User’s Guide to the

Western Root Disease Model,

Version 3.0

United States

Department

of Agriculture

Forest Service

Pacific Southwest

Research Station

General Technical Report

PSW-GTR-165
Abstract

Frankel, Susan J., technical coordinator. 1998. User’s guide to the Western Root Disease

Model, version 3.0. Gen. Tech. Rep. PSW-GTR-165. Albany, CA: Pacific Southwest

Research Station, Forest Service, U.S. Department of Agriculture; 166 p.

Effects of Armillaria spp., Phellinus weirii, Heterobasidion annosum, or bark beetles on stand

dynamics are represented by the Western Root Disease Model,Version 3.0. This model,

which operates in conjunction with the Forest Vegetation Simulator, can be used to

evaluate the effects of many silvicultural practices. This guide contains instructions for

use, detailed description of model functions, an overview of characteristics of root

diseases, a comparison of model predictions to real data, and special instructions for users

in British Columbia.

Retrieval terms: Armillaria, bark beetles, forest modeling, Forest Vegetation Simulator,

Heterobasidion annosum, Phellinus weirii

Technical Coordinator

Susan Frankel is a plant pathologist in the State and Private Forestry Staff at the Pacific

Southwest Region, USDA Forest Service (630 Sansome St., San Francisco, CA 94111).

Publisher:

Albany, California

Mailing address:

PO Box 245, Berkeley CA

94701-0245

510 559-6300

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

September 1998

Pacific Southwest Research Station

Forest Service

U.S. Department of Agriculture
Pacific Southwest

Research Station

USDA Forest Service

General Technical

Report

PSW-GTR-165

September 1998

User’s Guide to the

Western Root Disease Model, Version 3.0

Susan J. Frankel, Technical Coordinator

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Chapter 1

Characteristics and Consequences of Root Diseases

in Forests of Western North America . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Donald J. Goheen and William J. Otrosina

Laminated Root Rot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Armillaria Root Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Annosus Root Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Root Disease Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Root Disease Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Chapter 2

How to Use the Western Root Disease Model, Version 3.0. . . . . . . . . . . . . 11

Sarah J. Beukema and Werner A. Kurz

Section 1. Model Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Section 2. Running the Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Section 3. Initializing Root Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Section 4. Bark Beetle and Windthrow Models . . . . . . . . . . . . . . . . . . . . . . 33

Section 5. Changing Default Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Section 6. Managing Root Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Section 7. Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Section 8. Sample Keyword Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Section 9. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Chapter 3

Pointers for Using the Western Root Disease Model

Ellen Michaels Goheen and Matthew K. Thompson

Assessing Current Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Incorporating Information on Current Conditions into a

 Western Root Disease Model Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Adjusting Model Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 4

Detailed Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Sarah J. Beukema, Werner A. Kurz and Julee Greenough

Section 1. Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Section 2. Root Disease Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Section 3. Other Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Section 4. Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Section 5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Chapter 5

A Comparison Between Western Root Disease Model Predictions

and Real Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Susan J. Frankel, Larry J. Wilson and Garey Slaughter

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Chapter 6

Root Disease and the Western Root Disease Model

in British Columbia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

Sarah J. Beukema, Werner A. Kurz and John McLaughlin

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

Root Disease Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Use of the WRDM3 in British Columbia. . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Appendices

Index of Keywords. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Glossary of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Appendix to Chapter 2

List of Default Values by Tree Species and Variant . . . . . . . . . . . . . . . . . . 152

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

List of Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Appendix to Chapter 4

Detailed Calculation of Initial Root Disease . . . . . . . . . . . . . . . . . . . . . . . . 160

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

ii
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998s.

Preface

Purpose and Scope

This document contains background information and detailed instructions on how to use the WesternRoot Disease Model, Version 3.0 (WRDM3). The WRDM3 is an extension for the Forest Vegetation

Simulator (FVS), an individual-tree, distant-independent, growth and yield model produced by the

USDA Forest Service. This user’s guide assumes that the reader is generally familiar with FVS, and the

guide should be used in conjunction with the FVS (Prognosis) User’s Guide (Wycoff and others 1982).1

The WRDM3 was developed to account for the influences of pathogens and insects on stand

dynamics predicted by FVS. The model simulates the effects of Armillaria root disease (caused by

Armillaria spp. (Fr.:Fr.)), laminated root disease caused by Phellinus weirii (Murr. [Gilbn.]), or Annosus

root disease (caused by Heterobasidion annosum (Fr.) Bref.) and their interaction with bark beetles. The

model evolved from two earlier root disease models: The Western Root Disease Model, Version 1.0 (Stage

and others 1990)1

 and the Annosus Root Disease/Bark Beetle Model (Beukema and others 1995).1 All

these models were developed using a workshop process through which pathologists, entomologists, and

silviculturists contributed their knowledge of these biological systems.

This manual provides instructions on how to use the model and what to use it for, and a complete

description of model design. It is not meant to be read cover-to-cover. Instead, the reader is encouraged to

use the Table of Contents and Index to pull out information as needed.

Once you become familiar with the WRDM3 it becomes fairly easy to use; however, at first, trying to use

it can be overwhelming. If you are new to the model, we encourage you to get assistance from pathologists

and entomologists and FVS model specialists. To get the most recent version of the model or for answers to

technical questions contact: Judy Adams, Forest Health Technology Enterprise Team, USDA Forest Service,

3825 E. Mulberry Street, Fort Collins, CO 80524.

This manual is composed of six chapters, as follows.

Chapter 1 provides background information on the impacts and biology of root diseases. It describes

the biological phenomenon that the model is designed to capture.

Chapter 2 contains instructions on how to use the model. If you are anxious to get started with the

model, you may want to read this section first.

Chapter 3 outlines some pointers on how to use the model. It selects some important information that

will optimize the running of the model. Contents include a brief explanation of how to collect the root

disease information needed to run the model, how to adjust model functions to represent local conditions,

and examples of ways to enter root disease information into the model.

Chapter 4 is a detailed description of how the model was developed, and the functions and

assumptions it is based upon. It does not need to be read before using the model, rather it is presented for

those curious about how the model actually works.

Chapter 5 summarizes a test of the model that compared model performance to permanent plot

data. Short-term (13-year) model predictions were compared to real data from fir stands with Annosus

root disease in California. Results demonstrate that the model is, on average, making accurate projections.

Chapter 6 contains information relevant to model users in British Columbia (B.C.), Canada. A brief

overview of root disease models available in B.C. and their advantages and disadvantages is presented.

Technical Specifications

The Western Root Disease Model along with FVS is written using the Fortran 77 Standard. It currently

runs on the following computer platforms: AOS/VS (Data General), DOS (IBM compatible PCs) and AIX

(IBM Unix). It ports to other platforms easily.

System requirements for an IBM compatible PC are:

•IBM compatible PC, 386DX or better

iii

1. Anon. 1995. VDYP interactive application user guide, version 6.3. Victoria, BC:BC Ministry of Forests Resources Inventory

Branch; 44 p.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

•DOS 3.3 or above

•4 Mbytes of memory

•Minimum of 20.0 Mbytes of disk space. The model, depending on the variant of FVS being

used, requires 1.2 Mbytes to 3.5 Mbytes.

•To use the model along with a user interface (Submittal System or Suppose...) and postprocessors, 100 Mbytes of disk space are suggested.

Acknowledgments

The technical coordinator thanks Judy Adams for administrative assistance and support of this project.

This project was funded by a Technology Development grant from USDA Forest Health Protection—

Washington Office. Thanks to Charles G. (Terry) Shaw, III and Michael Marsden for reviewing the

manuscript.

Many people have given their time, energy, and expertise to help create and review the Western Root

Disease Model Version 3.0 and its precursors: the Western Root Disease Model Version 1.0 and the

Annosus Root Disease/Bark Beetle Model. In particular, we appreciate the work of Matthew Thompson

for code improvements and technical support. We gratefully acknowledge the contributions of:

iv

David Adams

Judy Adams

Stephen Arno

Fred Baker

Rick Barth

Jeff Beale

Jerome Beatty

W.J. Bloomberg

Jim Brickell

David Bridgewater

Martha Brookes

Jim Byler

Dick Castaldini

Jim Chew

Fields Cobb

Larry Cron

Jim Colbert

Nick Crookston

Robert O. Curtis

Lance David

Gregg DeNitto

Bov Eav

Robert Everitt

Dennis Ferguson

Greg Filip

John Fiske

Brain Geils

Don Goheen

Ellen Goheen

Julee Greenough

Tom Gregg

Lorne Greig

Sue Hagle

Everett Hansen

Dennis Hart

Alan Harvey

Bob Harvey

Paul Hessberg

Charles Hodges

Bruce Hostetler

Ralph Johnson

Richard E. Johnson

Alan Kanaskie

John Kliejunas

Richard Krebill

Jane LaBoa

F. Michael Landram

George Lightner

Blakey Lockman

Bob Loomis

Helen Maffei

Mike Marsden

Neil Martin

Geral McDonald

Peter McNamee

Hadrian Merler

William Merrihew

Duncan Morrison

John Muir

Ken Neiman

Earl Nelson

Bill Otrosina

Dick Parmeter

Robert Peterson

John Pronos

Susan Rainville

Don Robinson

Bruce Roettgering

Ken Russell

Robert Scharpf

Art Schipper

Richard Schmitz

John Schwandt

E. Michael Sharon

Charles G. Shaw, III

Katherine Sheehan

Greg Shubert

Tom Simonson

Garey Slaughter

Kathy Sleavin

Richard Smith

Dale Snedaker

Mike Srago

Al Stage

Glenn Sutherland

Walt Thies

Boris Tkacz

Tom Turpin

Roger Ward

Phil Wargo

Tim Webb

Douglas Welbourne

Elizabeth Willhite

Carroll Williams

Ralph Williams

Dave Wood

Paul Wood

Bill Woodruff

Ken H. Wright

Bill Wykoff

Stefan Zeglaen
Chapter 1
2 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 1 Characteristics and Consequences of Root Diseases in Forests ... Goheen, Otrosina
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 3

Chapter 1 Characteristics and Consequences of Root Diseases in Forests ... Goheen, Otrosina

Chapter 1

Characteristics and Consequences of Root Diseases in

Forests of Western North America

Donald J. Goheen1 William J. Otrosina2

Root diseases are somewhat mysterious. Operating as they do within the soil, it is difficult to actuallyview root pathogens or follow their progress in causing disease. The signs and symptoms that they

produce can be quite subtle and variable. Just identifying which pathogen occurs in a specific situation is

often challenging. Nevertheless, in the past two decades, forest managers have become increasingly

aware of the important roles that root disease organisms play in forests and the significant influences that

they exert on our ability to attain desired management objectives.

Root pathogens usually affect groups of neighboring trees in progressively expanding disease

pockets or centers. These centers generally contain dead trees that have died at different times over

several years and living but infected trees in various stages of decline. They are commonly characterized

by much lower stocking levels than surrounding healthy portions of stands, and they also may contain

numerous windthrown or broken trees on the ground. Root disease centers vary in size from those

involving only a few trees to those covering hundreds of acres. They may be very discrete or they may

involve a scattering of affected trees dispersed over a larger area.

On individual trees, crown symptoms associated with most root diseases are similar and include

reduced height growth, loss of needles, chlorotic foliage, death of branches, production of distressed

cone crops, and, ultimately, host mortality. Accurate identification of which root pathogen or pathogens

occur in an area usually requires digging to expose roots, and removing bark from roots and root

collars to reveal the inner wood. Often laboratory culturing or more sophisticated techniques such as

isozyme and DNA analyses are necessary to identify the biological species or strain of the causal fungi.

Although a number of root diseases are found in the West, three are considered to be the most

significant. These are laminated root rot, caused by the fungus Phellinus weirii; Armillaria root disease,

caused by Armillaria ostoyae; and Annosus root disease, caused by Heterobasidion annosum. The impacts

of these fungi on forest stands are modelled in the Western Root Disease Model, Version 3.0. A general

understanding of their biology is presented in the next few pages to enhance the reader’s ability to

input data and interpret outputs.

Laminated Root Rot

Laminated root rot caused by the fungus Phellinus weirii (Murrill) R.L. Gilbertson is distributed from the

Six Rivers National Forest in California north to northern Vancouver Island, British Columbia and east to

western Montana. It is probably the most important tree disease of coastal forests in the Pacific Northwest

and also has significant impacts in interior forests (Hadfield 1985, Hadfield and others 1986, Hagle and

others 1987, Nelson and others 1981, Scharpf 1993, Thies 1984, Thies and Sturrock 1995, Wallis 1976).

Laminated root rot is distinguished by the characteristic decay that it produces in roots and butts of

1Plant pathologist/entomologist, USDA Forest Service, Southwest Oregon Forest Insect and Disease Technical Center, 2606 Old Stage

Road, Central Point, OR 97529

2Supervisory research plant pathologist, USDA Forest Service, Southern Research Station, 320 Green Street, Athens, GA 30602
4 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 1 Characteristics and Consequences of Root Diseases in Forests ... Goheen, Otrosina

infected hosts. The decayed wood separates easily at the annual rings, is often heavily pitted on both

sides of each layer, and has diagnostic reddish-brown to pinkish setal hyphae between the layers. P. weirii

also forms a grayish-white to buff-colored mycelial sheath, referred to as ectotrophic mycelia, on root

surfaces of young trees and within bark crevices on older trees. Fruiting bodies of P. weirii are appressed

brown, crusty structures with numerous pores that occasionally form on the undersides of downed trees

or on exposed roots close to the ground. They are of little diagnostic value because of their rarity and

cryptic nature.

On highly susceptible hosts, Phellinus weirii causes extensive decay of roots and death of the tree by

destroying the roots’ ability to take up water and nutrients or predisposing them to windthrow or bark

beetle infestation. Less susceptible hosts develop butt rot. All western conifers can be infected by P.

weirii, but some tolerate the pathogen better than others. Douglas-fir, [Pseudotsuga menziesii (Mirb.)

Franco], white fir [Abies concolor (Gord. & Glend.) Lidl. ex Hildebr.], grand fir [Abies grandis (Dougl. ex

D. Don) Lindl.], Pacific silver fir [Abies amabilis Dougl. ex Forbes], and mountain hemlock [Tsuga

mertensiana (Bong.) Carr.] are highly susceptible and are readily infected and killed by the fungus.

Other true firs, spruces (Picea spp.), western hemlock [Tsuga heterophylla (Raf.) Sarg.], and western

larch [Larix occidentalis Nutt.] are considered to be intermediately susceptible; they are often infected

but rarely killed by P. weirii. Pines and cedars are tolerant or resistant; they are rarely infected and

almost never killed by the fungus. All hardwoods are immune to P. weirii.

Laminated root rot is a disease of the site. Phellinus weirii spreads very little if at all by windborne

spores. Virtually all spread involves mycelial growth on or in host roots. The fungus survives a long time

(as much as 50 years) in roots and stumps of dead or cut trees that were infected prior to death. The

fungus does not colonize dead wood, in part because it is not a good competitor with other fungi. In

infected trees that die or are cut, disease-induced resin impregnation of wood surrounding infections and

the ability of P. weirii to form a protective sheath around itself are responsible for the pathogen’s longterm survival. The fungus survives only in the portion of the host’s root system that it occupied at the

time of host death. The fungus spreads to new hosts when their roots contact colonized roots of infected

stumps or snags and the fungus grows onto and colonizes them in turn. Formation of disease centers

involves subsequent tree-to-tree spread via root contacts. P. weirii does not grow through soil but grows

on root surfaces at a rate of about 0.33 to 0.67 meter per year. Detailed information on this disease and its

biology, control, and management implications are given by Thies and Sturrock (1995).

Armillaria Root Disease

Armillaria root disease is widely distributed throughout the West and is common in many forest stands

(Hadfield and others 1986, Hagle and others 1987, Morrison 1981, Scharpf 1993, Shaw and Kile 1991,

Williams and others 1986). However, it acts differently in different areas and on different sites, even

some that are quite close to each other. In general, Armillaria root disease is more severe in interior than

in coastal forests.

Armillaria root disease is identified on the basis of the occurrence of thick, white mycelial fans

under the bark of roots and lower stems of affected trees. On trees that have been dead for some time,

mycelial fans fade in color to yellow and eventually disintegrate. However, they leave a fan-shaped

impression on the inner bark that can usually be seen for some time after the mycelium itself is gone.

Trees infected by A. ostoyae almost always exhibit various amounts of basal resin flow or brownish

leachate, often in very substantial amounts. Other species of Armillaria occur in forests primarily as

saprophytes. These Armillaria species also may produce mycelial fans under the bark of dying or dead

trees, but they are thinner and are not associated with resin flow. The combination of thick mycelial

fans and resinosis characterizes infection by A. ostoyae. Trees infected by A. ostoyae also frequently have

black string-like fungal structures called rhizomorphs on their roots or under their bark and exhibit a

decay that is yellow, stringy, moist, and malodorous. A. ostoyae produces honey-colored mushrooms in

autumn on or near dead trees and stumps.

Armillaria ostoyae kills the cambium of susceptible host roots and root collars, girdling the tree.

Infected trees are often predisposed to bark beetle attack and usually die standing. Windthrow is rare. All
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 5

Chapter 1 Characteristics and Consequences of Root Diseases in Forests ... Goheen, Otrosina

western conifers and some hardwood trees and shrubs can be infected by A. ostoyae. Off-site plantings,

wounded trees, and trees growing on compacted soils or in areas with drainage problems are particularly

likely to be affected. Tree susceptibility may differ with locale. In coastal forests, A. ostoyae is most

commonly observed killing Douglas-firs and true firs in plantations under 30 years old. After age 30,

trees become increasingly tolerant of the pathogen and are much less frequently killed. In interior forests,

A. ostoyae commonly causes more mortality than in coastal situations, and host susceptibility may differ

from site to site. True firs are usually the most susceptible hosts, Douglas-fir the second, and other species

tend to be tolerant or resistant. However, there are areas where pines are very susceptible and some sites

where almost all tree species are killed. Because of these differences, it is necessary to analyze host

susceptibility on a site-by-site basis. In interior forests, Armillaria root disease often is most evident in

partially cut stands where there are numerous wounded residual trees and compacted soils. It is often

clearly associated with the occurrence of large conifer stumps and, in some areas, with hardwood shrubs

or hardwood tree stumps.

Armillaria ostoyae may be spread on rare occasions by windborne spores that probably infect through

wounds or stump surfaces. However, in most cases, Armillaria root disease, like laminated root rot, is a

disease of the site. A. ostoyae survives for up to 35 years in previously infected stumps and snags. It

spreads to nearby susceptible regeneration by growth across root contacts or, less commonly, via

rhizomorphs that grow for short distances (about one meter) through soil. Tree-to-tree spread continues

by these same avenues at a rate of about 0.33 to 0.67 meter per year. Further details on the biology and

management of Armillaria root disease are presented by Shaw and Kile (1991).

Annosus Root Disease

Annosus root disease, caused by Heterobasidion annosum, occurs in conifer forests throughout the Northern

Hemisphere. It is common and widely distributed in western North America (Hadfield and others 1986,

Hagle and others 1987, Morrison and others 1986, Otrosina and Scharpf 1989, Scharpf 1993). However,

like Armillaria root disease, it acts differently in different areas.

Annosus root disease can be somewhat difficult to diagnose. The most reliable way to identify the

disease in the forest involves finding its fruiting bodies, called “conks.” Conks of Heterobasidion

annosum are formed in or on stumps, on the roots of windthrown trees, and sometimes in or just under

the duff layer on roots and root collars of standing hosts. Conks are shelf-shaped with dark

concentrically furrowed upper surfaces, creamy white sterile margins, and white lower surfaces with

numerous tiny pores. They are perennial and thus may have more than one pore layer. Dead and

symptomatic hosts as well as still healthy-appearing individuals associated with stumps, snags, or

trees exhibiting H. annosum conks will be infected in most cases.

Infection by Heterobasidion annosum is often characterized by reddish-brown staining in the roots

and lower stems of trees. Resinosis is often associated with infected tissues. Some hosts also exhibit

white or buff-colored fungal pustules on their roots. Advanced decay caused by the fungus is variable.

It is often laminated with pitting only on one side of the layers and no evidence of setal hyphae. It can

also be stringy and moist with white streaks and scattered black flecks. This latter appearance is most

common on hosts that develop butt rot. Where necessary, presence of H. annosum can be confirmed by

culturing wood chips on media and identifying the microscopic fruiting bodies of the asexual state of

the fungus, Spiniger meineckellus (Olsen) Stalpers.

The fungus is genetically complex. There are several biological species that are not morphologically

distinct but are genetically diverse (Otrosina and others 1992). These strains cannot be distinguished on

the basis of appearance, but they are intersterile and differ markedly in pathogenicity and host range. In

western North America, a “P-strain” that affects mainly pines and an “S-strain” that affects mainly true

firs, hemlocks, and Douglas-fir have been identified. On living trees, there is little or no host crossover

between the two strains. Unlike living trees, stumps offer no host specificity with respect to biological

species and may become infected by either the S or P strains.

These two strains, or biological species, and the variation between tree species and site factors

interact to make the host response to Annosus root disease infection highly variable. Heterobasidion
6 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 1 Characteristics and Consequences of Root Diseases in Forests ... Goheen, Otrosina

annosum kills resinous hosts directly by decaying roots and killing the cambium around the root collar.

Trees are often predisposed to bark beetle infestation and usually die standing. Some non-resinous hosts

are affected in the same way, but others develop extensive butt decay rather than being killed outright.

Older butt-rotted trees (150 years old or greater) often break and fall. White fir, grand fir, and Pacific

silver fir are often killed by the pathogen; other true firs and hemlocks usually suffer butt rot. Pines,

especially ponderosa pines, are widely infected; however, substantial mortality in these hosts is usually

limited to very dry sites. Douglas-fir is severely affected in portions of the Rocky Mountains, but is rarely

affected further west.

Heterobasidion annosum is capable of spreading over long distances. Conks release basidiospores that

are windborne and can be dispersed over distances of up to 100 miles. Spores that land on freshly cut stump

surfaces or fresh wounds germinate, and the fungus colonizes the tree or stump. Subsequently, the

pathogen can grow via root contacts into surrounding hosts, creating disease pockets. Rate of radial spread

across roots is about 0.5 meter per year, but the fungus does not grow through soil. New disease foci are

usually associated with large host stumps (0.5 meter or greater in diameter), and H. annosum can survive in

large stumps for considerable time periods (up to 60 years). Annosus root disease is especially common in

stands that have had partial-cut harvests. It is most severe in stands that have experienced several entries.

More detailed information on Annosus root disease may be found in Otrosina and Scharpf (1989).

Root Disease Impacts

It is difficult to find any forested area in the Western United States that has no root diseases. Though

West-wide surveys are lacking, numerous smaller surveys done at the stand, watershed, Ranger District,

or National Forest level suggest that laminated root rot, Armillaria root disease, and Annosus root

disease can be found singly or together over significant proportions of the areas occupied by their hosts.

It is estimated that the three major root diseases occur on 7 to 10 percent of the area occupied by coastal

forests and 10 to 25 percent of the area occupied by interior forests (Gedney 1981, Goheen and Hansen

1993, Smith 1984). These fungi have co-evolved with their hosts for eons and until a few decades ago

were in an equilibrium in forest ecosystems. However, it is apparent that root disease incidence is on the

increase. Fire exclusion and management activities such as partial-cut logging and planting of diseased

sites with susceptible hosts have favored their spread and intensification. In some areas, stand

manipulations have altered ecosystems, and the forest conditions where the trees and root disease fungi

were co-existing have been disrupted so they now favor the fungi. The result is similar to the introduction

of exotic pests—a rapid increase in pathogen populations and catastrophic effects on host mortality.

Root diseases influence species composition, stand structure, stand density, and direction and rate of

forest succession wherever they occur. They are agents of diversity, winnowers of weakened trees, natural

thinning agents, and, in some cases, arbitrators of age structures. Basically, root pathogens operate by

preferentially killing susceptible hosts. In affected areas, tree species and/or other plants that are tolerant,

resistant, or immune to the particular fungi are favored.

From the perspective of a forest manager, the significance of root diseases will differ with

management objective. Where timber production is a major concern, root diseases are viewed as being

very destructive. Surveys done in rotation age stands show that basal area of live trees in portions of

stands where root diseases are present is 10 to 55 percent less than that of adjacent healthy stands,

translating into significant volume losses (Bloomberg and Reynolds 1985, Filip and Goheen 1984, Goheen

and Hansen 1993, Lawson and others 1983). Root diseases also cause chronic, sub-lethal infections that

do not result in dramatic symptom expression but reduce tree growth and vigor. Furthermore, because

there are relatively small numbers of trees dying in any one year, salvage opportunities are not as great

with root diseases as they may be with other disturbance agents, such as insects or fires, that kill large

numbers of trees all at the same time.

Root diseases are also considered very damaging in developed recreation sites because of the high

degree of hazard represented by infected trees. Root-diseased trees pose considerable threats to facilities

and recreationists because of their increased windthrow potential.

Where wildlife habitat, visual quality, or watershed protection are major management objectives,
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 7

Chapter 1 Characteristics and Consequences of Root Diseases in Forests ... Goheen, Otrosina

root diseases may be viewed as damaging, innocuous, or even beneficial. Effects of the diseases such as

small openings in otherwise close-canopied stands, areas of plant species diversity, and groups of dead

and down trees can be quite desirable because of associated increases in browse, low cover, small animal

prey populations, cavity nesting sites, stand textural differences, and fall colors. However, root diseases

can also reduce canopy cover to below desired levels, cause loss of preferred large tree character, and

contribute to unacceptable fuel loads. Generally, root diseases are most likely to be considered

management concerns when infection centers are large and many trees are being killed over substantial

portions of a landscape.

Root Disease Management

There are silvicultural options for managing root diseases. The most common form of management

prescription involves species manipulation. In infected areas, tree species that are highly susceptible to

the particular root pathogen or pathogens present are discriminated against, and more resistant species

that are adapted to the site are favored. Species can be manipulated at the time of final harvest or during

intermediate stand entries. An alternative approach involves inoculum reduction. Infected stumps are

physically removed from the soil in infection centers, eliminating as much of their colonized root systems

as possible. This kind of treatment minimizes likelihood of new regeneration contacting infected material,

and, if conscientiously done, may allow sites to be regenerated with susceptible tree species. Some special

treatments aimed at certain root disease pathogens are also available. For example, infection of newly

created stumps by H. annosum can be prevented by treatment with borate compounds.

In making decisions about how to deal with root diseases in their prescriptions, planners need to

clearly define their management objectives and carefully consider the likely long-term effects of the

diseases in light of the desired future condition for the area under consideration. Clearly, planners will

benefit greatly from a predictive tool that enables them to compare future conditions in healthy stands

with those infected by root disease, the impacts of various levels of root disease, and the relative effects of

different management prescriptions on root diseases. The Western Root Disease Model is such a tool.

References

Bloomberg, William J.; Reynolds, Gordon. 1985. Growth loss and mortality in laminated root rot infection centers in secondgrowth Douglas-fir on Vancouver Island. Forest Science 31: 497-508.

Filip, Gregory M.; Goheen, Donald J. 1984. Root diseases cause severe mortality in white and grand fir stands of the Pacific

Northwest. Forest Science 30: 138-142.

Gedney, Donald R. 1981. The occurrence of laminated root rot on non-federal timberlands in Northwest Oregon. Res. Note

PNW-381. Corvallis, OR: Pacific Northwest Forest and Range Experiment Station, Forest Service, U.S. Department of

Agriculture; 7 p.

Goheen, Donald J.; Hansen, Everett M. 1993. Effects of bark beetles and pathogens in forests. In: Schowalter, R.D.; Filip G.M.,

eds. Beetle-pathogen interactions in conifer forests. San Diego, CA: Academic Press; 175-196.

Hadfield, James S. 1985. Laminated root rot, a guide for reducing and preventing losses in Oregon and Washington forests.

Portland, OR: USDA Forest Service, Pacific Northwest Region; 13 p.

Hadfield, James S.; Goheen, Donald J.; Filip, Gregory M.; Schmitt, Craig L.; Harvey, R.D. 1986. Root diseases in Oregon and

Washington conifers. Portland, OR: USDA Forest Service, Pacific Northwest Region; 27 p.

Hagle, Susan K.; Tunnock, Scott T.; Gibson, Kenneth E.; Gilligan, Carma J. 1987. Field guide to diseases and insect pests of Idaho

and Montana forests. Publication No. R1-89-54. Missoula, MT: USDA Forest Service, Northern Region; 123 p.

Lawson, Thomas T.; Berg, Allen B.; Handen, Everett M. 1983. Damage from laminated root rot at the Black Rock Management

Research Area in western Oregon. Forest Research Laboratory Res. Note 75. Corvallis: Oregon State University; 7 p.

Morrison, Duncan J. 1981. Armillaria root disease: a guide to disease diagnosis, development, and management in British

Columbia. BC-X-203. Victoria, BC: Environment Canada, Canadian Forestry Service, Pacific Forest Research Centre; 15 p.

Morrison, Duncan J.; Larock, M.D.; Waters, A.J. 1986. Stump infection by Fomes annosus in spaced stands in the Prince Rupert

Forest Region of British Columbia. Information Report BC-X-285. Victoria, BC: Canadian Forest Service, Pacific Forestry

Centre; 12 p.

Nelson, Earl E.; Martin, Neil E.; Williams, Ralph E. 1981. Laminated root rot of western conifers. Forest Insect and Disease Leaflet

159. Washington, DC: USDA Forest Service; 6 p.
8 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 1 Characteristics and Consequences of Root Diseases in Forests ... Goheen, Otrosina

Otrosina, William J.; Chase, Thomas E.; Cobb, Fields W., Jr. 1992. Allozyme differentiation of intersterility groups of

Heterobasidion annosum isolated from conifers in the western United States. Phytopathology 82: 540-545.

Otrosina, William J.; Scharpf, Robert F. 1989. Proceedings of the symposium on research and management of Annosus root

disease (Heterobasidion annosum) in western North America; 1989 April 18-21; Monterey, CA. Gen. Tech. Rep. PSW-116.

Berkeley, CA: Pacific Southwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture; 177 p.

Scharpf, Robert F., technical coordinator. 1993. Diseases of Pacific Coast conifers. Agric. Handb. 521. Washington, DC: Forest

Service, U.S. Department of Agriculture; 199 p.

Shaw, Charles G. III; Kile, Glen A. 1991. Armillaria root disease. Agric. Handb. 691. Washington, DC: Forest Service, U.S.

Department of Agriculture; 233 p.

Smith, Richard S. 1984. Root disease-caused losses in the commercial coniferous forests of the western United States. Report

No. 84-5. Fort Collins, CO: USDA Forest Service, Forest Pest Management, Methods Application Group; 21 p.

Thies, Walter G. 1984. Laminated root rot, the quest for control. Journal of Forestry 82: 345-356.

Thies, Walter G.; Sturrock, Rona N. 1995. Laminated root rot in western North America. Gen. Tech. Rep. PNW-GTR-349

[Resource Bulletin PNW-GTR-349]. Portland, OR: Pacific Northwest Research Station, Forest Service, U.S. Department of

Agriculture; 32 p. In Cooperation with Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre.

Wallis, Gordon W. 1976. Phellinus (Poria) weirii root rot, detection and management proposals in Douglas-fir stands. Forestry

Technical Report 12. Victoria, BC: Environment Canada Forestry Service; 16 p.

Williams, Ralph E.; Shaw, Charles G.; Wargo, Phil M.; Sites, William H. 1986. Armillaria root disease. Forest Insect and Disease

Leaflet 78. Washington, DC: USDA Forest Service; 8 p.
Chapter 2

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 11

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Chapter 2

How to Use the Western Root Disease Model, Version 3.0

Sarah J. Beukema1 Werner A. Kurz2

1.0 Model Overview

1.1 Model Background

The Western Root Disease Model, Version 3.0 (WRDM3), provides a dynamic representation of the spatial

epidemiology of Armillaria root disease, laminated root rot (caused by Phellinus weirii), or Annosus root

disease within a stand (fig. 1.1). Only one of the three root disease types may be simulated in a single stand.

However, for Annosus root disease, the model distinguishes between types of H. annosum that can co-exist

within a stand: P-type and S-type. This co-existence in the model is possible because each type infects

different tree species (P-type on pine, and juniper, and S-type on true firs and giant sequoia), and there is no

interaction between the two disease types.

The WRDM3 is based on the concept that the distribution of root disease in a stand can be characterized

as a number of circular root disease centers, each of which contains both infected and uninfected trees and

other inoculum sources. Trees can become infected within a center of the disease type to which they are

susceptible when the trees’ roots contact the infected portion of the roots of live and dead trees. Infection (as

measured by the proportion of the tree’s root radius that is infected) spreads at a constant rate through the

tree’s root system until the tree dies. After death, the model tracks and decays the inoculum level of the dead

tree. New Annosus root disease centers can be created through the infection of freshly cut stumps by airborne

spores, while new centers of Armillaria or Phellinus cannot. The effects of windthrow and bark beetles can

also be simulated in this model.

Details of the parameters, equations, and assumptions in all parts of the model are given in the Detailed

Model Description (Chapter 4). New users should note that many of the keywords should be used only by

users who are experienced with the model. These keywords are marked as “advanced” in the text box that

summarizes the keyword, and at the beginning of the section describing the keyword.

All Forest Vegetation Simulator (FVS) management options can be simulated, using the FVS keywords.

The model will respond to the management options in an appropriate manner. In addition, the model

contains two options that are specifically aimed at controlling root disease in the stand: (1) borate application

to prevent spore infection and the formation of new centers (when Annosus root disease is simulated), and

(2) removal of infected stumps.

1.2 Necessary Background Information

Several concepts are important to remember throughout this user’s guide, both to use the keywords and to

interpret the output. For basic model use, it is not necessary to understand these concepts fully; further

information on some of them is given in the Detailed Model Description (Chapter 4). These concepts are:

1. Identification of the root disease type

Throughout the keywords, the root disease type being simulated is often identified as a number.

These numbers are:

1Systems Ecologist, ESSA Technologies Ltd., Suite 300, 1765 West 8th Avenue, Vancouver, BC, V6J 5C6, Canada

2Senior Systems Ecologist, ESSA Technologies Ltd., Suite 300, 1765 West 8th Avenue, Vancouver, BC, V6J 5C6, Canada
12 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Year 0

Year 50: no root disease

Year 50: with root disease

A.

B.

C.

Figure 1.1 — Predicted changes in a pine stand: (A) Initial conditions; (B) 50 years later, without

considering bark beetles and root disease; (C) 50 years later, with root disease and bark beetles

accounted for.

1: P-type Annosus root disease

2: S-type Annosus root disease

3: Armillaria root disease

4: Phellinus weirii (laminated root rot)

Once the root disease type is selected by the user, it becomes the default for the rest of the

keywords, so the identifier is necessary only when simulating P- and S-type Annosus root

disease in the same stand. These instances will be clearly marked throughout this user’s guide.

2. Root systems are modelled as circles

All root systems are considered to be circles with the tree (or stump) in the middle. The infection

level is always given as the percentage of the circle’s radius which is infected. Because the root

system is a circle, root disease is assumed to expand or contract along the radius in all directions.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

3. Size and specification of root disease centers

A center is described as a circle that contains the total extent of diseased roots (inoculum

sources). The statistic of disease area is calculated as the total area inside all centers. Note that

the actual area with inoculum may be less than the area containing root disease (fig. 1.2). This is

especially important to remember when comparing the results of this model against field data.

4. All measures must be entered in imperial or metric units

The Western Root Disease Model has been developed to be used in the United States or in British

Columbia. One major difference, from a user’s viewpoint, is that British Columbia uses metric

units and the United States uses (generally) imperial units. Either set of units may be used in the

keywords in the model. The important point is that the units are consistent: the same units must

be used for all keywords in FVS and in the WRDM3. The default is imperial units in the United

States and metric units in British Columbia.

1.3 Formatting Conventions Used in this Manual

Throughout this manual, different fonts and formats are used for different purposes. Understanding these

may assist the reader in using the manual, or in finding items quickly in different sections. Descriptions of

these are given below.

1.3.1 Keywords

Keywords are identified in several ways. First, keywords are always in capital letters. In the text, they are

further marked by being in bold: KEYWORD. A detailed description of the meaning of the keyword, each of

its fields, and the default values of the fields is given in a table in the section which is most related to the

keyword. These tables have the following format:

KEYWORD One line keyword description

Field Description Default

1 Short description for the use of field 1 Value that the field has if left blank

2 Short description for the use of field 2 Value that the field has if left blank

...

7 Short description for the use of field 7 Value that the field has if left blank

Figure 1.2 – Diagram of root disease in a center in a

stand. Root diseased area (areas with inoculum) are

shaded, and the extent of the center is defined by the

outermost circle. Notice that parts of the center do not

contain inoculum.

Stand

center

infected

roots
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Keywords are also used frequently in examples. These will be discussed in the following section.

Finally, an index of keywords is contained at the end of the manual. Each use of the keyword, in text,

examples, or tables, is listed in the index. The bold page number in the index identifies the page on which the

keyword’s fields are defined in a table.

1.3.2 Examples

Numerous examples are given throughout the text. These are designed to give examples of the use of various

keywords, and to show some of the flexibility or limitations of different keywords. Examples are contained

within a box so that they will be easy to find, should users wish to refer to them at a later date, and to allow

others to skip over the examples completely. An example box looks like:

Example: title

Description of the example: what the example is trying to show and what the values mean in each

field of the example, if keywords are used.

KEYWORD1

KEYWORD2

Keywords are frequently used in the examples. They are distinguished from regular text by being in a

distinctive font. In the examples, if some fields are left blank, these fields will be identified using the symbol

“_”, since there is no attempt to maintain accurate spacing in the example keywords. Therefore, an example

keyword (always given within an example box) would have the general format:

KEYWORD 1 2 3__67

where the numbers (in this case) show the different fields and the “_” show two examples of fields left blank

in the example.

1.3.3 Units

As mentioned in Section 1.2, the model can be run in either metric or imperial units. The only difference in

the model is the actual numbers that are entered into each keyword and the values in the output tables.

Throughout the text, values are given in imperial units with their metric equivalent in square brackets [ ].

The examples are also in imperial units, but in each example, the metric interpretation of the keyword is

also given. Thus for example, if a field in a keyword is a 3, it may mean 3 inches in imperial units or 3 cm

in metric units.

2.0 Running the Model

2.1 Quick Start

Only three requirements exist for running the root disease model:

1. The variant of FVS being used must contain the Western Root Disease Model extension. Contact

your local FVS specialist if you are unsure whether you have the extension. (In British Columbia,

the WRDM3 is part of the Windows interface, and will be part of any run done from within the

Windows environment, or using the non-Windows executable version.)

2. Users should have a basic familiarity with FVS and with the use of keywords and management

options.

3. Users should have information on root disease either as FVS-ready tree inventories which
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 15

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

contain information about the root disease infection levels in the stand or as the size of root

disease centers and the density of live infected and uninfected trees within these centers.

As with FVS, the user communicates with the model through keywords which initialize the model,

change assumptions, define scenarios, and choose output files.

A minimum of four keywords is needed to run the model: RDIN, RRTYPE, RRINIT or RRTREIN, and

END. RDIN signals that the WRDM3 is being run and indicates the start of the WRDM3 keywords, RRTYPE

sets the type of root disease that is to be simulated in the stand, RRTREIN and RRINIT initialize the root

disease in the stand, and END signifies the end of the WRDM3 keywords. All keywords following the END

keyword will be interpreted as FVS keywords. Details about each of the keywords, including their full uses

and default values, will be given in this chapter. Some examples are given below.

Example: Minimum keywords for running the model

These keywords can be used to run the model for Armillaria root disease (the 3 after RRTYPE).

With the default conditions, the stand has 20 root disease centers totalling 25 acres [10.12 ha] of

infection in a 100-acre [40.47-ha] stand, and all infected trees and stumps are identified in the

inventory (so RRTREIN is used to tell the model to process the disease codes in the inventory) .

RDIN

RRTYPE 3

RRTREIN

END

If the inventory does not contain root disease damage codes, the RRINIT keyword must be used instead

of RRTREIN.

Example: Minimum keywords when there are no damage codes in the inventory

The following keywords can be used to run the model with Armillaria root disease (the 3 in field 1

of RRTYPE) with default root disease assumptions (i.e. the disease is in 20 root disease centers

totalling 25 acres [10.12 ha] of infection in a 100-acre [40.47-ha] stand). In this example, there are no

disease codes in the inventory so RRINIT is used instead of RRTREIN, and the model assumes

that 50 percent of all trees within centers are infected.

RDIN

RRTYPE 3

RRINIT

END

The RRINIT keyword, especially when used with other keywords, gives users a large amount of control

and flexibility over the initial levels of root disease in the stand. For details on the use of this keyword, and on

different methods of setting the infection levels in the model, see Section 3 of this chapter.

By default, the WRDM3 prints a summary output table at the end of the FVS main output file. Thus users

do not need to use extra keywords to get disease-specific output. Details about all available output files are

given in Section 7.

While the model is running using any of the sets of keywords given in the above examples, other parts of

the model, such as bark beetles, are also operational. Table 2.1 lists all the options which in the model are ON

by default (i.e., they will occur if the model is used, unless the user requests otherwise). Table 2.2 lists the
16 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 2.2 — Options which are OFF by default in the model. The second column gives the keyword that

can be used to turn on the relevant option. OFF by default means that these options do not occur unless

requested via a keyword.

Description Keyword

Armillaria or laminated root rot, s-type Annosus RRTYPE

Read disease information from tree list (plots or damage and RRTREIN, PLREAD,

severity codes) PLOTINF

Initial infected stumps RRTREIN, STREAD

“Jump” in root disease area after a cut RRJUMP

Bark beetle type 2 BBTYPE2

Windthrow WINDTHR

Management (borate application, stump pushing) BORATE, PSTUMP

Effect of Stand Density Index (SDI) on root radius SDIRMULT

Inside center Monte Carlo simulation (advanced option) INFSIMS

Output tables (except summary output table in main file) RRECHO, RRDOUT, BBOUT,

SMCOUT, INFSIMS

Table 2.1 — Options which are ON by default in the model. The second column gives the keyword that

is used to control the option (so that users can turn it off or change the defaults).

Description Keyword

P-type Annosus root disease in stand RRTYPE

Dynamic spread model (applying different spread rates SPREAD

to different centers)

Bark beetles types 1, 3, 4 BBTYPE1, BBTYPE3,

BBTYPE4

Spore model SPORE

Summary output table (in main output files)

options which the user must specifically request. Note that if no root disease type is specified, P-type

Annosus root disease will be modelled.

By the appropriate use of various keywords, users can change many of the assumptions used throughout

the model. Information about the various keywords is given in this document. An index to all keywords is

provided at the end of the document.

2.2 Controlling Model Execution

Several keywords are available that control various aspects of the model not directly connected with root

disease, management, or other agents. Three of these keywords are important, basic keywords. Discussion of

more advanced keywords which control the model is deferred until Section 5.3.

2.2.1 Identifying the WRDM3 in the Keywords

The two most important keywords are RDIN and END, both of which must be present in order for the model

to run. The keyword RDIN tells FVS that the keywords following are part of the Western Root Disease

Model. The END keyword marks the end of the WRDM3 keywords. The END keyword can also be used with

the COMMENT keyword (see below).
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 17

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

RDIN Beginning of the Western Root Disease Model keywords. This keyword is required.

Field Description

1-7 Not used. There are no fields associated with this keyword.

END End of WRDM3 keywords (or end of a comment). This keyword is required.

Field Description

1-7 Not used. There are no fields associated with this keyword.

Example: The use of RDIN and END

The keyword file to run the WRDM3 would contain:

various FVS keywords

RDIN

various root disease model keywords

END

more FVS keywords

Examples of the minimum keywords required to run the model are shown in Section 2.1, and examples of

complete keyword files are given in Section 8.

