United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Proceedings

RMRS-P-25

September 2002

Second Forest

Vegetation Simulator

Conference

Fort Collins, Colorado

February 12-14, 2002
The Compilers ______________________________________

Nicholas L. Crookston is an Operations Research Analyst with the USDA Forest Service, Rocky

Mountain Research Station in Moscow, ID. He has spent his career working on the base FVS system and

its extensions. His first contribution to FVS, in the late 1970s, involved creating the first extension — one

that represented mountain pine beetle population dynamics in lodgepole pine. Work on the development

of the Douglas-fir tussock moth and western spruce budworm extensions followed. These extensions

formed a template on which all FVS extensions have been based. Mr. Crookston conceived and built the

Event Monitor and, in a collaborative effort with Al Stage, built the Parallel Processing Extension to FVS.

His more recent efforts include designing and building the Suppose user interface and leading the effort

to build the Fire and Fuels extension.

Robert N. Havis is an Information Technology Specialist with the USDA Forest Service, Forest

Management Service Center (FMSC) in Fort Collins, CO. A civil and environmental engineer by training,

Mr. Havis has been involved in software support for FVS base code and model extensions since 1998. He

maintains the FVS base variants and implements new developments in FVS software. The FMSC is the

technology transfer center for the FVS model system, and the keepers of the FVS base code and the Fuels

and Fire Effects extensions. The FMSC distributes the FVS to the public, provides user training and hotline

support, and develops new geographic variants and enhancements to existing variants.

Acknowledgments ___________________________________

Thanks go to the conference committee: Michael VanDyck, Don Vandendriesche, Erin Smith, Chad

Keyser, Robert Havis, Gary Dixon, Nick Crookston, and Judy Adams, and for help with local arrangements

to Melissa Adams. Their efforts were rewarded in a well-received conference. Special appreciation is

extended to the many authors for their high quality manuscripts and cooperation through editorial process.

Louise Kingsbury and the Publishing Services staff at the Rocky Mountain Research Station are

recognized for special effort in publishing these proceedings in a timely and professional way.

Support for the conference and proceedings publication came from conference registration fees and

direct contributions from the Forest Health Technology Enterprise Team, the Forest Management Service

Center, the Joint Fire Science Program, and the Forest Ecosystem Process Research Work Unit of the

Rocky Mountain Research Station, all part of the USDA Forest Service.

Abstract –––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest Vegetation Simulator Conference; 2002 February 12-14;

Fort Collins, CO. Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research

Station. 208 p.

The Forest Vegetation Simulator (FVS) is a computer program that projects the development of forest stands in the United States

and British Columbia, Canada. The proceedings of the second FVS conference, held in Fort Collins, CO, includes 34 papers dealing

with applications of FVS that range from the stand-level through full-scale landscape analyses. Forecasts ranging from wildlife habitat

indicators through economic returns on investments take into account the effects of insects, pathogens, and fire. Also presented are

recent advances in computer visualization technology, the analysis of model prediction uncertainty, model development techniques,

software support tools, and the reconstruction of historical stand conditions.

Keywords: forest management, forest planning, landscape dynamics, vegetation dynamics, prognosis model, visualization, growth

and yield, forest health, habitat modeling
Second Forest Vegetation

Simulator Conference

Fort Collins, CO

February 12–14, 2002

Conference Endorsements

• The Society of American Foresters (SAF)

• The International Union of Forestry Research Organizations (IUFRO) Groups:

4.11 Statistical Methods, Mathematics and Computer

4.01 Mensuration Growth and Yield

4.02 Forest Resources and Inventory Planning

• The Midwest Mensurationists Organization.

Citation to the First Conference Proceedings

Teck, Richard; Moeur, Melinda; Adams, Judy, comps. 1997. Proceedings: Forest Vegetation Simulator conference;

1997 February 3-7; Fort Collins, CO. Gen. Tech. Rep. INT-GTR-373. Ogden, UT: U.S. Department of Agriculture,

Forest Service, Intermountain Research Station. 222 p.

Compilers:

Nicholas L. Crookston

Robert N. Havis

i

Contents_____________________________________________________

Page

Bov B. Eav, Ph.D. Collaborative Technology Development and Transfer:

Keynote at the Second Forest Vegetation Simulator Conference ....................... 1

Session 1. Tools ............................................................................................................................................ 3

John D. Shaw FVS Lessons on the Web: A Resource for Users and Instructors ...................... 5

James N. Long

Robert J. McGaughey Creating Visual Simulations of Fuel Conditions Predicted by the

Fire and Fuels Extension to the Forest Vegetation Simulator.............................. 8

Anthony W. Courter Southern Pine Beetle Keyword Component Addfile for Use with FVS .............. 14

Forrest L. Oliveria

James (Rusty) Rhea

Don Vandendriesche Pre-Suppose: Preprocessor to Suppose ........................................................... 20

Session 2. Reality Check ............................................................................................................................ 29

T. M. Barrett Classifying Stand Structure: A Comparison of SVS Images

F. G. Schurr with Plot Visits and FVS-Generated Metrics ...................................................... 31

K. L. O’Hara

Erin E. Smith-Mateja Validation of the Forest Vegetation Simulator Growth and

Carl W. Ramm Mortality Predictions on Red Pine in Michigan................................................... 38

Chad Keyser Calibrating FVS for the Chattahoochee-Oconee National

Ron Stephens Forest Planning Effort ........................................................................................ 45

Session 3. Landscape Applications .......................................................................................................... 51

Joy C. Roberts Using a Multichange Agent Approach with the Forest Vegetation

Simulator on the Boise National Forest, Idaho .................................................. 53

Andrew J. McMahan FVS-EMAP: A Simple Tool for Displaying FVS Output in ArcView® GIS .......... 57

Anthony W. Courter

Eric L. Smith

Eric L. Smith Landscape Analysis Application of the Westwide Pine

Andrew J. McMahan Beetle FVS Extension ........................................................................................ 62

Thomas Eager

Helen Maffei Methodology for Modeling the Spatial and Temporal Effects of

Brian Tandy Vegetation Management Alternatives on Late Successional Habitat in

the Pacific Northwest ......................................................................................... 69

S. Hummel Landscape Analysis with FVS and Optimization Techniques: Efficient

D. Calkin Management Planning for the Gotchen Late Successional Reserve ................ 78

J. Barbour

David Atkins Analyst Hazards When Assessing Fire, Insect, and Disease Hazard in

Renee Lundberg Montana Using FIA Data with FVS: Or Alligators We Didn’t See Coming ......... 83

iii
Glenn A. Christensen Simulating Fire Hazard Reduction, Wood Flows, and Economics of

Roger D. Fight Fuel Treatments with FVS, FEEMA, and FIA Data ............................................ 91

R. James Barbour

David L. Renner Using the Fuels and Fire Effects (FFE) and Economic (ECON) Extensions

Fred C. Martin to the Forest Vegetation Simulator (FVS) to Evaluate the Impacts of

Silvicultural Regimes.......................................................................................... 97

Albert R. Stage Using FVS and Its Fire and Fuels Extension in the Context of

Uncertain Climate ............................................................................................ 104

Paula J. Fornwalt Using the Forest Vegetation Simulator to Reconstruct Historical Stand

Merrill R. Kaufmann Conditions in the Colorado Front Range.......................................................... 108

Laurie S. Huckaby

Jason M. Stoker

Pamela E. Padgett Sensitivity of Forest Vegetation Simulations to Scale of the Input

Klaus H. Barber Data and Impact to Estimates of Key Habitat Indictors, Tree Size,

Andy Taylor and Density ...................................................................................................... 116

Julee Greenough Prognosis EI: A Detailed Watershed-Level Environmental

Sarah Beukema Indicators Model............................................................................................... 122

Donald Robinson

Werner Kurz

Nancy Densmore

Ralph Winter

Barry Snowdon

H. Temesgen Linking PrognosisBC to Aerial Attributes for Timber Supply Analysis in

V. LeMay British Columbia ............................................................................................... 126

Don Vandendriesche Using the Labeling Capabilities and Parallel Processing Extension

of the Forest Vegetation Simulator for Resource Supply Analysis .................. 127

Session 4. Model Development ............................................................................................................... 133

Robert E. Froese Incorporating Soil Parent Material into Prognosis ............................................ 135

Andrew P. Robinson

Kelsey S. Milner FVSBGC: A Dual Resolution Hybrid Model with Physiological

Dean W. Coble and Biometrical Growth Engines...................................................................... 141

Eric L. Smith

Drew J. McMahan

Eric L. Smith Eco-Physiology Approach to Projecting Tree Stress and Vigor in FVS ........... 146

Andrew J. McMahan

Gary Z. Wang

Abdel Azim Zumrawi Stand-Level Scaling of a Single-Tree, Distance-Independent,

Albert R. Stage Diameter-Growth Model: Interim Calibration of Prognosis for the

Barry Snowdon Southeastern Interior of British Columbia ........................................................ 151

Ronald E. McRoberts Diameter Growth Models for Inventory Applications ........................................ 158

Christopher W. Woodall

Veronica C. Lessard

Margaret R. Holdaway

iv
T. F. Gregg Assessing Sampling Uncertainty in FVS Projections Using a

S. Hummel Bootstrap Resampling Method......................................................................... 164

James W. Flewelling Comparing Methods for Modelling Tree Mortality ............................................ 168

Robert A. Monserud

Donald C.E. Robinson Spatial Statistical Model for the Spread of Dwarf Mistletoe Within and

Brian W. Geils Between Stands ............................................................................................... 178

John A. Muir

Session 5. FVS in Modeling Systems...................................................................................................... 187

J. Wang An Intelligent Information System for Forest Management:

W. D. Potter NED/FVS Integration ....................................................................................... 189

D. Nute

F. Maier

H. M. Rauscher

M. J. Twery

S. Thomasma

P. Knopp

Richard Bates Use of the Forest Vegetation Simulator in Determining Optimal Forest

Gary D. Kronrad Rotation Settings for Abandoned Mined Lands in Appalachia ......................... 196

Ching-Hsun Huang

Don Vandendriesche

Kevin R. Ceder Linking Tools of Forest and Wildlife Managers: Wildlife Habitat Evaluation

John M. Marzluff Using the Landscape Management System .................................................... 200

v

Session 1. Tools

Session 2. Reality Check

Session 3. Landscape Applications

Session 4. Model Development

Session 5. FVS in Modeling Systems

vii
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CAUTION: Pesticides can be injurious to humans, domestic animals,

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USDA Forest Service Proceedings RMRS-P-25. 2002 1

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Good morning!

I am delighted to have this opportunity to be with you at

this conference. And thank you for asking me to speak to a

topic that is of special interest to me, so special that my

career in the Forest Service began in this arena and continued for 9 years. In addition, the opportunity to visit with

former colleagues and friends is just as strong as my

interest in this subject. I worked side by side with many of

you at this conference to develop and incorporate insect and

disease models into the Prognosis Model, the original Forest

Vegetation Simulator (FVS). It’s good to see you all again.

Why FVS? _____________________

Today, more than ever, I am convinced that simulation

modeling is one of the most effective means of supporting

science-based decisions. Here are some of the compelling

reasons for FVS and other simulation models.

1. Simulation models inform decisionmakers. Decisions

are only as good as the quality of the predicted effects of

carrying out the decisions. If the consequences of decisions

are not known at all, then any decision is as good as any

other. The best tool to predict the consequences of land

management decisions is a simulation model such as FVS.

While models such as FVS cannot predict the effects of

decisions with certainty, they do provide good indications of

what to expect when decisions are carried out. With recent

advances in the realistic display of model outputs, effects of

decisions can be displayed in a way that decisionmakers and

everyone interested in the decisions can appreciate and

understand. This is of great value to the Forest Service and

other land management agencies as community participation in decisionmaking is a rule in our society.

2. Models and decision support tools can be used to

document the scientific basis for land management decisions. More and more, decisions made with respect to policies affecting livelihoods and communities across the country are being challenged and questioned on the grounds that

they are not science based. A well-documented simulation

Collaborative Technology Development and

Transfer: Keynote at the Second Forest

Vegetation Simulator Conference

Bov B. Eav, Ph.D.

Associate Deputy Chief for Research and Development

USDA Forest Service

model gives decision makers the confidence that their decisions are grounded in peer-reviewed scientific principles

and findings.

3. Simulation models are an excellent way to synthesize

knowledge relevant to decisionmaking process in a form that

is integrated with other analytical tools used by land managers. There is no single tool in the scientific world that

carries more potential than FVS in helping natural resource

managers take a peek into the future. FVS, with its extensions, allows us to synthesize what is known about the

development of forest vegetation through time and its response to management actions and natural disturbances.

Because of the exponential increase in the amount of information that resource analysts and decisionmakers must

consider in formulating their decisions, no single person or

even team could do the synthesis in their own heads. Tools

such as simulation models and decision support systems

must be developed as analytical aids to analysts and

decisionmakers.

4. During model development, scientists have an opportunity to discover knowledge gaps about how forest ecosystems work, which then leads to pathways for research.

The fact that the Forest Service and other land management organizations use FVS to develop forest and project

plans in all regions of the country and some parts of Canada

is a strong validation of its usefulness. The successful use

of FVS and other decision support tools that many of you in

this room participated in were clearly demonstrated in

resource management decisions at the large area plans

such as the Columbia River Basin Assessment and to many

Forest plans and numerous project level Environmental

Impact Statements.

These are four important reasons for continuing and

sustaining your work in FVS. When dollars are getting

scarce for R&D, it is incumbent upon us to find new ways of

getting the job done. I am convinced more than ever before

that FVS is one of the most important ways to bring science

to bear on informed decisionmaking.

Collaborative Research, Technology

Development, and Transfer _______

Working on models is a great way to promote collaboration

among researchers, academics, students, and practitioners

as demonstrated by the kind of people present at this

gathering.
2 USDA Forest Service Proceedings RMRS-P-25. 2002

Eav Collaborative Technology Development and Transfer: Keynote at the Second Forest Vegetation Simulator Conference

Collaboration works best when all participants have something to gain. The researchers want to see their research

results put into practice and perhaps get a few additional

publications; academicians are interested in having a good

teaching tool and having funds to support graduate students; the students need a problem to write their theses on

and a set of data that are either readily available or a sponsor

ready to pay for its collection; and the practitioners want the

best available knowledge and information to use as a basis

for sound decisions.

Collaboration promotes ownership and pride in the product. In 1987, I presented to the Director of Forest Health

Protection what I thought was a compelling case why Forest

Health Protection should invest in the development and care

and feeding of insect and disease models along with a plan

to accomplish the tasks. I was given the go-ahead and strong

encouragement to proceed with the plan. The catch is that I

was not given the requested dollars to get it done. I remember thinking to myself, “Thanks for nothing, Mr. Director.

Now what?” Looking back, it’s probably just as well that it

happened that way because what it forced me to do was to

build partnerships and coalitions with researchers, Regional Offices, and Forest staffs to go after the dollars and

jointly managed resources. If the Director gave me all the

required resources, the models developed would have been

Methods Application Group models, with inputs from others. They would not have had the ownership from National

Forests, Regions, and Research and Development that they

enjoyed. And their acceptance all throughout the Forest

Service may not have been as strong.

I believe the collaborative spirit displayed by all of you in

this room is an outstanding example of “what can be accomplished if we do not worry about who is getting the credit,” to

quote one of our former presidents.

I wish you a successful conference. And I thank you for

providing me with this opportunity to be with you and for

your attention.
USDA Forest Service Proceedings RMRS-P-25. 2002 3

Session 1. Tools

USDA Forest Service Proceedings RMRS-P-25. 2002 5

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

John D. Shaw is a Research Scientist and James N. Long is a Professor of

Silviculture, Department of Forest, Range, and Wildlife Sciences, Utah State

University, Logan, UT 84322–5230.

Abstract—A Web site (http://www.cnr.usu.edu/online/simulation)

has been developed to provide users of the Forest Vegetation

Simulator, Stand Visualization System, and Suppose an opportunity for self-instruction. Instructors may use the Web site as a

source of lessons and data that can be used in a variety of classroom

situations. Future expansion of the site includes a section highlighting cover types and silvicultural systems from across North America,

and development of distance-delivered courses.

The Forest Vegetation Simulator (FVS), and the related

programs Suppose and the Stand Visualization System

(SVS), have been widely adopted as the standard programs

for projecting forest growth on USDA Forest Service land.

These programs have received relatively little attention as

teaching tools in silviculture classes and other college courses

in which growth projections are used. In this paper we

describe our adoption of FVS, SVS, and Suppose as teaching

tools, and announce the availability of Web-based resources

available to users and instructors.

Using FVS in the Classroom ______

We started using FVS in the classroom in 1995 in a seniorlevel silviculture class (FR4250). Early in the semester,

students collected stand inventory data during field trips to

Utah State University’s T.W. Daniel Experimental Forest.

After a few weeks of class, the students were introduced to

FVS during a laboratory session. Data collected in the field

were pooled and used to create a tree list file for one

Engelmann spruce-subalpine fir stand. This stand served as

the example that was used to illustrate the effects of thinning, uneven-aged management, and other silvicultural

practices.

The use of class time was not too efficient in the first 2

years, for several reasons. We could not expect all students

in the class to be on par in computer skills. Few students

were familiar with the concept of writing scripts or program

code, so teaching them how to construct an FVS keyword file

required more effort than merely familiarizing them with a

text editor. A significant portion of the class time allotted to

learning FVS was used getting the students’ first simulaFVS Lessons on the Web: A Resource for

Users and Instructors

John D. Shaw

James N. Long

tions to run. However, students quickly learned to build

subsequent keyword files on their first working version, and

encountered fewer errors when adding new keywords to

their simulations. Hardware and software architecture in

the teaching laboratory was also problematic. Students did

not have personal disk space on the system, so they were

forced to direct FVS output to the floppy drive. This arrangement imposed limitations on output speed and volume. We

also used the DOS version of SVS, which required commandline modification of the video mode on the computers. This,

coupled with the need to run a conversion program to convert

FVS output files to SVS input files, added more teaching

overhead to the simulation lessons.

Despite the overhead time required to deal with technical

issues (in other words, everything not related to silviculture), FVS and SVS proved to be effective tools in the effort

to illustrate stand dynamics and the consequences (intended

and unintended) of silvicultural manipulation. We found

that, when given a verbal description of a silvicultural

prescription later in the semester (table 1), students were

generally able to deal with the technical issues effectively

and concentrate on silviculture.

Encouraged by 2 years of success and refinement, in 1997

we allocated 2 weeks of lecture and lab time (12 instruction

hours) to FVS, SVS, and the newly released Suppose interface. Our teaching lab had been upgraded with new computers and personal user space for each student, eliminating

many technical obstacles. The goal was to increase the

students’ proficiency with the programs, and increase the

number and complexity of silvicultural simulations to which

they were exposed. Advances in development of the programs, including the advent of Suppose and the Windows

version of SVS, not only required us to make changes in the

lesson plan, but also reduced the overhead requirement. We

were able to eliminate lecture time spent on all program

control keywords (for example, STATS, TREEDATA, PROCESS) and some keywords associated with stand data (for

example, STDINFO, DESIGN), although we continued to

briefly describe how keyword files were generated by Suppose. Most of the time previously spent on technical issues

was reallocated to learning the Suppose interface. With the

elimination of the hardware and data conversion issues

surrounding SVS, our time allocated to running SVS decreased dramatically. Because SVS has an easy-to-use interface and something of a gaming element (for example,

perspective modification, tree design, timber marking), students need only a brief introduction and tend to explore the

capabilities of the program on their own.

In early 2000, the Dean’s Office of the College of Natural

Resources released a request for proposals for development of “inquiry-based learning” projects. Although the
6 USDA Forest Service Proceedings RMRS-P-25. 2002

Shaw and Long FVS Lessons on the Web: A Resource for Users and Instructors

concept of inquiry-based learning appeared to be broadly

defined (similar to ecosystem management in its formative years), most of the existing examples involved the

independent exploration of topics on the worldwide Web

as a complement to some in-class exercise. We felt that the

FVS module in FR4250 was an ideal candidate for adaptation to this learning process, so we proposed and were

awarded start-up funds for Web-authoring software and

development time.

We deployed the Web site (fig. 1; http://www.cnr.usu.edu/

online/simulation) in fall of 2000 for use in FR4250. We

assigned introductory modules on each of the three programs as a pre-lab reading assignment, and students were

given a take-home quiz with a few questions on each module.

In addition, students were instructed to read about, and

attempt to create on their own user accounts, the Suppose

directory structure and Windows desktop shortcuts to Suppose and SVS. By this time, a required course on computer

applications in natural resources had been added to the

undergraduate curriculum, so we expected that the students

would have a higher computer aptitude and better capacity

for self-instruction.

Use of Web-based lessons had a substantial effect on the

progress made during the 2-week simulation module in

FR4250. Most students met our expectations of coming to

Table 1—Example of verbal silvicultural prescription converted into

FVS simulation by students in a lab exercise.

Shelterwood and Retention System Project

Objectives:

1. Additional experience with practical applications of FVS, SVS,

and Suppose.

2. Development of a “base case” shelterwood system for

ponderosa pine on the Black Hills National Forest

3. Explore implications of reserves

2000 Seedcut – thin from below; 4 to 999 inches; leave 20 TPA

Regeneration – natural regen; PP 700 TPA, 70% survival

after 10 years

2030 Final removal – thin from above, 999 to 3 inches, leave 0

TPA

PCT – thin from below, 0 to 10 inches, leave 300 TPA

2120 CT – thin from below when SDI approx. 270, leave 130

TPA, i.e., SDI ca. 125 if Dq ca. 10 inches)

2160 End of Rotation

Report:

1. yield table

2. SVS images for: a) current stand, b) 2040 (i.e., 10-yrs

after removal), c) EOR

Extra credit: Modify the removal to leave different numbers

of reserves: a) 0 TPA (you have already done this), b) 5

TPA, c) 10 TPA, d) 20 TPA

What are the implications of the reserves in terms of:

1. total yield of next rotation (i.e., must subtract out initial

volume of reserves)?

2. yield of “understory”?

3. growth and yield “costs” of reserves (if any)?

Figure 1—Simulation and visualization home page.

the first class of the module with a basic understanding of

the programs and their user accounts prepared for use. The

students who did not complete all the tasks required relatively little coaching, from us or their classmates, to catch up

with the rest of the class on the first day. The cumulative

effect of having students with better computer aptitude and

self-instruction modules available was being able to cover

the same amount of material in 1 or 2 fewer days. To date,

we have used the extra time for review, discussion of advanced topics, and completion of a trial (that is, no-grade)

assignment similar to the ones they are assigned during the

remainder of the semester.

Following the initial deployment of the Web site, we

considered possible additions to the site that would improve

its utility in other courses and for FVS users in general. We

considered two types of additions: those involving adaptation of existing materials, and those involving development

of new material. In both cases we would have the opportunity to take advantages of the unique qualities of the Web.

Expanding the Web Presence _____

During development of the silviculture module, it became

apparent that Web-based modules could be useful in other

classes or for any FVS users. We focused the next phase of

development on modules with general applicability. The

obvious candidate for inclusion was the FVS keyword reference manual.

We obtained the most current revision of the keyword

reference manual (Van Dyck 2000) from the Forest Management Service Center, converted the text to Web pages, and

added extensive hyperlinks throughout (fig. 2). The online

manual is more convenient to use than the hard copy

version, because users can quickly navigate among keyword

entries and create bookmarks using their Web browser. In

the future, the keyword manual may be integrated into the

Suppose interface.
USDA Forest Service Proceedings RMRS-P-25. 2002 7

FVS Lessons on the Web: A Resource for Users and Instructors Shaw and Long

Figure 2—Introduction to FVS keyword reference guide.

The Forest Management Service Center also maintains a

training guide (Van Dyck 2001) that covers a wide range of

FVS keywords and operations in Suppose. Although our

classroom modules do not require the depth provided by the

training guide, we anticipated that we or other instructors

might require some of the lessons in the future. We converted the training guide to a hyper-linked document, similar in format to the keyword manual.

Integrating the keyword and training manuals into the

Web site provided two important benefits: we saved time on

lesson development, and we were able to hyperlink our

existing Web pages to relevant entries in the two manuals.

Although a significant number of labor hours were required

to convert the manuals to a format consistent with the rest

of the Web site, we anticipate that future updates of the

documents will require relatively little effort.

One important distinction between the Forest Service

exercise guide and the modules we developed for class is that

the Forest Service manual focuses primarily on developing

technical competence with the software (that is, it is a “howto” manual), whereas we focus more on teaching silvicultural methods and only include the technical understanding

of the software that is necessary to accomplish the lesson. As

a result, the lessons we develop and the ones in the Forest

Service exercise guide are complementary.

The third area of expansion, which may prove to be of most

interest to instructors in general, is a section on cover types

from across North America (those areas covered by FVS

variants). This section was inspired by an article in the

November 2000 issue of the Journal of Forestry (McNab and

Ritter 2000) describing the 100–year history of the Old

Orchard plantation on the Biltmore Estate in North Carolina. We obtained data on the Old Orchard plots from the

authors and created two sets of FVS tree files. The first set

includes data from the 100–year inventory of the plots done

in 1999. We constructed the second data set using the site

data from the plots and descriptions of the stand when it was

planted in 1899. We also provide the user with a table of

treatments applied to the stands over the past 100 years.

The user has the opportunity to manage the 1899 stand for

100 years using FVS and compare results to inventory

conditions. We provide several questions that the user may

use as starting points for exploring the stands, but there are

many other approaches that instructors might use.

We hope to build the cover types section into a resource

that can be used by instructors everywhere. We hope to

obtain a wide range of data from local sources for inclusion.

We will not only provide data, but we will also provide some

background information about the stand or a detailed history such as the one that has been recorded for the Old

Orchard plantation. To date we have compiled data for

several cover types, including aspen, Douglas-fir, lodgepole

pine, longleaf pine, Engelmann spruce-subalpine fir, and

red spruce-eastern white pine. Visitors to the Web site who

wish to contribute data may contact the senior author.

Conclusion_____________________

We have developed a Web resource that can be used by

instructors wishing to incorporate FVS into their lesson

plan, or by other FVS users who want to improve their

expertise or have easy access to reference material. The

evolution of the Web site will be based on instructors’ current

and future needs, and future development of FVS, SVS, and

Suppose. We anticipate that we will use the Web site as a

component of distance-delivery courses in the near future.

We hope to continue development as FVS becomes a more

popular management tool and demand for education in its

use increases.

Acknowledgments ______________

We thank the Dean’s Office of the Utah State University

College of Natural Resources for providing the incentive to

develop the simulation Web site. We also thank M. Van

Dyck, USDA Forest Service, Forest Management Service

Center, for providing the source documents for the FVS

keyword and exercise manuals, and W.H. McNab, B.A.

Ritter, and R. Seymour for providing data for the cover type

examples section.

References _____________________

Crookston, N.L. 1997. Suppose: an interface to the Forest Vegetation Simulator. In: Teck, Richard, Moeur, Melinda; Adams, Judy,

comps. Proceedings: Forest Vegetation Simulator conference,

February 3–7, 1997; Fort Collins, CO. Gen. Tech. Rep. INT–GTR–

373. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Research Station.

Johnson, R.R. 1997. A historical perspective of the Forest Vegetation Simulator. In: Teck, Richard, Moeur, Melinda; Adams, Judy,

comps. Proceedings: Forest Vegetation Simulator conference,

February 3–7, 1997; Fort Collins, CO. Gen. Tech. Rep. INT–GTR–

373. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Research Station.

McGaughey, R.J. 1998. Techniques for visualizing the appearance

of forestry operations. Journal of Forestry 96:9–14.

McNab, W.H. and B.A. Ritter. 2000. The Old Orchard white pine

plantation at Biltmore. Journal of Forestry 98:18–23.

Van Dyck, M.G. 2000. Keyword reference guide for the Forest

Vegetation Simulator Forest Management Service Center, Fort

Collins, CO.

Van Dyck, M.G. 2001. Forest vegetation simulator: FY2001 class

exercises, Region 4. Forest Management Service Center, Fort

Collins, CO.
8 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Robert J. McGaughey is a Research Forester, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, University of

Washington, PO Box 352100, Seattle, WA 98195.

Abstract—The Fire and Fuels Extension (FFE) to the Forest

Vegetation Simulator (FVS) can help managers assess the risk,

behavior, and impact of fire in forest ecosystems. The FFE reports

surface fuel characteristics including the weight of woody material

in various size classes. Visualization software currently linked to

FVS can help managers better understand and communicate stand

structure and changes in stand structure resulting from growth,

mortality, and management activities. However, such visual simulations do not currently include surface fuels. This paper describes

the development of calibrated log models: models of individual

pieces of coarse woody debris that represent specific weights of fuels

in various sizes. Such models are used to represent surface fuels in

visual simulations. The paper presents motivations for the study

along with a description of the procedures used to measure and

photograph logs and an overview of the process for creating the

calibrated log models. An example log model is presented along with

visual simulations that depict fuel conditions.

Users of the Forest Vegetation Simulator (FVS) (Wykoff

and others 1982, Teck and others 1996) have been using

visual simulations produced by the Stand Visualization

System (SVS) (McGaughey 1997; http://www.fs.fed.us/pnw/

svs) to better understand and communicate the results of

their FVS projections. The Fire and Fuels Extension (FFE)

to FVS links changes in forest vegetation due to growth,

natural or fire-caused mortality, and management activities

to changes in expected fire severity and behavior (Beukema

and others 2000). Given the recognition of fire as an important component of forested ecosystems and the realization

that decades of fire suppression activities have resulted in

an increase in the amount of forest lands at risk of catastrophic fires, it makes sense to incorporate the results from

FFE into visual simulations designed to portray stand conditions.

This paper describes the development of calibrated log

models: models of individual pieces of coarse woody debris

that represent specific weights of fuels in various sizes. The

paper discusses motivations for the study along with a

description of the procedures used to measure and photoCreating Visual Simulations of Fuel

Conditions Predicted by the Fire and Fuels

Extension to the Forest Vegetation

Simulator

Robert J. McGaughey

graph logs and an overview of the process use to create

calibrated log models. An example log model is presented

along with visual simulations of fuel conditions produced

using EnVision (McGaughey, in press; http://www.fs.fed.us/

pnw/envision), a program that has several of the capabilities

of SVS.

The primary objective of this study is to build a set of

models that represent individual pieces of coarse woody

debris. These models, called calibrated log models in this

paper, represent the physical dimensions of a log and use

photographs to characterize the log ends, surface (bark), and

branches. Models provide sufficient detail to represent specific fuel weights in various size classes. EnVision uses the

models to produce visual simulations that depict standing

live and dead trees reported by FVS along with downed fuels

reported by FFE.

Motivations for this Study ________

This study was motivated by a desire to represent fuel

conditions and fire effects predicted by FFE in visual simulations commonly produced by FVS users. Previous efforts to

represent fuel conditions have focused on two methods:

photo series and visual modeling of individual fuel components.

The most common method used to illustrate fuel conditions relies on a library of images, called a photo series, to

depict conditions within various stand and grassland

ecotypes. For a specific ecotype, a photo series includes

images that illustrate a range of fuel conditions along with

quantitative data describing fuels. Photo series have great

appeal because users can directly compare their site conditions to photographs included in the series and select the

image that best matches the conditions they observe. They

can then use the quantitative information associated with

the image to estimate fuel loads for their site. The Fire and

Environmental Research Applications (FERA) Team within

the USDA Forest Service, Pacific Northwest Research Station, has produced a comprehensive photo series that includes single photos and stereo pairs for the United States.

For an example from this series, see Ottmar and others

1998.

Fuel specialists successfully apply the photo series approach to a variety of forest and grassland ecotypes. Users

typically relate well to photographs that depict actual sites.

However, their ability to correctly match conditions illustrated in a photograph to their specific site and fuel conditions remains untested. In addition, because a photo series
USDA Forest Service Proceedings RMRS-P-25. 2002 9

Creating Visual Simulations of Fuel Conditions Predicted by the Fire and Fuels Extension to the Forest Vegetation Simulator McGaughey

includes a limited number of images, users may not find

images that adequately represent the conditions they observe. An approach that produces images based on the

quantitative descriptions of fuel and stand conditions produced by FVS-FFE may be more appropriate for model

users.

Previous work by this author created visual models of fuel

conditions for stands in eastern Oregon being treated as part

of the Limber Jim Fuels Reduction Project using SVS

(McGaughey 1997). For that project, fuels were inventoried

using standard line transect methods. Computer programs

used the fuel data to create a population of plant and log

models to represent fuel conditions. Figure 1 shows example

images that depict pretreatment and posttreatment stand

conditions. This effort, while it produced images that were

useful for public orientation prior to site visits, did not

produce images that compared well with the typical photo

series. Limitations of the rendering system (SVS) made it

difficult to accurately represent fuels smaller than 3 inches

in diameter. The inability to represent fine fuels did not

greatly affect the visual simulations because most of the

downed logs were actually full-length tree stems with few

intact branches. In addition, the standard SVS viewpoint

(viewer located some distance from the stand and the image

contains most, if not all, of the 1 acre stand representation)

used for most images made it impossible to discern individual branches on downed trees.

The methods used in SVS do not adequately represent fuel

properties. First, SVS uses simple tree models that are not

detailed enough to represent individual branches or branch

patterns. The tree models used in SVS are abstract representations of trees that are recognizable as trees but do not

realistically model tree characteristics and do not generally

compare well to images of actual trees. The lack of adequate

branch representations makes it impossible to model a

known quantity of fine fuels because most of the fine fuels

are contained in tree branches. A second problem with tree

models used in SVS is that they all look “perfect.” That is, all

trees have straight stems and well-shaped crowns. These

models produce stand visualizations that communicate overall stand characteristics but do not represent the variety of

tree forms and condition classes needed to produce images

that compare favorably with on-site impressions. The third

problem with the process used for the Limber Jim project

was the algorithm used to place downed material. The

current algorithm included with SVS (TBL2SVS utility)

does not accurately model the interaction of downed pieces.

The result is that many of the downed logs appear to be

floating above the ground surface with no visible means of

support. This anomaly did not necessarily create problems

for the Limber Jim project because viewpoints were located

to provide overviews of the 1 acre stand; therefore, viewers

simply could not see the problem. When rendering the same

data in EnVision using a viewpoint that approximates the

view from the ground within the stand (fig. 2), the problem

is more apparent.

Existing methods used to represent fuel conditions are

adequate only in select situations. Photo series do not

include images that represent all conditions present within

an ecotype. In addition, users match images to site conditions and then use the corresponding fuel descriptions for

analysis and planning. A more desirable approach is to have

Figure 1—Visual simulations of pretreatment (top)

and posttreatment (bottom) conditions in a mixed

conifer stand in eastern Oregon. Pretreatment fuel

load for material 3 inches in diameter and greater is 54

tons per acre, and posttreatment fuel load is 10 tons

per acre.

Figure 2—Data from the Limber Jim project rendered

in EnVision using a near-ground viewpoint.

the FFE results directly influence the image because it is

unlikely that a photo series image will represent fuel quantities that exactly match the FFE results. Previous work

with SVS indicates that its rendering methods cannot sufficiently represent fuel properties for pieces less than 3 inches

in diameter.

Creating Calibrated Log

Models ________________________

Calibrated log models are created using field measurements and photographs of actual pieces of woody debris. The
10 USDA Forest Service Proceedings RMRS-P-25. 2002

McGaughey Creating Visual Simulations of Fuel Conditions Predicted by the Fire and Fuels Extension to the Forest Vegetation Simulator

intent of the log models is to provide accurate, visually

realistic representations of downed fuels in various size

classes. The models do not represent highly detailed log form

or individual branch locations or orientations.

Collecting Field Measurements and

Photos

Field measurements and photographs capture an adequate description of individual logs without collecting

more information than needed to produce visually realistic

models. Field measurements characterized the size and

general shape of the bole, branching patterns, and bole and

branch weights. Field measurements were divided into

three procedures: log characterization, branch inventory,

and photography.

Log characterization—Log characterization procedures

capture the log size and geometry. The overall procedure for

log measurement involved suspending the log from two

tripods rigged with a block-and-tackle for lifting the log and

a spring scale for weighing the log. After the log was suspended, field personnel recorded the weight from each scale

and visually divided the log into sections for measurement

and photography. Section breaks were located at major

bends in the log or were placed to keep sections shorter than

5 feet. Section markers (string and nails) were left in place

for photography. Orientation guides (2-foot diameter tempered hardboard discs with attachment points for reference

lines) were attached to each end of the log. Reference lines

(strings), used to measure the shape of the log, were attached

to the orientation guides.

The length, large and small end diameters (outside bark),

and the weight were measured for each log. Weight was

recorded with and without branches. For each log section the

following attributes were measured: length of section, large

and small end diameters (outside bark), and the distance

from the reference line to the actual log center at the small

end of the section. (Note: large end diameter was measured

for only the first section; for subsequent sections, the large

end diameter was the same as the previous section’s small

end diameter.) Section attributes were measured twice. The

second set of measurements was taken after the log had been

rotated 90 degrees clockwise. This produced two diameter

measurements that were averaged to compute the small end

diameter of the section and two deviations from centerline

that were used to calculate the rotations needed to properly

orient the log section in the final log model.

Branch inventory—The branch inventory was designed

to quantify the weight of small fuels present on the log. The

inventory was done for each log section to facilitate the

construction of new logs (different from those measured) by

stacking several sections and adding the fuel weights. The

branch inventory was a 100 percent sample. All branches

were removed from the logs and clipped at size break points

using a go/no-go gauge with openings for 0.25-, 1-, and 3-inch

sizes. As branches were clipped, they were placed in separate buckets to be weighed at the completion of each section.

A sample was collected from all branches on the log in each

size class to determine moisture content.

Photography—Three types of photographs were taken:

log section photos, branch photos, and log end photos. Log

section photos captured the surface (bark) of the bole and

some branches that were cropped from the final image. The

log section photos were digitally merged to create a texture

map for the conic frustum used to represent a particular log

section. Branch photos were taken with the entire log section

positioned at the bottom of the frame. Images were framed

to include all branches that originated on the log section.

Extensive overlap between branch images on adjacent sections was expected. The goal of branch images was to capture

all branches on the upper surface of the log and the portion

of the log section above its centerline. Four sets of images

were taken for each log section. Each set captured the view

of the log as it was rotated in 90-degree increments. Log end

photographs were taken to be used as texture maps for the

disk that closes the large and small end of the log.

Photographs were taken with a digital camera using a

resolution of 2048 by 1536 and 24-bit color. Images were

stored on the camera’s 64Mb storage cards (about 83 images

per card). Section markers were visible in both log and

branch images to assist when editing the images. A total of

two to four overview images, two log end images, and eight

images per log section (four log section images and four

branch images) were captured for each log.

Branch photographs were taken using a blue backdrop

held on a rigid frame (to minimize wrinkles) and positioned

behind the section being photographed. The consistent background color facilitated image editing.

Building the Log Models

Logs models are created using the solid modeling capabilities in EnVision. Logs models consist of a series of tapered

cylinders stacked end-to-end with spherical joints to hide

gaps between sections. A disc closes the ends of the first and

last sections. Branch planes are added using “wings” that

project from the log center. Figure 3 shows the EnVision

model file for a single log section (the large end section of the

log model shown in fig. 4). Figure 4 shows the geometric

primitives used to model a simple log containing three

sections.

Figure 5 shows an example log model with all texture

images applied.

Branch Representation

The method illustrated in figure 4 uses branch images

mapped onto flat plates. This approach produces a model

that appears correct when viewed from the side. However,

when viewed from the end, branches are not visible because

the “wings” have no thickness. The net result when rendering sets of log models to depict fuel conditions is that viewers

will not be able to see all of the branch material. One solution

to this problem is to twist the branch plates. Figure 6 shows

end views of the log shown in figure 5 with flat branch plates

and twisted branch plates. The viewpoint used in figure 6 is

raised slightly above the log end so branches mapped onto

the flat plates are visible. When viewed from the same

elevation as the log end, you cannot see the branch plates.
USDA Forest Service Proceedings RMRS-P-25. 2002 11

Creating Visual Simulations of Fuel Conditions Predicted by the Fire and Fuels Extension to the Forest Vegetation Simulator McGaughey

Using the twisted plates also affects the appearance of the

log when viewed from the side. The branches appear slightly

shorter but the appearance is still satisfactory.

When placing the log models and subsequently rendering

them, only half of the branches will be visible because most

logs will lay on the ground surface and half of the branches

will project below ground. In reality, when a tree falls down,

the branches on the portion of the stem closest to the ground

either break or bend. Few branches are driven into the

Figure 3—Sample EnVision model file for a single log section.

Figure 4—Log models consist of a series of geometric

primitives. The upper image shows the individual components and the lower image shows primitives combined to form a log model. Figure 5—Log model rendered in EnVision.

ground. Modeling the bending or breakage of branches

would be difficult given that the log models do not represent

individual branches, only collections of branches. However,

there needs to be some adjustment to account for the broken

or bent branches in a rendered image. One solution is to

simply double the number of branch plates on each log

model. For logs resting on the ground, this should result in

a viewer “seeing” the correct amount of branches. For logs

that are partially elevated, too many branches would be

visible. It is unclear just how many of the additional branches

would be visible since other logs would either partially or

completely screen these branches from view. Further experimentation will be needed to assess various branch representation options.

EPO 

1.0 

Log 001-section 1 

; do rotation for section 

rotate -1.4 y 

rotate -.24 x 

pushmatrix 

; draw log end 

settextureimage "001_large_end.bmp" 0 0 0 

disk 0 2.3 18 1 1 

; draw log 

rotate 90 z 

settextureimage "001_stem_sect1.bmp" 0 1 1 

cylinder 2.3 2.29 47.0541 18 1 1 

; draw joint 

translate 0 0 47.0541 

sphere 2.29 9 9 1 

popmatrix 

; branches 

pushmatrix 

settextureimage "001_branches_sect1alt.bmp" 1 0 0 

spiral 33 33 47.0541 0 12 1 .75 0 .25 1 

rotate 90 z 

spiral 36 36 47.0541 0 12 1 .5 0 .25 1 

rotate 90 z 

spiral 12 12 47.0541 0 12 1 .25 0 .25 1 

rotate 90 z 

spiral 12 12 47.0541 0 12 1 0 0 .25 1 

popmatrix
12 USDA Forest Service Proceedings RMRS-P-25. 2002

McGaughey Creating Visual Simulations of Fuel Conditions Predicted by the Fire and Fuels Extension to the Forest Vegetation Simulator

Simulating Fuel Conditions _______

The final step in producing visual simulations to represent fuel properties is the creation of a population of log

models that represents the specific fuel conditions reported

by FFE. This part of the project was not complete at the time

of this writing so the discussion that follows is subject to

change.

The fuel bed simulation program will accomplish two tasks.

First, it will sample from the collection of log models to build

a population of models that represents specific fuel conditions. Second, it will distribute the models throughout the

stand and calculate an appropriate orientation for each

model.

The first step of the sampling algorithm is to select an

appropriate subset of log models based on characteristics of

standing trees such as the species mix and size distribution.

After selecting a subset, the algorithm assembles a collection of models that matches the fuel characteristics reported

by FFE. The initial prototype of the fuel bed simulation

program will attempt to match the fuel weights in larger size

classes only. Experimentation with the program will likely

indicate the need for more complex logic. Most likely, the

algorithm will use a heuristic approach that looks at the

weight of material already added to the fuel bed and selects

a model with an appropriate mix of material weights in each

of the fuel size classes. It may be possible to fine-tune the fuel

weights by adding or subtracting branches from individual

log models or by assembling new log models using sections

from measured logs.

After creating an acceptable collection of log models, the

spatial positioning algorithm needs to distribute models

over the stand plot. There are two major issues to be resolved

when calculating the spatial distribution and orientation of

individual log models: log models should be placed so they do

not collide with standing trees, and models should be oriented so all logs appear to be stacked correctly. Solving the

first issue is relatively straightforward. The algorithm can

evaluate the position and size of the standing trees and place

log models so logs do not pass through standing trees. In

dense stands, placement could be difficult especially if the

log models are long. The stacking problem is more difficult

to solve. The new algorithm will need to look at how all logs

interact to determine a realistic stacking orientation.

Figure 6—End views of the log model shown in figure 5

with flat branch plates (left) and twisted branch plates

(right).

A simple example illustrates the potential for representing fuel conditions using the log models. Figure 7 shows a

sample fuel bed created using a single log model. This fuel

bed represents 3 tons per acre of material less than 3 inches

in diameter and 20 tons per acre of material 3 inches and

larger. Figure 8 shows the simulated fuel bed along with

standing trees.

Conclusions____________________

The ability to produce visual simulations that depict fuel

properties as well as stand conditions will help FVS-FFE

users communicate changes in stand structure and fire risk

that result from natural processes, management activities,

and fire. Preliminary experiments with the use of the calibrated log models described in this paper look promising.

The resulting visual simulations depict fuels in small size

classes represented by fine branches and larger fuels represented by large branches and boles. Remaining work in this

project includes measurement of many more logs and

branches, both burned and unburned, and subsequent construction of log models for use in the fuel bed simulation

program. Field crews will collect measurements during

spring 2002 and model building should be complete by late

Figure 8—Visual simulation that combines the surface

fuel layer shown in figure 7 with standing tree information.

Figure 7—Visual simulation of surface fuel layer representing 3 tons per acre of material less than 3 inches in

diameter and 20 tons per acre of material 3 inches and

larger.
USDA Forest Service Proceedings RMRS-P-25. 2002 13

Creating Visual Simulations of Fuel Conditions Predicted by the Fire and Fuels Extension to the Forest Vegetation Simulator McGaughey

2002. Work on the fuel bed simulation program is progressing and a prototype should be ready by mid-2002. Visual

simulations will be compared to photographs of areas where

fuel conditions have been measured. This will include comparison with images from the photo series produced by the

Fire and Environmental Research Applications Team of the

PNW Research Station. The results of these comparisons

will dictate further work on the fuel bed simulation program.

Acknowledgments ______________

The Joint Fire Science Program funded this research. The

author is indebted to Nick Crookston for technical information about FFE, for making changes to the SUPPOSE

interface to provide FFE information in the SVS tree list

files, and for providing leadership on the grant that funded

the research.

References _____________________

Beukema, S.J.; E. Reinhardt; J.A. Greenough; W.A. Kurz; N.

Crookston; D.C.E. Robinson. 2000. Fire and fuels extension:

model description. Prepared by ESSA Technologies Ltd.,

Vancouver, BC for U.S.D.A. Forest Service, Rocky Mountain

Research Station, Moscow, ID. 80 p. Draft copy is available at

http://forest.moscowfsl.wsu.edu/4155/ffe-fvs.html.

McGaughey, Robert J. 1997. Visualizing forest stand dynamics

using the stand visualization system. In: Proceedings of the 1997

ACSM/ASPRS Annual Convention and Exposition; April 7-10,

1997. Seattle, WA. Bethesda, MD: American Society for Photogrammetry and Remote Sensing. Vol. 4:248-257.

McGaughey, Robert J. [in press]. Seeing the forest and the trees:

Visualizing stand and landscape conditions. In: Proceedings of

Views from the Ridge: Considerations for Planning at the Landscape Level; November 2-4, 1999. Vancouver, WA. Portland, OR:

Western Forestry and Conservation Association & U.S. Department of Agriculture, Pacific Northwest Research Station.

Ottmar, Roger D.; Robert E. Vihnanek; Clint S. Wright. Stereo

photo series for quantifying natural fuels: volume I: Mixedconifer with mortality, western juniper, sagebrush, and grassland types in the interior Pacific Northwest. PMS 830. NFES

2580. Boise, Idaho: National Wildfire Coordinating Group. National Interagency Fire Center. 73 p.

Teck, R.M.; M. Moeur; B. Eav. 1996. Forecasting ecosystems with

the Forest Vegetation Simulator. Journal of Forestry 94(12):7-10.

Wykoff, W.R.; N.L. Crookston; A.R. Stage. 1982. User’s guide to the

Stand Prognosis Model. Gen. Tech. Rep. INT-133. Ogden, UT:

U.S. Department of Agriculture, Forest Service, Intermountain

Forest and Range Experiment Station. 112 p.
14 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Anthony W. Courter is a Systems Analyst with INTECS International,

Inc., Fort Collins, CO, contracting with the Forest Health Technology Enterprise Team, USDA Forest Service, 2150 Centre Ave. (NRRC) Bldg. A, Suite

331, Fort Collins, CO 80526. Forrest L. Oliveria is the Field Office Representative for Forest Health Protection Region 8 Alexandria Field Office, USDA

Forest Service, 2500 Shreveport Highway, Pineville, LA 71360. James

(Rusty) Rhea is an Entomologist with Forest Health Protection Region 8

Asheville Field Office, USDA Forest Service, P.O. Box 2680, Asheville, NC

28802.

Abstract—The Southern Pine Beetle Keyword Component Addfile

is a keyword set used within the Forest Vegetation Simulator (FVS)

to simulate the effects of southern pine beetles (SPB)(Dendroctonus

frontalis), Zimmermann (Coleoptera: Scolytidae). Individual stands

are evaluated with a hazard rating system to determine their

susceptibility to SPB depredation. SPB-induced mortality is determined, in part, by the stand’s hazard rating. This is a strategic

model that demonstrates potential pine timber and habitat loss due

to SPB. The user can proactively reduce the stand’s hazard level by

scheduling appropriate management actions within FVS. Once

preset conditions are met and a SPB outbreak is scheduled, losses

due to SPB will be estimated.

Modeling the effects of insects and pathogens within FVS

is conventionally achieved through the use of pest-specific

FVS Variant Extensions, which are supported by the staffs

of the USDA Forest Service Forest Health Technology Enterprise Team (FHTET) and the USDA Forest Service Forest Management Service Center. These extensions represent the state of the art in pest modeling and have been

developed for use exclusively with the Western variants of

FVS. These extensions require substantial investment of

time and money to build and maintain.

In 2000, the director of Forest Health Protection in USDA

Forest Service Southern Region requested assistance from

FHTET with FVS-Pest Model solutions for the South addressing southern pine beetle, oak decline, and littleleaf

disease. Due to the immediate need and budgetary constraints, it was decided to simulate pest impacts in the South

using FVS keyword sets, rather than develop fully integrated pest extensions. These keyword sets can then be

bundled into stand-alone Keyword ComPonent Addfiles

(.kcp files) to be included in FVS simulations as appropriate.

One of the advantages of developing pest models with FVS

keywords is that the code is stand alone; modifications can

be made to the FVS base model without requiring changes to

Southern Pine Beetle Keyword Component

Addfile for Use with FVS

Anthony W. Courter

Forrest L. Oliveria

James (Rusty) Rhea

the pest specific .kcp files. Occasionally, traditional pest

extensions might require immediate and extensive coding

changes if the base FVS code is altered in fundamental ways.

Another advantage is the ability of the developer/user to

quickly make changes in a single file that drastically streamlines the testing and development phase. The FVS Event

Monitor feature allows for the creation and evaluation of

user-defined variables that describe stand conditions. These

variables can then be used to schedule management actions

or growth/mortality functions with the “IF...THEN...ENDIF”

FVS keyword.

At its simplest level, the SPB keyword set “kills” a percentage of targeted pine species in a simulated stand by the use

of the FVS keyword “FIXMORT” if a drawn random number

is less than, or equal to, the estimated probability of a SPB

outbreak occurring in that multiyear FVS cycle. The SPB

keyword set comprised three geographically specific FVS

addfiles that cover the Coastal (SPB_Coas.kcp), Piedmont

(SPB_Pied.kcp), and Mountain (SPB_Mtn.kcp) subregions

in Region 8 (table 1). The Coastal and Piedmont SPB keyword files may be used with the Southern (SN) and Southeast (SE) FVS variants. The Mountain SPB keyword file

may be used with the SN, SE, and Northeast (NE) variants.

Coastal and Piedmont hazard ratings are determined in

accordance with USDA-Forest Service guidelines (Hess and

others 1997). The Mountain hazard rating was developed

through the Mountain Risk formula (Heddon 1983). The

estimated effects of SPB, as well as probability of outbreak

occurrence contained in these three addfiles, represent the

best professional judgment of entomologists in Forest Health

Protection Region 8 (FHP R8), USDA Forest Service.

Table 1—Targeted species by subregion

FVS Scientific

Subregion Pine species code name

Coastal Loblolly LP Pinus taeda

Shortleaf SP Pinus echinata

Slash SA Pinus elliottii

Longleaf LL Pinus palustris

Piedmont Loblolly LP Pinus taeda

Shortleaf SP Pinus echinata

Virginia VP Pinus virginiana

Mountain Shortleaf SP Pinus echinata

Virginia VP Pinus virginiana

Pitch PP Pinus rigida

Eastern white WP Pinus strobus
USDA Forest Service Proceedings RMRS-P-25. 2002 15

Southern Pine Beetle Keyword Component Addfile for Use with FVS Courter, Oliveria, and Rhea

Methods _______________________

Assumptions

The SPB keyword sets are based on the following assumptions as theorized by FHP R8 entomologists:

• Seven to eight SPB outbreaks in 120 years

• One catastrophic outbreak in 100 years

• SPB prefer sawtimber to poletimber

• SPB prefer poletimber to saplings

• SPB-induced mortality does not affect pine species less

than 1.0 inch d.b.h.

• A catastrophic outbreak applies heavy mortality to

diameter classes greater than or equal to 1.0 inch d.b.h.

• SPB-induced mortality is not treated as a release

In response to the current ongoing infestation in upland

stands, and upon request from FHP R8 entomologists, eastern white pine (Pinus strobus) (WP) was added to the

Mountain Keyword set. Because the effects of SPB on WP

are not well documented, it was decided that the SPB impact

on WP should be estimated as half that of the impact of SPB

on other target species. That is, if the mortality leveled

against shortleaf pine (Pinus echinata) is calculated to be 45

percent, then SPB-induced mortality on WP will be half that,

or 22.5 percent.

SPB Hazard

Potential SPB mortality, or percent of basal area loss

(PBALOSS), is determined by the SPB event monitor variable

HAZARD. A stand’s hazard classification, or rating, is defined by current stand conditions and determines the levels

of mortality applied to target pine sawtimber, if an outbreak

occurs. There are three SPB hazard classifications, low (1),

medium (2) and high (3), which are based on the stand’s

overall susceptibility to SPB depredation. Potential

PBALOSS of sawtimber for target pine species is shown in

table 2.

Actual PBALOSS is calculated as follows:

PBALOSS = A + (B – A) Random2

Where A equals the lower range limit

B equals the upper range limit

Random2 equals a pseudorandom number between

zero and one, or

(0 < Random2 <1)

For example, PBALOSS for a high hazard stand (table 2)

would be calculated as follows:

PBALOSS = .4 + [(.8 – .4) x Random2]

Calculating Hazard Near the Coast

SPB hazard in SPB_Coas.kcp is calculated by combining

the basal area of targeted pine species within the loblolly/

shortleaf and slash/longleaf pine communities, compared to

the stand mean top height. Low hazard (1) is the default

condition unless the stand meets any of the conditions

included in tables 3 and 4. A high hazard (3) classification for

the shortest tree range in the loblolly and shortleaf pine

community was added to the original R8 classification.

Table 2—PBALOSS (sawtimber) by hazard class

Hazard rating Percent basal area loss

1 - Low 0 to 10

2 - Moderate 20 to 40

3 - High 40 to 80

Table 3—Hazard ratings for coastal loblolly and shortleaf pine communities.

Loblolly and shortleaf pine combined basal area aMean greater than or equal to 50% stand total

stand top Pine basal area (ft2)

height (ft) < 70 70 to 80 80 to 90 90 to 100 > 100

106+ 1 2 2 2 3

76 to 105 1 2 2 3 3

66 to 75 1 1 2 3 3

56 to 65 1 1 2b 2b 3b

0 to 55 1 1 1 1 1

1 = Low hazard

2 = Medium hazard

3 = High hazard

a

Mean stand top height(ft) as calculated from the 40 largest trees by d.b.h.

b

Indicates change from original hazard classification parameters (Hess and others 1997).
16 USDA Forest Service Proceedings RMRS-P-25. 2002

Courter, Oliveria, and Rhea Southern Pine Beetle Keyword Component Addfile for Use with FVS

Calculating Hazard on the Piedmont

SPB hazard in SPB_Pied.kcp is calculated by combining the

basal area of targeted pine species within the loblolly and

shortleaf/Virginia pine communities, compared to the stand

mean top height. Low hazard (1) is the default condition

unless the stand meets any of the conditions included in

tables 5 and 6. Virginia pine was added to the shortleaf pine

community for a combined basal area with a more comprehensive determination of hazard risk in the Piedmont. We

added a high hazard (3) classification for the shortest tree

classification range in the loblolly community.

Calculating Hazard in the Mountains

SPB hazard in SPB_Mtn.kcp is calculated using the Mountain Risk formula (Hedden 1983), incorporating 5-year radial growth (RG) of targeted pines and the percentage of

targeted pine basal area (PER_PINE) relative to total stand

basal area. The 5-year radial growth of targeted pine species

is estimated using the FVS Event Monitor function,

SPMCDBH. SPMCDBH can be configured to return average

diameter growth, excluding bark. Diameter growth is then

divided by two to generate an approximation of radial

growth in the multiyear cycle. Average radial growth

(avgRG) is then calculated using a weighted mean average

for all targeted species. AvgRG is then divided by the Time

Interval (TI) and multiplied by 5 to generate a 5-year radial

growth (RG) variable, as per: RG = (avgRG / TI)

The variable STAT is then calculated by taking the natural log of the product of RG and 25.4, as per: STAT =

ALOG(RG• 25.4)

The Mountain Risk Score is then calculated as per:

SCORE = –1.90 –(3.97• PER_PINE)+(2.14• STAT)

The resulting SCORE is then used to determine the

stand’s hazard classification (table 7).

The Hazard Regulator

Because SPB-induced mortality is not treated as a Release, logic was built into the SPB keyword sets that assures

a stand’s hazard rating either stays the same or gets worse

over time, based on current stand conditions. Management

actions cause the hazard rating to be recalculated based on

current stand conditions. This logic was added after it was

observed that hazard went down after sizable SPB mortality

was applied in a preliminary SPB keyword simulation.

According to FHP R8 entomologists, this downturn contradicts what has been observed in the field. In nature, the

unaffected portion of the stand that had been attacked by

Table 4—Hazard ratings for coastal slash and longleaf pine communities.

Slash and longleaf pine combined basal area aMean greater than or equal to 70% stand total

stand top Pine basal area (ft2)

height (ft) < 70 70 to 80 80 to 90 90 to 100 > 100

106+ 1 1 1 1 2

76 to 105 1 1 1 2 2

66 to 75 1 1 2 2 2

0 to 65 1 1 1 1 1

1 = Low hazard

2 = Medium hazard

3 = High hazard

aMean stand top height(ft) as calculated from the 40 largest trees by d.b.h.

Table 5—Hazard ratings for coastal loblolly and pine communities.

Loblolly pine combined basal area aMean greater than or equal to 50% stand total

stand top Pine basal area (ft2)

height (ft) < 70 70 to 79 80 to 89 90 to 100 > 100

106+ 1 2 2 2 3

76 to 105 1 2 2 2 3

61 to 75 1 1 2 3 3

50 to 60 1 1 2b 2b 3b

0 to 49 1 1 1 1 1

1 = Low hazard

2 = Medium hazard

3 = High hazard

a

Mean stand top height(ft) as calculated from the 40 largest trees by d.b.h.

b

Indicates change from original hazard classification parameters (Hess and others 1997).
USDA Forest Service Proceedings RMRS-P-25. 2002 17

Southern Pine Beetle Keyword Component Addfile for Use with FVS Courter, Oliveria, and Rhea

SPB would be as susceptible to a subsequent SPB outbreak

as it was to the previous infestation.

SPB Outbreak

The SPB Event Monitor variable “Outbreak” determines

if a SPB infestation will occur, and if so, how severe its

consequences will be. Outbreak is an integer variable between zero and four, and represents the range of potential

outcomes for each FVS multiyear cycle (table 8).

No SPB-induced mortality is leveled against targeted pine

species in the simulated stand in a cycle where the variable

outbreak = 0 (no outbreak). As determined by the stand’s

hazard rating (table 9), SPB-induced mortality is differentially applied to various diameter classes in light (1) and

moderate (2) outbreaks.

For the purposes of the SPB Event Monitor, the diameter

classes are defined with lower d.b.h. breakpoints as sawtimber (10 inch), poletimber (3 inch), and saplings (1 inch). The

SPB Event Monitor does not affect pine species smaller than

1 inch.

Table 6—Hazard ratings for piedmont shortleaf and Virginiab pine communities.

Shortleaf and Virginiab pine combined basal area aMean greater than or equal to 50% stand total

stand top Pine basal area (ft2)

height (ft) < 70 70 to 80 80 to 90 90 to 100 > 100

106+ 1 2 2 2 3

76 to 105 1 2 2 3 3

56 to 75 1 2 3 3 3

45 to 55 1 2b 2b 2b 3b

 0 to 44 1 1 1 1 1

1 = Low hazard

2 = Medium hazard

3 = High hazard

aMean stand top height(ft) as calculated from the 40 largest trees by d.b.h.

bIndicates change from original hazard classification parameters (Hess and others 1997).

Table 7—Mountain risk score.

Score comparison Hazard classification

Score > 0.40 1

0.40 ≥ Score ≥ –0.56 2

Score < –0.56 3

Table 8—SPB outbreak variable values.

Outbreak severity Outbreak values

No outbreak 0

Light 1

Moderate 2

Heavy 3

Catastrophic 4

Table 9— Percentage basal area mortality by outbreak severity class.

Relative percentage of mortality applied.

Catastrophic Heavy Moderate Light

Size outbreak outbreak outbreak outbreak

class (4) (3) (2) (1)

Saplings 80 to 100 100 50 25

Poletimber 80 to 100 100 75 50

Sawtimber 80 to 100 100 100 100

SPB mortality will be evenly applied to sawtimber, poletimber,

and saplings during a heavy outbreak (3) condition. Regardless

of hazard classification, extreme SPB mortality will be applied

to all diameter classes in catastrophic outbreak (4) condition.

Catastrophic mortality will range between 80 and 100 percent

of basal area loss.

Yearly Probability of Outbreak Occurrence

Nonzero outbreak values are assigned if a drawn pseudorandom number between zero and one is less than or equal

to the estimated probability of that type of outbreak occurring in the multiyear FVS cycle. Yearly probabilities of SPB

outbreak occurrence are based on two assumptions: first, R8

entomologists expect a stand to endure seven to eight SPB

infestations over a 120-year period, and second, there will be

at least one catastrophic event during a 100-year period.

The probability that a catastrophic outbreak will occur is

set at 1 percent per year (one outbreak per 100 years). The

probability that an outbreak will occur at all (7.5 outbreaks

per 120 Years = 0.0625 or 6.25 percent) is the upper probability limit for a light outbreak in a single year. Likewise,

moderate and heavy outbreaks have been assigned a yearly

probability of occurrence of 5.0 and 2.5 percent, respectively.

Discounting Probability Logic

The Southern Pine Beetle keyword set uses discounting

logic to scale the probability of each outbreak event to the

length of the multiyear cycle. In this manner, the probability
18 USDA Forest Service Proceedings RMRS-P-25. 2002

Courter, Oliveria, and Rhea Southern Pine Beetle Keyword Component Addfile for Use with FVS

of outbreak occurrence can never exceed 100 percent regardless of the length of the user-defined cycle. This is

achieved by raising the probability of the nonevent to the

power of the number of years in a cycle, Time Interval (TI).

Therefore, the probability of a light outbreak in a cycle is

defined as:

ProbLightOutbreak = 1 – (ProbNoLightOutbreak) TI

By default, FVS operates in 5-year cycles in the Southern

Variant (TI = 5).

For example, if probNoLightOutbreak = 0.9375, the probability that a light outbreak will occur in a 5-year cycle is:

ProbLightOutbreak5Yr = 1 – (.9375)5

ProbLightOutbreak5Yr = 27.6 percent.

In each multiyear cycle, a pseudorandom number between

zero and one is compared to the calculated probability values

of each type of outbreak event — light, moderate, heavy, and

catastrophic. Determination of an outbreak event and its

associated outbreak severity is illustrated in table 10, in

which a drawn pseudorandom number between zero and one

is compared to the probability of the various nonevents,

raised to the power of the Time Interval (TI). Thus, a single

pseudorandom number (rN) can determine outbreak occurrence and outbreak severity (table 10). A (rN) with a value

of 0.223 in a particular FVS cycle will trigger a moderate

outbreak of SPB-induced mortality on targeted pine species.

A (rN) with a value of 0.333 in a subsequent cycle will

represent a No Outbreak scenario, and simulated trees will

continue to grow affected only with FVS calculated background mortality and competition effects.

Use of Random Numbers

The SPB keyword set is designed to run deterministically

based on the default FVS pseudorandom number seed

(55329). However, the user can easily edit the .kcp file in any

text-editing program to run the SPB keyword set stochastically. To do this the user must remove the comment character “!” from the line of code that reads

!RANNSEED 0

This establishes “0” as the replacement value for the pseudorandom number seed. Using “0” will cause the results to

vary among multiple runs of the same simulation file.

Also, the user can set his/her own pseudorandom number

seed by replacing the zero with any odd integer (Van Dyck

2002).

Applying Mortality

SPB mortality is leveled against targeted species in the

stand by use of the FVS keyword “FIXMORT.” In the

following example taken directly from the code, targeted

pine species are attacked in a moderate outbreak (2) using

the Mountain SPB keyword set (table 11). Stand hazard is

high (3), and PBALOSS is assigned at 60 percent. Poletimber

PBALOSS (POLELOSS) is then calculated at 45 percent,

and sapling PBALOSS (SAPLOSS) would be 30 percent.

White pine mortality in the same diameter classes is calculated at half that of the other targeted pine species.

WPBALOSS, WPPOLE, and WPSAP represent mortality on

white pine sawtimber, poletimber, and saplings, respectively.

The FIXMORT keywords are embedded within an event

monitor IF-THEN block and will execute only if an outbreak

is scheduled. The variable COUNT is used to assure sequential processing of the various code blocks within the SPB

keyword set.

The use of zero (0) in FIXMORT field 6 replaces calculated

FVS background mortality for targeted species, whereas the

use of one (1) in FIXMORT field 7 indicates mortality levels

will be applied on a point basis. This simulates SPB taking

discrete portions of the stand, thereby reducing release

effects among targeted pine species.

Output

The standard FVS output file contains a wealth of information about the simulation the user has created and run.

Table 10—Random number comparison assigning outbreak severity in

a 5-year cycle.

Pseudorandom Probability of

Outbreak number (rN) outbreak in a

severity comparison 5-year cycle (percent)

No outbreak 1.0 ≥ rN > 0.275 72.5

Light 0.275 ≥ rN > 0.226 4.9

Moderate 0.226 ≥ rN > 0.119 10.7

Heavy 0.119 ≥ rN > 0.049 7.0

Catastrophic 0.049 ≥ rN ≥ 0.0 4.9

!Test for an outbreak event and differentially

!apply mortality based upon the severity of the

!Outbreak

IF

OUTBREAK NE 0 AND COUNT EQ 10

THEN

! Apply mortality to saplings

FIXMORT 0 PARMS (SP,SAPLOSS,1,3.0,0,1)

FIXMORT 0 PARMS (PP,SAPLOSS,1,3.0,0,1)

FIXMORT 0 PARMS (VP,SAPLOSS,1,3.0,0,1)

FIXMORT 0 PARMS (WP,WPSAP,1,3.0,0,1)

! Apply mortality to poletimber

FIXMORT 0 PARMS (SP,POLELOSS,3,10.0,0,1)

FIXMORT 0 PARMS (PP,POLELOSS,3,10.0,0,1)

FIXMORT 0 PARMS (VP,POLELOSS,3,10.0,0,1)

FIXMORT 0 PARMS (WP,WPPOLE,3,10.0,0,1)

! Apply mortality to sawtimber

FIXMORT 0 PARMS (SP,PBALOSS,10,999,0,1)

FIXMORT 0 PARMS (PP,PBALOSS,10,999,0,1)

FIXMORT 0 PARMS (VP,PBALOSS,10,999,0,1)

FIXMORT 0 PARMS (WP,WPBALOSS,10,999,0,1)

ENDIF

Table 11—Applying SPB mortality in SPB_Mtn.kcp
USDA Forest Service Proceedings RMRS-P-25. 2002 19

Southern Pine Beetle Keyword Component Addfile for Use with FVS Courter, Oliveria, and Rhea

The user is urged to pay particular attention to the sections

“Stand Composition,” “Activity Summary,” and “Summary

Statistics” of the standard FVS output. The user can gain

direct insight into the inner workings of the SPB keyword set

with the two postprocessors that directly report all SPB

event monitor variables:

• Compute1—Table of Compute Variables (with headers)—good for general assessment and examination of

overall trends within the stand.

• Compute2—Table of Concatenated Compute Variables

(comma delimited)—presents the same data in commadelimited format, which can be imported into spreadsheet programs such as Microsoft Excel® or FVS-EMAP

(see these proceedings). SPB event monitor variables of

particular interest are HAZARD, OUTBREAK,

PER_PINE, STAND_BA, and PINEBA. Analysis of

event monitor results within the spreadsheet environment is helpful; the user can sort by one or more

variables (such as FVS-cycle year). It is relatively easy

to copy values from each cycle to individual worksheets

and calculate sum totals and/or averages.

Conclusion_____________________

The SPB keyword sets are three geographically specific

FVS Addfiles that emulate the effects of SPB upon a simulated stand over time. Current stand conditions are evaluated to calculate a SPB hazard rating for that stand in a

given cycle. If a drawn pseudorandom number is less than or

equal to the probability of an outbreak event, SPB mortality

is levied against targeted pine species in the stand, based on

the SPB hazard rating and/or the type of SPB outbreak

scheduled for the stand in that cycle. SPB-induced mortality

is not intended to act as a release for surviving trees.

Therefore, unless management actions are scheduled by the

user, the stand’s hazard rating can only remain the same, or

get worse over time. Appropriate management actions, as

portrayed by FVS keywords, can reduce a stand’s SPB

hazard rating and potentially reduce loss of pine basal area

due to simulated SPB mortality.

The three SPB keyword sets and User’s Guide are available at:

http://www.fs.fed.us/foresthealth/technology/products.htm

or by contacting:

Judy Adams

Program Manager

Forest Health Technology Enterprise Team

USDA Forest Service

2150 Centre Ave (NRRC), Bldg A, Suite 331

Fort Collins, CO 80526-1891

Voice: (970) 295-5846 Fax: (970) 295-5815

jadams04@fs.fed.us

Acknowledgments ______________

The authors thank the Forest Management Service Center (Fort Collins) staff for their ongoing advice and support:

Gary Dixon, Michael Van Dyck, Bob Havis, Chad Keyser,

Erin Smith, and Don Vandendriesche.

For their continuing support, we also wish to thank Judy

Adams, Program Manager, Forest Health Technology Enterprise Team, and Andrew J. McMahan, Systems Analyst,

INTECS International, Inc., Fort Collins, CO

References _____________________

Crookston, Nicholas L. 1990. User’s guide to the Event Monitor:

Part of the Prognosis Model Version 6. General Technical Report

INT-275. USDA Forest Service, Intermountain Research Station, Ogden UT. 27pp.

Hedden, R. L. 1983. Impact submodels for a southern pine beetle

pest management program. In: Mason, G. N.; Lorio, P. L.; Belanger,

R. P.; Nettleton, W. A. 1991. Integrated pest management handbook: rating the susceptibility of stands to southern pine beetle

attack. USDA Forest Service, Cooperative State Research Service, Agriculture Handbook No. 645: 20.

Hess, Nolan J.; Anderson, Robert L.; Dawson, Michael G.; Brown,

H. Daniel; Nettleton, Wesley A.; Hoffard, William H.; Martin,

William; Belcher, David M. 1997.

The Forest Health Decision Key for National Forests in Region 8:

hazard risks and recommendations; Version 4. U.S. Department

of Agriculture, Forest Service, State and Private Forestry, Southern Region. 52 p.

Van Dyck, Michael G. 2002. Keyword reference guide for the Forest

Vegetation Simulator. USDA Forest Service, Forest Management Service Center, Fort Collins, CO. 108 pp.
20 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Don Vandendriesche is a Forester, U.S. Department of Agriculture,

Forest Service, Forest Management Service Center, Vegetation Simulation

Group, Natural Resources Research Center, 2150A Centre Avenue, Fort

Collins, CO 80526.

Abstract—The Pre-Suppose program is designed to help analysts

organize inventory data for management planning. Pre-Suppose

rapidly generates preliminary statistics on a proposed data group.

This provides the ability to quickly evaluate the homogeneity of

selected forest strata. Plot attributes can be chosen from a variety

of code and range keys to designate a data group. Rapid processing

of plot summary values renders important statistical information

relevant to the keyed items. Once the user is satisfied with the

selected criteria and resultant output, support files can be created

that link directly to the Suppose interface for the Forest Vegetation

Simulator. Pre-Suppose has been programmed to work dynamically

with forest inventory data stored in a Microsoft Access database. A

case example using Forest Inventory Analysis data from the Black

Hills National Forest in South Dakota is presented to demonstrate

program capabilities.

The Suppose (Crookston 1997) interface presupposes that

inventory data are available in the proper format for the

Forest Vegetation Simulator (FVS) (Dixon 2001). Several

computer programs have been written to translate data

from stand examination and permanent plot inventories

into file formats needed for FVS. However, most translation

programs lack the ability to scrutinize data elements prior

to export. Users are frequently perplexed regarding how to

assemble inventory data items into comprehensive strata

needed for further analysis. The Pre-Suppose program addresses data attribute querying in the context of strategic

planning for large-scale forest ownerships.

Pre-Suppose was written for the Microsoft Windows operating system using the Visual Basic programming language.

“Any application that requires the user to perform a series

of steps to arrive at a predetermined result can benefit from

a Wizard” (Eidahl 1998). Pre-Suppose adheres to this programming tenet. Like most computer programs, Pre-Suppose has input and output components. The input components are addressed through three steps within the

Pre-Suppose Wizard. Once the user chooses the input elements, the proper output reports are generated. Following a

brief discussion of relevant forest planning aspects, associated functionality of the Pre-Suppose Wizard will be presented.

Pre-Suppose: Preprocessor to Suppose

Don Vandendriesche

Planning Framework _____________

Quite often, forest planning efforts require developing

estimates of conditions and outcomes by stand type. A stand

type is an aggregation of the physical, vegetative, and

developmental characteristics used to identify homogeneous

forest strata (Davis and Johnson 1987). Physical attributes

describe the site aspects of the forest, such as topography,

soils, and habitat type. Vegetative attributes characterize

the flora aspects of the forest, such as cover type species, size,

and density. Developmental attributes portray the humanuse aspects of the forest, such as roads, building, and

administrative boundaries. Stand types, by definition, are

nonspatial but comprise many geographically identifiable

forest stands.

Physical and developmental characteristics are initially

addressed by following a land classification scheme. For

example, suitable lands, according the USDA Forest Service

protocols, constitute the land base for determining the

allowable sale quantity and the vegetation management

practices associated with timber production. Suitable lands

must be available, capable, and technically favorable under

current management techniques.

Once the land has been classified, management prescriptions are formulated. Management prescriptions provide an

activity schedule that describes the conditions by which

vegetation will be treated to obtain desired outcomes. For

example, no treatment is a popular contemporary management option. Similar vegetation types often respond in a

predictable manner. Thus, vegetative characteristics are

defined by common overstory tree species, of similar size and

density. Understory components are increasingly important

and add the dimension of plant association or community

type. Typically, major forest cover types are derived from life

zones, plant biomes, or ecological geography (for example,

ecoregions, Bailey 1995). They depict the base vegetation

layer.

The Pre-Suppose program can be used to query the physical, vegetative, and developmental attributes within a forest

inventory data set. Layout of a schematic flow chart can be

easily accomplished by following the intuitive steps of the

Pre-Suppose Wizard. The Forest Inventory Analysis (FIA)

data set from the Black Hills National Forest in South

Dakota will serve as an example.

Forestland Classification _________

Winnowing the land base constitutes the initial step in

the development of stand types to be used in a forest

scheduling model. The derivation of the potential timber

base is extremely important insofar as it defines the basic

units, the building blocks for the analysis. Once the land

base is classified and incorporated into a scheduling model,

it is extremely difficult to alter. A change in the land
USDA Forest Service Proceedings RMRS-P-25. 2002 21

Pre-Suppose: Preprocessor to Suppose Vandendriesche

classification scheme could essentially mean starting from

the beginning and redoing the analysis.

The 1986 and 1999 measurements of the Forest Inventory

Analysis plot grids will be used as the empirical basis for the

Black Hills National Forest, Phase II Amendment of its 1997

Revised Land and Resource Management Plan. The FIA plot

locations will represent the various stand types within the

land classification scheme. This diagram will serve as a flow

chart for assigning stand types and associated acreages and

plot counts.

Candidate Lands in Timber Base

There are many possible ways to partition a forested area

into land classes that form the timber base. Experience has

shown that four major breaks need to be described: (1)

Administrative Availability – lands that are withdrawn by

statutes, regulations, or policies (that is, wilderness areas,

cultural, and special-use sites) are identified at the first level

of the land class hierarchy; (2) Physical Capability – lands

that are physically limited in their ability to sustain commercial stands of timber (for example, extremely dry or wet

sites, inadequate growth rate) are separated at the second

level of the land class hierarchy; (3) Ecological Suitability –

lands that are designated for resource values other than

timber production (that is, critical wildlife, watershed protection, visual corridors) compose the third level of the land

class hierarchy; and (4) Technical Favorability – lands that

are unable to be treated by current technology or restricted

by special treatment needs (that is, inaccessible areas, steep

terrain, insect, disease, fire areas) are excluded to form the

fourth level of the land class hierarchy. Figure 1 provides a

delineation of the forestland classification for the Black Hills

National Forest. Associated acreages and FIA plot counts

are presented for each stand type. Area and inventory

estimates are not given for nonsampled strata.

Stand Types Within the Timber Base

Stand types comprise forest land units that have the

same combination of physical, vegetative, and developmental

Figure 1—Forestland classification template.
22 USDA Forest Service Proceedings RMRS-P-25. 2002

Vandendriesche Pre-Suppose: Preprocessor to Suppose

characteristics to stratify as homogenous entities. Developmental and physical attributes have been addressed in

defining the resultant “Available – Capable – Suitable –

Favorable” forestland base. The vegetative characteristics

(type, size, density) can be described by the predominant

overstory forest canopy. Figure 2 provides a depiction of the

tentatively suitable forest land base for the Black Hills

National Forest. Acreage values were derived using Geographic Information System (GIS) software resident at the

Black Hills Supervisors Office. The Pre-Suppose program

was used to query the FIA data sets to assemble plot counts

for the various stand types.

Pre-Suppose Application _________

Pre-Suppose was developed to assist forest analysts in

developing stand types for management planning. The program leads users through a series of three input forms

(parameter, plot, and key screens). Upon conclusion, a

statistical report is presented to aid evaluation of a proposed

data group. A brief description of the processing sequence

used by Pre-Suppose follows.

Once the Pre-Suppose program has been invoked, a splash

screen is displayed, followed directly by an open database

dialog window. Pre-Suppose processes information stored in

Microsoft Access database files. A listing of internal data

tables will be presented during step 1 of the Pre-Suppose

Wizard.

The user can choose between any one of several locations

or time periods or both. For example, FIA data for the Black

Hills National Forest has been assembled in database format from the 1986 and 1999 measurements. During the

later, two complementary grids were installed. The primary

grid was monumented in accordance with a 5,000 m design.

The secondary grid was based on a centroid-hexagon layout.

Over time, the hexagon grid will become the principal

sampling design. Pre-Suppose easily accommodates the

varying data sources as depicted in figure 3. Checkboxes

within the listbox window provide the option of selecting the

preferred data sets.

Pre-Suppose allows an expanded search beyond the boundaries of a given ownership. Lets say that FIA plot sample size

is sparse in a specified locale as is true with many Eastern

National Forests. There are numerous cover types represented over the landscape. Plot intensity per cover type is

limited. Users could expand their search to FIA plots located

in proximity to the National Forest. So long as the treatment

and development histories are similar, these plots could be

Figure 2—Stand type delineation.
USDA Forest Service Proceedings RMRS-P-25. 2002 23

Pre-Suppose: Preprocessor to Suppose Vandendriesche

used as condition class samples to strengthen the inferences

related to the vegetation stand type. All that is needed is for

the FIA data to be loaded into the proper database for PreSuppose to access.

Step 1 of the Pre-Suppose Wizard is commonly referred to

as the parameter screen. Input parameters are gathered

that inform Pre-Suppose which database tables to use, how

to title and footnote the output reports, and which statistical

summaries to produce. Plot summary values can be generated for a variety of tree classes (such as live, harvest,

mortality trees). Parameter values are retained as defaults

for subsequent runs of Pre-Suppose. Thus, following initial

set up of the individual tabs, additional runs only require

proceeding by mouse clicking the Next command button.

Step 2 of the Pre-Suppose Wizard is commonly referred to

as the plot screen. Plots are specified for inclusion or exclusion from further processing. By default, all “Available

Plots” are used in the statistical analysis. However, there

are times when noncoded events affect the FIA data set. For

example, a recent catastrophic wildfire (Jasper Fire) burned

over 80,000 acres of the Black Hills National Forest. The FIA

plots within the fire perimeter were identified using GIS

software. A text file was generated that could be imported

into Pre-Suppose to populate the plot screen. Refer to

figure 4. A label can be included for additional identification

of the plot set.

Plot counts input through step 2 of the Pre-Suppose

Wizard can be used to directly populate the forestland

classification diagram. Note that the Jasper Fire was

identified as a subclass beneath the Available – Capable –

Suitable – Technically Restricted forest class. By including

only these plots, Pre-Suppose can produce a plot summary

statistical report relative to prefire conditions. Pre-Suppose

can also provide a direct link of these plots to the Suppose

interface. In contrast, in order to properly sort the Technically Unfavorable and Technically Favorable forest classes,

the Jasper Fire plots would need to be excluded from those

data run. Pre-Suppose readily handles this task. Mouse

clicking the Next command button will prompt Pre-Suppose

to continue.

Step 3 of the Pre-Suppose Wizard is commonly referred to

as the key screen. Any data item collected during the field

measurement or any value calculated during the office

processing can be accessed by the user to form forest strata

or stand types. Data items are assigned as either code or

range keys. Coded items are generally discrete or qualitative

variables. For example, ownership, land class, or forest type

are representative of coded data. Range items are generally

continuous or quantitative variables. For example, elevation, stand age, or site index are representative of range

data.

Keyed data items entered through step 3 of the PreSuppose Wizard eases the effort to built stand types as

designated in a forestland classification chart. For the Black

Hills example, to identify those FIA plots composed of the

Available – Capable – Suitable – Favorable forest class that

are Ponderosa pine overstory with White spruce understory,

the following steps would be used:

Pre-Suppose_Wizard – Step 3 of 3:

1. Add Code Key: Land Class 1 = Forest Land

2. Add Code Key: Forest Class 3333 = Available –

Capable – Suitable – Favorable

3. Add Code Key: Forest Type 211 = Ponderosa pine

4. Add Range Key: Forest Sub-Class 3 = White spruce

Figure 5 provides a graphic depiction of the process used

to enter the Ponderosa pine overstory code key. The Data

Index window operates in a similar fashion as the treeview

folder pane within the Windows Explorer program. The Plot

Record branch nodes are derived from the FIA plot record.

The Plot Record is further defined as either Classification

Data or Summary Data. Classification data are coded stand

measurements. Summary data are computed per acre stand

values. The Tree Record branch nodes are derived from the

FIA tree record. The Tree Record is further defined as either

Measurement Data or Calculated Data. Measurement data

are field measured tree attributes. Calculated data are

computed per tree values. Mouse clicking the Classification

Data branch under the Plot Record node will expand a list of

Figure 3—Parameter selection screen. Figure 4—Plot selection screen.
24 USDA Forest Service Proceedings RMRS-P-25. 2002

Vandendriesche Pre-Suppose: Preprocessor to Suppose

Figure 5—Key selection screen.

Figure 6—Plot summary statistical analysis report.

available data items. Selecting the Forest Type – Present

sub-branch will awaken the listview window in the upper

right corner of the step 3 window displaying available code

items. Mouse clicking the “211” code will designate ponderosa pine as the overstory forest type to be entered into the

selection process. The check symbol located in the lower

left corner of step 3 will record the query attribute.

Clicking the Finish command button will prompt PreSuppose to continue.

After gathering input through the three steps of the

Wizard, Pre-Suppose is ready to generate the output reports. Three text files are produced by Pre-Suppose. The first

report, Plot Summary Statistical Analysis (PSSA), contains

an abbreviated statistical analysis of the specified strata.

Figure 6 displays the PSSA output report for the ponderosa

pine/white spruce stand type residing within the tentatively

suitable forest land base on the Black Hills.

The top of the PSSA report provides identification information such as the database tables used, the plot basis, and
USDA Forest Service Proceedings RMRS-P-25. 2002 25

Pre-Suppose: Preprocessor to Suppose Vandendriesche

the sort key prescribed. The middle section of the report

provides a table that lists classical statistical measures for

trees per acre, average quadratic mean diameter, basal area

per acre, and cubic and board foot volumes per acre. Statistical inferences for volume growth are also displayed. Values

appearing in the statistics table are for trees 5 inches and

greater in diameter. The bottom of the PSSA report includes

information on the number of sample plots rendered by the

sort criteria, growth measurement length, average site index for the strata, and a seedling/sapling count distribution.

The second text file produced by Pre-Suppose is called the

Listing of Plot Attributes. As the name implies, this report

sequentially lists plot records that comprise the requested

strata. This listing can be used as a permanent record of the

plots that compose the data sort. It can also be used to examine

data relationships. For example, using the Black Hills data

set, there was a need to partition the dominant ponderosa

pine overstory by understory tree species components (that

is, juniper, oak, birch, aspen, spruce seedling–sapling counts).

Using Pre-Suppose to query the database by understory

species, a relationship between aspect and elevation was

noted. Given that ponderosa pine seedlings and saplings

occur on all aspects and all elevations, analysis showed that

juniper and aspen composed the understory on southerly

aspects. Juniper resided at lower elevations while aspen

occurred much higher. On northerly aspects, a progression

from low/dry to high/moist was observed. Juniper understory gave way to oak, oak to birch, birch to aspen, and aspen

to spruce. Much of this relationship was revealed by close

examination of the Listing of Plot Attributes report.

The third text file produced by Pre-Suppose is entitled the

Listing of Plot Values. A complete listing of plot summary

values (trees, quadratic mean diameter, basal area, cubic

foot, board foot) is presented on a per acre basis. Values are

listed for each plot that met the sort criteria. Annual cubic

and board foot volume growth is also included as well as

seedling and sapling stem counts. Understories values are

listed separately by timber and woodland species.

For visual interpretation of the output, a graphing option

is available. Users can pick any set of continuous variables

for the data group and produce a scatter plot diagram.

Sometimes, visual inspection of the data set can reveal

suspicious outliers. Data points can be selected on the graph

to identify associated plot numbers. Figure 7 provides an

example of this handy feature. Step 2, the plot exclusion

feature of Pre-Suppose, can be used to eliminate suspect

data points.

The value of the Pre-Suppose program for building stand

types is tremendous. As a result of its rapid processing of plot

summary records, resultant data group can be quickly generated. Output from trial runs can be evaluated based upon

statistical merits to determine if an adequate plot count has

been obtained with the input criteria. Building a defendable

forestland classification scheme for a strategic forest planning project is a snap with Pre-Suppose.

Additional Features______________

Suppose Linkage

The ultimate goal of Pre-Suppose is to develop forest

strata to relay to the Suppose interface. Pre-Suppose prompts

the user to enter titles for the Suppose.loc file and for the

Stand List File. These two files reside in the background to

assist Suppose in its operation. For strata that contain many

plots, the caption on the Print Preview windows (fig. 6) will

indicate which plot Pre-Suppose is currently processing.

This aids in determining that Pre-Suppose is still working

on the selected strata. Once the data group is available to

Suppose, using the “File/Open Locations File” menu option

will facilitate locating the newly created Suppose.loc file.

The “Select Simulation Stands” window within Suppose will

appear. By picking the “Location Title” and “Group Code” as

designated in Pre-Suppose, the associated plots will be

displayed in the “Available Stands” text box.

Tree Record Queries

Pre-Suppose generates the Plot Summary Statistical

Analysis Report based on data in the database plot tables.

These tables contain one record per plot. In contrast, the tree

tables contain one record per tree. Thus, the plot tables are

significantly smaller in size than the tree tables. Processing

of the plot tables requires less input/output of records, which

allows faster reporting. However, there are situations when

a forest analyst needs information at the tree level. In this

instance, the Plot Statistical Analysis Report would render

an improper result. It displays all tree records, not just the

records associated with the specified tree level attribute.

Suppose a particular tree species is threatened or endangered due to insect, disease, or human cause. No longer are

plot summary values of interest. Pre-Suppose can be used to

query plots that contain the tree species attribute.

Listing of Tree Records—Pre-Suppose produces a complete listing of each tree record that meets the tree level

attribute. Tree measurement data are concatenated with

tree calculated data to generate this report. This printout

can be large. Prior to printing the entire document, careful

examination of the number of pages to be printed is warranted. The Page frame at the bottom of the Print Preview

window (fig. 6) will indicate the total pages.

Figure 7—Graphical data display.
26 USDA Forest Service Proceedings RMRS-P-25. 2002

Vandendriesche Pre-Suppose: Preprocessor to Suppose

The “Listing of Plot Attributes” and “Listing of Plot Values” reports can be viewed by selecting the respective radio

buttons in the Plot frame on the Print Preview window (fig.

6). These listings contain the plot records with the tree level

attribute. These plots can be captured as the default input

plot file for step 2 of the Pre-Suppose Wizard.

Data Base Consideration

Four data tables per location per inventory compose the

background database for Pre-Suppose:

1. Plot Classification (_Plot_Clss)

2. Plot Summary (_Plot_Summ)

3. Tree Measurement (_Tree_Meas)

4. Tree Calculation (_Tree_Calc)

These categories populate the treeview window on step 3

of the Pre-Suppose Wizard. Each forms a branch node that

inherently contains the respective data items. These data

fields comprise the columns within the data tables.

There are two additional tables that Pre-Suppose uses for

operation: one for input, one for output. The Code_Definitions

table is developed in conjunction with the plot and tree

tables. This table provides the link to the other input tables

and is the main driver of the Pre-Suppose program. Users

can easily add additional data items to the database by

modifying the Code_Definitions table. The Presuppose table

is created internally by the Pre-Suppose program during the

processing phase and renders the output records of the

requested query.

Help Support

Pre-Suppose has an embedded help utility that has the

look and feel of other popular Microsoft products. Help for

Pre-Suppose can be invoked by using the Help menu option

or by simply pressing the F1 function key. Help topics can be

searched by contents or by index.

Applications of Pre-Suppose ______

The Pre-Suppose program has proven to be a valuable tool

for forest planning. In addition, Pre-Suppose has been used

for several regional assessments. Disturbance agents such

as insects, disease, and fire impact the forest landscape.

Assessing their potential risk is an important task and aids

decisionmakers in their planning efforts. To date, the PreSuppose program has been used for the following projects:

1. USFS R-1: Hazard-Risk Assessment for Montana

2. USFS R-2: Forest Plan Revision, Black Hills National

Forest, South Dakota

3. USFS R-3: Hazard-Risk Assessment for Arizona and

New Mexico

4. USFS R-4: Hazard-Risk Assessment for Idaho and

Utah

5. USFS R-8: Forest Plan Revision, Southern Appalachian. National Forests

6. USFS R-9: Forest Plan Revision, Chippewa - Superior

National Forests, Minnesota

7. USFS WO: FY-2000 Senate Appropriations Act Request

8. Bureau of Indian Affairs: Forest Plan Revision

9. Bureau of Land Management: Assessment Projects

10. Private: West Virginia Timber Company

USDA Forest Service Intranet Web access has been

developed for Region 8 (fig. 8). Immediate intentions are to

provide a similar mode of program and data file access for

the entire country. Internet links are being developed to

facilitate access for all public and private constituents. A

collaborative effort between FIA and FVS staffs has commenced to develop links from the FIADB (FIA presentation

database) (Miles 2001) to FVS ready input files.

Future Development

Anticipated future developments for the Pre-Suppose program include producing links to the FSVeg database and

addressing process methods for stand examination inventory data sets. Conceptual discussions have already begun.

Pre-Suppose Documentation

A User’s Guide (Vandendriesche 2001) is available for

Pre-Suppose from the author at the address listed on the

front-page footnote of this paper. The User’s Guide includes

a “How to ... ” section that contains a step-by-step tutorial.

A detailed description is given for each of the output reports.

The graphing option is more fully described. A rigorous

discussion of the process of linking Pre-Suppose output to

Suppose input is provided. An example that explains the

process of tree level sorting using Pre-Suppose is presented.

Consideration regarding the composition of the background

database is given along with an explanation on how to

dynamically modify its structure. It is an “everything you

wanted to know” guide.

References _____________________

Bailey, Robert G. 1995. Description of the Ecoregions of the United

States. 2d ed. Misc. Publ. No. 1391 (rev.), Washington, DC: U.S.

Department of Agriculture, Forest Service. 108 p.

