United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical 

Report

PNW-GTR-869

December 2012

Proceedings of the First 

Landscape State-andTransition Simulation 

Modeling Conference, 

June 14–16, 2011
Technical Editors

Becky K. Kerns is a research ecologist, USDA Forest Service, Pacific Northwest Research 

Station, Forestry Sciences Laboratory, 3200 SW Jefferson Way, Corvallis, OR, 97330. 

bkerns@fs.fed.us. Ayn J. Shlisky is a regional analyst, USDA Forest Service, Pacific 

Northwest Region, 333 SW First Ave, Portland, OR, USA 97204. ajshlisky@fs.fed.us. 

Colin J. Daniel is an ecologist, Apex Resource Management Solutions Ltd., 937 Kingsmere 

Ave, Ottawa, Ontario, Canada K2A 3K2. colin.daniel@apexrms.com.

Cover

A simplified schematic diagram of a state-and-transition simulation model (STSM) for a dry 

mixed-conifer forest, illustrating a subset of a vegetation successional states and natural and 

human-caused transitions. The papers in these proceedings provide more detail about STSM 

mechanics and specific applications across a range of ecosystem types. Cover design and 

drawings by Steven M. Wondzell.

Authors of each paper in this report are responsible for their content and accuracy. Opinions 

expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

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USDA is an equal opportunity provider and employer.
Proceedings of the First 

Landscape State-andTransition Simulation 

Modeling Conference, 

June 14–16, 2011

Becky K. Kerns, Ayn J. Shlisky, and Colin J. Daniel

Technical Editors

U.S. Department of Agriculture, Forest Service

Pacific Northwest Research Station

Portland, Oregon 

General Technical Report, PNW-GTR-869

December 2012

Abstract

Kerns, Becky K.; Shlisky, Ayn J.; Daniel, Colin J., tech. eds. 2012. Proceedings of the 

First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 

2011, Portland, Oregon. Gen. Tech. Rep. PNW-GTR-869. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 215 p.

The first ever Landscape State-and-Transition Simulation Modeling Conference was held 

from June 14–16, 2011, in Portland Oregon. The conference brought together over 70 users 

of state-and-transition simulation modeling tools—the Vegetation Dynamics Development 

Tool (VDDT), the Tool for Exploratory Landscape Analysis (TELSA) and the Path Landscape 

Model. The goal of the conference was to (1) provide opportunities for sharing experiences 

with different applications of the tools, (2) identify major existing conceptual or technological 

gaps, and develop goals for future state-and-transition simulation model (STSM) development, and (3) start building an international network of STSM users. Eighteen oral presentations and thirteen posters were presented. This proceeding includes thirteen papers that build 

on some key STSM concepts, applications, and innovations from that conference, and shares 

them with a wider audience. The goal of these proceedings is to provide a state-of-the-science 

reference for STSM modelers and users. All papers were peer-reviewed by two blind reviewers and one editor. The presentation of these papers reveals that the STSM approach has 

been applied to a wide range of management and land-use questions and ecosystems, with an 

equally wide variation in the amounts of scientific data and expert knowledge available for 

model parameterization.

Keywords: Climate change, FVS, ILAP, LANDFIRE, Path Landscape Model, state-andtransition simulation model, TELSA, vegetation dynamics, vegetation ecology, VDDT.

Conference sponsors:

Apex Resource Management Solutions 

ESSA Technologies

U.S. Forest Service, Pacific Northwest Research Station

The Nature Conservancy

Oregon University System’s Institute for Natural Resources

LANDFIRE project 
iv

Contents

1 Introduction

Becky K. Kerns, Ayn J. Shlisky, and Colin J. Daniel

5 Predicting Landscape Vegetation Dynamics Using State-and-Transition Simulation 

Models

Colin J. Daniel and Leonardo Frid

23 Use of State-and-Transition Simulation Modeling in National Forest Planning 

in the Pacific Northwest, U.S.A.

Ayn J. Shlisky and Don Vandendriesche

43 Modeling on the Grand Scale: LANDFIRE Lessons Learned

 Kori Blankenship, Jim Smith, Randy Swaty, Ayn J. Shlisky, Jeannie Patton, 

and Sarah Hagen

57 The Integrated Landscape Assessment Project

Miles A. Hemstrom, Janine Salwasser, Joshua Halofsky, Jimmy Kagan, and 

Cyndi Comfort

73 Using State-and-Transition Models to Project Cheatgrass and Juniper Invasion 

in Southeastern Oregon Sagebrush Steppe

Megan Kanaga Creutzburg, Joshua S. Halofsky, and Miles A. Hemstrom

85 Landscape Development and Mule Deer Habitat in Central Oregon

James A. Duncan, M.S., and Theresa Burcsu

103 Balancing Feasibility and Precision of Wildlife Habitat Analysis in Planning for 

Natural Resources

Anita T. Morzillo, Joshua S. Halofsky, Jennifer DiMiceli, Blair Csuti, 

Pamela Comeleo, and Miles A. Hemstrom

115 Forecasting Timber, Biomass, and Tree Carbon Pools With the Output of State and 

Transition Models

Xiaoping Zhou and Miles A. Hemstrom

123 Through a Glass, Darkly—Comparing VDDT and FVS

Donald C.E. Robinson and Sarah J. Beukema

143 Use of the Forest Vegetation Simulator to Quantify Disturbance Activities in 

State and Transition Models

Reuben Weisz and Don Vandendriesche

161 Approaches to Incorporating Climate Change Effects in State and Transition 

Simulation Models of Vegetation

Becky K. Kerns, Miles A. Hemstrom, David Conklin, Gabriel I. Yospin,

Bart Johnson, Dominique Bachelet, and Scott Bridgham

173 Modeling the Dynamic Responses of Riparian Vegetation and Salmon Habitat in the 

Oregon Coast Range With State and Transition Models

	 	 Steven M. Wondzell, Agnieszka Przeszlowska, Dirk Pflugmacher,

Miles A. Hemstrom, and Peter A. Bisson

197 Landscape Composition in Aspen Woodlands Under Various Modeled Fire Regimes

Eva K. Strand, Stephen C. Bunting, and Lee A. Vierling
1

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Authors

Becky K. Kerns is a research ecologist, USDA Forest 

Service, Pacific Northwest Research Station, Forestry 

Sciences Laboratory, 3200 SW Jefferson Way, Corvallis, 

OR 97330, bkerns@fs.fed.us. Ayn J. Shlisky is a regional 

analyst, USDA Forest Service Pacific Northwest Region, 

333 SW First Ave, Portland, OR 97204, ajshlisky@

fs.fed.us. Colin J. Daniel is an ecologist, Apex Resource 

Management Solutions Ltd., 937 Kingsmere Ave, Ottawa, 

Ontario, Canada K2A 3K2, colin.daniel@apexrms.com.

Introduction

The first ever Landscape State-and-Transition Simulation 

Modeling Conference was held from June 14–16, 2011, 

in Portland, Oregon. The conference was hosted by Apex 

Resource Management Solutions and ESSA Technologies, 

in collaboration with the U.S. Forest Service, The Nature 

Conservancy, the Oregon University System’s Institute 

for Natural Resources, and the LANDFIRE project. The 

conference brought together over 70 users of state-and-transition simulation modeling tools–the Vegetation Dynamics 

Development Tool (VDDT), the Tool for Exploratory Landscape Analysis (TELSA), and the Path Landscape Model. 

The goal of the conference was to (1) provide opportunities 

for sharing experiences with different applications of the 

tools, (2) identify major existing conceptual or technological gaps, and develop goals for future state-and-transition 

simulation model (STSM) development, and (3) start 

building an international network of STSM users. Eighteen 

oral presentations and thirteen posters were presented. This 

proceedings includes thirteen papers that build on some key 

STSM concepts, applications, and innovations from that 

conference, and shares them with a wider audience. Our 

goal is to provide a state-of-the-science reference for STSM 

modelers and users. As part of that goal, all papers were 

peer-reviewed by two blind reviewers and one editor. At 

least one reviewer was external to the USDA Forest Service. 

The proceedings begin with a paper by Daniel and 

Frid (2012) that outlines how STSMs can be used to 

project changes in vegetation over time across a landscape, 

explaining the theory and concepts behind the approach. 

Developing consistency in terminology was one goal 

identified at the conference; differences in terminology are 

apparent across the papers presented in these proceedings. 

Daniel and Frid include definitions and terminology for 

some of the most commonly used STSM features, setting 

the groundwork for more consistent usage of terminology 

in the future.

The following two papers focus on using STSMs 

in regional planning efforts and projects. Shlisky and 

Vandrendriesche (2012) describe how STSMs are used in 

national forest planning in the Pacific Northwest (PNW). 

They illustrate the use of STSMs to examine current and 

desired forest conditions, and the development of Forest 

Plan Environmental Impact Statement (EIS) alternatives, 

and evaluations of environmental effects, using examples 

from PNW forests. Blankenship et al. (2012) describe 10 

lessons learned from the LANDFIRE project. The LANDFIRE project developed a consistent and comprehensive set 

of STSMs for all major ecosystems of the United States.

The following five papers are all focused on a large 

regional assessment project—The Integrated Landscape 

Assessment Project (ILAP). Hemstrom et al. (2012) 

describe the overall ILAP project, which spanned four 

U.S. states, and produced information, models, data, and 

tools to help land managers, policy makers, and others 

examine mid- to broad-scale projections of land management actions, perform landscape assessments, and estimate 

potential effects of management actions for planning and 

other purposes. As part of the ILAP project, Creutzburg 

et al. (2012) used an STSM approach to project changes 

in sagebrush steppe vegetation across the landscape of 

southeastern Oregon. Duncan and Burcsu (2012) explore 

the ecological consequences of rural residential development and management on the spatial patterns of mule deer 

Introduction

Becky K. Kerns, Ayn J. Shlisky, and Colin J. Daniel 
2

GENERAL TECHNICAL REPORT PNW-GTR-869

and land use questions and ecosystems, with an equally 

wide variation in the amounts of scientific data and expert 

knowledge available for model parameterization. Over the 

years, an extensive suite of features have been added to 

STSMs that allow them to represent a range of dynamics 

important to landscape modeling (Daniel and Frid 2012), 

while maintaining a relatively user-friendly modeling 

platform. In part this is shown by many of the papers in 

this volume that highlight current development work and 

new applications in diverse locations. Compared to linear 

programming or optimization models, STSMs are proving 

to be a more realistic way to describe ecosystem dynamics 

and management interactions, and they have the potential 

to substantially improve communication between scientists, 

planners, land managers, and the general public about complex ecological processes and alternative solutions to natural 

resource problems. At the conference, attendees developed 

a list of possible future directions for the recently released 

Path Landscape Model, and the vast majority expressed 

interest in continuing to network and share lessons learned 

through future conferences, virtual meetings, and peer-topeer learning. Presently, developers are working on adding 

more features from VDDT, developing a spatially explicit 

raster simulator (to complement the polygon-based TELSA 

approach), creating tools for collaboration (e.g. Web portal, 

model library), exploring performance enhancements, and 

building the STSM community. The STSM framework will 

continue to evolve as a management tool and increasingly 

as a research tool. State-and-transition simulation modeling 

tools are being actively developed and improved, and are 

currently applied to new ecological questions every day.

References

Blankenship, K.; Smith, J.; Swaty, R.; Shlisky, A.; 

Patton, J.; Hagen, S. 2012. Modeling on the Grand 

Scale: LANDFIRE Lessons Learned. In: Kerns, B.K.; 

Shlisky, A.J.; Daniel, C.J., tech. eds. Proceedings of 

the First Landscape State-and-Transition Simulation 

Modeling Conference, June 14–16, 2011, Portland, 

Oregon. Gen. Tech. Rep. PNW-GTR-869. Portland, OR: 

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

Northwest Research Station: 43–56.

habitat in central Oregon. They used the spatially explicit 

STSM tool TELSA. Morzillo et al. (2012) present an analysis of tradeoffs in assessments of wildlife habitat within a 

multiple objectives based STSM framework. Finally, Zhou 

and Hemstrom (2012) use STSMs to model timber volume, 

biomass, and forest carbon over time in central Washington 

under two alternative management scenarios.

The next three papers are largely focused on methodological issues. Robinson and Beukema (2012) compare 

output from both VDDT and the Forest Vegetation Simulator (FVS), two modeling frameworks commonly used in 

land management planning. VDDT is a landscape-level 

model and FVS was developed as a stand-level model. Their 

comparison helps to identify differences in the assumptions of the two models and will hopefully result in more 

consistent and compatible use across the models. Weisz and 

Vandrendriesche (2012) expand on how FVS can be used 

to provide or calibrate rates of natural growth transitions 

under endemic conditions in STSMs, and also present methods that can be used to capture resultant vegetation stages 

following disturbance activities (natural or human caused). 

Kerns et al. (2012) present approaches for incorporating 

climate change effects in STSMs. Up until recently, most 

STSMs did not explicitly include climate considerations. 

Yet incorporating climate change into vegetation management for land managers and others has become increasingly 

critical.

The last two papers in the proceedings are focused on 

two STSM applications in very different environments. 

Wondzell et al. (2012) model the effects of various management and restoration practices on conditions of riparian 

forests, channel morphology, and salmonid habitat. Results 

are from the Wilson River in the Oregon Coast Range. 

Strand et al. (2012) developed a spatially explicit STSM to 

assess effects of current and historic wildfire regimes and 

prescribed burning programs on landscape vegetation composition and quaking aspen (Populus tremuloides) habitat in 

the Owyhee Mountains in Idaho. Quaking aspen habitat has 

been declining across the western United States, and is of 

particular management concern.

The presentation of these papers reveals that the STSM 

approach has been applied to a wide range of management 
3

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Creutzburg, M.K.; Halofsky, J.S.; Hemstrom, M.A. 

2012. Using state-and-transition models to project 

cheatgrass and juniper invasion in southeastern Oregon 

sagebrush steppe. In: Kerns, B.K.; Shlisky, A.J.; Daniel, 

C.J., tech. eds. Proceedings of the First Landscape Stateand-Transition Simulation Modeling Conference, June 

14–16, 2011, Portland, Oregon. Gen. Tech. Rep. PNWGTR-869. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station: 

73–84.

Daniel, C.J.; Frid, L. 2012. Predicting landscape 

vegetation dynamics using state-and-transition 

simulation models. In: Kerns, B.K.; Shlisky, A.J.; 

Daniel, C.J., tech. eds. Proceedings of the First 

Landscape State-and-Transition Simulation Modeling 

Conference, June 14–16, 2011, Portland, Oregon. 

Gen. Tech. Rep. PNW-GTR-869. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Research Station: 5–22.

Duncan, J.A.; Burcsu, T. 2012. Landscape development 

and mule deer habitat in Central Oregon. In: Kerns, 

B.K.; Shlisky, A.J.; Daniel, C.J., tech. eds. Proceedings 

of the First Landscape State-and-Transition Simulation 

Modeling Conference, June 14–16, 2011, Portland, 

Oregon. Gen. Tech. Rep. PNW-GTR-869. Portland, OR: 

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

Northwest Research Station: 85–101.

Hemstrom, M.A.; Salwasser, J.; Halofsky, J.S.; Kagan 

J.; Comfort, C. 2012. The Integrated Landscape 

Assessment Project. In: Kerns, B.K.; Shlisky, A.J.; 

Daniel, C.J., tech. eds. Proceedings of the First 

Landscape State-and-Transition Simulation Modeling 

Conference, June 14–16, 2011, Portland, Oregon. 

Gen. Tech. Rep. PNW-GTR-869. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Research Station: 57–71.

Kerns, B.K.; Hemstrom, M.A.; Conklin, D.; Yospin, 

G.; Johnson, B.; Bachelet, D.; Bridgham, S. 2012. 

Approaches to incorporating climate change effects in 

state and transition simulation models of vegetation.

In: Kerns, B.K.; Shlisky, A.J.; Daniel, C.J., tech. eds.

Proceedings of the First Landscape State-and-Transition 

Simulation Modeling Conference, June 14–16, 2011, 

Portland, Oregon. Gen. Tech. Rep. PNW-GTR-869. 

Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station: 161–171.

Morzillo, A.T.; Halofsky, J.S.; DiMiceli, J.; Hemstrom, 

M.A. 2012. Balancing feasibility and precision of 

wildlife habitat analysis in planning for natural 

resources. In: Kerns, B.K.; Shlisky, A.J.; Daniel, C.J., 

tech. eds. Proceedings of the First Landscape State-andTransition Simulation Modeling Conference, June 14–16, 

2011, Portland, Oregon. Gen. Tech. Rep. PNW-GTR-869. 

Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station: 103–114. 

Robinson, D.C.E.; Beukema, S.J. 2012. Through a glass, 

darkly—comparing VDDT and FVS. In: Kerns, B.K.; 

Shlisky, A.J.; Daniel, C.J., tech. eds. Proceedings of 

the First Landscape State-and-Transition Simulation 

Modeling Conference, June 14–16, 2011, Portland, 

Oregon. Gen. Tech. Rep. PNW-GTR-869. Portland, OR: 

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

Northwest Research Station: 123–141. 

Shlisky, A.J.; Vandendriesche, D. 2012. Use of stateand-transition simulation modeling in national forest 

planning in the Pacific Northwest, U.S.A. In: Kerns, 

B.K.; Shlisky, A.J.; Daniel, C.J., tech. eds. Proceedings 

of the First Landscape State-and-Transition Simulation 

Modeling Conference, June 14–16, 2011, Portland, 

Oregon. Gen. Tech. Rep. PNW-GTR-869. Portland, OR: 

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

Northwest Research Station: 23–41.
4

GENERAL TECHNICAL REPORT PNW-GTR-869

Wondzell, S.M.; Przeszlowska, A.; Pflugmacher, D.; 

Hemstrom, M.A.; Bisson, P.A. 2012. Modeling the 

dynamic responses of riparian vegetation and salmon 

habitat in the Oregon Coast Range with state and 

transition models. In: Kerns, B.K.; Shlisky, A.J.; Daniel, 

C.J., tech. eds. Proceedings of the First Landscape Stateand-Transition Simulation Modeling Conference, June 

14–16, 2011, Portland, Oregon. Gen. Tech. Rep. PNWGTR-869. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station: 

173–196.

Zhou, X.; Hemstrom, M.A. 2012. Forecasting timber, 

biomass and tree carbon pools with the output of 

state and transition models. In: Kerns, B.K.; Shlisky, 

A.J.; Daniel, C.J., tech. eds. Proceedings of the First 

Landscape State-and-Transition Simulation Modeling 

Conference, June 14–16, 2011, Portland, Oregon. 

Gen. Tech. Rep. PNW-GTR-869. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Research Station: 115–121.

Strand, E.K.; Bunting, S.C.; Vierling, L.A. 2012. 

Landscape composition in aspen woodlands under 

various modeled fire regimes. In: Kerns, B.K.; Shlisky, 

A.J.; Daniel, C.J., tech. eds. Proceedings of the First 

Landscape State-and-Transition Simulation Modeling 

Conference, June 14–16, 2011, Portland, Oregon. 

Gen. Tech. Rep. PNW-GTR-869. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Research Station: 197–214. 

Weisz, R.; Vandendriesche, D. 2012. Use of the Forest 

Vegetation Simulator to quantify disturbance activities 

in state and transition models. In: Kerns, B.K.; Shlisky, 

A.J.; Daniel, C.J., tech. eds. Proceedings of the First 

Landscape State-and-Transition Simulation Modeling 

Conference, June 14–16, 2011, Portland, Oregon. 

Gen. Tech. Rep. PNW-GTR-869. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Research Station: 143–160. 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Predicting Landscape Vegetation Dynamics 

Using State-and-Transition Simulation Models

Colin J. Daniel and Leonardo Frid

Authors

Colin J. Daniel is an ecologist with Apex Resource 

Management Solutions Ltd., 937 Kingsmere Ave, 

Ottawa, Ontario, Canada K2A 3K2, colin.daniel@

apexrms.com. Leonardo Frid is an ecologist with 

Apex Resource Management Solutions Ltd., 1110 

Lenora Drive, Bowen Island, British Columbia, Canada 

V0N 1G1, leonardo.frid@apexrms.com.

Abstract

This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes 

in vegetation over time across a landscape. STSMs 

are stochastic, empirical simulation models that use 

an adapted Markov chain approach to predict how 

vegetation will transition between states over time, 

typically in response to interactions between succession, disturbances and management. With STSMs a 

landscape is divided into a set of simulation cells, each 

cell is assigned to an initial vegetation state, and the 

model then predicts how each cell may change from 

one vegetation state to another over time. Over the 

years an extensive suite of features have been added 

to STSMs that allow them to represent a range of 

dynamics important to landscape modeling, including 

tracking age-structure, triggering transitions based on 

past events, setting targets for certain transitions, and 

varying transition rates over time. STSMs are also now 

able to represent spatial variability in two different 

ways: by dividing the landscape into spatial strata, 

typically defined by one or more important drivers 

of vegetation change, or alternatively by developing 

a spatially-explicit STSM, whereby transition events, 

such as fire or invasion by non-native vegetation, can be 

simulated to spread across the landscape.

Since their introduction in the early 1990s, STSMs 

have been applied to a wide range of landscapes and 

management questions, including forests, rangelands, 

grasslands, wetlands and aquatic communities, over spatial 

extents ranging from thousands to millions of hectares. 

Several software tools currently exist to support the 

development of STSMs; the most recent of these products, 

called the Path Landscape Model, is the latest in a lineage 

of STSM development tools that includes both the Vegetation Dynamics Development Tool (VDDT) and the Tool for 

Exploratory Landscape Analysis (TELSA).

Keywords: state-and-transition simulation model, 

STSM, ecological model, ecological restoration, ecosystem 

management, landscape ecology, vegetation dynamics, Path 

Landscape Model, TELSA, VDDT.

Introduction

Models are often used to predict vegetation conditions 

across a landscape over time. Since the early 1970’s a wide 

range of models have been developed for this purpose. As 

one might expect these models vary considerably in their 

approach; over the years several authors have attempted to 

classify these models, each using a slightly different set of 

criteria for distinguishing between approaches (Baker 1989; 

Keane et al. 2004; Scheller and Mladenoff 2006; Xi et al. 

2009). Common criteria that emerge from these reviews for 

distinguishing between models include:

1. Degree to which ecosystem processes, such 

as succession and disturbance, are simulated 

mechanistically (as opposed to being developed 

empirically);

2. Whether or not the models are deterministic (i.e. 

predict a single future) or stochastic (i.e. predict 

a distribution of possible futures);

3. Scale at which ecosystem processes are represented—e.g. gap (m2

), stand (ha), region (km2);

4. Extent to which the spatial dynamics of 

ecosystem processes are represented explicitly 

(e.g. disturbance spread over time);
6

GENERAL TECHNICAL REPORT PNW-GTR-869

5. Range of ecosystems to which the models can 

be applied (the majority of landscape models 

have been developed for forest ecosystems).

One technique for predicting landscape-level vegetation change is to use state-and-transition models, a term 

first introduced by Westoby et al. (1989) in reference to 

conceptual models describing the successional dynamics 

of rangeland vegetation over time. Through the use of 

box-and-arrow diagrams, these models describe a series 

of discrete states in which a parcel of land can find itself at 

any point in time, along with transitions, both natural and 

anthropogenic, that can move land between these states. 

Conceptual state-and-transition models provide a simple, 

flexible approach for describing and documenting one’s 

understanding of the vegetation dynamics associated with a 

particular ecosystem (for example, see Grant 2006; Bestlemeyer et al. 2009; Knapp et al. 2011).

A second form of state-and-transition models exists that 

extend the conceptual models described above by assigning 

probabilities to each of the transition pathways, leading 

to models that can simulate the states and transitions that 

might occur over time across a landscape. To distinguish 

these models from their conceptual counterparts we refer to 

them more specifically as state-and-transition simulation 

models (STSM).

Using the criteria outlined above, STSMs can be considered:

1. Strongly empirical: model relationships are 

typically fitted to data and/or understanding 

(including output from other models);

2. Stochastic: model dynamics involve probabilities, allowing for predictions that are distributions, rather than single values;

3. Stand or regional scale: models are typically 

developed to represent processes occurring at 

either a stand or regional scale;

4. Optionally spatially-explicit: a unique feature of 

STSMs is that models can be developed and run 

either with or without explicitly representing 

spatial processes;

5. Suitable for any ecosystem: due to their empirical 

nature, STSMs can be developed for any vegetation 

community.

In this article we provide an introduction to the STSM 

approach for simulating vegetation change across a landscape. We first explain the theory and concepts behind the 

approach, including definitions and terminology for some 

of the most commonly used STSM features. We then review 

software currently available for constructing and running 

STSMs, including a simple example of how the software 

can be used. Finally we provide an overview of some of 

the questions and landscapes to which STSMs have been 

applied.

Modeling Approach

STSMs are stochastic, empirical simulation models, 

whereby the vegetation across a landscape is classified into 

states, probabilities are assigned to possible transitions 

between states, and the landscape is then simulated through 

time using Monte Carlo simulation methods. Technically 

an STSM can be considered a Markov chain, whereby the 

probability of transitioning from one state to another at 

any given time depends only on the present state (Baker 

1989). However, as we shall see later in this section, current 

STSMs tend to push the Markov chain definition well 

beyond its usual “textbook” formulation.

For simple STSMs, a landscape is first divided into a 

set of n simulation cells; these cells can be any geometric 

shape and size (e.g. either raster pixels or polygons). The 

model then defines a discrete set of r states, S = {s1, s2, ..., 

sr}, in which each simulation cell can be found over time, 

and a discrete set of m transition types, U = {u1, u2, ..., um}, 

through which each state si can transition to sj. Transition 

probabilities, pm,i,j, specify the probability that state si

transitions to sj via transition type m in a single timestep 

of the simulation. Each non-zero transition probability is 

referred to as a transition pathway.

A simulation then tracks the state of each simulation 

cell, C = {c1, c2, ..., cn}, over a series of discrete timesteps; 

the duration of each timestep is user-defined (e.g. day, year, 

decade). A simulation begins by assigning each cell to an 

initial state for the first timestep. Each cell’s state is then 
7

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

subject to change from one timestep to the next according to 

the transition probabilities defined between states; a maximum of one transition can occur per cell in each timestep. A 

single iteration of a simulation is complete once the state of 

every simulation cell is calculated for a specified number of 

successive timesteps. Because the model uses probabilities 

to determine when and where transitions occur, the model’s 

predictions are stochastic, and the fate of any one simulation 

cell can vary from one run of the model to the next. Consequently, model runs are repeated for several iterations (i.e., 

Monte Carlo simulations), with the result being a distribution of projected outcomes for the state of each simulation 

cell over time (fig. 1).

There is a common misconception that Markov chains 

are too simple to represent many of the processes important 

to landscape vegetation modelling. However, as described in 

detail by Baker (1989), it is possible to represent a rich suite 

of dynamics using Markov chains by simply defining the 

state space appropriately. For example if transition probabilities for a particular state depend upon the time since a previous event, one can expand the state space to include a new 

state for each possible time since the event; with enough 

states any number of preceding events can be represented as 

a Markov chain. Markov chains can also be non-stationary, 

allowing probabilities to change over space and time; in 

this way external drivers, such as climate change or harvest 

demand, can be represented through changing probabilities; 

any non-stationary Markov chain can be made stationary by 

including enough new states to represent all the conditions 

across which the probabilities vary. Spatial processes, such 

as contagion of transitions between neighbouring simulation cells, can also be represented by allowing the transition 

probabilities to vary according to both the cell location and 

the previous state of neighbouring cells. Again, with a sufficiently large state space, these dynamics can ultimately be 

represented as a Markov chain: in the limit a model could 

be disaggregated such that each simulation cell and year is 

defined as a unique “state,” allowing complete control over 

how the transition probabilities vary across both space and 

time.

So while it is possible, in theory, to model quite 

complex dynamics using a Markov chain, the number of 

states required rises geometrically once the model includes 

one or more of the features outlined above; formulating a 

model as a traditional Markov chain can quickly overwhelm 

even the most proficient modeller. STSMs have been 

designed specifically to overcome these challenges: since 

the development of the first STSMs in the early 1990’s, 

features have been steadily added to the basic Markov chain 

formulation to shield modellers from this complexity. Over 

the years a number of features have been added to STSMs 

to allow users to rapidly and simply incorporate many of 

the complex dynamics outlined above, without the need 

to explicitly specify and track the full state space; some of 

these key features are described below. 

State Age and Deterministic Transitions

In addition to tracking the state (S) of each simulation cell, 

STSMs can also track a second state variable: the age, A = 

{a1, a2, ..., an}, of each cell. Each simulation cell is assigned 

an age at the start of the simulation (in addition to its state); 

by incrementing this age by one for each timestep of the 

simulation, the age of each simulation cell can be tracked 

over time. In addition, an upper and lower age limit can be 

specified for each state. Once the age for a simulation cell 

reaches the upper limit for its current state, the age is no 

longer incremented in future timesteps. A special form of 

Figure 1—Flowchart showing the STSM algorithm.
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GENERAL TECHNICAL REPORT PNW-GTR-869

transition pathway, referred to as a deterministic transition 

pathway, can be specified for each state, causing a transition 

to occur automatically to a new state once the age of the cell 

exceeds the state’s upper age limit; only one deterministic 

transition pathway is allowed per state. The lower age limit 

of the destination state associated with a deterministic 

transition pathway is then used to reset the age for the cell 

after a deterministic transition. Deterministic transitions are 

typically used to represent age-based vegetation succession 

in STSMs.

Transition Targets

Not all transition pathways are best represented in STSMs 

using probabilities. One example of this is the deterministic 

transition pathways that handle the dynamics of ageing 

between states. A second situation occurs frequently for 

management-oriented transition pathways, where the 

level of a transition is more appropriately expressed as 

a fixed target for the area treated each timestep, rather 

than as a probability of occurrence. Transition targets are 

typically specified by transition type for some portion of 

the landscape, and can vary over time (e.g. target for area 

harvested each year). To reformulate these transitions as a 

Markov chain, STSMs dynamically convert these targets to 

an equivalent probability for each transition pathway and 

timestep, based on the number of simulation cells eligible 

for the transition pathway during each timestep of the 

simulation.

Figure 2a shows an example of a very simple STSM. 

The pathway diagram defines the suite of possible states and 

transitions between states for the modelled system, where 

boxes represent states and arrows represent transition pathways. In this example “Fire” and “Invasion” are represented 

as probabilistic transitions (with annual probabilities), 

“Succession” is modelled as a deterministic transition (i.e. 

occurring with a probability of 1 once the age of the Shrub 

state reaches 4), and “Restoration” is set to have a transition 

target of 100 ha/yr. To begin the simulation the landscape is 

divided into simulation cells, with each cell being assigned 

an initial state and age. A single iteration of the model 

involves predicting the state of each simulation cell for a 

series of timesteps. Repeating the simulation for multiple 

iterations generates a distribution of predicted results for the 

state of each cell (fig. 2b).

While already more complicated than a basic Markov 

chain, the model shown in fig. 2 would still be considered 

Figure 2—Example of a simple STSM, illustrating (a) Pathway 

diagram and (b) Simulation results showing area over time 

in each state class (Shrub, Forest, Invaded) and area over 

time undergoing transitions (Fire, Invasion, Restoration) 

for 2 scenarios: 100 ha/yr restoration (solid) and 200 ha/

yr restoration (dashed). Model was run for 20 years and 100 

Monte Carlo iterations over a 10,000 ha landscape, divided 

into 1ha simulation cells, assuming equal area in each state 

and age class initially; graphs display the average values over 

all iterations.

(a) (b)
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

a very simple STSM; additional advanced features, which 

are commonly found in most applied STSMs, are described 

below.

Transition Pathway Age Range

By default a transition pathway specified for a particular 

state acts on all simulation cells in that state, regardless of 

the cell’s age. One exception is a deterministic transition, 

which is triggered only for cells that have exceeded the 

state’s upper age limit (and does so with a probability of 

1). For all other probabilistic transition pathways, however, 

it is possible in STSMs to change the default settings to 

constrain a particular transition pathway such that it only 

applies to those simulation cells that fall within a specified 

age range. For example, in a forest-based landscape one 

might specify a transition pathway called “thinning” that 

applies to a particular state (e.g., Forest), but only to those 

simulation cells within a specified age range (e.g., 70–90 

years old). 

Post-Transition Age

When a probabilistic transition occurs for a simulation cell, 

both the state and the age of the cell are updated according 

the associated transition pathway. By default the age of the 

cell is set to the start age of the pathway’s new destination 

state; however it is possible to specify alternative behaviours for the post-transition age: transition pathways can 

be specified to retain the age of the cell prior to the transition; they can also shift the age forward or backward. For 

example a transition pathway called “stand-replacing fire” 

might reset the age of the simulation cell to age 0, while a 

“surface fire” transition pathway might maintain the current 

age of the simulation cell.

Time-Since-Transition

In addition to tracking the current state (S) and age (A) of 

each simulation cell over the course of the simulation, 

STSMs can optionally track a third state variable: the number of timesteps since each type of transition last occurred 

for each simulation cell. This state variable is referred 

to as the time-since-transition (TST) for the cell, tstm,n, 

which is tracked for every simulation cell n and transition type m. Similar to the option to specify age ranges 

for transition pathways, TST can also be used to constrain 

which simulation cells are eligible for each transition 

pathway. For example, STSMs can represent the changing 

fire dynamics that occur on a landscape through the implicit 

build-up of fuels using the TST feature: simulation cells 

burned in a high severity wildfire can be modelled to not 

experience another high severity wildfire for the following 

20 years, the assumed time needed for sufficient fuels build 

up.

Temporal Heterogeneity

Because STSMs are stochastic, using probabilities to 

determine when and where transition occur, there will 

always be some variability between timesteps in the number 

of transitions that occur during a particular simulation. 

However there are often situations in which additional 

temporal variability is required in order to adequately 

capture landscape dynamics. For example, weather conditions and/or climate change may result in different patterns 

or trends of high and low fire risks from one year to the 

next, or insect outbreaks may show a cyclical pattern over 

many years. From an STSM perspective, capturing this kind 

of temporal variability involves varying the probabilities for 

certain transition pathways over time. This can be accomplished using transition multipliers, which scale the base 

probabilities associated with one or more transition pathways up and down over the course of a simulation according 

to an externally driven pattern. 

Spatial Heterogeneity

Often there is a requirement for STSMs to capture the 

spatial heterogeneity across a landscape. Biophysical factors, such as climate and soils, are often key determinants 

of vegetation dynamics; many disturbances, both natural 

and anthropogenic, tend to be aggregated spatially. Depending upon the requirements of any particular analysis, the 

amount of spatial variability that the model must capture 

will vary.

STSMs can represent three different forms of spatial 

variability. Firstly, as illustrated in fig. 2, a single set of 

pathway diagrams can be developed for the landscape as a 

whole—we refer to this as a whole landscape STSM. 
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GENERAL TECHNICAL REPORT PNW-GTR-869

A model in this form does not capture any of the spatial 

variability across the landscape; rather, it provides only 

predictions for the total area in each state over time. Information on the location and configuration of the simulation 

cells is not used: each cell is simulated independently of all 

others, essentially acting as a spatial “replicate” in a Monte 

Carlo simulation.

A second and more commonly used approach for STSM 

development is to stratify the landscape according to one or 

more criteria that are considered to be important external 

drivers of vegetation change, and then to develop a separate 

pathway diagram for each of these strata—we refer to this 

as a spatially-stratified STSM. As with whole landscape 

STSMs, each simulation cell within a stratum is simulated 

independently of its neighbours; however each simulation 

cell is assigned to a particular stratum at the start of the 

simulation. Strata are typically defined according to one or 

more important drivers of vegetation change. Biophysical 

drivers, such as soils, climate and topography are the most 

common: here the strata often follow existing ecological 

classification systems, examples of which include potential 

vegetation types (Chiarucci et al. 2010), biophysical settings 

(Long et al. 2006), ecological sites (Bestelmeyer et al. 2009) 

and biogeoclimatic zones (MacKenzie 2012). Stratification 

also often reflects differences in anthropogenic activity 

across the landscape, by dividing the landscape into zones 

with differing management practices (e.g., protected vs. 

unprotected, private vs. public). 

Stratifying the landscape allows the modeller to specify 

different assumptions–such as states, transitions, pathway 

probabilities and management targets—for each of the 

landscape strata, thus capturing some of the spatial variability in vegetation dynamics across the landscape (fig. 3). 

Model projections, in turn, can be displayed spatially across 

the entire landscape, classified according to the original 

strata polygons. Recently, modellers have begun to relax the 

Figure 3—Map showing ecological strata for the 46,000 ha Castle Creek landscape in Idaho. In a spatially stratified 

STSM, each stratum can be represented by a different pathway diagram.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

assumption that the stratum to which each simulation cell is 

assigned is static, and instead allow simulation cells to shift 

from one stratum to another over the course of a simulation (e.g., Provencher and Anderson 2011). This option has 

become an increasingly important tool for representing 

shifts over time in biophysical boundaries (e.g. due to 

climate change) that were historically considered fixed. 

The third and final approach for representing spatial 

variability is to develop what we refer to as a spatiallyexplicit STSM. These models typically begin as a spatiallystratified STSM with a number of important additions. 

Firstly, transition probabilities can be made specific to each 

simulation cell; for example an external driver, such as 

topography or climate, could influence the probability of 

a particular transition (e.g., fire) for each cell on the landscape. Secondly, in a spatially-explicit STSM each simulation cell is aware of the location and state of other cells; as a 

result the transition probabilities for any cell can be influenced by the past and present state of its neighbours (i.e. 

simulation cells are no longer independent). This feature is 

commonly used to “spread” transitions across a landscape, 

both within a timestep (e.g., fire) or between timesteps (e.g., 

invasives). Finally, a target frequency distribution of sizes 

for transition events on the landscape can be specified as 

a model input, guiding the number and size of contiguous 

transition “patches” that occur across the landscape. Figure 

4 shows the typical output of a spatially-explicit STSM: the 

result of a single Monte Carlo iteration is a prediction for 

the state of each simulation cell in every timestep; repeating 

simulations for multiple iterations results in distribution of 

predictions for the state of each cell. 

On the surface it would seem that spatially-explicit 

models would always be a modeller’s first choice, as they 

appear to produce the most ecologically relevant predictions. However there are important trade-offs to consider 

when deciding on the approach to use for capturing spatial 

variation (Mladenoff 2004). Firstly, the time and resources 

required to prepare and run a spatially-explicit model are 

generally much greater than that of a simpler spatiallystratified model. Deciding to use a spatially-explicit model 

often means making sacrifices in other areas of an overall 

modeling project: extra time spent preparing, running and 

analysing these models takes away from time available for 

exploring model behaviour in greater depth, particularly 

over larger landscapes. Secondly, the data and additional 

model parameters required to run a spatially-explicit STSM 

are not always available. Finally, many modelling projects 

do not require predictions be spatially disaggregated to the 

level of a simulation cell, in which case the added complexity of a spatially-explicit model may be of no value to the 

modeller.

Software

While many landscape vegetation models include some 

capabilities to represent Markov chains (Keane et al. 2004), 

over the past 20 years three software tools have been developed to support the STSM features described in this paper. 

The first of these is the Vegetation Dynamics Development 

Tool (VDDT; ESSA Technologies Ltd. 2007). Originally 

developed to support landscape modeling for a project in 

the Interior Columbia River Basin of the U.S. in the early 

1990’s (Hann et al. 1997), VDDT was the first software 

tool designed specifically to develop and run STSMs, and 

includes a simple visual editor for STSM pathway diagrams, 

a simulation engine for running models, and a graphics 

module for viewing model output. VDDT was originally 

designed to support the development of whole landscape 

STSMs, although with some effort technically adept users 

are able to adapt the software to run spatially-stratified 

STSMs. While the software is still available for free download, and support for using the software is still available, 

development of the product was discontinued in 2011.

A second software tool available for developing STSMs 

is the Tool for Exploratory Landscape Analysis (TELSA; 

Kurz et al. 2000; ESSA Technologies 2008). Originally 

developed to support forest management in British Columbia, Canada, TELSA provides the capability to run polygonbased spatially-explicit STSMs. TELSA was designed to 

work in conjunction with VDDT—typically users develop 

their STSM pathway diagrams first using the visual editor 

in VDDT, then import this information into TELSA. Within 

the TELSA software, users are able to prepare additional 

model inputs (including GIS maps using an ArcGIS 

extension), run Monte Carlo simulations, and view model 
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GENERAL TECHNICAL REPORT PNW-GTR-869

Figure 4—Sample output from a spatially-explicit STSM for the Castle Creek landscape of fig. 3. Results are shown for a single 

Monte Carlo iteration of a 20-year simulation. (a) Map showing cumulative area burned (in red) from year 0-20, superimposed upon 

ecological strata. (b) Map showing projected vegetation state in year 20. 

(a)

(b)
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

outputs (including maps in ArcGIS). Like VDDT, TELSA is 

available for free download, and continues to be supported 

and developed.

The last software tool for developing STSMs is the Path 

Landscape Model (Apex Resource Management Solutions 

2012). First released in 2009, Path was designed to merge 

the functionality of both VDDT and TELSA, allowing users 

to develop and run whole landscape, spatially-stratified, and 

spatially-explicit STSMs all from a single platform. One of 

the major objectives of Path was to simplify the process of 

developing models and analyzing the results. Some of the 

important new features found in Path include the ability to 

develop and run spatially-stratified STSM models, including an option to specify transitions between strata, and the 

ability to create and run spatially-explicit STSMs using an 

automated connection to the TELSA simulation engine. At 

present, Path is being actively developed and supported, and 

is available for free download.

Using a software tool such as the Path Landscape 

Model to develop and run a spatially-stratified STSM is 

relatively straightforward, and typically involves the following steps:

1. Specify a number of run control parameters for the 

simulation, including the total number of timesteps, 

the number of Monte Carlo iterations, the total area 

of the landscape and the number of simulation cells 

(fig 5). 

2. Divide the landscape into one or more ecological 

and/or land management strata and define a suite of 

possible states and transitions for each stratum. The 

list of possible strata, states and transitions is fully 

configurable by the user. Possible transitions between 

states are typically displayed as a pathway diagram 

for each strata. As discussed previously, there are 

three types of transition pathways that can be defined 

in STSMs: deterministic, probabilistic, and targets 

(fig. 6).

3. Specify the proportion of the landscape in each 

stratum and state (and optionally age) at the beginning of the simulation (fig. 7). For spatially-explicit 

simulations this information is derived directly from 

a user-specified polygon GIS map layer.

4. Run the simulation to generate model output. There 

are two basic outputs generated for every run: the area

in each state over time, and the area undergoing each 

type of transition over time (fig. 8). If multiple 

Monte Carlo iterations are simulated then a range of 

variability around each of these model outputs can 

also be calculated.

In addition to the model inputs described above, running a spatially-explicit STSM in Path involves specifying 

the following: 

1. A polygon GIS shapefile dividing the landscape into 

simulation cells, with an initial STSM stratum, state 

and age assigned to each polygon;

2. A frequency distribution for the size of STSM 

transition events.

With these additional model inputs, Path is then able 

to automatically configure and run a spatially explicit 

TELSA simulation. Additional details on the TELSA model 

algorithms can be found in Kurz et al. (2000) and ESSA 

Technologies (2008).

Applications

STSMs have been applied to a wide range of questions and 

ecological systems. While most other landscape simulation 

models are designed to work with only forested ecosystems, 

due to their empirical nature STSMs have no predetermined 

Figure 5—Sample run control settings for a STSM, as specified 

using the Path Landscape Model.
14

GENERAL TECHNICAL REPORT PNW-GTR-869

relationships and as such can be parameterized for any suite 

of vegetation communities. STSMs are often used in situations where landscapes include some non-forest vegetation 

types; they are also often used in situations where landscapes include non-native exotic vegetation (e.g., Forbis et 

al. 2006; Provencher et al. 2007; Frid and Wilmshurst 2009).

Figure 7—Sample STSM initial conditions, as specified using the Path 

Landscape Model.

Figure 6—Sample transition inputs associated with a STSM, as specified using 

the Path Landscape Model. (a) Transition 

pathway diagram showing state classes 

(boxes) and transitions ( arrows). (b) 

Deterministic and probabilistic transitions associated with a particular state 

class. (c) Transition targets and costs 

associated with management transitions.

(b)

(a)

(c)
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Typically, the pathways and probabilities in STSMs are 

determined through analysis of historical data regarding 

rates of succession, disturbance and management; in situations where data is lacking, however, STSMs can be readily 

parameterized using literature and/or expert opinion (e.g., 

Czembor et al. 2011, Price et al. 2012), making the approach 

suitable for projects that contain at least some vegetation 

communities for which data and/or knowledge is limited. 

Figure 8—Sample STSM output for the Castle 

Creek landscape of fig. 3, comparing two simulation scenarios: No Management (green) and 

Current Management (blue). Shading shows 1.96 

standard errors around the mean across 10 Monte 

Carlo iterations. (a) Area over time, across the 

entire landscape, in two of the model’s state 

classes: Pine Juniper Closed and Pine Juniper 

Open. (b) Area over time, across the entire landscape, undergoing two of the model’s transitions: 

Low Density Thinning and Replacement Fire. 

Finally with STSMs there are no hidden assumptions or 

relationships in the models, particularly when developed 

using software such as the Path Landscape Model, although, 

as with all models, it becomes increasingly challenging to 

keep track of all of the relationships as the complexity of 

the model increases. This makes the models well-suited for 

decision making in situations where stakeholder engagement and consensus are important.

(b)

(a)
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GENERAL TECHNICAL REPORT PNW-GTR-869

STSM modeling projects usually begin by developing 

a whole-landscape or spatially-stratified STSM; avoiding 

a spatially-explicit model in the early stages of a modeling 

project makes it much simpler to parameterize and validate 

an initial model. Many, but not all, modeling projects 

eventually convert their STSMs to a spatially-explicit form. 

Factors that tend to limit the development of spatiallyexplicit STSMs include: (1) the objectives of the analysis not 

requiring spatially-explicit predictions; (2) the spatial extent 

of the landscape being too large to run in a spatially-explicit 

form; (3) a shortage of spatial data and/or resources required 

to develop and run the more complicated spatially-explicit 

models.

The first known use of STSMs was in support of 

landscape-level vegetation modeling for the Interior Columbia River Basin Ecosystem Management Project in the 

U.S. Pacific Northwest (Hann et al. 1997). In this project, a 

spatially-stratified STSM was developed using the VDDT 

software. The STSM approach provided two key benefits 

to this project: firstly, it provided a modelling platform that 

could handle both forests and rangelands across a single 

landscape; secondly, it allowed input from both stakeholders 

and experts to be incorporated into the models (Kurz et al. 

1999).

Since this first project, STSMs have been applied to 

a wide range of management questions across a variety of 

ecological settings (table 1). The remainder of this section 

provides an overview of some of the questions for which the 

STSM approach has been used; additional examples can be 

found in Kerns et al. (2012b).

Forest Ecosystems

STSMs have been used extensively to inform the management and ecology of forest ecosystems. Klenner et al. 

(2000) were the first to develop a spatially-explicit STSM, 

using it to predict the combined effects of management 

actions and natural disturbances on old-growth habitat 

and patch size changes in British Columbia, Canada. 

Carlson and Kurz (2007) used a spatially-explicit STSM to 

explore the ability of alternative timber harvest strategies 

to approximate natural fire patterns in a boreal mixedwood 

forest of Alberta, Canada. Hemstrom et al. (2007) and 

Strand et al. (2009) used spatially-explicit STSMs to simulate the effects of alternative fire suppression and prescribed 

burning strategies on the structure and composition of 

forested systems in Idaho and Oregon. Klenner and Walton 

(2009) used a spatially-explicit STSM to predict the effects 

of alternative forest management treatments, such as partial 

cutting and fuel management treatments, on indicators of 

wildlife habitat, including understory productivity. Finally, 

a spatially-explicit STSM analysis was used to recreate the 

pre-European settlement structure and composition of the 

12 million hectare Great Lakes St. Lawrence forest region 

of Ontario (Ontario Ministry of Natural Resources 2010).

Ecological Restoration

A second major application area for STSMs has been their 

use in supporting ecological restoration, particularly in 

rangeland and grassland vegetation communities. Forbis et 

al. (2006) developed a spatially-stratified STSM to explore 

the effects of alternative restoration scenarios across a 4.6 

million hectare landscape of desert scrub (Atriplex spp. 

and others) and sagebrush (Artemisia spp.) communities in 

the Great Basin ecoregion of the western U.S. The model 

compared the effects of varying levels of fire suppression, 

livestock grazing, and restoration treatments on the predicted vegetation cover of native perennial understory and 

tree-invaded and weed-dominated states. Provencher et al. 

(2007) developed a spatially-explicit STSM to compare the 

cost and effectiveness of varying levels of managed fire, 

livestock grazing, and non-native species management in 

an effort to restore degraded vegetation types for a 140,000 

hectare rangeland landscape in eastern Nevada. An STSM 

was developed to explore the effectiveness of alternative 

control strategies for the management of the invasive 

crested wheatgrass (Agronpyron cristatum) in Grasslands 

National Park of Canada (Frid and Wilmshurst 2009); this 

spatially-explicit STSM included an explicit representation 

of the inter-annual spread of invasive vegetation across a 

landscape. 

The Nature Conservancy (TNC) has extended the 

application of STSMs for ecosystem restoration to help 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Table 1—Examples of past STSM applications

Spatial SpatiallyReference Ecosystems Application Geographic area extent (ha) explicit?

Provencher et al. 2008 Rangeland Natural range of variability Nevada USA 18,000 No

Strand et al. 2009 Forest Fire and aspen management Idaho USA 37,000 Yes

Frid and Wilmshurst 2009 Grassland Grassland invasive management Saskatchewan Canada 42,000 YesMiller 2007 Forest Wildfire regimes Idaho USA 50,000 YesWondzell et al. 2007 Aquatic/Riparian Disturbances and salmonid habitat Oregon USA 54,000 No

Klenner et al. 2000 Forest Management and wildlife habitat British Columbia Canada 63,000 Yes

Low et al. 2010 Rangeland/Forest Conservation planning California USA 76,000 No

Klenner and Walton 2009 Forest Management and wildlife habitat British Columbia Canada 100,000 Yes

Provencher et al. 2007 Rangeland Restoration and invasive management Nevada USA 142,000 YesHemstrom et al. 2007 Forest/Rangeland Fuel and wildfire management Oregon USA 178,000 No

Zweig and Kitchens 2009 Wetland Wetland hydrology management Florida USA 200,000 NoCzembor and Vesk 2009, Forest Restoration Victoria Australia 250,000 No Czembor et al. 2011Carlson and Kurz 2007 Forest Harvest and landscape pattern Alberta Canada 270,000 Yes

Price et al. 2012 Forest Forest management and climate change Michigan USA 272,000 YesForbis et al. 2006 Rangeland Restoration and invasive management Great Basin USA 4.3 million No

Ontario Ministry of Forest Pre-European settlement conditions Ontario Canada 11.9 million Yes

 Natural. Resources 2010Rollins 2009, Terrestrial Assessment of ecological departure Entire USA ~900 million No

 Swaty et al. 2011Kerns et al. 2012b Various 12 papers covering a range of Worldwide Various Yes and no

applications
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GENERAL TECHNICAL REPORT PNW-GTR-869

them determine how to get the best return on the restoration 

investment (Low et al. 2010). As part of their conservation 

planning process, TNC first uses an STSM approach to 

help predict how far departed a landscape is from “desired” 

condition; they then extend this reference STSM to explore 

how alternative future management actions will change 

the vegetation composition and structure of the landscape, 

and use this to predict which combination of management 

actions, on a per unit cost basis, will move the landscape 

most cost-effectively towards their desired condition.

Aquatic/Riparian Ecosystems

In addition to their long-standing application to upland 

vegetation communities, STSMs have also been developed 

for use in aquatic and riparian systems. A spatially-stratified 

STSM was developed to evaluate the effects of disturbances 

and land management practices, such as grazing, flooding, 

debris flows and wildfire disturbances, on riparian and 

aquatic vegetation for a river system in Oregon (Wondzell et 

al. 2007). In this STSM the states were defined by channel 

morphology and riparian vegetation, and model output was 

interpreted in terms of its suitability as habitat for anadromous salmonids. As a second example, Zweig and Kitchens 

(2009) developed a non-spatial STSM for predicting the 

effects of alternative hydrological regimes on wetland 

vegetation in the Florida Everglades. 

Regional Land Management

Finally, because of their flexibility in handling a wide range 

of vegetation types and varying data availability, STSMs 

are well suited for use in regional and national land management initiatives. For example, the LANDFIRE program, a 

5-year national, multi-agency project, developed over 1200 

STSM models to predict the pre-European settlement conditions for all of the major ecosystems of the United States 

(Rollins 2009, Swaty et al. 2011). The Integrated Landscape 

Assessment Project (ILAP), a 3-year project to prioritize 

land management actions in the western U.S., developed 

over 200 STSMs, one for every potential vegetation type 

(PVT) found across all lands in Arizona, New Mexico, 

Oregon and Washington (Hemstrom et al. 2012). 

Conclusions

STSMs provide a simple, flexible approach for predicting 

landscape-level vegetation change in response to both 

natural disturbances and management actions. They are 

stochastic, empirical simulation models, based on a Markov 

chain approach, that predict how vegetation will transition 

between states over time across a landscape. With STSMs a 

landscape is divided into a set of simulation cells (e.g. either 

raster pixels or polygons), each cell is assigned an initial 

vegetation state, and then the model predicts how the state 

of each cell changes over time. Spatial processes are typically represented in STSMs in one of two ways: by stratifying the landscape into zones that behave similarly with 

respect to vegetation change, and then representing each of 

these strata with its own non-spatial STSM; or alternatively, 

by creating a spatially-explicit STSM for the landscape in 

which the dynamics of every simulation cell is potentially 

dependent on its neighbours.

Over the past 20 years, the software available for 

developing STSMs has evolved considerably. Modellers 

are now able to represent an array of complex dynamics, 

including tracking and modifying the age-structure of 

the landscape, setting transitions to be contingent on past 

events, providing targets for transition areas (e.g. for forest 

harvest and management treatments), changing transition 

probabilities over time (e.g., due to climate change), and 

spreading disturbances across a landscape (e.g., for fire and 

invasive species).

As with all models, there are limitations to the use 

of STSMs for predicting vegetation change. Likely the 

most significant limitation is their purely empirical nature, 

whereby model relationships are typically fitted to existing knowledge and data. This makes it challenging to use 

the models to make predictions under novel conditions, as 

knowledge and data may not exist upon which to base the 

input parameters. To overcome this limitation users are 

increasingly turning to other, more specialized models to 

inform the model inputs for STSMs; for example Kerns 

et al. (2012a) have used the MC1 dynamic global vegetation 

model to incorporate climate change effects into STSMs, 

while others have used output from the Forest Vegetation 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Simulator (FVS) to calibrate transition probabilities in 

STSMs (Shlisky and Vandendriesche 2012; Weisz and 

Vandendriesche 2012). This approach allows modellers 

to combine the integrative capabilities of STSMs with the 

mechanistic capabilities of other models. A second limitation with STSMs is that all relationships must be ultimately 

be expressed in terms of a Markov chain. This restriction 

can be limiting in certain circumstances: for example while 

it might be desirable to represent post-fire succession as 

a Markov chain, the fire spread component of the model 

might be better represented using a more mechanistic 

simulation approach. At present it is not possible to create 

such hybrid modeling approaches with STSMs. Efforts are 

underway, however, to extend the capabilities of STSM software (i.e. the Path Landscape Model) so that it can dynamically link to other models; this would provide the capability 

to generate hybrid modelling approaches, whereby external 

models could be called upon to provide specialized predictions within an STSM model run.

The STSM approach has been applied to a wide range 

of management questions and vegetation communities. 

Because STSMs can be developed for any vegetation community, they are well suited for landscapes that include a 

range of vegetation types. Examples of ecosystems in which 

STSMs have been developed include those with forest, 

rangeland and wetland components; they have also been 

used extensively to represent the dynamics of non-native 

(i.e., invasive) species. The spatial extent over which STSMs 

have been applied ranges from a few thousand to millions of 

hectares. Finally, STSMs are well suited for use in situations with imperfect knowledge or data, as the models can 

be parameterized, when necessary, using expert opinion, 

and a wide range of scenarios can be simulated to represent 

uncertainty in model inputs.

Acknowledgments

Sarah Beukema and Werner Kurz were the lead architects 

for the original versions of VDDT and TELSA, the first 

tools for running STSMs. Alex Embrey, Tim Webb, Alan 

Sharpe, Don Robinson, Diana Abraham, Tom Roe, David 

Carr, Keti Milosheva, Katy Bryan, Philip Bailey and Julie 

Greenough have also contributed to the development of 

STSM tools over the years. We thank Paul Hessburg, 

Wendel Hann, Jim Byler, and Al Harvey for supporting the 

initial idea for VDDT, and the many users and participants 

of various workshops who patiently worked with earlier versions and provided suggestions and feedback that helped us 

to improve our tools. We especially thank Don Long, Kathy 

Schon, and the members of the Insect and Pathogens Working Group for their contributions to the success of the original tools. Jim Merzenich was a tireless supporter of VDDT, 

contributing many ideas for new features over the years. 

Kelly Pohl, Kori Blankenship, Jim Smith, Sue Hagle, Jim 

Byler, Andy Taylor, Susan Schilling, Ayn J. Shlisky, Louis 

Provencher, Greg Low, Jason Karl, Bob Unnasch, Don 

Major, Miles Hemstrom and Wendel Hann also provided 

ideas and suggestions that led to new features implemented 

in later versions of VDDT and Path. The development of 

TELSA has been championed by Walt Klenner and Russ 

Walton at the British Columbia Ministry of Forests; Wendel 

Hann of the U.S. Forest Service, along with Jim Smith and 

Jason Karl of The Nature Conservancy, were key supporters 

of the first version of the Path Landscape Model. Development of STSM software has been funded by many sources. 

Initial funding was provided by the Interior Columbia 

River Basin Assessment Project. Additional resources have 

been provided by LANDFIRE, the U.S. Forest Service, 

The Nature Conservancy, the Canadian Forest Service, 

Forest Renewal BC, The BC Ministry of Forests, The U.S. 

Geological Survey, North Carolina State University, Ontario 

Ministry of Natural Resources, Parks Canada, ESSA 

Technologies and Apex Resource Management Solutions.

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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Use of State-and-Transition Simulation Modeling in 

National Forest Planning in the Pacific Northwest, U.S.A.

Ayn J. Shlisky and Don Vandendriesche

Authors

Ayn J. Shlisky, regional analyst, USDA Forest Service 

Pacific Northwest Region, Portland, OR, USA, ajshlisky@

fs.fed.us. Don Vandendriesche, program manager, Vegetation Application Group, USDA Forest Service Southwestern 

Region, Albuquerque, NM, USA, dvandendriesche@fs.fed.

us.

Abstract

Effective national forest planning depends on scientifically 

sound analyses of land management alternatives relative to 

desired future conditions and environmental effects. The 

USDA Forest Service Pacific Northwest Region is currently 

using state-and-transition simulation models (STMs) to 

simulate changes in forest composition and structure for the 

revisions of five forest plans in Oregon and Washington. We 

illustrate the use of STMs to examine current and desired 

forest conditions, develop forest plan environmental impact 

statement (EIS) alternatives, and evaluate environmental 

effects, with examples from the Okanogan-Wenatchee 

National Forest (Washington, U.S.A.). Model parameters 

include ecosystem states and natural and human-caused 

disturbances, which were derived from empirical studies, 

published literature, and expert opinion. Forest growth 

rates were calibrated using Forest Vegetation Simulator 

(FVS) modeling of national forest inventory plot data and 

FVS post-processors, such as the Preside program. Preside 

was used to classify forest inventory plots into STM 

states, estimate mean residence times (within a state) and 

transition probabilities (between states), and summarize 

the alternative pathways between states. In some cases for 

the Okanogan-Wenatchee National Forest, Preside showed 

longer residence times for dense multi-story stands than 

assumed in previously developed STMs for the forest 

plan area. STMs are being used to simulate the effects of 

alternative combinations of forest treatments such as forest 

thinning, regeneration harvest, and prescribed burning. The 

effects of treatments on a suite of indicators and decision 

criteria, including forest structural states, departure from 

reference conditions, woody biomass yield, wildlife habitat, 

and fire severity and frequency, will be estimated using the 

output of STMs. STMs used in forest plan revisions have 

proved useful in testing assumptions, developing alternative 

restoration scenarios, and documenting current knowledge.