2.2.2 Comments

Users may add text or notes to the keyword file which will be printed in the keyword section of the main

output file. This is done using the keyword COMMENT.

COMMENT Start of a comment or text in the keyword file.

Field Description

1-7 Not used. There are no fields associated with this keyword.

Suppl. Comments can be entered in columns 1-80, in as many lines as

Record necessary. Comment terminates with the END keyword.

A comment must be at least one line (even if the line is blank), and may be as many lines as needed.

Comments terminate with the END keyword. All information entered between the COMMENT keyword

and the END keyword will be ignored by the model and will be written to the main output file. Note that

keyword sets which include a comment will also have two occurrences of the END keyword.

Example: The use of END with RDIN and COMMENT

The keyword file to run the WRDM3 would contain:

RDIN

root disease model keywords

COMMENT

First line of comment

Second line of comment

END

root disease model keywords

END
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

3.0 Initializing Root Disease

Many options are available to initialize the root disease in the stand, depending on the amount and type of

information available to the user. Various levels of root disease information can be entered in different

ways such as:

•Some combination of the area, number, size, and location of centers (Section 3.2);

•The infection status of sample plots (Section 3.3);

•The density of infected and uninfected trees, or the specific trees which are infected (Section 3.4);

and

•Infected stumps which may or may not be in the stand inventory (Section 3.5).

Each of these is discussed in more detail in the following sections. In addition, fig. 3.1 allows users to

determine which keywords should be used to initialize the amounts of infected and uninfected trees, given

the available information about the infection level in the stand.

One of the most important keywords is RRTYPE. This keyword tells the root disease model what type

of root disease is being simulated in the stand. If this keyword is entered first, all other keywords can use

the appropriate disease type as the default disease type. Thus, in many keywords, there would be one field

which could be left blank. Note that the RRTYPE keyword allows for the possibility of entering more than

one disease type. This is used only for Annosus root disease because S- and P-type Annosus can co-exist,

without interaction, in the same stand. For Armillaria root disease and Phellinus weirii, however, only one

disease is allowed in the stand, and neither disease may exist with Annosus root disease also present. If the

field is used inappropriately, the model will simply issue a warning message, and simulate only the

disease type in field 1.

Figure 3.1 — Flowchart to determine which keywords to use to initialize root disease levels. Note

that in all cases, the keyword RRINIT should also be used to set the number and size of centers

(Section 3.2), and the keyword RRTYPE should be used to set the root disease type being

simulated.

Tree

information

(damage codes)

Plot

information

Plot

information

Density

of infected

trees

RRTREIN

PLREAD

RRINIT

RRTREIN

RRINIT

RRINIT

(specify fields

3 and 4)

RRINIT

(at defaults)

PLOTINF

RRINIT

YES

YES

YES

YES

NO

NO

NO

NO
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

RRTYPE Enter the type of root disease in the stand.

Field Description Default

1 Type of root disease being simulated. 1

(1,2=P,S-type Annosus root disease, 3=Armillaria root disease, 4=Phellinus weirii)

2 Second type of root disease being simulated. This field can only be used if Annosus

root disease is being simulated (field 1 is a 1 or a 2).

3-7 Not used

Valid keywords possibilities are shown in the example below.

Example: Valid uses of the keyword RRTYPE

The first line sets the disease type to be both types of Annosus root disease. The second sets the

disease type to be Armillaria root disease, and the third asks the model to simulate Phellinus weirii.

RRTYPE 2 1

RRTYPE 3

RRTYPE 4

Note that if, RRTYPE is not entered in the keyword files, the model will assume that P- type Annosus

root disease is in the stand.

Users may enter the area of the stand using the keyword SAREA. The stand is assumed to be 100 acres [or

40.47 ha], unless otherwise specified. This keyword should be one of the first of the root disease model

keywords.

SAREA Set the area of the stand.

Field Description Default

Imp. Met.

1 Area of the stand (acres or hectares) 100 40.47

2-7 Not used

Imp. = imperial units, Met. = metric units.

3.1 Inventory Tips3

The most important step of running the root disease model is initializing it with reasonable infection levels.

There are numerous methods for doing this, but most depend on having the initial information about disease

levels in the stand of interest.

As with all models, the Western Root Disease Model is more sensitive to some options than to others. The

most important component for root disease initialization is the area inside centers (or the proportion of the

stand that is inside centers). In general, the more area inside centers, the more impact the disease will have.

This is true if the density of infected trees in the stand remains constant (fig. 3.2a) or if the density of infected

trees in the center remains constant (fig. 3.2b).

 3 This section is based, in part, on a similar chapter in the “User-Friendly User’s Guide to the Western Root Disease Model” by Goheen

and Thompson n.d.
20 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Figure 3.2 — Effect of center area on basal area. (A) Density of infected trees in the stand is constant between runs at 5 percent

of the trees in the stand (i.e., the density of infected trees inside centers varies). (B) Density of infected trees within the center

remains constant between runs at 50 percent of the trees in the center (i.e., the density of infected trees in the stand varies). The

different symbols used refer to the proportion of the stand that is inside root disease centers at the beginning of the simulation.

Note that the left y-axis is in imperial units (sq. ft./acre) and the right y-axis is in metric units (sq. m/ha).

A B

B

B

B

B

B

B B B B B B

H

H

H

H

H

H H H H H H

J

J

J

J J J J J J J J

T

T

T

T T T T

T

T T

T

1981 1991 2001 2011 2021 2031 2041 2051 2061 2071 2081

0

50

100

150

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Year

B No RR

H 10%

J 50%

T 100%

B

B

B

B

B

B B B B B B

H

H

H

H

H

H H H H H H

J J J J J J J J J J J

1981 1991 2001 2011 2021 2031 2041 2051 2061 2071 2081

0

50

100

150

200

250

300

0

23

46

69

BA

Year

B No RR

H 10%

J 100%

Figure 3.3 — A comparison of the new area generated during center spread. The open

circles are the original center and the shaded sections are the area that is newly incorporated

into the center after the center has spread. In each example, the radius increase is the same.

ABC

The reason for this effect is that (assuming the centers do not cover the entire stand) more area leads to

longer perimeters of centers. The rate of increase in the root disease area in a stand is proportional to the

length of the perimeter. Thus as the centers spread, more new area is incorporated into the centers, and more

trees become infected (fig. 3.3).

In general, the impact of the model on stand dynamics is independent of the number of centers in the

stand. Very high numbers of centers, however, may have a greater impact on stand dynamics than low

numbers of centers under some stand conditions (fig. 3.4).

When the entire stand is modelled as a single center, the density of infected trees becomes the critical

factor (fig. 3.5a). This density has little impact in mixed species or mixed-age stands in which the centers do

not cover the entire stand (fig. 3.5b).

The implication of these results on stand initialization is summarized in table 3.1. Effort should be made

to gather the important information, and not to spend as much time and effort on the items which are of less

importance. But, if the only available data does not cover the important points, any information is still better

than no information (see table 3.2).

Figure 3.1 relates the available information with the appropriate keywords.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 21

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

B

B

B

B

B

B B B B B B

H

H

H

H

H

H H H H H H

J

J

J

J

J

J J J J J J

T

T

T

T

T

T T T T T T

1981 1991 2001 2011 2021 2031 2041 2051 2061 2071 2081

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0

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B No RR

H 1 center

J 10 centers

T 20 centers

B

B

B

B

B

B B B B B B

H

H

H H H H H H H H H

J

J

J J J J J J J J J

T

T T T

T T T T T T T

1981 1991 2001 2011 2021 2031 2041 2051 2061 2071 2081

0

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100

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Year

B No RR

H 1 center

J 10 centers

T 20 centers

A B

Figure 3.4 — Effect of number of centers on basal area, in a stand with Armillaria root disease. (A) 10 percent of the

stand is inside centers. (B) 50 percent of the stand is inside centers. In both cases, the different symbols represent

different numbers of centers. Note that the left y-axis is in imperial units (sq. ft./acre) and the right y-axis is in metric

units (sq. m/ha).

Figure 3.5 — Effect of infected tree density on basal area in a stand with Armillaria root disease. (A) Entire stand is a

center. (B) 50 percent of the stand is inside a center. In both cases, the different symbols represent the different

density of trees infected. Note that the left y-axis is in imperial units (sq. ft./acre) and the right y-axis is in metric units

(sq. m/ha).

A B

B

B

B

B

B

B B B B B B

H

H

H

H H H H

H

H H

H

J

J J J J J J J J J J

T T T T T T T T T T T

1981 1991 2001 2011 2021 2031 2041 2051 2061 2071 2081

0

50

100

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0

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B No RR

H 5% infected

J 33% infected

T 50% infected

B

B

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B B B B B B

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J 33% infected
22 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

3.1.1 Gathering Information

Root disease information can be gathered in a variety of ways. Some of these are listed below.

1. Walk the stand. When doing regular stand exams, pay attention to the area between sample

points. If possible, try to draw rough maps of the stand showing areas with root disease.

2. Do intensive root disease sampling. In between each of the standard sample points in the stand

exam, place two supplemental plots that are just for observing root disease (such as the presence

or absence of root disease, the tree size and species infected, and damage and severity codes).

Again, try to draw rough maps of the stand showing areas with root disease.

3. Talk to a local pathologist to find out what sort of infection levels may be present in the stand.

The pathologist may be able to give some estimates about root disease area, number of centers, or

the density of infected trees.

4. See if there are any existing studies or guidelines for doing root disease surveys (such as the Root

Disease Management Guidebook published by the BC Ministry of Forests and BC Environment

[1995]).

When gathering the root disease information it is important that the data correspond to conventions used in

the model. Two examples of this are center area and density of trees inside centers. In the first case, a center is

measured in the model as a circle which encompasses the perimeter of all infected root systems. Other

definitions may be used in the field (fig. 3.6). When using the density of trees, the model assumes that

stocking density inside and outside centers is the same, unless damage codes are present in the inventory.

Thus without damage codes, the relative density of infected and uninfected trees inside centers will be the

information that is used inside the model rather than the actual density (see Section 3.2).

Table 3.2 — Sources of information. In each case, the information is listed in descending order of

desirability, i.e., the item at the top would give the best information.

Component Root disease in identifiable patches. Root disease not in clear patches

or in numerous tiny patches.

Area and number Stand maps Entire stand should be considered

of centers Information on the number of sample a single center

plots infected (if random plots only)

Density of infected Stand exam (including supplemental plots) Stand exam with individual tree

trees ‘Expert’ information damage and severity codes

Defaults if no other values available. Plot-level proportion of infection

‘Expert’ or observed information

Table 3.1 — Relative importance of various initialization components.

Component Root disease in identifiable patches. Root disease not in clear patches or

in numerous tiny patches.

Area in centers Important. Entire stand should be considered

a single center

Number of centers Little importance.

Density of infected trees Defaults should be adequate, if Important. Accuracy will improve

no other values available. further if individual tree

information (damage and severity

codes) is present.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 23

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

3.2 Entering Root Disease Center Information (area, number, size, location)

The keyword RRINIT can be used to initialize the number, size, and location of root disease centers. It can

also be used to enter information about infected trees (discussed further in Section 3.4).

RRINIT Define center information (number, area, location, proportion infected)

Field Description Default

1 Allow model to choose center locations and sizes (=0) or 0

ask model to read root disease center attributes from

supplemental records (=1)

2 Number of root disease centers in stand. (If field 1 = 1, 20

then this is also the number of supplemental records.)

3 Density or proportion of infected trees in root disease

 centers. Value will be ignored if RRTREIN is used or field 4

is blank. Default is 50 percent of trees inside centers.

4 Density or proportion of uninfected trees in root disease

centers. Value will be ignored if RRTREIN is used or field 3 is

blank. Default is 50 percent of trees inside centers.

5 Average proportion of root infection. 0.1

6 Total acres [or hectares] of root disease. Default is 25

percent of stand area. (-1 to calculate area from PLREAD)

7 Root disease type (1,2=P,S Annosus root disease, 3=Armillaria root 1, or type set by RRTYPE

disease, 4=Phellinus weirii)

There are two ways to specify the number and size of root disease centers, both cases using the keyword

RRINIT. The simplest case is if the user has information only about the number of centers and the total area

of root disease. In this case, the user can simply use RRINIT to enter this information.

Figure 3.6 — Definition of a center. On the left, the center is defined as

a circle which encompasses all the infected root systems (as is done in

the model). On the left, the center boundary is drawn from the center

of each infected root system (as is occasionally done in the field).

A/ B/
24 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Example: Initializing root disease with only the number of centers and diseased area

Suppose the stand has five centers (field 2) which combine to give 3 acres [3 hectares] (field 6) of

Phellinus weirii (field 7), but the user does not know the sizes or locations of the centers (the 0 in field

1). The keyword RRINIT is shown below, and a visual representation of the two stands (actual and

modelled) is shown in figure 3.7.

RRINIT 0 5 ___34

Figure 3.7 — Example of an actual (left) and simulated (right) stand. In

both cases, the stand has five centers (the shaded areas). Notice that

the model assumes that the stand is square and the centers are circles.

This method has the advantage that the total area inside root disease centers will be preserved, i.e., the

area seen in the field is the same as the area simulated in the model. Also, since FVS does not track the

location of each tree, little is lost by assigning center locations randomly.

Note that at most 100 centers in each disease type can be simulated. If the user requests more than 100

centers for a particular disease type, an error message will be printed in the main output file and only 100

centers will be used.

If a user does not know the amount of root disease in the stand but does know which plots are inside

centers, then the user can ask the model to determine the root diseased area in the stand. For details on this

option see the discussion of PLREAD (in Section 3.3).

In stands which have been more extensively studied, the location and size of each center may be known.

Instead of allowing the model to choose the size and location of the centers, the user can provide X-Y

coordinates and the radius of each center while still using the RRINIT keyword.

Example: Specifying the size and location of centers

The keyword to specify the size and location of 3 centers (field 2) in Phellinus weirii (field 7) is given

below. In this example, the first center is located at (100, 100) and has a radius of 44.3 feet [13.5

meters]. Note the ‘1’ in field 1 to tell the model that the center size and location are listed after the

keyword. A picture of the resulting centers is shown in figure 3.8.

RRINIT 1 3 ____4

100 100 44.3

550 430 150.5

400 200 75.1
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 25

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

In the example above, the “1” in field 1 indicates that the center locations will be entered following the

keyword. If this option is used, the user must enter one row for each center (the number of centers is given in

field 2) which contains, in this order, the X location (feet or meters), the Y location (feet or meters), and the

radius of the center (feet or meters). There is no method by which some centers can be entered manually and

others of the same root disease can be assigned randomly.

It is important to remember that stands are measured as a square. Most stands in the field are not actually

squares, and it is possible that, if the location is not modified, a center could end up being located outside the

stand (fig. 3.9). The model will print an error message to the main output file, and the coordinate that is

outside the stand will be set to the stand’s boundary. Also, the area in root disease centers may not be

preserved. In one stand, a center may be far from the boundaries, whereas, in the square stand, the center

may be on the edge. These considerations are not intended to discourage people from placing the centers in

the stand, but rather to ensure that all users have a clear understanding of the consequences of doing so.

Some users will want to run the entire stand as one root disease center. To do this, the keyword SAREA

must be used first to enter the area of the stand. In the keyword RRINIT, fields 1, 2, and 6 must be entered.

Example: Simulating the stand as one center

Suppose that a 10 acre [10 hectare] stand (field 6) is to be modelled as one (field 2) Armillaria root

disease center (field 7). The keywords to do this are:

SAREA 10

RRINIT 0 1 _ _ _ 10 3

If the entire stand is inside a root disease center, the PLREAD keyword (see Section 3.3) is irrelevant and

should not be used.

Note: If a center is present and does not include the entire stand, it will spread and infect trees.

This is true even if the model had no initial infected live or dead trees because the model

always assumes that the entire border of a center is infectious.

Figure 3.8 — Diagram of the centers entered in the example. The diagram shows the stand and

centers as the model sees them. (A) The model is run assuming that the keyword was in imperial

units so the stand is a square assumed to be 660 ft per side. (B) The model is assuming that the

keyword was in metric units and was 201 m per side. In both cases the centers are circles with the

radii measured in feet (A) or meters (B), as appropriate.

430

201

200

100

100 201

150.5

75.1

44.3

150.5

75.1

44.3

100

200

100 400 550

(ft) (m)

(ft)

(m)
26 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Figure 3.9 — Caveats when placing centers in a stand. On the left, a non-square stand with three centers. On the

right, the model’s square representation of the same stand. Notice the new location of the uppermost center. Also

notice that the stand in the model will have less area in root disease.

Note for Annosus root disease:

Since trees can be hosts of only one of the two types of Annosus root disease (S and P), it is possible to run

the stand with large areas in each of the types (fig. 3.10). A 10-acre stand can have up to 10 acres inside a Ptype root disease center and up to 10 acres inside an S-type center. There is no interaction between the two

disease types, except indirectly through the diseases’ effects on host trees.

3.3 Entering Plot-Level Information (whether plots are inside centers, outside

centers, or both)

In the tree inventory, the records are divided into sampling plots. If the user knows whether the plots were

entirely inside or outside root disease centers, the information can be given to the model so that the model can

more accurately assess which uninfected trees should be inside centers and which should be outside centers.

The keyword PLREAD identifies the plots which are inside centers of a specific disease type. This keyword

must be used with RRTREIN so that the model reads the damage codes in the infected plots. All trees in

uninfected plots are assumed to be outside centers, and any damage codes (see below) will be ignored. 1360

330

(140,1000)

(150,650)

(110,200)

660

660

(140,660)

(150,650)

(110,200)
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 27

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

PLREAD List the plots inside centers from which to read the damage code information

Field Description Default

1 Type of root disease contained in the plots 1, or type

(1=P-type, 2=S-type Annosus root disease, set by

3=Amillaria root disease, 4= Phellinus weirii). RRTYPE

2-7 Not used

Suppl. Diseased subplot number as a 4-digit integer,

Record right justified in the first 4 columns of the row.

The last row must be ‘-999’.

Occasionally the only information the user has about infection levels is that some of the trees (without

having specified which ones) in certain plots are infected. In this case the keyword PLOTINF can be used to

distinguish infected plots and their degree of infection. Note that this keyword should not be used with the

keyword RRTREIN (which reads tree infection information from the tree list). If there are damage codes in

the tree list and the infected plots are known, PLREAD provides a more accurate accounting of the disease.

PLOTINF List the plots inside centers and the proportion of infected trees in each plot.

Field Description Default

1 Type of root disease contained in the plots 1, or type

(1=P-type Annosus root disease, 2=S-type set by RRTYPE

Annosus root disease, 3=Armillaria root

disease, 4 = Phellinus weirii).

2-7 Not used

Suppl. Diseased subplot number as a 4-digit integer,

Record right justified in the first four columns of the

row. Then, the proportion of the plot which is

infected as a real number with 2 decimal places

in columns 6-9. The last row must be ‘-999’.

Figure 3.10 — Stand with S- and P-type

Annosus root disease centers. The dark

circles represent root disease centers of Ptype, and the light circles are S-type centers.

The black areas contain both types of root

disease. If each center were 1 acre, then this

stand would contain 3 acres of P-type and 2

acres of S-type Annosus.
28 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Both PLREAD and PLOTINF provide a list of infected plots. In addition, PLOTINF defines the proportion

(by number) of the trees in the plot that are infected. In the example below, the keyword set shows the plots

specified using PLREAD, and the second shows the plots entered using PLOTINF and specifying the

proportion of the trees in the plot which are infected. Note that only one of these keywords would be used for

initializing the root disease in the stand.

Example: Initializing root disease from plot information

In both cases, plots 1004, 1002, and 1007 (listed as supplemental records) contain P-type Annosus

root disease (code 1, after the keyword). The ‘-999’ signifies the end of the list of plot numbers. In

Example A, both PLREAD and RRTREIN are used, and the degree of infection in each plot is taken

from the disease codes in the tree inventory. In example B, all trees within plot 1004 are infected,

plot 1002 is 50 percent infected and plot 1007 is 25 percent infected. Note that only one of these

keywords (either PLREAD or PLOTINF) would be present in a keyword set.

Example A:

RRTREIN

PLREAD 1

1004

1002

1007

-999

Example B:

PLOTINF 1

1004 1.00

1002 0.50

1007 0.25

-999

When the keyword PLREAD is used, the model puts uninfected trees inside and outside centers

differently than when RRTREIN is used alone. In the first case, different tree densities may occur inside and

outside of centers while in the second case (i.e. RRTREIN is used alone), the density will be the same inside

and outside centers (fig. 3.11). In both cases, the correct trees are recognized as being infected. These

different densities inside and outside of centers will affect all aspects of the root disease simulation, including

spread rate and between-tree transmission.

If a user does not know the area inside root disease centers in the stand but does know which plots are

inside centers, the user can use the PLREAD keyword in conjunction with the RRINIT keyword and ask the

model to determine the root diseased area. The model assumes that plots were located systematically in the

stand so that all parts of the stand (inside and outside centers) have an equal chance of being sampled (i.e., the

plots were not selectively placed in diseased or disease-free areas). Additionally, the model assumes that the

proportion of infected plots is representative of the proportion of infected area in the stand.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 29

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Example: Initializing the root disease with only the number of centers and the plots with

infection

Suppose a series of random plots were placed in the stand and the user knew the number of infected

plots but not the area within centers. To ask the model to calculate the area, a “-1” is entered in field

6 of RRINIT.

RRINIT 0 5 - - -1 4

RRTREIN

PLREAD 4

1001

1002

1003

-999

Note that if PLREAD is used and there is not a “-1” in field 6 of RRINIT, the model will use the area given

by RRINIT. This option should be used if the plots were not selected at random (i.e., they overrepresent the

diseased area or if the area in centers is known.

3.4 Entering Tree-Level Information (damage and severity codes, density

infected or uninfected)

Some stand inventories include information about which trees are infected with root disease and the degree

of infection. In the tree list which is read by the model, infected trees may have been given a damage code,

indicating that they are infected with root disease. The model will recognize three different damage codes.

These are:

61 Armillaria root disease

62 Phellinus weirii

64 Annosus root disease (of either type)

Figure 3.11 — Pictorial representation of the difference in density using different keywords. On the

left is the stand when using only RRTREIN, with an even density inside and outside of centers. On

the right is the stand when using RRTREIN and PLREAD, showing a lower density inside centers and

a higher density outside the center. The overall stand density is the same in both stands.
30 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

They may also have been given a severity code as follows:

1 The tree was within 30 feet [9.1 m] of a tree with a deteriorating crown or killed by root

disease.

2 The tree has diagnostic signs or symptoms of root disease.

3 The tree has crown deterioration and is likely near death due to root disease.

The severity codes are used to assign a degree of infection to the trees. A mean infection level is used for

each severity code with some random variation to avoid clumpiness (Marsden and others 1993). Each

severity code is associated with a mean infection level: 1 = approximately 10 percent of the value at death, 2 =

approximately 65 percent of the amount at death, and 3 = approximately 75 percent of the amount at death.

The value at death is a tree-species-specific parameter. (See the Appendix for Chapter 2 infection levels at

death, for each tree species. If there are damage codes with no severity codes, the model will assume a

severity code of 1.

The keyword RRTREIN tells the model to read the damage and severity codes from the inventory to

initialize the root disease.

RRTREIN Assign infection based on damage and severity codes in inventory.

Field Description

1-7 Not used. There are no fields associated with this keyword.

Each line of the inventory has space for three damage code-severity code pairs (two of which are shown

in this example) since trees may be affected by multiple agents.

Example: Section of a tree inventory with root disease damage codes

The following is an example tree inventory entry for a stand containing Annosus root disease (and

measures in imperial units). Each line in this example contains a damage code (64 for Annosus root

disease) and a severity code (both codes are shaded) to identify infected trees:

4 47 13DF 18.9 .0 0 0 .0030 664 1 0 031 0 00 0

4 48 18DF 30.7 .0 0 0 .0064 148 8 0 031 0 00 0

4 49 16DF 4.0 .0 0 0 .0064 3 0 0 0 031 0 00 0

4 50 13DF 2.0 .0 0 0 .0030 364 1 0 031 0 00 0

In this example, Douglas-fir trees are infected. Douglas-fir is assumed to be 90 percent infected at

death (see the defaults for the INFKILL keyword in the appendix, or the Model Description).

Therefore, the three records with a severity code of 1 will be assigned an initial infection level of

around 9 percent (90 percent times 10 percent). The remaining record, with a severity code of 3,

will be given an initial infection level of around 90 percent times 75 percent, or approximately

67.5 percent.

Definitions for the other damage codes can be found in the user’s guides for other FVS model

extensions. Each extension, including the Western Root Disease Model, retains only information

about the relevant code.

The user may have only information about the approximate density of trees within root disease centers,

and how many of these trees are infected or uninfected. In this case, the user should use the keyword RRINIT

(fields 3 and 4, see Section 3.2), to give the density of infected and uninfected trees within centers or the

proportion of the trees inside centers which are infected or uninfected. The model will then divide each tree
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 31

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

record into infected and uninfected trees according to the relative densities given in the keyword (see the

Detailed Model Description for more information). Initial root disease will be assigned from a distribution

about the mean value given in field 5.

Example: Specifying the density of infected and uninfected trees in centers

The keyword to describe a stand which has 3 acres [3 ha] in five S-type Annosus root disease

centers (fields 6, 2, and 7) which have about 200 trees/acre [200 trees/ha] uninfected (field 4) and

100 trees/acre [100 trees/ha] infected (field 3), with a mean infection level of 20 percent (field 5) is:

RRINIT 0 5 100 200 0.2 3 2

Note that if the density of trees in the stand (and therefore also in the center) was different from 300

trees/acre, for example 600 trees/acre [600 trees/ha], then the model would use the relative density

of infected and uninfected trees. In this example, the model would assign 200 trees/acre infected

(600*100/300) and 400 trees/acre uninfected (600 * 200/300).

If the RRTREIN keyword is used in combination with RRINIT or is inadvertently added at this point, the

given values in fields 3 and 4 will be ignored and the model will assign all infection levels from the damage

codes in the inventory.

3.5 Entering Stump and Dead Tree Information

Throughout the model, the term “stump” refers to both the remaining part of a harvested tree (in the

standard sense of the word “stump”) and also any other dead trees. The model retains only information

about infected stumps, since they are a source of inoculum.

There are two methods for initializing the infected dead trees and stumps in the stand. A stump can be

entered as part of the tree list and given a tree history code which indicates that it has recently died (6, 7, 8, or 9),

and a damage code to indicate that the record is infected with root disease.

Example: Infected stumps in the inventory

In the sample inventory below (imperial units),

4 47 13DF 18.9 .0 0 0 .0030 664 1 0 031 0 00 0

4 48 18DF 30.7 .0 0 0 .0064 148 8 0 031 0 00 0

4 49 16DF 4.0 .0 0 0 .0064 3 0 0 0 031 0 00 0

4 50 13DF 2.0 .0 0 0 .0030 364 1 0 031 0 00 0

the second and third tree records have been coded as recently dead (shown by the 8 and the 6 in

bold), and infected with Annosus root disease (bold 64). The severity codes are ignored.

Note also that stumps may be coded in the tree list as trees with a height of 1.5 feet [0.457 m].

Stumps in the inventory are assumed to have been dead for 5 to 15 years, depending on their size

(larger stumps are assumed to have been dead longer). In the above example, the model assumes that the

30.7-inch stump has been dead for 15 years and the 4-inch stump for 5 years. The default times since death

are calculated by assuming that, in general, it takes about 10 to 20 years before most field crews can

recognize stumps as being infected with root disease. Using the default decay rates in the model, however,

many stumps smaller than 14 inches [35.6 cm] DBH are no longer present after 15 years. Thus, the smaller

stumps in the inventory must have been dead a shorter time. This assumption can be changed using the

keyword TIMEDEAD.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

TIMEDEAD Time (years) that the average infected tree/stump in the inventory has been dead.

Field Description Default

1 Time for size class 0-12 inch [0-30.5 cm] DBH. 5

2 Time for size class >12-24 inch [30.5-61 cm] DBH. 10

3 Time for size class >24-48 inch [61-121.9 cm] DBH. 15

4 Time for size class >48-100 inch [121.9-254 cm] DBH. 15

5 Time for size class >100 inch [>254 cm] DBH. 15

6 All size classes. If there is a value in this field, fields

1-5 are ignored

7 Not used

Example: Changing the time since death for stumps in the inventory

Suppose an inventory was done by a root disease specialist who was experienced at identifying

early stages of root disease infection. The pathologist knew stumps in the smallest size class (0-12

inches [0-30.5 cm]) could have been dead for only 2 years (field 1) and that stumps in the remaining

size classes were dead for only 5 years (fields 2-5). The keyword to tell the model this would be:

TIMEDEAD 2 5 555_

The second method for entering infected dead trees and stumps is to use the keyword STREAD. The user

must specify tree species, diameter class, number represented by this record, disease type, and time since

death, in that order.

STREAD Used for entering infected dead trees/stumps which were not in the inventory.

Field Description Default

1 Tree species code.

2 Diameter class (1=0-12 in., 2=>12-24 in., 3=>24-48 in.,

4=>48-100 in., 5=>100 in.) [in cm: 1=0-30.5, 2=>30.5-61,

3=>61-121.9, 4=>121.9-254, 5= >254]

3 Number of stumps with these characteristics

4 Disease type (1=P-type Annosus, 2=S-type Annosus,

3= Armillaria, 4=Phellinus)

5 Number of years stumps of this species and diameter

have been dead.

6 & 7 Not used

Any number of stumps may be entered using the keyword multiple times.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Example: Entering initial infected stumps

The example inventory above contained two Douglas-fir stump records (species code 3, field 1

below), one 30.7- inch diameter record and one 4-inch diameter record. Making some assumptions

about the number of stumps per acre each record represents, and using the information in the

keyword about the size class and age-class, the keywords to enter these stumps manually are:

STREAD 3 3 2 2 15

STREAD 3 1 3 2 5

Note that, in the model, stump diameters are assigned to the mid-point of the size class. For example, a

class 1 stump will be given a diameter of 6 inches [15 cm] and a class 2 stump will be given a diameter of 18

inches [45 cm]. These diameters are used for calculating how large the remaining root system is on the

stump, and therefore how much inoculum is left. If the stumps that are being entered are close to the

boundary of the class, then users should decide whether to put them in the lower class (and lose inoculum

more rapidly than expected) or the upper class (and retain inoculum in the stand for longer than would be

normally be expected).

4.0 Bark Beetle and Windthrow Models

4.1 Bark Beetles

The model can represent four different types of bark beetles. Each of these is triggered by the density of trees

(either all trees, trees with a certain degree of infection, or windthrown trees) above some specified size

threshold. Users can also specify the mortality rate which is applied to trees with various characteristics. Table

4.1 summarizes the differences between the beetle types and table 4.2 gives examples. Each letter in this table

indicates a user-controlled variable for each type of bark beetle, while the blank cells indicate values that are

not used for that type of beetle. For example, for beetle type 1 the user may specify the host species for the

beetle, the minimum diameter of trees affected by the beetle, a single mortality rate which is applied to trees

both inside and outside of root disease areas in the event of an outbreak, and the criterion for triggering the

outbreak. Alternatively, the user may choose to rely on default values for some or all of these variables. Some

of the differences between the bark beetle types is also shown pictorially in figure 4.1. See the Detailed Model

Description for more information about each beetle type.

Table 4.1 — A comparison of the four bark beetle types. Each letter indicates a variable under user control, while blank cells indicate values

that are not used for that type of bark beetle. Each different letter in a column is a different field in the keyword for that bark beetle type.

For Type 2 bark beetles, M* reminds the user that the mortality rate is specified relative to the number of windthrown trees. “D” is minimum

DBH and “I” is minimum root disease infection level. SDI is stand density index.

TYPE 1 BEETLES TYPE 2 BEETLES TYPE 3 BEETLES TYPE 4 BEETLES

Host H H H H

Min. DBH D D D D

Min. Infection I I

Outside Mortality M M* M1

Fringe Mortality M M* M2

Inside Uninfected M M* M3

Mortality

Inside Infected M M* M M4 if > I

Mortality M3 if < I

Outbreak Criteria Density of host > D or SDI Total density of host >D Density in centers Density in centers

(outside / whole stand, windthrown in the of live host of host > D and > I,

whole stand, living / alive current growth cycle. > D and > I. including host that

in last 2 yrs). died in last 2 years.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 4.2 — Example bark beetle types.

Species Type Type 1 Type 2 Type 3 Type 4

Beetle species Western pine beetle Douglas-fir Fir engraver Mountain pine beetle

Mountain pine beetle engraver

Host tree species Ponderosa pine Douglas-fir White fir Ponderosa pine

Lodgepole pine Grand fir

Figure 4.1 — A pictorial representation of some of the differences between bark beetle types.

Open circles are uninfected trees, lightly shaded circles represent trees with low root disease

infection levels, and darker shaded small circles represent trees with higher levels of infection.

The heavily shaded large circle represents a root disease center. “M” means mortality rate due to

bark beetle type.

Type 1 Type 2

Type 3 Type 4

m

m m

m 1

m 2

m 3

m 4

uninfected low infection level infected (moderate to heavy)
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Each of the four bark beetle types is represented by a keyword in which users specify the first year of

potential occurrence, the threshold density of trees of a specific size and disease level, and the mortality rates

to be applied. By default, bark beetle types 1, 3, and 4 are active in the model at their default values. The

keyword BBCLEAR (with no fields) allows the user to make the decision to use no bark beetles. If the user

wishes to use fewer, more, or different bark beetle types, a keyword (BBTYPE1, BBTYPE2, BBTYPE3, or

BBTYPE4) must be used for each desired type.

The bark beetle keywords are quite flexible, and with some planning, users should be able to accomplish

almost any desired beetle-induced mortality pattern. The same keyword can be repeated for any number of

tree species, thresholds, or mortality rates. Up to 33 different varieties of bark beetles (defined as a unique

combination of bark beetle type and parameters) may be active in any cycle.

BBTYPE1 Type 1 bark beetle.

Field Description Default

Imp Met

1 Earliest date that an outbreak can occur (year or cycle). 1 1

2 Tree species eligible for bark beetle attack (tree species number or character code). 10 10

3 Minimum DBH of trees eligible for attack by the bark beetle (inches or cm). 8 20.3

4 Threshold density of eligible trees/acre [or trees/ha] that must be exceeded for an 150 371

outbreak to occur.

5 Mortality rate applied to all eligible trees (fields 2 and 3) if an outbreak occurs. 0.1 0.1

6 Flag for whether the beetle model should remain on once it occurs (0 = yes; 1 = no). 0 0

7 Not used

Imp. = imperial units, Met. = metric units

BBTYPE2 Type 2 bark beetle (WINDTHR keyword must be present).

Field Description Default

Imp Met

1 Earliest date that an outbreak can occur (year or cycle). 1 1

2 Tree species eligible for bark beetle attack (tree species number or character code). 3 3

3 Minimum DBH of trees eligible for attack by the bark beetle (inches or cm). 0.0 0.0

4 Minimum number of windthrown trees/acre [trees/ha] necessary for an outbreak to occur. 10.0 24.7

 Eligible trees must meet requirements in fields 2 and 3.

5 Number of eligible trees (fields 2,3) to die in an outbreak for each windthrown tree 0.88 0.88

 meeting requirements in fields 2 and 3.

6 Flag for whether the beetle model should remain on once it occurs (0 = yes; 1=no). 0 0

7 Not used

Imp. = imperial units, Met. = metric units
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

BBTYPE3 Type 3 bark beetle.

Field Description Default

Imp Met

1 Earliest date that an outbreak can occur (year or cycle). 1 1

2 Tree species eligible for bark beetle attack (tree species 4 4

number or character code).

3 Minimum DBH of trees eligible for attack by the bark 10.0 2.5

beetle (inches or cm).

4 Threshold density of eligible trees/acre [trees/ha] that 10 24.7

must be exceeded for an outbreak to occur.

5 Minimum proportion of root radius infected for attack 0.30 0.30

by bark beetles.

6 Flag for whether the beetle model should remain on once 0 0

it occurs (0 = yes; 1=no).

7 Mortality rate applied to all eligible trees if an outbreak occurs. 0.88 0.88

Imp. = imperial units, Met. = metric units

BBTYPE4 Type 4 bark beetle.

Field Description Default

Imp Met

1 Earliest date that an outbreak can occur (year or cycle). 1 1

2 Tree species eligible for bark beetle attack (tree species 10 10

number or character code).

3 Minimum DBH of trees eligible for attack by the bark 4.0 10.2

beetle (inches or cm).

4 Threshold trees/acre [trees/ha] that must be exceeded 1.0 2.5

for an outbreak to occur.

5 Minimum proportion of infected root radius for trees to be 0.4 0.4

eligible for density calculation and to suffer the highest

mortality rate (field 7).

6 Flag for whether the beetle model should remain on once it 0 0

occurs (0 = yes; 1=no).

7 (Highest) mortality rate applied to eligible trees (fields 2,3,5) 0.75 0.75

when outbreak occurs.

Required Supplemental Record

1 Mortality rate applied to eligible trees (fields 2,3) inside centers 0.3 0.3

that are less than field 5 infected (‘inside-uninfected’ or low

infection trees).

2 Mortality rate applied to eligible trees (fields 2,3) outside 0.15 0.15

centers which are within an average root diameter of the

edge of the center.

3 Mortality rate applied to eligible trees (fields 2,3) outside 0.101 0.01

centers which are beyond the average root diameter away

from the edge of the center.

Imp. = imperial units, Met. = metric units
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BBCLEAR Simulate no bark beetles.

Field Description Default

1-7 Not used.

A key part of the bark beetle models is that more than one bark beetle type (or variety) may attack any

species or size of tree. It is possible that one bark beetle type can attack the same species of tree with different

threshold values and different mortality rates. Thus, the difference between endemic and outbreak levels of

bark beetles can be simulated. The example below shows a Type 1 bark beetle attacking ponderosa pine at

low levels and sets up outbreak conditions with a much higher threshold.

Example: Bark beetles attacking different size trees at different rates

In both keywords, the first two fields tell the model to start checking for beetle mortality in cycle 1

on ponderosa pine (species 10). The first line gives a low-level bark beetle mortality (such as

endemic mountain pine beetle): 1 percent mortality (field 5) will be applied to trees greater than 4

inches [4 cm] DBH (field 3) when there are at least 15 trees/acre [15 trees/ha] (field 4) of that size.

The second occurrence of the threshold is much higher (representing outbreak conditions): 100

trees/acre [100 trees/ha] greater than 12 inches [12 cm] DBH, and, at 20 percent, the mortality rate

is also greater.

BBTYPE1 1 10 4 15 .01

BBTYPE1 1 10 12 100 .20

The lower threshold will be met often, and only the low mortality rate will be applied. In the cases

in which the higher threshold is reached, the model will apply the higher mortality (20 percent) to

all ponderosa pine greater than the larger diameter limit (12 inches [12 cm]) and the lower,

background mortality (1 percent) to all trees between 4 inches [4 cm] and 12 inches [12 cm].

Bark beetles remain potentially active in all cycles after they have been switched on. Field 6 of each

BBTYPEn keyword allows the user to stop all bark beetles of a certain type (i.e., type 1, 2, 3, or 4). If the user

wants any of the varieties of bark beetle to remain on, they must be re-entered following the deactivation.

When the bark beetles of one type (type 3 in the example below) are deactivated, any other bark beetle types

are unaffected by the deactivation.

Example: Deactivating a bark beetle

Suppose that Type 3 bark beetle (such as fir engraver) was to be active on white fir (species code 4) in

all cycles (1-10), and that there was a different Type 3 bark beetle (such as mountain pine beetle)

attacking lodgepole pine (code 7) in cycle 5 only. The keywords to do this would be:

BBTYPE3 1 4

BBTYPE3 5 7 ___1_

BBTYPE3 6 4

The ‘1’ in the sixth field of the second bark beetle type turns off all Type 3 bark beetles at the end of

the cycle. Since the user wishes the bark beetle that attacks white fir (species code 4) to remain

active, the keyword must be re-entered for the next cycle.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Example: Deactivating a bark beetle type (2)

As an example, combine the keywords from the two previous bark beetle examples, but change the

last keyword from starting again in cycle 6 to starting again in cycle 7:

BBTYPE1 1 10 4 15 .01

BBTYPE1 1 10 12 100 .20

BBTYPE3 1 4

BBTYPE3 5 7 ___1_

BBTYPE3 7 4

Now look at the bark beetles that are potentially active in each cycle from cycle 1 to cycle 8:

Cycle 1: type1a, type1b, type3a

Cycle 2: type1a, type1b, type3a

Cycle 3: type1a, type1b, type3a

Cycle 4: type1a, type1b, type3a

Cycle 5: type1a, type1b, type3a, type 3b

Cycle 6: type1a, type1b

Cycle 7: type1a, type1b, type3a

Cycle 8: type1a, type1b, type3a

Notice that turning off the type 3 bark beetle after cycle 5 deactivated both type 3 bark beetles but

did not affect the type 1 bark beetles.

Type 2 bark beetles are attracted to stands in which windthrow has occurred. Therefore, although

bark beetle Type 2 has the same flexibility as the other bark beetle types, it can occur only if windthrow

(activated using the keyword WINDTHR) has occurred in the same cycle. Its threshold and mortality

rates are also totally defined by the number of windthrown trees, so if no windthrow occurs, the bark

beetles will not be active. If bark beetle Type 2 (BBTYPE2) is in the keyword file and windthrow

(WINDTHR) is not, a warning message will appear in the main output file, reminding users that bark

beetle Type 2 cannot occur.

All thresholds in the model are calculated on the basis of the trees/acre or trees/ha density of eligible

trees. The density will change depending on whether it is calculated on the basis of the entire stand, on the

area outside root disease centers, or on the area within root disease centers. In general, the area outside of

infected centers will have the highest density while the areas inside of centers will have a much lower

density because of the increased mortality. The density of the entire stand will be an average of the values.

Since the threshold for Type 3 and 4 bark beetles is based on infected trees only, the density is calculated

from the area in root disease, while for Type 2 bark beetles, it is based on the total area of the stand.

However, for bark beetle Type 1, the threshold may be based on any number of criteria. Users can choose to

calculate density by trees/acre or by Reineke’s stand density index (SDI). Density can also be calculated on

the basis of all trees in the stand (default) or only those that are outside root disease centers, and only living

trees (default) or all those alive within the last 2 years. The method of calculating stand density may be

changed using the keyword DNSCALC.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

DNSCALC Type of density calculation for type 1 bark beetles

Field Description Default

Imp Met

1 Method for calculating outbreak density threshold (0=trees/acre [or /ha], 1=SDI) 0 0

2 Areas to include in density calculation (0=entire stand, 1=area outside centers) 0 0

3 Trees to include in density calculation (0=currently living trees, 1=all trees alive in last 2 years). 0 0

4 Slope of the SDI function (used if SDI method is chosen in field 1). -1.605 -1.561

5-7 Not used.

Imp. = imperial units, Met. = metric units

Example: Changing the type of density calculation

Suppose a user wanted to have density calculated on the basis of the SDI (field 1) of all living trees

(field 3) outside centers (field 2), then the keyword would be:

DNSCALC 1 1 0 _

Note that this keyword affects only the BBTYPE1 keyword.

4.2 Windthrow

The windthrow submodel simulates a major blowdown event. The model will affect all dominant and

codominant trees (trees in the largest 20 percent of the basal area distribution are eligible), whether or not

they are infected. Users specify the proportion of these trees that would be windthrown, and the minimum

number of eligible trees for the event to occur, using the keyword WINDTHR.

WINDTHR Simulate a blowdown event.

Field Description Default

1 Earliest date a windthrow event can occur (calendar year

or cycle number).

2 Proportion of dominant and codominant trees to be 0.0

windthrown.

3 Minimum number of eligible tree/acre [trees/ha] necessary 0.0

for event to occur.

4-7 Not used.