Crookston, Nicholas L. 1997. Suppose: an interface to the Forest

Vegetation Simulator. In: GTR-375, Proceeding: Forest Vegetation Simulator Conference. Fort Collins, CO. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. p. 7-14.

Davis, Lawrence S.; Johnson, K. Norman. 1987. Forest Management. 3d ed. New York, NY: McGraw-Hill Book Company. 790 p.

Dixon, Gary E. 2001. Essential FVS: A User’s Guide to the

Forest Vegetation Simulator. GTR draft. U.S. Department of

Agriculture, Forest Service, Forest Management Service Center, Fort Collins, CO. 202 p.

Miles, Patrick D. 2001. The Forest Inventory and Analysis Database: Database Description and Users Manual Version 1.0. NCGTR-218. St. Paul, MN: U.S. Department of Agriculture. Forest

Service. North Central Research Station. 130 p.

Thayer, Rob; Eidahl, Loren. 1998. Visual Basic 6 Unleashed. Phoenix, AZ: Sams Publishing. 954 p.

Vandendriesche, Donald A. 2001. Selected Topics for the Forest

Vegetation Simulator. Internal Manuscript. Fort Collins, CO:

U.S. Department of Agriculture, Forest Service, Forest Management Service Center. 85 p.
USDA Forest Service Proceedings RMRS-P-25. 2002 27

Pre-Suppose: Preprocessor to Suppose Vandendriesche

Figure 8—Web Page link.

USDA Forest Service Proceedings RMRS-P-25. 2002 29

Session 2. Reality Check

USDA Forest Service Proceedings RMRS-P-25. 2002 31

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

T. M. Barrett is a Research Forester, Forestry Sciences Lab (FIA), USDA

Forest Service Pacific Northwest Research Station, P.O. Box 3890, Portland,

OR 97208. F. G. Schurr is Assistant Forest Manager, Blodgett Forest

Research Station, University of California-Berkeley, 4501 Blodgett Forest

Rd., Georgetown, CA 95634. K. L. O’Hara is Associate Professor, ESPM, 145

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

Abstract—FVS is frequently linked with the Stand Visualization

System (SVS) to provide computer-generated images of current and

projected forest structure for use in research articles, forest planning documents, and public presentations. A small pilot study in the

Sierra Nevada focused on whether observers classified stands in the

same way from SVS images as from site visits. This survey, conducted in fall 2000, used groups of forest visitors (45 individuals)

who toured seven 0.10 acre plots with a variety of stand structures.

Results showed these visitors classified tree size, age, and canopy

cover differently on real plots than from SVS images. Responses

from both field visits and image viewing also differed from commonly used metrics output from the FVS program.

In recent years computer graphic simulation of forests has

become more common, with uses varying from prototyping

harvest equipment (Block and Fridley 1990) to animated

“walk-throughs” of a forest environment (House and others

1998). Computer visualizations can range from the tree to

landscape level, and from the abstract to near photographic

quality. Several authors have noted the potential of computerized visualizations of forests to mislead as well as inform

(McQuillan 1998; Wilson and McGaughey 2001), a problem

that may be exacerbated when the goal of the visualization

is to produce an illusion of the actual environment (Orland

and Uusitalo 2001).

A substantial body of research on the use of computer

graphics, simulation, and photography exists in fields such

as landscape architecture, psychology, and urban and regional planning, but little is known about the effectiveness

of visualization for forestry applications. In particular, uses

of visualization specific to forestry, such as illustrating

different forest types or demonstrating the effects of silvicultural treatments, have had little research. This research

addresses one aspect of a forestry-related application, the

use of images to represent classified forest structure.

Although it has been argued that all forest simulations are

rhetorical in nature (Luymes 2001), compared to some

Classifying Stand Structure: A Comparison

of SVS Images with Plot Visits and

FVS-Generated Metrics

T. M. Barrett

F. G. Schurr

K. L. O’Hara

applications of virtual environments, users of virtual forest

representations may be more likely to be trying to provide

impartial information (Orland and Uusitalo 2001). A computer visualization to illustrate forest type or structure

could be characterized as this type of use; the image creator

may be trying to convey a particular piece of information

rather than achieve a preferred aesthetic reaction. This can

be especially useful for communicating to a lay audience that

has no experience with technical forestry terms such as

basal area, canopy cover, even-aged, seed tree, or shelterwood.

One program that has been used to convey information

about forest type and structure is the Stand Visualization

System (McGaughey 1998).

The Stand Visualization System, or SVS, is probably the

most commonly used visualization program for forestry in

the United States. Since the late 1990s, images generated by

the program have appeared in journals, conference proceedings, forest management plans and assessments, and numerous presentations. The program creates graphical images of trees and forest stands (fig. 1 to 3) using input of

individual tree species, diameter, height, crown width, crown

ratio, and tree location. The SVS program can also generate

tree location, and additional capabilities include the ability

to portray understory vegetation, snags, and logs.

A general question that may interest SVS users is “How

well do observers interpret the images?” Because of the

frequent use of SVS in illustrating forest structure (the

physical spatial distribution of tree vegetation), one indicator that might be useful in answering this question would be

to know if people classify SVS images similarly to how they

classify actual forested areas. To date, no research has been

done on this question. This paper reports the results of one

case study addressing this question. We surveyed three

groups of visitors to a research forest in fall 2000. The

visitors answered identical sets of questions about forest

structure for SVS images and for real plots. Although results

should not be extrapolated to a larger population or other

forests, it is hoped that managers will find this case study

useful in understanding some of the issues related to using

a data-driven visualization program to convey information

about forest structure.

Methods _______________________

Blodgett Forest, a 4,600-acre research forest for University of California-Berkeley, is located in the 4,000 to 5,000

feet elevation mixed-conifer region of the west slope Sierra

Nevada. The forest is used for research and demonstration,
32 USDA Forest Service Proceedings RMRS-P-25. 2002

Barrett, Schurr, and O’Hara Classifying Stand Structure: A Comparison of SVS Images with Plot Visits and FVS-Generated Metrics

and small groups of visitors occasionally stay at the forest

and use the conference center for meetings. The forest was

chosen for this case study because of the combination of

visitor use and silvicultural demonstration sites.

Seven 0.10 acre permanent plots from the forest’s continuous forest inventory (CFI) system were selected for use in the

survey, based on proximity to the visitor center. Although in

mature second-growth mixed conifer forest, the seven plots

provided some diversity of structure: two of the plots were in

stands managed as a shaded fuel break, two plots were in

unmanaged stands, and three plots were in stands that have

been managed using single tree selection.

Data for each plot were put into the 6.21 version of the

West Side Sierra Nevada variant of the Forest Vegetation

Simulator (FVS) (Dixon 1994; Wykoff and others 1982).

Measured values input into the program were tree species,

diameter, height, and live crown ratio; trees smaller than

4.5 inches dbh were not included. The FVS program was

used to estimate maximum crown width; this estimation

procedure uses a linear function of diameter, with speciesspecific coefficients. Using random spatial placement of

trees, the output from FVS was converted into input for the

Stand Visualization System 3.31 (McGaughey 2001) to

create images.

The SVS default parameters for coloring and branch

placement were used for all species, with the default ground

Figure 1—Tenth acre SVS image of plot 7. Figure 2—Tenth acre SVS image of plot 2 with logs.

surface. Tenth-acre images were created using a perspective

view and overhead view (fig. 1). Viewer azimuth, elevation,

and distance, lens focal length and aspect were kept constant for each 0.10 acre image. One plot, which contained

several large logs, had an additional image created with

graphical logs (fig. 2). Half-acre images, which duplicated

each individual tree five times, included perspective, profile,

and overhead views (fig. 3) and were only created for four of

the plots. Both types of images included four orange corner

posts scaled at 10 feet high and 2 feet in diameter. Each

image also included a written description of the size of the

plot and the diameter and height of the four orange corner

posts.

In fall 2000, three groups of visitors to the forest participated in the survey: 14 people attending a mycology meeting, 17 people finishing their Forest Service silviculturist

certification, and 14 people on a field trip for a college-level

silviculture class. Each group viewed a 20-minute PowerPoint

presentation of 12 SVS images: seven 0.10 acre images (one

for each plot), a 0.10 acre image of one plot with logs, and four

0.50 acre images (for four of the seven plots). For each image,

participants were asked questions on the tree size classification of the plot, whether the plot was even-aged or unevenaged, the canopy cover class of the plot, and whether the

image represented “old forest.” The questions on the survey

were:
USDA Forest Service Proceedings RMRS-P-25. 2002 33

Classifying Stand Structure: A Comparison of SVS Images with Plot Visits and FVS-Generated Metrics Barrett, Schurr, and O’Hara

tem (Mayer and Laudenslayer 1988). Two departures from

the system—(a) using average diameter instead of quadratic

mean diameter for size class and (b) using the term “unevenaged” instead of “multistory” —were made to increase participants’ understanding of the questions.

These questions were read aloud to the participants to

control the pacing of the survey; the 12 images were visually

separated from each other by showing a blank white screen

for a few seconds. Participants were not informed that some

of the images were created from the same plot data. Participants were verbally reminded of the size of each visualized

plot (0.10 or 0.50 acres), the size of the orange corner posts,

and to estimate canopy cover as “the percentage of the gray

square covered by tree canopy.”

Following the indoor presentation, each group was given

a guided walk to the set of forested plots; plots were separated by about 3 minutes walking time and screened from

each other by intervening forest. Trees on the plot had

trunks circled with yellow loggers tape, to provide a visual

cue separating the plots from surrounding forest. At each

plot location, participants answered the same set of questions that had been given for the SVS images. Time on the

plot was longer than in viewing the images, due to the

logistics of moving a group on and off a small plot with trees.

In addition, participants were free to move about the plot

and view it from different angles, whereas a single viewpoint

was presented in each SVS image. Although each group

viewed images and visited plots in the same order, the

arrangement of SVS images did not correspond with the

order of plot visitation.

Visitor estimates were compared with modeled and observed plot descriptors. Although not used for comparison

against visitor estimates, for additional information canopy

cover for each plot was also estimated with the FVS program

(table 1). The FVS canopy cover estimate uses the method

described by Crookston and Stage (1999) and accounts both

for overlap and for off-plot trees. Canopy cover was also

taken with a GRS densitometer using a grid within each plot

of 61 points spaced at 7 foot intervals. This field measurement included only canopy within plot boundaries, using

only trees whose stems were on the plot, to correspond to the

way participants were asked to estimate canopy cover.

Other plot level attributes, such as quadratic mean diameter, were also estimated with the FVS program (table 1).

Figure 3—Half acre image of plot 6.

1. Which of the following best describes this image?

a. Seedlings (average tree diameter less than 1”)

b. Saplings (average tree diameter between 1” and 6”)

c. Poles (average tree diameter between 6” and 12”)

d. Medium (average tree diameter between 12” and 24”)

e. Large (average tree diameter greater than 24”)

2. Which of the following best describes this image?

a. Even-aged

b. Uneven-aged

3. Which of the following best describes this image?

a. Sparse (10% to 24% canopy cover)

b. Open (25% to 39% canopy cover)

c. Medium (40% to 59% canopy cover)

d. Dense (60% to 100% canopy cover)

4. Please give your best estimate of the percent

canopy cover: ___________

5. Would you describe this image as representing

“old forest”? “Old forest” contains old, large trees.

____________ (Yes / No)

The first three questions correspond to the California Wildlife Habitat Relationship (CWHR) forest classification sysTable 1—Plot characteristics calculated with FVS (West-side Sierra Nevada variant).

Trees Stand Quadratic

per density mean Average Diameter Canopy

Plot acre Basal area index diameter diameter range covera CWHRb

ft 2/acre - - - - - - - - - - - - - inches - - - - - - - - - - - - - percent

1 60 258 314 28.1 26.7 13–36 53 5M

2 140 491 623 25.5 22.6 7–42 73 7M

3 80 218 290 22.4 21.3 12–32 50 5M

4 120 196 289 17.3 15.7 7–29 43 4M

5 180 219 342 14.9 13.6 6–24 54 6M

6 250 177 308 11.4 10.0 5–25 50 4M

7 320 185 334 10.3 9.1 5 –31 58 4M

a

Calculated with overlap correction (Crookston and Stage 1999)

b

CWHR class as calculated by FVS’s California Spotted Owl WHR postprocessor (VanDyck 2001)
34 USDA Forest Service Proceedings RMRS-P-25. 2002

Barrett, Schurr, and O’Hara Classifying Stand Structure: A Comparison of SVS Images with Plot Visits and FVS-Generated Metrics

Results ________________________

Canopy Cover

For all seven plots, the average canopy estimate from

participants for the 0.10 acre SVS image was lower than the

densitometer estimate (table 2). The discrepancy varied

from 14 to 29 percent, with an average difference of 21

percent across the seven plots. The densitometer should give

a relatively unbiased estimate of canopy cover compared to

most field instruments because it takes a single reading at

a vertical angle rather than using oblique angles. However,

61 points per plot result in only moderate precision (table 2).

The densitometer estimate gave canopy estimates higher

than for the SVS graphical method, even though a densitometer point that hit sky was not considered canopy regardless of whether it fell within the limits of maximum crown

width for the tree.

Mean estimates of canopy for the 0.10 acre images were

lower than for the corresponding 0.50 acre images for three

of the four plots; it should be expected that estimates for 0.10

acre images would be lower because the percentage of canopy

that falls outside of plot boundaries is higher for small plots.

The mycologist group, silviculture group, and the students

had similar estimates for canopy cover (fig. 4); the average

difference between group means of canopy cover was less

than 3 percent for both SVS images and the actual plots.

The participants’ mean field estimates of canopy cover

were higher than their estimates from the 0.10 acre images

for six of the seven plots. Small saplings on the plot that

weren’t included on the images, and overlapping canopy

from surrounding forest, could contribute to this, although

participants were asked to exclude these factors from their

estimates in the field. The participants’ estimates of canopy

cover for the actual plots in the field appeared to be at least

as variable as their estimates of canopy cover for the SVS

images (table 2).

Table 2—Mean canopy cover estimates by visitors for images (0.10 acre plots and 0.50 plots) and field visits

Survey responses

SVS 0.10 image SVS 0.50 image Field estimate Densitometer

Plot (with st. dev.) (with st. dev.) (with st. dev.) n (with 90% CI) SVS 0.10 acrec

- - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - -

1 40 (8) —a 46 (9) 45 64 (10) 54

2 46 (10) 71 (9) 71 (12) 45 64 (10) 55

3 29 (10) 41 (9) 36 (8) 44 44 (10) 41

4 28 (9) 40 (8) 42 (10) 44 56 (10) 34

5 46 (8) — 41 (11) 31b 61 (10) 56

6 45 (11) 43 (9) 67 (16) 45 66 (10) 50

7 48 (7) — 55 (15) 45 77 (9) 55

aPlots 1, 5 and 7 did not have a corresponding 0.50 acre image

b

One group did not make a field visit to plot 5.

c

Canopy cover from SVS image estimated with the graphical method without subplot

Figure 4—Means and standard deviations of canopy cover estimates for 11 SVS images

by the three groups of visitors.

Silviculturists Students Mycologists

Images for the seven plots

1 2 2 3 3 4 4 5 6 6 7

80

70

60

50

40

30

20

10

0

Canopy cover (%)
USDA Forest Service Proceedings RMRS-P-25. 2002 35

Classifying Stand Structure: A Comparison of SVS Images with Plot Visits and FVS-Generated Metrics Barrett, Schurr, and O’Hara

Plot 2 was used to create two 0.10 acre images, one with

logs (fig. 2) and one without; these two images had different

spatial placement of trees. All 45 participants estimated

higher canopy cover for the version with logs; the mean of

this difference was 26.3 percent. Estimates for the version

with logs were much closer to the estimates made in the field,

with a mean difference of less than 2 percent.

Because smaller image sizes have more canopy that falls

outside the graphical plot boundaries (fig. 2 and 3) one would

expect a greater correlation between canopy cover class

responses for the actual plots and for the 0.50 acre images

than between canopy cover class responses for the actual

plots and for the 0.10 acre images. This was the case, with

53 percent of 180 responses matching for the 0.50 acre

images and 36 percent of 300 responses matching for the

0.10 acre images.

The SVS program can compute the graphical cover for

each image. Comparing this to participants’ responses for

canopy cover classes, the overall percentage of responses

that were correct was 64 percent for the 0.10 acre images and

66 percent for the 0.50 acre images. For individual plots, this

percentage varied from a low of 9 percent correct for the 0.10

acre image for plot 4 to a high of 93 percent correct for the

0.50 acre image of plot 2. For the 169 of 480 cover class

responses that were incorrect, 92 percent assigned the

image to a smaller-than-actual cover class, and 8 percent

assigned the image to a larger-than-actual cover class.

Table 3—Participants’ responses for size class of trees: 0.10 acre image compared to

actual plot visit.

Visitor image estimate Visitor field estimate

larger than visitor larger than visitor

Plot Agreement field estimate image estimate n

- - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - -

1 61 7 32 44

2 47 2 51 45

3 53 9 58 45

4 58 7 36 45

5 52 3 45 31

6 40 2 58 45

7 47 36 18 45

Table 4—Participants’ responses for size class of trees: 0.50 acre image compared to

actual plot visit

Visitor image estimate Visitor field estimate

larger than visitor larger than visitor

Plot Agreement field estimate image estimate n

- - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - -

2 11 4 84 45

3 44 0 56 45

4 25 7 68 45

6 56 13 31 45

Size Class

There were large differences between estimated tree size

class for SVS images and in the field. For the 0.10 acre

images, the percent of responses that were identical for

the image and the real plot varied from 40 to 61 percent

(table 3). For six of the seven plots, individuals classified the

image as having smaller trees than the real plot. This

tendency was even stronger for the 0.50 acre images; for one

plot, 84 percent of individuals classified the real plot as

having larger trees than the image (table 4).

Based on quadratic mean diameter (qmd), two of the plots

would be in the large size class, three plots would be in the

medium size class, and two plots would be in pole size class

(table 1). Several of the plots had qmds or average diameters

that were near the division point for size class. Of all field

responses, 70 percent would have placed plots in categories

the same as would the qmd method, ranging from 41 to 94

percent for the different plots. Of all responses for the SVS

0.10 acre images, 56 percent would have placed plots in the

same category as the qmd method, ranging from 36 to 69

percent for the different plots.

As with canopy cover, there were differences in responses

between the two SVS images with and without the inclusion

of logs. While 51 percent of individuals placed the SVS 0.10

acre image for plot 2 in a smaller size category than they did

for the real plot, only 22 percent placed the 0.10 acre image
36 USDA Forest Service Proceedings RMRS-P-25. 2002

Barrett, Schurr, and O’Hara Classifying Stand Structure: A Comparison of SVS Images with Plot Visits and FVS-Generated Metrics

with logs in a smaller size class. The number of people

placing the image in a larger size class than the real plot

increased from 2 to 6 percent with the inclusion of logs. In

addition to the inclusion of logs, the two images had differences in the spatial placement of trees, and this could also be

a possible cause for differences in responses.

Structure: Even-Aged or Uneven-Aged

A total of 77 percent of responses showed identical

classification of structure (even-aged or uneven aged) for

the 0.10 acre image and the actual plot. The proportion

varied somewhat by plot but was similar between the 0.10

acre images and the 0.50 acre images. The type of discrepancy – whether calling the image uneven-aged and the real

plot even-aged, or calling the image even-aged and the real

plot uneven-aged – varied by plot. For example, 42 percent

of visitors called the SVS image shown in figure 3 evenaged but the real plot uneven-aged; and only 4 percent of

visitors called the image uneven-aged and the real plot

even-aged. In contrast, 11 percent of visitors called the SVS

image shown in figure 1 even-aged but the real plot unevenaged; but 33 percent of visitors called the image unevenaged and the real plot even-aged.

Adding logs to the 0.10 image of plot 2 appeared to have no

effect on the proportion of people who called the stand evenaged or uneven-aged.

Forest Age

Dominant trees in the forest surrounding Blodgett are

generally around 80 to 120 years old. The percent of responses for whether the real plots were “old forest” ranged

from 4 to 76 percent for the seven plots, perhaps indicating

the variety of forest structure present. For these visitors,

whether they answered identically for the real plot and the

SVS image, appeared to vary by plot and image size (fig. 5).

With the inclusion of logs in the 0.10 acre image for plot 2, the

percent of people calling the plot (but not the image) old

decreased from 22 to 4 percent, and the proportion of people

calling the image (but not the plot) old increased from 9 to 18

percent.

The three groups differed in responses to the question

about whether the plot was “old forest.” Fewer silviculturists

called plots old forest than students or mycologists (18

percent of all responses compared to 34 and 33 percent,

respectively). Participants were also asked whether they

considered themselves forestry professionals, and about

half of the respondents answered yes. Fewer of those who

responded affirmatively called real plots “old forest” (12

percent compared to 33 percent), and fewer called SVS

images “old forest” (25 percent compared to 38 percent).

Complete Classification System

The California WHR classification system includes size

class, canopy cover, and evenness of layering. When all

three of the questions related to this classification system

are included, for the plot with the greatest correspondence,

only 32 percent of responses were identical for the 0.10 acre

SVS images and the real plot. For the plot with the worst

correspondence, only 4 percent of the responses were identical.

Discussion _____________________

Tree size class, evenness of structure, and canopy cover

are common elements of many classification systems, just as

they are of the California WHR system used in this study. Of

these three elements, tree size class had the greatest discrepancy between participants’ answers for the field visit

and the SVS image.

Judging average diameter or qmd for a plot can be difficult, particularly for irregular or uneven size stands. Participants’ field responses identical to an “average diameter”

method of tree size classification were 69, 16, 51, 82, 94, 41,

and 76 percent for the seven plots, respectively. Participants’ field responses corresponding to CWHR tree size class

metrics generated by the FVS postprocessor (VanDyck 2001)

were 69, 84, 49, 82, 94, 59, and 13 percent for the seven plots,

Figure 5—Discrepancies for responses for whether images and real plots

were “old forest.”

0.6

0.4

0.2

0

Percentage of visitors

Plots

2 (qmd=25.4") 3 (qmd=22.3") 4(qmd=17.3") 6 (qmd=11.4") 

0.10 acre image is not “old forest”, but real plot is

0.50 acre image is not “old forest”, but real plot is

0.10 acre image is “old forest”, but real plot is not

0.50 acre image is “old forest”, but real plot is not
USDA Forest Service Proceedings RMRS-P-25. 2002 37

Classifying Stand Structure: A Comparison of SVS Images with Plot Visits and FVS-Generated Metrics Barrett, Schurr, and O’Hara

respectively. If real plots are difficult to classify “correctly,”

it is not surprising that there would be a variety of responses

for size class of images, where tree trunks may be only a few

pixels wide and where some trees may be hidden behind

others. However, although the difficulties of estimating size

class for both real plots and images might lead to a large

amount of noncorrespondence, it would not explain systematic differences in classification. Overall, 45 percent of the

525 responses gave a smaller size class to the images than to

the real plots, and only 8 percent gave a larger size class to

the image.

This research was intended as a pilot study, to identify

questions for further research and to estimate sample sizes

needed to answer such questions. Because this was a

noninferential case study, we cannot assume that misjudging

the size class of trees in SVS images is a typical problem for

other populations or other forests. But in some ways, these

participants may have been better prepared to correctly

interpret the size class of trees in SVS images than would be

typical for many viewers. As forestry professionals, forestry

students, or mycologists, the participants of this survey could

be expected to be more familiar with trees than would be

typical of the general public. In addition, the images contained a visual clue to tree size in the four corner posts, and

the attention of the participants was directed to these posts

both verbally and with written messages on each image.

It would be tempting to dismiss as an anomaly the one plot

where more participants thought the tree size on the SVS

image was larger than for the real plot. However, some

characteristics of the structure on this plot may have led to

this difference. This plot had a single large tree – 30 inches in

diameter and 140 feet tall – and a fairly dense understory of

smaller trees. In the SVS image, as can be seen in figure 1,

this single large tree is quite noticeable. On the plot, however, the upper portion of this tree is obscured by the

understory trees, and only the trunk is clearly visible. It is

possible that the complexity of what influences people’s

classification of forest structure, both on the ground and in

computer generated images, will make it difficult to draw

simple generalizations about the relationship between the two.

The differences in estimation of canopy cover, size class,

and age for the two 0.10 acre images of plot 2 – with and

without the inclusion of logs – was a surprising but interesting result. A few of the other plots also had logs and branches

on the forest floor, along with understory vegetation, or

small trees that were not included in the SVS images. How

the inclusion of these elements affects people’s interpretation might prove a fruitful topic of further research. In

addition, different spatial placement of trees in the images,

the effect of distance and angle of view, and different choices

for foliage or branches were not tested in this project, but

could affect interpretation. Given the increasing use of

computerized visualization tools, further research in this

area would be helpful.

The general question facing SVS users may be “How well do

observers interpret the images?” This research only addresses

a narrow aspect of this question, by looking at the similarity

of classifications of forest structure for images and real plots.

There are a number of other important questions that researchers might consider for future work: Do images effectively communicate the results of silvicultural treatments?

Can images be used to illustrate the dynamic nature of forest

structure? Do they help individuals have a greater understanding of the variation among forest types?

Many of the visualization programs being developed for

forestry are data-driven; in other words, the sizes of images of

trees are determined through actual forest inventory measurements. It is important for managers and researchers to communicate to the public that this data-based aspect of visualization

programs does not guarantee similarity between the images

and the actual forest. Managers who are using computer

visualization programs to illustrate the type of structures that

were, are, or will be on their forests may wish to make sure that

they consider this question: Is your audience interpreting the

information in the image as you intended?

Acknowledgments ______________

The authors thank the surveyees for participating, Blodgett

Research Forest for providing the survey sites, Rebecca

Richards for help with the survey design, and Jane Kapler

Smith, Robert McGaughey, and Klaus Barber for reviews of

a draft manuscript. This research was supported by the

University of Montana and the Forest Inventory and Analysis Program, Pacific Northwest Research Station, USDA

Forest Service.

References _____________________

Block, W. A.; Fridley, J. L. 1990. Simulation of forest harvesting

using computer animation. Transactions of the Amer. Soc. of Ag.

Engineers 33(3):967-975.

Crookston, N. L.; Stage, A. R. 1999. Percent canopy cover and stand

structure statistics from the forest vegetation simulator. Gen.

Tech. Rep. RMRS-GTR-24. Fort Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Research Station. 11 p.

Dixon, G. 1994. Western Sierra Nevada prognosis geographic variant of the forest vegetation simulator. Fort Collins, CO: WO-TM

Service Center, USDA Forest Service. 35 p.

House, D. H.; Schmidt, G. S.; Arvin, S. A.; Kitagawa-DeLeon, D.

1998. Visualizing a real forest. IEEE Computer Graphics and

Engineering 18(1):12-15.

Luymes, D. 2001. The rhetoric of visual simulation in forest design:

some research directions. In: Sheppard, S. R. J. and Harshaw, H.

W., eds. Forests and landscapes, linking ecology, sustainability

and aesthetics. New York, NY: CABI Publishing: 191-204.

Mayer, K. E.; Laudenslayer, Jr., W. F. (eds.). 1988. A guide to

wildlife habitats of California. Sacramento, CA: Cal. Dept. of

Forestry and Fire Protection. 166 p.

McGaughey, R. J. 1998. Computerized tools for visualizing the

impact of silviculture on multiple resources. In: Proceedings of

the SAF 1997 National Convention, 1997 October 4-8, Memphis,

TN; Bethesda, MD: Society of American Foresters: 192-199.

McGaughey, R. J. (2001, Sept. 27 – last update). SVS – Stand

Visualization System, [Online]. Available: http://www.fs.fed.us/

pnw/svs/ [March. 8, 2002].

McQuillan, A. G. 1998. Honesty and foresight in computer visualizations. Journal of Forestry 96(6):15-16.

Orland, B.; Uusitalo, J. 2001. Immersion in a virtual forest – some

implications. In: Sheppard, S. R. J. and Harshaw, H. W., eds.

Forests and landscapes, linking ecology, sustainability and aesthetics. New York, NY: CABI Publishing: 191-204.

VanDyck, M.G. 2001. User’s guide to the post processors for the

forest vegetation simulator. Fort Collins, CO: USDA Forest

Service, Forest Management Service Center.

Wilson, J. S.; McGaughey, R. J. 2000. Landscape-scale forest information: what is sufficient and what is appropriate. Journal of

Forestry 98:12:21-27.

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:

USDA Forest Service, Intermountain Research Station. 112 p.
38 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Erin E. Smith-Mateja is a Forester for the Forest Management Service

Center, USDA Forest Service, 2150 Centre Ave. Bldg. A, Suite 343, Fort Collins,

CO 80526. Carl W. Ramm (deceased) was Professor of Forest Biometric,

Department of Forestry, Michigan State University, East Lansing, MI 48824.

Abstract—Validating a model’s performance is an essential part of

model development and revision. Previously, the only subregional

validation of LS-TWIGS and FVS-LS was done on hardwood stands

on 5- and 10-year growth. This paper examines validation results for

red pine in Michigan. Red pine covers a large percentage of Michigan timberlands and is considered one of its most important commercial softwood species. Validation was run on two red pine sites

in Michigan. In simulations that included calibration (scaling),

FVS-LS almost always predicted d.b.h. within ±1 inch for projections up to 27 years long. FVS-LS predicted mortality and density

better for the Upper Peninsula site than the Lower Peninsula site.

The red pine timber industry is an important part of the

Michigan economy. In 1992 red pine production was valued

at $25.3 million (Potter-Witter 1995). Red pine covers 897,200

acres of Michigan timberland, 641,200 acres in the Lower

Peninsula, and 256,000 acres in the Upper Peninsula

(Leatherberry and Spencer 1996). Average net annual growth

(1980 through 1987) was 78,310,000 ft3

 and removals were

15,980,000 ft3 (Leatherberry and Spencer 1996).

Given the importance of red pine in the Lake States it is

important that landowners have a model that correctly

predicts growth to compare alternative treatments. The

Lake States TWIGS variant of the Forest Vegetation Simulator (FVS-LS) (Bush and Brand 1995) uses the growth and

mortality functions based on Lake States TWIGS. One

important difference is that Lake States TWIGS uses annual increments, while FVS-LS calculates 10-year growth

by multiplying the annual increment by 10 and then scaling

back if the growth period is less than 10 years. FVS-LS is an

individual tree distance independent model. This paper will

explore FVS-LS prediction of individual tree and stand level

attributes on two long-term red pine study sites in Michigan.

The only subregional validation of Lake State TWIGS for

Michigan involves upland hardwoods in the northern Lower

Peninsula (Guertin and Ramm 1996). It found 5-year diameter growth was predicted within ± 0.3 inch for the five

species studied. Mean errors for basal area projections were

Validation of the Forest Vegetation

Simulator Growth and Mortality Predictions

on Red Pine in Michigan

Erin E. Smith-Mateja

Carl W. Ramm

within ± 5 ft2/acre for all species and mean error for trees per

acre (TPA) was within ± 20 TPA for almost all species

studied. A followup study 5 years later on FVS-LS found that

10-year diameter growth was predicted within ± 0.5 inch

for all of the seven hardwood species (Canavan 1997). This

study is the first to validate red pine projections for Michigan.

Data __________________________

The validation data were collected from two long-term

study sites in Michigan, one on the Hiawatha National

Forest and one on the W. K. Kellogg Experimental Forest.

The Hiawatha National Forest is on the eastern side of the

Upper Peninsula. The Kellogg Experiment Forest, on the

Lower Peninsula in southwestern Michigan, is owned and

managed by Michigan State University’s Department of

Forestry.

The Hiawatha site was planted in 1938 with approximately 6 by 6 foot spacing. In 1962 it became a thinning

study set up as a randomized complete block design with

four blocks. Each block contained 16 treatments, with each

treatment being 0.10 acre (table 1). This study examined 12

of the treatments. Measurements were taken in 1962, 1965,

1969, 1976, 1982, and 1992 and thinned in 1962, 1969, 1976,

1982, and 1992, except for a few plots in which basal areas

were too low to thin in 1982 and 1992.

The Kellogg study site was planted in 1936 and 1937

with approximately spacing of 7 by 8 feet. In 1960 it

became a thinning study with nine thinning treatments

applied in a randomized complete block design with four

replications of each treatment (table 2). These treatments

Table 1—Hiawatha National Forest thinning treatment.

Treatment Description

BAr30 30 ft2/acre residual BA after thinning

BAr45 45 ft2/acre residual BA after thinning

BAr60 60 ft2/acre residual BA after thinning

BAr80 80 ft2/acre residual BA after thinning

BAr100 100 ft2/acre residual BA after thinning

BAr130 130 ft2/acre residual BA after thinning

BAr160 160 ft2

/acre residual BA after thinning

Row2 Every other row removed

Row3 Every third row removed

Row4,2 Every fourth row removed; at second thinning

remove center row of remaining 3 rows

Row4 Every fourth row removed

Control Control - no thinning
USDA Forest Service Proceedings RMRS-P-25. 2002 39

Validation of the Forest Vegetation Simulator Growth and Mortality Predictions on Red Pine in Michigan Smith-Mateja and Ramm

were also 0.10 acre each. Measurement and thinning

occurred in 1960, 1964, 1967, 1972, 1980, 1985, and 1991.

For both study sites diameter at breast height (d.b.h) of

each tree was measured, and height was measured on three

to five trees in each plot. Basal area and trees per acre were

calculated for each plot.

Methods _______________________

Two types of simulations were run for each treatment.

One with FVS calibration turned off, abbreviated ‘NA’ for no

ancillary data, meaning that past diameter growth data

were not used to scale the growth equations. The other type

of simulation included FVS calibration, abbreviated ‘D’. All

runs included plot name, age, site index (base age 50 years),

d.b.h. (1965, Hiawatha; 1964, Kellogg), height if measured,

and the year the tree was thinned. The ∆D simulations also

included diameter growth, which was calculated from the

diameter from the simulation start date and subtracting the

previous diameter measurement. The Hiawatha growth

measurements were from age 25 to 28, and Kellogg growth

measurements were from age 27 to 31. Hiawatha had a 3-

year growth measurement and Kellogg a 4-year growth

measurement period that FVS used to scale or calibrate the

growth equations to more closely match the growth that was

occurring in the plantation at that time. Three growth and

stand-level characteristics were used to calculate prediction

error by treatment and projection length. The three characteristics used were individual tree d.b.h., stand basal area,

and trees per acre. Each tree’s projected future d.b.h. was

compared to its actual d.b.h. Also, each stand’s projected

basal area and trees per acre were compared to its actual

basal area and trees per acre. Mean error and standard

deviation were then calculated from the difference between

actual and predicted measurements. In the FVS tree list, all

trees were coded with the species code “RP” indicating red

pine plantation trees.

Results ________________________

The Hiawatha simulations projected growth from 1965

through 1992, with cycle boundaries at 1969, 1976, 1982,

and 1992. The Kellogg simulations projected growth from

1964 through 1991, with cycle boundaries at 1967, 1972,

1980, 1985, and 1991.

D.B.H. Error

The sample size for each d.b.h. error calculation was

dependent on the treatment and cycle. Treatments with

heavy thinning had smaller sample sizes. Sample size also

decreased over time as trees were thinned during each cycle

(tables 3 and 4).

Hiawatha—FVS-LS on average predicted diameter

growth with better accuracy for the ∆D simulations than for

the NA simulations (table 3). For both types of simulations

FVS-LS overpredicted diameter growth, except in treatments with low residual basal area. Both simulation types

showed low mean errors (less than 1 inch) in almost all of the

treatments. Those treatments that did have an error over 1

inch were all in the 27-year projection period and tended to

be in treatments with larger residual basal areas. No treatment had an error greater than 1.78 inches in d.b.h. In

almost all the runs, as the length of the projection increased

so did the error.

Kellogg—Unlike the Hiawatha results, FVS did not necessarily predict diameter growth with better accuracy for

the ∆D simulations than for the NA simulations (table 4).

FVS-LS overpredicted growth for all trees in the NA projections except for the thin every fourth row treatment (row 4)

where growth was underpredicted. In both runs as projection length increased so did the error. The NA and ∆D

simulations with projections lengths of 3 and 8 years all

had a d.b.h. mean error of less than 1 inch. Almost all of the

∆D simulations had an error of less than 1 inch, except for the

longer projection lengths in the control, thin every fourth

row, and 70 ft2

/acre thinnings. Only treatments thin every

second row and thin to a basal area of 90 ft2

/acre of the NA

simulation had error of more than 2 inches. These errors

occurred in the 27-year interval.

Basal Area Error

Mean error and standard deviation were calculated for the

four plots for each treatment and projection length.

Hiawatha—∆D simulations projections were more precise than the NA simulations. The absolute mean error was

two to three times as great is the NA as in the ∆D simulations. As with the d.b.h. mean error projections, the ∆D

simulations were more likely to underpredict in treatments

with low residual basal areas, and tended to overpredict in

treatments with high residual basal areas (table 5).

Kellogg—Unlike with the Hiawatha results, in most

cases the NA simulations projected more precisely than the

∆D simulations. The ∆D projections were more likely to

underpredict basal area, while the only NA projection that

underpredicted was the thin every fourth row treatment.

The difference in the absolute mean error between the two

simulation types was not as large as that in the Hiawatha

simulations (table 6).

Trees Per Acre Error

As with the basal area calculations of mean error, the TPA

mean error is based on a sample size of four for each

treatment.

Table 2—W.K. Kellogg Forest thinning treatments.

Treatment Description

Row2 Cut every 2nd row, BA 85

Row3 Cut every 3rd row, BA 100,105

Row4 Cut every 4th row, BA 115, 120

Row4,2 Cut every 4th row, middle row, BA 110

BAr90 90-95 ft2

/acre residual BA after thinning

BAr70 70-75 ft2

/acre residual BA after thinning

BAr110 110-115 ft2

/acre residual BA after thinning

BAr130 130-135 ft2/acre residual BA after thinning

Control Control – no thinning
40 USDA Forest Service Proceedings RMRS-P-25. 2002

Smith-Mateja and Ramm Validation of the Forest Vegetation Simulator Growth and Mortality Predictions on Red Pine in Michigan

Table 3—Hiawatha mean error ( e ) and standard deviation (s) of mean error for estimated diameter

at breast height by treatment and projection length for the two types of simulation. Error

expressed as predicted value minus observed value.

NA simulation ∆D simulation

 Treatment Projection length Age n e s e s

BAr30 65-69 = 4yrs 32 59 –0.52 0.23 –0.47 0.27

65-76 = 11yrs 39 30 –0.77 0.30 –0.78 0.51

65-82 = 17yrs 45 21 –0.99 0.45 –0.97 0.74

65-92 = 27yrs 55 21 –0.59 0.62 –0.58 1.02

BAr45 65-69 = 4yrs 32 85 –0.28 0.25 –0.44 0.73

65-76 = 11yrs 39 46 –0.48 0.50 –0.37 0.49

65-82 = 17yrs 45 29 –0.91 0.62 –0.80 0.61

65-92 = 27yrs 55 29 –0.26 2.59 –0.11 2.56

BAr60 65-69 = 4yrs 32 121 –0.05 0.57 –0.04 0.56

65-76 = 11yrs 39 71 –0.26 0.97 –0.29 0.96

65-82 = 17yrs 45 47 –0.54 1.40 –0.60 1.42

65-92 = 27yrs 55 47 –0.30 1.72 –0.41 1.76

BAr80 65-69 = 4yrs 32 173 0.13 0.22 0.05 0.21

65-76 = 11yrs 39 110 0.07 0.40 –0.15 0.36

65-82 = 17yrs 45 73 –0.26 0.47 –0.56 0.45

65-92 = 27yrs 55 58 –0.24 0.63 –0.65 0.59

BAr100 65-69 = 4yrs 32 220 0.19 0.17 0.12 0.17

65-76 = 11yrs 39 146 0.28 0.31 0.08 0.34

65-82 = 17yrs 45 105 0.33 0.77 0.04 0.82

65-92 = 27yrs 55 84 0.62 1.51 0.22 1.55

BAr130 65-69 = 4yrs 32 302 0.29 0.38 0.17 0.38

65-76 = 11yrs 39 218 0.51 0.58 0.18 0.58

65-82 = 17yrs 45 162 0.53 0.97 0.08 0.98

65-92 = 27yrs 55 130 0.68 1.25 0.08 1.27

BAr160 65-69 = 4yrs 32 395 0.38 0.54 0.20 0.54

65-76 = 11yrs 39 305 0.73 0.71 0.31 0.72

65-82 = 17yrs 45 241 0.89 1.00 0.32 1.03

65-92 = 27yrs 55 200 1.27 1.37 0.53 1.41

Row2nd 65-69 = 4yrs 32 231 0.19 0.29 0.08 0.29

65-76 = 11yrs 39 138 0.26 0.53 –0.03 0.53

65-82 = 17yrs 45 96 0.21 0.73 –0.19 0.73

65-92 = 27yrs 55 79 0.56 1.54 0.00 1.53

Row3rd 65-69 = 4yrs 32 332 0.31 0.23 0.13 0.23

65-76 = 11yrs 39 212 0.56 0.47 0.10 0.49

65-82 = 17yrs 45 158 0.61 0.67 –0.03 0.72

65-92 = 27yrs 55 129 1.16 2.12 0.32 2.13

Row4,2 65-69 = 4yrs 32 331 0.36 0.46 0.19 0.45

65-76 = 11yrs 39 224 0.71 1.09 0.28 1.10

65-82 = 17yrs 45 159 0.97 1.75 0.36 1.80

65-92 = 27yrs 55 129 1.55 2.60 0.73 2.69

Row4th 65-69 = 4yrs 32 366 0.35 0.40 0.16 0.41

65-76 = 11yrs 39 218 0.69 0.82 0.17 0.87

65-82 = 17yrs 45 161 0.71 1.15 0.00 1.23

65-92 = 27yrs 55 118 1.06 1.92 0.10 2.03

Control 65-69 = 4yrs 32 470 0.36 0.33 0.18 0.34

(no thin) 65-76 = 11yrs 39 470 0.76 0.49 0.38 0.52

65-82 = 17yrs 45 470 0.95 0.77 0.54 0.81

65-92 = 27yrs 55 470 1.78 2.25 1.51 2.26
USDA Forest Service Proceedings RMRS-P-25. 2002 41

Validation of the Forest Vegetation Simulator Growth and Mortality Predictions on Red Pine in Michigan Smith-Mateja and Ramm

Table 4—Kellogg mean error ( e ) and standard deviation (s) of mean error for estimated diameter

at breast height by treatment and projection length for the two types of simulation. Error

expressed as predicted value minus observed value.

NA simulation ∆D simulation

 Treatment Projection length Age n e s e s

BAr70 64-67 = 3yrs 31 150 0.16 0.76 -0.35 0.77

64-72 = 8yrs 36 150 0.31 1.64 –0.52 1.87

64-80 = 16yrs 44 77 1.01 3.18 –1.29 3.31

64-85 = 21yrs 49 69 1.13 3.68 –1.15 3.48

64-91 = 27yrs 55 69 1.63 4.20 –1.67 4.39

BAr90 64-67 = 3yrs 31 168 0.21 1.46 0.00 1.48

64-72 = 8yrs 36 115 0.94 2.98 0.41 2.72

64-80 = 16yrs 44 91 1.77 3.86 0.85 3.92

64-85 = 21yrs 49 83 1.92 4.40 0.84 4.48

64-91 = 27yrs 55 83 2.17 4.68 0.97 4.77

BAr110 64-67 = 3yrs 31 205 0.08 1.01 –0.10 1.02

64-72 = 8yrs 36 135 0.26 1.68 –0.23 1.71

64-80 = 16yrs 44 104 1.23 3.45 0.36 3.49

64-85 = 21yrs 49 93 1.59 4.19 0.59 4.24

64-91 = 27yrs 55 93 1.98 4.56 0.85 4.61

BAr130 64-67 = 3yrs 31 255 0.18 1.14 0.01 1.14

64-72 = 8yrs 36 184 0.61 2.10 0.18 2.09

64-80 = 16yrs 44 147 1.61 3.54 0.85 3.53

64-85 = 21yrs 49 131 1.81 4.05 0.92 4.06

64-91 = 27yrs 55 131 1.94 4.32 0.96 4.34

Row2nd 64-67 = 3yrs 31 159 0.18 1.35 –0.08 1.35

64-72 = 8yrs 36 159 0.70 2.34 0.23 2.35

64-80 = 16yrs 44 106 1.38 3.55 0.27 3.72

64-85 = 21yrs 49 85 1.64 4.37 0.35 4.48

64-91 = 27yrs 55 85 2.08 4.62 0.63 4.87

Row3rd 64-67 = 3yrs 31 194 0.02 0.64 –0.25 0.64

64-72 = 8yrs 36 122 0.07 0.97 –0.65 0.97

64-80 = 16yrs 44 98 0.83 2.85 –0.47 2.90

64-85 = 21yrs 49 81 1.34 4.02 –0.22 4.05

64-91 = 27yrs 55 81 1.49 4.30 –0.30 4.34

Row4,2 64-67 = 3yrs 31 250 0.06 0.69 –0.18 0.69

64-72 = 8yrs 36 171 0.62 1.93 0.03 1.94

64-80 = 16yrs 44 123 1.09 3.02 0.00 3.09

64-85 = 21yrs 49 109 1.22 3.51 –0.07 3.61

64-91 = 27yrs 55 109 1.40 4.12 –0.04 3.86

Row4th 64-67 = 3yrs 31 232 –0.10 0.27 –0.21 0.19

64-72 = 8yrs 36 158 –0.18 0.74 –0.47 0.56

64-80 = 16yrs 44 110 –0.42 1.21 –0.97 0.74

64-85 = 21yrs 49 86 –0.75 1.45 –1.39 0.73

64-91 = 27yrs 55 86 –0.53 2.27 –1.23 1.72

Control 64-67 = 3yrs 31 293 0.18 0.69 –0.06 0.70

(no thin) 64-72 = 8yrs 36 293 0.45 0.80 –0.10 0.82

64-80 = 16yrs 44 293 1.09 1.57 0.25 1.64

64-85 = 21yrs 49 293 1.51 2.19 0.60 2.27

64-91 = 27yrs 55 293 1.96 2.76 1.04 2.83
42 USDA Forest Service Proceedings RMRS-P-25. 2002

Smith-Mateja and Ramm Validation of the Forest Vegetation Simulator Growth and Mortality Predictions on Red Pine in Michigan

Table 5—Hiawatha mean error ( e ) and standard deviation (s) of mean error for estimated trees per acre and basal area by treatment and

projection length for two types of simulation. Error expressed as predicted value minus observed value.

BA NA BA ∆D TPA NA TPA ∆D

Treatment Projection length Age e s e s e s e s

BAr30 65-69 = 4yrs 32 –7.06 2.44 –6.46 3.19 –0.18 0.02 –0.18 0.02

65-76 = 11yrs 39 –6.75 1.92 –6.69 4.51 –0.25 0.02 –0.25 0.02

65-82 = 17yrs 45 –7.20 1.76 –6.97 4.52 –0.27 0.03 –0.27 0.03

65-92 = 27yrs 55 –5.48 3.38 –5.22 8.14 –0.43 0.04 –0.43 0.04

BAr45 65-69 = 4yrs 32 –5.32 1.71 –4.02 1.14 –0.26 0.04 –0.26 0.05

65-76 = 11yrs 39 –6.32 1.73 –5.03 0.62 –0.38 0.06 –0.38 0.06

65-82 = 17yrs 45 –9.17 2.83 –8.12 1.57 –0.37 0.05 –0.37 0.05

65-92 = 27yrs 55 –6.50 5.90 –4.85 4.48 1.91 4.96 1.91 4.96

BAr60 65-69 = 4yrs 32 –2.25 2.62 –2.05 1.65 2.14 4.97 2.14 4.97

65-76 = 11yrs 39 –6.38 4.92 –6.91 4.12 1.91 4.93 1.91 4.93

65-82 = 17yrs 45 –10.01 3.83 –10.83 5.23 1.90 4.92 1.90 4.92

65-92 = 27yrs 55 –8.57 7.15 –10.32 10.21 1.55 4.88 1.55 4.88

BAr80 65-69 = 4yrs 32 4.45 2.25 1.96 0.71 –0.52 0.02 –0.52 0.02

65-76 = 11yrs 39 1.22 5.18 –4.38 1.29 –0.91 0.04 –0.91 0.04

65-82 = 17yrs 45 –5.87 3.55 –11.87 1.40 –0.93 0.03 –0.93 0.03

65-92 = 27yrs 55 –5.68 5.29 –13.36 2.29 –1.17 0.05 –1.18 0.05

BAr100 65-69 = 4yrs 32 7.01 3.98 3.09 1.56 –0.66 0.08 –1.10 0.85

65-76 = 11yrs 39 8.92 4.51 2.46 6.43 –1.21 0.14 –1.22 0.16

65-82 = 17yrs 45 6.94 3.81 –2.11 7.99 1.16 4.94 1.14 4.92

65-92 = 27yrs 55 11.63 7.34 1.75 14.33 3.30 5.68 3.28 5.66

BAr130 65-69 = 4yrs 32 14.33 2.06 7.55 1.66 –0.94 0.06 –1.05 0.12

65-76 = 11yrs 39 21.75 4.71 6.67 2.42 0.66 4.98 0.53 4.95

65-82 = 17yrs 45 17.55 4.07 –0.05 3.63 2.90 5.79 2.80 5.81

65-92 = 27yrs 55 20.09 4.79 –1.74 7.34 2.33 5.82 2.22 5.83

BAr160 65-69 = 4yrs 32 22.74 4.87 10.83 3.55 8.66 19.89 7.86 19.74

65-76 = 11yrs 39 40.34 5.60 14.23 9.06 7.28 19.84 6.26 19.70

65-82 = 17yrs 45 41.40 10.75 10.65 15.08 9.17 24.81 8.16 24.68

65-92 = 27yrs 55 53.92 18.01 16.45 22.81 10.62 23.40 9.63 23.05

Row2nd 65-69 = 4yrs 32 6.70 1.36 2.08 0.87 1.67 4.90 1.58 4.84

65-76 = 11yrs 39 6.80 6.07 –2.15 5.18 1.34 4.97 1.31 4.94

65-82 = 17yrs 45 3.64 9.68 –6.47 8.45 1.23 4.93 1.20 4.89

65-92 = 27yrs 55 9.25 13.25 –4.45 10.91 3.33 5.58 3.30 5.54

Row3rd 65-69 = 4yrs 32 15.91 2.03 5.93 1.01 1.14 4.76 0.52 4.43

65-76 = 11yrs 39 21.86 4.09 1.37 1.47 7.61 7.62 6.97 7.18

65-82 = 17yrs 45 18.78 5.80 –5.12 3.22 6.92 7.27 6.15 6.72

65-92 = 27yrs 55 28.68 15.58 –1.19 11.69 18.17 15.14 17.23 15.31

Row4,2 65-69 = 4yrs 32 17.58 3.03 7.59 2.28 8.66 19.84 7.99 19.55

65-76 = 11yrs 39 24.27 1.25 4.92 2.75 17.65 17.88 16.68 17.60

65-82 = 17yrs 45 19.98 5.89 –1.23 6.59 28.08 34.29 31.14 30.62

65-92 = 27yrs 55 30.73 9.90 1.55 12.46 44.24 37.51 43.47 36.87

Row4th 65-69 = 4yrs 32 19.18 2.32 6.90 2.55 6.04 9.56 4.91 9.39

65-76 = 11yrs 39 24.73 3.79 1.46 6.08 17.86 11.42 17.08 11.49

65-82 = 17yrs 45 18.32 5.32 –8.47 6.46 19.90 14.78 19.02 14.87

65-92 = 27yrs 55 20.64 3.44 –10.62 8.82 24.25 16.49 23.26 16.39

Control 65-69 = 4yrs 32 25.44 0.58 11.15 5.11 7.77 14.36 5.25 14.97

(no thin) 65-76 = 11yrs 39 57.49 1.85 23.65 9.55 10.16 22.68 2.75 25.55

65-82 = 17yrs 45 69.89 2.38 30.33 12.50 24.20 28.10 14.00 29.35

65-92 = 27yrs 55 89.13 9.46 65.13 18.05 177.55 51.76 179.50 63.05
USDA Forest Service Proceedings RMRS-P-25. 2002 43

Validation of the Forest Vegetation Simulator Growth and Mortality Predictions on Red Pine in Michigan Smith-Mateja and Ramm

Hiawatha—The FVS simulations had a mean error of ± 10

TPA for almost all treatments accept for the high residual

basal area treatments. The worst prediction accuracy for FVS

was the control treatment (no thinning). The control treatment, with a 27-year prediction interval, overpredicted by

Table 6—Kellogg mean error ( e ) and standard deviation (s) of mean error for estimated trees per acre and basal area by treatment and

projection length for two types of simulation. Error expressed as predicted value minus observed value.

BA NA BA ∆D TPA NA TPA ∆D

Treatment Projection length Age e s e s e s e s

BAr70 64-67 = 3yrs 31 0.70 6.04 –5.45 9.29 14.59 22.38 14.31 22.93

64-72 = 8yrs 36 4.49 6.04 –8.06 9.29 21.64 22.38 21.04 22.93

64-80 = 16yrs 44 7.47 6.00 –12.88 10.40 21.49 18.90 21.33 18.99

64-85 = 21yrs 49 6.73 6.91 –16.82 11.25 23.79 19.09 23.60 19.17

64-91 = 27yrs 55 14.85 11.35 –13.76 15.34 25.92 23.58 25.68 23.68

BAr90 64-67 = 3yrs 31 1.95 13.88 –5.11 13.95 14.54 33.12 14.29 33.23

64-72 = 8yrs 36 10.29 13.88 –3.93 13.95 36.64 33.12 36.39 33.23

64-80 = 16yrs 44 18.24 21.85 –4.95 20.09 46.03 44.37 45.64 44.58

64-85 = 21yrs 49 16.37 22.02 –9.99 20.38 45.67 44.44 45.22 44.67

64-91 = 27yrs 55 21.76 20.77 –9.84 19.26 45.11 44.51 44.60 44.76

.

BAr110 64-67 = 3yrs 31 –1.64 9.32 –9.39 9.52 14.44 23.65 14.14 23.77

64-72 = 8yrs 36 0.54 9.32 –14.59 9.52 16.51 23.65 16.09 23.77

64-80 = 16yrs 44 12.21 25.25 –12.06 23.58 41.12 43.78 40.47 43.73

64-85 = 21yrs 49 13.94 30.61 –13.20 26.91 45.59 48.59 44.98 48.25

64-91 = 27yrs 55 22.03 35.32 –10.39 31.29 47.46 51.33 46.75 50.88

BAr130 64-67 = 3yrs 31 3.14 19.88 –5.20 15.85 16.73 40.22 16.20 39.99

64-72 = 8yrs 36 11.13 19.88 –6.26 15.85 33.72 40.22 33.18 39.99

64-80 = 16yrs 44 27.11 38.89 –0.86 32.35 65.65 62.44 65.21 62.25

64-85 = 21yrs 49 25.00 42.41 –6.27 33.62 67.79 64.00 67.28 63.72

64-91 = 27yrs 55 71.86 122.48 35.42 116.64 99.33 118.96 98.72 118.77

Row2nd 64-67 = 3yrs 31 0.81 22.97 –6.81 24.12 14.63 62.45 14.53 62.58

64-72 = 8yrs 36 9.47 22.97 –5.67 24.12 36.76 62.45 36.51 62.58

64-80 = 16yrs 44 16.02 35.61 –13.83 31.92 41.22 71.95 41.05 71.88

64-85 = 21yrs 49 13.37 37.69 –16.82 31.93 41.15 71.77 41.00 71.65

64-91 = 27yrs 55 22.24 44.20 –14.57 38.96 43.25 76.55 43.06 76.39

Row3rd 64-67 = 3yrs 31 –0.57 5.28 –8.67 4.73 2.00 5.11 1.72 5.30

64-72 = 8yrs 36 0.10 5.28 –15.17 4.73 1.65 5.11 1.34 5.30

64-80 = 16yrs 44 9.77 12.20 –15.83 13.21 21.06 32.54 20.62 32.09

64-85 = 21yrs 49 12.49 17.68 –14.71 20.56 28.37 37.59 27.96 37.06

64-91 = 27yrs 55 15.41 18.63 –17.87 23.59 27.87 37.33 27.41 36.73

Row4,2 64-67 = 3yrs 31 1.23 12.66 –9.57 11.35 1.68 49.73 0.79 48.74

64-72 = 8yrs 36 10.00 12.66 –10.78 11.35 33.36 49.73 31.88 48.74

64-80 = 16yrs 44 10.91 18.50 –21.93 15.38 47.30 47.66 45.56 46.12

64-85 = 21yrs 49 8.45 18.75 –28.76 15.73 49.20 51.86 47.47 50.47

64-91 = 27yrs 55 12.66 20.42 –32.41 17.42 48.17 51.12 46.25 49.65

Row4th 64-67 = 3yrs 31 –5.12 19.59 –9.87 4.44 –2.39 4.43 –1.61 2.47

64-72 = 8yrs 36 –7.49 19.59 –17.45 4.44 –4.05 4.43 –2.92 2.47

64-80 = 16yrs 44 –13.12 30.82 –28.77 6.74 –2.72 2.70 –1.64 0.22

64-85 = 21yrs 49 –19.19 33.07 –35.04 5.99 –2.04 1.25 –1.51 0.20

64-91 = 27yrs 55 –16.36 40.27 –35.04 8.06 –0.01 5.73 0.58 5.11

Control 64-67 = 3yrs 31 6.79 3.73 –6.22 1.94 9.14 9.09 7.43 8.36

(no thin) 64-72 = 8yrs 36 21.12 3.73 –11.68 1.94 10.01 9.09 5.40 8.36

64-80 = 16yrs 44 48.39 9.64 –7.80 5.51 54.26 24.50 45.45 22.54

64-85 = 21yrs 49 60.88 16.26 –2.45 11.17 93.61 46.19 82.89 43.23

64-91 = 27yrs 55 72.89 13.61 6.56 9.13 133.44 15.76 121.49 14.45

more than 175 trees per acre. There was little difference

between the accuracy of the NA and ∆D simulations (table 5).

Kellogg—The two simulations showed little difference

in mortality prediction. NA and ∆D simulations showed an

increase in error as the projection intervals increased.
44 USDA Forest Service Proceedings RMRS-P-25. 2002

Smith-Mateja and Ramm Validation of the Forest Vegetation Simulator Growth and Mortality Predictions on Red Pine in Michigan

Treatments with higher residual basal areas, which often

had more mortality, showed the greatest error in the

prediction of mortality. The worst prediction accuracy for

FVS was the control treatment (no thinning). The control

treatment with a 27-year prediction interval overpredicted

by more than 130 trees per acre (table 6).

Discussion _____________________

Prediction was more accurate for the Hiawatha site than

the Kellogg site. This is probably because the original equations were developed from plantations in the northern portions of the Lake States (Miner and others 1988).

These validation runs used version 6.2 of the FVS-LS

variant with a revision date of 12/01/95. FVS variants are

continuously being updated and improved. These same data

runs through a current version of the model would yield

different results. There are many ways users can make

adjustments in an FVS simulation to produce more realistic

results. One example of this involves using serial correlation

of diameter growth. This feature improves estimates from

cycle to cycle by assuming that the error terms from a

previous cycle are correlated with error terms of the next

cycle. In other words, the error term is not randomly distributed at each cycle. So that trees that were growing well

previously continue to grow well, and those that are not

growing well continue to grow poorly (Dixon 2002). This

feature improves the overall distribution of diameter growth

in a stand. This feature was turned off by default in the

version used for this validation project. It could have been

turned on by the use of a keyword; however, it was not. This

feature is now on by default in a more current version of the

program.

Overall, the d.b.h. mean errors were better predicted for

runs that used past growth (∆D) information to scale the

equations than those that did not (NA). The d.b.h. mean

error for the ∆D simulations were on average ± 1 inch.

However, for the Kellogg site, prediction of density (BA) and

d.b.h. did not show much if any advantage to using the past

growth information to scale the equations. The scale factors

for Kellogg ranged from 0.42 to 0.75 with a mean of 0.56.

The Hiawatha scale factors ranged from 0.47 to 1.163 with

a mean of 0.78. On average, these were biased low. This is

due to the methodology of how the diameter growth equations were applied. The first thinning occurred (1962,

Hiawatha; 1960, Kellogg) at the start of the growth measurement period. If there was any release delay of the trees

(after the thinning) then FVS would not have captured the

impact of the thinning in the growth measurement period.

The scale factor applied to the thinned stand may have

scaled down growth too much for the thinned stand. A longer

measurement period may have given more accurate results.

However, for the Hiawatha site the calibrated runs did

improve the accuracy of the predicted d.b.h. of trees and the

density. A second reason that the scale factors were biased

low was because mortality trees were recorded as “dead”

trees in the input tree list instead of “recent mortality” trees.

If the trees had been recorded as recent mortality trees, then

FVS would have included them in the stand density calculations that then would have effected how the scale factors

were calculated. It is important that users realize the impact

that the scale factor has on future tree simulated growth.

The problem with the measurement periods in these simulations was that they were too short and reflected the

prethinned growth, therefore scaling down (slowing) the

original growth equations. In addition trees were marked as

“dead” instead of “recently dead,” making FVS incorrectly

predict past density. A third problem is that the repeated

growth measurements were not adjusted for bark. Because

diameter increments included bark growth, this added another bias to the results.

Mean error for trees per acre were similar between the

scaled and nonscaled simulations for both sites. It was

expected that FVS would overpredict mortality, because of

typically lower mortality in red pine plantations. However,

it actually underpredicted. The inventory history indicates

that some trees died by snow or ice storms, but no description

was given for other trees. However, from the previous

inventory, most trees seemed to put on good growth prior to

the cycle that death occurred. From this it is assumed that

many of the trees died from stochastic events such as wind,

ice, snow, insect, or disease. This suggests that when running simulations, in order to get reasonable estimates of

mortality, it is imperative to include stochastic mortality

events that are typical for the region and species modeled.

For the typical user of FVS who may not have a complete

understanding of the system, it is important to realize that

many adjustments can be made to FVS to produce more

accurate runs. FVS is a complicated growth and yield program. The more the user understands the capabilities of

FVS, and the process it uses to compute growth, the better

estimates the user will obtain. At the beginning of the

research, one of this study’s authors was a novice FVS user

and found that even without making many adjustments to

improve estimates of growth, FVS-LS did perform well in the

prediction of individual tree diameters, stand density, and

mortality in most cases.

Acknowledgments ______________

This research was supported by the Agricultural Experiment Station of Michigan State University and the McIntire

Stennis Cooperative Forestry Research Program.

References _____________________

Bush, R.R.; Brand, G. 1995. Lake States TWIGS Geographic Variant of the Forest Vegetation Simulator. Unpublished. Forest

Management Service Center, FVS Group, USDA Forest Service,

Fort Collins, CO.

Canavan, S.J. 1997. Evaluation of five and ten-year Lake States

FVS and TWIGS growth projections for Upland hardwoods in the

northern lower peninsula of Michigan. M.S. thesis, Department

of Forestry, Michigan State University.

Dixon, G.E. 2002. Essential FVS: A User’s Guide to the Forest Vegetation Simulator (Draft). Unpublished. Forest Management Service

Center, FVS Group, USDA Forest Service, Fort Collins, CO.

Guertin, P.J.; Ramm, C.W. 1996. Testing Lake States TWIGS: Five

Year Growth Projections for Upland Hardwoods in Northern

Lower Michigan. North. J. Appl. For. 13(4):182-188.

Leatherberry, E. C., Spencer, J.S. 1996. Michigan Forest Statistics,

1993. USDA Forest Service. Res. Bull. NC-170. 144 p

Miner, C.L. and others. 1988. A Guide to the TWIGS Program for the

North Central United States. USDA Forest Service. Gen. Tech.

Rep. NC-125. 105 p

Potter-Witter K. 1995. Status and Potential of Michigan Natural

resources: Michigan Agricultural Experimental Station. 171 p.
USDA Forest Service Proceedings RMRS-P-25. 2002 45

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Chad Keyser is a Forester/Statistician with the USDA Forest Service,

Forest Management Service Center, Vegetation Simulation Group, 2150A

Centre Ave., Suite 341a, Fort Collins, CO 80526. Ron Stephens is a Forest

Silviculturist and a member of the Plan IDT with the USDA Forest Service,

Chattahoochee-Oconee National Forests, 1755 Cleveland Highway,

Gainesville, GA 30501.

Abstract—The Forest Vegetation Simulator (FVS) is being used to

create yield tables for the Chattahoochee-Oconee National Forest

plan revision. The FVS Southern variant was calibrated and adjusted to produce reliable yield tables for this region. The large-tree

diameter increment model was calibrated using the self-calibration

feature of FVS and through FVS-computed mean correction scale

factors. Volume estimates were adjusted to the merchantability

specifications and defect estimates for the region. Adjustments to

the mortality and regeneration models were not done, but may be

needed after further analyses are conducted. This paper reports the

steps taken to calibrate the large-tree diameter increment model

and adjust the volume specifications for the Chattahoochee-Oconee

National Forest plan revision.

The Chattahoochee-Oconee National Forest is one of five

National Forests undergoing forest plan revision in the

Southern Region of the USDA Forest Service. The

Chattahoochee-Oconee National Forest covers 865,043 acres

in northern and central Georgia, of which 542,240 acres are

suitable for timber production (http://www.fs.fed.us/conf/

facts1.htm 2001). There are three predominate ecological

sections found on the forest: Blue Ridge Mountains, Southern Ridge and Valley, and Southern Appalachian Piedmont.

Forest Plan revision on the Chattahoochee-Oconee National Forest began in the late 1990s. Elements of the plan

revision include determining analysis units/strata and allocating National Forest land to these strata, determining

administrative layers, developing silvicultural prescriptions,

performing growth and yield forecasting, and evaluating the

management alternatives. As part of this process, the Forest

Vegetation Simulator (FVS) system was chosen to create the

yield tables needed for the growth and yield-forecasting

element of the forest plan revision. Output from FVS system

will be used as input in Spectrum, the analytical tool used to

evaluate forest management alternatives (Spectrum Overview 2001).

The FVS system is a suite of software tools designed to

translate forest data, simulate forest growth, and process

model outputs into usable forms for other analytical tools. At

the heart of this system is the FVS model. The FVS model is

Calibrating FVS for the ChattahoocheeOconee National Forest Planning Effort

Chad Keyser

Ron Stephens

an individual-tree, distant-independent growth and yield

model that has been calibrated to simulate forest growth for

most forest types of the United States (Dixon 2001). This

paper presents the FVS model calibration and adjustments

used to produce accurate yield tables for the forest plan

revision on the Chattahoochee-Oconee National Forest.

FVS Variant Selection and

Data __________________________

There are two FVS variants available for the

Chattahoochee-Oconee National Forest: the Southeastern

Variant (SE) and the Southern Variant (SN). The Southern

Variant was chosen for two reasons. First, it was fit to a

larger geographic region, which enabled all National Forests undergoing plan revision in the Southern Region to use

the same model. Second, the Southern Variant is more

reliable because it was built using FVS growth relationships, while the Southeastern Variant was based on equations in the Southeast TWIGS model (Lilly 1997; Donnelly

and others 2001).

The Southern Variant does not have insect or disease

extensions. However, the magnitude of possible insect and

disease impacts on the forest is being accounted for through

the use of FVS addfiles designed specifically to estimate the

severity of pests and pathogens, most notably the Southern

Pine Beetle addfile (Courter and others this proceedings).

This planning effort used the FVS-ready FIA data for the

Southeast available on the FMSC Intranet Web site. Each

Forest Inventory Analysis (FIA) plot contained all the input

data needed by FVS to make projections. In addition, diameter increment data were included on some tree records.

FVS Calibration and

Adjustments ___________________

Tree Growth Functions

By default, the FVS large-tree diameter increment model

and the small-tree height increment model undergo a process of self-calibration when sufficient growth increment

data are supplied with the input tree data. Self-calibration

is the process of scaling the predicted growth increments to

more accurately match the measured growth rates of trees

in the stand (Stage 1973). Wykoff and others (1982), explain

this procedure best:

The scaling procedure (Stage 1973), when stripped of statistical

condiments, is really quite simple. The affected models are both linear

and logarithmically scaled dependent variables. Therefore, the model
46 USDA Forest Service Proceedings RMRS-P-25. 2002

Keyser and Stephens Calibrating FVS for the Chattahoochee-Oconee National Forest Planning Effort

intercepts are, in effect, growth multipliers. An increment to match

each observed increment for a species is predicted, and differences are

sorted. The median difference is then added to the model for that

species, on the logarithmic scale, as an additional intercept term.

The diameter increment scale factors are attenuated over time. We

assume that, on long-term projections, the base model is a more stable

estimate of growth potential than is the scale factor. The attenuation

is asymptotic to one-half the difference between the initial value of the

scale factor and 1. The rate of attenuation is dependent only on time.

Self-calibration of the large-tree diameter increment model

occurs in the Southern Variant if, for a given species, there

are at least five tree records with backdated dbh greater

than 3 inches that contain measured diameter increments

(Dixon 2001; Wykoff and others 1982). Likewise, if there are

at least five tree records less than 5 inches dbh that contain

height increments, the small-tree height increment model is

calibrated for that species. If a species is able to undergo selfcalibration, a scale factor will be computed for the growth

model of that species. Because the plot data for this plan

revision do contain a sample of diameter increments, the

large-tree diameter increment model will be scaled automatically when the above conditions exist on any plot. No

height increment data were available in the data set for the

small-tree height increment model to self-calibrate.

For some species, consistent growth function bias may

occur within a given geographic area, ecological section,

forest type, and so forth. To correct for this bias, mean

correction scale factors may be calculated and added to the

simulations as multipliers. These multipliers allow the user

to simulate regional growth patterns on all plots, not just on

plots that self-calibrate. These multipliers remain in effect

for the entire simulation and do not attenuate over time.

For species exhibiting growth bias, mean correction scale

factors were calculated within each ecological section. The

Calibration Summary Statistics Post-Processor was used to

render the mean correction scale factors, which were subsequently entered into the simulations using the READCORD

keyword. Due to a low number of diameter growth samples,

some species had no or few large-tree diameter increment

scale factors computed. Therefore, mean correction scale

factors were not computed for these species.

The mean correction scale factors were determined for

each ecological section using the following six steps (see

Section 6.5.3, Dixon 2001):

1. Insert the CALBSTAT keyword into the keyword set

for each stand in the area and process the stands. This will

generate the auxiliary file.

2. Examine the values in this file and determine the

source of any values that seem extremely high or low.

3. For any stands with unresolved sources of error, or

with calibration values that you think are not reflective of

the area, remove the CALBSTAT keyword.

4. Rerun the set of stands.

5. Run the Calibration Statistics postprocessing program

to determine the average large tree diameter growth scale

factor and the average small tree height growth scale factor

for the area.

6. If these average scale factor values are different from

1.0, enter these values as multipliers using the READCORD,

READCORR, and READCORH keyword.

Table 1 lists the resulting species-specific large-tree diameter increment, mean correction scale factors by ecological

section. The mean correction scale factors reported are from

an updated Calibration Summary Statistics postprocessor.

Actual scale factors used in the forest plan are from a

previous version and differ slightly. Notice that there is a

general trend between ecological sections. For the most part,

species had mean correction scale factors less than 1.0

(stands growing slower than model prediction) for the Blue

Ridge Mountain and Appalachian Piedmont ecological sections and mean correction scale factors greater than 1.0

(stand growing faster than model prediction) for the Ridge

and Valley ecological section.

Volume Specifications and Adjustments

To get estimates of stand volume and volume harvested

consistent with the procedures on the Chattahoochee-Oconee

National Forest, merchantability limits and percent defect

estimates were set to commonly found values on the

Table 1—Mean large-tree diameter increment correction scale factors by species and ecological

section.

Blue Ridge Appalachian

Species name Species number Mountains Piedmont Ridge & Valley

shortleaf pine 5 ** ** 1.304

longleaf pine 8 * ** 1.087

eastern white pine 12 0.842 * *

loblolly pine 13 ** 0.720 1.065

virginia pine 14 0.675 0.586 0.851

hickory species 27   1.041

yellow poplar 45 0.992 **  **

scarlet oak 64 0.728  

chestnut oak 74 0.749 0.797 1.056

northern red oak 75 0.808 **  **

* Species does not occur in ecological section

** Insufficient data to create mean scale factor or no bias present.
USDA Forest Service Proceedings RMRS-P-25. 2002 47

Calibrating FVS for the Chattahoochee-Oconee National Forest Planning Effort Keyser and Stephens

Chattahoochee-Oconee National Forest (tables 2 and 3). The

BFVOLUME keyword was used to set the species merchantability specifications for sawlog portion of cubic feet and

board feet volume equations. The VOLUME keyword was

used to set the species merchantability specifications for

merchantable cubic feet volume. Sawlog defect was specified

using the BFDEFECT keyword (VanDyck 2001).

Additional Considerations ________

Regeneration Inputs

While the Southern Variant does simulate stump sprouts

from cut trees, it does not automatically simulate other types

of natural regeneration. Other than sprouting, users must

specify the species and quantity (trees per acre) of trees that

regenerate. Regeneration estimates may be obtained outside the FVS framework through the analysis of regeneration surveys and consultation with local experts. In addition,

within the FVS framework, users have the option of creating

regeneration addfiles based on current stand composition

with the REgeneration ImPUTation Extractor (REPUTE)

postprocessor (Vandendriesche 2001). Sprouting was turned

on in the simulations but no other types of regeneration were

input into the simulations.

Mortality Functions

When FVS mortality does not correspond to observed

mortality, FVS users may use mortality modifier keywords

to correct model outputs. Mortality modifier keywords include FIXMORT, MORTMULT, BAMAX, and SDIMAX (Van

Dyck 2001). FIXMORT allows users to replace or add to the

FVS calculated species-specific mortality rate for a given

diameter range. MORTMULT can be used to increase or

decrease the species-specific mortality rate for a given diameter range. BAMAX resets the maximum density of the

stand. SDIMAX allows users to reset the maximum stand

density index by species. There has not been a need to

calibrate the mortality functions in the ChattahoocheeOconee plan revision yet.

Example _______________________

The effects of using the above-mentioned FVS calibration

and adjustment techniques are depicted in table 4. In this

example, 12 plots from the Blue Ridge Mountains Ecological

Section, old growth community type 05, sapling age class

(11-40 years old) stratum (B05S) used in this plan revision

were selected. Four simulations were performed as follows:

Table 2—Default and adjusted cubic feet and board feet volume merchantability specifications for the

Chattehoochee-Oconee plan revision.

Minimum Minimum

merchantable top Stump

 Default settings Keyword Species dbh diameter* height

Merchantable Cubic Feet Volume all 4.0 4.0 1.0

Merchantable Board Feet BFVolume hardwoods 12.0 9.0 1.0

Merchantable Board Feet BFVolume softwoods 10.0 7.0 1.0

Minimum Minimum

merchantable top Stump

 Adjusted settings Keyword Species dbh diameter height

Merchantable Cubic Feet Volume hardwoods 6.0 4.0 0.5

redcedar 5.0 4.0 0.5

shortleaf pine 5.0 4.0 0.5

slash pine 5.0 4.0 0.5

longleaf pine 5.0 4.0 0.5

pitch pine 5.0 4.0 0.5

eastern white pine 6.0 4.0 0.5

loblolly pine 5.0 4.0 0.5

Virginia pine 5.0 4.0 0.5

hemlock 6.0 4.0 0.5

Merchantable Board Feet BFVolume hardwoods 12.0 9.0 1.0

redcedar 9.0 7.0 1.0

shortleaf pine 9.0 7.0 1.0

slash pine 9.0 7.0 1.0

longleaf pine 9.0 7.0 1.0

pitch pine 9.0 7.0 1.0

eastern white pine 10.0 7.0 1.0

loblolly pine 9.0 7.0 1.0

Virginia pine 9.0 7.0 1.0

hemlock 10.0 7.0 1.0

* Minimum top diameter is fixed in this variant and cannot be changed.
48 USDA Forest Service Proceedings RMRS-P-25. 2002

Keyser and Stephens Calibrating FVS for the Chattahoochee-Oconee National Forest Planning Effort

Table 3—Percent defect by species and diameter. Linear interpolation between diameters is used to calculate percent defect for a

given tree diameter.

Keyword Species 5 inch dbh 10 inch dbh 15 inch dbh 20 inch dbh 25 inch dbh

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

BFDefect white oak 0.0 0.0 0.5 3.5 3.6

swamp chesnut oak 0.0 0.0 0.5 3.5 3.6

chinkapin oak 0.0 0.0 0.5 3.5 3.6

cheerybark oak 0.0 0.0 2.5 2.5 2.5

northern red oak 0.0 0.0 2.5 2.5 2.5

Shumard oak 0.0 0.0 2.5 2.5 2.5

chestnut oak 0.0 0.0 0.5 10.0 6.3

post oak 0.0 0.0 0.5 10.0 6.3

scarlet oak 0.0 0.0 2.6 13.0 7.4

southern red oak 0.0 0.0 2.5 13.1 7.4

blackjack oak 0.0 0.0 2.6 13.1 20.0

hickory sp. 0.0 0.0 0.8 10.2 1.6

sugar maple 0.0 0.0 0.0 0.0 17.7

red maple 0.0 0.0 0.6 9.3 21.6

American beech 0.0 0.0 0.0 20.0 20.0

blackfum 0.0 0.0 5.8 20.0 25.0

yellow-poplar 0.0 0.0 0.4 0.0 2.1

black locust 0.0 0.0 24.7 65.7 90.1

Virginia pine 0.0 0.0 15.2 33.0 50.0

eastern white pine 0.0 0.0 0.0 0.0 20.0

Table 4—Mean stratum responses for simulations in the example at a mean stand age of 113

years. Standard errors (±1) are reported for Sawlog board feet.

Trees Ave. dbh Merchantable Sawlog Sawlog

Simulation* per acre (inches) cubic feet cubic feet board feet

1 276 9.5 6560 5334 33582 ± 1869

2 276 9.5 6395 4959 31142 ± 1762

3 278 9.5 6343 4920 30798 ± 1750

4 290 9.4 6309 4859 30313 ± 1771

* 1: Simulation with no volume adjustments, no self-calibraiton, and no mean correction scale factors.

2: Simulation with volume adjustments, no self-calibraiton, and no mean correction scale factors.

3: Simulation with volume adjustments, with self-calibraiton, and no mean correction scale factors.

4: Simulation with volume adjustments, with self-calibraiton, and with mean correction scale factors.

1. A simulation with no volume adjustments, no selfcalibration, and no mean correction scale factors.

2. A simulation with volume adjustments, no self-calibration, and no mean correction scale factors.

3. A simulation with volume adjustments, with self-calibration, and no mean correction scale factors.

4. A simulation with volume adjustments, with self-calibration, and with mean correction scale factors.

The stratum was grown for 150 years with no management with the FVSStand postprocessor combining the output by age into age-based yield tables. The plots within the

stratum were given equal weight in the analyses. The

resulting stratum data, presented in table 4, refer to the

output obtained when the average stand age within the

stratum was 113 years (up to 100 years of projection).

Simulation mean responses for trees per acre, average dbh,

merchantable cubic feet volume, sawlog cubic feet volume,

and sawlog board feet volume obtained from FVSStand are

displayed in table 4. The standard error of the mean sawlog

board feet volume is also included.

Discussion _____________________

Little difference was seen when self-calibration was employed, presumably due to either the attenuation of scale

factors over time or relatively few species having an adequate number of growth sample trees to self-calibrate. The

largest relative differences in the example came when volume adjustments and mean correction scale factors were

added to the simulations. Using the mean correction scale

factors allowed for scaling of species that would not have

been scaled due to too few growth samples in a particular

stand. The scaling affected diameter growth, which then had

secondary impacts on mortality. This is depicted in table 4

where the scale factors slowed down the diameter growth,

smaller average dbh, which then impacted the SDI based

mortality model, greater trees per acre.

Even though the relative growth differences are small,

adjusting and calibrating FVS is still warranted. First, this

example used a small sample from only one stratum out of

a possible 135. Other strata may in fact have large growth
USDA Forest Service Proceedings RMRS-P-25. 2002 49

Calibrating FVS for the Chattahoochee-Oconee National Forest Planning Effort Keyser and Stephens

differences. Also, practicality is of the essence. It makes

sense to adjust the volume merchantability specifications to

those used on your district or forest. It also makes sense to

calibrate the FVS growth functions to reflect past stand

growth. Using mean correction scale factors helps to alleviate systematic bias of the growth functions, especially when

self-calibration does not occur due to too few growth increments per species on a given plot. After further yield table

review, it might be necessary to further calibrate the model

by including regeneration estimates and modifications to

the mortality model. The authors believe that the FVS

adjustments and calibration techniques used in the

Chattahoochee-Oconee plan revision are necessary and

should be employed in other plan revision efforts.

References _____________________

Dixon, Gary E. 2001. Essential FVS: a user’s guide to the forest

vegetation simulator, DRAFT. U.S. Department of Agriculture,

Forest Service, Forest Management Service Center, Fort Collins,

CO. Interoffice publication.

Donnelly, Dennis, Barry Lilly, and Erin Smith. 2001. The southern

variant of the forest vegetation simulator. U.S. Department of

Agriculture, Forest Service, Forest Management Service Center,

Fort Collins, CO. Interoffice publication.

Forest Service, U.S. Department of Agriculture. 2000. Spectrum

Version 2.5. Washington, DC: Inventory and Monitoring Institute interoffice publication.

Lilly, Barry 1997. The southeastern variant of the forest vegetation

simulator. U.S. Department of Agriculture, Forest Service, Forest Management Service Center, Fort Collins, CO. Interoffice

publication.

Stage, Albert R. 1973. Prognosis model for stand development. U.S.

Department of Agriculture, Forest Service. Res. Pap. INT-137,

Intermountain Forest and Range Experiment Station, Ogden,

Utah 84401. 32 p., illus.

Vandendriesche, Don. 2001. Topic REGEN: regenerating the future. In: Select topics for the forest vegetation simulator. U.S.

Department of Agriculture, Forest Service, Forest Management

Service Center, Fort Collins, CO. Interoffice publication.

Van Dyck, Michael G. comp. 2001. Keyword reference guide for the

forest vegetation simulator. U.S. Department of Agriculture,

Forest Service, Forest Management Service Center, Fort Collins,

CO. Interoffice publication.

Wykoff, William R., Nicholas L. Crookston, Albert R. Stage. 1982.

User’s guide to the stand prognosis model. Gen. Tech. Rep. INT133. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Forest and Range Experiment Station. 112 p.

USDA Forest Service Proceedings RMRS-P-25. 2002 51

Session 3. Landscape Applications

USDA Forest Service Proceedings RMRS-P-25. 2002 53

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Joy C. Roberts is a Computer Specialist, U.S. Department of Agriculture,

Forest Service, Forest Health Protection, 1249 S. Vinnell Way, Boise, ID

83709.

Abstract—Permanent plots from the Boise National Forest, Idaho,

were established in 1982 and remeasured in 1987 and 2000 for

monitoring the effects of western spruce budworm (Choristoneura

occidentalis) on mixed conifer stands of grand fir (Abies grandis),

Douglas-fir (Pseudotsuga menziesii), subalpine fir (Abies lasiocarpa),

and Engelmann spruce (Picea engelmannii) tree species. The central

Idaho variant of the Forest Vegetation Simulator was used to make

two simulations that were then compared to the actual data. A

simulation without change-agent impacts causes an underestimation of mortality and overestimation of growth. A multichange

agent approach using a variety of pest model models and fixed

mortality functions simulates impacts from western spruce budworm and a variety of bark beetles. This simulation more closely

represents the mortality and growth indicated by the actual data.

Modeling the process of change in forests is challenging.

To assist in this task, forest managers use the Forest Vegetation Simulator (FVS) to simulate changes in stand structure

and composition over a specified timeframe. Traditionally,

simulating disturbances, referred to as change agents

(Steele and others 1996), is accomplished by linking one pest

model to the main FVS model. If Douglas-fir beetle

(Dendroctonus pseudotsugae) is the primary change agent,

then one will use the model with the Douglas-fir beetle

model. Similarly, other independent pest models are available for western spruce budworm (Choristoneura

occidentalis), mountain pine beetle (Dendroctonus

ponderosae), and Douglas-fir tussock moth (Orgyia

pseudotsugata). Only impacts from dwarf mistletoes

(Arceuthobium spp.) are simulated as part of the main FVS

model. Unfortunately, no single change agent can adequately

reflect the total actual mortality and growth impacts on

vegetation. Only a multichange agent approach can lead us

closer to simulating change.

Since 1991 a multichange agent scenario has been available for use with the central Idaho variant of FVS. The

Southwest Idaho Ecogroup Team has utilized it in landscape

and project planning on the Boise and Payette National

Forests in Idaho. For this analysis, permanent plot data

collected on the Boise National Forest in 1982 and remeasured

in 1987 and 2000 were used to evaluate the multichange

Using a Multichange Agent Approach with

the Forest Vegetation Simulator on the

Boise National Forest, Idaho

Joy C. Roberts

agent scenario. The outputs from the actual data were

compared to a no-change agent (nopest) simulation and a

multichange agent (pestadj) simulation.

Methods _______________________

To evaluate this multichange agent procedure, 88 permanent plots established in 17 stands in the Boise National

Forest in 1982 were analyzed. These stands were established to monitor the impacts of defoliation by western

spruce budworm on mixed conifer stands where an outbreak

event started in some stands as early 1971. The outbreak

grew and continued through 1987 when some unknown

event ended the outbreak in all stands. Species composition

includes grand fir (Abies grandis), Douglas-fir (Pseudotsuga

menziesii), subalpine fir (Abies lasiocarpa), and Engelmann

spruce (Picea engelmannii). Ponderosa pine (Pinus ponderosa), western larch (Larix occidentalis), and lodgepole pine

(Pinus contorta) tree species are also present but are not

primary hosts of western spruce budworm.

Originally, stands were selected based on presence of

defoliation, size of stand (20 to 65 acres), stand accessibility,

land base stratification, and defoliation stratification

(Beveridge and Cahill 1984). Remeasurements were taken

on stands in 1987 and 2000. Although these stands are

maintained to monitor impact from western spruce budworm, their species composition makes them suitable for a

multichange agent evaluation.

To prepare stand data for entry to the FVS model, the tree

data were compared in each year to correct small discrepancies in tree species and find unreasonable growth measurements. Stands were not included if trees grew smaller or

could not be found in every measurement. In the 17 stands

that were selected, only trees that were measured in all

three measurements were included in the simulated data

sets. Additionally, only trees equal to or greater than 1 inch

in diameter were included.

The following computer programs and files were used to

complete the project:

• Suppose Version 1.14—(Crookston 1997)

• Forest Vegetation Simulator—CIX.EXE (Wykoff and

others 1982; David 2001)

• Multichange agent keyword set (Roberts and Weatherby

1997)

• Event Monitor (Crookston 1990)

• Compute2 post processor (Van Dyck 2001)

• Excel spreadsheet—graphics (Microsoft© ExcelTM2000)

The CIX.EXE program offers the user a multilinked version

of FVS. In this version, the Douglas-fir beetle model, the

mountain pine beetle model in lodgepole pine, and the
54 USDA Forest Service Proceedings RMRS-P-25. 2002

Roberts Using a Multichange Agent Approach with the Forest Vegetation Simulator on the Boise National Forest, Idaho

western spruce budworm model are all available to the main

FVS program. Due to the low occurrence of mistletoe in these

stands, mistletoe was not included in the evaluation.

There is no programmed interaction between the pest

models. They run independently and in a sequence. Growth

is adjusted and mortality is applied according to each model’s

specifications. In addition, three mortality functions have

been defined to simulate mortality caused by fir engraver

beetle in grand fir (Schenk and others 1977), western pine

beetle in ponderosa pine (Stevens and others 1980), and

spruce beetle in spruce (Schmid and Fry 1976). These functions are executed from the multichange agent keyword set.

All instructions for determining vulnerability of trees to a

change agent are also included in the multichange agent

keyword set. A set of hazard rating rules is applied to each

stand using the event monitor. If the vulnerability is high,

based on these rules, the associated change agent model or

function is called. For the western spruce budworm model,

the Douglas-fir beetle model, and the mountain pine beetle

model, the tree mortality caused by the change agent is

checked against the background mortality computed by the

FVS model. The larger of the two is applied (Marsden and

others 1994). On the other hand, the fixed mortality functions for spruce beetle, fir engraver beetle, and western pine

beetle add mortality to the background mortality.

Two simulations were compared to the actual data in this

evaluation. Data from the actual measurements collected in

years 1982, 1987, and 2000 were submitted to FVS for

computing the initial stand characteristics for each stand for

each measurement year. Then a simulation that contained

no-change agent impacts (nopest) was made starting with

the 1982 actual data. The simulation grew each stands

forward to 1987 and then to 2000. The second simulation

(pestadj) started with the 1982 actual data and grew the

stands forward applying the rules, models, and functions

from the multichange keyword set. Both mortality and

growth are impacted by the change agents. The stand

summary data displayed in the Summary Statistics Table of

the FVS output were used as indicators for comparing

differences between the simulations and the actual data.

These summary data are recreated in tabular format using

the ECHOSUM keyword in the FVS and stored in an output

file (.sum).

The stand indicators that were compared are trees per

acre, basal area, stand density index, dominant height,

quadratic mean diameter, volumes, crown competition

factor, and mean annual increment. Additionally, some

indicators that were evaluated by species and tree size

class include trees per acre, basal area, quadratic mean

diameter, and dominant height. These outputs were produced using the event monitor, which is a part of the FVS

model. The event monitor keyword SPMCDBH was used to

extract the indicator information by species and diameter

class. The diameter classes consist of small trees between

1 and 5 inches and large trees greater than 5 inches. Two

output files were created using the postprocessor of Suppose. The data from both output files were imported into a

Microsoft Excel® spreadsheet program. Using the

spreadsheet’s pivotal table function and graphing capabilities, the data were summarized and displayed.

Results ________________________

In this paper, the basal area indicator will be discussed in

detail. By looking at the average basal area by species in

figures 1 and 2, it becomes clearer how the change agents

have impacted the stands. The bottom bar on the chart

represents the actual data, the middle bar is the multichange

agent simulation (pestadj), and the top bar is the no-change

agent simulation (nopest.) There is a decline in basal area in

all species from 1982 to 2000. The largest change in stand

species composition was in the subalpine fir species, which

declined from 17 percent in 1982 to 13 percent in 2000.

Grand fir, the largest component of this mix, remained

relatively unchanged, while both Engelmann spruce and

Douglas-fir improved position due to the decline in subalpine fir.

The average basal area for the no-change agent simulation (nopest) and the actual data are displayed in figure 3.

This no-change agent simulation might be viewed as the

best-case scenario. As each change agent is added as a line

on the graph, notice the move from the no-change agent

scenario toward the actual data. Modeling the change agents

explains some of the difference between a no-change agent

run and the actual data. Change agents that are not modeled

might explain some of the remaining difference. These

might include other insects, diseases, and impacts from

weather, drought, animals, or humans. Another factor that

might influence the remaining difference is in growth rates

used in the model. Unadjusted rates may be growing trees

faster than the actual growth rates, causing the basal area

Figure 1—Total basal area (ft2) and average basal area (ft2)

by species by simulation for 1982 to 1987 cycle. Species

codes are: gf = grand fir; wl = western larch; pp = ponderosa

pine; df = Douglas-fir; lp = lodgepole pine; af = subalpine fir;

es = Englemann spruce.
USDA Forest Service Proceedings RMRS-P-25. 2002 55

Using a Multichange Agent Approach with the Forest Vegetation Simulator on the Boise National Forest, Idaho Roberts

of the simulations to be higher. Other factors such as

competition of trees less than 1 inch that are not included in

the simulations might also have an impact.

Western spruce budworm, which impacts Douglas-fir,

Engelmann spruce, grand fir, and subalpine fir, causes

diameter growth loss, top kill, and mortality due to defoliation in host species (Beveridge and Cahill 1984). In the

multichange agent simulation, this is the only agent impacting subalpine fir. By year 2000, the overall difference in

subalpine fir from the no-change agent simulation to the

actual is 30 percent, of which 13 percent is attributed to

western spruce budworm. The remaining mortality in subalpine fir can be attributed to a complex of insects, pathogens, and environmental factors (Bennett and others 2001).

In addition to western spruce budworm, the Douglas-fir

beetle model in the multichange agent simulation moved

Douglas-fir basal area close to the actual data. There is a 16

percent difference in the actual basal area from the nochange agent simulation but only a 4 percent difference

between the multichange agent simulation and the actual

data in Douglas-fir.

The impacts from mountain pine beetle in lodgepole pine

move the 1987 basal area slightly lower than the actual

data. A study of the simulation indicates that outbreaks for

mountain pine beetle rated high in some stands in 1982,

which triggered an outbreak during the 1982 to 1987

growth cycle. A study of the actual data shows that a large

number of lodgepole pine trees actually died during the

1987 to 2000 cycle.

Although a stand may be vulnerable to an attack, it is most

difficult to predict when an outbreak might occur. During a

simulation using 10–year cycles, mountain pine beetle might

have an outbreak in highly vulnerable stands sometime

within a 20–year period. Which cycle the outbreak will occur

can only be simulated, not predicted. Although the length of

reoccurrence is different for different change agents, the

problem of predicting when an outbreak will occur applies to

all change agents.

