand Prognosis Model.

Gen. Tech Rep. INT-310. Ogden, UT: Intermountain Research Station, Forest Service, U.S.

Department of Agriculture; 9 p.

Hamilton, David A. Jr. 1997a. Guidelines for sensitivity analysis of FVS. In: Teck, Richard;

Moeur, Melinda; Adams, Judy, compilers. Proceedings: Forest Vegetation Simulator conference;

1997 February 3-7; Fort Collins, CO. Gen. Tech. Rep. INT-GTR-373. Ogden, UT: Intermountain

Research Station, Forest Service, U.S. Department of Agriculture; 33-38.

Hamilton, David A., Jr. 1997b. Guidelines for use of FVS multipliers. In: Teck, Richard; Moeur,

Melinda; Adams, Judy, compilers. Proceedings: Forest Vegetation Simulator Conference; 1997

February 3-7; Fort Collins, CO. Gen. Tech. Rep. INT-GTR-373. Ogden, UT: Intermountain

Research Station, Forest Service, U.S. Department of Agriculture; 39-42.

Horn, J.E.; Medema, E.L.; Schuster, E.G. 1986. User’s guide to CHEAPO 11 - Economic

analysis of stand Prognosis model outputs. Gen. Tech. Rep. INT-211. Ogden, UT:

Intermountain Research Station, Forest Service, U.S. Department of Agriculture; 38 p.

Johnson, Ralph. 1993. Blue Mountains geographic variant of the forest vegetation simulator.

Fort Collins, CO: Timber Management Service Center.

Johnson, Ralph. 1990. East Cascades Prognosis geographic variant of the Forest Vegetation

Simulator. Fort Collins, CO: Timber Management Service Center.

Moeur, Melinda. 1985. COVER: A User’s guide to the CANOPY and SHRUBS extension of

the Stand Prognosis Model. Gen. Tech. Rep. INT-190. Ogden, UT: Intermountain Research

Station, Forest Service, U.S. Department of Agriculture; 49 p.

Monserud, Robert A.; Crookston, Nicholas L. 1982. A user’s guide to the combined Stand

Prognosis Model and Douglas-fir tussock moth outbreak model. Gen. Tech. Rep. INT-127.

Ogden, UT: Intermountain Research Station, Forest Service, U.S. Department of

Agriculture; 49 p.

Patterson, Donald A.; Stiff, Charles T. 1987. An evaluation and comparison of two

distance-independent forest projection models in the inland northwest. In: Ek, Alan R.;

Shifley, Stephen R.; Burk, Thomas E., editors. Forest growth modeling and prediction, Volume

2. Proceedings of the IUFRO conference; 1987 August 23-27. Gen. Tech. Rep. NC-120. St.

Paul, MN: North Central Forest Experiment Station, Forest Service, U.S. Department of

Agriculture; 802-809.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 54

Shaw, Charles G., III; Stage, Albert R.; Webb, Timothy M. 1985. Development of a root disease

subroutine for use with stand growth models of western forests. In: Theis, Walter G.,

compiler. Western International Forest Disease Conference (33rd); Olympia, WA. Corvallis,

OR: Dept. of Printing, Oregon State University; 48-54.

Stage, Albert R. 1973. Prognosis model for stand development. Res. Paper INT-137. Ogden,

UT: Intermountain Forest and Range Experiment Station, Forest Service, U.S. Department of

Agriculture; 32 p.

Stage, Albert R. 1975. Prediction of height increment for models of forest growth. Res. Paper

INT-164. Ogden, UT: Intermountain Forest and Range Experiment Station, Forest Service,

U.S. Department of Agriculture; 20 p.

Stage, Albert R. 1976. An expression for the effect of aspect, slope, and habitat type on tree

growth. Forest Science 22(3):457-460.

Stage, Albert R. 1982. Regeneration modeling as a component of forest succession simulation.

In: Forest succession and stand development research in the northwest; 1981 March 26; Corvallis

OR: Forest Research Laboratory, Oregon State University; 24-30.

Stage, Albert R.; Renner, David L. 1987. Comparison of yield forecasting techniques using

long-term stand histories. In: Ek, Alan R.; Shifley, Stephen R.; Burk, Thomas E., editors.