If too few trees would be windthrown when the model calculates the proportion of dominant and

codominant trees, then the event will not occur, and the submodel will check eligibility again in the following

year. Once a windthrow event occurs, the submodel will be turned off.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

5.0 Changing Default Conditions

Many of the default parameter values used in the model can be changed by the user. The default values have

been chosen, however, by a group of experts to be the best (or most general) representation of what is

currently known about each of the root diseases, and should meet the needs of most users. Some of the

keywords are considered “advanced” and should be used only by those who are experienced users of the

model or who have experience with root disease. The advanced keywords are identified in each section, and

are primarily those which define the infection and inoculum parameters. Selected output files (see Section 7)

are also only useful for users who want detailed information about particular sections of the model.

It is important to note, before going further, that the processes within the model are highly interconnected.

While a single keyword may control one aspect of the model, changing that keyword may have implications

in other parts of the model. Table 5.1 shows some of the key processes in the model and some of the keywords

that affect them. The Detailed Model Description has additional details on the direct effects of changing some

of the parameters.

5.1 Spread of Infection

Infection can spread through a stand in a variety of ways. It spreads from infected trees and stumps to

uninfected trees, both inside and outside of root disease centers. For Annosus root disease, after a harvest the

newly formed stumps can be infected by airborne Annosus spores which may create new centers. The

keywords governing each of these means of infection are described below.

5.1.1 Infection Within Centers (Advanced)

Within centers, root disease spreads from infected trees (live and dead) and stumps to live healthy trees when

the roots of uninfected trees contact infected roots. Details of this process are given in the model description.

The calculation of root-to-root infection potentially involves a Monte Carlo simulation in which the infection

probability for each tree is calculated multiple times. The keyword INFSIMS tells the model the number of

times to perform the simulation (field 1), and allows the user to request detailed output about simulation results

(field 2; see Section 8 for details on the output). The Monte Carlo simulation can be performed up to 50 times for

each root disease type, but each additional simulation noticeably increases the model’s simulation time.

Table 5.1 — Summary of the main processes and keywords controlling or affecting them. The ‘X’ indicates keywords that control the process

directly. The ‘x’ indicates keywords which control the process indirectly, by affecting one of the other processes directly. For example,

TTDMULT (the time-to-death multiplier) directly controls the individual tree dynamics. Since the individual tree dynamics affect new

infections and center spread rate, the keyword has an indirect effect on these processes.

Keyword Individual tree Mortality Inoculum New Center New

dynamics (live) dynamics (dead) infection spread centers

INFMULT X x

TTDMULT X X xx

(and RRMINK)

INFKILL X X xx

INFCOLO X X xx

INOCLIFE X xx

SDIRMULT X Xx

SPREAD X

(and TDISTN)

RRJUMP X

INFSIMS X

SPORE X
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This option is for advanced users only, and is useful only at low inoculum densities. At high infection

levels the results from the Monte Carlo simulations usually show little variation and thus, the benefit is not

worth the increased overall simulation time.

INFSIMS Specify the number of times to do the inside center Monte Carlo simulation, and request

output of the results. (advanced)

Field Description Default

1 Number of times to perform the inside center Monte Carlo simulation. 1

2 Print an output file? (Any value greater than 0 means yes.) 0

3 Output file number. 25

4-7 Not used

5.1.2 Spread of Centers

The edge of a center is defined to be the outermost limit of a “patch” of infection. Thus, this edge is capable of

infecting uninfected trees outside the center and thereby causing the center to expand. The model assumes

that all centers can spread, and that they spread outwards in all directions at an equal rate (i.e., the circle

remains a circle). By default, the rate at which centers expand is a dynamic rate which is calculated on the

basis of the size, species, and density of trees outside of centers. It can change in each cycle. The model

determines the dynamic spread rate using a Monte Carlo simulation approach in which the spread rate is

calculated up to 50 times (see the Model Description for details, Chapter 4). Alternatively, users can specify a

static spread rate for each disease type which is applied to all centers of the appropriate type in all years or

until the user changes the rate.

SPREAD Specify type and parameters for the center spread rate calculations.

Field Description Default

Imp Met

1 First year for the parameters to take effect (cycle number or year). 1 1

2 Type of spread model (0=static spread, 1=dynamic, applying different 1 1

 spread rates to each center, 2=dynamic, applying the average spread

 rates to each center).

3 Fixed annual spread rate for the centers (used if field 2 is 0) 1.0 0.3

 in feet [meters]. (If both S- and P-type Annosus are being

 simulated, this is for P-type Annosus.)

4 Fixed annual spread rate for S-type centers (used if 1.0 0.3

field 2 is 0, and both S- and P-type Annosus are being

simulated) in feet [meters].

5 Number of times to simulate the Monte Carlo calculations 10 10

(used if field 2 is 1 or 2).

6 Disease type (0 = P- and S-type Annosus, 1=P-type Annosus, 11

2= S-type Annosus, 3 = Armillaria, 4 = Phellinus.

7

Imp. = imperial units, Met. = metric units

The SPREAD keyword allows users to control the type of spread rate and the number of times to do the

Monte Carlo simulation. Three types of spread rates exist (numbered according to their value in field 2 of the

keyword) and are pictured and described in figure 5.1.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

By default the model will apply the individual spread rates which result from each simulation to

different centers. This assumption has the consequence that some centers may stop growing if any of the

Monte Carlo simulation results indicate a zero spread rate which can occur if no infection occurs along the

center boundary. It also may allow some centers to grow much faster than others, because a range of the

possible spread rates are applied. See the model description for details on how these individual spread rates

are calculated and applied.

Figure 5.1 — Different types of spread rates. Within each diagram, two cycles of spread rates have been applied. (A)

Static spread rate; (B) Dynamic spread rate, applying different spread rates to each center; and (C) Dynamic spread

rate, applying the average spread rate to each center.

1. Dynamic spread rate, applying different spread rates to each center.

This takes the results from the Monte Carlo simulation and applies

different results to different centers. Thus, the radii of different centers

may enlarge at different rates from each other, and the rates may

change each cycle (fig. 5.1b).

0. Static spread rate.

This enlarges the radius of each center by a constant, user-defined

amount (fig. 5.1a)

2. Dynamic spread rate, applying the average spread rate to each center.

This takes the average of the results from the Monte Carlo simulation

and applies this average to each center. Thus, the radii of each center

will enlarge at the same rate, but these rates may still change each cycle

(fig. 5.1c).

x

x

x

x

x y

y

y

x

x

x

x
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Centers continue to spread and to be counted as individual centers even when they overlap with other

centers. The model does, however, account for the area within centers appropriately. See the model

description for details.

As mentioned above, the dynamic spread rate of centers is calculated on the basis of simulating the

spread of root disease through the uninfected part of the stand. By default, the Monte Carlo simulation is

done 10 times, and the results are applied to the various centers, either as the average of the simulations or as

individual spread rates (see the model description for more details). The SPREAD keyword allows users to

change the number of Monte Carlo simulations which are done.

Example: Applying a more representative spread rate

Suppose that the user wanted the spread rate to vary, but wanted the applied spread rate to be a

good representation of the stand conditions. The user might then decide to apply only the average

spread rate to all centers (a 2 in field 2) in all years (a 1 in field 1 to indicate that this type of spread

rate starts in the first year) and, to make the average more representative, to ask the model to do

more Monte Carlo simulations (field 5):

SPREAD 1 2 _ _ 40

The spread rate parameters may change in any cycle. Users may enter the keyword as many times as

desired, specifying the year that the change will take effect and the new values (see the example below). The

type of spread may be changed or the static spread rate may change.

Example: Changing the static spread rate parameters in different cycles

Suppose the centers in a stand grew at a static rate (the 0 in field 2) which was 1 ft/year [1 m/year]

(field 3) for the first 30 years (3 cycles, starting in cycle 1, field 1) and thereafter was only 0.25 ft/

year [0.25 m/year] (field 3). The SPREAD keyword would be used twice, once to tell the model to

start using the new parameters in the first cycle (field 1), and the second time to ask the model to

change to the new parameters in cycle 4 (or 30 years later; field 1):

SPREAD 1 0 1.0

SPREAD 4 0 0.25

Example: Changing the spread rate type in different cycles

Suppose the spread rates for the centers in a stand were known to be constant (at 0.75 ft/year [0.75

m/year]) for the first 20 years of the simulation. Thereafter (cycle 3 and later, field 1), the spread

rates are unknown, and the user wishes the model to calculate them, and to apply a range of spread

rates to the different centers (indicated by the 1 in field 2).

SPREAD 1 0 0.75

SPREAD 3 1

Note for Annosus root disease

Note that centers of both Annosus disease types (S and P) must spread using the same method. The model

cannot have centers of one disease type spreading using a static spread rate and centers of the other disease

type using a dynamic spread rate. The model also cannot apply the average spread rate to one type of center

but variable spread rates to the other type of center.
44 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Altering the spatial distribution of trees

Spread rates are determined from the density and placement of trees outside centers. The user has some

control over the spatial arrangement of trees outside centers using the keyword TDISTN. This keyword tells

the model whether trees are regularly spaced (as in a plantation) or randomly spaced (default). This keyword

can be used multiple times to change the distribution over time to represent the change from a natural forest

to regularly spaced conditions, or to represent the ingrowth of trees in a plantation.

TDISTN Spacing pattern of trees in the stand (used in center spread calculations only).

Field Description Default

1 Year in which the spatial distribution will change from

that specified in field 2 to the other type. Value must be

a calendar year (not a cycle number)

2 Current tree spatial distribution (0=random, 1=gridded). 0

3 Standard deviation about the mean distance between 1.0

trees in a gridded distribution.

4-7 Not used

Center spread after a clear-cut

After a stand is thinned or clear-cut, centers may expand and encompass neighboring trees (fig. 5.2).

Biologically, this occurs because the root systems of the trees immediately surrounding the centers are likely

to be in contact with some inoculum inside centers. Once trees have been cut, they can no longer defend

themselves and the infection is able to spread rapidly through a relatively healthy root system, leading to an

increase in center area. This is not common in all root diseases, and it does not occur by default for Annosus

root disease or Phellinus weirii. It does occur by default for Armillaria root disease. Users may request this

option using the keyword RRJUMP. When the keyword is used, the model assumes that the center will

expand after harvest by some multiple (as defined in the keyword) of the average root diameter in the stand.

Figure 5.2 — The “jump” in center size after a clearcut. The original centers are the large shaded areas

and root systems of trees are the smaller, open circles.

The dotted line shows the new edge of the center,

had a cut occurred.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 45

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

RRJUMP Set the “jump” in center radius after a harvest.

Field Description Default

1 Average root diameter multiplier for P-type Annosus 0

2 Average root diameter multiplier for the S-type Annosus 0

3 Average root diameter multiplier for Armillaria 1

4 Average root diameter multiplier for Phellinus 0

5-7 Not used.

Example: Setting the ‘jump’ in center size after a clear-cut

Suppose after a stand entry, P-type Annosus root disease centers tended to spread out through the ring

of trees immediately surrounding the center (i.e., one root system diameter, field 1), while S-type centers

only expanded through half of the root system (field 2). The keyword would be:

RRJUMP 1 0.5

5.2 New Centers: Annosus Root Disease Only

New centers can be generated only for Annosus root disease. Stands with Armillaria root disease and

Phellinus weirii will always have the same number of centers. However, root disease dynamics will continue

to reflect the fact that the root disease is not evenly distributed throughout the stand.

New centers can originate only from spore infection on large stumps created during a stand entry in a

previous cycle. Stumps from uninfected trees inside and outside of centers may be infected. Newly infected

stumps inside of existing centers will not create new centers but if the infected stumps are outside of existing

centers, they may create a small new center. Trees in centers created by spore-infected stumps take longer to

decay than other stumps, and are less infectious for the first FVS-cycle after creation. Spore infection will not

occur on stumps created from a windthrow event (WINDTHR keyword). Using the keyword SPORE, users

can control the minimum size stump that can be spore-infected (field 3), the proportion of these stumps that

can be infected (field 2), and the change in decay and infection rates (fields 4 and 5). Each of these parameters

can be changed for either disease type and in any cycle.

SPORE Change spore infection parameters (size of stumps, center characteristics).

Field Description Default

Imp Met

1 First year for parameters to take effect (cycle number or calendar year). 1 1

2 Proportion of newly cut stumps to be infected by spores. 0.1 0.1

A ‘0’ in this field will turn off the spore model.

3 Minimum stump diameter for forming new root disease 14 35.6

centers by spore infection (inches or cm).

4 Multiplier for time-to-death for trees in spore-initiated centers. 3 3

5 Multiplier for the probability of infection transmission, given root overlap, 0.5 0.5

 for spore-initiated centers.

6 Disease code (1=P-type, 2=S-type Annosus root disease). 1 1

7 Not used.

Imp. = imperial units, Met. = metric units
46 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Example: Changing the spore-infection parameters for Annosus Root Disease

Suppose that, starting in the first cycle (field1) after a stand entry about 5 percent (field 2) of the Stype trees (field 6) above 12 inches [12 cm] in diameter (field 3) were infected (and could potentially

create centers). When new trees are infected in the new centers, their time-to-death is the same as if

they had been infected in older centers (field 4). Also, suppose that no P-type spore infection

occurred (fields 2 and 6). The keywords would read:

SPORE 1 0.05 12 1 _ 2

SPORE 1 0.0 ___1

Spore infection occurs by default in the model. It can be turned off by setting the rate of infection to zero

for each disease type (as in the second keyword in the above example). Note that “SPORE” will show up as

“DONE” in the activity scheduler in the main output file in the cycle when the parameter values are changed,

to confirm that the change occurred.

5.3 Infection Parameters (Advanced)

All parameters that determine the course of infection in a live tree can be changed by the user (fig. 5.3). The

probability of infection given an instance of root overlap can be changed using the keyword INFMULT to

specify the species and the new probability of infection. Once a tree is infected, the disease spreads through its

root system at a rate calculated from the time between root infection and death (changed using the keyword

TTDMULT), and from the proportion of the root radius that is infected when death occurs (modified using the

keyword INFKILL). After the tree dies, the root disease immediately spreads through the tree’s root system to

some final level. The final proportion of the root radius infected is defined by the keyword INFCOLO. An

example showing the pattern of infection using actual default values is given in figure 5.4.

Figure 5.3 — Pattern of inoculum spread through a healthy root

system with parameters under user control labelled with the

keyword names. Proportion root radius infected

Years

TTDMULT

INFCOLO

INFKILL

prop after death

infection

at root

center

death

prop for 

death to occur

H

H

H

B

B

B

J

J

0%

20%

40%

60%

80%

100%

0 5 10 15 20 25 30

% root radius infected

Years since infection

H Armillaria B Annosus J Phellinus

Figure 5.4 — Default relationships for the progress of

infection in each of the root disease types. These

curves show the infection in a 15-inch [38 cm] DBH

grand fir. On each line the first point is the time of

death and the second is the percent of the radius that

is infected after death. Differences between the curves

represent difference in the default values for time to

death, and proportion infected at death and after death.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 47

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

INFMULT Probability of infection.

Field Description

1 Date (calendar year) in which parameters will be modified.

2 Disease type (1=P-type Annosus, 2=S-type Annosus , 3=Armillaria, 4=Phellinus). If this field is blank, the model

will use the disease type set by the keyword RRTYPE.

3 Tree species to be modified (number or character code). A ‘0’ or ‘ALL’ here indicates all species, and

requires a supplemental record.

4 Probability of infection for the species given in field 3. If field 3 indicates all species, this field will be ignored.

Defaults are given in Appendix 1.

5-7 Not used

Suppl. 11 values starting in column 1, each with a field width of five places (including the decimal).

Record All values must be entered. The supplemental record is only required if field 3 is 0 or ‘ALL’.

TTDMULT Multiplier for the time from infection at the tree’s midpoint to death.

Field Description

1 Date (calendar year) in which parameters will be modified.

2 Disease type (1=P-type Annosus, 2=S-type Annosus, 3= Armillaria, 4= Phellinus). If this field is blank, the model

will use the disease type set by the keyword RRTYPE.

3 Tree species to be modified (number or character code). A ‘0’ or ‘ALL’ here indicates all species, and

requires a supplemental record.

4 Time-to-death multiplier. If field 3 indicates all species, this field will be ignored. Defaults are given in

Appendix 1.

5-7 Not used

Suppl. 11 values starting in column 1, each with a field width of five places (including the decimal).

Record All values must be entered. The supplemental record is only required if field 3 is 0 or ‘ALL’.

INFKILL Proportion of the root system infected at death.

Field Description

1 Disease type (1=P-type Annosus, 2=S-type Annosus, 3= Armillaria, 4= Phellinus). If this field is blank, the model

will use the disease type set by the keyword RRTYPE.

2 Tree species to be modified (number or character code). A ‘0’ or ‘ALL’ here indicates all species, and

requires a supplemental record.

3 Proportion of the root system infected at death for the species given in field 2. If field 2 indicates all species,

this field will be ignored. Defaults are given in Appendix 1.

4-7 Not used

Suppl. 11 values starting in column 1, each with a field width of five places (including the decimal).

Record All values must be entered. The supplemental record is only required if field 3 is 0 or ‘ALL’.
48 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

INFCOLO Proportion of the root system infected after death.

Field Description

1 Tree species to be modified (number or character code). A ‘0’ or ‘ALL’ here indicates all species, and

requires a supplemental record.

2 Proportion of the root system infected after death for the species given in field 1. If field 1 indicates all

species, this field will be ignored. Defaults are given in Appendix 1.

3 Disease type (1=P-type Annosus, 2=S-type Annosus, 3=Armillaria, 4=Phellinus). If this field is blank, the model

will use the disease type set by the keyword RRTYPE.

4-7 Not used

Suppl. 11 values starting in column 1, each with a field width of five places (including the decimal).

Record All values must be entered. The supplemental record is only required if field 3 is 0 or ‘ALL’.

In the keywords in the following example, notice that INFMULT and TTDMULT can differ between

cycles. The ‘0’ in the first field indicates that these values should be used from the beginning of the

simulation. Also notice that the time to death is specified as a multiplier relative to a defined curve (see the

model description) which cannot be changed by the user.

Example: Changing root disease parameters

Suppose that a pathologist in Northern Idaho has local information for grand fir (species code 4)

that deviates from the default parameter values and wants to explore these alternative parameter

values. The pathologist believes that grand fir has only a 40 percent probability of being infected

(INFMULT, field 4), that it will take twice as long to die as defined by the reference curve

(TTDMULT, field 4), and that the tree will die when the root radius is 75 percent infected

(INFKILL, field 3). After death, the root disease will increase to 85 percent of the root radius

(INFCOLO, field 2). The series of keywords which will perform these changes is:

INFMULT 0 _ 4 0.4

TTDMULT 0 _ 4 2.0

INFKILL _ 4 .75

INFCOLO 4 .85

The effect of these changes can be seen in figure 5.5.

The relationship between the time from infection to death may not be linear with respect to DBH. The

keyword RRMINK is a way of allowing users to introduce some non-linearity. Users can specify a limit

below which the time-from-infection-to-death multiplier is not effective.

Figure 5.5 — Default and changed values for the

increase in infection in a 15-inch [38 cm] DBH

grand fir. On both lines the first point is the time

of death and the second is the percent of the

radius that is infected after death. Notice that

since the changed values, based on the keywords,

are independent of the root disease species, the

new values result in the same curve, no matter

which disease is simulated.

H

H

H

B

B

B

J

J

C

C

C

0%

20%

40%

60%

80%

100%

0 5 10 15 20 25 30 35

% root radius infected

Years since infection

Defaults C Changed
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 49

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

RRMINK Time-to-death below which modifiers in TTDMULT have no effect.

Field Description Default

1 Time-to-death below which the modifiers specified by the TTDMULT keyword 0

do not influence mortality and infection dynamics for trees infected with P-type Annosus.

2 Time-to-death below which the modifiers specified by the TTDMULT keyword 0

do not influence mortality and infection dynamics for trees infected with S-type Annosus.

3 Time-to-death below which the modifiers specified by the TTDMULT keyword 0

do not influence mortality and infection dynamics for trees infected with Armillaria.

4 Time-to-death below which the modifiers specified by the TTDMULT keyword 0

do not influence mortality and infection dynamics for trees infected with Phellinus.

5-7 Not used.

Example: Non-linearity in the time to death function

Suppose that an infected ponderosa pine (code 10) will live 25 percent as long as the reference

(TTDMULT, field 4), but that this multiplier will not apply to the first 20 years of infection (because up

to that point the predicted time to death is the same), but after that the multiplier will be added to reduce

the remaining time to death. This is done by specifying 20 years in field 1 of the RRMINK keyword.

TTDMULT 0 _ 10 .25

RRMINK 20

To interpret this, for example, suppose that the reference curve states that the tree would live for 40

years after infection. The tree with the above parameters would then live for 20 years (as given in

RRMINK) plus 20 * 0.25 years (the remaining 20 years of the 40 total, times the 25 percent specified by

the TTDMULT keyword), to give a total of 25 years from infection to death. Had the RRMINK

keyword not been present (but the TTDMULT keyword was present), the tree would have lived for 40

* 0.25 = 10 years, because the 25 percent multiplier would act on the entire number of years to death.

The RRMINK keyword can cause the tree to live either shorter or longer than it would otherwise,

depending on whether the multiplier is greater than or less than 1.0. The effect of RRMINK is shown

graphically in figure 5.6.

Figure 5.6 — Effect of the RRMINK keyword. These graphs are relative to the base curve for time to death. (A) is using a multiplier

of 2, and (B) is using a multiplier of 0.25. In each graph, the dark line is without the keyword, and the light line assumes that RRMINK

was set to 20 years. Note that the values on the top of the graph show the DBH in centimeters and the values on the bottom of the

graph show the DBH in inches.

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30

0 10 20 30 40 50 60 70

Years to death

dbh (in.)

dbh (cm)

with RRMINK @ 20 years

0

5

10

15

20

25

0 5 10 15 20 25 30

0 10 20 30 40 50 60 70

Years to death

with RRMINK @ 20 years

dbh (in.)

dbh (cm)

A B
50 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Figure 5.7 — Default decay pattern of infected

root radii, showing the aspects that can be

controlled by the keyword INOCLIFE.

fields

1 & 2

fields 3 & 4

years fields 5 & 6

infected root radius

5.4 Inoculum Parameters (Advanced)

The amount of inoculum in the stand is measured by the infected root radii of dead trees and stumps, and

inoculum reduction is calculated by decreasing the infected root radii. The model assumes that inoculum

decays at a constant rate. When the remaining root radius has decreased such that mainly the stump itself is

left, the radius is held constant for a number of years. Although decay continues, the radius of the remaining

inoculum is not altered until decay is complete. The keyword INOCLIFE allows the user to control various

aspects of the reduction in infected root radius (fig. 5.7).

INOCLIFE Set the parameters which describe the decay of inoculum (advanced).

Field Description Root Default

Disease Imp Met

1 Slope of the initial decay function as a function of infected root Annosus 0.02212 0.02212

radius (feet decay/year/feet root radius [m/year/m]). If the

slope is positive, trees with a larger root radius will Armillaria 0.0 0.0

decay more slowly than trees with a smaller root radius. Phellinus 0.0 0.0

2 Intercept of the initial decay function (feet/year or meters/year). Annosus 1.2032 0.3667

This is the decay rate at which a tree with almost no Armillaria 1.0 0.3048

infection would decay. Phellinus 1.0 0.3048

3 Slope of the function describing the relationship between DBH Annosus 0.1667 0.02

(inches or cm) and amount of root radius (feet or meters) Armillaria 0.297 0.0356

remaining when the root radius no longer decreases. Phellinus 0.2377 0.0285

4 Intercept of this relationship (feet or meters) All 0.0 0.0

5 Slope of the function describing the relationship between DBH Annosus 2.0 0.79

and the length of time that the radius will not change in size Arm. and Phel.

(years/inch or years/cm). ≤12 in. 1.1111 0.4374

>12 in. 0.5556 0.2187

6 Intercept of this relationship (years). Annosus 0.0 0.0

Arm. and Phel.

≤12 in. 0.0 0.0

>12 in. 6.6667 6.6667

7 Disease Type 1 or type 1 or type

set in set in

RRTYPE RRTYPE

Imp. = imperial units, Met. = metric units.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 51

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

The number of years that a stump’s root radius is decreasing is determined by the size of the infected root

radius and by the DBH of the dead tree, so that a large stump will retain some effective inoculum longer than

a small stump. Users can control the decay rate of the inoculum, the infected root radius at which inoculum

decay seems to stop, and the number of years at which the decline of the radius of infection is negligible (fig.

5.8). The overall decay pattern is shown in figure 5.9 while the length of time needed for the total decay of the

inoculum in stumps of different sizes is shown in figure 5.10.

Figure 5.8 — Relationships controlled by the keyword

INOCLIFE: (A) decay rate of inoculum; (B) root

radius at which to stop decline; (C) number of years

to stop decline. The default relationships for each of

the root disease types are shown. Notice that the

values are given in imperial units on the left y-axis and

the bottom x-axis; metric units are shown on the

right y-axis and the top x-axis.

A

B

C

0

10

20

30

40

50

60

0 5 10 15 20 25 30

0 10 20 30 40 50 60 70

Years

DBH [in or cm (top)]

Number of years to stop decline

Annosus

Armillaria & Phellinus

Field 5

(slope)

Field 6

0.8

1.0

1.2

1.4

1.6

1.8

0.24

0.34

0.44

0.54

0 2 4 6 8 10 12 14 16 18 20

0 12 3 4 56

Decay rate (ft/year)

(m/year)

Infected root radius [ft or m (top)]

Decay rate of inoculum

Field 2

Field 1

(slope)

Annosus

Armillaria & Phellinus

0

2

4

6

8

0.0

0.5

1.0

1.5

2.0

2.5

0 5 10 15 20 25 30

0 10 20 30 40 50 60 70

Root radius (ft)

(m)

DBH [in or cm (top)]

Root radius at which to stop decline

Annosus Armillaria Phellinus

Field 3 

(slope)

Field 4
52 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

0

5

10

15

20

0

1

2

3

4

5

6

0 10 20 30 40

Infected root radius (feet)

(meters)

Years

Annosus Armillaria Phellinus Figure 5.9 — The resulting decay pattern for each

disease type. The example is for a 15-inch [38-cm]

diameter dead ponderosa pine tree. Notice that

each disease type has an initially different infected

root radius. This is because each disease type infects

a different proportion of the tree’s root system after

death. Notice that the values are given in imperial

units on the left y-axis and metric units on the right

y-axis.

Figure 5.10 — The time required for

the inoculum in different sized stumps

to decay completely. In each graph,

the thick line is Douglas-fir and the

thin line is western red cedar.

Differences between species occur

primarily because of differences in the

amount of root infected (based on root

radius and the proportion of the radius

infected at death). Imperial units are

on the bottom x-axis and metric units

0 on the top x-axis.

20

40

60

80

0 10 20 30 40 50

0 20 40 60 80 100 120

Time (years)

DBH (in or cm (top))

Doug.-fir

Cedar

Armillaria

0

10

20

30

40

50

60

0 10 20 30 40 50

0 20 40 60 80 100 120

Time (years)

DBH (in or cm (top))

Doug.-fir

Cedar

Phellinus

0

20

40

60

80

100

120

0 10 20 30 40 50

0 20 40 60 80 100 120

Time (years)

DBH (in or cm (top))

Doug.-fir

Cedar

Annosus

A

B

C
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

This keyword is very flexible and allows the user to enter a wide variety of decay patterns. The first

example below shows how to change inoculum decay relationships without changing the pattern of the

decay. Alternatively, the user may choose to simulate a linear decay of inoculum without a change in decay

rate (see the second example below). Notice that Armillaria root disease and Phellinus weirii have default

rates that depend on whether the tree is greater than or less than 12 inches [30.5 cm] DBH. When these values

are changed in INOCLIFE, the same value is applied to both size classes (fig. 5.11).

Figure 5.11 — Default and changed relationships

in the inoculum decay example. The default

relationships are those given in figure 5.8, while

the changed relationships are those in the example

above. Imperial units are used on the left y-axis

and the bottom x-axis while metric units are used

on the right y-axis (except in the third graph in

which the units do not change) and the top x-axis.

A

B

C

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.24

0.34

0.44

0.54

0 2 4 6 8 10 12 14 16 18 20

0123456

Decay rate (ft/year)

(m/year)

Infected root radius [ft or m (top)]

Decay rate of inoculum

Default: Armillaria & Phellinus

Changed

Default: Annosus

0

2

4

6

8

0.0

0.5

1.0

1.5

2.0

2.5

0 5 10 15 20 25 30

0 10 20 30 40 50 60 70

Root radius (ft)

(m)

DBH [in or cm (top)]

Root radius at which to stop decline

Changed

Defaults

0

10

20

30

40

50

60

0 5 10 15 20 25 30

0 10 20 30 40 50 60 70

Years

DBH [in or cm (top)]

Number of years to stop decline

Armillaria & Phellinus

Changed

Default

Annosus
54 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Example: Changing inoculum decay

Suppose a user decides that the infected portion of a small stump decays faster than that of a large

stump (negative slope in field 1) and that all root radii (indicated by a 0 in fields 3 and 5) stop

declining for 25 years (field 6) when the radius reaches one foot (field 4). The keyword would be:

INOCLIFE -0.02 2.0 0.0 1.0 0 25

If the same relationship were to be derived in metric units, the keyword would become:

INOCLIFE -0.02 0.61 0.0 0.3 0 25

Figure 5.11 shows the new relationships for each component. Figure 5.12 shows the resulting

pattern of the decay.

Example: Setting a single inoculum decay rate

Suppose that a pathologist decided that the decay rate of the inoculum did not change, i.e., the

pathologist eliminates the final part of the decay line. This can be done by setting fields 3-6 to zero

(i.e., telling the model to stop radius decline at 0 feet for 0 years for all sizes of trees).

INOCLIFE _ _ 0.0 0.0 0.0 0

Shown in figure 5.13 is the resulting decay curve as it would affect each root disease type.

0

5

10

15

20

0

1

2

3

4

5

6

0 5 10 15 20 25 30 35

Infected root radius (ft)

(m)

Years

Annosus Armillaria Phellinus Figure 5.12 — Changed inoculum decay. This

example is for a 15-inch [38-cm] diameter

ponderosa pine stump. The default decay patterns

are shown in figure 5.9. The scale on the left is in

imperial units and that on the right is in metric

units. The results are slightly different for each

disease type because they each start with a

different proportion of their root radius infected.

0

5

10

15

20

0

1

2

3

4

5

6

0 5 10 15 20

Infected root radius (ft)

(m)

Years

Annosus Armillaria Phellinus Figure 5.13 — Inoculum decay with a single decay

rate, for the changed parameters. This example

is for a 15-inch [38-cm] diameter ponderosa pine

stump. The default pattern is shown in figure

5.9. Note that Phellinus has a minimum lifespan

of 20 years, so the decay rate is lower than

specified in the keyword. Metric units are given

on the right y-axis and imperial units are on the

left y-axis.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 55

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

It is important to realize that all values given in the keyword INOCLIFE assume that the tree is a resinous

species (see Model Description, Chapter 4, table 2.1) and that there is no minimum lifespan of inoculum. If the

tree is non-resinous, the decay rate will be increased slightly (fig. 5.14), while if there is a minimum lifespan,

the decay rate will be decreased.

The user has control over the minimum lifespan of inoculum by using the keyword INOCSPAN to

specify the desired minimum number of years for inoculum to be present. This minimum number of years

will slow the initial rate of decay, but not the number of years at which the size of the root radius remains

constant (fig. 5.15).

INOCSPAN Set the minimum number of years each type of inoculum is present.

Field Description Default

1 Minimum life span (years) for P-type Annosus. 0

2 Minimum life span (years) for S-type Annosus. 0

3 Minimum life span (years) for Armillaria. 0

4 Minimum life span (years) for Phellinus. 20

5-7 Not used.

0

2

4

6

8

10

12

14

0

1

2

3

4

0 10 20 30 40

Infected root radius (ft)

(m)

Years

Resinous Non-resinous Figure 5.14 — Comparison between the decay rates

of a resinous and non-resinous tree. Notice the

increased decay rate for the non-resinous tree. This

example shows the pattern for Annosus root disease

only, but the relationship is similar for the other

root disease types. Metric units are given on the

right y-axis and imperial units are on the left y-axis.

Figure 5.15 — The effect of minimum lifespan on the

decay pattern in Phellinus-infected stumps. The

minimum lifespan is 20 years. Smaller stumps would

normally decay in a shorter amount of time, but

since they must live at least 20 years, their initial rate

of decay is slower. Metric units are given on the right

y-axis and imperial units are on the left y-axis.

0

5

10

15

20

0

1

2

3

4

5

6

0 5 10 15 20 25 30 35

Infected root radius (ft)

(m)

Years

5in [13cm]

10in [25cm]

15in [38cm]

20in [51cm]
56 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

5.5 Assumptions About Trees

5.5.1 Host Tree Species (Advanced, Annosus Root Disease Only)

When Annosus root disease is simulated, the tree species are assigned as susceptible to P-type or S-type, and

each species is susceptible to only one of these types. Some advanced users may wish to use the model to

simulate a different root disease, and may therefore need to change the host tree species for a disease type.

This can be done using the keyword RRHOSTS. With this keyword, users can specify the new Annosus root

disease type for each tree species.

RRHOSTS Specify the host disease type for each tree species (Advanced, Annosus root disease only)

Field Description

1 Tree species to be modified (number or character code). A ‘0’ or ‘ALL’ here indicates all species, and

requires a supplemental record.

2 New disease type for which this tree is considered host (1=P-type Annosus, 2=S-type Annosus). This field is

ignored if field 1 indicates all species. Defaults are given in Appendix 1.

3-7 Not used.

Suppl. Eleven values starting in column 1, each with a field width of five places Record (including the decimal). All

values must be entered. The supplemental record is required only if field 1 is 0 or ‘ALL’.

Notice that if more than one species is to be changed, the keyword requires a supplemental record. This record is a

line following the keyword in which the user must enter a value (1 or 2) for each tree species (see the example below).

Example: Changing the Annosus type for more than one tree species

Suppose all species except white fir (code 4) and Douglas-fir (code 3) are to be considered host to Ptype Annosus root disease (code 1), the keyword would be:

RRHOSTS 0

11221111111

The ‘0’ in the first field of the keyword indicates that more than one species is to be changed. The ‘1’

or the ‘2’ tells the model which disease type the corresponding tree species will be assigned.

The keyword RRHOSTS can also be used to change the disease type of only one tree species. In this case, the

supplemental record is not used, and users specify the species code number (field 1) and the new disease type (field 2).

Example: Changing the Annosus root disease type for only one tree species

Suppose that the stand contains a tree species which is susceptible to S-type Annosus root disease

but is not one of the 10 specified species in FVS (and thus will be given species code 11, or ‘other’).

By default, the tree species in the ‘other’ category will be assumed to be susceptible to P-type

Annosus root disease. The keyword to change the Annosus root disease type from P-type (code 1)

to S-type (code 2) for tree species 11 (field 1) is:

RRHOSTS 11 2

5.5.2 Root Radius

Root radius is dependent on the DBH and species of a tree. The size of the root radius affects the spread rate of

centers and the infection rate of healthy trees within centers. Smaller root radii reduce the size of centers and the
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 57

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

number of infected trees. Using the keyword SDIRMULT, users can create a multiplier (ranging from 0.5 to 1.5)

that further changes root radius based on the density of the stand. By default this multiplier is not present.

SDIRMULT Allow the root radius to be modified by stand SDI.

Field Description Default

Imp Met

1 “Normal” Stand Density Index (SDI at which the multiplier should be 1). 369 826

2 Slope of the relationship between SDI and multiplier (negative slope means -0.0033 -.0013

the multiplier is small when SDI is large or that areas in crowded stands

have a smaller root system than expected).

3-7 Not used.

Imp. = imperial units, Met. = metric units

The assumption behind the default values in this keyword is that trees of similar DBH in dense stands

(those with SDI greater than 369 [826 in metric]) will have a smaller root radius than those in a “normal”

stand (SDI = 369 [or 826]) and that those in an open grown stand (SDI < 369 [< 826]) will have a larger root

radius (fig. 5.16). To use the default conditions, the keyword must be entered with no values in the fields.

Example: Changing the root radius based on density

Suppose a user running the imperial version believed that the effect of density should go in the

opposite direction (and be given a slope of 0.001, field 2), and that the SDI of a “normal” stand was

actually 450 (fig. 5.16), then the keyword would be:

SDIRMULT 450 0.001

If a different user was running the metric version of the model and wanted to use the keyword to

produce exactly the same effect, the keyword would be:

SDIRMULT 1112 0.0004

The effect of this change is shown in figure 5.17.

Figure 5.16 — Default curve for the SDI-based

root radius multiplier, with the parts that can

be changed with each field of the keyword

labelled. Imperial units are used on the bottom

x-axis and metric units are on the top axis.

0.0

0.5

1.0

1.5

2.0

0 200 400 600 800

0 300 600 900 1200 1500 1800

Root radius 

SDI

field 1

field 2

0.0

0.5

1.0

1.5

2.0

0 200 400 600 800

0 300 600 900 1200 1500 1800

Root radius multiplier

SDI

Default

Changed

Figure 5.17 — Effect of changing the parameter

values for the root radius multiplier. The

parameter values used are those in the

SDIRMULT keyword example. The default curve

is shown for comparison. Notice that the metric

SDI values are given on the top axis while the

bottom axis contains the imperial values.
58 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

5.6 Random Numbers

The Western Root Disease Model uses random numbers in simulating the spread rates of centers and in

calculating the infection that occurs inside centers. Each time a run is repeated with the same stand and the

same keyword set, the results will be identical because the model will be choosing the same set of random

numbers. To get some idea of the variability in behavior which results from the choice of random number,

users should choose a different starting point for the random number generator using the keyword RSEED.

RSEED Select a new ‘seed’ value for the random number generator.

Field Description Default

1 The new random number generator seed value (positive, odd integer). 889347

2-7 Not used

5.7 Compressing the Root Disease Tree Lists

The root disease model tree lists can contain at most 500 tree records. This is a smaller size limit than in FVS

(with no root disease model extension present) but is necessary to reduce the amount of memory used by the

tree lists (see the detailed model description for more information about the format of WRDM3 tree lists). By

default, the model keeps the tree list under 150 records. Users may change the number of tree records used in

the model by using the keyword RRCOMP. If any planting or regeneration occurs in the model, the tree lists

may exceed the specified tree limit, and compression will occur.

RRCOMP Maximum number of records in the tree list.

Field Description Default

1 The maximum number of records in the tree list. A value ≤500 must be present. 150

2-7 Not used.

6.0 Managing Root Disease

6.1 Introduction

Few options exist for managing root disease once it has entered a stand. Many of these options may,

however, be simulated using FVS and root disease model keywords. Table 6.1 lists several techniques, and

Table 6.1 — Options for managing root disease and their implementation in FVS.1

Effect Technique Keywords

Inoculum removal removing (pushing) stumps PSTUMP

“push-over” harvest FVS harvest keywords and PSTUMP

Species change plant less susceptible species FVS harvest and regeneration keywords

increase time before regeneration

Inoculum avoidance plant trees away from infection Cannot be modelled (but may be approximated by

using FVS regeneration keywords to plant at a lower

than normal density).

Chemical agents use chemicals to prevent infection BORATE (Annosus only)

1 Many of the effects and techniques in this table are based on those listed in the Root Disease Management Guidebook published by

the BC Ministry of Forests and BC Environment (1995).
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 59

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

the keywords that can be used to perform the operations. Example keyword sets are given below to

illustrate the different techniques.

Users should always be wary of thinning root diseased stands, especially those containing Armillaria root

disease and Annosus root disease. Thinning may often increase root disease in the stand because it allows the

disease to spread more rapidly through the root systems of the trees that were cut and may introduce new

infection courts. Some of this effect may be countered by some of the management techniques below.

6.2 Inoculum Removal

Inoculum removal is just that: removing the inoculum, in the form of infected roots, from the soil. This can be

done by pushing stumps currently present in the stand (i.e., stumps that were not necessarily created by a

harvest that year or any year), or by harvesting and removing the stumps as part of the same operation. In

both cases, the keyword PSTUMP must be used while in the second case, a harvest must first be done in FVS.

Note that the PSTUMP keyword affects all dead infected trees whether they are cut stumps or snags. The

keyword specifies the year or cycle in which stumps are pushed and the proportion of stumps above a certain

size to remove. Figure 6.1 shows an example of the effect of removing stumps.

Figure 6.1 — The effect of inoculum removal. Three runs are shown here: no root

disease ( ■ ), leaving the infected stumps in the stand ( ● ) and pushing 90 percent of

the infected stumps ( ▲ ). The stand was projected to be clear-cut in 2000 and

planted with Douglas-fir. 90 percent of stumps were pushed (see example). Metric

units (sq. m/ha) are given on the right y-axis and imperial units (sq. ft./acre) are on the

left y-axis.

B

B

B

B B

B

B

B

B

B

B

H

H

H

H H

H

H

H

H

H H

J

J

J

J J

J

J

J

J J J

1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080

0

50

100

150

200

250

300

0

23

46

69

BA

Year

B No RR

H Push 90%

J Leave Stumps

PSTUMP Simulate stump pushing to remove infected stumps.

Field Description Default

1 Year in which stump pushing is to occur (calendar year or cycle number). 1

2 Proportion of stumps to be removed. 1.0

3 Minimum stump diameter ( DBH of the tree that left the stump) for stump 0.0

pushing (inches or cm).

4-7 Not used.
60 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Example: Inoculum removal

Suppose that in 2000 a stand is clear-cut and the stumps and large roots of the cut trees are to be

removed. The keywords would be (see the FVS keyword guide for information about harvest

keywords):

THINABA 2000 0.0

RDIN

miscellaneous root disease model keywords

PSTUMP 2000 0.9 0.0

END

Note that 90 percent of all diseased stumps and infected standing dead trees in the stand will be

removed by this keyword, including those which were in the stand before the harvest.

6.3 Species Change

Tree species differ in their response to the various root diseases. Some trees are highly susceptible, while

others are more resistant. This is especially true for Annosus root disease, in which tree species are

susceptible only to S- or P-type Annosus, but not to both. The relative susceptibility of the different tree

species defined by the default values is listed in the appendix. Examples of very susceptible and resistant

species are listed in table 6.2.

There are two methods for implementing a species change:

1. Regenerate only with more resistant species (fig. 6.2).

2. Do not allow any regeneration on the stand for several years, i.e., long enough for the inoculum

to leave the site (fig. 6.3). This is considered a species change because other species (grasses, etc.)

will use the site in the interim.

Table 6.2 — Most resistant and susceptible tree species for each root disease type. These are based on the default values for the Inland Empire

and the Southern Interior variants. When more than one species is listed, they have the same (or very similar) default values.

Annosus Armillaria Phellinus ___________________________________________________________________________________________________

S-type P-type

Most resistant any P-type host any S-type host Western red cedar Western red cedar

Most susceptible Larch, Douglas-fir, Ponderosa pine True firs, Douglas-fir Douglas-fir, Grand fir,

Spruce, true fir Alpine fir

Figure 6.2 — Effect of planting different species

after a clear-cut. Four runs are shown here: no

root disease and planting Douglas-fir ( ■ ), no

root disease and planting red cedar ( ❑ ),

Phellinus weirii and planting Douglas-fir ( ▲ ),

and Phellinus weirii and planting red cedar ( ● ).

Notice that the root disease has little effect on

the red cedar (a more resistant species), and

more effect on Douglas-fir (a fast-growing but

susceptible species). Metric units (sq. m/ha) are

given on the right y-axis, and imperial units (sq.

ft./acre) are on the left y-axis.