In Engelmann spruce, the actual data show that the basal

area in this species declined only slightly and increased in

percentage composition from 17 percent in 1987 to 19 percent in 2000. Although spruce is present in the majority of

stands, no individual stand contained enough contiguous

spruce to trigger an epidemic event by spruce beetle. The minor

difference in basal area is caused by western spruce budworm.

Overall, grand fir had the largest difference of 24 percent

between the no-pest simulation and the actual data by 2000.

Of this, only 11 percent is modeled as fir engraver beetle. A

fixed mortality function based on risk ratings from Schmid

and Frye (1976) has been used to compute vulnerability and

outbreak mortality. The remaining mortality can be attributed to other change agents and environmental factors not

modeled.

There is minimal impact from western pine beetle as

ponderosa pine is a minor component in the stands. Western

larch is also a minor component, and although mistletoe is

modeled for this species, not enough mistletoe was present

to trigger an event. By year 2000, only 55 percent of the

difference in basal area between no pest and actual has been

explained by change agents. The remaining 45 percent

represents mortality that cannot be explained or are impacts

by change agents not simulated. Figure 3—Illustrates the change in basal area from 1982

to 2000 for each of the simulations and the actual data.

Figure 2—Total basal area (ft2 ) and average basal area (ft2

)

by species by simulation for 1987-2000 cycle. Species

codes are the same as used in figure 1.

1

A simulation that does not include any change agent impacts.

2

Impacts from western spruce budworm have been added.

3

Impacts from western spruce budworm and Douglas-fir beetle have been

added. 4

Impacts from western spruce budworm and Douglas-fir beetle, and

mountain pine beetle have been added. 5

Impacts from western spruce budworm and Douglas-fir beetle,

mountain pine beetle, western pine beetle, fir engraver beetle and spruce

beetle have been added. 6

Actual data
56 USDA Forest Service Proceedings RMRS-P-25. 2002

Roberts Using a Multichange Agent Approach with the Forest Vegetation Simulator on the Boise National Forest, Idaho

Conclusions____________________

In a no-change agent simulation, the model overestimates

indicators used in this analysis. In the actual data, certain

tree species were disturbed by some kind of change agent

that caused mortality and reduction in growth. Some of this

is explained by the individual change agents modeled in the

keyword set that processes the models and mortality functions in an independent and sequential procedure. In reality, change agents do not work independently and are in

concert with many other change agents including weather,

fire, and other insects and diseases (Weatherby and others

1997) not simulated in our analysis. This leaves some unexplained difference between the actual data and our changeagent simulation. Still, a multichange-agent approach offers

the modeler a simulation closer to actual change than does

a no-change agent approach.

There are several multichange agent methods available

besides the multichange agent keyword set used in this

analysis. The Western Root Disease Model (Frankel 1998)

and the Westwide Pine Beetle Model (Smith 1999) are also

available. Modelers are encouraged to work with insect and

disease modeling specialists who can assist in using a

multichange agent approach.

References _____________________

Bennett, D.; Guyon, J.; Halsey, D.; James, R.; Mocettini, P.; Munson,

S.; Progar, R.; Thier, R. 2001. Forest insect and disease conditions

in the Intermountain Region. 2000. Ogden, UT: U.S. Department

of Agriculture, Forest Service, Forest Health Protection. 2.

Beveridge, Ron L.; Cahill, Donn B. 1984. Western spruce budworm

feeding effects on conifers located on the Boise and Payette

National Forests, Report No. 84-7. Boise, ID: U.S. Department of

Agriculture, Forest Service, Forest Health Protection. 25 p.

Crookston, Nicholas L. 1990. User’s guide to the event monitor: part

of Prognosis Model Version 6. Gen. Tech. Rep. INT–272. Ogden,

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 27 p. [Electronic Version (modified

Sept.2001) Available on line: http://www.fs.fed.us/fmsc/fvs/documents/gtrs_event_monitor.php)]

Crookston, Nicholas L. 1997. Suppose: An Interface to the Forest

Vegetation Simulator. In: Tech, Richard; Moeur, Melinda; Adams,

Judy. 1997. Proceeding: Forest vegetation simulator conference.

3–7 February 1997, Fort Collins, CO. Gen. Tech. Rep. INT–GTR–

373. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Research Station.

Frankel, Susan J. 1998. User’s Guide to the Western Root Disease

Model, Version 3.0. PSW–GTR–165. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research

Station. 164 p.

Marsden, Michael A.; Eav, Bov B.; Thompson, Matthew K. 1994.

Gen. Tech. Rep. RM–250. User’s Guide to the Douglas-fir beetle

impact model. Fort Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Forest and Range Experiment

Station.

Roberts, Joy C.; Weatherby, Julie C. 1997. INT–GTR–373. Successional Simulations using the Forest Simulator and Multi-pest

Scenarios for Various Levels of Planning. In: Teck, Moeur, Adams.

Proceedings: Forest Vegetation Simulator Conference. Fort

Collins, CO, U.S. Department of Agriculture Intermountain

Research Station. 111 p.

Schenk, John A.; Moore, James A.; Adams, David L.; Mahoney,

Ronald L. 1977. A preliminary Hazard Rating of Grand Fir

Stands for Mortality by the Fir Engraver. Forest Science 23: 108.

Schmid, J. M.; Frey, R. H. 1976. Res. Note RM–309. Stand ratings

for spruce beetles. Fort Collins, CO; U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 4 p.

Smith, Eric. 1999. Westwide Pine Beetle Model User’s Guide, Fort

Collins, CO. U.S. Department of Agriculture, Forest Service,

Forest Health Technology Enterprise Team.

Steele, Robert; Williams, Ralph E.; Weatherby, Julie C.; Reinhardt,

Elizabeth D.; Hoffman, James T.; Thier, R. W. 1996. GTR–332.

Stand Hazard Rating for Central Idaho Forests. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. 1–4.

Van Dyck, Michael G. 2001. User’s Guide to the Post Processors for

the Forest Vegetation Simulator. Fort Collins, Colorado. U.S.

Department of Agriculture, Forest Service, Forest Management

Service Center. 27 p.

Weatherby, Julie C.; Roberts, Joy C.; Donnely, Stephen L.; Ogle,

Karen A.; Jacobsen, Glenn. 1997. FHP Report R4–97–04. Simulated successional pathways for a representative grand fir habitat type on the Payette National Forest. Boise, ID. U.S. Department of Agriculture, Forest Health Protection. 2 p.

Wykoff, William R.; Crookston, Nicholas L.; Stage, Albert R. 1982.

GTR–INT–133. User’s Guide to the Stand Prognosis Model.

Ogden, Utah: U.S. Department of Agriculture, Forest Service,

Intermountain Forest Range Experiment Station. 112 p.
USDA Forest Service Proceedings RMRS-P-25. 2002 57

Abstract—The FVS Event Monitor ArcView® Project (FVS-EMAP)

is a tool for moving simulation results from FVS into the ArcView®

GIS. Using the customized ArcView® project, users can create maps

depicting FVS output variables for a multistand landscape throughout simulated time. The tool can also display the maps in time

sequence, thereby “animating” the landscape through simulated

time. Users generate input for FVS-EMAP via the COMPUTE

keyword (part of the FVS Event Monitor), and the Compute2

postprocessor. An ArcView-based shapefile representing stand

boundaries is required as a “template” onto which FVS-EMAP will

map COMPUTE variable output. Besides functioning as an FVS

postprocessor and landscape analysis tool, the project also contains

a pre-processor that creates the supplemental record file (an FVS

input file) associated with PPE keyword AREALOCS.

The focus of forest management has recently shifted from

emphasizing stands to prioritizing ecosystems. “Landscape

analysis” is now an integral part of forest management.

Coincident with this shifting focus has been a surge of

computing and geographic information system (GIS) technology. Early development of landscape-scale modeling tools

within the Forest Vegetation Simulator (FVS) “family” of

models began with the Parallel Processing Extension (PPE;

Crookston and Stage 1991), which permits the simulation of

stands altogether (in parallel) through simulated time—

instead of serially (one stand after another)—thereby making possible the modeling of landscape-scale processes, such

as pest contagion (see for example Beukema and others

1997). Other tools have been developed that extend FVS

landscape analysis capabilities, including: Pre-Suppose, a

preprocessor that assists users in retrieving data for groups

of stands from a database (Vandendriesche, these proceedings); and the Event Monitor (Crookston 1990), a program

within FVS that allows user to more easily schedule activities across a simulated landscape.

In this paper, we describe a new FVS postprocessing tool,

the FVS Event Monitor ArcView® Project (FVS-EMAP),

that helps users create maps depicting FVS projection output on a multistand landscape. Such landscape scale maps

FVS-EMAP: A Simple Tool for Displaying

FVS Output in ArcView® GIS

Andrew J. McMahan

Anthony W. Courter

Eric L. Smith

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Andrew J. McMahan and Anthony W. Courter are Systems Analysts with

INTECS International, Inc., Fort Collins, CO. Eric L. Smith is the Quantitative Analysis Program Manager, Forest Health Technology Enterprise Team,

USDA Forest Service, 2150 Centre Ave. (NRRC) Bldg. A, Suite 331, Fort

Collins, CO 80526-1891.

can be useful for forest managers conducting landscape

analyses using FVS.

In addition to being a postprocessor, FVS-EMAP can also

be used as an FVS preprocessor to quickly generate the input

data file associated with the FVS/PPE keyword AREALOCS.

This supplemental record file contains geographical coordinates and areas of stands to be simulated. The FVS-EMAP

extracts this information from an ArcView® shapefile (defined below) and writes it to a file, appropriately formatted

for PPE.

Overview of FVS-EMAP __________

The FVS Event Monitor ArcView® Project (FVS-EMAP) is

a customized ArcView® project (an .apr file) that simplifies

the joining of FVS output data with a stand-based shapefile.

A shapefile is ArcView’s native spatial data format and

comprises at least three files, denoted by the extensions .shp,

.shx, and .dbf (it may comprise more than these three files).

Users having spatial data as ARC/INFO coverages, instead

of as shapefiles, can easily convert the data to a shapefile

format in ArcView®.

The FVS-EMAP is essentially a “blank” ArcView® project

that differs from a standard ArcView® blank project in that

it contains three additional scripts, written in ArcView’s

Avenue® programming language, that have been compiled

and embedded into the project. Each of these scripts performs a specific action or “function” (table 1). These functions are accessed via buttons on the project interface’s

buttonbar (fig. 1).

Table 1—Descriptions of the three functions embedded in FVS-EMAP.

Function What the function does:

1 (1) Prompts the user to locate a .cp2 file—an FVS output

file containing values of all Event Monitor COMPUTE

variables; (2) prompts the user to locate a shapefile; and

(3) joins the .cp2 file output to the shapefile.

2 “Animates” the active View by displaying each Theme in

the View one-at-a-time in two-second intervals, from top

to bottom (which will be in chronological order if the

Themes are positioned by Function 1).

3 Creates a supplemental record file for PPE keyword

AREALOCS. The function: (1) extracts polygon areas,

and x- and y-coordinates of polygon centers, and (2)

launches an external program which will rewrite the

extracted data into a text file, appropriately formatted

for FVS.
58 USDA Forest Service Proceedings RMRS-P-25. 2002

McMahan, Courter, and Smith FVS-EMAP: A Simple Tool for Displaying FVS Output in ArcView® GIS

Function 1 “loads” FVS COMPUTE variable output data

(from a .cp2 file) and joins those data to a shapefile. It is

designed to help the user quickly and easily make timeseries map displays portraying FVS simulation output data.

Function 2 “animates” the mapped data, portraying maps

one-at-a-time in short intervals in such a way as to show how

the modeled landscape might change over time. The third

function, unlike the first two functions, is a preprocessor

that extracts information from a shapefile and writes those

data to a file to be used in an FVS simulation along with the

FVS/PPE keyword AREALOCS. The bulk of this paper

focuses on function 1, joining .cp2-file output with a shapefile;

however, functions 2 and 3 are also discussed.

Using FVS-EMAP to create map displays of FVS output

variables involves three basic steps (fig. 2). The first two

steps occur at the FVS simulation set-up phase (before FVSEMAP is launched).

The first step is to incorporate the FVS Event Monitor

keyword COMPUTE, along with accompanying mathematical expressions, into an FVS simulation. This will generate

the output to be mapped. The second step is to invoke the

Compute2 postprocessor, which rewrites the COMPUTE

variable output to a form usable by the FVS-EMAP. The

third step is running the custom ArcView® project,

EMAP.apr. We only briefly discuss the first two steps;

further information about the Event Monitor and the Compute2 postprocessor can be found in Crookston (1990) and

Van Dyck (2001), respectively.

To use FVS-EMAP, users must have: (1) standard FVS

software and stand data files; (2) an appropriate FVS keyword file; (3) an ArcView® shapefile, which provides the

information needed to map the boundaries of the stands

being simulated; (4) ArcView® GIS software, version 3.2 or

higher (currently available for all USDA Forest Service

personal computers); and (5) the customized ArcView®

project, EMAP.apr, available from the Forest Health Technology Enterprise Team (FHTET; contact information provided below).

Using the Event Monitor to Generate

Input Data for FVS-EMAP _________

The COMPUTE keyword is an Event Monitor keyword

that provides the user the ability to insert into an FVS

simulation mathematical expressions that will be evaluated

for stands throughout the simulation. The mathematical

expressions incorporate predefined or previously defined

Event Monitor variables. Users can use the COMPUTE

keyword to have FVS report values of variables it (FVS) is

already keeping track of (for example, basal area per acre);

or it can be used to calculate elaborate user-defined mathematical expressions, such as a stand hazard rating scores

or habitat suitability indices. For example, the map depicted

in figure 3 depicts mountain pine beetle hazard ratings for

stands as defined by Randall and Tensmeyer (2000). This

hazard rating system was “coded” into the FVS simulation

using COMPUTE statements and included for all stands in

the simulated National Forest compartment. A number of

such Event Monitor keyword component files (.kcp files, or

“addfiles”) that calculate stand hazard ratings to various

insect pests are available from FHTET. Also, packaged with

FVS-EMAP is an addfile that generates output containing

all of the metrics in the main FVS stand summary statistics

output table, thus making these variables available to be

mapped. Further details about using the Event Monitor to

generate output variables of the user’s choice can be found in

Figure 1—The FVS-EMAP project interface. The project

interface resembles a typical “blank” ArcView® project.

Note the ”join“ button and tool tip (at cursor, upper left), and

the associated “help” information in the status bar (bottom).

Figure 2—Flowchart diagramming the flow of information from FVS and the Event Monitor into the FVS-EMAP

ArcView® projerct.
USDA Forest Service Proceedings RMRS-P-25. 2002 59

FVS-EMAP: A Simple Tool for Displaying FVS Output in ArcView® GIS McMahan, Courter, and Smith

Crookston (1990), as well as in the user’s guides accompanying the FHTET-provided addfiles.

In conjunction with the COMPUTE keyword, the Compute2 postprocessor must be used to generate a commadelimited output file reporting the COMPUTE variables in

a machine-readable format. The Compute2 postprocessor is

available in Suppose (FVS graphical user interface). When

included in a simulation, it automatically reads all COMPUTE variable output data (for all stands and all years in

which it was generated) from the main FVS output file. It

then rewrites the variables to their own comma delimited

output file (a .cp2 file), organized by stand identification code

(stand ID) and by year. This .cp2 file is the file that will be

read in by FVS-EMAP. Further details about the Compute2

postprocessor can be found in Van Dyck (2001).

These features of FVS are used to generate the data for

input into FVS-EMAP because:

1. The Event Monitor and the COMPUTE keyword can be

used to access a large number of simulated tree, stand, and

site variables.

2. The COMPUTE keyword allows users to mathematically manipulate any predefined or user-defined Event Monitor variable, thus allowing users to create a large number of

user-defined variables, such as hazard ratings or habitat

suitability indices.

3. The Compute2 postprocessor reads all user-requested

COMPUTE variables from the main FVS output file and

rewrites them to their own comma-delimited output file (a

.cp2 file). Having these variables written to their own file,

comma-delimited, and indexed by stand ID and simulation

year greatly facilitates the subsequent readability of the

data by ArcView® GIS.

Portraying FVS Data Spatially _____

After generating the desired FVS output data, the user

launches the ArcView® GIS project EMAP.apr. The “blank”

project interface will appear nearly identical to a “traditional” blank ArcView® project, except there will be at least

one new button on the button bar (fig. 1). This button

activates function 1.

Invoking function 1 begins the process of “loading” data

from a .cp2 file into the project and “joining” the data to a

shapefile. Once the user begins this process, the project will

prompt the user for information. There are five main steps

involved in the map-creation process. (The project may,

however, provide more than the following five queries,

depending upon how the user decides to have map legends

created.) The five steps, outlined briefly here, and discussed

below, are:

1. Input the FVS output data for analysis by “loading” the

appropriate .cp2 file.

2. Input a stand-based shapefile containing the boundaries of the simulated stands.

3. Select an output variable to map.

4. Identify the field in the shapefile’s .dbf table corresponding to the stand ID used in FVS.

5. Select a preformed map legend (if available), or begin

the legend-creation process.

After the user responds to these five prompts, FVS-EMAP

will create a map depicting the values of the output variable

for the simulated stands. The map—actually a multi-Themed

map View—will contain one map layer (a Theme) for each

year reported in the .cp2 file (fig. 3).

For step 1, the project presents a dialog box prompting the

user to navigate to the appropriate output file containing the

data to be displayed. Typically this will be a .cp2 file.

However, any appropriately formatted comma-delimited

file is “readable” by the project. Additionally, the project can

read in output files from the Westwide Pine Beetle Model

(Beukema and others 1997), a landscape-scale bark beetle

model extension to FVS.

For step 2, the project prompts the user to locate a standbased shapefile containing the boundaries of the simulated

stands. The user must provide the shapefile. The .dbf file

associated with this shapefile must have a field containing

data that are identical to the stand identification codes used

in the FVS simulation. That is, the stand IDs used in FVS

(which are the identifiers reported in a .cp2 output file) must

have an identical counterpart in the shapefile .dbf. It is these

matching stand identification codes that allow the project to

join the .cp2 file output data with the shapefile (see step 4

below).

For step 3, the project will provide a list of all of the output

variables that were read in from the .cp2 file. The user may

create map displays for only one output variable at a time.

For step 4, the project will offer a listing of all of the column

headings (field names) from the shapefile’s .dbf table. The

user must then identify the field name associated with the

column of data containing stand identification codes. There

must be a record for every stand reported in the .cp2 file. In

other words, for every FVS-simulated stand, there must

exist a polygon in the shapefile; the project will not allow the

mapping of output onto a nonexistent polygon.

Figure 3—Example map View created via FVS-EMAP.

The View will portray the FVS-simulated, user-chosen

COMPUTE variable of interest on the user-provided

shapefile. This map depicts mountain pine beetle hazard

in 1986 for a National Forest compartment. There are

map “layers” for each year in the output file. Predefined

map legends, if installed, can be automatically applied.
60 USDA Forest Service Proceedings RMRS-P-25. 2002

McMahan, Courter, and Smith FVS-EMAP: A Simple Tool for Displaying FVS Output in ArcView® GIS

Step 5, the legend-creation process, can occur different

ways depending upon what type of legend the user wants to

use. If a preformed legend file exists (an .avl file, in a

“Legends” subdirectory), with a filename matching the

variable name being mapped, then the project will offer to

use that legend. If no such corresponding preformed legend

exists, or if the user declines using the preformed legend,

then the project will create a legend. It offers the user the

choice of creating a “unique values” legend (for use with

discrete variables) or a “graduated” legend (for use with

continuous variables). If a graduated legend type is chosen,

the user is queried as to how many groups the data should

be categorized into. The project then queries the user to

choose a color scheme for the legend. Users can always

“manually” edit legends in ArcView®. Once a desired legend

is created, the user may save it to the “Legends” directory. If

the new legend is given a filename that corresponds to a

COMPUTE variable name, then it can be automatically

applied by FVS-EMAP next time that COMPUTE variable

is loaded and mapped.

After the FVS data View is created, two new buttons will

appear on the project interface (fig. 3). One of these buttons

will invoke the project’s second function: animating the

display. Selecting this button begins the process of displaying the map layers one by one, in two-second intervals. This

gives the user an animated “slices-in-time” view of the

landscape, thus providing users a convenient way to see how

the output variable changes over simulated time.

Generating an AREALOCS

Supplemental Record File ________

The AREALOCS supplemental record file is a file needed

by the FVS/PPE keyword AREALOCS. It contains data

regarding the location and area of stands. The four fields of

data in the supplemental record file are: Stand ID, xcoordinate of stand center, y-coordinate of stand center, and

stand area. These data are not necessarily needed by the

PPE but may be needed depending upon how one intends to

use the PPE. If for example a user wants to run the Westwide

Pine Beetle (WWPB) Model extension to FVS, keyword

AREALOCS, along with its appropriately formatted list of

supplemental records, is required.

The third function built into FVS-EMAP streamlines the

procedure for creating this supplemental record file. Together with a separate FORTRAN program—arealocs.exe

(which accompanies the FVS-EMAP package)—FVS-EMAP

will create the supplemental record file containing coordinates of stand centers, and areas for all of the polygons

(stands) in a shapefile.

Once a shapefile is loaded into FVS-EMAP and a View

created (regardless of whether or not FVS output data are

being portrayed), function 3 becomes available. This function:

1. Extracts stand centroid information (x- and y-coordinates of stand centers) and stand area from an ArcView®

shapefile (note that the shapefile must be projected in a

planar coordinate system, such as the Universal Transverse

Mercator [UTM] System).

2. Optionally writes this information into the shapefile’s

.dbf file.

3. Writes and saves a separate .txt file (comma delimited)

containing only the four fields of data needed as supplemental records with keyword AREALOCS.

4. Then launches a Fortran executable program that

reads the newly created .txt file and then writes the data,

appropriately formatted, to a file.

This new file can then be used as an AREALOCS supplemental record file in a PPE/FVS simulation.

Summary ______________________

FVS-EMAP is a tool that can be used to make maps

displaying results from FVS simulations on groups of stands.

Its usefulness becomes more significant as a collection of

stands representing a landscape is simulated. Once FVS

output data are loaded into ArcView®, various spatial analyses may be performed (using the ArcView® Extension Spatial Analyst, for example). This allows users to address

landscape-level and ecosystem-level management questions

(for an example, see Smith and others, in these proceedings).

Such ecosystem-scale and landscape-scale analyses are becoming increasingly important for forest managers, and

tools such as FVS-EMAP are playing an increasingly important role.

The FVS-EMAP project software, documentation, and

sample Event Monitor addfiles that calculate various stand

statistics are available free of charge from FHTET at:

http://www.fs.fed.us/foresthealth/technology/products

or by contacting:

Forest Health Technology Enterprise Team

USDA Forest Service

2150 Centre Ave (NRRC), Bldg A, Suite 331

Fort Collins, CO 80526-1891

Attn: Eric Smith (elsmith@fs.fed.us)

Acknowledgments ______________

The authors thank Vernon Thomas, Geographic Information Specialist, INTECS International, Inc., Fort Collins,

CO, for his assistance with Avenue® programming related

to function 3 of FVS-EMAP. For their continuing support, we

also thank Lance David (Systems Analyst) and Lowell Lewis

(Geographic Information Specialist), both with INTECS

International Inc.; and the following Forest Management

Service Center (Fort Collins, CO) staff: Gary Dixon, Bob

Havis, Chad Keyser, Erin Smith, and Don Vandendriesche.

References _____________________

Beukema, Sarah J.; Greenough, Julee A.; Robinson, Donald C.E.;

Kurz, Werner A.; Smith, Eric L.; Eav, Bov B. 1997. The Westwide

Pine Beetle Model: A spatially-explicit contagion model. In: Teck,

Richard; Moeur, Melinda; Adams, Judy, comps. 1997. Proceedings: Forest Vegetation Simulator conference; 1997 February 3-

7; Fort Collins, CO. Gen Tech. Rep. INT-GTR-373. Ogden, UT:

U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. p. 126-130.
USDA Forest Service Proceedings RMRS-P-25. 2002 61

FVS-EMAP: A Simple Tool for Displaying FVS Output in ArcView® GIS McMahan, Courter, and Smith

Crookston, Nicholas L. 1990. User’s guide to the Event Monitor:

Part of the Prognosis Model Version 6. Gen. Tech. Rep. INT-275.

USDA Forest Service, Intermountain Research Station, Ogden

UT. 27 p.

Crookston, Nicholas L.; Stage, Albert R. 1991. User’s guide to the

Parallel Processing Extension of the Prognosis Model. Gen. Tech.

Rep. INT-281. USDA Forest Service, Intermountain Research

Station, Ogden, UT. 88 p.

McMahan, Andrew J.; Courter, Anthony; Smith, Eric L. 2002.

User’s guide for FVS-EMAP: A simple tool for displaying FVS

output in ArcView®. FHTET 02-01. USDA Forest Service, Forest

Health Technology Enterprise Team, Fort Collins, CO. 30 p.

Randall, Carol B.; Tensmeyer, Greg. 2000. Hazard rating system for

mountain pine beetle in lodgepole pine using the Oracle database

and the Forest Service IBM platform. Forest Health Protection

Report 00-6. USDA Forest Service, Northern Region, Missoula,

MT. 5 p.

Van Dyck, Michael G. 2001. User’s guide to the post processors for

the Forest Vegetation Simulator. USDA Forest Service, Forest

Management Service Center, Fort Collins, CO. 27 p.
62 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Eric L. Smith is Quantitative Analysis Program Manager, Forest Health

Technology Enterprise Team, USDA Forest Service, 2150A Centre Avenue,

Fort Collins, CO 80521-1891. Andrew J. McMahan is a Systems Analyst,

INTECS International, Inc., c/o USDA Forest Service, 2150A Centre Avenue,

Fort Collins, CO 80521-1891. Thomas Eager is an Entomologist, USDA

Forest Service, 216 N. Colorado, Gunnison, CO 81230.

Abstract—Landscape level analyses of forest management projects

are necessary to consider many of the relevant future conditions and

project impacts. Applications of FVS to landscape analyses are often

hindered by incomplete inventory coverage of the landscape and the

difficulty of representing and modeling spatial interactions between stands. The Westwide Pine Beetle (WWPB) extension to FVS

operates on a multistand basis using the Parallel Processor Extension (PPE). The WWPB model simulates interactions of bark beetles

and stand characteristics both within stands and among stands

within a multistand landscape. To illustrate its functions, results

are presented from an application of the WWPB model to a proposed

forest management project on the Piney Analysis Area, Holy Cross

Ranger District, White River National Forest, Colorado. The problems encountered in this application, such as data availability, and

approaches for addressing them, are presented. Outputs from the

simulation include both stand and landscape level displays. Stand

results display projected tree mortality and stand structure changes

as a function of stand composition and beetle outbreak intensity.

Landscape outputs include both summary tables and ArcViewbased map displays. Effects of stand management are shown both

at the stand and landscape levels.

Forestry in Time and Space _______

In the past, forestry concentrated on tree growth over

time, using the stand as its basic unit of analysis. Spatial

analysis tools were not needed as long as growth and yield

was the major concern. However, over the past 20 years,

concern for spatial relationships between stands, and the

spatial relationship of stand management treatments, has

emerged. Wildlife ecologists and others have concentrated

on spatial relationships but have employed few temporally

dynamic tools.

As with other forms of wildlife, bark beetles move between

stands, and the population dynamics of these beetles encompass landscapes that include hundreds to thousands of

Landscape Analysis Application of the

Westwide Pine Beetle FVS Extension

Eric L. Smith

Andrew J. McMahan

Thomas Eager

stands. In environments where pine bark beetles have a

significant role in shaping forest structure, a stand-level

approach to modeling their impact and response to management is inadequate. The likely future impact of pine bark

beetles on a single stand depends on whether it is surrounded by a landscape of forests suitable or not suitable as

beetle habitat. The Parallel Processor Extension (PPE)

(Crookston and Stage 1991) of the Forest Vegetation Simulator (FVS) was developed to consider the spatial interactions between adjacent stands. The Westwide Pine Beetle

extension employs PPE for its ability to model stand interactions and to consider the spatial distances between stands.

The availability of Geographic Information Systems (GIS)

has also been a key factor in the feasibility of landscape

analysis. Not only do these systems allow easy storage and

display of spatial data, they incorporate powerful spatial

analysis routines needed for landscape analysis.

In this paper, we provide examples of how the Westwide

Pine Beetle model (WWPB; Beukema and others 1997;

FHTET in press)—a landscape-scale extension of the FVS—

can be a useful tool in performing landscape analyses. These

results illustrate the types of questions that may be addressed by the model and demonstrate features of model

behavior.

Scales and Complexity in

Landscape Analyses _____________

Landscape analyses can be conducted at a number of

different spatial and/or temporal scales. An analysis ought

to reflect the scale at which the model processes occur and

address analysis-specific questions.

We categorize these scales of landscape analysis into five

“levels” (table 1). These levels increase in complexity and

each relate to a different “category” of analysis-related

questions.

The first two levels are “nonspatial” in the sense that the

physical location of the units of the landscape is not accounted for. Level 1 of landscape analysis considers the

aggregate conditions of the stands in the landscape. For

example, estimating landscape average host basal area per

acre might suggest a landscape’s susceptibility to a bark

beetle epidemic.

Level 2 considers average stand characteristics for subsets

of the stands in the landscape, grouped, for example, by

cover type or structural stage. Characterization by subsets

of stands can provide information describing how heterogeneous a landscape is. Although these simple landscape

analyses do not consider spatial relationships between the
USDA Forest Service Proceedings RMRS-P-25. 2002 63

Landscape Analysis Application of the Westwide Pine Beetle FVS Extension Smith, McMahan, and Eager

stands, they do provide a summary at a spatial scale “above”

that of individual stand-level analyses. Such a landscape

analysis could prove to be more relevant than stand-level

analyses. For example, a stand that would have a high

hazard rating for beetle attack in a single-stand analysis

might be viewed differently if it was analyzed in the context

of an otherwise low hazard landscape.

Level 3—the simplest analysis using spatial data—considers the size distribution of contiguous conditions within a

landscape. This kind of analysis provides fragmentation and

connectivity statistics. For example, after categorizing stands

in a landscape according to some bark beetle risk rating

system, one could “coalesce” adjacent stands of similar

ranking into new units. The size distribution of these new

units of similar risk could provide insight, for example, into

the relative effectiveness of treatment alternatives to preserve unfragmented areas of wildlife habitat when faced

with tree mortality from beetle outbreaks.

Level 4 considers the spatial relationships among different conditions and can provide information about distance

between units, spatial patterns among units, amount of edge

within and among units, and so forth. Continuing from the

example above, one could analyze whether moderate risk

units are near high risk units or share substantial edge with

high risk units — findings that may have ecological significance. This more complex level of analysis—attained when

the relationships of all stands (or other landscape units) are

considered as elements of a landscape—may reveal ecologically important patterns and relationships that might otherwise be overlooked.

Even the most complex landscape analyses often stop at

this fourth level. A level 5, however, is also possible, in which

the location (or “place”) of stands or groups of stands within

a particular natural and cultural environment. Two different landscapes, for example, can be essentially alike in

terms of the trees they contain, but if one of them represents

critical habitat for an endangered animal species or a valued

scenic resource to an urban population center, the importance placed on these places requires an analysis of these

specific places in the landscape. Landscape averages or

aggregate indices are not adequate if they do not give specific

information about these specific places.

Input Data Considerations in

Landscape Modeling_____________

The techniques and models for spatial and landscape

analysis developed over the past two decades have grown

increasing sophisticated and complex (Sklar and Costanza

1991). These tools are not often employed in forestry, however. One of the reasons for this nonuse is that it can be

difficult to directly relate the results of the analyses to

management goals. For example, an analysis of habitat

fragmentation can rarely be used to accurately predict

future species population levels. Perhaps more important is

the problem of data quality and quantity. Stand-level inventories used as input data for FVS are not often available for

large, contiguous forest areas. For landscape applications of

FVS, lack of “wall-to-wall” stand data will remain a significant challenge for most users.

Meaningful landscape analyses can, however, still be

performed even without comprehensive inventories. Imprecision resulting from incomplete input data can be mitigated

in various ways. Even if complete stand inventories do not

exist, some information is generally available for the forested areas in the landscape. This information can be used

through imputation techniques to fill in these areas with

tree lists from inventoried stands. In our analyses, some

portions of the landscape were occupied by tree species that

are not susceptible to pine beetles. For areas of aspen

(Populus tremuloides Michx.) stands in the landscape, for

example, it was not too important to know precisely the

structure of these stands. In other cases, areas were known

to have been recently clearcut and regenerated with lodgepole pine (Pinus contorta Douglas var. latifolia Engelm.).

Simulation alternatives that thin stands to a target density

eliminate some of the uncertainty, because postthinned

conditions can be assumed to be close to the management

prescription. Stands with average size and stocking conditions for regeneration units of this age were assigned to

these areas. These trees will not be of sufficient size to be

Table 1—Levels of landscape analysis.

Increasing level

of complexity Example type Addresses questions and

Level of analysis issues pertaining to

1 Summary statistics about landscape conditions Average conditions

2 Analysis of landscape by stand condition classes Homogeneity

(e.g. proportion of landscape in each class) Heterogeneity

3 Analysis of the spatial distribution of the

stand condition classes; this possibly gives Connectivity

rise to the creation of new condition class units Fragmentation

(“emergent units”), via the aggregation of stand

level units

4 Analysis of spatial arrangement of emergent Edge/Configuration

condition class units

5 Location within natural and cultural environment “Place”
64 USDA Forest Service Proceedings RMRS-P-25. 2002

Smith, McMahan, and Eager Landscape Analysis Application of the Westwide Pine Beetle FVS Extension

attacked by pine bark beetles during the projection period,

so more precise data are not needed. In some cases, more

sophisticated imputation procedures could be employed to

populate the landscape where stand data are missing (Moeur

and Stage 1995).

Some times data will be missing from stands that are

central to the analysis. For these areas, a sensitivity analysis approach can be used (Ruckelshaus and others 1997). In

our example case, the sample stands that were simulated

represent the range of stand conditions in the landscape, in

the proportion they appeared in a sample inventory. The

sample selected may underestimate the proportion of high

hazard stands that actually exist in the landscape; in our

case, highly stocked stands with higher proportions of larger

diameter pine. To determine the sensitivity of the analysis

to this source of sample error, the sample-based simulation

results can be compared with those from a landscape populated by a higher (and lower) proportion of high hazard

stands.

Finally, some portions of the analysis area are likely to be

of more interest than others. For example, stands in a

specific location may be important components of wildlife

habitat, but stand inventories may not be available for all

the stands this area. Again, a sensitivity analysis may be

used to determine if simulated future habitat conditions in

this area are sensitive to the assumption that sampled

conditions also represent the unsampled conditions. If so,

then it may be possible to gather additional data in areas

where the simulation results are critical to decisions.

In all cases, one must remember to treat simulation model

results as imperfect summaries of present and future conditions. Stand inventory data are only samples of what actually exists, and FVS and the WWPB extension are only

statistical tools and abstractions of reality.

Westwide Pine Beetle Model ______

As an extension to the FVS—in conjunction with PPE—

the WWPB model simulates between-stand bark beetle

contagion processes across a landscape, as well as withinstand growth and mortality processes (FHTET, in press).

The importance of landscape-level mountain pine beetle

(MPB; Dendroctonus ponderosae Hopkins) populations has

been recognized as significant in determining risk at the

stand level (Shore and Safranyik 1992). The model is capable of simulating the effects of three beetle species: the

mountain pine beetle, the western pine beetle (WPB;

Dendroctonus brevicomis LeConte), and Ips species. Our

example applications simulate MPB in lodgepole pine.

Beetle populations per se are not simulated. Instead,

beetles are represented in terms of “Beetle Kill Potential” or

BKP. One unit of BKP represents an amount of beetles able

to kill 1 square foot of basal area. BKP is allocated to and

from stands annually. The amount of BKP allocated among

stands is a function of the amount of BKP present in each

stand, stand conditions in both the donor and recipient

stands, and the distance between donor and recipient stands.

Once allocated to a stand, BKP is assigned to specific tree

size classes based on stand conditions. With sufficient BKP,

trees are simulated as being killed. The amount of BKP in

the following year is primarily a function of the diameters

and numbers of trees that were killed the previous year.

Three spatial scales exist within the WWPB model:

1. Individual forest stands.

2. The collection of stands that compose the landscape.

3. A larger area (outside of, and encompassing, the landscape) referred to as the “outside world,” which serves as a

“source” and “sink” for landscape BKP.

The spatial relationships of stands within the landscape

are explicitly considered. Being a landscape-scale model, the

WWPB model is designed primarily to evaluate scenarios at

the landscape scale. That is, it can be used to address

questions such as:

• What proportion of a landscape might experience severe

beetle mortality (given a specific outbreak scenario)?

• What types of stands are most vulnerable to beetle

attack?

• If certain areas experience an outbreak, how quickly—

and how far—might an outbreak spread?

• How might specific management actions affect future

beetle dynamics?

At the stand level, the model may be used to address

questions such as:

• How will a severe bark beetle outbreak affect stand

structure?

• How long might stands experience high levels of beetles?

• What levels of mortality might some stands experience?

Note that these stand-level questions are posed in a

general way. The model is not designed to predict precisely

which individual stands in a landscape will be attacked or

impacted by bark beetles. Rather, the model is designed to

provide insight as to what kinds of stands in this landscape

might experience beetle mortality, and how much mortality

might occur. This can be simulated for different management and outbreak scenarios. In other words, although the

grain of the WWPB model involves stands, its extent is the

landscape, and that is the scale at which interpretation of

output is most meaningful. To the degree that the spatial

arrangement of stands in the landscape is known, some

spatial information is inherent in the model results. For

example, areas in the landscape that do not have host trees

will not experience mortality.

Objectives and Dimensions of this

Analysis _______________________

The objectives for this analysis are to use the WWPB

model to help address the following questions:

• How will MPB outbreaks of varying intensity affect the

landscape (defined below) as a whole?

• How will outbreaks of varying intensity affect the component stands?

• How will beetle-induced mortality manifest itself temporally and spatially across the landscape?

• How will specific management actions affect bark beetle

dynamics?
USDA Forest Service Proceedings RMRS-P-25. 2002 65

Landscape Analysis Application of the Westwide Pine Beetle FVS Extension Smith, McMahan, and Eager

We addressed these questions by organizing our simulations and analyses around the following dimensions of

analysis.

• Distribution of stands within the landscape

• Time (as it affects growth and mortality)

• Treatments (thinnings)

• Outbreak intensity scenarios

• Stand-level responses and conditions

• Landscape conditions

A thorough discussion of all of these dimensions is beyond

the scope of this paper. We address the first four dimensions—which are especially relevant during the simulation

set-up phase of the analysis—only in a cursory way. This

paper focuses primarily on the last two dimensions of the

analysis: stand-level and landscape-level responses to the

MPB, as projected by the WWPB Model.

Methods _______________________

Our analysis addresses a 23,000-acre landscape in the

Piney Analysis Area, Holy Cross Ranger District, White

River National Forest in Colorado. This area was the subject

of a previous forest health assessment that used the single

stand Mountain Pine Beetle FVS extension to analyze the

possible impact of this insect on selected stands (Angwin and

others 1996). Sources of information used for imputing stand

information and setting up the simulation included:

• Recent stage 2 stand inventories for the area

• Digital cover type maps for the area

• Maps of past treatment areas

• Known proposed treatments and treatment areas

• Knowledge of current beetle conditions and likely outbreak dynamics

• Online Vail, CO, climate information (to provide insight

into the frequency, duration, and magnitude of stress

events)

Lodgepole pine is the primary species (percent of total

stand basal area in host ranged from 66 to 100 percent), with

additional components of subalpine fir (Abies lasiocarpa

(Hook.) Nutt.), Engelmann spruce (Picea engelmannii Parry

ex Engelm.), and aspen. Stand basal areas ranged from 48 to

232 square feet per acre; quadratic mean diameters (Q.M.D.)

ranged from 2.6 to 14.2 inches. The area has experienced

recent MPB activity.

Because digital stand maps were not available, we subdivided the landscape into 221 “stands,” each 0.6 km by 0.7 km

(approximately 100 acres). Using digital vegetation-type

maps and hand-drawn district stand maps, we estimated

the actual percent of the landscape stocked to host pine

types, and the locations (in our simulated landscape) for

proposed silvicultural treatments and previous clearcuts.

We populated the stands with treelists from 19 representative stand inventories in such a way as to maintain a

quantitatively accurate distribution of cover type, basal

area distribution, and MPB risk rating across the landscape

(spatial accuracy was also maintained for cover type). “Replicating” inventories in the landscape served two purposes:

it facilitated analyzing the effect of stand location on beetle

Figure 1—Simulated beetle-induced mortality rates

over 5 years (2004-2009) as a percentage of the

preoutbreak (postthin) basal area. Each point represents one of the 163 simulated stands. Stands with

1999 basal areas of 80 and 100 sq ft per acre were

thinned (from below) to these levels in 1999.

dynamics, and it “filled in” the landscape to account for

uninventoried stands.

BKP was initialized in the simulated landscape at low

(endemic) levels. Under base case conditions for this landscape, the WWPB model projects gradually increasing endemic mortality over a 20-year period. One of the features of

the model is that “stress events,” which can simulate conditions such as droughts, can be imposed. These events cause

tree resistance to bark beetles to decline. Events were

simulated to create MPB outbreaks of two intensities: one

designated as a “severe” outbreak, another designated as a

“moderate” outbreak. The stress event variable (field 3 of

VARYRAIN keyword) has a default value of zero; it can be

changed to be positive or negative. When it is set to be

positive, tree vigor is increased and resistance to beetle

attacks is increased. This approach was used to create

outbreak scenarios that collapsed in realistic fashion.

Simulated thinning alternatives were constructed based

on actual management proposals for this site. Treatments

were simulated as thins-from-below to a residual basal area

of 80 or 100 square feet per acre. The base case for managed

scenarios applied treatments to about 10 percent of the area.

Simulations were run for 30 years, beginning in inventory

year 1994. Simulated MPB outbreaks commenced in 2004,

triggered by the use of the VARYRAIN keyword.

Results ________________________

Stand-Level Responses

Simulated MPB outbreaks differentially affected stands.

During a simulated “severe” outbreak, mortality rates ranged

from zero in the sparsest stands, to nearly 60 percent basal

area mortality over 5 years in the densest stands, with an

apparent “threshold” of somewhere between 100 and 120

square feet of basal area, below which only very low levels of

mortality occurred (fig. 1). Host basal area mortality rates

(10-year) sometimes exceeded 75 percent (fig. 2a). Peak 5-
66 USDA Forest Service Proceedings RMRS-P-25. 2002

Smith, McMahan, and Eager Landscape Analysis Application of the Westwide Pine Beetle FVS Extension

year percent basal area mortality was approximately 40

percent for the moderate outbreak (data not shown).

Within stands, beetle-caused mortality occurred primarily in the largest size classes for both severe and moderate

outbreak scenarios (fig. 2b). Resulting stand structures after

simulated outbreaks are noticeably different depending

upon the severity of the outbreak. Over time, smaller size

classes are attacked (fig. 3).

Within-stand temporal trajectories of beetle outbreak

varied significantly, stand to stand (fig. 4). Generally, denser

stands with larger trees experienced more rapid mortality

rates, followed by a relatively rapid decline of BKP levels.

Conversely, lower basal area stands with smaller Q.M.D.

experienced BKP pressure later in the simulation and for

longer periods, sometimes even well after the landscapewide outbreak had subsided.

Thinned stands experienced little if any beetle-caused

mortality (fig. 1 and 5). This thinning effect was observed in

spite of stands having, after thinning, higher proportions of

host and a larger host Q.M.D., characteristics that generally

are attractive to beetles.

Landscape-Level Responses

Simulated outbreaks took on different trajectories (landscape wide) depending upon how severe the outbreak was

(how negative of VARYRAIN values were used and for how

long), and how intensely the outbreak was “collapsed” (how

Figure 3—Landscape average diameter at breast height

of beetle-killed trees by year during the simulated “severe” outbreak.

Figure 4—BKP levels for three stands during a simulated

severe MPB outbreak. These “temporal trajectories” show

how landscape-scale outbreaks manifest differently in different stands. Legend indicates each stand’s 1984 (beginning

of simulation) basal area.

Figure 2—(a) Stand basal area by host and nonhost in 2004, and in 2014 after simulated “severe”

and “moderate” MPB outbreaks for one stand in the landscape. (b) Basal area by tree size class for

the same stand and time period as shown in (a).

0

25

50

75

100

125

150

175

200

Before

After Mod

After Sev

Stand Basal Area ( Sq Ft / A )

Host Non-host

0

20

40

60

80

3-66-9 9-12 12-15 15-18

Size Class (inches)

Basal Area ( Sq Ft / A )

Pre-Outbreak: 2004

After Moderate Outbreak: 2014

After Severe Outbreak: 2014

5

10

15

2004 2005 2006 2007 2008 2009

Average DBH Beetle-Killed

(Inches)

0

10

20

30

40

2000 2005 2010 2015

Year of Simulation

Mean % BA Beetle-Killed

232 184 160

a b
USDA Forest Service Proceedings RMRS-P-25. 2002 67

Landscape Analysis Application of the Westwide Pine Beetle FVS Extension Smith, McMahan, and Eager

positive of a VARYRAIN keyword was used). We present

four scenarios: a severe outbreak, collapsed; a severe outbreak, not collapsed; a moderate outbreak, collapsed, and a

moderate outbreak, not collapsed (fig. 6). The severe outbreak scenarios resulted in landscapewide BKP rate increases that were higher than in the moderate outbreak

scenarios. Rates of BKP increase appears to be controlled

more by the stress event or beetle dynamics rather than on

the stand conditions per se. Uncollapsed outbreaks—which

eventually subsided because relatively few adequate trees

remained in the landscape (data not shown)—took about

twice as long to subside (approximately 10 years) than did

the collapsed simulations.

Figure 4, which represents outbreak trajectories for individual stands in the severe outbreak, collapsed scenario,

suggests a landscape scale phenomenon not revealed by the

landscape-scale average graph (fig. 6). Although

landscapewide the outbreak appears to have subsided by

2010, notice that some individual stands are just then

attaining their peak BKP levels.

Distribution, landscapewide, of stand basal area per acre

changes significantly after simulated outbreaks (fig. 7). This

result follows from the individual stand phenomena suggested by figure 2b.

Figure 1 suggests the significance of stand location in the

landscape. As discussed, tree inventories used to populate

the stands in this landscape were replicated. Because replicates had similar stand basal area in 1999 (among themselves) before the simulated outbreaks, the difference in

beetle mortality experienced by identical stands in different

locations in the landscape is represented by the vertical

range among the replicates, which appear together in vertical lines in the figure.

Discussion _____________________

The review of the analysis presented here illustrates the

following:

1. Through use of the PPE extension, FVS can be used to

represent interstand contagion processes that occur over a

large number of stands (at least a few hundred) and that may

occur at time steps less than a typical FVS cycle.

2. Spatial processes that take place at a landscape scale

cannot be reliably modeled at the stand level; the stand-level

outcome can be affected by both the entire landscape conditions and the conditions immediately surrounding the stand.

3. The amount of meaningful analysis that can be performed on a landscape with FVS is limited by the amount of

data available for the landscape and the validity of the

models being employed; but through sensitivity analysis

and use of imputed data, useful landscape projections and

comparisons can still be made.

Figure 6—Landscape average BKP per acre over time

for four simulation scenarios, showing that simulated

outbreaks can have different temporal trajectories.

Figure 7—Frequency distribution of stand basal area

classes landscapewide, before and after a simulated

severe outbreak. (The sum of each of the two series

equals 100 percent.)

Figure 5—Stand basal area for two “replicate” stands,

identical at the beginning of the simulation, during the

severe outbreak simulation. One was thinned in 1999.

Beetle-induced basal area mortality (per year) also is

shown for the unthinned stand.

0

50

100

150

200

250

1995 2000 2005 2010 2015

Stand Basal Area (BA)

and BA Beetle-Killed

 (Sq Ft / A)

Unthinned Stand BA

Thinned Stand BA

BA Beetle-Killed per Year (Unthinned Stand)

0

5

10

15

2000 2005 2010 2015

Landscape Average BKP

( Sq Ft / A )

severe oubreak, collapsed

severe outbreak, not collapsed

moderate outbreak, collapsed

moderate outbreak, not collapsed

0%

25%

50%

75%

0-60 60-120 120-180 180-240

Basal Area (Sq Ft / A)

Percent of Landscape

Before Outbreak After Outbreak
68 USDA Forest Service Proceedings RMRS-P-25. 2002

Smith, McMahan, and Eager Landscape Analysis Application of the Westwide Pine Beetle FVS Extension

Spatially explicit analyses can be performed on WWPB

output by importing the results to a Geographic Information System such as ArcView®. Such analyses are not

meaningful for this example because only the types of

stands from the actual landscape, and not their spatial

arrangement, are represented. Specific functions included

in the WWPB software package facilitate moving output

directly into ArcView®. FVS-EMAP (McMahan and others,

this proceedings) is a flexible tool designed to perform this

function for general FVS applications.

Though not presented here, GIS analyses can be used to

describe the spatial effects of WWPB model outputs. A

relatively simple procedure is to dissolve boundaries between stands with similar characteristics (such as cover

type and age class) and display the distribution of sizes of

these contiguous areas. A number of more sophisticated

procedures have been developed and are routinely employed

by landscape ecologists (O’Neill and others 1988; Turner

1990; Cullinan and Thomas 1992).

Several other multistand processes and conditions, including fire, wildlife habitat, and watershed hydrology,

could be modeled with an approach similar to that used by

WWPB. Such models, including WWPB, could be linked so

that landscape impacts over time and space of landscape

disturbances could be better understood and displayed.

References _____________________

Angwin, P. A.; Johnson, D. W.; Eager, T. J.; Smith, E. L.; Bailey, W.

1996. 3430 Biological Evaluation R2-97-01. Piney Analysis Area

Holy Cross Ranger District White River National Forest, Forest

Health Assessment. USDA Forest Service, Renewable Resources,

Rocky Mountain Region, Gunnison Service Center, Gunnison,

CO. 80 p.

Beukema, Sarah J.; Greenough, Julee A.; Robinson, Donald D. E.;

Kurz, Werner A.; Smith, Eric L.; Eav, Bov B. 1997. The westwide

pine beetle model: a spatially-explicit contagion model. In: Teck,

Richard; Moeur, Melinda; Adams, Judy, comps. 1997. Proceedings: Forest Vegetation Simulator conference; 1997 February 3-

7; Fort Collins, CO. Gen Tech. Rep. INT-GTR-373. Ogden UT:

U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. 126-130

Crookston, N. L.; Stage, Albert R. 1991. User’s guide to the Parallel

Processing Extension of the Prognosis Model. USDA Forest

Service Gen. Tech. Rep. INT-GTR-281. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research

Station. 88 p.

Cullinan, V. I.; Thomas, J. M. 1992. A comparison of quantitative

methods for examining landscape pattern and scale. Landsc.

Ecol. 7(3): 221-227.

FHTET. In press. Westwide Pine Beetle Model: Detailed Description. Fort Collins, CO. U.S. Department of Agriculture, Forest

Service, Forest Health Protection, Forest Health Technology

Enterprise Team. 87 p.

Moeur, Melinda; Stage, Albert R. 1995. Most similar neighbor: an

improved sampling inference procedure for natural resource

planning. For. Sci. 41: 337-359.

Ruckelshaus, Mary; Hartway, Cynthia; Kareiva, Peter. 1997. Assessing the data requirements of spatially explicit dispersal

models. Conserv. Biol. 11(6): 1298-1306.

O’Neill, R. V.; Krummel, J. R.; Gardner, R. H.; Sugihara, G.;

Jackson, B.; Christensen, S. W.; Dale V. H.; Graham, R. L. 1988.

Indices of landscape pattern. Landsc. Ecol. 1(3): 153-162.

Shore, T. L.; Safranyik, L. 1992. Susceptibility and risk rating

systems for the mountain pine beetle in lodgepole pine stands.

Info. Rep. BC-X-336. Victoria, BC: Forestry Canada, Pacific

Forestry Centre. 12 p.

Sklar, Fred H.; Costanza, Robert. 1991. The development of dynamic spatial models for landscape ecology: A review and prognosis. In: Turner, M. G.; Gardner, R. H. eds. 1991. Quantitative

methods in landscape ecology: the analysis and interpretation of

landscape heterogeneity. Springer-Verlag, New York: 239-288.

Turner, M. G. 1990. Spatial and temporal analysis of landscape

pattern. Landsc. Ecol. 3(3): 153-162.
USDA Forest Service Proceedings RMRS-P-25. 2002 69

In: Crookston, Nicholas L.; Havis, R. N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Helen Maffei is the central Oregon Area Forest Pathologist, Deschutes

National Forest Supervisor’s Office, 1645 Hwy. 20 East, Bend OR 97701.

Brian Tandy is the District Silviculturist, Sisters Ranger District, Deschutes

National Forest, P.O. Box 249, Sisters, OR 97759.

Abstract—A 10-step methodology was developed to facilitate the

analysis and communication of vegetation management treatments

on short-term and long-term late successional habitat and overall

landscape stability in eastern Cascade forests. This methodology

was first used to evaluate proposed management effects on spotted

owl habitat for a large project area within a late successional reserve

(LSR) in central Oregon. Modeling tools and analysis techniques

included: stratifying and classifying the LSR by focal species

habitat and habitat stage; hazard rating systems, Forest Vegetation

Simulator (FVS), pest impact models, and most similar neighbor

analysis procedures. Data sources used include: stand exams, forest

inventory data, field surveys and a photo-interpreted forest layer.

Custom event monitor variables and keyword component files (kcp

files) were written to describe and evaluate project objectives,

proposed silvicultural treatments; and to model key disturbance

events where off-the-shelf FVS models were not available. FVS

projections to predict long-term effects were composed entirely of

kcp files, which were assigned to appropriate stands using specialized software. Results of these projections were imported into GIS

and displayed spatially, by treatment alternative, over time. Spatially explicit graphic and numerical displays of effects on spotted

owl habitat over time helped demonstrate the need, and develop a

base of support, for the management proposed by the project.

Over the past century, eastern Cascade grand fir and

Douglas-fir series communities have changed from relatively open stands dominated by large shade-intolerant

ponderosa pine (Pinus ponderosa Laws.) and Douglas-fir

(developed under a low intensity fire regime), to dense

multicanopy forests dominated by shade tolerant trees

(Hessburg and others 1994). Species such as spotted owl

(Strix occidentalis caurina) have found this combination of

large legacy trees and multilayered dense fir in-growth to be

suitable habitat and have established breeding populations

in these altered mixed conifer communities. Late successional reserves (LSRs) have been established in these areas

with the objective of protecting spotted owl and other species

Methodology for Modeling the Spatial and

Temporal Effects of Vegetation Management

Alternatives on Late Successional Habitat in

the Pacific Northwest

Helen Maffei

Brian Tandy

associated with late successional habitat (USDA Forest

Service and USDI Bureau of Land Management 1994).

The changes in mixed conifer communities that have

resulted in habitat conducive to northern spotted owl, have

also resulted in a vegetative shift toward greater instability.

Much of the “new” spotted owl habitat may be transient

because replacement Douglas-fir and ponderosa pine nest

trees are unlikely to develop given the present stand densities (Everett and others 1997). In addition, mixed conifer

communities are now much more susceptible to severe

impacts from insects, diseases, and wildfires (Agee and

Edmonds 1992; Hessburg and others 1994). Thus, broadscale losses of habitat can occur over very short periods. For

example, over a decade, we estimate that 70 percent of

spotted owl nesting, roosting, and foraging habitat (NRF)

was lost as a result of the combined effects of western spruce

budworm, root diseases, and bark beetles in the McCache

project area in Oregon (fig. 1).

Given the inherent transience of much of the spotted owl

habitat east of the Cascades and given that the USDA Forest

Service is directed to manage for spotted owl habitat over

time (USDA Forest Service and USDI Bureau of Land

Management 1994), the agency is faced with a delicate

balancing act for eastern Cascades spotted owl habitat that

is both spatially and temporally complex. The agency is

challenged with protecting and maintaining a portion of

the existing habitat, developing future habitat, and at the

same time, maintaining an overall condition of landscape

stability.

Silvicultural treatments such as thinning and prescribed

burning can be effective techniques to progress toward this

goal. However, traditional treatment proposals, analysis

procedures, and communication techniques have been inadequate for gaining support for these types of actions. The

impacts and benefits of the proposals to late successional

habitat are often not clear to wildlife biologists, line officers,

or the public. This lack of common understanding among

stakeholders has resulted in frustration, heightened controversy, and lawsuits.

To address this problem, the authors have developed a 10-

step analysis procedure to help land managers:

• Predict spatially explicit effects to habitat over time

• Communicate these effects

• Evaluate logic and rationale of proposed actions

• Make more informed resource management decisions

A number of analysis tools and data are used including:

(1) The Forest Vegetation Simulator (including the western
70 USDA Forest Service Proceedings RMRS-P-25. 2001

Maffei and Tandy Methodology for Modeling the Spatial and Temporal Effects of Vegetation Management Alternatives...

Figure 1—Northern spotted owl nesting roosting and

foraging habitat (NRF) mapped in the Cache LSR in 1985

and in 1995. The dramatic change in NRF illustrates the

rapid rate at which habitat can become nonhabitat in

unstable forest structures. The 1985 condition was developed by reconstructing tree lists for 1985 stands from

stand exams done in the early to mid 1990s.

root disease and dwarf mistletoe impact models), (2) stand

exams, (3) forest inventory data, (4) global data (in our case,

photo-interpreted data), and (5) Most Similar Neighbor

inference (MSN) (Moeur and Stage 1994). This 10-step

procedure was first applied to alternative analysis for the

15,350-acre McCache Vegetation Management Project area,

located in the Cache LSR, on the Sisters Ranger District,

Deschutes National Forest. The objective of this paper is to

describe the methodology using examples from this initial

McCache project analysis.

Methods _______________________

Part 1: Defining Existing Condition,

Project Goals, and Objectives

Step 1. Stratify the Late Successional Reserve by

focal species area. Focal species areas are portions of the

landscape that have the best potential biological capability

to provide late successional habitat for an identified focal or

indicator species. For example, in the mixed conifer plant

associations, the late successional focal species is northern

spotted owl. For the ponderosa pine plant associations the

focal species may be white-headed woodpecker (Picoides

albolarvatus) or northern goshawk (Accipiter gentiles). Many

eastern Cascades late successional reserves have more than

one focal species because they encompass plant communities with different late successional indicator species. The

Cache LSR assessment identified three major focal species

areas within the McCache project area. For most of the

mixed conifer plant associations the focal species is northern

spotted owl. For the ponderosa pine plant associations,

white-headed woodpecker is the focal species, and blackbacked woodpecker (Picoides arcticus)is the focal species for

the lodgepole (Pinus contorta var. latifolia) forest type.

Step 2. Describe the basic structural features of late

successional habitat in measurable terms. A shared

understanding of what you are trying to achieve is crucial to

any successful project. The second step of our analysis

methodology is, therefore, to describe late successional habitat in terms of measurable stand structure features. For the

Cache LSR northern spotted owl focal species area, spotted

owl habitat was quantified based on available scientific

literature, adapted to observed local spotted owl nesting and

dispersal behavior on the Deschutes National Forest

(Thomas and others 1990; Forsman and others 1984;

Buchanan and others 1995; Maffei and others 1997; Everett

and others 1997). Structural features of two types of spotted

owl habitat were described:

1. Nesting, roosting, and foraging habitat (NRF)

has all the structural characteristics required for successful reproduction. Spotted owls typically nest low in the

canopies of larger, dominant trees. Multilayered canopy

cover is required for protection from predators. For the

McCache Analysis, spotted owl NRF was described in

terms of a sliding scale of numbers of large overstory trees

(represented as trees over 21 inches d.b.h.) and canopy

cover (table 1).
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2. Dispersal habitat is used by spotted owls to travel

from one NRF area to another. It must have the structural

characteristics necessary for owls to travel safely between

NRF areas. For the McCache analysis, a stand was defined

as dispersal habitat if there was a minimum of 35 percent

canopy cover provided by trees greater than 6 inches d.b.h.

Step 3. Describe management objectives in

measurable terms. The following objectives probably apply to most eastern Cascades late successional reserves:

a. Protect all or a portion of the existing late successional habitat.

b. Promote the development of future late successional

habitat.

c. Promote an overall condition of productivity, stability,

and low risk of catastrophic disturbance.

For habitat that is inherently high risk, “protection” of

habitat is described in terms of the stability of the landscape

around it. We chose to depict vegetative stability on the

landscape in terms of hazard to “stochastic” disturbance

agents that are likely to promote sudden loss of habitat. For

mixed conifer systems we used hazard-rating models for

stand-replacing wildfire, western spruce budworm, and bark

beetles. Hazard rating for western spruce budworm was

based on Carlson and Wulf’s (1989) system, bark beetle

hazard on local stocking guides (Hall 1987). Stands were

rated as having a high potential for severe wildfire based on

vertical structure and fuel loading. Stands with high stocking levels and multiple canopies and/or stands with fuel

loads in excess of 20 tons/acre were rated as high hazard.

Forest diseases, if present and significant, could be described in terms of a hazard-rating system. However, we

chose to model the effects of these slow, progressive disturbance agents on habitat directly, using the western root disease

model and the dwarf mistletoe impact model. Achievement of

habitat is described in terms of the quantifiable features of

habitat or in terms of quantifiable benchmark indicators of

progress toward the desired habitat structural condition.

Step 4. Classify each stand in the focal species area

in terms denoting the stand’s present structure relative to the elements needed for habitat (habitat stage).

This classification could be approached any number of ways

but for our spotted owl focal species habitat areas we assigned each stand into one of four general groups (fig. 2):

1. Stands with sufficient numbers of large trees and levels

of canopy cover to presently fit the definition of NRF.

Table 1

Number of large Percent

trees per acre1 Canopy Cover2

8-10.9 60+

11-14.9 55-59.9

15-17.9 50-54

18-21.9 45-49.0

22+ 40-44.9

1

Large trees are as those with 21 inch or greater

d.b.h. 2

Canopy cover provided by trees larger than 6

inches d.b.h.

2. Sufficient canopy cover to fit the definition of dispersal habitat. Depending on the number of large trees

present, current dispersal habitat could develop into NRF

in a short time, a relatively long time, or may not ever

develop into NRF.

3. Sufficient large tree structure but not enough canopy

cover to be NRF. At this stage, not treating and allowing the

process of fir in-growth to continue could foster NRF.

4. Nonhabitat. Insufficient numbers of large tree structure or sufficient canopy cover to be classified as NFR or

dispersal habitat. These stands are the furthest temporally

away from being NRF because first the large tree structure

needs to be developed, followed by the development of a

layered, shade-tolerant understory.

Figure 3 shows this classification system applied to the

346 stands in the McCache project spotted owl focal species

area.

Step 5. For each habitat stage develop a list of

treatment objectives and prescriptions. For each habitat stage, assign a list of possible silvicultural treatment

objectives and associated treatments. No treatment is

always an option for each habitat category. Table 2 displays

assigned objectives and treatment possibilities based on

our four-category habitat classification for stands in northern spotted owl focal species areas. Note, that for nonhabitat

stands, the prescription to regrow habitat mirrors the longterm two-stage process that initially produced it (grow

large-tree-dominated fire climax forest first; then allow fir

to grow underneath).

Step 6. For each alternative, the Interdisciplinary

Team assigns a silvicultural treatment to each stand

(no treatment is considered a treatment in this case).

Proposed treatments are not assigned solely on individual

stand conditions. Broader landscape considerations will

also play an important role. For example, spatial concerns

and connectivity issues such as the need for dispersal

corridors or continuity of green fuel breaks, and unique

stand characteristics, may all drive the final proposed

stand treatments.

Part 2: Projection of Future Effects

Using FVS

The following steps involve using FVS to model spatially

explicit outcomes of the selected alternatives over time.

FVS (Stage 1973) and associated software (Crookston 1997;

McGaughey 1997) constituted the model of choice because:

• Event monitor allows for the creation of custom variables that tie directly to the project issues and objectives

• The model uses standard field inventory data

• Insect and disease impact models are available

• It is easy to project and summarize, and it spatially

displays large amounts of data

• Links to external databases and GIS

• Calibrated to central Oregon

• USDA Forest Service supported and maintained

Step 7. Fully populate the stand exam database for

each focal species area. In order to have a data-driven

spatial display for future scenarios, it is necessary for each

stand to be represented by a sample tree list so that it can
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Maffei and Tandy Methodology for Modeling the Spatial and Temporal Effects of Vegetation Management Alternatives...

C) D)

A) B)

Dead trees

Figure 2—Habitat Stage Classifications for the northern spotted owl focal species area: (A) Stand is nesting roosting and foraging

habitat (NRF); (B) Stand is dispersal habitat; (C) Stand has sufficient large trees but not enough canopy cover to be NRF; (D)

Stand is nonhabitat — it has neither enough large trees nor enough canopy cover to be considered NRF or dispersal habitat.
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Methodology for Modeling the Spatial and Temporal Effects of Vegetation Management Alternatives... Maffei and Tandy

Table 2

Northern spotted owl Probable stand

focal species habitat stage management objectives Prescription

NRF Desired condition has already been met No treatment

Dispersal Habitat Develop into NRF No Treatment or

Reduce fire hazard Thin from below to 20% below the upper

 management zone1

Maintain tree vigor and species composition.

Sufficient Large Trees Allow fir understory to develop No Treatment or

Fire hazard reduction Thin from below to 20% above the lower

Maintain health of large trees management zone1

Non-habitat Retain as travel corridor for other late No treatment or

 successional species

Restore large tree structure as the first Remove dead trees

 step of creating NRF long term Remove all true fir under 21 inches d.b.h.

Reduce fire hazard Thin residual trees or plant a mixture of

 ponderosa pine and Douglas-fir, if necessary

1

Stocking levels based on Cochran 1992

Figure 3—Northern spotted owl habitat stage classifications spatially mapped over the spotted owl focal species habitat

area for McCache Vegetation Management Project.
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Maffei and Tandy Methodology for Modeling the Spatial and Temporal Effects of Vegetation Management Alternatives...

be projected in FVS. For the McCache vegetation management project, nearly complete stand exam coverage “with

the exception of the plantations” was available. However,

partial stand exam coverage is more often the case. We are

currently working on another analysis area where only half

of the area was inventoried. Stands exams from stands of

similar structure (diameter class distribution), density (basal

area/acre), and species composition were assigned to the

noninventoried polygons to obtain the desired coverage. The

assignments were made using the most similar neighbor

analysis procedure (MSN) (Moeur and Stage 1994; Moeur

and others 1995). Interpreted aerial photography provided

the global data layer for the MSN analysis.

Step 8. Construct keyword component files for runstreams; calibrate and select appropriate models and

submodels. FVS runs using our technique are constructed

entirely of keyword component files (kcp files). These kcp

files can be composed of a combination of base and submodel

commands as well as custom variables created within the

event monitor. For each stand, these keyword component

files (kcp files):

• Calibrate the base model

• Call and calibrate appropriate FVS submodels

• Simulate important successional processes not available as off-the-shelf submodels with the user’s FVS

variant (for example, natural regeneration)

• Assign hazard ratings for important disturbance agents

over time

• Simulate proposed silvicultural treatments for each

alternative (including followup treatments in later years)

For example, each of the 346 stands in the McCache spotted

owl focal species area was assigned four or five kcp files. Kcp

files are available upon request:

• The first kcp file calibrated the base model (in terms of

site quality and growth); modeled natural regeneration

and endemic as well as density-induced mortality from

mountain and western pine beetle. In brief, this kcp file

addressed live tree regeneration, growth, and mortality

processes given the no action alternative.

• The second kcp file initialized and calibrated the western root disease model. Root disease was severe and

widespread throughout the area but was not adequately

represented in the stand exams. An estimated stand

root disease condition was, therefore, assigned based on

model ready keyword sets from forest inventory data

(Gregg and Goheen 1997) plots in the project area and

a 1980s root disease survey of a portion of the project

area.

• The third kcp file classified the stand in terms of spotted

owl habitat stage, by cycle.

• The fourth kcp file modeled hazard to stand-replacing

wildfire.

• The fifth kcp file modeled western spruce budworm

hazard.

• The sixth kcp file modeled the prescription assigned to

the stand by alternative, including any additional natural regeneration promoted by ground-disturbing activities. This kcp file was not included for untreated stands.

The inclusion of appropriate disturbance models for the

long-term projections is critical. For example, the spotted

owl focal species area in the McCache project is moderately

to severely infected with root disease. As such, root disease

will have a significant effect (fig. 4) on the outcomes of

proposed alternatives. Without modeling this important

disturbance process, future hazardous fuel loads will be

underestimated, and projections of spotted owl habitat will

be overoptimistic. Figure 4 compares projected habitat development and fuel buildup using and not using the root

disease model.

Step 9. Structure runs for processing multiple

stands, by alternative, with appropriate kcp files.

Because this is a spatially explicit landscape analysis, FVS

projection runs commonly consist of many hundreds of

stands. Thus, an efficient process for creating the run stream

is essential. We recommend that each project share a common Suppose.loc file, but that there be Stand List Files (SLF

files) for each alternative. Within each SLF file, kcp files are

assigned on a stand-by-stand basis. This assignment is

based on the individual stand’s focal species area, site

quality, insect and disease occurrence, and proposed management treatment. Given the number of stands involved, it

would be laborious to manually edit the SLF files and

Suppose.loc file to make the necessary kcp files assignments. For the McCache analysis, a project-specific visual

basic program was written (by Don Vandendriesche, Forest

Management Service Center) and used to expedite the

assignment of the kcp files to appropriate stands.

An interactive visual basic program to make these assignments for any project of this type has since been written (by

Leo Yanez, Deschutes National Forest). In order to assign

stands to groups and/or kcp files to stands, the program edits

the Suppose.loc and SLF files referencing user-provided

lists of stand exam names. These lists designate group

membership and kcp files assigned to the stands. For example, each stand in the McCache LSR project spotted owl

focal species area was assigned one of three levels of root

disease severity based on forest inventory and survey data.

Thus, there are three versions of the root disease kcp file that

initialize and calibrate the western root disease model. The

user specifies which stand goes with which version in the

form of three text file lists of stand names. The process is

repeated with other kcp files including the prescription kcp

file and the site/quality natural regeneration kcp file. This

program is available upon request.

Step 10. Make projection runs, and summarize and

display data. Projected variables for each stand can be tied

to GIS and mapped so that users can spatially view projected

outcomes over time. Tables of outcomes of effects by acres

can also be generated with ease as well. Figures 5 and 6 show

projections from the McCache LSR comparing the most

dramatic action alternative versus the no action alternative,

in terms of habitat stage and fire hazard over a 30-year

period. Figures 5 and 6 also illustrate the following:

• High fire hazard will persist long term without treatment.

• Damage will continue from disease and insect hazard

will increase without treatment.

• Treatment can promote greater stability and initiate

the process of developing new late successional habitat.

• Reduced fire hazard comes at the expense of dispersal

habitat.
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Methodology for Modeling the Spatial and Temporal Effects of Vegetation Management Alternatives... Maffei and Tandy

Figure 4—Predicted effects on two key components of

spotted owl habitat—large tree structure and percent canopy

cover provided by 6 inch d.b.h., or greater, trees and fuel

loading. A stand in the McCache project area was projected

over a four-decade period using FVS. Projections were

made both with and without incorporating the impacts of root

disease via the western root disease model. Levels of root

disease used for the projections were the actual levels found

in the stand.

Figure 5—Projected spotted owl habitat stage over time

for most dramatic treatment alternative versus the no

action alternative for the spotted owl habitat focal species

area in the McCache project area.

PREDICTED 30 YEAR EFFECT OF ROOT 

DISEASE ON STAND FUEL LOADING

0%

20%

40%

60%

80%

100%

123

Down Tons/Acre

Percent of Total 

Stands

No RD

RD

EFFECT OF ROOT DISEASE ON CANOPY

COVER

0

5

10

15

20

25

30

35

40

45

1980 2000 2020 2040 2060

YEAR

% CANOPY COVER RD

No RD

THE EFFECT OF ROOT DISEASE ON THE

NUMBER OF LARGE TREES

0

2

4

6

8

10

12

14

16

18

1980 2000 2020 2040 2060 2080

YEAR

TREES/ACRE OVER 21 

INCHES

RD

No RD

10 20 30
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Maffei and Tandy Methodology for Modeling the Spatial and Temporal Effects of Vegetation Management Alternatives...

Results ________________________

In the case of the McCache analysis, the clearly depicted

effects established agency and interagency support for the

most dramatic action alternative proposed. It demonstrated

that, without treatment, late successional habitat was unlikely to regrow on its own (primarily due to insect and

disease activity) and that susceptibility to severe wildfire

impacts would continue over the long term. The proposed

treatments would result in lower fire hazard and promote

redevelopment of late successional habitat. The decision

notice for this project was recently signed.

Figure 6—Projected fire hazard over time for most dramatic treatment alternative (A) versus the no action alternative (B) for

the spotted owl habitat focal species area in the McCache project area.

 Projection Year

 2010 2030 2060

B

A

Conclusions____________________

We believe this methodology is a helpful and objective

landscape analysis tool for evaluating vegetation management projects in late successional reserves and other old

growth areas. At the least, it can help convey to stakeholders

a common understanding of the logic and rationale driving

the proposed actions. It can also help clarify the expected

immediate and long-term benefits and tradeoffs between

alternatives in terms of the resource of interest, late successional habitat. Spatially explicit graphic and numerical

displays of effects on spotted owl habitat over time helped
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Methodology for Modeling the Spatial and Temporal Effects of Vegetation Management Alternatives... Maffei and Tandy

explain the logic and rationale of the project, demonstrate

the need for treatment, and communicate expected effects of

alternatives to stakeholders. As a result, understanding and

a base of support were developed among stakeholders for the

active management proposed by the project.

References _____________________

Agee, J. K. and Edmonds, R. I. 1992. Forest protection guidelines for

the northern spotted owl. In: Recovery plan for the northern

spotted owl-final draft, Vol 2. U.S. Dept. Int., Fish and Wild.

Serv., U.S. Govt. Printing Office, Washington, DC, pp. 181-244.

Buchanan, J. B.; Irwin, L. L.; McCutchen, E. L. 1995. Within-stand

nest site selection by spotted owls in the eastern Washington

cascades. Journal of Wildlife Management. 59 (2):301-309.

Carlson, Clinton E.; Wulf, William N. 1989. Silvicultural strategies

to reduce stand and forest susceptibility to the western spruce

budworm. Agricultural Handbook No. 676. U. S. Department of

Agriculture, Forest Service, Cooperative State Research Service.

31 p.

Crookston, Nicholas L. 1997. Suppose: An interface to the Forest

Vegetation Simulator. In: Tech, Richard; Moeur, Melinda; Adams,

Judy, comps: 1997. Proceedings Forest Vegetation Simulator

conference; 1997 February 3-7; Fort Collins, CO. Gen. Tech. Rep.

INT-GTR-3737. Ogden, UT: U.S. Department of Agriculture,

Forest Service, Intermountain Research Station: 7-20.

Everett, R.; Schellhaas, D.; Spurbeck, D.; Ohlson, P., Keenum, D.;

Anderson, T. 1997. Structure of northern spotted owl nest stands

and their historical conditions on the eastern slope of the Pacific

Northwest Cascades, USA. Forest Ecology and Management

94(1997) pp. 1-14.

Forsman, E. D.; Meslow, E. C.; Wight, H.M. 1984. Distribution

and biology of the spotted owl in Oregon. Wildlife Monograph

87. 64 p.

Gregg, Tommy F.; Goheen, Ellen Michaels. 1997. Current Vegetation Survey in the Pacific Northwest Region: A direct link to FVS

and the Insect and Disease Extensions. In: Teck, Richard; Moeur,

Melinda; Adams, Judy, comps. 1997. Proceedings Forest Vegetation Simulator conference; 1997. February 3-7; Fort Collins, CO.

Gen. Tech. Rep. INT-GTR-3737. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Research Station:

182-190.

Hall, Frederick C. 1987. Growth Basal Area Handbook. U.S. Department of Agriculture, Forest Service, Pacific Northwest Region. R6-ECOL-181b-1987. 84 p.

Hessburg, Paul F.; Mitchell, Russ G.; Filip, Gregory M. 1994.

Historical and current roles of insects and pathogens in eastern

Oregon and Washington forested landscapes. Gen. Tech. Rep.

PNW-GTR-327. 72 p.

Maffei, Helen; Gerdes, Mike; Beyer, Scott; Sandquist, Roger. 1997.

Use of the cover extension of Forest Vegetation Simulator to

identify northern spotted owl dispersal habitat in forest communities of Central Oregon. In: Teck, Richard; Moeur, Melinda;

Adams, Judy, comps. 1997. Proceedings: Forest Vegetation Simulator conference; 1997 February 3-7; Fort Collins, CO. Gen. Tech.

Rep. INT-GTR-3737. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station: 59-63.

McGaughey, Robert J. 1997. Quantifying stand structure using a

percent canopy cover model (PERCOV). In: Teck, Richard; Moeur,

Melinda; Adams, Judy, comps. 1997. Proceedings: Forest Vegetation Simulator Conference; 1997 February 3-7; Fort Collins, CO.

Gen. Tech. Rep. INT-GTR-3737. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Research Station:

133-138.

Moeur, Melinda; Crookston, Nicholas L.; Stage, Albert R. 1995.

Most similar neighbor analysis: a tool to support ecosystem

management. In: Thompson, J.E. (compiler. 1995. Analysis in

support of ecosystem management. Analysis Workshop III, April

10-13, 1995 Fort Collins, CO. Washington, D.C.: USDA Forest

Service Ecosystem Management Analysis: 31-44.

Moeur, Melinda; Stage, Albert R. 1994. Most similar neighbor: an

improved sampling inference procedure for natural resource

planning. Forest Science 41 (2).

Stage, A. R. 1973. Prognosis model for stand development. Res. Pap.

INT-137. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Forest and Range Experiment Station.

32 p.

Thomas, J. W.; Forsman, E. D.; Lint, J. B.; Meslow, E. C.; Noon, B.

R.; Verner, J. 1990. A conservation strategy for the spotted owl.

U.S. Gov. Printing Off., Washington, DC. 427p.

USDA Forest Service and USDI Bureau of Land Management,

1994. Record of Decision for the Amendments to Forest Service

and Bureau of Land Management Planning Documents Within

the Range of the Northern spotted Owl / Standards and Guidelines for Management of Habitat for Late-Successional and OldGrowth Forest Related Species Within the Range of the Northern

Spotted Owl. 228 p.
78 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

S. Hummel is a Research Forester, USDA Forest Service, Pacific Northwest Research Station, P.O. Box 3890, Portland, OR 97208, D. Calkin is a

Postdoctoral Researcher, University of Montana, Missoula, and J. Barbour is

a Research Forest Products Technologist, Pacific Northwest Research Station, Portland, OR.

Abstract—We combine simulation and optimization techniques to

test hypotheses about variable-intensity management in a 15,000-

acre forest reserve. The Forest Vegetation Simulator (FVS) and

integer optimization techniques together help identify tradeoffs

between two landscape resource goals: maintaining late successional forest and reducing the threat of wildfire. The FVS model

allows us to evaluate the contribution of stand-level treatments to

each landscape goal, whereas optimization identifies efficient solutions that allow us to test for complementary or competitive relations between the two goals over time and space. The methods we

discuss in this paper are adaptable to forested areas of various sizes

and with different resource goals. To illustrate the value and

innovation of our approach, an example demonstrates how uncertainty in FVS simulations might affect landscape solutions. This

example is the pilot application of a new FVS bootstrap model. Our

preliminary results indicate that we should change the definition of

late-successional forest used for our final analysis.

Concern about the persistence of species associated with

old forests of the U.S. Pacific Northwest led to the creation

of a regional forest reserve network in 1994 (Northwest

Forest Plan: USDA/USDI 1994). These reserves—called

late-successional reserves or LSRs—cover over 5 million

acres of Federal land throughout the range of the northern

spotted owl (Strix occidentalis caurina) in Washington,

Oregon, and California. The standards and guidelines for

management of LSRs are designed to protect late-successional forest ecosystems from “loss to large-scale fire, insect

and disease epidemics, and major human impacts” (USDA/

USDI 1994).

Coincident with establishment of the LSR network, aerial

detection surveys mapped a western spruce budworm

(Choristoneura occidentalis) outbreak in the eastern Washington Cascades. The area of the outbreak included the

Gotchen LSR, where budworm defoliation was mapped over

several thousand acres throughout the 1990s. By 2000,

ongoing defoliation and subsequent tree mortality created

conditions that caused forest managers and neighboring

private and tribal landowners to worry about wildfire. However, forest structures that support elevated budworm populations also contribute to desirable owl habitat, and this

apparent paradox raises questions about the role of active

management in the LSR.

Study Objectives ________________

The Gotchen LSR study was designed to test hypotheses

about differences in passive versus active management in

the 15,000-acre LSR landscape over time and space (Hummel

and others 2001). As part of the study, we are using both the

Forest Vegetation Simulator (FVS) and integer optimization techniques to characterize relations between the two

potentially competing landscape goals of maintaining late

successional forest (LSF) and reducing the threat of fire. The

FVS model allows us to evaluate the contribution of standlevel treatments to each landscape goal, whereas optimization allows us to identify what different combinations of

stand treatments might best meet LSR objectives over time.

We are also interested in exploring the potential effects of

within-stand variation on landscape-level results and are

therefore using a new bootstrap model that assesses uncertainty in FVS projections caused by variation in input data.

This paper documents our methods and their potential

utility to landscape analysis and planning.

Methods _______________________

Combining Simulation and Optimization

By integrating FVS with an optimization algorithm we

can derive benefits from both simulation and optimization

techniques. Simulations permit detailed analyses of relatively few outcomes while optimization techniques explore a

multitude of solutions at the expense of detail. Together,

these techniques enable us to generate and evaluate multiple combinations of treated and untreated stands in relation to landscape objectives, to identify efficient landscape

solutions, and to calculate net revenues associated with

these solutions. By “efficient” we mean that there are no

other solutions that increase the level of one resource goal (in

other words, LSF) without a corresponding decrease in the

other resource goal (in this case, fire threat reduction). A

Landscape Analysis with FVS and

Optimization Techniques: Efficient

Management Planning for the Gotchen

Late Successional Reserve

S. Hummel

D. Calkin

J. Barbour
USDA Forest Service Proceedings RMRS-P-25. 2002 79

Landscape Analysis with FVS and Optimization Techniques: Efficient Management Planning... Hummel, Calkin, and Barbour

“solution” is a set of selected treatment schedules. These

schedules represent the type and timing of treatments for

each stand within the LSR. Our objective is to identify a set

of solutions that maintain a specific amount of late-successional forest in the Gotchen LSR subject to a constraint of

minimizing a spatially explicit landscape fire threat index.

A “production possibility frontier” results from graphing the

efficient solutions, and this frontier can inform land managers, policymakers, and interested citizens about potentially

complementary or competitive relations among landscape

goals over time. The shape of the frontier is particularly

informative when relative resource values are unknown, as

in the Gotchen LSR.

Characterizing Stands in the Landscape

We began by characterizing current vegetation conditions

in the Gotchen LSR using photo-interpretation methods

developed for the Interior Columbia Basin Ecosystem Management Project (ICBEMP) and 1995 aerial photos (1:12,000)

(Hessburg and others 1999). This step resulted in 159

polygons, which we stratified into 15 “stand types” by structure class and potential vegetation (Hummel and others

2001). We then summarized the existing inventory and

stand exam data for each stand type; data collected before

1999 were discarded. Stand types that were originally under

sampled or unsampled were selected for sampling in 2001

using a randomized design with probability proportional to

size. All data were collected using the USDA Forest Service

Region 6 timber stand exam protocol and formatted as FVS

tree lists.

Creating Projection Units from Polygons

The size of some of the original polygons concerned us

because of both ecological and operational issues. We consider it vital that vegetation patterns be able to change over

time in our analysis and not be constrained by current

landscape geometry. For example, the large polygons in the

southern part of the Gotchen LSR, which resulted from fire

exclusion and selective logging, tend to be bigger than would

be expected from studies of regional disturbance ecology (for

example, see Edmonds and others 2000). We introduced the

ability for new patterns to emerge by substratifying the

original polygons into smaller “projection units.” We created

330 projection units (units) by considering biophysical, administrative, and operational factors provided by the Mt.

Adams Ranger District of the Gifford Pinchot National

Forest. These units represent the smallest area to which a

treatment in FVS is applied. FVS tree lists were randomly

assigned to unsampled units within the same stand type.

Developing an FVS Analysis Framework

We next created a four-level decision tree (tree) for our

FVS analyses. The criteria used for decisions within the tree

came from the photo-interpreted data, from consultation

with staff of the Gifford Pinchot National Forest and the U.S.

Fish and Wildlife Service, and from published information.

On the first level, all units were sorted into “dry,” “moist,” or

“cold” forest types, based on strata developed in the ICBEMP

process. On the second level, each forest type was further

sorted according to definitions for “late-successional

forest”(LSF), “nesting habitat” (NH) or “non-late-successional forest” (NonLSF). For our definition of LSF, we used

the “grand fir quantitative description of desired conditions”

from the Gifford Pinchot National Forest Late Successional

Reserve Assessment (Forest LSRA) (USDA 1997). This

definition includes a mean tree diameter of 18.6 inches

diameter at breast height (d.b.h.) and a minimum of 240 ft2

of basal area per acre. Nesting habitat, which is more

restrictive than LSF, was defined by local USDA Forest

Service and USDI Fish and Wildlife Service biologists, with

reference to Thomas and others (1990) and Buchanan and

others (1995). It requires, for example, that 25 percent of the

basal area requirement for LSF be in trees larger than 25

inches d.b.h.. Any unit not classified as either LSF or NH is,

by process of elimination, classed as NonLSF. On the third

level, these NonLSF units were further refined by key

structural characteristics, to segregate into potential treatment categories. There are various reasons a unit may not

meet the definition for LSF, which, in turn, influences the

logic of treatments designed to develop or perpetuate latesuccessional forest conditions. For example, a mixed-species, multistoried unit may not have sufficient basal area or

canopy closure, whereas a single-species, single-storied stand

would lack sufficient structural and compositional variety.

Applying Treatments in FVS and

Summarizing Results

In the first three levels of the decision tree, we grouped

units in ways relevant to the Gotchen LSR landscape goals.

In the fourth level, these groups are variously eligible for

different treatments. Units qualifying as nesting habitat or

cold forest types are automatically assigned the passive (no

action) treatment alternative. All other units are eligible for

treatment alternatives that are designed to (1) protect LSF,

(2) restore landscape patterns, or (3) reduce fire threat. The

optimization techniques we use require that each possible

treatment alternative be applied to each eligible unit. The

East Cascades variant of FVS simulates effects associated

with these alternatives and a no-action alternative over

five time intervals, each of which is a decade in length. If

three alternatives were available in each interval, there

would be a total of 1024 (45

) schedules per unit. We reduce

this number of schedules by using rules to evaluate whether

to apply a treatment to a given unit in a given interval. The

number of feasible alternatives varies by unit. We expect

that some units will qualify for very few (less than 10)

alternative schedules while some units will qualify for many

(up to 100).

We evaluate the outcome of every treatment applied in each

unit in each interval. First we use FVS compute statements

(Crookston 1990) and the Fire and Fuels Extension to FVS

(FFE-FVS) (Beukema and others 2000) to calculate values for

structure class (definition in Hummel and others 2001), flame

length, and torching and crown fire spread. We then use these

values to evaluate the status of each unit compared to our LSF

and fire threat definitions. The latter definition incorporates

adjacency criteria similar to that described in Wilson and

Baker (1998), adapted for the Gotchen LSR study by Dr. J.K.
80 USDA Forest Service Proceedings RMRS-P-25. 2002

Hummel, Calkin, and Barbour Landscape Analysis with FVS and Optimization Techniques: Efficient Management Planning...

Agee, Fire Ecologist at the University of Washington. We

calculate the fire threat to any unit “d” as a weighted combination of five factors: flame length, torching, and crown fire

spread in “d,” plus torching and crown fire spread in upwind

units “u.” In the Gotchen LSR, upwind units are those to the

east, as the conditions under which severe fire weather occurs

are mostly from low humidity, east winds. Our weighting of

the five factors considers unit characteristics more heavily

than adjacent units in calculating overall points; the weighting is not meant to be a linear function of fire risk. Rating

groups are summarized as low threat (1 to 3 points, fair

survival of residual stand, control likely), moderate threat

(3.3 to 6 points, some ponderosa pine [Pinus ponderosa] or

Douglas-fir [Pseudotsuga menziesii] residuals survive, control problematical), and high threat (6+ points, stand replacement fire likely, control unlikely). We next run the cut tree

lists from FVS through the Financial Evaluation of Ecosystem Management Activities (FEEMA) software (Fight and

Chmelik 1998) to evaluate the net revenues (dollars/acre)

associated with a treatment. These steps produce a text file

with “resource values” for LSF, fire threat, and net revenue

for each unit and each treatment schedule over the planning

horizon.

Evaluating FVS Output using Optimization

The resource values become input to an optimization

algorithm that identifies the schedule for each unit that best

achieves landscape goals, when evaluated together with all

other units in each interval. We initially considered using

the Parallel Processing Extension (PPE) of FVS (Crookston

and Stage 1991), which allows the comparison of alternative

management policies on a collection of stands. The PPE has

limitations, however, that affect its usefulness. The spatial

array of management activities in the Gotchen LSR is

important because management actions affect not only the

immediate stand, but also influence conditions, and thus

management decisions in neighboring stands. Spatial analysis of all possible treatment combinations requires techniques not available in the PPE. We explore this enormous

solution space using integer optimization techniques known

as heuristics (Reeves 1993).

For this paper, we did an exploratory optimization analysis using a simplified problem specification and FVS results

from a diameter-limit treatment. To select a diameter limit,

we first developed a regression equation from over 500 grand

fir (Abies grandis) trees measured in the Gotchen LSR. Our

equation relates d.b.h. to age as follows: d.b.h.= -0.212 +

0.194*AGE, when the standard errors of the estimates are

0.44 and 0.01, respectively (P<0.0001, R2

=0.66). Our treatment removed all grand fir trees under 12 inches d.b.h. in

eligible units; each unit could be treated only once or have no

treatments scheduled in the 50-year planning horizon. We

ran FVS simulations for each potential schedule on all

eligible units for 50 years.

Considering the Effects of Uncertainty

The FVS user can estimate variation attributable to

stochastic events over time by making multiple projections

of the same initial tree list, by using site appropriate insect,

pathogen, and fire model extensions, and by invoking different random numbers. We use the western root disease

(Frankel 1998) and fire models (Beukema and others 2000)

to incorporate stochastic events in FVS. These techniques

result in different realizations of stand development for the

same initial tree list. Simple summary statistics (mean,

standard deviation) can then be calculated for model results.

Another source of uncertainty in FVS outcomes comes

from sampling. A new bootstrap program (Gregg and

Hummel, this proceedings) now makes it easy to estimate

variation associated with sampling. This is important because the data used to create a FVS tree list come from a

sample of stand conditions. Information on the variation

between sample plots within a tree list enables the user to

provide additional information about the precision of FVS

projections. It also offers a useful way to evaluate the design

of the original stand stratification and sampling system.

Output from the bootstrap program includes prediction

intervals, which are intervals around a set of FVS predictions about a population. These intervals are based on

different components of variation. For example, one interval

available as program output is based on variation from the

stochastic elements in the FVS model. This interval, which

we refer to as the FVS prediction interval (FVSPI), is based

on the original FVS tree list, rerun as many times as the user

specifies, each time with a new random number seed. The

FVSPI mean is, therefore, the mean predicted value of the

total number of FVS runs made with the original tree list

and new random number seeds.

Another prediction interval available for the bootstrap

program is based on variation from sampling error. This

interval, which we refer to as the sampling error prediction

interval (SEPI), is based on resamples of the original tree

list, bootstrapped as many times as the user specifies. These

bootstrapped samples are generated by repeatedly sampling

the original tree list with replacement (Efron and Tibshirani

1993). The SEPI mean is the mean predicted value of the

total number of FVS runs made with bootstrapped samples

of the original tree list. Except in rare cases, the SEPI

interval also includes the random elements from the FVS

model. This is because in FVS the sequence of random

numbers changes with very small changes in the tree list.

Bootstrap resampling methods imply changes in the tree list

sufficient to generate new random number seeds in FVS over

multiple time intervals.

When using the bootstrap program, we change the random number and resample the original tree list. This ensures that the SEPI interval is consistently computed, even

in the rare cases when it might be theoretically possible to

separate sampling error from random events. All

bootstrapped prediction intervals assume that the model is

the correct form. If this assumption is not met, then associated bias will be included in the intervals.