Forest growth modeling and prediction, Volume 2. Proceedings of the IUFRO conference;

1987 August 23-27; Gen. Tech. Rep. NC-120. St. Paul, MN: North Central Forest Experiment

Station, Forest Service, U.S. Department of Agriculture; 810-817.

Stage, Albert R.; Shaw, Charles G.,III; Marsden, Michael A; Byler, James W.; Renner, David L;

Eav, Bov B.; McNamee, Peter J.; Sutherland, Glenn D.; Webb, Timothy M. 1990. User’s

manual for western root disease model. Gen. Tech. Rep. INT-267. Ogden, UT: Intermountain

Research Station, Forest Service, U.S. Department of Agriculture; 49 p.

Stage, Albert R.; Wykoff, William R. 1993. Calibrating a model of stochastic effects on diameter

increment for individual-tree simulations of stand dynamics. Forest Science 39(4): 692-705.

Teck, Richard; Moeur, Melinda; Eav, Bov. 1996. Forecasting ecosystems with the forest

vegetation simulator. Journal of Forestry 94(12):7-10.

Wykoff, William R. 1985. Introduction to the Prognosis Model—Version 5.0. In: Van Hooser,

Dwane D.; Van Pelt, Nicholas, editors. Proceedings - growth and yield and other

mensurational tricks: A regional technical conference. Gen. Tech. Rep. INT-193. Ogden,

UT: Intermountain Research Station, Forest Service, U.S. Department of Agriculture; 44-52.

Wykoff, William R. 1986. Supplement to the user’s guide for the Stand Prognosis Model

Version 5.0. Gen. Tech. Rep. INT-208. Ogden, UT: Intermountain Research Station, Forest

Service, U.S. Department of Agriculture; 36 p.

Wykoff, William R. 1990. A basal area increment model for individual conifers in the northern

Rocky Mountains. Forest Science 36(4): 1077-1104.

Wykoff, William R. 1997. Predicting basal area increment in irregular stands: A progress

report. In Teck, Richard; Moeur, Melinda; Adams, Judy, compilers. Proceedings: Forest

Vegetation Simulator conference; 1997 February 3-7; Fort Collins, CO. Gen. Tech. Rep.

INT-GTR-373. Ogden, UT: Intermountain Research Station, Forest Service, U.S. Department

of Agriculture; 199-205.

Wykoff, William R.; Crookston, Nicholas L.; Stage, Albert R. 1982. User’s guide to the Stand

Prognosis Model. Gen. Tech. Rep. INT-133. Ogden, UT: Intermountain Forest and Range

Experiment Station, Forest Service, U.S. Department of Agriculture; 112 p.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 55

Appendix C: CRYPTOS Bibliography

Krumland, Bruce E. 1987. A tree-based forest yield projection system for the north coast

region of California. Berkeley, CA: University of California; 188 p. Ph.D. dissertation.

Krumland, Bruce E.; Dye, J.; Wensel, Lee C.. 1978. Individual-tree mortality models for the

north coast region of California. Res. Note 6. Berkeley, CA: Co-op Redwood Yield Research

Project, University of California.

Krumland, Bruce E.; Wensel, Lee C. 1977. Height growth patterns and fifty-year base age site

index curves for young growth coastal redwood. Res. Note 4. Berkeley, CA: Co-op Redwood

Yield Research Project, University of California; 11 p.

Krumland, Bruce E.; Wensel, Lee C. 1979. Conversions for site index systems used in the

North Coast of California. Res. Note 10. Berkeley, CA: Co-op Redwood Yield Research

Project, University of California.

Krumland, Bruce E.; Wensel, Lee C. 1980. User’s guide to GENR - and interactive program

to generate inventory records of typical young growth stands in coastal California. Res.

Note 17. Berkeley ,CA: Co-op Redwood Yield Research Project, University of California.

Krumland, Bruce E.; Wensel, Lee C. 1981. A tree increment model system for North Coastal

California - design and Implementation. Res. Note 15. Berkeley, CA: Co-op Redwood

Yield Research Project, University of California; 56 p.

Krumland, Bruce E.; Wensel, Lee C. 1988. A generalized height-diameter equation for coastal

California species. Western Journal of Applied Forestry 3: 113-115.

Krumland, Bruce E.; Wensel, Lee C. 1982. CRYPTOS - User’s guide, Cooperative Redwood

Yield Project Timber Output Simulator, version 4.0. Co-op. Res. Note 20. Berkeley, CA:

Redwood Yield Research Project, University of California.