B

B

B

B B

B

B

B

B

B

B

H

H

H

H H

H

H

H

H

H H

J

J

J

J J J J

J

J

J

J

T

T

T

T T T T

T

T

T

T

1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080

0

50

100

150

200

250

300

0

23

46

69

BA

Year

B No RR (DF)

H Plant DF

J Plant C

T No RR (C)
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Example: Species change by planting a more resistant tree species

Suppose that in 2000 a stand infected with Phellinus weirii is clear-cut and red cedar, a relatively

resistant species, is planted. No root disease management keywords are necessary. See FVS and

Establishment model keyword guides for information about these keywords.

THINABA 2000 0.0

ESTAB 2000

PLANT 2003 6 400

TALLYONE 2007

END

RDIN

RRTYPE 4

miscellaneous root disease model keywords

END

Example: Species change by increasing the regeneration delay

Suppose that in 2000 a stand infected with Armillaria root disease is clear-cut and no planting

occurs for 30 years, at which time, Douglas-fir is planted (code 3). No root disease management

keywords are necessary. See FVS and Establishment model keyword guides for information about

these keywords.

THINABA 2000 0.0

ESTAB 2025

PLANT 2030 3 400

TALLYONE 2037

END

RDIN

RRTYPE 3

miscellaneous root disease model keywords

END

The delayed planting has an additional effect in the model. During the period in which no trees are in the

stand, the model starts reducing the size of the remaining centers (see the Model Description). At the time of

planting, all centers may have disappeared, which means the disease has disappeared, or they may just have

Figure 6.3 — Effect of planting several years

after a clear-cut. Two runs are shown here: no

root disease ( ■ ) and Armillaria root disease

(▲ ). In both cases, the harvest and planting

keywords were those shown in the example.

The 30-year delay in planting gives the stand a

chance to become free of root disease (so the

runs overlap in the graph). Metric units (sq. m/

ha) are given on the right and imperial units (sq.

ft./acre) are on the left y axis.

B

B

B

BBB B

B

B

B

B

H

H

H

HHH H

H

H

H

H

1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080

0

50

100

150

200

250

0

10

20

30

40

50

BA

Year

B No RR

H With RR
62 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

been reduced in size, which will also reduce the amount of infection which occurs after planting. Species

change is the only mechanism by which centers will disappear.

Note that the model does consider the effect of other competing vegetation such as grasses or shrubs on

the establishment success of the delayed planting. Additional site preparation may be required. In addition,

in natural systems natural regeneration may occur during the period in which planting is delayed. If the trees

that are regenerated naturally are susceptible to the root disease, they may carry the disease until the stand is

planted. This would likely remove the benefits to the delayed planting.

6.4 Other

The remaining two management options are: inoculum avoidance and chemical application. Inoculum

avoidance would be done in the field by planting trees far away from root disease infected stumps so that,

ideally, the root systems of the stump and the new tree would not overlap. Since the root disease model does

not model the location of each tree and stump, this option is not possible to simulate in the model. It may,

however, be simulated to some degree by telling FVS to plant at a lower density. Then, when the root disease

model simulates root-to-root infection, it will be less likely to place two root systems overlapping. The model

will likely still overestimate the amount of root disease infection occurring after the planting, if planting

crews in the field can indeed accurately determine the location and extent of inoculum.

Special Keyword for Annosus root disease

Chemical application is simulated in the model as borate application on stumps through the use of the

BORATE keyword. Borate can be applied to stumps after harvesting to reduce colonization by Annosus root

disease spores. By default, no borate application occurs, but using the keyword BORATE, the user can

simulate this management action. In the keyword, the user must indicate the minimum size stump and the

proportion of these stumps which will receive borate treatment. These parameters can be changed in any

cycle, hence borate can be applied after one harvest but not after a second. In the model, borate application

has a large impact on the number of centers in the stand but has little effect on the stand dynamics, at least for

the first several decades (fig. 6.4).

Figure 6.4 — Effect of borate application on basal area (left) and number of centers (right). The model was run using the keywords

in the borate example, with the exception of the second BORATE keyword (so that borate application will be done every time there

is a cut). Three runs are shown: no root disease ( ■ ), S-type Annosus root disease present with no borate ( ● ), and S-type Annosus

root disease present with borate application after both thinnings ( ▲ ). Note that since the borate was only 80 percent effective, the

number of centers increases even with the borate application. In the graph on the left, metric units (sq. m/ha) are given on the right

y-axis and imperial units (sq. ft./acre) are on the left y-axis.

B

B

B

B

B

B

B

B

B

B

B

H

H

H

H

H

H

H

H

H H H

J

J

J

J

J

J

J

J

J J J

1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080

0

50

100

150

200

250

300

0

23

46

69

BA

Year

B No RR

H Borate All

J No Borate JJJJ

JJJ

JJJJ

HHHH HHH

HHHH

1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080

0

5

10

15

20

25

30

35

40

45

50

Number of centers

Year

J No Borate

H Borate All
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

BORATE Simulate borate application to reduce number of new centers after a cut.

Field Description Default

Imp Met

1 Year in which to change the parameters (cycle number). 1 1

2 Proportion of newly cut stumps to receive borate treatment. 0.95 0.95

3 Minimum stump diameter for borate treatment (inches or cm). 12 30.5

4-7 Not used.

Imp. = imperial units, Met. = metric units.

It is not necessary to use the same cycle numbers in the thinning and borate keywords. This is because the

cycle number in BORATE indicates when the parameter values change, not when borate treatment occurs.

Example: Applying borate

Suppose that a thinning from below occurred in cycles 3 and 6, and that borate treatment was to

occur after the first thinning only. The keywords would be (see the FVS keyword guide for details

on harvesting keywords):

THINBBA 3 90

THINBBA 6 90

RDIN

miscellaneous root disease model keywords

BORATE 1 .8 14

BORATE 5 0 0

END

The same effect would occur by changing the first borate cycle number to be any integer between 1

and 3 and the second to be any integer between 4 and 6.

Note that borate application is reported as “DONE” in the activity scheduler in the main output file in the

cycle when parameter values are changed, not in the cycle in which borate is actually applied.

7.0 Output

Several output files provide information specific to the Western Root Disease Model, version 3.0.

Some of these files are printed automatically while others can be requested using one of the five relevant root

disease model keywords (table 7.1). Example output files are provided and described in the following sections.

Many of the keywords used for producing output (except RRDOUT) must be followed by the OPEN keyword.

Table 7.1 — Keywords controlling the model output tables.

Section Keyword Function Default file number

(field 1)

7.2 RRECHO Print summary table to separate file without headers 24

7.3 RRDOUT Print detailed output (to main output file) Not used

7.4 BBOUT Print bark beetle information. 27

7.5 SMCOUT Print center spread rate Monte Carlo results. 26

7.6 INFSIMS Print and control inside infection Monte Carlo results. 25 (field 3)
64 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

OPEN Open a file for printing output.

Field Description Default

1 File unit number (20-35) 24

2 Handling of blanks within a record: 0=spaces are zeros, 1=spaces are nulls. 0

3 File status: 0=unknown, 1=new, 2=old, 3=fresh (if file exists, delete and create new). 3

4 File record length (1-132) 132

5-7 Not used.

Suppl. Required.

Record Name of file to be opened.

Example: Opening a file for output

Suppose a user wishes to print the summary output information to a separate file called

“SUMMARY.OUT”; the required keywords are:

RRECHO 24

OPEN 24

SUMMARY.OUT

Further examples are given in the sample keyword set in Section 8.1.

In the following sections, sample output files are shown for both imperial and metric units. It is important

to notice that the only difference between the files is in the headings; otherwise, all columns contain the same

sort of information. The output files that have been chosen also demonstrate the difference between the

output files when they are reporting different root disease types. The output files will show a variety of cases:

both types of Annosus root disease, Armillaria root disease, or Phellinus weirii.

7.1 Input Information

The first section of the main output file (“Options selected by input”) contains a listing of each keyword in the

keyword file and a description of the values in each parameter field. It also gives other information such as

whether bark beetles were selected by default, whether the stand is being run as one center, and any errors

that occurred while reading the keywords.

The next section of input information is entitled “Summary of root disease information read in on input.”

If the RRTREIN keyword was used, this section reports the number of infected and uninfected tree records

and infected stump records inside root disease centers of each disease type. It also lists the number of

resinous and non-resinous infected stumps by size class and disease type (input either using STREAD or

RRTREIN, tables 7.2 and 7.3). If, after running the model, the results given in later tables seem unusual, these

sections provide good opportunities to check the input information and data.

Notice that there are two types of information under the heading “Summary of Disease Information Read

in on Input” (tables 7.2 and 7.3). The first few lines of this output give the number of records that were in the

inventory that had these characteristics (i.e. 2 infected tree records, 49 uninfected records, and 0 stumps in the

first half of table 7.2). The second part of the output summarizes the number of stumps that are present

initially (not the density). These will include the stumps in the stump records and the stumps entered under

the STREAD keyword.
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 7.2 — Summary of root disease information read in on input (imperial). In this example, the model is being simulated with both Pand S-type Annosus root disease, although the only stumps in the inventory were in S-type Annosus root disease.

-------------------------------------------------------------------------------------------------------------------------------------

SUMMARY OF ROOT DISEASE INFORMATION READ IN ON INPUT

-------------------------------------------------------------------------------------------------------------------------------------

DISEASE TYPE: P

NUMBER OF INFECTED TREE RECORDS IN DISEASE PATCHES= 2.

NUMBER OF UNINFECTED TREE RECORDS IN DISEASE PATCHES= 49.

NUMBER OF INFECTED STUMP RECORDS IN DISEASE PATCHES= 0.

NUMBER OF INFECTED STUMPS

SIZE CLASS RESINOUS NON-RESINOUS

---------------------------------------------------

0- 12" 0. 0.

12- 24" 0. 0.

24- 48" 0. 0.

48-100" 0. 0.

>100" 0. 0.

DISEASE TYPE: S

NUMBER OF INFECTED TREE RECORDS IN DISEASE PATCHES= 16.

NUMBER OF UNINFECTED TREE RECORDS IN DISEASE PATCHES= 97.

NUMBER OF INFECTED STUMP RECORDS IN DISEASE PATCHES= 5.

NUMBER OF INFECTED STUMPS

SIZE CLASS RESINOUS NON-RESINOUS

---------------------------------------------------

0- 12" 0. 100.

12- 24" 50. 0.

24- 48" 0. 0.

48-100" 0. 0.

>100" 0. 0.

-------------------------------------------------------------------------------------------------------------------------------------

Table 7.3 — Summary of root disease information read in on input (metric). In this example, the model is being simulated with Armillaria

root disease (disease type “A”), and both infected stumps and infected trees were present in the inventory.

-------------------------------------------------------------------------------------------------------------------------------------

SUMMARY OF DISEASE INFORMATION READ IN ON INPUT

-------------------------------------------------------------------------------------------------------------------------------------

DISEASE TYPE: A

NUMBER OF INFECTED TREE RECORDS IN DISEASE PATCHES= 153.

NUMBER OF UNINFECTED TREE RECORDS IN DISEASE PATCHES= 213.

NUMBER OF INFECTED STUMP RECORDS IN DISEASE PATCHES= 20.

NUMBER OF INFECTED STUMPS

SIZE CLASS RESINOUS NON-RESINOUS

---------------------------------------------------

0- 30 CM 506. 28.

30- 61 CM 0. 0.

61-122 CM 0. 0.

122-254 CM 0. 0.

>254 CM 0. 0.

-------------------------------------------------------------------------------------------------------------------------------------
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 7.5 — Standard root disease output table, with headings and in metric units. Only Phellinus weirii is present in this stand, and is

identified by the “W” in the column “TYPE.” This output table also shows an example of the entire 2-ha stand being within a single disease

center (the number of centers is 1, and the spread rate is 0).

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

GENERAL ROOT DISEASE MODEL

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

SUMMARY STATISTICS FOR ROOT DISEASE AREAS (PER HECTARE BASED ON DISEASED AREA ONLY)

-------------------------------------------------------------------------------------------------------------------------------------

DISEASE STATISTICS DEAD TREE CHARACTERISTICS LIVE TREE CHARACTERISTICS

STUMPS LOSSES

FROM DISEASE

--------------------------- ------------- -------------- --------------------------------------

UNINF INFECTED OVERALL NO OF VOLUME NO DISEASE SPREAD BA/ TREES LOSSES NO OF NO OF AVE MERCH BA/ IDENTIFIERS

OF AREA RATE TOTAL ACRE KILLED CU FT TREES TREES %ROOTS CU FT ACRE YEAR AGE TYPE CENTS HA M/YR /HA SQ M /HA /HA /HA /HA INFECTED /HA SQ M STAND IDMGMT ID

-------------------------------------------------------------------------------------------------------------------------------------

1992 26 W 1 2.00 .00 267.0 4.8 .0 .0 1431.0 217.0 56.6 26. 18. SUGAR NONE

2002 36 W 1 2.00 .00 .1 .0 231.1 19.0 911.9 390.3 18.0 107. 28. SUGAR NONE

2012 46 W 1 2.00 .00 .3 .0 57.6 6.4 687.9 424.6 32.6 179. 34. SUGAR NONE

2022 56 W 1 2.00 .00 224.5 6.7 191.7 56.7 390.3 462.2 24.8 213. 33. SUGAR NONE

2032 66 W 1 2.00 .00 183.2 6.4 158.4 55.8 197.4 457.8 27.8 234. 31. SUGAR NONE

7.2 Standard Output

The standard output table contains the summary information about the status of root disease in the stand. For the

root disease type(s) present, the model reports the number of centers, the area in root disease, and the average

spread rate, as well as the density of live infected and uninfected trees and the density and basal area of dead

infected trees and stumps. A more detailed description of each column is provided below, and examples are

given in tables 7.4 and 7.5. Note that all densities in this table are based on the area in root disease only.

Table 7.4 — Standard root disease output table, with headings and in imperial units. Both Annosus root disease types are present in this stand,

so information about both disease types is printed in the table. Had only S-type been present, for example, the P-type rows would not be printed.

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

GENERAL ROOT DISEASE MODEL

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

SUMMARY STATISTICS FOR ROOT DISEASE AREAS (PER ACRE BASED ON DISEASED AREA ONLY)

-------------------------------------------------------------------------------------------------------------------------------------

DISEASE STATISTICS DEAD TREE CHARACTERISTICS LIVE TREE CHARACTERISTICS

STUMPS LOSSES

FROM DISEASE

--------------------------- ------------- -------------- --------------------------------------

UNINF INFECTED OVERALL

NO OF VOLUME NO DISEASE SPREAD BA/ TREES LOSSES NO OF NO OF AVE MERCH BA/ IDENTIFIERS

OF AREA RATE TOTAL ACRE KILLED CU FT TREES TREES %ROOTS CU FT ACRE

YEAR AGE TYPE CENTS ACRES FT/YR /ACRE SQFT /ACRE /ACRE /ACRE /ACRE INFECTED /ACRE SQFT STAND IDMGMT ID

-------------------------------------------------------------------------------------------------------------------------------------

1990 100 P 5 1.03 .00 4.8 3.9 .0 .0 64.2 64.2 9.5 2336. 100. MIXED NONE

1990 100 S 5 1.04 .00 6.3 7.7 .0 .0 118.6 118.6 11.7 325. 36. MIXED NONE

2000 110 P 5 1.18 .50 70.0 33.3 65.8 874.4 57.9 4.5 38.2 2101. 78. MIXED NONE

2000 110 S 5 1.10 .19 5.9 7.3 102.3 112.8 109.5 24.8 48.6 525. 37. MIXED NONE

2010 120 P 5 1.50 .77 35.9 40.0 18.0 536.1 39.7 2.5 38.4 1420. 54. MIXED NONE

2010 120 S 5 1.22 .30 40.2 18.9 19.8 55.0 97.2 12.1 50.7 442. 29. MIXED NONE

2020 130 P 5 1.84 .77 27.4 38.3 12.4 277.8 31.7 1.8 39.7 1311. 49. MIXED NONE

2020 130 S 5 1.31 .27 22.0 10.7 12.9 33.7 89.7 8.6 36.8 481. 30. MIXED NONE

2030 140 P 5 2.11 .56 26.6 35.6 7.9 222.3 25.1 1.9 37.8 1189. 43. MIXED NONE

2030 140 S 5 1.45 .29 15.8 8.4 12.5 53.5 88.2 10.4 47.6 678. 36. MIXED NONE
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 67

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Year: The first year of the cycle for which the information is given (unless otherwise specified

below).

Age: Stand age. This will be the same as the age in the summary stand output table.

Disease Statistics:

Type: The type of root disease for which the information is given:

P P-type Annosus root disease

S S-type Annosus root disease

A Armillaria root disease

W Phellinus weirii (the W comes from weirii)

No. of Cents:

The number of centers in the stand. Note that if there are several centers, or if the centers are

large, parts of centers may overlap.

Disease Area:

Area in root disease centers (acres or hectares). This is the area in the stand that is considered

to be within centers (overlapping areas are counted only as being within one center). This is

not necessarily the same as the amount of area containing inoculum (fig. 1.2).

Spread Rate:

The rate of spread of the edge of centers (ft/year or m/year) in the previous cycle. For

example, if using 10-year cycles, then the spread rate printed in the year 2000 is for the period

1990-1999, and is the spread used to get from the area printed in 1990 to the area printed in

2000. This means that the spread rate in the first year will always be 0.

Dead Tree Characteristics:

Stumps:

Total: Density of dead infected trees and stumps within root disease centers.

BA: Basal area (sq.ft./acre or sq.m/ha) of these dead infected trees and stumps.

Losses from Disease:

No. of Trees Killed: Density of trees killed by root disease only. This number does not

include any trees killed by bark beetles, windthrow, or other agents (trees/acre or trees/

ha).

Volume Losses: The total volume of the trees killed by root disease only (cu ft/acre or cu m/ha).

Live Tree Characteristics:

Uninf: Density of uninfected host trees within centers (trees/acre or trees/ha).

Infected:

No. of Trees: Density of trees infected by this root disease type (trees/acre or trees/ha).

Avg. percent Roots Inf: The average percentage of the root radius of live infected trees that is

colonized by root disease.

Overall:

Merch: Merchantable cubic ft/acre or cubic m/ha of live trees within centers.

BA: Basal area (sq.ft./acre or sq.m/ha) of live trees within centers.

Identifiers:

Stand ID: Stand identification given in the FVS keywords.

Mgmt ID: Management identification given in the FVS keywords.

Each of the categories describing trees considers only host trees inside disease centers. Therefore, if the

entire stand is being run as one center, and non-host trees are present in the stand (such as when simulating

only one Annosus root disease type when trees that are susceptible to the other type are present), the density

of trees may not be the same as that in the standard FVS summary table.

This output table is always printed at the end of the main FVS output file and is not under user control.
68 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Using the RRECHO keyword, users may request that a machine-readable version of this information (i.e., no

headings, no blank lines, and values always present for both Annosus root disease types) be printed in a

separate file. This is useful as input for statistical and graphical software packages.

7.3 Detailed Output

A more detailed root disease output table (tables 7.6 and 7.7) can be requested using the keyword RRDOUT.

This information is also printed in the main FVS output file below the standard root disease output

information and contains column headings. All information in this table is applicable within root disease

centers only. Columns are self-explanatory but are described below.

Year: The initial year of the cycle.

Type: Root disease type

P P-type Annosus root disease

S S-type Annosus root disease

A Armillaria root disease

WPhellinus weirii (the W comes from weirii)

Area: Area (acres or hectares) inside centers of the appropriate disease type.

SP: Tree species. There may be several species of trees within one disease type, and the

information for each species will be listed separately.

Killed Trees: DBH distribution of trees killed by root disease. This is given as a percentile

distribution which lists the DBH which is greater than x percent of the trees. For

example, if the 90 percent DBH = 15.8, then 90 percent of the trees are 15.8 or

smaller.

Total: The total density (trees/root diseased acre [ha]) of this species of tree killed by root

disease.

Alive Trees: DBH distribution of living (infected or uninfected) trees within root disease

centers.

Uninfected: Density of uninfected trees (trees/root diseased acre [ha]).

Infected: Density of infected trees (trees/root diseased acre [ha]).

% roots infected: Percentage of the root radius of infected trees that is infected.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 69

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 7.6 — Example detailed output file (imperial). In this example, both Annosus root disease types are present, but each disease type

contains only one species of tree. If other species were present, they would be listed under the appropriate disease type (see table 7.7).

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

WESTERN ROOT DISEASE MODEL DETAILED OUTPUT VERSION 1.0

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

STAND ID= MIXED MANAGEMENT ID= NONE

STAND ATTRIBUTES INSIDE ROOT DISEASE PATCHES

-------------------------------------------------------------------------------------------------------------------------------------

ROOT KILLED TREES ALIVE TREES

DISEASE %TILE POINTS BY DBH (INCHES) TOTAL %TILE POINTS BY DBH (INCHES) TOTAL TOTAL

---------- ------------------------------------ KILLED -------------------------------------- UNINFECTED INFECTED %ROOTS

YEAR TYPE AREA SP 10 30 50 70 90 100 TREES/ 10 30 50 70 90 100 TREES/ TREES/INFECTED ACRE ACRE ACRE

-------------------------------------------------------------------------------------------------------------------------------------

1990 P 1.0

PP .0 .0 .0 .0 .0 .0 .0 4.7 6.0 8.2 10.8 16.2 50.0 64.2 64.2 9.54

S 1.0

WF .0 .0 .0 .0 .0 .0 .0 2.0 2.0 3.0 3.7 7.8 41.0 118.6 118.6 11.71

-------------------------------------------------------------------------------------------------------------------------------------

2000 P 1.2

PP 5.6 6.9 9.2 10.7 15.8 20.8 65.8 5.9 7.4 9.8 13.5 22.0 51.1 57.9 4.5 38.15

S 1.1

WF 2.1 2.1 2.1 3.9 6.5 7.4 102.3 2.1 2.1 3.9 7.0 9.8 43.2 109.5 24.8 48.63

-------------------------------------------------------------------------------------------------------------------------------------

2010 P 1.5

PP 6.5 7.6 10.2 11.8 17.6 52.3 18.0 6.6 8.9 11.3 15.2 21.2 52.3 39.7 2.5 38.39

S 1.2

WF 2.2 2.2 3.9 4.9 8.6 15.2 19.8 2.2 2.2 4.5 5.4 10.5 44.8 97.2 12.1 50.67

-------------------------------------------------------------------------------------------------------------------------------------

2020 P 1.8

PP 7.0 8.2 11.1 12.6 16.9 53.0 12.4 7.3 9.6 12.5 16.4 22.4 53.0 31.7 1.8 39.68

S 1.3

WF 2.3 2.3 5.1 6.0 8.2 18.6 12.9 2.3 2.3 5.1 6.1 11.6 46.8 89.7 8.6 36.77

-------------------------------------------------------------------------------------------------------------------------------------

2030 P 2.1

PP 7.5 9.4 12.0 13.4 18.2 53.9 7.9 7.9 11.5 13.4 17.4 23.8 53.9 25.1 1.9 37.80

S 1.4

WF 2.5 2.5 5.6 5.9 9.6 20.4 12.5 2.5 2.5 5.6 6.8 12.9 48.1 88.2 10.4 47.58
70 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 7.7 — Example detailed output file (metric). Armillaria root disease is being simulated. Several different species of trees are present

in the stand.

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

WESTERN ROOT DISEASE MODEL DETAILED OUTPUT VERSION 1.0

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

STAND ID= SUGAR92 MANAGEMENT ID= NONE

STAND ATTRIBUTES INSIDE ROOT DISEASE PATCHES

-------------------------------------------------------------------------------------------------------------------------------------

ROOT KILLED TREES ALIVE TREES

DISEASE %TILE POINTS BY DBH (CMS) TOTAL %TILE POINTS BY DBH (CMS) TOTAL TOTAL

---------- ------------------------------------ KILLED -------------------------------------- UNINFECTED INFECTED %ROOTS

YEAR TYPE AREA SP 10 30 50 70 90 100 TREES/ 10 30 50 70 90 100 TREES/ TREES/INFECTED HA HA HA

-------------------------------------------------------------------------------------------------------------------------------------

1992 A 2.0

WP .0 .0 .0 .0 .0 .0 .0 11.0 15.0 15.0 17.0 23.0 23.0 7.0 .5 50.00

L .0 .0 .0 .0 .0 .0 .0 7.0 11.0 13.0 17.0 21.0 35.0 7.0 1.0 60.00

DF .0 .0 .0 .0 .0 .0 .0 7.0 9.0 11.0 13.0 15.0 23.0 1244.0 202.5 56.92

WH .0 .0 .0 .0 .0 .0 .0 5.0 7.0 9.0 13.0 17.0 55.0 69.5 5.5 53.64

C .0 .0 .0 .0 .0 .0 .0 5.0 7.0 9.0 11.0 19.0 63.0 57.0 .5 50.00

LP .0 .0 .0 .0 .0 .0 .0 13.0 13.0 17.0 17.0 17.0 17.0 1.5 .0 .00

S .0 .0 .0 .0 .0 .0 .0 5.0 7.0 7.0 9.0 13.0 15.0 45.0 7.0 50.71

 2002 A 2.0

WP 6.4 6.4 6.4 6.4 6.4 6.4 .5 12.0 18.8 22.3 23.2 27.6 32.1 2.4 3.9 10.01

L 3.9 3.9 11.3 11.3 11.3 11.3 1.0 8.8 13.6 18.2 20.0 24.7 36.4 5.3 .6 3.23

DF 6.2 9.4 12.3 14.9 19.1 27.6 216.6 8.6 13.0 14.6 17.4 22.7 31.4 749.0 379.4 18.36

WH 4.0 4.4 4.4 5.6 5.6 9.0 5.5 8.3 11.5 16.1 17.8 24.7 60.6 60.9 3.6 4.87

C 5.9 5.9 5.9 5.9 5.9 5.9 .5 5.9 8.2 10.3 14.0 22.1 65.4 52.6 1.5 1.00

LP 5.9 5.9 5.9 5.9 5.9 5.9 .0 14.8 14.8 20.1 20.1 20.1 20.1 1.2 .1 .00

S 6.6 8.1 11.2 11.4 12.6 16.6 7.0 7.7 9.3 10.9 11.9 17.0 19.5 40.5 1.1 13.34

 2012 A 2.0

WP 14.4 14.4 14.4 14.4 14.4 14.4 .2 17.6 19.9 25.7 26.4 31.4 40.9 1.9 3.7 31.22

L 10.6 10.6 10.6 10.6 10.6 10.6 .0 10.7 16.7 21.7 22.7 28.0 37.9 4.2 .5 6.03

DF 11.9 12.5 15.1 17.7 20.8 21.1 56.9 12.0 16.5 18.2 21.5 26.2 37.9 542.3 412.3 33.11

WH 6.9 6.9 6.9 7.9 13.2 13.2 .2 10.7 17.8 20.4 22.2 29.8 62.3 53.4 4.7 8.18

C 6.9 6.9 6.9 7.9 13.2 13.2 .0 7.1 11.4 13.4 18.7 30.2 67.6 48.4 1.8 8.43

LP 6.9 6.9 6.9 7.9 13.2 13.2 .0 18.6 21.7 21.7 21.7 21.7 21.7 .8 .2 .00

S 13.4 13.4 13.4 13.4 13.4 13.4 .2 10.0 12.9 13.4 15.4 19.6 21.5 36.9 1.4 20.30
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 71

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

7.4 Bark Beetle Output

Detailed information about bark beetle activity is available through the keyword BBOUT. This information is

printed in a separate file (named using the keyword OPEN), and contains information about the species, size,

and type of tree killed in each year (tables 7.8 and 7.9). Since beetles can kill trees inside and outside of disease

centers, the output table reports the number of trees killed in the stand, not the density of trees. No

information about unattacked species is printed.

If no beetle activity occurred in a cycle, the output file will contain the initial year of the cycle and the

words “no beetle kill” (tables 7.8 and 7.9). If beetles were present in any cycle, the output will consist of groups

of three rows: one set for each species attacked. The first row in each set is labeled “in-inf” and contains

information about the infected trees inside disease centers that were killed. The second row, labeled “inuninf,” contains the number of uninfected trees within centers, while the third row, “outside,” contains the

number of killed trees outside of centers. A description of each column follows:

Period Ending: The first year of the cycle following a potential beetle attack (so 2000 means the

cycle from 1990-1999).

Tree Species: The species number of trees that were attacked. If a species is not mentioned, it was

not attacked.

Disease Status: The type of tree that was attacked (row 1: inside, infected; row 2: inside centers but

uninfected; row 3: outside centers).

DBH class: The density of trees killed in each species and tree location (inside a center

infected, inside a center and uninfected, or outside a center) and as in 5-inch (13-

cm) DBH classes.

Row Total: The total density of trees killed in each species and tree location (inside, infected,

inside uninfected or outside centers). The area used for each density calculation is

the area relevant to the type of tree, i.e., in rows 1 and 2 (trees inside centers), the

density is calculated from the area inside centers, while in row 3, the area is that

outside of centers.

Species Total: The total density of trees killed for each species. Note that this density is calculated on the

total stand area, while the other densities are calculated on the basis of the relevant area.

Total Before

Attack: The total density of trees in each species and tree location. This is included so that

the user can determine what proportion of trees of any type are being killed.

Again, the area used in the density calculations is the area relevant to the type of

tree in that row (inside or outside of centers).

Note that this output is for all types of bark beetles combined. If more than one bark beetle type was

active on the same species in a cycle, the information printed for that species would contain the resulting

impact of both beetle types. The activity summary in the main output file lists which beetle types were and

were not active in a given year.
72 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 7.8 — Bark beetle output file (imperial). In this example, bark beetle type 4 was active on species 10 (ponderosa pine), and bark beetle

type 3 was active on species 4 (grand fir).

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

GENERAL ROOT DISEASE MODEL

TREES PER ACRE KILLED BY BARK BEETLES IN EACH TIME PERIOD

-------------------------------------------------------------------------------------------------------------------------------------

Period Tree General DBH Class (in inches) Row Species Total

Ending Species Status ------------------------------------------------------------ Total Total Before

(year) 0-<5 5-<10 10-<15 15-<20 20-<25 25-<30 30+ Attack

-------------------------------------------------------------------------------------------------------------------------------------

2000 no beetle kill

2010 4 in-inf .00 12.23 4.18 1.00 .00 .56 .29 18.24 120.45

in-uninf .00 .00 .00 .00 .00 .00 .00 .00 9.03

outside .00 .00 .00 .00 .00 .00 .00 .00 1.82 233.92

10 in-inf .00 .00 .59 .08 .47 .84 .81 2.78 68.29

in-uninf .00 11.71 4.47 2.92 .33 .47 .58 20.49 34.16

outside .00 2.04 .80 .45 .05 .07 .09 3.51 5.49 125.72

2020 4 in-inf .00 4.23 3.73 .27 .00 .07 .13 8.42 118.37

in-uninf .00 .00 .00 .00 .00 .00 .00 .00 6.30

outside .00 .00 .00 .00 .00 .00 .00 .00 .84 227.72

10 in-inf .00 .00 1.10 .23 .54 .10 .18 2.15 59.50

in-uninf .00 6.83 5.78 3.41 .82 .37 .65 17.85 35.64

outside .00 1.55 1.33 .59 .14 .06 .11 3.78 5.41 117.78

2030 10 in-inf .00 .00 .29 .38 .86 .08 .21 1.82 58.42

in-uninf .00 5.57 6.19 3.47 1.29 .27 .73 17.53 37.76

outside .00 1.43 1.51 .67 .24 .05 .14 4.03 5.57 109.42

Table 7.9 — Bark beetle output file (metric). In this example, bark beetle type 1 was active on species 7 (lodgepole pine), and bark beetle type

3 was active on species 4 (grand fir).

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

GENERAL ROOT DISEASE MODEL

TREES PER HA KILLED BY BARK BEETLES IN EACH TIME PERIOD

-------------------------------------------------------------------------------------------------------------------------------------

Period Tree General DBH Class (in cm) Row Species Total

Ending Species Status ------------------------------------------------------------ Total Total Before

(year) 0-<13 13-<25 25-<38 38-<51 51-<64 64-<76 76+ Attack

-------------------------------------------------------------------------------------------------------------------------------------

1991 7 in-inf .00 7.28 24.00 .00 .00 .00 .00 24.28 242.83

in-uninf .00 3.52 18.03 .00 .00 .00 .00 21.55 215.53

outside .00 .00 15.83 .00 .00 .00 .00 15.83 23.32 458.78

2001 4 in-inf .00 .00 100.01 17.64 .00 .00 .00 117.65 133.67

in-uninf .00 .00 .00 .00 .00 .00 .00 .00 .00

outside .00 .00 .00 .00 .00 .00 .00 .00 23.53 .00

7 in-inf .00 4.60 18.38 .00 .00 .00 .00 22.98 229.78

in-uninf .00 2.23 9.37 .00 .00 .00 .00 11.60 115.98

outside .00 .00 32.70 .00 .00 .00 .00 32.70 33.17 326.96

2011 7 in-inf .00 .00 10.65 .00 .00 .00 .00 10.65 106.60

in-uninf .00 .00 5.65 .00 .00 .00 .00 5.65 313.33

outside .00 .00 25.34 1.31 .00 .00 .00 26.65 23.68 523.15
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 73

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

7.5 Spread Rate Output (Advanced)

Another optional output table, which is requested using the keyword SMCOUT, prints the results of the

Monte Carlo simulations of center spread (see Chapter 4, Section 2.4.1 of this guide for details about this

simulation). This information is printed in a separate file (named using the keyword OPEN), with column

headings (tables 7.10 and 7.11). Each year of output for each disease type present consists of three lines. Note

that although only the first 10 simulation results are printed (columns labelled #1-#10), the average and the

standard deviation of the spread rates are calculated from all simulations.

Year: The last year of the cycle in which the spread rate was applied.

Type: The root disease type:

P P-type Annosus root disease

S S-type Annosus root disease

A Armillaria root disease

W Phellinus weirii (the W comes from weirii)

Num: The number of Monte Carlo simulations which were done in the model.

Avg: The average spread rate (ft/year or m/year) from the simulation results. Note that this

average may be different from the spread rate listed in the standard output table

because the standard output table prints the average of the spread rates that were

applied. (See the Detailed Model Description for details on this calculation).

SD: The standard deviation of the spread rate.

Dens: The density of uninfected trees in the fringe around the outside of centers.

#1-10: Information about the first 10 simulations. If fewer than 10 simulations are done, some

of the columns will be blank.

Line 1: the calculated spread rate.

Line 2: the number of uninfected trees that were placed into the simulated area.

Line 3: the quadratic mean diameter of the trees in the simulation.
74 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 7.11 — Example spread rate simulation output (metric). This is for a stand which contains Armillaria root disease (“A”)

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

GENERAL ROOT DISEASE MODEL

SPREAD RATE MONTE CARLO SIMULATION RESULTS

-------------------------------------------------------------------------------------------------------------------------------------

YEAR TYPE NUM AVG SD DENS # 1 # 2 # 3 # 4 # 5 # 6 # 7 # 8 # 9 #10

-------------------------------------------------------------------------------------------------------------------------------------

2001 A 10 .14 .08 1648.2 .05 .11 .02 .20 .21 .14 .04 .17 .23 .23

NUM TREES IN MC SIM. 21 21 14 19 23 18 14 18 26 22

QMD OF TREES IN SIM. 11.3 10.8 12.2 11.4 13.8 12.7 9.6 12.7 13.1 11.3

2011 A 10 .21 .08 1237.8 .22 .27 .25 .26 .21 .16 .00 .26 .25 .22

20 22 22 29 17 22 10 24 22 22

15.8 15.7 20.3 14.7 16.8 14.9 14.6 15.2 14.7 14.9

2021 A 10 .25 .04 1008.6 .25 .21 .26 .28 .23 .29 .29 .24 .26 .16

18 14 19 20 17 25 21 19 16 17

18.6 19.6 21.1 19.3 17.6 17.1 22.8 19.4 18.7 20.3

Table 7.10 — Example spread rate simulation output (imperial). This example is for a stand which contains both S- and P-type Annosus root

disease.

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

ANNOSUS ROOT DISEASE MODEL

SPREAD RATE MONTE CARLO SIMULATION RESULTS

-------------------------------------------------------------------------------------------------------------------------------------

YEAR TYPE NUM AVG SD DENS # 1 # 2 # 3 # 4 # 5 # 6 # 7 # 8 # 9 #10

-------------------------------------------------------------------------------------------------------------------------------------

1999 P 10 .50 .36 128.30 .80 .06 1.05 .39 .32 .95 .59 .28 .00 .54

NUM TREES IN MC SIM. 19 24 20 17 16 26 15 19 20 17

QMD OF TREES IN SIM. 10.4 8.8 14.6 10.1 8.4 17.7 10.7 10.2 9.1 12.0

1999 S 10 .19 .18 237.21 .00 .37 .11 .31 .09 .53 .00 .16 .27 .02

17 22 23 16 21 20 15 19 23 26

3.7 5.4 5.7 4.8 7.0 7.7 3.6 7.1 4.7 5.3

2009 P 10 .77 .24 128.27 .84 1.07 .89 .37 .56 .89 .74 .95 .41 .95

27 19 30 19 21 26 20 22 17 24

13.9 14.5 15.7 10.7 10.4 12.5 13.3 15.4 10.6 14.2

2009 S 10 .30 .17 236.56 .15 .31 .11 .24 .30 .28 .61 .22 .57 .17

18 24 21 20 23 19 19 17 20 18

4.4 6.2 6.0 6.1 6.0 6.1 10.9 4.7 9.1 5.0

2019 P 10 .77 .28 109.03 .98 1.00 .67 .20 .65 .97 .76 1.11 .50 .87

18 22 19 19 19 23 19 18 18 22

15.0 13.8 10.7 12.4 11.9 13.0 14.2 20.5 11.6 12.6

2019 S 10 .27 .17 233.37 .20 .34 .49 .45 .36 .44 .03 .16 .25 .00

20 22 21 21 21 19 17 23 20 19

5.0 5.8 6.6 6.8 6.1 6.2 4.5 5.5 5.6 5.1

2029 P 10 .56 .35 105.98 .00 .65 .87 .56 .70 .85 .59 .00 1.02 .39

18 23 23 16 21 21 23 18 17 21

14.1 12.2 14.4 12.0 15.6 13.7 15.0 12.7 16.6 13.4

2029 S 10 .29 .26 231.59 .22 .18 .73 .00 .06 .62 .26 .00 .52 .37

20 19 24 20 20 21 19 19 24 20

5.9 6.6 12.9 6.2 6.3 13.0 6.0 5.3 10.2 6.5
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 75

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

7.6 Inside Infection Output (Advanced)

Output showing the results of the (optional) (Monte Carlo simulation of infection within centers can be

requested using the INFSIMS keyword (see Section 5.1 for keyword details). This information is printed to a

separate file which contains column headings (tables 7.12 and 7.13). Each year of output for each disease type

present consists of two lines. As in the center spread rate output file, although only the first 10 simulations

results are printed (columns labelled #1-#10), the average and the standard deviation of the number of trees

becoming infected are calculated from all simulations.

Year: The last year of the cycle in which the infection was calculated and applied.

Type: The root disease type:

P P-type Annosus root disease

S S-type Annosus root disease

A Armillaria root disease

W Phellinus weirii (the W comes from weirii)

Num: The number of Monte Carlo simulations which were done in the model.

Avg: The average number of trees that become infected.

SD: The standard deviation of the number of trees that become infected.

#Uninf: Total number of uninfected host trees within centers.

#1-10: Information about the results of the first 10 simulations.

Line 1: the number of uninfected trees which become infected.

Line 2: the number of inoculum sources (infected trees and stumps) which were put into a

simulation plot.

Any inoculum source can potentially infect any number of live trees, provided that the live tree’s root

system overlaps the inoculum. Thus, the number of uninfected trees which become infected may either be

greater than or less than the number of inoculum sources.
76 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Table 7.13 — Example output file from the Monte Carlo simulation of infection within centers (metric). This example is for a stand which

contains Phellinus infection.

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

WESTERN ROOT DISEASE MODEL

INSIDE CENTER INFECTION SIMULATION RESULTS

-------------------------------------------------------------------------------------------------------------------------------------

YEAR TYPE NUM AVG SD #UNINF # 1 # 2 # 3 # 4 # 5 # 6 # 7 # 8 # 9 #10

-------------------------------------------------------------------------------------------------------------------------------------

2001 W 10 994. 22. 2838. 961. 950. 979. 1098. 983. 936. 923. 969. 1026. 1115.

27 31 27 32 29 29 28 31 30 33

2011 W 10 493. 20. 1863. 398. 464. 465. 611. 518. 487. 514. 435. 478. 554.

12 13 11 19 16 13 15 12 13 17

2021 W 10 643. 19. 1401. 647. 609. 702. 644. 547. 708. 663. 613. 576. 717.

38 36 42 38 34 40 37 35 35 43

Table 7.12 — Example output file from the Monte Carlo simulation of infection within centers (imperial). This example is for a stand which

contains both S- and P-type Annosus root disease.

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 

WESTERN ROOT DISEASE MODEL

INSIDE CENTER INFECTION SIMULATION RESULTS

-------------------------------------------------------------------------------------------------------------------------------------

YEAR TYPE NUM AVG SD #UNINF # 1 # 2 # 3 # 4 # 5 # 6 # 7 # 8 # 9 #10

-------------------------------------------------------------------------------------------------------------------------------------

1999 P 10 7. 1. 66. 11. 5. 9. 7. 6. 4. 9. 9. 7. 5.

53 54 52 49 50 49 54 55 44 52

1999 S 10 10. 1. 123. 10. 11. 6. 9. 13. 6. 10. 10. 11. 12.

49 50 45 49 52 46 50 53 54 54

2009 P 10 12. 1. 68. 13. 13. 12. 16. 13. 11. 8. 13. 10. 10.

58 56 55 57 55 58 57 59 57 56

2009 S 10 16. 2. 120. 6. 13. 14. 17. 21. 21. 14. 22. 18. 14.

21 22 19 20 23 22 22 22 19 18

2019 P 10 5. 0. 60. 5. 4. 6. 5. 5. 5. 6. 4. 8. 7.

49 48 51 48 46 49 52 50 49 47

2019 S 10 11. 1. 118. 12. 9. 14. 12. 8. 11. 13. 14. 10. 13.

38 36 35 36 37 33 33 39 34 33

2029 P 10 5. 1. 58. 9. 5. 5. 3. 3. 4. 6. 4. 5. 7.

46 47 46 47 46 48 48 51 48 50

2029 S 10 6. 1. 117. 6. 5. 4. 6. 3. 11. 3. 7. 6. 6.

21 22 22 24 22 31 26 26 23 25

2039 P 10 3. 0. 53. 2. 3. 2. 3. 2. 2. 3. 4. 3. 5.

51 55 56 52 53 53 52 53 51 52

2039 S 10 12. 1. 128. 12. 11. 12. 11. 14. 9. 10. 9. 15. 13.

24 25 22 23 26 21 22 22 23 23
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 77

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

8.0 Sample Keyword Files

This section contains two sample keyword files, showing how some of the keywords can be used. Note that the

first example is in Imperial units and the second example uses metric units. Both use the Inland Empire variant.

Both examples are discussed in the same format. The keyword file is listed first, with line numbers for easy

reference (although note that line numbers are not part of an input keyword file). Each line containing a root disease

model keyword is described more fully. Finally, several pages list the output from the FVS main file. This output file

describes the parameters that have been read from the example keyword file.

8.1 File 1: Create Output Files, Enter Stumps, and Change Defaults (Imperial)

This keyword file can be used to create many of the output tables shown in Section 7. It is not the simplest

example since it is specifically designed to demonstrate how to create the more common output files. This set

of keywords provides information to the FVS base model about the stand, then initializes the root disease (as

both P- and S-type Annosus root disease) and specifies that only bark beetle types 3 and 4 should be active.

Each line in this keyword set is described more fully in the page facing the example.

Some other points that are demonstrated in this keyword set are:

1. Entering stumps which were not in the tree list (see the STREAD keyword, lines 14 and 15).

2. Turning off the default bark beetles and changing some of the default values for bark beetles to

occur (see BBTYPE3, line 17 and BBTYPE4, line 18).