The SEPI intervals help us consider how different results

might be if we consider variation from sampling error in

addition to that from stochastic events. In the Gotchen LSR

study, such information is essential to test whether differences in the predicted area of LSF associated with various

treatment combinations are indeed significant. It also helps

place within-stand variation in a landscape context. In this

pilot application of the bootstrap model, we ran it for 50

years on each untreated tree list in the Gotchen LSR.
USDA Forest Service Proceedings RMRS-P-25. 2002 81

Landscape Analysis with FVS and Optimization Techniques: Efficient Management Planning... Hummel, Calkin, and Barbour

Results ________________________

Implications of the Diameter-Limit

Treatment

The diameter-limit treatment resulted in many of the

units being categorized as LSF over time. Scheduling the

treatment on some units increases the amount of LSF up to

some point, which means that the relationship between

treatment and LSF is complementary. We used a simple

optimization algorithm to characterize this relationship

between harvest level and LSF. The amount of LSF in the

no-action alternative starts at 1,320 acres in the current

time period and grows to 3,350 acres in year 40 (the sum of

LSF for each of the five periods is 10,300 acres). The sum of

maximum acres of LSF obtained over the five planning

periods was approximately 40,020 and is achieved by harvesting (harvest levels of 15.8, 14.0, 4.4, 7.6, and 4.2 million

board feet in year 0, 10, 20, 30, and 40). Between the noaction alternative and the maximum LSF alternative there

are relative tradeoffs associated with timber harvest and

LSF. Cutting trees under 12 inches d.b.h. increases QMD

and thus often immediately created more LSF based on the

Forest LSRA definition. These results are inconsistent with

scientific literature on spotted owl habitat requirements,

which indicates some basal area in smaller diameter classes

is essential (see for example, Thomas and others 1990).

These results raise questions about the sensitivity of a

QMD-based LSF definition, and we therefore did not evaluate the financial implications of this example.

Implications of Uncertainty

Our bootstrap analysis underscores the importance of

considering different sources of variation when evaluating

FVS model output. We compared FVS output against the

Forest LSRA requirement of 18.6 inches mean tree diameter

(QMD) for two units within the Gotchen LSR. In unit 102,

the QMD after 10 years was estimated by the east Cascades

variant of FVS to be 22.3 inches d.b.h. Because the “critical

value” of 18.6 inches d.b.h. is smaller than this estimated

value, the unit would qualify as LSF, based on the Forest

LSRA definition. The empirical distribution generated by

the FVS bootstrapping program reveals that, indeed, 84

percent of the observations exceed the critical value (fig. 1a).

Confidence in the projected outcome is thus quite high. In

contrast, in unit 1111 the mean QMD after 40 years was

projected to be 17.1 inches d.b.h.. Because this outcome is

smaller than the 18.6 inches d.b.h. critical value, unit 1111

would not qualify as LSF based on the Forest LSRA definition. The bootstrapped distribution reveals, however, that

23 percent of the observations exceed the critical value

(fig. 1b). This means that almost a quarter of the time the

unit could qualify as LSF. Evaluations based purely on the

REPI predicted mean would not recognize this possible

outcome.

Figure 1—Identifying late successional forest (LSF) in FVS: implications of uncertainty.
82 USDA Forest Service Proceedings RMRS-P-25. 2002

Hummel, Calkin, and Barbour Landscape Analysis with FVS and Optimization Techniques: Efficient Management Planning...

In a landscape context, the potential implications of such

variation relate to the area of the units involved. For example, unit 102 includes just 50 acres. Thus, we have high

confidence in our prediction over a limited area. In contrast,

unit 1111 covers almost 800 acres. By failing to display a

range of outcomes associated with a simulated mean projection, we would underestimate the likelihood for this area to

contribute to landscape goals for LSF over time.

Our bootstrap analysis also suggested that the amount of

LSF appears highly sensitive to variation in FVS projections. The area of predicted LSF could as much as double if

values one standard deviation from the SEPI mean were

used rather than the mean value itself. This variation

associated with FVS projections increased over time. Together with results from our preliminary optimization

analysis, the bootstrap results also support changing the

QMD-based definition of LSF.

Conclusions____________________

We will use the results from this preliminary analysis to

modify the definitions and treatments we use in our landscape analysis. Our hypothesis is that reducing fire threat

and maintaining LSF may be complementary, or at least

nonconflicting, at low levels of threat reduction. In contrast,

we expect that a high level of fire threat reduction would

directly conflict with maintaining LSF. Figure 2 is a generalized production possibility frontier and represents our

hypothesized relationship between acres of LSF and fire

threat reduction. The uncertainty associated with FVS simulation is illustrated by the interval around the frontier,

whereas inefficient solutions lie within it. The frontier

identifies how stand-level treatments aggregate to affect the

landscape objective, and portrays the relative tradeoffs

associated with two landscape goals. Perhaps of even more

value to managers needing to set priorities with limited

resources, it also reveals if stand level treatments have a

measurable impact on landscape objectives.

References _____________________

Beukema, S.J.; Reinhardt, E.; Greenough, J.A.; Kurz, W.A.;

Crookston, N.; Robinson, D.C.E. 2000. Fire and fuels extension:

model description. Prepared by ESSA Technologies Ltd.,

Vancouver, BC, for the USDA Forest Service Rocky Mountain

Research Station, Moscow, ID. 80 p.

Buchanan, J.B.; Irwin, L.L.; McCutchen, E.L. 1995. Within-stand

nest site selection by spotted owls in the eastern Washington

Cascades. J. Wildlife Management. 59(2): 301-310.

Crookston, N. L. 1990. User’s guide to the event monitor: part of

Prognosis Model version 6. Gen. Tech. Rep. INT-275. Ogden, UT:

U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. 21 p.

Crookston, Nicholas L.; Stage, Albert R. 1991.

User’s Guide to the Parallel Processing Extension of the Prognosis

Model. Gen. Tech. Report-INT-281. Ogden, UT: U.S. Department

of Agriculture, Forest Service, Intermountain Research Station.

93 p.

Edmonds, R.L.; Agee, J.K.; Gara, R.I. 2000. Forest Health and

Protection. McGraw-Hill, 630 p.

Efron, B.; Tibshirani, R.J. 1993. An Introduction to the Bootstrap.

Chapman and Hill, NY. 436 p.

Fight, R.D.; Chmelik, J.T. 1998. Analysts guide to FEEMA for

financial analysis of ecosystem management activities. Gen.

Tech. Rep. FPL-GTR-111. Madison WI:

U.S. Department of Agriculture, Forest Service, Forest Products

Laboratory 5 p.

Frankel, Susan J., tech. coord. 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. 164 p.

Hessburg, P.F.; Smith, B.G.; Kreiter, S.G.; [and others]. 1999.

Historical and current forest and range landscapes in the interior

Columbia basin and portions of the Klamath and Great Basins.

Part I: Linking vegetation patterns and landscape vulnerability

to potential insect and pathogen disturbances. In: Quigley, Thomas, M. tech. ed.; Interior Columbia Basin Ecosystem Management Project: scientific assessment. Gen. Tech. Rep. PNW-GTR458. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station. 357 p.

Hummel, S.S.; Barbour, R.J.; Hessburg, P.F.; Lehmkuhl, J.F. 2001.

Ecological and Financial Assessment of Late-Successional Reserve Management. Res. Note. PNW-RN-531. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 25 p.

Reeves, C.R. 1993. Modern heuristic techniques for combinatorial

optimization. John Wiley and Sons, Inc., New York, NY.

Thomas, J.W.; Forsman, E.D.; Lint, J.B.; [and others]. 1990. A

conservation strategy for the northern spotted owl. U.S. Department of Agriculture, Forest Service; U.S. Department of the

Interior, Bureau of Land Management; Fish and Wildlife Service;

National Park Service. Washington, DC: U.S. Government Printing Office.

U.S. Department of Agriculture, Forest Service. 1997. Late-successional reserve assessment. Pacific Northwest Region. Gifford

Pinchot National Forest. http://www.fs.fed.us/gpnf/mgtdir. (March

15, 2000).

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

the Interior, Bureau of Land Management. 1994. Record of

decision for amendments to Forest Service and Bureau of Land

Management planning documents within the range of the northern spotted owl. [Place of publication unknown]. 74 p. [plus

Attachment A: standards and guidelines].

Wilson, J.S.; Baker, P.J. 1998. Mitigating fire risk to late successional forest reserves on the east slope of the Washington Cascade

Range, USA. Forest Ecology and Management 110: 59-75.

Figure 2—Hypothetical tradeoff between two landscape resource goals: late-successional forest (LSF)

and fire threat reduction.

(Z1+Z2+Z3)

MAX ∑i

∑j (Zij)

LSF

Fire Threat Reduction
USDA Forest Service Proceedings RMRS-P-25. 2002 83

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

David Atkins is a Forest Ecologist with the USDA Forest Service Northern

Region in Missoula, MT. During this project, his primary responsibilities

were Forest Health Monitoring Coordinator for the Northern and Intermountain Regions. Renee Lundberg is a Silviculturist/Ecologist with the Forest

Service Northern Region in Missoula, MT. Her primary responsibility for this

project was data analysis.

Abstract—The Forest Vegetation Simulator (FVS), with the Fuels

and Fire Effects and Insect and Pathogen Extensions, was used to

characterize forest health hazards in Montana. Forest Inventory

Analysis (FIA) data were processed through FVS to characterize

current and future hazards related to fire, insect, and disease. While

conducting this Statewide analysis, several weaknesses in the base

model and extensions were discovered. The paper presents problems encountered, how they were resolved, recommendations for

future FVS development, and research needs.

The potential for wildfires to put lives, property, and

environmental values at risk is a much discussed and

debated topic in recent years. It is important for managers,

policy makers, and the public to understand potential wildfire behavior and effects, along with the potential for insect

and disease activities and their potential interaction with

fire. Describing changes that may occur across an entire

State requires a consistent dataset across ownerships and

the tools to analyze these data. Using the Forest Inventory

and Analysis (FIA) Database and the Forest Vegetation

Simulator Model (FVS) program provides this opportunity.

Atkins and Lundberg (in preparation) used FIA plot data

processed through FVS (Wykoff and others 1982; FMSC

2001) to predict current and future hazards related to fire,

insect, and disease. In the process of conducting this Statewide analysis, several weaknesses in the base model and

extensions were observed. The objectives of this presentation are:

• To provide FVS users with an overview of our results.

• To share the problems we encountered and how we dealt

with them.

• To submit recommendations for future system development and research needs.

Analyst Hazards When Assessing Fire,

Insect, and Disease Hazard in Montana

Using FIA Data with FVS: Or Alligators We

Didn’t See Coming

David Atkins

Renee Lundberg

Overview of Results _____________

The original work had two objectives: (1) to develop methods to use FIA data and FVS, incorporating its Fuels and

Fire Effects (FFE) (Beukema and others 2000) and the

Insect and Pathogen (I&P) (FHTET 2001) Extensions, to

characterize current and simulated future forest conditions;

and (2) to use the methods developed to describe the conditions of Montana forests now and in 50 years.

Methods _______________________

We used FIA data for the State of Montana (USDA 2000;

Conner and others 1993; O’Brien and Conner 1991; USDA

1988) for this analysis. There are a total of 3,721 forested

plots from all ownerships, with the exception of National

Park Service lands that have not yet been inventoried. The

2,343 National Forest System (NFS) plots were inventoried

between 1993 and 1998. There are 1,378 plots on private and

other public lands, which were inventoried in 1988 and 1989.

For purposes of display, all plots were modeled with 2000 as

the inventory year. Our results do not reflect changes in the

forest condition from fires, insect, disease, or management

activities that may have occurred between the inventory

year and 2000.

Fire, insect, and disease potential are affected by a variety

of variables that describe and influence how much and

where hazardous conditions exist. We used these variables

to stratify the data:

• Forest type

• Potential vegetation groups

• Stand structure

o Size class

o Stand layering

• Ecoregions (fig.1)

The Eastern Montana (EM) and North Idaho (NI) variants

of FVS were used in modeling the FIA plots. The FVS model

was modified to allow both FFE and I&P extensions to model

fire, and insects and diseases simultaneously. This was

important because insect and disease processes directly

affect fuel loadings and subsequent fire behavior. Insect and

diseases behave somewhat differently in varying environmental settings (Hagle and others 2000). To account for

these variations we used the action probability indices

(APIs) from the results of Hagle and others (2000) to create
84 USDA Forest Service Proceedings RMRS-P-25. 2002

Atkins and Lundberg Analyst Hazards When Assessing Fire, Insect, and Disease Hazard in Montana Using FIA Data with FVS:...

the FVS keyword files by habitat type groups for east and

west of the Continental Divide.

The FVS Fuels and Fire Effects Extension provides indicators of fire effects including tree mortality, exposed mineral soil, and fuel consumption. We chose fire type and crown

index as our measures of fire hazard. Mortality is influenced

by the intensity of the fire, species of trees in the stand, and

their size, both diameter and height. We show fire type,

forest type, and basal area loss to illustrate potential fire

mortality. The I&P extensions simulate mortality from

various agents, including root disease, bark beetles, and

mistletoe, as FVS projects the forest through time. We show

bark beetle hazard as an example.

Fire behavior and effects vary considerably with shortterm weather and longer term climatic conditions (drought)

through time. Although these variables occur along a continuum, we chose to simulate two conditions. One represents

the average summer day weather and average climatic

conditions. The second represents extreme fire weather

conditions and very dry climatic conditions. They are displayed in table 1.

Down woody debris data were available on 88 percent of

the NFS plots. These measured fuel loadings were used in

the FVS simulations. For the plots without measured fuel

data, the default values from the FFE model were used. Fuel

loadings directly affect flame lengths, fire intensity, and fire

effects. By incorporating actual fuel loadings, the simulations better reflect the variation that exists in Montana

forests.

After the plots were processed by FVS, the output was run

through a custom program to extract specified report fields

and then loaded into an Access database, which facilitated

summarization and analysis.

Results ________________________

In order to understand and display the potential for fires,

insects and disease and their possible effects, we first summarized the inventoried conditions, forest type, structure,

layering, size class, and hazard rating for bark beetles. Then

we related these characteristics to FFE outputs: fire type,

crown index, and mortality. The results also included some

50-year projections.

Figure 2 shows that Douglas-fir (Pseudotsuga menziesii),

lodgepole pine (Pinus contorta), subalpine fir (Abies

lasiocarpa), and ponderosa pine (Pinus ponderosa) forests

are dominant types across the State. Douglas-fir and lodgepole pine compose over half of the forest types, and these four

types represent 83 percent of the forests in Montana.

Figure 3 shows that medium two storied stands are the

most common condition in the State. Figure 3 also illustrates

that two-storied and multistoried stands (layered stands

with fuel ladders) are abundant, representing approximately

12.3 million acres.

Figure 4 shows the relative abundance of the four size

classes. The medium size class is most abundant. The pole

class is next in abundance with the seedling/sapling and

large size classes present in lesser amounts. This size class

distribution is reflective of fire suppression and the logging

history. This distribution of size classes has important

ramifications for fire, insect, and disease hazard.

Table 1—Weather conditions used in FVS to represent average and

extreme fire scenarios.

Weather conditions Average fire Extreme fire

20 foot wind speed 15 40

Temperature 75 90

Fuel Moistures:

1 hour (0-1/4") 6% 4%

10 hour (1/4-1") 8% 6%

100 hour (1-3") 10% 7%

1000 hour (3"+) 15% 12%

duff 100% 65%

live 90% 60%

Figure 1— Ecoregions in Montana.

Figure 2—Percent of forested area in Montana by forest type.

0%

5%

10%

15%

20%

25%

30%

35%

Forested Area

PFGF/CHWDWLESWB/ALPPAF/MHLPDF

Forest Types in Montana 

PF–limber pine, GF/C-grand fir & cedar, HWD-hardwood,

WL-western Larch, ES-Englemann spruce, WB/ALwhitebark pine & subalpine larch, PP-ponderosa pine,

AF/MH- subalpine fir and mountain hemlock, LPlodgepole pine, DF- Douglas-fir
USDA Forest Service Proceedings RMRS-P-25. 2002 85

Analyst Hazards When Assessing Fire, Insect, and Disease Hazard in Montana Using FIA Data with FVS:... Atkins and Lundberg

Insects, particularly bark beetles, can have a substantial effect on fuel loading and stand structure over time.

Figure 5 illustrates that approximately 8.6 million acres

are in moderate and high hazard conditions from bark

beetles. These figures indicate stand conditions that could

support an outbreak given the right combination of conditions. Some of these acres have been, or are currently

being, attacked since the plot data were collected in the

Figure 3—Number of acres by structure group for forested

lands in Montana.

Figure 4—Number of acres by size class in Montana.

late 1980s to the late 1990s. It can be seen that lodgepole

pine and Douglas-fir forest types have the greatest amount

of hazard. This is partly a reflection of the size class

distribution illustrated earlier in figure 4.

The FFE simulates wildfire type based on the conditions

within the particular stand that is modeled. The FFE classifies fires behavior into three types:

• Surface fire: The fire burns on the ground through the

dead and down material, and crowns do not burn.

• Passive fire: The fire burns on the ground, and some

crowns will burn as individual trees or groups of trees

torch, but fire is not sustained in the crown.

• Active fire: The fire is a running through the crowns

burning all crowns, thus killing all trees.

The potential for fire is a function of weather, fuel conditions, and topography, and the interplay of these variables.

We modeled two weather conditions to provide a sense of

how weather influences fire behavior and effects. Figures 6a

and 6b display the percent of area by fire type for the

inventoried conditions, given average and extreme weather

parameters. With the extreme conditions, the amount of

active fires increased eight-fold, while the passive and surface fires decreased.

Stand layering has two somewhat contrary effects on fire

behavior. One is to add ladder fuels that facilitate flames

reaching into the canopy of the forest resulting in passive or

active fires. The second effect is to reduce the within-stand

wind speed on the fire, which can reduce the potential for the

flames to reach the forest canopy, thus reducing the amount

of passive and active fire. The FFE model accounts for this

interaction by using crown base height and canopy closure

calculations to determine if fire can get into the crown. If the

crown canopy is within reach of predicted flame lengths and

there are adequate ladder fuels, then torching and crowning

fires will result. The model uses percent canopy cover to

calculate mid-flame wind speed, thus incorporating density

effects on within-stand wind speed.

Figures 7a and 7b show the percentage of fire types for

average and extreme weather conditions. The amount of

Bark Beetle Hazard on Forest Lands in Montana

13,563

2,913

2,649

1,918

291

562

237

Bark Beetle Low to None

Medium Bark Beetle

Douglas Fir Beetle High

Mountain Pine Beetle High in LP

Mountain Pine Beetle High in PP

Mountain Pine Beetle High in WB

Spruce Beetle High

Thousands of Acres

Figure 5—Number of acres by bark beetle hazard rating for

forested lands in Montana. Note host species are indicated

where hazard rated as high (WB - whitebark pine, PP -

ponderosa pine, LP - lodgepole pine). Medium and low

hazard area include all host species forest types.

0

1,000

2,000

3,000

4,000

5,000

6,000

Thousand Acres

SS/NS

PS 

PT 

PM 

MS MT 

MM 

LS/VS

LT/VT

LM/VM 

Structure of Forested Lands in Montana

SS/NS–Seedling/Sapling & Non-stocked

P-Pole, M-Medium, L/V Large & Very Large

S-Single Story, T-Two Story, M-Multi-story

Example: MT – Medium Two Story

0

2,000

4,000

6,000

8,000

10,000

thousands of acres

SS/NSPML/VL

Size Classes in Montana 

SS/NS–Seedling/Sapling & Non-stocked

P-Pole, M-Medium, L/V Large & Very Large
86 USDA Forest Service Proceedings RMRS-P-25. 2002

Atkins and Lundberg Analyst Hazards When Assessing Fire, Insect, and Disease Hazard in Montana Using FIA Data with FVS:...

passive fire behavior in average conditions increases from 4

percent for single-storied stands to 14 percent for multistoried

stands. With extreme fire conditions, the active fire increases from 37 to 66 percent as layering increases from

single to multistoried stands. The fire intensity increases

with the number of layers, regardless of weather conditions.

The simulated fire type reflects fire behavior based on the

fire originating in that stand. It does not reveal how a fire

might spread into the stand from an adjacent stand. This is

an important point when interpreting the results. Fires that

burn hundreds to tens of thousands of acres are primarily a

function of fire spread from stand to stand.

Two fire indices are produced by the FFE model: crowning

index and torching index. The interplay of these indices,

with the specified weather and fuel loadings, results in a fire

type being assigned (Beukema and others 2000). The torching index indicates the wind speed needed to move the

Figure 6a—Percent of forested area in the three FVS

simulated fire types, using average fire conditions and

current/inventoried plot data.

Figure 6b—Percent of forested area in three FVS simulated fire types, using extreme fire conditions and current/inventoried plot data.

Figure 7a—Percent of forested area in the three FVS

simulated fire types, for single story, two story and multistory conditions, using average fire conditions and current/

inventoried plot data.

Figure 7b—Percent of forested area in the three FVS simulated fire types, for single story, two story and multi-story

conditions, using extreme fire conditions and current/inventoried plot data.

flames burning in the ground fuels into the tree crowns.

Crown base height is an important variable in computing

this index.

The crowning index indicates the wind speed needed to

sustain a crown fire. This is largely a function of crown bulk

density. The denser the canopy and the steeper the slope, the

less wind is needed to sustain a crown fire. Crowning index

gives an indication of the potential for fire to spread from

stand to stand, the behavior that drives medium to large

fires.

Figure 8 shows the percentage of forested area in three

crowning index classes. If a stand is in class 1, winds of 15

mph or less will sustain a running crown fire; in class 2,

winds of greater than 15 to 40 mph will sustain a crown fire.

This index is important because a stand classified as surface-fire type may have a low crowning index, which means

it lacks ladder fuels to help fire get into the crown. But once
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Analyst Hazards When Assessing Fire, Insect, and Disease Hazard in Montana Using FIA Data with FVS:... Atkins and Lundberg

in the crown, fire is easily sustained. Figure 9 shows the

acres in three crowning index classes by fire type for average

conditions. Note that 7.8 million acres of surface-fire type

would sustain a crown fire, spreading from another stand, if

winds were greater than 15 to 40 mph. An additional 1.8

million acres of crown-fire type could be sustained with wind

speeds less than 15 mph winds. Over 4.6 million acres of

passive-fire type would sustain a crown fire with winds

between 15 to 40 mph. In total over 15.7 million acres have

crown indices in the 0 to 40 mph wind classes, which

indicates 71 percent of Montana’s forests are at risk of crown

fire, even though modeled weather conditions relegated

them to the surface- or passive-fire type categories.

It is important to consider fire hazard using both fire type

and crown index. The fire type indicates the potential for fire

moving vertically within a stand, and the crown index shows

the potential for fire moving horizontally across the landscape as an active fire. Both aspects are important when

determining how to reduce fire hazard.

Figure 10 shows the basal area mortality for Montana’s

forest types by fire type. The variation in mortality illusFigure 8—Percent of area in three crown index classes,

using average fire conditions and current/inventoried

plot data.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

thousand acres

ACTIVE PASSIVE SURFACE

Crowning Index by Fire Type

2000 Average Conditions

Class 1 

0-15 mph

Class 2 

16-40 mph

Class 3 

40+ mph

Figure 9—Acres by crown index class for the three FVS

simulated fire types, using average fire conditions and

current/inventoried plot data.

trates that forest types dominated by species resistant to

fire have substantially less mortality. For example, a surface fire in the lodgepole type results in an average of 67

percent mortality, while the average is 32 percent in the

ponderosa pine type. For passive fires, there is less variation

in the mortality between forest types, but the trend between

types is similar.

Figure 11 illustrates that there is little difference in

mortality related to layering, which indicates that a similar

type of fire produces similar mortality regardless of layering. However, as illustrated in figure 7, layering does affect

the amount of fire type, with single layer stands having the

Figure 10—Average percent basal area loss by forest

type, for the three FVS simulated fire types; using

average fire conditions and current/inventoried plot data.

0%

20%

40%

60%

80%

100%

Percent Loss

Single Two Multi

Average Basal Area Loss 

by Layering and Fire Type

2000 Average Conditions

ACTIVE 

PASSIVE

SURFACE

Figure 11—Average percent basal area loss in single

story, two story and multi-story areas, for the three FVS

simulated fire types, using average fire conditions and

current/inventoried plot data.

Average Basal Area Loss 

by Forest Type and Fire Type

2000 Average Conditions

0%

20%

40%

60%

80%

100%

AF/MH

DFES

GF/CHWD

LP

PFPP

WB/AL

WL

Percent Loss

ACTIVE 

PASSIVE

SURFACE

PF–limber pine, GF/C-grand fir & cedar, HWD-hardwood,

WL-western Larch, ES-Englemann spruce, WB/ALwhitebark pine & subalpine larch, PP-ponderosa pine,

AF/MH- subalpine fir and mountain hemlock, LPlodgepole pine, DF- Douglas-fir
88 USDA Forest Service Proceedings RMRS-P-25. 2002

Atkins and Lundberg Analyst Hazards When Assessing Fire, Insect, and Disease Hazard in Montana Using FIA Data with FVS:...

least and multilayer stands the greatest distribution of

active and passive fire. Therefore, mortality is affected by

layering because it affects the type of fire simulated. Mortality does vary by size class as shown in figure 12. For surface

fires, mortality decreases from 66 percent in seeding/saplings to 34 percent in the large/very large class. The implication is that forests composed of fire resistant species and

larger size classes suffer less mortality given the same fire

type.

Analysis Problems/Challenges ____

In this section we describe the challenges we encountered,

how we calibrated the model, and suggest system developments that would improve and streamline analysis. We also

highlight research needed to improve model behavior.

Model Calibration and Improvements

During the analysis we encountered several problems

with default values and made adjustments, described below.

Other limitations were identified, and recommendations for

future improvements are presented.

Ingrowth and small tree mortality—Model runs with

default parameters resulted in outputs inconsistent with

our knowledge and that of others with considerable experience in Montana. The NI variant projected a 70 to 90 percent

decrease in understory trees per acre over 50 years. Experience and fire history studies (Smith and Arno 1999) indicate

an increase of small trees when fire has been excluded.

We used Flathead National Forest permanent growth plot

data (Northern Region, unpublished) to provide a basis for

adjusting model performance. Analysis of the data revealed

the base FVS model was killing too many tolerant species,

such as subalpine fir, and not enough of the intolerant

species, such as western larch. Also the permanent growth

plots indicated a substantially greater number of tolerant

species becoming established through time. We adjusted the

model using the MortMult and Estab keywords.

Our adjustments used only the Flathead growth plots

because they were the only data set available at the time. A

more thorough analysis of all the permanent growth plot

data is needed and consideration given to calibration of the

NI and EM variants of FVS based on this analysis.

Fuel load initialization—Actual fuel loading data were

available on most of the NFS plots. We used the FuelInit

keyword to initialize fuel loading for these plots. For plots

without data, default values were used. We compared the

average measured fuels to the default fuels and found the

default loadings were lower. As a result, the flame lengths

and subsequent fire behavior are most likely underestimated for the plots modeled with default values.

FIA fuel transect data could be used to refine the default

fuel loadings. Analysts are encouraged to use field measurements where available.

Decay rates—The decay rates of woody debris in the

version of the model that we used seemed high. We expected

to see an increase in fuel loading over time. The average fuel

loading during our 50-year projections remained about same,

even though no burning or harvesting activities occurred.

However, we would have expected fuel loadings to increase

due to ongoing insect, disease, and suppression mortality. As

a result, we feel fire behavior is underestimated for the 50-

year projections.

Another concern is the same decay rate applies to all

habitat types. Decay rates are a function of moisture and

temperature, so we would expect variation by habitat type.

Since our work was completed, the model has been adjusted.

However, the knowledge of decay rates is limited, indicating

an area needing further research.

“Other” species—FVS uses OT (other) for less common

species such as whitebark pine, alpine larch, and hardwoods. FVS’s definition of OT species depends on the variant. We need to be able to model and display all species. It is

becoming more important to be able to quantify and predict

the effects of fire, insect, and diseases for all species. This

may mean additional research in a number of areas including fire response, crown bulk densities, growth and regeneration, or others.

Programming Needs

Several improvements in data processing are needed to

make this type of analysis more efficient. Our project required substantial assistance from a several data managers

and programmers. The following is a list and discussion of

improvements that would streamline analysis.

Software that migrates FIA data into FSVEG—When

we started our project, FIA data were not available in FVS

format. The USDA Forest Service’s FSVEG database has

the capability to do this. However, the FIA data have not

been loaded into FSVEG at this time. All users need to be

able to access FVS-formatted FIA data for both NFS plots

and non-NFS plots.

Adding attributes to data sets—A programmer modified the FVS files by attaching attributes such as action

Figure 12—Average percent basal area loss by size class, for

the three FVS simulated fire types using average fire conditions and current/inventoried plot data. Seedling/Sapling

class includes seedling, sapling and non-stocked areas.

0%

20%

40%

60%

80%

100%

Percent Loss

ACTIVE PASSIVE SURFACE

Average Basal Area Loss 

by Fire Type and Size Class

2000 Average Conditions

Seedling/Sapling

Pole 

Medium

Large/Very Large
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Analyst Hazards When Assessing Fire, Insect, and Disease Hazard in Montana Using FIA Data with FVS:... Atkins and Lundberg

probability indices (for insect and diseases), inventoried

fuel conditions, and habitat type groups. This step greatly

simplified running the model. There is a need to develop

more automated tools that would give users the ability to

modify or supplement the FVS data without getting specialized help from programmers.

FVS output/reports—We used a custom program to

extract selected report fields. This program only works for

the output data fields we chose. This “extract” program

enabled us to load selected report fields into an Access

database, and to analyze and graphically display results.

There is a need for an FVS postprocessor that would put

user-defined fields in a flat-file format, so that large data

sets could be easily moved into other software applications.

This would greatly facilitate postmodeling analysis.

Postprocessing analysis—We wanted to quantify how

insects, diseases, growth, and mortality change forest types

and fire conditions, but we were unable to compare inventoried, FIA forest type with the simulated stand forest type

after 50 years. A postprocessor routine that reclassifies

forest type is needed for this type of analysis. This application is under development.

Mortality source identification—We were not able to

distinguish the cause of mortality between bark beetles, root

disease, or suppression. With current reports it is impossible

to discern the influence of insect and disease activity on fire

type, fuel models, and stand conditions. The way the I&P

extensions operate, more than one agent can kill the same

tree. To assess the impacts of insect and pathogens, and

determine the source of fuels, a methodology to track mortality by agent for each cycle would be useful.

Non-NFS plots do not have a National Forest—Some

FVS variants use National Forest (NF) in growth calibrations. The default for the NI variant is St. Joe NF, and the

EM variant uses the Custer NF. This was unsatisfactory for

our analysis since the plots were from across the whole

State. We solved this by assigning a National Forest based

on ecosection and county. This was added to the Montana

FVS stand list files. It is desirable to have a preprocessing

option available to allow all users to add National Forest.

Additional Research Needs

• It is apparent that permanent growth plot data and FIA

data, used separately and in conjunction with each

other, could be used to calibrate and improve FVS

predictions.

• Research on ingrowth, small tree mortality, fuel loading, decay rates, and crown dynamics is needed and

should be incorporated into FVS as information becomes available.

• Because crown bulk density is a critical factor in predicting and managing fire behavior, but not easily

measured, research is needed to find a readily applicable field technique to characterize it.

• There is a need to develop a better overall measure of

fire hazard that integrates fire type with crown index

and torch index.

Conclusions____________________

Our project demonstrates the utility of using FIA data in

combination with the FVS model, and its extensions, for

examining fire, insect, and disease hazard for large geographic areas, such as the State of Montana or subregions of

the State. Where data are available, they can also be applied

to a multi-State level. The flexibility of the model allows it to

be parameterized for a variety of different environmental

conditions. Our conclusion is the model and data provide a

powerful tool to help land managers and policymakers

assess conditions and provide a basis for making decisions.

The model was useful in describing specific indicators,

which allowed us to depict current conditions related to fire,

insect, and disease hazard. The results lead us to the following conclusions:

• An abundance of medium and pole sized forests exist,

and the fire hazard is not just a small tree problem.

There are too many medium sized trees as well.

• An abundance of forests with two or more layers exist,

which provide fuel ladders for fire.

• An abundance of forests exist with crowning index

ratings that indicate high potential for crown (active)

fires, which leads to the highest mortality.

• The extreme weather conditions resulted in much higher

amounts of active fire, and subsequently the mortality

and effects are greater than with average conditions.

• It is important for decisionmakers to consider both fire

type and crowning index when assessing the overall fire

hazard.

• Stand structure and composition that favor fire resistant species, larger diameter trees, lower crown bulk

densities (the major factor in crown index), and less

stand layering reduce fire intensity and mortality predicted when fires do occur. This gives managers options

for taking action to change hazard level, where desired.

• An abundance of forests are rated as moderate to high

hazard for bark beetle attack.

A number of caveats and limitations need to be recognized

by analysts and decisionmakers when using these tools and

data:

• Small tree mortality and ingrowth should be adjusted

for the forest conditions being modeled because they can

substantially change the results.

• On the non-NFS plots (37 percent of the plots), all of the

0 to 5 inch diameter trees may not have been recorded

because survey methodology was to determine which

plots were “stocked.” Therefore ladder fuels are underestimated, resulting in less active and passive fire

types.

• The default fuel loads appear to be underestimated,

which leads to underestimating fire type intensity.

• The decay rates appear to be too high, which reduces the

fuel load, which would tend to underestimate the intensity of the fire type.

• The use of only Flathead NF permanent growth plots for

calibrating the small tree mortality and ingrowth may

have resulted in overestimating ladder fuels for some

areas, which would tend to overestimate the intensity of

the fire type.
90 USDA Forest Service Proceedings RMRS-P-25. 2002

Atkins and Lundberg Analyst Hazards When Assessing Fire, Insect, and Disease Hazard in Montana Using FIA Data with FVS:...

Future analyses could be made more efficient by:

• Making model improvements, such as adding tree species and recalculating growth and mortality functions,

thereby producing more realistic results.

• Adding improved data input/output options to streamline data analysis, and reducing the number of helpers

needed to format and manipulate data.

Remember what George E. P. Box said, “All models are

wrong but some are useful!” Models are simplified abstractions of reality. We believe the results of this analysis are

useful for showing overall trends in hazards and the factors

that contribute to hazards. The model also provides information as to the potential effects that may occur. Therefore,

even though we know these simulations cannot predict

reality, they are useful for indicating patterns of likely

outcomes and options for managers to alter fire effects.

Acknowledgments ______________

The authors acknowledge the many individuals who contributed their knowledge and abilities to this project, each

playing a critical role in making this project possible: David

Browder, Renate Bush, Jimmie Chew, Glenn Christensen,

Nicholas Crookston, Lance David, Gary Dixon, Denny Edward, Susan Hagle, Robert Havis, Jeff Jones, Janet Krivacek,

George Lightner, Blakey Lockman, Elizabeth Reinhardt,

Don Vandendriesche, and Sharon Woudenberg. We extend

our thanks to the numerous others for their review and

advice. The Forest Health Monitoring Program, FIA Program, and the Northern Region Fire Program provided

funding for this project.

References _____________________

Beukema, S. J.; Reinhardt, E.; Greenough, J. A.; Kurz, W.A.;

Crookston, N.; Robinson, D.C.E. 2000. Fire and fuels extension:

model description. Prepared by ESSA Technologies Ltd.,

Vancouver, BC for USDA Forest Service, Rocky Mountain Research Station. Moscow, ID. 80 p.

Conner, Roger C.; O’Brien, Renee A. 1993. Montana’s forest resources. Res. Bull. INT-81. Ogden, UT: USDA Forest Service,

Intermountain Research Station 96 p.

Forest Health Technology Enterprise Team (FHTET). 2001. Forest

Vegetation Simulator, North Idaho and Eastern Montana variants with multiple insect and pathogen extensions (available

from FHTET). USDA Forest Service, FHTET, Fort Collins, CO.

Forest Management Service Center (FMSC). 2001. Forest Vegetation Simulator (FVS) available from FMSC @ www.fs.fed.us/

fmsc/fvs. USDA Forest Service, FMSC, Fort Collins, CO.

Hagle, Susan K.; Johnson, Terri L.; Stipe, Lawrence E.; Schwandt,

John W.; Byler, James W.; Kegley, Sandra J.; Bell-Randall, Carol

S.; Taylor, Jane E.; Lockman, I. Blakey; Sturdevant, Nancy J.

2000. Succession functions of pathogens and insects; ecoregion

sections M332a and M333d in North Idaho and Western Montana –

Volume 1: analysis methods. USDA Forest Service, Cooperative

Forestry-Forest Health Protection, Northern Region. FHP Report No. 00-10.

O’Brien, Renee A.; Conner, Roger C. 1991. Forest statistics for land

outside National Forests in eastern Montana, 1989. Res. Bull.

INT-71, Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Research Station. 45 p.

Smith, Helen Y.; Arno, Stephen F., eds. 1999. Eighty-eight years of

change in a managed ponderosa pine forest. Gen. Tech. Rep.

RMRS-GTR-23. Ogden, UT: U.S. Department of Agriculture,

Rocky Mountain Research Station. 55 p. plus 14 foldout photopoint

pages, one poster.

USDA Forest Service. 1988. Montana forest survey field procedures. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station, Forest Survey. 111 p.

USDA Forest Service. 2000. IWRIME inventory documentation and

terminology for Region 1 Montana National Forests.

Wykoff, William R.; Crookston, Nicholas L.; Stage, Albert R. 1982.

User’s guide to the stand prognosis model. Gen. Tech. Rep. INT-133.

Ogden, UT. U.S. Department of Agriculture, Forest Service,

Intermountain Forest and Range Experiment Station; 112 p.
USDA Forest Service Proceedings RMRS-P-25. 2002 91

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Glenn A. Christensen is a Forester (gchristensen@fs.fed.us), Forest Inventory and Analysis Program. Roger D. Fight is a Principle Economist

(rfight@fs.fed.us), Human and Natural Resource Interactions Program. R.

James Barbour is a Wood Scientist (jbarbour01@fs.fed.us), Focused Science

Delivery Program. All are at the USDA Forest Service, Northwest Research

Station, P.O. Box 3890, Portland, OR 97208.

Simulating Fire Hazard Reduction, Wood

Flows, and Economics of Fuel Treatments

with FVS, FEEMA, and FIA Data

Glenn A. Christensen

Roger D. Fight

R. James Barbour

Abstract—This paper demonstrates protocols to analyze and illustrate trends in the long-term effects of repeated fire hazard reduction entries at broad state-level scales. The objectives of this analysis are to determine the effectiveness of two stand treatment options

designed to immediately reduce and maintain lower wildfire hazards. Long-term effects of fire hazard reduction are reported for and

the stocking, size, and species mix of trees logs that might be

removed for wood products. We developed methods that use readily

available tools to provide this information and relate it to treatment

effectiveness in reducing fire hazard over time. The scope of the

project covers all forested areas of Montana and New Mexico.

Analysis is based on data collected by the Forest Service’s Forest

Inventory and Analysis (FIA) program for both States, but other

types of inventory data for smaller land areas also could be used. The

primary tools for this analysis are FVS, the Fire and Fuels Extension (FFE) model, and the Financial Evaluation of Ecosystem

Management Activities (FEEMA) model. Model output from more

than 1,000 plots is summarized using macros written for Microsoft

Excel, SAS statistical software, and Microsoft Access. These protocols can be used to simulate a variety of broad-scale management

options using stand level data that are readily available. This

information could be invaluable to evaluate future management

options over a wide range of spatial and temporal scales.

Substantial funding has recently been allocated to understanding options for long-term fire suppression effects

and hazard reduction options for the Intermountain West.

One such study is, “Assessing the Need, Costs, and Potential Benefits of Prescribed Fire and Mechanical Treatments to Reduce Fire Hazard in Montana and New Mexico”

(Barbour and others, unpublished paper). Some of the

methods used for that study are described in this paper.

The analysis is part of the Joint Fire Sciences Program to

develop protocols for use in determining hazard reduction

treatment needs, treatment cost, and associated benefits at

a State level. The objectives of the study were to (1)

quantify existing stand conditions for major forest types in

terms of density, structure, and species composition, and

prioritize by need for hazard reduction treatment, (2)

develop and compare alternative cutting and prescribed

burning prescriptions for reducing high-hazard conditions

in major forest types, (3) determine treatment costs, (4)

determine potential revenue from timber products generated from the hazard reduction harvest treatments, (5)

compare the future mix of timber products under alternative treatment scenarios, and (6) describe the potential for

analyzing noncommodity resources under treatment and

no-treatment scenarios (Barbour and others, unpublished

paper).

Our objective was to simulate broad-scale wildfire reduction options using readily available tools and stand-level

data. This paper describes the tools and analytical approaches used. The scope of the analysis includes all forested

lands in Montana and New Mexico. Our analysis determined and illustrated trends in the long-term effects of

repeated hazard reduction entries: the long-term effects in

terms of the stocking, size, and species mix of stands, and the

long-term effects, in terms of size and species mix, of trees

and logs that might be removed and utilized for wood

products. We also wanted to accomplish this analysis using

readily available models, software, and data typically collected as part of comprehensive forest inventories.

Flow of Analysis ________________

Analysis was based on data collected by the Forest Service’s

Forest Inventory and Analysis (FIA) program for both States,

but other types of inventory data could also be used such as

stand exam data. The primary tools for this analysis were

the Forest Service’s Forest Vegetation Simulator (FVS)

growth and yield model (Stage 1973; Wykoff and others

1982), the FVS Fire and Fuels Extension (FFE) (Beukema

and others 2000), and the Financial Evaluation of Ecosystem Management Activities (FEEMA) model (Fight and

Chmelik 1998). To provide exact control of treatment execution and specific output variables, the Event Monitor

(Crookston 1990) within FVS was used extensively. Microsoft

Excel and Access were used to format, sort, and summarize

data.

Our approach was to (1) extract the most recent forest

inventory data, (2) apply a hazard reduction treatment

using FVS, (3) use the FFE model to determine prefire and

postfire hazard conditions for treated stands, (4) use a listing

of removed trees from FVS as input to FEEMA (a customized

version) to buck each tree into logs, calculate volume, and
92 USDA Forest Service Proceedings RMRS-P-25. 2002

Christensen, Fight, and Barbour Simulating Fire Hazard Reduction, Wood Flows, and Economics of Fuel Treatments with FVS, FEEMA, and FIA Data

assign a dollar value, (5) store results from FVS, FFE, and

FEEMA in a database so that summary statistics for any

variable may be easily generated for any variable and

subtotaled by any desired classification, and (6) report these

data via standardized, tabular formats (fig. 1).

The large size of the study area poses challenges in

obtaining current inventory data for the entire area (for

example, for all ownerships) and in getting these data into

a useable format. Because several models are used, custom

linkages between them are needed to build an integrated

analytic system, and because hundreds, perhaps thousands,

of plots may be processed, this linkage needs to be as

automated and seamless as possible. After data from each

plot completes simulation, results need to be calculated. In

order for results to be based on any desired grouping or

subset, output from each plot for each year, each treatment,

and each tree needs to be tracked. Finally, all of this

information has to be distilled into a meaningful package

that provides enough detailed information to make informed

decisions, but not so detailed that extracting important

results is overly laborious. The following sections describe

how we overcame these obstacles and developed a methodology that can be readily used for analysis of forest lands

where multiple plots need to be evaluated. The analysis is

divided into three basic parts: data preparation, model

simulations, and output summarization.

Data Preparation ________________

The first and most important part of the analysis is to

obtain inventory data that reflects as accurately as possible

current vegetation for the area of interest. For our analysis

we used the latest FIA inventory covering all forested

ownerships of Montana and New Mexico. These data were

gathered with assistance from the Forest Service’s Forest

Management Service Center (FMSC) and the Rocky Mountain Research Station. A total of 1,278 plots, roughly half

from each State, are used in the analysis. These plots are not

necessarily on an evenly distributed systematic grid. To

Figure 1—General flow of simulation and result reporting.
USDA Forest Service Proceedings RMRS-P-25. 2002 93

Simulating Fire Hazard Reduction, Wood Flows, and Economics of Fuel Treatments with FVS, FEEMA, and FIA Data Christensen, Fight, and Barbour

compensate for this, results are summarized to provide

mean values weighted by each plot’s expansion factor. The

basic data needed to run FVS from the inventories include

plot level information such as slope, the number of acres

each plot represents (expansion factor or sampling factor),

specific sampling design information, and site index. Tree

level measurements included species, diameter at breast

height (d.b.h.), height, and crown ratio.

Before FVS could be used, inventory data had to be in a

specific FVS readable format, with one file per FIA plot. This

format differs by region and even within a region from one

inventory to the next. Using one of the conversion utilities

that have been developed and with help from the FMSC,

data were reformatted and the needed FVS files assembled.

Two types of files were created for FVS. The tree list file

contained measurements for every tree on a plot and the

stand list file contained attributes (for example, measurement date, file path to the tree data file, age, habitat type,

sampling information, and file path to any files with additional information) for every plot.

Next, stand list files were resorted to group plots into

“cases” (combinations of forest type, region, ownership,

slope, and current fire hazard). Silvicultural prescriptions

were developed that could be applied to each case. These

prescriptions are all formatted for FVS using a keyword file.

To facilitate tracking of simulation results in the final

database, all plots are renumbered using a system that

allows identification of the plot, case, year, and treatment. A

macro written in Excel was used to rename each plot and

build new stand list files for each case. The macro worked

from three worksheets: one listing a cross-reference of old to

new plot numbers, one that is a stand list file for all plots, and

one listing plot numbers desired in the new stand list file.

The macro produces a new worksheet that is saved as a space

delimited text file (FVS format) and used as the new stand

list file. The macro also provides the file path to a specific

keyword file used for that case. Treatment keyword files are

created using the Suppose interface (Crookston 1997) for

FVS. The keyword file includes the commands FVS uses to

apply thinning treatments, run the FFE model, and additional calculations needed such as trees per acre harvested

or posttreatment basal area.

Before plots could be assigned to a specific case, the

current fire hazard rating needed to be determined. We

accomplished this by running all plots through FFE. To

obtain an initial rating for this analysis, we used crowning

index as a proxy for potential fire hazard (Fielder and others,

unpublished paper). Crowning index is the wind speed

necessary to sustain a fire within the crown layer of a stand.

To ensure that all stands were current, FVS was used to

grow the stands to a common starting year of 2000. Stands

were rated as “low” or “high” hazard based on their crowning

index at year 2000.

Model Simulations ______________

All FVS and FFE simulations were initiated from the

Suppose interface. For every case, treatment and growth

were simulated for 100 years for each silvicultural prescription. For this analysis, we used two prescriptions, each a

thinning from below (a thinning that removes the smallest

trees first) to a diameter or basal area limit. A diameter limit

of 9 inches d.b.h. and a basal area limit of 50 percent were

used for both States. The diameter-limit treatment removed

all trees less than the target d.b.h. but was further bounded

by a minimum reserve basal area to ensure acceptable

stocking in the residual stand. The 50 percent basal area

treatment removed from below half the basal area. This

treatment has no upper diameter limit but is also bounded

by a minimum reserve basal area to ensure enough overstory trees remain. Minimum reserve basal area varied from

40 ft2 to 80 ft2 per acre, depending on forest type, State, and

geographic region within the State. To reduce fire hazard

(that is, to increase crowning index), thinning from below

reduced the overall stand density, helped remove ladder

fuels, and reduced crown bulk density. Crown bulk density

is one of the primary variables that determine crowning

index. In addition to thinning, each treated plot also received

a prescribed burn in association with each treatment interval. In some cases, plots that may have not been eligible for

thinning were still treated with a prescribed burn. For this

analysis we used the default prescribed burn values in FFE.

Thinning reentry and burning schedules are defined for

each case in the keyword file. If a plot was initially rated as

“low” fire hazard, treatment was delayed by one entry

interval.

The Event Monitor ensured that FVS applied each silvicultural prescription as intended. This allowed us to use IF/

THEN statements to evaluate each plot for treatment eligibility. If a plot met the initial criteria, the Event Monitor

provided precise control over which trees were removed.

Another advantage of using the Event Monitor is the COMPUTE keyword. By using this keyword, we were able to

create custom variables that calculate specific information

such as pre and posttreatment conditions (for example,

basal area, trees per acre, quadratic mean diameter) and

information on exactly what was removed (for example,

volume, trees per acre, and basal area by diameter class). By

using these variables in conjunction with IF/THEN statements, residual stand conditions could be calculated before

FVS applied the actual treatment.

After each case was processed through FVS and FFE,

output from both models was used as input for further

analysis with FEEMA (fig. 1). FEEMA uses a listing of

harvested trees, available via the CUTLIST and TREELIST

keywords in FVS, as input for bucking, log allocation, and

financial analysis. An import utility within FEEMA allows

direct importing of FVS tree list files to create FEEMA stand

files for each cut list. Note that a stand file in FEEMA is not

the same as a stand list file in FVS; in this analysis, a stand

in FEEMA is the same as a plot in FVS. Because stand file

names are concatenations of the plot name used in the FVS

run and the year of removal (indicated for each cut list

header), it is essential to adopt a plot naming convention. In

addition to using the tree list files from FVS, the output files

are also used to obtain results. FVS output files contain pre

and posttreatment plot variables calculated via the “COMPUTE” keyword and fire related data generated by FFE.

The FEEMA model was used to summarize volume and

species composition of utilized trees, volume of logs by size

class, and the net value of thinning treatments. To provide

information on the logs that would be available to industry

rather than specific products, the analysis was done with log
94 USDA Forest Service Proceedings RMRS-P-25. 2002

Christensen, Fight, and Barbour Simulating Fire Hazard Reduction, Wood Flows, and Economics of Fuel Treatments with FVS, FEEMA, and FIA Data

Figure 2—Example of residual stand characteristics report.

pricing rather than final product pricing. The customized

version of FEEMA allowed results to be processed through

a series of macros written in Excel that placed the needed

data items into one Excel worksheet for importing into an

ACCESS database.

Output Summarization ___________

At this point of the analysis all FIA plot data had been

processed with FVS, FFE, and FEEMA. FEEMA results have

been summarized and are now ready to be imported into

Access database tables (fig. 1). FVS output files have been

produced and are now ready to be summarized. Because FVS

writes its output report as a text file, macros were written

using a text editor capable of recording and playing macros

that manipulate these files. Macros were written to search

each FVS output file for specific data from each plot. These

raw data were then copied and pasted into a space-delimited

file amenable to import into Access. For this analysis, these

data included plot name, information in the FFE reports, and

all the COMPUTE variables.

Final summary reports were assembled from the Access

table of output. Each row in this database represents a tree

that can be tracked to a specific treatment, case, year

harvested, and plot. Reports on total number of plots and

acres treated can be easily generated by simple summation.

For this analysis, plots in Montana represent an average of

approximately 6,000 acres and for New Mexico approximately 6,600 acres; however, this value can vary from less

than 1,000 acres to more than 10,000 acres depending on the

forest strata. Thus, observations are weighted by plot expansion factors before summation. More complex summaries

are also possible—for example, mean and error statistics by

forest type, region, ownership, and so forth—through the

use of Access queries and reports. Examples of final tables

(fig. 2, 3, and 4) show the range of attributes that can be

summarized.
USDA Forest Service Proceedings RMRS-P-25. 2002 95

Simulating Fire Hazard Reduction, Wood Flows, and Economics of Fuel Treatments with FVS, FEEMA, and FIA Data Christensen, Fight, and Barbour

Figure 3—Example report of volume of utilized trees by diameter class.

Figure 4—Example report of financial results using FEEMA output.
96 USDA Forest Service Proceedings RMRS-P-25. 2002

Christensen, Fight, and Barbour Simulating Fire Hazard Reduction, Wood Flows, and Economics of Fuel Treatments with FVS, FEEMA, and FIA Data

Conclusions____________________

This analytic approach has been extended for other study

areas, including the Blue Mountains Demonstration Project

in Northeastern Oregon, where a detailed analysis of fuels

reduction treatments on three National Forests (Umatilla,

Malheur, and Wallowa-Whitman) is under way. The FIA

BioSum model, which seeks to evaluate potential biomass

available from fire hazard reduction treatments in the

Western United States, has significantly expanded the capability and complexity of this analytic framework by adding spatially explicit representation of biomass accumulation potential over existing road networks.

The analytic approach and software summarized here,

built on a foundation of publicly available data and models,

enable managers to address questions concerning forest

management over a large area with great detail. The scope

of silvicultural prescriptions that can be evaluated is limited

only by an analyst’s imagination and the capabilities of the

models. By using the Event Monitor within FVS, complex

treatments can be simulated. The FFE model allows analysis of wildfire potential, response to treatments, and use of

prescribed burning. If only these models were used, summary would be possible at a tree level, but by using FEEMA,

each harvested tree can be evaluated even further by accounting for treatment costs and conducting log level analyses. Finally, using Access database tables to store all raw

simulation output, results can be sorted and summed using

custom defined criteria and classes. This allows results from

hundreds or thousands of plots to be quickly compiled into

manageable, meaningful results. The report-writing feature

of Access can be used to automate result generation for

commonly requested table formats.

References _____________________

Barbour, Jamie; Fight, Roger; Fiedler Carl; Keegan, Chuck. 1999.

Project Title: Assessing the need, costs, and potential benefits of

prescribed fire and mechanical treatments to reduce fire hazard.

Unpublished paper on file at: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station, Forestry

Sciences Laboratory, Portland, OR. 20 p.

Barbour, R. James; Fight, Roger D.; Christensen, Glenn A.; Pinjuv,

Guy L.; Nagubadi, Venkatarao. 2001. Assessing the need, costs,

and potential benefits of prescribed fire and mechanical treatments to reduce fire hazard in Montana and New Mexico: A report

to the Joint Fire Sciences Program. Unpublished paper on file at:

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, Portland,

OR. 159 p.

Beukema, S. J.; Reinhardt, E.; Greenough, J. A.; Kurz, W. A.;

Crookston N.; Robinson, D. C. E. 2000. Fire and fuels extension:

model description. Working draft prepared by ESSA Technologies Ltd., Vancouver, B.C. for U.S. Department of Agriculture,

Forest Service, Rocky Mountain Research Station, Moscow, ID.

58 p.

Crookston, N. L. 1990. User’s guide to the event monitor: part of

Prognosis Model version 6. Gen. Tech. Rep. INT-275. Ogden, UT:

U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. 21 p.

Crookston, N. L. 1997. Suppose: An interface to the Forest Vegetation Simulator. In: Teck, Richard; Moeur, Melinda; Adams, Judy,

comps. 1997. Proceedings: Forest Vegetation Simulator conference; 1997 Februrary 3-7; Fort Collins, CO. Gen. Tech. Rep. INTGTR-373. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Research Station. 8 p.

Fiedler, Carl E.; Keegan III, Charles E.; Woodall, Chris W.;

Morgan, Todd A.; Robertson, Steve H.; Chmelik, John. 2001. A

strategic assessment of fire hazard in Montana. Unpublished

paper on file at: University of Montana, School of Forestry,

Missoula, MT. 34 p.

Fight, R. D.; Chmelik, J. T. 1998. Analysts guide to FEEMA for

financial analysis of ecosystem management activities. Gen.

Tech. Rep. FPL-GTR-111. Madison, WI: U.S. Department of

Agriculture, Forest Service, Forest Products Laboratory. 5 p.

Hasslen, Hayley; Alexander, Susan J. 2001. Summary report of the

effects of fire on recreation demand in New Mexico. [Homepage of

the Ecologically Sustainable Production of Forest Resources

Team], [Online]. Available: http://www.fs.fed.us/pnw/woodquality/

index.htm [2002, January 11].

Stage, A. R. 1973. Prognosis Model for stand development. Res. Pap.

INT-137. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Forest and Range Experiment Station.

14 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:

U.S. Department of Agriculture, Forest Service, Intermountain

Forest and Range Experiment Station. 112 p.
USDA Forest Service Proceedings RMRS-P-25. 2002 97

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

David L. Renner is a Forestry Consultant, Charis Consulting, 1130 E. 7th,

Moscow, ID 83843. Fred C. Martin is Forest Biometrician, Washington

Department of Natural Resources, Olympia, WA 98503.

Abstract—The FFE and ECON extensions to FVS were used to

evaluate the impacts of five silvicultural scenarios on a dry ponderosa pine (Pinus ponderosa) plantation and a moist, mixed-species

stand. Scenarios ranged from no treatments to traditional thinning

to thinning with prescribed fire management. Present net value

(PNV), total cubic foot volume productivity, and fire hazard, defined

by torching index, crowning index, and predicted fire type under

wildfire conditions, were computed. Greatest PNV difference among

all regimes was approximately $600 per acre at simulation end.

Greatest difference in total cubic foot volume production among

regimes at simulation end was approximately 2000 cubic feet per

acre. Productivity was greatest for mechanical treatment only

regimes. Regimes containing prescribed fire treatment generally

had lower total productivity, with multiple fire treatment regimes

generally producing less volume than single fire treatment regimes.

Greatest difference in fuel concentration was approximately 45 tons

per acre for the ponderosa pine plantation at 100 years. Surface fuel

concentrations, for both fine fuels and total fuels, were consistently

higher for the no treatment regimes for both stands, with marked

increases occurring when stands reached 60 to 100 years old. Using

the FFE and ECON extensions provides managers with quantitative measures for evaluating both the economic benefits and wildfire

hazards of different silvicultural scenarios.

Several extensions to FVS (Stage 1973; Wykoff and others

1982) have been developed over the years to provide managers with quantitative information about forest management

activities, beyond the basic dynamics of tree growth provided by FVS. The latest extensions are the Fuels and Fire

Effects Extension (FFE) (Beukema and others 2000) and the

Economic Extension (ECON) (Renner 2002, in preparation).

The FFE is designed to simulate effects of fire on stand

characteristics and stand development, and to simulate the

effects of stand development and management actions on

fuel dynamics, fire behavior, and fire effects. The FFE

consists of three major modules: (1) snag module that monitors decay and fall down of snags, (2) fuels module that

Using the Fuels and Fire Effects (FFE) and

Economic (ECON) Extensions to the Forest

Vegetation Simulator (FVS) to Evaluate the

Impacts of Silvicultural Regimes

David L. Renner

Fred C. Martin

simulates accumulation and decomposition of surface fuels,

and (3) fire module that simulates fire intensity, effects on

trees, smoke production, and mineral soil exposure.

The ECON extension computes economic measures associated with stand management activities including present

net value (PNV), soil expectation value (SEV), internal rate

of return, benefit/cost ratio, value of reproduction,

undiscounted cash flow, and the value of an existing stand.

ECON allows for three types of economic analyses: (1)

analysis of an existing stand over a finite investment horizon

(PNV), (2) analysis of an infinitely repeating management

regime (SEV), and (3) a combined analysis of an existing

stand followed by application of an infinitely repeating

management regime (PNVPLUS). ECON features include:

(1) computation of harvest revenues based on either tree

d.b.h. or log size, (2) evaluation of multiple rotation ages or

harvest timings within a single FVS simulation via the

PRETEND keyword, (3) full integration with FVS allowing

for dynamic specification of management activities based on

economic measures using the Event Monitor (Crookston

1990), (4) production of log stock tables with associated

revenues, and (5) flexible specification of fixed and variable

costs and revenues.

Case Studies ___________________

Two case studies illustrate the use of the FFE and ECON

extensions in evaluating the impacts of silvicultural regimes. Five silvicultural regimes, including a no treatments

option, were specified for each case study. Regime treatments, along with their associated economic costs and revenues, were provided by silviculturalists and fire management personnel currently involved in managing the stands.

PNV, total cubic foot volume productivity, fuel profiles, firerelated mortality, and three measures of fire hazard rating

were computed for each regime. The three fire hazard

metrics used were: (1) torching index defined as the 20-foot

wind speed (that is, wind speed at 20 feet above ground level)

required to carry a fire from the ground surface into tree

crowns; (2) crowning index defined as the 20-foot wind speed

required to maintain an actively crowning fire, thereby

killing all trees in the stand; and (3) predicted fire type under

severe burning conditions (that is, “surface” fire when the

fire is confined to the ground with no scorching of crowns,

“passive” fire when the fire scorches and consumes groups of

crowns resulting in scattered mortality, or “active” fire when

the fire consumes all crowns within the stand resulting in
98 USDA Forest Service Proceedings RMRS-P-25. 2002

Renner and Martin Using the Fuels and Fire Effects (FFE) and Economic (ECON) Extensions to the Forest Vegetation Simulator...

complete stand mortality). Prescribed fire treatment types

used in the study were: (1) throttle-back broadcast burn, (2)

low intensity “free to burn” broadcast burn, and (3) jackpot

fuel burn. The throttle-back prescribed fire is a broadcast

burn that affects the entire stand, with the fire confined to

thin strips. The free to burn broadcast burn is a low intensity

understory burn affecting the entire stand, while the jackpot

fuel burn affects only concentrations of fuels within the

stand.

Case Study 1 – Ponderosa Pine Plantation

The first stand used in the analysis was a ponderosa pine

plantation with the following characteristics: located in Nez

Perce National Forest, Idaho, 35 years old, Pseudotsuga

menziesii/Physocarpus malvacens habitat type, 20 percent

slope, southeast aspect, 4,500 feet elevation, 510 trees per

acre, composed of 70 percent ponderosa pine, 20 percent

Douglas-fir (Pseudotsugamenziesii), and 10 percent western

larch (Larix occidentalis) on a trees per acre basis. The stand

had no previous management treatments. The five silvicultural regimes applied to the stand are listed in table 1, and

the input economic parameters used are listed in table 2.

PNV for each regime, displayed in figure 1, shows that the

mechanical treatment only regime, 1b, generated the greatest PNV at simulation end, while the no treatment regime,

1c, had the lowest PNV. Regimes using prescribed fire

treatments, 1a, 2a, and 2b, generated intermediate PNV

values with slightly lower values for the regimes performing

a jackpot fuel burn in 2060 (1a and 2a). Total cubic foot

volume production, displayed in figure 2, shows that the

mechanical treatment only regime, 1b, generated the greatest total cubic foot production, while the regime applying

prescribed fire treatments at ages 30 and 60 years, 2a, was

Table 1—Silvicultural regimes for the ponderosa pine plantation. The stand was simulated beginning in year 2000 in order to include economic

costs associated with stand establishment.

Year Age 1a 1b 1c 2a 2b

2000 0 Plant Plant Plant Plant Plant

2020 20 Precommercial Precommercial No treatment No treatment No treatment

thin to 350 thin to 350 t/a;

trees/acre (t/a); no residue

no residue treatment treatment No treatment

2030 30 No treatment No treatment No treatment Throttle-back Throttle-back

understory burn understory burn

2060 60 Commercial thin Commercial thin No treatment Commercial thin Commercial thin

to 130 sq ft + to 130 sq ft; residue to 130 sq ft + to 130 sq ft; residue

jack-pot fuel burn removed from stand jack-pot fuel burn removed from stand

2120 120 Clear-cut Clear-cut No treatment Clear-cut Clear-cut

Table 2—Input economic parameters for ponderosa

pine plantation.

Economic parameter Value

Discount rate 5.0%

Annual cost $2.50/acre

Planting cost $175.00/acre

Burn site prep cost $200.00/acre

Precommercial thin fixed cost $100.00/acre

Harvest variable cost $25.00/mbf

Harvest fixed cost $100.00/acre

Average harvest revenue

Western white pine $339.75/mbf

Western larch $366.40/mbf

Douglas fir $366.40/mbf

Grand fir $339.75/mbf

Western hemlock $339.75/mbf

Western red cedar $366.40/mbf

Lodgepole pine $339.75/mbf

Englemann spruce $339.75/mbf

Sub-alpine fir $339.75/mbf

Ponderosa pine $384.00/mbf

mbf = thousand board-feet Figure 1—PNV for ponderosa pine plantation.

2000

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USDA Forest Service Proceedings RMRS-P-25. 2002 99

Using the Fuels and Fire Effects (FFE) and Economic (ECON) Extensions to the Forest Vegetation Simulator... Renner and Martin

least productive. The no treatment regime, 1c, was intermediate in productivity. Although PNV and cubic volume

differences among regimes were relatively small, greatest

differences occurred between the no treatment regime and

the more active management regimes.

Torching and crowning indexes for the ponderosa pine

plantation are displayed in figures 3 and 4, respectively.

Increasing values for both torching and crowning index

represent decreasing wildfire hazard. Fire hazard decreased

for all regimes during years 2022 through 2042 due to

reduction of ladder fuels as a result of thinning and understory burning (fig. 3). However, regimes with mechanical

treatment only at year 2020, 1a and 1b, resulted in a much

larger increase in torching index in years 2032 through 2042

compared to regimes specifying fire treatments at year 2030

(2a and 2b). Wildfire hazard for the no treatment regime, 1c,

was intermediate compared to the other regimes during this

period and is most likely due to increased height to the base

of crown that occurs as a result of self-pruning. Crowning

index declined and remained low for all regimes between

2020 and 2050 in spite of the precommercial thinning/

understory burn treatments at years 2020 and 2030 (fig. 4).

This suggests that decreased stand density resulting from

precommercial thinning/understory burn treatments was

not sufficient to offset posttreatment growth and expansion

of tree crowns. After 2060, however, thinning in all of the

active management regimes substantially reduced crown

fire hazard compared to the no treatment regime.

Predicted fire type under wildfire conditions, defined as 30

m.p.h. winds under very dry moisture conditions, indicate

all regimes were effective in reducing fire hazard between

2032 and 2052 as indicated by the transition from active to

surface fire type (table 3). However, treatments at 2062 had

a markedly different effect on the predicted wildfire type for

the duration of the simulation. The no treatment regime, 1c,

resulted in greater wildfire hazard as indicated by the active

fire type. Regimes 1a and 2a, which utilized prescribed fire

for treating fuels in 2062, provided the greatest reduction in

hazard as indicated by the surface fire type. Regimes 1b and

2b, which specified commercial thinning at 60 years without

prescribed fire, provided intermediate reduction in hazard

rating as indicated by the passive fire type.

With the exception of the no treatment regime, 1c, PNV of

the different silvicultural regimes varied little. Regimes

Figure 2—Total cubic foot volume production for

ponderosa pine plantation.

Figure 3—Torching index for ponderosa pine plantation. Values are identical for regimes 1a and 1b, and

2a and 2b, at year 2040.

Figure 4—Crowning index for ponderosa pine plantation.

0

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Renner and Martin Using the Fuels and Fire Effects (FFE) and Economic (ECON) Extensions to the Forest Vegetation Simulator...

that provided mechanical treatment only at age 60 years, 1b

and 2b, resulted in slightly higher PNV values at the end of

the simulation compared to regimes using a combination of

commercial thinning and fire-based fuel treatments at age

60, 1a and 2a. This slight difference appeared to result from

fire-induced mortality of larger, more valuable trees. PNV of

the no treatment regime, 1c, was lower at the end of the

simulation than other treatments, with an ending maximum difference of approximately $250 per acre among

treatments. Total cubic foot volume productivity was also

greatest for the mechanical treatment only regime, 1b.

Regimes combining commercial thinning with fire-based

fuel treatment at age 60 years, 1a and 2a, were least

productive with the no treatment option, 1c, being intermediate in value.

Silvicultural regimes with slightly greater total cubic foot

volume production and PNV values, 1b and 2b, also represented the scenarios of greater risk of loss under wildfire fire

scenarios (passive versus surface fire type). Wildfire hazard

risks were mixed, with the no treatments regime, 1c, having

the greatest risk by all three measures—predicted wildfire

type, torching index, and crowning index. While the use of

prescribed fire as a treatment alternative in the early stages

Table 3—Potential fire type under wildfire conditions for ponderosa pine

plantation silvicultural regimes.

Year Age 1a 1b 1c 2a 2b

2002 2 Active Active Active Active Active

2012 12 Active Active Active Active Active

2022 22 Active Active Surface Surface Surface

2032 32 Surface Surface Surface Surface Surface

2042 42 Surface Surface Surface Surface Surface

2052 52 Surface Surface Surface Surface Surface

2062 62 Active Active Active Active Active

2072 72 Surface Passive Active Surface Passive

2082 82 Surface Passive Active Surface Passive

2092 92 Surface Passive Active Surface Passive

2102 102 Surface Passive Active Surface Passive

2112 112 Surface Passive Active Surface Passive

2122 122 Passive Passive Active Passive Passive

of stand development afforded no major differences in fire

hazard risk compared to mechanical treatments, reduction

in fire hazard was predicted in later years for the regimes

that combined commercial thinning and fire-based fuel

treatment at age 60 years, 1a and 2a.

Case Study 2 – Mixed-Species, Multistory

Stand

The second case study was a mixed-species, multistory

stand with the following characteristics: located near Colville

National Forest in Eastern Washington State, approximately 60 years old, Abies grandis/Clintonia uniflorum

habitat type, level (0 to 5 percent) slope, north aspect, 2,500

feet elevation, 335 trees per acre, composed of 50 percent

lodgepole pine (Pinus contorta), 20 percent ponderosa pine,

20 percent western larch, and 10 percent Douglas-fir on a

trees per acre basis. The stand was commercially thinned in

1990. The five silvicultural regimes applied to the stand are

listed in table 4, and the input economic parameters are

listed in table 5.

PNV was greatest over the simulation period for mechanical treatment only regimes 1a and 1d (fig. 5). PNV for the no

treatment regime, 1c, was the lowest at the end of the

simulation, while regimes 1b and 1e were intermediate in

value. Regimes using prescribed fire, 1b and 1e, caused a

reduction in economic benefit compared to regimes 1a and 1d

that used mechanical only treatments. In addition, the

regime specifying two prescribed fire treatments, 1e, registered a smaller PNV than the regime composed of one fire

treatment, 1b. The effects of fire treatment on stand stocking

may explain this trend, as fire-related mortality was often

considerable even in larger diameter classes. Stocking reduction, particularly in the diameter classes that typically

represent the highest economic values, would be expected to

have such an adverse effect on economic value.

The mechanical treatment only regime, 1a, had the highest cubic foot volume productivity followed by the no treatment regime, 1c, and the mechanical treatment only regime

1d (fig. 6). Regimes applying prescribed fire treatments, 1b

and 1e, were least productive, with the regime containing

two fire treatments, 1e, registering the lowest total productivity at end of the simulation. The reduced volume in the

Table 4—Silvicultural regimes for the mixed-species stand.

Year Age 1a 1b 1c 1d 1e

1990 60 Commercial thin to Commercial thin to No treatment Commercial thin Commercial thin

300 trees/acre (t/a) 300 t/a + throttle-back to 300 t/a to 300 t/a

understory burn

1995 65 No treatment No treatment No treatment Sanitation thin Sanitation thin from

from below to below to 195 t/a +

195 t/a low intensity broadcast

understory burn

2010 80 Commercial thin Commercial thin No treatment Commercial thin Commercial thin

to 110 sq ft to 110 sq ft to 110 sq ft to 110 sq ft + low

intensity broadcast

understory burn

2030 100 Clear-cut Clear-cut No treatment Clear-cut Clear-cut
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Using the Fuels and Fire Effects (FFE) and Economic (ECON) Extensions to the Forest Vegetation Simulator... Renner and Martin

Table 5—Input economic parameters for the mixedspecies stand.

Economic parameter Value

Discount rate 5.0%

Annual cost $2.50/acre

Planting cost $175.00/acre

Burn site prep cost $200.00/acre

Precommercial thin fixed cost $100.00/acre

Harvest variable cost $25.00/mbf

Harvest fixed cost $100.00/acre

Average harvest revenue

Western white pine $268.00/mbf

Western larch $240.00/mbf

Douglas fir $240.00/mbf

Grand fir $200.00/mbf

Western hemlock $200.00/mbf

Western red cedar $200.00/mbf

Lodgepole pine $200.00/mbf

Englemann spruce $200.00/mbf

Sub-alpine fir $200.00/mbf

Ponderosa pine $285.00/mbf

Figure 5—PNV for mixed-species stand. Stand establishment costs are not included.

Figure 6—Total cubic foot volume production for

the mixed-species stand.

prescribed fire regimes was the result of fire-induced mortality of larger trees.

Torching index initially declined for all regimes at year

2000 (fig. 7). The no treatment and mechanical treatment

only regimes, 1a, 1c, and 1d, subsequently increased at year

2010 before declining again. Regimes using prescribed fire,

1b and 1e, remained low throughout the simulation. Crowning index increased for all regimes at year 2000, but then

declined in future years. Decreases in crowning index were

greater for regimes using prescribed fire, 1b and 1e, than for

other regimes.

Predicted fire types under wildfire conditions for the

mixed-species stand are listed in table 6. Wildfire conditions

were defined as 30 m.p.h. wind speed under moist conditions. Treatments applied at stand age 60 to 70 years

resulted in markedly different wildfire types. The no treatment regime, 1c, resulted in only surface fire conditions,

while regimes that applied commercial thinning with no

accompanying fire treatment, 1a and 1d, resulted in predictions of active crown fires. There were virtually no differences in fire hazard, however, at stand ages between 90 and

100 years. Regardless of silvicultural regime, active crown

fires were predicted to prevail under severe burning

conditions.

Figure 7—Torching index for the mixed-species stand.

Values are identical for regimes 1a and 1b, and 1d and

1e, at year 1990.

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300

400

500

600

700

800

900

1990 2000 2010 2020 2030 2040

Year

1a

1b

1c

1d

1e
102 USDA Forest Service Proceedings RMRS-P-25. 2002

Renner and Martin Using the Fuels and Fire Effects (FFE) and Economic (ECON) Extensions to the Forest Vegetation Simulator...

Table 6—Potential fire type under wildfire conditions for mixed-species

stand silvicultural regimes.

Year Age 1a 1b 1c 1d 1e

1990 60 Surface Surface Surface Surface Surface

2000 70 Active Passive Surface Active Passive

2010 80 Surface Active Surface Surface Passive

2020 90 Active Active Surface Active Active

2030 100 Active Active Active Active Active

Fire hazard trends for the mixed-species stand, as expressed by torching index, were somewhat unexpected. Major

reductions in fire hazard were observed for regimes using no

treatment or mechanical only treatments at year 2010 (1a,

1c, and 1d). Hazards were significantly greater during the

period between 2000 and 2020 for regimes containing prescribed fire treatments (1b and 1e). The rapid increase in fire

hazard at year 2000 was the result of a large impulse of

natural regeneration following the commercial thinning,

with the resultant lowering of height to crown base to ground

level and an increase in ladder fuels. The increase in torching

index at 2010 for the mechanical treatment only regimes was

likely due to the removal of understory ladder fuels at the

time of thinning.

Crowning index increased in year 2000 as a result of

thinning done in 1990. The thinning was effective in reducing crown bulk density and thus reducing the potential for

crown fires within the stand (fig. 8). This hazard reduction

was short lived for the single fire treatment regime, 1b, but

was maintained for several decades for the two-fire treatment regime, 1e. Reduction in crown bulk density for regime

1e, 4.5 times lower than that for regime 1b, was apparently

sufficient to provide a lower hazard level.

Discussion and Conclusions ______

Silvicultural regimes can have a significant effect on both

the economic benefits and the fire hazard status of forest

stands. The case study results presented here suggest a

significant tradeoff exists between the economic benefits

realized and the fire hazard risk associated with treating

and maintaining a forest structure through time that provides those benefits. In the ponderosa pine plantation case

study, there was little benefit in terms of fire hazard reduction from prescribed fire-based regimes early in the life of the

stand. This effect appeared to be important, however, in the

later stages of the stand’s development. Reduction in fire

hazard was associated with slightly reduced economic benefits as indicated by the lower PNV values for fire-related

regimes. Differences and similarities in trends existed for

the mixed species stand compared to the pine plantation. In

the mixed-species case study, mechanical treatment only

regimes, 1a and 1d, provided the largest PNV and total cubic

foot volume production values. However, in the mixedspecies stand, fire hazard reduction in response to treatment

is accomplished only for initial treatments at ages 60 and 65,

while subsequent fire treatment at age 80 provided no

hazard reduction. The steady influx of natural regeneration

in the mixed-species stand, along with its multistoried

characteristics, showed that the use of fire as a tool for

discriminating among tree classes and tree species is difficult. In both case studies the timing of fire treatments at

specific points in the development of the stands appeared to

be important in determining not only the economic benefits

realized but also the susceptibility to catastrophic fire losses.

In any event, results from these analyses suggest that any

reduction in fire hazard as a result of silvicultural treatment

is short lived. This is consistent with the findings of Fiedler

(Fiedler and others 2001) who observed that certain types of

silvicultural regimes provided only a temporary reduction in

fire hazard. Thus, management objectives calling for ongoing fire hazard reduction will likely entail repeated applications of fire as a silvicultural treatment. Managers considering a wide range of multiple-use objectives can use the FFE

and ECON extensions to FVS to better evaluate the impacts

of silvicultural scenarios on economic values and their effects on creating and maintaining a diverse forest structure

capable of reducing wildfire risk.

Acknowledgments ______________

The authors acknowledge the Joint Fire Science Program

and the Washington Department of Natural Resources for

their support of this project.

References _____________________

Beukema, S.J.; Reinhardt, E.D.; Kurz, Werner A.; Crookston, Nicholas L. 2000. An overview of the Fire and Fuels Extension to the

Forest Vegetation Simulator. In: Neuenschwander L.F. and Ryan,

K.C. (tech eds.). Proceedings from: The Joint Fire Science Conference and Workshop – “Crossing the Millennium: Integrating

Spatial Technologies and Ecological Principles for a New Age in

Fire Management.” Vol. II. Boise, ID, June 15-17, 1999. University of Idaho and the International Association of Wildland Fire.

pp. 80-85. Figure 8—Crowning index for the mixed-species stand. 20 Ft. Wind Speed (MPH)

0

5

10

15

20

25

30

35

40

1990 2000 2010 2020 2030 2040

Year

1a

1b

1c

1d

1e
USDA Forest Service Proceedings RMRS-P-25. 2002 103

Using the Fuels and Fire Effects (FFE) and Economic (ECON) Extensions to the Forest Vegetation Simulator... Renner and Martin

Crookston, Nicholas L. 1990. User’s guide to the event monitor: part

of Prognosis Model Version 6. Gen. Tech. Rep. INT-275. Ogden,

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 21 p.

Crookston, Nicholas L.; Beukema, Sarah; Robinson, Donald C.E.

In prep. Chapter 3 – User’s Guide. In: Reinhardt, Elizabeth,

Crookston, Nicholas L. (tech eds). The Fire and Fuels Extension

to the Forest Vegetation Simulator. Gen. Tech. Rep. RMRSGTR-000. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Fiedler, Carl E.; Keegan, Charles E. III; Woodall, Chris W.; Morgan,

Todd A.; Robertson, Steve H.; Chmelik, John T. 2001. A Strategic

Assessment of Fire Hazard in Montana. Report submitted to the

Joint Fire Sciences Program in cooperation with the U.S. Forest

Service, Pacific Northwest Research Station. 33 p.

Renner, David L. In prep. User’s guide to the ECON extension to

FVS. On file with Research Work Unit 4155, Moscow Forestry

Sciences Laboratory, U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station. 53 p.

Stage, A.R. 1973. Prognosis Model for Stand Development. Res.

Pap. INT-137. Ogden, UT: U.S. Department of Agriculture,

Forest Service, Intermountain Forest and Range Experiment

Station. 32 p.

Wykoff, W. R.; Crookston, N. L.; Stage, A. R. 1982. User’s Guide to

the Prognosis Model. Gen. Tech. Rep. INT-133. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 112 p.
104 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Albert R. Stage is Principal Mensurationist (retired), USDA Forest Service, Rocky Mountain Research Station, 1221 S. Main St. Moscow, ID, 83843.

Abstract—While the prospect of a static climate is no longer

tenable, the direction of change for particular localities is not yet

clear. Modelling vulnerability of silvicultural options to various

scenarios of climate change requires a modelling system that can

represent major processes affected by climatic variability. The

Forest Vegetation Simulator (FVS), through its Keywords and

Event Monitor commands modifying the underlying model, can be

used for such analyses. In this report, we document an FVS-based

analysis of four scenarios of climate change: warmer-dryer, warmerwetter, cooler-dryer, and cooler-wetter. Regeneration rates and

species composition of regeneration and rates of mortality and

accretion are expected to respond differently under the four scenarios. Development of forest stands in the example locales (northwestern Montana and central Washington) is strongly influenced by

fire, and conversely, fire behavior is influenced by stand structure.

We describe how analyses illustrating the interplay of these hypotheses were formulated using FVS and its Fire and Fuels Extension

(FFE). Our hypotheses of how processes might change in response

to varying climate are qualitatively consistent with our understanding of the ecosystem represented. Furthermore, the present range

of weather variability is sufficient to test each of the component

hypotheses independently by monitoring how their rates change in

response to weather variation.

Application of the Forest Vegetation Simulator for evaluating the effects of hypothesized changes in climate has been

described by Stage and others (2001). In that report, we

evaluated four possible scenarios: warmer-dryer, warmerwetter, cooler-dryer, and cooler-wetter. This report provides

more detail on how FVS and its Fire and Fuels Extension

(FFE) ( Beukema and others 1997) were used in the analysis.

Critical for the analysis were the capabilities of the Event

Monitor (Crookston 1990) to define variables and the use of

computed functions of those variables to modify the processes represented by the underlying FVS model.

The essence of the scenarios for stand development was

represented by differences in mean and variance of seasonal

moisture stress and length of growing season (table 1)

(Zahner and Stage 1966; Rehfeldt and others 1999).

These parameters of climate were hypothesized to affect

rates and species composition of regeneration, rates of

mortality and accretion, probability of wildfire, and rates of

fuel accumulation. Management options included thinning,

pruning, and prescribed burning. Separate keyword files

Using FVS and Its Fire and Fuels Extension

in the Context of Uncertain Climate

Albert R. Stage

were prepared for each combination of climatic scenario and

management alternative. Within the keyword file, management alternatives were defined by separate ADDFILES for

each alternative.

Several of the processes being modelled have large stochastic variation in addition to the variation in the climatic

parameters. These include wildfire frequency, regeneration,

and accretion. Therefore, the outcomes of each scenario were

represented by the mean and standard deviation of 40

replications of each simulation, initiated by reseeding the

random number generator.

Temporal Change in

Climatic Effects _________________

Four variables define the temporal progression from

present conditions to the conditions represented by the four

climatic scenarios: MS, the scenario mean moisture stress;

SIGMS, its annual standard deviation; STRESS, the stochastic realization of scenario stress applicable to the current cycle in the projection; and STR, the departure from

current stress including the 50–year transition from present

to future mean conditions. NYEAR is the length of each

cycle. Although usually 10 years, it was calculated to make

the conversion from annual to periodic rates explicit. Keywords implementing temporal change follow:

COMPUTE

DEV = BOUND (–2., NORMAL(0., 1.),+2)

NYEAR = CENDYEAR + 1. –YEAR

STRESS = MS + SIGMS*DEV/SQRT(NYEAR)

STR= (STRESS–9.2)*DECADE(0.1, 0.3, 0.5, 0.7, 0.9, 1.0)

END

FVS Processes Responding to

Scenarios ______________________

All three of the principal processes represented by FVS,

mortality, accretion, and regeneration were modified by the

moisture stress variables. In addition, the height increment

component of accretion was reduced for the cooler scenarios

to account for increased frost-damage to terminals.

Table 1—Scenarios of moisture stress.

Present MS = 9.2 ± 3.0 inches.

Warmer Cooler

Wetter 8 ± 3 5 ± 2

Dryer 13 ± 4 10 ± 4
USDA Forest Service Proceedings RMRS-P-25. 2002 105

Using FVS and Its Fire and Fuels Extension in the Context of Uncertain Climate Stage

Mortality

Response of mortality rates to the interaction of moisture

stress and stand stocking was derived from a thinning

study on the Flathead Reservation (Khatouri and Moore

1993; Cheng and Moore 1996). Those data, however, were

not sufficiently comprehensive for a completely new fitting

of the logistic mortality submodel. Therefore, the analysis

used the present northern Idaho variant (Hamilton 1986;

Wykoff and others 1982) as the null model. Then two new

terms modifying the argument of the exponential function

were added: a multiplicative term (P6= RIPMLT), and an

additive term (P7= RIPAD). P7 varied by moisture stress

and species.

Ham = linear function in NI variant

Mort = 1

1+ 6 7 exp(– * – ) P HAM P

P6 = 1.6482

P7 = –0.4683* STR – 2.0882 for Pondersa pine

P7 = –0.4683* (STR +1) –2.088 for Douglas-fir

P7 = –0.4683* (STR +2) –2.088 for Grand fir

Entering these two new parameters required modifying

FVS to add two additional fields to the MORTMULT keyword and to use the new parameters in the calculation of the

probability of mortality. Modifying the argument of the

exponential function in the probability of mortality submodel

permitted analysis of the mortality data in the same format

as the original analysis. Furthermore, the modified rates are

still bounded by zero and unity. Therefore, this approach

was considered a better solution than simply multiplying

the mortality rate, which was the original function of

MORTMULT.

Therefore, the keywords modifying mortality for ponderosa pine, Douglas-fir and grand fir were:

COMPUTE

RIPMLT = 1.648163

RIPAD = –0.468393*STR – 2.088163

RIPAD3 = RIPAD – 0.468393

RIPAD4 = RIPAD – 0.468393*2.

END

MORTMULT 0. PARMS(10,1.0,0.0,99.0,1.0,

RIPMLT,RIPAD)

MORTMULT 0. PARMS(3,1.0,0.0,99.0,1.0,

RIPMLT,RIPAD3)

MORTMULT 0. PARMS(4,1.0,0.0,99.0,1.0,

RIPMLT,RIPAD4)

Accretion

Basal area increment of large trees was assumed to

change by 5 percent for each inch of departure from mean

moisture stress (Zahner and Stage 1966). In the norhtern

Idaho variant, the submodel for height increment of large

trees includes diameter increment as a driving variable.

Therefore, no further modification for moisture stress was

required. However, predicted increment was reduced by an

additional 4 percent for normal top damage. Height increment of small trees, however, must be changed in concert

with the effects on height increment of the large trees. In the

logarithmic large-tree height increment model, the coefficient of logarithm of diameter increment is approximately

0.5. Therefore, a multiplier that is the square root of the

diameter multiplier is appropriate. The necessary keywords

are:

COMPUTE

GSTR=1.– (STR/20)

END

BAIMULT 0. PARMS(0., GSTR)

HTGMULT 0. 0. 0.96

REGHMULT 0. PARMS(0., SQRT(GSTR)*0.96))

Regeneration

Disturbances trigger the addition of regeneration to the

list of trees in the stand. In these analyses, there are three

kinds of disturbances: wildfire, prescribed fire, and thinning

with slash burning. The combinations of management actions and climatic scenarios were hypothesized to affect the

rates of stocking, the species composition, and the extent of

site preparation (proportion burned in these analyses).

Stocking rate is made quite sensitive to moisture stress

(Mika 2000). Then, conditional on the small regeneration

plot (1/300 acre) being stocked, the relative proportions of

drought-tolerant species are increased if moisture stress is

above present average, and conversely, decreased if moisture stress is below present average. The SPECMULTs are

shown for ponderosa pine (10), Douglas-fir (3), and grand fir

(4). Ponderosa pine was sparsely represented in the regeneration model for the northern Idaho variant. Therefore, its

presence was increased manyfold by the arbitrary 70, multiplying the stress effect.

ESTAB

STOCKADJ 0. PARMS(GSTR**2)

SPECMULT 0. PARMS(10, 70.0*EXP(STR*0.5))

SPECMULT 0. PARMS(3, EXP(STR*0.3))

SPECMULT 0. PARMS(4, EXP(STR*0.2))

END

Event Timing

Wildfire frequency (FIRFREQ) was calculated as a function of moisture stress using an approximation of the

return intervals shown in table 2 with a further increase of

40 percent to represent effects of suppression (Agee 1993).

The suite of management options also included prescribed

fire at intervals of 30 years. However, in the event of a

wildfire, the next prescribed fire would be rescheduled for

30 years after the wildfire (NXTFRYR2). If the wildfire

event is triggered for a cycle, then a particular year for the

fire to occur (FIRYEAR) is chosen at random within the

Table 2—Fire return intervals for four scenarios.

Warmer Cooler

Wetter 150 years 300 years

Dryer 20 years 40 years
106 USDA Forest Service Proceedings RMRS-P-25. 2002

Stage Using FVS and Its Fire and Fuels Extension in the Context of Uncertain Climate

cycle. This date is used in the regeneration keywords as the

date of disturbance. Site preparation for the regeneration

model is set to 100 percent for wildfire disturbances. Here

are the keywords:

COMPUTE

FIRFREQ = 1.4*EXP (7.39 – 0.34*(STR + 9.2) )

PFIRE= 1. – (1.– 1./FIRFREQ)**NYEAR

FIRYEAR = YEAR +INT( RANN*NYEAR )

RNDM = RANN

END

IF

PFIRE GE RNDM

THEN

COMPUTE 0.0

NXTFRYR2 = FIRYEAR + 29.9

END

ESTAB

TALLY 9.0 PARMS(FIRYEAR)

BURNPREP 9.0 100.

END

ENDIF

Prescribed fire keywords were filed in an ADDFILE along

with the silvicultural options. If a run was to consider

prescribed fire, it was invoked with the following keywords:

IF

(PFIRE LT RNDM) AND (YEAR GE NXTFRYR2)

THEN

FMIN

FIRETYPE 1.0 2.

FIRECOND 1.0 3. 4. 70. 1.

END

COMPUTE 2.0

NXTFRYR2 = YEAR + 30.

END

ESTAB

TALLY 9.0 PARMS(YEAR+1.)

BURNPREP 9.0 50.

END

ENDIF

Fire and Fuels

Duff and woody debris loading was initialized with default

loadings applicable to the habitat type. Ideally, the fuels

inventory should be obtained directly from the subject stand.

Modelled fuels are augmented by detritus from the growing stand using parameters in the Fuels extension. Then

they decay at rates specified by the FUELDCAY keyword

(table 3). In the runs reported earlier, fuel decay rates were

held constant at values specified in table 3 throughout the

span of simulated time. Hence, there is a discrepancy with

the trends in moisture stress, which followed a linear trend

over the 50–year transient between present mean and the

climate-scenario mean. A more realistic assumption would

use the moisture stress (STRESS) applicable to the current

cycle to interpolate among the values in table 3.

Model Execution in Suppose

The Suppose user interface for FVS was a critical adjunct

to the analysis beyond the obvious assistance with entering

routine keywords. The first step, “Select Simulation Stands,”

involved searching the inventory database for suitable stands

to be used as examples. The 40 replicates were created by

successively doubling the list of replications of the selected

stand. Then the “Edit Simulation File” operation was used

to open and append the keyword files for the particular

combination of climatic scenario and management options.

“Run Simulation” produced the output files, including those

requested by keyword. Finally, “Generate Report” was used

to parse the output for the variables to be displayed, averaged over the 40 replications, and transmit summary

statistics to a spreadsheet for further display.

Recommendations for Model

Modifications ___________________

FVS

• Provide two added parameters to Keywords modifying

probabilities as additions to, and multipliers of, the

argument in the exponential term in logistic function:

for example, MORTMULT, STOCKADJ, SPECMULT,

and so forth.

• Incorporate within-stand variability of stocking into

accretion and mortality models (Regeneration Establishment already has it) (Stage and Wykoff 1998).

• Update specifications for inventory design. FVS could

not accommodate Yakama Nation inventory without

revision. For example, use Byrne and Stage (1988)

protocols.

FFE

• Incorporate within-stand variability of stocking into

fire behavior model.

• Improve linkage between stand attributes and fire

behavior.

Table 3—Dead and downed fuel decay rates (proportion of weight loss per year) by fuel size.

Scenario Litter Duff 0-0.25” 0.25–1” 1–3” >3”

Warm-wet 1.000 0.0040 0.200 0.120 0.120 0.060

Warm-dry 0.650 0.0026 0.130 0.078 0.078 0.039

Cool-wet 0.350 0.0014 0.070 0.042 0.042 0.021

Cool-dry 0.250 0.0010 0.050 0.030 0.030 0.015
USDA Forest Service Proceedings RMRS-P-25. 2002 107

Suppose

• Provide capability to routinely replicate stands. (Needed

to address concerns of Hamilton (1991) that FVS users

do not recognize the implications of random variation.)

• Augment computation of means over multiple stands

with computation of their standard deviations.

Conclusion_____________________

These analyses describe how FVS was used to evaluate

silvicultural alternatives in the context of changing climate

in forests where fire is a significant process in stand dynamics. We reduce the problem of predicting future weather

variables such as daily temperature, humidity, and precipitation to the more accessible problem of predicting a few key

parameters—seasonal moisture stress and growing season

length. Response to changes in these parameters can and

should be estimated from direct observation of existing

forests. Furthermore, quantifying these responses to weather

variations would be useful in evaluating current model

biases.

References _____________________

Agee, J. K. 1993. Fire ecology of Pacific Northwest forests. Island

Press, Washington, D.C. 493 p.

Beukema, S. J.; Greenough, J. A.; Robinson, D. C. E.; Kurz, W. A.;

Reinhardt, E. D.; Crookston, N. L.; Brown, J. K.; Hardy, C. C.

and Stage, A. R. 1997. An introduction to the fire and fuels

extension to FVS. In: Teck, R.; Moeur, M. and Adams, J., comps.

Proceedings: Forest Vegetation Simulator conference; 1997

February 3–7, Fort Collins, CO. Gen. Tech. Rep. INT–GTR–373.

Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Research Station. 222 p.

Byrne, J. C. and Stage, A. R. 1988. A data structure for describing

sampling designs to aid in compilation of stand attributes. Gen.

Tech. Rep. INT–247. Ogden, UT: U.S. Department of Agriculture,

Forest Service, Intermountain Research Station. 20 p.