Keywords: Land use planning, state-and-transition 

model, alternative development, model calibration.

National Forest Management Planning in 

the United States

Management of national forests in the United States is 

guided by the strategic goals of the U.S. Department of 

Agriculture and USDA Forest Service, as well as laws, 

regulations, and agency policies. The Forest Service 

is responsible for managing the lands and resources 

of the National Forest System (NFS), which includes 

approximately 193 million acres in 44 states, Puerto Rico, 

and the Virgin Islands. The NFS is composed of 155 national 

forests, 20 national grasslands, and one national tallgrass 

prairie. Goals for managing NFS lands include (USDA FS 

2007):

•	 Restore, sustain, and enhance the nation’s forests and 

grasslands,

•	 Provide and sustain benefits to the American people,

•	 Conserve open space,

•	 Sustain and enhance outdoor recreation opportunities,

•	 Maintain basic management capabilities of the Forest 

Service,

•	 Engage urban America with Forest Service programs, 

and

•	 Provide science-based applications and tools for sustainable natural resources management.

The focus of National Forest System land management 

is continually evolving. Since the 1990s, the primary focus 

of NFS land management has shifted from optimization 
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GENERAL TECHNICAL REPORT PNW-GTR-869

and activities are, proposed, analyzed and carried out within 

the framework of the plan.

Currently, Forest plans consider the roles and capabilities of NFS lands within a complex matrix of climate 

change, increased forest densities, extended drought, 

uncharacteristic insect epidemics, intense wildfires, expansion of residential development into forest lands, and rapidly 

changing socioeconomic settings. Given the mix of ecosystem processes, management objectives, and an uncertain 

future regarding NFS lands, plus the need to meet NEPA 

requirements (e.g., to disclose the effects of Forest Service 

management in EAs and EISs), National Forest planners 

depend on scientifically sound modeling tools to analyze the 

effects of alternative management scenarios.

Forest plans are developed by first drafting a proposed 

plan that attempts to address known challenges, public 

concerns, and new information. This proposed plan is 

released for public comment. After analyzing comments on 

the proposed plan, alternatives to the proposal are included 

in a draft environmental impact statement (DEIS). Generally, the DEIS: (1) compares alternative ways of managing national forest lands; and (2) outlines the physical, 

biological, social, and economic effects of each alternative. 

Alternatives include a “no change” (or “no action”) proposal, which represents a continuation of current forest plan 

direction. After analysis of public comment on the DEIS, a 

final environmental impact statement (FEIS) and Record of 

Decision (ROD) are released along with a final forest plan 

which becomes management direction for the next 10 to 15 

years.

Table 1 summarizes the key national forest planning 

considerations and their relationships to ecological stateand-transition modeling (STM). Given the 2012 Planning 

Rule, science must be taken into account, appropriately 

interpreted, and applied when planning models are developed. Incomplete or unavailable information, scientific 

uncertainty, and risk are evaluated and disclosed as a part of 

model and forest plan documentation. Published research, 

1 The Final Programmatic EIS for the 2012 Forest Service planning rule is available at: 

http://www.fs.usda.gov/detail/planningrule/home/?cid=stelprdb5349164.

of commodity output toward the restoration of ecosystem 

function and resiliency. 

The framework for NFS land management plans (Forest 

plans) was established by Congress in the National Forest 

Management Act (NFMA) of 1976 (16 U.S.C. § 1604). 

Under NFMA, Forest plans:

1. Establish forest multiple-use goals and objectives;

2. Establish forest-wide standards and guidelines;

3. Establish management areas and direction applying to future activities;

4. Designate lands suitable and unsuitable for 

timber production; 

5. Evaluate potential wilderness areas; and 

6. Establish monitoring and evaluation

requirements.

NFMA is implemented under an agency planning 

rule, which establishes administrative procedures for 

developing, revising, and amending forest plans (36 Code 

of Federal Regulations Part 219). Current forest plans and 

plan revisions for all national forests follow guidance from 

the 1982 Planning Rule (47 Federal Register 43037, Sept. 

30, 1982). However, the Final Programmatic Environmental Impact Statement (EIS) for a new land management 

planning rule was released in January 2012;1

 future forest 

plan revisions will take place under this 2012 rule until a 

new rule is adopted. Forest planning must also adhere to 

the requirements of the National Environmental Policy Act 

(NEPA) of 1969 (42 U.S.C. § 4321), which set up procedural 

requirements for all federal government agencies to prepare 

environmental assessments (EAs) and EISs, which disclose 

the environmental effects of proposed federal agency 

actions.

Forest plans establish direction so that all future 

decisions on the forest will consider physical, biological, 

economic, and other sciences, and assure coordination of 

multiple-uses and a sustainable yield of products and services. Coupled with laws and regulations, forest plans create 

a management system for future decisionmaking. Projects 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

empirical studies, expert opinion, or combinations of each 

are used to define model states, transition probabilities 

between states, and temporal cycles of disturbance. 

Forest Plan Modeling in the Pacific Northwest

The Pacific Northwest (PNW) Region of the USDA Forest 

Service includes 16 national forests. Five national forests are 

currently revising their Forest Plans (fig. 1).

Two landscape level modeling applications are supported in the PNW Region for forest planning: the linear 

programming model Spectrum (USDA FS 1995) and 

the state-and-transition simulation model VDDT/Path 

(ApexRMS and ESSA Technologies Ltd. 2012; ESSA Technologies Ltd. 2007). The original planning efforts in the 

1980s and early 1990s aimed to meet goals and objectives to 

optimize or maximize net public benefit from various forest 

outputs. The first round of forest plans were developed 

using FORPLAN (the predecessor to Spectrum; Johnson et 

al. 1986, Kent et al. 1991). FORPLAN was used to choose 

the best mix of management options to meet specified goals 

and objectives given resource connstraints. 

Now, into a second round of planning, objectives for 

national forest management have changed along with social 

values and knowledge. These changes have also led to a 

shift in approach away from linear programming toward 

STM. National forest management goals currently emphasize restoring and maintaining ecosystem health, biodiversity, and resilience, while contributing to economic and 

social sustainability (USDA FS 2007). A linear programming approach generally has less applicability in addressing 

these types of goals. Further, forest planning staff now 

commonly use GIS to spatially analyze key issues, such 

as wildlife viability and habitat distribution, invasive 

species, and fire behavior. VDDT/Path is a state-andtransition modeling framework used in the PNW and 

elsewhere for examining the role of various disturbance 

agents and management actions in changing vegetation 

composition and structure. With STMs, users create and 

Table 1—Key National Forest Planning considerations and their relationship to ecosystem modeling

Forest planning consideration State-and-transition modeling task related to national forest planning

Desired conditions Define ecosystem states and transitions that are relevant to current, future, and desired 

conditions.

Simulate long-term reference conditions to serve as baselines.

Define relationships of key issues to desired ecosystem states and/or transitions (e.g., 

wildlife habitat, smoke production, watershed health).

Objectives Simulate management actions and outcomes that will move the forest toward desired 

conditions. This is done by management area and/or forest-wide.

Standards and Guidelines Use transition targets or adjust transitions probabilities to simulate effects of standards 

and guidelines.

Define states that are important to track relative to standards and guidelines (e.g., 

threatened and endangered species habitat).

Draft Proposed Action (PA) and Build PA and alternative scenarios based on desired conditions, natural processes, 

alternatives to the PA temporal variability, and varying management strategies and 

extents.

Analysis of effects of the PA Simulate alternative outcomes for key indicators and decision criteria.

 and alternatives Simulate cumulative effects of the PA and alternatives for ecosystem and social values 

across treatment types and land jurisdictions.

Monitor, adapt and amend plan Model parameters serve to document assumptions.

Adjust model probabilities, temporal and transition multipliers, initial conditions, and 

 other parameters through time as conditions change.

Best available science Appropriately use empirical data whenever possible.

Implement quality control on all models.
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GENERAL TECHNICAL REPORT PNW-GTR-869

test quantitative assumptions about vegetation dynamics, 

and simulate their effects on ecosystems into the future at 

the landscape level. Unlike linear programming models, 

STMs can address the interaction of many complex natural 

and human factors (e.g., tree harvests, fire, insects, pathogens, mammals, weather, growth, competition) and their 

combined effects over long periods, but they do not directly 

optimize a solution given ecosystem functions and management objectives. Principally, the VDDT/Path STM is: 

•	 Flexible

•	 In the public domain 

Figure 1—National Forests in the Pacific Northwest Region, USA, including five forests that are currently 

revising forest plans.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

shrub-steppe at its eastern edge. Annual precipitation varies 

widely from more than 70 inches along the crest to less than 

10 inches at its eastern edge.

The Okanogan and Wenatchee National Forests first 

developed their forest plans in 1989 and 1990, respectively. 

The forests were administratively combined into one forest 

in 2000. The plan revision process began in 2003 but was 

delayed by the development of a new U.S. Forest Service 

Planning Rule and subsequent litigation action between 

2005 and 2008. The Okanogan-Wenatchee National Forest 

faces at least the following challenges, which are being 

considered as they revise their forest plan for the next 

10–15 years:

•	 Climate is already a significant stressor in the 

Columbia River Basin and eastern Cascade Range 

and if predictions are correct, the land area affected by 

wildfire could double by 2040. Vegetation communities will also change, likely in unpredictable ways (CIG 

2009).

•	 Climate change, increasing pollution, spreading invasive plant and animal species, demand for natural 

resources, and human activities threaten to destabilize 

ecosystems.

•	 Fragmentation of wildlife habitat resulting from use 

patterns on lands adjacent to national forests, management activities, and increased demand of NFS lands 

is affecting the ability to manage for federally protected 

species, such as the northern spotted owl, Canada lynx, 

grizzly bear, and gray wolf.

•	 In the past ten or more years, there have been extensive outbreaks of, or increases in insects and disease 

(e.g., mountain pine beetle, western spruce budworm, 

balsam woolly adelgid, white pine blister rust), in 

some cases resulting in widespread tree mortality 

over large landscapes.

2 ILAP is a two-year project working on the watershed-level prioritization of land management actions based on fuel conditions, 

wildlife and aquatic habitats, economic values, and projected climate change across all lands in Arizona, New Mexico, Oregon, 

and Washington. The project creates a variety of analytical and graphical tools—including VDDT/Path models—that generate 

tables, graphs, and maps that land managers and planners can use to integrate and prioritize management activities. ILAP is a 

partnership between the U.S. Forest Service and Oregon State University. See http://oregonstate.edu/inr/ilap/ and Hemstrom et al. 

(2012) for more information.

•	 Relatively user friendly

•	 Able to model both deterministic and probabilistic 

processes

•	 A non-equilibrium model, which characterizes 

Ecosystems as constantly changing as a result of 

disturbance and other processes

•	 Compatible with the Tool for Exploratory Landscape 

Scenario Analysis (TELSA) spatial model (Kurz et 

al. 2000)

•	 Technically supported at the USFS PNW Regional 

and National levels

All five national forest plan revision efforts in the PNW 

Region are using STMs as their primary landscape level 

vegetation modeling framework in conjuntion with regiona/

local spatial and non-spatial data. The plan revision team 

in northeast Washington for the Okanogan-Wenatchee 

and Colville National Forests is using STMs compiled, 

standardized, and enhanced by the Integrated Landscape 

Assessment Project (ILAP2

). ILAP has produced VDDT/

Path STMs and associated GIS data seamlessly for all 

broad vegetation types across the PNW Region (Oregon 

and Washington), regardless of land ownership (Hemstrom 

et al. 2012). Three national forests in the Blue Mountains 

are revising their plans using models that were precursors 

to ILAP, in combination with locally derived existing and 

potential vegetation spatial data.

Throughout this paper, examples of concepts will be 

presented from the Okanogan-Wenatchee National Forest Plan revision effort that is currently underway. The 

Okanogan-Wenatchee National Forest encompasses more 

than 4-million acres in Washington State and stretches from 

the Canadian border to about 180 miles south, and from the 

Cascade Crest east into the Okanogan highlands. The forest 

is very diverse—from high, glaciated alpine peaks along the 

Cascade Crest, through valleys of old growth forest, to dry 
28

GENERAL TECHNICAL REPORT PNW-GTR-869

•	 Uncharacteristic wildfires are inherently dangerous 

and difficult to suppress. Associated costs are rising.

The Forests released a draft proposed plan for public 

comment in June 2011. At this point in time, the plan revision team is developing themes for management alternatives 

that will eventually be analyzed in a draft environmental 

impact statement. Hence, applications of STM to-date have 

been limited to setting up model structures, transitions, and 

initial conditions, and developing a reference condition scenario. Future efforts will result in the development of more 

complex model scenarios and outputs than are described in 

this paper.

Ecological Models

Currently, ecological modeling for forest plan revisions in 

the PNW Region start with ILAP state-and-transition models 

and data, which are consistent with national and regional 

vegetation mapping and classification standards. Individual 

Forest Plan revision teams adjust these regionally-compiled 

models to meet their needs, such as adding state classes, 

adjusting potential vegetation type (PVT) boundaries, using 

local vegetation data in lieu of regional data sets, condensing 

the number of model classes into fewer states or PVTs, or 

other adjustments. Figure 2 provides a generalized overview 

of key relationships between STMs and spatial and nonspatial data within the context of national forest planning. 

Potential vegetation types—

A potential vegetation type encompasses a group of 

plant associations that are characterized by a particular 

development pattern due to environment conditions and 

disturbance regimes (Henderson et al. 2011). Each VDDT/

Path model represents one PVT within a specific ecoregion. 

For the Okanogan-Wenatchee forest plan revision, the 

forested landscape is stratified into five potential vegetation 

type groups3

 that are each depicted by one or more PVT 

models, including dry forest (dry pine, dry mixed conifer 

PVTs), mesic forest (moist mixed conifer, cool-moist 

forest), cold-moist forest (Pacific silver fir), cold-dry forest 

3 Okanogan-Wenatchee revision efforts are currently focused on constructing forest and woodland VDDT/Path models. 

Non-forested ecosystems make up a relatively smaller portion of the National Forest.

(mountain hemlock), and alpine forest including subalpine 

parkland).

The Integrated Landscape Assessment Project has created spatial GIS data for PVTs across the Pacific Northwest 

Region, including all land ownerships (fig. 3). These PVT 

maps display the spatial distribution of each state-andtransition model. They were created by either cross walking 

existing plant association group maps (created from 2004 

to 2008 using a non-linear regression technique) to ILAP 

PVTs, or using a Random Forest Nearest Neighbor imputation process to map PVT distributions (completed in 2010). 

Potential vegetation type maps being used by the OkanoganWenatchee forest plan revision were created by cross 

walking a plant association group map created by nonlinear 

regression in 2004 to ILAP PVTs.

State-and-transition model structure—

Within each STM, states are defined by standardized combinations of cover type and structure (tree size, canopy density, and canopy layering). ILAP forested vegetation model 

state class specifications include at least the following:

•	 Dominant cover type (one, two, three, or mixed 

species dominance)

•	 Seven tree diameter classes (<1”, 1–5”, 5–10”, 

10–15”, 15–20”, 20–30”, >30”) 

•	 Four tree density (canopy cover) classes (<10 percent, 10–40 percent, 40–60 percent, 60 + percent)

•	 Two tree layer classes (single storied, or multiple 

storied)

The Okanogan-Wenatchee National Forest Plan revision 

is largely using state-and-transition models created by ILAP 

but has added or deleted a few model states based on local 

observations and empirical data availability. In some cases, 

there are close to 50 vegetation states for some ILAP PVT 

models. For the Okanogan-Wenatchee plan revision, giant 

tree size classes (30” + diameter) were added to a number 

of ILAP models. Also natural successional pathways were 

adjusted in the mountain hemlock model to capture the 
29

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Figure 2—Overview of relationships between state-and-transition models, and spatial and non-spatial data within the context of national 

forest planning.
30

GENERAL TECHNICAL REPORT PNW-GTR-869

Figure 3—Potential vegetation types of Washington and the Okanogan-Wenatchee National Forest as mapped by the Integrated Landscape Assessment Project (Hemstrom et al. 2021).

observed dynamics between vegetation states dominated 

by lodgepole pine cover, and bark beetle/fire disturbances, 

which frequently prevent forests from developing toward 

later seral stages. Consequently, to simplify analysis and 

interpretation, and create compatibility with existing data 

on historical reference conditions, forested structural 

states were combined into seven structural groups for most 

analyses. These seven classes were derived from the Interior 

Columbia Basin Ecosystem Management Project (Hessburg 

et al. 1999), and include: (a) Stand Initiation, (b) Stem 

Exclusion Open Canopy, (c) Stem Exclusion Closed Canopy, 

(d) Understory Re-initiation, (e) Young Forest Multi-Strata, 

(f) Old Forest Single Strata, and (g) Old Forest Multi-Strata. 

Figure 4 illustrates a small portion of the mesic (coolmoist) forest STM being used for the Okanogan-Wenatchee 

National Forest plan revision. This mesic forest model 

contains 34 vegetation states ranging from post-disturbance 

grass/forb conditions to closed canopy, multi-layer giant 

tree conditions. Transitions between states result from 

tree growth (increase in tree size and/or stand density) or 

disturbances (i.e., fire, insects, disease, severe weather, or 

management actions). Generally, growth transitions were 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

calibrated by running forest inventory plot data through 

the Forest Vegetation Simulator (FVS) (Dixon 2002). This 

process is explained in more detail below in the section 

titled “Empirical calibration of vegetation succession rates.” 

Fire transition probabilities were derived from Monitoring 

Trends in Burn Severity mapping data (Eidenshink et al. 

2007).

Existing vegetation type map and initial model 

conditions—

The PNW Region has a spatial database of existing 

vegetation that was produced by the USFS PNW Research 

Station and Oregon State University, in close collaboration with the Western Wildland Environmental Threat 

Assessment Center (WWETAC), Interagency Mapping 

and Assessment Project (IMAP—a precursor to the ILAP 

project), Northwest Forest Plan Effectiveness Monitoring 

program, Remote Sensing Applications Center (RSAC), 

and the Forest Inventory and Analysis (FIA) program at the 

PNW Research Station. Existing vegetation mapping was 

integrated with ongoing sample-based forest inventories 

conducted by FIA at the PNW Research Station and Current 

Vegetation Survey (CVS) of the USFS PNW Region and 

Bureau of Land Management (BLM) lands in western 

Oregon. Gradient imputation (Gradient Nearest Neighbor, 

or GNN; Ohmann and Gregory 2002) was used to map 

vegetation composition and structure for areas of forest and 

woodland. GNN uses multivariate gradient modeling to 

incorporate data from FIA field plots with satellite imagery 

and mapped environmental data. A suite of fine-scale plot 

variables is imputed to each pixel in a digital map, and 

regional maps can be constructed for many of the same 

vegetation attributes available for FIA plots. Nonforest 

areas were mapped by ILAP scientists using a similar GNN 

imputation method. All GNN map products are grid-based at 

30-m spatial resolution.

For the Okanogan-Wenatchee forest plan revision, 

these GNN existing vegetation maps were combined with 

the PVT layer, an ownership/land jurisdiction layer, and 

a management emphasis layer to determine initial forest 

conditions by PVT and management emphasis. Spatial 

land ownership data is used to delineate lands that fall 

Figure 4—A portion of the mesic forest STM model being used for the Okanogan-Wenatchee National Forest plan 

revision. The complete mesic (cool-moist) forest model includes 34 state classes and 207 probabilistic transitions. 

Tables 2a and 2b display examples of transitions between state classes.

State class codes. Cover type: DFmx = Douglas-fir/mixed species cover type. Structural stages: GF = grass/forb; 

S = shrubs; Yo = Seedling/sapling open density; Pm1 = pole, medium density, single story; Sm1 = small tree, medium 

density, single story; P2p = pole, multi=story, post-disturbance; S2p = small tree, multi-story, post-disturbance. 
32

GENERAL TECHNICAL REPORT PNW-GTR-869

under USDA Forest Service management jurisdiction and 

as such under Forest Plan revision direction. The spatial 

boundaries of non-NFS lands within the general planning 

area (e.g., other federal, state, private lands) are used to 

delineate areas that do not fall under forest plan direction, 

but may be important in analyses of cumulative effects 

of proposed management actions across all lands in the 

general planning area. The management emphasis layer 

for the Okanogan-Wenatchee plan revision divides NFS 

lands into “Wilderness,” “Timber Suitable,” and “Other” 

categories, each of which represent lands with similar forest 

management constraints and hence similar management 

transition probabilities. Initial conditions are the starting 

point for modeling alternative future scenarios representing 

forest plan revision EIS alternatives. The ownership/land 

jurisdiction classes used in the Okanogan-Wenatchee Plan 

revision are defined primarily by NFS versus other lands. 

The management emphasis layer is defined by the management emphasis being used for the forest plan revision, such 

as wilderness, timber suitable lands, and “other” management areas. It is expected that probabilities for management 

activities will generally differ between areas with differing 

management emphases.

Probabilistic transitions—

Natural and anthropogenic disturbances are characterized 

in STMs by deterministic and probabilistic transitions. 

A standardized suite of probabilistic transition types is 

used across the USFS PNW Region to maintain the ability 

to combine model parameters and ensure ease of model 

interpretation across geographical areas. Tables 2a and 

2b show deterministic transition probabilities (primary 

successional pathways), and examples of probabilistic 

transition probabilities of the mesic (cool-moist) forest STM 

being used for the Okanogan-Wenatchee National Forest 

plan revision, respectively. Table 3 lists all probabilistic 

transition types currently being used in STMs for forest 

planning in the USFS PNW Region. Probabilistic transitions 

between states result from tree growth, natural disturbances, 

and anthropogenic management actions. In general, VDDT/

Path STMs are attributed with average annual probabilities 

of transition. Transition multipliers are used to increase or 

decrease average transition probabilities by transition type 

across all states.

The Okanogan-Wenatchee National forest plan revision is using the probabilistic transition types created by 

ILAP, with corrections to some of the types of management 

treatments assigned to each state and adjustment of some 

natural disturbance probabilities (i.e. wildfire, insects and 

disease) to better reflect local situations. The forest plan 

revision effort is also using ILAP’s Monte Carlo temporal 

multipliers, which were developed from existing data and 

expert knowledge about temporal patterns in the frequency 

and intensity of fire4 and insect disturbances.

Compatibility with other models—

The National Forest Management Act (NFMA) requires 

that land management plans provide for diversity of plant 

and animal communities based on the suitability and 

capability of the land area while meeting overall multipleuse objectives. For terrestrial wildlife species, diversity is 

assessed through the use of a regional assessment procedure 

(Suring et al. 2011), which includes the identification 

of source habitat5 and modeling of viability relative to 

reference conditions. The structure of STMs in the PNW 

Region are designed, to the extent possible, to be compatible 

with these terrestrial species viability assessment models, 

at least for upland forests. First, the classes of potential 

vegetation types used to stratify national forests for forest 

planning are amenable to analysis of potential wildlife 

habitat for terrestrial species viability assessments. Second, 

structural states (e.g., tree size class and density) within 

forest planning vegetation models are generally compatible 

with the definition of the structural components of wildlife 

species source habitat. Third, the reference point used to 

assess ecosystem health, design forest plan objectives, 

as well as assess terrestrial species viability is partly 

determined by the “historical or natural range of variability”. 

4 ILAP uses data from the Monitoring Trends in Burn Severity (MTBS) database to develop temporal variation sequences and 

multipliers for wildfire disturbances.

5 Source habitat includes vegetation states that contribute to stationary or positive wildlife population growth (Wisdom et al. 2000).
33

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

From class (all states have a Douglas- Start End To class (all states have a Douglasfir/mixed tree cover type) age (years) age (years) fir/mixed tree cover type)

Grass/forb 1 20 Shrubs

Grass/forb post disturbance 1 20 Shrubs post disturbance

Shrubs 1 20 Seedling/sapling open cover

Shrubs post disturbance 1 20 Seedling/sapling open cover

Seedling/sapling medium cover 1 46 Pole closed cover single story

Seedling/sapling open cover 1 46 Pole medium cover single story

Seedling/sapling post disturbance 1 46 Pole medium cover single story

Pole single story post disturbance 47 75 Small tree single story post disturbance

Pole multi-story post disturbance 47 75 Small tree multi-story post disturbance

Pole closed cover single story 47 75 Small tree closed cover single story

Pole closed cover multi-story 47 75 Small tree closed cover multi-story

Pole medium cover single story 47 75 Small tree medium cover single story

Pole medium cover multi-story 47 75 Small tree medium cover multi-story

Small tree single story post disturbance 76 111 Medium tree medium cover single story

Small tree multi-story post disturbance 76 111 Medium tree medium cover multi-story

Small tree closed cover single story 76 111 Medium tree closed cover single story

Small tree closed cover multi-story 76 111 Medium tree closed cover multi-story

Small tree medium cover single story 76 111 Medium tree medium cover single story

Small tree medium cover multi-story 76 111 Medium tree medium cover multi-story

Medium tree single story post disturbance 112 167 Medium tree medium cover single story

Medium tree closed cover single story 112 167 Large tree closed cover single story

Medium tree closed cover multi-story 112 167 Large tree closed cover multi-story

Medium tree medium cover single story 112 167 Large tree medium cover single story

Medium tree medium cover multi-story 112 167 Large tree medium cover multi-story

Large tree single story post disturbance 168 300 Large tree medium cover single story

Large tree closed cover single story 168 300 Giant tree closed cover single story

Large tree closed cover multi-story 168 300 Giant tree closed cover multi-story

Large tree medium cover single story 168 300 Giant tree medium cover single story

Large tree medium cover multi-story 168 300 Giant tree medium cover multi-story

Giant tree single story post disturbance 301 500 Giant tree single story post disturbance

Giant tree closed cover single story 301 500 Giant tree closed cover single story

Giant tree closed cover multi-story 301 500 Giant tree closed cover multi-story

Giant tree medium cover single story 301 500 Giant tree medium cover single story

Giant tree medium cover multi-story 301 500 Giant tree medium cover multi-story

Table 2a—Deterministic transitions (primary successional pathways) of the mesic (cool-moist) forest 

STM being used for the Okanogan-Wenatchee National Forest plan revision. The mesic forest model 

contains 34 vegetation states ranging from postdisturbance grass/forb conditions to closed canopy, 

multi-layer, giant tree conditions

For more information on the use and limitations of the use 

HRV in establishing desired conditions, see the section 

below titled “Modeling Alternative Management Scenarios 

and their Environmental Consequences.”

Empirical calibration of vegetation succession rates—

Growth projections of forest inventory data using the 

Forest Vegetation Simulator (FVS) (Dixon 2002) provide 

empirically based information for validating model state 

age ranges and successional transition rates. Relatively 

large amounts of plot data from regional forest inventories 

are available for most modeled states in the PNW Region’s 

forested STMs. For the Okanogan-Wenatchee plan revision, 

a computer program called Preside (Vandendriesche 

2009) was used to classify data from over 3,200 U.S. 
34

GENERAL TECHNICAL REPORT PNW-GTR-869

Table 2b—A sample of probabilistic transitions of the mesic (cool-moist) forest STM being used for the 

Okanogan-Wenatchee National Forest plan revision. Probabilistic transitions between states result from tree 

growth, natural disturbances, and anthropogenic management actions

Annual Age shift Time since

From state classa To state classa Transition typeb probability (years)c transitions (years)d

Giant tree single story Grass/forb Stand-replacement 0.0040 0 0

 post disturbance wildfire

Giant tree closed cover Giant tree closed cover Non lethal wildfire 0.0008 5 0

 single story single story

Giant tree closed cover Large tree single story Spruce budworm 0.0100 0 0

 single story post disturbance outbreak

Grass/forb Shrubs Stand-replacement 0.0027 0 0

 wildfire

Grass/forb Seedling/sapling open Natural regeneration, 0.0075 0 0

 cover mid-seral species

Giant tree medium Giant tree medium Understory 0.0400 1 20

 cover single story cover multi-story development

Large tree single story Grass/forb Stand replacement 0.0012 0 0

 post disturbance wildfire

Large tree single story Large tree single story Non lethal wildfire 0.0015 5 0

 post disturbance post disturbance

Large tree single story Large tree medium Understory 0.0400 1 20

 post disturbance cover multi-story development

Large tree closed cover Large tree single story Spruce budworm 0.0100 0 0

 single story post disturbance outbreak

Medium tree single Grass/forb Stand-replacement 0.0012 0 0

 story post disturbance wildfire

Medium tree single Medium tree single Non lethal wildfire 0.0015 5 0

 story post disturbance story post disturbance

Medium tree closed Medium tree closed Non lethal wildfire 0.0011 5 0

 cover multi-story cover single story

Medium tree medium Grass/forb post Stand-replacement 0.0012 0 0

 cover single story disturbance wildfire

Pole single story post Pole multi-story post Understory 0.0400 1 20

 disturbance disturbance development

Pole multi-story post Grass/forb Stand-replacement 0.0015 0 0

 disturbance wildfire

Pole multi-story post Pole single story post Non lethal wildfire 0.0012 5 0

 disturbance disturbance

Shrubs Seedling/sapling Natural regeneration, 0.0038 0 0

 open cover mid-seral species

Shrubs post disturbance Shrubs post disturbance Non lethal wildfire 0.0015 5 0

Small tree multi-story Shrubs post disturbance Non lethal wildfire 0.0012 5 0

 post disturbance

Small tree multi-story Small tree closed cover Understory 0.0100 0 20

 post disturbance multi-story development

Small tree closed cover Shrubs post disturbance Spruce budworm 0.0100 0 0

 single story outbreak

Small tree single story Seedling/sapling post Natural regeneration, 0.0038 0 0

 post disturbance disturbance mid-seral species
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Table 2b—A sample of probabilistic transitions of the mesic (cool-moist) forest STM being used for the 

Okanogan-Wenatchee National Forest plan revision. Probabilistic transitions between states result from tree 

growth, natural disturbances, and anthropogenic management actions (continued)

Annual Age Shift Time Since

From State Classa To State Classa Transition Typeb Probabilistic (years)c Transitions (years)d

Seedling/Sapling Grass/Forb Stand replacement 0.0020 0 0

 medium cover wildfire

Seedling/Sapling post Seedling/Sapling post Non-lethal wildfire 0.0015 1 0

 disturbance disturbance

a

 All states of this mesic forest model have a Douglas-fir/mixed tree cover type.

b

 The mesic forest model contains 207 transitions between 34 vegetation states. Only a sample of natural disturbance probabilities is displayed here.

Anthropogenic management actions not shown here include various intensities of harvest, thinning, salvage, tree planting, and prescribed fire (see 

table 3).

c

 For transitions that accelerate forest development, “Age shift” refers to the number of years added to the age of the state class as a result of the 

transition.

d

 For transitions that are dependent on a certain number of years without prior disturbance, “Time since transition” refers to the number of years 

that a state must be “disturbance free” for the transition to occur.

Table 3—Natural and anthropogenic types of probabilistic transitions used in Forest Planning 

in the USDA Forest Service Pacific Northwest Region

Natural transition types Anthropogenic transition types

Wildland fire Prescribed fire

 (non-lethal, mixed severity, and stand replacement) (non lethal, mixed severity, and stand replacement)

Dwarf mistletoe Partial harvest salvage

Canopy growth Pre-commercial thin

Western pine beetle Selection harvest

Mountain pine beetle Group selection harvest

Spruce budworm Regeneration harvest

Douglas-fir beetle Salvage

High severity wind Planting

Understory development Livestock grazing

Natural regeneration Partial harvest (15 types, depending on current tree size

Alternative successional pathway and density)

Root disease

Forest Service forest inventory and monitoring plots6 into 

vegetation classes (i.e., cover type, size class, canopy cover, 

canopy layers) for subsequent FVS projection of the plot 

data into the future. The Preside program calculates the 

average time plots from a particular vegetation state stay in 

that state and the probability of movement to other model 

states.

The general sequence of steps being used to integrate 

FVS projections into STMs is:

1. Prepare the inventory data for projection by FVS.

2. Adjust FVS default parameters for growth, mortality, 

and regeneration for each PVT model.

3. Develop natural growth projections to estimate rates 

of forest succession.

4. Process FVS output through the Preside program 

and accumulate the results into a matrix summarizing mean residence times within states and transition 

probabilities between states.

5. Compare empirically derived transition rates (from 

FVS) to STM parameters and adjust the STM where 

necessary.

6 More information on forest inventory and analysis data for the USFS PNW region is available at: 

http://fia.fs.fed.us/library/fact-sheets/default.asp.
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GENERAL TECHNICAL REPORT PNW-GTR-869

Table 4—Inventory plot distribution by potential vegetation type for FVS calibration of STM models used 

in the Okanogan-Wenatchee forest plan revision

 Number of inventory plots at start

 of FVS simulation by tree size class

Total number of Grass/

Potentia inventory plots used forb Young Pole Small Medium Large Giant

vegetation type in FVS simulations (<1 in.) (1-5 in.) (5-10 in.) (10-15 in.) (15-20 in.) (20-30 in.) (>30 in.)

Dry forest 1,467 169 5 105 548 504 132 4

Mesic forest 911 24 13 92 368 287 120 7

Cold-moist forest 207 4 4 29 69 54 37 10

Cold-dry forest 622 45 8 144 309 80 36 0

Alpine forest 71 9 0 14 26 22 0 0

 Total plots 3,278 251 30 384 1,320 947 325 21

Table 4 summarizes the number of inventory plots 

used by tree size class to calibrate STMs for the OkanoganWenatchee forest plan revision. Preside analyses showed 

only a few discrepancies in growth transition rates for some 

vegetation states between FVS and the ILAP models. For 

example, in some cases Preside showed longer residence 

times for dense multi-story stands than related parameters 

in the ILAP models so growth rates for these states were 

consequently adjusted.

While not yet accomplished for the OkanoganWenatchee forest plan revision, FVS can also be used to 

validate or estimate transition pathways and/or probabilities 

for management, insects and disease, fire, or other natural 

or human-caused disturbances. It can also be used to report 

model attributes such as woody biomass volume or smoke 

emissions from fire. Additionally, climatic effects have 

recently been integrated into FVS to produce a new model 

called Climate–FVS, which provides a tool to allow climate 

change impacts to be incorporated in forest and project 

plans (Crookston et al. 2010).

Modeling Alternative Management 

Scenarios and their Environmental 

Consequences 

Forest plan alternatives are developed in-part by evaluating 

and current ecosystem and socioeconomic conditions, 

and designing alternative suites of management actions to 

achieve desired conditions. State-and-transition models 

are the primary framework used in the PNW Region for 

designing and testing Forest planning alternatives and 

analyzing some of their environmental effects.

For the Okanogan-Wenatchee National Forest plan revision, desired conditions are derived primarily from information on historical ranges of variation in vegetation structure 

(Hessburg et al. 1999; Landres et al. 1999). Hessburg et 

al. (1999) used sample-based aerial photointerpretation on 

the eastern slope of the Cascade Mountains in Washington 

State to build spatially continuous historical (1938–1956) 

vegetation maps for 48 randomly selected sub-watersheds. 

These data were used to build desired conditions used in 

the forest plan revision. While attempts to strictly recreate 

conditions of the past are often not desirable or feasible 

(e.g., due to climate change, non-native species invasion, 

soil erosion, social intolerance of fire frequencies at levels 

representative of Native American burning (Kay 2007), and 

future climatic changes may in-time reduce the relevancy 

of historical references, the HRV remains an objective 

reference for at least the short-term management of natural 

resources (Keane et al. 2009). The Okanogan-Wenatchee 

Forest Plan revision team is currently comparing likely climate change projections and “future ranges of variation” in 

ecosystem structure and function against HRV to improve 

forest plan development and implementation.

Objectives for vegetation management in the draft 

Okanogan-Wenatchee proposed plan were developed by 

comparing current and desired vegetation and fire regime 

conditions, and testing alternative suites of management 

practices (e.g., tree harvest, forest thinning, prescribed 
37

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

fire) to support a variety of resource objectives ranging 

from creating diverse wildlife habitats to maintaining 

scenic values while providing forest products. For example, 

relative to desired conditions, dry mixed conifer forests are 

generally deficit in stand initiation, stem exclusion/closed 

canopy, and old forest single story structures (Hessburg et 

al., 1999). Overall, proposed plan direction is intended to 

make ecosystems more resilient to disturbance driven by 

climate change, reduce impacts of insects and diseases, and 

produce quality forest commodities. 

The acceptability of these alternative management 

scenarios are also based on their feasibility within current 

budget assumptions and ability to ensure public safety. 

Development of these alternative suites of management 

practices—scenarios—begins with a STM that is parameterized with current annual disease, fire, and weather 

transition probabilities. Alternative sets of transition targets 

and transition multipliers are then iteratively used to test 

options—which integrate natural processes and management activities—for moving the forested landscape from 

current (initial) conditions toward desired goals. During 

scenario modeling, 10 to 30 Monte Carlo simulations are 

run to capture stochastic variability.

Comparisons of the environmental consequences of 

forest plan alternatives help support the selection of the final 

forest plan. For example, figure 5 illustrates the outcomes 

of two possible management scenarios for dry mixed 

conifer forest lands suitable for timber production on the 

Okanogan-Wenatchee National Forest. The “No Management” scenario excludes all management actions except 

fire suppression and allows all other natural disturbances 

to operate freely. The “Restoration” scenario represents 

one possible suite of regenerating stand densities harvest, 

thinning, and prescribed fire actions designed to move dry 

forest toward desired conditions. Forest harvest and thinning at a rate of 1-2 percent of timber suitable lands represents a level of activity consistent with historical budgets. 

As figure 5 demonstrates, over the 10-15 year planning 

horizon this restoration scenario moves dry mixed-conifer 

forests toward desired conditions (e.g., greater amounts of 

old forest single story; less dense, multistory conditions). 

However, this scenario also reveals that changes in management approach across large landscapes may take decades 

to effectively change ecosystem structure and composition 

due to the length of time needed for forest development, and 

constraints that limit the extent or intensity of management 

actions (e.g., budget, air quality regulations). Some of the 

environmental consequences of alternatives can also be 

estimated by calculating indicators that are a function of 

the model’s predicted area over time for state classes and 

transitions. Table 5 displays some common indicators used 

in forest planning in the PNW Region. Attributes such as 

forest structural groups, suitable wildlife habitat, or biomass 

volume can be assigned to one or more state classes, or 

calculated from model outputs.

Conclusions

The USFS PNW Region has extensive experience applying 

STMs and has an expanding model library. These models 

represent the integration of the best available science, albeit 

from a variety of sources including published research, peerreviewed literature, unpublished papers, and expert judgment. 

Scenario planning in general, and STMs specifically 

often incorporate a variety of quantitative and qualitative 

information and consideration of this diverse information 

in a systemic way frequently leads to better decisions 

(Peterson et al. 2003). On the other hand, no matter what 

the underlying data source, models carry with them a certain 

degree of error and uncertainty. Sources of error in STMs 

include, but are not limited to: 

•	 data or knowledge gaps 

•	 omitting states not currently present but which 

could occur on the landscape 

Table 5—Common monitoring indicators calculated 

from STM model output that are used in forest planning in the USDA Forest Service Pacific Northwest 

Region

Stand replacement fire hazard Wildlife habitat quality

Fire regime condition class Timber volume

Wildlife habitat abundance Biomass

Vegetation density class Revenue

Successional stage Cost

Single versus multi-layered Smoke production

Forest structural groups
38

GENERAL TECHNICAL REPORT PNW-GTR-869

Figure 5—Outcomes of 50-year STM simulations of two potential management scenarios for dry mixed conifer forest on lands 

suitable for timber production on the Okanogan-Wenatchee National Forest. Also shown is a graph of treatments used in the 

“restoration” scenario.
39

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

•	 application of transition probabilities from 

similar, but not identical processes or systems 

•	 inappropriately broad application of data from 

localized sources 

•	 using mean probability values for processes 

with wide or bimodal variability

•	 failure to incorporate temporal variation in 

disturbance processes

•	 failure to capture spatially controlled processes 

•	 failure to capture the effects of past land use on 

ecosystem structure and function

While the use of expert knowledge can introduce 

additional sources of model uncertainty, published literature 

alone rarely provides the detailed, site-specific information 

necessary to fully parameterize STMs. When empirical 

data is available, it is usually limited in extent and rarely 

includes an analysis of appropriate application scales or 

spatial heterogeneity (Bestelmeyer et al. 2011). Experts and 

practitioners are often the only source of information about 

ecosystem structure, function, and dynamics, especially at 

local scales (Drescher et al., 2008). 

Local resource management experts, including forest, 

fire, and insect and pathogen ecologists, have qualitatively 

reviewed STM parameters and modeled outcomes for the 

Okanogan-Wenatchee forest plan revision. These experts 

and managers generally concur that the models are fair 

representations of the landscape-level forest structure, 

composition and function for the purposes of broad forest 

planning. However, non-spatial STMs generally simplify 

spatial heterogeneity, particularly at resolutions finer than 

the analysis area (Bestelmeyer et al. 2011), and these limitations should be considered during the planning process and 

subsequent implementation. For example, riparian systems 

are generally under-represented in the suite of STMs 

currently available for national forest planning, and as such 

are often assumed to behave similarly to adjacent uplands 

during broad landscape analyses. Development of models 

for unique ecosystems, spatial modeling of systems and 

processes dependent on spatial constraints, and continual 

integration of lessons learned from forest plan and climate 

monitoring can be used to improve the accuracy of STM 

parameters through time. 

State-and-transition models are being used successfully 

to integrate resources for developing forest plan alternatives and analyzing the relative effects of those alternatives. These models can also be used to test assumptions 

within the complex matrix of climate change, increased 

forest densities, extended drought, uncharacteristic insect 

epidemics and fires, expansion of residential development 

into wild lands, and uncertainty in our understanding of 

ecosystem structure and function. The USFS PNW region’s 

library of STMs provide an opportunity to link landscape 

and forest-level analyses to broadscale analyses, while also 

establishing a framework for adaptive feedback between 

levels of analysis. This capacity is particularly important 

relative to the analysis of regional issues, such as conservation of old forest dependent species, effects of road density 

on habitat integrity, commodity production, and effectiveness of alternative ecosystem restoration strategies. Broader 

applications of STMs by other landowners, such as other 

federal agencies and state departments of natural resources, 

and development of consistent models across large geographic extents (such as ILAP’s suite of STMs for the PNW 

and Southwestern regions) are creating opportunities for 

integration of science and planning across larger geographic 

extents. Greater collaboration can help integrate multiple 

sources of scientific information, and improve our collective 

ability to effectively manage natural resources.

Acknowledgments

The authors would like to acknowledge Mark Loewen of 

the Okanogan-Wenatchee and Colville National Forests and 

two anonymous reviewers for their insightful comments 

and suggestions on earlier drafts. We are also indebted 

to Jim Merzenich (USFS PNW Region, retired), Bob 

Lee (USFS Washington Office), Jim Smith (The Nature 

Conservancy), Colin Daniel (ApexRMS), Leonardo Frid 

(ESSA Technologies), and Miles Hemstrom, Josh Halofsky, 

Treg Christopher, and others on the Integrated Landscape 

Assessment Project for their support and dedication to 

developing STM tools for land managers for regional, forest, 

and project planning.
40

GENERAL TECHNICAL REPORT PNW-GTR-869

References

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and pathogen disturbances. Gen.Tech. Rep. PNWGTR-458. Portland, OR: U.S. Department of Agriculture, 

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Keane, R.E.; Hessburgb, P.F.; Landres, P.B.; Swanson, 

F.J. 2009. The use of historical range and variability 

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FS]. 1995. Spectrum User Guide, Version 1.0. Prepared 

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Center, Fort Collins, Colorado. 324 p.

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www.fs.fed.us/emc/nfma/includes/overview.pdf].

Vandendriesche, D.A. 2009. Advanced FVS Topics 

for Forest Planning. Preside: Process Residence 

Times. Interoffice publication. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Forest 

Management Service Center. 129 p.

Wisdom, M.J.; Holthausen, R.S.; Wales, B.C.; Hargis, 

C.D.; Saab, V.A.; Lee, D.C.; Hann, W.J.; Rich, T.D.; 

Rowland, M.M.; Murphy, W.J.; Eames, M.R. 2000. 

Source habitats for terrestrial vertebrates of focus in 

the interior Columbia basin: broad-scale trends and 

management implications. Gen. Tech. Rep. PNWGTR-485. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 
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43

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Authors

Kori Blankenship is a fire ecologist, The Nature Conservancy, Bend, OR, kblankenship@tnc.org. Jim Smith is a 

LANDFIRE project lead, The Nature Conservancy, Jacksonville, FL, jim_smith@tnc.org. Randy Swaty is an ecologist, The Nature Conservancy, 101 S. Front St, Marquette, 

MI 49855, rswaty@tnc.org. Ayn J. Shlisky is a regional 

forest planning analyst, Pacific Northwest Region, USDA 

Forest Service, 333 SW First Ave., Portland, OR 97204, 

ajshlisky@fs.fed.us. Jeannie Patton is a program coordinator, The Nature Conservancy, 2424 Spruce St., Boulder, CO 

80302, jpatton@tnc.org. Sarah Hagen is a GIS analyst, The 

Nature Conservancy, 1101 West River Parkway, Suite 200, 

Minneapolis, MN 554, shagen@tnc.org.

Abstract 

Between 2004 and 2009, the LANDFIRE project facilitated 

the creation of approximately 1,200 unique state-andtransition models (STMs) for all major ecosystems in the 

United States. The primary goal of the modeling effort 

was to create a consistent and comprehensive set of STMs 

describing reference conditions and to inform the mapping of a subset of LANDFIRE’s spatial products. STMs 

were created by more than 700 experts through a series of 

modeling workshops, individual meetings and web conferences hosted around the country. While model- building 

speed, efficiency and consistency may have been enhanced 

by using a small group of project employees to develop 

STMs, our participatory approach to model development 

encouraged early engagement in the LANDFIRE project as 

a whole, helped to incorporate a broad spectrum of knowledge into the STMs and built modeling capacity. The depth 

and breadth of the LANDFIRE modeling effort provides an 

opportunity to learn about expert-based modeling efforts. 

In this paper we reflect on that effort and, based on our 

collective experience facilitating the development of the 

LANDFIRE STMs, we offer 10 lessons learned: (1) create a 

flexible modeling process, (2) incorporate a learn-by-doing 

method, but know that it takes work, (3) engage a broad 

spectrum of experts from the start, (4) agree on what is 

being modeled, (5) implement procedures to maintain quality control, (6) if possible, build from existing models, (7) 

thoroughly document results, (8) never forget the modeling 

purpose, (9) set realistic modeling goals, and (10) model to 

document known ecological information and identify gaps 

in understanding. In this paper, we discuss these lessons in 

detail and offer observations and examples from our experience to help others efficiently build more useful models for 

land management and planning efforts in the future.

Keywords: pre-settlement, vegetation ecology, vegetation dynamics, state-and-transition model, LANDFIRE, 

experts, VDDT, Vegetation Condition Class.

Introduction and Background

Between 2004 and 2009, the Landscape Fire and Resource 

Management Planning Tools Project (LANDFIRE; http://

www.landfire.gov) developed state-and-transition models 

(STMs) for all major Ecological Systems (Comer et al. 

2003) in the United States through an expert-based model 

development process (Rollins 2009). LANDFIRE (now 

the LANDFIRE Program) is a shared program between 

the U.S. Department of Agriculture Forest Service and the 

U.S. Department of the Interior that is chartered to develop 

a suite of more than 20 vegetation, fire and fuel related 

products (table 1) that support fire and land management 

activities at regional and national levels. The datasets were 

created using consistent methods and cover all lands, public 

and private, in the United States (Rollins 2009).

LANDFIRE developed STMs to estimate pre-settlement reference conditions and to inform the mapping of 

a subset of its spatial products (table 1). Pre-settlement reference conditions as applied in LANDFIRE refer to the estimated percent of the landscape within given seral stages for 

Modeling on the Grand Scale: 

LANDFIRE Lessons Learned

Kori Blankenship, Jim Smith, Randy Swaty, Ayn J. Shlisky, Jeannie Patton, and Sarah Hagen
44

GENERAL TECHNICAL REPORT PNW-GTR-869

an ecosystem that would have occurred prior to European 

settlement. The reference period included both the influence 

of Native Americans (e.g., use of fire) throughout much of 

the continental U.S. and the influence of Polynesian settlers 

(e.g., agriculture) in the Hawaiian Islands. The primary use 

of the STM generated reference conditions by LANDFIRE 

Table 1—LANDFIRE created and is continually updating its suite of more than 20 related fuel, 

vegetation and fire regime products. The STMs (called Vegetation Dynamics Models) are used 

directly and indirectly to create a subset of the spatial products

Fuel products Vegetation products Fire regime products

13 fire behavior fuel modelsa Existing vegetation typea Fire regime groupsb

40 fire behavior fuel modelsa Existing vegetation cover Mean fire return intervalb

Canadian forest fire danger Existing vegetation height Percent low-severity fireb

 rating system

Fuel characteristic classification Biophysical settingsa Percent mixed-severity fireb

 system fuelbeds

Fuel loading models Vegetation dynamics models Percent replacement-severity fireb

Forest canopy cover Environmental site potential Vegetation condition classb,c

Forest canopy height Vegetation departureb,d

Forest canopy bulk density Succession classese

Forest canopy base height

a

 Products that were developed using STMs and associated description documents as an ancillary data source. 

b

 Products that were generated directly by STMs in all versions of LANDFIRE except LANDFIRE National where they 

were used as inputs to the LANDSUM model (Keane et al. 2006) which generated these products. 

c

 Vegetation Condition Class was formerly called Fire Regime Condition Class. 

d

 Vegetation Departure was formerly called Fire Regime Condition Class Departure Index. 

e

 Product that was generated using rule sets in the STM description document.

Figure 1—The primary use of STMs by LANDFIRE was to compare reference and current 

conditions to calculate Vegetation Condition Class. This example compares reference conditions 

estimated from the Ozark-Ouachita Dry Oak Woodland STM (LANDFIRE 2012a) to current 

conditions (LANDFIRE 2012b) in Map Zone 44—Ozark and Ouachita Mountains.

was to calculate Vegetation Condition Class (formerly 

referred to as Fire Regime Condition Class or FRCC; 

Barrett et al. 2010), a metric which quantifies the difference 

in vegetation cover, height and type between reference and 

current conditions (fig. 1). The model documentation and 

model outputs for the reference scenario were also used 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

directly to provide mapping rule sets for developing the Fire 

Regime Group, Succession Class, Fire Frequency and Fire 

Severity spatial layers and as an ancillary data source for 

mapping Biophysical Settings, Existing Vegetation Type 

and Fire Behavior Fuel Models (Rollins 2009; table 1).

The primary objective of the LANDFIRE modeling 

effort was to create a consistent and comprehensive set of 

STMs describing reference conditions for every ecosystem 

mapped by LANDFIRE. In addition, we wanted to: 

• create a STM library as a foundation for future 

modeling efforts, 

• develop a sense of buy-in and ownership by the 

community of potential LANDFIRE data users, 

• train participants in the concepts and applications of STMs, and 

• provide a forum for scientific and land management networking. 

These objectives guided the model development process 

and modeling rules. The process we implemented served to 

market the LANDFIRE project as a whole, taking it from 

a top, own effort that delivered maps and models built by 

a small team of project employees to a participatory effort 

where user input was incorporated directly to build a subset 

of the products. While the former approach would probably 

have led to greater consistency in the STMs, it would have 

likely compromised training, outreach and networking 

objectives.

Each LANDFIRE STM represents a single ecosystem 

called a Biophysical Setting (BpS). A BpS is a vegetation 

concept mapped by LANDFIRE, based on the Ecological 

Systems classification (Comer et al. 2003), which represents 

the potential vegetation community that could exist on the 

landscape given the current biophysical environment (e.g. 

soils and precipitation) and an approximation of the historical disturbance regime (e.g. fire return interval and flooding 

frequency). A LANDFIRE STM consists of two related 

parts (fig. 2):

1. a quantitative state-and-transition model 

developed with the Vegetation Dynamics 

Development Tool (VDDT; ESSA Technologies 

Ltd. 2007) and 

2. a description document developed in the Model

Tracker Database (MTDB). 

VDDT was used to attribute each state within a BpS 

with an age range and probabilities for deterministic (i.e. 

succession) and probabilistic (i.e. disturbance) transitions. 

VDDT was then run for 1,000 years to estimate reference 

conditions (i.e. the percent of the landscape in each state) 

and the frequency of fire and other disturbances. VDDT was 

chosen as the modeling platform by LANDFIRE because it 

is in the public domain, relatively user-friendly, compatible 

with related spatial models and capable of running multiple 

iterations quickly. VDDT is also supported by some federal 

and state agencies as a land management planning tool. 

MTDB is a Microsoft Access database designed by 

LANDFIRE to document model development (fig. 3). The 

database was used by modelers to record:

• a description of the modeled ecosystem including geographic range, biophysical setting, disturbance regime, vegetation characteristics and 

dominant species, 

• mapping rules for each state (called s-class or 

succession class by LANDFIRE) in the STM, 

• STM results including the estimated reference 

condition (i.e. the percent of ecosystem in the 

various states) and the fire frequency and 

severity, and 

• relevant literature, model contributors, model 

reviewers and modeling assumptions. 

A report was generated from the MTDB which became 

the description document (metadata) that accompanies each 

STM. 

Modeling rules were established to ensure that the 

models would be consistent and comparable across the 

country and could be used to develop map products. For 

example, LANDFIRE models are consistent in resolution 

(they have five or fewer states), capture the main successional pathway without gaps or overlap in age and use a 

pre-defined subset of VDDT functions including: 

• a standardized set of definitions for cover types, 

structural stages, transition types and transition 

groups, 
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GENERAL TECHNICAL REPORT PNW-GTR-869

Figure 2—A LANDFIRE model consists of a STM developed in VDDT 

(A) and a description document developed in the MTDB (B).

A

B
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

• use of Time Since Disturbance (TSD) only with 

alternate succession pathways, and 

• use of relative Age (RelAge) limited to replacement severity disturbances occurring in the 

initial state (normally state A in LANDFIRE). 

These modeling rules created a number of weaknesses, 

including: 

• the need to develop crosswalks between differing vegetation classifications, 

• the potential loss of information available at 

resolutions finer than the project objectives, and

• constraint of expert modelers to a select suite 

of model functions, sometimes below their skill 

level. 

Figure 3—MTDB is Microsoft Access tool developed by LANDFIRE to document the modeling process.

Another limitation was imposed by the project schedule 

which limited the time available for STM development and 

review. During the National phase of the project, LANDFIRE developed, reviewed and revised seven unique STMs 

each week on average for 183 weeks. 

The STMs were developed through a series of more 

than 40 expert workshops held around the country, some 

35 web conferences and many more individual meetings. 

A modeling leader was designated for each of 13 geographic 

regions and provided funding for STM development activities in their area. LANDFIRE modeling leaders cast a wide 

net for experts in the fields of vegetation, fire and landscape 

ecology as well as land managers and stewards—in short, 

anyone with the training and/or experience necessary to 
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GENERAL TECHNICAL REPORT PNW-GTR-869

populate the ecological information in a STM, and who 

had supervisory support and funding for engagement in the 

project. Workshops were open to all interested individuals, 

and a modest amount of funds were available to support 

travel for a portion of participants. Model parameters were 

developed based on literature, local data and professional 

judgment. When disagreements occurred regarding model 

inputs, LANDFIRE modeling leaders consulted additional 

experts, reviewed the literature, performed sensitivity 

analysis and ultimately made the final decision about the 

parameters. Both the decision-making process and all the 

opinions were thoroughly documented in MTDB to ensure 

transparency. Written and verbal evaluations were solicited 

at each workshop to fuel to facilitate adaptive workshop 

planning.

During the Rapid Assessment phase of the project, 262 

coarse-scale STMs representing 242 BpS units mapped in 

the conterminous U.S. were developed. These STMs were 

then refined during the LANDFIRE National phase of the 

project to create 2,164 STMs representing 541 mid-scale 

(ranging from 10–1000’s of hectares in size) BpS units 

mapped in the U.S., including Alaska and Hawaii. In total, 

LANDFIRE engaged more than 700 experts from various 

sectors (fig. 4) to create 2,426 STMs (approximately 1,200 

of which were unique) representing 783 vegetation units in 

the U.S. (table 2). Not all of the STMs were unique because 

in some cases, based one expert feedback, one STM was 

Figure 4—More than 700 individuals from the government, non-profit and private sectors contributed to the development of LANDFIRE STMs. The category “Other” refers to individuals who 

did not affiliate with any group or organization, such as independent contractors and consultants.

Table 2—LANDFIRE created two sets of STMs: 1) Rapid Assessment models 

which were coarser scale and covered the conterminous U.S. and 2) National 

models which were finer scale and covered the entire United State including 

Alaska and Hawaii. The relationship between types mapped and types modeled is 

one-to-many because in some cases multiple STMs were needed to represent the 

geographic variation in widespread vegetation types. Of the 2,426 total LANDFIRE STMs, about 1,200 are unique because in some cases one STM was used to 

represent the same BpS in different Map Zones

Project phase Vegetation types mapped Vegetation types modeled

LANDFIRE Rapid Assessment 242 262

LANDFIRE National 541 2,164

TOTAL 783 2,426
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

used to represent the same BpS in different Map Zones 

(National Landcover Database Map Zones). However, 

the associated documentation in MTDB may have been 

adjusted to better represent the geographic variation in 

plant species or environmental gradients so a unique record 

was maintained for each Map Zone even when quantitative 

information in the STM was not changed. In some cases, 

the opposite situation occurred—a given BpS had multiple 

STMs associated with it to represent the geographic variation across Map Zones with quantitative changes in the 

model. For example, the Inter-Mountain Basins Big Sagebrush Shrubland BpS occured in 20 Map Zones and had at 

least three distinct STMs associated with it to represent the 

variability in successional rates and disturbance probabilities throughout its extensive range.

In this paper we offer 10 lessons learned based on 

our collective experience facilitating the development of 

LANDFIRE’s STM library. We discuss lessons related to all 

aspects of model building including developing a modeling 

process, eliciting expert input, defining modeling units, 

checking for model errors, documenting model results and 

setting appropriate expectations. These lessons may help 

others build more useful models for land management and 

planning in the future.

Lesson 1: Create a Flexible Modeling 

Process

Imagine embarking on a project to develop thousands of 

structurally-consistent, scientifically-sound vegetation 

STMs while also engaging hundreds of people with diverse 

knowledge, skills and personalities. Together these objectives necessitate a relatively large degree of flexibility in 

approach (e.g., to address a diversity of learning styles), 

while there may also exist many constraints on the modeling mechanics (e.g., to ensure each model is built at the 

appropriate scale of resolution). Being flexible in the modeling approach does not necessarily mean scientific quality 

will suffer; scientific quality may in fact be enhanced when 

the approach allows a greater diversity of experts to contribute their knowledge and skills. Flexibility in approach 

provides the wiggle-room necessary to work with individuals or organizations that have different styles or processes.

Goals for developing STMs for LANDFIRE included 

engagement of a large diversity of experts for the purposes 

of compiling the best available science on ecosystem 

structure and function, and capacity-building for the 

future application of completed models. Some modeling 

participants were interested in learning how to build and 

apply the STMs, as well as providing and/or compiling the 

best available science for translation into a STM format. 

Other participants were primarily focused on compiling the 

best available information and were not interested in being 

able to use the STMs themselves. Workshop participants 

also differed in learning styles. The “experiential learners” needed to run the models in a hands-on manner to 

understand how they worked; while others were “abstract 

learners” and could understand enough about the modeling 

process through lectures to meet the project objectives. 

While LANDFIRE, in part, aimed to build applied 

modeling capacity in each modeling participant, the 

diversity of learning styles and participant motivations 

necessitated a flexible approach. For example, we learned 

that if a small group of experts was expected to build a 

STM, it must include at least one person willing to listen 

openly and patiently to others and run the model software 

while also incorporating their own expert knowledge in an 

unbiased manner. If a group of experts lacked the skills and 

desire to build a STM in VDDT, they had to be provided 

more abstract methods to document the best available 

science on model parameters (e.g., flipcharts or forms where 

experts could fill-in tables of transition probabilities, or 

draw box-and-arrow diagrams) so that the STM could be 

built later. If experiential learners were willing to build 

STMs, but no other experts were available to assist, they 

had to be comfortable working individually, or be provided 

one-on-one support throughout the model-building process. 