3. Changing some of the default values for root disease and stand information (see lines 11-13,

SAREA and RRINIT).
78 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

1. NUMCYCLE 10

2. STDINFO 509 100 225 52 42

3. DESIGN -1 1 999.9 0

4. INVYEAR 1990

5. STDIDENT

6. MIXED

7. DELOTAB 1

8. DELOTAB 2

9. RDIN

10. RRTYPE 1 2

11. SAREA 10.0

12. RRINIT 0.0 5.0 1.00 1

13. RRINIT 0 5.0 1.00 2

14. STREAD 10 2 5.0 1 4

15. STREAD 4 2 6.5 2 6

16. BBCLEAR

17. BBTYPE3 8 5 0.10

18. BBTYPE4

19.

20. RRDOUT

21. RRECHO 24

22. OPEN 24

23. RRSUM.OUT

24. BBOUT 27

25. OPEN 27

26. BEETLE.OUT

27. END

28. PROCESS

29. STOP
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 79

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Line # Description

1 The number of cycles to simulate the model. Each cycle represents 10 years.

2-6 Miscellaneous FVS keywords giving information about the stand.

7-8 Instructions to FVS not to print certain output tables.

9 Begin reading the root disease model keywords. All keywords that are between RDIN

and END (line 27) must be root disease model keywords.

10 Root disease being simulated is Annosus, both S- and P-type.

11 Stand area is set to 10 acres.

12-13 Initialize the root disease area for each disease type to be five centers with an area of 1

acre. Default values are used for the initial density of uninfected and infected trees

inside centers.

14-15 Initialize some infected stumps with the following characteristics: 5 diameter class 2

ponderosa pine stumps (code 10) which have been dead for 4 years, and 6.5 diameter

class 2 white fir stumps (code 4) which have been dead for 6 years.

16 Turn off the default bark beetle types.

17 Request Type 3 bark beetles, with a different threshold criterion.

18-19 Request Type 4 bark beetles at the default values. Notice the blank line (line 19) which

must be present as a space holder for the supplemental records.

20 Print the detailed root disease output table. This will be printed to the main output file.

21-23 Print the root disease summary table in machine readable format to a separate file.

Notice that the OPEN keyword is use to open the file “RRSUM.OUT” for printing.

24-26 Print the bark beetle output table. Notice that the OPEN keyword is used to open the

file “BEETLE.OUT” for printing.

27 End of the root disease model keywords. All keywords which follow are not part of the

root disease model.

28-29 Process the stand, run the model, and then stop

The first part of the main output file is shown on the following pages.
80 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

1 FOREST VEGETATION SIMULATOR VERSION 6.2 — INLAND EMPIRE PROGNOSIS 12-15-1995 11:41:55

-------------------------------------------------------------------------------------------------------------------------------------

OPTIONS SELECTED BY INPUT

-------------------------------------------------------------------------------------------------------------------------------------

KEYWORD PARAMETERS:

-------------------------------------------------------------------------------------------------------------------------------------

NUMCYCLE NUMBER OF CYCLES= 10

STDINFO FOREST-LOCATION CODE= 118; HABITAT TYPE=260; AGE= 100; ASPECT AZIMUTH IN DEGREES= 225.;

SLOPE= 5 percent ELEVATION(100’S FEET)= 52.0; LATITUDE IN DEGREES= 42.

DESIGN BASAL AREA FACTOR= -1.0; INVERSE OF FIXED PLOT AREA= 1.0; BREAK DBH= 999.9

SEE “OPTIONS SELECTED BY DEFAULT” FOR PLOT COUNTS AND SAMPLING WEIGHT.

INVYEAR INVENTORY YEAR= 1990

STDIDENT

STAND ID= MIXED

DELOTAB TABLE NUMBER 1 SPECIFIED. STAND COMPOSITION TABLE WILL BE DELETED.

DELOTAB TABLE NUMBER 2 SPECIFIED. SAMPLE TREE TABLE WILL BE DELETED.

RDIN GENERAL ROOT DISEASE KEYWORDS:

RRTYPE P TYPE AND S TYPE WILL BE SIMULATED.

SAREA STAND AREA = 10.00 ACRES

RRINIT ROOT DISEASE IS P TYPE

ROOT DISEASE INITIALIZATION KEYWORD; ROOT DISEASE CENTERS ASSIGNED RANDOMLY

INITIALIZATION WILL BE WITH USER-SUPPLIED DATA UNLESS THE RRTREIN KEYWORD IS USED IN THE RUNSTREAM

NUMBER OF CENTERS= 5

50 percent OF TREES/ACRE IN THE DISEASED AREAS ARE INFECTED

50 percent OF TREES/ACRE IN THE DISEASED AREAS ARE UNINFECTED

PROPORTION ROOTS INFECTED= .10 ;DISEASE AREA = 1.00 ACRES

RRINIT ROOT DISEASE IS S TYPE

ROOT DISEASE INITIALIZATION KEYWORD; ROOT DISEASE CENTERS ASSIGNED RANDOMLY

INITIALIZATION WILL BE WITH USER-SUPPLIED DATA UNLESS THE RRTREIN KEYWORD IS USED IN THE RUNSTREAM

NUMBER OF CENTERS= 5

50 percent OF TREES/ACRE IN THE DISEASED AREAS ARE INFECTED

50 percent OF TREES/ACRE IN THE DISEASED AREAS ARE UNINFECTED

PROPORTION ROOTS INFECTED= .10 ;DISEASE AREA = 1.00 ACRES

STREAD 5.0 P TYPE STUMPS OF DIAMETER CLASS 2 OF SPECIES 10 AND 4 AGE ADDED.

STREAD 6.5 S TYPE STUMPS OF DIAMETER CLASS 2 OF SPECIES 4 AND 6 AGE ADDED.

BBCLEAR NO BARK BEETLES WILL BE ACTIVE

BBTYPE3 TYPE 3 BARK BEETLE TO BE ATTEMPTED IN DATE/CYCLE= 1; SPECIES = 4.; MINIMUM DBH=10.00 INCHES

MINIMUM ELIGIBLE STEMS FOR EVENT= 10. STEMS/ACRE ; MORTALITY RATE = .88

MINIMUM ROOT DISEASE INFECTION= .30

BBTYPE4 TYPE 4 BARK BEETLE IS ON EVERY YEAR SPECIES =10.; MINIMUM DBH= 4.00 INCHES

MINIMUM ELIGIBLE STEMS FOR EVENT= 1. STEMS/ACRE ; INFECTED MORTALITY= .750

 MINIMUM ROOT DISEASE INFECTION= .40 UNINFECTED-WITHIN-PATCH MORTALITY= .30

UNINFECTED-FRINGE MORTALITY= .15 UNINFECTED-NON-PATCH MORTALITY= .01

RRECHO MACHINE READABLE OUTPUT ON LOGICAL UNIT 24

OPEN DATA SET REFERENCE NUMBER = 24; BLANK=ZERO; STATUS=FRESH

MAXIMUM RECORD LENGTH (IGNORED ON SOME MACHINES) = 132; FILE FORM= 1 (1=FORMATTED, 2=UNFORMATTED)

DATA SET NAME = RRSUM.OUT

BBOUT DETAILED BARK BEETLE MORTALITY OUTPUT ON LOGICAL UNIT 27

OPEN DATA SET REFERENCE NUMBER = 27; BLANK=ZERO; STATUS=FRESH

MAXIMUM RECORD LENGTH (IGNORED ON SOME MACHINES) = 132; FILE FORM= 1 (1=FORMATTED, 2=UNFORMATTED)

DATA SET NAME = BEETLE.OUT

END END OF ROOT DISEASE KEYWORDS

PROCESS PROCESS THE STAND.

-------------------------------------------------------------------------------------------------------------------------------------

-------------------------------------------------------------------------------------------------------------------------------------
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 81

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

OPTIONS SELECTED BY DEFAULT

-------------------------------------------------------------------------------------------------------------------------------------

TREEFMT (I4,T1,I7,F6.0,I1,A3,F4.1,F3.1,2F3.0,F4.1,I1,3(I2,I2),2I1,I2,2I3,2I1)

DESIGN BASAL AREA FACTOR= -1.0; INVERSE OF FIXED PLOT AREA= 1.0; BREAK DBH= 999.9

NUMBER OF PLOTS= 9; NON-STOCKABLE PLOTS= 0; STAND SAMPLING WEIGHT= 9.00000

-------------------------------------------------------------------------------------------------------------------------------------

 SUMMARY OF DISEASE INFORMATION READ IN ON INPUT

-------------------------------------------------------------------------------------------------------------------------------------

DISEASE TYPE: P

NUMBER OF INFECTED STUMPS

SIZE CLASS RESINOUS NON-RESINOUS

-----------------------------------------

 0- 12" 0. 0.

>12- 24" 5. 0.

>24- 48" 0. 0.

>48-100" 0. 0.

>100" 0. 0.

DISEASE TYPE: S

NUMBER OF INFECTED STUMPS

SIZE CLASS RESINOUS NON-RESINOUS

-----------------------------------------

0- 12" 0. 0.

>12- 24" 0. 7.

>24- 48" 0. 0.

>48-100" 0. 0.

>100" 0. 0.

-------------------------------------------------------------------------------------------------------------------------------------

CALIBRATION STATISTICS:

PP --

---- ----

NUMBER OF RECORDS PER SPECIES 45 16

NUMBER OF RECORDS WITH MISSING HEIGHTS 32 12

NUMBER OF RECORDS WITH BROKEN OR DEAD TOPS 0 0

NUMBER OF RECORDS WITH MISSING CROWN RATIOS 35 15

NUMBER OF RECORDS AVAILABLE FOR SCALING

THE DIAMETER INCREMENT MODEL 9 1

RATIO OF STANDARD ERRORS

(INPUT DBH GROWTH DATA : MODEL) 1.14 1.00

WEIGHT GIVEN TO THE INPUT GROWTH DATA WHEN

DBH GROWTH MODEL SCALE FACTORS WERE COMPUTED .75 .00

INITIAL SCALE FACTORS FOR THE

DBH INCREMENT MODEL .75 1.00

NUMBER OF RECORDS AVAILABLE FOR SCALING

THE SMALL TREE HEIGHT INCREMENT MODEL 0 0

INITIAL SCALE FACTORS FOR THE SMALL TREE

HEIGHT INCREMENT MODEL 1.00 1.00

NUMBER OF RECORDS WITH MISTLETOE 0 0
82 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

8.2 File 2: Thin, Plant, and Push Stumps (Metric)

This keyword file gives examples of harvesting and planting. A highly susceptible species is removed

preferentially and a more resistant species is planted. Stump pushing is done to remove the diseased stumps.

All values are given in metric units.

In this example, instructions are first given to FVS to harvest and plant. Armillaria is the root disease being

simulated, and the disease information is read from the tree list (RRTREIN). Some additional output files are

requested.

1. NUMCYCLE 10

2. METRIC

3. INVYEAR 1990

4. STDINFO 104 26

5. DESIGN 4 999 8

6. STDIDENT

7. SUGAR1

8. THINABA 2001 20 1.00

9. SPECPREF 2001 3 99

10. ESTAB 2001

11. PLANT 2003 6 250 99

12. TALLYONE 2008

13. END

14. NOAUTOES

15. ECHOSUM

16. DELOTAB 1

17. DELOTAB 2

18. RDIN

19. RRTYPE 3

20. SAREA 4

21. RRINIT 0 30 1.2

22. RRTREIN

23. RRDOUT

24. PSTUMP 2001 0.9 15.0

25. BBOUT 27

26. OPEN 27

27. BEETLE.OUT

28. RRECHO 24

29. OPEN 24

30. RRSUM.OUT

31. END

32. PROCESS

33. STOP
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 83

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

Line # Description

1 The number of cycles to simulate the model. Each cycle represents 10 years.

2 Tell the model that all input and output will be in metric units.

3-7 Miscellaneous FVS keywords giving information about the stand.

8-9 FVS thinning keywords which ask the model to thin from above to 20 sq.m./ha basal

area, preferentially selecting Douglas-fir (species code 3).

10-14 Establishment model keywords to plant 250 trees/ha of species 6 (western red cedar)

directly after the harvest. Also, turn off the automatic regeneration model.

15 Print the FVS summary table in a machine readable form in a separate output file.

16-17 Instructions to FVS not to print some of the FVS tables.

18 Begin reading the root disease model keywords. All keywords that are between RDIN

and END (line 31) must be root disease model keywords.

19 Root disease type is Armillaria (code 3). Notice that this keyword is one of the first root

disease model keywords used.

20 Stand area is set to 4 ha.

21 Initialize the centers and their area. RRINIT places 1.2 acres of root diseased area into

30 centers. Notice that field 7 is left blank. Thus, the model will assume that the

initialization is for the current disease type: Armillaria.

22 Read the infected trees and stumps from the inventory. Use the severity codes to set the

initial infection levels for each tree.

23 Print the detailed root disease output table.

24 Push all diseased stumps greater than 15 cm in diameter, with a 90 percent efficiency.

25-30 Print the results from bark beetles to a file titled “BEETLE.OUT”, and the machine

readable root disease output to the file titled “RRSUM.OUT”.

31 End of the root disease model keywords. In the main output file, note that after the

END keyword, the model prints information about the values used for the bark beetles

which are active by default.

32-33 Process the keywords, run the model, and stop.

The first part of the main output file is shown on the following pages.
84 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

1 FOREST VEGETATION SIMULATOR VERSION 1.0b — INTERIOR DOUGLAS FIR PROGNOSIS 12-13-1995 23: 7:10

-------------------------------------------------------------------------------------------------------------------------------------

OPTIONS SELECTED BY INPUT

-------------------------------------------------------------------------------------------------------------------------------------

KEYWORD PARAMETERS:

-------------------------------------------------------------------------------------------------------------------------------------

NUMCYCLE NUMBER OF CYCLES= 10

METRIC INPUT AND OUTPUT UNITS ARE METRIC

INVYEAR INVENTORY YEAR= 1990

STDINFO FOREST-LOCATION CODE= 104; HABITAT TYPE= 260; AGE= 26; ASPECT AZIMUTH IN DEGREES= 0.; SLOPE=

5 percent ELEVATION(METERS)= 1158.2; LATITUDE IN DEGREES= 45.

DESIGN BASAL AREA FACTOR= 9.2; INVERSE OF FIXED PLOT AREA= 4.0; BREAK DBH= 999.0

SEE “OPTIONS SELECTED BY DEFAULT” FOR PLOT COUNTS AND SAMPLING WEIGHT.

STDIDENT

STAND ID= SUGAR1

THINABA DATE/CYCLE= 2001; RESIDUAL= 20.00; PROPORTION OF SELECTED TREES REMOVED= 1.000

DBH OF REMOVED TREES WILL RANGE FROM .0 TO 999 CMS, AND

HEIGHT OF REMOVED TREES WILL RANGE FROM .0 TO 999.9 METERS.

SPECPREF DATE/CYCLE= 2001; SPECIES= DF (CODE= 3); THINNING SELECTION PRIORITY= 99.

ESTAB REGENERATION ESTABLISHMENT OPTIONS:

DATE OF DISTURBANCE= 2001

PLANT DATE/CYCLE= 2003; SPECIES= CW (CODE= 6); TREES/HA= 250.; PERCENT SURVIVAL= 99.00

AGE= .0; AVE. HEIGHT= .0; SHADE CODE= .0

TALLYONE DATE/CYCLE= 2008; DATE OF DISTURBANCE= 2001.

END END OF ESTABLISHMENT KEYWORDS

NOAUTOES TALLIES AND INGROWTH WILL NOT BE ADDED AUTOMATICALLY.

STOCKADJ IS SET TO ZERO, AND NO SPROUTING WILL BE SIMULATED.

DELOTAB TABLE NUMBER 1 SPECIFIED. STAND COMPOSITION TABLE WILL BE DELETED.

DELOTAB TABLE NUMBER 2 SPECIFIED. SAMPLE TREE TABLE WILL BE DELETED.

RDIN GENERAL ROOT DISEASE KEYWORDS:

RRTYPE ARMILLARIA WILL BE SIMULATED.

SAREA STAND AREA = 4.00 HA

RRINIT ROOT DISEASE IS ARMILLARIA

ROOT DISEASE INITIALIZATION KEYWORD; ROOT DISEASE CENTERS ASSIGNED RANDOMLY

INITIALIZATION WILL BE WITH USER-SUPPLIED DATA UNLESS THE RRTREIN KEYWORD IS USED IN THE

RUNSTREAM

NUMBER OF CENTERS= 30

50 percent OF TREES/HA IN THE DISEASED AREAS ARE INFECTED

50 percent OF TREES/HA IN THE DISEASED AREAS ARE UNINFECTED

PROPORTION ROOTS INFECTED= .10 ;DISEASE AREA = 1.20 HA

RRTREIN ROOT DISEASE INITIALIZATION IS FROM TREELIST

RRDOUT DETAILED ROOT DISEASE OUTPUT WILL BE PRINTED

PSTUMP DATE/CYCLE= 2001; EFFICIENCY = .90; MIN SIZE = 15.00 CMS DBH

BBOUT DETAILED BARK BEETLE MORTALITY OUTPUT ON LOGICAL UNIT 27

OPEN DATA SET REFERENCE NUMBER = 27; BLANK=ZERO; STATUS=FRESH

MAXIMUM RECORD LENGTH (IGNORED ON SOME MACHINES) = 132; FILE FORM= 1 (1=FORMATTED, 2=UNFORMATTED)

DATA SET NAME = BEETLE.OUT

RRECHO MACHINE READABLE OUTPUT ON LOGICAL UNIT 24

OPEN DATA SET REFERENCE NUMBER = 24; BLANK=ZERO; STATUS=FRESH

MAXIMUM RECORD LENGTH (IGNORED ON SOME MACHINES) = 132; FILE FORM= 1 (1=FORMATTED, 2=UNFORMATTED)

DATA SET NAME = RRSUM.OUT

END END OF ROOT DISEASE KEYWORDS

******* WARNING: NO BARK BEETLES WERE SPECIFIED AND THE BBCLEAR KEYWORD WAS NOT USED.

BARK BEETLES ARE ON USING THEIR DEFAULT VALUES AS FOLLOWS

BBTYPE1 TYPE 1 BARK BEETLE IS ON EVERY YEAR. SPECIES =10.; MINIMUM DBH=20.32 CMS

MINIMUM ELIGIBLE STEMS FOR EVENT = 371. STEMS/HA; MORTALITY RATE = .100

BBTYPE3 TYPE 3 BARK BEETLE IS ON EVERY YEAR SPECIES = 4.; MINIMUM DBH=25.40 CMS

MINIMUM ELIGIBLE STEMS FOR EVENT= 25. STEMS/HA; MORTALITY RATE = .88

MINIMUM ROOT DISEASE INFECTION= .30
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Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

BBTYPE4 TYPE 4 BARK BEETLE IS ON EVERY YEAR SPECIES = 10.; MINIMUM DBH= 10.16 CMS

MINIMUM ELIGIBLE STEMS FOR EVENT= 2. STEMS/HA ; INFECTED MORTALITY= .750

MINIMUM ROOT DISEASE INFECTION= .40 UNINFECTED-WITHIN-PATCH MORTALITY= .30

UNINFECTED-FRINGE MORTALITY= .15 UNINFECTED-NON-PATCH MORTALITY= .01

PROCESS PROCESS THE STAND.

-------------------------------------------------------------------------------------------------------------------------------------

OPTIONS SELECTED BY DEFAULT

-------------------------------------------------------------------------------------------------------------------------------------

TREEFMT (I4,T1,I7,F6.0,I1,A3,F4.1,F3.1,2F3.0,F4.1,I1,3(I2,I2),2I1,I2,2I3,2I1)

DESIGN BASAL AREA FACTOR= 9.2; INVERSE OF FIXED PLOT AREA= 4.0; BREAK DBH= 999.0

NUMBER OF PLOTS= 8; NON-STOCKABLE PLOTS= 0; STAND SAMPLING WEIGHT= 8.00000

-------------------------------------------------------------------------------------------------------------------------------------

ACTIVITY SCHEDULE

 STAND ID= SUGAR1 MANAGEMENT ID= NONE

-------------------------------------------------------------------------------------------------------------------------------------

 CYCLE DATE EXTENSION KEYWORD DATE PARAMETERS:

 ----- ---- --------- ------- ----

-------------------------------------------------------------------------------------------------------------------------------------

 1 1990

RDIS BBTYPE1 1990 10.00 20.32 370.65 .10 .00

RDIS BBTYPE3 1990 4.00 25.40 24.71 .88 .30 .00

RDIS BBTYPE4 1990 10.00 10.16 2.47 .75 .40 .30 .15 .00

 2 2000

BASE THINABA 2001 20.00 1.00 .00 999.00 .00 999.00

BASE SPECPREF 2001 3.00 99.00

RDIS PSTUMP 2001 .90 15.00

ESTB PLANT 2003 14.83 101.17 99.00 .00 .00 .00

ESTB TALLYONE 2008 2001.00

 3 2010

 4 2020

 5 2030

 6 2040

 7 2050

 8 2060

 9 2070

 10 2080

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86 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 2 How to Use the Western Root Disease Model, Version 3.0 Beukema, Kurz

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SUMMARY OF DISEASE INFORMATION READ IN ON INPUT

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DISEASE TYPE: A

NUMBER OF INFECTED TREE RECORDS IN DISEASE PATCHES= 153.

NUMBER OF UNINFECTED TREE RECORDS IN DISEASE PATCHES= 213.

NUMBER OF INFECTED STUMP RECORDS IN DISEASE PATCHES= 20.

NUMBER OF INFECTED STUMPS

SIZE CLASS RESINOUS NON-RESINOUS

-----------------------------------------

0- 30 CM 1012. 56.

>30- 61 CM 0. 0.

>61-122 CM 0. 0.

>122-254 CM 0. 0.

>254 CM 0. 0.

-------------------------------------------------------------------------------------------------------------------------------------

 CALIBRATION STATISTICS:

PW LW FD HW CW SE PL

—— —— —— —— —— —— ——

NUMBER OF RECORDS PER SPECIES 16 16 221 53 44 33 3

NUMBER OF RECORDS CODED AS RECENT MORTALITY 5 0 8 5 0 11

NUMBER OF RECORDS WITH MISSING HEIGHTS 11 16 212 48 44 32 2

NUMBER OF RECORDS WITH BROKEN OR DEAD TOPS 0 0 0 0 0 00

NUMBER OF RECORDS WITH MISSING CROWN RATIOS 11 16 213 48 44 32 2

NUMBER OF RECORDS AVAILABLE FOR SCALING

THE DIAMETER INCREMENT MODEL 0 0 0 0 0 00

 RATIO OF STANDARD ERRORS

 (INPUT DBH GROWTH DATA : MODEL) 1.00 1.00 1.00 1.00 1.00 1.00 1.00

WEIGHT GIVEN TO THE INPUT GROWTH DATA WHEN

DBH GROWTH MODEL SCALE FACTORS WERE COMPUTED .00 .00 .00 .00 .00 .00 .00

INITIAL SCALE FACTORS FOR THE

DBH INCREMENT MODEL 1.00 1.00 1.00 1.00 1.00 1.00 1.00

NUMBER OF RECORDS AVAILABLE FOR SCALING

THE SMALL TREE HEIGHT INCREMENT MODEL 0 0 0 0 0 00

INITIAL SCALE FACTORS FOR THE SMALL TREE

HEIGHT INCREMENT MODEL 1.00 1.00 1.00 1.00 1.00 1.00 1.00

NUMBER OF RECORDS WITH MISTLETOE 0 0 0 0 0 00

9.0 References

BC Ministry of Forests and BC Environment. 1995. Root disease management guidebook. Forest Practices Code of British Columbia.

Victoria, BC. 58 p.

Goheen, E.M.; Thompson, M.K. n.d. User friendly user’s guide to the western root disease model. Version 1.1. Portland, OR: USDA

Forest Service; 49 p. and appendix.

Marsden, M.A.; Eav, B.B.; Thompson, M.K. 1993. Modeling initial conditions for root rot in forest stands: Random proportions.

Research Note RM-524. Fort Collins, CO: Rocky Mountain Forest and Range Experiment Station, Forest Service, U.S. Department of

Agriculture; 12 p.
Chapter 3

Chapter 3 Pointers for Using the Western Root Disease Model Goheen, Thompson

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 89

1 Pathologist, USDA Forest Service, Southwest Oregon Forest Insect and Disease Technical Center, 2606 Old Stage Road, Central

Point, OR 97529

2 Information Systems Analyst, formerly at Management Assistance Corporation of America, 3825 E. Mulberry Street, Fort Collins,

CO 80524

Chapter 3

Pointers for Using the Western Root Disease Model

Ellen Michaels Goheen1 Matthew K. Thompson2

Assessing Current Conditions

What Is Important to the Western Root Disease Model

The impacts of root diseases on forest stands vary considerably depending upon the root disease(s)

present, inoculum loads, the area of the stand with diseased roots, configuration of root disease patches,

host species, size, and distribution, site characteristics, and other factors. Root disease impacts as

described by WRDM3 outputs will also vary depending upon how initial conditions are configured in the

model. Certain model components are more sensitive than others; knowledge of how these components

drive the model should guide how information used to describe the initial conditions of root disease is

gathered in the field.

The WRDM3 is most sensitive to the root disease being simulated. Basic model functions that control

infection, tree mortality, inoculum survival, and inoculum decay differ depending upon whether the

user is modeling laminated root rot, Armillaria root disease, or P-type or S-type Annosus root disease. It

is therefore important to know which root disease occurs in the stands being modeled.

How initial root disease conditions in the field are described for the model is also critical. The most

important component of root disease initialization is the area inside root disease centers: the proportion

of the stand that is inside centers. In general, the greater the area inside centers, the greater impact the

disease will have.

If root disease occurs in distinct centers or patches, accurately estimating the area inside the patches

is more important than counting the actual number of patches. However, when acres in root disease are

equal, very high numbers of centers may have a greater impact on stand dynamics than low numbers,

under some stand conditions. Where distinct patches occur, the numbers of infected and uninfected trees

inside the patches have little impact on stand dynamics, particularly in mixed species and mixed age

stands. Defaults for these entries in the keyword (RRINIT) for root disease initial conditions are

adequate. If local knowledge indicates that distinct patch patterns are associated with particular root

disease/site combinations, efforts to inventory root disease can then be focused on evaluating the area in

root disease rather than individual tree characteristics. For example, laminated root rot is frequently

found in distinct centers in Douglas-fir stands west of the Cascade Mountains in Oregon and Washington.

Therefore, in west-side Douglas-fir stands, estimating root disease area would be a more powerful and

efficient inventory method than collecting individual tree disease information.
Chapter 3 Pointers for Using the Western Root Disease Model Goheen, Thompson

90 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

If root disease is confined to only a portion of a stand, a post-inventory stratification of the stand

into root-diseased and non-root-diseased portions that are simulated separately may provide more

realistic outputs. In the WRDM3, root disease centers are randomly placed within a stand unless

coordinates are provided. By simulating the strata separately, root disease impacts will be more

accurately accounted for in the root diseased portion of the stand, and the “healthy” portion of the

stand will not be unnecessarily affected.

When root disease occurs in numerous tiny centers or when the edges of centers are not readily

discernible on the ground, the stand should be modeled as one root disease patch. Then, having an

accurate estimate of the numbers of infected and uninfected trees becomes critical. Accuracy of the model

run will be further improved if individual tree information regarding root disease infection and severity

are obtained in the field and included (via RRTREIN) in the run. Dead trees with root disease should be

included in the tree list to serve as inoculum and adjacent live trees need to be rated for root disease

severity. Severity ratings include determining if trees are within 30 feet (10 meters) of known root

disease, if they are infected but asymptomatic, or if they are infected and symptomatic.

Acquiring Root Disease Information

In the Western United States, several techniques are used to assess root disease impacts. These include

many types and intensities of ground-based surveys as well as interpreting aerial photography to

estimate root disease severity. Some of the methods have been developed specifically to acquire the

information needed to describe initial root disease conditions for the WRDM3. Regardless of origin, a

shared objective of these strategies is to reasonably describe root disease infection levels in a stand.

When information on root disease distribution is not available prior to the survey, comprehensive

approaches to root disease survey are necessary. A common approach is to systematically examine a

stand, mapping the extent and area in root disease, combined with plot-based sampling of the

characteristics of live and dead trees. The mapping distinguishes distinct centers from scattered infection.

Plot locations can be geographically tied to root disease centers. Sampling individual trees provides

damage and severity information that can be input into the model as a part of the tree list.

Care must be taken that both the mapping and plot grids are intense enough to discern unevenly

distributed root disease centers. Sampling intensities used to gather tree characteristics in healthy stands

will likely be less than adequate for root diseased stands. To efficiently and economically combine an

intensive root disease examination with a standard stand inventory, two sampling grids can be used. A

standard plot grid is used to gather information on live and dead trees to supply information to the

treelist. These plots can provide the height and growth information that is time-consuming, and therefore

expensive, to gather. A more intensive grid is then overlaid on the stand to sample trees in those areas

where root disease occurs. Only information necessary to determine proportions of infected and

uninfected trees is collected. By using this method, more plots are taken, but efforts are focused in

portions of the stand of interest.

Often stand inventory data are available but do not contain root disease information. A systematic

walk-through of the stand can provide the necessary root disease information: the area affected and the

distribution of centers. The walk-through can also be used to gather information on inoculum sources,

such as numbers of infected stumps, that might otherwise not be gathered during the stand inventory.

Interpretation of aerial photography may be used to assess root disease conditions. This procedure is

most useful where root disease is the predominant cause of tree mortality seen in the photos and where root

disease occurs in a scattered distribution such that the entire stand is considered a single center. Stands are

assigned root disease severity ratings on the basis of the number of dead trees in the photographs. These

ratings are translated into proportions of infected and uninfected trees and incorporated into inventory

keywords. Ground-truthing the severity ratings and other assumptions regarding root disease

characteristics is a vital part of this methodology. The intensity of ground-truthing that is adequate will

depend on the variability of the forest stands surveyed.

As mentioned in the previous section, any information that can indicate whether root disease occurs in

distinct patches or as numerous small centers can help to focus the inventory effort. If scattered infection is
Chapter 3 Pointers for Using the Western Root Disease Model Goheen, Thompson

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 91

found in certain stand types, such as with S-type Annosus root disease in mixed conifer stands, tree level

information is important. Surveys can be designed that focus on intensive sampling of trees and stumps

infected by root disease. If discrete patches are the norm, mapping strategies for root disease inventory are

more important. Root disease specialists with knowledge of local conditions may be able to provide guidance.

A basic description of root disease initial conditions is essential to a realistic WRDM3 simulation.

However, more information is not necessarily better. Data acquisition efforts should be selective; they

should take into account the sensitivity of the model to different initialization components, information

on local conditions supplied by area experts, and how initialization keywords interact with one another.

Incorporating Information on Current Conditions into a

Western Root Disease Model Simulation

It is possible to execute a successful simulation using the WRDM3 without providing information on the

actual root disease conditions that exist in the stand of interest. The model has default values that

describe a stand of a certain size with a particular configuration of root disease centers. However,

running the model without entering information about actual root disease conditions is not

recommended. It is highly unlikely that these values represent the actual conditions for your stand. The

better the user can describe initial conditions according to available information, the more realistic the

output from the run.

There are several root disease inventory keywords to choose from depending upon the type of

information available. The user must also be aware that some keywords override others and that

keywords used in different combinations have different results.

Essential Inventory Keywords

SAREA: Specifies the stand area. Root disease is simulated spatially; SAREA provides

boundaries for spread.

RRINIT: Describes initial root disease conditions including the number of root disease centers

in the stand, the area that is affected, and the level of infection within that area.

RRTYPE: Designates the root disease being simulated.

RRTREIN: Specifies that the model should look to the tree-level data for specific root disease

information. Proportion of infected and uninfected trees inside the centers will come

from the treelist. Root infection levels will be based on root disease severity ratings

given to individual trees. RRTREIN should be used only if root disease information

was collected and stored with individual tree data.

Helpful Inventory Keywords

STREAD: Allows the user to enter inoculum sources not included in the treelist. Infected or

dead trees and stumps are categorized by species and diameter class. The number

entered is the total number for the area, not the density.

PLREAD: Lists the plots (by number) inside root disease centers from which to read individual

tree-level damage information from the treelist. The model will ignore all tree-level

information on plots not listed.

PLOTINF: Lists the plots (by number) inside the centers and the proportions of infected trees in

each plot. This keyword should be used only if RRTREIN is not available.
Chapter 3 Pointers for Using the Western Root Disease Model Goheen, Thompson

92 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Inventory Keyword Combinations

RRTREIN, RRINIT:

Values calculated from stand examination data where tree-level root disease

information is known (when RRTREIN is used) will override values entered in

RRINIT. The number of uninfected and infected trees per acre will be calculated from

the tree data. The proportion of root system infected will be based on the root disease

severity rating assigned to a tree and the species of tree.

RRINIT, PLREAD:

When RRINIT is used in combination with PLREAD, the model will use the area in

root disease provided by RRINIT. If the user does not know the area in root disease,

a “-1” can be placed in the field designated for area in root disease in RRINIT. The

area in root disease will then be calculated as a proportion of the total area based on

the proportion of plots infected out of the total number of plots.

PLREAD, RRTREIN:

PLREAD must be combined with RRTREIN. RRTREIN provides the information

that PLREAD needs to determine infection levels inside centers.

PLOTINF, RRTREIN, PLREAD:

PLOTINF should NOT be used with RRTREIN. If information in RRTREIN is

available, PLREAD should be used. PLOTINF is not as powerful a keyword as

PLREAD combined with RRTREIN because it is based on an estimate of the

proportion of infected trees, rather than the actual trees that are infected.

Examples of Initializing Root Disease Conditions

The stand of interest encompasses 32 acres. In the field, a walk-through examination provides

information on current root disease conditions. Laminated root rot is present in five distinct centers.

The centers encompass 7 acres. Information from local pathologists suggests that the default, a 10

percent average root infection level, is low for the area; 45 percent average root infection represents the

local conditions better.

Within the WRDM3, the keywords SAREA and RRINIT are used to describe current conditions.

SAREA equals 32 acres. Input to RRINIT includes that the root disease centers are located randomly

(there is no information on coordinates of individual root disease centers available), the number of

centers is 5, no information was collected on the number of infected and uninfected trees per acre inside

the centers (defaults are used), the average percent root infection is 0.45, the number of acres with root

disease is 7, and the disease simulated is laminated root rot (type 4).

The portion of the keyword set that directs the WRDM3 looks like this:

RDIN

RRTYPE 4

SAREA 32

RRINIT 0 5 (blank) (blank) .45 7 4

END
Chapter 3 Pointers for Using the Western Root Disease Model Goheen, Thompson

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 93

In another area, a 45-acre stand is intensively inventoried. Root disease centers are well represented by

the plot data. Root disease damage codes and appropriate severity levels are designated for individual

trees. Additional information on infected stumps outside of plots was also collected during the inventory.

Four laminated root rot centers totaling 16 acres were found.

WRDM3 inventory keywords used are SAREA, RRINIT, RRTREIN, and STREAD.

RDIN

RRTYPE 4

SAREA 45

RRINIT 0 4 (blank) (blank) .1 16 4

RRTREIN

STREAD 4 1 45

STREAD 2 1 33

STREAD 4 2 11

STREAD 4 3 5

END

A 39-acre stand is examined. Tree-level root disease information is not collected; however, an effort

is made to estimate the proportion of trees affected by root disease on every plot. Root disease pockets

are mapped in relation to stand inventory plots. Three laminated root disease centers were found, each

of which is associated with a different inventory plot (Plots 1, 5, and 11), and within each plot, 30

percent of the trees are estimated to be infected. Based on the map, the three plots are estimated to

represent a total of 9 acres.

WRDM3 keywords used to represent these current conditions are SAREA, RRINIT, and PLOTINF.

PLOTINF tells the model the proportion of trees, on a given plot, that are infected.

RDIN

RRTYPE 4

SAREA 39

RRINIT 0 3 (blank) (blank) .1 9 4

PLOTINF 4

1 0.30

5 0.30

11 0.30

-999

END

Adjusting Model Functions

The functions inside the WRDM3 that describe mortality and growth loss, spread rate, inoculum survival,

etc. are based on best estimates for West-wide averages, or are specific to locations where reliable data

were available at the time the model was built. Outputs based on defaults are not meant to represent

conditions on all sites. Local conditions may differ. Species susceptibility or site factors may not be well

represented by model defaults. However, the model can be adjusted to more closely represent these

differences.
Chapter 3 Pointers for Using the Western Root Disease Model Goheen, Thompson

94 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Most of the functions that guide model behavior can be modified using keywords. By adjusting one

or more of these keywords, changes in model behavior may better reflect local conditions. For example,

in some areas, Armillaria root disease readily kills ponderosa pine in all age and size classes. In other

locations, ponderosa pine is little affected and would be a good species to plant when managing

Armillaria root disease. When modeling Armillaria root disease in areas where ponderosa pine is a

resistant species, defaults specific to ponderosa pine can be changed. These may include increasing the

time required for ponderosa pine trees to die, increasing the proportion of colonized root system

necessary for ponderosa pine death, and making it more difficult to transmit the fungus from one

ponderosa pine root system to another.

Model-modifying keywords should be used with caution. Some are much more powerful than

others; small adjustments in certain keywords have large effects on model output. There are logical

combinations of keywords and combinations that override one another. Model adjustments should be

based on experience and local information. Local root disease specialists should be consulted to determine

under which circumstances, and to what degree, these adjustments need to be made. West-wide efforts

are underway to calibrate the model for local conditions. In many cases, data are available to support

adjustments; in other situations, default values are adequate.

The keywords listed below are frequently used to modify model functions:

INFKILL: In the model, trees die before their entire root system becomes colonized by the root

disease-causing fungus. The proportion of colonized root system necessary for tree death differs for each

root disease fungus and each tree species. INFKILL controls this proportion. The larger the proportion,

the more fungal colonization required for tree death, and hence, the longer trees live.

INFMULT: Infection probability describes how well root disease is transmitted from one tree to

another, assuming root system overlap. This probability is controlled by INFMULT and is specific to root

disease and tree species. INFMULT is the most powerful model-modifying keyword to use to reflect

changes in species susceptibility.

TTDMULT: The time-to-death multipliers in the model describe how long it takes for a tree to reach

the proportion of root system infection required for death from the time it first becomes infected. These

multipliers are specific to root disease and tree species. They are controlled by the keyword TTDMULT.

Other model-modifying keywords are available. They are not as powerful as the ones described

above. These other keywords control specific equations in the model such as how inoculum decays

(INOCLIFE), the minimum number of years the inoculum is present (INOCSPAN), and the proportion

of root system infected after death (INFCOLO).

Modeling Complexes

Modeling Stands with Multiple Root Diseases

In many stands, more than one root disease is present. However, with the exception of Annosus root

disease where both P-type and S-type can be modeled simultaneously, the WRDM3 can simulate only

one root disease at a time. Model behavior is different depending upon which fungus is being modeled.

Modeling the effects of root disease complexes can be done by using combinations of keywords that

modify model assumptions to fit the root disease dynamics of the complex. Any reference to root disease

type becomes academic. Infection parameters, inoculum lifespan, species susceptibility, etc. are adjusted

to represent how the fungi work together. Calibration data or experience with local conditions are

required to be successful. Local experts can provide guidance on which keywords to use and how to

adjust them to represent stand conditions.
Chapter 3 Pointers for Using the Western Root Disease Model Goheen, Thompson

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 95

Bark Beetles

Bark beetles are often found in association with root diseased trees; beetles are attracted to trees with

reduced vigor. They can also affect root disease dynamics by killing trees. Tree death reduces stand

density and, thus, influences root disease spread. Bark beetles may also cause mortality that is not related

to root disease; the beetle activity is triggered when stand density thresholds are exceeded or by sudden

increases in down wood.

The WRDM3 offers several options for including bark beetle activity in stand simulations with root

disease. Bark beetles that are dependent on density and/or root disease operate at all times during a

simulation, unless they are turned off using the keyword BBCLEAR. Bark beetles that respond to

windthrow will be included in the run if windthrow occurs and other parameters are met. Both endemic

and outbreak populations of bark beetles can be simulated.

The user should examine the assumptions associated with bark beetle activity to determine whether

defaults used to describe each bark beetle type in the model (BBTYPE 1-4) correspond to known behavior

of local bark beetle populations. Entomologists with bark beetle expertise should be consulted. Bark

beetle parameters can be easily changed by adjusting the keyword specific to the bark beetle type to be

modified (BBTYPE 1-4). The user can change minimum tree diameters associated with bark beetle attack,

threshold stand densities, threshold down tree densities, the proportion of root system infected that

triggers bark beetle attack, as well as species susceptibility and mortality rates. But, unless there is good

reasoning behind it, bark beetle functions, as with all other model functions, should remain at default

settings during simulations.

Windthrow

Major blowdown events can be simulated within the WRDM3. The keyword WINDTHR is used to set

how soon windthrow occurs in the run and the proportion of trees affected. The user designates a

minimum threshold of trees to be windthrown. This keyword must be present to activate bark beetles

requiring windthrow (BBTYPE2).

Chapter 4

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 99

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Chapter 4

Detailed Model Description

Sarah J. Beukema1 Werner A. Kurz2 Julee Greenough3

This chapter is organized into four sections. Section 1 discusses some of the model’s basic conceptsand definitions. The second section describes details of root disease dynamics. The final two

sections describe other options in the model: bark beetles, windthrow, and management options.

This chapter explains the functions contained in the Western Root Disease Model, version 3.0. For

instructions explaining how to use the model, see Chapter 2, How to Use the Western Root Disease

Model, Version 3.0.

1.0 Basic Concepts

1.1 Order of Calculations

Both FVS and the Western Root Disease Model operate on the same time step or “growth cycle.” In each

cycle, after FVS predicts the amounts of growth and mortality and before it applies them to the tree

records, the root disease model simulates the dynamics of root disease and other agents and adjusts the

FVS-predicted growth and mortality values to account for impacts from bark beetles, root disease, and

windthrow. Table 1.1 presents the basic order of calculations in the model.

1.2 Tree Lists

The root disease model keeps track of stand information using a variety of tree lists. The structure and

number of records in the standard FVS list of live trees is replicated three times. The content of each

element in each new tree list varies depending on its disease status. These new tree lists are:

•Trees outside root disease centers (as trees per acre [or hectare] of area outside centers);

•Trees which are uninfected but which are inside a center (as number of trees); and

•Infected trees inside centers (as number of trees), stratified by the source of infection (from a

center expanding or from being infected inside a center).

For each of these lists, the model also keeps track of the species, dbh, and root radius of each element.

When Armillaria root disease or Phellinus are simulated, all tree species are potential hosts to the disease.

For Annosus root disease, each tree species is capable of being host to only one of the two Annosus root

disease types (S or P). A list of the number of dead infected trees and stumps stratified by Annosus root

disease type (if Annosus root disease is being simulated), wood type (resinous or non-resinous), and

diameter size class is also maintained (table 1.2). Note that the model does not distinguish between dead

trees and stumps.

Since the model must update infection levels in both live infected trees and dead infected trees and

stumps based on either age or time since death, the list of live infected trees contains information about

1 Systems Ecologist, ESSA Technogolies Ltd., Suite 300, 1765 W. 8th Ave., Vancouver, BC, Canada V6J 5C6

2 Senior Systems Ecologist, ESSA Technologies Ltd., Suite 300, 1765 W. 8th Ave., Vancouver, BC, Canada V6J 5C6

3 Systems Ecologist, ESSA Technogolies Ltd., Suite 300, 1765 W. 8th Ave., Vancouver, BC, Canada V6J 5C6
100 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Table 1.1–Order of calculations in the Western Root Disease Model, including sections of

FVS. Italicized activities are part of FVS.