Cheng, Hailong and Moore, J. A. 1996. An approach for relating

climatic information with individual tree morality in managed

uneven aged stands. A report to BIA for the Project of Global

Warming. University of Idaho, Dept. of Forest Resources. 26 p.

Crookston, N. L. 1990. User’s guide to the Event Monitor: part of the

Prognosis Model Version 6. Gen. Tech Rpt. INT–275. Ogden, UT:

U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. 21 p.

Hamilton, D. A., Jr. 1986. A logistic model of mortality in thinned

and unthinned conifer stands of northern Idaho. Forest Science

32: 989–1000.

Hamilton, D. A., Jr. 1991. Implications of random variation in the

Stand Prognosis Model. Res. Note INT–394. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. 11 p.

Khatouri, M. and Moore, J. A. 1993. Climatic variation affects

growth and mortality more in unmanaged ponderosa pine plots

compared to managed plots. Internal report, University of Idaho,

College of Forest, Wildlife and Range Sciences.

Mika, P. G. 2000. The seedling establishment study: seedling

mortality and growth as related to soil moisture deficits and soil

chemical characteristics. Report presented at the IFTNC Technical Committee Meeting, Sept. 25, 2000. Intermountain Forest

Tree Nutrition Cooperative, College of Natural Resources, University of Idaho, Moscow, Idaho USA.

Rehfeldt, G. E.; Cheng C. Ying; Spittlehouse, D. L. and Hamilton, D.

A., Jr. 1999. Genetic responses to climate in Pinus contorta: niche

breadth, climate change, and reforestation. Ecological Monographs. 69(3): 375–407.

Stage, A. R.; Moore, J. C.; Renner, D. L. 2001. Modelling silvicultural options in the context of uncertain climate: Using the

forest vegetation simulator and its fire and fuels extension.

Jour. Mathematical Modelling and Scientific Computing. 13(3–4):

249–259.

Stage, A. R. and Wykoff, W. R. 1998. Adapting distance-independent forest growth models to represent spatial variability: Effects

of sampling design on model coefficients. Forest Science. 44(2):

224–238.

Wykoff, W. R.; Crookston, N. L. and Stage, A. R. 1982. User’s guide

to the stand prognosis model. Gen. Tech. Rep. INT–133.Ogden,

Utah: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 112 p.

Zahner, R. and Stage, A. R. 1966. A procedure for calculating daily

moisture stress and its utility in regressions of tree growth on

weather. Ecology. 47(1): 64–74.
108 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Paula J. Fornwalt and Laurie S. Huckaby are Ecologists, Merrill R.

Kaufmann is a Research Forest Ecologist, and Jason M. Stoker is a GIS

Specialist at the Rocky Mountain Research Station, 240 West Prospect Road,

Fort Collins, CO 80526.

Abstract—Presettlement ponderosa pine/Douglas-fir forests of the

Colorado Front Range were open and heterogeneous. Logging,

grazing, and fire suppression over past 100 to150 years have altered

stand structure by changing diameter distributions and increasing

overstory density. In an effort to guide forest restoration toward

presettlement conditions, we are currently using the Forest Vegetation Simulator (FVS) to validate estimates of 1900 forest structure,

and calibrate the model for future use, for an unmanaged landscape

(unlogged, ungrazed, but fires suppressed since 1900). In 1997, tree

data were collected in 15 0.1-ha plots at Cheesman Lake. For trees

larger than 1.4 m tall, diameter at breast height, height, species,

live/dead status, age, and diameter growth increment were recorded; for trees less than 1.4m tall, basal diameter, height, species,

live/dead status, age, and height growth increment were measured.

FVS was first calibrated with diameter and height growth increments from the plots so that it more adequately depicted our study

area. A 1900 tree data file was derived from data collected in 1997

for each plot by including only the tree records that pre-dated 1900,

and measuring 1900 d.b.h. on increment cores. The plot was then

grown forward to 1997, with natural regeneration added each

decade to represent ingrowth in the plot since 1900. The modeling

results were compared to the actual data. Growth and mortality

multipliers were modified and FVS was rerun until FVS-generated

1997 diameter distributions, stand densities, and canopy covers

were similar to those observed in 1997. Results from this intermediate phase of research will help to guide restoration activities in the

South Platte Watershed, Colorado, and will also be critical in future

stages of our research.

Stand structure in ponderosa pine/Douglas-fir (Pinus

ponderosa/Pseudotsuga menziesii) forests of the Colorado

Front Range has changed dramatically in the past 150 years.

Prior to Euro-American settlement in the late 19th century,

these forests were open and heterogeneous in age and size

(GLO Field Notes 1879, 1882; Jack 1900). Logging, grazing,

and fire suppression over the past 100 to 150 years have

Using the Forest Vegetation Simulator to

Reconstruct Historical Stand Conditions in

the Colorado Front Range

Paula J. Fornwalt

Merrill R. Kaufmann

Laurie S. Huckaby

Jason M. Stoker

altered stand structure by removing many of the large or

old trees and by allowing more regeneration to become

established (Marr 1961; Veblen and Lorenz 1986; Kaufmann

and others 2000). As a result, the current forest structure

is younger, denser, and more homogeneous, and shadetolerant Douglas-fir has become more prevalent on the

landscape.

Restoring ponderosa pine/Douglas-fir ecosystems to historical stand conditions is vital if we wish to maintain

ecological sustainability and reduce the risk of catastrophic,

stand-replacing forest fires. Treatment projects intended

to restore ponderosa pine ecosystems to a historical condition require an understanding of what this historical

condition was.

We are fortunate to be able to study at the Cheesman Lake

landscape, a relatively undisturbed 35- km2

 ponderosa pine/

Douglas-fir forest in the Upper South Platte Watershed of

the Colorado Front Range. Cheesman Lake, a reservoir

created on the South Platte River at the beginning of the 20th

century, and the surrounding land are owned and managed

by Denver [City] Water. Unlike most montane forests in the

Colorado Front Range, this landscape has experienced only

minimal, localized logging (mostly below the current water

line of the reservoir during the time of dam construction).

At the time of dam completion in 1905, a six-strand barbedwire fence was erected around the property, which has

excluded domestic grazing. Wildfires, however, have been

suppressed since the early 1900s.

We used the Forest Vegetation Simulator (FVS; SpruceFir submodel of the Central Rockies variant, version 6.31) to

“de-grow” plots on the current Cheesman Lake landscape to

1900, just prior to the effects of fire suppression during the

20th century (fig. 1). FVS was first calibrated with diameter

and height growth increments so that it more adequately

depicted our study area. A 1900 tree data file was then

derived from data collected in 1997 for each plot by removing

all trees that germinated after 1900, and estimating the

1900 diameter for all pre-1900 trees. Each 1900 plot was

grown forward to 1997, with natural regeneration added

each decade to represent ingrowth in the plot since 1900. The

modeling results were compared to the current data. Growth

and mortality multipliers were modified, and FVS was rerun

until FVS-generated 1997 diameter distributions, stand

densities, and canopy covers were similar to those observed

in 1997. Results from this research will help guide restoration activities in the South Platte Watershed, and will also

be critical in future stages of our research.
USDA Forest Service Proceedings RMRS-P-25. 2002 109

Using the Forest Vegetation Simulator to Reconstruct Historical Stand Conditions... Fornwalt, Kaufmann, Huckaby, and Stoker

Estimating Plot Condition

in 1900 ________________________

In 1997, we established 15 0.1-ha (0.25 acre) plots within

a 4-km2

 portion of the Cheesman Lake landscape (Kaufmann

and others 2000). Five plots were randomly located in the 4

km2

 area on each of three topographic categories: northfacing slopes, south-facing slopes, and east- or west-facing

slopes. In each plot, for all trees taller than breast height

(1.4 m), tree diameter at breast height (d.b.h.), species, and

live or dead status were recorded. Basal diameter, species,

height, and 5-year height growth increment were measured

for trees less than breast height tall. Also in each plot, 20

randomly selected live trees were cored for age at 30 cm

above the ground; every tree in the plot that appeared to be

over 200 years old was also cored (Kaufmann and others

2000). Tree germination dates were then determined by

crossdating with a local chronology (Stokes and Smiley

1968; Kaufmann and others 2000). If a tree’s germination

date was not measured, it was predicted using a kernel

density function (Venables and Ripley 1999). We also measured 10-year diameter growth increments on the cores.

We relied on our increment cores to reconstruct the 1900

stand condition. For all increment cores taken from trees that

germinated prior to 1900, we measured the distance from

the pith to the ring that was laid down in 1900. This 1900

radius was doubled to get the 1900 diameter (inside the

bark and at a coring height of 30 cm). We then estimated

the tree’s 1900 diameter at breast height (outside the bark)

from allometries developed by VanDeusen (1975).

For trees that were not cored but whose estimated germination date was prior to 1900, we ran backward stepwise

regressions to predict 1900 d.b.h. from age, 1997 d.b.h., and

1997 height for the rest of the pre-1900 trees. Only variables

that were significant at the alpha = 0.15 level were retained

in the backward stepwise model.

We also relied on our increment core data to estimate tree

recruitment that occurred in the plots since 1900. For trees

that germinated after 1900, we determined the number of

trees, by species, that germinated in each decade between

1900 and 1997.

The pre-1900 stand conditions were entered into FVS as

tree data files (‘.fvs’). The tree data files contained the

species, live or dead status, and 1900 d.b.h. for all trees that

germinated prior to 1900. We used the ‘NATURAL’ keyword

to enter the number of trees (by species) that germinated in

each decade between 1900 and 1997. To compensate for trees

that were alive at some time between 1900 and 1997 but

have since died, we assumed a mortality rate of 2 percent per

decade.

Calibrating, Parameterizing and

Running FVS ___________________

Several keywords were used to calibrate and parameterize FVS. These keywords can be divided into two groups:

those whose values were fixed for all FVS runs for an

individual plot, and those whose values were adjusted for

each FVS run so that the FVS simulation results more

closely matched what was observed in the plot.

The keywords that were fixed for all simulation runs of a

plot were ‘READCORD,’ ‘READCORR,’ and ‘SDIMAX’.

‘READCORD’ is a multiplier for the large tree diameter

growth model. The values for this keyword were determined

by FVS, based on diameter increment measurements.

‘READCORD’ was 0.651 for Douglas-fir and was 0.749 for

ponderosa pine. ‘READCORR’ is a multiplier for the small

tree height growth model. FVS determined the values for

this keyword from height growth measurements on small

trees. ‘READCORR’ was 0.708 for Douglas-fir and 0.928 for

ponderosa pine. The ‘SDIMAX’ keyword changes the maximum stand density index (SDI) for a stand, and how maximum SDI affects a stand’s density dependent growth and

mortality rates. While we did not alter maximum SDI, we

did decrease the percent of maximum SDI where density

dependent mortality begins (field 5) from 55 to 40 percent.

We also decreased the percent of maximum SDI where the

stand reaches maximum density (field 6) from 85 to 70

percent. A stagnation effect, where tree growth stagnates

when plot density exceeds 70 percent of the maximum SDI,

was also invoked (field 7). These alterations to ‘SDIMAX’

were based on our plot data.

The keywords whose values were changed for any individual simulation run were ‘REGHMULT,’ ‘FIXDG,’ and

‘MORTMULT’. ‘REGHMULT’ is a multiplier for the height

growth of small trees. ‘FIXDG’ is a multiplier for diameter

growth of large trees. We entered this keyword into our

simulation file five times, so that growth rates of trees 0 to

10 cm d.b.h., 10 to 20 cm d.b.h., 20 to 30 cm d.b.h., 30 to 40 cm

Figure 1—Flow chart depicting the methods used to

“degrow” the plots at Cheesman Lake.

Estimate 1900 stand conditions from

increment cores collected in 1997 

Develop regeneration files for trees that 

germinated between 1900 and 1997

Calibrate and Parameterize FVS

Evaluate success of modeling run by 

comparing modeled 1997 conditions to 

actual conditions

Successful Unsuccessful

Modeling done!

Run FVS
110 USDA Forest Service Proceedings RMRS-P-25. 2002

Fornwalt, Kaufmann, Huckaby, and Stoker Using the Forest Vegetation Simulator to Reconstruct Historical Stand Conditions...

d.b.h., and greater than 40 cm d.b.h. could be adjusted

independently of each other. ‘REGHMULT’ and ‘FIXDG’

varied from 0.1 to 1.9 (plus or minus 90 percent of the

default value); ‘MORTMULT’ varied from 1.0 to 2.0. Note

that these values were not changed during a simulation

(that is, from decade to decade), but were changed for any

individual simulation. During a simulation, the values for

these parameters remained constant.

During each simulation, we computed the number of trees

per hectare in 10 cm d.b.h. classes (0 to 10 cm, 10 to 20 cm,

20 to 30 cm, 30 to 40 cm, 40 to 50 cm, and greater than 50 cm

d.b.h.). We also computed total canopy cover. The ‘Compute

2’ postprocessor was invoked so that the output file (‘.cp2’)

could be read into SAS for evaluation with the observed plot

data (SAS release 8.1, SAS Institute Inc.).

Evaluating the Success of the

FVS Run _______________________

To determine whether an FVS run was successful at

producing the observed 1997 plot conditions from a given set

of inputs, we evaluated our FVS run in four ways. The first

way was qualitative; histograms of the d.b.h. distributions

for the plot and the FVS output were visually compared. If

the histograms appeared similar, the modeling run was

deemed successful by this method. Second, we calculated a

10 percent “confidence interval” for each d.b.h. class; if the

FVS-generated output was within these confidence intervals for at least four of the six d.b.h. classes, the modeling run

successfully passed this test. The third way we compared the

FVS output to the plot data was with a chi-square goodnessof-fit test. An FVS modeling run successfully mimicked the

plot data if the p-value was greater than or equal to 0.80.

Fourth, percent canopy cover for the plot and the FVS output

were also compared, and if the FVS-generated canopy cover

differed from the plot canopy cover by 10 percent or less, then

the modeling run was successful by this method.

When an FVS run successfully passed all four “tests,” it

was considered acceptable. If all of the above conditions were

not met, then the keyword parameters were altered and FVS

was rerun until the conditions were met. We will use one

plot, ‘CL0111,’ as an example, and describe each of these

evaluations for both an unparameterized (default) FVS run,

and a parameterized (modified) run.

We generated histograms for a graphic comparison of the

plot and the FVS-generated data, shown in figures 2a, b,

and c. Figure 2a shows the number of trees per hectare by

d.b.h. class observed in plot CL0111 in 1997. Plot CL0111

has the classic inverse-j shaped curve characteristic of uneven-aged stands. While the diameter distribution of our

unparameterized stand (where the keywords ‘REGHMULT,’

‘MORTMULT,’ and ‘FIXDG’ are all equal to the default

values) does not really mimic what is on the ground, our

parameterized run looks similar to real life (fig. 2b, c).

We calculated a 10 percent “confidence interval” for the

number of trees per hectare in the plot in each diameter

class. It is important to note that what we calculated was not

a true confidence interval; rather, it was the number of trees

per hectare by d.b.h. class observed in the plot, plus or minus

10 percent. Results from an unparameterized FVS run and

a parameterized run are shown in tables 1a and 1b. While

the number of trees predicted by FVS for each d.b.h. class is

never within 10 percent of those observed in the plot (never

‘OK’) in the unparameterized run, the parameterized run is

‘OK’ in five of the six d.b.h. classes.

The third way that we evaluated the success of our

modeling run is with a chi-square goodness-of-fit test. Again,

our FVS run with the default parameters does not match

observed conditions as well as the parameterized FVS run

(unparameterized run: p=0.0005; parameterized run:

p=0.9882).

Evaluating canopy cover is important for us because

canopy cover serves as a link to apply the results from our

plots to the rest of the Cheesman Lake landscape in the next

phase of our research. Canopy cover (calculated by FVS) for

CL0111 in 1997 was 38.6 percent; the unparamaterized

model estimated canopy cover to be 45.8 percent, while the

parameterized model estimated it to be 40.6 percent. Again,

the FVS run with the modified parameters more closely

mimicked what is actually on the ground.

Preliminary Results and Their

Application To Date ______________

Table 2 shows modeled 1900, observed 1997, and modeled

1997 canopy covers for all plots, averaged by aspect. Values

for our example plot, which is east-facing, are also included.

Average historical forest density for our plots was estimated

to range from 13 to 22 percent canopy cover, while average

current forest density (actual data) ranged from 39 to 65

percent. For plots that were successfully modeled, FVS

canopy cover was within 10 percent of the actual data for all

aspects.

Table 3 lists the parameter values for all the keywords

that were used in each FVS simulation. Five plots could not

be successfully degrown, given the bounds we put on our

parameters. Of these five plots, two were very sparse (14 or

fewer trees per plot, or 140 trees per hectare), and one was

very dense (canopy cover was greater than 70 percent).

While we are generally satisfied with the results of our

modeling runs, we realize that several of our assumptions

may be affecting their success. Our assumption that parameters are constant for all decades in a simulation may not be

valid. These parameters may change over time as stand

density and tree size increases through the decades. Also,

these parameters may change over time because of changes

in climate.

We mapped 2,500 polygons from 1:6000 color infrared

aerial photographs for the entire Cheesman Lake landscape,

based on canopy closure and diameter class distribution

(Huckaby and others 2001). Of these polygons, 580 were

within the 4-km2

 study area.

Based on the preliminary results from this modeling work

and the polygon coverage for the 4-km2

 study area, we

developed relationships between 1997 canopy cover and

1900 canopy cover for each of the 585 polygons. Aspect and

an aspect-by-1997 percent canopy cover interaction variable

were initially considered in the regression but were removed

because they were not significant predictors. While the

correlation between 1900 and 1997 canopy covers is not

strong (Adj. R2

 = 0.19, s.e. = 8.83), an initial version of

estimated 1900 canopy cover for the 4-km2

 study area (top),
USDA Forest Service Proceedings RMRS-P-25. 2002 111

Using the Forest Vegetation Simulator to Reconstruct Historical Stand Conditions... Fornwalt, Kaufmann, Huckaby, and Stoker

Figure 2—Example output for plot Cl0111. The histograms of diameter class distribution were used to evaluate the

success of an FVS run. Figure 2a is the histogram for the actual plot data in 1997. Figure 2b is the histogram for the

unparameterized FVS run, and figure 2c is the histogram for the parameterized FVS run. The diameter distribution

produced by the parameterized FVS run better reproduces the actual data.

2a—Observed

DBH(cm)

00-10

10-20

20-30

30-40

40-50

50+

123.41

71.99

41.14

30.85

20.57

10.28

2c—Model with modified parameters DBH(cm)

00-10

10-20

20-30

30-40

40-50

50+

120

77

41

28

23

12

0 25 50 75 100 125 150 175 200

trees/hectare

2b—Model with default parameters DBH(cm)

00-10

10-20

20-30

30-40

40-50

50+

102.08

108.01

51.25

10.82

25.62

17.96
112 USDA Forest Service Proceedings RMRS-P-25. 2002

Fornwalt, Kaufmann, Huckaby, and Stoker Using the Forest Vegetation Simulator to Reconstruct Historical Stand Conditions...

along with the 1997 estimates from air photos (bottom) is

shown in figure 3. Changes in canopy cover from 1900 to

1997 are dramatic. These preliminary results indicate that,

in 1900, approximately half of the 4-km2

 study area had a

canopy cover of 10 percent or less, and none of the polygons

had a canopy cover exceeding 40 percent. Conversely, less

than one quarter of the 4-km2

 study area had a canopy cover

less than 10 percent in 1997, while more than one quarter of

it had a canopy cover exceeding 40 percent. Although this

research work is in an intermediate stage of development,

these results are supported by historical photographs taken

at Cheesman Lake (fig. 4; Denver Water Archives, Denver,

CO), by written records from the turn of the century (GLO

Field Note 1879, 1882; Jack 1900), and by what we know

about the fire history for the 4-km2

 area (Brown and others

Tables 1a and b—“Confidence interval” evaluation for plot CL0111. Table 1a (top) is the output for the unparameterized FVS run, and table 1b

(bottom) is the output for the parameterized FVS run. We calculated a 10 percent “confidence interval” for each d.b.h. class; if

the FVS-generated output was within these confidence intervals for at least four of the six d.b.h. classes, the modeling run was

successful. While the number of trees predicted by FVS for each d.b.h. class is never plus or minus 10 percent of those observed

in the plot (never ‘OK’) in the unparameterized run, the parameterized run is ‘OK’ in five of the six d.b.h. classes.

D.B.H. Trees per ha Trees per ha Low ‘confidence High ‘confidence High, low,

class for actual plot estimated by FVS interval’ (–10%) interval’ (+10%) or OK?

a. Unparameterized Run

0 to 10 cm 123 102 111 136 Low

10 to 20 cm 72 108 65 79 High

20 to 30 cm 41 51 37 45 High

30 to 40 cm 31 11 28 34 Low

40 to 50 cm 21 26 19 23 High

50 cm plus 10 18 9 11 High

b. Parameterized Run

0 to 10 cm 123 120 111 136 OK

10 to 20 cm 72 77 65 79 OK

20 to 30 cm 41 41 37 45 OK

30 to 40 cm 31 28 28 34 OK

40 to 50 cm 21 23 19 23 OK

50 cm plus 10 12 9 11 High

Table 2—Canopy cover, averaged by aspect class, for

1900, 1997Plot, and 1997FVS. Percent canopy

covers for our example plot, CL0111, are also

listed. 1900 and 1997Plot percent canopy cover

averages are for all plots, not just plots that were

successfully degrown; 1997FVS percent canopy

covers are only for the plots that were successfully

degrown (this number of plots is reported in

parentheses next to the canopy cover value).

Average percent canopy cover

Aspecta 1900 1997 Plot 1997 FVS

N 21.5 65.4 66.7 (n = 4)

E/W 15.7 45.3 43.0 (n = 2)

CL0111 18.0 38.6 40.6

S 12.9 40.1 40.8 (n = 4)

a

E/W= East/West, N=North, and S=South.

Table 3—Parameter values, by plot. Multiplier values of ‘—’ indicate that the plot was not successfully degrown.

READCORD READCORR REGH FIXDG FIXDG FIXDG FIXDG FIXDG MORT

Plot Aspecta DFb PPc DF PP MULT 0-10 10-20 20-30 30-40 40+ MULT P-value

- - - - - - - - - - - - - - - - - cm - - - - - - - - - - - - - - - - -

CL0111 E/W 0.65 0.75 0.71 0.93 0.70 1.00 1.10 1.50 0.60 0.20 1.50 0.9882

CL0113 N 0.65 0.75 0.71 0.93 0.60 1.30 1.30 0.80 0.80 0.80 1.00 0.9194

CL0420 E/W 0.65 0.75 0.71 0.93 — ————— — —

CL0701 N 0.65 0.75 0.71 0.93 0.40 0.80 1.70 0.70 0.90 0.60 2.00 0.9946

CL0904 N 0.65 0.75 0.71 0.93 0.70 1.10 1.20 0.95 0.50 0.50 2.00 0.9503

CL1208 E/W 0.65 0.75 0.71 0.93 — ————— — —

CL1312 S 0.65 0.75 0.71 0.93 1.00 1.00 1.00 1.00 0.70 1.10 2.00 0.9505

CL1632 N 0.65 0.75 0.71 0.93 — ————— — —

CL1711 S 0.65 0.75 0.71 0.93 0.80 0.80 1.00 1.00 0.40 0.10 1.00 0.9653

CL1916 E/W 0.65 0.75 0.71 0.93 — ————— — —

CL2202 S 0.65 0.75 0.71 0.93 1.20 1.20 1.20 1.00 0.65 0.60 1.50 0.9671

CL2316 S 0.65 0.75 0.71 0.93 — ————— — —

CL2408 S 0.65 0.75 0.71 0.93 0.50 0.83 1.90 0.70 1.00 0.90 1.20 0.9221

CL2503 E/W 0.65 0.75 0.71 0.93 1.00 1.00 1.00 1.00 1.00 1.00 1.70 0.9745

CL2613 N 0.65 0.75 0.71 0.93 1.00 1.00 1.70 0.80 0.30 0.30 1.00 0.9562

a

E/W= East/West, N=North, and S=South.

bDF= Douglas-fir.

c

PP= ponderosa pine.
USDA Forest Service Proceedings RMRS-P-25. 2002 113

Using the Forest Vegetation Simulator to Reconstruct Historical Stand Conditions... Fornwalt, Kaufmann, Huckaby, and Stoker

Figure 3—Map of percent canopy cover at

Cheesman Lake. The 1900 map (top) was developed from a correlation between 1900 and 1997

percent canopy cover for our 15 plots. Using this

correlation, we were able to calculate 1900 canopy

cover from the 1997 canopy covers assigned to

each polygon. The 1997 map (bottom) was developed from aerial photograph interpretation of 1:6000

color infrared photographs, based on canopy cover

and d.b.h. distributions.

Figure 4—Paired photographs showing forest condition

along the South Platte River, just below the Cheesman Lake

Dam. The photo on the top was taken in 1903 (courtesy

Denver Water Archives), and the photo on the bottom was

taken in 1999 (courtesy Laurie Huckaby).

1999). The fire history indicates that most or all of this area

burned in 1851 as a mixed-severity or stand-replacing fire,

where many trees were killed. Consequently, canopy covers

in 1900 would be likely to be low, and our results reflect this.

Future Research ________________

The research effort outlined here is an intermediate stage

in our overall goal of estimating historical stand conditions,

and also in determining what current forest conditions

would be at Cheesman Lake if 20th century fires had not been

suppressed. Future research will involve incorporating data

for 51 additional 0.1 ha plots that are distributed across the

entire 35-km2

 Cheesman Lake landscape, and degrowing all

of our plots using methods similar to those outlined above,

while incorporating the two following changes.
114 USDA Forest Service Proceedings RMRS-P-25. 2002

Fornwalt, Kaufmann, Huckaby, and Stoker Using the Forest Vegetation Simulator to Reconstruct Historical Stand Conditions...

First, we will begin working with a test version of FVS that

allows tree age measurements to be incorporated into an

FVS tree data file (‘.fvs’). Incorporating a tree’s actual age

into an FVS run will allow us to modify growth rates based

on age, rather than d.b.h., and will likely increase our

modeling success. While it is generally true that age and

d.b.h. are related, they are often not well correlated, especially when growing conditions are extreme and highly

variable. Our increment core data indicate that patterns of

tree growth rates are more closely related to tree age than to

tree size. In the Colorado Front Range, it is not uncommon

for two ponderosa pine trees of the same d.b.h. to differ in age

by 100 years or more. For example, the two ponderosa pine

trees in figure 5 have similar diameters, but differ in age by

nearly 130 years.

We will also use a program written by Don Vandendriesche

at the Forest Management Service Center in Fort Collins,

CO, to perform sensitivity analyses for the keywords that we

modify (Hamilton 1997a). This program, named the ‘Keyword Iteration Navigator’ (‘KIN’), allows a user to specify

minimum, maximum, and step increments for various keyword parameters that are being used in a simulation

(Vandendriesche 2001). In our work for this paper, we varied

parameters for seven keywords (‘REGHMULT,’ five ‘FIXDG’

keywords, and ‘MORTMULT’). To vary these keywords in

the ranges used for this paper by increments of 0.1 would

require hundreds of thousands FVS runs per plot. Using

KIN, we will generate all possible parameter combinations

and run them through FVS in much less time and with much

less effort than if we did it manually. We will import the

KIN output into SAS to determine which of the simulation(s)

successfully mimic what we know to exist in the plot.

Keyword combinations that are illogical, but are successful

by our criteria, will need to be eliminated manually

(Hamilton 1997b). Doing this may give us multiple “pathways” with which to grow our forest.

Once we have reparameterized the current 15 plots and

the 51 additional plots using the age-enhanced FVS variant

and KIN, we will attempt to determine the 2000 forest

condition at Cheesman Lake, under a plausible natural fire

regime. Based on the Cheesman Lake fire history and

knowledge of fires that were suppressed, we suspect that

there were 4 years that had potential to be big fire years at

Cheesman Lake during the 20th century (Brown and others

1999). These fire years are 1912, 1954, 1963, and 2000.

Trees in Cheesman Lake were scarred by fire in all of these

years, but the fires were probably suppressed. The 1912

fire scarred trees in the southeastern portion of Cheesman

Lake, and the 1954 fire scarred trees in the northeastern

portion of Cheesman Lake; the extent of these fires is

unknown. Fire scars and written records indicate that the

1963 fire burned in the southeastern corner of the Cheesman

Lake property and burned 50 to 100 ha before it was

suppressed. The 2000 fire burned in the northwestern portion of Cheesman but was suppressed before it exceeded

10 ha. We speculate that if these four fires had not been

suppressed, based on their ignition locations and what we

know about historical fire size, they would each have burned

approximately half of the Cheesman Lake property, so that

the entire property would have burned twice (with variable

intensity) in the past 100 years. This is consistent with the

fire history for the area (Brown and others 1999). We hope to

Figure 5—Photograph of an older tree (left) and a

younger tree (right) growing side by side. While both of

these trees are of similar d.b.h. and height, the older

tree has a pith date of 1761 (d.b.h. is 33.6 cm or 13.2

inches), and the younger tree has a pith date of 1889

(d.b.h. is 31.7 cm, or 12.4 inches). The photograph is

courtesy of Laurie Huckaby.

use the results of this study, along with our plot data, the

1997 polygon coverage, FVS, and the Fire and Fuels Extension of FVS to help depict the landscape just before and after

each of these fires, with forest structure reflecting the effects

of these four fires had they been allowed to burn as natural

fires.

Results from this research and previous research using

FVS are currently being used to guide restoration efforts on

the South Platte Restoration Project (Kaufmann and others

2001). The Upper South Platte Watershed is important in

the Colorado Front Range because it provides 70 percent of

Denver’s water supply and provides the people of the greater

Denver area with access to fishing, hiking, and other recreational activities (Foster Wheeler 1999). Because of the

watershed’s proximity to Denver, it also contains a large

area of urban-wildland interface. Two stand-replacing fires

well outside the range of historical variability have occurred

in the Upper South Platte Watershed since the mid-1990s.

The 1996 Buffalo Creek fire, which burned 4,820 hectares

(11,900 acres), and the 2000 Hi Meadows fire (4,250 hectares,

or 10,500 acres) illustrate the huge risk of wildfire and
USDA Forest Service Proceedings RMRS-P-25. 2002 115

Using the Forest Vegetation Simulator to Reconstruct Historical Stand Conditions... Fornwalt, Kaufmann, Huckaby, and Stoker

postfire erosion in these dense forests. Future research

results will better guide restoration activities and reduce the

risk of catastrophic wildfires such as Buffalo Creek and Hi

Meadows, and the research will promote ecological sustainability in these ponderosa pine/Douglas-fir ecosystems.

Acknowledgments ______________

We owe thanks to several people and organizations for

their help with this research project. Don Vandendriesche

has been especially generous with his time and expertise.

Rudy King offered advice on the methodology and statistical

analysis. The Upper South Platte Watershed Protection and

Restoration Project helped to fund this project. Denver

Water Board also provided funding and allowed us to conduct research on their land.

References _____________________

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VanDeusen, J.L. 1975. Estimating breast height diameters from

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Veblen, Thomas T. and Diane C. Lorenz. 1986. Anthropogenic

disturbance and recovery patterns in montane forests, Colorado

Front Range. Physical Geography 7(1): 1-24.

Venables, W.N. and B.D. Ripley. 1999. Modern applied statistics

with S-Plus (Third edition). Springer, New York. 501 p.
116 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Pamela E. Padgett is a Research Plant Biologist, USDA Forest Service,

4955 Canyon Crest Dr. Riverside, CA 92507 ppadgett@fs.fed.us. Klaus H.

Barber is the Regional Analyst, USDA Forest Service, 1323 Club Drive,

Vallejo, CA 94592 kbarber@fs.fed.us. Andy Taylor is the Mendocino Forest

Analyst, USDA Forest Service 825 N Humboldt Avenue Willows, CA 95988

ataylor@fs.fed.us

Abstract—We demonstrate that the results of landscape-level

vegetation projections using FVS differ depending on the strategy

used to aggregate the input data. To illustrate the point, we picked

landscape-level variables that are important to addressing habitat

components for old forest-dependent species, a contemporary forest

management issue for the Sierra Nevada Bioregion, California. The

approach was to compare FVS results using: (1) Traditional strata

maps developed by USDA Forest Service Region 5 for regional

planning where the “average” condition of all the relevant FIA plots

is aggregated into a series of timber strata representative of large

landscapes. (2) Modified strata maps developed by calculating the

“mode” of the relevant FIA plots but also aggregated into timber

strata. (3) Projections of the individual FIA plots without aggregation. Interpretation of two continuously valued variables we studied did not vary significantly between aggregation methods, but the

interpretation of the discretely classed variables greatly differed.

Our inspection of the results suggested that the unaggregated data

gave more realistic projections of size and density distribution that

seemed to best represent fundamental ecological processes. Nonetheless, we argue that using aggregated data is necessary when the

spatial integrity of the analysis needs to be maintained. The

implications of our observations on landscape analysis are discussed.

The paradigm for management of the California National

Forest lands has shifted away from management for resource production such as timber and mining and into

management strategies for preserving and enhancing

habitat characteristics (Committee of Scientists 1999). The

change is driven by the conflicts among the legal requirements for species preservation, reflecting a shift in societal

perspectives, a burgeoning population (approaching 35 million within the State alone), the popularity of the diverse

recreational activities offered by National Forest lands, and

Sensitivity of Forest Vegetation Simulations

to Scale of the Input Data and Impact to

Estimates of Key Habitat Indictors, Tree

Size, and Density

Pamela E. Padgett

Klaus H. Barber

Andy Taylor

the continued need of the forests to provide water and other

historic resources in support of local communities.

In an effort to reconcile the complicated interactions and

conflicts among the many stakeholders, the Sierra Nevada

Framework Project was established. The framework’s original charge was to combine the best of the scientific knowledge within the Forest Service’s Pacific Southwest Research

Station and the best of the management experience within

the interdisciplinary teams from Region 5 to prepare an

Environmental Impact Statement for modifying the forest

plans of the 10 National Forests and the Lake Tahoe Management Unit that occupy the Sierra Nevada Bioregion

(Final Environmental Impact Statement 2000).

The work was intended as an outgrowth of the Sierra

Nevada Ecological Project (Sierra Nevada Ecosystem Project

1996) submitted as a Congressional report. Out of that

project, five key topics emerged as the most serious threat to

the health and sustainability of Sierran ecosystems (Record

of Decision 2001):

• Protect, increase, and perpetuate old forest ecosystems

and provide for the viability of native plant and animal

species associated with old forest ecosystems.

• Protect and restore aquatic, riparian, and meadow ecosystems and provide for the viability of native plant and

animal species associated with these ecosystems.

• Manage fire and fuels in a consistent manner across the

National Forests, coordinate management strategies

with other ownerships, integrate fire and fuels management objectives with other natural resource management objectives, address the role of wildland fire, and

set priorities for fire and fuels management actions.

• Reduce and, where possible, reverse the spread of noxious weeds.

• Maintain and enhance hardwood forest ecosystems in

the lower westside of the Sierra Nevada.

The charge of the Framework was to use all available tools

to define, analyze, evaluate, and present probable consequences of eight independent management alternatives

developed to address the five key topics. To do so, the Forest

Vegetation Simulator (FVS) was used in combination with

several other models to produce a set of scenarios where the

needs of forest, woodland and riparian habitats were balanced with needs of fire protection, while attempting to

return Sierran ecosystems to a more natural fire regime and

ecological balance.
USDA Forest Service Proceedings RMRS-P-25. 2002 117

Sensitivity of Forest Vegetation Simulations to Scale of the Input Data and Impact to Estimates... Padgett, Barber, and Taylor

The Sierra Nevada Bioregion occupies roughly 15 million

acres, of which the Forest Service manages 11.5 million

acres. The remaining lands are a polyglot of private, State,

National Parks, and other Federal agency ownership. The

primary range extends for about 800 miles, roughly northsouth and about 120 miles in the east-west direction. The

elevations start at near sea level on the western edge and

climb to the highest peak in the continental United States at

Mount Whitney at over 14,000 feet. Because of the huge

diversity in geological formations leading to widely varying

landscapes, California contains the highest number of individual ecosystems of any U.S. State. Half of that natural

diversity in California resides in the Sierra Nevada Bioregion

(Sawyer and Keeler-Wolf 1995). Human population forecasts indicate between 1.8 and 2.0 million people will occupy

the region in 2040, mostly at elevations below 7,000 feet

(Duane 1996). The population densities are heaviest in the

central and southern part of the bioregion. In addition to

permanent residents, the Sierra Nevada draws millions of

visitors every year. Like the permanent settlements, visitor

days tend to be heaviest in the central part of the region. The

management emphasis on several of the individual National

Forests has slowly shifted from resource extraction to recreation, both of which can have significant impacts on habitat

quality (Final Environmental Impact Statement 2000).

Sierran ecosystems are highly heterogeneous with patches

of dense, young trees immediately adjacent to open stands of

very old trees, large trees emerging from shrub patches, and

an interdigitation of ecosystems that makes accurate mapping and identification difficult at a scale appropriate for the

characterization of habitat for many animals. Understanding and projecting vegetation characteristics that are key to

habitat values is a relatively new science. By far the most

challenging aspect of habitat projections is simply identifying key vegetation characteristics required by specific species in a quantitative manner. And, obviously, key characteristics are species-dependent, thus highly variable in scale.

What might be an important requirement for foraging habitat for the Sierran fisher would be of little quantitative use

in understanding habitat for yellow-legged frogs.

Many of the metrics used in standard FVS modeling such

as tree size and density were useful for vegetation modeling

for habitat characteristics. Other metrics need to be modified or developed such as distribution patterns of trees and

growth projections for nontree species. This being said,

however, the FVS offers the best and most comprehensive

model for landscape-scale evaluation of forest responses to

management alternatives.

Two primary sources of input data are available for

Sierrawide modeling — the Forest Inventory Assessment

(plot-based) data, and the Region 5 strata maps, developed

from satellite imagery. The two data systems have been coordinated so that strata map information corresponds to plot

information and vise-a-versa. The strata maps use broad

forest-type categories and for the most part, underrepresent

nontimber types. For example, the presence of as little as 10

percent conifers in a stand of oaks is enough to have the

stand classified as a conifer-type. However, the maps are

important visual tools for decisionmaking and for presentation to stakeholders and the public at large. The FIA plot

data, on the other hand, includes species composition,

vegetation distribution patterns, and historical information

but can only represent a small sampling of particular places.

It is impossible to derive map products from the FIA data

alone. In the Sierra Nevada, because of the scale and heterogeneous nature of the forests, the statistical variation within

a cluster plot may exceed the variation across plots.

An effort was made to combine the two data sets in a way

that allowed for reclassification of the forest strata data

based on the FIA distribution information and then use FVS

projection of this new hybrid data to estimate changes over

time. An interesting problem occurred during this hybridization process: the results of vegetation projections for the

eight proposed alternatives differed substantially depending on whether the strata average, strata mode, or pure FIA

plot data were used for the input data source.

What follows is a summary of the approach for combining

map-based information with the FIA data, the results of the

different data reduction schemes and a brief discussion of

the use of the FVS in evaluation of key habitat indicators.

This paper explores the sensitivity of a number a continuous

variables such as the number of large trees, and class

habitat data such as serial stage, under various level of data

aggregation from the plots, to the stand, the stratum, and

the landscape. The implications for using these data at

different scales are discussed.

Methods and Materials ___________

Vegetative Mapping and Stratification

The forests within Sierra Nevada Bioregion were mapped

from Thematic Mapper satellite imagery. Map polygons

representing forest species type, tree size classes, and crown

cover density were defined by processing the satellite imagery data by a combination of supervised and unsupervised

classification systems. Vegetation maps were produced first

using spectral analysis of data combined with ecological

modeling. Polygons representing stands of trees defined by

major forest type, tree size, and canopy closure density class

were the primary sampling units for assignment of the

ground plot data used to describe these strata. These vegetation-type polygons are the basic unit for which management

decisions are made and are the unit areas for which wildlife

habitat type is typically assigned within Region 5.

Linking Ground Plot Data to Map

Polygons

Approximately 2,900 Forest Inventory and Analysis (FIA)

plots were distributed across the 10 National Forests in

Region 5. The individual sample plots are aggregated into

vegetation classifications according to a vegetation map

defined above. This map is the primary bookkeeping tool for

tracking vegetation on the forests, based on summaries of

the plots sampled within each strata.

Forest inventory ground data were linked to forest type or

polygon maps through the strata label attribute. That is, the

unique vegetation labels from the FIA data were aggregated

into similar classes and then ground plots were allocated to

vegetation polygons. Traditionally, these strata were defined for the purpose of sampling timber volume. Each

vegetation polygon was then assigned to a stratum which
118 USDA Forest Service Proceedings RMRS-P-25. 2002

Padgett, Barber, and Taylor Sensitivity of Forest Vegetation Simulations to Scale of the Input Data and Impact to Estimates...

had an associated set of FIA plot data. The total number of

acres on a particular national forest associated with a given

stratum label was estimated by summing among map polygons with identical stratum labels. For example, the total

number of large trees (those greater than 30 inches d.b.h.)

associated with map areas within a given stratum label were

estimated by calculating the per acre average from the

inventory data and multiplying by total number of acres as

determined from the vegetation type map.

The Region 5 method of using vegetation-type maps to

stratify and assign ground plots was designed to describe

general conditions, relative to timber, especially stocking

measured by cubic volume. There was no intent to provide

detail description of vegetation condition in any specific

place, much less all places. As a consequence, data from

2,978 plots used in this analysis cannot be used to describe

any other polygon than the one sampled.

CWHR System

The California Wildlife Habitat Relationships System

(CWHR) is a generalized model of habitat relationships for

643 resident or regularly migrant terrestrial vertebrate

species in California (CWHR 1988). The University of California at Berkeley and the California Department of Fish

and Game developed it, cooperatively. The CWHR models

were originally designed to closely approximate the timber

strata presented in FIA system. The system allows a user to

predict the occurrence and habitat quality for any of these

species based upon the presence of specific habitat types or

habitat elements. It includes species notes for each species

including life history, range maps, legal status, habitat

requirements, and so forth. The CWHR habitat system, like

many other vegetation classification systems, uses the combination of plant species, size, and density to classify habitats. The CWHR system then uses this habitat classification

to identify habitat relationships between the vegetation and

the wildlife specie likely to be found in that area.

The CWHR models are designed to evaluate wildlife

response to habitat change over relatively large spatial and

temporal scales. CWHR was considered an appropriate

approach to a quantitative analysis at the bioregional scale

for a large array of species that occur in the Sierra Nevada.

The accuracy of this model for many species is often enhanced with information on special habitat elements such as

snags or dead and down material. CWHR was conceived to

operate using existing timber type maps. Thus for the most

part, the definition related to size and canopy closure was

similar to those used for FIA.

Differing Needs, Conflicting Strategies —

One Forest

One objective when developing a vegetation classification

system is to create enough classes so that management

activities can be modeled, while at the same time restraining

the number of classes so that the modeling does not become

excessively complex. The Region 5 planning efforts found

that creating 16 to 50 vegetation classes or forest strata

within an individual National Forest planning unit is the

most appropriate for modeling activities over time at that

scale. Without a stratification scheme, each of the Sierran

Forests would contain between 350 and 800 individual

vegetation types. Other than making the modeling effort

cumbersome, collecting plot-level data for 350 to 800 independent vegetation types is unrealistic under the current

FIA protocols and enormously expensive for the individual

Forests to undertake on their own. Lumping vegetation

types, however, reduces the depth of information available

to conduct ecosystem assessments for key species or critical

habitats. Many of the ecosystems (or habitat types) of greatest concern occupy small land areas, thus perhaps lost in the

large classification schemes. Therefore, the variation among

plots within strata is equally important as the average trend

of the strata. We tracked this variation by classifying each

plot in a consistent manner and then tabulating the number

of plots of each class found within each stratum. Plots, which

are stand-level characterizations, were classified first into

forest types according to a standardized rule set developed

by Region 5, and then into size and canopy closure classes

based on the CWHR typing scheme (table 1).

size class 0 size class 1 size class 2 size class 3 size class 4 size class 5 size class 6

non-stocked seedling sapling poles small tree large tree

multicanopy

<1-in DBH

 1-5.9"-

DBH

 6-10.9"-

DBH

11"-23.9"-

DBH

 >23.9"-

DBH

size 5 over 

4 or 3

no cover

<10% 

canopy 

cover

nonstocked

stocked n/a n/a n/a n/a n/a

sparse cover (S)

10-24% 

canopy 

cover

n/a 2S 3S 4S 5S n/a

open cover (P)

25-39% 

canopy 

cover

n/a 2P 3P 4P 5P n/a

moderate cover (M)

40-59% 

canopy 

cover

n/a 2M 3P 4M 5M n/a

dense cover (D)

60-100% 

canopy 

cover

n/a 2D 3D 4D 5D 6

C

L

O

S

E

D

CWHR HABITAT STAGES

YOUNG MATURE

O

P

E

N

1x

Table 1—Definition of California Wildlife Habitat Relations tree classifications.
USDA Forest Service Proceedings RMRS-P-25. 2002 119

Sensitivity of Forest Vegetation Simulations to Scale of the Input Data and Impact to Estimates... Padgett, Barber, and Taylor

Application of the Forest Vegetative

Simulator to Habitat Assessment

The FVS is the basic tool for simulating forest dynamics at

the plot or stand level. Projections of management activities

on changes in tree size class and density are a critical part

of the National Forest analytical process for habitat assessment. Inspection of initial projections using ecological

principles indicated a bias in the results and suggested that

the bias might be due to the original starting condition data

set. To test this hypothesis, the starting condition was

adjusted to reflect three statistical approaches:

Stratum Average Condition: This is the traditional

way of modeling forest dynamics by aggregating like FIA

plot data from the same vegetation zone into a single stratum. Projections were then made using data that were

aggregated by strata label as the initial condition.

Stratum Modal Condition: This method applies the

most frequently encountered value (the mode) of all plots,

rather than the arithmetic average. This value is then

applied to the stratum. It most closely reflects timber type

maps from which the CWHR stand classifications were

based. Projections in FVS were based on the aggregated

strata as the initial condition.

Plot Average Condition: Each vegetation-type plot was

treated separately. Proportionate weight of the plots relative to the landscape is based on the total area represented

by the plot within the stratum. For example, if there were six

plots used to describe a stratum of 12,000 acres, each plot

would carry a weight of 1/6 of 12,000 or 2,000 acres. Each of

these 2,000 acre “plots” were then projected individually,

rather than beginning with an aggregated strata. Following

each period of projections, the total number of acres was

reapportioned based on the percent of land occupied by that

plot within the stratum, and then a new round of modeling

would begin.

Results ________________________

Three examples of outcomes from FVS modeling of Sierran

forests based on differing input data are shown in figure 1.

Each panel represents a tree size and stand density that was

identified as suitable habitat for old forest dependent species.

The size classes for the trees are species dependent. In

general, size class 4 would be saw timber less than 24 inches

dbh, class 5 would be trees greater than 24 inches dbh. The “D”

in the delineation indicates a dense stand. Size class 6 is

unique in that it takes into account understory structure as it

contributes to a multilayered stand of tall, large trees with

smaller trees and large shrubs in the understory. The measure used in the analysis was acres occupied by each size class

and density class, regardless of the species composition.

These data demonstrate three patterns in modeling results. The size class 4D begins with a relatively similar

number of acres across the different input data; the plotbased data are slightly lower than the strata and mode data

(which are nearly identical) (fig. 1A). Over the 150-year

projection, the number of acres consistently decline, but the

rates of decline differ so that projections 150 years out using

the plot data indicate more acres than either the strata mean

or mode data. The example shown for the 5D class begins

with different starting acres (fig. 1B). Neither the strata

mean nor the mode data include any initial acres in this

class, while the dataset that maintains the richness of the

individual plots indicates roughly 16,500 acres. The projections for future acres also differ substantially. The projections using the modal data indicate no change in acreage

until the eighth decade, an increase to about 10,000 acres,

and a subsequent decline back to near zero by the end of the

period. The strata mean-based data starts with a 25,000

increase in the first two periods and then a drop back to

nearly the starting point. This type of outcome seems most

prevalent in class-based data; the fewer the classes and the

bigger the categories, then the more likely stands are prone

Figure 1—Projected changes in acres of tree size

classes and tree densities assumed to be important to

old forest dependent species.

180

160

140

120

100

80

60

40

20

0

50

45

40

35

30

25

20

15

10

5

0

200

180

160

140

120

100

80

60

40

20

0

0 2 4 6 8 10 12 14 16

Periods (10 years)

Acres (thousands)

Size class 4D

Size class 5D

Size class 6

PLOT BY PLOT

STRATA

MODAL

a

b

c
120 USDA Forest Service Proceedings RMRS-P-25. 2002

Padgett, Barber, and Taylor Sensitivity of Forest Vegetation Simulations to Scale of the Input Data and Impact to Estimates...

to sudden jumps from one class to another. A second jump

occurs in the seventh period, and the number of acres

remains fairly stable, or perhaps decreases slightly. The

more detailed plot data illustrates an entirely different

result. Not only is the starting point much greater, acres

increased over the modeled period. A loss of more than

10,000 acres is shown in year 70, but the loss seems to be

recovered 20 years later. Using acres classed as 6, the

starting data differs by about 2.5 fold, but all three datasets

converge at 55,000 acres after 150 years (fig. 1C). All results

indicate a decline in acres. The plot-based dataset starts at

about 57,000 showing the smallest decline, while the averaged strata data decline to less that half of its starting acres.

In contrast, the continuous variables (number of trees

greater than 30 inches and number of snags) show little

dataset-based differences (fig. 2). A slight increase in the

number of large trees is observed for the plot data. The mean

and mode data indicates a slight increase in the first seven

decades and subsequent decline back to the starting point by

year 150. Snags (an admittedly difficult parameter to model,

for lack of empirical data) increase by about 13 percent in all

cases.

If it is assumed that the total acres in forested lands do not

change over time, by plotting all size classes and densities

together in a single figure one can ascertain (within the

model) how stands change over time (fig. 3). In comparing

the projections by input dataset, changes based on plot data

are smoother than either of the other datasets. Both the

6

5

4

3

2

1

0

Periods (10 years)

PLOT BY PLOT

MODAL

STRATA

4

3

3

2

2

1

1

0

Large Trees (>30 inches)

Snags

Number (millions of snags) Number (millions of trees)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 3—Combined changes in all size classes and

tree densities over a 150-year modeled period.

Figure 2—Projected changes in the number of large trees

and snags, continuous variables, over a 150-year modeled

period.

1X 2X 3S 3P 3M 3D 4S 4P 4M 4D 5S 5P 5M 5D 6

900

800

700

600

500

400

300

200

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

900

800

700

600

500

400

300

200

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

900

800

700

600

500

400

300

200

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Period (10 years)

Strata Mode

Strata Mean

Plot data

Acres (thousands)

strata mean and strata mode start with larger inventories of

4M (smaller saw timber in moderately dense stands), smaller

inventories of seedlings, and sparse and light stands, and

particularly in the strata mean approach size classes 5D and

6. Most of the difference between the methods occurs in the

first half of the modeling period.

Discussion _____________________

Each input source had its own strengths and weaknesses

depending on the objective of the projections and the needs

of the analysis. Using nonaggregated plot data allows us to

maintain the data richness of the plots, but we lost spatial

integrity. When aggregations based on the mean and modes

were used, we lost track of very small or very large size

classes and densities but maintained the spatial integrity of

large geographic images.
USDA Forest Service Proceedings RMRS-P-25. 2002 121

Sensitivity of Forest Vegetation Simulations to Scale of the Input Data and Impact to Estimates... Padgett, Barber, and Taylor

The problem with assessing habitat condition using the

individual FIA plots as the input data source is that there

are more polygons within the stratum than there are FIA

plots to represent the polygons. This means that the majority of polygons are not sampled on the ground and therefore,

no direct linkage between the mapped data and the plot data

can be maintained. Because the plot data cannot be mapped

as a continuum, one loses the geographic integrity of a map.

Averaging plots, on the other hand, may create a model

“vegetation stratum” that does not actually occur, especially

if the distribution of plot stand conditions is not a normal

distribution. However, the geographic coverage is maintained and a map product can be produced. An alternative to

averaging plot data is to use the statistical mode of the plot

data. Under this scheme, polygons are classified based on

the most frequent or modal condition found within or between stands within the same stratum. This method most

closely imitates the traditional interpretation of aerial photos used to develop timber types maps that were most of the

source material when CWHR system was developed.

Forest Vegetation Simulator projection of continuous variables such as the number of large trees or the number of

snags indicated close correspondence among the three data

input variants. In contrast we found large differences among

the three approaches when discrete data sets, such as size

and density classes, were projected. Stratum averages or

modes tend to promote large numbers of acres into the next

class, resulting in gains or losses of particular classes in

timeframes that seem unrealistic from an ecological perspective.

Scale is a key concept in habitat analysis. However,

managers, biologists, and other specialists often fail to

adequately consider the impacts of scale especially when

developing desired future condition or rules to limited or

restrict activities. Improvements and ease in digital processing vegetative mapping and plot data have made conducting

analyses at appropriate scales and resolution both more

important and at the same time more difficult. Increasingly,

forest managers are expected to assess the implications of

management activities beyond the site being treated and to

broader landscape impacts. This necessitates sampling and

using remote means to provide maps and descriptions of

various habitats. This can be problematic when variables

such as species, size, density, and spatial arrangement of the

vegetation are considered important. Maps and related data

tables can be valuable tools for understanding; however,

they may mislead investigators. Analysts share the responsibility to ensure that habitat information used in

decisionmaking are instructive generalizations and not misrepresentations.

In conclusion, forest management objectives shift in response to population pressure and changes in the public

desires from their lands. In California forests, and other

regions adjacent to large urban centers, human activities

and expectations of the National Forest System are often

conflicting. The ability to understand and project the outcomes of management activities and decisions is crucial to

planning. Not only are habitat and ecosystem assessments

used within the forest management teams but also for

interactions with the public. The Forest Vegetation Simulator is a powerful information tool but care must be taken in

understanding the scale at which the primary data are used

and interpreted.

References _____________________

CWHR. 1988. Guide to the California Wildlife Habitat Relations

System. Wildlife Management Division California Department of

Fish and Game, Sacramento, CA

Committee of Scientists, USDA. 1999. Sustaining the People’s

Lands. Recommendations for Stewardship of the National Forests and Grasslands into the Next Century. United States Department of Agriculture, Washington DC.

Duane, T.P. 1996. Human settlement, 1850-2040. In: Volume II.

Sierra Nevada Ecosystem Project. Final Report to Congress.

Centers for water and wildland resources University of California, Davis. pp. 235-360.

Final Environmental Impact Statement. 2000. Sierra Nevada

Forest Plan Amendment. United States Department of Agriculture, Washington DC.

Record of Decision. 2001. Sierran Nevada Forest Plan Amendment

Environmental Impact Statement. United States Department of

Agriculture, Washington DC.

Sawyer, J.O.; Keeler-Wolf, T. 1995. A Manual of California Vegetation. California Native Plant Society, Sacramento CA.

Sierra Nevada Ecosystem Project. Science Team. 1996. Status of the

Sierra Nevada, final report to Congress of the Sierra Nevada

Ecosystem Project, 3 vols. Wildland Resources Center Report

Number 36, Univ. Calif. Davis
122 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Julee Greenough and Donald Robinson are Systems Ecologists and

Sarah Beukema is a Senior Systems Ecologist, all at ESSA Technologies

Ltd. 300-1765 W. 8th Ave, Vancouver, BC V6J 5C6. Werner Kurz, formerly of

ESSA Technologies Ltd. is a Senior Research Scientist, Canadian Forest

Service, 506 W. Burnside Rd. Victoria, BC V8Z 1M5. Nancy Densmore is a

Silviculture Planning Forester (Forest Development Section), Ralph Winter

is a Stand Management Officer (Silviculture Practices Section), and Barry

Snowdon is a Complex Stand Development Specialist (Silviculture Practices

Section), all with the British Columbia Ministry of Forests, P.O. Box 9513 Stn.

Prov. Gov’t. Victoria, BC V8W 9C6.

Abstract—The new Prognosis Environmental Indicators model

(Prognosis EI) was designed to assist forest management planning.

The model provides detailed, quantitative environmental impact

projections and expected timber flows under user-defined scenarios.

Prognosis EI is a linked set of models: the base FVS variant, the

western root disease extension, the fire and fuels extension (FFE),

and the new environmental indicators model (EI). These components operate within the Parallel Processing Extension (PPE). The

model produces all the standard outputs normally produced by FVS,

the FFE, and the PPE. As well, it generates indicators specific to the

EI model. These include stand structural indicators such as understory condition; species-specific wildlife indicators such as measures of foraging, nesting, roosting, and denning habitat for pileated

woodpeckers, bats, ungulates, grizzly bears, and black bears; landscape spatial indicators such as patch size distribution or amount of

interior old growth; water quality indicators, visual quality indicators, and active roads. Model outputs can be viewed and analyzed

using a flexible custom software interface that requires ArcView

GIS. Prognosis EI has been used in a case study to evaluate 21

alternative management and natural disturbance scenarios in the

West Arm Demonstration Forest in southeastern British Columbia,

Canada. In addition to providing information with which to evaluate the merits of alternative scenarios, the simulation results

suggest a number of general conclusions regarding the impacts of

root disease, snag retention practices, unauthorized firewood cutting, and recurrent underburns.

Prognosis EI: A Detailed Watershed-Level

Environmental Indicators Model

Julee Greenough

Sarah Beukema

Donald Robinson

Werner Kurz

Nancy Densmore

Ralph Winter

Barry Snowdon

Forest managers are increasingly being asked to develop

forest management plans that demonstrate the ability of the

forest to meet a wide range of timber and nontimber goals

over a long period. Assessing the consequences of different

actions on various indicators can be complex and challenging. Models can help the decisionmaking process, but few

models exist that combine the ability to project both forest

conditions and environmental characteristics. The goal of

the Environmental Indicators (EI) model was to create a

single tool with which to simulate the projection of timber

and nontimber attributes at the landscape level. The approach was to integrate appropriate existing Forest Vegetation Simulator (FVS; Stage 1973, Wykoff and others 1982)

tools, and to create additional quantitative indicators where

necessary, based primarily on information already available

in the other FVS tools.

The Model _____________________

The overall system combines the Southeastern Interior

variant of FVS (Robinson 1998) with the Fire and Fuels

Extension (FFE, Beukema and others 2000), the Western

Root Disease Model (WRDM, Beukema and others 1998),

and the new Environmental Indicators (EI) Model. These

four components operate within the Parallel Processing

Extension (Crookston and Stage 1991). To predict nontimber

attributes, the EI model uses the projected data about trees,

fuels, and snags from the other three components as well as

EI-specific input data containing information about stand

adjacencies and groupings, roads, and understory vegetation.

As part of the EI model, two ArcView GIS postprocessing

tools were also created. One tool uses the projected information and performs various spatial analyses to produce output on landscape characteristics. The other tool allows users

to easily display maps and graphs of various indicators over

time or at selected points in time.

Indicators ______________________

The overall model produces three general types of indicators: stand-level, spatial, and species-specific. Stand-level

indicators include all the standard output from the base FVS

model, the FFE, and the WRDM, such as tree information
USDA Forest Service Proceedings RMRS-P-25. 2002 123

Prognosis EI: A Detailed Watershed-Level Environmental Indicators Model Greenough, Beukema, Robinson, Kurz, Densmore, Winter, and Snowdon

(from FVS), snags (from the FFE), and coarse and fine woody

debris (from the FFE). Descriptions of the type of outputs

and the methods by which they are calculated can be found

in the appropriate documentation. The EI part of the model

produces additional stand-level indicators about stand

developmental stage and the percent cover of nontree vegetation such as herbs, shrubs, epiphytes, mosses, lichens,

and liverworts. The stand developmental stage is calculated

using information about BEC variant, habitat unit, stand

age, percent crown cover, and percent cover of selected

species (Greenough and others 1999a). The developmental

stage is then used to look up the predicted amount and types

of understory vegetation (Greenough and others 1999a). The

change in vegetation is not simulated explicitly. FVS will

predict changes to stand structure due to growth, mortality,

and management, which can cause a change in developmental stage, which in turn will affect the predicted understory

vegetation.

All spatial indicators are produced either by the EI model

during the simulation or by the EI’s postprocessing spatial

analysis tool. These indicators include the location, amount,

and size distribution of age-based patches or of interior old

growth, age and seral stage distributions, length of active

roads, conditions of designated viewsheds, and 11 measures

related to water quality. The definition of which ages define

a patch or define old growth, the buffer required to be

considered “interior,” the road network (including stream

crossings or adjacencies, and length of roads on unstable

soils), the viewsheds, and some of the water-related information are all defined by the user. Once the road network is in

place, management activities simulated by FVS will activate appropriate road segments. Roads are assumed to be

deactivated after a user-defined period. The water quality

indicators that are simulated are 11 of the ones specified by

the Interior Watershed Assessment Procedures guidebook

(BC Ministry of Forests and BC Ministry of Environment

1995). Many of these depend on knowledge of the active road

and stream network, as described by Greenough and others

(1999a, b).

The third category of indicators applies to four wildlife

groups: ungulates, pileated woodpeckers, bats, and bears.

For ungulates, the model predicts the amount of winter

foraging habitat based on stand attributes such as BEC

zone, aspect, age, crown closure, tree size, and presence of

particular understory forage species. Predefined management units can be used to track the effects of management

strategies. Two indicators are predicted for bats: edge foraging habitat and roosting trees. Edges are defined as any

boundary between stands with a low height and those with

a much greater height and a large amount of basal area. Bat

roosting trees are large, hard snags found in stands containing at least five such snags per hectare. There are three snag

and habitat indicators for pileated woodpeckers. The model

determines the number of drumming snags as large hard

snags that are sufficiently taller than the main canopy

height. Winter foraging snags are those that are a minimum

size and a given species and that are within a certain

distance of a large hard snag. Nesting habitat quality is

determined by the amount of large snags that are present.

Finally, bear habitat quality is calculated as a function of

habitat type and stand developmental stage, while denning

locations (trees and snags) are measured as a function of

BEC variant, stand slope, and tree or snag size and type. The

specific criteria that are used to measure each of these

indicators are described by Greenough and others (1999a).

Case Study

The model was developed using a case study. The study

area is in the West Arm Demonstration Forest near Nelson,

BC, located on the north side of the west arm of Kootenay

Lake. The study area included the complete Redfish Creek

watershed and two adjacent face units. The area was about

3,300 ha (8,154 acres), with five ecological zones. The simulations used between 699 and 1,507 stands depending on the

management scenario.

For input data, the model required maps with the various

management zones and the road network. For the stand

data, few sample tree lists were available, but some

general characteristics, such as slope, aspect, elevation,

habitat, and cover, were known about all locations. The

sample and summary information was used with a Most

Similar Neighbor Analysis (Moeur and Stage 1995) to assign

a tree list and initial fuel levels to each stand. Root disease

levels were then assigned based on habitat type, age, and

species composition.

The case study simulated 21 scenarios, which explored a

range of different management systems and harvest schedules (Greenough and others 1999c). The scenarios also used

different assumptions about the presence of root disease or

underburns, removal of snags, and road activation.

Example Results ________________

Output was produced for each of the indicators discussed

above. This section does not attempt to provide a comprehensive analysis of the 21 scenarios (see Greenough and

others 1999c for the complete results). Rather, it demonstrates some of abilities of the model and the methods of

examining the output.

One of the key requirements of the model was that it

provide planners with a single tool that can show the results

of both timber and nontimber indicators in a way that can be

used for evaluating the tradeoffs between different assumptions. One method of doing this is to produce a table with

some of the key indicators. For example, table 1 compares

the values of seven indicators from four scenarios. The “best”

and “worst” values are highlighted for each indicator. An

analyst can immediately tell that there is no single scenario

that is best for all indicators, and can see some of the key

questions that will need to be considered. For example, if it

is crucial to be able to produce some volume from the

landscape, then scenario 4 is not an option, even though it

produces the highest values for three out of the seven

indicators. If the minimum requirement for old seral stage

is below 29 percent, then scenario 9 may be a good option

because it is best for three of the indicators and intermediate

for three others.

The EI model has an ArcView-based postprocessing tool

that allows users to select one or more indicators and one or

more scenarios and to produce graphs or maps. Graphs are
124 USDA Forest Service Proceedings RMRS-P-25. 2002

Greenough, Beukema, Robinson, Kurz, Densmore, Winter, and Snowdon Prognosis EI: A Detailed Watershed-Level Environmental Indicators Model

produced for indicators that vary over time, and multiple

scenarios can be plotted on a single graph (fig.1 and 2). This

can readily be used to assess how values change over time

and how the relative merit of each scenario may differ over

time. For example, in the figures both indicators show

relatively little difference between the scenarios for the first

20 to 30 years, implying that in the short-term, the choice of

scenario may not affect these indicators. By the end of the

simulation, however, the scenarios are each different, and

the choice of “best” scenario is precisely opposite for each

indicator. The unmanaged scenario produces the most denning snags but the least number of woodpecker foraging

snags.

The tool automatically produces several maps in one of

three ways: showing one indicator and one scenario at

several points in time, showing one indicator for several

scenarios at one point in time, or showing several indicators

for one scenario at one point in time. This facilitates comparison between indicators and scenarios.

Applying the EI Model in Other

Locations ______________________

The EI model was designed and tested in southeastern

British Columbia, Canada. Like all other FVS extensions,

with some customization it can be applied to areas in other

regions for which an FVS variant exists. The minimum

requirement is that the appropriate variant of FVS and the

EI model are linked to the PPE. If indicators that require

knowledge of snags or coarse woody debris are desired, the

corresponding FFE variant would also need to be linked into

the PPE framework.

Once linked to the PPE, and with the appropriate input

data, the EI model will run for the selected landscape. Many

indicators, however, depend on the understory species composition look-up table, on the developmental stage logic

developed for the case study, or on the ecological zones

represented in the SEI variant of FVS. Each of these would

need to be redefined and coded into the new EI variant.

Many of the other relationships in the model, such as the tree

species or minimum sizes of snags required by different

wildlife species, can be controlled through keywords

(Beukema and others 1999).

Table 1—Example values for seven indicators from four scenarios. The “best” value for each indicator is shaded in grey, while the “worst” value is

circled. This table is for demonstration purposes only. Many more scenarios and indicators are available in the case study (Greenough

and others 1999c).

Patch-cut

Partial-cut

Unmanaged

Large clearcut

600

500

400

300

200

100

0

2000 2020 2040 2060 2080 2100

Year

Number of snags ('000)

Figure 2—Number of black bear denning snags in four

scenarios.

Figure 1—Number of pileated woodpecker foraging snags

in four scenarios.

Patch-cut

Partial-cut

Unmanaged

Large clearcut

1400

1200

1000

800

600

400

200

0

2000 2020 2040 2060 2080 2100

Year

Number of snags

1: patch 723 244 75 925 195 43 7.3

4: partial 490 221 198 244 126 60 5.5

7: unman 0 746 80 245 142 64 4.0

9: Lg CC 917 288 292 395 264 29 5.9

Merch. vol. Bear den Deer Old seral High concern

harvest sites winter Bat forage Wood-pecker stage watersheds

Scenario (’000 m3/ha)(number) forage (ha) edges (km) snags (’000) (% area) (number)
USDA Forest Service Proceedings RMRS-P-25. 2002 125

Prognosis EI: A Detailed Watershed-Level Environmental Indicators Model Greenough, Beukema, Robinson, Kurz, Densmore, Winter, and Snowdon

One of the greatest challenges faced when doing a landscape analysis is the availability of spatial data. The EI

model requires input about several spatial relationships

that provide, for each stand:

• A list of other stands that are within a given distance of

the center of the focal stand.

• A list of other stands that share a border with the focal

stand.

• Which deer management unit the stand belongs to, if

any (optional).

• Which viewshed the stand belongs to, if any.

• The length of stream within the stand, if any.

• The road segment that is within the stand, if any.

Many of these can be produced by a GIS technician if the

applicable landscape, stream, road, viewshed, and deer

management unit maps are provided.

In addition to these stand-based maps, the model also

requires information about the road network. It assumes

that the roads in the landscape have been divided into

different segments that join to form the complete road

network. For each road segment the model needs to know, at

a minimum, the length of the segment and the road to which

it connects. If the watershed indicators are desired, the

model also needs to know for each road segment the length

on erodable soils, number of stream crossings, and length of

the road within 100 m of a stream (see Beukema and others

1999 for more details).

With this information, in addition to a treelist for every

stand, the EI model would be able to run in other areas. If

some information is missing, then the indicators could

simply be ignored. For example, if the watershed assessment rules are not appropriate for a different area, then the

model would not need to know anything about the relationship between roads and streams or soils. Deciding before

doing a simulation which indicators may be important could

facilitate the process of gathering the required information.

Conclusions____________________

The EI Model combines FVS, the FFE, and WRD with the

PPE and a new tool to project timber and environmental

indicators at the stand and landscape level. It combines all

the forecasting and simulation abilities of each of the individual components (such as the growth and mortality of

trees, creation of snags and coarse woody debris, and prediction of management options) and provides additional output

on new stand and landscape indicators such as wildlifespecific habitat quality or quantity, active roads, seral stage

distributions, or water quality measures. The model has

been applied successfully in southeastern BC and the results

are summarized in several reports. The EI model can be

readily applied in other locations using regional parameter

sets and the appropriate variant of each of the FVS tools.

References _____________________

BC Ministry of Forests; BC Minisry of Environment. 1995. Interior

Watershed Assessment Procedures Guidebook. Forest Practices

Code of British Columnbia. BC Ministry of Forests and BC

Ministry of Environment, Victoria, BC. 99 pp.

Beukema, S. J.; Reinhardt, E. D.; Kurz, W. A.; Crookston, N. L. 2000.

An overview of the fire and fuels extension to the Forest Vegetation Simulator. pp. 80-85. In: L.F. Neuenschwander, K.C. Ryan,

G.E. Gollberg, and J.D. Greer (eds.). Proceedings: The Joint Fire

Science Conference and Workshop: Crossing the Millennium:

Integrating Spatial Technologies and Ecological Principles for a

New Age in Fire Management. Vol II. 1999 June 15-17, Boise, ID.

U of ID and International Assoc. of Wildland Fire. 312 pp.

Beukema, S. J.; Robinson, D. C. E.; Greenough, J. A.; Kurz, W. A.

1999. User’s Guide to the Prognosis Environmental Indicators

Model. Prepared by ESSA Technologies Ltd., Vancouver, BC.

86 pp.

Beukema, S. J.; Kurz, W. A.; Greenough, J. A. 1998. Detailed model

description. In: S. Frankel (tech. coord.). User’s Guide to the

Western Root Disease Model, Version 3.0. Gen. Tech. Rep. PSWGTR-165. Albany, CA: Pacific Southwest Research Station, Forest Service, US Department of Agriculture, 164 pp.

Crookston, N. L.; Stage, A. R. 1991. User’s Guide to the Parallel

Processing Extension of the Prognosis Model. USDA Forest

Service, Intermountain Research Station, Ogden, UT. Gen. Tech.

Rep. INT-281. 88 pp.

Greenough, J. A.; Robinson, D. C. E.; Beukema, S. J.; Kurz, W. A.

1999a. Prognosis Environmental Indicators Model Description.

Prepared by ESSA Technologies Ltd., Vancouver, BC. 66 pp.

Greenough, J. A.; Robinson, D. C. E.; Beukema, S. J.; Kurz, W. A.

1999b. Prognosis Environmental Indicators Model: WADF Case

Study Methodology. Prepared by ESSA Technologies Ltd.,

Vancouver, BC. 24 pp.

Greenough, J. A.; Robinson, D. C. E.; Kurz, W. A.; Beukema, S. J.

1999c. Prognosis Environmental Indicators Model: WADF case

study. Prepared by ESSA Technologies Ltd., Vancouver, BC.

90 pp.

Moeur, M.; Stage, A. R. 1995. Most similar neighbor: an improved

sampling inference procedure for natural resource planning. For.

Sci. 41:337-359.

Robinson, D. C. E. 1998. Prognosis southeast interior (SEI 1.0)

geographic variant description. Draft report prepared by ESSA

Technologies Ltd., Vancouver, BC for the BC Ministry of Forests,

Victoria, BC. 162 pp.

Stage, A. R. 1973. Prognosis Model for stand development. Res. Pap.

INT-137. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Forest and Range Experiment Station.

32 pp.

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:

U.S. Department of Agriculture, Forest Service, Intermountain

Forest and Range Experiment Station. 112 pp.
126 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

H. Temesgen is Research Associate and V. LeMay is Associate Professor, Department of Forest Resources Management, University of British

Columbia, 2045-2424 Main Mall, Vancouver, BC. Email: temesgen@

interchange.ubc.ca and lemay@interchange.ubc.ca.

Abstract—In British Columbia, the forestland is divided into

various strata based on forest cover types and aerial attributes for

forest inventory purposes. To manage forest resources in perpetuity, several growth and yield models have been developed and used

in various parts of the province. Among these models, PrognosisBC

(adapted from the northern Idaho variant of FVS) is used in the

southeastern interior of the province. However, the estimates from

this model have not been linked to aerial attributes nor have they

been used to update the existing inventory databases. This paper

discusses how PrognosisBC can be linked to aerial attributes and

inventory databases when there are large and widely varied land

Linking PrognosisBC to Aerial Attributes for

Timber Supply Analysis in British Columbia

H. Temesgen

V. LeMay

bases and insufficient ground data. Differences in observed and

estimated species composition, tree, and stand attributes are also

discussed.

A revised version of the paper presented at the conference

is to be published elsewhere: Temesgen, H.; LeMay, V.M.;

Froese, K.L.; Marshall, P.L. In press. Imputing tree-lists

from aerial attributes for complex stands. For. Ecol. &

Mgmt.
USDA Forest Service Proceedings RMRS-P-25. 2002 127

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Don Vandendriesche is a Forester, U.S. Department of Agriculture, Forest

Service, Forest Management Service Center, Vegetation Simulation Group,

Natural Resources Research Center, 2150A Centre Avenue, Fort Collins, CO

80526.

Abstract—“Label Processing” is a little known feature within the

Forest Vegetation Simulator (FVS). When used in combination with

the Parallel Processing Extension (PPE), it can provide a powerful

analytical tool. Labels within FVS link forest stands to activities.

These activities can be either silvicultural or managerial in context.

The PPE can be used for several purposes, including resource supply

analysis where labels relate activities to groups of stands that are

being managed to meet a specific objective. An example from the

Flathead National Forest is presented that employs the labeling

and parallel processing capabilities of FVS to solve a resource

supply problem.

Many forest planning problems can be solved using the

policy labeling capabilities of the Parallel Processing Extension (PPE) (Crookston and Stage 1991) of the Forest Vegetation Simulator (FVS) (Dixon 2001). The advantage of using

these tools in combination would be that inventory information and associated silvicultural prescriptions would already be set up within the projection model [FVS]. Preparing

data and developing stand management options are prerequisites to any planning exercise. Labeling and the PPE

facilitate the final planning step — namely decision analysis

for multi-stand processing. This paper provides an introduction to policy labeling rules and demonstrates the utility of

the Parallel Processor in solving resource supply problems.

Policy Labeling Feature __________

Specifying management policies using rules is a little

known feature embedded within the FVS model. This process is spun through every aspect of FVS: the Suppose

interface, the base model, the event monitor, and the model

extensions. There are three levels of labels within FVS.

Beginning with individual stand polygons and working

toward a landscape perspective, they are:

• Stand Policy

• Activity Group Policy

• Multistand Policy

Using the Labeling Capabilities and

Parallel Processing Extension of

the Forest Vegetation Simulator

for Resource Supply Analysis

Don Vandendriesche

You may not have heard of stand policy labels, but if you

have used the Suppose interface (Crookston 1997), you

certainly have unknowingly encountered them. Stand Policy

Labels are synonymous with Grouping Codes. Grouping

codes come into play when you invoke the “Select Simulation

Stands” command button from the main Suppose window.

Grouping codes populate the middle windowpane on the

Select Simulation Stand screen (fig. 1). They are used to help

identify common characteristics among individual stands.

In figure 1, the DF grouping code is used to equate forest

stands that are predominantly Douglas-fir forest cover type.

Unbeknown to most users, this is also a Stand Policy Label

for this grouping of forest stands.

The Suppose interface obtained these grouping codes from

the Stand List File, Record Type C. Data translation programs such as Pre-Suppose (Vandendriesche, these proceedings) can assist in assigning grouping codes to common

plot sets.

Silvicultural activities can be related to groups of stands

using Activity Group Policy labels. Activity Group Policy

labels were incorporated into FVS with the introduction of

the Event Monitor (Crookston 1990). A keyword, AGPLABEL,

is used to designate an Activity Group Policy label. This

keyword is used in conjunction with conditionally scheduled

silvicultural events such as thinnings and plantings. In

other words, when a user inserts an “If...Then...EndIf”

sequence in an FVS projection, by default, an Activity Group

Policy label is associated with this treatment. Refer to figure

Figure 1—FVS Stand Policy Labels are built by

Suppose from grouping codes.
128 USDA Forest Service Proceedings RMRS-P-25. 2002

Vandendriesche Using the Labeling Capabilities and Parallel Processing Extension of the Forest Vegetation Simulator for Resource Supply Analysis

2 for an example of Activity Group Policy labeling within a

conditionally scheduled event.

Implementation of a forest plan requires setting standards and guidelines for distinct management areas. Many

stands may be affected by a policy decision established at the

programmatic level of planning. An optimal prescription for

a given stand may not be feasible when viewed in the context

of the entire forest setting. There are many planning models

available to forest analysts. To simplify solving such rich

problems with FVS, the Parallel Processing Extension was

developed. As with any FVS entity, there is an associated set

of mnemonic keywords. To handle management directions

involving many stands, Multistand Policy Labels were created. For the resource supply example demonstrated in this

paper, four inputs direct the PPE. The first involves the

management objective or target for the resource. The second

involves the criteria for selecting which stand among many

will contribute to meeting the management goal. The third

involves the unit value of the contributing factor. The fourth

is the text string defining the Multistand Policy Label (fig.

3). For example, if the management policy objective were to

harvest a specified allowable sale quantity (ASQ) from a

particular group of stands within a designated land type, a

predetermined selection criterion would dictate the order in

which stands are harvested (such as oldest stands first). The

unit value would be in terms of volume rendered (such as

cubic feet, board feet). The multistand policy label associates

the stands and activities that may be scheduled to meet the

objectives of the policy.

Parallel Processing

Capabilities ____________________

Lets expand this discussion to the second facet of this

paper, the Parallel Processing Extension of the Forest Vegetation Simulator. At this time, the PPE has the capability

to address four types of problems:

1. Multistand treatment scheduling

2. Resource supply trends

3. Contagion in pest dynamics

4. Decision trees

Multistand treatment scheduling involves tradeoff analysis. For example, suppose a forested area comprising many

stands was used for elk hiding cover and timber production.

Favoring one entity impacts the other. The PPE allows

examination of these tradeoffs. Resource supply analysis

involves gaming analysis. For example, suppose a forest

analyst wanted to determine if a specified level of timber

harvest were sustainable. The PPE, based on resource

supply and public demand, can determine the level of diminishing return. A detailed example will be presented later in

this paper. Contagion in pest dynamics deals with spatial

analysis. For example, suppose a mountain pine beetle

epidemic was devastating an area. The PPE can be used to

chart the progression of an insect or disease pathogen from

one stand to another. Decision trees are used for decision

analysis. For example, suppose a silviculturalist was trying

to decide on the timing options for a thinning treatment. The

PPE could be used to replicate a stand along several pathways to determine the best solution.

For each of these solution processes, labeling plays a vital

role. Through the use of unions and intersections of the

various text labels (in other words, stand policy, activity

group policy, multistand policy), the Parallel Processing

Extension performs its magic.

Flathead National Forest

Example _______________________

In conjunction with the passage of the fiscal year 2000

Forest Service budget, the Senate Appropriations Committee requested a report summarizing current timber growth,

inventory, and mortality, including projections for the next

10, 20, and 50 years for the National Forest System. To the

extent practicable, the report was to include age class and/

or structural stage distribution of the stands represented by

the data. The Flathead National Forest in northwestern

Montana was chosen by the Forest Management Service

Center as a representative example in responding to this

inquiry.

Figure 2—The Activity Group Policy Label is specified

using the AGPLABEL keyword.

Figure 3—Multistand Policy Label is used to associate a

multistand policy to a group of stands and the activities

used to achieve the policy’s objectives.
USDA Forest Service Proceedings RMRS-P-25. 2002 129

Using the Labeling Capabilities and Parallel Processing Extension of the Forest Vegetation Simulator for Resource Supply Analysis Vandendriesche

A systematic approach was developed to perform the

required analysis. A framework similar to that used for

forest management planning was employed. The major

steps in the process were: formulating a forestland classification strategy, prescribing silvicultural choices, forecasting growth and yield, evaluating management polices, and

allocating resource assignments. Relevant aspects of each of

these steps are presented.

Forestland Classification

The Forest Inventory and Analysis (FIA) data set was

used as the empirical basis for the analysis. The systematic

sampling design of FIA provided acreage values as well as

per acre estimates of important plot and tree attributes. A

strategy was developed to guide forestland classification

assignments. Removing nonforest parcels from the acreage

base and stratifying by administrative availability and management suitability rendered a hierarchal flow for data

sorting. Physiographic class and condition class further

defined each of the major stand types. Refer to figure 4.

Stand types were developed within each land unit using the

Pre-Suppose program. Stand Policy Labels (grouping codes)

embedded in the Stand List Files guided the assignments

(fig. 1).

Silvicultural Options

Silvicultural treatments followed a conceptual framework. Rule sets for vegetation management were developed

based on historic fire regimes. Departure of current vegetation due to missed fire return intervals resulted in an

invasion of shade-tolerant tree species. Treatment opportunities were postulated to achieve an ecosystem in proper

balance with regard to natural fire frequency. The diagram

in figure 5 defines condition classes by major forest cover

type and size class. Mechanized treatments and prescription

fire compose the available tools for stand management.

Disturbance agents such as insects, disease, and wildfire

have an impact on resultant forest structure. Each of these

factors were identified and addressed.

Condition Classes—Four prescription (Rx) options were

recognized: maintenance, restoration, conversion, and deferment. A brief description of each follows.

Maintenance (Maintain) Rx: Applied to young stands.

Favorable species exist on a given habitat type. Missed one

or fewer fire intervals.

• Precommercial thin overstocked stands.

• Prescribe fire for fuel reduction.

• Deferring of activities, let grow.

• Commercial thinning as stands mature to regulate

stocking and maintain health.

Restoration (Restore) Rx: Applied to older stands. Favorable species exist on a given habitat type. Missed two or fewer

fire intervals. Slight invasion of shade tolerant species.

• Improve stand composition to seral species.

• Precommercial thin overstocked stands.

• Prescribe fire for fuel reduction.

• Commercial thinning as stands mature to regulate

stocking and maintain health.

Conversion (Convert) Rx: Applied to older stands. Unfavorable species exist on a given habitat type. Missed two or more

fire intervals. Massive invasion of shade-tolerant species.

• Improve stand composition to seral species.

• Precommercial thin overstocked stands.

Figure 4—Forestland classification diagram.
130 USDA Forest Service Proceedings RMRS-P-25. 2002

Vandendriesche Using the Labeling Capabilities and Parallel Processing Extension of the Forest Vegetation Simulator for Resource Supply Analysis

• Prescribe fire for fuel reduction.

• Commercial thinning as stands mature to regulate

stocking and maintain health.

• Regeneration harvest to shade-intolerant species as

opportunity allows based on habitat.

Deferment (Defer) Rx: Applied to old growth, defined as

stands with 10 or more trees per acre greater than 20 inches

dbh.

• Deferring of activities, let grow.

• Monitor stand age and structure.

Silvicultural Prescriptions—Mechanical treatments

such as precommercial thinning, commercial thinning, and

regeneration harvesting could only be implemented on the

Suitable forestland base. The following parameters were

used in designing the keyword sets for projection with the

Forest Vegetation Simulator.

Pre-Commercial Thinnings (PCT): Thin to 500 trees per

acre. Favor retention of seral species.

• Stands less than 80 years old.

• Stands less than 5 inches quadratic mean diameter.

• Stands greater than 1,500 trees per acre.

Commercial Thinnings (CT): Thin to 70 square feet, basal

area. Favor retention of seral species.

• Stands at least 20 years old.

• Stands at least 5 inches quadratic mean diameter.

• Stands at least 2,000 bd. ft./acre cut volume.

• A 40-year re-entry period.

Regeneration Harvest (RH): Even-aged methods. Favor

establishment of seral species.

• Stand at least age 80 for LP, age 90 for other forest types.

• Stands at least 5 inches quadratic mean diameter.

• Stands at least 2,000 bd. ft./acre cut volume.

• Leave nine Legacy Trees for snag replacement.

Let Grow (LG) Rx: Defer young and old growth stands.

• Young stands less than 40 years old (generally).

• Young stands less than full stocking.

• Old stands greater than 120 years old (generally).

• Old stands with 10 trees per acre greater than 20 inches

dbh.

Prescription Fire—Controlled burns could be used on

both Suitable and Unsuitable forest areas as a low cost

treatment to enhance species composition. Although a Fire

and Fuels Extension had been developed for the FVS model,

linkage with the Parallel Processor Extension had not been

made at the time. The PPE was used as the modeling tool to

bring the associated analytic pieces together. Keyword sets

that simulated low and moderate intensity prescription fire

scenarios were developed. Attributes of the prescription fire

keyword sets were as follows:

Prescription Fire—Low Intensity

Applicable only to shade-intolerant and LP types (that is,

PP, WL-DF, WBP, QA, LP). Used “FixMort” keyword to

simulate fire-induced mortality.

• 50 percent mortality in trees 0 to less than 3 inches dbh.

• 30 percent mortality in trees 3 to less than 5 inches dbh.

• 20 percent mortality in trees 5 to less than 9 inches dbh.

• 10 percent mortality in trees 9 to less than 16 inches dbh.

• 0 percent mortality in trees greater than 16 inches dbh.

• Indiscriminate to all species.

• Applicable only to Suitable_Maintain and Restore forestland classes.

• Targeted 2,500 acres annually.

Prescription Fire - Moderate Intensity

Applicable only to shade mid-tolerant and tolerant types

(that is, DF, GF, ES, ES-SAF). Use “FixMort” keyword to

simulate fire-induced mortality.

• 80 percent mortality in trees 0 to less than 3 inches dbh.

• 60 percent mortality in trees 3 to less than 5 inches dbh.

• 50 percent mortality in trees 5 to less than 9 inches dbh.

• 30 percent mortality in trees 9 to less than 16 inches dbh.

• 20 percent mortality in trees greater than 16 inches dbh.

• Indiscriminate to all species.

• Applicable only to Unsuitable_Convert forestland class.

• Targeted 3,500 acres annually.

Forest Health—Forest pests can significantly impact

the current and future development of a stand. The USDA

Forest Service Northern Regional Office developed forest

pest risk maps for all Montana forests including the Flathead National Forest. Root rot, bark beetle, and dwarf

mistletoe incidence were recorded on the FIA plots. Overlaying risk maps with the FIA plots enabled forecasting potential hazard sites. The Northern Region Forest Health Specialists developed associated FVS keyword file sets to simulate

pest effects. By using the Western Root Disease Extension,

these health issues were incorporated into the projections.

Fire Impacts—The Flathead National Forest had compiled a report of recorded wildfires during 10 years from

1985 to 1994. Based on this information, FVS keyword file

sets were developed to randomly burn the approximate

acreage as cited. The following parameters were used:

Figure 5—Silvicultural options.
USDA Forest Service Proceedings RMRS-P-25. 2002 131

Using the Labeling Capabilities and Parallel Processing Extension of the Forest Vegetation Simulator for Resource Supply Analysis Vandendriesche

Wildfire - 1985-1994 period

____________________________________________

Forest Acres Low Moderate High

land burned intensity intensity intensity

class 10 years 15% 65% 20%

__________________________________________________________________

Suitable 12,250 1,837 7,962 2,450

Unsuitable 7,650 1,148 4,973 1,530

Wilderness 2,600 390 1,690 520

__________________________________________________________________

Total: 22,500 3,375 14,625 4,500

FVS keyword sets were written to describe each of these

facets. Activity Group Policy Labels were assigned within

conditionally scheduled events (If ... Then ... EndIf keyword

sequences) to link grouping of stands with their associated

treatment/disturbance activity. Refer to figure 2 for an

example.

Yield Forecast

Stand projections were performed using the Forest Vegetation Simulator for a 100-year time horizon. Yield estimates were compared and calibrated to measured data as

observed from the Flathead FIA data set.

Management Direction

The essence of the Senate Appropriations Committee’s

request was that given a specified level of harvest based

upon current production output, what would be the resultant condition of the forest in terms of growing stock and

mortality rates over the next 50 years. An inference of the

current harvest activity needed to be made.

Sale Quantity—The most recent Forest Plan for the

Flathead National Forest cited an allowable sale quantity of

54 million board feet per year from suitable lands. However,

due to market constraints, pending litigation, threatened

and endanger species considerations, and other unforeseen

issues, the programmed financed sell had been 23 MMBF

during the period from 1995 to 1999. This was the harvest

level used for the analysis.

Implementation Strategy—Intermediate harvests

(commercial thinnings) were targeted for the younger merchantable size classes of the maintain and restore condition

classes (target 5.0 MMBF). Regeneration harvests were

targeted for the older merchantable size classes of the

convert condition class (target 18.0 MMBF). Also, high bark

beetle hazard plots were prioritized for regeneration treatment. Stand improvement treatments (such as

precommercial thinnings) were restricted to 10 percent of

the potential area to aid in metering out the acres treated.

Resource Allocation

The Parallel Processing Extension was used to solve the

forest trend inquiry. For resource supply analysis, PPE is

easy to implement.

PPE Set up—The Suppose interface distinguishes FVS

keywords for PPE as one of three component types: top,

bottom, and report. Relevant keywords used for resource

allocation were as follows:

Top Components

Exact - Instructs PPE to use partial stands to meet target

objectives.

Bottom Components

MsPolicy - Signals that a multistand policy will be

entered.

LabWts - Request that labels and weights be written to

an auxiliary file. Used for post processing of stand

structure statistics.

Report Components

Yields - Prints composite yield statistics table.

The MsPolicy keyword establishes the parameters associated with the Multistand Policy (fig. 3). This label is used to

link groups of stands (Stand Policy) and their proposed

silvicultural treatment (Activity Group Policy). For example,

within the suitable forest land class on the Flathead, there

were 29 Douglas-fir stands (fig. 1). The convert condition

class was comprised of a subset of 12 stands (fig. 4). Regeneration harvest can only be applied to the suitable_convert

activity group (fig. 2). The overall multistand-ASQ target

for the suitable_convert forest land class was 18.0 MMBF

(fig. 3). The priority for stand selection to meet target

objectives was to harvest the oldest stands first. Their

contribution to meeting the target goal was a factor of the

stands’ removal board foot volume and its size in acres.

Therefore, if Douglas-fir stand within the suitable_convert

forestland class was the oldest stand available at that point

in the projection, it would be selected for regeneration

harvest to meet the target ASQ objective of 18.0 MMBF.

PPE Reports—Within the main output report of FVS,

two important PPE tables related to resource supply analysis are generated. The first, the Targeted Resources Table

(fig. 6), displays a listing of stands selected to meet the

Figure 6—Targeted resources table.
132 USDA Forest Service Proceedings RMRS-P-25. 2002

Vandendriesche Using the Labeling Capabilities and Parallel Processing Extension of the Forest Vegetation Simulator for Resource Supply Analysis

management policy objective. Note that the Priority column

is ordered sequentially from left to right then down in terms

of descending stand age. The Credit column equates to the

selected stands’ harvested board foot volume multiplied by

the number of acres within the stand. If only part of the stand

was needed to meet the exact ASQ goal, then the remaining

stand acres would be carried to the next time period. Split

stands are denoted by a series of two asterisks in the Select

column within the Targeted Resources table.

The second important PPE table to review is the Composite Yield Statistics Table (fig. 7). Average removal volume

per acre is displayed. Total Sample Weight [acres] and

Fraction of Area Treated are also presented. Multiplying

these terms together will render the targeted volume goal.

Figure 8—Flathead National Forest, board foot volume trends.

Figure 7—Composite yield statistics table.

Acknowledgments ______________

The complexities of the Flathead National Forest example

could not have been fully developed without the input and

oversight provided by Doug Berglund, Forest Silviculturist,

Flathead National Forest. Doug’s consummate knowledge of

local history, ecology, and silviculture proved invaluable.

Barry Bollenbacher, Northern Region Silviculturist, and

George Lightner (retired), Northern Region Inventory Specialist, provided preliminary support data and direction.

References _____________________

Crookston, Nicholas L. 1990. User’s guide to the event monitor: part

of Prognosis Model version 6. Gen. Tech Rep. INT-275. U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. Ogden, UT. 21 p.

Crookston, Nicholas L. 1997. Suppose: an interface to the Forest

Vegetation Simulator. In: GTR-375, Proceeding: [1st] Forest Vegetation Simulator Conference. Fort Collins, CO. U.S. Department

of Agriculture, Forest Service, Intermountain Research Station.