Wensel, Lee C.; Krumland, Bruce E. 1986. A site index system for redwood and Douglas-fir in

California’s north coast forest. Hilgardia 54: 1-14.

Appendix D: CACTOS Bibliography

Biging, Greg S. 1983. Volume tables for young-growth mixed-conifers of northern California

based upon stem analysis data. Res. Note 7a. Berkeley, CA: Northern California Forest

Yield Cooperative, University of California.

Biging, Greg S. 1984. Taper equations for second-growth mixed conifers of Northern

California. Forest Science 30: 1103-1117.

Biging, Greg S. 1985. Improved estimates of site index curves using a varying-parameter

model. Forest Science 31: 248-259.

Biging, Greg S. 1988. Estimating the accuracy of volume equations using taper equations of

stem profile. Canadian Journal of Forest Research 18: 1002-1007.

Biging, Greg S.; Meerschaert, Walter J. 1987. STAG User’s Guide: The forest stand generator

for mixed species in California (Version 3). Res. Note 21. Berkeley CA: Northern California

Forest Yield Cooperative, University of California.

Biging, Greg S.; Robards, Timothy A. 1989. Individual tree mortality models for northern

California mixed conifer species. Res. Note 24. Berkeley, CA: Northern California Forest

Yield Cooperative, University of California.

Biging, Greg S.; Wensel, Lee C. 1984. A photographic technique for use with stem analysis.

Forest Science 30: 715-729.

Biging, Greg S.; Wensel, Lee C. 1984. Site index equations for young-growth mixed-conifers

of northern California. Res. Note 8a. Berkeley, CA: Northern California Forest Yield

Cooperative, University of California.

Biging, Greg S.; Wensel, Lee C. 1988a. STAG: a forest stand generator for producing complete

CACTOS stand descriptions. In: Ek, Alan R.; Shifley, Stephen R.; Burk, Thomas E., editors.

Forest growth modeling and prediction, Volume 1. Proceedings of the IUFRO Conference

1987 August 23-27. Gen. Tech. Rep. NC-120. St. Paul, MN: North Central Forest Experiment

Station, Forest Service, U.S. Department of Agriculture; 47-53.

Biging, Greg S.; Wensel, Lee C. 1988b. The effect of eccentricity on the estimation of basal

area and basal area increment of coniferous trees. Forest Science 34:621-633.

Biging, Greg S.; Wensel, Lee C. 1990. Estimation of crown form of six conifer species of

northern California. Canadian Journal of Forest Research 20: 1137-1142.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 56

Daugherty, Peter J.; Wensel, Lee C.; Meerschaert, Walter J. 1987. YDAVG user’s guide: The

CACTOS yield file averager Version 2.0. Res. Note 18. Berkeley, CA: Northern California

Forest Yield Cooperative, University of California.

Koelher, James R.; Wensel, Lee C. 1986. The use of pseudo- stochastic effects in a tree growth

projection system. Res. Note 14. Berkeley, CA: Northern California Forest Yield Cooperative,

University of California.

Meerschaert, Walter J. 1986. Compare: A computer program for comparing actual growth

with CACTOS predictions. Res. Note 17. Berkeley, CA: Northern California Forest Yield

Cooperative, University of California.

Meerschaert, Walter J. 1988. An overview of CACTOS: the California Conifer Timber Output

Simulator. In: Ek, Alan R.; Shifley, Stephen R.; Burk, Thomas E., editors. Forest growth

modeling and prediction, Volume 2. Proceedings of the IUFRO Conference; 1987 August

23-27; Gen. Tech. Rep. NC-120. St. Paul, MN: North Central Forest Experiment Station,

Forest Service, U.S. Department of Agriculture; 992-997.

Meerschaert, Walter J.; Wensel, Lee C. 1986. SDAVG user’s guide: The CACTOS stand

description file averager. Res. Note 19. Berkeley, CA: Northern California Forest Yield

Cooperative, University of California.

Robards, T.A.; Biging, Greg S. 1989. A change in the height-to-crown base model for six

conifer species of the California mixed conifer region. Res. Note 29. Berkeley, CA: Northern

California Forest Yield Cooperative, University of California.

Wensel, Lee C. 1977. Volume tables for young-growth conifers in the northern regions of

California. Bulletin 1883. Berkeley, CA: Agricultural Experiment Station, University of

California.