In general, based on written workshop evaluations by 

participants, most LANDFIRE modelers appreciated the 

in-person, facilitated workshop approach and the opportunity to interact with other experts. However, where time, 

travel budgets, modeling skills and/or a desire for increased 

modeling capacity was lacking, first iteration “straw man” 

models were built by LANDFIRE staff which could be 

reviewed individually by experts on their own time.
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GENERAL TECHNICAL REPORT PNW-GTR-869

We suggest holding in-person workshops and one-onone meetings whenever possible, using techniques that the 

modeling leaders and modelers are comfortable with. We 

used multiple techniques to build models often within the 

same workshop, including:

• facilitated modeling using flip charts and/or 

VDDT software,

• modeling individually or in small groups, 

• modeling by pairing an expert with a VDDT “driver” 

who could run the software but did not necessarily 

understand the ecology, and

• LANDFIRE staff creating straw man models which 

were later critiqued by experts during or outside a 

workshop event.

Online web conferences can be effective when in-person 

meetings are not feasible. 

Essentially, the need to build structurally consistent 

STMs using the best available science (see Lesson 5) does 

not preclude taking a flexible modeling approach. A flexible 

modeling approach facilitates engaging the broadest suite of 

learning styles and personal motivations possible. 

Lesson 2: Incorporate a Learn-By-Doing 

Method, but Know That it Takes Work

Building STMs requires a general understanding of modeling concepts and specific knowledge of modeling tools such 

as VDDT. While these concepts and tools can be taught 

through a didactic approach, we found that an experiential 

learning approach, where users learned directly by doing, 

facilitated two of our project objectives: building many 

STMs in a short amount of time and building modeling 

capacity within our expert community. While this constructivist-guided approach is well documented (two publications 

by Jean Piaget, attributed as “father” of constructivism, 

have over 7,000 citations in Google Scholar), it requires (1) 

teamwork, (2) motivation, and (3) preparation on the part of 

the facilitator. 

Team learning , such as building STMs in small groups 

as was done at most LANDFIRE workshops, has proven to 

be valuable in virtually every educational setting (Daniels 

and Walker 2001). Team situations provide opportunities 

for reflective observation (i.e., asking “why?”), and further, 

learning is often motivated by conflict (Kolb 1993). The 

teams in the LANDFIRE modeling effort were selected for 

expertise, not necessarily for agreement in learning styles, 

age or type of experience. In one example from a LANDFIRE modeling workshop in Michigan, a young college professor was paired with an older ecologist from The Nature 

Conservancy. The professor was very comfortable with both 

the modeling software and the ecosystem from literature 

review, whereas The Nature Conservancy ecologist was 

relatively uncomfortable with the software but had decades 

of field experience. The two experts often questioned each 

other—the ecologist questioning how the professor ran the 

model; the professor questioning the ecologist’s field-based 

observations. The tension forced both modelers to alternate 

between the four modes of experimental learning: reflection, action, feeling and thinking (Daniels and Walker 

2001). We did not test the experts, but both stayed engaged 

with LANDFIRE, built a nuanced and complete model and 

most importantly, commented that they “learned a lot” from 

the experience.

Addressing the built-in tensions between people, 

the challenges of quantifying ecosystem processes with 

substantial levels of uncertainty, high expectations and 

simply “being away from the office” required motivation. 

Motivation was both internal to the experts and created on 

site during workshops and meetings. The simple fact that 

experts prioritized their work to be involved often indicated 

that there was motivation and that the topic at hand had 

immediate relevance. However, some experts may have 

been directed to attend by a supervisor, for example. It is 

important that leaders do not assume adequate motivation 

among participants. In the LANDFIRE modeling process, 

motivation was developed through several means: (1) 

immediate engagement (e.g. minimizing lectures and moving quickly to hands-on modeling), (2) creation of a “safe” 

environment where risks of questioning and being questioned were kept to a minimum, (3) accountability based on 

STM review and (4) fun (see below). 

Working with many people of varied backgrounds 

requires structure and preparation. Corroborating many of 

Vella’s 12 fundamental principles of adult learning (Vella 

1994), we found that for the processes to be effective there 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

had to be physical comfort (plenty of food, quiet location, 

etc.), clear expectations and clear, but evolving roles. In 

LANDFIRE workshops, the facilitator took on the leadership role in establishing the aforementioned “motivational 

setting,” but as the process matured the facilitator would 

often be replaced as the leader by experts. It was apparent to 

us that peer-to-peer learning and collaboration increased the 

value of the workshop approach over STMs being developed 

by individuals working independently.

Finally, it was helpful to throw in some fun whenever 

possible. For example, we used an acronym contest, where 

we learned that TNC, i.e. The Nature Conservancy, also 

can mean “Totally Non-Confrontational.” Administering an 

“Are you a lumper or a splitter?” quiz to workshop participants not only brought laughter, but helped participants 

recognize their potential modeling strengths and weaknesses.

Lesson 3: Engage a Broad Spectrum of 

Experts from the Start

We believe that engaging a broad spectrum of experts in the 

development and review processes results in more robust 

and useful SMTs. Consider inviting individuals who will 

be critical to building future support for the use of STMs. 

Research has shown that experts are the greatest source of 

variation in the modeling process (Czembor 2011) but for 

many ecosystems expert knowledge is virtually the only 

information source available. If variation is inevitable, 

modeling leaders need to increase the sample size, that is, 

identify and engage as many experts as possible within time 

and resource constraints. LANDFIRE modelers included 

scientists, managers and resource specialists from all the 

major U.S. land management agencies (e.g., Forest Service 

and National Park Service), teachers and students from 

academic institutions and foresters, ecologists, botanists, 

managers and others from a variety of non-governmental 

organizations (e.g., The Nature Conservancy and NatureServe; fig. 4). 

Once experts are involved, managing their input in 

constructive ways is the key to successful engagement. 

Through experience we developed several techniques 

for responding appropriately to issues encountered when 

working with a diverse group of experts (table 3). When 

modeling is complete, it is important to follow up with 

modeling participants to communicate project results and 

the importance of their efforts to the success of the project. 

Lesson 4: Agree on What is Being 

Modeled 

Defining what is being modeled and communicating that 

explicitly are essential to the modeling process. This 

includes both the vegetation concept (e.g., BpS) and the 

individual vegetation units to be modeled (e.g., Alaska 

Arctic Wet Sedge-Sphagnum Peatland). When explaining 

the vegetation concept, we found it helpful to discuss it 

within the context of various other potential vegetation 

classifications familiar to our experts such as Potential 

Natural Vegetation Type (e.g., Schmidt et al. 2002), Habitat 

Type (e.g., Daubenmire 1968, Pfister et al. 1977), Land Type 

Association (e.g., ECOMAP 1993) and others. 

After the modeling concept is defined and understood, 

the individual modeling units themselves must be examined 

and modelers must come to agreement on the distinction 

between related and sometimes overlapping ecosystems. For 

example, LANDFIRE created STMs for seven California 

chaparral ecosystems: California Maritime Chaparral, 

California Mesic Chaparral, California Montane Woodland 

and Chaparral, California Xeric Serpentine Chaparral, 

Mediterranean California Mesic Serpentine Woodland and 

Chaparral, Northern and Central California Dry-Mesic 

Chaparral and Southern California Dry-Mesic Chaparral. 

While time consuming, examining similar ecosystems like 

those listed above before initiating modeling is essential to 

preventing confusion during model development and later 

model use.

Lesson 5: Implement Procedures to 

Maintain Model Quality

Maintaining model quality through standards, rules and 

error checking are critical when STMs are to be comparable 

across ecosystems and/or if they are to be used in other 

software programs (e.g., LANDSUM) or applications (e.g., 

mapping state classes). LANDFIRE developed modeling 
52

GENERAL TECHNICAL REPORT PNW-GTR-869

standards and rules to maintain the quality and consistency 

of its STMs and to ensure their compatibility with mapped 

products. Standards, such as modeling forested ecosystems 

with a standardized set of state classes including one early, 

two open and two closed states, create a sense of unity 

among the LANDFIRE models. While this standard was 

followed most, but not all, of the time, LANDFIRE had a 

set of rules that were applied to the entire model set such as 

using five or fewer states in a STM to create consistency in 

resolution and prohibition of the use of Monte Carlo multiplier files to capture temporal variation in disturbances. 

The VDDT software includes functionality for setting the 

temporal variation in disturbances, but it was not incorporated into the project design because of large gaps in data or 

knowledge about temporal variation of natural disturbances 

geographically and for particular disturbance types (see 

lesson eight). 

An automated and a manual set of quality control 

checks were developed to ensure that LANDFIRE modeling 

rules were followed and errors were minimized. Keeping 

models as simple as possible (see lesson nine), makes finding and fixing errors easier. Modeling efforts like LANDFIRE should clearly communicate the benefits of standards 

or rules such that modelers and reviewers can understand 

how the rules may work in their favor in the long term. 

Lesson 6: If Possible, Build from Existing 

Models 

Starting with an existing STM and modifying it as needed 

to represent a new ecosystem can promote modeling 

efficiency and may help build modeling confidence among 

experts with little modeling experience. Working with a 

variety of experts we found that most preferred to modify 

an existing STM rather than start from scratch. This seemed 

to be particularly helpful for individuals who had no 

previous modeling experience or who had not used VDDT 

before—the case for most LANDFIRE modelers. 

Table 3—Successfully engaging experts and eliciting the information required to build STMs involves developing ways to constructively mange expert input and work with experts with diverse backgrounds and skills

Issue Potential solution LANDFIRE example

Working with “lumpers” and

“splitters” (i.e., individuals 

who tend to focus on 

similarities and define fewer 

ecosystems vs. those who 

tend to focus on differences 

and define more ecosystems)

Managing experts with 

agendas

Building modeling 

confidence and capacity

Limited budget for 

compensating experts

Illuminate individual tendencies to 

prevent either over-simplification or 

wasted time tracking down unnecessary information.

Respectfully mange their input so as 

to separate agendas from science.

Make sure all participants feel 

valued and acknowledge that only 

some experts will be willing to learn 

modeling tools such as VDDT.

Payment may increase motivation 

and timeliness. Use web conferences 

instead of in-person meetings when 

budgets are limited.

We gave a light-hearted “Are you a lumper or a 

splitter?” quiz to identify individual tendencies 

early on in the model development process.

MTDB provided a place where all opinions 

could be documented and robust model review 

helped ensure that the best available science was 

incorporated into each STM.

We paired experts who were comfortable modeling with those who were not, and/or provided 

other devices to record expert input such as flip 

charts or cheat sheets. We started by eliciting 

information within the expert’s area of interest 

and worked towards less familiar information.

We used limited financial support to engage 

key experts and ensure completion of all STMs, 

especially for rare ecosystems and ecosystems 

for which little research existed.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

In addition to helping modelers get started, working 

from a similar, existing STM aided efficiency by allowing users to focus on the quantitative differences between 

ecosystems (see lesson seven). For ecosystems with little 

research from which to develop STM parameters, starting 

with a related ecosystem’s STM and focusing on relative 

differences in those numbers was an effective strategy. The 

downside of starting with an existing STM is that, to reveal 

hidden bias, it becomes extremely important to question the 

existing model’s assumptions within the context of the new 

ecosystem. 

Lesson 7: Thoroughly Document Results

Documenting model results promotes later evaluation, 

understanding and application of the model set by identifying information sources, stating assumptions and identifying knowledge gaps. The use of LANDFIRE’s MTDB as a 

place to document this information promoted transparency 

in the modeling process, which supported scientific confidence in the STMs. This was particularly important in cases 

where there was disagreement between experts on model 

parameters and/or where there was little research from 

which to glean succession and disturbance rates. In addition 

to its use internally, documentation makes models more 

transferable and readily useable by others. When a modeler 

starts with an existing model (see lesson 6), and understands 

its assumptions, documentation of new model parameters 

can be facilitated by merely editing existing documentation. 

Lesson 8: Never Forget the Modeling 

Purpose

Modeling is generally undertaken to achieve a specific 

objective and this objective should help guide modeling 

decisions. The LANDFIRE project used its STMs primarily 

to estimate reference conditions and to assist with mapping 

vegetation and fuel spatial products. Keeping these goals 

in mind allowed us to focus on the required outputs and 

minimize issues that did not impact the results the project 

needed. For example, we found it was often difficult to 

quantify infrequent disturbances (with return intervals 

of 1,000 years or more such as severe insect outbreaks or 

weather events) without the use of Monte Carlo multipliers, 

a VDDT function not used in LANDFIRE STMs (see lesson 

five). Without the use of multipliers, disturbances with 

long return intervals occur in the model more frequently 

(because at every time step there is a probability of their 

occurrence) but at a lower intensity (i.e., the disturbance 

affects fewer pixels or landscape area) at any given time 

than would be expected by the real world event. For short 

duration simulations, the loss of variability could have a 

significant effect on the results but by running the STMs 

for a long time period (1,000 years) and, taking the average of the outputs for that period, the impact on the results 

needed by LANDFIRE was minimal so we could document 

our assumptions and move on without delay. However, this 

example illustrates the trade-offs between modeling rules 

set in place by the project for consistency and the ability to 

model some complex ecological phenomena. The modeling 

purpose impacts the modeling rules; it can help determine 

what to include and what to leave out of a model. 

Lesson 9: Set Realistic Modeling Goals

As a matter of practicality and philosophy, we recommend 

keeping STMs as simple as they can be while still meeting 

the project goals. Philosophically, modelers must remember 

that every model is an intentional simplification of reality, 

and that it is the modeler’s responsibility to decide how 

much simplification is appropriate to meet his or her objectives. Practically, modelers should remember that STMs 

must be parameterized and understood to be useful, and the 

more complex the STM, the more difficult both these tasks 

are. There are at least two levels of simplification that we 

recommend, what to model and how to model. 

To decide what to model, identify those things that are 

important to your project. For instance, in LANDFIRE, the 

significance of fire and fire regime was paramount, although 

not exclusive. When the list of BpS to be modeled was 

defined by the experts involved, we asked them to “lump” 

and “split” modeling units intelligently based upon LANDFIRE’s needs. If two vegetation systems were very similar 

ecologically and had very similar fire regimes, such as 

riparian types, the distinction between them was not critical 

to LANDFIRE, so we asked them to “lump” the two BpS 

into a single STM. If a BpS occurred in two variants that 
54

GENERAL TECHNICAL REPORT PNW-GTR-869

had significantly different fire regimes, such as Douglas-fir 

at different elevations or on different aspects, we asked the 

experts to “split” the system into two distinct STMs. Within 

the context of the modeling objectives, the simpler the 

model, the easier it is to maintain model quality. 

There are many reasons to keep the content and structure of each STM as simple as possible, such as parameter 

specification, model over-specification and model exploration. A vegetation STM is composed of states, transitions 

and transition parameters (frequency and destination at 

minimum). A STM with five states and five transitions 

has at least 50 potential parameters to specify, and each 

transition has five possible destinations. Imagine a second 

STM with 10 states and 10 transitions. This STM has 100 

or more potential parameters to specify, and each transition 

has 10 possible destinations that must be sorted out. Often 

the reference information or experience that is needed to 

specify all these model parameters is not substantial, and is 

spread very thin indeed for more complex models. It is also 

possible to over-specify a STM. Consider a system with two 

types of flooding disturbances: one has a return interval of 

50 years and the other’s is 500 years. It is highly likely that 

the second type of flooding disturbance may not add useful 

information to a STM being used in a 100-year planning 

process. Finally, it is much easier to understand and explore 

a simpler STM. By minimizing the number of states and 

transitions in a STM, errors are found and diagnosed more 

quickly, and the interpretation process is more thorough and 

efficient. 

Lesson 10: Model to Document Known 

Ecological Information and Identify Gaps 

in Understanding

The process of quantitatively modeling every mid-scale 

ecosystem in the United States helped us identify the many 

gaps in our collective ecological understanding. We found 

that ecosystems with commercial value such as ponderosa 

pine and longleaf pine forests tend to have more research 

associated with them, allowing for more robust estimates of 

succession and disturbance rates. In contrast, noncommercial and/or rare ecosystems such as California chaparral or 

Great Lakes alvar (limestone plains with sparse vegetation) 

have comparatively less information from which to build 

quantitative models. Our efforts highlighted research needs 

in many ecosystems. 

The modeling process was often as beneficial as the 

STM results. For example, the process allowed us to: 

• document what is known about ecosystems, 

• identify areas where information is lacking 

about ecosystems, 

• test assumptions about ecosystem function, 

• look at relative differences between ecosystems, 

• create a shared understanding about ecosystem function, and

• stimulate collaborative learning. 

These were valuable outcomes of the modeling process 

above and beyond the creation of STMs. 

The creation of a comprehensive, national STM library 

led by LANDFIRE in collaboration with hundreds of 

experts across the country represents a significant contribution to the understanding and synthesis of information 

related to pre-settlement ecosystems across the entire U.S. 

In addition to their use in understanding and setting reference conditions, the models can be adapted to represent current or desired conditions, to predict future conditions and/

or test land management strategies (sensu Low et al. 2010, 

Pohl et al. 2001, Shlisky et al. 2005, Shlisky and Vandendriesche 2012, Weisz et al. 2009). The LANDFIRE STMs 

combined with these lessons learned can serve as a solid 

foundation for future model development efforts related to 

land management and planning in the United States. 

Acknowledgments

This work was funded by LANDFIRE through Cooperative Agreement 10-CA-11132543-054. The authors would 

also like to thank the LANDFIRE regional modeling leads 

who facilitated model development (Mike Babler, Elena 

Contreras, Keith Fisher, John Foster, Susan Hickey, Darren 

Johnson, Maria Melnechuk, Kelly Pohl, Louis Provencher, 

Colleen Ryan, Christopher Szell and Douglas Zollner) and 

the more than 700 contributors who created or reviewed 

models for LANDFIRE. 
55

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

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Authors

Miles A. Hemstrom is a research ecologist (retired), 

Forestry Sciences Laboratory, 620 SW Main St. Portland, 

OR 97205. He is now a faculty research associate, Institute 

for Natural Resources, P.O. Box 751, Oregon State University, Portland, OR, 97207, miles.hemstrom@oregonstate.

edu. Janine Salwasser is project coordinator, Institute for 

Natural Resources, Oregon State University 210 Strand 

Agricultural Hall, Corvallis, OR 97331-2208, Janine.salwasser@oregonstate.edu. Joshua Halofsky is a landscape 

ecologist, Washington Department of Natural Resources, 

PO Box 47000, 1111 Washington Street SE, Olympia, WA 

98504-7000, joshua.halofsky@dnr.wa.gov. Jimmy Kagan

is director of the Oregon Biodiversity Information Center, 

Institute for Natural Resources, Portland State University, 

P.O. Box 751, Portland, OR 97207-0751, Jimmy.kagan@

oregonstate.edu. Cyndi Comfort is a former research 

assistant, Institute for Natural Resources, Oregon State 

University 210 Strand Agricultural Hall, Corvallis, OR 

97331-2208, Cyndi.comfort@oregonstate.edu.

Abstract

The Integrated Landscape Assessment Project (ILAP) is a 

three-year effort that produces information, models, data, 

and tools to help land managers, policymakers, and others 

examine mid- to broad-scale (e.g., watersheds to states and 

larger areas) prioritization of land management actions, perform landscape assessments, and estimate potential effects 

of management actions for planning and other purposes. 

ILAP provides wall-to-wall, cross-ownership geospatial 

data and maps on existing, potential and future vegetation 

conditions, land ownership and management allocation 

classes, and other landscape attributes. State and transition 

models integrate vegetation development, management 

actions, natural disturbances, and climate change to allow 

users to examine the mid- and long-term effects of alternative management, disturbance, and climate scenarios. Stateand-transition model (STM) outputs are used to produce 

information on many landscape characteristics, including 

vegetation conditions, disturbance regimes, fuel conditions, 

wildlife habitats, and economic values of natural resourcerelated products in Arizona, New Mexico, Oregon, and 

Washington. The project consists of science delivery (e.g., 

state and transition models, spatial data) and knowledge discovery (e.g., new linkages to wildlife habitat relations, fuel 

treatment economics, aboveground carbon pools, biomass, 

water supplies, and trends in wildfire and fuel conditions) 

that are integrated through decision support systems. The 

spatial data, state and transition models, model outputs, 

and interpretations cover all major upland vegetation types, 

including forests, woodlands, shrublands, grasslands, and 

deserts. To date, more than 50 GIS layers and 250 unique 

state and transition models have been produced across the 

4-state area (over 117 million hectares). ILAP data, models, 

and tools will be accessible through a Western Landscapes 

Explorer portal to be publicly launched in 2012 (INR and 

OSU Libraries 2012). Products from ILAP can be used by 

land managers, program managers, analysts, planners, and 

policymakers to evaluate management strategies that reduce 

wildfire risk, improve habitat, generate revenues, benefit 

rural communities, and inform restoration investment 

decisions. Because it allows for integration of many natural 

resource management objectives, ILAP facilitates collaborative landscape planning over very large areas. ILAP 

methods should be widely applicable for all lands.

Keywords: landscape assessment, science delivery, 

knowledge discovery, vegetation models, decision support.

Introduction

Fire suppression, vegetation management activities, grazing, climate change, and other factors produce constantly 

changing vegetation, fuel, and habitat conditions across 

millions of hectares in the western United States. In recent 

years, the size and number of large wildfires has grown, 

threatening lives, property, and ecosystem integrity. At the 

same time, habitat for species of concern is often becoming less suitable, the economic vitality of many natural 

The Integrated Landscape Assessment Project

Miles A. Hemstrom, Janine Salwasser, Joshua Halofsky, Jimmy Kagan, and Cyndi Comfort
58

GENERAL TECHNICAL REPORT PNW-GTR-869

resource-dependent human communities is declining, and 

resources available for natural resource management are 

tight. Techniques are needed to prioritize where natural 

resource management activities, such as fuel treatments, 

could be most effective and most likely to result in desirable 

conditions. Solutions driven by single resource concerns 

have proven problematic in most cases, since ecological 

and human systems are necessarily intertwined. More than 

$5.5 million of funding from the American Recovery and 

Reinvestment Act in 2009 provided the opportunity to hire 

a team of more than 50 technical experts to provide an 

integrated approach to assess landscape conditions and forecast potential future effects of alternative natural resource 

management strategies in Arizona, New Mexico, Oregon, 

and Washington. This paper summarizes the approach and 

methods used in the ILAP. Examples of applications can 

be found elsewhere (Creutzburg et al. 2012, Morzillo et al. 

2012, Shlisky et al. 2012, Zhou and Hemstrom 2012).

The Integrated Landscape Assessment Project (ILAP) 

produces databases, reports, maps, analyses, and other 

information showing mid- to broad-scale (thousands to 

millions of hectares) vegetation conditions and trends, key 

wildlife habitat conditions and trends, potential economic 

value of products that might be generated during vegetation 

management, and other critical information for all lands and 

all major upland vegetation types in Arizona, New Mexico, 

Oregon, and Washington (fig. 1).

ILAP integrates these landscape attributes into databases, reports, and maps that show a continuum of integrated priority areas (from high to low priority) considering 

a combination of vegetation trends, treatment costs, and 

likely effects of treatments and climate change impacts on 

key wildlife habitat, fuel conditions, and other landscape 

characteristics. ILAP gathers and consolidates key information and filled in data gaps across the 4-state area. The 

project packages and delivers knowledge in usable ways and 

allows for the development of new knowledge. In addition, 

ILAP is modular and allows updates or exchange of vegetation data sets, including incorporation of new plot data, 

resource interpretations, and other elements as knowledge 

improves.

ILAP relies on regional advisory groups—with representation from state and federal land management agencies, 

conservation organizations, and industry groups—for 

definition of goals and priorities. With their input, a set of 

resource management questions have been defined relating 

to all major upland ecological systems in the 4-state area:

1. What are the existing vegetation conditions across 

forests, woodlands, shrublands, grasslands, deserts, 

and other ecological systems?

2. What are the implications of vegetation and natural 

disturbance trends on key wildlife habitats, wildland fuel conditions, nonnative invasive plant 

species, and other landscape characteristics?

3. How might those trends play out in the future under 

alternative land management approaches and in the 

face of climate change?

While ILAP models and data cover all major upland 

vegetation types in the 4-state area, the project also works 

with collaborative groups involved with restoration decisionmaking in focus landscape areas to demonstrate utility 

and refine the landscape analysis process. At present, focus 

areas include the Tapash Sustainable Forest Collaborative in 

Figure 1—Integrated Landscape Assessment study area.
59

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

central Washington and the Sky Islands/FireScape group in 

southeastern Arizona (fig. 2). While management questions 

are specific to each focus area, example questions include:

1. What general kinds of treatments might produce the 

desirable combinations of fuel reductions and wildlife habitat conditions? Is it possible to slow or stop 

the upward spiral of fire suppression costs through 

fuel treatments? What are the economic costs of 

such an approach?

2. How likely are fuel treatments to generate valuable 

economic products? Can the treatments pay for 

themselves?

3. What areas and management regimes might be 

most likely to produce high combined potential to 

reduce critical fuels, improve or not degrade key 

wildlife habitat, and generate positive economic 

value?

Figure 2—The central Washington and Sky Islands landscape focus areas within the Integrated Landscape Assessment Project.
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GENERAL TECHNICAL REPORT PNW-GTR-869

4. How will projected climate scenarios affect future 

vegetation, habitat, and fuel conditions over the 

long term (100 years)?

Because ILAP is funded by the American Recovery 

and Reinvestment Act, the project will be completed in 

a relatively short time (about 3 years). Both the project 

timeframe and the size of the four state project area (>117 

million hectares) necessitate reliance on existing information (vegetation data, state and transition models, etc.) rather 

than development of extensive new information. The 4-state 

project area was selected because of the state and transition models and collaborations that were already in place 

between Region 3 of the USDA Forest Service (based in 

Albuquerque, New Mexico), Region 6 of the Forest Service, 

and the Forest Service PNW Research Station (based in 

Portland, Oregon). Because ILAP aims to be an “all-lands” 

approach, new data sets and models are developed to fill 

data gaps or where models do exist, as is the case for much 

of the arid land. Existing data and models are refined as new 

insights into ecological interactions, natural resource conditions and trends, potential climate change effects, economic 

and social interactions, and other topics become apparent.

Organization

The project is a collaborative effort and incorporates 

expertise from several institutions and disciplines (fig. 3). 

An oversight team, composed of representatives from the 

funding agency and major collaborators (Institute for Natural Resources, Oregon State University College of Forestry, 

USDA-FS PNW Research Station, and USDA-FS Region 3) 

provides overall direction at monthly meetings. Two groups 

of project advisors, one from Oregon and Washington and 

one from Arizona and New Mexico, connect the project 

goals, objectives, and products to state agencies, federal 

Figure 3—Organization of science delivery and knowledge discovery partners in the Integrated Landscape Assessment Project.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

agencies, non-profit organizations, private contractors and 

industries, universities, and the interested public by providing comment, feedback, and review at twice-yearly working 

sessions. The project lead scientist and project coordinator 

oversee the technical and outreach aspects of project work. 

Science delivery, as a whole, is jointly led by scientists from 

the Institute for Natural Resources and the Washington 

Department of Natural Resources. Each science delivery 

module has a lead investigator and production team, as 

necessary. Knowledge discovery modules are led by several 

universities and nonprofit organizations and each module 

has a lead scientist and, as appropriate, a production team. 

User involvement is critical to establishing project priorities 

and developing useful products.

Science Delivery and Knowledge 

Discovery

The project is separated into two major components: science 

delivery and knowledge discovery. Science delivery teams 

generally work with existing methodologies to develop landscape-level information (primarily relating to vegetation 

conditions) while the knowledge discovery teams develop 

and apply new methodologies to develop, project, and 

integrate associated landscape-level information on wildlife 

habitat, fuel conditions, treatment economics, community 

impacts, and climate change impacts using STM simulation 

outputs. From a timing perspective, the science delivery 

teams produce the foundational data and model outputs that 

are then used by the knowledge discovery teams as inputs 

to their models and tools to enable a multidimensional 

approach to assess landscapes and inform management 

priorities for restoration over very large areas. The ILAP 

team is organized into 12 module teams: GIS, vegetation 

modeling, wildlife habitat, fire and fuels, fuel treatment 

economics, community economics, climate change and 

vegetation, climate change and watersheds, climate change 

and fire, EMDS decision support, optimized decision 

support, and data portal.

Science Delivery Modules

ILAP’s science delivery modules include the Geographic 

Information System (GIS), State and Transition Modeling 

(STM), and Ecosystem Management Decision Support 

(EMDS) modules. 

Geographic Information System Module

The GIS module provides spatial data to the other ILAP 

modules. These data include current and potential vegetation conditions, watershed boundaries, ownership 

categories, management activities, and others. The GIS 

team gathers data from various public and private sources, 

merges and appends it into seamless datasets, combines 

attribute data into consistent formats, creates detailed 

documentation, and provides data to the broader ILAP team 

and partners. Much effort focuses on standardizing datasets 

across administrative units and between modeling regions. 

Data is delivered in raster/grid and polygon formats. The 

GIS team uses a long-term data management process to 

facilitate the incorporation of any data updates or use of 

new and improved datasets, as well as maintenance of 

original datasets. GIS data on current and potential natural 

vegetation are developed by using imputation methods and 

geo-referenced plot data from various sources. Much of the 

plot data, especially for forested vegetation types, are from 

permanent inventory plots on federal lands, such as the 

Forest Inventory and Analysis (FIA) program (USDA FS 

2012a). A significant plot gathering and database compilation effort in the Southwest is under sub-contract with 

Ecosystem Management, Inc. (EMI) and Natural Heritage 

New Mexico at the University of New Mexico. Local field 

offices of many federal agencies (USFS, NRCS, BLM, 

etc.) are visited to collect plot data. Often times, these plot 

data are not in a digital form or georeferenced, so efforts 

are made to select the highest priority data to digitize 

and compile. Existing or current vegetation and potential 

vegetation types (PVTs) are mapped using gradient nearest 

neighbor imputation (GNN; Ohmann and Gregory 2002) for 

forested vegetation and a combination of GNN and random 

forest nearest neighbor imputation (RFNN; Breiman et 

al. 2006) for arid lands, both of which rely on a combination of remotely sensed information and other geographic 

data. The resulting spatial data are 30 m grids that contain 

information on key attributes of existing vegetation and an 

assignment of potential vegetation types (PVT) in Oregon 
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and Washington and potential natural vegetation types 

(PNVT; PVT for simplicity) in Arizona and New Mexico. 

The vegetation data cover all major vegetation types across 

all wildlands (forests, woodlands, shrublands, grasslands, 

and desert). Riparian areas, minor upland types, urban, 

agricultural and other developed areas are excluded.

State and Transition Modeling Module

The state and transition modeling (STM) module collects, 

assembles, and builds models for forest and arid land 

vegetation types. Using input datasets from the GIS module 

(existing vegetation cover and structure, potential vegetation, ownership and management data layers, and watershed 

boundaries), the STM module projects future landscape 

conditions for all major upland vegetation types using a “no 

management” scenario (no management other than continued wildfire suppression on all lands and continued grazing 

in arid lands) and for the landscape focus areas according to 

a few example alternative management scenarios.

ILAP builds on STMs currently used by various 

organizations for federal land management planning, 

restoration planning, and ecoregional assessments in the 

4 state area and elsewhere (e.g., Forbis et al. 2006, Hann 

et al. 1997, Hemstrom et al. 2007, Holsinger et al. 2006, 

Merzenich and Frid 2005, Weisz et al. 2009). The STM 

approach treats vegetation as states, with each state defined 

as a combination of cover type and structural stage within 

potential vegetation types. Transitions among states 

represent natural disturbances, management actions, and 

vegetation growth and development. At present, STMs are 

implemented in the Vegetation Development Dynamics 

Tool (VDDT) (ESSA Technologies Ltd. 2012) and run using 

the Path Landscape Modeling Framework (Apex Resource 

Management Solutions Ltd. 2012). STMs are adapted from 

existing models available from the USDA Forest Service, 

Pacific Northwest and Southwest Regions, The Nature 

Conservancy, and LANDFIRE (LANDFIRE 2012). In some 

cases, STMs consistent with project methods are not available and new models are constructed using similar existing 

models as templates. STMs are developed for each PVT 

within each modeling region (fig. 4), resulting in 124 STMs 

for Oregon and Washington and 90 STMs for Arizona and 

New Mexico. Transitions are developed from a combination of expert opinion, available literature, and empirical 

data analysis (e.g., the Monitoring Trends in Burn Severity 

data—for wildfire probabilities; MTBS 2011). Transitions 

include all major natural disturbances, including wildfire 

(low, mixed, and high severity), insect outbreaks, wind 

disturbance, drought mortality, and others as appropriate to 

the ecological system being modeled, as well as a variety of 

management activities.

The 4-state area is stratified by combinations of land 

ownership, land allocation classes, and potential vegetation 

types within modeling regions. Fifth-code (Hydrologic 

Unit Code; HUC; USGS 2012) watersheds were used to 

further stratify results to improve spatial resolution. HUC 

boundaries within a modeling region do not affect ecological relationships in the models but allow modelers to better 

target management treatments to relatively small areas (e.g. 

1000s of hectares). STM are run on each modeling region, 

PVT, land ownership/allocation, and HUC stratum. Alternative land management scenarios are generated by changing 

assumptions about vegetation management treatments 

and rates by modeling region, PVT, land ownership, land 

use allocation and HUC. Resulting forecasts of vegetation 

conditions, management activities, and natural disturbances 

are linked to wildlife habitat characteristics, economic 

values, and other important conditions (Barbour et al. 

2007, Hemstrom et al. 2007, Reeves et al. 2006, Wales et 

al. 2007). In this fashion, model simulation results forecast 

potential future amounts and distributions of important 

landscape characteristics at the scale of modeled strata 

without implying pixel or stand-level accuracy.

Ecosystem Management Decision Support 

(EMDS) Module

The Ecosystem Management Decisions Support (EMDS) 

module integrates the separate factors of vegetation, fuels, 

wildlife habitat, and economic conditions into a combined, 

flexible assessment and prioritization process. Likely future 

trends are included in the prioritization process along with 

important ancillary data (e.g. wildland-urban interface 

boundaries, roads, key watershed delineation, etc.). The 

EMDS tool helps managers and others explore and set 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

priorities using maps, tables, and reports based on different 

combinations of characteristics that best reflect their values.

Knowledge Discovery Modules

ILAP’s knowledge discovery modules include fire and fuel 

characterization, wildlife habitat, fuel treatment economics, 

community economics, climate change, and an optimized 

decision support system.

Fire and Fuel Characterization Module

The fire and fuel characterization module evaluates potential future fire hazard, focusing on how land management 

and natural disturbances might affect fuels and answering 

question such as: How do fuel characteristics vary across 

the four states? How might different forest management 

scenarios affect fuel conditions and fire hazard across landscapes? To what extent can fuel treatment programs reduce 

fire hazards over the long term? Fuel beds (descriptions 

of burnable biomass extending from the forest floor to the 

canopy) have been built for each vegetation state in forested 

STM using inventory and other plots classified into each 

state class. Resulting fuel beds allow users to assess current 

conditions and trends in fuels and potential fire behavior. 

Deliverable module products include: (1) characterized 

fuel properties from inventory plots, describing a range of 

conditions in each STM state class and (2) characterization 

of fire hazard for each land ownership and PVT stratum by 

watershed, including both qualitative characterization (fire 

Figure 4—Modeling regions used in the Integrated Landscape Assessment Project.
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GENERAL TECHNICAL REPORT PNW-GTR-869

potential on a scale of 0 to 9) and quantitative characterization (output such as simulated fire flame length, rate of 

spread, and crown fire potential). One application of this 

module’s outputs is to assess the likelihood of crown fire 

given different management approaches.

Fuel Treatment Economics Module

The fuel treatment economics module assesses the financial feasibility of proposed forest vegetation management 

treatments. This module estimates potential supplies of 

timber and biomass (by diameter classes and tree species 

groups) and aboveground, tree-based carbon pools by STM 

state class for forested lands in Oregon and Washington. In 

addition, methods and data from this module allow users to 

conduct financial analyses that compare alternative vegetation treatment scenarios. Methods are tested by comparing 

a base “no management” scenario to a hypothetical restoration scenario in the central Washington landscape area. This 

example uses STM simulation outputs of removed products 

from proposed treatments over time to develop cost-benefit 

analyses. It considers harvesting cost associated with each 

treatment using a fuel reduction cost simulator (FRCS) 

(USDA FS 2012b), transportation cost to the desired mills, 

products prices, and other economic factors. Deliverable 

products include: (1) data and methods for examining 

available biomass and timber across all forested lands in 

Oregon and Washington, (2) methods and data for examining potential timber and biomass removals associated with 

a wide variety of alternative management scenarios for forested lands in Oregon and Washington, and (3) an example 

analysis of economic attribute trends and variability for a 

no management and an alternative restoration management 

scenario in the central Washington landscape area, and 

(4) documentation of modeling methods and results. The 

outputs of this module can help land managers and others 

evaluate prospective areas for timber and forest product 

extraction and assess watersheds where forest management 

treatments may have the largest economic potential in terms 

of revenue and jobs for communities or where those products may help offset the costs of management treatments.

Wildlife Habitats Module

The wildlife habitat module generates look-up tables for 

STM state classes to estimate potential habitat area for 

more than 50 species and habitats in Arizona, New Mexico, 

Oregon, and Washington. The module develops databases 

that allow users to derive aggregate area of vegetation 

composition for each modeling region, PVT, ownershipland allocation, and watershed stratum and match this 

information to species-habitat relationships to determine 

potential aggregate habitat area within each stratum. 

Deliverable products include: (1) a list of focal specieshabitat relationships (Oregon and Washington) or habitats of 

interest (Arizona and New Mexico), (2) example generation 

of wildlife habitat analyses based on STM outputs for a 

no-management scenario and a restoration management 

scenario in the central Washington landscape area, and (3) 

written documentation of methods, results, and findings. 

The outputs of this module provide land managers and planners with an ability to evaluate how specific habitats may 

be impacted by various land management decisions and 

proposed policies across modeled lands in the 4-state area.

Community Economics Module

The community economics module addresses the question 

of to what degree can large-scale forest vegetation treatment 

programs support economic activity and contribute to wellbeing in communities that have been negatively impacted 

by recent federal forest policy changes. Essentially, this 

module asks: How would priorities for fuel treatment 

areas be affected by including community well-being as a 

criterion for treatment prioritization, along with fire hazard 

reduction and wildlife habitat quality? Communities are 

assigned an “Impact Score” based on their level of socioeconomic distress, their ability to utilize harvested forest 

materials, and whether they have been impacted by changes 

in federal forest policy, such as the Northwest Forest Plan. 

One application of this module’s output is to help describe 

the potential for fuel treatments to produce economic 

benefits to nearby communities for the forested landscapes 

in Arizona, New Mexico, Oregon, and Washington.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Climate Change

The three ILAP climate change modules address potential 

climate change impacts on vegetation, watershed conditions, 

and fire probabilities. Together, these modules evaluate 

potential future effects that climate change might have on: 

(1) mid- to broad-scale vegetation conditions and wildfire 

regimes, (2) hydrology at the watershed scale, and (3) local, 

stand-scale vegetation and wildfire interactions.

Climate change and vegetation module—

The climate change and vegetation module provides estimates of potential future climate change on major vegetation types and wildfire conditions in study areas in central 

Oregon and eastern Arizona (fig. 5). In normal usage, the 

areal extent of potential vegetation types and associated 

STM remain constant over time (Kerns et al. 2012). In the 

future, however, climate change is expected to alter the 

mix and distribution of PVTs. This module builds “megamodels” in which many individual STMs are combined. 

Landscape area can move among PVTs over time as a 

function of changes in climatic conditions and wildfire. The 

module gathers vegetation change and wildfire trend data 

from simulations of three global climate model and emissions scenarios (MIROC-A2, CSIRO-A2, and Hadley-A2) 

run with the MC1 dynamic vegetation model (Bachelet et 

al. 2001). That information is used to build new transitions 

that cross PVT boundaries in response to vegetation and 

wildfire trends from MC1 (fig. 6). Potential future wildfire 

trends under different climate change scenarios are also 

included under the assumption that wildfire will be a major 

contributor to climate change effects. The resulting models 

will allow users to answer questions such as: How might the 

forests and arid lands in the study areas change in the future 

given the three different climate scenarios? What kinds of 

management activities might exacerbate climate change 

effects on vegetation conditions, natural disturbances, and 

associated resource values? Conversely, are there suites of 

management activities that might foster relatively resilient 

vegetation communities? Module products will include a set 

of “climatized” STMs for the two study areas, simulations 

of the three climate scenarios at 4 km grid scale for all of 

the modeling regions in the four state area, GIS tools to 

extract hydrography and other data from dynamic global 

vegetation model output, and methods that can be used to 

construct similar models in other areas.

Climate change and watersheds module—

The climate change and watersheds module applies and 

enhances the NetMap system (Earth Systems Institute 

Figure 5—The central Oregon and eastern Arizona climate change 

prototype area within the Integrated Landscape Assessment 

Project.
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GENERAL TECHNICAL REPORT PNW-GTR-869

2012) to generate estimates of erosion hazards, in-channel 

habitat conditions, and other important watershed characteristics for all 5th code hydrologic unit (U.S. Geological 

Survey 2012) watersheds that contain national forest lands 

in Oregon and Washington. Based on a computer model 

of stream systems, different inputs such as bank slope and 

sediment erosion potential are used to create maps of priority restoration areas. It also provides estimates of potential 

future changes in watershed condition under climate change 

by analyzing the likely effects of changes in precipitation 

amount and seasonality along with changes to wildfire and 

vegetation conditions that might result from alternative 

climate change scenarios. NetMap is free downloadable 

software that can be overlaid on Google Earth to help 

managers identify areas where changes are most likely, and 

where restoration activities may be most effective.

Climate change and fire probabilities module—

The climate change and fire probabilities module provides 

refined insight at the stand scale into the variation of 

wildfire probabilities and vegetation dynamics with climate 

change in a prototype area of the upper Deschutes sub-basin 

in Oregon. This module uses the FireBGCv2 computer 

model (USDA FS 2012c) to generate data and maps of fire 

ignition, spread, frequency and severity and the resulting 

shifts in vegetation arrangement and distribution. In the 

future, results from the FireBGCv2 and similar models will 

help to inform STMs by characterizing vegetation change 

and altered fire regimes under a range of potential climate 

change conditions.

Optimized Decision Support Module

The optimized decision support module integrates fuels, 

wildlife habitat, and economic conditions into a spatiallybased analytical decision support process. Input criteria, 

such as preserving habitat for a particular wildlife species, 

generating revenue from forest products, or encouraging 

desirable future forest conditions will be variously weighted 

in the optimization process. The analysis procedure is 

Figure 6—Conceptual process for connecting a Dynamic Global Vegetation Model (MC1) to STM.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

computationally intensive and often limits the size of 

the landscapes that can be analyzed using optimization 

methods. This module will explore the geographic limits of 

optimization methods and provide a spatial representation 

of the integrated landscape components.

Forecasting Future Conditions Process

While many users are satisfied w ith information on current 

landscape conditions contained in various ILAP data sets, 

ILAP STM can be used for examining likely future outcomes of alternative management strategies. For example, 

land management planners may use STM to see if a 

proposed set of management activities are likely to produce 

desired conditions. Alternatively, resource managers can 

use a gaming approach to generate management approaches 

that provide an acceptable mix of natural resource conditions and values. This scenario forecasting process requires 

several steps (fig. 7).

• Assemble base data needed to run state and transition models. This involves aggregating and crosswalking existing vegetation, potential vegetation 

types, watershed boundary, and land ownership/

allocation data into the strata used for modeling. 

Cross-walking and aggregation are not trivial tasks 

given the potential variety of data that may be useful. Aggregation of finer-scale grid or polygon data 

to coarser modeling strata reduces spatial detail, but 

often improves data compatibility. Spatial detail is 

critically dependent on the scale and accuracy of 

input data. It is important to understand the 

limitations of input data.

Figure 7—Process flow for landscape analysis and prioritization in the Integrated Landscape Assessment Project.
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GENERAL TECHNICAL REPORT PNW-GTR-869

• Generate management scenarios. Users are consulted about the issues with which they are most 

concerned. Most often, this process results in a 

series of natural resource management and socioeconomic questions that relate to one another. 

These questions must be translated into assumptions about vegetation management treatment 

types and rates, differences among the vegetation 

and management objectives of different land 

owners and on different land allocations, and the 

kinds of resulting landscape conditions of interest. 

The combination of assumptions and desired 

products is a management scenario. Developing a 

management scenario is not a trivial step and 

often takes several in-depth discussions with users 

to clarify their intent and interests.

• Run STM simulations for many decades or longer 

with management scenarios of interest. ILAP 

typically runs models for 50 to 100 years or more. 

Simulations usually include 30 Monte Carlo 

sequences of randomly drawn wildfire years and 

insect outbreaks as well as the prescribed management activities.

• Analyze model output and link to interpretations 

of fuel conditions, wildlife habitat, economic 

values, and other forecast landscape characteristics. The modeling process generates output by 

year, state class, and transition. In essence, every 

state class and every transition is output for every 

year. Output data are linked by look-up tables to 

the combinations of state classes (area by year by 

model stratum) and transitions (area by year by 

model stratum) that are interpretations of key 

wildlife habitats, fuel conditions, economic values, 

and other landscape characteristics.

• Integrate and prioritize. Separate analyses of fuels, 

wildlife habitats, and economics provide useful 

information for examining mid- and broad-scale 

trends, but the real power of the project comes 

through integrating these separate factors into a 

combined, flexible prioritization process. ILAP uses 

decisions support systems to help managers and 

others interact with combinations of characteristics 

that best answer their particular questions. Outputs 

are maps, tables, databases, and reports.

• Product delivery. ILAP products range from relatively simple geographic and other data sets to integrated landscape analyses, to white papers and 

science journal articles. ILAP products are 

accessible from the Western Landscapes Explorer 

portal (INR and OSU Libraries 2012). Most basic 

geographic data and many landscape analysis 

reports will be freely available. Some data sets and 

analyses may be available for only limited distribution if they contain data that users deem sensitive.

Conclusion

ILAP creates a variety of analytical and graphical tools 

that will help land managers and planners integrate and 

prioritize management activities. The project’s publications, 

models, maps, data, and tools will be available online and 

archived so that scientists and managers in years to come 

will be able to use and build on the project’s products. 

The project will also create a web-enabled decision support system if time and resources permit. Land managers, 

planners, analysts, scientists, policymakers, and large-area 

landowners can use the project’s tools and information for 

many applications including, but not limited to:

• Watershed restoration strategies

• Land management planning

• Statewide assessments and bioregional plans

• National forest plan revisions

ILAP data and models are currently in use by the 

Okanogan-Wenatchee and Colville National Forests to support current forest plan revisions. Forest planning analysts 

are running the ILAP models with help from the ILAP 

team and the Forest Service regional planning analyst. 

Other national forests in the Pacific Northwest (Deschutes, 

Ochoco, and Fremont-Winema) are using the ILAP data 

and models to support wildlife species viability analyses. 

National forests in the southwest are using local versions of 

ILAP models for forest plan revisions. Several other USDA 

Forest Service regions use STMs for land management 

planning and assessments and those regions could be easily 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

included in ILAP. Remaining Forest Service regions may 

not have STMs except for LANDFIRE models, but more 

detailed STMs suitable for planning and assessments could 

be developed rather quickly. In addition, the USDA Natural 

Resources Conservation Service and USDI Bureau of Land 

Management often use STMs in ecological site descriptions. 

The ILAP process could be applied to natural resource 

management issues in any ecological system as long as that 

system can be described in state and transition terms.

As the first phase of ILAP concludes, a strong foundation of landscape-level data, STMs, tools and expertise has 

been built that can be efficiently applied to other landscapes 

in the West. The goal is to have complete Western coverage 

of ILAP data and STMs to support regional and statewide 

issues of importance by groups such as the Western Governors’ Association, Western Forestry Leadership Coalition, 

and Landscape Conservation Cooperatives.

Acknowledgments

The Integrated Landscape Assessment Project was funded 

by the American Recovery and Reinvestment Act, the 

USDA Forest Service PNW Research Station, the USDA 

Forest Service Pacific Northwest Region, and the USDA 

Forest Service Southwest Region. The Western Wildland 

Environmental Threat Assessment Center provided partial 

funding for the climate change module. The USDA Forest Service Washington Office and USDA Forest Service 

Intermountain Research Station provided project advice 

and review. The USDI Bureau of Land Management in 

Arizona, New Mexico, and Oregon/Washington contributed plot data and other valuable information. Several 

state agencies, including the Arizona Division of Forestry, 

Arizona Department of Fish and Wildlife, New Mexico 

Division of Forestry, Oregon Department of Forestry, and 

Washington Department of Natural Resources provided 

data, input on project priorities, and review of products. The 

Nature Conservancy in Arizona, New Mexico, Oregon, and 

Washington provided data and review of products. Oregon 

State University, the University of Arizona, University of 

New Mexico, and University of Washington provided data, 

product review, contracted work on science delivery and 

knowledge discovery, and other valuable input. Ecosystem 

Management Inc. contracted for plot data, wildlife habitat 

information, and other data in the southwest.

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2003 Symposium Portland, OR: U.S. Department of 

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M.A. 2012. Balancing feasibility and precision of 

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Authors

Megan Creutzburg is a research ecologist, Institute for 

Natural Resources, Oregon State University, PO Box 751, 

Portland, OR, 97207-0751, megan.creutzburg@oregonstate.

edu. Joshua Halofsky is a landscape ecologist, Washington 

State Department of Natural Resources, 1111 Washington 

St. SE, Olympia, WA, 98504-7016, joshua.halofsky@

dnr.wa.gov. Miles A. Hemstrom is a research ecologist 

(retired), Forestry Sciences Laboratory, 620 SW Main St. 

Portland, OR 97205. He is now a faculty research associate, Institute for Natural Resources, P.O. Box 751, Oregon 

State University, Portland, OR, 97207, miles.hemstrom@

oregonstate.edu.

Abstract 

Many threats are jeopardizing the sagebrush steppe of the 

Columbia Basin, including the spread of invasive species 

such as cheatgrass (Bromus tectorum L.) and the expansion 

of western juniper (Juniperus occidentalis Hook.) into 

historic shrub steppe. Native sagebrush steppe provides 

productive grazing lands and important habitat for many 

wildlife species, and managers are in need of landscapescale tools to assess shrub steppe conversion risk and 

management options to maintain native shrub steppe. We 

used a state-and-transition modeling approach to project 

changes in sagebrush steppe vegetation across the landscape 

of southeastern Oregon. Models were constructed using 

both empirical data, including empirically derived fire 

probabilities, and expert opinion for processes that are still 

poorly documented, such as livestock grazing effects. With 

unrestricted grazing and no restoration treatments, future 

invasion by exotic annual grasses in warm, dry sagebrush 

steppe and juniper expansion into cool, moist sagebrush 

steppe are likely to accelerate in the next 50 years under 

current climatic conditions. Invasions are also likely to be 

Using State-and-Transition Models to Project 

Cheatgrass and Juniper Invasion in Southeastern 

Oregon Sagebrush Steppe

Megan K. Creutzburg, Joshua S. Halofsky, and Miles A. Hemstrom

spatially heterogeneous, depending on the mix of sagebrush 

steppe environments, current rangeland condition, disturbances, and management activities across the landscape. 

We conclude that state-and-transition models provide a useful framework for conceptualizing vegetation dynamics of 

sagebrush steppe systems, identifying gaps in knowledge, 

projecting future vegetation conditions, and identifying 

potential areas for restoration at landscape scales.

Keywords: Bromus tectorum, cheatgrass, invasive 

species, western juniper, Juniperus occidentalis, rangeland, 

sagebrush steppe, state-and-transition modeling.

Introduction 

Sagebrush steppe ecosystems across much of the West have 

experienced significant declines over the last few decades 

(Connelly et al. 2004, Hemstrom et al. 2002). Among the 

major threats, intensive livestock grazing, species invasions, 

altered fire regimes, development, and climate change are 

all thought to contribute to the decline of shrub steppe 

(DiTomaso 2000, Jones 2000, Mack 1981, Miller et al. 

2005). Rangelands provide an important source of forage 

for livestock, and degradation of shrub steppe may reduce 

the ability of rangelands to support livestock (Belsky 1996, 

Young and Clements 2009). Conversion of sagebrush steppe 

also occurs against a backdrop of increasing concern about 

loss of habitat for sagebrush-obligate species such as greater 

sage-grouse (Centrocercus urophasianus) (Connelly et al. 

2004). Restoration of degraded shrub steppe can be exceedingly difficult due to the complex and often unpredictable 

interaction of site potential, fire, climate, invasive species, 

and management practices such as grazing (Di-Tomaso 

2000, McIver and Starr 2001).

Exotic species invasions and native juniper expansion 

in particular have dramatically changed the landscape in 

eastern Oregon over the last century. Exotic annual grasses, 
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GENERAL TECHNICAL REPORT PNW-GTR-869

such as cheatgrass (Bromus tectorum L.), ventenata (Ventenata Koeler spp.), and medusahead (Taeniatherum caputmedusae (L.) Nevski), have invaded many warm-dry sites, 

and have changed the vegetation structure and fire regime 

by forming dense, dry grass stands and promoting frequent 

fire (Pellant 1996, Whisenant 1990). Another contemporary 

threat to shrub steppe ecosystems comes from expansion 

of western juniper beyond its historic range. Western 

juniper (Juniperus occidentalis Hook.) is native to eastern 

Oregon but has expanded rapidly in the past 130 years 

due to fire suppression, reduction of fuels from livestock 

grazing, changes in precipitation patterns, and other factors 

(Burkhardt and Tisdale 1976, Miller et al. 2005). Juniper 

trees can deplete soil water, alter species composition and 

biodiversity of shrub steppe, increase soil erosion, reduce 

stream flows, and reduce forage production for livestock 

(Miller et al. 2000, 2005). Because of these complex threats 

and the vast extent of shrub steppe ecosystems, there is a 

need for a broad, multi-ownership perspective to examine 

landscape-scale trends in vegetation and effects of rangeland management. 

One approach to examine vegetation dynamics, natural 

disturbances, and management across large areas is through 

the use of state-and-transition models (STMs). STMs are 

widely used in land management across both forest and 

rangeland landscapes (Forbis et al. 2006, Hemstrom et al. 

2004, Holmes et al. 2010, Weisz et al. 2010). The models 

provide a conceptual framework for understanding ecological dynamics (Bestelmeyer et al. 2009, Briske et al. 2006, 

Stringham et al. 2003, Westoby et al. 1989), and challenge 

ecologists to define their assumptions in terms of vegetation 

composition (states and phases) and processes that cause 

vegetation change (transitions). In the process of building 

STMs, existing literature and data can be explored and gaps 

in our knowledge and data are revealed as areas for future 

study. STMs allow the user to easily test alternative hypotheses about vegetation dynamics and change by evaluating 

different models, and allow managers to compare alternative management strategies in terms of desired outcomes. 

In this study, we construct a suite of detailed STMs designed 

to capture the contemporary dynamics of southeastern 

Oregon shrub steppe ecosystems, and use them to project 

vegetation change 50 years into the future. 

We focus on two major sagebrush steppe ecosystems of 

southeastern Oregon. The most common sagebrush sites in 

southeast Oregon are warm, dry lowland sites (called warmdry sites) primarily occupied by Wyoming big sagebrush 

(Artemisia tridentata Nutt. ssp. wyomingensis Beetle and 

Young), basin big sagebrush (Artemisia tridentata Nutt. ssp. 

tridentata), bluebunch wheatgrass (Pseudoregeneria spicata

(Pursh) A. Löve), Thurber needlegrass (Achnatherum 

thurberianum (Piper) Barkworth), and needle-and-thread 

(Hesperostipa comata (Trin. and Rupr.) Barkworth). In 

these areas, exotic annual grasses have invaded many sites 

and partially or wholly converted the shrub steppe to exotic 

grass. The second major sagebrush system is characterized by cool, moist upland sites (called cool-moist sites) 

primarily occupied by mountain big sagebrush (Artemisia 

tridentata Nutt. ssp. vaseyana (Rydb.) Beetle), low sagebrush (Artemisia arbuscula Nutt.), Idaho fescue (Festuca 

idahoensis Elmer), and bluebunch wheatgrass. Cool-moist 

sagebrush sites are more productive and less susceptible to 

invasion by exotic grasses, but many are rapidly converting into woodlands as western juniper expands its range. 

As part of the Integrated Landscape Assessment Project 

(ILAP), we project changes in sagebrush steppe vegetation 

under unrestricted livestock grazing and no restoration 

treatments, and focus on cheatgrass and juniper invasion as 

indicators of contemporary landscape change.

Methods

Study Area 

We modeled sagebrush steppe vegetation types across a 5.3 

million hectare (13.2 million acre) portion of southeastern 

Oregon, bounded by the Blue Mountains to the north and 

the foothills of the Cascade Mountains to the west (fig. 1). 

This area roughly corresponds to the Malheur High Plateau, 

Humboldt Area, and Owyhee High Plateau Major Land 

Resource Areas (MLRA) that are contained within the 

state of Oregon. The study area was comprised primarily 

of warm-dry sagebrush sites (59.8 percent) and cool-moist 

sagebrush sites (18.8 percent), with salt desert shrub, 

woodlands, playas, and other minor systems comprising 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

the remaining undeveloped landscape. The Bureau of Land 

Management (BLM) was the primary land steward in the 

region, covering 72.7 percent, followed by private land and 

state agencies. 

Spatial Layers 

Three spatial layers were used to define our modeling units: 

potential vegetation type (PVT), ownership/allocation, 

and watershed. PVT described the vegetation potential 

of a site based on soils, climate, and disturbance regime. 

PVTs defined the spatial extent of each vegetation type 

as a modeling unit, and each STM simulated vegetation 

dynamics of alternative vegetation communities within a 

single PVT. We modeled five major PVTs, including two 

warm-dry types (Wyoming big sagebrush with and without 

juniper encroachment potential), and three cool-moist PVTs 

(mountain big sagebrush with juniper encroachment potential and low sagebrush with and without juniper encroachment potential) (fig. 1). Where plot data did not differentiate 

subspecies of big sagebrush, an elevation cutoff of 1200 m 

was applied, assuming that the mountain subspecies would 

occur above this elevation and the Wyoming subspecies 

would occur below this elevation. Potential vegetation was 

modeled using a random forest nearest neighbor imputation 

(RFNN) method (Crookston and Finley 2008, Ohmann 

and Gregory 2002), which related plant association data 

to grids of climate (PRISM climate group, Oregon State 

University) and topographic (National Elevation Dataset) 

environmental variables. Because projections of future 

vegetation condition must allow for expansion of juniper 

beyond its historic range, Wyoming big sagebrush and low 

sagebrush PVTs were divided into areas with and without 

potential for juniper encroachment in the PVT map using 

RFNN predictions of juniper cover. Mountain big sagebrush 

was considered susceptible to juniper invasion across its 

entire extent. All PVTs in this study were considered as 

sagebrush steppe potential, and thus any juniper presence 

was considered to be expansion beyond its historic range. 

The second spatial layer used to define modeling units was 

ownership/allocation, which categorized the landowner or 

Figure 1—The sagebrush steppe in southeastern Oregon is divided into two major site types and five potential vegetation types 

(PVTs). Blank (white) areas represent other PVTs not modeled for this study, barren areas, urban areas, or agriculture.
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GENERAL TECHNICAL REPORT PNW-GTR-869

land steward (BLM, Forest Service, private, tribal, state, 

and other) and management intent, ranked into five categories based on the intensity of intended use. For this study, 

the same STMs were run across all ownership/allocation 

levels, but the ownership/allocation layer will be used to 

inform varying grazing and restoration treatment levels for 

future studies. Third, we used 5th-field (10 digit) Hydrologic 

Unit Codes (HUCs) to define watersheds, downloaded from 

the United States Geological Survey (http://water.usgs.gov/

GIS/huc.html). The combination of PVT, ownership/allocation, and watershed thus provided the spatial basis of our 

modeling, allowing us to stratify our model output in terms 

of site characteristics, management intent, and hydrologic 

unit location. 