Ogden, UT. p. 7-14.

Crookston, Nicholas L.; Stage, Albert R. 1991. User’s guide to the

Parallel Processing Extension of the Prognosis Model. Gen. Tech.

Rep. INT-281. U.S. Department of Agriculture, Forest Service,

Intermountain Research Station. Ogden, UT. 88 p.

Dixon, Gary E. 2001. Essential FVS: A User’s Guide to the Forest

Vegetation Simulator. GTR draft. U.S. Department of Agriculture, Forest Service, Forest Management Service Center, Fort

Collins, CO. 202 p.

Summary ______________________

By using the labelling capabilities and Parallel Processing Extension of the Forest Vegetation Simulator, a comprehensive analysis of a resource supply problem was

addressed. These tools were used to prepare reports showing trends in forest stocking as impacted by changes in

growth, harvest, and mortality. Figure 8 displays the

projected outcome relative to board foot volume over the

next 50 years. Live tree stocking showed an ever-increasing accumulation as a result of forest growth outpacing

harvest and mortality components.
USDA Forest Service Proceedings RMRS-P-25. 2002 133

Session 4. Model Development

USDA Forest Service Proceedings RMRS-P-25. 2002 135

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Robert E. Froese is a Ph.D. degree candidate, Department of Forest

Resources, University of Idaho, Moscow, ID 83844-1133. Andrew P. Robinson

is an Assistant Professor, Department of Forest Resources, University of

Idaho, Moscow, ID 83844-1133.

Abstract—The Prognosis large-tree basal area increment model is

being adapted to include soil parent material (lithology) as a new

predictor variable. In this paper, the critical issue of how to aggregate diverse geologic units into a set that is meaningful for growth

model prediction is examined. We fit the model in a pilot study to

compare subjective (concept-based) and objective (data-based) approaches to aggregation. When compared using Akaike’s Information Criterion, the best models were those using data-based approaches. Concept-based approaches produced groupings that are

least useful for modelling, and data-based approaches produced

groupings that were least meaningful biologically and geologically.

Poor data distribution and a narrow data set are likely causes of the

discouraging result.

Soil properties have an important and well-recognized

influence on tree and forest dynamics. Soils are reservoirs of

both water and the mineral nutrients that are required by

plants. The properties of soil are substantially defined by the

properties of the parent material – usually bedrock – from

which soils are derived. Over the past decade a number of

studies have revealed empirical evidence of a cause/effect

relationship between soil parent rock, soil properties, and

forest dynamics. The Intermountain Forest Tree Nutrition

Cooperative (IFTNC) has identified a number of interactions between fertilizer type, soil parent rock, forest growth,

and mortality using an extensive set of forest fertilization

trials (for example, Hayek and others 1999; Garrison and

Moore 1998; Mika and Moore 1990). Soil parent rock has also

been related to mortality and basal area increment (Shen

and others 2001, 2000) in models closely related to those

used in the Prognosis Model (Wykoff and others 1982). These

studies suggest inclusion of soil parent rock may be a useful

operational revision to Prognosis.

Lithology is the branch of geology concerned with the

study of the properties of rock. This paper presents an

overview of issues involved in using the lithology of soil

parent rock as a predictor, and a pilot study that involved

incorporating lithology into the Prognosis large-tree basal

area increment model.

Incorporating Soil Parent Material into

Prognosis

Robert E. Froese

Andrew P. Robinson

Link Between Lithology and Tree

Growth ________________________

Five factors define soil attributes: time, climate, biota,

topography, and parent material (see for example Buol and

others 1989). Parent material is important because the

mineralogy of parent rock defines the potential supply of

most mineral nutrients used by plants, with nitrogen being

the most notable of several exceptions (Garrison and Moore

1998). The physical properties of parent material are also

important because they define rock weatherability. In turn,

weatherability affects the rate of nutrient release as well as

physical properties of soil particles that affect nutrient and

moisture holding capacity.

Classification systems are used to categorize the expected

particle size and nutrient status of a soil derived from a given

parent material. Results for a relatively simple classification developed by the Intermountain Forest Tree Nutrition

Cooperative (IFTNC) are presented in table 1. Though

granites have moderate mineral nutrient contents, they

weather to sandy soils, which have poor moisture and

nutrient holding capacity, and thus produce soils of medium

to low fertility. In contrast, basalts weather to soils that are

richer in clay minerals and produce soils with high nutrient

holding capacity (Buol and others 1989). Metamorphosed

sedimentary rocks (metasediments) weather to soils with

low nutrient holding capacity and low nutrient content,

producing soils of generally poor fertility (Garrison and

Moore 1998). Thus, soil parent material may be a useful

general predictor of the inherent fertility of soils.

Unfortunately, precisely identifying soil parent material

at a given location is problematic. Parent material may be

field sampled, but this is inefficient at operational scales and

is perhaps impossible where soils are developed from complex mixtures of parent material, such as glacial till, fluvial

deposits, or loess. Imputing parent material using geologic

maps of bedrock lithology is a practical solution, though

again, where soils are not developed in situ but rather from

transported materials, underlying bedrock is obviously not

representative of soil parent material. For these and other

reasons, the use of bedrock lithology map data is not straightforward.

Using Lithology _________________

Technical difficulties complicate geological mapping. For

example, vegetation, water, surficial deposits, and soil development complicate the identification of underlying lithology. Surficial deposits, such as loess, glacial till, or alluvium,

may be deep, making identification of underlying rock diffi-
136 USDA Forest Service Proceedings RMRS-P-25. 2002

Froese and Robinson Incorporating Soil Parent Material into Prognosis

cult. In some situations, bedrock lithology maps show surficial

deposits themselves as the lithologic unit. Objective and

absolute standards for classification are difficult even when

bedrock is visible, because the composition, age, and process

of formation of rocks may be extremely variable.

Geologic maps, and specifically maps of bedrock lithology,

can be based on different classification schemes that reflect

the biases of the mapping geologist or differences in emphasis that relate to intended use of the resulting data. For

example, rocks may be classified based on their geochemistry, their petrography, resistance to weathering, or on the

process that formed the rock; the latter are the familiar

“igneous,” “sedimentary,” and “metamorphic” processes.

Thus, geologists commonly provide detailed descriptions

that include various degrees of all these characteristics. The

objective, perspective, and bias of the mapmaker are not

necessarily clear but have a significant impact on the maps

produced.

Systems of classification that are meaningful to geologists

are likely suboptimal for modelling forest growth. Interpreting and redefining a geological system to make it useful is

challenging for geologists, who are unfamiliar with the

language and science of forest growth, and conversely for

modellers and physiologists who are unfamiliar with the

language and science of geology. For example, how is a

“metamorphosed, siliciclastic sedimentary rock” related to

potassium available to trees at a given forest plot? Descriptive geological classifications need to be reduced to a set of

lithologic classes that are simple enough that they can be

understood and analysed by modellers but retain as much

information as possible so the effect of the information can

be explored. Merging geology and forestry is a challenge in

interdisciplinary science.

Research on the effects of lithology on forest dynamics has

employed inconsistent approaches, as little precedent exists. Results have nonetheless demonstrated clear and statistically significant influences of parent material on forest

dynamics that appear to be the consequence of nutrient

Table 1—Expected soil nutrient status with respect to characteristics of different parent rock

types and subclasses common to the Inland Empire. After Garrison and Moore

(1998). Used with permission.

Potassium Expected soil Expected soil

Lithology status particle size nutrient status

Igneous

 Plutonic (granites) medium coarse – sandy medium to low

 Volcanic (basalts) good fine – clayey high

Metamorphic

 Schists medium fine – silty medium to low

 Gneisses poor coarse – sandy medium to low

 Metasediments poor variable low

Mixed

 Glacial till medium variable variable

 Loess medium to poor fine – silty medium to high

Sedimentary

 Shales medium to poor fine – clayey medium to low

 Sandstones medium to poor variable variable

 Carbonates poor variable poor

supply (for example Shen and others 2000; Garrison and

Moore 1998; Hamilton 1998). These results were achieved

using relatively simple systems of lithologic classification,

suggesting that further study may reveal opportunities for

improvement. Possibilities may be to reduce:

1. Inconsistency – geologic units were aggregated into

types sometimes defined by parent material attributes, and

sometimes by soil properties (for example Hamilton 1998).

2. Generality – two to five qualitative classes were used to

define lithology (for example Shen and others 2000) or soil

nutrient status (for example Garrison and Moore 1998).

3. Uncertainty – hypotheses about causal factors may be

tested by examination against a more extensive data set.

A greater understanding of functional relationships between stand dynamics and lithology would guide efficient

classification schemes, bolster the credibility of model products, and guide extrapolation to conditions outside of the

range of the data used in model development (for example

Hamilton 1990) by increasing biological and physical credibility (Rykiel 1996).

Why Not Use Soils? _____________

Soil properties associated with lithology would ideally be

obtained directly from soil maps. Unfortunately, high-quality soils data for Northern Rocky Mountain forestlands are

inconsistently available. The Natural Resources Conservation Service Soil Survey Geographic Database (SSURGO) at

present covers little of the National Forest area in northern

Idaho and north-western Montana. Because most data suitable for fitting Prognosis growth equations in the Northern

Rocky Mountains are obtained from National Forests, using

SSURGO data would be impossible. Using soil maps would

also involve many of the same difficulties that apply to

lithology, including the need to aggregate to a manageable

set of classes and the possibility that a classification scheme
USDA Forest Service Proceedings RMRS-P-25. 2002 137

Incorporating Soil Parent Material into Prognosis Froese and Robinson

Then remaining classes were grouped with the most similar

founding members, based on minimizing the increase in

residual sums of squares.

Data and Methodology ___________

Forest Plots

Data from the USDA Forest Service Region 1 permanent

plot program were available at the time of the pilot study.

This program is a network of plot clusters installed in

treated stands across National Forests in Region 1, which

includes central and northern Idaho as well as Montana

portions of the overall study area. Plot establishment

began in the early 1980s, with a target measurement cycle

of 5 years; thus, up to three remeasurements were available

on some plots. Measurements in a given stand comprise

three or more plot clusters installed in treated areas, paired

with a single control cluster in an untreated area. Three

measurement plots are included in each cluster. Region 1 is

in the process of cleaning and certifying the permanent plot

database, and clean data were only available for the Idaho

Panhandle and Flathead National Forests, which restricted

the geographic extent of the exercise. Only data for the four

species most abundant in the database were used in the

analysis: Douglas-fir (Pseudotsuga menziesii), grand fir (Abies

grandis), western larch (Larix occidentalis), and lodgepole

pine (Pinus contorta).

Lithology

The USGS preliminary lithology map covered the northern Idaho portions of the Bonners Ferry, Sandpoint,

Coeur d’Alene, St. Maries, Thompson Falls, and Wallace

1-by-2 degree quadrangles. The total coverage was approximately 60 percent of the entire study area. Lithologic diversity within the map was extensive, including

784 unique lithologic units and nearly 37,000 individual

polygons. Permanent plot locations were intersected with

the USGS digital lithology map to impute lithology at

plot locations.

Model

The Prognosis large tree basal area model formulation

described by Wykoff (1990, 1986) was the base model used in

the analysis. This model already uses the Wykoff t-test

matrix algorithm to aggregate habitat type and location

effects on the model intercept and as interactions with some

predictors. Because the objectives of this study were to

explore aggregation techniques themselves and reveal patterns in the dataset related to lithology, a modified version

of the model was specified to focus on these questions. This

version, in which mean annual precipitation was substituted for habitat and location, was used principally in the

analysis. The Wykoff (1990) formulation was subsequently

fit, applying aggregation techniques to lithology only, to

examine whether using the modified version affected the

results. In the modified model, mean annual precipitation is

a continuous variable and was imputed at each plot from 2

km digital precipitation grids. Precipitation and lithology

deemed optimal for soil scientists is substantially suboptimal for modelling tree growth. Indeed, studies of the relationship between soils and forest productivity are not rare in

the literature, and despite extensive analytical effort generally produce weak or poorly generalizable results (see

Monserud and others 1990 or Kayahara 1989 for an extensive review).

Pilot Study _____________________

The objectives were to fit the Prognosis large tree basal

area increment model, using lithology as a new predictor,

and explore ways to aggregate the large set of lithology units

present in the preliminary map into a smaller set that would

be appropriate for applying the model in management. We

hypothesized that aggregation would improve model fit and

reveal patterns in the data that were geologically and

biologically meaningful. This hypothesis was tested in two

ways. Aggregated units were compared for consistency across

species and against a priori hierarchies of geologic similarity

suggested by first principles expectation of soil properties.

Final models fit using different aggregation methodologies

were also tested for quality of fit using Akaike Information

Criterion (AIC), which is a model-independent metric of

quality fit. This exercise used a digital lithology map generated by the U.S. Geological Survey (USGS), which was

merged with the database of forest plots.

Aggregation Methodologies_______

Developing objective criteria for aggregating across classification variables is not a new problem in forestry. Most

literature concerns growth modelling in tropical forests,

where an often-overwhelming number of tree species necessitates the reduction into fewer groups for purely technical

reasons (for example, Alder and others 2000; Kohler and

Huth 1998; Leech and others 1991; Vanclay 1991). Aggregation methodologies generally follow one of two approaches:

subjective (concept-based) or objective (data-based). Concept-based grouping may be either based entirely on

a priori beliefs about function or combined with statistical

techniques that suggest initial groupings (Alder and others

2000; Kohler and Huth 1998; Leech and others 1991). A

common theme to each of these three examples is that they

allow or require a predetermined number of classes to be set

as an initial step in the methodology.

Data-based grouping emphasizes the use of statistical

techniques to objectively elucidate structure in the classification variable (for example, Vanclay 1991; Meldahl and

others 1985; Wykoff and others 1982). Wykoff and others

(1982) aggregated diverse ecosystem units into similar sets

based on “statistical similarity,” defined as groups where

none of the estimated coefficients that are grouped into a

class differs from any other at the 50 percent level of

significance. Wykoff’s technique attempts to aggregate only

intercept effects, fit uniquely by group, rather than the

entire model. Vanclay (1991) introduced arguably the most

objective methodology. Classes were sorted by decreasing

order of data set size, and whole model F-tests conducted

recursively sensu Leech and others (1991) to identify “founding classes” that were statistically different from all others.
138 USDA Forest Service Proceedings RMRS-P-25. 2002

Froese and Robinson Incorporating Soil Parent Material into Prognosis

were introduced simply as linear terms and not as interactions with any other variables.

Aggregation Schemes

Six aggregation schemes were tested:

1. Null — the model is fit without separate intercepts,

effectively combining the effect of all lithology classes in the

intercept.

2. Full — the model is fit with separate intercepts for all

lithology classes.

3. Shen — set of five classes (granite, basalt, metasediment,

sediment, mixed) from Shen and others (2000).

4. IFTNC — the USGS classes are interpreted following

table 1 and placed into classes “good,” “medium,” and “poor.”

5. Wykoff and others (1982) t-test matrix.

6. Vanclay (1991) paired F-test method.

Schemes 1 through 4 were implemented by introducing a

class variable in standard linear regression functions in

S-Plus, Version 6. The effect is to estimate parallel functions

with separate intercepts for the class variables. The final

two schemes were implemented by writing S-Plus scripts

that coded their respective algorithms.

Results ________________________

Over 45,000 individual tree observations were available

for model fitting after processing to generate the variables

used in the model. However, since up to three successive

measurements were made in some cases, only 124 unique

stands were represented in the data (table 2). The sample of

stands was distributed unevenly across the 14 lithology

classes. In terms of the three broad geologic types: eight were

granitic (6 percent), 78 were metamorphic (63 percent), and

the remaining 38 were fluvial (31 percent). Nine of the 14

classes (64 percent) were represented by seven or fewer

stands; three classes were represented by a single stand. The

four species most prevalent in the data set were used for

analysis (table 2).

Of the six aggregation methods, all but the Vanclay and

Wykoff methods were completely determined a priori and

were by definition constant across species. The Shen and

IFTNC systems show some correspondence (table 3), mostly

because of their simplicity. For example, metasedimentary

types are all rated “poor” in the IFTNC system. However, the

data contained only two IFTNC types; no units classified as

“good” were present. The Wykoff approach, which tended to

produce groups with only two or rarely three members,

produced more classes than Vanclay method (tables 3, 4).

Aggregated groups of up to five members were produced

with the Vanclay method. Results from both the Wykoff and

Vanclay algorithms were inconsistent across species, especially in comparison to the other aggregation methods.

Consider the metasedimentary types, which are grouped

together in both the Shen and IFTNC systems. In the Wykoff

approach, none of the five metasedimentary types were

grouped for any species, with the exception of two classes

aggregated for grand fir. Results were only marginally more

consistent using the Vanclay method. The same two classes

were aggregated for grand fir, and three of five classes

aggregated for lodgepole pine. Otherwise, few patterns are

apparent. Notably, every set aggregated under the Wykoff

system was also aggregated under the Vanclay system with

one exception, though not vice versa.

Comparing models by AIC showed that generally a greater

the number of classes resulted in a better fit (table 5). Only

a minor improvement resulted from using any of the aggregation schemes compared to using all classes. The lowest

AIC, and therefore presumably best model, was that developed with the Vanclay approach for Douglas-fir and grand

fir, and the Wykoff approach for lodgepole pine and western

larch.

Table 2—Summary of parent material classes from USGS lithology map by stand and tree species in the fitting data.

Domlith No. No. trees

USGS mapped dominant lithology code stands DF1 GF L LP

glacial sediments2 glac1 7 325 84 923 1531

glaciofluvial glac2 21 436 10 1588 4104

granitoid gran2 6 365 231 180 497

granodiorite gran3 2 329 0 46 7

meta-claystone meta1 1 28 4 22 2

metamorphosed siliciclastic sedimentary rocks meta11 14 772 695 470 180

meta-mudrock or “shale” (>50% silt and clay) meta3 17 1146 599 687 1125

metamorphosed carbonate/siliciclastic sedimentary meta7 4 191 24 396 150

metamorphosed siliciclastic/carbonate sedimentary meta8 29 2774 1703 963 1144

mixed metamorphic/igneous plutonic mixe3 1 12 0 286 181

natural unconsolidated sediments natu 14 857 545 1328 130

older alluvium olde 1 14 237 0 0

semi-pelitic rocks semi 3 83 116 41 43

unconsolidated sediments unco 4 22 164 0 518

totals 124 7354 4412 6930 9612

1

 DF = Douglas-fir; GF = grand fir; L = western larch; LP = lodgepole pine.

2 Though the class “glacial sediments” and some others here are strictly geomorphologic and not lithologic descriptors, they are the descriptive terms assigned to the

bedrock lithology polygon by the USGS and are used accordingly.
USDA Forest Service Proceedings RMRS-P-25. 2002 139

Incorporating Soil Parent Material into Prognosis Froese and Robinson

Table 5—Akaike Information Criterion values, by species, for

models fit using different aggregation methodologies.

Model DF1 GF L LP

base 16320.1 10039.4 15505.8 22101.4

all 15934.1 9759.7 15084.2 21437.0

Shen 16076.1 9977.7 15378.7 21767.6

IFTNC 16143.4 9975.9 15407.8 21871.0

Vanclay 15920.5 9751.8 15094.8 21431.1

Wykoff 15924.7 9752.4 15082.2 21427.7

1

DF = Douglas-fir; GF = grand fir; L = western larch; LP = lodgepole pine.

Table 3—Results of aggregating classes using the Shen, IFTNC and

Wykoff methods. Units with the same name or number are

grouped together.

Domlith

code Shen IFTNC DF1 GF L LP

glac1 mixed medium 1 1 1 1

glac2 mixed medium 2 2 2 2

gran2 granite medium 3 3 3 3

gran3 granite medium 4 1 4

meta1 metased poor 1 4 4 5

meta11 metased poor 5 5 5 6

meta3 metased poor 2 5 6 3

meta7 metased poor 6 3 7 2

meta8 metased poor 7 6 8 7

mixe3 granite medium 8 9 5

natu mixed medium 9 7 10 3

olde mixed medium 6 8

semi metased poor 4 8 11 8

unco mixed medium 6 5 4

1

DF = Douglas-fir; GF = grand fir; L = western larch; LP = lodgepole pine.

Table 4—Results of aggregating classes using the Shen, IFTNC, and

Vanclay methods. Units with the same name or number are

grouped together.

Domlith

code Shen IFTNC DF1 GF L LP

glac1 mixed medium 3 5 1 1

glac2 mixed medium 1 4 1 1

gran2 granite medium 2 3 5 3

gran3 granite medium 2 3 4

meta1 metased poor 3 5 5 3

meta11 metased poor 2 2 1 3

meta3 metased poor 1 2 3 3

meta7 metased poor 4 3 5 1

meta8 metased poor 1 1 2 2

mixe3 granite medium 3 4 3

natu mixed medium 1 3 1 3

olde mixed medium 4 4

semi metased poor 2 4 5 2

unco mixed medium 4 2 4

1

DF = Douglas-fir; GF = grand fir; L = western larch; LP = lodgepole pine.

Similar results are produced when the Wykoff (1990) base

model is used instead of the modified model, which substitutes precipitation for habitat type and location. Overall,

when the Wykoff (1990) base model is used, AIC values are

1 to 3 percent lower than under the modified model, but the

relative ranking of aggregation methodologies is nearly

identical. In either case, within and across species few

patterns are apparent in the grouping of lithology classes by

the Vanclay or Wykoff methods, and little correspondence is

found between the final result for both methods using either

base model.

Discussion _____________________

Data-based and concept-based approaches to aggregating

classification variables have different strengths. Objective

statistical techniques may reduce the potential for bias as

well help elucidate the correct interpretation of the functional relationship between class variables and the modelled

system. This is most useful when little is known about the

relationship in advance. Yet when knowledge about function

is available, schemes that incorporate knowledge may be

superior to statistical techniques that produce some hierarchies that are an artifact of random data patterns (Vanclay

1991).

Our goal was to test the hypothesis that biologically

meaningful patterns could be discerned. The results are not

encouraging. This is principally because data-based techniques have suggested groupings that are not biologically

sensible, and because groupings that are biologically or

geologically sensible were not useful in modelling. If lithology is in fact not related to tree growth, or our interpretation

of what is biologically sensible is inappropriate, this result

is not surprising. Nonetheless, the analysis was limited by

a data set that may not contain a sufficient breadth of

information to discern meaningful relationships. A larger

data set would include more lithologic diversity, and independent data would permit a more extensive evaluation.

Data structure is a better explanation for the results than

causal links between lithology groupings and tree growth for

this data set. Any signs of model improvement through the

addition of lithology may be due more to overfitting than a

true increase in explanatory power. This is evidenced by the

chaotic aggregation results from the data-based techniques,

which are at odds with even the simplest conceptual expectations, and inconsistent results across species, aggregation

method, and underlying base model formulation. Lithology

may be acting as a proxy for unmeasured site values, given

the poor data distribution across lithology classes, in some

cases with a class represented by a single site. Again,

model development and testing using a broader data set is

necessary.

The results do offer some insights into the tradeoffs

between aggregation methodologies. The Wykoff and Vanclay

data-based techniques were able to identify similar data

patterns, albeit only within single species. AIC comparisons

suggest that the two techniques produce models of similar

quality. However, the Wykoff technique allows for the final

decision to aggregate to be made subjectively, which may be

desirable especially where some a priori knowledge is available to guide modelling. Vanclay’s technique, which was
140 USDA Forest Service Proceedings RMRS-P-25. 2002

Froese and Robinson Incorporating Soil Parent Material into Prognosis

initially proposed for modelling of tropical species, would

have an advantage where the number of groups was overwhelming or where little information is available in advance

that could guide subjective decisionmaking.

A key objective of the pilot study was to produce some

results that could guide and refine future work, and to this

end the study has been useful. Clearly a broader data set

with more lithologic diversity is needed. Lithologic units

were also simplistic; greater diversity should include descriptions and information that are useful for linking lithology conceptually to soil properties and tree growth.

Acknowledgments ______________

This research was supported in part by a research joint

venture agreement (USDA FS RMRS-99541-RJVA) between

the USDA Forest Service Rocky Mountain Research Station

and the University of Idaho Forest Biometrics Laboratory.

We thank Nick Crookston, Bob Havis, Dennis Ferguson, and

Bill Wykoff, all of the USDA Forest Service, for helpful

review and comments. Our special thanks go to Bill Wykoff

for his patient, durable support of Prognosis work at the

Forest Biometrics Lab.

References _____________________

Alder, D., F. Oavika, M. Sanchez, J. N. M. Silva and H. L. Wright.

2000. A method for calibrating growth models for tropical moist

forest with minimal data. Submitted to For. Ecol. Mgmt (July

2001 - in review).

Buol, S. W., F. D. Hole and R. J. McCracken. 1989. Soil genesis and

classification, 3rd edition. Iowa State University Press, Ames.

Garrison, M. T. and J. A. Moore. 1998. Nutrient management: a

summary and review. Int. For. Tree Nut. Coop. Sup. Rep. 98-5.

Univ. Idaho, Moscow.

Garrison-Johnston, M. T. and J. A. Moore. 2001. North Idaho

nutrition guidelines by rock type: Version 1.0. Int. For. Tree Nut.

Coop., Univ. Idaho, Moscow.

Garrison-Johnston, M. T., J. A. Moore and G. J. Niehoff. 2001.

Douglas-fir outbreaks are associated with certain rock types in

the inland Northwestern United States. Int. For. Tree Nut. Coop.

draft manuscript, July 20, 2001. Univ. Idaho, Moscow.

Hamilton, D. 1990. Extending the range of applicability of an

individual tree mortality model Can. J. For. Res. 20: 1212-1218.

Hamilton, D. 1998. Prognosis (FVS) yield simulations using new

individual tree mortality prediction models that include geologic

classification variables. Presentation Int. For. Tree Nut. Coop.

1998 Ann. Meet.

Hayek, J. C., J. A. Moore and J. M. Mandzak. 1999. Multi-nutrient

fertilizer response differs by rock type in central Idaho forests.

Int. For. Tree Nut. Coop. Sup. Rep. No. 4. Univ. Idaho, Moscow.

Kayahara, G. 1989. The relationship of forest site factors and

derived site units to forest productivity: a literature review with

commentary. B.C. Min. For. Cont. Rep., Vancouver, B.C.

Kohler, P., and A. Huth. 1998. The effects of tree species grouping

in tropical rainforest modelling: simulations with the individualbased model FORMIND. Ecol. Model. 109: 301-321.

Leech, J. W., R. L. Correll and A. K. Myint. 1991. Use of principalcoordinate analysis to assist in aggregating species for volumetable construction. For. Ecol. Mgmt. 40: 279-288.

Meldahl, R. S., M. Eriksson and C. E. Thomas. 1985. A method for

grouping species – forest type combinations for the development

of growth models for mixed species stands. In: Proceedings of the

3rd Biennial Southern Silvicultural Research Conference, 7-8

Nov. 1984, Atlanta, GA. Edited by E. Shoulders. USDA For. Serv.

Gen. Tech. Rep. SO-54. pp. 422-428.

Mika, P. G. and J. A. Moore. 1990. Foliar potassium status explains

Douglas-fir response to nitrogen fertilization in the Inland Northwest, USA. Water, Air and Soil Poll. 54: 477-491.

Monserud, R. A., U. Moody and D. W. Breuer. 1990. A soil-site study

for inland Douglas-fir. Can. J. For. Res. 20: 686-695.

Rykiel, E. J. Jr. 1996. Testing ecological models: the meaning of

validation. Ecol. Model. 90: 229-244.

Shen, G., J. A. Moore and C. R. Hatch. 2001. The effect of nitrogen

fertilization, rock type, and habitat type on individual tree mortality. For. Sci. 47: 203-213.

Shen, G., J. A. Moore and C. R. Hatch. 2000. The effect of habitat

type and rock type on individual tree basal area growth response

to nitrogen fertilization. Can. J. For. Res. 30: 613-623.

Vanclay, J. K. 1991. Aggregating tree species to develop diameter

increment equations for tropical rainforests. For. Ecol. Mgmt. 42:

143-168.

Wykoff, W. R. 1990. A basal increment model for individual conifers

in the Northern Rocky mountains. For. Sci. 36: 1077-1104.

Wykoff, W. R. 1986. Supplement to the user’s guide for the stand

prognosis model: Version 5.0. USDA For. Serv. Gen. Tech. Rep.

INT-218.

Wykoff, W. R., N. L. Crookston and A. R. Stage. 1982. User’s guide

to the Stand Prognosis Model. USDA For. Serv. Gen. Tech. Rep.

INT-133.
USDA Forest Service Proceedings RMRS-P-25. 2002 141

In: Crookston, Nicholas L.; Havis, R. N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Kelsey S. Milner is Professor of Forestry, University of Montana, Missoula

59812. Dean W. Coble is Assistant Professor of Forestry at Stephen F. Austin

State University, Nacogdoches, TX 75962. Eric L. Smith is Program Manager

and Drew J. McMahan is Ecologist at the Natural Resources Research Center,

Fort Collins, CO 80526-1891.

Abstract—This paper describes the physiological model, StandBGC, and how it was linked to the Forest Vegetation Simulator

(FVS) base code as a system extension. Using a set of Stand-BGC

keywords, a user can invoke the extension in an FVS simulation and

grow a stand using the physiological growth engine. A daily climate

file and information on soil depth and texture must be provided.

Understory vegetation can be input via keywords or by running the

Cover extension. When the Stand-BGC extension is running, all

vegetation is grown so that grass and shrubs directly impact tree

growth. Regardless of the growth engine selected, standard FVS

formatted output is produced. The Stand-BGC extension also generates various reports containing elements of the water and carbon

balance calculations at tree and stand levels at daily and yearly time

intervals. The Stand-BGC extension may be roughly calibrated to

FVS through user-controlled multipliers on annual precipitation

and canopy photosynthesis. After calibrating Stand-BGC so that

predicted top height after 30 years matched that from FVS, the two

growth engines produced similar tree and stand attributes. The

daily and yearly tree and stand level physiological variables produced should be useful in describing ecosystem function and for

driving other models requiring higher resolution and climate sensitive inputs. Results suggest that more sophisticated communication

between the two types of models is possible.

Ecosystem management, by definition, requires information on ecosystem processes. Generally, the current suite of

mensurational models in common use by forest management organizations are not constructed to provide such

information. Models such as the Forest Vegetation Simulator (FVS) simulate tree and stand dynamics through statistical correlation of the results of underlying processes that

operate at higher temporal and spatial resolutions. These

underlying processes are driven by factors not relevant at

the resolution of such a biometrical model. Thus, our forest

management models are insensitive to climate and other

site variables at the resolutions needed to assess ecosystem

functions.

FVSBGC: A Dual Resolution Hybrid Model

with Physiological and Biometrical Growth

Engines

Kelsey S. Milner

Dean W. Coble

Eric L. Smith

Drew J. McMahan

In the past decade or so, physiological models, such as

Stand-BGC (Milner and Coble 1995), have been developed

that explicitly represent some of these underlying ecosystem

processes at the higher resolutions needed. There is, of

course, still plenty of empiricism; it just exists at a higher

level of resolution in the hierarchical system. However, with

this higher resolution comes the increased difficulty of

simulating reliable system behavior at the resolutions useful to forest management. The underlying processes modeled are still approximations, and the complex linkages

within and between scales are not well understood. For

many forest management needs, it is perhaps a truism that

a physiological model will never be able to achieve the

prediction accuracy of a well-defined, biometrical model fit

to adequate data. Moreover, physiological models are generally designed neither to process the kinds of data collected in

forest inventories nor to simulate the activities that need to

be analyzed in forest management.

The challenge then is to combine the two types of models

into one system that contains the strengths of both types of

models. The work described in this paper is an attempt to

achieve such a linkage between FVS and Stand-BGC.

Stand-BGC Description __________

Stand-BGC (Milner and Coble 1995) is an individual

entity, distance independent, version of the whole stand

physiological model, Forest-BGC (Running and Coughlan

1988). The term “entity” is used because grasses and shrubs

are input and grown, as whole stand entities, right along

with individual tree entities. As with Forest-BGC, StandBGC is a climate-driven, carbon and water balance model

that operates at a daily resolution for basic production

processes and at an annual resolution for carbon allocation.

Unlike Forest-BGC, Stand-BGC scales the “big leaf” level

physiology to individual entity subcanopies rather than to

the whole stand. Combined with stand structural information, the resulting interacting subcanopies provide the mechanism for competition for site resources among entities.

Details of the basic physiological modeling are contained in

papers by Running and Gower (1991) and Running and

Hunt (1993) and will not be repeated here.

Like FVS, Stand-BGC was designed to process tree lists

from standard forest inventories. In addition to standard

tree measurements such as height, diameter, and crown

ratio, cover and canopy depth of understory vegetation, by

lifeform or species, should be provided. A daily climate file
142 USDA Forest Service Proceedings RMRS-P-25. 2001

Milner, Coble, Smith, and McMahan FVSBGC: A Dual Resolution Hybrid Model with Physiological and Biometrical Growth Engines

containing maximum and minimum temperatures, relative

humidity, solar insolation, and atmospheric transmissivity

must be supplied. Soil depth and texture must also be

supplied. A default set of physiological parameters is provided that may be altered by the user.

Stand dynamics result from competition for site resources

(light and water) among entities. The differential access to

these resources arises from how water and light are modeled

and from assumptions as to how different lifeforms acquire

each resource. Based on the input vegetation list, foliar

biomass equations, and assumptions about the geometry of

entity crowns, leaf area index for any subcanopy of an entity

can be calculated. The vertical position of any subcanopy,

relative to those of its neighbors is also known (fig. 1). Either

a fixed distance (user defined), or one defined by entity tops

and crown bases, is used to define the subcanopy layers

through which light will pass. Individual entities then absorb light according to a subcanopy’s LAI based on a Beer’s

Law formulation (fig. 2). The presence and amount of leaf

area in canopy zones above a particular entity directly affect

its access to the light resource. Thus, there is shading within

and between canopies.

Entities access water according to their rooting depth and

the size of their “water bucket” (fig. 3). The latter is proportional to the amount of leaf area an entity supports. Precipitation events provide water to the topsoil layer first. If and

when the top layer’s water holding capacity is exceeded,

water moves to the lower soil layer. If both layers are filled,

the excess leaves the site as overland flow. Entity subcanopies

transpire from bottom to top, sequentially removing water

from the entity’s water bucket. Small trees and understory

vegetation are limited to the top soil layer of their water

buckets, while large trees tap water in either layer according

to which has the least negative water potential. At the end

of each day, a site water balance is calculated for each soil

layer, and all water buckets equilibrate to the site water

potential. Thus, large trees dominate the site water balance. Small trees compete with understory vegetation for

water in the top layer until they become “large,” at which

time they access water in the lower soil layer. Competition

intensifies when droughty conditions exist. Different “leafon” and “leaf-off” dates, transpiration rate parameters, and

stomatal conductance parameters also influence realized

competition.

Figure 1—Schematic showing how stand canopy

structure is abstracted. Individual tree crowns are

right circular cones. Crowns for per hectare grass and

shrub entities are cylinders. Foliar biomass is assumed to be uniformly distributed in the crown volume.

Figure 2—The light absorption model in Stand-BGC. A

Beer’s Law calculation provides the solar radiation that

drives photosynthesis and transpiration at the subcanopy

level.

Figure 3—The water access model in Stand-BGC. Large

trees (greater than 2 m in height) can access water in either

soil layer. Small trees and herbaceous vegetation are restricted to water in the top layer.
USDA Forest Service Proceedings RMRS-P-25. 2001 143

FVSBGC: A Dual Resolution Hybrid Model with Physiological and Biometrical Growth Engines Milner, Coble, Smith, and McMahan

At the end of each year, a carbon balance is calculated for

each entity, and carbon in excess of respiration and turnover

is allocated to leaf, stem (including branches), and roots

using the approach described by Running and Gower (1995).

Leaves and roots receive their allocation first, based on

accumulated moisture stress during the year and some

constraining allometric ratios. Whatever is left over is allocated to stems. In dry years, more carbon is allocated to roots

and less to leaves.

Following carbon allocation, entity dimensions are updated. For trees this means converting the stem carbon

allocation to diameter and height growth. After converting

carbon mass increment to wood volume increment, a volume

equation and a height/diameter equation are used to change

height and diameter (fig. 4). For understory entities, percent

canopy cover and canopy depth are updated by inverting the

appropriate biomass equations. When site resources are

plentiful, entities have carbon in excess of turnover and

maintenance respiration costs and growth occurs. When

total stand leaf area approaches site capacity (hydrologic

equilibrium), competition for resources intensifies. Those

entities of lesser social status cannot access sufficient resources to balance turnover and respiration, and their leaf

area declines. Mortality ultimately occurs when leaf area

goes to zero. Currently, an entity dies and is removed from

the projection when respiration costs exceed net canopy

photosynthesis for more than 3 years successive.

Output from Stand-BGC currently includes an updated

entity list annually along with attributes of the carbon and

water balance calculations at entity and stand levels at daily

and annual intervals.

Model Linkage __________________

We linked Stand-BGC to FVS as a system extension to the

base FVS code (Milner and others 2002; McMahan and

others 2002). Thus, all variants can access the Stand-BGC

extension. Users invoke and control the extension through a

set of FVS keywords. The linkage is illustrated in figure 5.

When a user invokes Stand-BGC as part of an FVS run, FVS

initializes Stand-BGC with its tree list at the start of the

projection. Each model then grows the tree list forward

independently: FVS at about 10-year growth steps, StandBGC at daily and yearly time steps. Stand-BGC increments

are aggregated to the cycle length specified for the FVS

projection. At each FVS cycle boundary, the models can

exchange growth increments, mortality, and change in crown

ratio. If the user has specified, via keyword, that FVS should

use the Stand-BGC increments, the FVS tree list is updated

with these increments. Otherwise the tree list is updated

with FVS increments. Any scheduled FVS management

activities are implemented and the updated tree list, which

also contains any new trees from the FVS Regeneration

Model, is passed from FVS to Stand-BGC. Each model

generates its standard suite of reports and the next cycle

begins.

If the user has specified that Stand-BGC increments are

to be used in FVS, only the new trees from FVS are processed

into the carbon pools needed by Stand-BGC. The end of cycle

carbon pools for the trees remaining after any thinnings in

FVS are already present in Stand-BGC. Thus, the StandFigure 4—Procedure for updating tree height and diameter from the volume increment predicted by Stand-BGC.

Figure 5—Schematic of the linkage between StandBGC and FVS. FVS initializes Stand-BGC at the start of

a simulation and at FVS cycle boundaries. The user

may choose to have growth and mortality predicted by

FVS or Stand-BGC.

BGC output is identical to that from a stand-alone StandBGC projection of the initial tree list. The standard FVS

reports show stand dynamics and growth and yield information as usual; the trees have just been grown using the

climate-driven physiological growth engine.

If the user decides to use the FVS increments, the FVS

output is unaffected by Stand-BGC. Stand-BGC is completely reinitialized at the start of each FVS cycle, and the

Tree Stem Increments

• v = starting cvts = f(h,d)

• V = new cvts = v + ∆v

• ∆v = from BGC

• D = d*10[log(V/v)/(c1*b2+b1)]

• H = h*10[log(V/v)/(b2+b1/c1)]

• bi = coefficients from

allometric H:D equation

• ci = coefficients from

cvts equation d

h H

D

∆v

FVS

Stand-BGC

FVS

Stand-BGC

Reports

Reports Management

Inventory

Climate

Soils

FVS-BGC Linkage

Cycle 0 Cycle 1

Keywords

Parameters
144 USDA Forest Service Proceedings RMRS-P-25. 2001

Milner, Coble, Smith, and McMahan FVSBGC: A Dual Resolution Hybrid Model with Physiological and Biometrical Growth Engines

end-of-cycle Stand-BGC output just provides a suite of

physiological variables for that FVS cycle.

An Example ____________________

An FVS tree list was prepared from plot data in an evenaged lodgepole pine stand in western Montana. The stand

had the following characteristics:

— QMD = 7.4 inches

— TPA = 302

— BA/acre = 90 square feet

— Top height = 45 feet

— Slope = 15 percent, aspect = NE, elevation = 3800 feet

— Habitat type = PSME/PHMA

— Grass cover = 30 percent

— Soil was a sandy loam, 1 meter deep

A 30-year climate file was prepared from a nearby valley

bottom weather station and extrapolated to the site using

the MT-CLIM climate model (Nemani 1988). The stand was

projected forward for 30 years with and without the StandBGC growth increments and mortality so the two models

could be compared. All Stand-BGC parameters were the

defaults. The model was calibrated to FVS by setting a

multiplier on net canopy photosynthesis so that top height

from Stand-BGC matched that from FVS after 30 years. For

these runs, the multiplier reduced photosynthesis by 10

percent. The Stand-BGC keywords for the simulation were

as follows:

Keyword Explanation

BGCIN Signals the start of Stand-BGC keywords

and invokes the Stand-BGC growth engine. Matched with an END keyword.

BGCGROW Indicates that Stand-BGC increments

are to replace FVS increments when growing trees.

VEGOPT 2 The keyword signals how understory vegetation is to be entered. A ‘1’ means

output from the COVER extension will

be used. A ‘2’ means vegetation data will

be entered in a supplemental record.

UNDERVEG Signals the start of vegetation data.

G gr 1.0 30 G = grass life form, gr = species label,

1.0 = grass canopy depth, 30 = percent

cover of grass.

ENDENT Signals the end of vegetation data.

END Signals the end of Stand-BGC keywords.

The summary yield tables from FVS, with and without the

Stand-BGC increments, for the 30-year projection of the

unthinned stand, are quite similar (table 1). The most

noticeable difference is in the amount and pattern of predicted mortality. FVS killed a few more trees, with the

mortality occurring in a gradual pattern; Stand-BGC killed

fewer trees but all in the same period.

A variety of physiological variables are produced by StandBGC so that a user can view the underlying processes. For

example, tables 2 and 3 show annual estimates of photosynthesis, respiration, and transpiration at stand and entity

levels, respectively. Other tables (not shown) are produced

that give these variables at daily resolutions, show light

absorption by canopy layer, provide details of the daily stand

and tree level water balance calculations, give estimates of

mortality, and show updated entity lists.

Discussion _____________________

Assuming that FVS provided a reliable forecast over the

30-year period, the fact that Stand-BGC can, with minimal

calibration, produce similar results, suggests that the underlying physiological processes may be reliably modeled as

Table 1—Summary statistics from FVSBGC using FVS vs. Stand-BGC

growth increments.

Year TPA BA/ac QMD Top Ht CVTS/ac

1984 302 90 7.4 45 1853

1994 285 111 8.5 50 2517

2004 269 132 9.5 57 3272

2014 251 148 10.4 61 3977

Summary statistics when using Stand-BGC Increment

Year TPA BA/ac QMD Top Ht CVTS/ac

1984 302 90 7.4 45 1853

1994 302 119 8.5 52 2807

2004 302 135 9.1 56 3437

2014 267 145 10 61 4051

Summary statistics when using FVS Increments

Table 2—Annual stand level processes from Stand-BGC

FVS Year Psn Trans Mresp Gresp Grass Tree Total Stem C

Cycle (KgC/ha) (m3/ha) (KgC/ha) (KgC/ha) LAI LAI LAI (tC/ha)

1 0 0 0 0 0 0.21 6.9 7.1 42.9

1 1 11843 1882 2997 3358 0.19 6.7 6.9 45.0

1 2 11604 1997 3063 3217 0.17 6.8 6.9 48.2

1 3 8318 1378 3055 2325 0.16 6.1 6.2 50.0

1 4 9831 1482 2921 2783 0.14 6.6 6.8 51.6

1 5 10820 1806 2788 3013 0.13 7.1 7.2 53.9

1 6 9088 1463 2780 2486 0.14 6.8 6.9 56.0

1 7 14271 2551 2978 4076 0.17 8.5 8.6 58.9

1 8 10876 1575 3411 3024 0.15 7.8 8.0 61.7

1 9 12628 1745 3454 3553 0.13 8.4 8.6 64.2

1 10 16429 2558 3219 4768 0.13 9.7 9.9 68.5

Annual stand-level output from Stand-BGC
USDA Forest Service Proceedings RMRS-P-25. 2001 145

FVSBGC: A Dual Resolution Hybrid Model with Physiological and Biometrical Growth Engines Milner, Coble, Smith, and McMahan

well. While significant work is needed to establish this

reliability for the Stand-BGC model, the possibilities are

clear. Such a linked model can provide a means for analyzing

impacts of climatic variation on forest growth and yield,

provide variables at resolutions perhaps more suitable for

driving pest models, and provide insights into the mechanisms of stand responses to thinning treatments.

The way in which FVS and Stand-BGC were linked in this

study is quite simple; tree lists and predicted increments are

merely passed back and forth. A more exciting approach

would be to have each of the two models contribute to one

combined forecast. The complete suite of state variables for

each model is available to the other at FVS cycle boundaries.

Thus, one can envision communication between models

ranging from creating weighted averages of some predicted

states (such as basal area increment) to alteration of underlying model parameters. For example, the strong dimensional relationships in FVS could be used to calibrate or

constrain carbon allocation fractions and leaf turnover rates

in Stand-BGC. Alternatively, the climate sensitive rates of

photosynthesis, respiration, and water stress could be used

to modify FVS mortality parameters.

References _____________________

Hungerford, R. D., R. R. Nemani, S. W. Running, and J.C. Coughlan.

1989. MTCLIM: A Mountain Microclimate Simulation Model.

Research Paper INT-414. Intermountain Research Station, Ogden,

Utah. 52 p.

McMahan, A.J., K.S. Milner, and E.L. Smith. 2002. FVS-BGC:

User’s Guide to Version 1.0. Forest Health Technology Enterprise

Team report FHTET 02-02. USDA Forest Service. Fort Collins

CO. 51 p.

Milner, K.S. and D.W. Coble. 1995. A mechanistic approach to

predicting the growth and yield of stands with complex structures. In: Uneven-aged management: opportunities, constraints,

and methodologies, K.S. O’Hara, ed., pp. 144-166. University of

Montana, Missoula.

Milner, K.S., D.W. Coble, E.L. Smith, and A.J. McMahan. 2002.

FVSBGC: A hybrid of the physiological model Stand-BGC and the

Forest Vegetation Simulator. Canadian Journal of Forestry Research.

Nemani, R.R. and S.W. Running. 1989. Testing a theoretical climate-soil-leaf area hydrologic equilibrium of forests using satellite data and ecosystem simulation. Agric. For. Meteorol. 44:245-

260.

Running, S.W. and J.C. Coughlan.. 1988. A general model of forest

ecosystem processes for regional applications. I. Hydrologic balance, canopy gas exchange and primary production processes.

Ecol. Modeling 42:125-154.

Running, S.W. and S.T. Gower. 1991. FOREST-BGC, A general

model of forest ecosystem processes for regional applications II.

Dynamic carbon allocation and nitrogen budgets. Tree physiology 9:147-160.

Running, S.W. and E.R. Hunt Jr. 1993. Generalization of a forest

ecosystem process model for other biomes, BIOME-BGC, and an

application for global-scale models. In: Scaling physiological

processes: Leaf to globe. Academic Press, New York, NY. pp. 141-

158.

Table 3—Annual entity level processes from Stand-BGC

FVS Year Entity SP Psn Transp Mr esp Gresp Leaf Stem Root Tur nover

Cycle (KgC) (m3) (KgC) (KgC) (KgC) (KgC) (KgC) (K gC )

1 0 1 Lpp 0.0 0.0 0.0 0.0 3.0 43.6 3.0 0.0

1 0 2 Lpp 0.0 0.0 0.0 0.0 1.7 23.2 1.7 0.0

1 0 3 Lpp 0.0 0.0 0.0 0.0 5.2 95.4 5.2 0.0

1 0 53 gr 0.0 0.0 0.0 0.0 85.3 0.0 85.3 0.0

1 1 1 Lpp 12.5 1.8 3.0 3.5 2.9 45.8 3.5 3.3

1 1 2 Lpp 6.9 0.9 1.9 1.9 1.6 24.4 1.9 1.8

1 1 3 Lpp 21.8 3.0 5.1 6.2 5.1 99.2 6.2 5.8

1 1 53 gr 163.9 95.0 315.8 0.0 76.7 0.0 42.6 51.2

1 10 1 Lpp 16.5 2.3 3.5 4.7 4.0 68.3 2.9 3.3

1 10 2 Lpp 6.9 0.8 2.0 1.9 1.7 33.2 1.2 1.4

1 10 3 Lpp 29.0 3.8 5.8 8.4 7.2 139.7 5.2 5.9

1 10 53 gr 94.1 39.7 45.0 28.1 53.6 0.0 11.3 17.2

Annual Entity level output from Stand-BGC
146 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Eric L. Smith is Quantitative Analysis Program Manager, Forest Health

Technology Enterprise Team, USDA Forest Service, 2150A Centre Avenue,

Fort Collins, CO 80521-1891. Andrew J. McMahan is a Systems Analyst and

Gary Wang is a Biometrician, INTECS International, Inc., c/o USDA Forest

Service, 2150A Centre Avenue, Fort Collins, CO 80521-1891.

Abstract—Tree stress and vigor affect, and are affected by, damage

agents such as insects and pathogens. Stand density and tree

growth rates serve as indicators of stress and vigor, but drought

events and other disturbances are also important components of

stress and vigor status. Site factors must also be considered when

relating stand density to stress. In this paper, the FVS-BGC hybrid

model has been used to analyze tree and stand vigor at different time

scales, under a number of hypothetical management, climate, and

site scenarios for lodgepole pine stands in the Central Rocky Mountains. Results suggest a quantifiable relationship between stand

density, soil moisture, and tree vigor that can be projected using the

FVS-BGC model. Example simulations using FVS-BGC relates

stand density, site, and precipitation with a stem growth/leaf area

vigor index. The timing of simulated moisture stress within a year

appears to be correlated with the mountain pine beetle flight period.

Results related to stand basal area appear to agree with management guideline to lower mountain pine beetle hazards. This approach may be useful in enhancing FVS insect and pathogen

extensions and has the potential to better explain the limiting

factors to growth for different sites and stand structures.

Consideration of insect and pathogen effects on stand

projections is a component of the Forest Vegetation Simulator (FVS) system (Wykoff and others 1982). Tree stress and

vigor affect, and are affected by, damage agents such as

insects and pathogens, so direct estimates of these biological

conditions could improve FVS projections. We use the term

“stress” to refer to conditions that inhibit normal tree functioning, and “vigor” to generally refer to the efficiency with

which a tree or stand is utilizing its resources.

Stress factors, such as limited soil moisture, affect basic

physiological processes such as photosynthesis and allocation. Although these processes can be difficult to track

individually, the net effects of these processes can be described in terms of growth efficiency, or vigor. In this study,

we analyze the relationships between two stress factors—

soil water availability and competition—and tree vigor. We

represent vigor in terms of an index proposed by Waring and

others (1980). Soil moisture availability is directly simuEco-Physiology Approach to Projecting Tree

Stress and Vigor in FVS

Eric L. Smith

Andrew J. McMahan

Gary Z. Wang

lated; competition is expressed in terms of stand density (as

basal area per acre).

Pine bark beetles are important forest mortality agents

that appear to be sensitive to vigor conditions. It has long

been held that tree “vigor” is an important factor in pinebark beetle interactions (Eaton 1941; Johnson 1951). Moisture conditions are often limiting in pine forests and are thus

a factor in determining tree and stand vigor (Berryman

1976). Stand moisture conditions are the result of site

conditions including soil texture and depth, and of interplant competition, damage from other agents, and climate

including variation in precipitation. Forest entomologists

have developed semiempirical stand hazard rating systems

for tree-vigor sensitive insects such as pine bark beetles. The

systems primarily use measures of basal area, trees per

area, or both, to represent the vigor status. Other factors,

such as tree diameter or phloem thickness, have been included to represent suitable beetle habitat once the tree is

successfully attacked. The problem with these systems is

that they poorly account for variation in site and climate

conditions, and not at all for annual variation in weather

(Amman and Anhold 1989). More data-intensive rating

systems include phloem thickness and changes in relative

growth rates, based on ring measurements. This captures

some of the variation in weather factors, but even these

cannot be easily used to project future conditions because

empirical growth projection systems, such as FVS, use

average, multiyear time steps that ignore within- and between-year variability.

Tree growth is generally assumed to be directly correlated

to photosynthetic production. However, not all of the fixed

carbon is used for growing stems, roots, and leaves. In some

trees, one important use is to produce oleoresin compounds

(pitch), which are used by the tree to seal wounds and defend

against insect attacks. The toxic, sticky resin produced by

the tree can flow out through holes produced by bark beetles,

and kill the beetles. It can also play a role in limiting the

spread of beetle-introduced fungi through the phloem.

In pine trees, resin is produced under two conditions. It

may be preformed in resin canals in the phloem, or it may be

induced as a response to wounding. There is evidence that

production of preformed resin may occur during times of

moderate moisture stress (Lorio 1986, 1993; Lorio and

Sommers 1986; Wilkens and others 1998), and that induced

resin is more available during times when high levels of

carbohydrates are moving through the phloem to the roots

(Herms and Mattson 1992), in times of low moisture stress.

In times of high moisture stress, little or no photosynthesis

takes place, and the tree must rely on whatever preformed

resin was produced earlier. The preformed resin reserves
USDA Forest Service Proceedings RMRS-P-25. 2002 147

Eco-Physiology Approach to Projecting Tree Stress and Vigor in FVS Smith, McMahan, and Wang

are limited and will be exhausted if the tree is attacked by a

large number of beetles. The relationship of tree vigor and

resin defenses has been used to relate tree vigor to mountain

pine beetle attacks (Waring and Pitman 1985).

In recent years, a number of tree physiology growth

models have been developed that simulate photosynthesis

processes (Battaglia and Sands 1998). Some of these models

provide outputs that correlate with tree vigor status and

defensive resin production. Stand-BGC (Milner and Coble

1995) is a process-based model that has been designed to

simulate the allocation of total stand photosynthate back to

individual trees. FVS-BGC (Milner and others, these proceedings; McMahan and others 2002) is an adaptation of the

Stand-BGC model designed to function as an extension of

the Forest Vegetation Simulator (FVS) system (Wykoff and

others 1982). FVS-BGC links the process-based modeling

approach from Stand-BGC to the FVS empirical growth

approach, and uses the management options in FVS to

simulate stand manipulations.

The FVS-BGC extension provides a suite of carbon- and

water-cycle output files at the tree and stand levels, at both

daily and yearly time steps, as well as standard FVS stand

tables derived from the process model. The inputs to FVSBGC include the primary factors that determine tree and

stand vigor (namely, site and climate), and the outputs

correspond to observable indicators of stress and vigor

status. We have used the FVS-BGC model to demonstrate its

usefulness for analyses of stand and tree vigor as it relates

to pine bark beetles.

Methods _______________________

FVS-BGC requires two sets of inputs: those required for a

standard FVS simulation; and BGC specific inputs, which

include climate files, soils data, and physiology parameters.

The simulation was performed on a lodgepole pine stand,

from the White River National Forest, in central Colorado.

The initial stand, chosen to be typical of the area, had these

characteristics:

Basal area: 174 ft2

/acre [≈ 40 m2 / ha]

Trees per acre: 534 [≈ 216 / ha]

Q.M.D.: 7.7 inches [≈ 19.6 cm]

Mean top height: 71 ft [≈ 21.6 m]

FVS thinning keywords were used to create five additional

levels of stand density. Stands were created having basal

areas of 80, 100, 120, 140, and 160 ft2

/acre. These stands

were created (using FVS keyword THINDBH) by removing

a constant proportion from every tree record in the tree list;

consequently, the stand’s diameter distribution structure

was not altered.

A single year’s daily climate input from Vail, CO, was used

as input. The year chosen had a total annual precipitation

close to the 20-year mean for that site (μ=22.0; σ = 4.5

inches); it also had similar monthly means, hence the

intraannual distribution of moisture was similar to patterns

under which the stand developed.

The MTCLIM model (Hungerford and others 1989) was

used to adjust the weather data for the elevation of the

stand. Seven separate weather input files were created

representing a range of dry to wet conditions. These files

were created by scaling each daily precipitation event by a

constant so that the year’s total precipitation would sum to

60, 75, 90, 100,110, 125, or 140 percent of the long-term

average annual precipitation. (This range captures most of

the observed variability, and represents approximately two

standard deviations on either side of the mean annual

precipitation.) The simulations were run for 3 years each. All

simulations used the average precipitation file in the first

year, followed by 2 years of one of the proportionally adjusted

precipitation files. Soil data were obtained from a forest soil

survey.

Results ________________________

Within-Year Stress

Figure 1 displays the relationship between daily precipitation and simulated soil water potential for one of the

modeled combinations of stand density and precipitation

during the summer months. Before July 1, soils in this area

generally have high water availability (less negative water

potentials) from snowmelt. The simulated drying process

results from evapotranspiration exceeding precipitation.

Figure 2 displays the simulated soil water potential for

three of the modeled precipitation levels during the summer

months, for the 120 ft2

/acre stand. Mountain pine beetles

(MPB; Dendroctonus ponderosae, Hopkins) in this area

primarily emerge and attack sometime between early July

and early September, with the attack period generally concentrated in the middle of the that time. Actual MPB flight

times can vary year to year and can occur over small time

intervals. Note that the amount of time this stand is under

moisture stress varies significantly during the normal beetle

attack period depending upon the amount of precipitation.

The possible temporal correspondence of MPB flight to the

period of moisture stress might influence the successfulness

of MPB attacks.

Figure 3 displays the simulated soil water potential for

three of the modeled stand density levels, all simulated

using the average (100 percent) precipitation level throughout the simulation. This range of stand densities bracket the

Figure 1—Daily precipitation (PPT, in millimeters

[bars]) and simulated soil water potential (SWP, in

megapascals [line]) for July through September for

an “average” precipitation year.
148 USDA Forest Service Proceedings RMRS-P-25. 2002

Smith, McMahan, and Wang Eco-Physiology Approach to Projecting Tree Stress and Vigor in FVS

levels that are regarded as having low to high risk for beetle

infestation. Again, note that the amount of time this stand

is under moisture stress varies significantly for this range of

stand densities.

Annual-Level Vigor

Waring and colleagues (Waring and others 1980; Larsson

and others 1983; Mitchell and others 1983; Waring and

Pitman 1985) performed a series of studies involving pines

and mountain pine beetle susceptibility that were based on

the assumption that photosynthate allocation is hierarchical. They proposed that allocation to defensive oleoresins

was lower in the hierarchy than either leaf or stem growth.

They produced a vigor index (Waring and others 1980) that

they defined as:

grams/year of stemwood produced

m2 leaf area (projected)

The Waring vigor index (WVI) was based on the premise that

the ratio of stem growth to leaf area was indicative of the

amount of photosynthate available for oleoresin production.

As applied in field studies by Waring and others, this

value used stemwood production estimated from diameter

increment, and leaf area estimated from sapwood area. In

FVS-BGC, stemwood growth and leaf area are available as

simulation outputs, so we can estimate the WVI with the

model.

A field study (Waring and Pitman 1985) related the

estimated WVI of lodgepole pine trees on an Oregon site to

mountain pine beetle attacks. The study showed a distinct

threshold at about 100 grams of stemwood production per m2

of leaf area. Trees below this level suffered many attacks;

with a positive linear relationship between the WVI below

this level and the number of attacks it took to kill the tree.

Above this level, few trees were attacked, none successfully.

Figure 4 shows WVI values for individual trees from FVSBGC simulations for two stand densities, at average precipitation levels, of our example lodgepole stand. In these

simulation scenarios, trees in the less dense stand fix more

net carbon per unit of leaf area than trees in denser stand.

The 80 ft2

/acre represent the thinning density recommended

for this area to lower mountain pine beetle hazard (Angwin

and others 1996); 180 ft2

/acre for this stand structure would

be considered to be at high risk. Note that most of the trees

in the 80 ft2

/acre stand fall above the 100 WVI level, and all

of the trees in the 180 ft2/acre stand are below it.

The six levels of stand densities and the seven levels of

precipitation were simulated for all 42 combinations, and

the stand level WVIs calculated. Figure 5 shows the results

of WVI values when plotted, and isopleths estimated, to

Figure 2—Soil water potentials in year 3 for the 120 ft2/

acre basal area stand under three precipitation regimes.

Increasing simulated precipitation delayed the onset

date of moisture stress; decreasing simulated precipitation quickened the onset of moisture stress. The period

over which the mountain pine beetles are known to fly in

this area is shaded.

Figure 3—Soil water potentials in year 3 for stands

of differing densities during an “average” precipitation year. Decreasing stand density delayed the

onset of moisture stress; increasing stand density

quickened the onset of moisture stress. Compare

with figure 2.

Figure 4—Simulated Waring vigor index values

for individual trees from two stands with different

stand basal areas (BA). These values are from

year 3 of a simulation using the average precipitation for a consecutive 3 years. Note the “threshold”

Waring vigor index at 100 g/m2

.

0

100

0 5 10 15 20

DBH (inches)

Stemwood Production

per unit Leaf Area (g / sq m)

BA 80 BA180
USDA Forest Service Proceedings RMRS-P-25. 2002 149

Eco-Physiology Approach to Projecting Tree Stress and Vigor in FVS Smith, McMahan, and Wang

show lines of equal vigor conditions. For this site and stand

structure, the 100 g/m2

 WVI level corresponds, under average precipitation conditions, to about 92 ft2

/acre basal area.

One should not consider the 100 g/m2 WVI level as being a

well-defined threshold because it is based on estimates of the

independent variables and the study has not been replicated. These simulation results, however, suggest that the

100 g/m2

 value does correspond to local guidelines where

stand conditions would be considered on the border of being

at risk.

The soil conditions used for these simulations represent

one of two major soil types found in the area from which the

example stand was taken. A second soil type in the area,

which is deeper and has a different texture, provides more

available water to plants. As a way of simulating the effects

of varying site qualities, we ran a second set of the same 42

combinations of stand densities and precipitation levels

using the second soil type as FVS-BGC input. The results are

displayed in figure 6. Note that for average precipitation

conditions, the 100 g/m2

 WVI level corresponds to a stand

density of approximately 125 ft2/acre basal area in this

better soil.

Conclusions____________________

The physical and biological outputs from the FVS-BGC

model can be compared to measurable values obtained from

field studies. This creates the potential for validation studies

that directly compare simulated results and measured valFigure 5—Waring vigor index isopleths by stand density and precipitation for a given site and stand structure. Isopleths connect lines of equal vigor and were

generated using SigmaPlot®, from vigor index estimates from 42 simulations (six stand densities times

seven precipitation regimes).

Figure 6—Vigor isopleths generated from 42 simulations that used the same stands and climate files

used in the previous example, but simulated on a

different site, one having deeper soil with higher total

available water. Compared with figure 5, isopleths

have shifted to the right and down, indicating generally higher Waring vigor indices for any given moisture/density combination.

ues for the same stand conditions. The simulation results

produced by these examples are consistent with the findings

of other studies and with local management guidelines. If

further testing confirms that the model has the ability to

reliably represent stress and vigor conditions for forest

stands, the model’s outputs may be used to supplement base

FVS information and enhance insect and pathogen models.

The ability to separate the contributions of site components

to both growth and vigor may allow the development of more

biologically insightful hazard rating systems. Use of a BGCbased vigor rating system from within FVS also has the

potential to improve current FVS insect impact extensions,

especially the Mountain Pine Beetle model and the Westwide

Pine Beetle model.

References _____________________

Amman, G. D; Anhold, John A. 1989. Preliminary evaluation of

hazard and risk rating variables for mountain pine beetle infestations in lodgepole pine stands. In: Amman, G. D. ed. Symposium on the management of lodgepole pine to minimize losses to

the mountain pine beetle: proceedings; July 12-14, 1988, Kalispell,

MT. Gen. Tech. Rep. INT-262. U.S. Department of Agriculture,

Forest Service, Intermountain Forest and Range Experiment

Station: 22-27.

Angwin, P. A.; Johnson, D. W.; Eager, T. J.; Smith, E. L.; Bailey, W.

1996. 3430 Biological Evaluation R2-97-01. Gunnison, CO. Piney

Analysis Area Holy Cross Ranger District White River National

Forest, Forest Health Assessment. USDA Forest Service, Renewable Resources, Rocky Mountain Region, Gunnison Service Center. 80 p.
150 USDA Forest Service Proceedings RMRS-P-25. 2002

Smith, McMahan, and Wang Eco-Physiology Approach to Projecting Tree Stress and Vigor in FVS

Battaglia, M.; Sands, P. J. 1998. Process-based forest productivity

models and their application in forest management. For. Ecol.

Manage. 102: 13-32.

Berryman, Alan A. 1976. Theoretical explanation of mountain pine

beetle dynamics in lodgepole pine forests. Environmental Entomology. 5(6): 1225-1233.

Eaton, C. B. 1941. Influence of the mountain pine beetle on the

composition of mixed pole stands of ponderosa pine and white fir.

J. Forestry. 39: 710-713.

Herms, D. A; Mattson, W. J. 1992. The dilemma of plants: to grow

or defend. Quart. Rev. Biol. 67: 283-335.

Hungerford, Roger D.; Nemani, Ramakrishna R.; Running, Steven

W.; Coughlan, Joseph C.. 1989. MTCLIM: A mountain microclimate simulation model. Res. Pap. INT-414. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. 52 p.

Johnson, Philip C. 1951. Application of risk ratings for western pine

beetle control in the Inland Empire. Northwest Science XXV, Feb:

32-37.

Larsson, S.; Oren, R.; Waring, R. H.; Barrett, J. W. 1983. Attacks of

mountain pine beetle as related to tree vigor of ponderosa pine.

Forest Sci. 29(2): 395-402.

Lorio, P. L., Jr. 1986. Growth-differentiation balance: a basis for

understanding southern pine beetle-tree interactions. For. Ecol.

Manage. 14: 259-273.

Lorio, P. L., Jr. 1993. Environment stress and whole-tree physiology. In Schowalter, T. D.; Filip, G. M., eds. Beetle-pathogen

interactions in conifer forests. Academic Press, London: 81-101.

Lorio, P. L., Jr.; Sommers, R. A. 1986. Evidence of competition for

photosynthesis between growth processes and oleoresin synthesis in Pinus taeda L. Tree Physiol. 2: 301-306.

McMahan, Andrew J.; Milner, Kelsey S.; Smith, Eric L. 2002. FVSBGC: User’s Guide to Version 1.0. FHTET 02-02. Fort Collins, CO.

U.S. Department of Agriculture, Forest Service, Forest Health

Protection, Forest Health Technology Enterprise Team. 51 p.

Milner, K. S.; Coble, D. W. 1995. A mechanistic approach to predicting the growth and yield of stands with complex structures. In:

O’Hara, K. S., ed. Uneven-aged management: opportunities,

constraints, and methodologies. University of Montana, Missoula,

MT: 144-166.

Mitchell, R. G.; Waring, R. H.; Pitman, G. B. 1983. Thinning

lodgepole pine increases tree vigor and resistance to mountain

pine beetle. Forest Sci. 29(1): 204-211.

Waring, R. H.; Pitman, G. B. 1985. Modifying lodgepole pine stands

to change susceptibility to mountain pine beetle attack. Ecology.

66(3): 890-897.

Waring, R. H.; Theis, W. G.; Muscato, D. 1980. Stem growth per unit

of leaf area: A measure of tree vigor. Forest Sci. 26(1): 112-117.

Wilkens, R. T.; Ayres, M. P.; Lorio, P. L., Jr.; Hodges, J. D. 1998.

Environmental effects on pine tree carbon budgets and resistance

to bark beetles. In: Mickler, R. A.; Fox, S. eds. The productivity

and sustainability of Southern forest ecosystems in a changing

environment. Springer-Verlag, New York: 591-616

Wykoff, William R.; Crookston, Nicholas L.; Stage. Albert R. 1982.

User’s Guide to the Stand Prognosis Model. Gen. Tech. Rep. INT133, Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Forest and Range Experiment Station. 112 p.
USDA Forest Service Proceedings RMRS-P-25. 2002 151

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Abdel Azim Zumrawi is a Growth and Yield Biometrician, Research

Branch, B.C. Ministry of Forests and a Visiting Faculty, Department of Forest

Resources Management, UBC, 2045-2424 Main Mall, Vancouver, B.C. V6T

1Z4. Albert R. Stage is Principal Mensurationist (retired), USDA Forest

Service, Rocky Mountain Research Station, 1221 S. Main St., Moscow, ID,

83843. Barry Snowdon is a Timber Supply Analyst, Timber Supply Branch,

B.C. Ministry of Forests, 595 Pandora , Victoria, B.C. V8W 9C3.

Abstract—The main feature of the 1998 release of PrognosisBC is

an interim calibration of its large-tree diameter-growth model. The

calibration is based on the development of multiplier functions that

predict the ratio between growth rates inherent in the northern

Idaho variant of the Prognosis Model (now called the Forest Vegetation Simulator in the United States) and those rates observed in

long-term permanent sample plots (PSP) in the Nelson, Kamloops

and Cariboo forest regions of British Columbia, Canada. The selfcalibration feature of the Prognosis Model was used to generate the

calibration statistics. These statistics were then modelled as a

function of variables whose effects were hypothesized to change as

the model range is expanded northward. This paper discusses the

methodology and presents the results of this stand-level calibration

of an individual-tree, diameter-growth model.

The Forest Vegetation Simulator (FVS) (Stage 1973; Wykoff

and others 1982) — FVS is a new name for the Prognosis

Model — is a single-tree, distance-independent model of

forest-stand dynamics. The model also provides linkage to

other submodels of various components of the ecosystem

such as root disease and understory development. The

architecture of the model is such that the model is applicable

to a wide range of timber types and stand conditions. During

execution of the model, growth measured in the current

inventory can be used to scale its projections, providing a

smooth link between past experience and the future projections. This feature also provides a convenient means to

extend the range of model applicability.

The British Columbia (B.C.) Ministry of Forests in Canada

is currently adapting the northern Idaho (NI) variant of FVS

Stand-Level Scaling of a Single-Tree,

Distance-Independent, Diameter-Growth

Model: Interim Calibration of Prognosis for

the Southeastern Interior of British

Columbia

Abdel Azim Zumrawi

Albert R. Stage

Barry Snowdon

to the Southeastern Interior of British Columbia. This variant of the model was selected as a basis because of its

geographic proximity and its use of classes of potential

vegetation or habitat types (Daubenmire and Daubenmire

1968) as part of its index of productivity.

An initial approach to extending the model northward was

to map the B.C. Biogeoclimatic Ecosystem Classification

(BEC) classes (Meidinger and Pojar 1991) onto the

Daubenmire habitat types represented in the northern Idaho

variant (McKenzie 1991; Sargent and McKenzie 1997).

BEC units represent groups of ecosystems under the

influence of the same regional climate. Zones are divided

into subzones (actually the subzones are the basic units and

aggregate to zones). The notation used is ZONEab, where

ZONE is abbreviation of the zone (ICH, IDF...and so forth)

and “a” and “b” are two letters that define the subzone based

on precipitation and temperature, respectively. The codes

for “a” could be x (very dry), d (dry), m (moist), w (wet), or v

(very wet). The codes for “b” could be h (hot), w (warm),

m(mild), k (cool), c (cold), and v (very cold). A subzone may

further be divided into variants (a geographic location identifier) indicated by a number that follows the zone and

subzone, for example, ICHmw2 (the Schuswap moist warm

Interior Cedar-Hemlock variant). A subzone or variant is

divided into Site Series (01, 02...and so forth). Site Series

group ecosystems within a subzone or variant by environmental properties (soil moisture and nutrient regimes) and

potential climax vegetation. The 01 is the zonal (typical)

series of the variant. Site Series with numbers less than 4

(for example, 02 and 03) are (generally) drier and poorer.

Site Series with numbers greater than or equal to 4 (04,

05,06...and so forth) generally occur on wetter, richer sites.

However, the soil moisture or nutrient regime of a given

series relative to the zonal (01) may vary from variant to

variant.

The prevailing BEC zones in Southeastern B.C. that were

included in this study are the Engelmann Spruce-Subalpine

Fir (ESSF), the Interior Cedar Hemlock (ICH), the Interior

Douglas-fir (IDF), the Montane Spruce (MS), and the Ponderosa Pine (PP). This approach of mapping the BEC units to

habitat types permitted B.C. analysts to evaluate the potential usefulness of the Prognosis architecture in their

decisionmaking context.
152 USDA Forest Service Proceedings RMRS-P-25. 2002

Zumrawi, Stage, and Snowdon Stand-Level Scaling of a Single-Tree, Distance-Independent, Diameter-Growth Model: ...

The longer term objective is to have a variant of Prognosis

for Interior British Columbia that is based entirely on local

data. However, that objective may require more specific data

than are presently available. In the interim, useful projections seem possible with a model that uses our limited B.C.

data to adjust for the major ecological differences between

northern Idaho and Southeastern B.C. This adaptation

consists of a series of steps, ordered by the sensitivity of the

model to our ecological differences. Each step provides an

interim model with improved representation of B.C. forests:

1. Convert the NI variant from Imperial units to metric

units of measure including the B.C. static tree-volume equations (Robinson 1997).

2. Modify the large-tree diameter-increment component

to recognize BEC classes, and determine appropriate scaling

factors using diameter-increment data from B.C. permanent sample plots (PSP’s). The methods used in this step are

the primary feature of this report and are the basis for the

1998 release of PrognosisBC.

3. Modify the individual-tree mortality model to agree

with B.C. PSP data.

4. Modify the large-tree height-increment model to use

the BEC classes in place of NI habitat types.

5. Construct a height-increment submodel for small trees

(less than 12.5cm.).

6. Construct a submodel for regeneration.

Steps 3, 4, 5, and 6 are in process and are expected to form

the basis for a future release of PrognosisBC in early 2002.

Calibration of the diameter-increment submodel was the

first step in modifying the dynamic (growth and mortality)

aspects of the northern Idaho variant. This paper presents

the interim calibration of the diameter-increment submodel

for the Southern Interior of British Columbia.

Background ____________________

In the northern Idaho (NI) variant of the Forest Vegetation Simulator (FVS), the overall model behavior is sensitive

to the diameter-growth component for trees larger than 12.5

cm. This sensitivity is a consequence of the model’s use of

diameter at breast height (d.b.h.) and diameter increment in

the prediction of changes in other tree attributes (height

growth, mortality, and crown length). The NI variant of

Prognosis is thus predominantly a diameter-driven growthand-yield model as opposed to height-driven models that use

a potential height-growth based on site index. Therefore, we

started the calibration of FVS to the Southern Interior of

British Columbia with the diameter-increment model. The

diameter-increment model predicts 10-year diameter increment in trees greater than 12.5 cm in d.b.h. The model uses

tree and stand attributes, site and location variables to

predict the change in inside-bark d.b.h. The model modifies

this prediction for trees less than 25 cm to provide a smooth

transition between the large-tree estimates and the diameter-increment estimates derived from height increment for

trees less than 12.5 cm.

The form of the diameter-increment model (Wykoff 1986)

is as follows:

Ln(DDS) = HAB + LOC + b1*SL*cos(ASP) + b2*SL*sin(ASP) +

b3*SL + b4*SL2 + b5*EL + b6*EL2 + b7*ln(DBH) + b8*CR +

b9*CR2

 + b10*(BAL/100) + b11*BAL/(DBH+1) + b12*DBH 2 +

b13*(CCF/100) (Eq. 1)

Where:

Ln(DDS) = Natural log of 10-year change in squared

inside-bark diameter

HAB = Constant term that is dependent on habitat type

LOC = Constant term that is dependent on location

SL = Stand slope ratio

ASP = Stand aspect (degrees)

EL = Stand elevation (hundreds of feet)

CCF = Stand crown competition factor

CR = ratio of crown length to total tree height

DBH = diameter at breast height

BAL = Stand basal area in trees larger than the subject

tree

b1,.., b13 = Species-specific regression coefficients

The regression coefficients in the NI variant of FVS, at the

time the model was adopted for calibration in B.C., were

given in Wykoff (1986).

Data __________________________

Permanent sample plots (PSP) have been established

throughout the operable land base in B.C. (B.C. Forest

Productivity Council Minimum Standards Manual 1995).

The PSPs used in the calibration were selected from the

Nelson, Kamloops, and the Cariboo forest regions based on

the following criteria:

• The PSP has been measured at least twice.

• The plot has been ecologically classified to the site series

using the Biogeoclimatic Ecological Classification system.

• The plot Biogeoclimatic Ecological Classification at the

site-series level has been mapped to a corresponding

northern Idaho habitat type.

• Measurement periods were excluded if the plots were

disturbed or partially cut between any successive measurements.

• Only measurement periods of 9, 10, or 11 years were

selected. This was intended to minimize the effects of

interpolating to a 10-year growth cycle.

Repeated measurements on the selected plots were used

to provide the initial tree and stand conditions and a 10-year

diameter increment. The distribution of PSP data used in

the interim calibration by forest region is given in tables 1

and 2.

In addition to the tree-list data, a keyword list was also

prepared for each sample plot. The tree list and keyword list

were used to project the stand between successive measurements. For the purpose of these calibration projections, the

plot elevation, slope and aspect were set to the average

conditions in the northern Idaho data used to develop the

variant. This substitution results in a reduced model where

the aspect, slope and elevation terms become part of the

model constant terms (equation 1).

The plot site series from the British Columbia

Biogeoclimatic Ecological Classification (BEC) were mapped

to nearest corresponding NI habitat type. The mapping of

the BEC site series in the Nelson and Kamloops forest
USDA Forest Service Proceedings RMRS-P-25. 2002 153

Stand-Level Scaling of a Single-Tree, Distance-Independent, Diameter-Growth Model: ... Zumrawi, Stage, and Snowdon

regions of B.C. to the corresponding northern Idaho habitat

types is given in tables 3 and 4. The tables show the habitattype codes used in FVS. For more details on the habitat types

represented by these codes, see Wykoff and others (1982).

Calibration Statistics ____________

Wykoff and others (1982) gave a brief description of the

FVS model selfcalibration process. The calibration process

scales growth predictions of each species to match the input

growth data from the PSP repeated measures. Calibration

statistics represent the magnitude of the scaling factor

needed such that the plot totals of the model-based growth

estimates will equal the totals as measured on the plot. The

adjustment includes an empirical-Bayes-based averaging of

the estimates from the a priori, imbedded-increment model

and the a posteriori stand sample of increments. The version

of FVS used in this study reports the unadjusted values of

the calibration statistics. Newer versions of FVS now report

the modified calibration statistics.

The diameter-increment scale factors are expressed as a

multiplier to the change in squared diameter and are in

effect multipliers of the tree-basal-area increment. The

calibration statistics were computed for each projectedgrowth-measurement period.

Table 1—PSP distribution by BEC site series, number of plots and average number of

trees in the plot in the Kamloops and Cariboo forest region.

Site series

Subzone/variant 03 04 01 05 06

ICHmk1 7 (65) 4 (42)

ICHmw2 n/m 1 (14) 47 (43) 2 (21)

ICHmw3 No Data n/m 24 (40)

IDFdk1 n/m 17 (53) 18 (53)

IDFdk2 20 (50) n/m 37 (66)

IDFdk3 n/m 6 (54) 32 (53) n/m 11 (49)

IDFdm1 n/m 2 (55) 1 (24)

IDFmw1 n/m 3 (62)

IDFxh1 n/m 2 (53) 8 (63)

IDFxh2 n/m n/m n/m 4 (74) 2 (78)

MSdm1 n/f 5 (27)

PPxh2 3 (27)

n/m = Site series not mapped to a corresponding habitat type.

n/f = Site series not forested

Table 2—PSP distribution by BEC site series, number of plots and average number of trees in the plot

in the Nelson forest region.

Site series

Subzone/variant 02 03 04 01 05 07

ESSFdk n/m n/m 9 (48) 11 (29)

ESSFwc4 4 (49) 3 (78) n/m n/m

ESSFwm No Data 3 (38) 1 (29) 9 (29)

ICHdw n/m n/m No Data 45 (44)

ICHmk1 n/m 30 (57) 15 (55) 25 (71) 2 (65)

ICHmw1 n/m n/m n/m 1 (110)

ICHmw2 n/f 82 (61) 69 (41) 22 (36) 2 (45)

ICHmw3 n/m 2 (43) 6 (26) 6 (25) 8 (30) 1 (26)

ICHwk1 n/f n/m 1 (31) No Data

IDFdm1 n/f n/m 12 (32) 25 (30)

IDFdm2 n/f n/m 13 (48) 43 (62)

MSdk n/f 5 (51) 49 (83) 15 (43)

MSdm1 No Data No Data 2 (23) No Data

PPdh2 n/f n/m n/m 5 (59)

n/m = Site series not mapped to a corresponding habitat type.

n/f = Site series not forested.
154 USDA Forest Service Proceedings RMRS-P-25. 2002

Zumrawi, Stage, and Snowdon Stand-Level Scaling of a Single-Tree, Distance-Independent, Diameter-Growth Model: ...

Calibration Methods _____________

The next step models the calibration statistics obtained

for each plot. Effects of stand elevation, slope, and aspect on

tree growth are expected to change as the model’s geographical scope is expanded northward. Setting the plot elevation,

aspect, and slope to the average NI conditions during the

calibration runs allowed us to examine the correlation between the model-generated calibration statistics and the

actual elevation, aspect, and slope of the plot. The stand

stocking, expressed as CCF, was also chosen as a potential

predictor variable. The inclusion of CCF in a regression

model to predict plot-level calibration statistics provides a

stand-level adjustment to the species-specific CCF regression coefficients in the original NI model.

Table 3—Mapping of the BEC site series to habitat types in the Nelson forest region.

Site series

Subzone/variant 03 04 01 05 05 07

FVS Habitat Type Codes

PPdh1 PIPO/AGSP

PPdh2 PIPO/AGSP

IDFdm1 PSME/CARU PSME/VACA ABLA/VACA

IDFdm2 PSME/VACA

IDFxh1 PSME/CARU PSME/CARU PSME/SYAL

ICHdw THPL/OPHO TSHE/CLUN

ICHmw2 TSHE/CLUN TSHE/CLUN THPL/OPHO THPL/OPHO

ICHmw3 PSME/VACA TSHE/CLUN THPL/OPHO

ICHmk1 PSME/VACA ABLA/VACA

ICHwk1 THPL/OPHO

ICHvk1 THPL/OPHO

MSdm1 PSME/VACA

ESSFdc1 ABLA/VASC

Table 4—Mapping of the BEC site series to habitat types in the Kamloops forest region.

Site series

Subzone/variant 03 04 01 05 05 07

FVS Habitat Type Codes

PPxh1 PIPO/AGSP PIPO/AGSP PSME/SYAL PSME/SYAL

PPxh2 PIPO/AGSP

IDFxh1 PSME/CARU PSME/CARU

IDFxh2 PSME/CARU PSME/CARU

IDFdk1 PSME/CARU PSME/CARU

IDFdk2 PSME/CARU PSME/VACA

IDFdk3 PSME/CARU

IDFdm1 PSME/CARU PSME/CARU THPL/ATFI

IDFmw1 PSME/VACA

ICHmw2 TSHE/CLUN TSHE/CLUN THPL/OPHO THPL/OPHO

ICHmw3 PSME/VACA TSHE/CLUN THPL/OPHO

ICHwk1 THPL/OPHO

ICHmk1 PSME/VACA ABLA/VACA

ICHvk1 THPL/OPHO

MSdm1 PSME/VACA

Msxk PSME/CARU

ESSFdc1 ABLA/VASC

Mapping of the BEC units to corresponding NI habitat

types was not a one-to-one mapping. It was a subjective

process based on the ecologist’s best knowledge of the vegetation characteristics of the BEC unit and the habitat type.

Differences between the two systems of classification could

result in more than one habitat type as a possible match to

a given BEC site series (McKenzie 1991; Sargent and

McKenzie 1997), so that two or more site series are mapped

to the same habitat type. The inclusion of the BEC site series

as a predictor variable is expected to explain that source of

variation in the calibration statistics that may be due to

differences between a BEC site series and the habitat type

to which it had been mapped.
USDA Forest Service Proceedings RMRS-P-25. 2002 155

Stand-Level Scaling of a Single-Tree, Distance-Independent, Diameter-Growth Model: ... Zumrawi, Stage, and Snowdon

The regression model of plot-level FVS-generated calibration statistics as a function of stand aspect, slope, elevation,

BEC site series, and CCF is:

Ln (Scale) = SITE + c1*SL*sin(ASP) + c2*SL*cos(ASP) +

c3* SL + c4*EL + c5*EL2 + c6*(CCF/100) (Eq 2)

Where:

Scale = Prognosis-generated plot-level scale factor for 10-

year change in squared diameter

SITE = Constant term that is dependent on the BEC site

series

C1 to C6 are species-specific regression coefficients.

All other variables appear in equation (1) and are defined

there.

The natural-log transformation of the dependent variable

was selected to have a model that is consistent with the

original NI model (equation 1). The growth multiplier becomes an additive term on the log scale. In this formulation,

the CCF coefficient C6 in equation (2) becomes a stand level

adjustment to the original CCF term (b13) of equation (1).

The aspect, slope and elevation terms with coefficients C1 to

C5 in equation (2) are in effect adjustments to the constant

term that resulted from the use of the average NI aspect,

slope, and elevation conditions in the model runs that

generated the scale factors. The model in equation (2) was fit

to each of 10 coniferous species included in the original

model. Of the 11 species covered in the NI version, only

mountain hemlock was not included in the analysis due to

lack of PSP data. A list of the tree species included in the

analysis is given in table 5.

Results and Discussion __________

The fit statistics for equation (2) are shown in table 6. The

adjusted R2

 values ranged from 0.10 for hemlock and western white pine to 0.56 for subalpine fir. Note that low R2

values indicate species for which the NI model required little

change. Standard errors of the estimate ranged from 0.29 to

0.46 on the log scale. For Douglas-fir, for example, the

standard error on the log scale is 0.36, which results in a

lower and upper one-standard deviation of 0.70 and 1.43,

respectively, or 30 and 43 percent error.

Least squares estimates of the regression coefficients of

equation (2) are presented in tables 7 and 8. The negative

significant CCF coefficients (C6) for Douglas-fir and lodgepole pine indicate that the two species are more negatively

affected by stand density in Southeastern B.C. than in the

northern Idaho variant of Prognosis. The positive effect of

elevation on growth is offset by a significant EL2

 term for the

species that occur in a wide range of elevation, indicating

that an optimum elevation does exist for these species. The

magnitude of the EL2

 coefficient is higher for the two species

that occur mostly in the higher elevations of the Englemann

Spruce Subalpine Fir (ESSF) zone, namely spruce and

subalpine fir. For all other species, C5 of equation (2) is not

significantly different from zero.

Constant terms for site series are given in table 7. These

coefficients represent site differences as well as an adjustment for any possible mapping mismatch between the site

series and the NI habitat type deemed most appropriate.

Because of this confounding effect, the coefficients for a

given species cannot be interpreted to indicate site differences between the different site series for that species. We

could add the intercept given for the root habitat type to the

new intercept for each BEC class. However, as the intercept

also includes the mean times regression coefficient for each

of the other variables in the model, its interpretation as

relative productivity is still clouded.

For each species, residual analysis by BEC site series

showed that there were no significant biases in the predicted

scale factors across the site series.

The large-tree diameter-growth model implemented in

the current release of PrognosisBC is a combination of equation (1), in which aspect slope and elevation were fixed to the

average of these conditions in NI, and equation (2). Preliminary verifications of the model’s performance (Temesgen

and Lemay 1999) indicated no serious bias in the overall

behavior of the model. Furthermore, and because diameter

growth is an important predictor of height growth, Temesgen

and LeMay (1999) found that the large-tree height model

performed well in the site series where the diameter model

was calibrated.

Table 5—Tree species included in the large-tree diameter-growth

model calibration.

Common name Code Scientific name

Western white pine Pw Pinus monticola

Western larch Lw Larix occidentalis

Douglas-fir Fd Pseudotsuga menziesii

Western hemlock Hw Tsuga heterophylla

Western redcedar Cw Thuja plicata

Lodgepole pine Pl Pinus contorta

Engelmann spruce Se Picea engelmannii

Grand fir Bg Abies grandis

Subalpine fir Bl Abies lasiocarpa

Ponderosa pine Pp Pinus ponderosa

Table 6—Fit statistics for equation (2).

No. of Standard error

Species plots (N) Adjusted R2 of estimate

Douglas-fir 1130 0.20 0.3562

Lodgepole pine 779 0.26 0.2995

Western larch 592 0.20 0.4614

Western redcedar 541 0.14 0.4033

Western hemlock 368 0.10 0.3509

Western white pine 209 0.10 0.3616

Engelmann spruce 167 0.20 0.4033

Subalpine fir/Grand fir 73 0.56 0.3779

Ponderosa pine 58 0.38 0.4441
156 USDA Forest Service Proceedings RMRS-P-25. 2002

Zumrawi, Stage, and Snowdon Stand-Level Scaling of a Single-Tree, Distance-Independent, Diameter-Growth Model: ...

Table 7—Species-specific coefficients in the calibration function for the large-tree, diameter-increment model (equation 2).

Species

Variable PW LW FD HW CW PL SE BL/BG PY

Overall constant –1.20012 0.03164 –0.33768 –0.53158 0.30382 –0.57696 –1.46710 –0.65511 –2.01572

Sl.Cos(ASP) C1 0.00083 –0.08531 –1.11471

SL.Sin(ASP) C2 –0.74662

SL C3 –0.82000

EL C4 0.01724 0.01048 0.04685 0.01065 0.07223 0.07129 0.06151

EL2 C5 –0.00068 –0.00090 –0.00099

CCF C6 –0.07463 –0.16348

Site series

BEC variant

2

Constant

2

Constant

Western white pine Western hemlock

ICH 0.97080 —0.33966

mw2 0.55088 —0.32891

mw3 0.54472 —0.38468

1.57930 —0.35852

Western larch —0.34059

ICH —0.24679 —0.33842

—0.11546 Western redcedar

mk1 —0.45001 —0.67998

IDF 0.16060 —0.53091

dm2 —0.53506 —0.24884

—0.83091 —0.45763

mw1 —0.28032 mw3 —0.94850

MS —0.47719 —0.26425

—0.13477 —0.36550

Douglas-fir —0.74451

ICH 0.22398 —0.50328

mk1 0.15358 —0.85966

mw2 0.26506 —0.93040

0.12485 Lodgepole pine

mw3 0.52539 0.14517

0.37126 0.21038

0.16271 0.10317

wk1 0.29088 0.24878

IDF 0.13588 —0.29809

dk2 0.28110 —0.44302

dk3 —0.17949 —0.53275

—0.73119 —0.38840

—0.18901 —0.31680

dm1 0.31994 Engelmann spruce

0.11163 0.26510

dm2 0.14139 0.72027

mw1 0.35498 0.30629

xh1 —0.20460 mk1 —0.21132

xh2 Subalpine fir

MS 0.22099 —0.68074

Grand fir 0.26881

ESSF —0.68074 ICH —1.32980

wm1 0.26881 —0.99113

ICH —1.32980 mw2 —1.08110

mk1 —0.99113 mw3 —0.77432

mw2 —1.08111 —0.49646

mw3 —0.74432 Ponderosa pine

MS —0.49646 IDF —0.44823

xh1 —0.41938

xh2 —0.68892

Table 8—Species-specific, BEC constant terms in the calibration function for the large-tree, diameter-increment model

(equation 2). The shaded cell(s) indicate the site series or group of site series for which the constant term applies.
USDA Forest Service Proceedings RMRS-P-25. 2002 157

Stand-Level Scaling of a Single-Tree, Distance-Independent, Diameter-Growth Model: ... Zumrawi, Stage, and Snowdon

References _____________________

Anonymous. 1995. Minimum standards for the establishment and

measurement of permanent sample plots in British Columbia.

Unpublished report. Victoria, B.C: Secretariat, Forest Productivity Council of BC. 22 p.

Daubenmire, R.; Daubenmire, J. B. 1968. Forest vegetation of

eastern Washington and northern Idaho. Technical Bulletin 60.

Pullman, Washington: Washington Agric. Exper. Station. 104 p.

Crookston, N. L. 1985. User’s guide to the Event Monitor: an

addition to the Prognosis Model. Gen. Tech. Rep. INT-196. Ogden,

UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 36 p.

McKenzie, E. 1991. A computer correlation for Prognosis of B.C.

ecosystems and U.S. habitat types using a similarity coefficient

program. Unpublished report. Victoria, B.C: Ministry of Forests.

Meidinger D. and J. Pojar. 1991. Ecosystems of British Columbia.

B.C. Special Report Series 6, Ministry of Forests. Victoria, Canada.

330 pp.

Sargent, P.; McKenzie, E. 1997. Re-evaluation of B.C. site unit-U.S.

habitat type correlations for Prognosis and tree species regeneration predictions for the Nelson forest region site series. Unpublished report. Victoria, B.C: Ministry of Forests. 16 p.

Stage, A. R. 1973. Prognosis model for stand development. Res. Pap.

INT-137. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Forest and range Exp. Station. 32 p.

Temesgen, H.; LeMay, V. 1999. Report on the examination of

diameter growth model modified for SEI variant. Unpublished

report. Victoria, B.C.: B.C. Ministry of Forests, Resources Inventory Branch.

Wykoff; W. R.; Crookston, N. L.; Stage, A. R. 1982. User’s guide to

the stand Prognosis model. Gen. Tech. Report. INT-133. Ogden,

UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Exp. Station. 112 p.