Wensel, Lee C. 1982. The Coop Yield Model: background and architecture. Res. Note 4.

Berkeley, CA: Northern California Forest Yield Cooperative, University of California.

Wensel, Lee C.; Biging, Greg S. 1988. The CACTOS system for individual-tree growth

simulation in the mixed conifer forests of California. In: Ek, Alan R.; Shifley, Stephen R.;

Burk, Thomas E., editors. Forest growth modeling and prediction, Volume 1. Proceedings of

the IUFRO Conference; 1987 August 23-27; Gen. Tech. Rep. NC-120. St. Paul, MN: North

Central Forest Experiment Station, Forest Service, U.S. Department of Agriculture; 175-183

Wensel, Lee C.; Daugherty, Peter J.; Meerschaert, Walter J. 1986. CACTOS user’s guide: the

California Conifer Timber Output Simulator. Bulletin 1920. Berkeley, CA. Agricultural

Experiment Station, University of California.

Wensel, Lee C.; Koelher, James R. 1985. Tree DBH and height growth equations for California

conifers (from stem analysis data). Res. Note 12. Berkeley, CA: Northern California Forest

Yield Cooperative, University of California.

Wensel, Lee C.; Meerschaert, Walter J.; Biging, Greg S. 1987. Tree height and diameter growth

models for northern California conifers. Hilgardia 55: 1-20.

Wensel, Lee C.; Robards, Timothy A. 1989. Revised parameter estimates for CACTOS growth

models. Res. Note 23. Berkeley, CA: Northern California Forest Yield Cooperative, University

of California.

Appendix E: ORGANON Bibliography

Curtis, Robert O. 1994. Some simulation estimates of mean annual increment of Douglas-fir:

Results, limitations, and implications for management. Res. Paper PNW-RP-471. Portland,

OR: Pacific Northwest Research Station, Forest Service, U.S. Department of Agriculture. 27 p

Hann, David W.; Larsen, David R. 1991. Diameter growth equations for fourteen tree species

in southwest Oregon. Res. Bulletin 69. Corvallis: Oregon State University, Forest Research

Laboratory; 18 p.

Hann, David W.; Olsen, Christina L.; Hester, Arlene S. 1992. ORGANON user’s manual: Edition

4.0 southwest Oregon version and Edition 1.0 western Willamette Valley version. Corvallis:

Oregon State University, Department of Forest Resources; 113 p.

Hann, David W.; Olsen, Christina L.; Hester, Arlene S. 1994. ORGANON user’s manual: Edition

4.3 southwest Oregon version and Edition 1.3 western Willamette Valley version. Corvallis:

Oregon State University, Department of Forest Resources; 113 p.

Hann, David W.; Ritchie, Martin W. 1988. Height growth rate of Douglas-fir: A comparison

of model forms. Forest Science 34: 165-175.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 57

Hann, David W.; Scrivani, John A. 1987. Dominant-height-growth and site-index equations

for Douglas-fir and ponderosa pine in southwest Oregon. Res. Bulletin 59. Corvallis: Oregon

State University, Forest Research Laboratory; 36 p.

Hann, David W.; Walters, David K.; Scrivani, John A. 1987. Incorporating crown ratio into

prediction equations for Douglas-fir stem volume. Canadian Journal of Forest Research 17:

17-22.

Hann, David W.; Wang, Chao-Huan. 1990. Mortality equations for individual trees in southwest

Oregon. Res. Bulletin 67. Corvallis: Oregon State University, Forest Research

Laboratory; 17 p.

Hester, Arlene S.; Hann, David W.; Larsen, David R. 1989. ORGANON: Southwest Oregon

growth and yield model user manual, Version 2.0. Corvallis: Oregon State University, Forest

Research Laboratory; 59 p.

Kershaw, John A. Jr.; Maguire, Douglas A.; Hann, David W. 1990. Longevity and duration of

radial growth in Douglas-fir branches. Canadian Journal of Forest Research 20: 1690-1695.

Larsen, David R.; Hann, David W. 1985. Equations for predicting diameter and squared

diameter inside bark at breast height for six major conifers of southwest Oregon. Res.

Note 77. Corvallis: Oregon State University, Forest Research Laboratory; 4 p.