Additionally, we initialized our STMs with spatial 

maps of current vegetation. Current vegetation was modeled 

using a RFNN method. The mapping method was similar to 

PVT mapping, but in this case the RFNN method imputed 

field plot data to pixels using the association between field 

data (species composition and cover), grids of environmental data, and LANDSAT TM (thematic mapper) imagery 

from 2000. Vegetation communities in the current vegetation map were linked to states and phases in the STMs 

using a series of rule sets that allocated every pixel in the 

landscape into a state or phase. 

State-and-Transition Models (STMs) 

We constructed STMs using the Vegetation Dynamics 

Development Tool (VDDT) (ESSA Technologies 2007) to 

characterize vegetation dynamics of the major sagebrush 

ecosystems in southeastern Oregon and project future vegetation change. VDDT allows users to divide the landscape 

into distinct combinations of vegetation cover and structure 

(states and phases), linked together by processes (transitions) such as succession, disturbance, and management 

activities. Users define a pathway for each transition and 

its annual probability of occurring, and VDDT uses Monte 

Carlo simulations to project landscape change over time. 

VDDT is a non-spatial model, and tracks each simulation 

cell independently of neighboring cells. Simulations were 

run in the Path Landscape Model (Apex RMS and ESSA 

Technologies), which uses VDDT as a simulation engine but 

allows the user to run multiple STMs and scenarios (such 

as alternative management options) in a single landscape 

analysis. 

One STM was constructed for each PVT, describing alternative vegetation states and phases within each 

potential vegetation unit. STMs developed by the BLM for 

the Malheur High Plateau MLRA in southeastern Oregon 

(Evers 2010) and STMs built by the USDA Forest Service 

for the Blue Mountains of northeastern Oregon1 were used 

heavily to aid in constructing and parameterizing models. 

Conceptually, our sagebrush STMs can be divided into 

a few broad states (large boxes, fig. 2), with community 

phases (smaller boxes within states, fig. 2) describing varying combinations of cover and structure (Bestelmeyer et al. 

2009). Major states include shrub steppe, exotic grass, juniper woodland, juniper with exotic grass, and seeded grass. 

Semi-degraded phases represent disturbance-impacted 

vegetation that is recoverable to native conditions (dashed 

lines, fig. 2) but is at-risk of crossing a threshold to an alternative state. Note that each STM varies; not all states and 

phases in figure 2 are present in each model, and transition 

probabilities vary substantially among STMs, particularly 

between PVTs on warm-dry and cool-moist sites. 

Specific criteria were used to define the vegetation 

composition and structure of each state and phase within 

each STM. Herbaceous composition was used as an indicator of native, semi-degraded, or exotic-dominated condition. 

Exotic grass states were defined by a minimum absolute and 

relative cover of exotic annual grasses, primarily cheatgrass 

and other invasive bromes, ventenata, medusahead, vulpia 

(Vulpia bromoides (L.) Gray), and others. Native states 

were defined by a minimum absolute and relative cover of 

8 grass species sensitive to disturbance (decreasers such as 

bluebunch wheatgrass, Idaho fescue, needle- and-thread, 

some Achnatherum and Elymus species, and others). Cover 

1

 Personal communication: Dave Swanson, former area ecologist, USDA Forest Service, Baker City, OR. 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

thresholds are variable among models. In PVTs where seeding of nonnative species occurs, seeded states were defined 

by Agropyron species and others that are commonly seeded 

in rangelands. Anything that did not meet the minimum 

threshold for these indicator species was considered to be in 

a semi-degraded state. Depleted shrub was defined as high 

shrub cover (>25 percent) and low grass cover (<5 percent) 

and is only present in warm-dry sagebrush models. Juniper 

woodlands were divided into phases I, II and III based on 

Miller (2005), where phase I represented shrub steppe with 

scattered juniper, phase II represented codominant juniper 

and shrubs/grasses, and phase III indicated mature woodlands where juniper was dominant. Juniper cover classes 

of 2–10 percent, 11–20 percent and >20 percent were used 

to distinguish phases I, II and III, respectively, based on 

feedback from expert reviewers. 

Disturbance dynamics, including succession, natural 

disturbance (e.g., wildfire, drought, and insects) and management transitions (e.g., seeding, cutting, and prescribed 

fire) were modeled by specifying transition pathways 

between boxes and defining annual probabilities of growth 

or disturbance events occurring (table 1). Models were 

constructed so the primary mechanism for degradation 

(exotic grass invasion) into sagebrush steppe was through 

the interaction of grazing with fire or drought disturbance 

(Curtin 2002, Evers 2010, Loeser et al. 2007). In warm-dry 

sites, recovery of native species in exotic grass states was 

slow and required rest from grazing disturbance before it 

Figure 2—A conceptual state-and-transition model (STM) diagram of vegetation dynamics in the 

Wyoming big sagebrush with juniper potential vegetation type (PVT), showing major model states 

(large boxes), representative phases within states (small boxes), and transitions that link states and 

phases (arrows). Transitions include succession (S), fire (F), juniper establishment (J), grazing (G), 

insect outbreaks (I), drought (D), recovery (R), and management treatments (T). Dashed lines separate 

native from semi-degraded condition, and represent reversible thresholds where recovery to native 

condition is more likely. Transitions between states, however, are often irreversible without management intervention or major disturbance, and low probability recovery transitions are shown as thin 

arrows. Juniper phases II and III represent increasing juniper dominance (Miller et al. 2005), and phase 

I juniper woodlands are omitted for simplicity. Each individual STM varies and this figure represents a 

simplified model for a single PVT.
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GENERAL TECHNICAL REPORT PNW-GTR-869

would begin to occur. In cool-moist sites, recovery from 

exotic grass states was modeled to occur automatically 

unless it was heavily grazed, reflecting the higher competitive ability of native bunchgrasses in mesic sites. Fire 

probabilities varied among states and phases based on the 

cover of exotic grass species (see “Fire Probabilities”). 

Insect outbreaks and severe drought affected vegetation 

by thinning shrub cover, and juniper establishment events 

(where applicable) occurred from late-successional shrub 

steppe into phase I woodlands (Evers 2010). Management 

transitions were built into the models but were deactivated 

for this study to evaluate future landscape condition without 

active management. 

Fire Probabilities

We used the Monitoring Trends in Burn Severity (MTBS) 

data to derive fire probabilities and interannual variability 

in fire year (Eidenshink et al. 2007, www.mtbs.gov). The 

MTBS data set is a publicly-available, 25-year record of 

all fires >405 hectares (>1,000 acres) in size across the 

United States from 1984 to 2008. It includes fire perimeters 

and burn severity ratings for each fire occurring in the 

study area from 1984 to 2008. For this study, we used fire 

perimeters for ~250 fires to infer the proportion of the 

landscape in each PVT group burned annually. We overlaid 

the yearly maps of fire perimeters with a map of PVT 

groups (warm-dry and cool-moist) and exotic grass cover 

groups in a GIS. Exotic grass cover was derived from our 

current vegetation map, and divided into three groups: 0-10 

percent, representing places with little to no invasion; 10-25 

percent, representing areas that are semi-degraded; and 

>25 percent, where exotic grasses dominate the herbaceous 

layer. We extracted the landscape proportion burned and 

calculated annual fire probabilities for each combination of 

PVT group and exotic grass group (table 2), and assigned 

these probabilities to wildfire transitions in the STMs. Fire 

return intervals (FRIs) were calculated as the inverse of fire 

probability. 

Running Simulations 

Simulations were run in the Path model to project vegetation change 50 years into the future. One model was run for 

Table 1—Transitions used to model sagebrush steppe vegetation dynamics in southeastern Oregon

Transition Description

Replacement fire Wildfire that results in return to early-successional phases. Replacement fire is modeled in all

 states and phases.

Mosaic Fire Patchy fire that thins shrubs or trees. Mosaic fire occurs in most phases except those dominated 

 by exotics, closed or depleted shrub, and phase III juniper.

Surface fire Surface fire that burns the woodland understory (phase III juniper with exotic grass only).

Maintenance grazing Low-impact grazing that does not affect plant community composition or structure.

Moderate grazing Grazing that causes successional change by increasing shrub cover but is not severe enough to

 promote exotic grass invasion.

Graze degrade Heavy grazing that causes degradation from native to semi-degraded to exotic grass-dominated 

 shrub steppe. The transition probability is low, as we assume that the interaction of grazing 

 with disturbance (Post-disturbance graze degrade) is more likely to lead to degradation.

Post-disturbance Heavy grazing after major disturbance, leading to semi-degraded condition or exotic grassGraze degrade dominated states. This transition can only occur within two years following a fire or drought, 

 and the transition probability is 10-fold higher than Graze degrade.

Drought Moderate multi-year drought that does not cause vegetation change.

severe drought Drought severe enough to kill shrubs, causing a transition to early-successional shrub steppe. 

 This transition occurs only once every 100-200 years.

Natural regeneration Recovery of native herbaceous vegetation in a degraded site by natural regeneration. 

 This transition usually requires rest from grazing to occur.

Juniper establishment Juniper establishment that converts shrub steppe to phase I juniper.

Insect Cyclical outbreaks of sagebrush-defoliating insects, occurring once occur every 20-30

Outbreaks years.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Table 2—Annual fire probabilities and corresponding fire return intervals derived from Monitoring 

Trends in Burn Severity (MTBS) data for the two major sagebrush steppe site types and three levels 

of exotic grass cover. Numbers reflect fire return intervals under current levels of fire suppression

Site Type Exotic grass cover Annual probability Fire return interval

Cool-moist sagebrush steppe 0-10 percent 0.0068 148

10-25 percent 0.0089 112

>25 percent 0.0173 58

Warm-dry sagebrush steppe 0-10 percent 0.0063 160

10-25 percent 0.0114 88

>25 percent 0.0179 56

each modeling unit (combination of PVT, watershed, and 

ownership/allocation). Where modeling units were <405 

hectares (<1,000 acres) in size, a rule set was applied to 

combine small units within a watershed with other similar 

vegetation or management types. Where small units did not 

meet the criteria to combine with others, they were dropped 

from the analysis. The study area consisted of 889 modeling 

units in the major sagebrush steppe PVTs that were large 

enough to be retained for analysis, with <5 percent of the 

landscape not modeled due to small modeling unit size. 

Each STM was run for 30 Monte Carlo simulations with 

random draws of fire severity year and insect outbreak 

occurrence, and we reported average trends. 

Model Output 

To simplify results for graphical purposes, we combined 

states and phases into seven groups, including native, semidegraded, and exotic shrub steppe, exotic grass, and phase 

I, II, and III juniper. Seeded states were not included for 

simplicity. Although VDDT is a non-spatial model, current 

and future projections of exotic grass and juniper woodlands can be summarized and mapped back to our spatial 

modeling units. We summarized the percent of pixels 

within each modeling unit that contained exotic grass or 

juniper woodland states, and displayed a single value scaled 

between 0 (low invasion level) and 1 (high invasion). Exotic 

grass maps displayed all exotic grass phases, and juniper 

woodland maps depicted woodlands in phases II and III 

only, since phase I juniper is similar to sagebrush steppe.

Results

Current and Projected Future Conditions

Our imputed current vegetation conditions (2000) for 

warm-dry sites across the extent of the study area indicate 

that much of the sagebrush steppe (~70 percent) was semidegraded, with exotic grass encompassing ~15 percent of 

the landscape. In cool-moist sites, current vegetation maps 

show that half of the landscape was semi-degraded shrub 

steppe, one-third was native shrub steppe, and juniper 

encroachment affected <15 percent of the cool-moist shrub 

steppe. Where juniper had encroached it was still largely in 

phase I, with shrubs and grasses still dominant. 

STM projections to year 2050 indicate a decline in 

rangeland condition in both warm-dry and cool-moist 

sites, assuming unrestricted grazing and no restoration 

treatments (fig. 3). Much of the current semi-degraded 

sagebrush steppe is projected to convert to exotic grass, 

increasing to nearly half of the landscape in warm-dry sites. 

In cool-moist sites, model projections indicate an increase 

in juniper woodlands to more than half of the extent of 

cool-moist PVTs, with rapid expansion of phase I in the first 

25 years and conversion to phase II in the second half of 

the simulation. In both site types, native and semi-degraded 

shrub steppe decline as they are converted to exotic grass or 

juniper woodlands.

Invasion Maps 

STM projections suggest that much of the landscape is 

likely to convert to either exotic grass or juniper woodland, 
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GENERAL TECHNICAL REPORT PNW-GTR-869

but that the severity of invasion is highly variable across 

the landscape (fig. 4). We report results at the modeling unit 

level because the STMs are non-spatial, and thus pixel-bypixel projections are not possible. 

Figure 4—Exotic annual grass (top) and juniper woodlands (bottom) as mapped in 2000 (left) and projected for year 2050 using 

state-and-transition models (right). Colors depict the percent of each mapped modeling unit in an invaded state. Blank (white) areas 

were not modeled or represent PVTs where invasion cannot occur. 

Figure 3—Projected vegetation change from 2000-2050 for warm-dry sagebrush steppe (left) and cool- moist sagebrush steppe 

(right). Graphs show average modeled landscape proportion across 30 Monte Carlo simulations for southeastern Oregon. 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Discussion

Model projections indicate that much of the sagebrush 

steppe landscape of southeastern Oregon is likely to experience invasion by exotic grasses or juniper under a scenario 

of unrestricted grazing and no restoration treatments. Exotic 

grass in particular is projected to expand across a large 

extent of southeastern Oregon, whereas juniper is more 

limited by site potential in drier (warm-dry) sites. However, 

these projections represent a worst case scenario, and 

managing grazing (particularly by limiting grazing during 

and after major disturbances such as fire and drought) or 

implementing restoration treatments could result in much 

improved landscape condition. Invasion risk maps show that 

the projected level of exotic grass and juniper invasion is 

highly heterogeneous across the landscape of southeastern 

Oregon. This heterogeneity stems from varying susceptibility of each PVT to invasion and variation in current vegetation condition at the initialization of the model runs (year 

2000 conditions). Although the maps provide coarser-scale 

projections summarized at the modeling unit level instead 

of individual pixel-by-pixel predictions, they can nonetheless aid in large landscape-level assessment of rangeland 

condition and invasion risk, and provide a broader context 

for management decisions and prioritization across the 

study area. 

The trends in exotic grasses largely reflect grazing 

effects and the interaction between grazing and major 

disturbance (fire and drought). We assumed that heavy 

grazing in warm-dry sites leads to degradation by reducing 

the presence of native grasses while providing a competitive 

advantage to nonnative species, which is exacerbated under 

conditions of abiotic stress. Under heavy grazing, a feedback loop is created whereby grazing leads to more exotic 

grasses, which in turn leads to more frequent wildfires, and 

leads to an even greater exotic grass presence. Furthermore, 

once range condition has deteriorated to semi-degraded 

conditions, some disturbances even in the absence of 

grazing can lead to dominance by exotic grasses. Grazing 

also removes grasses in sites that are susceptible to juniper 

invasion, which provides greater opportunity for juniper 

establishment under existing shrubs and reduces fuel that 

would historically cause establishing juniper woodlands to 

periodically burn (Burkhardt and Tisdale 1976, Miller and 

Wigand 1994). Much of the landscape that was historically 

shrub steppe is now considered to be vulnerable to future 

juniper expansion (fig. 1), and our projections suggest rapid 

juniper expansion, as has been documented on many of 

these sites (Miller et al. 2005). 

STMs have been adopted by many land management 

agencies because of their useful characteristics for organizing ecological knowledge and informing management. They 

provide a relatively simple and intuitive modeling framework that managers can use as a mid or broad-scale land 

management tool. STMs can be used as conceptual models 

as well as predictive models, and they force ecologists to 

formalize their assumptions about landscape dynamics. 

They are easily incorporated into sensitivity analysis to 

test the importance of different processes under a certain 

set of assumptions, and can challenge and expand ideas 

about rangeland ecosystem dynamics and management. 

Constructing models can also be valuable for highlighting 

areas where little empirical data exists. We used a variety 

of data sources to construct our STMs, including empirical 

data to construct fire probabilities (Eidenshink et al. 2007) 

and drought frequencies (Knapp et al. 2004), published 

(Evers 2010) and unpublished STMs, and several experts 

to construct our models. The STM framework can readily 

accommodate new data and information as it becomes 

available to test our assumptions and understanding of 

sagebrush steppe ecosystems.

A novel aspect of our study was the inclusion of MTBS 

fire perimeter data to quantitatively derive fire return 

intervals for each site type and varying levels of exotic 

annual grass invasion (table 2). It was particularly important 

to capture the effects of exotic grass in our analysis, since 

exotic grasses can dramatically increase fire frequency and 

severity (Pellant 1996, Whisenant 1990) and fire probabilities are expected to vary among states and phases in 

the STMs. Our analysis assumes a similar level of exotic 

grass cover over the 25-year record, but is likely to be more 

robust to interannual variability in grass productivity and 

cover since we group exotic grass cover into three broad 
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GENERAL TECHNICAL REPORT PNW-GTR-869

categories. Consistent with previous studies, we detected an 

increase in fire with increasing exotic grass cover, although 

fire return intervals are not as frequent as some previous 

studies suggest (Evers 2010, Pellant 1996, Whisenant 

1990). Because the MTBS record has captured fires under 

a fire suppression policy, our model projections assume a 

fire suppression policy and effectiveness similar to that of 

recent decades. The MTBS data provides the most detailed 

spatial record of wildfire occurrences we are aware of, but 

it is likely that wildfires are underreported to some degree, 

particularly on nonfederal lands and earlier in the recorded 

history (1980s). Although the MTBS data set has several 

limitations, we maintain that the benefits of using over two 

decades of spatial, quantitative data outweigh the limitations of the data set. 

Although STMs have proven useful to many land 

managers and rangeland scientists, various drawbacks to the 

approach limit the interpretations we can make with STMs. 

Non-spatial STMs by nature cannot model spatial processes 

explicitly or incorporate fine-scale site variation, resulting 

in generalized predictions that can only be applied at mid 

to broad spatial scales. STMs are also not mechanistic, and 

rely upon the modeler to determine the effects of disturbance and management processes and how they cause state 

and phase change. Most STMs, including those presented 

here, rely at least in part on expert judgment to determine 

transition pathways and probabilities, and therefore each 

expert will likely build a slightly different model. Even 

where some data are available, it is generally not available across large landscapes, adding uncertainty about 

the effects of environmental heterogeneity on transition 

probabilities. Given these limitations, we frame STMs as 

working hypotheses that describe the state of the knowledge 

about each ecological system given various assumptions. 

They are designed to conceptualize and project vegetation 

dynamics across broad spatial scales, and should be coupled 

with field studies to refine local vegetation dynamics where 

possible. Lastly, our models do not address climate change 

effects, as our projections are relatively short-term (to year 

2050), but methodology is being developed as part of ILAP 

to incorporate climate change effects in our STMs (Kerns et 

al. 2012). 

In this study, we demonstrate the utility of STMs 

for evaluating the risk of sagebrush steppe conversion to 

exotic grass or juniper across the landscape of southeastern 

Oregon. Although we projected large increases in both 

exotic grasses and juniper, we only ran a worst-case scenario of no restoration treatments and unrestricted grazing. 

With the models and data available we can now begin to 

incorporate alternative management scenarios to address 

a range of questions such as: given a limited budget, what 

combination of fuel treatments, seeding, grazing levels, and/

or juniper control could maintain or improve current levels 

of good condition sagebrush? Where should we prioritize 

such treatments? How do our projections relate to habitat 

for species such as sage-grouse? How might our answers 

differ under a changing climate? The resulting projections 

and maps of model output can be useful to public and 

private land managers in answering important management 

questions and providing a broader context for landscape 

treatments and restoration. 

Acknowledgments 

Funding for the Integrated Landscape Assessment Project 

was provided by the American Recovery and Reinvestment Act. Dave Swanson (USFS) and Louisa Evers (BLM) 

created the base models upon which ILAP models were 

based; many thanks to both of them for sharing models with 

this project. Treg Christopher and Theresa Burcsu created 

the potential vegetation map, and Emilie Henderson created 

the current vegetation map. Melissa Whitman developed 

the ownership/allocation map, and Mike Polly conducted 

the wildfire analysis. Joe Bernert, Jenny DiMiceli and Matt 

Noone provided GIS support. Jimmy Kagan, Louisa Evers, 

Paul Doescher, and Rick Miller evaluated the STMs and 

made many helpful suggestions. 

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Authors

James A. Duncan was a graduate student in the Department of Geosciences, 104 Wilkinson Hall, Oregon State 

University, Corvallis, OR 97331. He is now a consultant at 

the World Bank Institute, World Bank, Washington, DC, 

jimduncan4@gmail.com. Theresa Burcsu is a former 

research ecologist, Pacific Northwest Research Station, 

Focused Science Delivery Program, 620 SW Main Street, 

Suite 400, Portland, OR 97205. She is now a faculty 

research associate with the Institute of Natural Resources, 

Oregon State University, P.O. Box 751, Portland, OR 

97207-0751, theresa.burcsu@oregonstate.edu.

Abstract

This research explored the ecological consequences of rural 

residential development and different management regimes 

on a tract of former industrial timberland in central Oregon 

known as the Bull Springs. Forage quality and habitat suitability models for mule deer (Odocoileus hemionus) winter 

range were joined to the outputs of a spatially explicit 

vegetation dynamics model under two management scenarios. In one scenario, the tract was managed as a working 

forest excluding development, and in the other, development was allowed to occur at historical rates. Landscape 

pattern analysis was used to measure differences between 

the outcomes of the two scenarios. Our efforts showed that 

allowing development on the tract could potentially lead 

to greater isolation, smaller habitat patches, and decreased 

extensiveness of patches used for foraging across mule deer 

winter range. Patches providing multiple habitat functions also became more isolated and less numerous in our 

simulations. Although neither scenario prevented habitat 

degradation, restricting development on Bull Springs had 

slightly more favorable simulated outcomes for forage and 

multifunctional habitat conditions. Management of this 

tract as a working forest in a region under pressure for 

more residential development could reduce the negative 

effects of development on an iconic species in the region. 

This research provides insight into how the land use change 

trajectory of a small portion of the landscape can influence 

the larger ecological conditions of a region undergoing rapid 

rural residential development.

Keywords: Mule deer; landscape ecology; rural 

residential development; alternative development scenarios; 

habitat suitability.

Introduction 

Low-density housing has expanded into rural lands and the 

wildland-urban interface (WUI) across the United States 

(Theobald and Romme 2007) and represents an accelerating phenomenon in the West (Brown et al. 2005) capable 

of altering social and ecological landscapes. The relative 

permanency of development distinguishes it from extractive land uses such as logging and grazing (Hansen et al. 

2005), and its impacts extend beyond the walls of individual 

homes. Disturbance regimes, biodiversity, and myriad other 

ecosystem services have all demonstrated sensitivity to 

the extent and nature of residential development on rural 

lands (Dale et al. 2005, Hansen et al. 2005, Rindfuss et al. 

2004). For example, individual residences create localized 

disturbance zones for wildlife (Theobald et al. 1997), and 

developments propagate disturbances, especially fire and 

invasive species, into adjacent undeveloped lands (Hansen 

and DeFries 2007). The cumulative effects of individual 

land use change decisions can lead to substantial ecological 

impacts (Theobald et al. 2005), and uncoordinated residential development over time can have disproportionate effects 

on potential wildlife habitat (Spies et al. 2007) and disrupt 

migration corridors (Hansen and Defries 2007).

Central Oregon has exemplified the western American 

trend of residential development taking place on forested 

land previously managed for timber and other non-residential uses. When plans emerged to develop housing on a 

Landscape Development and 

Mule Deer Habitat in Central Oregon

James A. Duncan, M.S. and Theresa Burcsu, Ph.D.
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GENERAL TECHNICAL REPORT PNW-GTR-869

13 000-ha private tract of former timberland just west of 

Bend, the region’s largest city, questions arose about how 

different management actions on the tract would affect the 

region’s natural resources. Although the tract, known as 

the Bull Springs, was zoned for forest use and a minimum 

lot size of 97 ha at the time of the proposal, there was 

a precedent of converting land zoned for forest use to 

residential use in other parts of the state (Lettman 2002, 

2004). Furthermore, the tract overlaps a substantial portion 

of the observed winter range for mule deer (Odocoileus 

hemionus). One potential outcome of land use, ownership, 

and management changes in Central Oregon is a shift in the 

amount, quality, and spatial pattern of habitat for wildlife 

such as mule deer. Mule deer use higher elevation woodlands in the summer, however, their movement to lower 

elevation valleys and sagebrush in the winter could bring 

them into contact with the proposed development. As mule 

deer require large home ranges (up to 500 ha for solitary 

bucks) and long dispersal distances (often exceeding 1 

km), the proposed development could fragment important 

habitat patches or disrupt migration corridors. Development in the winter range also has potential to impact mule 

deer foraging spaces as well as valuable hiding habitats 

that provide camouflage from predators and thermal cover 

habitats that protect them from wind and sun (Csuti et al. 

1997, Johnson and O’Neil 2001, Oregon Department of Fish 

and Wildlife (ODFW) 2003). An iconic species in the region 

and an important source of game (ODFW 2003), mule deer 

and their winter range requirements have often been at the 

center of the debate over the possible policy options for 

guiding land use change on the Bull Springs tract following 

its ownership change, and so we chose mule deer habitat 

as the indicator of the ecological outcomes of alternative 

policies for this study. 

To better understand the impacts of shifts in the landscape patterns resulting from changes in land ownership 

and land use on mule deer winter range habitat, we analyzed 

differences in the landscape patterns between two management options by examining potential habitat configurations 

simulated under each option. We present methods that 

quantify the landscape consequences of differing policy 

options and, by providing a means to weigh alternative 

policy scenarios, may be useful to decision makers. Two 

alternative development scenarios for the Bull Springs tract 

were considered plausible in the future:

(1) The tract is managed as a working forest and not 

developed (WF). 

(2) The tract becomes developed for residential use 

at historical rates (DEV). 

Development was allowed to occur outside of the Bulls 

Springs regardless of the scenario. 

To understand how different policy options might 

influence the spatial arrangement of mule deer habitat, we 

used spatial pattern metrics to quantify change under both 

scenarios for a mule deer forage quality model and a habitat 

suitability model. These types of analyses can help identify 

the effects of working forest management on mule deer 

habitat in the region, and show the role the Bull Springs 

tract could play in the larger suite of developments forecast 

to occur within the region. 

Data and Methods 

Vegetation dynamics in the region surrounding the Bull 

Springs tract and mule deer winter range were simulated 

under two scenarios for 60 years. The two scenarios were 

based on the proposed options for the tract arising in the 

public policy debate in Oregon. The Working Forest (WF) 

scenario excluded development from the Bull Springs tract 

and managed it for restoration goals. The Development 

(DEV) scenario allowed development to occur at historical 

rates on the tract. We classified the initial (2000) and final 

(2060) vegetation conditions into forage and habitat suitability categories using wildlife-habitat relationship models 

(described below). These categorical maps were then analyzed using spatial pattern metrics to quantify the changes 

in mule deer habitat and compare the ecological outcomes 

of the scenarios. Analyses were conducted at three spatial 

extents: the full study area, mule deer winter range, and 

Bull Springs boundary (fig. 1). 

Study Area 

Centered on the Bull Springs tract, the study area is 430 000 

ha of the dry, eastern slopes of the Cascade Range and 

the western edge of Oregon’s high desert and ranges in 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

elevation from 590 m to 3150 m (fig. 1). High elevation 

forests are dominated by spruces (Picea spp.) and firs (Abies

spp.) whereas lower regions of the mountain slopes consist 

primarily of ponderosa pine (Pinus ponderosa C. Lawson) 

and lodgepole pine (Pinus contorta Douglas ex Louden). 

East of the foothills, the landscape is dominated by juniper 

(Juniperus occidentalis Hook.) and sagebrush (Artemisia

spp.) plant communities. With precipitation ranging from 

25.4 cm to 239.7 cm annually with large seasonal variation 

(Thorson et al. 2003) and soils with low water retention 

capacity, available soil water is often the limiting factor for 

plant growth (Simpson 2007). The ownership landscape is 

a mixture of federal and private ownerships. The USDA 

Forest Service (FS) and the U.S. Bureau of Land Management (BLM) administer much of the land in the study area, 

with 29.4 percent in private industrial and nonindustrial 

ownership. The mapped mule deer winter range1 consists of 

133 100 ha (30.7 percent) of the total study area. The Bull 

Springs tract covers 3.0 percent of the total study area and 

9.8 percent of the observed mule deer winter range (fig. 1). 

Vegetation Dynamics

Spatially-explicit state and transition models (STMs) were 

used to simulate residential development and vegetation 

Figure 1—The study area and its ownership context. The solid black line shows the boundary of the study area used in the 

STM; the dashed line shows the observed extent of the mule deer winter range. The Bull Springs tract is shown in the center of 

the modeling area.

1

 Personal communication with Glenn Ardt, Biologist, Oregon Department of Fish and Wildlife (ODFW).
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dynamics. STMs have been applied to various ecological 

systems in which multiple stable states are possible 

(Westoby et al. 1989, Hemstrom et al. 2007, Vavra et al. 

2007, Petersen et al. 2009). Vegetation was simulated for 60 

years (year 2000 to 2060) using a suite of STMs representing individual development stages, or states, within potential vegetation types (PVTs) for climax communities. PVTs 

are vegetation assemblages that take into account physical 

settings and species communities (Hall 1998). States are 

defined by a cover type, typically the dominant species or 

vegetation type, and a structural stage that describes the 

vegetation size class, canopy cover, and vertical layering. 

Transitions, such as wildfire, management activities, and 

development, defined possible pathways between states. 

Base transition rates control the speed at which vegetation assemblages change from one state to another given a 

particular transition, however, rates may be modified using 

multipliers to increase or decrease base rates to represent, 

for example, different intensities of the same management 

type among ownership types. The order, occurrence, and 

location of transitions are determined stochastically for each 

annual time step. Other transition-inducing mechanisms 

modeled were defoliators (e.g., western pine beetle) and 

parasites (e.g., mistletoe). We used the Tool for Exploratory 

Landscape Scenario Analyses (Version 3.06) to model 

landscape dynamics and the Vegetation Dynamics Development Tool (Version 6.0.25) to design, build, and calibrate 

the STMs (ESSA 2007, ESSA 2008). Models were designed 

by local ecologists or derived from previous work (Hemstrom et al. 2007) and ongoing planning activities. Fourteen 

potential vegetation types were modeled, three shrub steppe 

types and 11 forest types. PVTs modeled were: mountain 

shrub/meadow, Wyoming big sage/juniper, mountain big 

sage/juniper, ponderosa pine dry (pumice soils), ponderosa 

pine dry (hot dry; residual soils), ponderosa pine/lodgepole

pine, lodgepole pine dry (pumice soils), lodgepole pine 

wet, mixed conifer dry (pumice soils), mixed conifer dry 

(other soils), mixed conifer moist, Shasta red fir (dry), upper 

montane (cold), and subalpine parklands. 

Initial conditions were created by intersecting spatial 

data sets that represented vegetation stand boundaries, PVT 

boundaries, ownership/allocation boundaries, and development zones. The vegetation stand boundaries were derived 

using segmentation in eCognition 5 (Baatz et al. 2004) over 

a multi-image stack, where image segments represented 

homogeneous patches of vegetation, such as stands. The 

multi-image stack was composed of individual Landsat 5 

and NAIP bands and image texture. Vegetation cover and 

structure attributes assigned to vegetation stands came 

from a vegetation layer developed using a gradient nearest 

neighbor (GNN) analysis technique where imputation is 

used to assign plot-level vegetation data to pixels (Ohmann 

and Gregory 2002; LEMMA 2011). PVT boundaries were 

determined from plant association maps developed by the 

USDA FS. Plant associations were grouped to form PVTs 

and represented the entire geographic extent over which 

PVTs could occur rather than the mean. Ownership-allocation boundaries were developed by the Oregon Department 

of Forestry. Residential development was restricted to development zones derived from projections of future building 

densities based on development rates from the 1970s to 

2000 and environmental factors such as slope, elevation, 

distance to roads, zoning, and distance to other buildings 

(see Kline 2005 and Kline et al. 2010). For this study, we 

assumed PVT distribution, ownership-allocation, and 

development zone boundaries remained static over time. We 

used the TELSA Voronoi tessellation algorithm (Kurz et al. 

2000) to subdivide our landscape into simulation polygons 

with an average size of 1 ha. 

Development and Land Management

To model development, we determined the rates of change 

within five development density classes based on initial and 

ending development patterns determined by Kline et al. 

(2010), and applied these rates to our models as the annual 

probabilities for development. In other words, for each 

development density class, we defined development transitions and a probability for a development event to occur; 

the probabilities were determined from work by Kline et al. 

(2010). Development was assumed to occur linearly so that 

a patch must be developed at the lowest density class before 

being developed to a higher density and must pass through 

all density classes, in ascending order, before reaching the 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

highest density class possible given the initial development zone. Exact locations of development were assigned 

stochastically within the development zones. Development 

density classes were defined, going from lowest to highest 

density, as more than 194 ha per structure (NotDev), 97 to 

194 ha per structure (D2), 32 to 97 ha per structure (D3), 4 

to 32 ha per structure (D4) and less than 4 ha per structure 

(D5). Most of the landscape began in the lowest development class (NotDev). When housing densities exceeded 

one house per 97 ha, active forest management ceased, as 

sustainable forest management has been shown to decrease 

with parcelization (Germain et al. 2007). Development 

analyses are presented for the whole study area below, but 

model outcomes were examined at all three spatial extents. 

Modeled management activities varied by ownership 

(e.g., private industrial), federal land allocations (e.g., 

wilderness), and vegetation types and were applied to 

the landscape based on ownership class and development 

density. Federal lands were managed primarily for restoration and to reduce fuel loads, with lower rates of treatments 

than private lands. Management activities modeled for 

federal lands included pre-commercial thinning, commercial thinning, prescribed fire, and other harvest types; 

the frequencies at which management occurred varied 

by ownership, vegetation type, cover type, and structure. 

Overall, private land without residential development was 

assumed to be managed using similar methods to federal 

lands, but at higher intensities. For example, salvage activities were assumed to be 50 times more likely to occur on 

private ownership types than on USDA Forest Service land. 

Under the working forest scenario, the Bull Springs tract 

was managed for restoration of open ponderosa pine stands 

typical of the region under historical conditions prior to 

Euro-American settlement (Youngblood et al. 2004). 

Quantifying Changes in Mule Deer Habitat 

To represent the landscape in terms of mule deer habitat, the 

states in the initial and final vegetation maps were classified 

into habitat categories using a wildlife-habitat relationship 

(WHR) model (Johnson and O’Neil 2001). WHR models 

map the habitat of a particular wildlife species to the 

landscape based on vegetation type and structure, and have 

been compiled for many species in Oregon (Johnson and 

O’Neil 2001). With these models, the spatial structure of the 

landscape is linked to ecologically relevant life history traits 

such as home range size and dispersal distances, allowing 

species-specific responses to changes in landscape structure 

over time to be inferred (Johnson and O’Neil 2001). 

The wildlife-habitat model for mule deer for the central 

Oregon region was developed in consultation between the 

USDA FS and wildlife biologists from ODFW. Vegetation 

type and structure were classified in three dimensions: 

forage quality (poor/none, low, moderate, high), thermal 

cover (yes, no), and hiding cover (yes, no). In addition, these 

dimensions were combined into a single rating model called 

the habitat suitability index (HSI). Classifications drew 

mainly on natural history information and sources reviewed 

above, but some modifications were made based on the 

expert knowledge of area biologists. We limited our pattern 

analysis of habitat area and patches to the observed mule 

deer winter range. All outputs generated by the vegetation 

dynamics model were converted to raster data sets with a 

30-m cell size to coincide with the nominal grain size of 

the GNN vegetation data and analyzed in FRAGSTATS 3.3 

(McGarigal et al. 2002). Habitat patches were defined in 

FRAGSTATS as adjacent cells sharing a cell boundary. 

Forage Quality 

Mule deer forage quality is related to a combination of 

forest structure (corresponding to structural stages in the 

STMs) and dominant tree species (cover types in the STMs). 

High-quality forage patches were typically open, with 

grass/forb, closed shrub, and seedling/sapling conditions in 

areas that supported most conifer tree species or were older 

stands of very large trees with multistory, open canopies. 

The exceptions were mesic stands of high elevation mixed 

conifer species, ponderosa pine, white fir (Abies concolor 

(Gord. and Glend.) Lindl. ex Hildebr.), and lodgepole pine, 

which were considered to be of low-quality due to snow 

accumulation in the winter. Moderate-quality forage consisted of younger stands of mostly conifer species and open 

canopies, with the exceptions listed above. Development 

densities less than one structure per 97 ha were considered 

moderate-quality forage provided the cover type was a tree 
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GENERAL TECHNICAL REPORT PNW-GTR-869

species and the site was not mesic and high in elevation. 

All closed-canopy stands were considered to be low-quality 

forage. Forage quality in states with development higher 

than one structure per 97 ha or in sagebrush, juniper, or 

grassland trajectories were classified as poor/none, following the concept of disturbance zones (Theobald et al. 1997). 

These classifications are summarized in table 1. 

Habitat Suitability Index 

To capture change in modeled states that provide multiple 

habitat benefits or functions, we defined a habitat suitability 

index. The index combined forage quality habitat classes 

with thermal and hiding cover classes to provide a means 

to consider all habitat types together. Hiding cover was 

based on vegetation structure. Seedling/sapling stands, 

denser stands of poles, and all stands with a multilayered 

canopy and trees greater than 25 cm in diameter provided 

hiding cover. Closed shrub conditions in aspen types and 

dry ponderosa types were also considered hiding cover. 

All other modeled states, including all development states, 

provided no hiding cover. Canopy closure was the primary 

determinant of thermal cover, as it provides both shade 

in the summer and reduced wind exposure in the winter. 

All areas with coniferous tree species larger than 25 cm in 

diameter and canopy closure exceeding 40 percent provided 

adequate thermal cover. Numerical equivalents were given 

to each level of habitat classification: forage quality received 

scores of 0 through 3 corresponding to each level defined 

above from poor/none to high, while hiding and thermal 

cover were each given a score of 0 when absent and 3 when 

present. These numerical equivalents were then summed 

to generate the HSI (table 1). This formulation of habitat 

suitability distinguished between vegetation states that 

Table 1—Vegetation conditions defining each forage quality rating level and the habitat suitability index 

scores obtained when these levels are combined with hiding and thermal cover classifications (see text for 

definitions of hiding and thermal cover conditions). Certain combinations were not possible in these models 

due to the structural requirements for habitat to be called hiding or thermal cover, and are indicated with ‘--’

in the table

 Habitat suitability index scores when combined with 

 hiding and thermal cover

Hiding and Neither 

Forage thermal Hiding Thermal hiding nor 

quality Description of forage quality ratings cover cover cover thermal cover

High Structure: open, grass/forb, closed shrub, -- 6 -- 3

 seedling/sapling or older very large trees 

 with multistory, open canopies

Composition: all species except mesic, high 

 elevation stands of mixed conifer species 

 (ponderosa pine, white fir, and lodgepole 

 pine), where snow accumulates in winter

Moderate Structure: younger and open canopies -- 5 -- 2

Composition: mostly conifer species

Low Structure: closed canopy stands 7 -- 4 --

Composition: any species when canopy is 

 closed, and any occurrence of mesic, high

 elevation stands of mixed conifer species

 (e.g., ponderosa pine, white fir, and

 lodgepole pine)

Poor/none Structure and composition: sagebrush, 6 3 3 0

 juniper, grassland or development 

 densities higher than 97 ha per structure
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

provided just one habitat function (single-function classes) 

from those that provided 2 or 3 habitat functions (multifunctional classes). A rating of 2, for example, corresponded 

to only moderate-quality forage being present, whereas a 

rating of 7 indicated that both hiding and thermal cover 

were present in combination with low forage quality. HSI 

values of 2 or 3 only reflected forage conditions (except for 

very large multistory stands of open-canopy white fir, which 

provided only hiding cover), while HSI values of 4, 5, 6, and 

7 represented multifunctional classes. Values of 0 signified 

no habitat provision and values of 8 and 9 were not possible, 

as all states that are moderate- or high-quality forage are 

inherently poor thermal and hiding cover; similarly, HSI 

= 1 does not occur because low-quality forage conditions 

provide hiding or thermal cover. 

Landscape Pattern Analysis

Landscape pattern analysis can quantify the effects of 

human activities on the spatial arrangement of the landscape, and many metrics have been developed for measuring 

these arrangements (McGarigal et al. 2002, Turner 2005, 

Turner et al. 2001). Various studies in the field of landscape 

ecology have used landscape pattern metrics to study the 

relationship between ownership, land use change, and 

ecological processes (McComb et al. 2007, Stanfield et al. 

2002, Swenson and Franklin 2000). Landscape structure 

can be measured within habitat types (class level) or across 

all habitat types (landscape level), and depends on the spatial extent considered and the grain size of the smallest unit 

of area. Metrics were calculated at the class and landscape 

levels, where habitat quality and HSI values represented 

classes, using FRAGSTATS 3.3 and were chosen to minimize redundancy while capturing the broadest set of landscape characteristics possible. Within the observed mule 

deer winter range, patches were defined as adjacent pixels 

of the same forage and HSI classes. At the class level, we 

calculated mean patch size, total class area, mean Euclidean 

nearest neighbor, mean radius of gyration and the number 

of patches (table 2). At the landscape level, we calculated 

the Shannon’s Diversity Index (SHDI), contagion, and LPI 

(table 2). These metrics were calculated for each habitat 

model component for the initial and final conditions under 

Table 2—The landscape metrics used, a description of what they measure, and their units

Level Metric Definition Ecological interpretation Units

Class Mean patch area Average area in hectares of Home range size needed by an Hectares

 all patches in the same class individual for mating, breeding, 

 and foraging

Mean patch radius Average distance between each Connectivity and corridor Meters

of gyration cell in a patch and the patch characteristics of the

 centroid, averaged over all landscape

 patches

Mean Euclidean Average distance from the edge Overall isolation of patches in Meters

 nearest neighbor distance of a patch to the edge each class

 of the nearest neighbor of the 

 same type for all patches in that 

 class

Number of patches Total number of patches in each Fragmentation or consolidation of Count

 class a class

Landscape Shannon’s diversity richness and evenness of the Sensitive measure of rare patch None

 index distribution of patch types types

Largest patch index percentage of the landscape Homogeneity and the dominance Percent

 occupied by the single largest of patches within the 

 patch

Contagion The likelihood that adjacent cells General aggregation of the Percent

 will be of the same class type patches in the landscape

 and connectedness
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GENERAL TECHNICAL REPORT PNW-GTR-869

each scenario within the mule deer winter range. Percentage changes and absolute changes from present to future 

were calculated. In addition, change in developed land and 

vegetation states were calculated to provide context for 

interpreting the results of the pattern analysis. Our approach 

did not include a weight for neighborhood or proximity to 

high quality habitat. For example, moderate quality patches 

located beside high quality patches did not receive a higher 

habitat value than those far from high quality patches.

Results

The two scenarios generated different spatial patterns of 

development (fig. 2) and affected differences in vegetation 

structure and cover combinations after 60 years of simulation. Overall, the DEV scenario showed greater amounts of 

land conversion from lower to higher densities of development than the WF scenario. Urban densities increased in 

both scenarios (1.0 percent in WF and 0.7 percent in DEV), 

and undeveloped land area decreased by 1.2 percent and 

2.5 percent in the WF and DEV scenarios respectively. In 

the observed mule deer winter range 5.8 percent of the land 

moved from undeveloped into the lowest developed density 

(D2, 97 to 142 ha per structure) in DEV as compared to 

only 3.0 percent in WF (fig. 3). In contrast, 42 percent 

of the Bull Springs tract converted from undeveloped to 

developed land (D2) under the DEV scenario, with 3670 ha 

of undeveloped land converted to D2 and 1890 ha converted 

to development densities of 32 to 97 ha per structure (D3) at 

the end of 60 years. On the Bull Springs tract alone, nearly 

twice as much land entered the lowest density class in the 

DEV scenario as compared to the WF scenario. 

Forage Quality

Mapping the initial and future forage quality conditions 

illustrated differences in the locations and nature of 

land use conversions between scenarios (fig. 4A) and the 

resulting distributions of forage quality levels (fig. 4B). 

Figure 2—Spatial distribution of development classes for initial conditions and working forest (WF) and development (DEV) 

scenarios at 60 years. NotDev = greater than 194 ha/structure; D2 = 97 to 194 ha/structure; D3 = 32 to 97 ha/structure; D4 = 

4 to 32 ha/structure; D5 = less than 4 ha/structure.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Landscape level metrics did not change dramatically from 

initial conditions for either scenario or show substantial 

differences between scenarios (table 3). Overall, low-quality 

forage experienced the greatest negative change from the 

initial conditions for both scenarios in all landscape metrics 

(fig. 5). The largest total increases in patch area, abundance, 

and extensiveness occurred in high- and moderate-quality 

classes, with slightly larger increases in total area, largest 

patch index, patch area, and extensiveness in the WF 

scenario. Nonforage patches were less numerous, less 

extensive, and closer together under the WF scenario 

compared to DEV; under the DEV scenario nonforage 

patches became smaller and constituted a larger percentage 

of the landscape. Moderate-quality forage patches became 

larger and more extensive under WF compared to DEV, but 

they also were more isolated, according to nearest neighbor 

distances. High-quality forage patches became smaller 

on average in both scenarios. The decrease in mean highquality patch area was less pronounced in the WF scenario, 

but fragmentation through the creation of more patches was 

more pronounced. High-quality patches also became less 

isolated and more extensive, with somewhat greater gains 

under the WF scenario compared to DEV. The largest differences between the scenarios were an increase in nearest 

neighbor distances of low-quality forage patches and a 

reduction in the patch abundance under DEV compared to a 

moderate increase in patch number under WF. 

Habitat Suitability Index

Similar to outcomes for forage quality, the spatial mapping 

of the habitat suitability index showed subtle differences 

between the initial conditions and the two scenarios in 

terms of the location and nature of habitat quality changes 

(fig. 6A), as well as the overall distribution of habitat suitability levels in the two scenarios (fig. 6B). Landscape-level 

metrics were fairly similar between scenarios (table 3), but 

at the class level, differences were more apparent (fig. 7). 

For single-function habitat types (HSI = 2 or 3), the mean 

patch size increased in both scenarios although the increase 

was slightly smaller in DEV. Other metrics showed little 

difference between the two scenarios. In contrast, multifunctional habitat types (HSI = 4–7) displayed substantial 

differences between scenarios. For all multifunctional 

habitat types, mean radius of gyration was slightly lower 

under DEV than under WF. The patch isolation (mean 

Figure 3—Proportions of development in five classes within mule 

deer winter range for the development (DEV) scenario and the 

working forest (WF) scenario. Winter range comprises 9800 ha 

of the Bull Springs tract and 133 100 ha of the entire study area. 

NotDev = greater than 194 ha/structure; D2 = 97 to 194 ha/structure; D3 = 32 to 97 ha/structure; D4 = 4 to 32 ha/structure; D5 = 

less than 4 ha/structure. As NotDev comprises most of the winter 

range, the vertical axis begins at 50 percent to better illustrate 

the differences in the other development classes.

Table 3—Landscape level metrics calculated for the forage and habitat suitability index landscapes

 Forage HSI

Metric Initial Development Working Forest Initial Development Working Forest

Largest patch 19.60 19.68 19.69 19.61 19.68 19.69

 index 

Contagion 53.22 61.95 63.33 52.22 63.33 61.95

Shannon’s 1.57 1.32 1.27 1.57 1.32 1.27

 Diversity Index
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GENERAL TECHNICAL REPORT PNW-GTR-869

Figure 4—(A) Initial forage quality conditions (left) as well as the simulated forage quality conditions for the working forest 

scenario (middle) and the development scenario (right). The conditions within the mule deer winter range are highlighted and the 

location of the Bull Springs tract is shown in each map, as well as the major urban areas in the landscape. (B) Trajectories of the 

total amount of land in each forage quality class over the simulation.

(A)

(B)
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

nearest neighbor distances) under DEV nearly doubled 

for HSI values of 4, 5 and 7. These habitat types provide 

low- or moderate-quality forage, in combination with either 

hiding or thermal cover. Our results suggest that vegetation 

conditions supplying more than one habitat function on the 

landscape would become more isolated, less extensive, and 

smaller in the future, with these effects amplified under 

DEV in most cases. 

Interestingly, the outcomes for HSI values of 6 differed from those of the other multifunctional classes. Total 

class area remained within 10 percent of initial conditions, 

but both scenarios resulted in very large increases in the 

number of patches and little change in isolation and extensiveness. Coupled with the overall decrease in patch size, 

this indicates greater fragmentation of this class compared 

to the other multifunctional classes. There were also marked 

differences between scenarios in terms of nearest neighbor 

distance, with WF resulting in increased isolation compared 

to decreased isolation under DEV for this value. 

Discussion

Our spatial models demonstrated that rural residential 

development and forest management have the potential to 

alter the Bull Springs tract and the surrounding landscape 

regardless of the development fate of the Bull Springs 

Figure 5—Differences in patch metrics between the initial conditions and modeled scenarios indicate changes in the 

spatial patterns of mule deer forage quality classes. The calculated values of the landscape metrics are given for the 

mean patch area (top left), the number of patches (bottom left), the mean Euclidean nearest neighbor (top right), and the 

mean radius of gyration (bottom right).
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GENERAL TECHNICAL REPORT PNW-GTR-869

Figure 6—Maps of the habitat suitability conditions initially (left) and after 60 years under the working forest (WF, center) and 

development (DEV) scenarios (right). The conditions within the mule deer winter range are highlighted and the location of the Bull 

Springs tract is shown in each map, as well as the major urban areas in the landscape.

(A)

(B)
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

tract. Results from both modeled scenarios suggest that 

landscape-wide changes projected to occur in the next 

several decades have potential to affect mule deer habitat, 

largely because development is expected to continue, but the 

differences may be subtle at the landscape scale. However, 

due to the Bull Springs’ landscape position, large proportion 

of mule deer winter range, and large size relative to other 

private single-ownership tracts, preventing development on 

Bull Springs may offset landscape-level habitat degradation 

that could result from nearby rural development. Moreover, 

the modeled interaction of management and changes in 

forest structure and composition due to forest maturation 

suggests improved forage and multifunctional habitat 

conditions for mule deer over the next several decades if 

the land is managed as a working forest. This expectation 

was exemplified by increased patch abundance and area of 

high-quality forage patches in the working forest scenario 

when compared to the development scenario. Though 

more total area of high quality forage was added under the 

working forest scenario, both scenarios created the same 

number of high-quality forage habitat patches and resulted 

in decreased isolation of these patches relative to the initial 

conditions. This relationship suggests that mule deer may 

find more high-quality forage patches in the future and 

have to travel shorter distances between patches once they 

find the high-quality forage patches. The overall shift from 

multifunctional to single-function HSI ratings in both 

scenarios signals a possible stratification of the landscape 

Figure 7—Class-level measures of the spatial pattern of the mule deer habitat suitability index. HSI = 0 has no habitat functions, HSI = 2 or 3 represent single-function habitat classes, HSI > 3 represent multifunction habitat classes. 

The calculated values of the landscape metrics are given for the mean patch area (top left), the number of patches 

(bottom left), the mean Euclidean nearest neighbor (top right), and the mean radius of gyration (bottom right).
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GENERAL TECHNICAL REPORT PNW-GTR-869

and could lead to higher energetic costs if mule deer must 

travel farther distances between patches of different habitat 

types to obtain all needed resources. In contrast, if the 

single-function patches are closely interspersed and all 

habitat requirements are met within short travel distances, 

energetic costs may decline. 

Mule deer is an iconic species in eastern Oregon, and 

maintenance of the herd for aesthetic and hunting purposes 

is a stated goal; our results suggest habitat supply and 

arrangement will be impacted by Bull Springs tract management decisions. Based on the limits of the observed winter range, there is evidence from the simulation that lower 

rates of development on the tract could enhance mule deer 

habitat conditions in the future and offset or ameliorate the 

impacts of development elsewhere in the region. Promotion 

of mule deer persistence in their winter range will likely 

require attention to the location of residential development 

outside the Bull Springs tract. Attention must also be paid to 

the potential for isolating high-quality forage and multifunctional habitat conditions and converting these to less 

suitable types because restricting development within the 

Bull Springs may increase development pressures elsewhere 

in the landscape. 

Conversion of forested land to rural residences is only 

one process affecting vegetation dynamics and landscape 

change. Decision makers are often expected to respond 

to landscape-scale processes, but may not have access to 

landscape-scale information to ensure the broader policy 

governing land use change balances wide-ranging land 

use conversion with gains made through more localized 

conservation actions. Landscape simulation modeling provides one approach for investigating the numerous effects 

future policy choices may have in a region by defining the 

ecological significance of a particular piece of land in a 

given landscape as well as the limits of conservation on a 

single tract when considering the larger suite of changes 

taking place elsewhere in the landscape. By coupling 

spatial vegetation dynamics modeling that incorporates 

both human- and disturbance-driven modifications of the 

landscape with landscape pattern analysis, we can inform 

the broader debates on planning for land use changes in the 

future. Moreover, landscape simulation modeling results 

such as these can be extended to provide supporting data 

to a decision support system designed to prioritize scenario 

outcomes. Another extension of the work presented here 

that could benefit decision support would be to relate the 

landscape metrics for forage quality and HSI classes to mule 

deer carrying capacity. Incorporating the role that proximity 

to various habitat types plays in carrying capacity on the 

landscape would be another powerful means to understand 

the complex interactions of human activity and wildlife 

requirements. Landscape models and analyses such as the 

ones presented here provide a means for increasing knowledge of dynamic landscapes that are important for many 

management objectives. 

Limitations

A major limitation of this study is that the allocation of 

development and simulation of final vegetation states were 

done using a single simulation, rather than multiple simulations, which limits the conclusions that can be drawn from 

the predictions. While we performed limited uncertainty 

and sensitivity analyses with the nonspatial and spatial 

vegetation dynamics models, we are limited in our ability 

to distinguish between the impacts of development and artifacts arising from the spatial simulation method; our results 

must be tempered by this uncertainty. In addition, the 

pattern metrics used in this study represent a set of assumptions about what makes “good” mule deer habitat, both in 

defining patches and in deciding what spatial aspects are 

more important than others. We also used a limited set of 

independent and complementary spatial pattern metrics for 

simplicity, but these are dependent on the scale and grain 

of measurement (Li and Wu 2004) and can often be highly 

correlated with cumulative changes in habitat area (Fahrig 

2003). While these metrics were chosen to relate to life history requirements of mule deer, they are just one set of measurements of landscape structure in the region, and could 

not capture all aspects of the spatial structure of habitat, 

such as inter-class spatial relationships. Other limitations 

arise from not including other forms of human-wildlife 

interactions such as subsidized food supplies, suppression of 

predators, and climate change in the models. 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

With regards to the input data, the base GNN vegetation data layer was generated from a statistical model. When 

combined with the spatial vegetation dynamics model there 

is the potential for nontrivial classification errors (Ohmann 

and Gregory 2002). As such, spatial results from these 

products should be considered at the regional level, not the 

site level. These tools could not predict landowner behavior 

and changes in management approaches or ownership 

boundaries, and are limited to our historical understanding 

of and assumptions about these phenomena. Human and 

ecological processes and interactions were simplified in 

our modeling, but while the results must be viewed as only 

a small subset of many possible outcomes, they provide a 

point of departure for understanding the different outcomes 

that could be experienced under alternative policy and 

management regimes. 

Conclusion

Interactions between land use change, shifts in management priorities, and natural disturbance processes can drive 

landscape dynamics and resulting patterns. The habitat 

of mule deer and other wildlife are determined by these 

dynamics and patterns. Using wildlife-habitat relationships, spatial analysis, and spatial vegetation dynamics 

modeling, it is possible to provide concise measurements of 

landscape change and facilitate ecological interpretations 

of differences between alternative land use policy futures. 

Our analysis of a large private tract in central Oregon and 

surrounding landscape showed that alternative management 

regimes might affect mule deer habitat, but more information is needed to understand the effect on carrying capacity. 

Notably, working forest management on the Bull Springs 

could result in somewhat better mule deer habitat outcomes, 

particularly with respect to higher quality forage and multifunctional habitat types. That said, simulated habitat was 

degraded to some extent under both scenarios, but marginal 

gains due to conservation over dispersed rural development 

could arise through working forest management, according 

to our simulation outcomes. Managing this tract as working forest could reduce the negative ecological effects of 

residential development and other land use changes within 

the region. Future management decision-making should 

pay continued attention to where residential development 

is expected to occur outside the Bull Springs tract and 

consider the isolating effect development might have on 

important habitat types. Managing the use and conditions 

of a single portion of the landscape can only do so much 

when that portion is embedded in a larger context of change, 

but this research provides critical insight into how land 

use policies on a specific tract can influence the ecological 

trajectory of change in the broader landscape. 

Acknowledgments

This work was possible only through the support of Miles 

Hemstrom as the project and science lead, James Merzenich 

for developing the STMs, Allison Reger and Peter Heinzen 

for technical guidance, Hannah Gosnell for advice and 

analytical guidance, and S. Mark Meyers for computing 

resources. The Deschutes Land Trust was instrumental in 

defining the working forest scenario, and Glen Ardt of the 

Oregon Department of Fish and Wildlife and Barbara Wales 

were essential to the construction of the mule deer habitat 

data and habitat models. Interagency Mapping and Assessment Project partners Oregon Department of Forestry, 

USDA Forest Service Pacific Northwest Region, USDA 

Forest Service Pacific Northwest Research Station, Washington Department of Natural Resources, and The Nature 

Conservancy provided necessary resources leading to the 

completion of this project as did the Department of Geosciences at Oregon State University.

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Authors

Anita T. Morzillo is a landscape ecologist, Oregon State 

University, Department of Forest Ecosystems and Society, 

321 Richardson Hall, Corvallis, OR 97331, anita.morzillo@

oregonstate.edu. Joshua S. Halofsky is a landscape ecologist, Washington State Department of Natural Resources, 

Olympia, WA 98504, joshua.halofsky@dnr.wa.gov. Jennifer DiMiceli is a systems manager, Arizona Game and 

Fish Department, Phoenix, AZ 85086, jdimiceli@azgfd.gov. 

Blair Csuti is a wildlife ecologist, Oregon State University, 

Corvallis, OR 97331, blair.csuti@oregonstate.edu. Pamela 

Comelaeo is a former research assistant, Oregon State 

University, Corvallis, OR 97331. Miles A. Hemstrom is a 

research ecologist (retired), Forestry Sciences Laboratory, 

620 SW Main St. Portland, OR 97205. He is now a faculty 

research associate, Institute for Natural Resources, P.O. Box 

751, Oregon State University, Portland, OR, 97207, miles.

hemstrom@oregonstate.edu.

Abstract

Wildlife conservation often is a central focus in planning for 

natural resource management. Evaluation of wildlife habitat 

involves balancing the desire for information about detailed 

habitat characteristics and the feasibility of completing 

analyses across large areas. Our objective is to describe 

tradeoffs made in assessments of wildlife habitat within 

a multiple-objective vegetation-based state-and-transition 

model (STM) framework. Species-habitat relationships 

derived from STM vegetation characteristics require careful 

interpretation for several reasons. First, the observational 

unit for wildlife analysis is habitat, which does not provide 

information about actual species occurrence or distribution. 

Second, subjectivity exists in researcher interpretation of 

species-habitat relationships derived from past literature, 

particularly qualitative descriptions. For quantified specieshabitat relationships that exist, only information that 

matches output criteria directly may be used for analysis. 

Third, visual interpretation of results may vary based on 

Balancing Feasibility and Precision of Wildlife 

Habitat Analysis in Planning For Natural Resources

Anita T. Morzillo, Joshua S. Halofsky, Jennifer DiMiceli, Blair Csuti, Pamela Comeleo, and Miles Hemstrom

scale of analysis used in STMs. When preparing wildlife 

habitat information from STM output and its application 

to natural resource planning, there is a need to focus on 

consistent and defensible information and emphasize the 

limitations of knowledge derived from data analysis.

Keywords: fuels management, habitat, land use planning, state-and-transition model, wildlife management.

Introduction

Policymakers and planners often consider tradeoffs between 

resources available and the information gained through 

resource investment when making decisions about natural 

resources management. State-and-transition models (STMs) 

can play an important role in assisting natural resource 

managers and policymakers with such decisions. STMs 

allow users to create and test descriptions of vegetation 

dynamics, management regimes, and natural disturbances 

by simulating them simultaneously across a landscape 

of interest through time. Vegetation-based STMs have 

been used in many applications, including wildlife habitat 

analysis (e.g., Evers et al. 2011, Wisdom et al. 2002).