Read all keywords and inventory

Establish initial root disease conditions

For Each Growth Cycle

Apply stand management

Apply borate to newly created stumps (Annosus root disease only)

Calculate spore infection of stumps (Annosus root disease only)

Update dead infected list and stump list

Estimate normal growth and mortality for each tree list element

Compute radial extent of roots

Calculate effects from bark beetles and windthrow

Update dead infected tree list

Push stumps

Calculate new infection within centers

Calculate new centers (from spore infection, if Annosus root disease is present)

Spread or shrink centers (including the “jump” after harvesting)

Update infection levels

Calculate actual tree mortality and growth

Update all tree lists

Decay the inoculum in dead infected trees and stumps

Call Establishment Model

Update Stand Attributes (by applying the growth and mortality rates)

Display Indicators

End Growth Cycle Loop

Table 1.2–Stratification of dead infected trees and stumps. This list is further stratified by year of death

(not pictured here).

Disease Type* Resinous Diameter size class

0-12 in. >12-24 in. >24-48 in. >48-100 in. >100 in.

[0-30.5 cm] [>30.5-61 cm] [>61-122 cm] [>122-254 cm] [> 254 cm]

P yes

no

S yes

no

*Annosus root disease only
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 101

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

the year in which infection occurred, and the list of dead trees and stumps retains the year that the

infected tree died.

All dead trees (i.e., not just dead infected trees) are recorded in an array for potential use outside the

root disease model (e.g., in the simulation of snag dynamics). This array contains information about the

number of trees that died, the “official” cause of death (root disease, bark beetles, windthrow, or

“natural” causes), and whether the tree was infected at the time of death. This array is not used or

manipulated at any point within the root disease model.

Note for Annosus Root Disease

When Annosus root disease is simulated, information about newly created, uninfected stumps is

maintained for two cycles after a harvest, for use by the spore model (see Section 2.6). These lists contain

the same information as the list with dead infected trees and stumps. After 20 years the information in

these lists is either discarded or, if stump infection occurred, added to the list of dead infected trees and

stumps.

Transfer between the various lists occurs through harvesting, infection, and mortality. A pictorial

summary of the processes is shown in figure 1.1.

1.3 Representation of Roots, Centers, and Stands

The root disease model makes several simplifying assumptions about roots, centers, and stands. These

simplifications are designed to capture the essence of the geometry but still retain some generality and

ease of calculation.

Figure 1.1–Mechanisms which transfer trees between tree lists (shown in

bold). “Other mortality” mechanisms are those not predicted by the

root disease model. The “All dead” list includes those trees contained in

the Dead Infected list and the two stump lists. Note that not all uninfected

trees which are harvested will become spore-infected, and only stumps

created during a simulation with Annosus root disease present can

potentially become infected.

center spread

bark beetles

windthrow

other mortality

bark beetles

windthrow

other mortality

harvest harvest

infection

spore

infection

spore

infection

all

mortality

center

spread

outside uninfected

inside

infected

dead

infected

outside

stumps

uninfected

inside

stumps

all dead trees and stumps
102 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

1.3.1 Roots

The model assumes that in each tree, the extent of all roots large enough to become infected by root

disease is modeled as a circle with the stem of the tree as its center. The radius of this circle is a linear

function of dbh for trees greater than 3.5 inches [8.9 cm] dbh:

Radius = F1 ∗ F2 ∗ dbh

where

Radius is the root radius in feet;

F1 is an FVS parameter which is normally equal to 1.0;

F2 is an FVS species-specific parameter which equals 14.26 for all species except western

hemlock (14.5) and western red cedar (9.35); and

dbh is the dbh in feet (i.e., inches/12) or meters (i.e., cm/100).

For trees less than 3.5 inches [8.9 cm] dbh, the root radius depends on tree height and stand basal area:

(Imperial) Radius = e(0.6 ln(HT)+0.4 ln(BA)-0.81)

or (Metric)Radius = e(0.6 ln(HT)+0.4 ln(BA)+2.75)

where

Radius is the root radius in feet or meters;

HT is the height in feet or meters; and

BA is the stand basal area in sq.ft./acre or m2/ha.

These equations both assume that the roots are growing under some average condition. Since the root

radius may be different in stands which are more or less crowded than average, the model includes a

keyword, SDIRMULT,4 which allows users to have the root radius change as the density of the stand

changes. This keyword invokes a multiplier which depends on the Reineke’s stand density index (SDI)

(Reineke 1933) and which ranges from 0.5 to 1.5 (fig. 1.2). When this keyword is used at the default

values, the root radius will be larger in less dense stands, and smaller in denser stands, where the

“normal” density is defined by the users. The maximum and minimum values of the multiplier are not

under user control.

4 All model keywords are listed in bold. They are explained in detail in Chapter 2, How to Use the Western Root Disease Model,

Version 3.0.

Figure 1.2–Root radius multiplier activated

using the SDIRMULT keyword. The bottom

axis is in imperial units (trees/acre) and the

top axis is in metric units (trees/hectare).

0.0

0.5

1.0

1.5

2.0

0 200 400 600 800

0 300 600 900 1200 1500 1800

Root radius multiplier

SDI
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Since root systems are defined as circles, infection levels and the amount of inoculum in the stand are

defined by the length of the root radius that is infected in live trees, dead trees, and stumps. This method

for defining infection means that while the proportion of the radius that is infected increases linearly, the

amount of area that is considered infected increases quadratically (fig. 1.3). Section 2 contains detailed

descriptions of the dynamics of the infection.

1.3.2 Centers

A second model assumption is the definition of a root disease center. Centers are assumed to be circles

encompassing the outermost point of infection, i.e., the perimeter of the infected root systems. Note that

this definition may be different from the one used often in the field in which the center boundaries are

drawn by connecting stems of infected trees. This assumption has two ramifications. The first is that the

area inside root disease centers may be much larger when calculated by the model than what is seen in

the field (fig. 1.4). The second is that while the center is considered to be one large infection zone, it has a

certain level of inoculum (as represented by infected live trees, dead trees, and stumps) which may be

patchy. Thus it is possible for a center to cover 2 acres, for example, with two old stumps whose infected

roots each have a 2-foot radius as the only source of inoculum (fig. 1.5). The stand would seem heavily

Figure 1.3–Area within a root system

that is infected. Lighter shading

indicates areas infected in more recent

time steps. Notice that each concentric

ring is the same width (the radius

increases linearly), but the area

represented by each ring increases as

the distance from the center of the

circle increases (i.e., the area increases

quadratically.)

Figure 1.4–Boundary of a center. The heavy line

indicates the edge of the center (i.e., the area

inside the area defined by the line represents the

area inside centers), the black dots are the stems

of trees, and the light circles are the extent of the

root disease in each tree. (A) Calculation of the

boundary by the model. (B) One method of

calculating the boundary in the field.

Figure 1.5–Difference between the center area and area

with inoculum. The square represents the stand

boundary, the heavy circle outlines the center boundary,

and the shaded circles show the area with inoculum.

A B

center boundary

infected roots

stand

area inside center

area with inoculum
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

infected from the amount of area inside the center but would actually contain very little inoculum or

potential for infecting healthy trees.

The disease centers are defined by their location in the stand and their size. Location is defined by

assigning coordinates to the mid-point of each center either randomly by the model or specified by the

user at the time of initialization. The size of each center is entirely defined by its radius. Any center may

overlap other centers or may extend outside the stand boundary (fig. 1.6); the model accounts for the

actual area inside centers in the stand correctly (see Section 2.4 for details). For Annosus root disease,

when both types of root disease are in the stand, S- and P-type Annosus root disease centers are

maintained separately.

Throughout all reports, the part of the stand infected by root disease (of the type of interest) is

referred to as “inside” (as in inside a disease center), while the part of the stand not infected by that root

disease is termed “outside” (fig. 1.6). For example, for Annosus root disease, a tree that is an S-type host

is considered “inside” if it is within an S-type center, but is considered “outside” in all other areas of the

stand, including areas which are inside P-type centers.

1.3.3 Stands

All stands are represented as squares. Stand dimensions (measured in feet or meters) are used for two

purposes: for calculating the total area in root disease centers inside the stand and for placing centers into

the stand. While the mid-point of each center must be inside the stand, part of a center may extend

outside of the stand boundaries. The part outside the stand is not considered as part of the root disease

area in the model. Thus, when calculating the total area inside centers, the model needs to know the

location of the edges of the stand so as not to account for any of the area outside of the stand. The model

also needs to know the stand’s dimensions when determining the location of centers (as assigned by the

user or by the random number generator) to ensure that the mid-point of the center is within the stand.

Users should be aware of some of the consequences of manually placing centers in a stand. Most

stands in the field are not actually squares so a center which is inside the stand in the field may be outside

the stand in the model. The model will change the coordinates of any centers which are outside of the

stand to be on the stand border (fig. 1.7). As a consequence, the area inside centers may not be preserved.

A center which is on the border will have less area inside the stand than the center that was totally within

the original non-square stand.

1.4 Effect of Infection

Root disease affects trees in three ways: by reducing their height and diameter growth, by increasing

their susceptibility to other agents (bark beetles and windthrow), and ultimately by killing the trees. The

effect of the root disease on growth is a function of the proportion of the root radius infected (fig. 1.8):

Figure 1.6– Areas considered inside centers. Notice that some

centers extend outside the stand, and other centers overlap.

counted only

once in the model

not included in

the model

"inside" center

"outside" center
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Growth = −2 ∗ infection + 1 if infection ≤ 0.5

Growth∗=−0 if infection > 0.5

where

Growth is the proportion of growth that is realized; and

infection is the proportion of the root radius that is infected.

This proportion of growth realized is applied to the height and diameter growth increments predicted by

FVS. Since a FVS tree record may be made up of a mixture of infected and uninfected trees, the amount

that a record actually grows is the average of the height or diameter increment for each tree in the record.

Figure 1.7–Caveats when placing centers in a stand. On the left, a non-square

stand with three centers. On the right, the model’s square representation of

the same stand. Notice the new location of the uppermost center. Also

notice that the stand in the model will have less area in root disease because

of the overlap in the two centers near the top edge of the stand.

Figure 1.8–Percentage of growth realized in

infected trees.

0%

100%

0% 100%

Percent of Growth Realized

Percent of Root Radius Infected

1360

330

(140,1000)

(150,650)

(110,200)

660

660

(140,660)

(150,650)

(110,200)
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

1.5 Mortality Reconciliation

Both the root disease model and FVS predict mortality. FVS calculates an expected probability of

mortality for each tree record in the tree list at the beginning of each growth cycle. The FVS mortality

functions were, in theory, developed from data that include all causes of tree death, including root

disease, bark beetles, and windthrow. Because the root disease model explicitly calculates the mortality

from these sources, and because the FVS-predicted mortality is a minimum value, the actual mortality

applied must be a combination of the mortality predicted by the two models.

The two estimates of mortality are reconciled on a record-by-record basis. FVS mortality applies to

the entire tree record. The root disease model identifies, for each record, trees that are infected, uninfected

inside centers, or outside centers (by definition uninfected). The model calculates a specific mortality rate

for each category of trees. The objectives of the reconciliation process are to apply the mortality

differentially to each category while killing at least as many trees in the entire record as the background

mortality predicted by FVS.

Two cases can apply: total mortality predicted by FVS can either be greater than (or equal) or less

than that predicted by the root disease model.

1. FVS mortality is greater than (or equal to) root disease model mortality

For infected trees, the root disease model first applies the greater of the root disease or FVS predicted

mortality. Then it applies the remaining mortality to trees outside centers and, if necessary, to uninfected

trees inside centers. If the mortality of infected trees predicted by the root disease model is greater than

that predicted by FVS, then the mortality of uninfected trees must be reduced in proportion (except when

bark-beetles or windthrow caused mortality of uninfected trees that was greater than that predicted by

FVS). For example, imagine a record of 36 trees, 12 in each of the three categories. FVS predicted a

mortality of 9 trees, i.e., 25 percent of the record. For infected trees, the FVS prediction of mortality is

12∗0.25 = 3 trees. Assume that the root disease model predicted that 7 of the 12 infected trees died, and

that there was no mortality of uninfected trees due to bark beetles or windthrow (table 1.3). First, 7 of the

12 infected trees are killed. The total mortality predicted by FVS was 9, so 2 more trees must be selected

from the uninfected trees inside or outside centers. Since the stand density is higher outside centers, the

remaining mortality is first applied to trees outside centers. FVS predicted that 25 percent or 3 of the trees

outside centers would die, but in this case only 2 trees would be killed according to the model. None of

the uninfected trees inside centers would be affected.

2. FVS mortality is less than root disease model mortality

As in the previous example, the root disease model first applies the greater of the root disease or FVSpredicted mortality to infected trees. Then it applies the remaining mortality to trees outside centers

and, if necessary, to uninfected trees inside centers. For example, imagine a record of 36 trees, 12 in

each of the three categories. FVS predicted a mortality of 9 trees, i.e. 25 percent of the record. The FVS

prediction for mortality of infected trees is 12∗0.25 = 3 trees. Assume that the root disease model

predicted that 7 of the 12 infected trees died, and that 4 trees outside centers and 2 uninfected tree

inside centers were killed by bark-beetles (table 1.4). Since total mortality predicted by the root disease

model is greater than that predicted by FVS, the mortality predicted by the root disease model in each

category of trees will be applied.
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Table 1.4–Example of mortality reconciliation when FVS predicts lower mortality than

the root disease model.

Total Infected Inside- Outside

Uninfected

Number of trees 36 12 12 12

FVS-predicted mortality 9 3 3 3

Root disease-predicted mortality 13 7 2 4

Applied mortality 13 7 2 4

Table 1.3–Example of mortality reconciliation when FVS predicts higher mortality than

the root disease model.

Total Infected Inside- Outside

Uninfected

Number of trees 36 12 12 12

FVS-predicted mortality 9 3 3 3

Root disease-predicted mortality 7 7 0 0

Applied mortality 9 7 0 2

2.0 Root Disease Dynamics

2.1 Introduction

The root disease model simulates the dynamics of root disease in a tree’s root system from the time of

infection, through the spread of the disease across the root system, to the complete decay of the inoculum

(fig. 2.1). Infection can occur in live trees through contact with inoculum or in stumps through spores.

Each step is explained in more detail below.

2.2 Initial Infection in the Stand

The two principal options for simulating stand dynamics with the model are stand establishment on bare

ground or starting with an existing stand. A new stand is established through planting or natural

regeneration and the inoculum originates from infected stumps remaining from the previous stand

(entered either using a keyword or through the tree list). When the model is used to simulate an existing

stand, infection can be initialized in many different ways (see Chapter 2, Section 3). Accurately portraying

the existing status of the root disease in the actual stand in the model is both difficult and important. In

the field, trees will show a variety of infection levels: some trees will have recently been infected, others

may have just been killed by the disease, and others will have infection levels between these stages.

Depending on how the model is initialized, the model will attempt to capture some of this heterogeneity

of infection levels.

2.2.1 Initializing from the Tree List

The root disease can be initialized using information in the tree list, either disease code information, plot

information, or both. When the keyword RRTREIN is used, codes in the tree list identify infected tree
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

records. All other records are assumed to be uninfected, either inside or outside of centers. If PLREAD is

used, the model assumes that all the records in the plots listed in the keyword are inside centers. Those

records that do not have root disease damage codes will be assumed to be uninfected. All records which

are in plots not identified in the keyword are assumed to be uninfected trees outside disease centers (even

if a disease code is present, i.e., the disease codes are ignored).

When PLREAD is used, each element in the FVS tree list represents trees in only one of the root

disease model tree lists. To account for density correctly, the model scales each of the root disease model

tree lists by the ratio of the stand area to appropriate area (area inside centers for tree lists inside centers

and disease-free area for the outside tree list). This keyword allows the model to have different stand

densities inside and outside centers. The Appendix for Chapter 4 shows the equations which adjust the

densities in each of the tree lists, and provides users with an example.

2.2.2 Choosing Trees to Infect (When No Damage Codes are in the Tree List)

The choice of which tree records to infect becomes more complicated when RRTREIN is not present and

the model is initialized via the keyword RRINIT. This keyword allows users to define the amount of area

within centers and the proportion of trees in centers which are infected or uninfected. The model assumes

that the distribution of infected trees by species should be based in part on the likelihood of becoming

infected so that, all else being equal, the center should contain more infected trees of a species which has

a high probability of being infected. Next, the model uses the average time from infection to death for the

trees (by species) in the center; species with long times to death will be more likely to be represented as

infected in the center than those whose time to death is very short. Combining this information, the

model calculates the number of trees of each species to be infected in diseased areas. A more detailed

explanation (with equations and examples) is given in the Appendix for Chapter 4.

Figure 2.1–Summary of the dynamics of root disease. The first row is for a live tree, and the second

and third are for a dead tree or stump. In all cases, infection is represented by the shaded area, the

stem or stump by a black dot, and the maximum extent of the root system by the outermost

circle. Note that spore infection occurs only for Annosus root disease.

live

tree

dead

tree

death

initial

infection

infection reaches

center and

grows outwards

infection reaches

level to kill tree

infection spreads

to amount

colonized at death

decay of

inoculum

radius decline stops for some period

spore

infection

infection

spreads to 50%

of root radius
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

2.2.3 Degree of Infection

Trees may initially be infected at different levels. When RRTREIN is used, the severity code connected

with the damage code determines the initial proportion of root radius that is infected (see Chapter 2 for

the exact definition of each severity code). The severity codes define a proportion relative to the amount

infected at death; thus the same severity code may lead to different proportions of infected root radii in

different species.

If information about the damage or severity codes is unavailable and the keyword RRINIT or

PLOTINF was used, the infection level is assigned differently. Users may define the average proportion

of root radius which is infected in the keyword RRINIT. For each tree record which is infected initially,

the root disease model will choose a proportion infected from a distribution about the defined root

radius. The average proportion of root radius infected will usually be close to the requested value (see the

root disease summary table in the main output file), but deviations occasionally occur because of the

random component. Assigning a range of initial infection levels assures some variability in the timing of

the death of infected trees. This avoids a peak in mortality near the beginning of the model simulation,

which is created by a cohort of trees infected at identical levels.

2.3 Spread of Infection Within a Center

2.3.1 Infection within Root Disease Centers

Live trees become infected through root contact with sources of inoculum (modeled as the infected

portion of the root systems of either live or dead infected trees and stumps). Because FVS is not spatially

explicit, the root disease model does not know the location of trees in the stand. To estimate the likelihood

of becoming infected, the model explicitly simulates a small portion of the stand (with average

characteristics of inside centers). The locations of a representative sample of live infected and uninfected

trees and dead infected trees and stumps (fig. 2.2) are assigned at random. The sample plot is used to

estimate the new infections resulting from root contacts. The results from these simulations are then

applied to the entire stand.

Figure 2.2–Example sample plot used for

calculating infection rates inside centers.

The shaded circles are the sources of

inoculum (the infected roots of live and

dead infected trees and stumps), while

the open circles are the root systems of

uninfected trees.

For each disease type, the sample plot is set up with representative trees. In order to control execution

time, the model attempts to simulate 20 trees, and adjusts the area which is simulated accordingly (so a

dense stand would use a smaller plot than a less dense stand). The model estimates the number of trees

from each record in each infected tree list (live and dead infected) that should be placed into the plot,

based on the target number of trees (20), and the resulting plot area. Then, the number of trees equal to

the integer number of trees in each entry is placed in the simulated plot. If there is a fractional number of

trees remaining, a random number is drawn which determines whether to include another tree from that
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Table 2.1–Examples of parameters defining root disease dynamics for Annosus root disease (A), Armillaria root disease (Ar) and laminated root

rot (Ph). The parameters given here are those used in the Inland Empire and Southern Interior variants of FVS. Details on these parameters for

other species and other variants can be found in the Appendix for Chapter 2.

Prob. of infection Relative time Prop. of root radius Prop. of root radius Wood

to death infected at death colonized after death type1

____________________ _________________ __________________ __________________

Species A Ar Ph A Ar Ph A Ar Ph A Ar Ph

White pine 0.4 0.1 0.1 1.0 1.8 3.0 0.6 0.3 0.8 0.8 1.0 0.8 R

 Larch 0.4 0.05 0.2 1.0 2.0 1.5 0.9 0.85 0.6 0.95 1.0 0.6 R

 Douglas-fir 0.4 0.5 0.4 1.0 1.0 1.0 0.8 1.0 0.3 0.95 1.0 0.3 R

 Grand fir 0.5 0.6 0.4 1.5 0.75 1.0 0.8 0.75 0.85 0.9 1.0 0.85 NR

 Western hemlock 0.5 0.1 0.2 1.75 0.9 1.5 0.8 0.8 0.8 0.9 1.0 0.8 NR

 Western red cedar 0.0 0.1 0.02 1.0 1.2 1.0 1.0 0.8 0.85 1.0 1.0 0.85 R

Lodgepole pine 0.4 0.2 0.1 1.5 1.8 3.0 0.5 0.8 0.8 0.75 1.0 0.8 R

 Spruce 0.4 0.5 0.2 1.0 1.1 1.1 0.9 0.6 0.8 0.95 1.0 0.8 NR

 Alpine fir 0.5 0.5 0.4 1.5 0.75 1.0 0.8 0.8 0.75 0.9 1.0 0.75 NR

 Ponderosa pine 0.5 0.2 0.1 0.5 1.8 3.0 0.5 0.8 0.65 0.75 1.0 0.65 R

 Other 0.0 0.1 0.4 1.0 0.9 1.5 1.0 0.6 0.8 0.86 1.0 0.8 NR

1

R = resinous, NR = non-resinous

Table 2.2–Keywords used to change the parameters defining root disease dynamics. Each of these keywords uses essentially

the same format (see Chapter 2). Note that these keywords will rarely be used by users other than pathologists.

Parameter Disease type Probability of Relative time Proportion of root Proportion of root

infection to death radius infected at death radius colonized

 after death

Keyword RRHOSTS INFMULT TTDMULT INFKILL INFCOLO

element. If the random number is less than the fractional number, another tree will be added. For

example, if the model computes that an element should contribute 3.4 trees, 3 trees would be added, and

there would be a 40 percent chance of including one more tree (because in a uniform random number

generator 40 percent of the number will be less than 0.4, the fractional number of remaining trees).

At the time these inoculum trees are chosen, they are also randomly assigned an X and Y coordinate.

The model then loops over each tree in the inside uninfected live tree list with the appropriate disease

type, placing each one in the simulation plot.

2.3.2 Probability of Infection

Infection can occur when the infected portion of the root system of a diseased tree contacts the root

system of a healthy tree. The probability of infection, given a single contact, varies among tree species

and root disease species (table 2.1) and can be controlled using the INFMULT keyword (table 2.2).

The probability of infection is defined to be the probability in a 10-year period: i.e., in 10 years, what

is the probability that a tree becomes infected, given that its root system contacts the infected portion of

the root system of an infected live or dead tree? The model properly adjusts the probabilities of infection

if a time step other than 10 years is simulated. (This was not the case in earlier versions of the model).

Thus, considering infection rates only and assuming no additional effects from the change in time step, if

the model was simulated in 5-year steps, the same amount of infection should occur after 30 years as

when simulating in 10-year steps.
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

2.3.3 Multiple Overlaps

For each tree, the model determines the number of overlaps between the uninfected root system and the

infected root system, the length of each overlap, and the probability of no infection occurring, given the

number of overlaps:

Pno = (1 − Pinf)

overlaps

where:

Pno is the probability of no infection occurring;

Pinf is the probability of infection occurring at a single contact in the cycle (table 2.1); and

overlaps is the number of infected root systems which contact this uninfected one.

This information is then averaged within a tree record to determine the likelihood that the tree has

been infected.

2.3.4 Location of Infection

For each newly infected tree, the model also assigns the initial amount of infection if the two root systems

overlap. The model assumes that the point of infection is the contact point closest to the centers of the

uninfected tree (fig. 2.3, example A). In the subsequent years, the infection will first travel to the center of

the tree and then spread in concentric circles throughout its root system. The model assigns a negative

proportion of root radius (-r) to indicate that the infection is growing towards the center. At this stage, the

tree does not infect its neighbors. In some cases, the center of the uninfected tree is surrounded by the

inoculum from the infected neighbor (fig. 2.3, example B). If this is the case, the model assumes that the

infection has reached the center of the uninfected tree and has spread outward to infect the proportion (r)

of its root system.

The entire process of determining the new infection of trees within a center is by default done only

once each cycle for each disease type. The keyword INFSIMS, however, allows the users to specify that

the process be repeated within a cycle and that the average infection rates are calculated from multiple

simulations. Output information about the results from each simulation is also available with this

keyword. Sensitivity analyses indicate that in most conditions a single simulation is adequate. In lowdensity stands, users may want to increase the number of simulations to 5 or 10 (fig. 2.4).

Figure 2.3–Radius that is considered infected when root overlap and infection

occur. The shaded circle is an infected root system, and the open circle is an

uninfected root system. The heavy black line represents the radius of the uninfected

root system that becomes infected. In both cases, the distance from the center of

the uninfected tree to the perimeter of the infected tree represents the newly

infected root-radius. (A) The overlap did not include the center, so the tree is

given a negative radius of infection, -r. (B) The overlap included the stem, and the

tree is given a positive radius of infection, r.

infected

-r

infected

r

A/ B/
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

2.3.5 Progress of Infection Through a Live Root System

Infection can occur at any point along the root system. If the infection occurs outside the center of the

root system, the disease must grow inwards toward the center along the radius. The tree cannot infect

others until infection has reached its center. Once a tree is infected at its center, the model assumes that

root disease spreads through the root system from the stem of the tree, radiating outwards in concentric

rings (fig. 1.3).

The rate at which the disease spreads through the root system of an infected tree is determined from

the number of years it takes root disease to kill a tree (a function of dbh, tree species, and root disease

type, table 2.1), the root radius of the tree (primarily a function of dbh and tree species, see Section 1.3.1)

and the proportion of the root radius that must be infected to cause death (function of tree species and

root disease type, table 2.1). The equation which relates these is

Thus, increasing only the root radius or the proportion of the root radius at which the tree dies will

increase the rate at which the disease spreads through the root system (because the disease will have

farther to travel in the same amount of time). Even though the time to death increases with increasing

dbh, the rate of spread through the root system also changes with dbh. Figure 2.5 shows this for several

tree species and different tree diameters.

Figure 2.4–Coefficient of variation of the new

infections in stands with different inoculum

densities. The inoculum density is the percentage

of total stems that are infected (live and dead)

inside centers.

E

E

EE E

E

E

E

E

E

E

E

E

E E E

E

E E

E

E

E

E

E

E

E

E

E

E E

E

E

E E

E

E

E

E

E

E

0.00

0.05

0.10

0.15

0.20

0 10 20 30 40 50 60 70

Coeff. of Variation

average % inoculum density

New infections occuring inside centers

Figure 2.5–Rate of spread of the infection through root

systems. Each species has a different time-to-death

multiplier, and some have different parameters used in

the calculation of root radius. This example was created

using parameters for Armillaria root disease from the

Inland Empire variant of the WRDM3. Metric units are

given on the top axis (dbh in cm) and on the right axis

(spread rate in m/yr), while Imperial units are on the

left axis and the bottom axis.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 10 20 30 40 50

0 20 40 60 80 100 120

Spread rate (ft/yr)

(m/yr)

DBH (in)

(cm)

white pine cedar hemlock Doug-fir

Rate of spread Root Radius Proportion infected at death

Time to death 

 = ( ) ∗( )
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Time-to-death, i.e., the number of years it takes for root disease to kill a tree, is defined as the length

of time it takes the disease to grow from the center of the root system to the proportion of the root radius

at which the tree dies. The number of years to death is a linear function of dbh (fig. 2.6) and is defined

relative to a basic curve which is the reference curve for all tree species and root disease types. This basic

curve is defined such that a 4-inch [10 cm] dbh tree will take 5 years to die and a 35.4-inch [90 cm] dbh tree

will require 40 years, with linear interpolation between these points. The time to death varies for different

tree species and different root disease types and is defined using a multiplier which is applied to the basic

curve (table 2.1). Ponderosa pine, for example, when infected with Annosus root disease, takes half as

long to die as predicted by the reference curve (as indicated by the 0.5 as the relative time to death), so the

values would be 2.5 years for a 4-inch [10 cm] dbh ponderosa pine and 20 years for the 35.4-inch [90 cm]

ponderosa pine. This multiplier can be controlled using the keyword TTDMULT (table 2.2) and can be

changed for all species. The effect of changing the time to death is explored in Section 2.5.

Figure 2.6–Number of years from infection at the

center to death for the base curve (e.g., Douglas-fir),

for a species which takes half as long to die (e.g.,

ponderosa pine with Annosus root disease), and for

a species which takes 50 percent longer to die (e.g.,

larch with Phellinus).

0

10

20

30

40

50

60

0 10 20 30 40

0 20 40 60 80 100

Years to death

dbh (in)

(cm)

Base 0.5x 1.5

Each species of tree has a different tolerance to root infection and will be killed at different degrees of

infection. Trees die when a pre-defined proportion of their root radius is infected (table 2.1). After death

the disease will continue to spread for a short time, until a pre-determined amount of the root system is

colonized. The default values (table 2.1) vary for the different root disease species (table 2.3). Note that the

proportion of the root radius which is infected at death is changed by the user (using the keyword

INFKILL), the proportion of the root radius infected after death does not change automatically; users

must make the change using the INFCOLO keyword. The effect of changing the proportion of the root

radius infected at death is explored in Section 2.5.

Table 2.3—Amount that each root disease will spread through

the root system after death.

Root disease Amount of spread after death

Annosus 50 pct of the remaining root system

Armillaria 100 pct of root system

Phellinus No spread
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

2.4 Spread of Root Disease Centers

2.4.1 Calculating the Spread Rate

Root disease centers can expand by infecting trees that were previously outside disease centers but whose

roots now contact infected roots. Therefore, the rate of spread of centers is based on the size, density, and

species of host trees outside of centers. Stands with large numbers of host trees likely contain trees whose

roots extend into the center and become infected, increasing the chance of the center spreading.

As with the simulation of new infection within a center, the calculation of the spread rate of the center

is based on a spatially explicit simulation of a small portion of the stand. In this case, the model simulates

the dynamics of infection within the plot for a number of years and calculates the distance traveled by the

disease in that time period. This simulation is repeated several times (default is 10), and the resulting

spread rates are applied to centers as discussed below.

For the spatially explicit simulation, trees are selected from the list of live trees outside disease

centers in the same way that trees were selected in the inside center simulation (Section 2.3.1). These trees

are placed in the plot either randomly or on an almost uniform grid. In general, the random pattern (the

default) is most useful here since grid patterns of planted trees rarely occur, especially when there is

some natural regeneration. This flexibility, however, allows users to investigate the effects of different

distribution patterns on the spread of root disease through the stand. Users can change the distribution

pattern at any time in the root disease model run by using the keyword TDISTN.

The bottom border of the spatially explicit plot represents the edge of the root disease center and is

assumed to be infected (fig. 2.7). Therefore, any tree whose roots overlap this border can become infected

with some probability (table 2.1). In all cases of infection, the disease must grow to the stem of the tree

before radiating outwards in concentric circles. The trees are not infectious until the disease has reached

the center of the root system. In each time step of the spatially explicit simulation, the radius of infection

in each infected tree is updated, and some trees may die. Each live non-infectious tree (uninfected trees or

those where the disease has not reached the center) in the simulation is checked against each of its

overlapping neighbors to see if the neighbor can infect it. If the tree is uninfected and in contact with

infection, it can become infected with some probability for each tree contacted. If the tree is already

infected, the new source of overlap may cause it to be infected at some point closer to the center of the

tree. This new point of contact will cause a new infection on the tree with the usual probability, thus

potentially accelerating the rate at which infection reaches the stem. All the basic parameters used for

tracking infection in trees (as in table 2.1) are used in the spread rate simulations.

This spatially explicit sub-model is simulated for twenty 5-year time steps or until the infection has

reached the opposite side of the simulation plot. In either case, the spread rate (in feet/year or meters/

year) is calculated as the distance traveled (in feet or meters) divided by the number of years that it took

to get there (fig. 2.7). A separate spread rate is calculated for each disease type. During this spatially

explicit Monte Carlo simulation, trees themselves do not grow.

Figure 2.7–Example of a plot used to calculate the spread rate of a

disease center. The circles define root systems. The shaded sections are

the infected portions of the root systems. Notice that the trees along the

southern border are infected. The distance “d” is the maximum distance

(in feet or meters) that the disease traveled in the simulation time, so the

spread rate would be d/year.

Infectious border

of center

d
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

The entire calculation of spread rate is normally performed 10 times. Each resulting spread rate is

remembered. Using the SPREAD keyword, users can control the number of Monte Carlo simulations (to

a maximum of 50), and apply the average spread rate to each center or apply each of the spread rates that

were calculated from each simulation to a different center (see below). Output on the results from the

simulations is available with the SMCOUT keyword.

The spread rate is based only on the density and root system size of host trees outside of centers. By

default the size of the root system of a tree is independent of the density of the stand. The keyword

SDIRMULT (Section 1.3.1) modifies the root radius of a tree on the basis of the total density of the stand.

This is particularly important for S- and P-type Annosus root disease, where the total stand density is

composed of both host and non-host tree species. The spread rate simulation model cannot distinguish

between a low-density, pure host stand, and a high-density stand with a low proportion of host (fig. 2.8).

The keyword SDIRMULT thus accounts for some of the effects of non-host trees on host trees. When this

keyword is used, the spread rate will be different in stands with the same density of host but with

different overall stand density.

2.4.2 Spreading the Centers

The amount of spread (in feet or meters) is determined by multiplying the spread rate (feet/year or

meters/year) by the model time step (the cycle length, in years). The spread is applied by increasing the

radius of centers by this amount.

As mentioned above, two methods exist for applying spread rates. The first is that the average spread

rate of all the Monte Carlo simulations is applied to each center. This method is based on the assumption

that since the model does not know which trees are in which center, the size of each center is not

important, and all can spread with the average rate. The second is that different spread rates (based on

the simulated values) are assigned to each center. This is based on the assumption that having different

centers spread at different rates will result in a more accurate portrayal of the dynamics of the area in root

disease in the stand. This method also is more likely to allow some centers to stop spreading for some

time. Two methods exist for assigning the individual spread rate simulation results to different centers,

based on the number of simulations and the number of centers:

1. The number of centers is at least as large as the number of simulations. In this case, each

spread rate is applied to a center until all the spread rates have been used, and the process is

repeated until all the centers have a spread rate. For example, if the stand had 14 centers

(labeled C1, C2, ... , C14) but only 10 spread rates (represented as s1, s2, ... , s10) were

Figure 2.8–Effect of SDIRMULT on root system size.

In the diagram, each circle represents the root system

of a host tree. With the SDI effect on root radius, the

trees in the diagram have either a smaller root system

(in a dense stand) or a larger root system (in a more

open stand) than would be predicted by the basic root

radius equation. The effect is drawn more extreme

than would actually occur.

h o st tre e s

d e fa u lt

d e n se sta n d

o p e n sta n d
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

calculated, the spread rates that would be used would be: C1 = s1, C2 = s2, ..., C10 = s10, C11 =

s1, ... , C14 = s4. This method preserves the range of values that occurred from the spread-rate

simulations, but it may not preserve the mean.

2. The number of centers is less than the number of simulations. In this case, averages of the

spread rates are calculated such that all spread rates are used and each is used in only one

average. For example, if the stand had 3 centers but 10 Monte Carlo simulations (represented

by s1, s2, ... ,s10) were done, the spread rates that were used would be: average[s1,s2,s3],

average[s4,s5,s6], average[s7,s8,s9,s10]. This method preserves the mean of the Monte Carlo

simulation results, but may not preserve the range of values. Also, any zero spread rates that

occurred will likely be lost during the averaging.

Two constraints are considered when applying spread rates. The first is that centers which received a

zero spread rate in the previous cycle are more likely to receive a zero spread rate in the current cycle,

provided one exists. The second is that centers which are shrinking in this time step (see Section 2.8) are

not assigned a spread rate from these calculations, and are not included in the number of centers when

determining which method to use.

2.4.3 Calculating Infected Area

As the centers expand, they may start to overlap other centers of the same disease type or they may

extend outside the boundaries of the stand. When describing the total area in root disease, the model

ignores any areas outside the stand and avoids double accounting of the areas within multiple disease

centers. The total area within root disease centers of each type is estimated by laying a fine grid over the

stand and determining how many cells of this grid lie inside each disease type’s centers (fig. 2.9). The

proportion of points in the grid that fall inside the centers of each type is a measure of the proportion of

the stand that is infected by this type. The number of grid cells is always the same, regardless of the size

of the stand. This means that the area within centers is more accurately calculated in smaller stands, in

which the grid cell size is smaller.

Figure 2.9–Method for calculating total

area inside centers. Each grid cell is marked

as ‘inside’ (shaded), or ‘outside’ (unshaded),

and the proportion of cells ‘inside’ the grid

is used to determine the proportion of the

stand area infected.

New trees are infected on the basis of the increase in diseased area. The model processes each element of

the appropriate disease type in the live, outside (uninfected) tree list to determine the number of trees

that have moved from being outside all centers of that disease type to inside the center. A proportion of

these (equal to the probability of becoming infected, table 2.1) is considered to be infected at the center of

the tree’s root system. The remaining new trees inside centers are uninfected. For example, suppose that

the centers expanded to include an additional 0.1 acres, and that an outside tree record had 100 trees/

acre. Of these, 100∗0.1 = 10 trees would now be inside centers. If the probability of becoming infected was

30 percent, then 3 trees (10∗0.3) would be infected and the remaining 7 trees would be uninfected.

2.4.4 Infecting New Trees
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Trees infected by expanding centers are always infected at the stem, i.e. the mid-point, of their root

system, and they are immediately infectious. Unlike infection occurring within root disease centers

(Section 2.3), the disease does not need to travel to the stem base before radiating outwards. The

assumption behind this is that centers expand only after the infection has crossed past the stem of the

tree. Therefore, by definition, trees that were infected through center expansion are infected at the stem.

2.4.5 Center Expansion After a Harvest

At the time of a clearcut, the model assumes that each disease center is surrounded by a ring of trees

whose roots have just been infected (those trees whose stems would be inside the disease center after the

center expands). When these trees are cut, the infection will spread very rapidly through the root systems

of these trees; in effect, causing the center to “jump” in size. The model simulates this effect by estimating

the mean diameter of all root systems outside of disease centers at the time of the cut and using this as the

distance by which the radii of all disease centers increase.

This option can be controlled using the RRJUMP keyword. This keyword also allows users to change

the number of root diameters by which the center expands. The default behavior is different for each root

disease (table 2.4) and is active only for Armillaria root disease.

2.5 Summary of the Effects of Parameters

Root disease dynamics in live trees are almost entirely determined from three parameters: the probability

of infection, the time from infection at the stem base to death, and the proportion of the root radius that is

infected at death. These parameters control, among other functions, the number (or rate) of new infections

in the stand, the amount of mortality, and the area of root disease.

To demonstrate the effects of these parameters, a series of simulations was performed with the

model. The simulations changed each parameter independently from the others in the following way:

•no change,

•double the time to death,

•double the probability of infection (to a maximum of 1.0), and

•halve the proportion of the root radius which is infected at death.

The runs were done on six different types of stands, representing a range of conditions (table 2.5). Each

stand was 10 acres, 5 of which were inside Armillaria root disease centers. All other parameters and

assumptions, except those specifically mentioned, were at default values. Each simulation lasted for 50

years (5 cycles). At the end of the simulation, the effect of each parameter change was assessed on a

number of different root disease indicators. The summary of the effects of changing the parameters is

given in table 2.6.

2.5.1 Time-to-Death

When the time to death is increased, the obvious effect is that trees take longer to die, so less mortality

occurs, and the density of currently infected trees is higher (i.e., the infected trees are living rather than

Table 2.4–Defaults for the jump in center size after a clear-cut for

each root disease. The distance multiplier defines the increase in

center radius as a multiple of the mean root system diameter of

the trees outside centers.

Root disease Jump occurs? Distance Multiplier

Annosus No not applicable

Armillaria Yes 1

Phellinus No not applicable
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

dead). Two secondary effects also occur. The first is that the proportion of uninfected trees which become

infected is lower (“new infections” in table 2.6) because, although there are more live trees which are

infected, less of their root system is infected. For example, suppose that, after 6 years, a tree has 6 feet of

infected root radius using the default time-to-death values. When the time to death is doubled, the same

tree would have only 3 feet of its root radius infected. This reduction in root radius causes a 75 percent

reduction in area infected (table 2.7). Roots from uninfected trees will be less likely to contact the smaller

area containing the infection.

The other secondary effect is that centers are generally smaller when the time-to-death is increased,

assuming the dynamic spread rate model is used. This occurs because spread rate of centers is calculated

on the basis of the basic parameters defined above (see Section 2.4.1 for details about spread rate

calculations). If the disease is traveling slower inside individual trees, fewer trees are dying, and fewer

new infections are occurring, the disease will spread more slowly throughout the stand.

Table 2.5–Types of stands used in the simulations. The pine stand contained only ponderosa pine,

the fir stand contained Douglas-fir, and the mixed-species stand contained Douglas-fir, grand fir,

western hemlock, and ponderosa pine.

Species ________________________________________________________________________________________________________

Single __________________________________________________________________

Pine Fir Mixed

Even-aged

Young X

Older X XX

Uneven-aged X X

Table 2.6–Effect of increasing the given parameter. A ‘-’ means that the indicator was decreased

relative to the run on the same stand but with no change in parameter values, a ‘+’ means that the

indicator increased, an ‘=’ means that the indicator displayed essentially no change (but the value in

brackets indicates the direction of what little change occurred), and the ‘?’ means that the result was

inconclusive. Note that simulations which examined the effect of the proportion of the root radius

infected at death simulated a decrease in infection level. The results were therefore reversed before

being entered in the table.

Indicator: Root Mortality from New Number of

Increase in: disease area root disease infections infected trees

Time to death −− − +

Probability of infection = (+) + + +

Infected radius at death + + + ?

Table 2.7–Example of the effect of doubling the time to death on the area with infected roots.

This example is for a 10-inch dbh tree which uses the basic time-to-death curve (i.e., the

multiplier is 1.0). Rate of spread inside the root system is estimated assuming that most of

the radius is infected at death.

Time-to-death Rate of spread Radius infected Area of infected

(yr) (ft/yr) after 6 years (ft) roots (ft2)

Default 11.7 1.0 6 113.1

Double 23.4 0.5 3 28.3
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

2.5.2 Probability of Infection

Increasing the probability of infection means that more infection will occur in each time step, especially

as centers spread. This higher infection rate will lead to higher disease-caused mortality. It will also

generally mean that there will be more live infected trees in the stand at any time step (even if the trees

are all the same age). A less intuitive effect is that the higher probability of infection has little impact on

center spread and center area. There are two reasons for this. First, when the spread rate calculation is

performed, only the farthest distance traveled in the simulation is used to calculate the rate of spread.

Increasing the number of infected trees does not guarantee that the disease will travel farther in the

specified direction. Second, not all probabilities were doubled because many default probabilities were

greater than 0.5, and the maximum possible probability was 1.0. The magnitude of the changes in center

area may have been small because some probabilities were not increased by as much. It is likely that a

greater impact would be noticed if the probabilities of infection were decreased (halved). What little

impact on center area occurred caused an increase in center area.

2.5.3 Infected Radius at Death

The proportion of the radius infected at death controls the amount of live inoculum in the stand.