Larsen, David R.; Hann, David W. 1987. Height-diameter equations for seventeen tree species

in southwest Oregon. Res. Paper 49. Corvallis: Oregon State University, Forest Research

Laboratory; 16 p.

Larsen, David R.; Hann, David W.; Steams-Smith, Stephen C. 1987. Accuracy and precision of

the tangent method of measuring tree height. Western Journal of Applied Forestry 2: 26-28.

Maguire, Douglas A.; Hann, David W. 1987a. Equations for predicting sapwood area at crown

base in southwestern Oregon Douglas-fir. Canadian Journal of Forest Research 17: 236-241.

Maguire, Douglas A.; Hann David W. 1987b. A stem dissection technique for dating branch

mortality and reconstructing past crown recession. Forest Science 33: 858-871.

Maguire, Douglas A; Hann, David W. 1988. Prediction of branch diameter and branch

distribution for Douglas-fir in southwestern Oregon. In: Ek, Alan R.; Shifley, Stephen R.;

Burk, Thomas E., editors. Forest growth modeling and prediction, Volume 2. Proceedings of

the IUFRO Conference; 1987 August 23-27; Gen. Tech. Rep. NC-120. St. Paul, MN: North

Central Forest Experiment Station, Forest Service, U.S. Department of Agriculture; 1029-1036.

Maguire, Douglas A.; Hann, David W. 1989. The relationship between gross crown dimensions

and sapwood area at crown base in Douglas-fir. Canadian Journal of Forest Research 19:

557-565.

Maguire, Douglas A.; Hann, David W. 1990. Bark thickness and bark volume in southwestern

Oregon Douglas-fir. Western Journal of Applied Forestry 5: 5-8.

Maguire, Douglas A.; Hann, David W. 1990a. Constructing models for direct prediction of

5-year crown recession in southwestern Oregon Douglas-fir. Canadian Journal of Forest

Research 20: 1044-1052.

Maguire, Douglas A.; Hann, David W. 1990b. A sampling strategy for estimating past crown

recession on temporary growth plots. Forest Science 36: 549-563.

Maguire, Douglas A.; Kershaw, John A. Jr.; Hann, David W. 1990. An approach to predicting

the effects of silvicultural regime on branch size and crown wood core in Douglas-fir. In:

Research in forest mensuration, growth and yield. Publication No. FWS-2-90. Blacksburg,

VA: School of Forestry and Wildlife Resources, Virginia Polytechnic Institute & State

University; 85-94.

Maguire, Douglas A.; Kershaw, John A. Jr.; Hann, David W. 1991. Predicting effects of

silvicultural regime on branch size and crown wood core in Douglas-fir. Forest Science

37: 1409-1428.

Paine, David P.; Hann, David W. 1982. Maximum crown-width equations for southwestern

Oregon tree species. Res. Paper 46. Corvallis: Oregon State University, Forest Research

Laboratory; 20 p.

Ritchie, Martin W.; Hann, David W. 1984. Nonlinear equations for predicting diameter and

squared diameter inside bark at breast height for Douglas-fir. Res. Paper 47. Corvallis:

Oregon State University, Forest Research Laboratory; 12 p.

Ritchie, Martin W.; Hann, David W. 1985. Equations for predicting basal area increment in

Douglas-fir and grand fir. Res. Bulletin 51. Corvallis: Oregon State University, Forest

Research Laboratory; 9 p.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 58

Ritchie, Martin W.; Hann, David W. 1986. Development of a tree height growth model for

Douglas-fir. Journal of Forest Ecology and Management 15: 135-145.

Ritchie, Martin W.; Hann, David W. 1987. Equations for predicting height to crown base for

fourteen tree species in southwest Oregon. Res. Paper 50. Corvallis: Oregon State University,

Forest Research Laboratory; 14 p.

Ritchie, Martin W.; Hann, David W. 1990. Height growth rate equations for six conifer species

in southwest Oregon. Res. Paper 54. Corvallis: Oregon State University, Forest Research

Laboratory; 12 p.

Stearns-Smith, Stephen C.; Hann, David W. 1986. Forest soil associations of southwest Oregon.

Corvallis: Oregon State University, Forest Research Laboratory.

Walters, David K.; Hann, David W. 1986a. Predicting merchantable volume in cubic feet to a

variable top and in Scribner board feet to a 6-inch top for six major conifers of southwest

Oregon. Res. Bulletin 52. Corvallis: Oregon State University, Forest Research

Laboratory; 107 p.