Because wildlife habitat is not the observational unit 

of focus for vegetation STMs, but rather a representation of 

habitat based on pre-determined vegetation characteristics, 

mismatches between data and inferences made from those 

data can have drastic consequences for species and habitat, 

as well as for land use managers who rely on planning and 

policy decisions for the sustainable management of natural 

resources. For example, fine-scale details such as relative 

habitat quality and spatial distribution of habitat may not be 

available from coarse-level aggregated state class characteristics of STMs. Therefore, our objective is to highlight 

a number of considerations that are necessary for accurate 

interpretation of wildlife habitat analyses from STMs. First, 

we discuss two reasons why wildlife habitat analyses are 

important in policy and planning decisions that may involve 

use of STM tools, and how resulting policies may vary 

across jurisdictional levels. Then, we evaluate four factors that affect the interpretation of habitat information as 
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derived from STMs: (1) the observational unit of analysis, 

(2) available versus desired species-habitat information, (3) 

interpretation of data by different researchers, and (4) scale 

of analysis. We conclude by highlighting factors to consider 

when balancing feasibility and precision of wildlife habitat 

analysis with interpretation of results for policy decisions, 

and provide some basic guidelines for making inferences 

from wildlife habitat analyses.

Why Apply STMs to Wildlife Habitat 

Analysis?

Wildlife habitat analysis involves conditional inferences 

and, when placed within a broader natural resource policy, 

can conflict with other natural resource management objectives. Despite potential conflicts, we highlight two motivations for including wildlife in policy analyses and linkages 

with STMs.

Wildlife Is an Indicator of Ecosystem Conditions

Each wildlife species depends on a range of environmental 

and habitat features for activities such as foraging, roosting, 

nesting, denning, and hiding from predators (Bolen and 

Robinson 1999). Presence or absence of a species in a location that contains characteristics linked to that species may 

provide clues about both habitat condition and the integrity 

of ecological processes (Grimm 1995).

Habitat characteristics for species span both non-living 

and living features within ecosystems. Non-living features 

may include ground moisture, particular soil conditions, 

water, leaf litter or detritus, and down and dead wood. For 

example, snags provide roosting locations for many bat species as well as sources of foraging for woodpeckers (Saab 

et al. 2004). Like non-living features, living features such 

as shrubs and other understory features provide shelter, 

nest locations, cover from predators, as well as foraging 

opportunities for wildlife. For example, a variety of forest 

ecosystems can provide seasonal food needs for black bears 

(Ursus americanus), such as berries and mast (Baldwin and 

Bender 2009, Hébert et al. 2008). Species such as marten 

(Martes americana), fisher (Martes pennanti; Powell et al. 

2003), and flying squirrels (Glaucomys sabrinus; Mahan et 

al. 2010) rely on complex canopy structures for life activties. Presence of smaller species, such as flying squirrels, is 

a necessary condition for related predators, such as northern 

spotted owl (Strix occidentalis caurina; e.g., Forsman et al. 

2005). Therefore, management for particular features can 

result in habitat benefits for many species.

Wildlife species also are indicators of ecosystem 

processes. For example, tree regeneration and seed dispersal 

can be highly dependent on mammals and birds (e.g., Auger 

et al. 2002, Gilgert and Zack 2010, Schupp 1993, Siepielski 

and Benkman 2007, Stiles 2000). Dam building by beavers 

(Castor canadensis) enhances soil nutrients and species 

richness of wetlands (Wright et al. 2002) and provides 

habitat for fish and waterfowl (McCall et al. 1996, Pollock et 

al. 2004). Therefore, evidence of the processes that species 

help maintain provides clues about the overall ecosystem 

function.

Wildlife Has Social Value

Wildlife holds social value in both consumptive and 

non-consumptive contexts. Utilitarian value (Decker et al. 

2001) often is associated with fees generated for hunting 

and fishing opportunities, and in some instances can total 

tens of thousands of dollars for trophy species (Booth 

2009). Naturalistic value (Decker et al. 2001) is associated with activities such as wildlife viewing. Several US 

national parks, including Yellowstone, Olympic, and the 

Great Smoky Mountains, cite the participation in wildlife 

viewing activities as an important reason for park visitation 

(Manni et al. 2007, Papadogiannaki et al. 2009, Van Ormer 

et al. 2001). Bird watching is a global industry that attracts 

participants with diverse backgrounds and motivations 

(Curtain 2010, Green and Jones 2010, Lindsey et al. 2007). 

Thus, similar to wildlife as indicators of ecosystem conditions, there is opportunity to relate STMs to social wildlife 

indicators, although we are unaware of any current applications of STMs in this area.

The social value of wildlife also is illustrated by the 

wealth of non-profit groups focusing on wildlife. For example, the World Wildlife Fund (http://www.worldwildlife.

org) and National Audubon Society (http://www.audubon.

org) rely on donations for operating revenue. In such cases, 
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donors may seek to promote existence of particular species 

regardless of ever having the personal chance to see them 

(e.g., World Wildlife Fund conservation program for giant 

pandas; Ailuropoda melanoleuca), suggesting anthropomorphic or moral values (Decker et al. 2001).

Factors Affecting Interpretation of Wildlife 

Habitat Derived from STMs

Ecological and social values related to wildlife influence 

policy at federal, regional, and state jurisdictional levels. 

At the federal level, the Endangered Species Act (USFWS 

2011) and National Environmental Policy Act (NOAA 

1969) provide guidance for potential impacts to wildlife and 

wildlife habitat. Regional and state policies may complement or supplement federal guidelines for wildlife, or focus 

on regional priorities. For example, the U.S. Forest Service 

Northwest Forest Plan (NWFP) for the Pacific Northwest 

provided goals for sustainable forest management (NWFP; 

USDA FS 1997) through a vision of fish and wildlife habitat 

conservation, such as recovery of the northern spotted owl 

and its habitat (USDA FS 1997). However, mismatches 

between geopolitical boundaries and geographic ranges 

of wildlife species can result in inconsistent designations 

of wildlife. For example, western gray squirrel (Sciurus 

griseus) populations are stable in Oregon and California, 

but have declined in Washington, where three genetically 

isolated populations now exist. The Washington Department of Natural Resources has instituted a recovery plan for 

the species, the objectives of which include restoring and 

protecting habitat in appropriate areas of the state (Linders 

and Stinson 2006). In those cases, STMs may not only assist 

species management at the state level, but also interpretation of available knowledge may be necessary at multiple 

scales of analysis for decision-making across multiple 

geopolitical boundaries across the geographic range of the 

species.

STMs allow managers to evaluate tradeoffs and risks 

associated with land management decisions that may 

impact ecological and social wildlife values across multiple 

jurisdictional scales. However, regardless of scale and tools 

used, all wildlife models are the result of inferences made 

from evaluations of data about organisms and their habitat. 

In other words, wildlife habitat models are representations 

of species-habitat relationships that are limited to the scope 

of the information used to construct them. Therefore, time 

sensitive needs and variation in spatial scale of the policy 

context result in a need to balance feasibility of analyses 

necessary for decision-making with precision of the data 

used to make those decisions. Here we highlight four factors 

that affect habitat evaluation and the resulting inferences 

made from those evaluations.

Observational Unit

The observational unit (or unit of observation) is the unit 

used for analysis. For wildlife management, the unit of 

analysis can span a range of observations: an individual, a 

population, multiple populations, or a species. There often 

is a mismatch between the observational unit of wildlife and 

STM output, which can limit inferences that can be drawn 

from STMs. Analyses based on STMs may consider mere 

habitat presence as defined by particular characteristics, but 

may not be able to provide a precise assessment of habitat 

quality. For example, open canopy and recent disturbance 

may be used to identify presence of habitat or potential 

habitat for the black-backed woodpecker (Picoides arcticus). 

Ability to incorporate snag density into STMs would allow 

for a finer assessment of habitat quality because a greater 

density of snags is directly related to higher habitat quality 

(Saab et al. 2009). Similarly, habitat area is another variable 

that may be considered, but scale of analysis (discussed 

below) may limit interpretation of this variable. For 

example, assessment of habitat area at the watershed scale 

may provide information about the aggregated number of 

acres of black-backed woodpecker habitat available within 

a watershed. However, because those habitat data are 

aggregated at the watershed scale, there is limited ability to 

evaluate the spatial distribution of that habitat within each 

individual watershed. The lack of information on habitat 

arrangement would then affect the ability to assess home 

ranges, connectivity, and other landscape assessments (e.g., 

see analyses in Dudley and Saab 2007). In general, the 

selection of the observational unit will not only affect the 

analysis that can be completed, but also the interpretation 

and knowledge derived from the analysis.
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Matching “What We Know” to “What We Need”

Habitat models are simplified representations of complex 

ecological relationships. Building habitat models from 

empirical wildlife data allow researchers to control selection 

of variables they perceive as important to a species based on 

existing knowledge. For example, habitat suitability indices 

(HSIs) are models that use an inductive process based on 

data from observed species-habitat relationships. However, 

habitat conservation may only be one consideration within 

a broader management objective and, therefore, would be 

evaluated using a framework and context derived from a 

perspective other than wildlife. For instance, a STM built 

with the primary purpose of evaluating vegetation growth 

and succession likely will contain parameters that align well 

with variables important to vegetation dynamics. However, 

those important vegetation variables may or may not overlap with variables important for a particular wildlife species 

and potentially result in a less-than-optimal relationship 

between model output and wildlife habitat. Ultimately, such 

inconsistencies, particularly at the fine scale, are important 

to interpret and convey clearly and precisely.

If modelers are interested in using existing literature 

to link species-habitat relationships to STMs, how state 

classes are defined in STMs will affect the ability to derive 

habitat information from published knowledge. We found 

that although numerous studies may exist for particular 

wildlife species, information often was not recorded or 

reported in ways that matched directly with the definitions 

of STM state classes (e.g., fig. 1). Either the observational 

unit of the study did not match the STM observational unit, 

or the variables incorporated into STMs were not recorded 

in the wildlife habitat information. The outcome resulted in 

limited data for even for the “most studied” species.

Consensus Among Researchers

Wildlife habitat modeling efforts often include a number 

of individuals working as a team, which can lead to differences in interpretation of habitat information. At the most 

basic level, ability of an individual researcher to define and 

match those relationships consistently may be influenced 

by life history traits of a species. Habitat specialists are 

restricted to a narrow range of habitat characteristics. For 

example, red tree voles (Arborimus longicaudus) reside 

Figure 1—Evaluation of wildlife habitat within a framework of vegetation dynamics creates difficulty in matching habitat information 

to vegetation attributes. In this example, a) Vegetation characteristics categories used for development of STMs were limited to tree size 

(dbh), canopy closure (percent) and canopy structure (layers). b) When evaluating habitat for the American marten from past research, 

a large number of variables cannot be used because of inconsistencies between important vegetation and wildlife habitat variables. 

For example, if the output from STMs such as that illustrated here are used to evaluate American marten habitat, only overhead cover 

(percent; in bold text) can be used from Payer and Harrison (2003).

Habitat variable Units of measurement

Volume of snags m3/ha

Volume of stumps m3/ha

Volume of exposed root masses m3/ha

Volume of downed logs m3/ha

Basal area of live deciduous m3/ha

Basal area of live coniferous trees m3/ha

Density of live trees number/ha

Tree height m

Overhead cover Percent

Litter depth cm

Understory deciduous stem density number/ha

Understory coniferous stem density number/ha

Foliage density <0.5 m percent

Foliage density 0.5-2.0 m percent

Size Class (inches) Canopy Closure (%) Canopy Layers (#)

Pole Smtree Mdtree Lgtree Gttree GrassForb Open Medium Closed Single Multi

(5-10) (10-15) (15-20) (20-30) (>30 in) (<10) (10-40) (40-60) (>60) 1 >1

a

b
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

among the canopy of mature Douglas-fir forests south of the 

Columbia River, and rely on conifer needles for both forage 

and water (e.g., Forsman et al. 2009). Those narrow habitat 

traits are relatively easy to identify consistently from STM 

output. Conversely, both white-tailed (Odocoileus virginianus) and mule deer (O. hemionus) are habitat generalists, 

and forage among a wide variety of ecosystems and forest 

types. Those complexities in habitat use may result in 

differences among researchers if each researcher focuses 

on a different habitat component (e.g., forage versus cover 

information).

Multiple researchers also can interpret the same 

information differently, which can confound efforts to 

match species-habitat relationships with STM state classes. 

For example, the northern spotted owl is associated with 

relatively large trees and closed and complex canopy structure (Forsman et al. 2005). Those habitat characteristics are 

relatively straightforward to identify among vegetation STM 

state classes such as (tree) size class (diameter at breast 

height; dbh), canopy cover (or percent closure), and canopy 

structure (single versus multiple layers; fig. 2). Therefore, 

it is expected that a research team would have a relatively 

easy time reaching consensus on state classes to be defined 

as habitat, particularly if quantitative measures are provided 

within references. However, qualitative descriptions such 

as “large,” “closed,” and “complex” are more difficult to 

interpret if metrics are not also defined quantitatively. For 

example, the pileated woodpecker (Dryocopus pileatus) 

inhabits forests with a variety of size classes and diverse 

canopy structures (Aubry and Raley 2002). In this case, 

there are many possible definitions of habitat, which may be 

difficult to evaluate if not defined with quantitative metrics 

and can lead to inconsistencies among a research team. 

Therefore, developing decision rules to account for potential 

inconsistencies among a team can play an important role 

in decreasing probability of error when describing habitat 

characteristics, as well as defining guidelines for inferences 

made from those data.

Figure 2—Individual interpretation of information can lead to inconsistent definition of species-habitat relationships, such as illustrated 

for four hypothetical members of a research team “A,” “B,” “C,” and “D.” Cover and structure states identified as habitat for a given 

species are designated with a “1”, whereas those considered not habitat are designated with a “0”. a) The first species (top) is an oldgrowth habitat specialist with a narrow range of habitat characteristics, which results in relatively easy consensus among researchers. 

b) The second species is a forest habitat generalist with a broad range of possible habitat characteristics, which results a need to discuss 

inconsistencies in information interpretation among researchers.

a

b

Cover Structure A B C D

Ponderosa Pine Large tree, low density, single story 0 0 0 0

Douglas-fir/White Fir Large tree, low density, single story 0 0 0 0

Grand Fir/Englishman Spruce Large tree, low density, single story 0 0 0 0

White Fir Large tree, low density, single story 0 0 0 0

Ponderosa Pine Large tree, medium density, single story 1 1 1 1

Douglas-fir/White Fir Large tree, medium density, single story 1 1 1 1

Grand Fir/Englishman Spruce Large tree, medium density, single story 1 1 1 1

White Fir Large tree, medium density, single story 1 1 1 1

Cover Structure A B C D

Ponderosa Pine Large tree, low density, single story 0 0 1 1

Douglas-fir/White Fir Large tree, low density, single story 0 0 0 0

Grand Fir/Englishman Spruce Large tree, low density, single story 1 0 1 1

White Fir Large tree, low density, single story 0 0 0 0

Ponderosa Pine Large tree, medium density, single story 0 1 0 1

Douglas-fir/White Fir Large tree, medium density, single story 1 0 0 0

Grand Fir/Englishman Spruce Large tree, medium density, single story 0 0 1 1

White Fir Large tree, medium density, single story 0 1 0 0
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Scale

Scale is the spatial or temporal dimension of a pattern or 

process. The scale of analysis can be coarse (e.g., continent 

or geologic period) or very fine (individual grains of and or 

milliseconds). Regardless of size, scale used for STM analysis affects interpretation of data and potential comparisons 

of output to other wildlife habitat models. For example, the 

range of the black-backed woodpecker includes the east side 

of the Cascade Mountains as well as upper-elevation portions of west-side slopes (Dixon and Saab 2000); presentation of that information at the 30-meter scale illustrates this 

succinctly (fig. 3a). If the same results are presented at the 

watershed scale as devised from STMs (fig. 3b), the species 

range distribution includes the extent of all watersheds with 

boundaries along the Cascade crest. Although the actual 

geographic range of the species did not change for the two 

analyses, the visual interpretation of the species-habitat 

relationships data in the STM output is distorted, such 

that it appears to include a larger total area than the finer 

scale map. Without fully appreciating the role of scale or 

general ecological information about the species, STM data 

users may misinterpret the output and overestimate the 

habitat needs of and/or available habitat for black-backed 

woodpeckers and inaccurately emphasize the importance of 

ecosystems outside of this species’ geographic range. Thus, 

without proper understanding, mapped output could lead to 

policy decisions that overly conserve, or do not sufficiently 

conserve, habitat.

Scale of analysis also affects comparison of STM data 

to results of other habitat mapping approaches. For example, 

the initial Gap Analysis Program (GAP), an effort by the US 

Geological Survey, involved use of wildlife-habitat relationship models (WHR) to assess underrepresented species 

and habitats for conservation planning (Scott et al. 1993). 

Initial GAP efforts used 30-meter resolution land cover data 

to map WHRs by estimating habitat presence from known 

species distribution and data derived from individual pixels 

(fig. 4a). Using land cover and habitat relationship data, the 

range and distribution of individual species were mapped 

and modeled to determine relative amount of habitat on and 

off protected lands.

A second and more recent approach, the Gradient Nearest Neighbor method (GNN; Ohmann and Gregory 2002), is 

an imputation designed to map vegetation characteristics at 

the 30-meter scale based data derived from forest inventory plots. Characteristics of the data collected for each 

plot, along with other biophysical attributes and satellite 

imagery, are used to interpolate vegetation data across the 

entire landscape where plot information is absent (fig. 4b). 

Although GNN is a powerful tool for evaluating vegetation 

characteristics, it does not consider fine-scale landscape 

attributes and geographic features important for wildlife 

species considered by the GAP approach. As a result, some 

aggregation to coarser scales (e.g., 1-hectare) is necessary in 

order to account for imputation bias.

Current efforts combining GAP and GNN data layers 

provides the ability to integrate the complex vegetation 

characteristics of GNN with the fine-scale presence of 

geographical features and land cover of GAP. The result is 

a more-precise process for ranking habitat for conservation priority based on known species distributions, and is 

currently being used in analyses such as for the Northwest 

Regional Gap Analysis Project (Re-GAP; http://gap.uidaho.

edu/index.php/gap-home/Northwest-GAP; fig. 4c).

Confusion can result if decision-makers then attempt 

to compare results from STMs to GAP, GNN, Re-GAP 

or other moderate and coarse-scale assessments. For 

example, the American marten is a mature-forest species 

with a distribution across much of northern North America 

(Powell et al. 2003). In Oregon, GAP results predict habitat 

in occupied watersheds within mountain ranges (fig. 5a). 

More specifically, the observational unit (habitat) is predicted at the 30-meter scale only for watersheds that include 

known occurrence of martens. What if STM analysis 

evaluates distribution of marten habitat across Oregon, with 

habitat as the observational unit, but evaluation includes all 

watersheds regardless of marten occurrence? The discrete 

difference in how watersheds were selected (marten occurrence versus complete enumeration based on habitat characteristics) greatly affects interpretation of results (fig. 5b). 

Specifically, the evaluation based on complete enumeration 

appears to resemble historic range of marten as identified by 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Figure3a—Black-backed woodpecker range presented at the 30-meter scale illustrates 

mainly dry forest habitat relationships for this species in Washington and Oregon, with some 

extension to the west side of the Cascade Mountains (INR 2011a). 

Figure 3b—Visualization of results at the HUC5 scale suggest a larger total range area than 

when visualized at the 30-meter scale because of aggregations of habitat information presented in relation to extent of associated watershed boundaries. Appreciation of both species 

ecology and scale of inference are needed for accurate assessment of habitat information.
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GENERAL TECHNICAL REPORT PNW-GTR-869

GAP analysis (fig. 5c) rather than current known distribution. The GAP and STM analyses both focused on the 

same observational unit and incorporated GNN data, but 

the slightly different objectives, specific selection process 

of observational units, and scales of analysis results in 

two different presentations of results. As a result, clarity 

of objectives and acknowledgement of differences of scale 

allows for understanding of why results may vary across 

potentially seemingly similar evaluations.

Conclusion

The objectives of this paper were to establish the importance of wildlife habitat analysis in policy and planning 

decisions and highlight some factors to be considered 

when balancing feasibility and precision of wildlife habitat 

analysis using STM data. Wildlife is important to policymaking for both ecological and social reasons. Evaluation 

of wildlife habitat is a complex process, and analysis can 

vary based on the observational unit, the ability to match 

species-habitat relationships with data related to other planning objectives, individual evaluation of information used 

to construct habitat models, and scale used for analysis. 

Even the most-studied species provide researchers limited 

information because of the complexities inherent in ecological systems. Therefore, results must be interpreted carefully 

with appropriate scrutiny based on the scope of the data.

Acknowledgments

The authors thank Joseph Bernert, Stephanie Graham, 

Maggie Massie, Emilie Henderson, three anonymous 

reviewers, and the technical editors of this general technical 

report. This research is part of the Integrated Landscape 

Assessment Project funded by the USDA Forest Service, 

PNW Research Station, under the American Recovery and 

Reinvestment Act.

Figure 4—Research objectives, data used, and scale of analysis 

affect interpretation of data. Illustrated here are HUC5 watersheds 

in proximity to the Three Sisters and Mt. Washington Wilderness 

areas of the Cascade Mountains in Oregon. a) GAP used 30-meter 

resolution land cover data to extrapolate habitat based on known 

species distribution and data derived from individual pixels. Geographic features are preserved from land cover data. b) The GNN 

approach imputes vegetation characteristics based on focal and 

neighboring FIA plot data. Geographic features are not preserved 

in the imputation process c) Combined GAP and GNN provides 

the ability to integrate vegetation complexity and preservation of 

geographic features derived from raw land cover data. Darker gray 

colors indicate relatively “better” habitat than white and lighter 

gray colors. (Source: Oregon Natural Heritage Information Center 

2005 updated ODFW Conservation Straegy GAP Analysis).

B

C

A
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Figure 5—Comparing results from different 

habitat modeling efforts can have mixed results 

if research objectives, data used, and scale of 

analyses are not consistent between analyses. 

a) In Oregon, GAP predicts American marten 

habitat among mountain ranges of both states 

(INR 2011b). In a second analysis, results 

visually represent broader distribution because 

of varying objectives, data used, and scale of 

analysis from those of GAP. c) In fact, projected 

distribution of marten by within the second 

analysis "b" suggests closer relationship to 

historical distribution of the species than 

present occurrence (INR 2011b).

b)

c)

a)
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Authors

Xiaoping Zhou is a research economist, xzhou@fs.fed.us. 

Miles A. Hemstrom is a research ecologist (retired), Forestry Sciences Laboratory, 620 SW Main St. Portland, OR 

97205. He is now a faculty research associate, Institute for 

Natural Resources, P.O. Box 751, Oregon State University, 

Portland, OR, 97207, miles.hemstrom@oregonstate.edu.

Abstract

The Integrated Landscape Assessment Project (ILAP) 

uses spatial vegetation data and state and transition models 

(STM) to forecast future vegetation conditions and the 

interacting effects of natural disturbances and management 

activities. Results from ILAP will help land managers, planners, and policymakers evaluate management strategies that 

reduce fire risk, improve wildlife habitat, and benefit rural 

communities. This case study illustrates the methodology 

for modeling timber volume, biomass estimates, and the 

forest carbon over time with the output of STM simulations. 

It presents how the Forest Inventory and Analysis data 

were applied to assist the interpretation of STM simulation 

results and derives useful information to the public. The 

method was applied in the central Washington study area to 

project the timber production, biomass supply potential, and 

aboveground carbon stock for two alternative management 

scenarios.

Keywords: State and transition model, biomass estimation, timber volume, aboveground tree carbon.

Introduction

The Integrated Landscape Assessment Project (ILAP) 

forecasts potential future vegetation conditions under longterm landscape management scenarios using Path (http://

www.apexrms.com) and Vegetation Dynamics Development 

Tool (VDDT) (ESSA 2007), a state and transition modeling 

(STM) platform. Future vegetation conditions and the disturbance information from STM simulations are summarized 

in terms of acres by management allocation (ownership and 

management types), watershed, and vegetation state classes. 

The simulation output does not carry the key variables 

for estimating volume and biomass over time during the 

simulation such as tree species, diameter, and height. The 

vegetation conditions are summarized by management 

allocation for each watershed (table 1a), and the disturbance 

effects are also displayed by acres with detailed information 

about the state class changes associated with disturbance 

types (table 1b). The challenge for people who are interested 

in information about timber products and biomass is how 

area-only-related outputs can be translated to the desired 

attributes. The approach developed for this study uses 

Forest Inventory and Analysis (FIA) plot information to 

build look-up tables with volume and biomass attributes 

linked to each state class for each region. This paper will 

describe procedures to develop look-up tables for calculating timber volume and biomass as well as tree carbon from 

VDDT output. The method is demonstrated for central 

Washington to get the estimates of timber volume, biomass, 

and aboveground live tree carbon for both management and 

non-management scenarios. The method can be applied to 

other landscapes in the ILAP study regions.

Study Area

The study area is in central Washington and encompasses 

2.65 million acres of mostly forested land in 25 5th hydrologic unit code (HUC) level watersheds and 16 combinations of ownership and management types (fig. 1). The 

terrain is mountainous with the highest peaks at over 7,874 

feet (2400 m) elevation and the lowest valley bottoms at less 

than 1,640 feet (500 m). Forested vegetation ranges from 

very dry environments that support ponderosa pine (Pinus 

ponderosa Lawson and C. Lawson) stands to upper elevation forests of subalpine fir (Abies lasiocarpa (Hook.) Nutt.) 

and mountain hemlock (Tsuga mertensiana (Bong.) Carrière). Much of the forest consists of mixed-conifer stands 

Forecasting Timber, Biomass, and Tree Carbon 

Pools with the Output of State and Transition Models

Xiaoping Zhou and Miles A. Hemstrom
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GENERAL TECHNICAL REPORT PNW-GTR-869

dominated by Douglas-fir (Pseudotsuga menziesii (Mirb.) 

Franco), grand fir (Abies grandis (Douglas ex D. Don) 

Lindl.), and ponderosa pine.

Data and Methodology

The existing vegetation conditions in ILAP came from 

Gradient Nearest Neighbor (GNN) imputation of inventory 

plots to 98-feet (30-m) pixels (Ohmann and Gregory 2002) 

http://www.fsl.orst.edu/lemma/method/methods.php) and 

other geographic information system (GIS) layers. Each 

30-m pixel was associated with an FIA inventory plot. To 

develop the look-up table with the attributes of volume 

and biomass, the plots initially assigned to the GNN pixels 

for the modeling region were retrieved from the inventory 

database with the detailed individual sample tree information including species, diameter, and height. The following 

steps were taken to build the volume/biomass look-up table 

and estimate the volume and biomass:

• Step 1: Collect the plot data (tree list) from the

 FIA periodic or annual data used in GNN.

• Step 2: Assign a state class to each plot using 

predefined rules.

• Step 3: Calculate the volume and biomass for 

each plot associated with the state class using 

the equations documented in Zhou and 

Hemstrom (2010).

• Step 4: Summarize volume and biomass attributes for each plot and by product groups or 

for the total.

• Step 5: Compute average volume and biomass 

attributes for each state class and build a 

look-up table.

Table 1a—VDDT inventory output example—dry mixed conifer PVT 

for general management in the Washington East Cascades ecoregion

Time Watershed code Ownership State class code Acres

1 104 Forest Service 22210 21.91

1 104 Private 322705 30.11

1 105 Washington state 182705 43.00

1 131 Yakama Nation 332705 8.91

... ...

5 104 Forest Service 372705 20.50

5 104 Washington state 462235 15.00

5 105 Forest Service 452235 50.90

5 106 Private 192210 18.71

5 131 Yakama Nation 462235 110.21

Table 1b—VDDT Output example for disturbances—dry mixed conifer PVT model for general 

management on Forest Service lands in the Washington East Cascades ecoregion

Watershed From state To state Treated

Time code Disturbance class code class code acres

1 104 Partial harvest, small—medium size 242210 182210 40.5

 and medium density

1 105 Partial harvest, small—medium size 312210 262210 31.9

 and medium density

1 106 Partial harvest, small—medium size 292705 282705 11.1

 and high density

2 105 Partial harvest, small –medium size 212705 182705 20.4

 and high density

3 105 Partial harvest, large—medium size 362705 352705 7.8

 and high density

3 106 Partial harvest, small—medium size 282210 262210 16.0

 and medium density
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

• Step 6: Fill data holes for state classes with 

inadequate plot representation using a hierarchical rule set for state class similarity.

• Step 7: Calculate volume and biomass by 

matching the state class from the VDDT output 

to the look-up table to generate estimates of 

volume and biomass by landscape stratum and 

year.

• Step 8: Calculate volume and biomass 

removals based on the difference in each state 

class before and after treatment assuming that 

the treatment happens middle of the year and 

there is no growth during the treatment year.

Look-up tables are built for each different landscape 

eco-region. Table 2a shows an example of total volume and 

biomass for each state class and potential vegetation type 

(PVT) for the Washington East Cascades (WEC). Volume 

attributes include total volume (for trees with a diameter at 

breast height (dbh > 1 in), merchantable tree volume (dbh 

> 5 in) and sawtimber volume (dbh > 9 in for softwood and 

dbh > 11 in for hardwood); biomass attributes include biomass of stem, branch, bark and leaf; the total biomass is the 

sum of all these parts. Merchantable biomass is computed 

separately. Table 2b is an example of a look-up table with 

detailed product classes for the WEC ecological region. Five 

products are defined in this study based on tree diameters: 

(1) small tree—dbh <5 in; (2) chip tree—5 in ≤dbh <7 in; 

Figure 1—Central Washington landscape study area.
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(3) pole tree—7 in ≤dbh <9 in for softwood and 7 in ≤dbh 

< 11 in for hardwood; (4) small sawtimber—9 in ≤dbh 

<20in for softwood and 11 in ≤dbh <20 in for hardwood; 

and (5) large sawtimber—dbh ≥20 in.

Two management scenarios were simulated for 300 

years using STMs in ILAP. The no-management scenario 

assumed no-management treatments except continued current levels of wildfire suppression; unsuppressed wildfires, 

and other natural disturbances were allowed to run their 

course. The current management scenario was based on 

estimated forest management treatment rates currently in 

place by land ownership and management types. Management type differed widely among ownerships and land 

allocations from no treatment (except wildfire suppression) 

in wilderness and similar reserved areas to commercial 

timber harvest on private timberlands. We worked with 

local collaborative groups to gather estimated treatment 

rates by ownership and land allocation. Treatments included 

regeneration harvests, commercial thinning, precommercial 

thinning, tree planting, prescribed fire, and mechanical fuel 

treatments. Wildfire probabilities were computed by PVT 

(dry, moist, and cold forest) using data from the Monitoring Trends in Burn Severity study (http://www.mtbs.gov, 

accessed on 11/29/2011) and reflect wildfire occurrence in 

the Washington East Cascades ecological region for the 

1984 to 2008 time period.

Table 2b—Volume (cubic feet/acre) and biomass (pounds/acre) look-up table example by product groups in 

the Washington East Cascades ecoregion

Potential State Product Total Merchantable Stem Branch Total

vegetation type class code group volume volume ... biomass biomass biomass

cf/ac cf/ac lb/ac lb/ac lb/ac

Dry mixed conifer 332705 Small tree 27 0 326 1,942 3,988

Dry mixed conifer 332705 Chip tree 29 9 278 140 577

Dry mixed conifer 332705 Pole tree 42 11 312 93 502

Dry mixed conifer 332705 Small saw 489 375 10,303 2,176 14,826

Dry mixed conifer 332705 Large saw 602 582 ... 15,024 2,821 20,703

Pacific silver fir 392710 Small tree 131 0 2,325 2,625 6,651

Pacific silver fir 392710 Chip tree 150 92 3,378 1,027 5,396

Pacific silver fir 392710 Pole tree 248 204 6,011 1,543 9,012

Pacific silver fir 392710 Small saw 3,630 3,470 93,675 21,125 132,599

Pacific silver fir 392710 Large saw 6,642 6,420 ... 171,187 39,810 246,362

Table 2a—Volume (cubic feet/acre) and biomass (pounds/acre) look-up table example (total) in the 

Washington East Cascades ecoregion

Potential State Total Merchantable Stem Branch Total

vegetation type class code volume volume ... biomass biomass biomass

cf/ac cf/ac lb/ac lb/ac lb/ac

Dry mixed conifer 332705 1,031 977 24,886 3,778 40,597

Dry mixed conifer 342705 3,835 3,642 97,080 15,004 147,451

Dry mixed conifer 352705 2,732 2,582 67,771 10,765 105,275

Dry mixed conifer 362705 8,584 8,242 237,485 36,817 341,420

Dry mixed conifer 372705 1,866 1,767 ... 43,816 6,260 66,699

Pacific silver fir 342710 7,286 6,960 178,732 27,411 274,912

Pacific silver fir 352710 1,203 1,144 31,421 6,303 58,604

Pacific silver fir 392710 10,733 10,186 262,566 38,511 400,021

 Pacific silver fir 422710 4,492 4,324 106,910 17,916 159,528
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Results

The Biomass Removals From 

Fuel Reduction Treatment

The materials removed by fuel reduction treatment include 

merchantable timber products, and small trees or other 

non-merchantable parts of the trees such as branches, 

bark, and tops, which might be used as biomass for energy 

production or other purposes. Of course, depending on how 

the biomass is defined, potential biomass removals will be 

different. For simplicity, we considered biomass removals to 

include everything but foliage; a definition that can be easily 

changed since biomass components are computed separately. Simulated removals from Forest Service lands and 

Yakama Tribe lands increase gradually over the first two to 

three decades and then stabilize. The removals from private 

lands are relatively low during the entire simulation period 

because of the low acreage of privately owned lands in this 

study region (fig. 2). The removals from Washington state 

forests initially are about 67 percent of the total removals 

from all ownerships, it decreases dramatically for the first 

three decades to less than 40 percent of the total, ultimately 

stabilizing at 20 to 25 percent of the total biomass removals. Conversely, biomass removals from Forest Service and 

tribal lands increase steadily. The removals from Forest 

Service lands average 11 percent at the beginning and 

reach 25 percent at the end of 50 years, and removals from 

tribal lands during the same period increase from 19 to 35 

percent. The removals from private forest land are projected 

to increase from 3.2 to 5.3 percent of the total biomass 

removals for the first 50 simulation years.

The Merchantable Tree Volumes

The total standing merchantable tree inventory increased 

rapidly under both management and no-management 

scenarios during the first three decades, stabilized for 10 to 

20 years, and then decreased (fig. 3). The inventory under 

the current management scenario peeks at 10.9 billion cubic 

feet around year 33, about 13.5 percent higher than the 

initial inventory, and then declines. The inventory under 

the no-management scenario reaches its maximum, 11.5 

billion cubic feet, around year 40, 20 percent more than the 

beginning inventory; remains steady for about a decade, 

then declines. In both scenarios, the inventories at the end 

of simulation period are above original levels although the 

merchantable tree inventory under the no-management scenario is projected to be 9.7 percent higher than the inventory 

under the management scenario.

Figure 2—Total tree biomass removals by human disturbances (fuel treatments).
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Aboveground Tree Carbon Stock Over 

Simulation Period

We estimate the tree carbon pool by multiplying whole-tree 

biomass by 0.5 (Heath 2007). Although other carbon pools, 

such as those in dead trees and soil, are equally important 

on forest lands, we calculate only the tree-based, aboveground carbon pool. The aboveground tree-based carbon 

stock varies over time under different management scenarios. The average total aboveground, tree-based carbon 

stock increased for 21 of the 25 watersheds during the first 

50 years (fig. 4). Carbon pools decline slowly in 11 watersheds during the second fifty year and remain relatively 

constant in the rest. The aboveground live tree carbon stock 

was relatively stable after the first century for almost all the 

watersheds.

Discussion and Conclusion

State and transition models can project vegetation structures 

and landscape condition over time for different management 

assumptions across a variety of vegetation types, land ownerships, and land management allocations. Forest inventory 

Figure 4—Aboveground carbon stock over time (tons/acre).

Figure 3—Total merchantable tree inventory in central Washington.
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data provide plot and tree level information to build look-up 

tables with the attributes of tree volume and biomass that 

can be attached to STM output. This process provides 

useful information to forest managers and other decision 

makers for forest management planning. Our methods could 

be applied to generate similar look-up tables for soil carbon, 

dead tree biomass, and other landscape attribute estimates. 

These estimates can be used to examine the potential longterm effects of various management approaches on forest 

vegetation conditions (including wildlife habitats, fuels, and 

others) and general amounts, types, and production locations of economically valuable forest products.

Acknowledgments

This project was funded by a grant from the American 

Recovery and Reinvestment Act, the USDA Forest Service 

Pacific Northwest (PNW) Research Station. Several other 

entities provided information and data, including the Washington Department of Natural Resources and the Tapash 

Collaborative. Inventory plot data from the Forest Inventory 

and Analysis effort at the PNW Research Station and the 

Pacific Northwest Region of the USDA Forest Service were 

invaluable.

References

ESSA Technologies Ltd. 2007. Vegetation Dynamics 

Development Tool user guide, Version 6.0. Vancouver, 

BC. 196 p.

Heath, L.S. 2007. Section 3.1—Methodology for estimating 

net carbon stock changes in forest lands remaining forest 

lands. In: Inventory of U.S. greenhouse gas emissions 

and sinks: 1990–2005. Unpublished document. On file 

with: Northern FIA Program, Durham, New Hampshire.

Ohmann, J.; Gregory, M.J. 2002. Predictive mapping of 

forest composition and structure with direct gradient 

analysis and nearest neighbor imputation in coastal 

Oregon, U.S.A. Canadian Journal of Forestry. 

32: 725–741.

Zhou, X.; Hemstrom, M.A. 2010. Timber volume and 

aboveground live tree biomass estimations for landscape 

analyses in Pacific Northwest. Gen. Tech. Rep. PNWGTR-819. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 31 p. 

http://www.fs.fed.us/pnw/pubs/pnw_gtr819.pdf.
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123

Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Authors

Donald C. Robinson and Sarah J. Beukema are senior 

systems ecologists, ESSA Technologies Ltd, 600-2695 

Granville Street, Vancouver, BC, V6H 3H4, Canada, 

drobinson@essa.com and sbeukema@essa.com. 

Abstract

Land managers commonly use FVS and VDDT as planning 

aids. Although complementary, the models differ in their 

approach to projection, spatial and temporal resolution, 

simulation units and required input. When both are used, 

comparison of the model projections helps to identify differences in the assumptions of the two models and hopefully 

will result in more consistent results across models.

We used the transition probability matrix as the basis 

for comparing the two models, using side-by-side simulations with FVS and VDDT to project 250 mixed conifer 

stands. We designed and carried out a simulation experiment with managed and unmanaged scenarios, to explore 

the consequences of different approaches to filtering FVS 

outputs by removing censored and rare observations, as 

well as smoothing out side-effects such as jitter which result 

from creating categories from continuous data.

Our analysis includes verification of the Preside system, 

comparison of matrix behavior, as well as the behavior of 

VDDT with FVS runs imported into VDDT. Three useful 

conclusions are that: (1) including rare transitions from FVS 

is important to getting reasonable temporal dynamics; (2) 

initialization and censoring issues can be ignored; and (3) 

smoothing and jitter can also be ignored.

One surprising conclusion is that very different 

assumptions about regeneration cause VDDT and FVS 

results to be profoundly different for species, size and 

canopy structure. One nagging question is “how can we tell 

which model is right?” Field observations would help, and 

iterative model revision of both FVS and VDDT models is 

also helpful to a point. Our best advice is to use each model 

to challenge and improve the assumptions of the other, 

using each model to illuminate the “blind spots” of the 

other.

Keywords: Forest Vegetation Simulator, Vegetation 

Dynamics Display Tool, FVS, VDDT, scale, simulation.

Introduction

Land managers use simulation tools to help them analyze 

and understand how different management scenarios or 

disturbance regimes will affect future landscape conditions. Two commonly used tools are the Forest Vegetation 

Simulator (FVS; Stage 1973, Crookston and Dixon 2005) 

and the Vegetation Dynamics Development Tool (VDDT1; 

ESSA Technologies Ltd. 2007). The foundations of FVS lie 

in four decades of empirical growth and yield modeling and 

forest management, while the roots of VDDT are found in 

the lineage of probabilistic ecosystem succession models. 

Although both may be used to simulate the same landscape 

and vegetation, the models differ in their approaches to 

projecting vegetation change, in the spatial and temporal 

resolution of projections and in the fundamental simulation 

units, processes and required input data.

The decision to use FVS or VDDT can depend upon 

circumstances such as data availability, disturbance dynamics, forest management strategies, the presence of nonforested conditions, and the scale and type of issues that are 

being addressed. When there is an opportunity to use both 

models to project the same landscape, it raises the possibility of comparing the projections. Such comparisons can 

draw attention to differences in the projections, which can 

result in a more thorough understanding of the two modeling systems. Going deeper, they also create the opportunity 

to compare specific common parameters; to consider the 

reasons for any difference and to decide whether there are 

Through a Glass, Darkly—Comparing VDDT and FVS

Donald C.E. Robinson and Sarah J. Beukema

1 VDDT simulations can also be carried out using the Path Landscape model 

(http://pathmodel.com), in which the VDDT state transition model is embedded.
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GENERAL TECHNICAL REPORT PNW-GTR-869

compelling reasons to transfer a parameter estimate made 

by one model, for use in the other. If good reasons exist for 

modifying previously-accepted VDDT parameters based on 

FVS, then this could be termed “calibrating” VDDT with 

FVS.

At the finest resolution, FVS projects the change in 

density, diameter- and height-growth of a collection of 

individual trees (the stand) and simulates the development 

of a non-spatial inventory over time, and often with management activities. When needed, attributes of the whole 

stand (e.g., percent canopy cover, quadratic mean diameter, 

basal area) can be easily derived from the finer scale 

attributes of individual trees. FVS simulations frequently 

use a 5- or 10-year timestep. In its most basic use, VDDT 

uses state-transition probabilities to simulate temporal 

changes in vegetation classes on an annual timestep. These 

probabilities are often developed in collaborative workshops 

at which ecologists synthesize published literature and 

their experience with a particular landscape or ecosystem, 

to define the vegetation classes found on the landscape, 

and the transitions between the classes. VDDT models 

can range from very simple models with a few classes, 

to very complex models with dozens of vegetation states, 

along with the disturbance and succession probabilities that 

control how vegetation classes change. The fundamental 

unit of resolution of VDDT is a physical abstraction called 

the pixel2. Pixels can represent almost any vegetation state 

(e.g., “open single-story ponderosa pine dominant” or 

“mature tall-grass prairie”), but do not explicitly represent 

an element or cell of any particular size or location in a 

spatial landscape map. Rather, each pixel represents an 

instance of one of the possible vegetation states defined for 

the landscape. Frequently, VDDT users use 1,000 pixels, 

but the actual number is arbitrary and is chosen to provide 

enough detail for the management questions under study. 

Over time, pixels change between different vegetation states 

in response to succession or disturbance using user-defined 

deterministic or probabilistic rules.

VDDT models usually define vegetation classes and 

transition probabilities based on a mixture of literature and 

expert judgment. Because of this subjective element, some 

land managers believe that VDDT models could be put on 

a stronger footing by incorporating parameters estimated 

from FVS simulations, thus providing more defensible land 

use decisions. In this way, the empirical basis of FVS might 

serve as an additional source of guidance for VDDT.

The key step in bridging the two models is to translate 

FVS results so that the two models have a common basis. 

This is achieved by post-processing FVS landscape simulations to create vegetation classes that are identical to those 

used by VDDT, then computing transition probabilities 

based on the class changes that take place during the 

FVS stand simulation. A software program called Preside 

(Moeur and Vandendriesche 2009)3 has been created for 

this translation step, using the FVSSTAND keyword and the 

FVSStand (Vandendriesche 1997) post-processer. Tabular 

results from FVSStand are processed by Preside to create a 

classification of stand structure over time, classes that are 

user-defined and compatible with the class definitions used 

to create VDDT models for the same landscape. Once the 

classification step is complete, Preside creates a matrix of 

the occurrence and frequency of transitions between different states, similar to the transition matrix created by VDDT.

Transitions defined through FVS are the result of its 

internal dynamics combined with post-simulation classification, while those found in VDDT are defined by a 

model developer. In the case of VDDT, the model developer 

defines all possible transitions in advance, while transition 

pathways and probabilities predicted by FVS are derived 

from the detailed growth, mortality and regeneration of 

individual trees in stands simulated over time. 

However they were created, the results of the VDDT 

and FVS simulations use a common set of vegetation 

classes, and the transitions between those classes should 

be comparable. That said, the differences between the two 

models—the continuously varying world of FVS and the 

2 Pixel is technically a misnomer and is unrelated to “picture elements” or remotely sensed data or Geographic Information 

Systems. This label for VDDT simulation units was coined many years ago and has come into common parlance.

3 Available at: http://www.fs.fed.us/fmsc/ftp/fvs/software/pre_post/Preside_z.exe.
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class- and transition-based world of VDDT—are not simple 

to overcome. While class models are usually much simpler 

in structure, both models have subtle assumptions that can 

produce large effects, and the introduction of class boundaries to FVS simulations can create significant artifacts. As 

this paper shows, removing these artifacts is not trivial, and 

can introduce changes to the “signal” of FVS-based transition parameters.

Materials and Methods

As key factors in this study, we created simple managed 

and unmanaged scenarios in both FVS and VDDT and went 

on to analyze those simulations in different ways. Facing 

the challenges of comparing two models with markedly 

different approaches, we tested a variety of approaches for 

presenting the data from each model in ways that fostered 

comparisons. As there are inherent issues with trying 

to classify a continuous landscape, we performed extra 

analyses using the unmanaged FVS scenario to explore the 

sensitivity of our classification boundaries.

Scenario Design

To compare the landscapes predicted by the native-VDDT 

and FVS outputs converted to VDDT transitions, we made 

300 year simulations of Unmanaged and Managed scenarios 

(see table 1) using both VDDT and FVS. Other scenarios 

were explored as well, as described below. 

Table 1—High level summary of the simulation 

scenarios 

Management 

Scenario VDDT FVS

Unmanaged Succession + Unmanaged

Natural Disturbance

Managed Succession + Restoration

Natural Disturbance + management

Data Simplification Scenarios 

All Use all observed transitions

Sub Use a subset, removing transitions that 

 occur fewer than 5 times

Base Include all timesteps

Drop Zero (DZ) Remove transitions in the first timestep

Management scenarios—

Two FVS scenarios were compared with matching VDDT 

simulations. After the results of the FVS simulations were 

processed with FVSStand, some analyses used a custom 

Excel spreadsheet to implement different smoothing 

methods (described below), which were then exported 

and formatted to conform to the Preside input file format. 

VDDT outputs were similarly treated, so that both modeling 

systems were processed identically as much as possible. 

Preside was configured to use the classification rules shown 

in table 2, and the information about the state of each stand 

at each timestep was used to create VDDT-like transition 

probability matrices. These matrices contained between 42 

and 58 different state classes, depending on the scenario 

and model.

Data simplification scenarios—

One of the outputs of the Preside model is an estimate of the 

annual probability of changing from one vegetation class to 

another, based on FVS outputs. It is an attempt to classify 

FVS in the language of VDDT. Using options in Preside, 

we examined two different approaches to calculating the 

probability of different transitions. The first approach we 

used—All—accepts all observed transitions; even those 

that are not observed very frequently. This allows greater 

successional complexity to be captured, but rare transition 

parameter estimates may not be very precise. The second 

approach we examined—Sub—removed transitions that 

were observed fewer than 5 times: less than 1 percent of the 

total number of transitions. The implicit hypothesis underlying this approach is that more frequent transitions are more 

important to the overall successional pattern and that less 

frequent transitions can be ignored. 

As a second part of this exercise we explored the 

impact of removing a model initialization artifact from the 

FVS results. The first approach—Base—includes the initial 

timestep in Preside’s calculation of classes and transitions. 

The second treatment—Drop Zero—accounts for the fact 

that two kinds of initialization artifacts can incorrectly 

influence the estimation of transition probabilities. The first 

is caused by the internal calibration of FVS growth and 

mortality to local site conditions during model initialization. 
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Program Configuration

FVS— 

We used 500 FVS inventories (two inventories of 250 stands 

taken at different times) taken from mixed conifer dry 

site stands from the East Cascades of Washington, USA, 

compiled and prepared as part of the IMAP study (Miles 

Hemstrom, pers. comm.). These stands are principally made 

up of Douglas-fir and ponderosa pine. The 500 FVS inventories were projected using 10 year timesteps for 200 years 

using the East Cascades FVS variant (model date: 04/07/09). 

Ingrowth in open stands was simulated by using the Repute 

Event Monitor keyword package (Vandendriesche, 2009). 

The FVS main output file and the supplementary detailed 

output file created by the FVSStand keyword were then 

post-processed using the FVSSTAND Alone program, 

creating a set of input files for Preside.

After an initial analysis of the inventory, the FVS 

East Cascades variant was calibrated by adjusting small 

and large tree growth increments and maximum SDI and 

basal area (see Appendix 1 for details). In the unmanaged 

scenario, FVS was calibrated and run with the Repute-based 

Table 2—Stand classification variables defined for the Preside program

Variable Variable

Name Type Class Definition

DOMTYPE Categorical Classification of the dominant tree species for the landscape.

 PP early seral—LAOC, PICO, PIPO or TEIX leading

 DW late seral—DF, WF or any other species leading

QMD Continuous Quadratic mean diameter (cm) of the >80th percentile largest-diameter trees

 Y <5cm—young seedling/sapling

 P 5-10cm—pole

 S 10-15cm—small tree

 M 15-20cm—medium tree

 L 20-30cm—large tree

 G >30cm—giant tree

CANCOV Continuous Canopy cover (percent)

 n <10 percent—non-stocked

 o 10-40 percent—open canopy

 m 40-70 percent—medium canopy

 c 70 percent—closed canopy

 — Unclassifiable

STORY Categorical Number of canopy layers

 1 one story or poorly defined

 2 two story

— Unclassifiable

This adjustment is reported in the first simulation timestep, 

and can create “pseudo-transitions” that are unrelated to 

natural succession. The second artifact is caused by the fact 

that the first transition observation lacks any information 

about how long the stand was in its initial vegetation state 

prior to the transition. 

Smoothing scenarios—

Classifying a landscape made up of continuous variables 

(such as height or QMD) into discrete variables carries the 

risk that the classification can change in more than one 

direction, especially if any variable is near a classification 

boundary. In this case a stand can move back and forth 

between two classes, creating jitter. We developed and 

tested two different ways to smooth the classification of 

FVS stand output to see if any would reduce the jitter or 

impact the landscape-level results. Detailed descriptions 

of the these methods and their results are in found in 

Appendix 2.
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regeneration information described above. For the managed 

scenario we implemented a simple management system 

with a restoration focus in which stands are thinned from 

below whenever they exceed 65 percent of the maximum 

SDI, removing smaller stems until the SDI is reduced to 

50 percent of the maximum SDI, preferentially retaining ponderosa pine. These limits were selected based on 

Cochrane and others (1994), who recommend that stands be 

managed between 50 percent and 75 percent of full stocking. With these two thresholds, stands are re-entered every 

20 to 40 years. Over time, this system creates open forests 

of large, older ponderosa pine, using smaller trees for low or 

moderate amounts of timber. Like the unmanaged scenario, 

management simulations were run with the calibrated 

model using 10 year timesteps for 200 years. 

VDDT—

VDDT diagrams were defined for the landscape and 

included numerous pathways representing succession, natural disturbances from bark beetles, mistletoe, wild fire and 

management (J. Merzenich, pers. comm.). For the unmanaged simulation, all management pathways were disabled. 

Since base FVS mortality rates include mortality from 

background disturbance, we kept most disturbance pathways active to make the FVS and VDDT simulations more 

comparable. The only disturbance type that was excluded 

from the unmanaged simulations was stand-replacing fire, 

since this level of disturbance is not consistent with the FVS 

assumptions.

Simulations of 1000 pixels were initialized by assigning 

pixels evenly over most of the VDDT vegetation classes. 

We then simulated VDDT for 300 annual timesteps; saving 

information about the transitions that occurred in each 

timestep and printing the detailed landscape state class 

output every 10 timesteps to emulate the temporal resolution of FVS. We then formatted the output so that it could 

be provided to Preside in the same way as FVS. Finally, we 

ran Preside using the output generated by VDDT, allowing 

Preside to independently calculate the transition probabilities, in order to verify the Preside algorithms.

4 Although stochastic transitions are commonly found in VDDT models, VDDT can also be parameterized for other modes of 

state change, including combinations of deterministic and stochastic rules.

The VDDT management scenario was based on the 

same model diagram and initial conditions as the unmanaged scenario, activating some of the management pathways 

and simulating these pathways using a file of input multipliers which defined multipliers for a private lands scenario. In 

order to keep the managed scenario as similar as possible to 

its FVS management counterpart, we excluded the simulation of prescribed fire. As with the unmanaged VDDT runs, 

we ran the model for 300 years, saving transition information in all years, and state information every 10 years. 

Preside—

Preside takes classification rules provided by the user and 

applies those criteria to the output of FVS landscape projections, classifying stand structure in a way that is compatible 

with VDDT. When processing is complete, FVS stand 

structure is classified at each timestep with user-defined 

rules corresponding to VDDT vegetation states, along with 

the probability of changing from one vegetation state to 

another. Preside organizes these transition probabilities into 

a matrix that can be compared with a matrix of transition 

probabilities created by VDDT for the same landscape4

Following a comparison step, VDDT transition probabilities 

can be adjusted to be more FVS-like, if desired.

To faithfully recreate the VDDT classification for the 

mixed dry landscape, we configured Preside to classify 

each stand and timestep using the classification variables 

and breakpoints shown in table 2. Preside labelled each 

stand’s state at each timestep, constructing a label from the 

concatenation of the four variables:

DOMTYPE + QMD + CANCOV + STORY

Using this coding, the label PPGo2 indicates a pineleading (PP) stand of giant (G) diameter class, with an open 

canopy (o) having two (2) stories.

The first step taken by Preside is the classification 

of each stand, so we began by verifying its classification 

algorithm. To do this, we exported results from the FVS 

simulation of the unmanaged landscape scenario simulation 

to a database using the FVS Database Extension (Forest 
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GENERAL TECHNICAL REPORT PNW-GTR-869

probabilities through the separate transformations for the 

diagonal terms of the matrix (Eqn. 1: probability of remaining in the same vegetation state) and for the off-diagonal 

elements (Eqn. 2), in which M' is the resulting square 

matrix of 1-year transition probabilities: 

[Eqn 1]

[Eqn 2]

In cases where a diagonal entry is absent from the 

matrix (i.e., in which all pixels in vegetation class i change 

class in a single timestep), we found that row totals calculated by Preside do not always sum to 1.0, a necessary condition for accounting for all transitions over the landscape. 

However, the impact of this problem is small since VDDT 

does not actively make use of the probability of remaining 

in a vegetation class, and uses only the probability of changing state. Hence any imbalance in the transition calculations 

will be offset by the implicit value of the diagonal, which 

will result in an implicit row total of 1.0.

However, the annualized probabilities of M' computed 

using Eqn. 1 and 2 do not account for transitions to intermediate classes at the 1-year time scale, which in the case of 

high transition probabilities (i.e., fast transitions) can lead 

to different vegetation classes from those calculated at the 

10-year timestep. It is therefore possible that when simulated at a faster timestep, the Preside time-scaling method 

will incorrectly simplify transitions that lead to different 

pathways.

As an alternative method, we used a classical matrix 

analysis approach to change from the decadal to annual 

time scale, to compare the matrices produced by FVS and 

VDDT. Beginning with the matrix M produced by Preside, 

we transformed to the annual probability matrix M' by 

transforming M to diagonal form (Eqn. 3), where V and λ

are the matrix of eigenvectors and array of eigenvalues of 

M, respectively. 

 [Eqn. 3]

Eqn. 4 then rescales the eigenvalues of the diagonal 

matrix D from a decadal to an annual interval leaving the 

eigenvectors unchanged.

 [Eqn. 4]

M = V -1 D(λ) V

M' = V -1 D(e

1n(λ) /10) V

Vegetation Simulator Staff 2003). We then classified those 

results using our own SQL queries and compared our stand 

classification with the classification created by Preside. 

Using the same stratification and breakpoints used by Preside, our checks agreed with the Preside classification. After 

further checks we also concluded that Preside’s calculation 

of class-transitions were correct.

Consistency of VDDT and VDDT—Preside—

VDDT Transitions

As in internal consistency check of our arithmetic and of 

the methodology for processing transitions with Preside, 

we compared the distribution of vegetation classes created 

directly from VDDT against VDDT output that had been 

processed through Preside and then simulated by VDDT. 

We expected that after allowing for stochastic variation, 

a comparison of the results these simulations would show 

them to be identical, and that this would verify the mathematical steps upon which the translation of FVS results 

are based.

We began this check by performing an Unmanaged 

VDDT simulation lasting 300 years, printing the landscape 

state every 10 years to match the output interval from FVS. 

This decadal output was then processed into a format that 

could be read by Preside, which then independently recalculated the transition probabilities using the same algorithms 

employed for processing FVS output. These probabilities 

were provided as input to a new VDDT simulation, treating 

all transitions as purely probabilistic. The results of the two 

VDDT simulations were independently simulated five times 

and then compared. 

Transforming the Time Scale

Preside (Version 2010.06) uses decadal FVS output, conceptually creating an initial square matrix M of 10-year transition probabilities. Each diagonal element of this matrix 

represents the probability of remaining in vegetation class i

after 10 years, and each off-diagonal element represents the 

probability of changing from vegetation class i to class j in 

the decade. Zero values are allowed, but each row must sum 

to 1.0. Preside converts these values to annual transition 

M' i,j | i=j = e

1n(λ) Mi,j/10

M' i,j | i#j = 1–e

1n(1-Mi,j )/10
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This method avoids the separate equations used by 

Preside, scales correctly between any two time intervals, 

and produces row sums of 1.0 for all possible timesteps.

Matrix Analyses 

The properties of the transition matrices were used to compare VDDT and FVS simulations over the scenarios and 

treatments described above. Each row and column of the 

transition probability matrix represents a unique vegetation 

class (e.g., PPGo1). Depending on the scenario, there were 

42 rows and columns in the FVS matrix and up to 58 rows 

and columns for the VDDT simulations. In the case of the 

VDDT simulations, the extra 16 vegetation states represent 

post-disturbance classes which are impossible to distinguish 

from other vegetation classes in FVS. 

Matrix properties were characterized in two ways. First 

we found the dominant eigenvector, which is the theoretical equilibrium composition of the landscape. Secondly, 

we explored the behavior of the matrix system over time 

through simulation. One method of analyzing the temporal 

behavior was to run the matrix in VDDT. The other method 

we used was to characterize temporal dynamics through 

Figure 1—Comparison of the number of pixels in each state class after 300 years for the original VDDT simulation and for 

the VDDT simulation using transitions and probabilities exported and recalculated by Preside. Error bars are from 5 Monte 

Carlo simulations.

5 Supplemental information about the simulation methodology and detailed results are available at: 

http://essa.com/wp-content/uploads/RobinsonBeukema2012Supplement.pdf.

Monte Carlo simulation of the matrix directly (i.e., not 

in VDDT) through the calculation of the approach to the 

theoretical stable equilibrium, using the time to reach 

99.9 percent of the equilibrium as a consistent measure of 

system dynamics. This is an arbitrary condition that will 

never be seen in physical landscapes. However, it provides 

a standardized way of comparing the behavior of different 

transition matrices.5

Results

Verification of Preside Calculations

A comparison of the results from the simulation of the 

unmanaged scenario in VDDT and a previous run of VDDT 

exported and processed through Preside shows that both 

runs are very similar (fig. 1). In both runs the most frequent 

10 classes are identical and represent about 75 percent of 

the total area. Only two of the classes differ by more than 

1 percent of the total landscape area. This result confirms 

that Preside transforms results in a consistent way, and that 

the transition probabilities are comparable between the two 

models, other things being equal. Indirectly, these results 

also show that for this landscape, the differences of the two 
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methods of transforming from decadal to annual timestep 

are not large in this landscape.