Decreasing the proportion of the radius which is infected at death has a similar effect to increasing the

time to death. In both cases, the disease will travel a shorter distance along the root radius in the same

amount of time (table 2.8), leading to less area containing inoculum from infected roots. However, some

important differences arise. The change in proportion of root radius infected at death does not necessarily

change the number of live trees which are infected because infected trees will die in the same number of

years. Decreasing the radius at which death occurs results in the stand containing a much smaller area of

inoculum from living trees. Infection, when it occurs, will occur later and will occur primarily from the

dead trees. If Phellinus were being simulated instead of Armillaria root disease, even fewer new infections

would occur because, with Phellinus, the disease does not continue to expand through the roots after the

tree has died. To maximize the effect of changing the infected radius at death, it would be useful to also

adjust the parameter which controls the amount of the root system infected after death.

2.6 Creation of New Centers: Annosus Root Disease Only

Root disease centers are created in two ways: at model initialization using keywords (see Chapter 2,

Section 3), and from Annosus root disease spores infecting freshly-cut, uninfected stumps. After a stand

entry, uninfected stumps anywhere in the stand may be colonized with spores from the appropriate

Annosus root disease type. Only infected stumps which are above a diameter limit are simulated by the

model. Some of these newly infected stumps are outside of all centers, and, in the second time step after

infection, may create new centers (fig. 2.10). The coordinates for each new center are chosen at random,

with the constraint that the new center must be outside of all existing centers. The radius for the new

center is set to 50 percent of the stump’s root radius, similar to the proportion of the root radius that is

considered to be infected. All large, spore-infected stumps inside centers are added to the main list of

dead infected trees and stumps 20 years after creation. Thus they increase the amount of inoculum

without affecting the area with root disease.

Table 2.8–Example of decreasing the radius which is infected at death. This example is for

a tree which takes 10 years to die. Note that table 2.6 shows the effect of increasing the

infected root radius.

Radius infected Rate of spread Radius infected Area of infected

at death (ft) (ft/yr) after 6 years (ft) roots (ft2)

Default 10 1.0 6 113.1

Halved 5 0.5 3 28.3
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

The spore model has the potential of creating a large number of small centers, especially if the stumps

are large and the proportion of stumps becoming infected is high. To avoid excessively long execution

times, the model allows a maximum of 200 centers, 100 in each disease type. If the spore model attempts

to create more than 100 centers in a disease type, the area of an existing center will be increased to include

the area which normally would have been in the new center.

Spore-created centers are different from other centers for the first time step after creation. Infected

trees die slower within this center (by default three times as slow), and the probability of infection from

root overlap is only 50 percent of the value in other centers. Since the model does not know which trees

are in which centers, it cannot track mortality and infection rates by center. Thus, the infection and

mortality rates which are actually used in the model are weighted by the amount of area in spore-created

or other centers, by disease type.

In nature, small stumps usually do not become active root disease centers. To capture this effect users can

control the minimum diameter at which stumps can potentially create centers, and the proportion of these

stumps which become infected using the keyword SPORE. The mortality and infection rate multipliers can

also be adjusted in this keyword. Since the spore model is active by default, new centers may be created each

time a cut occurs on large trees. Spore infection may be de-activated by setting the proportion of stumps

becoming infected to 0. This, and any other change in spore model parameters, may be made in any cycle.

Spores can create centers only for Annosus root disease. New centers cannot be created if Armillaria

root disease or Phellinus is being simulated.

2.7 Dynamics of Infection Within Dead Trees and Stumps

Once an infected tree has died from any cause, the roots are assumed to be at maximum colonization within

a year. The proportion of the root radius colonized after death is a function of tree species and root disease

type (table 2.1). The default values for the proportion colonized after death for each species are defined as

given in table 2.3, and can be modified using the INFCOLO keyword. The only exception to this is that

stumps that have been infected from spores (Annosus root disease only) are assumed to have 50 percent of

their root radius infected, regardless of the tree species. This assumption is not under user control.

The model assumes that inoculum decays at a constant rate, and represents this by reducing the

radius of the infected roots. When the remaining root radius has decreased such that mainly the stump

itself is left, the radius of infection is held constant for a number of years. Thus, the inoculum remains

active close to the stump for the longest period of time.

Figure 2.10–Centers created from spore infection. The light shading in the left figure

are the old centers, while the darker circles are the spore-infected stumps. Those

stumps outside of centers become new centers (right figure). The original centers

also have increased inoculum due to the newly infected stumps.

spore-infected stumps
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

The default decay rate is given by the linear equation:

decay = a ∗ radius + b

where:

decay is the decay rate (feet/year or meters/year);

radius is the radius of the infected root system (feet or meters), and

a and b are root disease-specific constants shown in table 2.9.

This rate of decay is applied until the inoculum is reduced to the amount given by the linear equation:

radius = c ∗ dbh + d

where:

radius is the radius of the infected root system (feet or meters);

dbh is the dbh associated with the infected stump (inches or centimeters); and

c and d are root disease-specific constants shown in table 2.9.

Finally, the inoculum stays at this level for a number years, determined by:

years = g ∗ dbh + h

where:

years is the radius of the infected root system (feet or meters);

dbh is the dbh associated with the infected stump (inches or centimeters); and

g and h are root disease-specific constants shown in table 2.9.

All constants, including the values governing decay rate, are controlled using the keyword INOCLIFE.

Table 2.9–Default constants for inoculum decay, by root disease species.

a b c dg h

(Imperial) ft decay /yr ft/yr ft/inch feet yr/inch yr

/ft radius

Annosus 0.02212 1.2032 0.01667 0 2 0.0

Armillaria 0 1 0.297 0 1.1111* 0.0*

0.5556 6.6667

Phellinus 0 1 0.2377 0 1.1111* 0.0*

0.5556 6.6667

(Metric) m decay /yr m/yr m/cm m yr/cm yr

/m radius

Annosus 0.02212 0 0.3667 0 0.79 0.0

Armillaria 0 0.3048 0.0356 0 0.4374* 0.0*

0.2187 6.6667

Phellinus 0 0.3048 0.0285 0 0.4374* 0.0*

0.2187 6.6667

∗

The first value is for trees < 12 inches [30.5 cm] dbh, while the second is for trees ≥ 12 inches [30.5 cm] dbh.

For Annosus root disease, the values given in table 2.9 were derived using the following assumptions.

The root radius at which the decline in infected root radius stops is (by default) equal to twice the

diameter of the stump (when measured in feet), so a 15-inch (1.25-foot) stump will stop declining when

the root radius reaches 2.5 feet. The number of years that the length of the infected root radius stops

decreasing is also equal to twice the diameter (when measured in inches). For example, if a dead tree has
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

a dbh of 15 inches, then the infected portion of the root system will not change for 30 years. After this

period, the stump and the remainder of its infected root system disappear. For Armillaria root disease

and Phellinus, the initial decay of the inoculum (as defined by infected root radius) is independent of tree

or stump size (a=0).

Resinous vs. Non-resinous

The constants in the equation for the decay rate are for resinous tree species only (see table 2.1 for

species assignments). In non-resinous tree species, the decay rate will be faster by a third. For example,

suppose a tree of a resinous species has an inoculum decay rate of 1.5 feet/year. A non-resinous tree

with the same infected root radius will use a decay rate of 1.5 / 0.75 = 2.0 feet/year. All the other decay

constants (as defined in table 2.9) are unchanged. Thus, the roots of a non-resinous tree will stop

decaying sooner than the roots of a resinous tree of the same size, and the overall lifespan of the

inoculum in the non-resinous tree will be shorter (fig. 2.11).

Figure 2.11–Comparison between the inoculum decline of a

resinous and a non-resinous tree of the same size. This

example is for Annosus root disease, but the difference in

behavior is similar for Armillaria root disease and Phellinus.

Imperial units are on the left y axis (ft) and metric units are

on the right y axis (m).

Under most circumstances, inoculum decays according to the equations and parameters above. The

model contains an option, however, which specifies the minimum amount of time that the inoculum

must be present (table 2.10). If the default parameters above predict that the inoculum would live for less

than the minimum number of years, the initial decay rate is reduced so that the inoculum will be present

for the full period (fig. 2.12). If the inoculum is predicted to live longer than the minimum time, the decay

rate remains unchanged from the predicted value. By default, only Phellinus weirii uses this option.

Table 2.10–Default minimum lifespan of inoculum

for each disease type.

Root disease Minimum lifespan (years)

Annosus 0

Armillaria 0

Phellinus 20

Minimum Lifespan

0

2

4

6

8

10

12

14

0

1

2

3

4

0 10 20 30 40

(m)

Years

Resinous Non-resinous

infected root radius (ft)
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Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

2.8 Shrinking Centers

Occasionally a stand has fewer live trees within centers than it has centers. When this condition occurs,

some centers must not contain any live trees, although they may still contain some dead infected trees

and stumps. Assuming no new infection, these centers will start to shrink as the inoculum decays, and

the centers may disappear (fig. 2.13).

Shrinking occurs in three stages:

1. An initial rapid decline in which the decrease in center radius is proportional to the reduction

in root radius of stumps and dead trees at the perimeter of the center.

2. A period when the center radius remains constant. This conservatively assumes that the

biggest stumps in the center are at the edge of the center, and that they have reached the stage

in which the size of their root systems are remaining constant. During this period the

inoculum potential inside the center continues to decrease as smaller stumps inside the center

decay completely.

3. The center disappears as all stumps completely decay.

The model does not know which stumps are in which centers. Since big resinous stumps take longer to

decay than smaller stumps, the model chooses the biggest stump in the stand and calculates the stages of

shrinking based on this stump’s decay pattern. The model chooses the smallest centers to start shrinking,

since by their size, they are less likely to have any live trees in them.

If stand conditions change (such as new trees being planted) so that there would be enough live trees

to have at least one tree per center, all shrinking centers will be eligible to start spreading. In some cases

(see description of spread rate calculations above), only some of the centers will start spreading again.

The model chooses the biggest centers to start spreading, since these will be more likely to have a higher

level of inoculum present and to infect new trees.

Figure 2.12–Comparison between the decay of

Phellinus inoculum in trees of different sizes. Note

that the smaller two trees (5 and 10 inches) have a

slower, initial decay rate than the larger two trees (15

and 20 inches).

0

5

10

15

20

0

1

2

3

4

5

6

0 5 10 15 20 25 30 35

Infected root radius (ft)

(m)

Years

5in [13cm]

10in [25cm]

15in [38cm]

20in [51cm]

Figure 2.13–Diagram of a shrinking center. The heavy circle represents the center boundary, and the shaded circles are the

inoculum surrounding infected stumps. The dotted lines show where the center boundary used to be.
124 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

3.0 Other Agents

3.1 Bark Beetles

Four functional types of bark beetle can be simulated in the model. The differences between the four

types are described in detail below and are summarized in table 4.1 of Chapter 2. Each type of bark beetle

is controlled by a separate keyword. The user may implement any combination of types of beetles in any

time step, including using multiple versions (“varieties”) of any type of bark beetle. For example, the user

may want to have Type 1 beetles cause 20 percent mortality when there are 100 trees/acre above the dbh

limit but cause 60 percent mortality when there are more than 200 trees/acre above the dbh limit. The

same type of beetle can also act on more than one species of tree. These scenarios can be achieved by

entering the keyword for that beetle type two or more times with different parameter values. The model

will implement up to 33 beetle varieties in each growth cycle.

The model evaluates the criteria for each beetle type (or variant) before any mortality has been

applied. Thus, in any time step the threshold conditions for any bark beetle are not affected by mortality

caused by preceding bark beetle types. For any beetles that meet or exceed the thresholds, the model then

calculates the mortality occurring on each element of each live tree list for each beetle variety. The

mortality that is actually applied is the maximum mortality that would occur on any tree list element (i.e.,

bark beetle mortality is not additive within tree list elements; see table 3.1).

By default, stand conditions are re-evaluated for a possible outbreak of each beetle variety in every

growth cycle after the first cycle specified by the beetle’s keyword. However, the keywords for each

beetle type provide an option to inactivate all current varieties of that type of beetle after the specified

growth cycle. If some of the varieties affected by this procedure are to remain potentially active, their

keywords must be re-entered for the following growth cycle (see Chapter 2).

Bark beetle types 1, 3, and 4 are active by default in the model. The keyword BBCLEAR turns off all

bark beetles. Type 2 is not active by default and must be entered individually.

Table 3.1–Application of bark beetle mortality to trees. This table assumes that all the bark beetles were

active in the same time step on the same species of trees. The number in each cell is the percentage mortality

predicted for the trees of that type. Numbers in bold are the mortalities which were actually applied.

Outside Inside Uninfected Infected _________________ __________________ ________________________

dbh (in.) < 4 4 - 8 > 8 < 4 4 - 8 > 8 < 4 4 - 8 >8 -10 > 10

Type 1 0 0 10 0 0 10 0 0 10 10

Type 3 0 0 0 0 0 0 0 0 0 88

Type 4 0 1 1 0 30 30 0 75 75 75

Applied 0 1 10 0 30 30 0 75 75 88

Type 1 Beetles

In the event of an outbreak, Type 1 bark beetles apply the same mortality rate to all host trees larger than

a threshold diameter (dbh). The user may choose to trigger the outbreak by either the density (trees/acre

[stems/hectare]) of host trees larger than the threshold diameter, or the current stand density index (SDI)

which considers all species and all sizes of trees present. Whichever measure is chosen, the user may

further specify either that it is calculated for areas outside centers only or that it is averaged over the

entire stand, and that it is calculated for either live trees only or for all trees that were alive within the last

2 years. An outbreak will be triggered when the resulting value is greater than a specified threshold.

The defaults for Type 1 beetles are that they will kill 10 percent of all ponderosa pine more than 8

inches [20.32 cm] dbh whenever the density of such trees exceeds 150 live stems/acre [371 live stems/ha]

over the stand as a whole. The keyword BBTYPE1 will change the default parameters, and the DNSCALC
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 125

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

keyword changes the method of calculating the threshold value. Type 1 bark beetles are active by default

in the model. Examples of Type 1 beetles are western pine beetle (Dendroctonus brevicomis) on ponderosa

pine and mountain pine beetle (D. ponderosae) on lodgepole pine.

Type 2 Beetles

Like Type 1 beetles, Type 2 beetles apply the same mortality rate to all host trees larger than a threshold

dbh. However, Type 2 beetles are triggered by the density of host trees that are windthrown in the

current time step and that are larger than this dbh. The actual mortality rate that occurs in the event of an

outbreak depends on the number of windthrown trees: provided that enough live trees are available, a

specified number of trees will be killed by the beetle for each windthrown host tree that was larger than

the threshold dbh. The user inputs this “number of trees to kill for each tree windthrown,” not the actual

mortality rate. If the number of trees to kill is more than the number of live trees available, then all host

trees above the threshold dbh will be killed.

The defaults for Type 2 beetles are that they will kill 0.88 Douglas-fir for every windthrown Douglasfir, of any size, whenever the density of windthrown Douglas-fir exceeds 10 stems/acre [24.7 stems/ha]

over the growth cycle. The keyword to change these defaults is BBTYPE2. Type 2 bark beetles are not

active by default in the model, and, even if requested, they will occur only if windthrow has been

scheduled for the same time step. An example of a Type 2 beetle is Douglas-fir beetle (D. pseudotsugae) on

Douglas-fir.

Type 3 Beetles

Type 3 bark beetles affect only those host trees that are larger than a threshold dbh and have a sufficient

proportion of their root radius infected with root disease. The outbreak is triggered by the density of live

trees with these characteristics in the root disease areas that infect the host species. In the event of an

outbreak, a specified mortality rate is applied to all trees that met the criteria.

The defaults for Type 3 beetles are that they will kill 88 percent of all white fir (or grand fir in some

FVS variants) larger than 10 inches [25 cm] dbh and with more than 30 percent of their root radius

infected with root disease. Mortality will occur whenever the density of such trees exceeds 10 live stems/

acre [24.7 trees/ha] in root disease areas. The keyword to change these defaults is BBTYPE3. Type 3 bark

beetles are active by default in the model. An example of a Type 3 beetle is fir engraver (Scolytus ventralis)

on white or grand fir.

Type 4 Beetles

Like Type 3 beetles, Type 4 beetles are triggered by the density of sufficiently large host trees that have a

sufficient proportion of their root system infected with root disease. However, any such trees that were

alive within the last 2 years, and not just currently living trees, contribute to triggering an attack of a Type

4 beetle. Furthermore, Type 4 beetles may apply different mortality rates to the trees that exceed the

threshold dbh in each of the following categories:

1. Trees that have more than the threshold proportion of their root radius infected (i.e., “insideinfected” trees).

2. Trees that have less than the threshold proportion of their root radius infected but are inside a

root disease area of the type that infects the beetle’s host (i.e., “inside-uninfected” trees and

inside-infected trees with low infection levels).

3. Trees that are outside of root disease areas of the type that infects the beetle’s host, but are

within a distance d of such areas (i.e., “fringe” trees), where d is the average root diameter of

all the trees outside of those disease areas that are susceptible to that type of root disease.

4. Trees that are more than the distance d from root disease areas of the type that infects the

beetle’s host (i.e., “outside” trees).
126 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Before applying mortality rates, the model must determine which trees fall into each of the four

categories. Categories 1 and 2 can be calculated from the knowledge of the trees inside centers and their

infection levels. Fringe trees (category 4) are determined differently, since the model does not have any

spatial representation of the trees in the stand. The model calculates the average root diameter of all trees

outside centers that are susceptible to the disease type of the center. This distance is d. This value is then

used to determine the amount of area that would fall within d of the center boundary, i.e., the torus

surrounding the center. Finally the model uses the density of trees outside centers to determine which

trees will be in this fringe area. Thus before the bark beetles attack the trees, the density of trees in the

fringe is the same as in the rest of the area outside centers. “Outside” trees are all remaining trees outside

centers.

The defaults for Type 4 beetles are that they will attack ponderosa pine greater than 4 inches [10.2 cm]

dbh when there is at least one [2.5] such tree[s] with more than 40 percent of the root radius infected per

acre [hectare] of disease area. The resulting mortality rates will be 75 percent for “inside-infected” trees,

30 percent for “inside-uninfected” trees, 15 percent for “fringe” trees, and 1 percent for “outside” trees.

The keyword to change these defaults is BBTYPE4. Type 4 bark beetles are active by default. An example

of a Type 4 beetle is mountain pine beetle (D. ponderosae) on ponderosa pine.

3.2 Windthrow

The windthrow model simulates major blowdown events and can affect all live trees, whether or not they

are infected. Once the model knows the magnitude of the event (defined as the proportion of dominant

and codominant stems to windthrow), it can allocate the effect between species according to their relative

susceptibility to windthrow, and then among individuals of a species.

The windthrow model operates on all stems which are considered to be dominant or codominant.

Trees are defined as dominant or codominant if the basal area percentile for their tree record is greater

than or equal to 80 percent. Therefore, the total number of susceptible stems is the sum of all trees that

meet this dominance criterion. The number of stems to windthrow is a proportion of the total susceptible

stems. At this point, one of two conditions are possible:

1. The number of stems to windthrow is less than a minimum value. Windthrow will not occur

in that time step, and the windthrow model will be called each time step until the number of

stems to windthrow is above the minimum value.

2. The number of stems to windthrow exceeds the threshold value. In this case, windthrow will

occur, and the effects will be allocated among the various stems.

Once windthrow has occurred, it will not occur again, unless the user has entered the keyword again for

a later cycle.

Allocating the effects among the different tree records is a three-step procedure. First, the number of

stems of each species that will be windthrown is calculated as:

S E R

E R

S s s

j j

j l

nsp =

=

∑

where

Ss is the number of stems of species s to windthrow;

Es is the number of eligible stems of species s to windthrow; and

Rs is the relative susceptibility of species s to windthrow (default is 1 for all species);

nsp is the total number of species; and

∑EjRj is the sum over all species (j=1 to nsp) of the number of eligible stems of

 species j times the relative susceptibility of species j.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 127

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

Second, the number of stems to windthrow from each tree record i of each species s is computed by:

M

SN L P

SN L P

S i

n si si si

s sk sk sk

k l

, t

,, ,

,, ,

= ( ) +

( ) + =

∑

1

1

where

Ms,i is the number of stems in tree record i to windthrow;

Ns,i is the number of stems in tree record i;

Ls,i is the crown length of tree record i expressed as a percentage of total height;

Ps,i is the proportion of the root radius of tree record i infected by root disease; and

t is the number of tree records in the tree list;

k is a record counter for summing over the number of records.

This calculation assumes that stems with a larger proportional crown length are more susceptible to

windthrow, and that stems with greater root disease infection are more prone to windthrow.

Third, the model determines the number of stems that tip over as opposed to snapping off. Infected

windthrown stems which tip over are assumed to remove the major portion of their root system from the

soil, and therefore cannot continue to spread the root disease. Infected windthrown stems that snap off

become infected stumps and can continue to serve as an inoculum source. In the model, the probability of

a stem being uprooted is equal to the proportion of its root system that is infected, so that more heavily

infected trees are more likely to tip over than to break off.

The WINDTHR keyword controls the windthrow model. It specifies the proportion of dominant and

codominant stems which are windthrown and the minimum number of eligible stems for windthrow to

occur.

4.0 Management

The root disease model contains two management actions (borate application and stump removal) that

are specific to the model. All the usual FVS-level stand management options are also available and may

affect root disease development.

4.1 Borate (Annosus Root Disease Only)

When Annosus root disease is in the stand, the user can simulate the application of borate to newly

created stumps. Borate application reduces the number of stumps that are eligible for colonization by

spores and thus reduces the number of new disease centers and the spread of root disease. If the spore

sub-model is not being used, then borate application has no effect. Also, if most of the stand is already

inside root disease centers, the application will have less effect than in a stand with less root disease.

Finally, borate application has the biggest effect on large diameter stumps: those that have the potential

to create a relatively large disease center and to contribute significantly to the inoculum in the stand.

Borate is applied in the year that the harvest occurs. Using the BORATE keyword, users must specify

the minimum stump diameter for borate to be applied (current default is 12 inches [30.5 cm]), and the

proportion of stumps on which the borate treatment prevents colonization (current default is 95 percent).

These values may be changed in any year, and the treatment with the specified parameters will be used

for all harvests until the values are changed again. By default, borate application does not occur. Once

requested, borate application occurs with each harvest unless the proportion of stumps on which the

treatment is successful is set to 0.
128 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 4 Detailed Model Description Beukema, Kurz, Greenough

4.2 Stump Removal

Pushing stumps removes stumps with root disease. A proportion of all infected stumps of all ages are

removed. Using the keyword PSTUMP, users can specify in which year or cycle to remove the stumps,

and what proportion of stumps above a given size is removed. By default, stump removal does not occur.

If stump removal is requested, the default is that all stumps will be removed.

4.3 Regeneration

Adding new trees to a stand is a standard FVS option through use of the Establishment Model (Ferguson

and Crookston 1991), and in some variants is a default condition. In the Root Disease Model, when new

trees are added (as part of FVS), all the trees are uninfected. They are placed both inside and outside of

centers in proportion to the size of the relative areas. New trees are “immune” to the infection in the time

step in which they are planted, and will not show up in the root disease model-specific output until the

subsequent time step.

5.0 References

Ferguson, D.E.; Crookston, N.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.

Reineke, L.H. 1933. Perfecting a stand density index for even-aged forests. Journal Agricultural Research 46:627-638.
Chapter 5

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 131

Chapter 5 A Comparison Between ...Model Predictions and Real Data Frankel, Wilson, Slaughter

Chapter 5

A Comparison Between Western Root Disease Model

Predictions and Real Data

Susan J. Frankel1 Larry J. Wilson2 Garey Slaughter3

Introduction

How accurate are the WRDM3’s simulations of the impacts of root disease and bark beetles on forest

stands? The WRDM3 was not derived from empirical data, instead it is based upon a group effort to

capture a complex biological system through a series of assumptions. Although its design makes

intuitive sense to the forest pathologists and entomologists who created it, does it make reasonable

predictions of the impacts of root disease on stand development?

To determine whether the model is accurately simulating the impacts of root disease on forest stands,

we ran the model in many different types of stands under heavy, moderate, and light infection levels. We

tested every keyword to see if the response from the model made intuitive sense. These behavior analyses

(Zhang and others, 1997, Beukema and Kurz 1995) increased our confidence in the model’s ability to

predict the impacts of root disease in a reasonable manner.

To quantitatively test the model, comparisons of model predictions to data from permanent plots are

useful. In this analysis we compared model predictions to data from permanent plots, tracking the

impact of Annosus root disease in fir stands. We entered the data from 1981, the year of plot establishment,

into the model, ran the model for a 13-year simulation period, and compared the output to the data from

our most recent year of data (1994). Unfortunately, we could use only 26 plots, but for this limited data set

we found that the S-type Annosus functions of the WRDM3 are, on average, making accurate predictions.

Methods

Pest Damage inventory plots measured in 1981 were remeasured in 1994. The 0.1-acre plots in fir stands

on the Stanislaus and Eldorado National Forests are centered around trees that died in 1972-73. In 1981,

tree species, diameter at breast height (dbh), height, crown ratio, and root disease severity were recorded

and trees tagged and stem mapped.

In 1994, these plots were revisited, and all inventory measurements required by the USDA Forest

Service, Pacific Southwest Region (USDA FS 1994) were recorded. Tree numbers were matched so actual

growth measurements could be determined from 1981 to 1994. In some cases, a 1994 plot tree could be

matched to a tree in the 1981 plot, but diameter was not measured on that tree in 1981 (in 1981 the tree was

smaller than 10 cm, or not picked up by the 5 BAF prism). For these trees the 1981 dbh was determined by

subtracting the plot average dbh growth from its 1994 dbh. On 20 plots, there occurred one or more

instances of a tree recorded as dead in 1994 that could not be matched with a 1981 tree record. In these cases,

1 Plant pathologist, USDA Forest Service, State and Private Forestry, 630 Sansome Street, San Francisco, CA 94111.

2 Forester, Pacific Evergreen Forestry, P.O. Box 1318, Guerneville, CA 95446.

3 Researcher, Department of Plant Pathology, University of California, Davis, CA 95616.
132 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 5 A Comparison Between ...Model Predictions and Real Data Frankel, Wilson, Slaughter

the plot was dropped from the analysis. One other plot was discarded because the stand conditions were

not characteristic of fir stands in the Sierra Nevada.

For the remaining 26 plots, the 1981 data were translated and processed with the Forest Vegetation

Simulator (FVS) with and without the Western Root Disease Model. Default values were used for all

parameters of the Western Root Disease Model. In order to eliminate variation due to inaccurate height

measurements, the Pacific Southwest Region height dubbing routines in FVS were used to assign heights

to both the 1981 and 1994 data sets.

The changes in tree mortality and basal area from 1981 to 1994 were calculated from the actual plot data

and compared to the 1994 predicted values from the FVS summary table. Mortality in trees per acre, increase

in basal area in square feet per acre, and volume killed in cubic foot mortality per acre were compared.

Results and Discussion

The average actual mortality calculated from the data over the 13-year period was 657 cu. ft. of mortality

per acre, from 35 trees per acre. On average, the model predicted 650 cu. ft. of mortality per acre from 49

trees per acre. Average increase in plot basal area from 1981 to 1994 was predicted to be 23 sq. ft. per acre;

the actual plot average was 29 sq. ft. per acre.

We also ran the FVS model without the WRDM3 extension for comparison. Without the WRDM3,

running the Western Sierra Nevada (WESSIN) variant of the FVS model overpredicted growth by 20 sq.

ft. per acre (41 percent) underestimated the mortality by 497 cu. ft. per acre (76 percent) and 17 trees per

acre (49 percent).

Mortality and basal area were simulated for each plot, and the results were averaged (see tables 1 and 2

for individual plot values). The model did not accurately predict the mortality on a plot-by-plot basis.

The plots are very small, which makes the model simulations more variable: one tree killed or not killed

on a plot is a high percentage of the volume of a very small plot.

These plots come from a small geographic area, and it is unknown how well they represent Annosus

root disease in other areas. It tests only the functions of S-type Annosus; the functions for P-type Annosus,

Armillaria and Phellinus are significantly different and need to be analyzed separately. We are currently

performing a similar analysis in P-type Annosus infecting ponderosa pine in northeastern California.

Only 26 plots were analyzed, a small number on which to base the accuracy of a model, but at least in

this project area the WRDM3 on average made an accurate prediction of the impact of Annosus root

disease in fir stands after 13 years of development. When making runs with the WRDM3, it is best to plan

the analysis and review the results with a forest pathologist or entomologist familiar with pathogens and

insect activity in the analysis area.

Summary

Simulations of 26 permanent plots in fir stands infected with Annosus root disease (caused by

Heterobasidion annosum) demonstrate that the Forest Vegetation Simulator with the Western Root Disease

Model is, on average, accurately predicting mortality for 13 years. Data from 1981 were processed using

the Forest Vegetation Simulator (FVS)—with and without the Western Root Disease extension model—

then compared to measurements made in 1994/95. The 0.1-acre plots, from the Pest Damage Inventory

on the Stanislaus and Eldorado National Forests, are centered around dead true firs infested with

Annosus root disease. Enlargement of tree mortality centers has been mapped and monitored at these

plots from 1981 to 1995. On average, the actual mortality calculated from the data over the 13-year period

was 657 cu. ft. mortality per acre. The FVS model with the Western Root Disease Model predicted 650 cu.

ft. per acre. The FVS model without the root disease model underpredicted mortality, calculating

mortality at 160 cu ft. per acre.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 133

Chapter 5 A Comparison Between ...Model Predictions and Real Data Frankel, Wilson, Slaughter

Table 1—Individual plot data for mortality and basal area change for a 13-year interval (1981-1994) on

26 Annosus root disease plots in fir stands in the Sierra Nevada.

Plot Trees Basal Change in Change in Volume

numbers per acre area mortality basal area mortality1

1981 1994 1981 1994

ft2/acre trees/acre ft2/acre ft3

6 St055 360 320 259 325 40 66 410

7 St077 280 230 109 140 50 31 370

8 St070 80 50 52 47 30 -5 390

9 St050 140 110 47 65 30 18 130

11 St105 130 110 276 253 20 -23 2240

13 St126 170 170 139 181 0 42 0

14 St127 250 240 92 150 10 58 20

15 St114 170 160 137 161 10 24 180

16 St120 220 212 107 120 8 13 447

17 St086 410 380 312 307 30 -5 2300

18 St024 250 200 337 348 50 11 650

20 St034 350 280 99 140 70 41 130

21 St038 170 140 153 144 30 -9 1230

31 El002 200 120 232 162 80 -70 3260

32 El023 320 310 208 208 10 0 3400

64 El018 80 70 126 151 10 25 140

65 St058 240 240 197 249 0 52 0

67 St056 330 310 186 247 20 61 60

71 St047 290 260 222 280 30 58 36

72 St045 250 210 241 277 40 36 100

73 St080 270 260 136 207 10 71 10

81 St081 410 290 216 269 120 53 730

83 St111 110 100 148 195 10 47 30

84 St119 150 140 90 156 10 66 0

86 St125 350 310 302 373 40 71 140

87 St091 510 410 212 245 100 33 680

Averages 250 217 178 208 35 29 657

1

Volume mortality uses 1981 dimensions of trees that died during the period, per acre.
134 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 5 A Comparison Between ...Model Predictions and Real Data Frankel, Wilson, Slaughter

Table 2—Western Root Disease Model predictions for mortality and basal area change from 1981 to 1994 (13 years)

on 26 Annosus root disease plots in fir stands in the Sierra Nevada.

Annosus Off1 RRINIT2,3 RRTREIN3,4

_____________________________________________ ____________________________________________ ________________________________________________

Plot ∆5 in ∆ in Volume ∆ in ∆ in Volume ∆ in ∆ in Volume

numbers mortality basal area mortality6 mortality basal area mortality6 mortality basal area mortality6

trees/acre ft2/acre ft3 trees/acre ft2/acre ft3 trees/acre ft2/acre ft3

6 St055 59 65 538 65 51 1768 64 54 1489

7 St077 8 55 23 23 45 105 21 43 105

8 St070 2 31 16 14 15 212 15 4 326

9 St050 5 45 16 39 19 296 32 23 178

11 St105 6 38 198 16 24 252 20 18 323

13 St126 6 40 54 23 37 54 6 38 54

14 St127 9 55 26 14 54 26 9 55 26

15 St114 6 51 56 49 5 938 38 31 202

16 St120 8 65 51 44 27 667 37 17 831

17 St086 45 54 423 77 2 1556 87 -31 2690

18 St024 26 27 420 46 15 657 43 21 478

20 St034 12 60 32 81 28 227 87 17 283

21 St038 6 45 58 24 20 416 26 18 513

31 El002 6 67 125 55 -17 1955 77 -33 2079

32 El023 12 41 70 98 13 250 86 11 304

64 El018 2 50 85 15 16 896 21 35 221

65 St058 8 39 50 31 34 83 27 34 93

67 St056 29 52 168 41 49 192 34 51 179

71 St047 26 52 260 72 11 1169 42 41 286

72 St045 32 45 333 48 40 320 37 44 337

73 St080 9 77 49 47 37 756 46 36 694

81 St081 49 44 324 104 9 796 104 -7 1032

83 St111 4 38 78 26 -5 1180 19 15 472

84 St119 6 47 42 49 11 584 42 -2 791

86 St125 39 50 402 65 29 805 67 19 1044

87 St091 57 51 254 105 18 728 125 -6 1081

Averages 18 49 160 49 23 650 47 21 620

1

“Annosus off” means tree data for 1981 were projected for 13 years using the WESSIN (Western Sierra Nevada

Variant) of the Forest Vegetation Simulator with the Western Root Disease Model turned off. 2

“RRINIT” displays results of Western Root Disease Model simulations; root disease was entered into the model

using the RRINIT keyword. 3

Plots are run as 0.1-acre stands, with one fir-type Annosus root disease center, 30-foot radius (0.065 acre).

4

“RRTREIN” displays results from simulation done with the Western Root Disease Model, with the root disease

information read from the plot inventory information using the RRTREIN keyword. 5

∆ = change 6

Volume mortality is taken from FVS summary table and applies to the whole plot.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 135

Chapter 5 A Comparison Between ...Model Predictions and Real Data Frankel, Wilson, Slaughter

References

Beukema, Sarah J.; Kurz, Werner A. 1995. Comparison of the behavior of Armillaria and Phellinus in the Western Root Disease

Model and the Combined Root Disease Model, further analyses. ESSA Technologies, Ltd. Vancouver, B.C. B.C. Prepared for

Ministry of Forests, Victoria, B.C.; 98 p.

USDA Forest Service. 1994. Forest inventory and analysis user’s guide. San Francisco, CA: Pacific Southwest Region; 431 p.

Zhang, Shaoang; Smith, Eric L.; David, Lance. 1997. Sensitivity analysis of the revised Annosus Root Disease Model. FHTET 96-

18. Fort Collins, CO: Forest Health Technology Enterprise Team, Forest Service, U.S. Department of Agriculture; 41 p.

Chapter 6

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 139

Chapter 6 Root Disease and the...Model in British Columbia Beukema, Kurz, McLaughlin

Chapter 6

Root Disease and the Western Root Disease Model in

British Columbia

Sarah J. Beukema1 Werner A. Kurz2 John McLaughlin3

Introduction

This chapter gives an overview of the use of the Western Root Disease Model Version 3 (WRDM3) in

southeastern British Columbia (BC) for the benefit of current and potential model users in BC. This

chapter will:

•Give an overview of the history of the development and testing of the model and of the

involvement of BC pathologists,

•Compare this model to Tree and Stand Simulator/ROTSIM,

•List some of the BC-specific features of the model, and

•Discuss some of the applications and limitations to using the model in BC.

After reading this chapter, readers should have a better understanding of the applicability of the WRDM3

to ecosystems in southeastern BC, and of some of the issues relating to its use. Descriptions of how to use

the model and details of model behavior will not be provided because they are available in other

chapters.

Root diseases, especially those caused by Armillaria ostoyae and Phellinus weirii (laminated root rot),

are common throughout the Kamloops and Nelson Forest Regions (Morrison and others 1992; fig. 1). In

some areas, more than 50 percent of the stands surveyed contain some level of identifiable root disease.

Armillaria root disease is found in many different biogeoclimatic ecosystem classification zones (BEC)

(Meidinger and Pojar 1991, Pojar and others 1987) whereas laminated root rot is found only in the Interior

Douglas-fir (IDF) and Interior Cedar Hemlock (ICH) zones (table 1). Neither of the root diseases is found

in the Ponderosa Pine zone (PP) or in the higher elevations of the Englemann Spruce Subalpine Fir (ESSF)

zones (Morrison 1997). Heterobasidion annosum occasionally occurs in the cool, wet ICH zones in the

Nelson region (Braumandl and Curran 1992) but is not of major concern in BC.

These diseases create serious complications for forest management, especially since the recent

introduction of the Forest Practices Code which specifies forest management guidelines in British

Columbia (Government of British Columbia 1995). For example, the Code calls for Silviculture

Prescriptions and Stand Management Prescriptions to identify the forest health implications of conducting

planned silviculture treatments (e.g., clear-cut vs. partial cut or juvenile spacing). Likewise, restraint on

timber harvesting arising from the need to manage for other forest values (e.g., scenery, wildlife, cultural

heritage) has resulted in the reduction of the Annual Allowable Cut (AAC). The degree to which this

AAC reduction can be mitigated through treatment of sites infected with root disease must be quantified.

1 Systems Ecologist, ESSA Technologies Ltd., Suite 300, 1765 West 8th Avenue, Vancouver, BC, V6J 5C6, Canada

2 Senior Systems Ecologist, ESSA Technologies Ltd., Suite 300, 1765 West 8th Avenue, Vancouver, BC, V6J 5C6, Canada

3 Forest Pathologist, BC Ministry of Forests, 31 Bastion Square, 3rd Floor, Victoria, BC, V8W 3E7, Canada
140 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 6 Root Disease and the...Model in British Columbia Beukema, Kurz, McLaughlin

Phellinus weirii

Vancouver Nelson

Kamloops

Armillaria ostoyae

Kamloops

Vancouver Nelson

Table 1—Summary of distribution of Armillaria ostoyae and Phellinus weirii, by BEC1

zones, in the Kamloops Forest Region and the Nelson Forest Region, BC. In the Kamloops

Forest Region, an ‘X’ marks those zones with moderate to severe infestations (Lloyd and

others 1990). In the Nelson Forest Region, an ‘X’ indicates those zones with “probable and

persistent incidences” (Braumandl and Curran 1992, Morrison 1997, Norris 1997).

ESSF MS SBS ICH IDF PP

Kamloops Region

A. ostoyae X2 X X XX3

P. weirii X X3

Nelson Region

A. ostoyae4 X5 X na6 X X

P. weirii na6 X X

1

The BEC zones listed here are: ESSF = Englemann Spruce-Subalpine Fir, MS=

Montane Spruce, SBS = Sub-boreal Spruce, ICH = Interior Cedar Hemlock, IDF = Interior

Douglas-fir, and PP = Ponderosa Pine (Pojar and others 1987, MacKinnon and others

1992). 2

Only known in the dv and wc subzones.

3

Not known in (or speculated to be in) the xh1 and xh2 subzones.

4

Listed as a “broad spectrum agent” infecting all common tree species.

5

Lower elevations only.

6

na = zone does not exist in the Nelson Forest Region.

Figure 1—Extent of Phellinus weirii and Armillaria ostoyae in British Columbia (from Morrison and others 1992)
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 141

Chapter 6 Root Disease and the...Model in British Columbia Beukema, Kurz, McLaughlin

For these and other reasons, the ability to model the behavior and impact of root disease in southeastern

BC was recognized as a priority by the BC Ministry of Forests, and funding was allocated to assist in

model development.

Addressing forest health issues with extensions to the Forest Vegetation Simulator (FVS) was an

attractive solution because FVS is widely used in the United States and many add-on tools are available

that address forest health, stand visualization, wildlife/habitat relationships, and other issues at the

stand and landscape level. Two forest health extensions to FVS were used in the United States to simulate

the dynamics of root disease and its interaction with forest management activities: the Western Root

Disease Model version 1.0 (WRDM) (Stage and others 1990), which simulates root disease caused by

Phellinus weirii or Armillaria ostoyae, and the Annosus Root Disease/Bark Beetle Model (Beukema and

others 1995), which evolved from the WRDM1. Following the recommendations from a workshop with

model users and pathologists from the United States and Canada, the British Columbia Ministry of

Forests decided to fund the merger of these models into a single model: the WRDM3.

Forest health extensions to the FVS, such as the WRDM3, have been developed over a number of

years using a workshop approach (Holling 1978). These workshops brought together pathologists,

entomologists, silviculturalists, and others to identify the important relationships to be included in the

model and to review model behavior. Several pathologists from BC participated in initial workshops in

which the first version of the model was created (the Western Root Disease Model, Version 1) (Stage and

others 1990), and have been involved in workshops developing the current version. With the advent of

the SI (Southern Interior) variant of FVS, a variant applicable to a range of ecosystems in the Kamloops

and Nelson Forest Regions, interest from BC pathologists has increased. Upon completion of model

development, pathologists from BC and the United States defined a series of stand conditions and stand

management scenarios to be used in behavior analyses of the model. The results of these simulations

have been reviewed by the pathologists. Although the model behavior was generally endorsed, it was

also realized that the development and testing of default parameter sets applicable to specific regions are

important.

Root Disease Models

With the introduction of the WRDM3 into BC, two root disease models are supported by the BC

Ministry of Forests: the WRDM3 and ROTSIM (Bloomberg 1988). Each model is an extension to a

different base model that is used to simulate the growth and yield of trees. ROTSIM is an extension to

the Tree and Stand Simulator (TASS; Mitchell 1975), a distance-dependent, even-aged growth model

that simulates pure-species stands (table 2). Mixed-species stands are simulated as smaller blocks of the

pure species independently and in proportion to the species distribution in the mixed stand. Results

are then combined to obtain per hectare estimates. The WRDM3 is an extension to FVS (Wykoff and

others 1982), a distance-independent, even or uneven-aged growth model that simulates pure or mixed

species stands. The Southern Interior (SI) variant of FVS is applicable to ecosystem types in British

Columbia’s southern interior regions. Both base models allow users to simulate different types of

thinning and harvest options.

The two root disease models simulate the dynamics of the root disease using different approaches

(table 2). ROTSIM has a detailed, three-dimensional representation of the uninfected and Phellinus weiriiinfected portions of root systems of trees and the location of these trees in the stand. Users of the WRDM3

can select one of three root diseases. The model carries information about the average species composition,

size distribution, and density of trees inside and outside root disease centers. In both root disease models,

the root disease will affect the growth and survival of infected trees.

The Kamloops and Nelson Forest Regions contain many uneven-aged and multi-species stands, and

have large areas infected with Armillaria root disease. For these regions, therefore, the WRDM3 is the

more useful of the two models because of the ability of the base model to portray these types of stands.

For areas in which there are mostly even-aged stands and for which TASS has been calibrated, ROTSIM

may be the more appropriate choice.
142 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 6 Root Disease and the...Model in British Columbia Beukema, Kurz, McLaughlin

Use of the WRDM3 in British Columbia

Model Features Unique to the BC Version

The biology of the trees and root diseases in southeastern BC is similar to those in the adjacent states

(Idaho, Montana, and eastern Washington). Thus, the WRDM3 is the same model as that linked to the

Northern Idaho variant of FVS used by the USDA Forest Service with the exception that all input and

output are in metric units.

To facilitate use of the model, the BC Ministry of Forests has developed a user-friendly interface. This

interface will allow users to select from options that they are familiar with (such as BEC zone, thinning

terminology, etc.) and will create the appropriate annotated keyword files. In addition, this front end will

make the cross-reference between the BEC zone entered by the user to the U.S. Daubenmire habitat

classification system (Daubenmire and Daubenmire 1968) used inside the model.

Table 2—Comparison between main components of the WRDM3 and ROTSIM. A brief comparison

of the base tree growth and yield model used with each (FVS or TASS) is also included.

Root disease model

WRDM3 ROTSIM

Species Armillaria ostoyae, Phellinus weirii

Phellinus weirii,

Heterobasidion annosum,

some representation of bark beetles

Centers Root disease centers contain average Root disease centers are self

stand densities and inoculum levels. forming, due to spatial

modeling of entire stand

Disease Simulations of probability of root Root contact as estimated

spread contact based on tree species, root from the number of root

radius and stand density elements per cubic meter

of soil.