Walters, David K.; Hann, David W. 1986b. Taper equations for six conifer species in southwest

Oregon. Res. Bulletin 56. Corvallis: Oregon State University, Forest Research Laboratory; 36 p.

Walters, David K.; Hann, David W.; Clyde, Merlise A. 1985. Equations and tables predicting

gross total stem volumes in cubic feet for six major conifers of southwest Oregon. Res.

Bulletin 50. Corvallis: Oregon State University, Forest Research Laboratory; 36 p.

Wang, Chao-Huan; Hann, David W. 1988. Height-diameter equations for sixteen tree species

in the central western Willamette Valley of Oregon. Res. Paper 51. Corvallis: Oregon State

University, Forest Research Laboratory; 7 p.

Zumrawi, Abdel Azim; Hann, David W. 1989. Height to crown base equations for six tree

species in the central western Willamette Valley of Oregon. Res. Paper 52. Corvallis: Oregon

State University, Forest Research Laboratory; 9 p.

Zumrawi, Abdel Azim; Hann, David W. 1993. Diameter growth equations for Douglas-fir

and grand fir in the western Willamette Valley of Oregon. Res. Contribution 4. Corvallis:

Oregon State University, Forest Research Laboratory; 6 p.

Appendix F: DFSIM Bibliography

Bare, B. Bruce. 1986. Douglas-fir yield forecasting systems: what’s available. In: Oliver,

Chadwick Dearing; Hanley, Donald P.; Johnson, Jay A., Editors. Douglas-fir: Stand management

for the future. Contribution No. 55. Seattle, WA: College of Forest Resources, University of

Washington; 344-349.

Curtis, Robert O. 1987. Levels -of-growing stock cooperative study in Douglas-fir: Report

No. 9-Some comparisons of DFSIM estimates with growth in the levels-of-growing-stock

study. Res. Paper PNW-RP-376. Portland, OR: Pacific Northwest Research Station, Forest

Service, U.S. Department of Agriculture; 34 p.

Curtis, Robert O. 1994. Some simulation estimates of mean annual increment of Douglas-fir:

Results, limitations, and implications for management. Res. Paper PNW-RP-471.

Portland, OR: Pacific Northwest Research Station, Forest Service, U.S. Department of

Agriculture; 27 p.

Curtis, Robert O.; Clendenen, Gary W.; DeMars, Donald J. 1981. A new stand simulator for

coast Douglas-fir: DFSIM user’s guide. Gen. Tech. Rep. PNW-128. Portland, OR: Pacific

Northwest Forest and Range Experiment Station, Forest Service, U.S. Department of

Agriculture; 79 p.

Curtis, Robert O.; Clendenen, Gary W.; Reukema, Donald L.; DeMars, Donald J. 1982. Yield

tables for managed stand of coast Douglas-fir. Gen. Tech. Rep. PNW-135. Portland, OR:

Pacific Northwest Forest and Range Experiment Station, Forest Service, U.S. Department of

Agriculture. 182 p.

Curtis, Robert O.; Marshall, David D. 1993. Douglas-fir rotations—time for reappraisal?

Western Journal of Applied Forestry. 8: 81-85.

Fight, Roger D.; Chittester, Judith M.; Clendenen, Gary W. 1984. DFSIM with economics: a

financial analysis option for the DFSIM Douglas-fir simulator. Gen. Tech. Rep. PNW-175.

Portland, OR: Pacific Northwest Forest and Range Experiment Station, Forest Service, U.S.

Department of Agriculture.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-174. 1999. 59

Miller, Richard E.; Clendenen, Gary W.; Bruce, D. 1988. Volume growth and response to

thinning and fertilizing of Douglas-fir stands in southwestern Oregon. Gen. Tech. Rep.

PNW-GTR-221. Portland, OR.: Pacific Northwest Research Station, Forest Service, U.S.

Department of Agriculture. 39 p.

Mitchell, Kenneth J. 1986. Comparison of McArdle, DFSIM, and TASS growth and yield

models. In: Oliver, Chadwick Dearing; Hanley, Donald P.; Johnson, Jay A., editors. Douglas-fir:

Stand management for the future. Contribution No. 55. Seattle, WA: College of Forest

Resources, University of Washington; 350-359.