Presence and Absence of Transitions 

The transition matrices from the Unmanaged VDDT and 

FVS simulations were analyzed using a simple count of 

the presence or absence of different transitions (table 3). 

Remarkably, FVS predicts many fewer total transitions than 

VDDT and only one quarter can be matched with a transition in the Unmanaged VDDT landscape. A further 115 

transitions are found only in the post-disturbance classes 

that are unique to VDDT. Considering all transitions, there 

is about a two-to-one disparity. From this simplified aggregation we conclude that VDDT shows a much wider range 

of behavior, in spite of the fact that we might expect FVS to 

account more explicitly for growth, mortality and regeneration in multi-species stands. As described in more detail 

below, we find that much of the complex behavior of VDDT 

can be traced to detailed transitions driven by disturbance 

agents in VDDT. 

FVS Transitions Caused by Calibration and 

Regeneration

Study of the transitions that are absent from VDDT but 

present in FVS shows that a number of them occur when 

a continuous classifier (QMD or CANCOV) switches to 

a smaller class. Of the 11,000 timesteps in the projection, 

such events take place in about 7 percent of timesteps: 226 

times for QMD and 505 times for CANCOV. We found 

that these changes are primarily caused by regeneration 

in the FVS model. For QMD this happens when a pulse of 

regeneration grows large enough to be classified among 

the largest 20 percent of trees. The diameter threshold then 

drops to include the numerous smaller trees and the indicator declines markedly. 

We examined sixteen stands that showed the most 

extreme decline in CANCOV or QMD. Of these sixteen 

cases, ten undergo the transition in the first model timestep, 

indicating a marked change from the inventory condition 

to the first projected state. Examples of results showing this 

behavior are shown in figure 2, which shows vegetation 

class over time for 10 stands. In this example, nine of the 

stands undergo a transition between timestep 0 and timestep 

1. Our analysis shows that the first kind of declining transition is a simulation artifact produced by the initial FVS selfcalibration adjustment and expressed as large tree mortality. 

Fortunately, transitions occurring this way can be screened 

out using the Drop Zero method, as described below.

The second kind of declining transition typically 

happens later in the simulation, as regeneration begins to 

reach a size where it can be in the top 20 percent of trees, 

contributing noticeably to canopy cover. It can then trigger a 

change in the dominant overstory layer, as the stand moves 

from larger older trees to include smaller younger trees. 

This kind of transition is not inherently unreasonable, but 

underscores the role of regeneration in stand dynamics and 

in measures related to stand maturity, and signals the need 

for further consideration of how the indicator is intended 

to be used. If QMD or CANCOV alone do not adequately 

capture the qualities of a mature stand and the stand truly 

remains in a mature state, then novel attributes (perhaps 

combinations of existing attributes or heuristic rules) may 

be called for. Alternatively, these transitions caused by 

regeneration also underscore the need to better consider the 

role of small trees when defining states and state transitions 

in VDDT.

Jittery Transitions

Besides declining transitions, there are frequent cases of 

back-and-forth switching between two classes (see two 

lower horizontal boxes in fig. 2), occasionally accompanied 

by a change in dominant cover type (PP or DW in our study 

landscape). We examined nine stands with jitter effects, and 

Table 3—Summary of vegetation class transitions 

present in Unmanaged FVS and VDDT models. The 

two simulation systems have few vegetation states 

in common

 Observed Transitions

Vegetation FVS VDDT FVS and 

Class only only VDDT

Regular 127 145 43

Post 0 115 0

Disturbance

Total 127 260 43
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

found that jitter is also closely associated with regeneration, 

generally taking place when QMD or CANCOV are close 

to a transition threshold. In these cases the regeneration 

contribution, while small, is sufficient to push the indicator over the class boundary. In the examples we studied, 

significant regeneration mortality can occur in the following 

cycle, causing the classifier value to drop and return back 

across the threshold a second time. If there is another round 

of regeneration, the classification can change yet one more 

time. Jitter ends when there is no regeneration for two 

cycles, allowing the stand to grow substantially beyond the 

boundary condition.

A classification change due to small back and forth 

movement across a boundary has a large impact on the 

average residency time, expressed as the annual probability 

of changing from Class X to Class Y. A stand that alternates between two classes each 10-year timestep will have 

numerous predicted residence times of 10 years. If the classification was more flexible or the stand was farther from a 

boundary, it might have residence times of 20 or 40 years. 

For example, consider a stand with nearly 70 percent canopy 

cover for four cycles: 69.5, 70.1, 69.6 and 70.3 percent. In 

this example, the stand would be classified as: X-Y-X-Y. If 

the values were all slightly less (e.g., ranging from 68–69 

Figure 2—Vegetation classes are shown for 10 stands from the unmanaged FVS landscape simulation. Each class 

is represented by a single letter and state changes are shaded for clarity. The heavy box at left highlights the large 

number of transitions that take place between timestep zero (inventory) and one. The three horizontal boxes show 

examples of rapid state change (upper box) and classification jitter (two lower boxes).

percent), they would all remain in class X, resulting in a 

longer residence time with no transitions, and if they were 

slightly higher, they would all be in class Y.

This kind of classification problem is not new. A nearly 

identical case is addressed by Stage (1997) in the context 

of stand structure classification, transition probability 

and declining transitions. Stage’s suggested approach to 

the problem is to move the boundary by one-sixth of the 

class width whenever a retrogressive (declining) transition 

is predicted, thus avoiding the transition. This might be 

reasonable for individual cases, but it often only moves the 

boundary problem to another value or causes it to emerge 

in another stand, and does not remove it entirely. Other ad 

hoc solutions that involve visualization of a stand’s state 

space and its proximity to a border may be suitable for 

single stands, but are not feasible for the automatic classification of thousands of stands over dozens of timesteps. 

Ideally, what are needed are algorithms that solve the logical 

problem of the classification boundary and which can be 

implemented programmatically. In Appendix 2 we present 

different approaches to smoothing and further analyze the 

consequence of these smoothing methods on measures 

of the equilibrium behavior of the system. Generally, our 

smoothing methods resulted in landscapes that were quite 
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different from both FVS and VDDT landscapes; leading us 

to conclude that smoothing is not a panacea that will easily 

reconcile model differences.

Comparison of Native and FVS–Derived VDDT

Extending the analysis described earlier, we compared a 

Base Unmanaged VDDT simulation with an FVS simulation processed through Preside into matrix form and then 

simulated in VDDT. The results are notably different, but 

consistent with the results presented in table 3. Since VDDT 

defines a set of up to 16 post-disturbance classes that are 

not possible to define within FVS, some differences are 

to be expected. The key differences are unrelated to those 

novel classes, however, and the most common classes, 

accounting for 70 percent of the landscape in each model, 

are completely different. As shown in the left panel of fig. 

3, FVS generally predicts a less complex landscape that 

consists primarily of “Large” or “Giant” multiple story 

stands. In contrast, VDDT predicts stands in which sapling 

and medium size trees are more common. 

When the Managed FVS and VDDT transition matrices 

are compared, the same types of differences can be seen, 

with FVS predicting more big trees and VDDT predicting 

smaller trees (fig. 3, right panel). Both VDDT and FVS 

predict open to medium canopies, however. 

The goal of the management regime was to preferentially retain open forests of large, older ponderosa pine. 

This was successful, and PP-dominated vegetation classes 

are more common in the managed landscape than in the 

unmanaged landscapes for both models. The FVS landscape 

contains more than 3 times as much ponderosa pine in 

the managed run compared to the unmanaged case, and 

represents just over half of the landscape, with the majority 

of the vegetation classes in large open or moderately open 

Figure 3—Left and right panels compare the 

composition of Unmanaged (left) and Managed 

(right) landscapes near equilibrium. In each 

panel the FVS-derived landscape composition 

is shown on the left (light grey bars) and the 

VDDT-derived composition is shown in on 

the right (dark grey bars). The horizontal grey 

line separates vegetation classes common to 

both models at the top, from those found in 

VDDT only, at the bottom. Key to bar labels: 

the first two letters are the dominant species; 

letters (Y,P,S) are small trees and (M,L,G) are 

medium, large or giant trees; the fourth letter 

represents open, medium or closed canopy, and 

the final number is the number of strata. Labelling details are in table 2. Vegetation classes <4 

percent are not shown.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

stands. The managed VDDT landscape also shows significantly more ponderosa pine stands. However, the managed 

VDDT stands are generally much younger than in either the 

unmanaged VDDT run or the managed FVS run.

Changes in Temporal Scale

Two methods—Preside and matrix diagonalization—exist 

for scaling from the decadal to annual timestep. The 

outcomes of these two methods are compared in figure 4, 

using example transition matrices for Unmanaged FVS 

and VDDT landscapes. The symmetry of the two examples 

indicates that the long-term behavior is very similar for 

both methods, usually with maximum differences of a few 

percent in vegetation categories. In the VDDT example in 

Figure 4—Decade-annual transformation 

differences are shown for Unmanaged FVS 

(left panel) and VDDT (right panel) simulations. Within each panel, horizontal bars show 

the landscape composition near equilibrium as 

calculated through matrix diagonalization on 

the left (light grey bars) and by Preside on the 

right (dark grey bars). The horizontal grey line 

separates vegetation classes common to both 

models at the top, from those found in VDDT 

only, at the bottom. Bar labels are defined in 

the preceding figure and vegetation classes <4 

percent are not shown.

figure 4, there is a small excess in PP vegetation classes 

predicted using the Preside method, compared to the 

diagonalization method. In transition matrices with faster 

transitions or more transitions (i.e., in which the transition 

matrix was less sparse) the differences between the two 

calculation methods would probably be more significant.

Comparative Matrix Analysis of FVS and VDDT

The important results from the analysis of the equilibrium 

of some of the treatment matrices are summarized in table 

4; with more complete results found in Appendix 2 and in 

supplementary material.5 Since they show the distribution 

of vegetation states very near equilibrium, these results are 

not identical (and sometimes not even very similar) to those 

5 Supplemental information about the simulation methodology and detailed results are available at: 

http://essa.com/wp-content/uploads/RobinsonBeukema2012Supplement.pdf.
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GENERAL TECHNICAL REPORT PNW-GTR-869

shown in figure 1 or figure 3 or figure 4, all of which show 

the distribution of vegetation classes farther from equilibrium.

The Base and Drop Zero treatments in table 4 are 

very nearly identical for both the managed and unmanaged 

FVS scenarios, regardless of whether all transitions (All) 

or a subset of transitions (Sub) are used. Data Simplification treatments that include rare transitions take at least a 

century or more to approach equilibrium (table 5).

A set of simplified complementary views of the 

equilibrium results is found in table 6, which regroups the 

data of table 4 into three groups of two strata each. Viewed 

through the perspective of these coarser strata, the overall 

disparity between the VDDT and FVS simulations is 

striking. When using the All-transitions treatment, VDDT 

is primarily small to medium Douglas-fir with medium to 

closed canopy at equilibrium, while FVS is primarily large, 

open ponderosa pine. When rare transitions are excluded, 

Table 4—The percent of the landscape in different state classes near equilibrium for Unmanaged and 

Managed VDDT and FVS simulations. The FVS simulations were analyzed with transitions using all years 

(FVS-Base), and ignoring the transition between years 0 and 1 (FVS- DZ). Within those treatments each matrix 

contained either all transitions (All), or the most frequent transitions (Sub) as described in the Materials section. Vegetation classes <4 percent are excluded. The transition classes shown in the two bottom rows are 

post-disturbance classes that have no analogue in FVS. The bottom row shows the sum within each column, 

and indicates the importance of rare vegetation classes in VDDT simulations

Unmanaged Managed

VDDT FVS-Base FVS-DZ VDDT FVS-Base FVS-DZ

Sub All Sub All Sub All Sub All Sub All Sub All

DWSn_ 6 6

DWPm_ 7 7

DWPo_ 13 13

DWSc1 15 15 8 4 4

DMMo2

DWMc2 9 9

DWMc1 9 8

DWLc2 19 19

DWLc1 4 4

DWLm2 6 5

DWLo2 6 8

DWGo2 38 39 6 6

DWGo1 30 31

PPSn_ 9 9

PPPo_ 28 28

PPSm2 4 4

PPSm1 9 6 6

PPSo1 6 6

PPMm2 5 5

PPMo1 5

PPLm2 13 13

PPLm1 11 11

PPGo2 23 93 18 93 76 35 80 36

PPGo1 7 4 7 19 9 20 9

DWPp1 4

DWSp1 10 10

Total 73 76 100 100 100 100 81 81 100 86 100 88
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the species mix is more similar, but not the size or degree 

of canopy closure. Dropping the first transition makes little 

difference to the results.

Comparing simulations made using all transition 

probabilities and those with fewer transitions gives a 

fairly consistent result for timing and complexity: simpler 

stochastic matrices generally reach a near-equilibrium state 

two to three times faster than their more complex counterparts, and also tend to reach a simpler end-state with a 

small number of final vegetation classes. That said, the nearequilibrium (climax vegetation) end states can be remarkably different: PP in some cases and a mix of DW and PP in 

others. As a result of this, the attempt to simplify transitions 

only adds to our woes and makes it seem unlikely that FVS 

and VDDT simulations can be easily reconciled.

Table 5—The median number of years required to 

reach near-equilibrium for the Unmanaged and Managed VDDT and FVS simulations. The FVS simulations were analyzed using All timesteps (Base) 

and excluding the first timestep (Drop Zero). These 

treatments were further divided to use all observed 

transitions (All) and to exclude rare transitions (Sub)

 Unmanaged Managed

VDDT FVS VDDT FVS

Base Sub 223 38 142 138

All 209 208 140 368

Drop Zero Sub 165 156

All 209 342

Table 6—Simplified near-equilibrium states for Unmanaged and Managed VDDT and FVS simulations, 

showing the percentage in different groupings by dominant species, QMD class and Cover class. 

Some columns do not add to 100 percent due to rounding

 Unmanaged Managed

 VDDT FVS VDDT FVS

 Base Base DZ Base Base DZ 

Sub All Sub All Sub All Sub All Sub All Sub All

Species PP 2 3 32 100 23 100 66 63 100 71 100 72

DW 88 88 68 78 29 29 24 23

QMD P,S,M 63 64 9 9 95 92 5 1 2

L,G 27 27 91 100 92 100 95 94 98 95

Cover n,p,o,_ 23 24 91 100 100 100 68 68 100 61 100 64

m,c 67 67 9 1 24 24 34 31

Summary and Recommendations

Although we have not been able to develop ways to easily bridge FVS and VDDT, we have learned some useful 

lessons. We have independently confirmed that Preside’s 

methodology for calculating transitions and their probabilities is correct, and that identical results are obtained in 

VDDT when using VDDT-defined pathways and probabilities directly, and when VDDT results are output as FVS-like 

results which are then imported back into VDDT using 

Preside-calculated pathways and probabilities. This gives us 

confidence that if the issues caused by classification boundaries and model behavior can be resolved, accurate FVSgenerated information can be provided to VDDT. Although 

we found that the algorithm for converting FVS decadal 

transition probabilities into annual VDDT probabilities is 

not correct, the consequences of this error are small when 

the transition matrix is sparse and probabilities are small. 

Correcting this error is conceptually straightforward.

Even after taking care to calibrate FVS and to make 

the FVS and VDDT simulations as similar as possible, we 

found two notable sources of discordance. First, the FVS 

simulation contains complex regeneration rules which 

have a significant influence on the types of transitions 

that emerge from the model. And for its part, VDDT also 

contains assumptions about regeneration that are implicit 

in the user-defined pathways (e.g., when moving from a 
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GENERAL TECHNICAL REPORT PNW-GTR-869

single-story to a multistory stand), but which are nonetheless obscure. These different regeneration assumptions 

work themselves out in different ways and result in different 

stand structures that cannot easily be reconciled. Improvements could possibly be made in the VDDT regeneration 

arena by more careful and explicit attention to regeneration 

and its temporal and structural consequences, which might 

require additional detail in the state models. FVS would 

benefit from a thorough review of the Repute model which 

was used to drive regeneration in the FVS simulation. 

Secondly, VDDT contains explicit rules about low levels of 

natural disturbances (insects, pathogens and low-level fire) 

which cumulatively keep the landscape from reaching an 

old giant forest state. FVS has a set of assumptions about 

the role of natural disturbance which are embedded in its 

mortality model, but these rules generally have less impact 

on the size or structure of the forest.

Because of the underlying differences in the biological 

processes that are included, classifying FVS outputs into 

discrete VDDT-like vegetation states will always present difficulties. Simple approaches such as smoothing or 

moving a class boundary such as QMD, simply transfers 

the classification problem to another type of stand structure. 

Because of the way they are defined, the algorithms which 

define QMD and Cover remain sensitive to the appearance of any new regeneration, and the jitter issue therefore 

remains unresolved as long as standard definitions and 

categorical variables are used to stratify stand structure. 

There are other intrinsic difficulties for comparing the two 

models since in this landscape at least, VDDT contains a 

number of vegetation states that are undefined and therefore 

never observable in FVS. In the Managed case using all 

transitions, about 20 percent of the VDDT landscape is in 

vegetation classes that are out-of-scope for FVS.

Given the initial jump in stand vegetation class often 

produced by FVS self-calibration, combined with the 

censoring that takes place between the initial inventory 

condition and the end of the first timestep, the Drop Zero 

treatment should produce more reliable results than the control Base treatment. It is therefore surprising to note that the 

dynamics of these two treatments are very similar (table 6). 

Regardless, removal of the first transition is recommended 

to reduce initialization artifacts.

Finally, we explored a further layer of data treatments 

through scenarios which either retained or removed rare 

transitions. We discovered that the removal of the less frequent transitions results in stochastic matrices with overly 

simplistic pathways, along with the side effect that the 

landscape dynamics are sped up, often dramatically, in their 

approach to equilibrium. This speed seems unreasonable in 

forests marked by century-scale life spans, and suggests that 

rare transitions should always be retained.

We also examined smoothing methods that might result 

in less jittery FVS transition dynamics which we hoped 

would be more transferrable to VDDT. This exploration 

was neither simple nor uniformly successful. The methods 

we tested often resulted in system dynamics that were 

quite different from the comparatively similar Base and 

Drop Zero treatments described above. We were unable to 

find similarities between any smoothed scenario and the 

VDDT landscape dynamics that were consistent across the 

unmanaged and managed landscapes. A Preside option that 

allowed the calculation smoothed classes would provide a 

way to examine the benefit of reducing jumpy transitions, 

leaving it up to the modeler to decide whether this created 

better agreement between the two models.

Because of the differences in intent and design, no one 

model can capture all aspects of stand or landscape dynamics. Our findings show that FVS can be used to capture 

changes due to stand processes: tree growth, mortality, and 

regeneration and the associated changes in size and structure, and that these transitions can be transferred to VDDT. 

Modelers should consider these transitions as candidate 

versions of reality, just as VDDT transitions are different 

candidate versions of reality. Given the many possible 

differences between the two models, it might be helpful to 

develop automated methods of comparing large matrices 

which highlight transition probabilities that differ between 

the two models. Tools that allowed modelers to quickly 

simulate stable states might also provide insight into model 

differences that would highlight, for example, the role of 

regeneration and disturbance in creating and maintaining 

different pathways. When doing a landscape level analysis 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

using VDDT however, including FVS transitions by themselves are not sufficient, and it remains necessary to add 

the important effects of landscape level disturbances such 

as beetles and fire. In this way, one is using the strengths in 

both models to create the best possible VDDT model. 

Given the challenge of reconciling the two model 

paradigms, we have proposed and examined some possible 

approaches to aide in model comparison. Using the common 

framework of the transition matrix, its dynamics and stable 

states are a useful way of comparing two complex models, 

producing compact summaries—two vectors—which allow 

the quick identification of similarities and differences. 

Identifying these contrasts is the first step toward understanding the underlying mechanisms which create different 

projections. Although it is possible to compare the two 

transition matrices directly, landscape dynamics are based 

on the aggregate behavior of the entire system, making it 

misleading to consider the automated insertion of specific 

transitions from one model into another without careful 

examination of the consequences.

Acknowledgments

We express our great appreciation to the numerous people 

who provided ideas and guidance for this project. In alphabetical order, they are: Miles Hemstrom, Jim Merzenich, 

Melinda Moeur, Don Vandendriesche and Reuben Weisz. 

Don Vandendriesche also provided support through the FVS 

calibration exercise. Funding for this project was supplemented by Joint Venture Agreement PNW 08-JV-11261976-

362, between the USDA Forest Service Pacific Northwest 

Research Station and ESSA Technologies, and by internal 

research funding provided by ESSA Technologies. 

References

Crookston, N.L.; Dixon, G.E. 2005. The Forest Vegetation 

Simulator: a review of its structure, content, and 

applications. Computers and Electronics in Agriculture. 

49: 60–80.

ESSA Technologies Ltd. 2007. Vegetation Dynamics 

Development Tool User Guide, Version 6.0. Prepared by 

ESSA Technologies Ltd., Vancouver, BC. 196 p.

Forest Vegetation Simulator Staff. 2003. Users guide 

to the Database Extension of the Forest Vegetation 

Simulator Version 2.0. Fort Collins, CO. U.S. 

Department of Agriculture, Forest Service, Forest 

Management Service Center. (July 2009 revision). 60 p.

Moeur, M.; Vandendriesche, D.A. 2009. Calibration of 

state and transition models with FVS. In: T. Jain, R.T. 

Graham and J. Sandquist, tech. eds. 2010. Integrated 

management of carbon sequestration and biomass 

utilization opportunities in a changing climate: 

Proceedings of the 2009 National Silviculture Workshop; 

2009 June 15–18; Boise, ID. Proceedings RMRS-P-61. 

Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station: 275–287.

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. 36 p.

Stage, A.R. 1997. Using FVS to provide structural class 

attributes to a forest succession model (CRBSUM). In: 

R. Teck, M. Moeur and J. Adams (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. 139—147 p.

Vandendriesche, D. 1997. FVSSTAND Alone: generate 

dynamic yield reports from the Forest Vegetation 

Simulator. In: R. Teck, M. Moeur, and J. Adams 

(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. 168–177.

Vandendriesche, D.A. 2009. Repute–regeneration 

imputation extractor. http://www.fs.fed.us/fmsc/ftp/fvs/

software/pre_post/repute_z.exe. (6 August 2009).
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GENERAL TECHNICAL REPORT PNW-GTR-869

Van Dyck, M.G.; Smith-Mateja, E.E. (compilers) 2000.

Keyword reference guide for the Forest Vegetation 

Simulator. Revision: March 2012. Fort Collins, CO: 

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

Management Service Center. 119 p.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Figure 5—FVS keywords used to implement a simple Restoration Management regime that retains large ponderosa pine.

Table 7—Parameters of the ReadCord and ReadCorH keywords used to adjust baseline diameter 

and height growth. Empty fields have coefficients 

of 1.0

Species Diameter Height

WP

WL 0.799

DF 1.056 2.730

SF

RC

GF 0.575

LP 0.861 0.461

ES

AF 0.637

PP 0.824

OT

Table 8—Parameters of the TreeSzCp keyword 

used to adjust survival and dimension constraints 

for larger trees

DB Mortality Height

Species (in) (10y–1) (ft)

WP 28 0.374 120

WL 26 0.118 120

DF 32 0.103 125

SF 12 0.129 70

RC 26 0.159 95

GF 22 0.291 120

LP 18 0.259 95

ES 16 0.223 100

AF 12 0.243 70

PP 34 0.135 120

OT 10 0.464 70

to globally reflect pooled diameter and height calibration 

data from the full set of stands. Mortality was also adjusted 

using the TreeSzCp keyword (Van Dyck and Smith-Mateja, 

2000) to reduce survival of larger trees.

Additional keywords were needed to implement the 

restoration management scenario (fig. 5). These keywords 

instruct FVS to thin the stand to 50 percent of SDI when 

the stand is more than 65 percent of maximum SDI, and to 

preferentially retain larger ponderosa pine.

SpecPref

SpecPref

SpecPref

SpecPref

SpecPref

SpecPref

SpecPref

SpecPref

SpecPref

If 0

BSDI GT (BSDIMAX*0.65)

Then

ThinSDI

Endif

WP 10

WL 10

DF 10

SF 10

RC 10

GF 10

LP 10

ES 10

AF 10

OT 10

0 Parms ( (BSDIMAX8.500, 1, 0, 0, 999,1)

Appendix 1: Calibration of FVS

This Appendix contains the details of the parameters that 

were changed or added to calibrate FVS for these simulations.

As described in the body of the paper, site quality was 

constrained to a maximum SDI of 380 and maximum basal 

area of 164 ft2 ac

–1. As part of the FVS calibration process, 

the large tree diameter increment model and small tree 

height increment model were also adjusted (table 7 and 8) 
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GENERAL TECHNICAL REPORT PNW-GTR-869

Appendix 2: Impacts of Smoothing

As described in earlier sections, the use of boundaries to 

classify FVS stands into VDDT classes can create cases 

of jitter: rapid sequences of change between different 

Table 9—The near-equilibrium states of the Unmanaged and Managed FVS simulations, expressed as a percentage of the landscape 

in each vegetation class using the two smoothing methods. FVS 

Base results are provided from table 4 for comparison. “All” and 

“Sub” indicate analyses that include all transition probabilities or 

exclude rare transitions, as described in table 1. Minor vegetation 

classes (<4 percent) have been excluded. No Managed Conditional 

smoothing simulations were carried out

Base Running Conditional

All Sub All Sub All Sub

Unmanaged

DWSm2 5

DWMm2 14

DWMo2

DWLm2 34

DWLm1 11

DWLo2 13

DWGo2 38

DWGo1 30

PPSm1 9 100 16

PPMo1

PPLm2 6

PPLm1

PPLo2

PPGo2 23 93 84 93

PPGo1 7 7

Total percent 100 100 100 83 100 100

Managed

DWSm2

DWMm2 4 5

DWMo2

DWLm2 6 17 15

DWLm1 8 7

DWLo2 6 12 10

DWGo2 6

DWGo1

PPSm1

PPMo1 5

PPLm2 13 20 20

PPLm1 11 16 16

PPLo2 6 5

PPGo2 76 35

PPGo1 19 9

Total percent 100 86 83 78 – –

classes. We experimented with two different methods for 

smoothing jittery transitions. The first method—Running 

Smooth—uses an evenly weighted running smoother (t–1, t, 

t+1) to average the CANCOV and QMD variables that are 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Table 10—The median number of years required to 

reach near-equilibrium for the Unmanaged and Managed 

FVS simulations for different smoothing methods. FVS 

simulations were analyzed using all timesteps (Base), 

subdividing those results to use all observed transitions 

(All) and to exclude rare transitions (Sub)

 Unmanaged Managed

Base Sub 38 138

All 208 368

Running Sub 155 183

All 247 178

Conditional Sub 223 –

All 211 –

Table 11—Simplified near-equilibrium states for Unmanaged and Managed FVS simulations for different smoothing methods, using all transitions (All) and excluding rare transitions (Sub). FVS Base 

results are provided from table 4 for comparison. Vegetation classes are grouped as in table 4, 

showing the percent-age in different groupings by dominant species, QMD class and Cover class. 

Some columns do not add to 100 percent due to rounding; those marked with ‘–’ were not simulated

Unmanaged FVS Managed FVS

 Base Running Conditional Base Running Conditional

Sub All Sub All Sub All Sub All Sub All Sub All

Species PP 32 100 100 10 36 100 100 71 51 51 – –

DW 68 86 64 24 47 45 – –

QMD P,S,M 9 100 22 4 5 1 7 12 – –

L,G 91 100 74 96 100 95 94 91 84 – –

Cover n,p,o,_ 91 100 21 96 100 100 61 30 26 – –

m,c 9 100 75 4 34 68 70 – –

used to assign vegetation classes to the output of each FVS 

timestep. The second method—Conditional Smooth—uses 

a simple rule which is applied at each timestep t. The rule 

is: if a stand’s change in QMD or CANCOV in timestep 

t causes it to be assigned to a class that differs from the 

one to which it was assigned in timestep t-1, or would be 

assigned in the timestep t+1, then the classification boundary must be exceeded by at least 1/6 of the class width for 

the new classification to be accepted. Thus, patterns such as 

A-B-A would likely become A-A-A, but patterns of A-B-B 

would likely stay A-B-B. We examined the impact of the 

application of these two kinds of smoothing methods to the 

behavior of the resulting transition matrices.

The results of applying these smoothing methods are 

shown in table 9 (the distribution of vegetation states near 

equilibrium), table 10 (a measure of the time required to 

approach equilibrium) and table 11 (a condensed comparison of the vegetation states). The Base FVS scenario 

is included with each table to facilitate comparison with 

a non-smoothed simulation. The general conclusion from 

these simulations and comparisons is that the smoothing 

methods have significant effects upon the behavior of the 

transition matrices, but that they are as different from the 

Base FVS simulation as they are from any of the VDDTbased simulations.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Authors

Reuben Weisz is a regional analyst (retired), USDA Forest 

Service Southwestern Region, Albuquerque, NM, USA, 

reuben@weisz.org. Don Vandendriesche is a program 

manager, Vegetation Application Group, USDA Forest 

Service Southwestern Region, Albuquerque, NM, USA, 

dvandendriesche@fs.fed.us.

Abstract

The Forest Vegetation Simulator (FVS) has been used to 

provide rates of natural growth transitions under endemic 

conditions for use in State and Transition Models (STMs). 

This process has previously been presented. This paper 

expands on that work by citing the methods used to capture 

resultant vegetation states following disturbance activities; 

be it of natural causes or human induced. Forest Inventory 

and Analysis (FIA) plots were stratified by representative Potential Natural Vegetation Types (PNVTs) of the 

southwestern United States. Structural states within the 

Vegetation Dynamics Development Tool (VDDT) (i.e. STM 

used for forest planning) were defined based on dominant 

tree size, canopy cover density, and vertical story layering. 

A standard set of silvicultural and fire prescriptions were 

evaluated using FVS projections. Outputs such as post treatment vegetation states, harvest volumes, and snag generation were captured and linked to transitions in VDDT. A 

case study involving the ponderosa pine/bunchgrass forest 

ecosystem will be presented to demonstrate features of the 

modeling approach.

Introduction

State-and-transition models (STM) have the ability to integrate multiple interactions between ecological processes and 

land management strategies and as such are increasingly 

being used to guide land management decision making. 

However, owing to their complexity, some state characteristics and transition probabilities are largely constructed from 

expert opinion because of a lack of empirical data or lack 

of tools to test STM performance against empirical data. 

Recently, the Forest Vegetation Simulator (FVS) (Dixon 

2002) has been used to provide rates of natural growth 

transitions under endemic mortality conditions for use in 

Vegetation Dynamics Development Tool (VDDT) (ESSA 

2006) models (Henderson 2008, Moeur and Vandendriesche 

2010, Weisz et al. 2010, Shlisky and Vandendriesche 2012). 

In our previous work in analyzing landscapes for forest 

plan revision, we divided the southwestern United States 

into terrestrial ecosystems that range from dry grasslandsshrublands, to semi-arid woodlands, to moist forestlands. 

Each ecosystem is representative of a Potential Natural 

Vegetation Type (PNVT) (Schussman and Smith 2006). 

Each PNVT, which is depicted within separate VDDT 

models, was then further broken into vegetation states. A 

vegetation state is a composite of cover type (preeminent 

species composition) and stand structure (dominant tree 

size, canopy cover density, and vertical canopy layering). 

The vegetation states developed for the ponderosa pine/

bunchgrass (PPG) ecosystem are illustrated in table 1.

Vegetation states can transition to other states in the 

absence of disturbance due to natural processes. Tree 

establishment, growth, and mortality comprise the main 

components for natural succession. For example, the 

straight green lines connecting model states in figure 1 

represent deterministic pathways (i.e., natural transitions in 

the absence of disturbance).

Change in vegetation states can also result from 

management activities, insect and disease outbreaks, and 

wildfire occurrence. The blue lines in figure 1 represent the 

myriad of stochastic pathways (i.e., probabilistic transitions 

due to disturbance events). State-and-transition models 

(STMs) such as VDDT can be used to evaluate natural 

succession and disturbances effects that result in vegetation 

change on the landscape (He 2008). The objective of this 

Use of the Forest Vegetation Simulator to Quantify 

Disturbance Activities in State and Transition Models

Reuben Weisz and Don Vandendriesche
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GENERAL TECHNICAL REPORT PNW-GTR-869

paper is to illustrate the expanded use of FVS for estimating outcomes from disturbance agents within the context 

of national forest land management planning. The general 

process that we used for FVS analysis is described in the 

sidebar. The red line between step 4 and 5 demarks the 

break between our initial effort to capture natural growth 

succession and our subsequent work to quantify resultant 

vegetation states due to disturbance activities. The later 

steps will be elaborated in the following sections.

Figure 1—Conceptual pathways diagram for the ponderosa pine/bunchgrass PNVT. Boxes represent model 

states and arrows represent transitions due to natural growth and other natural and anthropogenic transitions 

such as management activities, fires, and insects and disease. In the key to the conceptual diagram we see the 

Cover Type (Ponderosa Pine = PP), State Name (1) and the State Class or Code (VCS = Very-large, Closed, 

Single story).

Table 1—Stratification of figure 1 ponderosa pine/bunchgrass PNVT vegetation states A 

through N, according to key attributes of dominant tree size, canopy cover, and canopy 

layering

 Tree diameter

GFB 0-5 in. 5-10 in. 10-20 in. 20+ in. Canopya Canopy

 cover layering

A or Nb B C D E Open Single

F G H I Closed Single

 Jc Kc Open Multi

L M Closed Multi a

 Except for States A and N, “Open” states have 10 to 30 percent canopy cover and “Closed” states have greater than 30 percent 

canopy cover. States A and N have less than 10 percent canopy cover.

b

 States A and N are grass, forbs, brush, and shrub states (GFB). State A is the characteristic state which existed in reference 

conditions. State N is the uncharacteristic state resulting when stand-replacing fires occur in closed canopy states. (Smith 2006)

c The desired condition is an open multi-layered (> 5 age classes) state with average diameter varying by site productivity 

with State J occurring on low productive sites and State K occurring on high productivity sites. (Triepke et al. 2011)
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Methods

Coarse and Fine Filter Plot Datasets

During our initial phase for forest planning, “coarse 

filter” field data plots consisted of all Forest Inventory and 

Analysis (FIA) plots meeting habitat type (USDA FS 1997) 

specifications for each PNVT1. Table 2 provides a listing 

of the habitat types associated with the PPG PNVT. For 

our current work, we began by using the coarse-filter plot 

set. However, during the process, and under the scrutiny 

of silviculturists and fire ecologists, questions arose as to 

whether the coarse filter plots were homogeneous enough to 

evaluate the effects of management activities in the VDDT 

models. Some of the arguments against using coarse filter 

plots exclusively were that they contained:

• plots that were designated with incorrect habitat 

types,

• plots that represented inclusions or ecotones, within 

or between PNVTs, and

• plots that included non-stockable areas (i.e., rock 

outcrops, perennial water).

These contentions indicted that coarse filtered plots 

would add unrealistic effects to our results because they 

didn’t represent the type of vegetation that typically would 

be affected by our treatment activities. Classifying forestland with respect to its capacity for timber production has 

been a requirement of the National Forest Management 

Act and subsequent implementation regulations since the 

early 1980’s (Youtz 2006). Determining timber suitability 

is a stepwise process that separates National Forest System 

lands into various classification categories. (Refer to figure 

2.) Separating our data set into proxies of land generally not 

suitable and land generally suitable for timber production 

or harvest is analogous to applying finer resolution to the 

coarse filter data set.

We therefore created fine filters to remove the non-representative plots from the coarse filtered data set using rules 

determined by our silvicultural and fire ecology specialists. 

As an example, by using the PNVT—habitat type crosswalk 

1 The terms “habitat type” and “plant association” are synonymous in the southwestern region. A PNVT is composed of several 

habitat types.

Sidebar

The conceptual method for estimating transition 

rates and destination states for STMs involved using 

inventory data, FVS model runs, and post processing software. The analysis process culminated with 

formal evaluation and adjustment of model results 

where needed. The steps utilized were:

Step 1. Assemble inventory data for FVS projections. 

Regional strategic inventories such as FIA provide an 

excellent data source to represent vegetation stratification schemes. Classification attributes can be used 

to filter plot sets for finer resolution. In the absence of 

adequate sample, FVS can be used to grow plots into 

model states.

Step 2. Adjust FVS parameters to the current inventory trends (Vandendriesche 2010). The default 

model context for FVS is to forecast stand development to full site occupancy. If endemic or epidemic 

conditions are to be portrayed, the FVS model needs 

to be adjusted toward those ends.

Step 3. Develop natural growth projections to estimate parameters for deterministic pathways without 

disturbance. The goal here is to capture ecological 

processes that represent stand development over at 

least one life cycle.

Step 4. Process successional progressions through 

the Preside program (Vandendriesche 2009). 

Vegetation classification attributes are compiled into 

a report from which mean residence times within 

states and transition probabilities between states are 

computed.

Step 5. Develop management activity projections

to estimate parameters for stochastic pathways. 

Construction of a treatment matrix is beneficial in 

assigning silvicultural prescriptions and fire activities 

to each model state.

Step 6. Process probabilistic transitions through the 

Preside program. Resident and resultant vegetation 

states for pre and post treatment/activity are captured. Average stand conditions and reporting 

attributes are summarized.

Step 7. Review model results and make adjustments

in relation to conceptual expectations. When necessary, account for knowledge obtained from ancillary 

literature and professional judgment.
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GENERAL TECHNICAL REPORT PNW-GTR-869

Table 2—Habitat type codes associated to the ponderosa pine/bunchgrass PNVT

Habitat type code Common name

011092 Ponderosa pine/Arizona fescue/blue gramma

011093 Ponderosa pine/Arizona fescue/Gambel oak

011330 Ponderosa pine/mountain muhly

011340 Ponderosa pine/screwleaf muhly

011341 Ponderosa pine/screwleaf muhly/Gambel oak

011350 Ponderosa pine/Indian ricegrass

011380 Ponderosa pine/black sagebrush

011390 Ponderosa pine/screwleaf muhly-Arizona fescue

011391 Ponderosa pine/screwleaf muhly-Arizona fescue/blue gramma

011392 Ponderosa pine/screwleaf muhly-Arizona fescue/Gambel Oak

011400 Ponderosa pine/kinnikinnik

011470 Ponderosa pine/Arizona walnut

Figure 2—Timber suitability screening process.
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as defined for coarse filter plots, we identified 477 FIA 

plots associated with the ponderosa pine/bunchgrass forest 

ecosystem. The following fine filters were applied:

1. Must have 60 at least percent canopy cover in 

ponderosa pine trees

2. No canopy cover restrictions on:

a. junipers

b. pinyons

c. bristecone/limber pine

d. aspen

3. Must have less than 5 percent canopy cover for all other 

tree species except:

a. Must have 0 percent canopy cover for corkbark 

fir, Engelmann spruce, blue spruce

b. Must have 0 percent canopy cover narrowleaf 

cottonwood, other hardwoods

c. Must have less than 2 percent canopy cover 

Chihuahua pine, other softwoods

4. Stockability: 85 percent or greater

5. Site Productivity: Ponderosa Pine Site Index within 

the range of 45 to 105.

This resulted in a subset of 283 plots meeting the fine 

filter criteria (approximately 60 percent of coarse file plot 

set). These were used in FVS projections to evaluate the 

effects of management activities. Figure 3 characterizes the 

winnowing of fine filtered plots from the coarse filtered data 

set.

Supplementing Plot Sample Sizes 

With Synthetic Datasets

When processing FIA plots to compute transition probabilities for STMs, on occasion there were either too few plots 

(i.e. less than 10) or no plots at all to represent particular 

model states. In these cases, confidence in the results from 

FVS simulations was relatively low. Having a small sample 

within a vegetation state increased the likelihood that a 

borderline plot (i.e. plots at the edge of the shaded box in 

fig. 3) would have undue effects on the resultant transition 

probabilities. For example, if one plot (of three) responded 

to treatment by transitioning to an unlikely vegetation 

state, the relative impact would suggest that 33 percent of 

representative acres within the state would transition to 

this suspect destination state. With a larger sample size, the 

Figure 3—Use of expert opinion rules 

to define fine filter plots sets.
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GENERAL TECHNICAL REPORT PNW-GTR-869

transition probabilities would be buffered against unlikely 

end states. Occasionally, this problem was encountered with 

coarse filtered plot sets; however, it was more likely to occur 

with the reduced subset of fine filtered plots.

We set the minimum sample size for a vegetation state 

to greater than or equal to 10 plots. If this criterion was 

not met, we created synthetic plots and added those to the 

original inventory sample. Having 30 or more representative plots in a vegetation state is desirable. An FVS post 

processing program (named Synthesize2) was developed to 

create modeled plot sets. The process involves first using 

FVS to grow existing inventory plots into vegetation states 

with a deficiency of existing plot data. Next, the Synthesize 

program reads the output tree list files created by FVS and 

extracts the plot and tree information of specified vegetation 

states (fig. 4). This extracted information is then converted 

into input plot and tree records in database files. These 

databases are used as the plot data input source for deficient 

vegetation states. With respect to the PPG PNVT, there 

were no vegetation states that lacked an adequate inventory 

sample. There was a need to augment the inventory sample 

with synthetic plots in other PNVTs in the southwestern 

region.

Validation of Disturbance Transitions

Two types of disturbance were considered during this phase 

of modeling analysis: human induced and naturally caused. 

Silvicultural treatments in the form of tree removal were 

the primary source of human intervention. Depending on 

the ignition source and outcome, fire can be considered 

either or both types of disturbance activity. Planned fires 

(i.e. prescribed) served as a secondary source of human 

induced disturbance. Unplanned fires (i.e. wildfires) for our 

purposes were considered the primary natural disturbance 

agent. Inferences regarding insects, disease, wind, and other 

sources of natural disturbances outside of pervasive impacts 

were not modeled independently due to time constraints.

The Forest Vegetation Simulator and Fire and Fuels 

Extension (FVS-FFE; Rebain 2010) were used to simulate 

the effects of using tree cutting, and planned and unplanned 

fires as restoration tools for the various PNVTs of the 

Southwest. A standard set of management activities were 

applied to each model state. Species composition and 

structure (i.e. vegetation state) were compared pre and post 

treatment. The resultant conditions were then used to assign 

the transition pathways for each disturbance type in VDDT.

Land managers should consider a full “toolbox” of 

treatment methods in order to work toward achieving 

desired conditions. However, in an effort to reduce the 

modeling workload, eight silvicultural prescriptions and 

three burning intensities were evaluated (table 3).

Silvicultural prescriptions—

Cutting parameters were adjusted in the FVS model runs 

to fit each PNVT and the intent of the particular treatment method. Knowledge of species silvics and ecological 

objectives were considered and adjusted for each PNVT as 

appropriate. Residual target basal areas (BAs) were assigned 

according to cutting prescriptions as shown in table 4 for 

PPG.

Tree species cut/leave preferences were set within the 

FVS model to fit the native species mix and emphasized 

Figure 4—The Synthesize post processing program can be used to 

select deficient vegetation states to increase sample representation.

2 Synthesize, Version 2011.01, programmed by D. Vandendriesche while working with USDA Forest Service, Forest Management Service Center, Forest Vegetation Simulator Staff, Fort Collins, CO. Inquiries should be directed to the developer.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

restoring each PNVT to reference conditions.3 Where 

certain tree species, such as aspen and oaks, were desired 

to retain they were either favored in the species preference 

settings or as a percentage of the total target residual BA. 

Trees with recorded dwarf mistletoe infection were selected 

for removal in silvicultural treatments aimed at improving 

overall stand vigor and health.

Cutting treatments were to be followed by a cool 

prescribed burn as part of the same restoration entry. 

These follow-up burns were not modeled in FVS-FFE. 

The thought being that these surface fires would treat the 

understory ground/shrub level and not have an impact on 

the resultant vegetation state. Prescribed fire treatments 

were modeled in FVS-FFE as the sole method of thinning 

trees in areas where tree cutting is not possible.

Fire modeling parameters—

Fire behavior is a combination of fuels, weather, and 

topography. The FVS-FFE model accepts fuel and weather 

Table 3—Standard treatments used to evaluate the 

effects of management activities within each VDDT 

Model State

Management activity Code

Natural endemic growth in the absence of A

 disturbancea

Free thin, all sizes to target basal area B

Thin-from-below to target basal area C

Thin under a 16” diameter cap to target basal area D

Group selection with matrix thin to target basal area E

Shelterwood seed cut to target basal area F

Clearcut with non-regeneration objective legacy G

 trees

Clearcut/coppice for hardwood regeneration H

Prescribed fire, low intensity burning conditions J

Prescribed fire, moderate intensity burning K

 conditions

Prescribed fire, high intensity burning conditions L 

 conditions

Thin under a 9 inch diameter cap M

a Specifying a “No Treatment” activity implies by default a management 

decision.

Table 4—Prescribed residual basal area targets by 

cutting method for the ponderosa pine/bunchgrass 

PNVT

Cutting method PPG

B = Free Thin All Sizes to Target BA 50 BA

C = Thin From Below to Target BA 70 BA

D = Thin Under 16” Diam. Cap to Target BA 60 BA

E = Group Selection, with Matrix Thin to Target BA 60 BA

F = Shelterwood Seed Cut to Target BA 30 BA

G = Clearcut with Non-regen. obj. Legacy Trees na

H = Clearcut /Coppice with Non-regen. Legacy Trees na

M = Thin Under 9” Diam. Cap for MSO Recovery na

parameters that mimic environmental conditions at the time 

of an ignition. The resulting fire behavior, such as type of 

fire (surface, passive, or crown fire), flame length, and torching and crowning index are then estimated by FVS-FFE. 

These fire behavior parameters were applied to our model 

states and FVS-FFE then estimated mortality and survival 

of the vegetation by species and size. One fire disturbance 

was applied at the beginning of the growth cycle with 

each fire modeled at low, moderate, and high conditions of 

weather and fuel moisture.

Environmental conditions used to simulate the low, 

moderate, and high fire conditions are based on historic 

weather data from the Alpine, Arizona, Remote Automated 

Weather Station (RAWS). The Alpine RAWS has the most 

complete and accurate data of all the weather stations on the 

Apache-Sitgreaves National Forests, and was used for FVSFFE modeling of the ponderosa pine/bunchgrass PNVT.

Weather data were sorted using FireFamilyPlus4 (v4.1) 

to produce a Percentile Weather Report. This percentile 

report was used to determine the 15th, 75th, and 90th

percentile weather for the past twenty years (1990–2009). 

Weather data used were from the period of April 1 through 

October 15 each year, representing a typical fire season. The 

15th percentile represents natural fire season conditions for 

a low intensity fire; the 75th is moderate conditions; and, the 

90th is high intensity fire conditions (table 5).

3 The concept of ‘desired conditions’ and ‘forest restoration’ strategies for the ponderosa pine and dry mixed conifer PNVTs of 

the southwestern region is documented in Triepke et al. 2011.

4 USDA Forest Service, Rocky Mountain Research Station, Missoula, MT. Software and User’s Guide available online at: 

http://www.firemodels.org/index.php/firefamilyplus-software/firefamilyplus-downloads.
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GENERAL TECHNICAL REPORT PNW-GTR-869

These percentile environmental conditions were used 

to represent both natural fire conditions, such as wildfires 

that may be managed to move vegetative conditions toward 

desired conditions, as well as burning treatments that may 

be used for management ignited prescribed fires. These 

environmental conditions approximate conditions under 

which a natural fire may burn. They represent a good 

starting point for development of a management burning 

prescription.

Winds are recorded at the RAWS each day at 1:00 p.m. 

and while they capture wind speed and direction at the 

average hottest time of the day, this does not represent wind 

gusts adequately. Consequently, the wind speeds generated 

from analysis of the historical weather were considered 

too low to reflect wind gusts effecting fire behavior, so 10, 

15, and 20 mph winds were substituted for low, moderate 

and high wind speeds at 20’ above the main vegetation 

canopy (where RAWS wind speeds are measured). Based on 

analysis of the weather data and professional judgment, 60, 

75, and 90 degrees were used respectively for air temperature. Duff moisture is also not produced by the percentile 

weather report. These were derived using FVS-FFE defaults 

for duff moisture under moist (125 percent duff moisture), 

dry (50 percent), and very dry (15 percent) conditions 

(Rebain, 2010). These conditions were used across all 

vegetation types to provide consistency.

Treatment Matrix

A group of silviculturists and fire ecologists was assembled 

from throughout the southwestern region for the purpose of 

providing input to the forest plan revision effort. Recall that 

this group influenced the decision regarding the base datasets used for modeling management activities. This same 

group collaboratively developed a standard set of prescriptions (eight silvicultural treatments, three burning activities) 

that were used to address the management alternatives put 

forth by planning teams on the various National Forests. 

For each PNVT, a “0–1 Treatment Matrix” was created 

that designated in which model states FVS would be used 

Table 5—Percentile Weather Report derived from FireFamilyPlus program

Station: 020401: ALPINE, AZ Variable: ERC Model: 7G2PE2

Data Years: 1990–2009 Date Range: April 1–October 15 Wind Directions: S, SW, W

Percentile, Probabilities and Mid-Points

Variable/Component Range Low Moderate High

Percentile Range 0-15 16-89 90-97

Climatological Probability 15 75 7

Mid-Point ERC 15-15 48-48 90-90

Number Observations 61 82 61

Calculated Spread Component 4 10 16

Calculated ERC 16 49 91

Percent Area Burned 60 70 80

Fuel Moistures/Weather

Variable Low Moderate High

1 Hour Fuel Moisture 11.17 4.46 2.42

10 Hour Fuel Moisture 15.39 6.15 2.81

100 Hour Fuel Moisture 19.11 10.39 4.36

1000 Hour Fuel Moisture 21.81 13.95 6.06

Herbaceous Fuel Moisture 108.83 60.23 39.72

Woody Fuel Moisture 166.06 105.34 60.00

Duff Moisture 125 50 15

Temperature 60 75 90

20’ Windspeed 10 15 20

3772 Weather Records Used, 2200 Days.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Table 6—The “0–1 Treatment Matrix” for the ponderosa pine/bunchgrass PNVT. Model state “H” is discussed 

in more detail in the text

 VDDT model states

RX Management activities A B C D E F G H I J K L M N

B Free thin all sizes to target Basal Area (BA) 0 0 1 1 1 1 1 1 1 1 1 1 1 0

C Thin-from-below to target BA 0 0 1 1 1 1 1 1 1 1 1 1 1 0

D Thin under 16-inch diameter cap to target BA 0 0 0 1 1 0 0 1 1 1 1 1 1 0

E Group selection with matrix thin to target BA 0 0 0 1 1 0 0 1 1 1 1 1 1 0

F Shelterwood seed cut to target BA 0 0 0 1 1 0 0 1 1 1 1 1 1 0

G Clearcut with non-regeneration objective 0 0 0 0 0 0 0 0 0 0 0 0 0 0

 legacy trees

H Clearcut/Coppice for hardwood regeneration 0 0 0 0 0 0 0 0 0 0 0 0 0 0

I Artificially plant seedlings (modeled in 0 0 0 0 0 0 0 0 0 0 0 0 0 0

 VDDT alone)

J RX fire in low intensity burning conditions 0 1 1 1 1 1 1 1 1 1 1 1 1 0

K RX fire in moderate intensity burning 0 1 1 1 1 1 1 1 1 1 1 1 1 0

 conditions

L RX fire in high intensity burning conditions 0 1 1 1 1 1 1 1 1 1 1 1 1 0

M Thin under 9-inch diameter cap to target BA 0 0 0 0 0 0 0 0 0 0 0 0 0 0

to determine the effects of management activities. The 0–1 

Treatment Matrix for the PPG PNVT is shown in table 6.

A “1” in a cell indicated that we used FVS to simulate 

the effects of that treatment in that model state given this 

was an option to be considered in the analysis of management alternatives. A “0” in the treatment matrix meant that 

this type of treatment was either not likely to be used in 

the landscape analysis process for forest planning, or that 

it could be readily evaluated with professional judgment. 

FVS was not used for these cells. A national forest could 

decide independently whether or not to use specific cells 

within the “0–1 Treatment Matrix”. Adjusting the transition 

probabilities within VDDT for particular vegetation states 

allows “turning on” or “turning off” movement between 

model states.

“Transition probability multipliers” are values within 

VDDT that increase or decrease the average probability 

for one or more transition types. This feature simplified 

sensitivity testing of the probabilities and allowed exploring 

“what if” scenarios (for example, “what if fires were twice 

as frequent?”). To perform this type of sensitivity testing 

without multipliers, we would have needed to edit the probability of each fire transition in each model state individually. Alternatively, we specified a transition multiplier of “2” 

for fire transitions, and the VDDT software automatically 

doubled the values of all fire probabilities.

Here is an example of the computational process 

for determining the transition probabilities following a 

disturbance activity. Within the PPG PNVT, there were 40 

FIA plots that represented vegetation state H (i.e. medium 

tree size class, closed canopy, single storied). According to 

table 6, silvicultural prescriptions B-F and fire intensities 

J-L would apply to vegetation state H. The FVS model was 

run eight times to represent these disturbance activities. 

Table 7 summarizes the destination state (post treatment) 

for each management activity. Dividing the total number 

of FIA plots (40) into the FIA plot counts by prescription 

(across rows in table 7) produces the proportion of plots that 

stayed within the original vegetation state and those that 

transitioned to alternant states (table 8). These transition 

probabilities are then used in the VDDT model.

Summary Reports by State Class

Several FVS post-processing steps were bundled together 

to produce aggregate summaries for each vegetation state. 

Table 9 provides vegetation characteristics of State H. These 

attributes were computed for each model state by growing 

the representative FIA plots forward, in the absence of 

disturbance, for 150 years and summarizing the results. 
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Figure 5—Stand Visualization System images of vegetation state H, 

the Medium Size, Closed Density, 

Single Story state.

Table 7—Destination states of 40 plots initially residing in vegetation state H as the result of FVS 

simulations of management activities

 Destination states

RX Management activities A C D E G H J L Total

B Free thin all sizes to target Basal Area (BA) 4 34 2 40

C Thin-from-below to target BA 26 2 1 10 1 40

D Thin under 16-inch diameter cap to target BA 36 2 1 1 40

E Group selection with matrix thin to target BA 36 3 1 40

F Shelterwood seed cut to target BA 5 2 32 1 40

J RX fire in low intensity burning conditions 11 25 4 40

K RX fire in moderate intensity burning conditions 13 23 1 3 40

L RX fire in high intensity burning conditions 16 20 1 3 40

Table 8—Transition probabilities based on the proportions of 

FIA plots remaining and moving to alternant states as a result

of management activities in vegetation state H

 Destination states

RX A C D E G H J L Total

B .10 .85 .05 1.00

C .65 .05 .025 .25 .025 1.00

D .9 .05 .025 .025 1.00

E .9 .075 .025 1.00

F .125 .05 .8 .025 1.00

J .275 .625 .1 1.00

K .325 .575 .025 .075 1.00

L .4 .5 .025 .075 1.00
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Table 9—Composite characteristics of ponderosa pine/bunchgrass PNVT, vegetation state H

Vegetation characteristics Value for State H

Vegetation structure variable examples

Dominance type PIPO

Canopy layers 1

Stand age in the overstory 139

Total plot activity count 1424

Proportion of stockable acres .99

Stand-stock variable examples

Seedlings per acre less than an inch in diameter 53.2

Trees per acre greater than an inch in diameter 249.7

Basal area per acre greater than an inch in diameter 151.6

Canopy cover percent 47.2

Annual growth: cubic feet/acre >5 inches in diameter 42.4

Annual mortality: cubic feet/acre >5 inches in diameter 20.3

Wildlife habitat variable examples

Standing snags 8 to 12 inches in diameter 8.23

Standing snags 12 to 18 inches in diameter 6.24

Standing snags >18 inches in diameter 2.04

Pestilence disturbance examples

Dwarf mistletoe awareness indicator (plot count) 278

Mountain pine beetle hazard 3

Wildfire risk examples

Crown bulk density .07

Crown bulk height 19.33

Crowning index 30.14

Torching index 32.54

Fuel load in the duff layer 4.16

Biomass and carbon examples

Tree biomass in dry weight of live and dead boles and crown 64.5

Total stand carbon above and below ground 55.5

Figure 5 provides graphical depictions of vegetation state H 

using the Stand Visualization System (McGaughey 1997), 

which can be obtained directly from FVS output.

Timber volume calculations—

In addition to evaluating the effects of disturbance activities on post treatment states and determining average stand 

conditions by model state, FVS simulations also were used 

to quantify timber volumes required by the National Forest 

Management Act of 1976 (NFMA). For each combination 

of standard silvicultural treatment, model state, and PNVT, 

a variety of per acre wood volume statistics were produced 

in a cut-volume spreadsheet. Table 10 illustrates the harvest 

volumes for silvicultural prescription B in State H. When 

these per acre values are multiplied by the acres treated 

outputs from VDDT, total timber volume removal can be 

estimated.

Species composition—

A topic of interest was estimating the changes in species 

composition resulting from alternative treatment options. 

Relative to other management treatments, silvicultural 

prescription D “Thin under a 16” diameter cap to target 

basal area” (table 3) is controversial. Refer to Triepke et al. 

2011 for a more in-depth discussion of the impasse; specifically, on page 4, Diameter Cap discussion. Each PNVT has 

a set of desired conditions as developed by Southwestern 

Regional resource specialists to guide management practices as specified in forest plan revisions. For example, 

the ponderosa pine/bunchgrass forest ecosystem includes 
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Figure 6—Tree frequency of white fir following alternative management activities in the Mixed Conifer 

Dry PNVT.

characteristics such as open canopy, uneven-aged structure, 

desired species composition, etc. In the mixed conifer dry 

PNVT, the goal for species composition is to mimic reference conditions under frequent, low-intensity fire regimes 

(i.e. open uneven-aged forests). White fir is susceptible to 

fire and is shade tolerant (favored by infrequent fire and 

closed canopy conditions). Historically, white fir was absent 

or rare in this forest type but over time has increased in 

abundance due to fire suppression and high canopy densities. FVS predicted that prescriptions B, C, E, F, and L left 

less white fir than did the 16-inch diameter cap prescription. 

Figure 6 summarizes post treatment white fir densities for 

the mixed conifer dry PNVT.

Discussion

Using one model’s output (FVS) as input to another model 

(VDDT) poses a valid question: Are the estimation error 

Table 10—Example of a summary of wood volume removed per acre for silvicultural 

prescription B in vegetation state H

Type of wood volumes per acre (cubic feet, board feet, and tons) Net value

Softwoods

5-9” DBH Cubic Feet (CF) for allowable sale quantity (ASQ) 153.91

> 9” DBH (CF) for ASQ 534.30

Total CF of ASQ contribution on suitable lands (excludes aspen) 688.21

0-5” DBH (tons of biomass) not ASQ 10.63

5-9” DBH (tons of biomass) not ASQ 2.22

> 9” DBH (tons of biomass) not ASQ 8.23

> 9” DBH (board feet) not ASQ 2,384.87

Nonindustrial Species > 5” diameter (PJ, etc.) (CF) not ASQ 8.14

Nonindustrial Species > 0” diameter (Tons of Biomass) not ASQ .15

Hardwoods

Aspen > 5” DBH (industrial CF), optional ASQ addition 8.53

Aspen > 0” DBH (tons of biomass) not ASQ .12
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

associated with the first model being passed on and perhaps 

compounded within the second model. Truly, inferences 

associated from the second model can only be as good, 

at the base level, as the information/parameters derived 

from the first model. To address this potential dilemma, we 

crafted the FVS model runs to forecast in accordance with 

observed trends from permanent plot inventory data (FIA) 

as based on contemporary climate. Our previously published paper (Vandendriesche 2010) stresses the importance 

of knowing the modeling context and making the proper 

adjustments in the FVS model to achieve viable estimations 

that are empirically based.

Based on variation in the availability of measurement data and scientific information, we recognized the 

importance of striking a balance between the FVS predictions, expert opinion, and research literature. This truism 

came into play regarding the data sets used to populate the 

FVS model. Based on feedback from resource specialists, 

criteria for fine filter rules were developed to apply against 

the coarse filtered, habitat type based PNVT plot sets. 

Additionally, when professional judgment indicated that we 

didn’t have a sufficient number of plots in a vegetation state, 

we generated synthetic plots. In addition to the methods 

discussed above, further adjustments were made during the 

process based on lessons learned.