Root Root systems are two-dimensional Root systems modeled

systems circles in three dimensions

Management Stump removal Stump removal

actions Borate application

Stand Pure or mixed species Pure or mix-prorated species

types Even or uneven-aged Even-aged

 11 species—interior only, 7 tree species

No hardwoods

Growth FVS TASS

model Distant independent Distance dependent

Diameter driven Height driven

Individual tree growth is Individual tree growth is

adjusted to account for relative adjusted to account

position in stand. for competition from

neighbors.

Root disease reduces tree growth Root disease reduces tree

and increases mortality. growth and increases

mortality.

Actions Thinning and harvesting Thinning and harvesting

modelled Planting Planting, Pruning

Region Southern Interior Province-wide
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 143

Chapter 6 Root Disease and the...Model in British Columbia Beukema, Kurz, McLaughlin

Potential Applications

The root disease model produces output on a number of variables, with various levels of detail about the

stand or root disease. As such, it is designed to be used for a wide variety of purposes, including (but not

limited to): research, designing silviculture prescriptions, and providing input to the development of

timber supply reviews. For example, the model can be used to project how a stand will respond to

various treatments under different assumptions about root disease. Detailed results about a stand are

available, or the results from many stands can be processed outside FVS to get summary information.

The model can be run with different options. If data are available, the model can simulate existing

stands from the time the inventory information was obtained. Users also have the option of running

‘bareground’ simulations, as can be done with WinTIPSY (the Windows version of the Table Interpolation

Program for Stand Yields; Mitchell and others 1995) and the Variable Density Yield Prediction System

(VDYP) (Anon. 1995).

In BC, the WRDM3 (and FVS) has been used to explore partial harvesting scenarios in areas that may

be subject to a decline in wood supply (Robinson and Kurz 1996). Simulations explored the response of

mean annual increment (MAI) to combinations of partial harvest entry age, re-entry age, and basal area

removed, with different assumptions about root disease levels. In addition, post-processed output from

the model was subsequently used with FSSIM, an optimizing landscape projection model used by the BC

Ministry of Forests to conduct timber supply analyses at the district level.

Data Requirements

To run the root disease model (and FVS) from existing stands, some basic information about the stand

and site is required. At a minimum, this information must include lists of representative trees with their

species, diameter at breast height (dbh), and sample weight. Some knowledge about the measure of

disease incidence in the stand is also necessary. The model will yield more realistic results if more

information, such as site information (BEC unit, slope, elevation, aspect), height, crown ratio, disease

status, and recent growth increments, is included as part of the input data. Note that the SI variant of the

model uses site information (not site index) to modify growth projections.

Current standard surveys do not supply the necessary model input data (table 3), and some modifications

to these methods are necessary to provide at least the minimum input data required for the model. For

example, when a pixel survey is done, some information about the live trees in the stand can also be collected

(using prism sweeps or more detailed plot exams). New surveys suitable for collecting data for partial cutting

and for meeting the data requirements of existing and future models are under development by the Ministry.

Table 3—Brief summary of the types of data collected by standard surveys used in BC. Note that the only data mentioned in the table

are those that are part of the minimum information needed for running FVS and the WRDM3.

Survey method Collected Not collected / Comments

Stocking Species and density, some root disease information dbh

Free-growing Species and density, some root disease information dbh

Pre-stand Species, number, dbh (by size class) dbh usually not accurate

tending Disease damage Survey method is being phased out

Timber Data about commercial species of merchantable size No information collected on

cruise other species or other sizes.

Multi-story Percent species by crown layer, some root disease dbh

silviculture information

survey

Pixel Distribution and severity of disease No detailed stand information
144 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Chapter 6 Root Disease and the...Model in British Columbia Beukema, Kurz, McLaughlin

Opportunities to Improve

Although the WRDM3 has many uses in BC, some obstacles to more general use remain:

Few long-term data sets which include information on both the living uninfected trees and the

root-disease-infected trees and stumps are available in BC. This situation has limited the

amount of model calibration and testing that can be done with the model. Current model

calibration has focused on behavior analyses of the model that are reviewed

for ‘reasonableness’ by local pathologists.

Many stands in the region contain significant numbers of hardwood species, which are not

included in the Southern Interior variant of the FVS model at the present time. This limits the

use of the WRDM3 model in these stands. In addition, no brush and shrub species, which act as

a host for A. ostoyae, are included in the model, which may result in underestimates of the

impact of the root disease in early successional stands.

It is not uncommon to find Armillaria root disease and laminated root rot in the same stand,

infecting the same species of trees. The WRDM3 cannot accommodate more than one root

disease type in a stand (with the exception of S- and P-type Annosus root disease), independent

of whether the root disease is infecting the same tree species. Although it is possible to revise

the model to accommodate multiple root diseases in the same stand, it is uncertain whether the

biological understanding about the interactions of different root disease species is adequate to

parameterize such a revised model.

Future steps in WRDM3 model development and validation are needed to establish and expand the

usefulness of the model in BC. These include:

•Addressing some of the obstacles listed above (such as performing additional simulations

and comparing the results to expert opinion and data, promoting the development of longterm data sets, adding hardwood species to both the root disease model and the base model,

and developing a version of the model that includes the ability to model more than one root

disease in the same stand);

•Adding other root diseases (such as Tomentosus root rot);

•Refining existing biological relationships in the model and adding new ones (such as

increasing the “flushing” effect that occurs after a stand entry and adjusting the parameters of

the root disease relative to the vigor of the tree);

•Adding additional management treatment options (such as “pop-up” spacing);

•Further refinement of the user-friendly front-end.

Conclusions

The WRDM3 is the first growth and yield model that simulates stand dynamics in mixed species, even or

uneven-aged stands that are affected by root diseases in southeastern BC. The model simulates the

impact of root disease on stand dynamics, including reduction in growth rates, increased mortality,

changes in species composition, and increased disease levels after a stand entry. The model has been

tested for selected ecosystem types in these regions and its behavior is generally considered reasonable
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 145

Chapter 6 Root Disease and the...Model in British Columbia Beukema, Kurz, McLaughlin

by expert reviewers. The lack of long-term plot data in managed and unmanaged stands with and

without root disease precludes more formal model calibration and validation at this time.

Acknowledgments

Hadrian Merler, Duncan Morrison, and Don Norris have been and continue to be active participants in the

design and testing of the WRDM3. John Muir and Jeff Beale have been instrumental in the introduction of

the WRDM3 in BC, and without their long-term support, this model would not be available to BC users. We

thank Neil Bow, Robert Macdonald, Pat Martin, Ken Mitchell, Duncan Morrison, Don Norris, Paul Rhesler,

Don Robinson, and Ralph Winter for providing additional input on sections of this document, and Duncan

Morrison, John Muir, Don Norris, and Barry Snowdon for reviewing earlier drafts.

Funding for the development, testing, and documentation of the WRDM3 has been provided jointly

by the BC Ministry of Forests, Forest Renewal BC, and the USDA Forest Service.

References

Anon. 1995. VDYP interactive application user guide, version 6.3. Victoria, BC:BC Ministry of Forests Resources Inventory

Branch; 44 p.

Beukema, Sarah J.; Kurz, Werner A.; Greenough, Julee A. 1995. Annosus root disease model: user’s guide and model description.

Prepared by ESSA Technologies Ltd., Vancouver, BC; 95 p. + appendices.

Bloomberg, W.J. 1988. Modeling control strategies for laminated root rot in managed Douglas-fir stands: model development.

Phytopathology 78: 403-409.

Braumandl, T.F.; Curran, M.P., eds. 1992. Field guide for site identification and interpretation for the Nelson Forest Region.

Land Management Handbook #20. May 1992. Victoria, BC: BC Ministry of Forests; 214-226.

Daubenmire, R.; and Daubenmire, J.B. 1968. Forest vegetation of eastern Washington and northern Idaho. Technical Bulletin 60.

Pullman, WA: Washington Agricultural Experiment Station; 104 p.

Government of British Columbia. 1995. Forest Practices Code of British Columbia: regulations. Victoria, BC: Government of

British Columbia.

Holling, C.S., ed. 1978. Adaptive environmental assessment and management. Wiley International Series on Applied Systems

Analysis, Vol. 3. Chichester, UK: Wiley; 377 p.

Lloyd, D.; Angove, K.; Hope, G.; Thompson, C., ed. 1990. A guide to site identification and interpretation for the Kamloops

Forest Region, February 1990. Victoria, BC: BC Ministry of Forests; 379 p.

Meidinger, Del; Pojar, Jim. 1991. Ecosystems of British Columbia. Victoria, BC: BC Ministry of Forests. 330 p.

Mitchell, K.J. 1975. Dynamics and simulated yield of Douglas-fir. Forest Science. Monograph 17; 36 p.

Mitchell, K.J.; Grout, S.E.; Macdonald, R.N. 1995. User’s guide for producing managed stand yield tables with WinTipsy

Version 1.3 under Microsoft Windows. Unpublished report. Victoria, BC: BC Ministry of Forests, Research Branch. FPDS

Section. 96 p. + Appendices.

Morrison, Duncan, Research Forest Pathologist, Pacific Forestry Center, Victoria, BC. [Telephone Conversation with Sarah

Beukema]. 10 July 1997.

Morrison, D.; Merler, H.; Norris, D. 1992. Detection, recognition and management of Armillaria and Phellinus root diseases in

the southern interior of British Columbia. FRDA Report 179. Victoria, BC: Forestry Canada and BC Ministry of Forests; 25 p.

Norris, Donald. Forest Pathologist, BC Ministry of Forests, Nelson, BC. [Telephone Conversation with Sarah Beukema]. 10 July

1997.

Pojar, J.; Klinka, K.; Meidinger, D. 1987. Biogeoclimatic ecosystem classification in British Columbia. Forest Ecology Management

22: 119-154.

Robinson, D.C.E.; Kurz, W.A. 1996. Exploring partial harvesting options in the Nelson Region using Prognosis. Prepared by

ESSA Technologies Ltd., Vancouver, BC for Nelson Forest District, BC Ministry of Forests, Nelson, BC, 23 p.

Stage, A.R.; Shaw, C.G.; Marsden, M.A.; Byler, J.W.; Renner, D.L.; Eav, B.B.; McNamee, P.J.; Sutherland, G.D.; Webb, T.M. 1990.

Users 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.

Wykoff, W.R.; Crookston, N.L.; Stage, A.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.

Appendixes Appendices

USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 149

Appendix

Index of Keywords1

BBCLEAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35, 37, 95, 124

BBOUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 63, 71

BBTYPE1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 35, 37, 38, 39, 95, 124

BBTYPE2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 35, 38, 95, 125

BBTYPE3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 35, 36, 37, 38, 77, 95, 125

BBTYPE4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 35, 36, 77, 95, 125, 126

BORATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 58, 62, 63, 127

COMMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 17

DNSCALC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38, 39, 124

END . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15, 16, 17, 60, 61, 79, 83, 92,93

INFCOLO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 46, 48, 94, 110, 113, 120, 153

INFKILL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30, 40, 46, 47, 48, 94, 110, 113, 154

INFMULT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 46, 47, 48, 94, 110, 155

INFSIMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 40, 41, 63, 75, 111

INOCLIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 50, 51, 53, 54, 55, 94, 121

INOCSPAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55, 94

OPEN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63, 64, 71, 79

PLOTINF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 18, 27, 28, 91, 92, 93, 109

PLREAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,18, 24, 25, 26, 27, 28, 29, 91, 92, 108, 160, 161

PSTUMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 58, 59, 60, 128

RDIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15, 16, 17, 83, 92, 93

RRCOMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

RRDOUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 63, 68

RRECHO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 63, 64, 68

RRHOSTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56, 110, 156

RRINIT . . . . . . . . . . . . . . . . . . . . . . . 15, 18, 23, 24, 25, 28, 29, 30, 31, 77, 83, 89, 91, 92, 93, 108, 109, 134, 161

RRJUMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 40, 44, 45, 117

RRMINK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 48, 49

RRTREIN . . . . . . . . . . . . . . . . . . . . . . 15, 16, 26, 28, 29, 30, 31, 64, 82, 90, 91, 93, 107, 108, 109, 134, 160, 161

RRTYPE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15, 16, 18, 19, 47, 91

RSEED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

SAREA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19, 25, 77, 91, 92, 93

SDIRMULT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 40, 57, 102, 115

SMCOUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 63, 73, 115

SPORE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 40, 45, 46, 120

SPREAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 40, 41, 43, 115

STREAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 32, 33, 64, 77, 91, 93

TDISTN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 44, 114

TIMEDEAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31, 32

TTDMULT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 46, 47, 48, 49, 94, 110, 113, 156

WINDTHR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 35, 38, 39, 45, 95, 127

1

 Page numbers in boldface type indicate where keyword is defined.
150 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Appendix

Glossary of Abbreviations

A: Armillaria root disease

Ann: Annosus root disease

approx.: approximately

Arm: Armillaria root disease

ave: average

ba: basal area

BC: British Columbia

BEC: biogeoclimatic ecosystem classification zone

cents: centers

cm: centimeters

cu: cubic

dbh: tree diameter measured at breast height

Dens: density

ft: feet

FVS: Forest Vegetation Simulator

ha: hectares

ID: identifier

IMP: imperial units

in: inches

in-uninf: tree is inside root disease center, but is uninfected

in-inf: tree is inside root disease center and is infected

m: meters

MAI: mean annual increment

MC: monte carlo

met: metric units

merch: merchantable

na: not applicable

no: number

nr: non-resinous

Num: number
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 151

Appendix Chapter 2 Beukema, Kurz

outside: tree is outside root disease center and is therefore uninfected

%tile: percentile

p. pine: ponderosa pine

Phel.: laminated root disease caused by Phellinus weirii

prob: probability

prop: proportion

P-type: Biologial species of Annosus root disease that damages pine, juniper,

incense cedar and other tree species.

QMD: quadratic mean diameter

r: resinous

ROTSIM: Root Rot Simulation model developed in BC and used with the Tree

and Stand Simulator (TASS)

RR: root rot

SD: standard deviation

SDI: Stand density index

SI: Southern Interior variant of FVS (used in British Columbia)

SIM: simulation

SORNEC: Southern Oregon Northeastern California variant of FVS

sp: tree species

sq. ft.: square feet

S-Type: Biological species of Annosus root disease that damages true fir, giant

sequoia and other tree species.

uninf: uninfected

US: United States

W: laminated root disease caused by Phellinus weirii

WESSIN: Westside Sierra Nevada variant of FVS

WRDM3: Western Root Disease Model, Version 3.0

x: times

yr: year
152 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Appendix Chapter 2 Beukema, Kurz

Appendix to Chapter 2

List of Default Values by Tree Species and Variant

Species Abbreviation Table

The non-italicized abbreviations are for all variants except the Southern Interior

(SI) (BC) variant. The species abbreviations for this SI variant are given in italics

next to the main abbreviation.

Species Abbreviation Species Name Species Abbreviation Species Name

AF B or BL Alpine Fir MH Mountain Hemlock

AS Aspen OC Other conifers

BO Black Oak OH Other hardwood

BS Blue Spruce OS Other softwood

C C Western Red cedar OTH Other species

CB Corkbark Fir PP PY Ponderosa Pine

CW Cottonwood RF Red Fir

DF FD or F Douglas-fir S S Spruce

ES Engelmann Spruce SF Pacific Silver Fir

GF BG Grand Fir SP Sugar Pine

GS Giant Sequoia TO Tanoak

IC Incense-cedar WB Whitebark Pine

J Juniper WF White Fir

JP Jeffrey Pine WH HW Western Hemlock

L L Larch WL LW or L Western Larch

LM Limber Pine WP PW White Pine

LP LP Lodgepole Pine WS White Spruce

Western Root Disease Model Species Tables

Variants and Associated Species Table

Species Code (as represented in tree data and output)

Variant 1 2 3 4 5 6 7 8 9 10 11

Inland Empire WP L DF GF WH C LP S AF PP OTH

Southern Interior PW L FD BG HW C PL S BL PY OTH

S.Cen.Oregon/NE Calif. WP SP DF WF MH IC LP S RF PP OTH

Eastern Montana WB L DF GF WH C LP S AF PP OTH

East Cascades WP WL DF SF C GF LP ES AF PP OTH

 Blue Mountains WP WL DF WF WH GF LP ES AF PP OTH

Western Sierra Nevada OC SP DF WF GS IC BO JP RF PP TO
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 153

Appendix Chapter 2 Beukema, Kurz

Keyword Defaults by Species and Variant

INFCOLO

This keyword defines the proportion of the root system that is colonized after

death (Section 5.3 of Chapter 2).

Species Code (as represented in tree data and output)

Variant 1 2 3 4 5 6 7 8 9 10 11

ANNOSUS

Inland Empire 0.8 0.95 0.95 0.9 0.9 1.0 0.75 0.95 0.9 0.75 0.86

Southern Interior 0.8 0.95 0.95 0.9 0.9 1.0 0.75 0.95 0.9 0.75 0.86

S.Cen.Oregon/NE Calif. 0.8 0.8 0.95 0.9 0.9 0.95 0.75 0.95 0.9 0.75 0.86

Eastern Montana 0.8 0.95 0.95 0.9 0.9 1.0 0.75 0.95 0.9 0.75 0.86

East Cascades 0.8 0.95 0.95 0.9 0.9 0.9 0.75 0.95 0.9 0.75 0.86

Blue Mountains 0.8 0.95 0.95 0.9 0.9 0.9 0.75 0.95 0.9 0.75 0.86

Western Sierra Nevada 0.9 0.8 0.95 0.9 0.95 0.95 0.0 0.75 0.9 0.75 0.0

ARMILLARIA

Inland Empire 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Southern Interior 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

S.Cen.Oregon/NE Calif. 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Eastern Montana 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

East Cascades 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Blue Mountains 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Western Sierra Nevada 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

PHELLINUS

Inland Empire 0.8 0.6 0.3 0.85 0.8 0.85 0.8 0.8 0.75 0.65 0.8

Southern Interior 0.8 0.6 0.3 0.85 0.8 0.85 0.8 0.8 0.75 0.65 0.8

S.Cen.Oregon/NE Calif. 0.8 0.6 0.3 0.3 0.8 0.85 0.8 0.8 0.3 0.65 0.8

Eastern Montana 0.8 0.6 0.3 0.85 0.8 0.85 0.8 0.8 0.75 0.65 0.8

East Cascades 0.8 0.6 0.3 0.85 0.8 0.85 0.8 0.8 0.75 0.65 0.8

Blue Mountains 0.8 0.6 0.3 0.3 0.8 0.85 0.8 0.8 0.75 0.65 0.8

Western Sierra Nevada 0.8 0.6 0.3 0.3 0.8 0.85 0.8 0.8 0.9 0.65 0.8
154 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Appendix Chapter 2 Beukema, Kurz

INFKILL

This keyword defines the proportion of the root system that is colonized at death

(Section 5.3 in Chapter 2).

Species Code (as represented in tree data and output)

Variant 1 2 3 4 5 6 7 8 9 10 11

ANNOSUS

Inland Empire 0.6 0.9 0.9 0.8 0.8 1.0 0.5 0.9 0.8 0.5 1.0

Southern Interior 0.6 0.9 0.9 0.8 0.8 1.0 0.5 0.9 0.8 0.5 1.0

S.Cen.Oregon/NE Calif. 0.6 0.6 0.9 0.8 0.8 0.9 0.5 0.9 0.8 0.5 1.0

Eastern Montana 0.6 0.9 0.9 0.8 0.8 1.0 0.5 0.9 0.8 0.5 1.0

East Cascades 0.6 0.9 0.9 0.8 1.0 0.8 0.5 0.9 0.8 0.5 1.0

Blue Mountains 0.6 0.9 0.9 0.8 0.8 0.8 0.5 0.9 0.8 0.5 1.0

Western Sierra Nevada 0.75 0.6 0.9 0.8 0.9 0.9 0.0 1.0 0.8 0.5 0.0

ARMILLARIA

Inland Empire 0.3 0.85 1.0 0.75 0.8 0.8 0.8 0.6 0.8 0.8 0.6

Southern Interior 0.3 0.85 1.0 0.75 0.8 0.8 0.8 0.6 0.8 0.8 0.6

S.Cen.Oregon/NE Calif. 0.3 0.85 1.0 0.3 0.75 0.85 0.8 0.6 0.75 0.8 0.6

Eastern Montana 0.8 0.85 1.0 0.75 0.8 0.8 0.8 0.6 0.8 0.8 0.6

East Cascades 0.3 0.85 1.0 0.65 0.8 0.75 0.8 0.6 0.8 0.8 0.6

Blue Mountains 0.3 0.85 1.0 0.3 0.8 0.75 0.8 0.6 0.8 0.8 0.6

Western Sierra Nevada 0.8 0.85 1.0 0.3 0.8 0.85 0.8 0.8 0.75 0.8 0.6

PHELLINUS

Inland Empire 0.8 0.6 0.3 0.85 0.8 0.85 0.8 0.8 0.75 0.65 0.8

Southern Interior 0.8 0.6 0.3 0.85 0.8 0.85 0.8 0.8 0.75 0.65 0.8

S.Cen.Oregon/NE Calif. 0.8 0.6 0.3 0.3 0.8 0.85 0.8 0.8 0.3 0.65 0.8

Eastern Montana 0.8 0.6 0.3 0.85 0.8 0.85 0.8 0.8 0.75 0.65 0.8

East Cascades 0.8 0.6 0.3 0.85 0.8 0.85 0.8 0.8 0.75 0.65 0.8

Blue Mountains 0.8 0.6 0.3 0.3 0.8 0.85 0.8 0.8 0.75 0.65 0.8

Western Sierra Nevada 0.8 0.6 0.3 0.3 0.8 0.85 0.8 0.8 0.9 0.65 0.8
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 155

Appendix Chapter 2 Beukema, Kurz

INFMULT

This keyword defines the probability of infection, given root contact (Section 5.3

in Chapter 2).

Species Code (as represented in tree data and output)

Variant 1 2 3 4 5 6 7 8 9 10 11

ANNOSUS

Inland Empire 0.4 0.4 0.4 0.5 0.5 0.0 0.4 0.4 0.5 0.5 0.0

Southern Interior 0.4 0.4 0.4 0.5 0.5 0.0 0.4 0.4 0.5 0.5 0.0

S.Cen.Oregon/NE Calif. 0.4 0.4 0.4 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.0

Eastern Montana 0.4 0.4 0.4 0.5 0.5 0.0 0.4 0.4 0.5 0.5 0.0

East Cascades 0.4 0.4 0.4 0.5 0.0 0.5 0.4 0.4 0.5 0.5 0.0

Blue Mountains 0.4 0.4 0.4 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.0

Western Sierra Nevada 0.45 0.4 0.4 0.5 0.5 0.5 0.0 0.5 0.5 0.5 0.0

ARMILLARIA

Inland Empire 0.1 0.05 0.5 0.6 0.1 0.6 0.2 0.5 0.5 0.2 0.1

Southern Interior 0.1 0.05 0.5 0.6 0.1 0.6 0.2 0.5 0.5 0.2 0.1

S.Cen.Oregon/NE Calif. 0.1 0.1 0.5 0.6 0.1 0.5 0.2 0.5 0.5 0.2 0.1

Eastern Montana 0.1 0.05 0.5 0.6 0.1 0.6 0.2 0.5 0.5 0.2 0.1

East Cascades 0.1 0.05 0.5 0.6 0.6 0.6 0.2 0.5 0.5 0.2 0.1

Blue Mountains 0.1 0.05 0.5 0.6 0.1 0.6 0.2 0.5 0.5 0.2 0.1

Western Sierra Nevada 0.1 0.1 0.5 0.6 0.1 0.1 0.0 0.1 0.5 0.2 0.2

PHELLINUS

Inland Empire 0.1 0.2 0.4 0.4 0.2 0.02 0.1 0.2 0.4 0.1 0.4

Southern Interior 0.1 0.2 0.4 0.4 0.2 0.02 0.1 0.2 0.4 0.1 0.4

S.Cen.Oregon/NE Calif. 0.1 0.1 0.4 0.4 0.4 0.4 0.1 0.2 0.4 0.1 0.4

Eastern Montana 0.1 0.2 0.4 0.4 0.2 0.02 0.1 0.2 0.4 0.1 0.4

East Cascades 0.1 0.2 0.4 0.4 0.02 0.2 0.1 0.2 0.4 0.1 0.4

Blue Mountains 0.1 0.2 0.4 0.4 0.2 0.2 0.1 0.2 0.4 0.1 0.4

Western Sierra Nevada 0.4 0.1 0.4 0.4 0.4 0.4 0.0 0.02 0.4 0.1 0.1
156 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Appendix Chapter 2 Beukema, Kurz

TTDMULT

This keyword gives a multiplier for the basic time-to-death function (Section 5.3

in Chapter 2).

Species Code (as represented in tree data and output)

Variant 1 2 3 4 5 6 7 8 9 10 11

ANNOSUS

Inland Empire 1.0 1.0 1.0 1.5 1.75 1.0 1.5 1.0 1.5 0.5 1.0

Southern Interior 1.0 1.0 1.0 1.5 1.75 1.0 1.5 1.0 1.5 0.5 1.0

S.Cen.Oregon/NE Calif. 1.0 1.0 1.0 1.5 1.75 1.75 1.5 1.0 1.5 0.5 1.0

Eastern Montana 1.0 1.0 1.0 1.5 1.75 1.0 1.5 1.0 1.5 0.5 1.0

East Cascades 1.0 1.0 1.0 1.5 1.0 1.5 1.5 1.0 1.5 0.5 1.0

Blue Mountains 1.0 1.0 1.0 1.5 1.75 1.5 1.5 1.0 1.5 0.5 1.0

Western Sierra Nevada 1.0 1.0 1.0 1.5 1.75 1.75 99.0 0.5 1.5 0.5 99.0

ARMILLARIA

Inland Empire 1.8 2.0 1.0 0.75 0.9 1.2 1.8 1.1 0.75 1.8 0.9

Southern Interior 1.8 2.0 1.0 0.75 0.9 1.2 1.8 1.1 0.75 1.8 0.9

S.Cen.Oregon/NE Calif. 1.8 1.8 1.0 0.75 0.9 0.9 1.8 1.1 0.75 1.8 0.9

Eastern Montana 1.8 2.0 1.0 0.75 0.9 1.2 1.8 1.1 0.75 1.8 0.9

East Cascades 1.8 2.0 1.0 0.75 1.0 0.75 1.8 1.1 0.75 1.8 0.9

Blue Mountains 1.8 2.0 1.0 0.75 0.9 0.75 1.8 1.1 0.75 1.8 0.9

Western Sierra Nevada 0.9 1.8 1.0 0.75 0.9 0.9 99.0 0.2 0.75 1.8 1.8

PHELLINUS

Inland Empire 3.0 1.5 1.0 1.0 1.5 1.0 3.0 1.1 1.0 3.0 1.5

Southern Interior 3.0 1.5 1.0 1.0 1.5 1.0 3.0 1.1 1.0 3.0 1.5

S.Cen.Oregon/NE Calif. 3.0 3.0 1.0 1.0 1.5 1.5 3.0 1.1 1.0 3.0 1.5

Eastern Montana 3.0 1.5 1.0 1.0 1.5 1.0 3.0 1.1 1.0 3.0 1.5

East Cascades 3.0 1.5 1.0 1.0 1.0 1.0 3.0 1.1 1.0 3.0 1.5

Blue Mountains 3.0 1.5 1.0 1.0 1.5 1.0 3.0 1.1 1.0 3.0 1.5

Western Sierra Nevada 1.5 3.0 1.0 1.0 1.5 1.5 99.0 10 1.0 3.0 3.0

RRHOSTS

This keyword allows users to change the default Annosus root disease type to

which the tree species is host (Section 5.5 in Chapter 2). This keyword can be

used only for Annosus.

Species Code (as represented in tree data and output)

Variant 1 2 3 4 5 6 7 8 9 10 11

Inland Empire 1 2 2 2 2 2 1 2 2 1 1

Southern Interior 1 2 2 2 2 2 1 2 2 1 1

S.Cen.Oregon/NE Calif. 1 1 2 2 2 1 1 2 2 1 1

Eastern Montana 1 2 2 2 2 2 1 2 2 1 1

East Cascades 1 2 2 2 2 2 1 2 2 1 1

Blue Mountains 1 2 2 2 2 2 1 2 2 1 1

Western Sierra Nevada 1 1 2 2 2 1 1 1 2 1 1
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 157

Appendix Chapter 2 Beukema, Kurz

List of Figures in Chapter 2

1.1: Predicted changes in a pine stand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.2: Diagram of root disease in a center in a stand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1: Flowchart to determine which keywords to use to initialize root disease levels . . . . . . . . . . . . . . 18

3.2: Effect of center area on basal area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3: A comparison of the new area generated during center spread . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.4: Effect of number of centers on basal area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.5: Effect of infected tree density on basal area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.6: Definition of a center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.7: Example of an actual and simulated stand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.8: Diagram of the centers entered in the example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.9: Caveats when placing centers in a stand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.10: Stand with S- and P-type Annosus root disease centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.11: Pictorial representation of the difference in density using different keywords . . . . . . . . . . . . . . . 29

4.1: A pictorial representation of some of the differences between bark beetle types . . . . . . . . . . . . . 34

5.1: Different types of spread rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.2: The “jump” in center size after a clear-cut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.3: Pattern of inoculum spread through a healthy root system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.4: Default relationships for the progress of infection in each

of the root disease types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.5: Default and changed values for the increase in infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.6: Effect of the RRMINK keyword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.7: Default decay pattern of infected root radii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.8: Relationships controlled by the keyword INOCLIFE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.9: The resulting decay pattern for each disease type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.10: Time required for inoculum to decay completely . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.11: Default and changed relationships in the inoculum decay example . . . . . . . . . . . . . . . . . . . . . . . . 53

5.12: Changed inoculum decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.13: Inoculum decay with a single decay rate, for the changed parameters . . . . . . . . . . . . . . . . . . . . . . 54

5.14: Comparison between the decay rates of a resinous and non-resinous tree . . . . . . . . . . . . . . . . . . 55

5.15: The effect of minimum lifespan on the decay pattern in Phellinus-infected stumps . . . . . . . . . . . 55

5.16: Default curve for the SDI-based root radius multiplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.17: Effect of changing the parameter values for the root radius multiplier . . . . . . . . . . . . . . . . . . . . . 57

6.1: The effect of inoculum removal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6.2: Effect of planting different species after a clear-cut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

6.3: Effect of planting several years after a clear-cut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

6.4: Effect of borate application on basal area and number of centers . . . . . . . . . . . . . . . . . . . . . . . . . . 62
158 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Appendix Chapter 2 Beukema, Kurz

List of Tables in Chapter 2

2.1: Options which are ON by default in the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2: Options which are OFF by default in the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.1: Relative importance of various initialization components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2: Sources of information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.1: A comparison of the four bark beetle types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2: Example bark beetle types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.1: Summary of the main processes and keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

6.1: Options for managing root disease and their implementation in FVS. . . . . . . . . . . . . . . . . . . . . . . 58

6.2: Most resistant and susceptible tree species for each root disease type . . . . . . . . . . . . . . . . . . . . . . 60

7.1: Keywords controlling the model output tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

7.2: Summary of root disease information read in on input (imperial) . . . . . . . . . . . . . . . . . . . . . . . . . . 65

7.3: Summary of root disease information read in on input (metric) . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

7.4: Standard root disease output table, with headings and in imperial units . . . . . . . . . . . . . . . . . . . 66

7.5: Standard root disease output table, with headings and in metric units . . . . . . . . . . . . . . . . . . . . . 66

7.6: Example detailed output file (imperial) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

7.7: Example detailed output file (metric) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

7.8: Bark beetle output file (imperial) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

7.9: Bark beetle output file (metric) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

7.10: Example spread rate simulation output (imperial) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

7.11: Example spread rate simulation output (metric) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

7.12: Example output file from the Monte Carlo simulation of infection within centers (imperial) . . 76

7.13: Example output file from the Monte Carlo simulation of infection within centers (metric) . . . . 76
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 159

Appendix Chapter 2 Beukema, Kurz

List of Examples in Chapter 2

Minimum keywords for running the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Minimum keywords when there are no damage codes in the inventory . . . . . . . . . . . . . . . . . . . . . . . . . . 15

The use of RDIN and END . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

The use of END with RDIN and COMMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Valid uses of the keyword RRTYPE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Initializing root disease with only the number of centers and diseased area . . . . . . . . . . . . . . . . . . . . . . 24

Specifying the size and location of centers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Simulating the stand as one center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Initializing root disease from plot information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Initializing root disease with only the number of centers and the plots with infection . . . . . . . . . . . . . 29

Section of a tree inventory with root disease damage codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Specifying the density of infected and uninfected trees in centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Infected stumps in the inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Changing the time since death for stumps in the inventory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Entering initial infected stumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Bark beetles attacking different size trees at different rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Deactivating a bark beetle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Example: Deactivating a bark beetle type (2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Changing the type of density calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Applying a more representative spreadrate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Changing the static spread rate parameters in different cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Changing the spread rate type in different cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Setting the ‘jump’ in center size after a clear-cut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Changing the spore-infection parameters for Annosus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Changing root disease parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Non-linearity in the time to death function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Changing inoculum decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Setting a single inoculum decay rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Changing the Annosus type for more than one tree species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Changing the Annosus type for only one tree species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Changing the root radius based on density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Inoculum removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Species change by planting a more resistant tree species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Species change by increasing the regeneration delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Applying borate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Opening a file for output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
160 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Appendix Chapter 4 Beukema, Kurz, Greenough

Appendix to Chapter 4

Detailed Calculation of Initial Root Disease

Density Calculation Using Disease Codes

Density Calculation Using RRTEIN

The keyword RRTREIN is used to tell the model to read disease codes from the tree list. These codes

identify tree records that contain some root disease. The model assumes that each record represents a

density of trees that covers the entire stand. Thus, each record represents trees inside centers (either

infected or uninfected, depending on the presence or absence of a disease code) and trees outside centers.

The densities of trees in both locations (inside and outside centers) are assumed to be the same. Thus for

each record, the model sets:

Infected = RecordDensity or Infected= 0

UnInf = 0 or UnInf =RecordDensity

Outside = Infected+UnInf

where:

Infected is the density of infected trees inside centers (trees/acre or trees/hectare);

UnInf is the density of uninfected trees within centers (trees/acre or trees/hectare);

Outside is the density of uninfected trees outside centers (trees/acre or trees/hectare);

RecordDensity is the density of the current record;

The overall stand density is equal to the sum of the density for each record.

Density Calculation Using PLREAD

The keyword PLREAD is always used in conjunction with RRTREIN. When PLREAD is used, each

record is either entirely within a center or outside of a center, and those records within centers are further

identified as infected or uninfected. However, initial density as determined by FVS is calculated as the

density of the record over the whole stand. Thus, the model scales each record in the tree lists by the ratio of

the total area in the stand to the area in root disease centers, using one of the following equations

Infected = RecordDensityinf ∗ StandArea / RRArea

UnInf = RecordDensityun ∗ StandArea / RRArea

Outside = RecordDensityout ∗ StandArea / (StandArea − RRArea)

where:

RecordDensity is the density of the current record, either infected (inf), uninfected inside centers

(un), or outside centers (out);

StandArea is the total area of the stand; and

RRArea is the area inside root disease centers.

The overall stand density is then calculated either by summing the total RecordDensity or by summing the

total density in each list (infected, uninfected, and outside center) and using the following formula:

(Infectedtot + UnInftot) ∗ RRArea + Outsidetot ∗ (StandArea–RRArea) StandDensity =

StandArea
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 161

Appendix Chapter 4 Beukema, Kurz, Greenough

Comparison of using RRTREIN and PLREAD

Imagine a stand with 10 plots, each of which has two tree records. Four of the plots contain one tree

record with disease codes, and in these plots the density is 15 trees/acre for each record. When the

keyword PLREAD is used, these four plots are considered to be inside centers. The remaining six plots

have no disease codes, have 25 trees/acre for each record and are considered outside centers when using

PLREAD.

Plot No. 1 2 3 4 5 6 7 8 9 10

No. Records 1 1 1 1 1 1 1 1 1 1

Disease Code yes yes yes yes no no no no no no

Density of each record 15 15 15 15 25 25 25 25 25 25

No. Records 2 2 2 2 2 2 2 2 2 2

Disease Code no no no no no no no no no no

Density of each record 15 15 15 15 25 25 25 25 25 25

Infected Uninfected Outside Total Stand

RRTREIN only 60 360 420 420

RRTREIN & PLREAD 200 200 429 420

Next assume that the stand is 10 acres, with 3 acres inside disease centers. Using the above formulas, and

summing across all records, the resulting densities can be calculated and are:

Choosing the Trees to Infect (detailed explanation)

When the keyword RRINIT is used for setting initial infection levels, the steps for calculating which tree

records will be infected are:

1. Place trees in centers. This is done by determining how much of the stand is inside root

disease centers, and then placing that proportion of each tree record inside centers. For

example, if centers cover 30 percent of the stand, 30 percent of each tree record will be inside

a center.

2. Determine the relative proportion of trees of each species which should be infected. This is

calculated using the formula:

PropInf Pinf AvgTTD

Pinf AvgTTD sp

sp sp

sp sp sp

= ∗

∑ ∗

11
162 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Appendix Chapter 4 Beukema, Kurz, Greenough

where:

PropInfsp is the proportion of trees of species sp which should be infected;

Pinfsp is the probability of infection for species sp; and

AvgTTDsp is the average time to death for the trees of species sp which are present in the

center.

When Annosus root disease is being simulated, the denominator of the equation is based

only on the species which are host to the same disease type. For the other root diseases, all

trees are host so all species would be considered.

For example, imagine a hypothetical stand with nine tree records, three each for ponderosa

pine, lodgepole pine, and incense cedar. Each species has one record that is 5 inches dbh, one

that is 10 inches and one that is 20 inches, and each record within a species contains the same

number of trees. Given the basic time to death of an infected tree (i.e., a tree with a time-todeath multiplier of 1) is: 6.12 years for a 5-inch tree, 11.69 years for a 10-inch tree, and 22.84

years for a 20-inch tree, with an average time to death of 13.55 years, and using Armillaria

root disease in the Inland Empire variant, the following calculations can be made:

Species Pinf TTD mult. AvgTTD Pinf*TTD PropInf

Basic 1.0 13.55

Ponderosa pine 0.2 1.8 24.39 4.88 0.39

Lodgepole pine 0.6 0.75 10.16 6.10 0.48

Incense cedar 0.1 1.2 16.26 1.63 0.13

Total 12.61 1.00

3. Determine the density of trees of each species in the center which should be infected. The

pieces of information required for this step are: the total number of trees inside centers, the

proportion of each species to be infected (from step 2 above), and the proportion of trees in

the center which should be infected.

NumInfsp = NumTrees ∗ PropCenterInf ∗ PropInfsp

where:

NumInfsp is the desired number of trees of species sp infected;

NumTrees is the total number of trees inside the center of a disease type;

PropCenterInf is the proportion of the center which should be infected; and

PropInfsp is the proportion of each species to be infected (from step 2).

For example, consider the stand in step 2. If there were 75 trees and 30 percent of the trees in

centers were to be infected, then 8.8 ponderosa pine trees would be infected, 10.8 lodgepole

pine, and 2.9 incense cedar.
USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998. 163

Appendix Chapter 4 Beukema, Kurz, Greenough

Ponderosa Lodgepole Incense

Pine Pine Cedar Total

Number of Trees 30 15 30 75

PropInfsp 0.39 0.48 0.13 1

NumInfsp @ 30 percent 8.8 10.8 2.9 22.5

Actual Number Infected 8.8 10.8 2.9 22.5

If too few trees of a species are present in the center, then all of the trees of that species will be

infected and the remainder of the ‘desired’ number of trees will be distributed evenly among

the other species. If the stand in the previous example were to be 50 percent infected, the

model would want to initialize 18 lodgepole pine as infected. However, since the centers

contain only 15 lodgepole pine, all of them will be infected and the remaining three will be

divided equally between the two remaining species (ponderosa pine and incense cedar).

4. Infect the trees. A proportion of each tree record inside centers will be infected on the basis of

the proportion calculated from the number infected in step 3. The proportion is applied

across all size classes; the size variation of trees within centers is accounted for in the

calculation of time to death, since small trees die faster than large ones.

In the first example given in step 3, 8.8 ponderosa pine out of 30 total will be infected. Thus

29 percent (8.8/30) of each ponderosa pine record will be considered infected. A similar

calculation can be done for the lodgepole pine and the incense cedar.

Ponderosa Lodgepole Incense

Pine Pine Cedar Total

Number of Trees 30 15 30 75

PropInfsp 0.39 0.48 0.13 1

NumInfsp @ 50 percent 14.6 18 4.9 37.5

Actual Number Infected 16.1 15 6.4 37.5
164 USDA Forest Service Gen. Tech. Rep. PSW-GTR-165. 1998.

Appendix Chapter 4 Beukema, Kurz, Greenough

List of Tables in Chapter 4

1.1: Order of calculations in the Western Root Disease Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

1.2: Stratification of dead infected trees and stumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

1.3: Example of mortality reconciliation when FVS predicts higher mortality than the root

disease model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

1.4: Example of mortality reconciliation when FVS predicts lower mortality than the root

disease model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

2.1: Examples of parameters defining root disease dynamics for Annosus root disease,

Armillaria root disease, and laminated root rot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

2.2: Keywords used to change the parameters defining root disease dynamics . . . . . . . . . . . . . . . . . 110

2.3: Amount that each root disease will spread through the root system after death . . . . . . . . . . . . 113

2.4: Defaults for the jump in center size after a clear-cut for each root disease . . . . . . . . . . . . . . . . . . 117

2.5: Types of stands used in the simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

2.6: Effect of increasing the given parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

2.7: Example of the effect of doubling the time to death on the area with infected roots . . . . . . . . . 118

2.8: Example of decreasing the radius which is infected at death . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

2.9: Default constants for inoculum decay, by root disease species. . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

2.10: Default minimum lifespan of inoculum for each disease type . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

3.1: Application of bark beetle mortality to trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

List of Figures in Chapter 4

1.1: Mechanisms which transfer trees between tree lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

1.2: Root radius multiplier activated using the SDIRMULT keyword . . . . . . . . . . . . . . . . . . . . . . . . . 102

1.3: Area within a root system that is infected . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

1.4: Boundary of a center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

1.5: Difference between the center area and area with inoculum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

1.6: Areas considered inside centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

1.7: Caveats when placing centers in a stand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

1.8: Percentage of growth realized in infected trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

2.1: Summary of the dynamics of root disease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

2.2: Example sample plot used for calculating infection rates inside centers . . . . . . . . . . . . . . . . . . . 109

2.3: Radius that is considered infected when root overlap and infection occur . . . . . . . . . . . . . . . . . 111

2.4: Coefficient of variation of the new infections in stands with different inoculum densities . . . 112

2.5: Rate of spread of the infection through root systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

2.6: Number of years from infection at the center to death . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

2.7: Example of a plot used to calculate the spread rate of a disease center . . . . . . . . . . . . . . . . . . . . 114

2.8: Effect of SDIRMULT on root system size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

2.9: Method for calculating total area inside centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

2.10: Centers created from spore infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

2.11 Comparison between the inoculum decline of a resinous and a non-resinous

tree of the same size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

2.12: Comparison between the decay of Phellinus inoculum in trees of different sizes . . . . . . . . . . . . 123

2.13: Diagram of a shrinking center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

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PSW-GTR-165

General Technical ReportResearch StationPacific SouthwestForest Serviceof AgricultureDepartmentUnited States User’s Guide to the

Western Root Disease Model, Version 3.0