Wykoff, W. R. 1986. Supplement to the user’s guide for the stand

Prognosis model. Gen. Tech. Report. INT-208. Ogden, UT: U.S.

Department of Agriculture, Intermountain Forest and Range

Exp. Station. 36 p.
158 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Ronald E. McRoberts is a Mathematical Statistician, Christopher W.

Woodall is a Research Forester, and Margaret R. Holdaway is a Mathematical

Statistician (retired), North Central Research Station, USDA Forest Service,

1992 Folwell Ave., St. Paul, MN 55108. Veronica C. Lessard is a Statistician,

Natural Resource Conservation Service, 1992 Folwell Ave., St. Paul, MN

55108.

Abstract—Distant-independent, individual-tree, diameter growth

models were constructed to update information for forest inventory

plots measured in previous years. The models are nonlinear in the

parameters and were calibrated using weighted nonlinear least

squares techniques and forest inventory plot data. Analyses of

residuals indicated that model predictions compare favorably to

another regional diameter growth model and may be considered

unbiased for their intended application. In addition, the models

provide for estimation of model prediction uncertainties and may be

easily recalibrated. The final model calibrations were by species

within each of three ecological provinces in the North Central

United States.

The U.S. Renewable Forest and Rangeland Resources

Planning Act of 1978 requires that the Forest Service, an

agency of the U.S. Department of Agriculture (USDA),

conduct inventories of the nation’s forest land to determine

its extent and condition and the volume of standing timber,

timber growth, and timber removals. The U.S. Agricultural

Research Extension and Education Reform Act of 1998

further requires that in conducting these inventories, the

Forest Service measure a proportion of the plots in each

State annually. In response, the Forest Inventory and Analysis (FIA) program of the Forest Service has initiated the

Enhanced FIA program that features a systematic sampling

design with plots assigned to panels and individual panels

selected for measurement on a rotating basis.

One objective of the FIA program at the North Central

Research Station (NCRS), USDA Forest Service, has been to

establish the capability of annually producing standard

inventory estimates. Three approaches to calculating annual estimates using inventory data collected under the

Enhanced FIA program have been proposed (McRoberts

1999). The simplest approach is to use the data from the

most recently measured panel of plots. Although the resulting estimates would reflect current conditions, their low

precision may be unacceptable for some variables because of

Diameter Growth Models for Inventory

Applications

Ronald E. McRoberts

Christopher W. Woodall

Veronica C. Lessard

Margaret R. Holdaway

the small sample size. A second approach is to use the most

recent plot measurements for each panel, regardless of the

date, and then employ a moving average estimator. The

advantage of this approach is that precision is increased

because data for all plots are used for estimation; the

disadvantage is that the estimates reflect a moving average

of conditions over past years and may lag current conditions

in the presence of temporal trends. A third approach is to

update to the current year data for plots measured in

previous years and then base estimates on the data for all

plots. If the updating procedure is sufficiently unbiased and

precise, this approach provides nearly the same precision as

using all plots but without the adverse effects of using outof-date information.

Two models are currently available for predicting individual tree survival and growth in the North Central region

of the United States: STEMS (Belcher and others 1982), and

two variants of the Forest Vegetation Simulation (FVS). The

FVS variant for the Lakes States (Bush and Brand 1995) and

the variant for the Central States (Bush 1995) both use the

TWIGS models (Miner and other 1998), which, in turn are

based on the STEMS models. For over a decade, the FIA

program at NCRS has used the STEMS (Belcher and others

1982) survival and diameter growth models for updating

inventory plot information. A decision to construct new

models was motivated by several limitations in the STEMS

models:

1. Calibration data for the STEMS models were collected

primarily from long-term research plots in selected locations

and may not represent forest conditions across the North

Central region as do FIA plots.

2. The mathematical form of the STEMS models includes

a potential growth component that is difficult and cumbersome to calibrate, primarily because potential growth is

unobservable.

3. The STEMS diameter growth models were calibrated

using a two-step process in which a subset of the model

parameters were estimated first and then held constant

while the other parameters were estimated, a procedure

that would be difficult to defend statistically today.

4. The STEMS calibrations did not use weighted regression, did not estimate model parameter covariances, and did

not retain estimates of residual variances, thus precluding

estimation of the precision of model predictions.

The discussion that follows outlines and reviews progress to

date by FIA scientists at NCRS in constructing new individual tree, distant independent, diameter growth models

for updating inventory plot information.
USDA Forest Service Proceedings RMRS-P-25. 2002 159

Diameter Growth Models for Inventory Applications McRoberts, Woodall, Lessard, and Holdaway

Methods _______________________

Data

The models were calibrated using inventory data for trees

with diameter-at-breast height (d.b.h.) (4.5 ft. above ground)

greater than 5 inches for all ownership categories on lands

capable of producing 20 ft3

/ac/yr of industrial wood (FIA

timberland). For States in the North Central inventory

region and some adjacent States, plot data were obtained for

two consecutive periodic inventories: Illinois - 1985, 1998;

Indiana - 1986, 1998; Iowa - 1974, 1990; Kansas - 1981, 1994;

Kentucky -1974, 1987; Michigan - 1980, 1993; Minnesota -

1977, 1990; Missouri - 1972, 1989; Nebraska - 1983, 1994;

North Dakota - 1980, 1994; Ohio - 1978, 1990; South Dakota

- 1980, 1995; Tennessee - 1989, 1996; and Wisconsin - 1983,

1996. Data for a random selection of 75 percent of plots were

assigned to a model calibration data set; the remaining data

were assigned to a model validation data set and were

excluded from use for initial model calibration.

Calibration analyses focused on the suite of plot- and treelevel variables identified in preliminary analyses reported

by Holdaway (2000) and Lessard (2000a,b). Plot-level variables included latitude (LAT) and longitude (LON) of the

plot center, plot basal area (BA), and physiographic class

(PC). PC is a measure of site soil and water conditions that

affect tree growth and is coded as follows: 3-xeric; 4-xeromesic;

5-mesic; 6-hydromesic; 7-hydric; and 8-bottomland (NCRS

1998). Tree-level variables included observed average annual d.b.h. growth, which was calculated as the ratio of the

difference in d.b.h. measurements for the two inventories

and the number of growing seasons between measurements

and was used as a surrogate for observed annual d.b.h.

growth. For the purpose of consistency in notation and model

expression, DBH and ∆DBH are used to denote variables

quantifying d.b.h and annual change or growth in d.b.h.,

respectively.

Other tree-level variables included crown ratio (CR), plot

basal area in trees larger than the subject tree (BAL), and

crown class (CC) at the time of the initial inventory. CC is a

measure of a tree’s dominance in relation to adjacent trees

in the same stand and is coded as follows: 1-open grown;

2-dominant; 3-codominant; 4-intermediate; and 5-overtopped

(NCRS 1998). BA and BAL are calculated as the sum of

cross-sectional areas of live tree boles at breast height, are

scaled to a per unit area basis, and when used to calibrate the

models were calculated as the average value for the two

inventories. Although current BA and BAL would be used as

the value of these predictor variables when applying the

models, average BA and BAL over the rather long (12 to 17

years) inventory remeasurement interval for the calibration

data better reflect the growing conditions over the entire

interval than do BA and BAL at the initial inventory. The

values of CC and PC are categorical, but their coding is

ordered with respect to their expected effects on growth, and

they were treated as continuous predictor variables in the

model formulation.

Although calibration data were selected from plots with no

disturbance or minimal disturbance over the measurement

interval, some plots had substantial mortality or harvesting

before the measurement interval. Nevertheless, this disturbance is expected to have little impact on model prediction

bias because predictor variables such as plot basal area

partially account for disturbance and because the proportion

of extensively disturbed plots was small.

Annual Forest Inventory System Models

The diameter growth models constructed for updating

FIA plot information are designated the Annual Forest

Inventory System (AFIS) models. Their mathematical form

consists of the product of two components, an average

component that predicts average ∆DBH with respect to DBH

for the calibration area, and a modifier component that

adjusts ∆DBH predictions in accordance with local plot and

tree conditions. The average component is based on a twoparameter gamma function with a constant multiplier and

uses DBH as the predictor variable. The modifier component

consists of the product of exponential factors of which each

expresses a multiplicative effect on growth predictions in

terms of departures from the mean over the calibration area

for a single predictor variable. The mathematical form of the

AFIS growth models is

E(∆DBH) = Ave(DBH)Mod(X) [1]

where E(.) denotes statistical expectation, ∆DBH is annual

d.b.h. growth, and X represents the set of selected predictor

variables. Further,

 Ave(DBH) = β1exp(β2DBH)DBHβ3 [2]

and

Mod(X) = exp[β4(X4 – γ4)]exp[β5(X5 – γ5)]...exp[βp(Xp – γp)]

= exp β γ jj j

j

p

(x − ) 













 =

∑4

[3]

where the Xs are predictor variables, the γs are constants

representing mean values for the associated predictor variables over the calibration area, the βs are parameters to be

estimated, and p is the number of model parameters. After

substituting [2] and [3] into [1], mathematically expanding

the resulting expression, and combining constants where

possible, the AFIS growth model form may be expressed as,

E DBH DBH DBH Xj j

j

p

( ) exp( ) exp ∆ = 









 =

β β ∑ β 1 2 3 β

4

[4]

where the βs are still parameters to be estimated, although

not the same parameters as for [3]. The mathematical form

of the d.b.h. growth models is generally similar to the forms

used by STEMS and by Wykoff (1990) for modeling basal

area increment in northern Idaho.

Analyses

Three sets of analyses were performed to evaluate the

AFIS models: (1) the AFIS and STEMS model predictions

were compared; (2) the AFIS models were calibrated by

species for three ecological provinces in the North Central

United States, and an analysis of the residuals was conducted; and (3) the utility of the AFIS models for updating

FIA plot information was evaluated.

AFIS/STEMS Comparisons—The objectives of the first set

of analyses were to compare both the forms and calibrations
160 USDA Forest Service Proceedings RMRS-P-25. 2002

McRoberts, Woodall, Lessard, and Holdaway Diameter Growth Models for Inventory Applications

of the STEMS and AFIS models using their predictions for

trees on a subset of a plots in northeastern Minnesota

(Holdaway 2000, Lessard 2000a,b, McRoberts 2000).

These analyses were conducted early in the model construction process and used the mathematical form of the

models as expressed in [1], [2], and [3]. Variables were

selected for inclusion in the model using the criterion, C,

proposed by Linhart and Zucchini (1986),

C=SSe + 2p*MSe, [5]

where p is the number of model parameters, and SSe and

MSe are the sum of squared residuals and mean square

residual, respectively. This criterion simultaneously accounts for unexplained residual variability and the number

of model parameters. Models with lower values of C are

judged to provide better fits to data. The final set of predictor

variables considered for inclusion in the models was restricted to DBH, CR, BAL, BA, CC, PC, LAT, and LON. If the

estimate of a parameter corresponding to a variable included zero in its asymptotic 95 percent confidence interval,

the parameter estimate was set to zero in the model, effectively excluding any effect of the predictor variable.

Heterogeneity of residual variance was accommodated by

weighting each observation by the inverse of the estimated

residual standard deviation, σˆ res, which was adequately

described by,

E DBH res ln ˆ ln ˆ [ ] (σ αα ) = +1 2 (∆ ) [6]

where E(.) denotes statistical expectation, ∆DBHˆ is predicted

diameter growth from the models, and the αs are parameters to be estimated (McRoberts and others 2000).

To compare the form of the STEMS models to the form of

the AFIS models, the STEMS models were first recalibrated

using FIA data. Because of the complexity and time-consuming nature of this task, the recalibration was restricted to

four commonly occurring species in northeastern Minnesota: two conifers, red pine and balsam fir, and two hardwoods, quaking aspen and paper birch. The recalibrated

STEMS models were designated the STEMS-FIA models

and were calibrated with data for 37,550 trees on 2,434 plots.

The analyses consisted of three-way comparisons of the

∆DBH predictions on the selected plots using the existing

STEMS models, the STEMS-FIA models, and the AFIS

models. The comparisons were based on median residuals

and correlations, r2

, between observed and predicted ∆DBH.

AFIS Model Calibrations for Ecosystem Provinces—

The AFIS models were calibrated for each of the three

largest ecosystem provinces defined by Bailey that occur in

the North Central region: (1) Province 212, Laurentian

mixed forest, characterized as a transitional zone between

the boreal and broadleaf deciduous forests; (2) Province

222, Eastern broadleaf forest (continental), characterized

by dominance of broadleaf deciduous species favoring

drought-resistant oak-hickory associations; and (3) Province 251, Prairie parkland (temperate), characterized by

intermingled prairie, groves, and strips of deciduous species (Bailey 1995) (fig. 1).

The model fitting, verification, and validation procedures

were similar to those for the first analyses and as reported

by Lessard and others (2001) with two exceptions: first, [4]

was used as the mathematical form of the model rather than

[1], [2], and [3], and second, the parameters were tested for

statistical significance using an F test based on the extra

sum of squares principle (Ratkowsky 1983). Validation analyses were conducted using the independent validation data

sets and consisted of analyses of standardized residuals, ε,

calculated as,

ε

σ = ∆ ∆ DBH DBH −

res

ˆ

ˆ [7]

where ∆DBH is observed average annual d.b.h. growth,

∆DBHˆ is the corresponding model prediction, and σˆ res is the

estimate of residual standard deviation obtained from [6].

The standardized residuals were analyzed by evaluating

their distributions by species within ecosystem provinces for

the validation data sets. Following the validation analyses,

the calibration and validation data were pooled to form final

calibration data sets, the models were calibrated again, and

the residuals were evaluated using the same procedures.

Updating—The AFIS models were evaluated with respect to bias and precision for updating data for FIA plots

measured in previous years. Because this task required

additional analyses to estimate the uncertainty in model

parameters for each species, the evaluations were restricted to data for Province 212 plots that included only

trees of the four species, red pine, jack pine, quaking aspen,

and balsam fir.

An annualized 11-year database of plot and tree variables

was constructed using data for the selected inventory plots.

The year of first inventory measurement for each plot was

designated year 0, growth for individual trees was distributed equally over the observed remeasurement intervals,

Figure 1—Ecosystem provinces for the North Central region of the United States.
USDA Forest Service Proceedings RMRS-P-25. 2002 161

Diameter Growth Models for Inventory Applications McRoberts, Woodall, Lessard, and Holdaway

and years of mortality, harvest, and in-growth for individual

trees were randomly selected using a uniform distribution

over the remeasurement intervals. The resulting annual

database included data for 2,900 trees on 185 plots for years

0 to 10. Details regarding construction of the annualized

database are documented in McRoberts (2001).

Monte Carlo simulations were used to obtain estimates of

uncertainty for model parameter estimates, annual mean

plot BA estimates, and annual inventory estimates of mean

plot BA and the standard error of the mean. Before the

simulations could be implemented, uncertainty had to be

quantified for all the relevant components. Measurement

error and sampling variability in predictor variables were

ignored and assumed to be negligible. Estimates of residual

variability were obtained from [6] as by-products of calibrating the models.

Model parameter covariances reflect uncertainty in the

parameter estimates and must be included as a component

of total uncertainty whenever the precision of model predictions is to be estimated. When the models are relatively

simple (for example, linear), parameter covariance estimates may be easily calculated using analytical methods.

However, when the models are complex and nonlinear, then

Monte Carlo simulations are appropriate for reliably estimating these covariances. Distributions of model parameter

estimates from which covariances may be derived were

obtained using a three-step Monte Carlo procedure:

1. Simulated d.b.h. growth observations were calculated

as the sums of two components: first, ∆DBH predictions

obtained using the models with the original parameter

estimates and observations of the predictor variables, and

second, simulated residuals randomly selected from a

Gaussian distribution with zero mean and standard deviations obtained from [6].

2. Model parameter estimates were obtained by fitting

the models to data sets consisting of the simulated ∆DBH

observations obtained from step 1 and the observed values

of the predictor variables.

3. Simulated distributions of model parameter estimates

were obtained via 250 repetitions of steps 1 and 2.

Estimates of the uncertainty in ∆DBH predictions and in

estimates of derived BA variables were obtained using

Monte Carlo simulations. The simulation approach was

designed to mimic the sampling procedures of the Enhanced

FIA program. The selected plots were ordered with respect

to their plot numbers for each State and distributed among

five equal-sized panels by systematically assigning every

fifth plot to the same panel. Because FIA plot numbers had

been assigned sequentially on the basis of their geographic

locations, the panel assignments approximated the systematic, interpenetrating feature of the sampling design. Annual inventory estimates of mean plot BA and the standard

error of the mean were calculated with three methods: (1) the

SAMPLE20 estimates were based on measurements for

plots in the current year’s 20 percent panel; (2) the MOVING

estimates were based on the most recent measurements for

all plots; and (3) the UPDATE estimates were based on

measurements for plots in the current year’s 20 percent

panel of plots and updated data obtained using the AFIS

growth models for plots in the four panels measured in

previous years.

Annual estimates of mean plot BA and the standard error

of the mean were obtained using a four-step Monte Carlo

procedure:

1. Year 0:

a. Measurement of all plots was simulated by selecting the year 0 values of DBH, CR, CC, PC, LAT, and

LON from the annualized database.

b. Simulated values of BA and BAL were obtained

from the simulated d.b.h. observations by calculating BA for each plot and BAL for each tree on each

plot; mean plot BA and the standard error of the

mean were calculated and recorded.

c. A set of model parameter estimates for each species

was randomly selected without replacement from

the simulated distributions previously constructed.

2. Subsequent years:

a. For panels selected for measurement, plot field

measurement was simulated by replacing values

for each tree with values from the annualized

database for the appropriate year.

b. For panels not selected for measurement, an updated value of DBH for each tree was calculated as

the sum of previous year’s DBH, predicted ∆DBH,

and a simulated residual randomly selected from a

Gaussian distribution with zero mean and standard deviation obtained from [6].

c. BA was calculated for each plot, and BAL was

calculated for each tree on each plot; mean plot BA

and the standard error of the mean were calculated

and recorded for all three methods.

3. Step 2 was repeated 10 times to obtain estimates for

years 1 through 10.

4. Steps 1 through 3 were repeated 250 times to obtain

distributions of mean plot BA and the standard error of

the mean for each method for each year.

The standards of comparison for evaluating bias and the

contribution of uncertainty in model predictions to the

uncertainty in estimates of mean plot BA were the annual

estimates of mean plot BA and the standard errors of the

means calculated from the annualized database values. For

comparison purposes, the latter estimates represent a current year sample of the entire area of interest and are

regarded as being without measurement error. These estimates use 100 percent of the sample plots and are designated the SAMPLE100 estimates.

Bias and uncertainty in the annual inventory estimates of

mean plot BA and standard errors of the means were

evaluated using the medians of the distributions of simulated estimates. Comparisons of median estimates of mean

plot BA for the SAMPLE20, MOVING, and UPDATE methods to the annual SAMPLE100 estimates of mean plot BA

provide the bias check. Comparisons of the medians of

distributions of estimates of the standard error of mean plot

BA for the UPDATE method with the SAMPLE100 estimates reveals the effects of uncertainty in model predictions

on annual inventory estimates of mean plot BA.
162 USDA Forest Service Proceedings RMRS-P-25. 2002

McRoberts, Woodall, Lessard, and Holdaway Diameter Growth Models for Inventory Applications

0 2 4 6 8 10

20

40

60

80

100 Basal area (sq ft/ac)

SAMPLE100 mean

SAMPLE100 mean " 2SE

SAMPLE20 mean

MOVING mean

UPDATE mean

Year

Results ________________________

AFIS/STEMS Comparisons

The distributions of residuals for the STEMS, STEMSFIA, and AFIS predictions were not substantially different

(table 1). Median residuals for all species were less in

absolute value than 0.015 inch. Values of r2

 were also

comparable for the three models, although values for the

AFIS models were largest for three of four species, while

values for the STEMS models were smallest for three of

four species.

Ecosystem Province Models

The distributions of standardized residuals were consistently slightly skewed with longer tails in the positive

direction and with slightly negative medians. These results

are attributed to the combination of a lower bound, ∆DBH=0,

for observed average annual d.b.h. growth and the lack of an

analytical (not a physiological) upper bound for average

annual d.b.h. growth. In no case, however, was this skewness considered severe enough to affect the robustness of

least squares techniques to slight violations of the normality

assumption.

The analyses of residuals focused on median and mean

residuals for the validation and final calibration data sets.

Standards of 0.20 inch for absolute values of medians and

0.05 inch for absolute values of means were arbitrarily

selected. The analyses sought to identify any species group

with residual statistics that consistently failed to satisfy

these standards for reasons that could not be attributed to

small sample sizes or skewness in the distributions as

previously discussed. For Province 212, absolute values of

mean residuals were less than 0.05 inch for 17 of 25 species

groups for the validation analyses and for all 25 species

groups for the final calibration analyses. Only yellow birch

exhibited any consistent failures, although the mean residual for the final calibration data set was less than 0.01

inch For Province 222, absolute values of mean residuals

were less than 0.05 inch for the validation data sets for only

10 of 26 species groups, a result attributed primarily to small

validation sample sizes. For the final calibration data sets,

absolute values of mean residuals were less than 0.01 inch

for all species groups. For Province 251, absolute values of

mean residuals were less than 0.05 inch for five of nine

species groups for the validation data sets. These results are

partially attributed to overall small sample sizes and the

necessity of pooling data over larger geographic areas. Absolute values of mean residuals were less than 0.01 inch for all

species groups for the final calibration data sets. Parameter

estimates by species within ecosystem provinces are reported by McRoberts and others (submitted).

Updating

The 250 simulations were deemed adequate based on the

observation that coefficients of variation for estimates had

stabilized by 100 to 150 simulations and were virtually

unchanged for the final 50 simulations. Bias in the annual

inventory estimates of mean plot BA was evaluated by

comparing the medians of the distributions of the SAMPLE20,

MOVING, and UPDATE estimates to the SAMPLE100

estimates (fig. 2). The medians of the SAMPLE20 estimates

deviated considerably from the SAMPLE100 estimates due

to the SAMPLE20 small sample size, while the medians of

the MOVING estimates exhibited consistent bias due to the

trend in the SAMPLE100 estimates. The medians of the

distributions of the UPDATE estimates tracked the

SAMPLE100 estimates quite closely, a result confirmed by

the failure of the Wilcoxon Signed Ranks test to detect

Table 1—STEMS versus AFIS comparisons for validation data sets.

Species Model No. trees* Median residual r2

Red pine STEMS 501 0.004 0.480

STEMS-FIA 496 –0.002 0.462

AFIS 496 0.005 0.469

Balsam fir STEMS 1,924 –0.012 0.201

STEMS-FIA 1,922 –0.012 0.324

AFIS 1,922 –0.010 0.363

Quaking

 aspen STEMS 5,348 –0.005 0.133

STEMS-FIA 5,345 –0.001 0.197

AFIS 5,345 –0.009 0.228

Paper birch STEMS 1,708 –0.005 0.066

STEMS-FIA 1,704 –0.006 0.123

AFIS 1,704 –0.005 0.144

*

The number of trees represents only the 25 percent of the data randomly

selected for the validation data sets; slight differences in number of trees by model

for a particular species reflects analyses of residuals conducted at different times.

Figure 2—Annual inventory estimates of mean plot

basal area.
USDA Forest Service Proceedings RMRS-P-25. 2002 163

Diameter Growth Models for Inventory Applications McRoberts, Woodall, Lessard, and Holdaway

statistically significant differences (α=0.05). The medians of

the distributions of the UPDATE estimates of the standard

errors of the means were only slightly larger than the

SAMPLE100 estimates, indicating that uncertainty in model

predictions of d.b.h. growth has only a slight negative impact

on the uncertainty of annual inventory estimates of mean

plot BA. This result is attributed primarily to the observation that variability among plots is much greater than model

prediction uncertainty.

Summary ______________________

The AFIS models were constructed to update information

for FIA plots measured in previous years. Predictions using

the AFIS and STEMS models were comparable, but the

AFIS models remedy the noted limitations in the STEMS

models. Based on the general observation that average

annual d.b.h. growth for the North Central region is approximately 0.1 inch and that time intervals between inventories

for the calibration and validation data ranged between 12

and 17 years, the small mean and median residuals obtained

with the AFIS models indicate that they may be regarded as

unbiased for updating d.b.h. for trees measured in previous

years. In addition, annual inventory estimates of mean plot

BA obtained using the AFIS models to update information

for plots measured in previous years were superior to estimates using only the current year’s panel of data and

estimates using a five-panel moving average. Finally, the

AFIS models may be easily recalibrated as new data becomes available or as regional growth conditions change due

to climatic changes, management practices, or forest succession.

References _____________________

Bailey, R.G. 1995. Description of the ecoregions of the United

States. Ed. 2. Revised and expanded (1st ed. 1980). USDA For.

Serv. Misc. Publ. No. 1391 (rev.), Washington, DC. 108 p. with

separate map.

Belcher, David W., Margaret R. Holdaway, and Gary J. Brand. 1982.

A description of STEMS-the stand and tree evaluation and

modeling system. General Technical Report NC-79. St. Paul, MN:

U.S. Department of Agriculture, Forest Service, North Central

Research Station. 18 p.

Bush, Renate. 1995. The Central States TWIGS variant of the

Forest Vegetation Simulator. Internal Report. Fort Collins, CO:

U.S. Department of Agriculture, Forest Service, Forest Management Service Center. 28 p.

Bush, Renate, and Gary J. Brand. 1995. The Lakes States TWIGS

variant of the Forest Vegetation simulator. Internal Report. Fort

Collins, CO: U.S. Department of Agriculture, Forest Service,

Forest Management Service Center. 30 p.

Holdaway, Margaret R. 2000. The AFIS tree growth model for

updating annual forest inventories in Minnesota. In: Proceedings

of the IUFRO conference, 1998 August 16-20, Boise, ID. Hansen,

M.H. and T.E. Burk (eds.). General Technical Report NC-212. St.

Paul, MN: U.S. Department of Agriculture, Forest Service, North

Central Research Station. pp. 507-514.

Lessard, Veronica C. 2000a. Calibration of the STEMS diameter

growth models using FIA data. In: Proceedings of the IUFRO

conference, 1998 August 16-20, Boise, ID. Hansen, M.H. and T.E.

Burk (eds.). General Technical Report NC-212. St. Paul, MN: U.S.

Department of Agriculture, Forest Service, North Central Research Station. pp. 525-532.

Lessard, Veronica C., 2000b. Diameter growth models using FIA

data from the Northeastern, Southern, and North Central Research Stations. In: Proceedings of the First Annual Forest

Inventory and Analysis Symposium, 1999 November 2-3, San

Antonio, TX. McRoberts, R.E., G.A. Reams, and P.C. Van Deusen

(eds.). General Technical Report NC-213. St. Paul, MN: U.S.

Department of Agriculture, Forest Service, North Central Research Station. pp. 37-42.

Lessard, Veronica C., Ronald E. McRoberts, and Margaret R.

Holdaway. 2001. Diameter growth models using Minnesota forest inventory data. Forest Science 47(3):301-310.

Linhart, Heinz, and Walter Zucchini. 1986. Model selection. John

Wiley and Sons, Inc. New York. 301 p.

McRoberts, Ronald E. 2001. Imputation and model-based updating techniques for annual forest inventories. Forest Science

47(3):322-330.

McRoberts, Ronald E. 1999. Joint annual forest inventory and

monitoring system: the North Central perspective. Journal of

Forestry 97(12):27-31.

McRoberts, Ronald E., Margaret R. Holdaway, and Veronica C.

Lessard. 2000. Comparing the STEMS and AFIS growth models

with respect to the uncertainty of predictions. In: Proceedings of

the IUFRO conference, 1998 August 16-20, Boise, ID. Hansen,

M.H. and T.E. Burk (eds.). General Technical Report NC-212.

General Technical Report NC-212. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research

Station. pp. 539-548.

McRoberts, Ronald E., Christopher W. Woodall, and Veronica C.

Lessard. (submitted). Individual-tree diameter growth models

for three ecological provinces in the North Central region of the

United States. Forest Science.

Miner, Cynthia L., Nancy R. Walters, and Monique L. Belli. 1988.

A guide to the TWIGS program for the North Central United

States. General Technical Report NC-125. St. Paul, MN: U.S.

Department of Agriculture, Forest Service, North Central Research Station. 105 p.

North Central Research Station (NCRS). 1998. North Central

Region Forest Inventory and Analysis Field Instructions, First

Edition. North Central Research Station, USDA Forest Service,

St. Paul, MN.

Ratkowsky, David A. 1983. Nonlinear regression modeling. Marcel

Dekker, New York. 276 p.

Wykoff, William R. 1990. A basal area increment model for individual conifers in the northern Rocky Mountains. Forest Science

36(4): 1077-1103.
164 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

T. F. Gregg recently retired from the USDA Forest Service, Region 6 in

Portland, OR. He was the Regional Biometrician for the Forest Insects and

Disease Group. S. Hummel is a Research Forester, USDA Forest Service,

Pacific Northwest Research Station, P.O. Box 3890, Portland, OR 97208.

Abstract—The Forest Vegetation Simulator (FVS) lets users project

changes in forest stands associated with different initial conditions

and silvicultural treatments. Our objective is to develop tools that

help model users estimate the precision of FVS projections. A

technique called bootstrap resampling (bootstrapping) allows us to

approximate the sampling distribution of any variable simulated by

FVS. To use the technique, the original FVS tree list is sampled

repeatedly, with replacement, to build hundreds of bootstrapped

tree lists. These bootstrapped tree lists are then used to make

several hundred FVS projections. Each projection is thus based on

a resample of the original tree list. The resulting empirical distribution provides information on the sampling uncertainty associated

with the original tree list, which is important for making statistical

inferences about FVS model outcome. This paper introduces a new

bootstrapping program (FVSBoot) and describes its purpose and

potential value.

“...The oldest and simplest device for misleading folks is

the barefaced lie. A method that is nearly as effective and

far more subtle is to report a sample estimate without any

indication of its reliability...” (Freese 1967)

The Forest Vegetation Simulator (FVS) (previously known

as the Prognosis Model for Stand Development; Wykoff and

others 1982), lets model users project forest stand development through time. To use the FVS model information about

the initial condition of a stand must first be formatted into

a tree list. A tree list typically is created from a sample of

forest conditions (stand exam or forest inventory) and is the

basis for projecting the development of vegetation in FVS.

Hence, at least two potential sources of uncertainty exist in

FVS projections and should concern anyone who uses either

the model or its results. The sources of uncertainty are (1)

variation in the parameters and model forms that build the

simulation and 2) variation in the sample data used to create

tree lists.

The FVS model is generally considered deterministic

rather than stochastic (Hamilton 1991). It is important to

remember that a single realization from a single FVS projection is only one of several possible outcomes for the simulated

Assessing Sampling Uncertainty in FVS

Projections Using a Bootstrap Resampling

Method

T. F. Gregg

S. Hummel

development of that tree list. To explore “stochastic” (or

“random”) variation, the FVS user should make several

projections (also referred to as “runs” or “simulations”), each

of which has the same simulation file tree list but a new

random number seed. Model outcomes generated this way

can be summarized and analyzed; descriptive statistics

include the computation of upper and lower bounds on 95

percent (α=0.05) of the FVS outcomes. We define the interval between these bounds as the FVS prediction interval

(FVSPI). FVS users have been able to compute FVSPI to

characterize uncertainty associated with variation in parameters and models forms for several years.

The Issue ______________________

Results from FVS projections are, in our opinion, too often

reported without disclosing distributions of predicted outcomes or associated statistics. At issue is the ability to

interpret the precision of FVS simulations.

FVSPI is already available to users via the RANNSEED

keyword (Van Dyck 2000). There are several reasons why

FVSPI is not often reported, many of which relate to computational intensity. One obvious reason is simply the cost of

making a large number of projections. This was especially

true prior to the 1990s when FVS users had to pay by the run

on time-share systems. Another reason is the time associated with making large numbers of projections on old computers. However, the improved power in desktop systems

has made fast and cheap computing possible, and thus these

reasons are no longer insurmountable. Users can now easily

report FVSPI and simple summary statistics such as the

mean and standard deviation.

A different matter altogether is the challenge of reporting

distributions that include sampling uncertainty in FVS

projections. It hasn’t been possible to compute the effects of

sampling uncertainty because classical statistical methods

are not available to make inferences about FVS projections.

A variance estimator is not available for the results of

simulation. Our objective is to develop tools that characterize sampling uncertainty in FVS projections so that users

can easily report this important component of variation.

Bootstrapping __________________

Technological advances helped to resolve the problems of

time and money in making multiple FVS projections, and we

felt technology could also help us to tackle the problem of

assessing sampling uncertainty in FVS projections. Indeed,
USDA Forest Service Proceedings RMRS-P-25. 2002 165

Assessing Sampling Uncertainty in FVS Projections Using a Bootstrap Resampling Method Gregg and Hummel

a technique called bootstrap sampling (“bootstrapping”)

allows us to empirically approximate the sampling distribution of any statistic for which we want to make inferences

and thus avoid the lack of a variance estimator. In essence,

bootstrapping is data-based simulation. The statistical theory

underlying bootstrap sampling dates to the 1930s but the

computer capacity necessary to apply it to problems such as

ours has only recently become widely available. Bootstrapping

allows us to substitute computational power for theoretical

analysis (Efron 1982).

Starting in the 1970s, Efron introduced bootstrapping as

a generalized, computer-based method for estimating standard error for any parameter estimate of interest (Efron

1979 and 1982; Efron and Tibshirani 1993). Since then, a

flood of statistical research and reports on practical applications using bootstrapping has been published. The bootstrap

method has gained popularity among scientists working

with complex problems where estimating standard error

and confidence intervals is difficult, inaccurate, or impossible using classical statistical methods. Because of the

generalized nature of bootstrapping, it has been applied to a

wide range of statistical applications for estimating error

rates (Chernick 1999). These include discriminate analysis,

multiple, logistic, and nonlinear regression analysis, and

complex surveys. It has also been applied in a variety of

disciplines, including psychology, all the major physical

sciences, economics, medicine, and engineering. Our goal

was to apply bootstrapping techniques to FVS simulations.

We set three specific tasks:

1. Develop a method using bootstrap sampling techniques

for assessing uncertainty in FVS projections caused by

variation in input (tree list) data.

2. Create a module compatible with Suppose (Crookston

1997) and FVS to implement the method.

3. Illustrate the method using tree lists from the Gotchen

Late Successional Reserve study on the Gifford Pinchot

National Forest in Washington State.

Methods and Results ____________

For the first task, we take our original sample of the stand

(n) and replace the unknown population characteristics with

the known empirical distribution derived from repeatedly

resampling n (Chernick 1999). Because n comprises multipleplots (that FVS pools to create a tree list), by resampling

with replacement we “reshuffle” the combination of plots

that create the FVS tree list for that stand. There are just

two steps required:

• Generate a new sample of stand conditions by sampling

the original plots with replacement to create a bootstrap

sample, also of size n.

• Compute a bootstrap mean (Θ*) from the bootstrap

sample for any FVS attribute.

By repeating this process k times, we generate a Monte

Carlo approximation of the distribution of Θ*. The standard

deviation of this Monte Carlo approximation is the bootstrap

estimate of the true standard deviation for the population.

For the second task, we wrote a program (FVSBoot) to

implement the process described above within FVS and

Suppose. Output from the bootstrap program includes intervals around a set of FVS predictions. One interval available

as FVSBoot output is based on variation from the parameters and model forms of FVS (FVSPI). For example, we

made 201 FVS projections of stand 1022144 (using the

original tree list and random number seed plus an additional

200 projections seeding new random numbers). Figure 1

Figure 1—FVSBoot output of basal area (BA) for stand 1022144 at year 30 based on

the same initial tree list projected 200 times with a new random number each time.

Stand ID = 1022144

 Management ID = NONE

 Model output data from FVS SUMMARY Table = Cycle( 3), BA

 FVS-PI mean = 129.56

 Number of FVS runs = 200

 Standard Deviation = 1.30

 Median = 130.00

 Max = 133.00

 Min = 126.00

 Range = 7.00

 Frequency distribution for ( 201 ) samples for "Cycle( 3), BA" from FVS.

 Interval Midpoints Counts

 -------- --------- ---+------------------------------------------------------------

 1 126.50 12 |IIIIIIIIIIII

 2 127.50 25 |IIIIIIIIIIIIIIIIIIIIIIIII

 3 128.50 64 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 4 129.50 51 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 5 130.50 37 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 6 131.50 10 |IIIIIIIIII

 7 132.50 2 |II

 -------- --------- ---+------------------------------------------------------------
166 USDA Forest Service Proceedings RMRS-P-25. 2002

Gregg and Hummel Assessing Sampling Uncertainty in FVS Projections Using a Bootstrap Resampling Method

Figure 2—FVSBoot output of the mean basal area for stand 1022144 at year 30 based on 500 bootstrapped tree lists

projected 500 times.

illustrates how the FVSBoot program displays FVSPI output for square feet of basal area per acre (BA) after the third

cycle (Cycle (3)) for stand 1022144. It shows that the FVSPI

mean for BA is 129.56, and the standard deviation is 1.30.

The frequency distribution reveals that the midpoint value

in 64 of 201 counts was 128.50. Another interval available

from FVSBoot is based on variation in the sample data used

to create tree lists. We refer to it as the sampling error

prediction interval (SEPI). For comparison with FVSPI,

figure 2 illustrates how the FVSBoot program displays SEPI

Data from FVS Model: SUMMARY STATISTICS.

 Stand ID = 1022144

 Management ID = NONE

 FVS Variable = Cycle( 3), BA

 FVS-PI

 Mean = 129.56

 Number of samples = 201

 Standard Deviation = 1.295

 SEPI:

 Number of samples = 500

 Mean = 131.87

 Standard Deviation = 14.00

 Bootstrap Median = 132.00

 Max outcome = 175.00

 Min outcome = 93.00

 Range of outcomes = 82.00

BOOTSTRAP SAMPLING ERROR PREDICTION INTERVALS

 Variable Mean Percent Lower Upper

 ---------------- -------- ----- -------- --------

 Cycle( 3), BA 129.56 68 118.00 146.00

 80 114.00 150.00

 90 109.00 157.00

 95 105.00 162.00

 99 99.00 173.00

 -----------------------------------------------------------

 Frequency distribution for ( 500 ) bootstrap samples for "Cycle( 3), BA" from FVS.

 Interval Midpoints Counts

 -------- --------- ---+------------------------------------------------------------

 1 95.05 2 |II

 2 99.15 3 |III

 3 103.25 9 |IIIIIIIII

 4 107.35 13 |IIIIIIIIIIIII

 5 111.45 20 |IIIIIIIIIIIIIIIIIIII

 6 115.55 31 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 7 119.65 44 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 8 123.75 40 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 9 127.85 50 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 10 131.95 83 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 11 136.05 54 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 12 140.15 43 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 13 144.25 31 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 14 148.35 31 |IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

 15 152.45 17 |IIIIIIIIIIIIIIIII

 16 156.55 9 |IIIIIIIII

 17 160.65 12 |IIIIIIIIIIII

 18 164.75 4 |IIII

 19 168.85 2 |II

 20 172.95 2 |II

 -------- --------- ---+------------------------------------------------------------
USDA Forest Service Proceedings RMRS-P-25. 2002 167

Assessing Sampling Uncertainty in FVS Projections Using a Bootstrap Resampling Method Gregg and Hummel

output for BA after the third cycle (Cycle (3)) for stand

1022144. In this example, the SEPI mean is the mean

predicted value basal area per acre (BA) from 500 FVS runs

made with 500 bootstrapped samples. The SEPI mean is

131.87, and the standard deviation is 14.00. The 95 percent

SEPI for BA ranges from 105-162 ft2

 per acre. Except in rare

cases, the SEPI interval also includes the variation in the

parameters and model forms of FVS. This is because in FVS

the sequence of random numbers changes with very small

changes in the tree list. Bootstrapping implies changes in

the tree list sufficient to generate new random number seeds

in FVS over multiple time intervals. For this reason the

FVSPI is also reported with the SEPI statistics in the

FVSBoot output. In figure 2, the FVSPI summary statistics

are based on 201 projections, as described for figure 1.

For the third task, we pilot-tested FVSBoot using stand

exam data from the Gifford Pinchot National Forest. We

created 200 bootstrap samples for each tree list, ran the East

Cascades variant of FVS for 50 years, and evaluated the

projected values for variation in quadratic mean diameter.

Details, results, and potential implications for two tree lists

are reported in Hummel and others (this proceedings).

Summary ______________________

The FVSBoot program offers model users a tool that can

strengthen both the analysis of potential treatment effects

on stand development and the design of forest inventories.

If users rely on only one FVS projection of a tree list to

characterize stand development, then other outcomes possible from different combinations of sample data or from

other sequences of random events are not considered. We

think it is useful to characterize “extreme” outcomes as well

as “average” projected outcomes for FVS variables of interest because analyses that consider the likelihood of different

outcomes are more informative than analyses that do not.

The value of this additional information will depend on the

real or perceived risk associated with rare events. In addition to strengthening FVS analysis for management planning, FVSBoot creates the possibility for computer-based

experiments. By generating intervals around FVS output, it

is possible to determine whether differences among treatments on projected stand development are statistically significant. Further, it provides a way to investigate assumptions about the distribution of populations and patterns of

variation through time.

Another promising area for using FVSBoot is in the evaluation of sample design. We could, for example, characterize

the standard deviation from presampling and then calculate

the desired number of samples for a given level of acceptable

error. We could also ask whether within-stratum variation is

greater or less than that among strata to test the value of

different stratification methods.

FVSBoot does not cover all potential sources of variation

(climate change, for example), nor does it answer whether

statistically significant differences are, in fact, biologically

significant. Despite these shortcomings, we offer FVSBoot

as a new tool that can provide information about important

components of uncertainty in FVS model projections.

Acknowledgments ______________

This work benefited substantially from ideas and comments

offered by Jim Alegria, Nick Crookston, Gary Dixon, Tim

Max, Melinda Moeur, Bob Monserud, and Al Stage. Errors

and omissions remain our responsibility.

References

Chernick, Michael R. 1999. Bootstrap Methods, A Practitioner’s

Guide. John Wiley & Suns, Inc., New York.

Crookston, Nicholas L. 1997. Suppose: An Interface to the Forest

Vegetation Simulator. In: Teck, Richard; Moeur, Melinda; Adams,

Judy. 1997. Proceedings: Forest vegetation simulator conference.

1997 February 3-7, Fort Collins, CO. Gen. Tech. Rep. INT-GTR373. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Research Station.

Dixon, G.E. 2001. Essential FVS: A User’s Guide to the Forest

Vegetation Simulator. USDA Forest Service, Forest Management Service Center, Fort Collins, CO.

Efron, B. 1979. Bootstrap Methods: Another Look at the Jackknife.

Ann. Statist., 7:1-26.

Efron, B. 1982 The Jacknife, the Bootstrap, and Other Resampling

Plans. Society for Industrial and Applied Mathematics.

Philadelphia.

Efron, Bradley; Robert J. Tibshirani. 1993. An Introduction to The

Bootstrap. Chapman and Hill, New York.

Freese, Frank. 1967. Elementary Forest Sampling. Agriculture

Handbook No. 232, U.S. Department of Agriculture, Forest Service.

Hamilton, David A. Jr. 1991. Implications of Random Variation in

the Stand Prognosis Model. Research Note-INT-394 Ogden, UT:

U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. 12 p.

Wykoff, W.R.; N.L.Crookston; and A.R. Stage. 1982. User’s Guide to

the Stand Prognosis Model. USDA Forest Service Research Paper

INT-133. Intermountain Forest and Range Experiment Station,

Ogden, UT. 112 p.

Van Dyck, M. (comp.). 2000. Keyword Reference Guide for the

Forest Vegetation Simulator. USDA Forest Service Forest Management Service Center. Ft. Collins CO.
168 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

James W. Flewelling is a Consulting Biometrician, 26724 51st Place S.,

Kent, WA 98032. Fax: +1-253-520-6733. E-mail: jimflew@eskimo.com.

Robert A. Monserud is Chief Biometrician, Pacific Northwest and Rocky

Mountain Research Stations, located at 620 SW Main St., Suite 400, Portland,

OR. Fax: +1-503-808-2020. E-mail: rmonserud@fs.fed.us

Abstract — Our goal is to compare different methods for estimating

the parameters of individual tree mortality models. We examine

general methods including maximum likelihood and weighted nonlinear regression, and a few specialized methods including Proc

LOGISTIC in SAS, and an implementation of the Walker-Duncan

algorithm. For fixed period lengths, almost all methods for fitting

the logistic mortality model should work. The LOGISTIC procedure

in SAS is quite robust and the easiest to use. For unequal period

lengths, either a weighted nonlinear least squares or a maximum

likelihood formulation is needed to specify the annualized logistic

mortality model: NLIN(wtLS) and NLIN(LOSS) in SAS. Of course,

other statistical packages that mimic these procedures should give

the same results.

Introduction ____________________

Mortality remains one of the least understood yet important components of growth and yield estimation (Hamilton

1986). Great success in modeling mortality is rare, perhaps

because the focus is on modeling the occurrence of rare

events. Realistically, mortality modelers mostly hope to

capture the average rate of mortality and relate it to a few

reliable and measurable size or site characteristics (Keister

1972; Hamilton and Edwards 1976; Monserud 1976;

Hamilton 1994; Monserud and Sterba 1999).

A few preliminary observations provide some context,

several of which conspire to increase the difficulty of mortality modeling in forestry. First, mortality is a discrete, rare

event. A common rule of thumb for both temperate and

boreal forests is that roughly 0.5 to 1.5 percent of the trees

are expected to die in a given year (background mortality).

It therefore follows that a large sample is needed to observe

enough occurrences of mortality for modeling the process

adequately. Using this rule of thumb, we expect that a

sample of 10,000 trees/yr (or 2000 trees/5 yrs) would be

needed to observe approximately 50 to 150 deaths, which is

a relatively small sample of the event of interest. Second,

observations of the same individual tree at two points in

Comparing Methods for Modelling Tree

Mortality

James W. Flewelling

Robert A. Monserud

time are necessary to observe the survival or mortality

status of a tree; this requires remeasured permanent plots.

Estimates of the date of death from temporary plots are

notoriously unreliable and should not be used. Third, we

usually do not know tree age, so we cannot use Survival

Analysis methods (Allison 1995) that have been successful

in medical research. Furthermore, remeasured tree survival data are usually both left and right censored (Allison

1995; Meeker 1998): we only know that some of the trees died

sometime during the sampling interval, and the others

failed to die. Fourth, it is not uncommon to have widely

varying period lengths, which precludes the proper use of

some estimation programs that assume equal period lengths,

such as Proc LOGISTIC in SAS (SAS Version 8.1 is a product

of the SAS Institute, Cary, NC 27513).

Simulator architecture determines how mortality must be

calculated and simulated. Mortality is discrete in a spatial

simulator such as TASS (Mitchell 1975) or FOREST (Ek and

Monserud 1974), with the tree either completely dead or

alive. This mortality process is stochastic. Furthermore, the

costs of misclassification are not equal in spatial models.

Misclassifying a live tree as dead can never be corrected, but

misclassifying a dead tree as live can be corrected in the

future. Mortality can be continuous in a nonspatial simulator such as FVS (Stage 1973; Wykoff and others 1982;

Hamilton 1994; Teck and others 1997) or ORGANON (Hann

and others 1997), with the mortality rate smoothly reducing

the number of trees each sample tree represents. Therefore, it is much easier to predict mortality rates over large

areas with nonspatial models.

Our goal here is to compare several methods for estimating the parameters of individual tree mortality models. Note

that our objective is not to find the best mortality model for

a given data set. We will examine several methods: maximum likelihood, weighted nonlinear regression, and a few

specialized programs (Proc LOGISTIC in SAS, and the

Walker-Duncan algorithm of Hamilton 1974). Our motivation is that all too often authors do not provide enough

details on methods to reproduce results. Although our immediate context is modeling in the FVS environment, our

results apply to any other forest simulation system.

We have a few caveats. We will not address:

• Correlation of trees within a plot. They will have the

same stand density, and they could all be exposed to the

same unobserved mortality agent.

• Plot size effects on model parameters (see Stage and

Wykoff 1998).

• Simultaneous fitting of mortality and the growth equations (Hasenauer and others 1998; Cao 2000)
USDA Forest Service Proceedings RMRS-P-25. 2002 169

Comparing Methods for Modelling Tree Mortality Flewelling and Monserud

The Algebra of Mortality

Because a tree can only die once, mortality is not a Markov

process. Survival is, however. This property requires that all

algebra be mediated in terms of survival, not mortality. The

probabilities of survival (Ps) and mortality (Pm) are connected by the standard identity:

Survival = 1 – Mortality: Ps = 1 – Pm

We must use the compound interest formula to convert

survival to other period lengths:

1-year: Ps

1

 = 1 –Pm1

n-year: Ps

n

 = Ps

1

 Ps

1

 Ps

1

... Ps

1

= (Ps

1 )

n

 = (1 – Pm1 )

n

(The Markov property)

1-year: Pm1

 = 1 – Ps

n

1/n = 1 – (1 – Pmn )1/n

Statistical Considerations

The modeling of tree mortality requires fundamentally

different statistical methods than those used for most of the

other components of forest growth models. This is because

the dependent variable is dichotomous rather than continuous: 0 or 1 (live or dead). It then follows that the variance is

Binomial and is not constant: Var = P(1–P) = PmPs. Maximum Likelihood (ML) methods are the proper estimation

procedure. The likelihood of a sample of observations is the

joint probability of that sample (Mendenhall and Shaeffer

1973). The ML estimation problem is to find the values of the

unknown parameters that maximize this likelihood, which

is a product of individual probabilities. The log of this

product is used instead because it is a monotonically increasing function of the Likelihood and is much easier to

differentiate and maximize (Mendenhall and Shaeffer 1973).

Although seemingly complicated, ML estimates are quite

intuitive. They have the desirable properties of consistency,

asymptotic unbiasedness, and efficiency. The independence

of the observations is an underlying assumption, which is

violated if there are correlations between trees on the same

permanent plot, resulting in the underestimation of confidence intervals for parameters. We do not examine the

independence assumption.

A seminal paper by Grizzle and others (1969) demonstrated that Weighted Least Squares (with the weight ∝1/var)

could be an equally appropriate estimation method for

categorical data analysis (Agresti 1990). The method is

widely available and is asymptotically equivalent to maximum likelihood (Agresti 1990). Fisher’s Discriminant Analysis also works but quickly becomes unwieldy as the problem

becomes increasingly multivariate.

To model the probability of mortality (or survival), all that

is needed is a flexible function bounded by 0 and 1. The

Logistic equation is most often used, although any sigmoid

or cumulative distribution function would work. The logistic

for the probability of survival (Ps) is:

Ps = 1

1

1

+ 1 0 11 2 2

= + ′ ++ + e e bX b b x b x K

where X is a vector of independent variables, and b'X is a

linear combination of the n parameters bi and the xiindependent variables. Invoking the identity Ps = 1 – Pm and some

algebraic manipulation leaves us with the closely related

logistic form for the probability of mortality (Pm):

Pm = e

e

b X

b X

′

+ ′ 1

The inverse transform has a long history (McCullagh and

Nelder 1983):

log(Pm/(1–Pm)) = b'X

It is recognizable as the “logit” or the “log odds ratio.” This

equation provides the link between the unconstrained function b'X and the logistic probability prediction, which is

bounded by [0,1].

In the foregoing, Pm and Ps are interchangeable. The

function to model Pm could be used equally well to model Ps;

the only difference is that the signs in the coefficient vector

b would be reversed. This amounts to switching the 0-1

coding on the dependent variable.

We always want to know how good the model is. Usual

measures of residual variation (R2) are useless for dichotomous variables. It does not matter how close a predictor is to

0 or 1 as long as it can accurately estimate expectations. We

will simply use the sum of the log likelihoods, with the

standard factor of –2 that results from maximizing the

logarithmic linearization; we will label this –2LL. A closely

related statistic for model comparison is Akaike’s Information Criterion (AIC). It penalizes –2LL by the number of

parameters in the model (Chatfield 1996). The Chi-square

statistic is also appropriate, with the calculation based on

dividing important predictor variables into classes. Hosmer

and Lemeshow (2000) discuss these and other statistics.

Methods _______________________

We concentrate on a comparison of several methods for

estimating parameters with a fixed logistic model. Five

fitting techniques are described:

1. Logit Model for Proportions { REG } — This model, also

referred to as the linear logistic model, is ascribed to several

authors in the 1940s and 1950s (McCullagh and Nelder

1983). It requires that the data be grouped into classes of X

values. Within each class, the predictions are identical for all

observations. The observed proportion in each class should

be between zero and one, and the expected numbers of events

and nonevents in each class should be at least five. The

procedure is to transform the independent variable by computing its logit and then regressing that transformation

against X.

2. Least squares { NLIN (LS) } — Unweighted least

squares minimizes the sums of squared errors, where the

error is calculated as ε = Y – Pm, where Y equals one for

mortality and zero otherwise, and Pm is the predicted probability of mortality. Because of the logistic link function, Pm

is not a linear function of the parameters. Therefore, nonlinear regression is needed.

3. Walker-Duncan {RISK } — The Walker-Duncan algorithm (Walker and Duncan 1967) is a sequential approach to

weighted least squares that was developed in an era of

limited computer resources. It is claimed to be asymptotically equivalent to the maximum likelihood fit of the logistic
170 USDA Forest Service Proceedings RMRS-P-25. 2002

Flewelling and Monserud Comparing Methods for Modelling Tree Mortality

model. This routine was coded by Hamilton (1974) in the

RISK program and extended to deal with variable periodlength data.

4. Weighted Least Squares {NLIN (wtLS), LOGISTIC } —

Weighted least squares minimizes the weighted sums of

error squared, using weights that are inversely proportional

to the estimated variance: Var = P(1-P). McCullaugh and

Nelder (1983) show that the minimization of this weighted

sums of squares produces the maximum likelihood solution.

That proof assumes that the link function is linear in the

parameters, which is not true for the variable length period

regressions to be discussed later. Nonetheless, in every test

regression that we have examined, the weighted least squares

method does produce the ML solution, a result supported

by Agresti (1990). The weighting is iterative because the

weights depend on the parameter vector b, which is being

estimated. The iterative weighting can prevent some fitting

techniques from fully converging. A conservative strategy is

to hold the weights constant while iterating toward a least

squares solution, then resetting the weights and proceeding

to further rounds of minimization. Most LOGISTIC programs use iteratively reweighed least squares to obtain the

ML solution.

5. Maximum Likelihood { NLIN (LOSS) } — Maximum

likelihood solutions can be derived directly by iterating the

model parameters until the likelihood is maximized, or

equivalently until the negative log likelihood is minimized.

This is done by writing the LL function shown in the

appendix and incorporating that computation into an

optimizer. The SAS procedure NLIN and equivalent procedures in other statistical packages can perform the optimization. In NLIN, the keyword _LOSS_ defines the objective

criteria.

The above programs are all procedures within SAS, except

for RISK, which was developed by Hamilton (1974). We use

the notation “LS” to indicate the least squares objective

function normally employed in nonlinear regression. The

notation “LOSS” indicates where we employ an explicitly

defined loss function.

Data Sets

Four data sets are used to illustrate various regression

methods. The data sets are referred to as data A, B, C, and

D; they are summarized or listed in their entirety in tables

1 through 4, respectively. Data sets A and B offer simple

examples suitable for event estimation (tables 1 and 2);

period length is not involved. Data set C is real data

(64,121 observations) from young unthinned plantations of

Douglas-fir in coastal Washington, Oregon, and British

Columbia (summary in table 3). The growth rates in those

data are from regressions of change in DBH versus initial

DBH fit to the data one plot and one growth period at a time.

Growth periods are from 1 to 4 years. Data set D is a

simulated data set, which also has variable period lengths

(table 4). It consists of four 0.5 acre plots, each with 10

arbitrary selected tree sizes, arbitrary frequencies, and

arbitrary growth rates. The mortality was simulated for all

trees in all years using the ML regression fit to data set C.

Because data set D was selected to have high densities and

Table 1—Data set A - categorized data

distributed as an example for

the RISK program.

Y X1 X2

012

121

112

122

012

011

021

021

122

122

122

121

112

021

111

122

122

112

121

112

112

122

111

022

022

121

111

011

112

011

112

021

111

112

022

011

021

111

121

111

An alternative formulation of the same

data set shows N observation on each

line, with NY1 of them indicating an

event (Y=1):

X1 X2 N NY1

1 1 10 6

1 2 10 8

2 1 10 5

2 2 10 7

long periods, the predicted mortality rates are high, allowing

us to better see the differences due to the various fitting

methods. As with data set C, the growth rates are best

thought of as having been predicted by a diameter growth

function.
USDA Forest Service Proceedings RMRS-P-25. 2002 171

Comparing Methods for Modelling Tree Mortality Flewelling and Monserud

Results ________________________

Fitting with Fixed Period Length

The examples presented use techniques that are suitable

for predicting mortality in a period of fixed length, where all

of the data are from remeasurement periods of that same

length. In these circumstances, the length of the period does

not enter into the prediction process.

Data set A is used to illustrate the fitting of a simple

logistic regression. Code for three methods is shown in the

appendix. Coefficients and -2 LL’s are in table 5; predictions

for all X values are in table 6. RISK, REG, and LOGISTIC

solutions all have nearly identical likelihoods, even though

there are minor differences in the coefficients. RISK predicts

there will be 25.90 events, whereas the other methods have

prediction closer to the observed 26 events; this offers a hint

that the RISK solution may not be the true maximum

likelihood solution. The results from RISK may vary a bit if

the order of observations is changed. The conclusion is that

for classed data, with event (or nonevent) expectations as

low as 2, all of the fitting methods produce results that are

statistically indistinguishable from one another.

Table 2—Data set B. A simple data set

illustrating low-probability events.

The data set is shown in its

classed formulation. It could also

be shown as 100 observations,

with 11 observations having Y = 1

and 89 observations having Y =

0. All models using these data

use the expanded form (100 observations) unless stated otherwise.

X1 N NY1

0.0 10 3

0.1 10 1

0.2 10 2

0.3 10 1

0.4 10 1

0.5 10 2

0.6 10 0

0.7 10 0

0.9 10 1

1.0 10 0

Table 3—Summary of data set C, remeasurement data from fixed-area

plots in young Douglas-fir plantations. The data set contains

64,121 observations.

Variable Mean Minimum Maximum

DBH (in) 4 0 14

Height (ft) 22 2 74

BA (ft2

/ac) 62 3 172

Age 14 5 22

Period length (years) 3.4 1 4

Mortality proportion 0.014

Data set B (table 2) is another simple data set. It is

included here to show some lower probability events, as are

typical in mortality data sets. Code is presented for four

regression procedures in the appendix. Unweighted least

squares is implemented with NLIN (LS); weighted least

squares is implemented by NLIN (wtLS) and LOGISTIC;

direct maximum likelihood is implemented with NLIN

(LOSS). In the NLIN(wtLS) code, note how the iterative

reweighting is implemented in two passes: first, an

unweighted regression returns preliminary estimates of

event probabilities; second, a weighted regression then uses

the earlier estimated probabilities in assigning weights. The

solutions are nearly identical for all methods (table 7). The

trivial differences in -2LL are due to incomplete convergence

for NLIN(LOSS) and to the fundamental consideration that

NLIN(LS) is not using the theoretically correct weighting.

Fitting with Variable Period Length

One complication in mortality modeling is unequal period

lengths. In the Algebra subsection, we saw how the compound interest formula (Markov property) can bring mortality or survival rates for any period length to an annual basis.

It is important to have this flexibility to simulate any

reasonable period length. However, not all estimation procedures allow for variable period lengths. The LOGISTIC

procedure in SAS is a case in point. Beyond the mechanical

difficulties of fitting, there may be serious differences between how the regression predictions are applied to the data

set, and how they will be made within a simulator.

Data set C has variable period lengths of 1 to 4 years

(table 3). An annual mortality model is fit for these data. The

independent variables generally take on values as assigned

at the start of the growth period. One variant on the regression process uses midpoint values for the independent variables, a technique that is useful for building annual simulators from multiyear data (Hyink and other 1985). The linear

combination used in the Logit is:

b'X = b0 +b1DBH +b2BAL/BA + b3BA

where BA is the total basal area per acre, and BAL is the

basal area of larger trees. Although these variables were a

good set of predictors for this particular data set, they are not

a recommended set for other applications.

The procedures applied to data set C include RISK, LOGISTIC, and NLIN(LOSS). The latter procedure is run once

with start-of-period X values and once with midpoint values.

Results are in table 8. The results from NLIN(LOSS) are the

best (the lowest -2LL), and RISK is the poorest. Surprisingly,

the LOGISTIC results are almost the best, even though they

rely on a statistically incorrect model. In this application,

each period of YIP years is treated as YIP independent

observations, one of which may result in mortality. For

situations where mortality rates are very low (as in data set

C), the LOGISTIC procedure can produce good estimates of

parameters. However, all the fit statistics (including -2LL)

and confidence intervals returned by the LOGISTIC regression program are incorrect. The NLIN (LOSS) regression

using midpoint values is significantly poorer than those

using start-of-period values. The use of midpoint values was

expected to be an improvement; the lack of improvement

might indicate a deficiency in our choice of independent
172 USDA Forest Service Proceedings RMRS-P-25. 2002

Flewelling and Monserud Comparing Methods for Modelling Tree Mortality

Table 4—Data set D. Simulated data set based on models from data set C.

PLOT SIZE YIP DBH DBHRATE NTREES GROUP BAL BA TPA NSURV BA_FINAL TPAFINAL

1 0.5 8 2.8 0.80 40 1 1.710 17.191 800 37 220.410 688

1 0.5 8 2.6 0.78 40 2 4.896 17.191 800 35 220.410 688

1 0.5 8 2.4 0.76 40 3 7.627 17.191 800 35 220.410 688

1 0.5 8 2.2 0.74 40 4 9.940 17.191 800 36 220.410 688

1 0.5 8 2.0 0.72 40 5 11.868 17.191 800 36 220.410 688

1 0.5 8 1.8 0.70 40 6 13.448 17.191 800 33 220.410 688

1 0.5 8 1.6 0.68 40 7 14.713 17.191 800 37 220.410 688

1 0.5 8 1.4 0.64 40 8 15.699 17.191 800 35 220.410 688

1 0.5 8 1.2 0.62 40 9 16.441 17.191 800 31 220.410 688

1 0.5 8 1.0 0.60 40 10 16.973 17.191 800 29 220.410 688

2 0.5 12 2.8 0.80 40 1 1.710 17.191 800 34 296.201 456

2 0.5 12 2.6 0.78 40 2 4.896 17.191 800 29 296.201 456

2 0.5 12 2.4 0.76 40 3 7.627 17.191 800 27 296.201 456

2 0.5 12 2.2 0.74 40 4 9.940 17.191 800 31 296.201 456

2 0.5 12 2.0 0.72 40 5 11.868 17.191 800 30 296.201 456

2 0.5 12 1.8 0.70 40 6 13.448 17.191 800 26 296.201 456

2 0.5 12 1.6 0.68 40 7 14.713 17.191 800 22 296.201 456

2 0.5 12 1.4 0.64 40 8 15.699 17.191 800 12 296.201 456

2 0.5 12 1.2 0.62 40 9 16.441 17.191 800 8 296.201 456

2 0.5 12 1.0 0.60 40 10 16.973 17.191 800 9 296.201 456

3 0.5 8 7.8 0.50 30 1 9.955 156.883 600 24 283.609 434

3 0.5 8 7.6 0.50 30 2 29.361 156.883 600 24 283.609 434

3 0.5 8 7.4 0.50 30 3 47.772 156.883 600 20 283.609 434

3 0.5 8 7.2 0.50 30 4 65.214 156.883 600 22 283.609 434

3 0.5 8 7.0 0.50 30 5 81.714 156.883 600 23 283.609 434

3 0.5 8 6.8 0.50 30 6 97.298 156.883 600 22 283.609 434

3 0.5 8 6.6 0.50 30 7 111.991 156.883 600 24 283.609 434

3 0.5 8 6.4 0.50 30 8 125.821 156.883 600 17 283.609 434

3 0.5 8 6.2 0.50 30 9 138.813 156.883 600 21 283.609 434

3 0.5 8 6.0 0.50 30 10 150.993 156.883 600 20 283.609 434

4 0.5 12 7.8 0.50 30 1 9.955 156.883 600 20 326.410 356

4 0.5 12 7.6 0.50 30 2 29.361 156.883 600 23 326.410 356

4 0.5 12 7.4 0.50 30 3 47.772 156.883 600 15 326.410 356

4 0.5 12 7.2 0.50 30 4 65.214 156.883 600 20 326.410 356

4 0.5 12 7.0 0.50 30 5 81.714 156.883 600 18 326.410 356

4 0.5 12 6.8 0.50 30 6 97.298 156.883 600 18 326.410 356

4 0.5 12 6.6 0.50 30 7 111.991 156.883 600 17 326.410 356

4 0.5 12 6.4 0.50 30 8 125.821 156.883 600 16 326.410 356

4 0.5 12 6.2 0.50 30 9 138.813 156.883 600 14 326.410 356

4 0.5 12 6.0 0.50 30 10 150.993 156.883 600 17 326.410 356

Variables for data set D

Plot An arbitrarily assigned plot number

Size Plot size in acres

YIP Years in the period

DBH Initial diameter (inches)

DBHRATE DBH growth rate (in/yr)

NTREES Initial number of trees of this size.

GROUP Sequence number assigned to a group of trees of same size.

BAL Initial basal area of larger trees (including half of current group)

BA Initial basal area (sq. ft/ac)

TPA Initial trees per acre

NSURV Number of surviving trees at end of growth period

BA_FINAL Final basal area for the plot (sq. ft/ac)

TPAFINAL Final trees per acre.

Table 5—Regression results, data set A.

Regression b0 b1 b2 –2LL

LOGISTIC –.011508 –.462517 .908641 49.558

REG –.002985 –.47223 .91406 49.558

RISK .0530757 –.494347 .889641 49.562
USDA Forest Service Proceedings RMRS-P-25. 2002 173

Comparing Methods for Modelling Tree Mortality Flewelling and Monserud

Table 6—Predictions from three methods for the probability of an event using data set A.

X1 X2 N NY1/N P (RISK) P (LOGISTIC) P (REG)

1 1 10 .6 .61025 .60698 .60798

1 2 10 .8 .79216 .79302 .79461

2 1 10 .5 .48851 .49302 .49166

2 2 10 .7 .69924 .70698 .70696

Wt’d Sum 40 26 25.90 26.00 26.01

Table7—Regression results, data set B.

Regression b0 b1 –2LL

LOGISTIC –1.1115 –2.6602 64.3675

NLIN(LS) –1.1219 –2.6270 64.3682

NLIN(wtLS) –1.1120 –2.6586 64.3675

NLIN (LOSS) –1.1219 –2.6270 64.3682

Table 8—Regression results, data set C.

Regression b0 b1 b2 b3 –2LL

LOGISTIC1 –3.2956 –.9826 –1.5666 0.0410 8049

NLIN(LOSS) –3.4217 –1.0068 –1.38 0.0425 8047

NLIN(LOSS) – Midpoint –2.3943 –1.1032 –2.3609 0.0423 8068

RISK –3.1513 –.90577 –1.6717 0.0391 8096

1

The regression reports –2LL as 10363, based on an inflated number of observations.

variables. The RISK procedure shows the poorest -2LL

value, possibly indicative of convergence problems.

Data set D is an artificial data set of four plots, each with

10 tree sizes. Four variations of the same model are examined: a Compound survival model, a Midpoint model, an

Interpolated model, and a Simulation model. The differences between the models arise from different assumptions

for calculating the same X variables. The Compound model

uses the initial values of X’s for each survival prediction, so

a compound interest formula for survival is appropriate. The

Midpoint model uses the average of starting and ending

values for the independent variables and applies the same

compound survival formula as for the previous method. The

Interpolated model uses different X values for each year in

the growth period; these are from linear interpolation. For

both the midpoint and interpolated models, BAL/BA is not

updated from the start of the period because doing so would

require information on tree mortality. The Simulation model

uses different X values for each year; these are iteratively

recomputed based on predicted survival (for example, the

computation of each year’s BAL is dependent on the predicted mortality in earlier years). The NLIN (LOSS) procedure is used for fitting in all four cases. The Midpoint model,

the Interpolated Model, and the Simulation model all take

advantage of knowing something about DBH growth rates

or end-of-period values. If the DBH growth rates are in fact

predictions from a growth model, their use does not invalidate the independence of the X values. The use of the end-ofperiod basal area by the Midpoint and Interpolated models

is a not a statistically valid procedure, but may still produce

good results (Hyink and others 1985).

The Compound survival model for data set D uses the

same code as given in the appendix under the heading: “C -

NLIN(LOSS)”. This is a reasonable approach for data where

the period lengths are similar. With period lengths in the

data of 8 and 12 years, this model could reasonably be

applied to a 10-year period. The Midpoint model uses the

same code as indicated for “C - NLIN (LOSS), Midpoint X

values.” The Interpolated X model uses linear interpolation

code shown in the appendix. Code for the Simulation model

is specific to this problem and relies upon a particular data

structure (see appendix).

Results of fitting data set D are in table 9, where two

values are shown for -2LL, the “fit” value as reported by the

regression program and the “true” value that would be

obtained within the context of an annual simulation. The

Compound model would not be applied within an annual

simulation, nor can it be evaluated for the 10-year steps

where it is likely to be applied. Thus, its true -2LL is

unknown. The best fit is expected to be from the Simulation

model. Here the mortality predictions within the regression

are identical to those made within the simulator, and a
174 USDA Forest Service Proceedings RMRS-P-25. 2002

Flewelling and Monserud Comparing Methods for Modelling Tree Mortality

Table 9—Regression results, data set D. The NLIN (LOSS) procedure is used for fitting in all four cases.

Two values of –2LL are shown: those from the fitting routine, and the “true” –2LL values that

would be obtained by using the coefficient solutions in an annual growth simulation.

Model b0 b1 b2 b3 –2LL (fit) –2LL(true)

Compound1 0.1956 –1.6602 –2.1140 .058730 1606

Midpoint2 –2.5369 –1.4604 –2.3461 .063074 1550 1596

Interpolated X3 –4.9798 –.9577 –1.4003 .04970 1538 1544

Simulation4 –2.9387 –1.0529 –1.7037 .0437 1538 1538

1

Predicted annual survival rate is compounded for YIP years; X’s are initial values. Suitable for 10-yr step.

2

Annual model relying on midpoint X values. Reported –2LL =1550

3

Annual model relying on annually interpolated X values.

4

Annual model, with iteratively simulated annual X variables.

maximum likelihood solution is chosen; we do not address

any interaction with maximum density constraints that

FVS may employ. The Interpolated X method has a -2LL

value that is almost as good; the Midpoint model is considerably poorer. A partial explanation for the interpolated X

method being so good, is that a linear DBH growth model is

assumed; a nonlinear model would not interpolate as well.

Another result, not shown in the table, is the total number

of mortality trees that would be predicted by an annual

simulation. The observed mortality was 433 trees. The

predicted mortality for the Midpoint, Interpolated X, and

Simulation model fits were 518, 474, and 459, respectively.

There is a tendency to overpredict mortality. Normally that

might be indicative of model misspecification. Here there is

the additional possibility that the simulation that created

the data set was somehow defective.

Summary ______________________

Fitting a fixed-period length mortality model is straightforward. The model should directly predict the probability of

survival (or mortality) for the entire period, without any

requirement to compound the survival rate. Any of the

methods presented here should be adequate for this task,

with the exception that the Logit model for proportions

should not be used unless the data are easily summarized

into cells with a constant mortality rate. We offer this Logit

model for proportions for historical completeness, not as a

viable general alternative. The LOGISTIC regression procedure is the most robust. It is the easiest to apply, should not

have convergence problems for typical data sets, and should

normally be the preferred methodology. Each tree is one

observation, and weighting is not used. Weighted least

squares, NLIN(wtLS), should also be problem-free but is a

bit more work. NLIN(LOSS) is the maximum likelihood

option. It should provide good solutions, although convergence may slow as the number of parameters increases.

RISK is a product of its time: a clever approximation to

maximum likelihood when mainframe computer resources

were limited. A FORTRAN compiler is required if model

transformations cannot be done beforehand in an external

data structure. Finally, a fixed-length survival model can be

formulated as a direct prediction, as we have done here, or

it could be formulated as a compound annual survival model

for a fixed-length application. The appropriate methodology

for fitting the latter formulation would be the same as for a

variable-period length model, using initial X values.

For variable-length periods, the first decision is whether

the model will be used in an annual simulation or in a

simulation with a fixed step size of greater than 1 year. For

the fixed step size, the data should be limited to growth

periods of approximately that duration. The appropriate

regression model is the Compound model in table 9, or the

equivalent NLIN(LOSS) regression in table 8. For an annual

simulation model, the best fit is assured with the Simulation

model in table 9. In a real fitting exercise, the assumed DBH

growth rates would be replaced by predicted growth rates

(Cao 2000). As alternatives to a full Simulation model,

reasonable approximations may be achieved with Interpolated X’s or Midpoint X’s. It is impossible to know in advance

whether one of these approximate methods will be adequate.

Due to the difficulty in programming the full simulation

method, the use of midpoint X’s may be the preferred method

for model screening, and the use of interpolated X’s may be

adequate for final fitting. If the Midpoint or Interpolated X

model is used, the Simulation model should be used to verify

that the total predicted mortality is close to the observed

total. This verification may be part of the validation of a

completed growth simulator. Regardless of the formulation

of the X’s, the actual fitting method should be either NLIN

(wtLS) or NLIN(LOSS).

In the foregoing, NLIN(wtLS) and NLIN(LOSS) are presented as having similar capabilities; in general, they both

produce the ML solution. For the SAS system, NLIN(wtLS)

seems to have better convergence properties than the

NLIN(LOSS). For other statistical packages, this observation may not hold.

Acknowledgments ______________

The Stand Management Cooperative, headquartered at

the University of Washington, Seattle, provided the Douglas-fir data. Bill Wykoff, David Hamilton, Mark Hanus,

and David Hann provided assistance with the RISK program. Additionally, Bill Wykoff patiently explained the

possible ways for bringing mortality predictions into FVS.

We thank Mark Hanus and Temesgen Hailemariam for

helpful review comments.
USDA Forest Service Proceedings RMRS-P-25. 2002 175

Comparing Methods for Modelling Tree Mortality Flewelling and Monserud

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Flewelling and Monserud Comparing Methods for Modelling Tree Mortality

Appendix ________________________________________________________

B - NLIN(LOSS)

PROC NLIN ;

 PARMS B0 = -.5 B1 = -2 ;

 LOGIT = B0 + B1 * X1;

 Yhat = EXP(LOGIT) / (1 + exp(LOGIT)) ;

 IF Y = 1 then N2LLIKE = -2*LOG(Yhat);

 else N2LLIKE = -2*LOG(1-Yhat);

_LOSS_ = N2LLIKE;

MODEL Y = YHAT;

OUTPUT OUT=Filename P = PredY;

C - LOGISTIC

PROC LOGISTIC ORDER=INTERNAL ;

 MODEL MORT/YIP = DBH BAL_BA BA ;

note: YIP = years in period.

MORT=1 if tree dies, else 0.

BAL_BA = BAL / BA

C - NLIN(LOSS).

PROC NLIN METHOD=DUD MAXITER=130;

 PARMS

 B0 = -3 B1 = -1 B2 = -1 B3 =0.05 ;

 LOGIT = B0 + B1 *DBH + B2 * BAL_BA + B3*BA;

 P_annual = exp(LOGIT) / (1 + exp(LOGIT) );

 P_surv = ( 1 - P_annual)**YIP;

 P_mort = 1 - P_surv ;

 IF MORT = 1 then N2LLIKE = -2*log(P_mort);

 else N2LLIKE =-2*log(1 - P_mort) ;

 _LOSS_ = N2LLIKE;

 MODEL MORT = P_mort;

 OUTPUT OUT=OUTF P=Pmort;

The above does not converge satisfactorily. It was followed up by

several regressions with B1 fixed at various values, including B1 = -

1.38, and improved initial guesses for the other parameters.

note: YIP = years in period.

MORT=1 if tree dies, else 0.

BAL_BA = BAL / BA

C - NLIN (LOSS), Midpoint X values.

Same as the above code, but preceded by a data step redefining the

independent variables:

BAL_BA unchanged

F = (YIP/2 -0.5)/YIP ;

BA = BA + F*(BAfinal - BA) ;

DBH = DBH + F * (DBHgrown - DBH)

where BAfinal is the basal area at the end of the period, and DBHgrown

is from a by-plot regression of ending DBH (for the surviving trees)

versus the initial DBH.

C - RISK

RISK with SMC data for DF

1. 1. 4. 64121. 15. 0. 0.

0. 999999. 5. 20.

8

0000000000000000

(8F9.5)

 A0BAL/B BA DBH MORT 1 1 step

1 2 34 5 67 8

Note: This is the control file that was actually used. The resultant

coefficients are in a different order than is shown in our results

(Table 8).

Code for selected regression examples, by data set (A, B, C, D).

A - LOGISTIC

Proc LOGISTIC for data set A (40 observations)

PROC LOGISTIC ORDER=INTERNAL DESCENDING;

 MODEL Y = X1 X2 ;

A - REG

Logit Model for proportions, using classed formulation of data set A

Data Step

 P = NY1 / N ;

 LOGITY = LOG(P/(1-P)) ;

PROC REG;

MODEL LOGITY = X1 X2;

WEIGHT N ;

A - RISK

Control lines for running data set A (40 records) with the RISK program.

TEST PROBLEM 1

1 1 3 40 8

9999 3 5.0

4

00000

(4F2.0)

 PROB XONE XTWO

000010000200003

B- LOGISTIC

PROC LOGISTIC ORDER=INTERNAL DESCENDING;

 MODEL Y= X1;

 OUTPUT OUT=fileout P = PRED;

PROC TABULATE;

 CLASS X1;

 VAR Y PRED;

 TABLE X1 ALL, SUM*( Y PRED);

B - LOGISTIC (for classed data set of 11 records)

PROC LOGISTIC;

 MODEL NY1 / N = X1;

B - NLIN (LS)

PROC NLIN ;

 PARMS B0 = -.5 B1 = -2 ;

 LOGIT = B0 + B1 * X1;

 Yhat = EXP(LOGIT) / (1 + exp(LOGIT)) ;

 MODEL Y = YHAT;

B- NLIN (wtLS)

PROC NLIN ;

 PARMS B0 = -.5 B1 = -2 ;

 LOGIT = B0 + B1 * X1;

 Yhat = EXP(LOGIT) / (1 + exp(LOGIT)) ;

 MODEL Y = YHAT;

 OUTPUT OUT=File2 P=Pred1 ;

PROC NLIN data=File2;

 PARMS B0 = -.5 B1 = -2 ;

 LOGIT = B0 + B1 * X1;

 Yhat = EXP(LOGIT) / (1 + exp(LOGIT)) ;

 VAR = Pred1 * (1-PRED1);

 _WEIGHT_ = 1/VAR ;

 MODEL Y = YHAT;

 OUTPUT OUT=File3 P=Pred2 ;

And optionally, one more iteration
USDA Forest Service Proceedings RMRS-P-25. 2002 177

Comparing Methods for Modelling Tree Mortality Flewelling and Monserud

D - Interpolated X.

Data Step

 BAL_BA=BAL/BA;

 DO I=1 to NSURV;

 MORT=0;

 OUTPUT;

 END;

 NDEAD = NTREES-NSURV;

 if NDEAD> 0 then DO I=1 to NDEAD;

MORT=1;

OUTPUT;

END;

PROC NLIN METHOD=DUD ;

 PARMS B0 =-3 B1=-1 B2 =-1 B3=.04;

PER_SURV = 1.0 ;

Label PER_SURV=’Pr{Surv for Period}’;

 DO I = 1 to YIP;

 BAi = BA + (I-1)/YIP * (BA_FINAL-BA);

 DBHi = DBH + (I-1) * DBHRATE ;

 F = B0 + B2* BAL_BA + B3*BAi + B1*DBHi;

 P_mort = exp(F) / (1 + exp(F) );

 P_surv = ( 1 - P_mort);

 PER_SURV = PER_SURV * P_SURV;

 END;

PER_MORT = 1.0-PER_SURV;

IF MORT = 1 then N2LLIKE = -2*log(PER_mort);

else N2LLIKE =-2*log(PER_SURV) ;

_LOSS_ = N2LLIKE;

MODEL MORT = PER_mort;

OUTPUT OUT=OUTF P=Pmort;

D - simulation model

* Collapse to one record per plot;

DATA A;

 SET [original data]; by PLOT;

 ARRAY VDBH{10} DBH1-DBH10 ;

 ARRAY VRATE{10} RATE1-RATE10;

 ARRAY VN{10} N1-N10;

 ARRAY VTPA{10} TPA1-TPA10;

 ARRAY VNSURV{10} NSURV1-NSURV10;

 ARRAY VBAL{10} BAL1-BAL10;

 VDBH{GROUP} = DBH;

 VRATE{GROUP} = DBHRATE;

 VN{GROUP} = NTREES;

 VTPA{GROUP} = NTREES/SIZE ;

 VNSURV{GROUP} = NSURV;

 VBAL{GROUP} = BAL ;

 DEATHS = (TPA-TPAFINAL)*SIZE;

 if LAST.PLOT then output;

 RETAIN DBH1-DBH10

RATE1-RATE10

N1-N10

TPA1-TPA10

NSURV1-NSURV10

BAL1-BAL10 ;

KEEP PLOT SIZE YIP TPA BA DEATHS

DBH1-DBH10

RATE1-RATE10

N1-N10

TPA1-TPA10

NSURV1-NSURV10

BAL1-BAL10;

PROC NLIN METHOD=DUD ;

 PARMS B0 =-3 B1=-1 B2 =-1 B3=.04;

ARRAY VDBH{10} DBH1-DBH10;

ARRAY VRATE{10} RATE1-RATE10;

ARRAY VN{10} N1-N10;

ARRAY VTPA{10} TPA1-TPA10;

ARRAY VNSURV{10} NSURV1-NSURV10;

ARRAY VBAL{10} BAL1-BAL10;

ARRAY GDBH{10} GDBH1-GDBH10;

ARRAY GTPA{10} GTPA1-GTPA10;

ARRAY GBAL{10} GBAL1-GBAL10;

 * START-UP before growth;

 DO I=1 to 10;

GDBH{I}=VDBH{I};

GTPA{I}=VTPA{I};

GBAL{I}=VBAL{I};

 END;

 GBA = BA;

DO IYEAR = 1 to YIP;

BASUM=0;

DO I = 1 to 10;

 F = B0 + B2* GBAL{I}/GBA + B3*GBA + B1*GDBH{I};

 P_mort = exp(F) / (1 + exp(F) );

 P_surv = ( 1 - P_mort);

 GTPA{I} = GTPA{I} * P_SURV;

 GDBH{I} = GDBH{I} + VRATE{I};

 BAgroup = GTPA{I} * 0.005454154 *GDBH{I}*GDBH{I};

 GBAL{I} = BASUM + BAGROUP/2;

 BAsum = BAsum + BAgroup ;

END;

 GBA= BAsum;

END;

* Evaluate all the losses;

 N2LLIKE=0;

 PRDeaths = 0;

 DO I = 1 to 10;

 PR_SURV = GTPA{I}/VTPA{I} ;

 PR_MORT = 1-PR_SURV;

 PrDEATHS = PRDEATHS + VN{I} * PR_MORT;

 CNT_DEAD = VN{I} - VNSURV{I};

 CNT_LIVE = VNSURV{I};

 THISLOSS = CNT_DEAD * ( - 2*log(PR_MORT))

+CNT_LIVE * (- 2* log(PR_SURV)) ;

 N2LLIKE = N2LLIKE + THISLOSS;

 END;

 _LOSS_ = N2LLIKE;

 MODEL DEATHS = PRDEATHS;

 ID GTPA1-GTPA10;

 OUTPUT OUT=OUTF P=Pred;

PROC PRINT;
178 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Donald C.E. Robinson is a Systems Ecologist, ESSA Technologies Ltd.,

Third Floor, 1765 West 8th Avenue, Vancouver, B.C., Canada. V6J 5C6.

drobinson@essa.com. Brian W. Geils is a Research Plant Pathologist, USDA

Forest Service, Rocky Mountain Research Station, Southwest Forest Science

Complex, 2500 South Pine Knoll Drive, Flagstaff, AZ, U.S.A. 86001-6381.

bgeils@fs.fed.us. John A. Muir is a Forest Pathologist, Silviculture Practices

Section, B.C. Ministry of Forests, PO Box 9513, Stn. Prov. Govt, Victoria, B.C.,

Canada. V8W 9C2. john.muir@gems1.gov.bc.ca

Abstract—Dwarf mistletoes affect the growth and survival of

many conifer species in North America. Interactions between the

host and pathogen influence spread, intensification, and stand

development. These interactions are especially complex in stands

with several tree species, multiple storys, and irregular patterns of

tree distribution. For such stands, we developed a model of mistletoe

dynamics linked to FVS. The model simulates the development and

dispersal of mistletoe seeds and describes infection distribution by

dwarf mistletoe rating (DMR). The stand-level model includes

several important features such as the spatial arrangement of trees,

the spatial autocorrelation of trees with similar amounts of mistletoe infection, the dynamics of seed flight, the effect of crowns

intercepting and inhibiting infection spread, and the light environment that strongly affects the life history of dwarf mistletoes. Model

input requirements are modest: at a minimum, only an FVS tree list

that includes DMR is required. Users may either enter parameters

for the patterns of tree distribution and mistletoe patchiness or

accept default values. User-determined values can be computed

from many standard survey designs or set from experience with

similar situations. Default values represent many typical stands.

Simulations based on data from uneven-aged ponderosa pine and

coastal western hemlock stands show reasonable projections of

stand-level changes in mistletoe incidence and severity over several

decades, and plausible responses to silvicultural management scenarios. Recent work is also described that provides a theoretical

basis for simulating spread between stands. This between-stand

model is currently being implemented using the Parallel Processing

Extension to FVS.

Dwarf mistletoes (Arceuthobium) are among the most

widespread and serious conifer pathogens in western North

American forests. Up to half of lodgepole pine and Douglasfir forests and over one-third of ponderosa pine forests in

the Central and Southern Rockies are affected by dwarf

mistletoe (Hawksworth and Scharpf 1984). Infection by

mistletoe reduces growth, survival, and reproductive potential of many commercially important tree species, resulting

Spatial Statistical Model for the Spread of

Dwarf Mistletoe Within and Between Stands

Donald C.E. Robinson

Brian W. Geils

John A. Muir

in estimated losses of more than 11 million m3 per year of

merchantable timber in the United States (Drummond 1982).

Development of silvicultural prescriptions to control the

tree-to-tree spread of mistletoes within stands has been the

subject of extensive research efforts for many years (Van

Sickle and Wegwitz 1978; Hawksworth and others 1987;

Hawksworth and Johnson 1989). The goal of these prescriptions has been to limit the intensification of already established infections and to reduce the potential to spread to

uninfested trees. Spread across stand discontinuities is also

recognized as an important factor when designing spatial

cutblock patterns in variable retention silviculture.

While control of mistletoe can sometimes be achieved

silviculturally, planning cost-effective treatments must be

done within the larger context of landscape- and stand-level

management planning. Increased use of forest growth and

yield models to assist forest managers in planning silvicultural operations and their expected consequences on economic returns have created a need for accurate prediction of

the dynamics of dwarf mistletoe response to stand-level

management, and the ability to accommodate nonclearcutting

practices. Our objectives were to: (1) develop a model of the

mistletoe infection dynamics that can be used to assess

stand-level silvicultural activities for mistletoe management; and (2) extend the conceptual model to accommodate

the simulation of spread across stand discontinuities.

Dwarf Mistletoe Spread and

Intensification __________________

Two aspects of the life history of dwarf mistletoe are

critical for predicting mistletoe spread and intensification:

first, the mechanics of seed dispersal, and second, the establishment, incubation, and reproduction of mistletoe plants

on their hosts. Generally, there is a unique relationship

between the host tree species and its associated dwarf

mistletoe species, and seeds will not successfully infect a tree

of another species. Dwarf mistletoes are dioecious plants

that reproduce only from seeds borne on shoots. Seed dispersal begins when a mature fruit explosively discharges its

seed in ballistic flight. Unlike other mistletoes, which are

primarily dispersed by birds, the dwarf mistletoes rely

almost exclusively on ballistic spread. Birds and mammals

are important for the rare, long-distance dissemination of

seeds to new infection centers.

The physical process of dispersal brings the mistletoe seed

to the location where it establishes a new infection. The

incubation period of newly established plants varies greatly

but is typically 3 to 4 years (Hawksworth and Wiens 1996).
USDA Forest Service Proceedings RMRS-P-25. 2002 179

Spatial Statistical Model for the Spread of Dwarf Mistletoe Within and Between Stands Robinson, Geils, and Muir

Generally, 1 to 2 years then elapse from shoot appearance to

initial flowering. Individual plants may persist for many

decades. Because they rely upon the host for nutrition and

because reproductive success does not require annual seed

production, mistletoes can persist without aerial shoots, as

latent infections. Little is known of the physiological mechanisms regulating shoot production, but it appears that shoot

production and flowering are suppressed in the low light and

nutrition environment of shaded lower crowns (Shaw and

Weiss 2000). Opening the canopy by removing surrounding

trees, as is often done to thin or regenerate a stand, commonly results in a proliferation of mistletoe shoots on the

residual trees.

Ballistic dispersal and parasitism are critical to mistletoe

population dynamics. Ballistic dispersal is effective for

short-range dissemination only, and parasitism requires a

living host. Consequently, mistletoe plants are found clustered within trees, and infected trees are clustered into

infested groups. The spatial pattern of mistletoe populations

therefore operates across a range of scales: the tree, neighborhood, and stand. The average level of infection is usually

described by an index of the amount of host crown affected:

the dwarf mistletoe rating (DMR) system (Hawksworth

1977). Each crown third is rated 0 if it is apparently not

infected, 1 if less than half is infected, or 2 if more than half

is infected. The DMR system allows infection to be quantified in various ways: by crown thirds, for the whole tree, or

by averaging across a collection of trees, for a group or stand

of trees.

We define spread as the establishment of mistletoe infection in previously uninfected trees, and intensification as

the establishment of new mistletoe infections within infected trees. Spread and intensification are influenced by

the same factors and are complementary aspects of the same

process. Dispersal is primarily affected by the physical

configuration of the tree: crown density and distribution;

and by stand species composition. Establishment, incubation, and reproduction are determined by weather, genetic,

and other biological factors.

The Model _____________________

A number of dwarf mistletoe models have been developed

to assist forest managers in planning silvicultural operations and estimating effects on yield. These models range

from projecting whole-stand impacts (for example, Myers

and others 1971, Edminster and others 1991) to the explicit

simulation of the effects of the spatial arrangement of trees

(for example, Strand and Roth 1976, Bloomberg and Smith

1982).

The model described in this paper simulates dwarf

mistletoe spread and intensification at an intermediate

spatial resolution in which the transmission of dwarf mistletoe is the outcome of spatial relationships between infected

and uninfected trees. In the model, height and canopy

relationships are explicit and stem location relationships

are statistical, based on the spatial distribution of trees at

the 14-m scale of ballistic spread. Those parts of the model

that are explicitly spatial operate at a 2-m resolution. The

model operates on an annual time step and is able to

simulate infection dynamics in patchy multispecies,

multistoried stands. It does not simulate long-distance transmission of mistletoe and is restricted to spread from ballistic

seed flight. It is linked to the Dwarf Mistletoe Impact

Modelling system (DMIM; Hawksworth and others 1995),

an extension to the FVS model (Stage 1973; Wykoff and

others 1982). Dwarf mistletoe impacts, reduced diameter

increment, and reduced survival for severely infected trees,

are modelled using the DMIM system.

Model users are provided with the means to parameterize

the model. The detailed model structure and user-control

are fully documented in Robinson and others (1994) and in

Hawksworth and others (1995). Modelling behavior studies

are described in Robinson and others (2000) and in Robinson

and Geils (in preparation). The main features of the model

are summarized below.

Life History

In the simplest case, new infections progress from an

immature through an incubation stage and finally produce

an active (flowering) adult that contributes to the DMR

classification. The passage from immature to incubation

status is modeled with a default delay of 4 years. Passage to

the reproductive stage depends on the amount of light

available in the crown third. If light decreases to the point

that an active infection cannot be sustained, the infection

regresses to a nonflowering suppressed state. These lifehistory dynamics and stages are summarized in figure 1.

Light plays an important role in most of the transitions

between life history stages and takes the form of a heightdependent extinction curve. As described below, this curve is

dynamically generated using a simulated stem map and a

model of the canopy.

Light and Opacity

The presence or absence of light is critical to the maturation of incubating infections and to the activation or suppression of mature flowering plants. The model uses the concept

of stand-average opacity to simulate the reduction of light by

foliage, branches, and stems. In doing so, it is able to

estimate the proportion of incident light at different heights

above the ground, and by the same mechanism account for

the interception of seeds in ballistic flight. Stands may be

made up of trees of different species, height, crown shape,

length, and foliage opacity. To account for this complexity,

stand-average opacity is computed by Monte Carlo simulation. At the beginning of each time step, a 1-ha stand

simulation is made. Each tree is represented in proportion to

its density in the stand. Beginning at the ground and

extending upward in 2-m steps, the structure of each layer

is simulated with simplified circular discs of canopy, each

representing the crown diameter of trees at that height.