Vegetation Responses Drawn from the Literature—

Our initial intent was to quantify all STM transitions based 

on the results of FVS simulations using FIA plots. However, 

although endemic insect and disease activity (such as dwarf 

mistletoe) was included in our FVS projections, the probability of major insect and disease outbreaks was taken 

from the literature including Lynch et al. (2010) and Smith 

(2006). As mentioned, we did not model these effects with 

FVS because of time limitations in the land management 

planning process as implemented by the Southwestern 

Region. Beginning in the last quarter of 2005, each National 

Forest and Grassland was programmed to work through 

forest plan revision within a three year time period (fig. 7). 

This requirement focused efforts to meet required benchmarks in the planning process. Overall modeling analysis 

needed to be tapered to achieve the specified time goals.

Professional Judgment: Fire Effects—

Interdisciplinary teams on each national forest needed to 

review the results of our modeling work to differentiate 

acres belonging to the characteristic versus uncharacteristic 

“grass, forbs, brush, shrub” states (A versus N, respectively). Our mid-scale vegetation mapping products (Triepke 

2005, Weisz et al. 2010) did not delineate between these two 

vegetation states. Consequently, when tabulating the results 

of fire burning under high intensity conditions (Rx L, 

table 3), all FVS plots destined for the “grass, forbs, brush, 

shrub” stage were initially assigned to state A. Following 

the interdisciplinary review, plots were reallocated between 

states A and N based on local terrestrial ecological unit 

data, historical fire data, and professional judgment.

As an example, FVS results of a prescribed fire burning 

under high intensity conditions in state H resulted in 40 percent of the plots transitioning to state A. However, a review 

of historical fire data and fire effects data on the ApacheSitgreaves National Forest suggested that 27 percent the 

plots would go to state A and 13 percent of the plots would 

go to state N. It should be expected that each national forest 

and PNVT has a unique division of plots between states 

A and N resulting from fires burning under high intensity 

conditions in closed canopy states.

We decided that the effects of unplanned ignitions 

would be the same as the effects of planned ignitions. 

Essentially, we assumed that unplanned nonlethal ignitions 

would have the same destination states as Rx J; unplanned 

mixed severity ignitions would have the same destination 

states as Rx K; and, unplanned stand replacing ignitions 

would have the same destination states as Rx L. This was 

based on an interdisciplinary review of the results of the 

FVS-FFE modeling process.

Professional Judgment: Group Selection—

At the start of our FVS modeling process, we made two 

simplifying assumptions. The first assumption was that a 

14-box model with states classified on the basis of cover 

type, dominant trees size, percent canopy cover, and 

storiedness (single story versus multi-story) was sufficient 

to represent most forest conditions and management activities (table 1). The second assumption was that for modeling 
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GENERAL TECHNICAL REPORT PNW-GTR-869

Figure 7—Southwestern Region forest plan revision timetable.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Table 11—Transition probabilities from the group 

selection prescription based on FVS projections

 To Destination States

From

State A D E I J K Total

D .95 .05 1.00

E 1.00 1.00

H .90 .08 .03 1.00

I 1.00 1.00

J .06 .41 .53 1.00

K .50 .10 .40 1.00

L .23 .30 .06 .23 .17 1.00

M .09 .73 .18 1.00

Table 12—Transition probabilities from group selection prescriptions based on professional judgment

 To Destination States

From

State A D E I J K Total

D 1.00 1.00

E 1.00 1.00

H 1.00 1.00

I 1.00 1.00

J 1.00 1.00

K 1.00 1.00

L 1.00 1.00

M 1.00 1.00

the effects of management activities, we only needed to use 

FVS to categorize conditions before and after treatment. As 

we completed our initial FVS process, it became apparent 

that these assumptions needed to be modified for the group 

selection treatment type (Rx E, table 3).

Table 11 lists the destination states immediately following implementation of the group selection prescription (Rx 

E). Post-treatment destination states and the resulting wood 

volume estimates describe the immediate effects of one 

group selection entry rather than a number of entries that

would create several groups over several cutting cycles. A 

major objective for applying a group selection silvicultural 

system is to convert existing even-aged stands to unevenaged structures. A minimum of five entries in succession 

on the same stand acres is needed to be successful. A more 

realistic modeling approach of group selection silviculture 

would have resulted in a more complex VDDT model with 

more model states and many more transitions (tracking 

relative age, time since disturbance, etc.).

Instead, we pursued a simplified approach. We assumed 

that the group selection management system would provide 

the “predominance of uneven-aged dynamics” which is 

characterized by states J and K (table 1; Triepke et al. 2011). 

These assumptions were implemented in the VDDT model 

as displayed in table 12. The results were consistent with the 

group selection FVS runs applied over time to develop the 

Long Term Sustained Yield Capacity (LTSYC) for each of 

our PNVTs (Youtz 2011).

Applications

The methods and models described in this paper are currently being used by the national forests in Arizona to assist 

in their development of revised land management plans 

(for example, Higgins and Kleindienst 2011). They are also 

currently being utilized as a point of departure for large 

area assessments (Hemstrom 2012) and are being linked to 

global climate change models to assess effects of climate 

change on forest ecosystems (Kerns et al. 2012). The main 

contribution of this work has been to demonstrate the use of 

the FVS model and related software to estimate the effects 

of disturbance activities in an STM. It is our intent that 

others can benefit from this process.

Acknowledgments

The authors would like to recognize the contributions of 

Monica Boehning1

, Mitchel White1, Chris Nelson1, Judith 

Palmer1, Jack Triepke2, Jim Youtz2, Linda Wadleigh2, 

Tessa Nicolet2, Pam Bostwick2, Wayne Robbie2, Melinda 

Moeur3

,

 Michael Pitts4, Mike Manthei5, Bruce J. Higgins6, 

Dan Mindar7, and Chris Frye8 in the development of the 

processes described in this paper. Without the expertise of 

these individuals, the scope to the accomplishments would 

not have been achieved.

1. USDA Forest Service, Apache-Sitgreaves NF, 

Springerville, AZ 85938

2. USDA Forest Service, Southwestern Region, 

Albuquerque, NM 87102
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GENERAL TECHNICAL REPORT PNW-GTR-869

3. USDA Forest Service (retired), Contractor, Portland, 

OR 97201

4. USDA Forest Service (retired), Contractor, Medford, 

OR 97501

5. USDA Forest Service, Coconino NF, Flagstaff, AZ 

86001

6. USDA Forest Service, Kaibab NF, Williams, AZ 86046

7. USDI National Park Service, Springerville, AZ 85938

8. USDA Forest Service (retired), Placitas, NM 87043

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Approaches to incorporating climate change effects in 

state and transition simulation models of vegetation

Becky K. Kerns, Miles A. Hemstrom, David Conklin, Gabriel I. Yospin,

Bart Johnson, Dominique Bachelet, and Scott Bridgham

Authors

Becky K. Kerns is a research ecologist, USDA Forest 

Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 3200 SW Jefferson Way, Corvallis, OR, 

97330, bkerns@fs.fed.us. Miles A. Hemstrom is a research 

ecologist (retired), Forestry Sciences Laboratory, 620 SW 

Main St. Portland, OR 97205. He is now a faculty research 

associate, Institute for Natural Resources, P.O. Box 751, 

Oregon State University, Portland, OR, 97207, miles.hemstrom@oregonstate.edu. David Conklin is a climate change 

modeler, dave.conklin@commonfutures.biz. Dominique 

Bachelet is a senior climate change scientist, Conservation 

Biology Institute, 136 SW Washington Avenue, Suite 202, 

Corvallis, OR 97330, dominique@consbio.org. Gabriel I. 

Yospin is a post-doctoral fellow, Montana State University, 

Institute on Ecosystems, 106 AJM Johnson Hall, Bozeman, 

MT 59717, Gabe.Yospin@montana.edu. Bart R. Johnson is 

an Associate Professor, Department of Landscape Architecture, bartj@uoregon.edu. Scott D. Bridgham is a professor, 

Institute of Ecology and Evolution, University of Oregon, 

Eugene, OR 97403, bridgham@uoregon.edu. 

Abstract 

Understanding landscape vegetation dynamics often 

involves the use of scientifically-based modeling tools that 

are capable of testing alternative management scenarios 

given complex ecological, management, and social conditions. State-and-transition simulation model (STSM) 

frameworks and software such as PATH and VDDT are 

commonly used tools that simulate how landscapes might 

look and function in the future. Until recently, however, 

STSMs did not explicitly include climate change considerations. Yet the structure of STSMs makes them highly 

conducive to the incorporation of any probabilistic phenomenon. The central task in making a STSM climate-sensitive 

is describing the relevant processes in terms of probabilistic 

transitions. We discuss four different approaches we have 

implemented to inform climate-induced changes in vegetation and disturbance probabilities in STSMs using the 

dynamic global vegetation model MC1. These approaches 

are based on our work in several landscapes in the western United States using different modeling frameworks. 

Developing STSMs that consider future climate change 

will greatly broaden their utility, allowing managers and 

others to explore the roles of various processes and agents 

of change in landscape-level vegetation dynamics. However, 

numerous caveats exist. Regardless of how STSMs are 

made climate-sensitive, they neither simulate plant physiological responses directly nor predict landscape states by 

simulating landscape processes mechanistically. They are 

empirical models that reflect the current understanding of 

system properties and processes, help organize state-ofthe-art knowledge and information, and serve as tools for 

quickly assessing the potential ramifications of management strategies. As such, they highlight the need for new 

research, while providing projections based on the best 

available information. 

Keywords: climate change, coupled models, dynamic 

global vegetation models, state-and-transition simulation 

model, vegetation dynamics.

Introduction 

Across the globe, plant communities are already experiencing the effects of climate change: warmer temperatures, 

earlier springs and earlier snowmelt, reduced snowpack, 

changes in fire regimes, and higher concentrations of CO2

(Parry et al. 2007). There is increasingly strong evidence 

that climate change will profoundly alter vegetation structure and composition, ecosystem processes, and the future 

delivery of ecosystem goods and services (Parry et al. 

2007). Coupling climate change projections with landscape 

vegetation dynamics is a promising approach that involves 
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the use of scientifically based modeling tools that are 

capable of testing alternative management scenarios given 

complex ecological, management, and social conditions. 

State and transition simulation models (STSMs) are one 

tool for simulating how landscapes might look and function in the future and thus guide decisionmaking (Daniel 

and Frid 2012). With vegetation STSMs, different potential 

vegetation types are grouped into discrete state classes. 

Transitions from one state class to another may occur 

probabilistically or are empirically based; regardless, they 

represent the effects of ecological processes such as succession and wildfire and management actions (Daniel and Frid 

2012). Although factors such as drought and frost kill have 

been included as probabilistic disturbances within STSMs 

(e.g., Evers et al. 2011), up until recently most STSMs did 

not include climate change considerations. Incorporating the 

effects of future climate change would increase the utility 

of STSMs as a common platform to collectively define the 

roles of various processes in projecting landscape-level vegetation dynamics. To the best of our knowledge, there are 

only a handful of studies where changing climate has been 

explicitly being incorporated into STSMs (e.g., Costanza et 

al. 2010, Hemstrom et al. in press, Provencher et al. 2009, 

Provencher and Anderson 2011, Yospin 2012). 

Climate change can affect vegetation by altering the 

future abiotic and biotic conditions under which plant 

species establish, survive, reproduce and spread. Increased 

temperature, longer growing seasons, less snow, and more 

frequent drought conditions may increase plant stress 

and decrease a species’ ability to survive in the drier and 

warmer parts of its range (Allen and Breshears 1998, Allen 

et al. 2010). Changes in abiotic conditions and subsequent 

effects on individual species reproduction, establishment 

and growth may in turn substantially alter plant competitive 

dynamics (Pfeifer-Meister et al. 2008). Rising CO2 concentrations will also directly affect plant growth and productivity through a variety of mechanisms (Nowak et al. 2004). 

But climate change modifications of disturbance regimes, 

such as wildfire and insect and disease outbreaks, might 

be the most important factors for forcing future vegetation 

responses (Brown and Westerling 2004, Taylor and Beatty 

2005, Westerling et al. 2006, Raffa et al. 2008, Pennisis 

2009). Therefore, STSMs that consider changes in successional trajectories and disturbance regimes in response to 

changing climate are needed. 

Considerations for Creating ClimateSensitive State and Transition Simulation 

Models 

The structure of STSMs makes them highly conducive 

to the incorporation of any probabilistic phenomenon 

using information generated from another source, dataset, 

modeling framework, or even expert knowledge (Daniel 

and Frid 2012). The central task in making a STSM climatesensitive is describing the relevant processes in terms of 

new assumptions and empirical relationships, including 

probabilistic transitions. This can be a challenge because 

it is not always clear how information from general circulation models (GCMs) and climate-sensitive vegetation 

models can be reduced to a set of empirical relationships, 

particularly to the fine spatial grain and level of detail at 

which many STSMs are typically developed. Information regarding potential future climate changes can be 

generated by more than twenty different GCMs that give 

variable projections of future climate (Littell et al. 2011). 

However, GCMs are highly complex mechanistic models 

that estimate potential future climatic trends on grids at 

resolutions of thousands or tens of thousands of square 

kilometers. Each grid-cell represents average conditions 

within its boundaries, producing daily to yearly estimates 

of a variety of climate attributes. Because GCMs estimate 

potential future climatic trends using coarse spatial grids, 

they are frequently spatially downscaled using a variety of 

quantitative techniques (Littell et al. 2011). General circulation models are all ‘‘forced’’ with scenarios of greenhouse 

gas emissions that reflect different assumptions about future 

global economic activity and fossil fuel use (Nakicenovic 

et al. 2000). Thus a single emission scenario can generate 

multiple future climate scenarios using different GCMs. 

Alternately, a single GCM can project multiple future 

scenarios under different emissions scenarios. Ideally, 

output from multiple GCMs and emission scenarios would 

be used for input into a climate-sensitive vegetation model 

to capture the available range of future conditions and 
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simulate their consequences for ecosystems. However, this 

ensemble approach is computationally intensive (Littell et 

al. 2011) and furthermore requires that all climate variables 

needed to run the vegetation model are readily available. 

One approach to avoid this problem is to pair scenarios 

based on a gradient of risk (Kerns et al. 2009, Littell et al. 

2011, Mote and Salathé 2010). For example, one could pair a 

scenario combination (GCM and emission scenario) at two 

extremes (e.g., less warming vs. lots of warming; precipitation increases vs. precipitation decreases). However, the risk 

framework may only be specific to a single resource issue. 

That is, a high risk scenario for potential changes in wildfire 

may be different than a high risk scenario for potential 

changes in an endangered species habitat. 

Downscaled future climate data are becoming increasingly available at scales useful for land managers (e.g., 0.6 

km2, Rogers et al. 2011). However, it is important to assess 

whether downscaling has exceeded the resolution supported 

by observations, recognizing that finer-scale projections 

are not always more reliable (Littell et al. 2011). Moreover, 

climate change data alone are not usually useful input 

data for a STSM. Climate data can be used to develop the 

empirical relationships required to modify STSM transition 

probabilities (e.g., fire frequencies, insect and disease outbreaks, changes in tree growth rates) for projecting changes 

in potential vegetation, or as input into other models that 

explicitly incorporate climate information and produce 

output that can then be used by STSMs. Provencher and 

Anderson (2011) used projections of future CO2, precipitation and temperature, to create trends in STSM disturbance 

transition probabilities. The authors also used information 

about species regeneration and simulated range shifts from 

the literature to develop a series of hypothetical range shifts 

for vegetation in Nevada. Costanza et al. (2010) modeled 

the effects of climate change by altering fire frequencies 

using the spatially explicit TELSA model (Kurz et al. 2000). 

Historic (1979–2010) climate and fire occurrence data were 

used to hindcast relationships between the acres burned and 

climate variables (i.e., temperature and precipitation). Those 

relationships were then incorporated as a multiplier on fire 

transition probabilities in the TELSA model runs. A statistical approach may be one method for generating projections, 

but may miss the important interactions between climate, 

disturbance, and succession that will drive changes in 

vegetation over the coming century. 

A commonly used approach for landscape analysis is 

to stratify the landscape according to one or more criteria 

that are considered to be important external drivers of 

vegetation change, and then to develop a separate pathway 

diagram (STSM) for each stratum (the spatially stratified 

state-and-transition simulation model approach, Daniel and 

Frid 2012). Biophysical drivers, such as soils, climate, and 

topography are typically used to define a stratum, based on 

existing ecological classification systems, such as potential 

vegetation types (PVTs) (Chiarucci et al. 2010) or biophysical settings (Long et al. 2006). However, modelers assume 

that the landscape stratification is then fixed over planning 

horizons of several decades or centuries, although strata 

can move through numerous state classes according to the 

defined STSM transition pathways. But the assumption that 

the present-day landscape stratification will remain constant 

over time is only valid if the underlying site conditions that 

define the stratum boundaries on the landscape remain constant. Because this is unlikely with future climate change, 

climate-sensitive models must by definition incorporate 

many spatially-stratified STSMs with transition pathways 

between strata. For example, such a model would allow 

transitions from a broadleaf forest PVT to a mixed conifer 

forest PVT and in turn to a conifer forest PVT. Others have 

operationally referred to such STSMs as a “mega-model” 

(i.e., Hemstrom et al. in press). Likewise, we will refer 

to these large spatially stratified STSMs with transition 

pathways between strata as mega-STSMs. 

A number of climate-sensitive vegetation models 

are available that can be used to create climate sensitive 

STSMs. These models are either empirical and typically 

species-specific (e.g., Rehfeldt et al. 2006, 2008; Iverson et 

al. 2008), or mechanistic (i.e., process-based, or physical) 

(Keane et al. 2004) and usually not species-specific (e.g., 

Bugmann 2001, Bachelet et al. 2003). Empirical models fit 

parameters to observations and use statistical methods to 

make projections. By contrast, mechanistic models typically 

try to represent underlying physiological processes, and 

thus can incorporate complex and novel interactions. The 
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capacity to include novel interactions allows a model to 

yield unexpected outcomes, which is a critical consideration 

for planning across a wide range of potential future conditions. However, mechanistic models are highly complex 

and require extensive training to use them correctly. They 

typically cannot incorporate the current vegetation as an 

initial starting condition (i.e., they require an extensive 

‘spin-up’ period), cannot directly incorporate management, 

and produce outputs as plant physiognomic types instead of 

actual species. Thus, these models provide a fairly abstract 

view of potential vegetation in a landscape under a set of 

climatic conditions. Incorporating output from mechanistic 

models into STSMs would better allow their results to be 

used for management purposes.

Using MC1 to Build Climate Sensitive 

State-and-Transition Simulation Models 

In the following section we present challenges and four 

approaches for incorporating climate-induced vegetation 

changes in STSMs using the dynamic global vegetation 

model MC1: (1) modifying potential vegetation using annual 

probabilities and transition multipliers calculated from 

MC1; (2) modifying potential vegetation by developing 

spatially explicit changes in vegetation using regression 

equations between MC1 output variables and site index and 

the Forest Vegetation Simulator (FVS); (3) modifying wildfire probabilities using annual probabilities and transition 

multipliers calculated from MC1; and (4) using MC1 output 

to develop a simple spatially-explicit rule-base to attenuate 

growth potential across a landscape. These approaches are 

based on our collaborative work across several landscapes 

in the western United States (Hemstrom et al. in press, 

Yospin 2012) using different modeling frameworks, and 

described in more detail in the following section. 

Dynamic global vegetation models (DGVMs) use 

climate projections from GCMs to simulate vegetation 

potential, vegetation growth, carbon and nutrient dynamics, and some natural disturbance regimes (e.g., wildfire) at 

relatively coarse resolution (Bachelet et al. 2003). Output 

is usually at a coarser spatial grain than that at which 

land managers make decisions. Furthermore, DGVMs do 

not usually include species-specific information, detailed 

vegetation dynamics such as seed dispersal, fire adaptations 

of various species, or the effects that various land management activities might have on vegetation dynamics. MC1 

is a DGVM that simulates plant type mixtures and broad 

vegetation types; pools and fluxes of carbon, nitrogen, 

and water through ecosystems; and fire disturbance. MC1 

routinely generates century-long, regional-scale simulations 

on relatively coarse-scale data grids (Bachelet et al. 2003, 

2005; Lenihan et al. 2008). 

MC1 (Bachelet et al. 2001) is a good candidate for 

incorporating climate-induced vegetation changes in 

STSMs because the model is mechanistic, incorporates disturbance dynamics, and projects future vegetation mechanistically based on changes in climate and biogeochemistry. 

MC1 combines a biogeography model (MAPSS), a model 

to simulate fire disturbance (MC-FIRE), and a biogeochemistry model (Century). Therefore MC1 can provide relevant 

output about future changes in potential vegetation and 

wildfire regimes that can in turn can be used to alter site 

potential and wildfire probabilities in a connected suite of 

STSMs that make up a landscape. 

Although MC1 produces projections of future changes 

in vegetation, it does so by predicting the life form or plant 

functional types mixtures, which are then classified into 

potential vegetation classes. A common challenge for using 

output from DGVMs such as MC1 is that these classes are 

not directly comparable to most locally defined STSM strata 

such as PVTs (fig. 1, table 1). Thus a key methodological 

issue in using MC1 output to build a climate-sensitive 

STSM is how to relate or “cross-walk” the local strata 

within a study area, such as a PVTs, to the more broadly 

defined potential vegetation classes simulated by MC1. 

Typically MC1 potential vegetation classes combine 

numerous species and structural conditions into single 

entities (table 1). For example, “temperate needleleaf forest” 

is an important MC1 potential vegetation class simulated 

for many western U.S. forested landscapes. In the interior 

Pacific Northwest, this broad class would correspond to 

a variety of strata, including ponderosa pine, lodgepole 

pine, Douglas-fir, and grand fir. Because most STSM PVTs 
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currently in use represent relatively fine-scale ecological 

conditions, their relation to a MC1 potential vegetation class 

is often many to one (table 1). 

Given this constraint, one crosswalk approach is to 

select a single representative STSM stratum for each MC1 

potential vegetation class for a landscape. Throughout this 

document, we will often provide examples and illustrate 

processes using the PVT concept for STSM stratum. With 

this approach, numerous aggregated PVTs will crosswalk to 

a particular MC1 vegetation class (Hemstrom et al. in press). 

Historical and future simulations with MC1, compared to 

locally derived maps, can guide the selection of the most 

representative PVTs for each landscape. It is important to 

consider whether or not the representative PVT fits the MC1 

Figure 1—Model output for 

vegetation types in a central Oregon 

area (30-arc sec grid, 800-m2): (A) 

STSM potential vegetation types 

generated from imputed data (B) 

MC1 potential vegetation classes 

projected for the historical period 

(30-year mode vegetation), and (C) 

MC1 potential vegetation classes 

projected for the last part of the 21st

century (30-year mode vegetation, 

MIRCO A2 scenario).
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plant functional type concept. It may also be possible to fine 

tune MC1 so that local vegetation is better reflected in the 

broad vegetation classes (Hemstrom et al. in press). When 

developing representative PVTs to reflect the dynamics of 

vegetation in the future, additional PVTs that might become 

more common in the future need to be added to the megaSTSM. Adjacent regions that may represent potential future 

conditions in the area of interest can be assessed for relevant 

candidates. This approach assumes that extant PVTs already 

approximately represent the vegetation dynamics that MC1 

simulates. The selected PVTs are, therefore, surrogates for 

future potential vegetation types that are assumed to have 

generally similar successional and disturbance dynamics. 

The resulting climate-sensitive mega-STSM would then 

consist of a combination of representative PVTs based on 

output from MC1 from the historical and future simulation 

periods. Transitions in the mega-STSM could then allow 

portions of the landscape to move among the previously 

independent PVTs according to output from MC1 run with 

the selected climate change scenarios. 

Once the strata for a mega-STSM have been selected, 

and incorporated into a single model, there are a number 

of ways in which output from MC1 can inform changes 

in strata. For example, MC1 projects changes in potential 

vegetation classes for a particular climate change scenario 

(an emission scenario combined with a GCM). These 

changes, in turn, can be converted into probabilities, which 

can then be used to inform the transition probabilities in 

the mega-model (Hemstrom et al. in press). However, MC1 

vegetation types can change quickly and unrealistically 

from year to year, so implementing simple annual changes 

among strata does not necessarily lead to reasonable model 

output, especially at a fine spatial or temporal scales grain. 

A feature of STSMs is that they can be configured to change 

transition probabilities over time; using transition multipliers, average transition rates can be shifted up or down in 

any year by proportions that range from zero (no transition 

occurs that year) to greater than one (the transition is larger 

than the long-term simulation period average that year). 

Modelers can compute the average annual transition rate for 

Table 1—Example showing the relationship between selected MC1 potential vegetation classes and locally

 representative potential vegetation types from a study area in eastern Oregon

MC1

Potential 

vegetation class Description STSM potential vegetation type

Subalpine forest Subalpine forests in cold, upper • Mountain hemlock (Tsuga mertensiana

 elevation environments. (Bong.) Carrière) – cold, dry

• Shasta red fir (Abies × shastensis (Lemmon) Lemmon 

[magnifica × procera]) – dry

• Subalpine woodland

Cool needleleaf Mixed conifer forests in relatively • Mixed conifer – moist 

 forest moist mid- to upper-elevation • Lodgepole pine (Pinus contorta Douglas ex Louden) – 

 forested environments. wet

• Mixed conifer – cold dry

• Cold dry forest

• Cool moist forest

Temperate Mixed conifer forests in relatively • Ponderosa pine (Pinus ponderosa C. Lawson

 needleleaf forest dry mid- to lower elevation var. ponderosa)/lodgepole pine – dry

 forested environments. • Mixed conifer – dry

• Ponderosa pine – xeric

• Mixed conifer – dry (pumice soils)

• Grand fir (Abies grandis (Douglas ex D. Don) Lindl.) – 

dry

• Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) – dry

• Lodgepole pine – dry
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each potential vegetation climate change transition over the 

entire MC1 simulation period, then develop transition multipliers to shift the average annual rate up or down according 

to MC1 output for that climate change transition in that 

year. Using this technique, it is possible to reproduce the 

long-term average and the year-to-year variation simulated 

by MC1 for transitions between PVTs in the mega-STSM. 

Provencher and Anderson (2011) use a different method 

for incorporating climate change into their STSMs, but 

make similar use of transition multipliers. The conditions 

under which the STSM allows transitions due to climate 

change should be carefully considered and ecological 

constraints may be necessary to produce plausible dynamics. One approach is to only allow transitions between PVTs 

within the STSM following a stand-replacing disturbance 

(Provencher and Anderson 2011, Hemstrom et al. in press, 

Yospin 2012); alternatively, transitions from one potential 

vegetation type to another may occur under a broader set 

of circumstances. 

A second approach to adjust transition probabilities for 

successional changes among strata and states over time in a 

mega-STSM is to use additional MC1 output beyond just its 

projected plant functional types. For example, Yospin (2012) 

developed a regression equation between MC1 output variables (e.g., soil carbon) that correlated reasonably well (r2

 = 

0.55, p < 0.001) with site index, a measurement of the height 

to which a Douglas-fir will grow in 50 years. Forest stands 

representing current and potential plant communities were 

run through the Forest Vegetation Simulator (Crookston 

and Dixon 2005) at a wide range of site index values. The 

rates at which trees within these stands transitioned from 

one STSM state to another under different site indexes were 

then converted to annual transition probabilities. Using 

the regression equation, MC1 output was used to project 

future site index in each location over time, which in turn 

was used to select the appropriate transition probabilities 

for each location at each time step. Because site index and 

MC1 data were spatially explicit, this approach allows for 

spatially explicit simulations of climate change effects on 

site productivity. 

These two types of adjustments to STSM transition 

probabilities account for changes in plant growth potential 

due to climate change, but do not capture the role of other 

climate-related effects. The impact of climate change 

on other stand-replacing disturbances also needs to be 

accounted for, and this is the focus of our third approach. 

Presently, MC1 does not provide projections regarding 

disturbance types other than fire, although researchers are 

currently working to incorporate insect and disease effects. 

Hemstrom et al. (in press) used projections for wildfire 

occurrence directly from MC1 and incorporated these into 

a mega-STSM. First, annual trends in wildfire probabilities 

from MC1 were calculated using the annual fraction of cells 

burned each year. For simplicity, and to reduce uncertainty, 

output was combined for several STSM strata (e.g. forest 

types, arid land types). MC1 can run without or with fire 

suppression using a set of algorithms that only allow intense 

stand-replacing fires to spread (Rogers et al. 2010). If MC1 

is run without fire suppression, the projections for area 

burned are considerably higher than would be expected with 

fire suppression, but future projections of fire area burned 

can be scaled down using empirical datasets (e.g., the 

Monitoring Trends in Burn Severity data-set, Eidenshink et 

al. 2007, http://www.mtbs.gov). Alternately, MC1 can run 

with fire turned off during the historical period, the future 

period, or both periods. In this case, a separate statistical 

or mechanistic fire model would be required to provide 

projections of fire disturbance to the mega-STSM. By using 

a fire model outside of MC1, carbon and biogeochemical 

pools simulated by MC1 are decoupled from the fire effects, 

missing fire mortality and biomass consumption the model 

normally calculates. Furthermore, an external fire model 

would disregard the build-up of fuel and fuel moisture variability that serve as index to trigger fires in MC1. 

There are also many other parameters within a STSM 

or mega-model that could change dynamically in response 

to changing climatic conditions. Mortality probabilities 

may need adjustment for drought-stressed trees under 

some climate scenarios (for examples, Provencher et al. 

2009, Provencher and Anderson 2011). Yospin (2012) used 

a simple spatially-explicit rule base to attenuate growth 

potential across a landscape. The rule base restrictions 

prevent forests from growing larger trees or denser forest 

stands when MC1 indicated that climate did not support 
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GENERAL TECHNICAL REPORT PNW-GTR-869

such growth, without summarily imposing mortality for 

the stand. This ecological restriction on both growth and 

mortality is a conservative approach to making the STM 

climate-sensitive. Simulations using a STSM parameterized 

with MC1 with the fire module turned completely off are 

one way to test the effect of climate independently from fire 

disturbance. For example, the direct effects of increased 

CO2 and increased plant water use efficiency may accelerate 

some successional pathways, or allow larger amounts of 

carbon to be stored on the landscape, although this effect 

may be only marginal for some ecosystems. Users may also 

need to define additional states to capture such phenomena, 

or allow another model to specify those transitions. 

One advantage of performing spatially-explicit simulations of vegetation dynamics with a STSM (e.g., Yospin 

2012) is that spatially explicit land management actions can 

then be simulated in conjunction with climate-sensitive 

ecological succession. For example, the STSM developed 

by Yospin (2012) is being incorporated as a module within 

a larger modeling system named Envision. Envision is an 

agent-based model of landscape change that allows individual agents, representing different types of landowners, to 

make probabilistic land use and land management decisions 

based on the availability of resources, feedbacks from past 

actions, and in response to user-defined behaviors (Envision 

is a new model based on Evolan, Bolte et al. 2006, Guzy 

et al. 2008). Envision is one example from the broad array 

of agent-based social decision simulation models, many of 

which rely on simple STSMs of vegetation. Incorporating 

climate change into STSMs of vegetation may be an effective way to bring climate change effects into simulation 

modeling of landscape management. 

Discussion and Conclusions 

Developing vegetation STSMs that incorporate the possible 

effects of future climate change will broaden and enhance 

their utility, allowing managers and other users to explore 

the roles of various processes and agents of change on 

landscape-level vegetation dynamics. The empirical basis of 

STSMs makes possible a variety of approaches for incorporating the effects of climate change. We describe common 

challenges and four approaches using output from the 

DGVM MC1 to create climate-sensitive STSMs (Hemstrom 

et al. in press, Yospin 2012). These approaches hold promise 

because the DGVM can mechanistically project potential 

vegetation changes and fire with changing climate, while 

the mega-STSMs can apply these changes to locally relevant 

potential vegetation, impose realistic management actions, 

and mitigate the rapid rates of change allowed under 

DGVMs like MC1. We are currently producing example 

extrapolations of possible vegetation change from several 

climate change scenarios in different case study landscapes 

using these approaches. We expect these methods to be of 

considerable interest to others who use STSMs as well. 

However, it is essential to recognize numerous caveats 

about all STSM-based approaches. Regardless of how 

STSMs are made climate-sensitive, they neither simulate 

physiological responses of vegetation nor project landscape 

states by simulating landscape processes mechanistically. 

Rather they are empirical models that must draw from a 

combination of other models and expert judgment to reflect 

the current understanding of system properties. In doing so, 

they can help researchers organize state-of-the-art knowledge and information, and serve as tools for assessing the 

potential ramifications of alternative management strategies. Because STSMs are probabilistic, a series of repeated 

simulations can be used to bracket a potential range of 

future conditions under changing climate. The results from 

these models can be informative for land managers working at a variety of spatial grains and scales. We see these 

approaches as promising avenues for improving landscape 

planning and assessments under the projected trends and 

uncertainties of climate change.

Acknowledgments

The USDA Forest Service Western Wildland Environmental 

Threat Assessment Center providing the initial funding 

to pursue these projects. Funds were also provided by the 

American Recovery and Reinvestment Act, the USDA 

Forest Service PNW Research Station, the USDA Forest 

Service Pacific Northwest Region, and the USDA Forest 

Service Southwest Region. Some of this material is based 
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upon work supported by the National Science Foundation 

Dynamics of Coupled Natural and Human Systems Program under Grant No. 0816475.

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Authors

Steven M. Wondzell is a research aquatic ecologist, Forestry Sciences Laboratory, 3625 93rd Avenue SW, Olympia, WA 98512-9193, swondzell@fs.fed.us. Agnieszka 

Przeszlowska is a research ecologist, Forestry Sciences 

Laboratory, 3625 93rd Avenue SW, Olympia, WA 98512-

9193, agnesp@gmail.com. Dirk Pflugmacher1 graduate 

research assistant, Department of Forest Ecosystems and 

Society, Oregon State University, 321 Richardson Hall, 

Corvallis, OR 97331, dirk.pflugmacher@geo.hu-berlin.

de. Miles A. Hemstrom2 is a research ecologist (retired), 

Forestry Sciences Laboratory, 620 SW Main St. Portland, 

OR 97205. Peter A. Bisson is a research fish biologist, 

Forestry Sciences Laboratory, 3625 93rd Avenue SW, 

Olympia, WA 98512-9193, pbisson@fs.fed.us.

Abstract

Interactions between landuse and ecosystem change are 

complex, especially in riparian zones. To date, few models 

are available to project the influence of alternative landuse practices, natural disturbance and plant succession on 

the likely future conditions of riparian zones and aquatic 

habitats across large spatial extents. A state and transition 

approach was used to model the effects of various management and restoration practices on conditions of riparian 

forests, channel morphology, and salmonid habitat. We 

present results of model analyses for the Wilson River 

in the Oregon Coast Range. We focus on critical habitat 

for spawning and rearing salmon and how habitat quality 

might be influenced by alternative land-use practices over 

the next 50 years, especially contrasting the outcomes of 

passive vs. active habitat restoration strategies. Results of 

our simulations suggest that active restoration of large wood 

in streams may accelerate habitat improvement relative to 

recovery projections under a passive restoration strategy. 

Active restoration seems to be a more viable approach for 

species such as coho salmon in the Wilson River watershed, 

which has limited potential spatial distribution in the 

drainage network, and where a significant proportion of 

the available habitat is in poor condition. In contrast, using 

active restoration techniques to improve habitat for a widely 

distributed species such as steelhead seems less feasible. 

Steelhead habitat is abundant throughout the basin and at 

least some of it is currently in good or excellent condition. 

Thus, large portions of the Wilson River would need to 

be restored to substantially increase the proportion of the 

stream network that is in good or excellent condition for 

steelhead.

Very little data are available with which to validate 

models at this scale. Results of our model simulations 

appeared reasonable wherever field and lidar data were 

available for comparison, however we caution that these 

comparisons do not validate all the factors simulated in our 

models. Consequently, the results of the model simulations 

should be interpreted as hypotheses of likely outcomes from 

management strategies at the scale of a large watershed 

(one or several 5th-field hydrologic units or HUC5s) or a 

large portion of a USFS ranger district. Nevertheless, the 

approach holds promise for simulating physical and biological responses of aquatic organisms and their habitats to 

alternative restoration approaches.

Keywords: state and transition models, riparian management, stream habitat, Oregon Coast Range, Tillamook 

burn, large wood addition, coho, steelhead, lidar.

Modeling the Dynamic Responses of Riparian 

Vegetation and Salmon Habitat in the Oregon 

Coast Range With State and Transition Models

Steven M. Wondzell, Agnieszka Przeszlowska, Dirk Pflugmacher, Miles A. Hemstrom, and Peter A. Bisson

1

 Now at Department of Geography, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany.

2 Now at Institute for National Resources, Oregon State University, Portland, Oregon, miles.hemstrom@oregonstate.edu.
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Introduction

Pacific salmon and steelhead have declined in abundance 

or have been eliminated from large parts of their historical 

range (Nehlsen et al. 1991), and many populations are now 

listed under the U.S. Endangered Species Act (USDA and 

USDI 2000). Multiple factors have contributed to these 

declines, including the degradation of spawning and rearing 

habitat in tributary streams (Federal Caucus 2000). Maintenance of existing high-quality habitat and restoration of 

degraded habitat have become cornerstones of many salmon 

recovery efforts (NRC 2002). However, there is a critical 

need for information relating how management activities 

in riparian areas will interact with natural processes to 

create and maintain salmon habitat and how this habitat 

will change over time—particularly over the broad spatial 

extents that are relevant to recovering salmonid populations.

Landscape-scale perspectives of historical, current, and 

potential future conditions of upland, riparian, and aquatic 

systems resulting from plant succession, natural disturbances, and land-use practices can help inform policy directions. Further, such information is essential to developing 

strategic restoration policies for large regions (e.g., Oregon 

Plan for Salmon and Watersheds; http://www.oregon-plan.

org/ accessed 15 November 2011) that make the most of 

limited funds. However, providing relatively detailed 

information on current and future riparian conditions for 

large watersheds (e.g., one or several 5th-level hydrologic 

units sometimes referred to as HUC5 or HUC6 watersheds; 

USGS and USDA, 2009) over large areas poses substantial 

logistical and technical challenges.

Mapping and classifying riparian zones using a combination of remotely sensed and field-collected data offers 

a means of assessing the current condition of riparian areas 

at fine detail over large areas. However, these assessments 

only provide a static “snapshot” of a watershed at a single 

point in time. The objective of this project was to develop 

state and transition models that could use this “snapshot” as 

a starting point and then project changes in salmonid habitat 

resulting from ecological processes that shape streams, 

riparian vegetation, and the upland systems to which they 

are connected.

The state and transition models developed here were 

designed to forecast changes at the watershed scale (one 

to several 5th-field HUCs) resulting from plant succession, 

hydrogeomorphic processes, and natural disturbances. 

Simulations using a background natural disturbance regime 

were used to “hindcast” the historical condition and to forecast the outcomes of “passive restoration”, i.e., the strategy 

of allowing natural ecosystem processes to dictate the pace 

and trajectory of habitat recovery without human intervention. The models also accommodate land use activities such 

as logging, stream and riparian restoration activities (e.g., 

large wood additions to streams or planting of conifers in 

riparian areas to facilitate conversion from hardwoods to 

conifer dominated stands), and episodic disturbance events 

(e.g., debris flows, windthrow, and wildfires) that shape 

stream channels and valley floors. The models were used to 

forecast the outcome of alternative management policies, 

specifically contrasting a passive restoration strategy to an 

active strategy of restoring large wood to forested stream 

reaches where large, in-stream wood is currently lacking.

Methods

The aquatic-riparian state and transition models described 

in this paper were designed to simulate the temporal 

dynamics of riparian vegetation in the mountain stream 

networks of the Oregon Coast Range. To apply these 

models, the stream network must be delineated into relatively homogeneous stream reaches classified into “potential 

geomorphic types.” The riparian zone around each reach 

must then be mapped and assigned to a “potential vegetation type.” Each reach polygon must also be attributed with 

the current vegetation type to provide a starting point for 

the model simulation. These steps are described, below. 

Additional information on the methods we used, along with 

the aquatic-riparian state and transition models and supporting data are available from the project website (accessed 

7 November, 2011): http://www.fs.fed.us/pnw/lwm/aem/

projects/ar_models.html.

Stream Network Delineation and Classification

The stream network was delineated with the NetStream 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

tool available from Earth Systems Institute3 and hydraulic 

geometry coefficients for Pacific maritime mountain 

streams (Castro and Jackson 2001). A 5-m Lidar DEM and 

literature derived threshold values for drainage area and 

channel gradient were used to trace the stream network and 

provide a preliminary classification of channel reaches into 

six geomorphic types described by Montgomery and Buffington (1997, 1998): (1) colluvial, (2) cascade, (3) cascade 

with wood-forced step-pools, (4) step-pool, (5) plane-bed, 

and (6) pool-riffle (fig. 1). We dropped non-fish-bearing 

stream reaches from our analysis (i.e., bedrock and colluvial 

reaches and those with channel gradient > 20 percent or 

drainage areas < 1.5 km2). We compared our preliminary 

classification with field-classified geomorphic types from 

29 sampled reaches (described below). The final classification correctly assigned 86 percent of the 29 sampled 

reaches in the Wilson River watershed into their respective 

Montgomery and Buffington potential geomorphic types. 

The final stream network was generated with field-derived 

coefficients for bankfull width and depth and the boundary 

of the riparian zone around each classified stream reach was 

then delineated using path-distance thresholds generated 

from ArcGIS path-distance tools. This method identifies a 

geomorphically delineated riparian zone based on a “cost 

threshold” evaluated from a combination of distance and 

elevation from the active channel. We verified riparian zone 

delineations by comparing the path-distance derived boundaries to boundaries mapped at our 29 field sites based on 

distance, elevation, and slope breaks to adjacent hillslopes.

The geomorphically defined riparian zone boundary 

is likely to underestimate the influence of stream-adjacent 

trees in areas where steep hillslopes bound narrow valley 

floors. In these locations, trees growing on lower hillslopes 

may significantly affect riparian conditions in the stream. 

Thus, the limits of the riparian polygons were defined in 

two ways: the actual valley-floor delineation was done using 

the path-distance methods described above and a 30-m 

3 These tools are available from the NETMAP website: http://www.netmaptools.org.

Note: The use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. 

Department of Agriculture of any product or service.

Figure 1—Classification of stream reaches into potential geomorphic types following the stream classification system of 

Montgomery and Buffington (1997 and 1998).
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GENERAL TECHNICAL REPORT PNW-GTR-869

buffer was also drawn on both sides of the stream. The final 

“riparian” polygon used for vegetation classification was 

defined as the larger of the two methods.

Potential Riparian Vegetation

We based our classification of riparian potential vegetation 

types (PVT) on the adjacent plant association groups (PAGs 

obtained from the Northwest Oregon Ecology Group, Corvallis, Oregon) and upland PVTs from the Siuslaw National 

Forest, Oregon (table 1) because potential vegetation type 

spatial data with previously classified riparian vegetation 

were not available. The dominant PVTs in the Wilson River 

watershed are Sitka spruce/western hemlock wet PVT and 

Douglas fir/western hemlock PVT. We added a meadow 

riparian PVT to characterize riparian areas in the northeast 

portion of the Wilson watershed where deep-seated landslides created low gradient valleys with high water tables, 

and present-day flooding by beaver dams tends to prevent 

forested vegetation types. The meadow riparian PVT is 

characterized by PAGs in the Douglas-fir/western hemlock 

PVT as well as alder, willow and sedge functional groups. 

Combining the 3 PVTs with the potential geomorphic types 

(figure 1) resulted in a suite of 11 separate state and transition models for the Wilson River watershed (table 2).

Field Sampling of Selected Reaches

Sampling was designed to characterize three aspects of 

selected study reaches: (1) channel, steambank, and valley 

floor geomorphic conditions of the entire reach; (2) vegetation zones within the reach; (3) vegetation composition and 

structure in quantitative sub-plots. We randomly selected a 

stratified sample of 30 stream reaches from the preliminary 

delineation and classification. We generated fine-scale 

hillshade and canopy height maps for each reach and field 

sampling protocols were designed around these maps. We 

sampled 29 of these reaches during the summer of 2009 

to provide data from which current conditions could be 

classified and mapped for the entire stream network within 

the study watershed.

Channel and valley floor geomorphic conditions— 

At each reach, the channel morphology was classified into 

channel types following the Montgomery and Buffington 

(1997, 1998) classification. Similarly, the overall plant association group and potential vegetation type was determined 

and the current vegetation structure was classified following 

state-classes as defined in the state and transition models 

(described below). Current land use, evidence of herbivory, 

and the overall condition of the channel were also noted. 

The longitudinal gradient was measured using an auto-level 

Table 1—Cross-walk between upland potential vegetation types (PVTs), plant association groups (PAGs), 

and the final riparian PVTs used for our model development within the Wilson River watershed

 PAG

Riparian PVT PAG description code Upland PVT description

not modeled Sitka spruce/ wet nonforest 991 nonforest

not modeled W. hemlock/dry non-forest 1971 nonforest

not modeled W. hemlock/wet non-forest 1991 nonforest

Sitka spruce/W. hemlock wet Sitka spruce/oxalis-swordfern-moist 902 Sitka spruce (coastal)

Sitka spruce/W. hemlock wet Sitka spruce/salal-mesic 901 Sitka spruce (coastal)

Sitka spruce/W. hemlock wet Sitka spruce/salmonberry-wet 903 Sitka spruce (coastal)

Sitka spruce/W. hemlock wet W. hemlock/oxalis-swordfern-moist 1907 W. hemlock moist (cascades)

Sitka spruce/W. hemlock wet W. hemlock/salmonberry-wet 1908 W. hemlock wet (coastal)

W. hemlock intermediate Pacific silver fir/Alaska huckleberry-wet 2207 silver fir intermediate (high)

W. hemlock intermediate Pacific silver fir/oxalis-high precipitation 2208 silver fir intermediate (high)

W. hemlock intermediate W. hemlock/Alaska huckleberry/oxalis 1909 W. hemlock cool (cascades)

W. hemlock intermediate W. hemlock/Oregon grape-salal 1906 W. hemlock intermediate

 (cascades)

W. hemlock intermediate W. hemlock/vanilla leaf-cool 1905 W. hemlock cool (cascades)

W. hemlock intermediate W. hemlock-warm, transitional to 1903 W. hemlock hyperdry (SW)

 Douglas fir
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

and stadia rod. Measurements were taken over a 100-m long 

length of channel wherever possible although dense vegetation sometimes limited the survey length.

The limits of both the active valley floor and the total 

valley floor were delineated on the hillshade and canopy 

height maps. We defined the active valley floor as the area 

with flood return intervals of 2 to 5 years, and determined 

the limits of this zone based on evidence of scouring, 

sediment deposition, and vegetative change outside of the 

bankfull width of the channel. We defined the total valley 

width as the limit of valley floor inundation which might 

occur in a 100-year return interval flood. Practically, the 

total valley floor width was determined by distance from, 

and height above, the bankfull channel and often coincided 

with obvious slope breaks with adjacent hillslopes or terraces.

Within each reach, we established five cross-channel 

transects which were marked on the hillshade and canopy 

height maps. The first transect was located randomly, using 

a random number generator, and located within the first 20 

percent of the reach length. The remaining transects were 

spaced at equal intervals along the remaining 80 percent of 

the reach length. All pools, pool-structure, large wood, and 

over-hanging vegetation was inventoried between transects 

1 and 5. The length, type, forming agent, total depth, tailout depth (residual depth by difference) was recorded for 

each pool. All pieces of large wood were also inventoried. 

Lengths and diameters of each piece of large wood, in or 

suspended above, the bankfull channel, were estimated and 

a subset was measured. Also, we recorded percent of total 

length of large wood within the bankfull channel and the 

proportion that would be in the water at bankfull flows.

We measured the bankfull width, right and left bank 

elevation, thalweg depth, and two additional bed depths to 

provide a coarse cross-sectional profile at each cross-channel transect. Bankfull depth was calculated as the difference 

between the bankfull elevation and the thalweg depth. The 

elevation measurements were made using a stadia rod and 

inclinometer and provided reasonably accurate measurements (a system we field tested at the beginning of the field 

season and provided repeatable elevation measurements 

accurate to within a few centimeters). Bank characteristics 

were recorded for both the left and right banks, for a 2-m 

wide swath centered on the transect tape (1-m upstream 

and 1-m downstream). Within this zone, the stream bank 

was ranked either as stable or unstable. The percent length 

of undercut bank and average undercut depth was also 

recorded as was the ground cover on the bank and the 

percent of the swath with overhanging vegetation. Stream 

shade was evaluated at the center of the channel by estimating the percent of sky obscured by vegetation within a 

20-cm diameter ring, held at arm length, 60° above a level 

Table 2—List of the 11 aquatic-riparian state and transition models developed for the Wilson River watershed. 

Potential Riparian Percent Percent

vegetation Length area total total riparian

Model type Channel type (km) (ha) length area

sx_cscd Sitka spruce Cascade 4.1 27.4 2 2

sx_cscd_sp Sitka spruce Cascade / w-f step-pools 28.1 174.1 12 13

sx_sp Sitka spruce Step-pool 43.0 262.1 19 19

sx_pb Sitka spruce Plane-bed 26.8 162.6 12 12

sx_pr Sitka spruce Pool-riffle 35.1 212.9 16 15

me_pr Meadow Pool-riffle 11.7 70.4 5 5

df_cscd Western hemlock Cascade 6.2 40.2 3 3

df_cscd_sp Western hemlock Cascade / w-f step-pools 18.7 114.2 8 8

df_sp Western hemlock Step-pool 31.4 192.4 14 14

df_pb Western hemlock Plane-bed 12.4 75.2 6 5

df_pr Western hemlock Pool-riffle 7.4 44.4 3 3

Grand total 224.9 1,375.9 100.0 99.0

Note: “w-f” in “Cascade / w-f step pools” denotes “wood-forced” – steep channels where steps and pools are formed where large wood obstructs the 

channel and that, lacking wood, would otherwise have a cascade morphology.
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GENERAL TECHNICAL REPORT PNW-GTR-869

horizon facing due south (the approximate location of the 

sun at solar noon on the solstice at the latitude of the study 

sites).

Reach and sub-reach summaries of vegetative 

conditions— 

While in the field, discrete, homogenous vegetation zones 

within each study reach were delineated and mapped based 

on composition, size and structure of vegetation. We distinguished the following vegetation strata within each zone: 

(1) upper or overstory canopy layer; (2) secondary canopy 

layer; (3) sapling and tall-shrub layer (2 to 6-m height); 

(4) short shrub layer (0.5 to 2-m height); (5) herbaceous 

layer (<0.5 m); and (6) exposed ground. The total canopy 

cover and the relative cover by species were recorded for 

each layer. For tree layers the mean diameter breast height 

(DBH) was recorded from a subsample of trees present; 

for the shrub layers, average shrub heights were recorded. 

Herbaceous cover was characterized by functional groups 

(grasses, sedges, rushes, forbs, and ferns) rather than 

species. Ground cover was the percent of area not covered 

by herbaceous vegetation (soil, litter, rock/boulder, wood, 

water). Herbaceous and ground cover sum to 100 percent.

Quantitative sub-plots of vegetation composition and 

structure—

Eight to ten quantitative sub-plots were located in each 

study reach and data from these were used to develop lidar 

classification techniques for vegetation mapping. To allocate 

field plots, lidar canopy height rasters were segmented 

using eCognition software and classified into height classes. 

Field plot locations were then randomly selected based on 

height classes. These locations were then mapped onto 

the hillshade and canopy height maps for use in the field. 

Plots were located in the field using handheld GPS units 

and the detailed maps and then sampled to characterize the 

structure and composition of the vegetation within each 

sub-plot. The actual number of plots sampled was limited 

by the time available to sample each reach, but at least 5 of 

the 10 sub-plots were always sampled. We used sub-plots of 

3 sizes: 5-m radius for tree plots; 2-m radius for shrub plots; 

1-m radius for herbaceous plots. On tree plots, we recorded 

the species and DBH of all trees with DBH greater than 

12.5 cm. We measured tree heights of at least 3 trees within 

each of three canopy layers (when present): (1) the upper or 

overstory canopy layer; (2) the secondary canopy layer; (3) 

the sapling layer. For trees between 2.54 and 12.5 cm DBH, 

all stems were counted and the mean height of the trees was 

estimated. For shrub plots, the mean height, canopy cover 

and species composition was recorded. For herbaceous 

plots, the height, cover, and composition were recorded. 

Dominant species were identified to species if possible.

Mapping Current Vegetation

To derive detailed information on current vegetation composition and structure for each riparian polygon we followed 

a two-step approach. First, using discrete-return lidar data 

(>8 pulses m-2), a high-resolution raster map was created 

with a spatial resolution of 5 m. The map classification 

consists of 12 classes: 5 non-tree classes (water, barren, herbaceous vegetation, shrubs) and 7 tree classes distinguishing between conifer and hardwood, and tree sizes. For the 

tree classes an additional sub-class was derived separating 

stands with and without understory. In the second step, we 

calculated the area proportions of each (raster) class within 

each riparian polygon, and then labeled each polygon using 

a defined classification logic. Here, we briefly describe the 

classification methods and results. A more detailed description lies outside the scope of this paper which is focused on 

using state-and-transition models to simulate the dynamics 

of riparian vegetation.

The vegetation raster map was classified using 

measurements from the quantitative vegetation sub-plots 

for model training and validation. For each field plot, we 

extracted lidar point clouds and calculated 41 potential 

predictor variables from the lidar height and intensity 

distributions similar to Hudak et al. (2008). For estimation 

of continuous variables such as tree DBH we used multiple 

linear regression and for classification of categorical map 

attributes we used RandomForest (Breimann 2001). First, 

we used a canopy height model (fig. 2) and a height threshold of 0.5 m to separate woody vegetation (shrubs and trees) 

from herbaceous vegetation, barren, and water. Then we 

classified herbaceous vegetation, barren surfaces, and water 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

using lidar height and intensity metrics. However, lowstature shrubs (< 0.5 m) could not be reliably distinguished 

from herbaceous vegetation.

We then used the lidar-point data to estimate tree height 

and compared those estimates to the field-measured tree 

heights which showed a strong correlation for both conifers 

(r = 0.91) and hardwoods (r = 0.85). Because structural 

states in the state-and-transition models are based on tree 

diameters rather than height, tree height and canopy cover 

data derived directly from lidar were converted to tree 

diameter at breast height, using the following equations for 

hard woods and conifers:

dbhH = exp (0.537+0.147 * H95PCTlog + 0.809 * 

HMEDIANlog + 0.003 * CANCOV)

dbhc = exp (0.2127 + 1.1045 * H95PCTlog)

Where dbhH and dbhC is maximum plot-level dbh for 

hardwoods and conifers, respectively, H95PCT is the 

95th percentile of lidar vegetation heights, HMEDIAN, 

median lidar height and CANCOV is lidar canopy cover 

(number of returns above 2m divided by total number of 

returns). Model accuracy was acceptable (RMSE = 30-36 

percent), but generally lower than observed in studies of 

more uniform forest stands. When converted to categorical 

size classes, overall classification accuracy was 64 percent 

(63 percent for hardwoods and 64 percent for conifers). 

For conifer trees, confusion (omission/commission error) 

between medium and large tree classes was roughly 40 

percent, but balanced. For hardwood plots, there was a 

tendency of large trees to be mapped as medium tree class. 

Further, tall shrubs were misclassified as young hardwood 

trees. Thus, we combined the two classes into a single class.

A classification of hardwood and conifer forests was 

derived from lidar metrics using a combination of field and 

photo-interpretation plots. Analysis of the field data alone 

showed high omission errors for conifers, partly resulting 

from an unbalanced data set dominated by hardwood plots. 

Thus, to increase sample size, we collected additional 

reference data by means of photo-interpretation of nearinfrared airphotos from the National Agriculture Imagery 

Program (NAIP). We generated 150 random plots (5 m 

diameter), 75 within and 75 outside the riparian zone. The 

best RandomForest model showed an overall accuracy of 85 

percent (out-of-bag, boot-strapping estimate). Classification 

of tall understory shrubs showed an overall accuracy of 67 

percent.

To estimate canopy density, we used lidar-derived 

estimates of canopy cover (vegetation returns above 2 m 

divided by all returns) without any transformation. We 

were unable to derive an acceptable relationship between 

tree cover estimates from the quantitative sub-plots and the 

lidar-derived canopy cover. However, at the polygon level 

Figure 2—Example of a lidar-derived canopy height map overlaid 

on a hillslope-shaded topographic map prepared for field sampling 

a selected reach on the South Fork Wilson River. Lines contrast 

preliminary NetStream delineations (solid) with field-drawn delineations (white-dashed). Note, the right boundary of the riparian 

zone is caused by a road grade.
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GENERAL TECHNICAL REPORT PNW-GTR-869

the agreement between simple (uncalibrated) lidar cover 

and field-based estimates was high (80 percent).

To derive the current vegetation layer for the stateand-transition modeling, the vegetation raster map was 

summarized to describe the vegetation within each riparian 

polygon. Polygons were classified into bare (<=15 percent 

herbaceous) or herbaceous (>15 percent) when the proportion of shrub pixels within the valley floor polygon was 

less than 5 percent, or open (5–15 percent), medium (15–40 

percent), and dense shrub (40–100 percent) otherwise. To 

account for a wider influence zone for trees we summarized 

forest structure attributes using a buffer zone of 30 m for 

stream orders 1–4, and a 30-m buffer in addition to the 

valley floor boundary for stream orders greater than 5. Here, 

we distinguished between nonforest (<15 percent), open 

forest (>15 percent) and dense forest (>40 percent). Forest 

polygons were classified as mixed conifer/hardwood when 

neither of the two forest types made up greater than 65 

percent of the forest area. Further, we defined multi-layer 

stands when more than one tree size class occupied greater 

than 10 percent of the area. Canopy density was derived 

from lidar estimates of canopy cover (the number lidar 

returns above 2 m divided by the total number of returns) 

without any transformation.

The classification description for each riparian polygon 

was compared to the classification made during the field 

visit (table 3). The attributes of the overstory tree canopy 

were classified correctly approximately two-thirds of the 

time. However, the lidar-based methods had difficulty classifying understory attributes. The density of the understory 

shrub canopy agreed with the field observations in only 38 

percent of the cases. Similarly, the agreement in the number 

of canopy layers was only 48 percent at the polygon level. In 

eight of the 10 erroneous classifications, the lidar failed to 

detect a multistory canopy observed in the field.

It is quite difficult to assess the overall classification 

accuracy. The lidar-based classification of the overstory tree 

types were quite accurate for homogeneous forest types 

(either conifer or hardwood) as evidenced by the 85 percent 

accuracy for the quantitative sub-plots. Summarizing the 

5-m raster map to describe the heterogeneous riparian polygons was more challenging. For example, in only 3 of the 

29 sampled plots (10 percent) did the lidar classification of 

all 5 attributes completely agree with the field classification. 

In only 8 of 29 plots (41 percent) did all 4 canopy attributes 

(excluding shrub canopy density) agree between the lidar 

and field classifications (table 3).

While the overall classification accuracy appears quite 

low, it does not distinguish between classification errors 

among ecologically similar classes (e.g., medium hardwood and large hardwood) and distinctly different classes 

(herbaceous versus forest). Overall, misclassified polygons 

tended to be classified into relatively similar classes (table 

3). For example, in the hardwood plots, the regression equations relating height and diameter resulted in a tendency to 

map large diameter alders as medium diameter. While the 

lidar data were quite accurate in assessing canopy height, 

converting height to diameter classes added additional 

uncertainty caused by large variations in tree physiognomy. Similarly, the field sampling included 4 polygons 

with mixed conifer-hardwood overstory, of which, 3 were 

correctly classified by lidar. But the lidar also classified 6 

hardwood or shrub dominated polygons as mixed. Thus, 

misclassified polygons were often classified into relatively 

similar classes.

The assessment of overall classification accuracy is 

further complicated because we do not have an accuracy 

assessment of our field classifications. Some attributes were 

easily observed in the field, but others were quite difficult 

to estimate. Estimating average canopy density for multiple 

canopy layers was especially problematic in large riparian polygons with heterogeneous vegetation. We used a 

40-percent canopy cover threshold to distinguish between 

open and closed canopies for both overstory and understory 

tree layers, as well as the shrub layers. It is possible that 

the lidar-based classification, trained from the relatively 

homogeneous quantitative sub-plots, made more accurate 

classification than was possible in the field.