Canopy geometry is computed by the COVER model (Moeur

1985) (fig. 2). After simulating all the stem locations, the

stand-average opacity at each height is a function of the

amount of space unoccupied by canopy, combined with the

canopy opacity of the space occupied by foliage. Standaverage opacity is then used to reduce the amount of light
180 USDA Forest Service Proceedings RMRS-P-25. 2002

Robinson, Geils, and Muir Spatial Statistical Model for the Spread of Dwarf Mistletoe Within and Between Stands

transmitted from the top of the stand to the ground. Because

light extinction is cumulative, a decay curve results.

Stem Clumpiness

Seeds spread ballistically from a source tree to a target

tree. For an infection to be transmitted successfully, two

trees must have compatible heights and be in the same

neighborhood. The local neighborhood of each target tree

can be regular, random, or clumped. As the left part of

figure 3 shows, the model simulates stem spacing by placing a target tree at the center of a bulls-eye, surrounded by

2 m rings. Source trees are then selected and simulated as

if they were located in each of the concentric rings. Placing

a target at the center of a bulls-eye with source trees at

Figure 1—The model simplifies dwarf mistletoe life history into four stages. Light drives the progress of incubating

infections to reproductively active individuals, and to and from the suppressed pool.

Figure 2—An estimate of stand-average opacity is

made by simulating the crown diameter at each height

in the stand. Light penetrating the stand from above is

blocked by crowns, and less light is available at lower

heights (right panel). The ability of crowns to block light

varies with stem density and by species. For example,

pines allow more light to pass than western hemlock.

Figure 3—At left, source trees, S, surrounding each

target tree, T, are selected based on an underlying

spatial clumping function (for example, binomial).

Samples in each ring are based on the conditional

distribution functions produced by overlapping inside

and outside discs, ri

 and ro. The example shows one

possible way in which a ring with two source trees

could be simulated from samples from inside and

outside discs. Actual positions around the disc are not

necessary for the simulation.

discrete distances allows the infection field of each source

tree to overlap the canopy of the target, simulating the

dynamics of spread.

For each of the concentric rings, the expected number of

stems is the average stem density of the whole stand multiplied by the area in the ring. A ring is defined by its inner and

outer radius, ri and ro. The distribution for an inner disc with

radius ri (the ri disc) is derived first, giving the probability of

sampling x=0,1,2,3... trees in the inner disc. The same

method is followed for the larger ro disc, as shown in figure

3. The expected distribution of trees in the ring is then

defined by the distribution of trees found in disc ro that are

not already in disc ri. The distribution is generated by

counting all the combinations of tree numbers on the two

discs that would give rise to an observation of a particular

number of trees in the ring (equation 1). In equation 1, the

probability generating function, P, can be binomial, poisson,

or negative binomial. The upper tail of the distribution drops

T

S

S

S

r i r o

Overstory Stand

Opacity

% of full sun

Understory

.01

.05

.08

.05
USDA Forest Service Proceedings RMRS-P-25. 2002 181

Spatial Statistical Model for the Spread of Dwarf Mistletoe Within and Between Stands Robinson, Geils, and Muir

off rapidly, and in practice the summation over j and k

usually converges with fewer than 20 terms.

Px n r r

Px j r Px j n r

Px j r Px j k r

n i o

o i

j o

o i

k o j k

( = ) =

( = ) ( = )

( = ) ( = )

> =

∞

=

∞

=

∞

∑

∑∑

| ,

| –|

| –|

; 0 [1]

The denominator term scales the probability distribution.

This scaling is necessary because the two disc samples are

not independent. The ri disc lies completely inside the ro disc,

and therefore some tree counts are impossible. For example, if a sample of size 3 is observed in the inner disc ri,

a sample of 2 or fewer can never be observed in the outer

disc ro. The double summation in the denominator adjusts

the distribution so that the probabilities over all sample

sizes sum to one.

Infection Patchiness

In addition to creating a local neighborhood around each

target tree, the model simulates positive autocorrelation

among infection classes, resulting in patches of infection.

The autocorrelation results in an increased likelihood that

trees of similar DMR are neighbors compared to trees of

unlike DMR. The stem-mapped Log Cabin plot shown in

Dixon and Hawksworth (1979) and the Wind River site

(Shaw and others 2000) both give good examples of such

patches. Although the existence of patches is well documented, modelling their explicit arrangement is not reasonable for a nonspatial growth model. We have therefore

modelled the autocorrelation structure of DMR with a simple

function (equation 2), an exponential decay curve driven by

the magnitude of DMR difference between a target and its

potential neighbors. Equation 2 gives a modified density

estimate, ρ’i,for each DMR class, i, surrounding a target tree

of DMR t.

ρ ρ

ρ

ρ

α

α i i

t i

j

j

j

t j

j

e

e

' |–|

|– |

= =

=

∑

∑

0

6

0

6 [2]

This difference-driven weighting assigns greater density to

neighboring source trees with DMR similar to the DMR of

the target tree, and less density to trees with unlike DMR.

It allows the local DMR to vary in different target tree

neighborhoods, while maintaining the observed stand-average DMR. To preserve stand-level statistics, reducing the

likelihood of finding a source tree having a different DMR

class implies a corresponding increase in the density of

similar-DMR sources in other neighborhoods. Equation 2

preserves this symmetric property of target-source differences in DMR: trees “taken away” from one neighborhood

are “put back” in another neighborhood, so that the stand

density of each DMR class is preserved. These properties are

illustrated in table 1 (setting α = –0.5).

The term can be estimated from stem-mapped data by

comparing the density of each source DMR class observed

around each target DMR tree. Predictions made by equation

2 are compared against observed stem map densities, generating a range of χ2

 over a range of values for α. Minimizing

χ2 then gives the best choice of α. Results from an analysis

of six sites (four from a long-term study in the Grand

Canyon National Park; one from Fort Valley, and one from

the Wind River site; J.J. Smith, personal communication)

are shown in table 2. Three of the sites have repeated

measures but show no consistent change in α over time. The

mean of all 28 observations is α = –0.103 (SD = 0.075).

Ballistic Seed Dispersal

Because of physical constraints on seed flight, infection is

usually transmitted laterally and downward from an infection source. An infection in a higher tree will usually expose

a lower neighbor to infection, but not the reverse. The model

uses encoded and simplified ballistic seed trajectories

based on the following characteristics: 24 m s–1 initial

velocity (Hinds and Hawksworth 1965); 7.5 m s–1 terminal

velocity (Hawksworth 1959); uniform random angle of discharge; and aerodynamic drag represented as a quadratic

function of velocity. These assumptions produce the dispersal pattern shown in figure 4. The 14-m lateral limit is

consistent with field observations (for example, Hawksworth

1961).

Table 1—An example of spatial autocorrelation setting α = –0.5 The left side of the table shows the stand-average density of each DMR

class. Rows of the right table show the neighborhood density of source trees around each target DMR class (columns). In

each case, the overall stand density is preserved. Weighted sums by neighborhood density (right-most column) show that

the stand-average DMR densities are also preserved.

Stand

avg. Source Target DMR class Wtd.

DMR density DMR 0123456 density

0 45 0 80 47 29 21 17 15 13 45

1 25 1 27 43 26 20 16 14 12 25

2 32 2 21 33 56 41 33 29 25 32

3 17 3 7 11 18 36 29 26 22 17

4 12 4 3 5 8 16 34 30 26 12

5 3 5 0 1 1 2 5 12 11 3

6 5 6 0 1 1 2 5 12 29 5

Total 139 Total 139 139 139 139 139 139 139
182 USDA Forest Service Proceedings RMRS-P-25. 2002

Robinson, Geils, and Muir Spatial Statistical Model for the Spread of Dwarf Mistletoe Within and Between Stands

Examples of Model Behavior ______

Grand Canyon —Ponderosa Pine

In 1950, Frank Hawksworth initiated a study of dwarf

mistletoe control in several fixed area plots of moisturelimited ponderosa pine on the south rim of the Grand

Canyon. We used data from plot GC12, an untreated 4-ha

fixed area plot that was stem-mapped and monitored for its

tree and mistletoe characteristics (tree condition, dbh, height,

DMR). Stand examinations were made in 1950, 1955, 1961,

1966, 1970, 1979, 1982, 1986, 1990, and 1997. The oldest

trees are currently about 250 years. Ingrowth had been

minimal for most of the period. Stand basal area of original

trees declined from 11.5 m2

 ha–1 in 1950 to 5.3 m2 ha–1 in

1990. (The most recent 1997 inventory was not included in

this analysis and includes the recent ingrowth.)

We carried out numerous model simulations with these

data to examine the model’s sensitivity to opacity, clumping,

and patchiness, comparing those runs to the empirical

observations. The results in figure 5 show the influence of α,

which controls the patchiness of infections. When α = –2.50,

infection patches are tightly focused, resulting in high mortality, limited spread to uninfected trees, and declining

stand DMR. Conversely, with α = 0.0 the infections are

evenly spread throughout the stand, resulting in extensive

spread. When α = –0.30 the predicted stand DMR follows

the inventory estimates quite closely. The mean estimate

over the 50-year period is α = –0.18 (see table 2).

Vancouver Island—Western Hemlock

Through the courtesy of Weyerhaeuser Canada Ltd., we

were provided with a number of 1-ha inventories from

Vancouver Island, B.C. The stands are predominantly

western hemlock and range from dwarf mistletoe-free to

moderately infested. To study response to management,

we selected a young, lightly infected stand (age = 53 years

in 1987; SI = 24 m; QMD = 15 cm; BA = 44 m2

 ha–1; stand

DMR = 1.2). The stand was initially understocked. An initial

planting was therefore simulated, to give a target stocking

level of 1,200 stems ha–1.

The simulations follow a block design, with two retention

scenarios and three regeneration scenarios. Shelterwood

harvests left 15 and 25 percent of the basal area following an

entry at age 100. Three regeneration scenarios were simulated: (1) no manual planting following entry; (2) planted

using the original species mixture following entry; and (3)

planted using an 80:20 hemlock:cedar mix following entry.

In all three scenarios a small amount of simulated natural

regeneration was added in each cycle. At the time of stand

entry, 15 percent mortality was applied to the advanced

regeneration, and natural regeneration was cancelled in the

planted stands for 20 years. Finally, the residual overstory

was removed 20 years after the stand entry, allowing the

regenerating stand to continue growing. Some results of

these simulations are shown in figure 6. The figure shows

two measures of infection: stand DMR in the upper panels

and infected trees/ha in the lower panels.

In each of the simulations, the first entry is responsible for

a dramatic decline in DMR and in the number of infected

Table 2—Observed values of α, the autocorrelation parameter, at six

sites.

Stand

Year GC11 GC12 GC22 GC06 Ft. Valley Wind R

1950 –0.13 –0.07

1952 0.06

1955 –0.12 –0.06 0.00

1961 –0.15 –0.08 –0.01

1966 –0.14 –0.07 –0.03

1970 –0.15 –0.03 –0.04

1979 –0.16

1982 –0.20 –0.07 –0.10

1986 –0.25

1988 –0.13

1990 –0.27 –0.02 –0.14

1997 –0.07 –0.18 –0.12 –0.15

Figure 4—The aerodynamic simulation of ballistic flight

produces a field of trajectories. The maximum lateral

discharge is about 14 m, and the maximum rise above the

point of discharge (20 m in the figure) is about 6 m.

Discharge is heaviest near the outer margin of the discharge envelope. The scale is log-transformed relative

seed density.
USDA Forest Service Proceedings RMRS-P-25. 2002 183

Spatial Statistical Model for the Spread of Dwarf Mistletoe Within and Between Stands Robinson, Geils, and Muir

trees. The decline is less precipitous in the unplanted scenario for two reasons. First, the scenario continues to regenerate in the period after the first entry and therefore carries

more mistletoe infection in the advanced regenerating trees.

Second, the two planting scenarios “dilute” stand DMR with

large numbers of initially uninfected seedlings. After thinning, infection levels and DMR both rebound as the infection

is transferred from the residual overstory to the new understory trees. As expected, the 25 percent retention scenarios

always transmit more mistletoe because they contain almost twice as many overstory trees as the 15 percent scenario. Likewise, regeneration scenarios that incorporate

western redcedar all result in lower transmission success.

This reduction is achieved through the physical blocking of

Figure 6—The model’s sensitivity to management actions are shown in simulations from the Vancouver Island

plot: variable levels of retention, different regeneration

scenarios, and different species mixtures. The representation of overstory and understory relationships is responsible in large measure for this sensitivity. See text

for further interpretation. Dashed line — unplanted following entry; solid line — planted with original mixture

following entry; dotted line — planted with cedar mixture

following entry. See text for additional details.

Figure 5—Sensitivity to the choice of the

autocorrelation parameter, α (see equation 2) is

shown in simulations from the GC12 plot. The heavy

line in each pane shows the inventory-based estimate of stand DMR over time. Lighter lines show the

model projection of DMR, starting at each inventory

date. See text for further interpretation.

seeds and because the infection measures are based on

summing over both western redcedar (which is never infected) and western hemlock.

The success of the postentry mistletoe flush is transient in

most of the simulations and is nearly always quenched by

the subsequent second entry removal of the residual overstory. The young regenerating stands appear to “outrun” the

new infestation because they are adding sufficient height

annually so that they outgrow and then suppress the more

slowly advancing mistletoe. In some scenarios — notably the

unplanted — the infection is successfully transferred to the

next generation because of its greater incidence in the more

abundant advanced regeneration.

Spread Across Boundaries _______

To meet habitat conservation goals, group retention harvesting systems are being considered in some harvest plans

(Bill Beese, Weyerhaeuser Canada Ltd., Nanaimo, B.C.;

personal communication). In these scenarios, retained

patches containing dwarf mistletoe have the potential to

spread across patch boundaries and into young regrowing

areas. The need to understand and project the future state

of these regrowing stands motivated us to adapt the spatial

statistical framework to accommodate spread across boundaries. As figure 7 shows, the first step in extending the

existing framework is to superimpose a boundary line across

the local neighborhood of target trees. Conceptually, the

stand structure of the adjacent stand then contributes any

dwarf mistletoe influence onto trees in the index stand.

0

0

1987 1987 2037 2037 2087 2087 2137 2137

2

Stand DMR (all trees) Trees/ha infected

500

4

1000

6

1500

25% Retention 15% Retention

1950 1960 1970 1980 1990 2000

2.0 3.0 4.0 5.0

alpha= -2.50

1950 1960 1970 1980 1990 2000

2.0 3.0 4.0 5.0

alpha= -0.30

1950 1960 1970 1980 1990 2000

2.0 3.0 4.0 5.0

Year

alpha= 0.0

Stand DMR
184 USDA Forest Service Proceedings RMRS-P-25. 2002

Robinson, Geils, and Muir Spatial Statistical Model for the Spread of Dwarf Mistletoe Within and Between Stands

Figure 8 shows a target tree, T, adjacent to a stand boundary. The likelihood of finding a tree in this location depends

on the proportion of the stand area within a given “edge

band.” If a target lies in a band, the influence of source trees

in the adjacent stand then depends on the amount of overlap

into the adjacent stand. These relationships are being modelled using the programming infrastructure of the Parallel

Processing Extension (PPE: Crookston and Stage 1991),

combined with a map preprocessing step using spatial numerical integration to derive the measures of relative and

absolute area in edge bands and in adjacent stands.

Conclusions____________________

A spatial statistical model was developed to represent

several important features of the life history and distribution of dwarf mistletoe in complex stands—namely, mistletoe latency and mortality, stem clumpiness, and infection

patchiness. Several example situations demonstrate that

parameter estimation is not an impediment to use and that

projections produce reasonable results that are useful for

assessing management alternatives within infested stands.

The spatial statistical framework has been extended to a

slightly larger scale and is able to model the spread of dwarf

mistletoe across stand or treatment discontinuities.

Figure 8—In this example, the likelihood that a

target tree, T, lies within 6 to 8 m of the adjacent

stand depends upon the proportion of stand area in

that edge region. The amount of area found in ring

samples in the adjacent stand (for example, the 8

to 10 m overlap region) depends on the amount of

overlap of the target tree neighborhood. DM present

in source trees found in the adjacent stand can then

be included for the target tree.

Figure 7—Between-stand spread is simulated using the

same framework as the single-stand model, superimposing a boundary across the local neighborhood of the

target tree, T. Source trees (S) and stand structure of the

adjacent stand then contribute to the spread of DM onto

trees in the index stand.

Acknowledgments ______________

We express our appreciation to the more than 30 foresters,

plant pathologists, and modellers (see Robinson and others

1994 for a complete listing) who have contributed their time

and expertise to the development of this model.

Bill Beese and Nick Smith (Weyerhaeuser Canada Ltd.,

Nanaimo) provided us with suggestions for hemlock management systems and with updated inventory data. Mark

Perdue (Department of Forest Sciences, UBC) provided us

with guidance during the development and testing of plausible regeneration and stocking scenarios for the Vancouver

Island simulations. Glenn Sutherland carried out the

Vancouver Island simulations.

We thank Kelly Robson at ESSA Technologies, who prepared the figures.

T

S

S

S

S

S

S

S

S

S

S

S

Adjacent stand

Index stand

Adjacent

stand

Index stand

6-8 m edge region

12-14 m

overlap 

region

8-10 m

overlap 

region

T
USDA Forest Service Proceedings RMRS-P-25. 2002 185

Spatial Statistical Model for the Spread of Dwarf Mistletoe Within and Between Stands Robinson, Geils, and Muir

References _____________________

Bloomberg, W. J.; Smith, R. B. 1982. Measurement and simulation

of dwarf mistletoe infection on second-growth western hemlock

on southern Vancouver Island. Can. J. For. Res. 12: 280-291.

Crookston, N. L.; Stage, A. R. 1991. User’s guide to the Parallel

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Dixon, G. E.; Hawksworth, F. G. 1979. A spread and intensification

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Drummond, D. B. 1982. Timber loss estimates for the coniferous

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Olsen, W. K.; Hawksworth, F.G. 1991. GENGYM: A variable

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Hawksworth, F. G. 1961. Dwarfmistletoe of ponderosa pine in the

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Hawksworth, F. G. 1977. The 6-class dwarf mistletoe rating system.

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Hawksworth, F. G.; Johnson, D. 1989. Biology and management of

dwarf mistletoe in lodgepole pine in the Rocky Mountains. Gen.

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the effects of dwarf mistletoe in coastal western hemlock stands.

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G.N. Jones subsp. tsugense) aerial shoots in an old-growth Douglas-fir/western hemlock forest. Northwest Science 74: 306–315.

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INT-137. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 32 p.

Strand, M. A.; Roth, L. F. 1976. Simulation model for spread and

intensification of western dwarf mistletoe in thinned stands of

ponderosa pine saplings. Phytopathology 66: 888-895.

Van Sickle, G. A.; Wegwitz, E. 1978. Silvicultural control of dwarf

mistletoe in young lodgepole pine stands in Alberta and British

Columbia. Report BC-X-180. Canadian Forestry Service, Victoria,

B.C. 11 p.

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USDA Forest Service Proceedings RMRS-P-25. 2002 187

Session 5. FVS in Modeling Systems

USDA Forest Service Proceedings RMRS-P-25. 2002 189

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

J. Wang, W. D. Potter, D. Nute, and F. Maier are with the Artificial

Intelligence Center, 111 Boyd Graduate Studies Research Center, University

of Georgia, Athens, GA, 30605. Contact jwang@ai.uga.edu. H. M. Rauscher,

M. J. Twery, S. Thomasma, and P. Knopp are with the USDA Forest Service,

Asheville, NC, and Burlington, VT

Abstract—An Intelligent Information System (IIS) is viewed as

composed of a unified knowledge base, database, and model base.

This allows an IIS to provide responses to user queries regardless of

whether the query process involves a data retrieval, an inference, a

computational method, a problem solving module, or some combination of these. NED-2 is a full-featured intelligent information

system for the sustainable management of forestlands. It is designed to help managers plan for wildlife, ecology, water, and

landscape objectives as well as timber production. FVS is one of the

integrated decision support model components in NED-2. We provide the FVS simulation agent and “wrapper” that permit communication between FVS and NED-2. We are developing a metaknowledge base. Simulation agents can use it to set up and execute

external simulation models. This paper will briefly describe the

NED decision process, the NED-2 architecture, and discuss the

design issues explaining the integration of NED-2 and FVS.

Owners and managers of forestlands in the United States

are being challenged as never before to produce an increasingly complex set of benefits as a variety of costs increase.

Getting the most money from your forestland through timber harvesting is by itself a challenging goal. But a growing

number of landowners want much more than money from

their forestlands. They may want to create or maintain

certain desirable ecological conditions such as a grove of “old

growth forest” or a scenic, parklike environment. They may

want to restore portions of their property to more natural

conditions to enhance the habitat for many creatures well

into the future. Knowing that one is being a good steward of

the land may also be part of the rewards of forestland

ownership. And many landowners have said for years that

they are more concerned with enhancing wildlife habitat,

An Intelligent Information System for Forest

Management: NED/FVS Integration

J. Wang

W. D. Potter

D. Nute

F. Maier

H. M. Rauscher

M. J. Twery

S. Thomasma

P. Knopp

increasing bird population diversity, or protecting rare and

endangered species than they are with high returns from

timber harvesting. Landowners want to manage their forestlands for more and more goods, services, and environmental conditions than ever before. However, providing for

these increasingly complex benefits often increases management costs and the cost of ownership.

Researchers at the USDA Forest Service, Northeastern

and Southern Research Stations, in cooperation with many

collaborators, have been developing a computer software

system called the NED decision support system. This system

is designed to help forestry consultants and their private

landowner clients develop goals, assess current and potential conditions, provide a way to study and select from

different scenarios, and produce management plans for

their forest properties.

In this paper, we will briefly describe the NED decision

process, the NED-2 architecture, and how we have used the

theory of Intelligent Information Systems (IIS) to link FVS

into the NED-2 DSS.

The NED Decision Support

Process _______________________

The purpose of modern forest management is to achieve

diverse goals selected by the landowner. This perspective is

critical and central to the NED Decision Process. It cannot

be overemphasized that without goals, reasoned management cannot be practiced.

Surprisingly, identifying and choosing a good set of goals

is the most difficult part of the entire decision process. A

difficult part about choosing suitable goals is that you have

to be able to tell whether you have achieved them or not. For

example, one of the goals for the Deer Hill case study in

South Carolina was to focus on producing Wild Turkey

Habitat. Well, you can’t just walk into the forest, pull out

your Wild Turkey Habitat measurement gauge, swing it

around, and get a reading on it. There is, of course, no such

gauge and that is because Wild Turkey Habitat is an abstract

concept that unifies many factors about the birds and their

needs. So how do we measure it? Well, first we have to

further define what we mean by Wild Turkey Habitat. We

talked with turkey management experts and decided that
190 USDA Forest Service Proceedings RMRS-P-25. 2002

Wang, Potter, Nute, Maier, Rauscher, Twery, Thomasma, and Knopp An Intelligent Information System for Forest Management: NED/FVS Integration

we needed three things for good turkey populations: (1) a

favorable landscape pattern, (2) forestland with all sizes of

trees represented, and (3) turkey food. That list certainly

helps, but we are still not able to go out and measure any one

of these three things. So what do we do? We define what we

mean by each of the three subdivisions. A favorable landscape pattern for turkey is defined as: (1) parklike, open

forests that cover at least 80 percent of the area, (2) scattered

small fields that make up more than 10 percent of the area,

(3) parklike large hardwood forests that are present near

small fields, and (4) parklike large pine forests that cover at

least 10 percent of the area. So now, finally, we have

something we can measure in our forests. By going through

this process, we create a list of goals in sequence with

immeasurable but valuable top level goals at one end and

measurable conditions that define the top level goals at the

other end of the hierarchy.

Having defined our goals, we next need to learn about our

property. Many landowners do not possess an inventory of

the trees on their land and have only a general idea of the mix

of vegetation, soil, and topographic features. Obtaining an

inventory is an expensive proposition yet a cost that must be

paid prior to the beginning of serious efforts to establish a

cost effective management program. Knowing this, the NED

Team developed a relatively inexpensive and yet quite

complete inventory procedure that provides good estimates

of the large trees, the smaller trees, shrubs, and herbs, and

allows for a rapid assessment of wildlife habitat conditions

using measures such as the presence or absence of dead

standing and dead fallen trees. We look for perches for birds,

whether moss, ferns, or grasses cover the ground, whether

there is permanent water available for all creatures and

especially salamanders and turtles, and so on. Although we

look for many things, we have designed a process that takes

approximately a half hour per forest stand.

Given the set of goals and an understanding of the current

forest conditions, we can turn our attention to figuring out

what we might do to our land (if anything) so that it better

achieves the goals. We want to create a small number of

different strategies for managing our land while satisfying

the goals. These strategies are called management scenarios. Each is a different road to get to the same place. In

sustainable forest management there is rarely a single, best

road to follow to achieve a given set of goals. What we can

realistically do is design several different ways (roads) to get

to our goals and then compare them. At each cycle through

this process, we learn more about our own values and goals,

about our land, and about the things we can do with it. For

Deer Hill, we designed three management scenarios (fig. 1):

1. A Timber Only scenario: maximum profit from timber operations consistent only with best management practices for sustainable timber management and the CRP

requirements. Wildlife is not specifically addressed, and no

revenue from wildlife operations is expected. All open areas

will be planted to loblolly pine. The pine-hardwood stands

will be commercially chipped and converted to loblolly pine

plantations as soon as feasible. Loblolly pine plantations

will have two thinnings (age 10 to 12 and 20 to 25) and a final

harvest at age 30 to 40. And the plantation size class

distribution will be spread out to get a more even flow of

income.

Figure 1—Computer models allow the forest manager to predict the results of

different management options
USDA Forest Service Proceedings RMRS-P-25. 2002 191

An Intelligent Information System for Forest Management: NED/FVS Integration Wang, Potter, Nute, Maier, Rauscher, Twery, Thomasma, and Knopp

2. A Timber and Extra Products scenario: maximum

profit from nontimber, nonextractive human use of the land.

Leave 400 acres of existing pine plantations alone and

continue to manage for maximum timber. Take 100 acres of

pine plantation and manage for big pine over long rotations.

The pine-hardwood stands will be turned into park savannahs

with large hardwood trees spread out over a 30 by 30 foot

grid. Islands of regenerating hardwoods will be created in

these open forests. Wildlife food plots will be established in

all open fields, and the wildlife row-crop planting (corn) will

continue. Many different camping, nature education, hunting, and other sporting activities will be developed as income

producing activities.

3. A Timber and Hunting scenario: maximum profit

from timber and hunting operations. Leave all pine plantations as they are and manage for maximum production. Rent

hunting rights to highest bidder. Thin pine-hardwood and

hardwood stands to promote acorn production. Keep open

fields open and food plots productive.

Having developed the three management scenarios for

Deer Hill, we had to pretend to carry each of them out over

our 40-year time frame and then compare them to each

other. We did this by using a forest growth forecasting

software package called the Forest Vegetation Simulator

(FVS). FVS was created and is maintained by the USDA

Forest Service, Forest Management Service Center in Fort

Collins, CO. This system covers all forest types in the entire

United States. It is, however, fairly complicated to use and

requires at least 1 week training before users feel comfortable with the software. NED integrates FVS but only for

regions east of the Mississippi. FVS is available on the

Internet for downloading, free of charge, at www.fs.fed.us/

fmsc/fvs.

We are almost at the end of the NED decision process now.

To recap: We set our goals; learned about the current

condition of our property; figured out some alternative ways

we could manage our land; and projected those alternatives

over a 40-year time horizon to get an estimate of how each

forest is likely to look 40 years in the future. Now we can go

back to our goals, find our measurable conditions, and

evaluate them against each of our simulated future forests

(fig. 2). For example, in the Wild Turkey Habitat goal, we

needed parklike hardwood forests with large trees near

small fields. By evaluating the three simulated scenarios,

we can determine that only the Timber and Extra Products scenario (#2) will provide us with that condition. Mass

producing oak forests, on the other hand, are found in both

the Timber and Extra Products scenario (#2) and the

Timber and Hunting scenario (#3). Comparisons were

continued for each of the measured conditions. It is then a

relatively simple matter to rate each scenario against each

measurement condition and then determine which scenario

does best in satisfying the top-level goals.

We usually learn a lot from this process. We may find that

a goal that we selected at the beginning turns out to be

unrealistic. We learn this because no matter what we do in

any scenario, we simply cannot achieve this particular goal

given the resources we are willing to spend and the time we

are willing to commit. We then may wish to change our goals

or maybe see if we can achieve them in 60 years instead of 40

and thus leave a legacy for our grandchildren. We may also

discover through discussions some other ways to manage

our property, thus creating another alternative scenario.

Such changes are OK, because now it is inexpensive to play

“what-if” games with the NED software; to look at different

Figure 2—By comparing the outcomes of

different management scenarios, you can

choose the right management plan to meet

your goals.
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Wang, Potter, Nute, Maier, Rauscher, Twery, Thomasma, and Knopp An Intelligent Information System for Forest Management: NED/FVS Integration

futures and different goals until we are comfortable with our

“final decision.” This final decision is of course to be used, but

it is only tentative. It is likely that next year or the year after,

we as well as our world will have changed enough that our

“final decision” may be outdated. But because we have done

our homework, know our land, and have increased our

understanding, we can go through the NED process again

quickly and efficiently.

We have described the NED decision process. This process

is currently being field tested in case studies with real users.

The NED-2 decision support system is being developed to

automate the NED decision process and make it as efficient

and easy to implement as possible. Next, we will provide a

brief overview of Intelligent Information Systems theory, a

description of the NED-2 architecture, and then discuss the

implementation issues about how we integrated FVS into

the NED Intelligent Information System.

Intelligent Information Systems and

the NED-2 Architecture ___________

We view an Intelligent Information System (IIS) as composed of a unified knowledge base, database, and model

base. This allows an IIS to provide responses to user queries

regardless of whether the query process involves a data

retrieval, an inference, a computational method, a problem

solving module, or some combination of these (Potter and

others 2000). The integration of information sources, whether

local or remote, should be transparent to the user. That

means after getting user queries, the system itself decides

which source(s) need to be accessed.

To add new functionality to a software system, one approach is to integrate additional autonomous, heterogeneous information sources. Because each source is designed

to perform a particular task using its own data model, we

might need to deal with these issues. First, the data format

of the information source may need to be translated to

communicate with the other sources. Second, the information source that is developed in a different programming

environment should be invoked by appropriate control codes.

We might also need to consider how to perform transparent

processing so that the users would just focus on their queries

without having to consider where the results could be gotten.

There are many existing approaches that provide intelligent

integration of information, such as a federated database

approach (Sheth and Larson 1990), a hierarchical mediator

approach (Papakonstatniou and others 1996), a descriptionlogic-based approach (Levy and others 1996), or an ontologybased approach (Cheung and Cheng 1996). Instead of designing and building NED-2 based on a monolithic and

predefined solution, we approach the development by creating a loosely organized system that consists of smaller

components. Each smaller component performs its own

functions within the larger problem-solving framework.

Essentially, every component is an external heterogeneous

information source composed of an intelligent agent, its

associated knowledge base, and whatever source it accesses.

By consulting the knowledge base, an external model management agent knows when and how to perform its tasks.

Therefore, the use of intelligent agents and knowledge bases

makes integration of heterogeneous information sources

easier and more flexible.

NED-2 is a blackboard system with semiautonomous

intelligent agents. Its blackboard integrates a Microsoft

Access database and a set of Prolog clauses. Inventory data

and other information are stored in the database. NED-2

includes a user interface, databases, simulators, knowledge

bases, hypertext documents, geographical information systems, and visualization tools. Simulators and other external

modules are integrated into NED-2 via their intelligent

agents (fig. 3).

In an agent-based blackboard system, each agent makes

a contribution to the problem-solving process. Agents communicate with each other through a blackboard. Tasks that

need to be done are posted to the blackboard. Agents also

post most of the intermediate results of their activities to the

blackboard. Agents watch the blackboard continually. The

information on the blackboard will prompt an agent to do

some work. If an agent performs some task listed on the

blackboard, it will erase that task from the task list. An

agent may place a report that the task has been performed

on the blackboard after it completes the task. If an agent

begins a task, then discovers that something needs to be

done that is beyond its capability, it can put the new task on

the blackboard and wait until another agent performs it

before completing its original task.

In NED-2, Prolog, a high-level logic programming language, provides the primary implementation platform for

agents, knowledge bases, and inference engines. The user

interface is implemented in Microsoft Visual C++. The

databases are implemented in Microsoft Access. To perform

its tasks, an agent may need to retrieve and update the core

data on the blackboard. The Prodata LPA Prolog interface

(http://www.lpa.co.uk/ind_pro.htm) is used to implement

the database access. The Prodata interface provides a tight

coupling between LPA Prolog for Windows and all Database

Management Systems (DBMSs) that support a sufficient

level of Open Database Connectivity (ODBC) compliance to

be used with Microsoft ODBC 2. Prodata allows database

tables to be accessed from Prolog as though they existed

Agents 

Inference Engines 

Prolog 

Clauses 

NED-2 

Database 

Knowledge Models 

Meta-knowledge Bases 

Temporary Data Files 

Simulations 

GIS 

Visualizations 

HTML Reports 

User Interface Modules 

Blackboard 

Figure 3—The NED-2 architecture
USDA Forest Service Proceedings RMRS-P-25. 2002 193

An Intelligent Information System for Forest Management: NED/FVS Integration Wang, Potter, Nute, Maier, Rauscher, Twery, Thomasma, and Knopp

within Prolog’s environment as unit ground clauses (facts).

All routine database functions — such as creating tables or

updating/retrieving records — may be achieved via normal

Structured Query Language (SQL) commands. When an

agent needs to get something from the blackboard or put

something onto the blackboard, it calls a particular predicate designed to retrieve or update information. If information is already present as facts on the blackboard, it is easy

to be accessed or changed. However, if information is stored

in the core database, a Prodata SQL query will be constructed to select, update, add, or delete the data values in

the database.

The FVS Agents, Wrappers and

Meta-Knowledge Bases __________

The first growth and yield models integrated with NED-2

are the Northeastern and Southern variants of FVS (Teck

and others 1997). Simulation agents are designed to set up

and call these growth and yield models. The NED user

develops management plans using a drag-and-drop scheduling screen (fig. 4). Treatments are selected and dropped onto

individual stands. Management plans are stored in the core

database. A set of default treatment parameters and userspecified treatment parameters are stored in the core database also. A set of rules is included in the simulation

knowledge base. According to these rules, a simulation

agent for an external model will determine all the conditions

that must be satisfied for the model to function. For example,

FVS needs stand information such as stand year of origin,

site index species, site index, and basal area. If a condition

is not satisfied, the agent will go to the blackboard to see if

the message it needs is there. If not, the agent will post a

request on the blackboard. Eventually the required message

will be posted. Once all conditions are satisfied, the agent

will run the external model. Currently the FVS agent needs

to invoke the FVS wrapper in order to access FVS. The FVS

wrapper creates the FVS key file and FVS tree data file, runs

FVS, and then converts the FVS simulation results and

inserts them back into the NED-2 database (fig. 5).

The FVS Agents

There are two agents related to FVS: the baseline agent

and the FVS simulation agent. The baseline agent generates

data of the baseline year if necessary (when inventory data

are collected in a previous year from the specified baseline

year). The baseline agent gets the baseline year value from

the blackboard and then checks every stand in the specific

management unit under consideration. If the last inventory

year for a stand is the baseline year, the baseline agent does

not need to take any action. However, if the inventory year

is different from the baseline year, the baseline agent will set

up a request for the FVS wrapper to run FVS in order to

generate the data for the baseline year.

The primary functions of the FVS simulation agent are to

retrieve the user’s treatment plan and manage the FVS run

via the wrapper. A treatment plan includes which stands

will be simulated, how long the simulation will run, how to

implement the treatment, and when to treat the stand. The

FVS simulation agent gets the treatment parameters from

the blackboard, then requests the FVS wrapper to simulate

the plan.

In addition to these primary functions, there are other

functions such as determining which variant to use for FVS

and in some cases which region within a variant. If this

information is not available on the blackboard, the FVS

agent will put a request on the blackboard and the variant

agent will respond to this request. Currently, the variant

agent is responsible for providing the proper FVS variant

information via a graphical interface. It is designed to allow

the user to select an appropriate FVS variant (and region).

We plan to incorporate a knowledge base into the variant

agent that would suggest a variant to the user based on

known conditions (such as knowing the State where the

stand is located; that is, if the stand is in Vermont, it is likely

that the northeastern variant would be utilized). If the user

Figure 5—NED/FVS integration.

Figure 4—The NED management plan screen.

 Blackboard

 FVS agent + FVS wrapper

FVS simulator
194 USDA Forest Service Proceedings RMRS-P-25. 2002

Wang, Potter, Nute, Maier, Rauscher, Twery, Thomasma, and Knopp An Intelligent Information System for Forest Management: NED/FVS Integration

chooses a variant that is not consistent with the user’s

inventory data, the agent will issue a warning window to the

user to allow an alternate variant to be selected or a warning

override. Once the variant is decided, the agent will post

appropriate messages on the blackboard.

The FVS Wrapper

In intelligent information systems, a wrapper is an important component. Usually, information sources are heterogeneous (and sometimes distributed). They could (1) be multimedia (video, sound, images, and text), (2) be stored in

diverse formats (databases, flat files, or Internet sites), (3)

have different data meanings across sources (for example,

grade point average might be based on a 4.0 system in one

database and a 10.0 system in another), (4) differ in temporal

and spatial dimension, and/or (5) be application programs.

By building wrappers “around” the heterogeneous information sources, we can communicate with them easily. The

function of a wrapper should be to accept the caller’s queries

and data, convert them into the target-source format, and

then pass them on to the target-source for execution. After

execution, the results are captured by the wrapper that will

transfer them into the format of the caller.

NED-2 is written in LPA Prolog and Microsoft Visual C++.

Agents, the callers, are written in Prolog. FVS is written in

FORTRAN. We provide an FVS wrapper that permits communication between FVS and agents, and is also written in

Prolog. Because the input and output of FVS consists of only

plain text files, and NED stores all the data in a Microsoft

Access database, the FVS wrapper needs to transfer the data

between these two formats.

The FVS wrapper includes three main modules: the MDB

to FVS module, the FVS to MDB module, and the NED

calculation module. It (1) accesses related stand data in the

NED-2 database to create FVS keyword and stand files, (2)

runs FVS, (3) converts the FVS simulation results and

inserts the results back into the NED-2 database, and (4)

runs the NED calculation module to calculate simulation

results at the stand level.

MDB to FVS Module —This module takes the stand

data, plot data, and tree data from the blackboard and

creates a keyword file (*.key) and a set of FVS tree data files

(*.fvs).

One of the FVS input files is the keyword file. In NED-2,

we completely by-pass the SUPPOSE interface that allows

direct FVS users to specify treatments and treatment parameters when running FVS. The values that are included

in the keyword file can be retrieved from the NED-2 database and treatment plan.

For keyword related thinning, the parameters will be

decided by the treatment plan. The MDB to FVS module

takes the plan and then retrieves the corresponding thinning parameter from the treatment parameter database.

For example, if the treatment plan is “light thinning from

below using basal area in FVS in 2010,” and the default

values for light-thinning are residual_ba(120), min_dbh(1),

and max_dbh(7) in the treatment parameter database, the

keyword thinBBA will look like “ThinBBA 2010 120 1 1 7 0

999”.

FVS to MDB Module—After running FVS, we have

results of the simulation. They are saved in a tree list file

(*.trl). This module takes the tree list file as input and

inserts appropriate data back to the NED-2 database. The

FVS to MDB module selects each line in the tree list file. It

checks if the data record for this tree is duplicated. If it is, the

record is ignored and processing continues with the next tree

record. If it is not, the module gets the values of tree ID, tree

species code, tree diameter at breast height, and tree stems

per acre. These items are converted to

the NED-2 database format and sent back to the NED-2

database.

NED-2 Calculation Module—The function of this module is to run the NEDCalc.dll. In NED-2, trees are identified

by the stand, cluster, and plot that they belong to. Tree data

are stored in the overstory and understory tables according

to d.b.h. To facilitate the retrieval from the NED-2 database,

the FVS wrapper runs the NED calculation module before

calling the MDB to FVS module. The NED-2 calculation

module then runs several routines that will create a

“pseudo_stand” combining all trees in a cluster into one

“pseudo_plot”. After running the FVS to MDB module, the

FVS wrapper calls the NED-2 calculation module again.

This time the NEDcalc.dll shuffles the simulation results

into the overstory and understory tables, and then computes

the other tree data at both the tree level and stand level.

Meta-Knowledge Bases

To make the simulation agents more intelligent, we are

creating a meta-knowledge base for simulation agents. Metaknowledge is knowledge about knowledge. Different simulators require different formats for input data and generate

output data differently. Wrappers can provide data translation. But on the one hand, for each specific simulator, we

have to build a corresponding wrapper in order to set up and

run the simulator. On the other hand, these simulator

wrappers include some common procedural knowledge.

Therefore, it is desirable to develop a meta-knowledge base.

Simulation agents will use this meta-knowledge base to

know when and how to use a knowledge source. For example,

by consulting the meta-knowledge base, the simulation

agents will know how to convert NED-2 data into the format

FVS can accept, how to set up control codes for FVS, and how

to convert output from the FVS format back into the NED2 format. So all procedural knowledge for running simulators will be provided by the simulation agent, and specific

knowledge needed to run individual simulators will be

stored in the meta-knowledge bases.

Conclusions____________________

We have described the FVS integration process for NED2. Our blackboard architecture based on intelligent agents

makes integration of information sources easy, fast, and

more powerful. Our future work is to build more intelligent

simulation agents. Other simulation models, such as Silvah

(Marquis and Ernst 1992), will be integrated in NED-2.

Simulation agents will consult meta-knowledge to know

when and how to access related sources (FVS, Silvah, or
USDA Forest Service Proceedings RMRS-P-25. 2002 195

An Intelligent Information System for Forest Management: NED/FVS Integration Wang, Potter, Nute, Maier, Rauscher, Twery, Thomasma, and Knopp

other integrated simulators). NED-2 will be able to respond

to high level queries, such as “Show me how Stand 10 will

look in 25 years if I remove all hardwoods under 6 inches in

diameter today.” The simulation agent will reformat the

original user query and run an appropriate growth and yield

model according to the meta-knowledge base. This allows

seamless database, knowledge base, and model base interaction that is transparent to the user. The point is to increase

the intelligence of the software to reduce the complexity that

the user has to overcome. For further information on NED,

please visit NED Web site at http://www.fs.fed.us/ne/

burlington/ned.

References _____________________

Cheung, W.; Cheng, H. 1996. The Model-Assisted Global query

system for multiple databases in distributed enterprises. ACM

Transactions on Information systems, 14(4): 421-470.

Levy, A.; Rajaraman, A.; Ordille, J. 1996. Querying heterogeneous

information sources using source descriptions. Proceedings of the

22nd VLDB Conference, pp.251-262. Bombay, India.

Marquis, D. A.; Ernst, R. L. 1992. User’s guide to SILVAH: stand

analysis, prescription, and management simulator program for

hardwood stands of the Alleghenies. Gen. Tech. Rep. NE-162.

Radnor, PA: U.S. Department of Agriculture, Forest Service,

Northeastern Forest Experiment Station. 124 p.

Papakonstatniou, Y.; Garcia-Molina, H.; Ullman, J. 1996. MedMaker:

A mediation system based on declarative specifications. IEEE

12th Int. Conference on Data Engineering, pp. 132-141. New

Orleans.

Potter, W. D.; Deng, X.; Somasekar, S.; Liu, S.; Rauscher, H. M.;

Thomasma, S. 2000. Forest Ecosystem Management via the NED

Intelligent Information System. Proceedings of the 13th Int.

Conference on Industrial & Engineering Applications of Artificial

Intelligence and Expert Systems, IEA/AIE’2000, pp. 629-638.

New Orleans.

Sheth, A.; Larson, J. 1990. Federated database systems for managing distributed, heterogeneous and autonomous database. ACM

Computing Surveys, 22, 3, pp. 183-236.

Teck, R.; Moer, M.; Eav, B.1997. The forest vegetation simulator: a

decision-support tool for integrating resources science. http://

www.fs.fed.us/ftproot/pub/fmsc/fvsdesc.htm
196 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Richard Bates is a Master’s Candidate, Dr. Gary D. Kronrad is Professor

of Forest Resources Economics, and Dr. Ching-Hsun Huang is a Research

Associate, Arthur Temple College of Forestry, Stephen F. Austin State

University, Nacogdoches, TX 75962-6109. Don Vandendriesche is a Forester,

USDA Forest Service, Forest Management Service Center, Fort Collins, CO

80526-1891.

Abstract—Approximately 1 million acres of abandoned mined

lands exist in the Appalachian coal region. Reclamation and afforestation of these sites may be conducted if it is profitable or if losses

can be minimized. Calculating profitability requires a determination of the optimal thinning and harvesting schedule that maximizes financial return. The Forest Vegetation Simulator was used

to determine the best management regime by simulating stand

growth for northern red oak (Quercus rubra). Soil expectation value,

calculated with real alternative rates of return ranging from 0.0 to

12.5 percent, was used as the investment criterion. Results indicate that when reclamation costs are low, afforestation can be

profitable only when a landowner’s real alternative rate of return

is 0.0 percent and one thinning is conducted with a final harvest

age of 100, at 2.5 percent with two thinnings and a final harvest age

of 100, and at 5.0 percent with one thinning and a final harvest at

80 years. Afforestation can be profitable with high reclamation costs

only when the alternative rate of return is 0.0 percent and one

thinning is conducted with a final harvest at age 100, and at 2.5

percent with two thinnings and a final harvest age of 100.

Appalachia and Abandoned Mined

Lands _________________________

The Appalachian coal region of the Eastern United States

extends from Pennsylvania to Alabama. Of this region,

according to the Office of Surface Mining (OSM) (USDI-OSM

2000), there are approximately 1 million acres of land

classified as abandoned mined lands (AML) (fig. 1). AML

sites are inadequately reclaimed mine sites occurring before

passage of the 1977 Surface and Mining Control and Reclamation Act (SMCRA); no Federal or State laws exist that

require any further reclamation responsibility to any company or individual (USDI-OSM 2002).

Use of the Forest Vegetation Simulator in

Determining Optimal Forest Rotation

Settings for Abandoned Mined Lands in

Appalachia

Richard Bates

Gary D. Kronrad

Ching-Hsun Huang

Don Vandendriesche

The many problems associated with Appalachian AML

sites range from dangerous highwalls causing landslides to

underground mine fires causing the release of smoke, haze,

heat, or hazardous gases. In an effort to combat the problems

present on these lands, OSM has set up the following five

AML reclamation priority levels:

• Priority 1—the protection of public health, safety, general welfare, and property from extreme danger of

adverse affects of past mining practices.

Figure 1—Abandoned mined lands within Appalachia.
USDA Forest Service Proceedings RMRS-P-25. 2002 197

Use of the Forest Vegetation Simulator in Determining Optimal Forest Rotation Settings ... Bates, Kronrad, Huang, and Vandendriesche

• Priority 2—the protection of public health, safety, general welfare, and property from adverse affects of past

mining practices.

• Priority 3—the restoration of land and water resources

and the environment previously degraded by adverse

effects of past mining practices including measures for

the conservation and development of soil, water, woodland, fish, wildlife, recreation resources, and agricultural productivity.

• Priority 4—the protection, repair, replacement, construction, or enhancements of public facilities such as

utilities, roads, recreation, and conservation facilities

adversely affected by past mining practices.

• Priority 5—the development of publicly owned land

adversely affected by mining practices including land

acquired for recreation and historic purposes, conservation, and reclamation and open spaces (USDI-OSM

2002).

The correction of these problems can be costly, in some

cases more than $100,000 per acre. Therefore, OSM has

become interested in developing environmentally sound

least-cost reclamation methods. Planting trees can be a

perfect reclamation tool to achieve this goal for several

reasons. First, the Appalachian region has the largest contiguous, temperate hardwood forest in the world (Smith

1995), so restoration with trees can create a landscape that

is closer to the premined state. Reclamation with trees can

also increase environmental benefits to the surrounding

communities by increasing soil pedogenetic processes, enhancing recreational opportunities, and creating more wildlife habitat. Another reason why trees can be presented as a

perfect reclamation tool is their ability to withstand pH

levels lower than that of other vegetation (Vogel 1981). Trees

can also provide landowners with cyclical revenues from

forest product sales and effectively reduce the overall cost of

reclamation. As a result, economic stimulus can be created

within surrounding communities.

Methods _______________________

Reclamation and afforestation of AML sites at a least

cost requires the determination of the optimal forest rotation setting that maximizes financial return. This study

was designed to determine the optimal thinning and harvesting schedules for northern red oak (Quercus rubra),

one of the major commercial tree species within this region,

on AML sites in Appalachia. The Forest Vegetation Simulator (FVS) was used to simulate stand growth data on

diameter, height, and volume, for stands from establishment to final harvest. FVS, in conjunction with the Keyword Iteration Navigator (KIN) (an FVS preprocessor that

allows a user to input minimum, maximum, and increment

values for any one keyword), was combined with an economic program to calculate cash flow tables, net present

worths (NPW), and soil expectation values (SEV). This

dynamic program was developed to simultaneously determine the financially optimal timing and intensity for

thinning(s) and the financially optimal rotation age for

northern red oak.

Site index 80 (base age 50) land was examined in this pilot

study. The maximum rotation age was set at age 100 with a

choice of zero, one, two, or three thinnings during the

rotation. The method used for thinnings was a percent of

basal area removal from below. The first thinning could not

be conducted until the stand was at least 30 years of age. The

minimum years between thinnings, or between a thinning

and final harvest, could not be less than 15 years. For all of

the dynamic computer runs, a “thinning and final harvest”

regime would be considered to be operable only if (1) each

thinning or final harvest yielded a minimum of 10 cords

(960 ft3

), (2) each thinning or final harvest yielded a minimum of 1,500 Doyle board feet (1995 Scribner board feet), or

(3) each thinning or final harvest yielded a minimum combination of 5 cords (480 ft3

) and 750 Doyle board feet (997

Scribner board feet). A minimum of 80 residual trees was

required after each thinning operation. Four thinning intensities, 25, 30, 35, and 40 percent basal area removal from

below, were conducted for each possible thinning regime.

Six real alternative rates of return (ARR) were chosen for

the economic analyses: 0.0, 2.5, 5.0, 7.5, 10.0, and 12.5

percent. The annual real price increase of sawtimber and

pulpwood were assumed to be 2.5 and 2.0 percent, respectively. The price of sawtimber was assumed to be $255 per

1,000 Doyle board feet ($191.25 per 1,000 Scribner board feet),

and pulpwood was priced at $9.00 per cord ($0.09375 per ft3

).

The cost for marking and administering a pulpwood or

sawtimber sale was assumed to be 10.0 percent of the gross

sale. Two sets of establishment costs were used in these

analyses. The first set was for the case when little to no

reclamation was conducted, and a cost of $225 per acre for

seedlings and planting was used. The second set of establishment costs included a $1,100 per acre reclamation cost used

to correct erosion and pH problems, and a $225 per acre cost

for seedlings and planting. It was assumed that the environmental engineering problems associated with some abandoned mined lands have been corrected, and these lands are

ready for forestry practices.

Economic Approach

SEV was used as the efficiency criteria to select the most

profitable thinning and harvesting schedule. SEV is the

NPW of bare forestland for timber production over a perpetual series of rotations. NPW is the sum of the discounted

revenues minus the sum of the discounted costs of a project

(Gregory 1987). SEV is used to compare forest investments

of different rotation lengths. For each real ARR, the management regime with the highest SEV was chosen as the

optimal forest rotation setting.

Results ________________________

A total of 144,066 thinning and harvesting combinations,

NPWs, and SEVs were calculated. The optimal forest rotation settings that maximize SEV for each real ARR are

presented in table 1, and the NPWs for each real ARR with

low reclamation costs and high reclamation costs are presented in tables 2 and 3, respectively. All monetary values

are presented on a per acre basis.
198 USDA Forest Service Proceedings RMRS-P-25. 2002

Bates, Kronrad, Huang, and Vandendriesche Use of the Forest Vegetation Simulator in Determining Optimal Forest Rotation Settings ...

0.0 Percent Real ARR

The optimal forest rotation setting when the landowner’s

ARR is 0.0 percent requires that a thinning should be

conducted at stand age 51, removing 25 percent of the basal

area from below, and the final harvest should be conducted

at age 100 (table 1). This optimal management regime

would generate an NPW of $70,470 per acre when reclamation costs are low (table 2) and an NPW of $69,370 per acre

when reclamation costs are high (table 3). These NPW

Table 1—Optimal forest rotation settings that maximize soil expectation

value for northern red oak site index 80 (base age 50).

Real alternative Thinning Final harvest

rate of return year(s) Thinningb year

percent percent

0.0 51 25 100

2.5 47-82a 30 100

5.0 59 25 80

7.5 41 40 74

10.0 40-55 40 70

12.5 40-55 40 70

a

First number indicates first thinning year; second number indicates second

thinning year. b

Basal area removed from below during thinning(s).

Table 2—Net present worth of the optimal forest rotation settings for

northern red oak site index 80 (base age 50) with low

reclamation costs.

Real alternative rate of return Net present worth

percent dollars per acre

0.0 70,470

2.5 6,108

5.0 448

7.5 –110

10.0 –206

12.5 –219

Table 3—Net present worth of the optimal forest rotation settings for

northern red oak site index 80 (base age 50) with high

reclamation costs.

Real alternative rate of return Net present worth

percent dollars per acre

0.0 69,370

2.5 5,008

5.0 –652

7.5 –1,210

10.0 –1,301

12.5 –1,319

values indicate that the landowner who followed this management regime, over one rotation, would earn 0.0 percent

on every dollar invested plus $70,470 per acre if reclamation costs are low, or 0.0 percent plus $69,370 per acre if the

reclamation costs are high.

2.5 Percent Real ARR

For landowner’s who have a real ARR of 2.5 percent, the

optimal thinning and harvesting schedule requires the first

thinning at age 47 and the second thinning at age 82. At each

thinning, 30 percent of the basal area should be removed.

The final harvest should be conducted at age 100. The NPWs

for this thinning and harvesting schedule are $6,108 per

acre with low reclamation costs and $5,008 per acre with

high reclamation costs.

5.0 Percent Real ARR

For landowners who have real alternative rates of return

of 5.0 percent, the optimal thinning and harvesting schedule requires that 25 percent of the basal area should be

removed at age 59, and a final harvest should be conducted

at stand age 80. With low reclamation costs, the NPW for the

optimal forest rotation setting is $448 per acre. If reclamation costs are high, the NPW would be –$652 per acre.

7.5 Percent Real ARR

If a landowner’s real ARR is 7.5 percent, the optimal

thinning and harvesting schedule requires that a thinning

should be conducted at age 41, in which 40 percent of the

basal area should be removed. The final harvest should be

conducted at age 74. This optimal schedule would generate

an NPW of –$110 per acre when reclamation costs are low,

and an NPW of –$1,210 per acre when reclamation costs are

high.

10.0 Percent Real ARR

If a landowner has a real ARR of 10.0 percent, the optimal

forest rotation setting shows that the first thinning should

be conducted at stand age 40, a second thinning at age 55

(removing 40 percent of the basal area during thinnings),

and a final harvest at age 70. This thinning and harvesting

schedule would yield an NPW of –$206 per acre when

reclamation costs are low, and an NPW of –$1,301 per acre

when reclamation costs are high.

12.5 Percent Real ARR

When 12.5 percent is the landowner’s real ARR, the

optimal thinning and harvesting schedule is the same as the

10.0 percent ARR; a first thinning is conducted at age 40

and a second thinning at age 55. At each thinning, 40 percent

of the basal area should be removed. The final harvest

should be at stand age 70. The NPWs for this thinning and

harvesting schedule are –$219 per acre for low reclamation

costs and –$1,319 per acre for high reclamation costs.
USDA Forest Service Proceedings RMRS-P-25. 2002 199

Use of the Forest Vegetation Simulator in Determining Optimal Forest Rotation Settings ... Bates, Kronrad, Huang, and Vandendriesche

Conclusions____________________

The profitability of reclaiming and afforesting AML sites

is highly dependent on the landowner’s real ARR and the

site’s productivity, or site index. The use of the optimal forest

rotation setting methodology allows a landowner to answer the two basic questions in any profit-maximizing

forestry related project: (1) How should I manage my

stand, and (2) How profitable will it be? Results indicate

that reclaiming and afforesting AML with northern red oak

can be a profitable investment when a landowner’s real ARR

is 0.0 or 2.5 percent, regardless of reclamation costs, or 5

percent when reclamation costs are low. Although there is a

net loss in profit when higher ARRs are used, the positive

externalities generated from reclaiming these sites may

exceed the negative environmental effects present on these

AML sites.

Every site has specific reclamation needs. Successful reclamation and afforestation requires a thorough understanding

of critical species-site relationships. Future analyses will

investigate the potential of reclaiming AML sites with northern red oak and white oak (Quercus alba) for site indices

ranging from 40 to 80 (base age 50) across the Appalachian

coal region.

References _____________________

Gregory, G.R. 1987. Resource economics for foresters. New York.

John Wiley & Sons, Inc. 477 p.

Smith, D.W. 1995. The southern Appalachian hardwood region. In:

Barrett, J.W., ed. Regional silviculture of the United States,

Third Edition; New York. John Wiley & Sons, Inc.: 173-225.

U.S. Department of the Interior, Office of Surface Mining (USDIOSM). 2000. Internal documents.

U.S. Department of the Interior, Office of Surface Mining (USDIOSM). 2002. AML inventory definitions. http://www.osmre.gov/

aml/inven/zqadefin.htm (28 Jan. 2002).

Vogel, W.G. 1981. A guide for revegetating coal minesoils in the

Eastern United States. General Technical Report NE-68.

Newtown, PA: U.S. Department of Agriculture, Forest Service,

Northeastern Forest Experiment Station. 190 p.
200 USDA Forest Service Proceedings RMRS-P-25. 2002

In: Crookston, Nicholas L.; Havis, Robert N., comps. 2002. Second Forest

Vegetation Simulator Conference; 2002 February 12–14; Fort Collins, CO.

Proc. RMRS-P-25. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Kevin R. Ceder is a Forest Technology Specialist, Rural Technology

Initiative and John M. Marzluff is an Associate Professor of Wildlife Science,

College of Forest Resources, University of Washington, Box 352100, Seattle,

WA 98195

Linking Tools of Forest and Wildlife

Managers: Wildlife Habitat Evaluation Using

the Landscape Management System

Kevin R. Ceder

John M. Marzluff

Abstract—Natural resource managers are increasingly being asked

to consider values outside their fields. This is especially evident with

regards to wildlife habitat changes caused by forest management

activities. Forest managers are being asked to balance both wildlife

habitat and forest product outputs from the forest. Our approach of

implementing a Habitat Evaluation Procedure as a module of the

Landscape Management System is an example of how forest growth

and yield models can be integrated with existing wildlife models to

expand the forest manager’s tool set. The Landscape Management

System uses the Forest Vegetation Simulator to simulate forest

growth and changes caused by silvicultural activities on the Satsop

Forest ownership, located in southwestern Washington State. The

Habitat Evaluation Procedure module then calculates Habitat

Suitability Indexes and Habitat Units for Cooper’s hawk (Accipiter

Cooperii), pileated woodpecker (Drycopus pileatus), southern redbacked vole (Clethrionomys gapperi), and spotted towhee (Pipilo

erythrophthalmus) from the resulting projected forest inventories.

The result is a tool that allows forest managers to assess changes in

wildlife habitat caused by potential forest management at the stand

and ownership levels. Because the Landscape Management System

produces summaries of a variety of forest outputs, both tabular and

visual, the results can then be used in analyses of existing and

proposed forest management plans. On a stand-by-stand basis,

multiple silvicultural pathways can be tested to assess which

pathways meet varying desired habitat and forest product outputs.

Through the use of stand and ownership level simulations and

analyses of multiple target outputs, forest managers and

decisionmakers are able to better understand output tradeoffs at

the landscape and watershed levels.

The public has become increasingly concerned over the

past three decades about potential negative effects on wildlife caused by development and other modifications of wildlife habitat. Conversion of naturally regenerated forests to

intensively managed plantations for timber production has

raised concerns about habitat for species that are associated

with these forest structures at the present time and in the

future. While planning current and future forest management activities, forest managers are being asked to address

how management will affect forest systems in the coming

decades.

As concern grows and more species are studied, many of

these species become candidates for special consideration

ranging from a “species of concern” at the State level, such

as the pileated woodpecker (Dryocopus pileatus) in Washington State, to “threatened” or “endangered,” such as the

northern spotted owl (Strix occidentalis caurina) in the

Pacific Northwest, the red-cockaded woodpecker (Picoides

borealis) in the Southeast, and the Kirtland’s warbler

(Dendroica kirtlandii) in the Lake States region, at both the

State and Federal levels. Listing of these species resulted in

regulatory constraints on forest management. Changes in

Federal forest management in the Pacific Northwest under

the Northwest Forest Plan to protect old forest habitat and

the spotted owl exemplify the regulatory constraints. Harvest on Pacific Northwest National Forests has virtually

stopped.

Technology has increased greatly during this time as well.

Computing power has greatly increased, and forest growth

and yield models, such as the Forest Vegetation Simulator

(FVS), have been developed to predict forest growth and

development though time. Using these tools, forest managers can estimate potential harvest volumes and tree sizes in

the future. From initial forest inventory data and simulated

future data, managers create forest management plans

based on criteria such as allowable harvest volume or stand

structures, now and in the future, calculated from stand

attributes such as tree species, sizes, and volumes. As

demands on forests change, managers must estimate effects

on other forest outputs such as wildlife habitat. Using a

simulation system that includes wildlife habitat models, it

may be possible for managers and other interested parties to

gain insight into how current forest management may affect

future forest outputs and ensure forests are managed in a

sustainable manner.

This study implements a Habitat Evaluation Procedure

(HEP; USDI 1980a) within the Landscape Management

System (LMS) for two reasons: first, to develop tools to

support analysis of new management alternatives for Satsop

Forest. Any analysis must be consistent with the original

Satsop Forest HEP (Curt Leigh personal communication) to

ensure comparable results. The original Satsop Forest HEP

was performed on Satsop Forest, formerly the Satsop Nuclear

Site, to assess losses of wildlife habitats caused by construction of two nuclear power plants and to analyze management

plans to mitigate for lost habitats. Second, because Habitat

Suitability Index (HSI) models and the HEP use, primarily,

tree-based measures, the HEP is used in this study to

demonstrate linking wildlife habitat models with forest

growth models within a forest simulation system.
USDA Forest Service Proceedings RMRS-P-25. 2002 201

Linking Tools of Forest and Wildlife Managers: Wildlife Habitat Evaluation using the Landscape Management System Ceder and Marzluff

Background ____________________

Several methods have been used to assess changes in

quality and quantity of wildlife habitat caused by forest

management and disturbances. These have included HSI

models implemented within a GIS, optimization systems,

population density models, and the HEP. Kliskey and others

(1999) used GIS-based HSI models for woodland caribou

(Rangifer tarandus) and pine marten (Martes americana) in

the North Columbia Mountains of British Columbia, Canada.

This study examined changes in habitat quality and quantity for both species as well as harvested volume under four

simulated forest management scenarios to assess amounts

of habitat generated by each scenario, tradeoffs of habitats

among species for each scenario, and tradeoffs between

habitat quantity and harvested volume. Moore and others

(2000) used an optimization technique, which employed a

“genetic algorithm,” to optimize harvest scheduling on a

simulated landscape based on bird populations derived from

population models for hypothetical species. Beavers and Hof

(1999) took a different approach by spatially optimizing the

amount of edge habitat to maintain populations of both edge

and interior habitat species. Hansen and others (1995)

constructed population density response models for 16 species of birds in the Central Oregon Cascades that used

densities of trees in specific diameter classes to estimate

population densities. With these models several silvicultural pathways were simulated with the ZELIG growth

model (Urban 1992) and the outputs were used to estimate

the resulting population densities. The HEP is a methodology used to assess impacts to wildlife habitats caused by

projects such as power plant construction where wildlife

habitat is used as “currency” in an accounting system.

Initially developed in the 1970s, this system is still in use

today.

Study Location

Satsop Forest encompasses roughly 1,400 acres south of

the Chehalis River in southwestern Washington State

(fig. 1) owned by the Grays Harbor Public Development

Authority (PDA). About 840 acres are forested, and 220

acres are in grassy meadows, shrubs, or riparian areas

and not included in timber management analysis. The

remaining 340 acres are part of the developed infrastructure of the Satsop Development Park. Elevation is relatively constant at 250 feet, and forest productivity is

moderate to high (56 percent of the forest is Site Class II,

Site Index 115 to 135 feet (King 1966)). Stands range from

pure conifer to pure hardwood, but most are complex

mixtures of Douglas-fir (Pseudotsuga menziesii), big leaf

maple (Acer macrophyllum), western hemlock (Tsuga

heterophylla), red alder (Alnus rubra), and western

redcedar (Thuja plicata). Most of the area is covered by

forest 5 to 10 years old; forests greater than 120 years are

present in small proportion on the site (Marzluff and

others 2002).

Currently, Satsop Forest management is dictated by a

wildlife mitigation agreement. This agreement was put in

place to mitigate for wildlife habitat losses caused by the

partial construction of Washington Public Power Supply

System’s Nuclear Plants No. 3 and No. 5. Even though this

agreement was created prior to the PDA’s acquisition of the

land, it is still in force, providing management prescriptions

through the year 2040.

The PDA’s management objectives for Satsop Forest extend beyond strictly the creation and enhancement of wildlife habitat (Jim Walls, personal communication). There is a

desire to create a demonstration forest where alternative

silvicultural practices that create both wildlife habitat and

income can be showcased to other landowners with the

primary focus being nonindustrial private forest (NIPF)

owners. Income is important because the trees on forestland

are often an investment for the owner.

Original Satsop Forest HEP (WPPSS 1994)

Performing the original HEP on Satsop Forest involved

several steps. First a vegetation cover type inventory of the

area was undertaken using aerial photographs with associated criteria (percent canopy closure, percent conifer/deciduous, average d.b.h., average height, total trees per acre,

trees per acre greater than 21 inches d.b.h., and number of

canopy layers) used to determine cover types. Next, a set of

species for the analysis was selected, followed by Habitat

Suitability Index (HSI) model selections and a habitat attribute inventory. Several potential management scenarios

were then drafted for the area, with forest cover type changes

estimated. Habitat Suitability Index values were then calculated for each cover type that was expected to be found on

Satsop Forest at specific future target years. For each target

year, cover type acreages, HSI, and Habitat Unit (HU)

values for each species were calculated. For the life of each

Figure 1—Location of Satsop Forest in southwestern

Washington.
202 USDA Forest Service Proceedings RMRS-P-25. 2002

Ceder and Marzluff Linking Tools of Forest and Wildlife Managers: Wildlife Habitat Evaluation using the Landscape Management System

alternative, Annual Average Habitat Unit (AAHU) values

were calculated to estimate average available habitat quantities. AAHU values were compared between alternatives to

select the “preferred” management alternative.

We found 21 cover types on Satsop Forest including

“Developed” and “Barren” ground that are not considered as

wildlife habitat. There are three riparian cover types as well

as ponds, grass, and brush. Nonriparian forested areas that

can be managed fit into 13 cover type classifications.

Five species and associated HSI models were selected for

the HEP analysis: Cooper’s hawk (Accipiter cooperii; USDI

1980b), southern red-backed vole (Clethrionomys gapperi;

Allen 1983), pileated woodpecker (Dryocopus pileatus;

Schroeder 1983), spotted towhee (Pipilo erythrophthalmus;

USDI 1978), and black-tailed deer (Odocoileus hemionus

columbianus; WDFW 1991). Each species was chosen for a

specific reason (WPPSS 1994). Cooper’s hawks tend to prefer

hardwood and mixed conifer-hardwood forests in both upland and riparian habitats. Southern red-backed voles were

chosen to represent small forest rodents. They prefer mature

and older forest structures and are a prey species for forest

raptors and owls. Pileated woodpeckers were selected to

represent cavity nesters. They are the largest of the woodpeckers and require larger snags than other cavity nesters;

and they are listed as a Washington State “species of concern.” If habitat exists for pileated woodpeckers, it is assumed that smaller cavity users such as nuthatches, flying

squirrels, and bats will have habitat as well. Spotted towhees prefer open structures with dense shrub layers, such

as brush lands and young forests. Black-tailed deer use

multiple habitats and are of concern to the public and

wildlife management agencies as a game species.

All HSI models chosen for the Satsop Forest HEP require

tree-based measures, such as canopy closure, overstory

d.b.h., and number of trees with d.b.h. greater than 21 inches

per acre, and all the HSI models, except the Cooper’s hawk,

also require non-tree-based measures, such as downfall

litter, downed logs per acre, and grass cover. All attributes

were inventoried on Satsop Forest in 1991 and used in the

original HEP analysis. Based on average habitat attribute

values for each cover type, HSI values were calculated for

each species for each cover type on Satsop Forest. Cover

type acreages were calculated for all the target years

based on estimated forest changes caused by growth and

potential management alternatives. These acreages were

used with the HSI values to calculate HU values and

AAHU values for each species. Changes in AAHU values

between alternatives were used as the deciding factor in

selecting the preferred mitigation alternative for the mitigation agreement.

Landscape Management System

The Landscape Management System (LMS) is an integrated forest management simulation and decision analysis software package developed as a cooperative effort

between the Silviculture Laboratory, College of Forest

Resources, University of Washington, and the USDA Forest Service (McCarter and others 1998). LMS is an evolving

application designed to assist in stand and landscape

ecosystem analyses by coordinating the processes of forest

growth and management simulations, tabular data summarization, and stand and landscape visualization. Implemented as a Microsoft WindowsTM application, many separate programs integrate these tasks. These programs include

forest growth models, harvest simulation programs, and

data summary programs, as well as stand and landscape

level visualization software.

Underlying data for LMS are consolidated into a landscape portfolio. These data include forest inventory data

(stand-by-stand tree list); stand level data (such as site index

and age), and topographic data (slope aspect and elevation),

as well as geographic information system (GIS) data in the

form of a digital terrain model (DTM), ESRI (Environmental

Systems Research Institute, Inc., Redlands, CA) shapefiles

of stand boundaries, and other features such as streams and

roads. This assemblage of data is then used by LMS to

simulate, analyze, and communicate the effects of forest

management on the landscape.

Summary output tables from LMS range from standard

inventory tables, to stand structural stages, to harvested

and standing volumes. All tables are summaries of current

and projected inventories for analyses of predicted future

conditions and forest outputs. The large array of tables

allows analyses of proposed forest management from many

perspectives.

Habitat Evaluation Procedure (HEP)

Implementation within LMS

Given the similarities between HSI model input data and

LMS output data and the modular, integrative nature of

LMS, implementation of an HEP was a process of codifying

the graphical HSI models and cover typing rules and creating the computer programs needed to read LMS output files,

generate model input data, perform cover typing and HSI

calculations, and create HEP output files. This was done

using the Python programming language (URL: http://

python.org). The LMS implementation of the Satsop HEP

consists of cover typing and four of the five original HSI

models. Because of the spatial nature of the black-tailed deer

model and the lack of spatial metrics in LMS output, it

cannot be implemented directly in LMS.

HSI Model Implementation

HSI models contain several component variables that

contribute to the total habitat suitability for a species. Each

component variable has its own graphical relationship between that habitat attribute and that attribute’s contribution to the overall HSI value. Each of these component HSI’s

varies from 0.0 to 1.0. When each component HSI has been

estimated, an overall HSI is determined by geometric averages or minimum values, depending on the relative importance of attributes to the particular species. The HSI model

for Cooper’s hawk (fig. 2) is an example of the graphical

model.

To be used in a computerized simulation system, HSI

models must be converted into piecewise equations. An
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Linking Tools of Forest and Wildlife Managers: Wildlife Habitat Evaluation using the Landscape Management System Ceder and Marzluff

example of this are the equations for the Cooper’s hawk HSI

model as implemented in the Satsop HEP module of LMS:

Variable 1 (V1) – % Canopy closure

Input – % Canopy closure (CC)

V1 = 1/60 * CC, where CC ≤ 60

V1 = 1, where CC > 60

Variable 2 (V2) – Overstory size class

Input – Average DBH (ADBH)

V2 = 0.2, where ADBH ≤ 6

V2 = 0.6, where 6 < ADBH ≤ 10

V2 = 0.9, where 10 < ADBH ≤ 20

V2 = 1, where ADBH > 20

Variable 3 (V3) – % Conifer canopy closure

Input – % Conifer canopy closure (CCC)

V3 = 0.8 + 0.2/10 * CCC, where CCC ≤ 10

V3 = 1, where 10 < CCC ≤ 30

V3 = 1 – 0.8/50 * (CCC – 30), where 30 < CCC ≤ 80

V3 = 0.2, where CCC > 80

HSI = Lesser of: (V1 * V2)1/2 or V3

Figure 2—Cooper’s hawk Habitat Suitability Model from

USDI (1980b)

Cover Type Rule Implementation

Generating the algorithm to perform the cover typing

required writing a series of “if” statements based on the rules

in table 1. Within each “if” is a series of “and” statements to

test all criteria required for each cover type. If all criteria are

satisfied, the stand is assigned that cover type. If any one

criterion is not satisfied, the algorithm moves to the next

cover type. The process continues until the stand satisfies all

criteria for a cover type or ends in the catchall “Brush” cover

type.

HEP Input Data Generation

Cover types and HSI model implemented within LMS

require LMS generated data. These data come from two

sources: (1) Tree-based measures (total canopy closure,

conifer canopy closure, percent conifer/deciduous, dominant

height, average d.b.h., overstory d.b.h., trees per acre, trees

per acre with d.b.h. greater than 21 inches, and canopy

layers) are calculated from initial and simulated future tree

lists. (2) Non-tree-based data (downfall litter, grass cover,

stumps per acre, logs per acre, snags per acre with d.b.h.

greater than 21 inches, average d.b.h. of snags with d.b.h.

greater than 21 inches, total ground cover, and a Shrub

Suitability Index) are taken from a lookup table based on the

cover type of the stand.

Calculation of tree-based data is performed using algorithms within LMS plus implementations of the canopy

closure equation published by Crookston and Stage (1999)

and the canopy layers algorithm published by Baker and

Wilson (2000). All tree based measures are calculated then

stored for use by the cover typing and HSI calculation

algorithms.

Extracting the non-tree-based measures from the lookup

table is done after the stand has been assigned a cover type.

Within the lookup table there are habitat attribute values,

from the 1991 habitat attribute inventory, presented as

cover type averages. The non-tree-based data extraction

algorithm reads the cover type of a stand then retrieves the

non-tree-based values and stores them to be used by HSI

calculation algorithms.

After all HSI model inputs have been generated, HSI

calculations are performed for each species chosen for the

analysis. The results are relative measures, on a 0.0 to 1.0

scale, of habitat suitability for each stand for each species.

Habitat suitability is analogous to habitat quality. Estimating the amount of potential available habitat is done by

multiplying the HSI value by the acreage of the stand

resulting in HU values. HU values are a relative measure of

available habitat where a value of 50 may be 50 acres of

optimal habitat, with HSI of 1.0, or 500 acres of poor habitat,

with an HSI value of 0.1. HU values can then be summed

across the entire landscape to get a relative measure of

available habitat for the species used in the analysis. Estimating the amount of habitat available over the life of the

simulation is done with Average Annual Habitat Units

(AAHU).
204 USDA Forest Service Proceedings RMRS-P-25. 2002

Ceder and Marzluff Linking Tools of Forest and Wildlife Managers: Wildlife Habitat Evaluation using the Landscape Management System

Table 1—Timbered cover type thresholds for Satsop Forest from the original HEP.

Cover Canopy Percent Percent TPA Avg. Avg. Canopy

 type Description closure conifer deciduous TPA >21" d.b.h. d.b.h. height layers

- - - - - - - - - - Percent - - - - - - - - - - Inches ft

C4 Conifer late-successional >70 >75 20 >21 >40 3

C4T Conifer late-successional, thinned <70 >75 >21 >40

C3 Mature conifer >70 >75 12-21

C3T Mature conifer, thinned <70 >75 12-21

C2 Conifer pole/sapling >50 >75 4-12

C1 Early-successional conifer >50 >75 >150 1-4

M3 Mature mixed >70 <75 <75 12-21 >40

M2 Mixed pole/sapling >50 <75 <75 4-12

M1 Early-successional mixed >50 <75 <75 1-4

H3 Mature deciduous >50 >75 12-21 >40

H2 Deciduous pole/sapling >50 >75 4-12

H1 Early-successional deciduous >50 >75 1-4

B Brush <50

Satsop Forest HEP Output Tables

Output tables from the HEP analysis performed by LMS

are designed to be compatible with the tables used in the

original Satsop Forest HEP. In addition, there are other

summary tables and tables formatted to be imported into

ESRI ArcView for mapping of cover types and habitat

suitabi1ty values. Four cover type output tables are available: cover type designation for each stand for each year of

the simulation with acreage for each stand; summary of

acreage in each cover type for each year of the simulation;

cover type by stand for each year of the simulation and the

acreage summary; and a table to be imported into ESRI

ArcView. Seven habitat-related output tables are available:

HSI, HU, By Species, HSI Summary, AAHU, All HEP tables,

and an HSI table to be imported into ESRI ArcView for

mapping habitat suitability. This suite of tables will allow

the analysts and managers to answer many questions regarding habitat suitability and availability.

Validation

To assess the performance of the models implemented in

LMS, it was necessary to use data, both tree-based and nontree-based, from the original HEP to calculate HSI values for

the four species. When compared with the HSI values

calculated during the original HEP, differences range from

–0.009 to 0.001. A paired t-test on the differences between

HSI values reported in the original HEP and those calculated with LMS using data from the original HEP was

performed using the statistical analysis software package

SPSS 10.0. Mean differences range from 0.000 – 0.001 with

all 95 percent confidence intervals containing 0.0 (fig. 3).

Results from the Cooper’s hawk HSI model, using the original HEP data, could not be analyzed using the t-test because

the model predicted the exact HSI values that were reported

in the original HEP analysis. The resulting mean difference

of 0.0 with a standard error of the mean of 0.0 does not allow

the paired t-test to be used. Because all 95 percent confi-

-0.002

-0.001

0.000

0.001

0.002

0.003

0.004

Hawk Vole Woodpecker Towhee

Species

Figure 3—Mean difference between HSI values reported

in original HEP and values calculated by LMS using

original HEP data, with 95 percent confidence intervals.

dence intervals contain 0.0, the models predict as well as the

original HSI models at the 0.05 level.

Given these results it can be said that the HSI models, as

implemented in LMS, predicted HSI values as well as the

original HSI models and using updated forest inventory

data summarized on a per stand basis made no significant

difference in HSI values for each cover type.

Applications of the Satsop Forest

HEP Using LMS _________________

LMS with the HEP module is currently being used to

develop potential management alternatives for Satsop Forest that will be used in wildlife mitigation agreement renegotiations. Development of management alternatives involves two processes: stand-level analyses and

landscape-level analyses. These analyses allow the manager to examine tradeoffs between habitat values and other
USDA Forest Service Proceedings RMRS-P-25. 2002 205

Linking Tools of Forest and Wildlife Managers: Wildlife Habitat Evaluation using the Landscape Management System Ceder and Marzluff

outputs from many stand-level silvicultural pathways and

landscape level management plans. Performing stand-level

analyses allows planners and managers to screen potential

silvicultural pathways based on habitat quality, potential

harvest volume, or other criteria. Preferred pathways are

assigned to specific stands to create a landscape-level management plan. Landscape-level analyses of the management

plan assess potential consequences of applying the plan on

the landscape.

Management of Satsop Forest is constrained by the requirements of a wildlife mitigation agreement. Any alternative management plans must meet the habitat levels of the

current management plan. Screening potential alternative

silvicultural pathways was done with four stands: two 10-

year-old stands, with 435 t.p.a. and 1350 t.p.a, and two 90-

year-old stands, open stand with a single canopy layer and

dense stand with multiple canopy layers. Thirty pathways

were simulated using the young stands, and 21 pathways

were simulated using the older stands. Many of the pathways were taken from previous studies that assessed the

effect of timber harvesting on habitats or populations in

other areas of the Pacific Northwest (CCEM 1993; DeBell

and Curtis 1993; McComb and others 1993; Hansen and

others 1995; Carey and others 1996; Barbour and others

1997). Based on the results of these simulations, the pathways that produced the highest habitat levels for individual

species or highest harvest volume, regardless of habitat

provided, were selected. Examples of the pathways that

produce the highest levels of habitat and harvest volume are

shown in table 2. It should be noted that different pathways

provided different amounts of habitat or harvest volume. If

a land manager is to balance providing habitat for many

species and produce harvest volume, one silvicultural pathway cannot be applied on all acres of a landscape. Designing

landscape level plans from simulated pathway results can

be done with a spreadsheet known as “the Toggle Program”

(Johnson 2001) that allows managers to assess tradeoffs

between many objectives by apportioning different amounts

of the landscape acreage to different pathways.

Landscape-level management alternatives are analyzed

to assess consequences of applying the management plan on

the landscape. Questions such as “How much habitat is

available?” and “How much timber volume is being harvested?” can be answered for the entire landscape. Tradeoffs

between average harvest volume and average available

habitat for each species can be assessed for the life of the

management plan (table 3). Flows of habitat can be examined as well (fig. 4). Flows of habitat are important to avoid

“boom-and-bust” cycles of habitat and species populations.

AAHU values do not address potential changes in habitat

availability over the life of the management plan.

Advantages of Integrating HEP and LMS

Implementing the HEP calculations in LMS provided

several advantages. First, because an empirical growth

model is used, all assumptions are held constant for all forest

Table 2—HSI and harvest volume (m.b.f.) values and top five pathways for individual pathway simulations using young

dense stand.

Hawk Vole Woodpecker Towhee Harvest volume

Max 0.614 0.968 0.651 0.078 260

Min 0.200 0.580 0.112 0.012 0

Average 0.383 0.863 0.486 0.024 96

Top five pathways

Barbour15-150 Barbour30-250 YSTD-Light Hansen0-40 Hansen0-80

Barbour30-150HL YSTD-Light BarbourNT CC40_PCT McComb_CC

Barbour30-75 BarbourNT Hansen0-120 CC60_PCT_CT CC80_PCT_2CT

Barbour15-75 Hansen0-120 Barbour30-250 Hansen0-80 CC40_PCT

Barbour30-150 CareyBDPS 0_NA CC80_PCT_2CT Hansen0-40

Table 3—Comparison of annual harvested volumes (mbf/yr) to percent changes in wildlife habitat over an 80-year simulation

(bold = maximum, italics = minimum).

Southern red- Pileated

Annual Cooper’s hawk backed vole woodpecker Spotted towhee

harvest 2038 2078 2038 2078 2038 2078 2038 2078

mbf/yr - - - - - - - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - - - - - - - -

1. No Action 0.0 –10 –19 65 75 93 112 –80 –86

2. 45-yr rotation 914.4 –71 –71 47 56 –22 –20 –45 –47

3. Moderate enhancement 47.1 –10 –19 65 75 98 112 –80 –86

4. Intensive enhancement 478.4 –53 –60 116 146 90 135 –70 –86

5. Mixed management 337.5 –31 –35 32 67 39 58 –65 –77
206 USDA Forest Service Proceedings RMRS-P-25. 2002

Ceder and Marzluff Linking Tools of Forest and Wildlife Managers: Wildlife Habitat Evaluation using the Landscape Management System

growth simulations, allowing alternatives to be compared

without bias. Also, with the HSI models hard-coded into the

Satsop HEP module, any bias in HSI model application is

held constant as well.

Using stand-by-stand forest inventory, compared to cover

type averages, allows the variability within each cover type

to be included in the analysis. Cover type classification rules

allow a wide range of tree sizes and canopy closures within

Figure 4—Flows of habitat for four species under five

management alternatives

C. Pileated Woodpecker Habitat Units

0

100

200

300

400

500

600

700

800

1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Year

Habitat Units (HSI * hectares)

No Action

Timber Intensive

Moderate Enhancement

Intensive Enhancement

Mixed Management

B. Southern Red-backed Vole Habitat Units

0

100

200

300

400

500

600

700

800

1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Year

Habitat Units (HSI * hectares)

No Action

Timber Intensive

Moderate Enhancement

Intensive Enhancement

Mixed Management

A. Cooper's Hawk Habitat Units

0

100

200

300

400

500

600

700

800

1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Year

Habitat Units (HSI * hectares)

No Action

Timber Intensive

Moderate Enhancement

Intensive Enhancement

Mixed Management

D. Spotted Towhee Habitat Units

0

100

200

300

400

500

600

700

800

1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Year

Habitat Units (HSI * hectares)

No Action

Timber Intensive

Moderate Enhancement

Intensive Enhancement

Mixed Management

C. Pileated Woodpecker Habitat Units

0

100

200

300

400

500

600

700

800

1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Year

Habitat Units (HSI * hectares)

No Action

Timber Intensive

Moderate Enhancement

Intensive Enhancement

Mixed Management

B. Southern Red-backed Vole Habitat Units

0

100

200

300

400

500

600

700

800

1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Year

Habitat Units (HSI * hectares)

No Action

Timber Intensive

Moderate Enhancement

Intensive Enhancement

Mixed Management

A. Cooper's Hawk Habitat Units

0

100

200

300

400

500

600

700

800

1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Year

Habitat Units (HSI * hectares)

No Action

Timber Intensive

Moderate Enhancement

Intensive Enhancement

Mixed Management

D. Spotted Towhee Habitat Units

0

100

200

300

400

500

600

700

800

1998 2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073 2078

Year

Habitat Units (HSI * hectares)

No Action

Timber Intensive

Moderate Enhancement

Intensive Enhancement

Mixed Management

each cover type. Selecting only cover type average attribute

values, instead of stand-level forest inventory data, may

neglect variability and result in lower habitat quality estimations.

The most significant advantage of automating these calculations in LMS is the ability to develop and analyze many

alternatives quickly. Both landscape level and stand level

simulations can be made to assess and communicate the

effects of proposed management regimes. Working at the

stand level, several alternative silvicultural pathways can

be compared to assess potential benefits. Using hypothesistesting framework, pathways can be selected that may

closely meet management goals in the present and the

future, before they are applied on forest landscapes.

Many other analyses are available to the user of LMS.

More analysis tables are included with LMS that are created

by programs within LMS, which summarize the current and

projected future inventories in different ways. Tables created in this process have been developed to answer questions

relating not only to levels of habitat and volumes but also

other forestry measures such as inventory tables and stand

summary tables and other measures such as carbon sequestration and stand structures.

Other assessments include landscape-level and standlevel visualizations using the Stand Visualization System

and EnVision programs (developed by Robert McGaughey,

Research Forester, USDA Forest Service, Pacific Northwest

Research Station, University of Washington, Seattle, WA)

that are included with LMS. These visualizations are especially important when some parties involved in planning

have backgrounds other than forestry and therefore do not

understand standard forestry metrics such as volume, tree

sizes, and stand structures, allowing managers to communicate forest changes caused by forest management activities.

Limitations of Integrating HEP and LMS

This approach also has several limitations. A primary

limitation is the HSI models themselves. All models used in

this study are knowledge-based (expert opinion) models

designed for the entire ranges of the Cooper’s hawk, pileated

woodpecker, southern red-backed vole, and spotted towhee.

The primary weakness of these models is lack of validation

and local calibration. None of the HSI models used were

validated using data to assess their performance prior to use

in the original Satsop Forest HEP. Results from the HSI

models and HEP may not actually reflect actual habitat use

for the species in southwestern Washington without validation and calibration to local habitat uses.

Because understory, snag, and downed wood data are

needed for calculating HSI values for all models used in this

study, with the exception of Cooper’s hawk, the applicability

of these models is limited to the Satsop Forest ownership.

Applying these models elsewhere would require collection of

the appropriate understory, snag, and downed wood data,

and calculations of average values based on cover types

classified by the cover type classification algorithm.

Understory relationships in Pacific Northwest forests

have been studied in great depth. Studies have focused on

developing relationships between overstory and understory

structure and composition, as well as responses of the
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Linking Tools of Forest and Wildlife Managers: Wildlife Habitat Evaluation using the Landscape Management System Ceder and Marzluff

understory to disturbances, including harvesting, in old

growth, mature, and young forests (Halpern and Spies 1995;

Bailey and others 1998; Van Pelt and Franklin 2000). Relationships have been developed into models for other regions

of the Pacific Northwest (McPherson 1992; Klinka and

others 1996; Mitchell and Popovich 1997), but none were

found for southwestern Washington. Forest inventory that

is limited to tree measures will have little success in estimating wildlife habitat using the Satsop Forest HEP module of

LMS because both the spotted towhee and southern redbacked vole require understory metrics as well as tree

metrics. Development of understory models for other regions demonstrates that it can be done, and building models

for western Washington would increase the applicability of

the models to other areas of the region.

Snags and downed wood are important habitat attributes

for pileated woodpecker and southern red-backed vole models. A snag and downed wood model is scheduled to be

included in future versions of LMS. Application of this model

will allow the models to estimate habitat with variability of

snags and downed wood within each cover type included. It

is hoped that including snag and downed wood in the models

will increase their sensitivity and accuracy.

Spatial arrangement and size of habitat patches are

important for some species. Pileated woodpeckers need a

home range of up to 600 acres (Schroeder 1983). In the case

of Satsop Forest, with an average polygon (habitat patch)

size of 5.2 acres, the ability to arrange harvest activities, in

such a way that minimum home range requirements are

met, is through iterative applications of silvicultural pathways to various stands. After an initial management plan

simulation, there may be 600 acres of pileated woodpecker

habitat, but it may be arranged in a series of small patches

that are not of use for the woodpeckers. This is less of a

concern for a species with a small home range such as the

southern red-backed vole with a home range size of up to

approximately 3.5 acres (Allen 1983).

Conclusion_____________________

This study provide the conceptual process of linking forest

growth models with wildlife habitat models within a simulation system that brings many advantages over the old

process of separate habitat and forest growth simulations

but also many limitations. Given the limitations of the HEP

within LMS and the variability between growth model

outputs and actual tree and stand growth, this system

should be used in an adaptive management context. Ensuring stands are developing on the desired trajectory will

require managers to revisit the results after several years to

verify that silvicultural pathways are providing the desired

results. If the silvicultural pathways are not producing the

desired results, silvicultural pathways and assessment tools

can be adjusted to produce the needed forest outputs.

HSI models used in this study, which are limited by their

lack of verification and validation, do not provide estimates

of actual species populations, only measures of potential

habitat quality and quantity. Thus, if population estimates

are required, managers will need to perform field surveys to

determine if animals are actually using the habitats that are

expected to exist on the landscape. If species are using

habitats different from the model’s predictions, the models

may be modified using the monitoring data to predict habitat suitability indices better. Alternatively, if the models are

found to be completely inadequate and better models are

constructed, these models can be implemented within LMS

to expand and refine the wildlife habitat analysis capabilities of LMS.

Even with the limitations of the HEP within LMS, using

this type of simulation system in a gaming context will allow

people to gain valuable insights into tradeoffs of various

forest outputs under many potential forest management

plans with all assumptions being held constant for all

analyses. Even though results of the models may only be an

abstract representation of reality, relative differences between the outputs of compared silvicultural pathways can be

examined. Comparing these differences for a large array of

management objectives between many alternatives developed using LMS will help to avoid unintended consequences

of forest management plans.

References _____________________

Allen, A. W. 1983. Habitat suitability index models: Southern redbacked vole (western U.S.), USDI Fish and Wildlife Service: 14.

Bailey, J. D.; C. Maysohn; others. 1998. Understory vegetation in

old and young Douglas-fir forests of western Oregon. Forest

Ecology and Management 112: 289-302.

Baker, P. J.; J. S. Wilson. 2000. A quantitative technique for the

identification of canopy stratification in tropical and temperate

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The U.S. Department of Agriculture (USDA) prohibits discrimination in all its

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ROCKY MOUNTAIN RESEARCH STATION

RMRS

The Rocky Mountain Research Station develops scientific information and technology to improve management, protection, and use of

the forests and rangelands. Research is designed to meet the needs

of National Forest managers, Federal and State agencies, public and

private organizations, academic institutions, industry, and individuals.

Studies accelerate solutions to problems involving ecosystems,

range, forests, water, recreation, fire, resource inventory, land reclamation, community sustainability, forest engineering technology,

multiple use economics, wildlife and fish habitat, and forest insects

and diseases. Studies are conducted cooperatively, and applications

may be found worldwide.

Research Locations

Flagstaff, Arizona Reno, Nevada

Fort Collins, Colorado* Albuquerque, New Mexico

Boise, Idaho Rapid City, South Dakota

Moscow, Idaho Logan, Utah

Bozeman, Montana Ogden, Utah

Missoula, Montana Provo, Utah

Lincoln, Nebraska Laramie, Wyoming

*Station Headquarters, Natural Resources Research Center,

2150 Centre Avenue, Building A, Fort Collins, CO 80526