Large In-Stream Wood and Missing State Classes

To fit the current conditions to the VDDT state class 

structure (described below), reaches were also assigned 

to one of three in-stream wood classes: large wood (>20 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

in. dbh), small wood (≤20 in. dbh), or no wood. Unfortunately, we were unable to determine the abundance and 

size of wood present in each stream reach from lidar data. 

Therefore, reaches within each potential geomorphic type 

were assigned to each class in the proportion of each wood 

class determined from field sampling. Additionally, we 

assumed that local recruitment of in-stream wood would 

occur in large and giant tree state classes, and therefore, 

these structural states were assigned to the large wood state 

classes. Overall, this would tend to overestimate the amount 

of in-stream wood present in the stream network because 

little wood was present in many of the sampled reaches even 

where medium- and large-sized trees were growing in the 

riparian zone or on lower hillslopes. However, very little 

of the stream network currently falls into the large or giant 

state classes. These were seldom observed in the field and 

none of the sampled plots were field classified into large 

or giant tree size classes. Thus, errors in the classification 

Table 3—The classification accuracy for current vegetation in the 29 sampled riparian polygons based on a 

comparison of the lidar-based classification versus the classification made during field sampling

Reach Lidar Field All Canopy

ID classa classa Type Size Canopy Strata Shrub attributes attributes

1169 HMD2D HMD2D 1 1 1 1 1 1 1

1371 XMD1O HSO1O 1 1

6872 XMO1D HMD2D 1 1

8808 XSO1D HSD1O 1 1

9280 XLO2D S---O

10202 XMO1O XMD2D 1 1

10444 XMD1O XMD2D 1 1 1 1

10929 HMD1O HMD2D 1 1 1

11448 HMD1O HMD1O 1 1 1 1 1 1 1

11749 XMD1O CMD2D 1 1 1

11805 HLD2D HMD1D 1 1 1

14925 HMD1O HMD1D 1 1 1 1

14957 HLD2D HMD2D 1 1 1 1 1

15588 HMD1O HMD2D 1 1 1 1

15764 HMD1O HMD1D 1 1 1 1 1

15816 HMD2D HMD2D 1 1 1 1 1 1 1

15898 HMD1D HMD1O 1 1 1 1

16594 HMD1O HMD1D 1 1 1 1

17049 HMD1D HMD2D 1 1 1 1

17061 HLD2D HMD1D 1 1 1

17548 HMO1D HSO2O 1 1

17807 CLD2D XMD2D 1 1 1

18206 HMO2O HMD2O 1 1 1 1

18615 XMD1D S---O

19058 XSO1D S---O

19266 XLD2D XMD2O 1 1 1

19726 HMD1O HMD1D 1 1 1 1 1

19843 HSO1O S---O 1

20152 CMO1D S---D 1

Number correct 19 17 19 14 13 3 8

Percent correct 66 59 66 48 45 10 28 a Classifiation codes are as follows for each character in the 5 character code:

First character: S=shrub; H=hardwood; C=conifer; X=mixed conifer and hardwood. Second character: S=small; M=medium; L=large.

Third character: O=open; D=dense.

Fourth character: 1=single-story; 2=multi-story.

Fifth character: O=open; D=dense.
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GENERAL TECHNICAL REPORT PNW-GTR-869

of the current conditions caused by assuming that large 

in-stream wood present in large and giant stands were likely 

quite small.

The lidar classifications generated a number of structural state classes that were not present in the models we 

developed. For example, we only simulated open canopy, 

single-storied stands and closed canopy, multi-storied 

stands for conifer dominated riparian polygons. Hardwood 

dominated stands were all simulated as closed canopy, 

single-storied stands and all mixed conifer and hardwood 

stands were simulated as closed, multi-storied structural 

states. Such simplifications are necessary to minimize the 

complexity of our models and are reasonable approximations of the potential forest structural state classes. The 

lidar methods we used to classify current conditions, 

however, were not restricted to just those structural states 

present in our models. Thus, the lidar classified 144 mixed 

conifer and hardwood polygons as either open-canopy or as 

single-storied. These polygons were reclassified into closed 

canopy multi-storied states. Similarly, the lidar classification generated 194 hardwood-dominated polygons classified 

either as open canopy or multi-storied all of which were 

reclassified as closed canopy single-storied states. In total, 

current conditions in 422 polygons out of the 1554 polygons 

(27 percent) needed to be reclassified into the most similar 

state class that occurred in the models.

Aquatic-Riparian State and Transition Models

We intersected the classified stream network map with the 

potential vegetation map of the Wilson River watershed to 

identify all possible combinations of channel and vegetation 

types (table 2). We excluded the coastal plain in the lower 

watershed from our analyses because the area had been 

drastically altered by agriculture and development. Models 

for the remaining area included five geomorphic types 

in three PVTs: (1) the Sitka spruce/western hemlock wet 

PVT in the lower parts of the watershed and low elevation 

riparian zones, (2) the Douglas-fir/western hemlock PVT in 

the upper parts of the watershed and high elevation riparian 

zones (fig. 3), and (3) the meadow PVT present only in the 

extreme eastern portion of the watershed (area of deepseated landslides in Devils Lake Fork not shown in figure 3 

because their spatial extent is too limited to be visible at the 

scale of the figure). Conceptually, the models do not limit 

Figure 3—Classification of upland vegetation into Potential Vegetation Types as used in the state and transition models 

developed for the Wilson River watershed.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

the overstory canopy to a single dominant tree species. For 

example, in the Sitka spruce/western hemlock wet PVT, we 

recognize that either mixed species stands, or stands dominated by either Douglas-fir or western hemlock are likely to 

occur thus we abbreviate this PVT as “sx”, denoting a Sitka 

spruce—miXed conifer overstory.

We built separate state and transition models for each 

of the 11 combinations of PVT and geomorphic channel 

types using the Vegetation Development Dynamics Tool 

(VDDT; Beukema et al. 2003; http://essa.com/tools/vddt/ 

accessed 15 November 2011). These VDDT models were 

developed from upland models for the northern Oregon 

Coast Range and the Washington Coast Range (please see 

the ILAP project web page: http://oregonstate.edu/inr/ilap/ 

as well as the following link for downloads of data, models, 

and documentation ftp://131.252.97.79/ILAP/Index.html). 

We expanded these models to include riparian shrub and 

hardwood state classes, hydrogeomorphic processes, and 

riparian restoration practices (table 4). We also expanded 

the pathways and transition probabilities to characterize 

the historic and current land use activities present in the 

watershed. The completed models simulated a large number 

of possible states of a stream reach, from recently disturbed 

states resulting from stand replacing disturbances such as 

logging, landslides or wildfire to states where stand replacing disturbances have not occurred for long periods of time.

Individual states within each model are defined on the 

basis of the potential vegetation type (PVT), the dominant 

overstory trees, the vegetation structure, and the presence 

and size of in-stream wood. Tree sizes are based on diameter breast height, in inches, as follows: young 0-1; small 

1-10; medium 10-20; large 20-30; giant > 30. In-stream 

wood is recruited from forested states in two size classes. 

Small wood constitutes pieces with large-end diameters 

≤ 20 inches; large wood is > 20 inches. Thus, in-stream 

wood recruited from medium-sized tree stands falls into the 

small-wood class whereas wood from large- or giant-sized 

tree stands falls into the large-wood state class. Nonforest 

state classes have tree canopy cover < 15 percent; open 

canopy state classes have tree canopy cover between 15 

percent and 40 percent; closed canopy state classes have 

tree canopy cover > 40 percent. Open shrub states have 

shrub cover < 40 percent; closed shrub states have shrub 

cover > 40 percent. Shrub cover in Sitka spruce/western 

hemlock wet PVT and Douglas-fir/western hemlock PVT 

is dominated by Rubus spectabilis (salmonberry), while 

Salix spp characterize shrubs in the riparian meadow PVT. 

In forested states, we assume that conifer-dominated states 

have open shrub understories whereas alder-dominated or 

mixed conifer-alder stands have closed shrub understories. 

Sitka spruce/western hemlock wet and Douglasfir/western hemlock PVTs— 

Nonforested early seral states used in the models are either 

barren from post-debris flow conditions or shrub states. 

Barren conditions are rapidly colonized by salmonberry, 

transitioning into an open shrub state after 3 years (table 

4). Salmonberry grows rapidly and these open shrub 

states transition to dense shrub states after 2 more years. 

Wildfires transition directly to the open shrub states which 

then require 5 years before they reach the dense shrub state 

class. Forested states can only be initiated from barren 

or shrub states through tree regeneration. Alder is highly 

favored early seral tree species in these riparian models 

(table 4). Conifer regeneration is much lower than that of 

alder. Further, once salmonberry grows into a dense shrub 

layer, regeneration rates are reduced by 50 percent for alder 

and nearly a factor of 10 for conifers. Following conifer 

regeneration, successional development is simulated as a 

deterministic process with intermediate seral states defined 

on the basis of dominant tree sizes rather than age (see 

table 4). We only simulate open single-story and closed 

multi-story conifer stands where canopy growth in small, 

medium, large and giant tree sizes leads to canopy closure 

(table 4) which is assumed to be accompanied by regeneration of shade-tolerant tree species that form a secondary 

canopy layer.

Alder regeneration results in a successional sere dominated by alders (table 4). We use a probabilistic transition 

in year 20 of the small-alder state that forces 25 percent of 

these state classes into a mixed conifer-alder successional 

sere. We also simulate relatively slow rates of conifer 

regeneration in alder-dominated medium-sized tree states. 
184

GENERAL TECHNICAL REPORT PNW-GTR-869 Table 4—Table of annual transition probabilities and probability multipliers used in the models. (continued) Nonforest vegetation Conifer-dominated forest vegetation Open Dense Young Medium Large Giant Vegetative structural state Barren shrub shrub tree Small tree tree tree tree Age (years) 0-3 0-5 6-200 0-10 11-35 36-65 66-110 111-200+ Base probabilistic transitions Tree regeneration (alder) 0.6 0.6 0.3 Tree regeneration (conifer) 0.04 0.04 0.005 Canopy growth (open to closed) 0.05 0.03 0.03 0.01 Mixed severity wildfire (open canopy) 0.0027 0.0019 0.0009 Mixed severity wildfire (closed canopy) 0.0045 0.0045 0.0018 0.0027 0.0036 Stand replacing wildfire (open canopy) 0.0023 0.0023 0.0009 Stand replacing wildfire (closed canopy) 0.0045 0.0045 0.0023 0.0023 0.0036 Small wood decay to no wood 20 years Large wood decay to small wood 100 years

 Windstorm1 MLow,High MLow,High MLow,High MLow,High

Transitions controlled with multipliers Debris flow source channels M3 M2 M2 M2 M1 M1 M1

 Debris flow receiving channels MNo,S,L MNo,S,L MNo,S,L MNo,S,L MNo,S,L MNo,S,L MNo,S,L MNo,S,LManagement controlled with multipliers Post-wildfire salvage logging M M

 Post-wildfire or logging conifer planting M Pre-commercial thinning M Partial harvest M M M M

 Regeneration harvest M M M M Large wood addition M M M
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Table 4—(continued) Table of annual transition probabilities and probability multipliers used in the models. Nonforest vegetation Alder-dominated forest vegetation

Open Dense Young Medium Large Giant

Vegetative structural state Barren shrub shrub tree Small tree tree tree tree

Age (years) 0-5 6-20 21-50

Probabilistic transitions

 Tree regeneration (conifer) 0.0125

 Alternate succession to mixed conifer – 0.25 in

 alder stand YR 20

 Stand-replacing wildfire (closed canopy) 0.0045 0.0045 0.0045

 Windstorm MHigh MLow,High

Transitions controlled with multipliers

 Debris flow source channels M2 M2 M2

 Debris flow receiving channels MNo,S, MNo,S, MNo,S,L

Management controlled with multipliers

 Hardwood conversion – Plant conifers M M

 Non-forest vegetation Conifer-alder mixed forest vegetation

Open Dense Young Medium Large Giant

Vegetative structural state Barren thrub thrub tree Small tree tree tree tree

Age (years) 21-65 66-110 110-200

Probabilistic transitions

 Stand-replacing wildfire (closed canopy) 0.0045 0.0045 0.0036

 Windstorm MLow,High MLow,High MLow,High

Transitions Controlled with Multipliers

 Debris flow source channels M1 M1 M1

 Debris flow receiving channels MNo, S,L MNo, S,L MNo, S,L

a High severity windstorms only occur in the Sitka spruce / western hemlock, wet PVT. See text for details.

Note: the transition probabilities shown here are from the Douglas-fir / western hemlock PVT. The Sitka spruce PVT will use slightly different probabilities. Also, a number of transitions 

(especially natural disturbance processes where underlying rates are poorly documented and all management-based transitions) rely on a file of multiplier values (shown here as an “M”) to 

determine the rates of these processes in the models. A multiplier of zero effectively turns off the process.
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Mixed stands eventually become entirely conifer-dominated 

after 200 years.

Riparian meadow PVT— 

This model characterizes areas of the northeastern portions 

of the watershed where beaver might be active and contribute to the development of wetlands and ponds. The main 

difference between this model and the two other PVTs are 

additional wet meadow/beaver pond states (MeW). The rest 

of the model (structure and probabilistic transitions) is very 

similar to the Douglas-fir/western hemlock PVT except that 

we assume that the non-MeW states are more mesic than 

structurally similar Douglas-fir/western hemlock states.

The MeW wet meadow/pond state persists when beaver 

are present and is characterized by grasses/sedges/forbs 

or riparian shrubs. Floods or beaver extirpation transition 

wet meadows to relatively drier conditions (MeD). Without reoccurrence of dam building by beaver, MeD states 

transition to forested stands of Douglas-fir/western hemlock 

or alder. As in the other PVTs, alder is an early seral tree 

species with much higher probability of regeneration than 

conifers. Salix spp. dominate in younger alder or conifer 

states while salmonberry is more abundant in older tree 

states.

Natural disturbances in all PVTs— 

We simulate a variety of probabilistic disturbances (table 4). 

We simulate several stand replacing disturbances that cause 

state transitions to the open shrub state. These include stand 

replacing wildfire and clearcut logging. The overall annual 

wildfire probability in any forested state is 0.0045 which 

is equivalent to a 220-year fire return interval. In youngand small-sized tree stands, only stand-replacing wildfire 

occurs. In stands with larger trees, both mixed-severity and 

stand-replacing fires occur, and the actual probabilities vary 

with tree size and canopy closure. Mixed-severity fires transition closed-canopy state classes to open canopy states or 

maintain pre-existing open-canopy states. Mixed-severity 

fires also drive recruitment of wood into the streams—

recruiting small wood from the medium-sized states and 

large wood from the large- and giant-sized states.

High severity wind storms are also simulated as stand 

replacing disturbances but only occur in the low-elevation, 

Sitka spruce/western hemlock wet PVT—thus preferentially 

affecting wet sites in low-elevation mountain valleys located 

nearest to the Pacific coast. We parameterized the models 

with high-severity windstorm probabilities equal to 0.0083 

per year (for a return interval of 120 years). We assume the 

shrub layer will be minimally disturbed by high-severity 

windstorms so that the postdisturbance stand inherits an 

open or dense state class from the predisturbance state 

class.

State transitions resulting from low-severity wind 

storms are similar to those caused by mixed-severity fire 

in that these wind storms transition closed-canopy state 

classes to open canopy states or maintain preexisting opencanopy states. They also drive recruitment of small wood 

from the medium-sized states and large wood from the 

large- and giant-sized states. We parameterized the models 

with high-severity windstorm probabilities equal to 0.0100 

per year (for a return interval of 100 years).

We only simulate decay and loss of large, in-stream 

wood in the dense shrub state class where we require 100 

years for the large-wood state class to transition into a 

small-wood state, and 20 years for a small-wood state class 

to transition into a no-wood state class. Wood decay cannot 

be easily tracked in the other state classes because there 

are multiple transition pathways and disturbances that can 

recruit additional wood and the VDDT modeling software 

prevents tracking the age of specific attributes independently of the age of the underlying state class.

The occurrence of debris flows are controlled using a 

multiplier file. We start by assuming that the probability of 

landslide-caused debris flows is limited by the potential rate 

of hillslope hollow refilling which suggests that landslides 

might occur approximately 1 in 500 years from colluvial 

hillslope hollows. This would give an expected base rate of 

debris flows of 0.002 per year. There is substantial evidence, 

however, that debris flows are more likely to occur soon 

after stand-replacing disturbances. Thus, for channel types 

where debris flows are likely to originate (cascade and 

cascade with wood-forced step pools), we created three 

categories: Debris Flow1 (multiplier M1 in table 4) occurs 

in mature and old-growth forest and accounts for 10 percent 

of all debris flows; Debris Flow2 (multiplier M2 in table 4) 
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occurs in dense shrub, young, and small forest state classes 

and accounts for 30 percent of all debris flows; Debris 

Flow3 (multiplier M3 in table 4) occurs only in the open 

shrub state class which results from wildfire or clearcutting 

and accounts for 60 percent of all debris flows. Because 

the underlying base probability given to all debris flows in 

the state-and-transition models is 0.01, we use the following multipliers to drive debris flow transitions in models 

for cascade or cascade-with-wood-forced-step channels: 

M1=0.02; M2=0.06; M3=0.12.

Larger channels lower in the network (step-pool, planebed, and pool-riffle) have longitudinal gradients too shallow 

for origination of landslides. Instead, they are impacted by 

debris flows moving down the stream network. Because 

many headwater channels converge to form larger streams, 

and because the larger stream can be impacted by debris 

flows in any of the headwater channels, the probability of 

a debris flow increases. Consequently, we assumed that 

debris flows impacted step-pool channels 1 in 100 years and 

1 in 50 years for planebed and pool-riffle channels.These 

debris flows may originate in an area lacking large wood 

and deposit only sediment, or may travel through the reach 

removing all large wood. Alternatively, they could deposit 

either small wood or large wood. Thus we created three 

categories: Debrisflow no wood (multiplier MNo in table 4) 

where the debris-flow-impacted channel is left free of wood; 

Debrisflow small wood (multiplier MS in table 4) where the 

debris flow impacted channel is left with abundant small 

wood; Debrisflow large wood (multiplier ML in table 4) 

where the debris-flow-impacted channel is left with abundant large wood. In the simulations reported here, these 

three classes of debris flows were given equal weightings. 

Again, because the underlying base probability given to all 

debris flows in the state-and-transition models is 0.01, we 

use the following multipliers to drive debris flow transitions 

in models for all step-pool channels: MNO = MS = ML = 

0.3300. We use the following multipliers for all planebed 

and pool-riffle channels: MNO = MS = ML = 0.6700. Future 

model runs can be made iteratively, and the relative proportion of no wood, small wood, and large wood debris flows 

could be based on the tree size and large wood abundance in 

the debris flow source areas.

Management transitions in all PVTs—

The models we developed include a large number of 

possible management practices. Logging transitions 

(post-wildfire salvage logging, precommercial thinning, 

partial harvest and regeneration harvest, or clearcutting) 

were inherited from the upland models we used as a starting 

point for the aquatic-riparian models (table 4). In all cases, 

these transitions are initialized with a base probability of 

0.0100 and must be controlled using an external multiplier 

file. We have not simulated forest harvest in any riparian 

stands so in the simulations reported here, all forest harvest 

transitions probabilities were set to zero.

The models also include several planting or restoration 

transitions, including planting of conifers in the open shrub 

state class following stand-replacing disturbances, large 

wood additions to the stream, and hardwood conversion of 

small alder stands by planting of conifers. These restoration 

transitions are also initialized with a base probability of 

0.0100 and must be controlled using an external multiplier 

file. Additional management practices or prescriptions could 

be added to the models if need is demonstrated by managers 

or model users. Similarly, natural disturbance pathways 

could be edited and transition probabilities altered with 

new probabilities, or modified by using static or temporal 

multipliers.

Channel Conditions and Aquatic Habitat Quality

We used a 4-factor scale for each state class in the models 

to qualitatively rank their channel morphologic conditions: 

shade, erosion, undercut banks, large wood, pools, large 

pools, off-channel habitat, width-depth ratio, and riparian shrub abundance. We inferred the relative abundance 

of large wood, pools, undercut banks, and erosion from 

the cover type and structural stage of each state class in 

the models. These variables were then used in an expert 

systems model to rank the habitat quality (poor, fair, good, 

excellent) for migration, spawning, summer and winter 

rearing of coho salmon and steelhead. We assumed that 

coho habitat would be characterized by low gradient reaches 

(< 3 percent gradient), abundant large pools, and low 

erosion (low concentration of fine sediment) for spawning. 
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We used the abundance of large pools as an indicator of 

pool-riffle morphologies that would provide appropriate 

spawning riffles. Where pools were lacking, low -gradient 

reaches were assumed to be in a plane-bed state, with few 

distinct riffles. We assumed that Coho rearing habitat would 

be characterized by abundant pools, a combination of either 

undercut banks or large wood, and abundant off-channel 

habitat in areas with low erosion (low concentration of fine 

sediment). Thus, states with low gradient (i.e. pool-riffles) 

and abundant in-stream large wood, either recruited by 

stand-altering disturbances or self-recruited from large and 

giant trees, were assumed to provide the most favorable 

coho habitat.

We assumed that steelhead habitat would encompass 

nearly all coho habitat in low gradient reaches (i.e. poolriffle morphologies) but would also extended into higher 

gradient reaches (including step-pool reaches) as long as 

pools and in-stream wood were abundant. We assumed 

that steelhead spawning requirements would be similar to 

those of coho, that is, they would utilize riffles in poolriffle reaches where large pools were abundant and erosion 

(concentration of fine sediment) was low. We assumed that 

steelhead rearing habitat would be much more extensive 

within the watershed because this species was better able to 

occupy steeper reaches (i.e., > 3 percent gradient) with fast 

flowing water and little off-channel habitat. Also, steelhead 

typically spend two years rearing in freshwater instead of 

the single year that is the norm for coho. Thus we assumed 

that steelhead would be larger bodied in their second year, 

and would require larger pools and deeper water than in 

their first year.

We combined our qualitative habitat rankings with an 

independent evaluation of potential habitat quality, using 

Intrinsic Potential (IP) models (Burnett et al. 2007). IP 

scores are based on channel gradient, valley floor width, 

and drainage area and thus reflect the underlying physical 

“potential” to support the species of interest. The IP scores 

do not change over time, regardless the type and magnitude 

of disturbances that may alter other channel characteristics 

that influence the current quality of the stream habitat. We 

used the IP scores as a coarse filter through which we could 

identify portions of the stream network that had the potential to provide quality habitat for rearing, i.e., reaches where 

coho IP ≥ 0.60 (~18 km of the stream network) and steelhead IP ≥ 0.75 (~150 km of the stream network). Thus, large 

portions of the watershed, especially the coastal plain which 

has been converted to agricultural uses, mainstem reaches 

confined by bedrock gorges or high alluvial terraces, and 

headwater reaches too steep to support fish were excluded 

from our analyses.

Analysis and graphical display of model output—

We developed the 4-factor ranking scale for both channel 

attributes and fish habitat quality in MS Excel but using 

spreadsheet templates from VDDT and then imported the 

ranked attributes into the VDDT models. Thus, the native 

graphic capabilities of VDDT or PATH (Path Landscape 

Model, VDDT’s sister state-and-transition simulation 

model; http://www.apexrms.com, accessed 15 November, 

2011) and other analytical tools can be used to analyze the 

model outputs. However, we found it easier to conduct these 

analyses outside of the VDDT/PATH platform where we 

could code SAS (Statistical Analysis Software) to perform 

specific detailed analyses needed to answer the questions in 

which we were interested. Further, we coded SAS to format 

output datasets that could be pasted directly into a graphic 

template in SigmaPLOT and thus quickly analyze and 

display results of individual model runs.

Model Application

The state and transition models were applied to the mountainous portion of the Wilson River watershed (500 km2

) in 

the northern Oregon Coast Range to examine: (1) current 

conditions relative to the historic condition; (2) likely 

trajectories of aquatic and riparian habitats given current 

and expected land-use practices; (3) the potential of passive 

restoration to meet recovery goals; and (4) the potential of 

active restoration to accelerate recovery. As a first step in 

our analysis, we simulated the Tillamook Burns to evaluate 

the ability of the models to successfully “hindcast” current 

conditions resulting from forest regrowth following these 

large, stand-replacing fires. We then used the models to 

compare future projections of habitat quality resulting from 
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active restoration with large wood addition to that resulting 

from passive restoration.

Simulating the Effect of the Tillamook Burns

The history of the Tillamook Burns—a series of large stand 

replacing wildfires that burned large portions of the northern Oregon Coast Range in 1933, 1939, 1946, and 1951—

provides an opportunity to examine the ability of our state 

and transition models to simulate a time series of episodic 

disturbance and subsequent recovery of the riparian forests. 

We lack detailed records of the conditions of the Wilson 

River watershed prior to these major fires. However, large 

portions of the watershed were burned in high-intensity 

fires that essentially reset vegetation structure and composition to post-wildfire states. Thus, we initialized our model 

runs with initial conditions reflecting the long-term average 

historical condition of riparian vegetation within the watershed. The historic condition was projected from a 1000-year 

model run in which all anthropogenic effects were turned 

off and natural disturbance rates reflected the presumed 

historical rates of wildfires, windstorms, and debris flows. 

We lack detailed records of the exact portions of the Wilson 

River watershed burned in the Tillamook fires as well as 

historical records of postfire salvage logging and efforts to 

replant the burned areas. We do know that the 1933 fire was 

the largest of the “Tillamook Burns,” so we used a series 

of temporal multipliers to force major wildfires in 1933 and 

1934 and again in 1938. Each of these fires burned approximately 75 percent of the watershed so that by the late 1930s 

the structure and composition of the watershed had been 

nearly entirely reset to the earliest seral stages.

We simulated postfire recovery (1940–2010) by preventing stand-replacing wildfires (which did not occur over 

this time period), but continued to simulate background 

disturbances from wind, mixed-severity wildfire, and debris 

flows. Because salmonberry is a highly successful earlyseral shrub throughout the northern Oregon Coast Range 

and resprouts readily after wildfire, in our models, wildfires 

forced transitions to salmonberry-dominated state classes 

(fig. 4). Subsequently alder rapidly colonized these states 

Figure 4—Comparison of historic condition (HC), the time series of changes resulting from the 

Tillamook Burns and subsequent forest regrowth through 2010, and the lidar-derived current 

condition (CC) for approximately 2010. The model projection includes a 10-year period from 1930 

to 1940 where the watershed burned three times (red arrows), with each burn covering approximately 75 percent of the watershed. During the post-fire recovery (1940–2010), stand-replacing 

wildfires did not occur, but the models continued to simulate background rates of wind disturbance, mixed-severity wildfire, and debris flows.
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resulting in a rapid shift toward alder dominance, followed 

by growth and aging of established trees over time (table 5). 

By the end of the simulation period in 2010, forest structure 

and composition resembled the 2010 lidar-derived current 

conditions (fig. 4).

Current vegetation (both simulated and lidar-derived), 

however, is markedly distinct from the long-term average 

historic condition (fig. 4, table 5). Current conditions (CC; 

fig. 4) were determined through classification of lidar 

imagery. Giant conifer (DBH > 30 inches) dominated stands 

are notably lacking in the riparian zone. Historically, our 

simulations project that some 25 percent of the stream 

network would have supported riparian vegetation dominated by conifers larger than 30 inches DBH. Conversely, 

alder-dominated stands and mixed conifer-alder stands of 

medium- or large-sized trees are overrepresented in the 

riparian zone (fig. 4). These trends are expected, given 

the history of large, stand-replacing wildfires within the 

watershed.

A more detailed analysis of forest composition, separating the forest types into alder-dominated forest, coniferdominated forests, and mixed forests where conifers are 

beginning to over-grow previously alder-dominated forests, 

is shown in table 5. There are some discrepancies between 

the simulated structure and composition in 2010, compared 

to the lidar-derived current vegetation. For example, we 

under predict the amount of large, conifer-dominated forest 

(table 5). However, much of the area within the Tillamook 

Burn was planted with conifers in the decade following the 

burns. We did not simulate postdisturbance planting in our 

model runs. First, our models only allow postdisturbance 

planting over a short time window following disturbance 

which is typical of Burned Area Emergency Response 

(BAER) treatments currently employed within the region. 

Second, we do not know if the riparian zones were successfully planted in the Tillamook Burn area. Certainly, our 

observations in the field and the composition of the lidarderived current vegetation are generally in agreement with 

the simulation results which indicate that large areas of the 

riparian zone remain in alder-dominated stands. Overall, 

Table 5—Summary table showing the percent of the riparian area in each vegetation composition and structural group within the Wilson River watershed

we conclude that the models, as parameterized, provide an 

Non- Salmon- Alder Alder Alder Mixed Mixed 

Mixed Conifer Conifer Conifer Conifer Conifer

Time step Forest berry young small medium medium large giant young small medium large giantHC 1.1 0.8 2.4 6.80 18.96 16.5 11.12 12.86 0.2 0.5 0.8 1.76 26.18

Post-burn 6.9 66.2 21.6 0.3 0.8 0.8 0.6 0.6 0.9 0 0.1 0.2 1.1

1940 1.6 3.2 65.2 21.6 1.2 1 0.5 0.4 3.8 0.2 0.1 0.2 1.1

1950 1.3 0.4 0.9 78.5 8.7 3.9 0.5 0.5 0.3 3.8 0 0.2 1.11960 1.5 0.4 0.9 14.7 60.4 15.7 0.5 0.5 0.1 4 0.1 0.1 1.21970 1.6 0.5 0.8 4.5 61.7 24.2 0.5 0.5 0 2.5 1.7 0.1 1.21980 1.6 0.5 0.9 2.7 56.3 31.1 0.5 0.5 0 0.8 3.6 0.1 1.2

1990 1.7 0.4 0.9 2.7 50.4 36 1.4 0.6 0.1 0.5 4.1 0.1 1.32000 1.7 0.4 0.9 2.6 45 38 4.6 0.6 0 0.3 3.4 1.3 1.3

2010 1.6 0.4 0.9 2.6 40.3 36.1 11.1 0.6 0.1 0.2 2.2 2.7 1.3LIDAR-CC 0.3 0 0 0.1 42.2 11.4 29.4 0.5 0 0.5 5 10.1 0.6

Note: The data shown here subdivide the coarser vegetation classes shown in figure 4. HC denotes the simulated long-term average condition of the stream network resulting from a long-term (1,000-year) simulation of natural disturbance and plant succession. Post-burn denotes the composition and structure of the riparian zone in the year following the last simulated burn. LIDAR-CC denotes the current condition of the riparian zone, based on the lidar-derived vegetation structural classes and subsequent classification into model state classes.
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acceptable simulation of the riparian vegetation dynamics in 

the riparian zones of the Wilson River watershed.

Changes in Coho and Steelhead Habitat

Coho—

Only 18 km of the stream network within the mountainous 

portions of the Wilson River watershed have high intrinsic 

potential to provide quality rearing habitat for juvenile coho 

salmon (IP score ≥ 0.60). Comparisons of the simulated 

historical condition and with lidar-derived current condition 

of riparian vegetation show large departures from historical 

conditions. The historical condition (HC) was projected 

from 500-year model runs in which all anthropogenic 

effects were turned off but natural disturbances continued 

to occur. Current conditions (CC) were determined through 

classification of lidar imagery. Under current conditions, 

giant-tree structural states are almost entirely lacking and 

nearly two-thirds of the riparian areas are dominated by 

medium-sized tree structural states (fig. 5a). Simulations 

of the historical condition suggest that nonforest vegetation comprised as much as 10 percent of the riparian zone. 

Today, nonforest vegetation comprises less than 5 percent of 

the riparian zone. Changes within the watershed have also 

substantially influenced the quality and abundance of rearing habitat available for coho salmon (fig. 5b). Our historical 

simulation suggests that nearly two-thirds of the potentially 

useable habitat would have been ranked as good or excellent 

quality. Today, less than 25 percent of that stream habitat is 

ranked as good or excellent.

Future conditions were projected from the aquaticriparian VDDT models with all anthropogenic activities 

turned off (i.e., no forest harvest in riparian zones, no 

salvage logging, and no riparian planting or other restoration treatments). This simulation is effectively a “passive 

restoration” scenario where no active management occurs. 

Simulations suggest that, over time spans of 5 to 10 years, 

changes in vegetation structure are relatively slow. Over the 

longer term, in the absence of episodic disturbances, the 

growth of conifers in mixed stands will lead to a slow but 

steady increase in the abundance of large trees. However, 

even after another 50 years (by ca. 2060) riparian forest 

conditions will still remain distinct from their historical 

conditions (fig. 5a). Similar patterns are seen in the projections for habitat abundance and quality for coho salmon, 

with changes accumulating slowly, but steadily, so that 

conditions are markedly improved after 50 years. However, 

Figure 5a—Comparison of historical condition, current condition, and projected future conditions of the 

riparian vegetation resulting from passive restoration (i.e., all anthropogenic activities turned off) in areas 

where the intrinsic potential score for coho is greater than 0.60.
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even after 50 years of “passive restoration,” comparison 

with the historical condition shows that there is substantially less habitat ranked as excellent and more ranked as 

fair (fig. 5b) than in the simulated historical condition.

Steelhead—

The portions of the stream network with the highest potential to provide high-quality rearing habitat for steelhead 

(IP score > 0.75) are much more extensive than for coho, 

encompassing most of the modeled reaches of the mainstem 

Wilson River as well as all of the larger tributaries within 

the watershed. Collectively, more than two-thirds of the 

modeled stream network has the potential to provide highquality rearing habitat for steelhead.

Comparisons between the simulated historic condition 

and lidar-derived current condition of riparian vegetation 

for steelhead are very similar to those for coho salmon in 

which giant-tree structural states are almost entirely lacking 

and nearly two-thirds of the riparian areas is dominated by 

medium-sized tree structural states (fig. 6a). The quality 

and abundance of rearing habitat available for steelhead 

under current conditions is also substantially different from 

the simulated historical condition (fig. 6b). Our historical 

simulation suggests that nearly two-thirds of the high IP 

Figure 5c—Comparison of rearing habitat quality under historic, current, and projected future 

conditions resulting from an active restoration from large wood addition in areas where the 

intrinsic potential score for coho is greater than 0.60. Note that large wood addition has no effect 

on the structure and composition of the adjacent riparian vegetation, thus vegetation structure is 

not shown for this simulation.

Figure 5b—Comparison of rearing habitat quality under historicall, current, and projected 

future conditions resulting from passive restoration in areas where the intrinsic potential score 

for coho is greater than 0.60.
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habitat would have been ranked as good or excellent habitat 

quality. Today, only 20 percent of that stream habitat is 

ranked as good or excellent.

Simulations of passive restoration suggest that, over the 

short term, changes in vegetation structure are relatively 

slow. Over the longer term, the models simulate substantial 

growth of riparian trees, however, even after another 50 

years (by ca. 2060) riparian forests remain distinct from 

their historical conditions (fig. 6a). As with coho, similar 

patterns are seen in the projections for habitat abundance 

and quality for steelhead, with changes accumulating 

slowly, but steadily, so that conditions are markedly 

improved after 50 years. However, even after 50 years of 

“passive restoration,” comparison with the historical condition shows that there is substantially less habitat ranked as 

excellent and more ranked as fair or poor (fig. 6b) than in 

the simulated historical condition.

Stream Restoration Through Large Wood 

Augmentation

Our model simulations project substantial recovery is 

likely to occur over the next 50 years in the absence of 

major episodic disturbance, however, even after 50 years, 

the quality and abundance of stream habitat for coho and 

Figure 6a—Comparison of historic condition, current condition, and projected future conditions 

of the riparian vegetation resulting from passive restoration (i.e., all anthropogenic activities 

turned off) in areas where the intrinsic potential score for steelhead is greater than 0.75. Legend 

follows figure 5a.

Figure 6b—Comparison of rearing habitat quality under historic, current, and projected future 

conditions resulting from passive restoration in areas where the intrinsic potential score for 

steelhead is greater than 0.75.
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steelhead remains distinct from the simulated historical 

condition. One active restoration approach would be to add 

large wood to stream reaches where it is currently lacking 

to accelerate the recovery of habitat quality. The initial 

conditions we used to start our model runs for the 18 km of 

river with high intrinsic potential for coho salmon indicated 

that 61 percent of those 18 km had little or no stream wood 

and 24 percent had abundant large wood at the beginning 

of our simulation (ca. 2010). In the active recovery scenario, 

we simulated active large wood addition, treating approximately 1 km of stream network per decade. After 50 years, 

the portion of those 18 km where large wood was abundant 

increased from 53 percent to 64 percent. These large wood 

addition treatments, however, led to a small increase in the 

availability of coho rearing habitat ranked good or excellent after 50 years. Some 57 percent of the stream network 

was ranked good or excellent under the passive restoration 

scenario and 67 percent ranked in those categories in the 

active restoration scenario (fig. 5b versus 5c). Because the 

availability of high-quality habitat for coho is presently very 

limited within the Wilson River watershed, treating only 5 

km of stream channel through large wood addition results 

in a modest improvement in simulated habitat quality.

We conducted a similar series of model simulations 

for the 150 km of stream network with high intrinsic 

potential to support steelhead. Our simulations showed 

that to get an improvement similar to coho in the amount 

of habitat ranked good or excellent required treatment of 

approximately 44 km of stream channel which increased 

the amount of the stream network ranked good or excellent 

from 41 percent under passive restoration to 52 percent 

under active restoration over the 50-year model simulation 

(fig. 6b versus 6c). The active restoration scenario for steelhead would be much more expensive than for coho because 

it would require adding large wood to approximately 9 km 

of stream per decade.

The abundance of large wood was not readily quantified from the remote sensing techniques employed in this 

study. We did make estimates of large wood abundance 

from our field sampling plots (n = 29) which allowed us 

to specify the initial conditions for our model simulation. 

However, we emphasize that our 29 sample plots are too 

few to realistically estimate the abundance of large wood. 

Actual stream inventory data would help set more realistic 

initial conditions for our model simulation.

Discussion

The results presented here demonstrate the utility of our 

state and transition models for exploring a variety of questions related to landuse decisions and the effect of alternative management scenarios on riparian vegetation, channel 

morphology, and stream habitat condition for salmonids. 

Figure 6c—Comparison of rearing habitat quality under historic, current, and projected 

future conditions resulting from active restoration scenario with large wood addition in 

areas where the intrinsic potential score for steelhead is greater than 0.75. Note that large 

wood addition has no effect on the structure and composition of the adjacent riparian 

vegetation, thus vegetation structure is not shown for this simulation.
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The restoration scenarios examined here represent only a 

few of the large number of management questions that could 

be explored using these models.

State and transition models are relatively “transparent” in that other users can pick up a package of existing 

models, and with a minimum effort begin working with 

those models. This ease of use is facilitated by the fact that 

the VDDT and PATH software (http://www.apexrms.com/

path, accessed 15 November 2011) needed to run these 

models are publicly available and relatively easy to use. 

And while the size of some of our aquatic-riparian state 

and transition models may make them appear daunting at 

first, the software user interface makes it relatively easy 

to revise the models to meet a wide variety of alternative 

assumptions. The true value of these models is that alternative assumptions or “alternative management scenarios” can 

then be readily tested and the model outputs used to provide 

hypotheses of likely outcomes to explore the ways in which 

policy decisions may influence the future condition of 

riparian zones.

Our models are broadly portable. We developed models 

specifically for the Wilson River watershed in the northern 

Oregon Coast Range. However, similar potential vegetation 

and geomorphic types occur throughout the central and 

northern Oregon as well as the southern Washington Coast 

Range. Consequently, our models are likely to be directly 

applicable to these larger regions. Further, the general 

model structures and the rather exhaustive list of transition 

processes included in the models provides a template from 

which models for other areas within the region could be 

readily constructed.

Although the results of our model simulations have 

appeared reasonable wherever data were available for 

comparison, we caution that these comparisons do not 

provide detailed validation of all the factors simulated 

in our models. Consequently, the results of the model 

simulations should be interpreted cautiously. The models 

are not intended to provide detailed predictions of specific 

outcomes at the scale of a single reach or for a specific 

restoration project involving 100s or a few kilometers of 

stream channel. Rather, the results of the model simulations 

should be interpreted as hypotheses of likely outcomes from 

management directions at the scale of a large watershed 

(one or several 5th-field hydrologic units or HUC5s) or a 

large portion of a USFS Ranger District.

References

Beukema, S.J.; Kurz, W.A.; Pinkham, C.B.; Milosheva, 

K.; Frid, L. 2003. Vegetation Dynamics Development 

Tool, User’s Guide, Version 4.4c. Vancouver, BC, 

Canada: ESSA Technologies Ltd. 239 p.

Breiman, L. 2001. Random Forests. Machine Learning, 

45:5–32. Burnett, K.M.; Reeves, G.H.; Miller, D.J.; 

Clarke, S.; Vance-Borland, K.; Christiansen, K. 2007. 

Distribution of salmon-habitat potential relative 

to landscape characteristics and implications for 

conservation. Ecological Applications. 17: 66–80.

Castro, J.M.; Jackson, P.L. 2001. Bankfull discharge 

recurrence intervals and regional hydraulic geometry 

relationships: patterns in the Pacific Northwest, USA. 

Journal of the American Water Resources Association. 

37: 1249–1262.

Federal Caucus. 2000. Vols. 1–2. Conservation of 

Columbia basin fish, final basinwide salmon recovery 

strategy. Portland, OR: Federal Caucus/Bonneville 

Power Administration. http://www.salmonrecovery.gov/

Final_Strategy_Vol_1.pdf, http://www.salmonrecovery.

gov/Final_Strategy_Vol_2.pdf. (June 9, 2003).

Gatziolis, D.; Fried, J.S.; Monleon, V.S. 2010. Challenges 

to estimating tree height via LiDAR in closed-canopy 

forests: A parable from western Oregon. Forest Science. 

56: 139–155.

Hudak, A.T.; Crookston, N.L.; Evans, J.S.; Hall, D.E.; 

Falkowski, M.J. 2008. Nearest neighbor imputation of 

species-level, plot-scale forest structure attributes from 

LiDAR data. Remote Sensing of Environment. 

112: 2232–2245.

Montgomery, D.R.; Buffington, J.M. 1997. Channel-reach 

morphology in mountain drainage basins. Geological 

Society of America Bulletin. 109: 596–611.
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Montgomery, D.R.; Buffington, J.M. 1998. Chapter 

2: Channel processes, classification, and response. 

In: R.J. Naiman and R.E. Bilby, eds. River Ecology 

and Management—Lessons from the Pacific Coastal 

Ecoregion. New York: Springer-Verlag: 13–42. 

National Research Council (NRC). 2002. Riparian areas: 

functions and strategies for management. Washington, 

DC.: National Academy Press. 428 p.

Nehlsen, W.; Williams, J.E.; Lichatowich, J.A. 1991.

Pacific salmon at the crossroads: Stocks at risk from 

California, Oregon, Idaho and Washington. Fisheries. 

16: 4–21.

Pflugmacher, D.; Cohen, W.B.; Ohmann, J.L.; Kennedy, 

R.E. 2011. Final report to the Oregon Watershed 

Enhancement Board, for grant OWEB # 208-8007-5781. 

Mapping Current Conditions and Modeling the Dynamic 

Responses of Riparian Vegetation and Salmon Habitat in 

Oregon: Mapping Component. Submitted 6 June 2011.

U.S. Department of Agriculture, Forest Service; 

U.S. Department of the Interior, Bureau of Land 

Management. 2000. Interior Columbia basin 

supplemental draft environmental impact statement. 

The Interior Columbia Basin Ecosystem Management 

Project. Washington, DC: U.S. Government Printing 

Office. [Irregular pagination].

U.S. Geological Survey and U.S. Department of 

Agriculture, Natural Resources Conservation 

Service. 2009. Federal guidelines, requirements, and 

procedures for national Watershed Boundary Dataset: 

U.S. Geological Survey Techniques and Methods 11-A3, 

55 p.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Authors

Eva K. Strand, National Interagency, Fuels, Fire, and 

Vegetation Technology Transfer Team, evas@uidaho.edu. 

Stephen C. Bunting, sbunting@uidaho.edu and Lee A. 

Vierling, Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID 83844, USA, leev@

uidaho.edu.

Abstract 

Quaking aspen (Populus tremuloides) is declining across 

the western United States. Aspen habitats are diverse plant 

communities in this region and loss of these habitats can 

cause shifts in biodiversity, productivity, and hydrology 

across spatial scales. Western aspen occurs on the majority 

of sites seral to conifer species, and long-term maintenance 

of these aspen woodlands requires periodic fire. We use 

field data, remotely sensed data, and fire atlas information 

to develop a spatially explicit landscape simulation model 

to assess the effects of current and historic wildfire regimes 

and prescribed burning programs on landscape vegetation 

composition in the Owyhee Mountains, Idaho. The model 

is run in the Tool for Exploratory Landscape Scenario 

Analyses (TELSA) environment. Model outputs depict the 

future structural makeup and species composition of the 

landscape at selected time steps under simulated management scenarios. Under current fire regimes and in the 

absence of management activities, loss of seral aspen stands 

will continue to occur. However, a return to historic fire 

regimes, burning 12–14 percent of the modeled landscape 

per decade, maintains the majority of aspen stands in early 

and mid seral woodland stages and minimizes the loss of 

aspen. A fire rotation of 70–80 years was estimated for the 

historic fire regime while the current fire regime resulted in 

a fire rotation of 340–450 years. Implementation of prescribed burning programs, treating aspen and young conifer 

woodlands according to historic fire occurrence probabilities, are predicted to prevent conifer dominance and loss of 

aspen stands. 

Keywords: Aspen, Populus tremuloides, VDDT, 

TELSA, succession, disturbance, fire regime 

Introduction 

Region-wide decline of quaking aspen has caused concerns that human alteration of vegetation successional and 

disturbance dynamics jeopardize the long-term persistence 

of these woodlands. (Bartos 2001, Kay 1997, Shepperd et 

al. 2001, Smith and Smith 2005). Aspen are an important 

component that provides ecosystem diversity in the conifer 

dominated western mountains. Aspen ecosystems provide 

a disproportionately diverse array of habitats for flora and 

fauna for its relatively small area on the landscape (Bartos 

2001, Jones 1993, Kay 1997, Winternitz 1980). In the semiarid western U.S., aspen commonly occurs as a disturbancedependent species, seral to conifer species (Bartos 2001, 

Kaye et al. 2005, Smith and Smith, 2005). It is well known 

that in mixed aspen and conifer stands, periodic fires 

prevent conifer dominance and possible loss of the aspen 

stand (Baker 1925, Bartos and Mueggler 1981, DeByle et 

al. 1987). Although the aspen is a prolific seed producer, 

the conditions required for successful seed germination and 

establishment are rare in the American West (Mitton and 

Grant 1996). Aspen clones in the region reproduce primarily via vegetative suckering and therefore it can be concluded that an aspen clone lost is not likely to re-establish 

via seed. An example of recent successful establishment of 

aspen seedlings occurred in response to the severe fires in 

1988 in Yellowstone National Park (Romme et al. 2005). All 

aspen stands are however not seral to conifers. Aspen stands 

in certain biophysical settings and away from a conifer seed 

source have been observed to exist as self-regenerating even 

and uneven aged stands that do not appear to be at risk of 

Landscape Composition in Aspen Woodlands 

Under Various Modeled Fire Regimes

Eva K. Strand, Stephen C. Bunting, and Lee A. Vierling
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GENERAL TECHNICAL REPORT PNW-GTR-869

rapid decline due to conifer expansion even in the absence 

of fire (Mueggler 1989, Rogers et al. 2010, Strand et al. 

2009). Mortality in these stable aspen stands has however 

been observed over the past decade (Worall et al. 2008). 

This mortality has been correlated with rising temperatures 

and drought in the southwestern U.S. (Huang and Anderegg 

2011; van Mantgem and Stephenspn 2007, van Mantgem 

et al. 2009) potentially caused by hydraulic failure of roots 

(Anderegg et al. 2012). 

Successional rates within pure and mixed aspen stands 

and interactions with fire and herbivory have been studied 

at the stand level, however, little work has examined these 

dynamics across larger landscapes over decades. Computer 

simulation models may be a means to better understand 

these dynamics in landscapes where aspen is present. 

Such landscape level succession/disturbance models have 

been used for evaluating habitat patterns in forests and 

woodlands (e.g., Klenner et al., 2000; Bunting et al. 2007) 

and assessment of fire regimes and management scenarios 

(Bunting et al. 2007, Franklin et al. 2001, Keane et al. 1997). 

In response to the need for better understanding of 

interactions between aspen/conifer succession and fire 

regimes across larger landscapes over decadal time scales, 

we simulated a number of aspen management scenarios 

using a conceptual state-and-transition model developed 

for aspen/conifer woodlands (fig. 1, Strand et al. 2009) and 

the Tool for Exploratory Landscape Scenario Analyses 

(TELSA, ESSA Technology 2003). We utilized field and 

remotely sensed data combined with spatially explicit 

modeling to estimate the effects of current and historic fire 

regimes on landscape vegetation composition and structure, 

emphasizing aspen woodland dynamics. Although prescribed fire has been suggested and applied to mitigate the 

frequent fire events common in the western mountains of 

the past, with the goal of maintaining and restoring aspen 

woodlands (Bates et al. 2004, Brown and DeByle 1989, 

Miller et al. 2005, Shepperd, 2001), little is known about 

how such management affects the vegetation composition 

and structure spatially and temporally. We therefore also 

incorporate prescribed burning scenarios into our modeling 

runs. In particular, we address the following four research 

questions: (1) Can we simulate the fire regime that maintained aspen stands prior to Euro-American settlement?; (2) 

What extent and frequency of fire is required to stabilize the 

current land cover composition within aspen woodlands?; 

(3) What is the structural composition of aspen woodlands 

under historic and current fire occurrence probabilities, and 

under prescribed burning scenarios?; and (4) What is the 

Figure 1—Simplified pathway diagram for upland aspen/conifer communities that served as the 

conceptual model for vegetation dynamics in the Owyhee Mountains.
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

effect of fire size on the long-term maintenance of aspen 

woodlands? 

Methods

Site Description

The mountain ranges of the Owyhee Plateau in SW Idaho 

(116.4° W, 43.0° N) contain vegetation communities 

representative of many semi-arid mountains of the western 

U.S.A. We include two study areas in this research: the 

South Mountain study area encompassing 17,000 ha and 

the Silver City Range covering 20,000 ha. Western juniper 

woodlands (Juniperus occidentalis ssp. occidentalis) and 

sagebrush (Artemisia spp.) steppe dominate the landscape 

above 1700 m altitude, interspersed with pockets of aspen, 

mountain shrub species, and meadows. Western juniper is 

gradually replaced by Douglas-fir (Pseudotsuga menziesii 

ssp. glauca) above 1850 m in both mountain ranges. Aspen 

stands are commonly located on cool northeast facing 

slopes, in concave snow and moisture accumulation areas. 

Soils that support aspen include deep fine-loamy and loamyskeletal mixed pachic or typic cryoborols, rich in organic 

material with high water-holding capacity (USDA NRCS 

1998). In the area, aspen occurs in three distinctly different 

biophysical settings with different successional trajectories 

and rates; pure aspen on south-facing aspects above 1900 

m, aspen on wet micro sites, and aspen/conifer stands on 

mountain hillsides (Strand et al., 2009). Areas that support 

aspen receive 400-1000 mm annual precipitation (Oregon 

Climate Service 1999) in the form of rain in the spring and 

fall, and snow during the winter. Summer and early fall are 

warm and dry with an average high temperature in July of 

26.7o C (WRCC 2003). 

Field Data Collection

A total of 82 aspen clones along elevational and succesional gradients were sampled across the study areas. 

Site characteristics were recorded: slope, elevation, aspect, 

and Universal Transverse Mercator (UTM) coordinates. 

We further collected stand characteristics: canopy cover 

of aspen and conifers in the crown and below 2-m height, 

increment cores from the five tallest mature aspen and 

conifer trees (thought to be among the oldest), stem counts 

of aspen and conifers in three height classes (< 2 m, 2 m 

up to 75 percent of the stand height, and trees taller than 

75 percent of the stand height). The increment cores were 

mounted and sanded, and the annual growth rings counted 

in a stereo-microscope for the age estimate. Faint annual 

rings in aspen were stained with phloroglucinol solution 

before ring counting (Patterson 1959). 

Model Requirements and Assumptions

TELSA (Essa Technology 2003) is a spatially explicit landscape dynamics model environment, allowing the user to 

explore the effect of natural and anthropogenic disturbances 

on landscape composition. Input data to this model include 

potential natural plant communities, initial vegetation types 

and structural stages, along with natural and anthropogenic 

disturbance agents and pathways. Succession is treated as a 

deterministic variable with a constant pre-determined time 

period between successional states. 

Successional rates in upland aspen stands are based on 

models developed by Strand et al. (2009). They discovered 

that the successional development in upland aspen/conifer 

woodlands on the Owyhee Plateau can be characterized 

with a positive exponential function where the proportion 

conifer in the stand is fit against time since conifers were 

introduced to the stand: 

 f(t) = A e kt (0 < f(t) < 1) (1) 

where f(t) is the proportional cover of conifers in the aspen 

stand (e.g. conifer cover divided by total cover of all tree 

species), which is close to 0 at t = 0 and approaches 1 at 

complete conifer dominance, and the constant k represents 

the successional rate. The best model estimate (R2

 = 0.63, 

F=114.4, p<0.001) was: 

 f(t) = 0.0177 e 0.0315* t 0 < f(t) < 1 (2) 

where the model constant A = 0.0177 and successional rate 

k = 0.0315. Time since the initiation of conifer establishment was the only variable that significantly affected the 

successional rate in this data set although environmental 

variables such as terrain attributes, soil and climate data 

were included in model development (Strand et al. 2009). 

This model was developed using only upland aspen/conifer 

stands, and does not apply to aspen in riparian areas nor 
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GENERAL TECHNICAL REPORT PNW-GTR-869

areas around meadows and springs. An exponential increase 

in the conifer dominance occurs 50–60 years after conifers 

were initiated to the aspen stand, as prolific conifer seed 

production and spread begins (see Strand et al. 2009). This 

exponential increase in conifer dominance marks the transition of mid seral aspen into late seral aspen (fig. 1). 

In TELSA, disturbance is treated as a stochastic variable driven by user-defined probabilities. This stochastic 

component in landscape models results in many possible 

landscape configurations given the same input variables, 

allowing the range of variability in landscape composition 

to be explored statistically. 

Spatially explicit simulations in TELSA require 

information in the form of GIS data layers (digital maps) of 

the study area. Each landscape unit in the map must be classified hierarchically in a potential vegetation type (PVT), 

current cover type, and current structural class. PVTs are 

groupings of habitat types or ecological sites with similar 

overstory composition in the absence of a disturbance and 

similar environmental requirements. For the sagebrush 

steppe/juniper woodlands we employed the PVT classification developed by Bunting et al. (2007) in the same general 

study area. As mentioned earlier, aspen woodlands are 

potentially present in three PVTs (Strand et al. 2009): 

pure aspen, aspen/western juniper, and aspen/Douglas-fir. 

In the simulation, aspen stands on pure aspen PVTs 

represent stands that can be expected to self-regenerate 

and persist as uneven aged aspen stands for decades into 

the future. Over time, aspen on aspen/western juniper and 

aspen/Douglas-fir PVTs become outcompeted by western 

juniper and Douglas-fir, respectively, and in the absence of 

a disturbance within a certain time period will permanently 

convert to pure conifer stands (Wall et al. 2001, Strand et al. 

2009). Aspen/conifer stands that burn prior to permanent 

conversion to conifer stands are assumed to return to stand 

initiation aspen stands (fig. 1). 

Each landscape unit is characterized by its PVT, but 

also by the current cover and structure. The current cover 

map represents the vegetation currently present on the 

ground and includes the climax vegetation classes represented by the PVTs with the addition of seral cover types 

such as grasslands, shrublands, and young woodlands. The 

structural classes within aspen succession include: stand 

initiation aspen, young aspen woodlands, mature aspen 

woodlands, aspen woodlands with conifers, and conifer 

woodlands. We used input GIS layers previously developed 

for the Owyhee Plateau by Strand (2007) depicting the PVT, 

current vegetation and structural stages, see table 1 for 

landscape distribution of cover types and table 2 and figure 

2 for distribution of PVTs. 

Table 1—Areas of mapped cover types within the South Mountain and Silver 

City Range study sites on the Owyhee Plateau in SW Idaho 

Silver City

South Mountain Range

Cover type Area (ha) Area (ha) 

Aspen woodland (pure aspen) 496 236

Aspen/Douglas-fir woodland 1371 2002

Aspen/Western juniper woodland 745 527

Bare/Rock 2 72

Ceanothus/Mesic shrub 299 365

Douglas-fir 298 923

Juniper woodland/Low sage open 1635 787

Juniper woodland/Low sage closed 1056 141

Juniper woodland/Mountain big sage open 4062 3321

Juniper woodland/Mountain big sage closed 3451 1259

Curlleaf mountain-mahogany 227 1983

Low sagebrush steppe 1335 2343

Mountain big sagebrush steppe 1729 5992

Wet meadow 42 189
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

In general, we make the assumption that PVTs are 

static, and consequently a landscape unit occupied by a PVT 

at the beginning of the simulation will stay within that PVT 

throughout the simulation. The land cover and structural 

vegetation stage within the landscape unit may change via 

the successional time step or revert to an earlier seral stage 

via disturbance (i.e. fire). This static view of PVT works 

well in most ecosystems within reasonable time periods. In 

the aspen ecosystem, however, this static view is limited for 

two reasons. First, aspen has been observed to expand into 

adjacent areas with low canopy cover such as grasslands 

and sagebrush steppe. Such expansion of aspen clones was 

observed during field assessments during this study and 

has also been reported by other researchers (Manier and 

Laven 2001). Expansion of aspen could not be incorporated 

directly in the TELSA simulations, but upper limits of 

aspen expansion were estimated based on expansion rates 

and the length of currently available aspen/sagebrush edge. 

The rate of aspen expansion into adjacent cover types, was 

estimated by recording the decrease of aspen stem age along 

four transects perpendicular to the aspen/sagebrush steppe 

ecotone during the 2006 field season in the nearby Jarbidge 

Mountains. The four transects show similar expansion rates 

of approximately 0.5 m per year (20 m expansion in 40 

years). We assume here that the aspen expansion rates are 

similar in the Jarbidge and Owyhee Mountains, because 

the two mountain ranges are located at similar latitudes and 

span similar altitudes. Second, it is currently not known 

how long and under what conditions an aspen clone can persist after conifers dominate a site. It has been suggested that 

aspen clones can be sustained for decades in the absence 

of mature ramets maintained only by transient suckers 

Table 2—Areas of mapped potential vegetation types (PVT) within the study area 

Silver City

South Mountain Range

Cover type Area (ha) Area (ha) 

Aspen woodland 496 236

Aspen/Douglas-fir woodland 1669 2925

Aspen/Western juniper woodland 745 5 27

Bare/Rock 2 7 2

Ceanothus/Mesic shrub 299 365

Juniper woodland/Low sage 4028 3272

Juniper woodland/Mountain big sage 9240 10571

Curlleaf mountain-mahogany 227 1983

Wet meadow 42 189

Figure 2—Potential vegetation maps of the South Mountain (left) and the Silver City (right) 856 areas of the 

Owyhee Mountains in SW Idaho.
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GENERAL TECHNICAL REPORT PNW-GTR-869

(Despain 1990). This hypothesis has not yet been tested 

(Hessl 2002); and we assume here that old mixed aspen/

conifer stands permanently transition to conifer stands 120 

years after aspen regeneration has diminished due to conifer 

dominance within a stand (Strand et al., 2009). In such 

stands we do not expect a fire event to return the landscape 

unit to young aspen woodland but rather to young conifer 

woodlands, resulting in permanent loss of aspen within the 

landscape unit (fig. 1). 

The current wildfire size distribution was calculated 

from a fire database provided by the Interior Columbia 

Basin Ecosystem Management Project (http://www.

icbemp.gov/) for the interior Columbia River basin between 

1986–1992. The maximum allowable area burned in 

prescribed fires was set to 1000 ha per year in scenarios that 

included prescribed fire. 

Current wildfire probability of occurrence in each PVT 

and structural stage was computed from an overlay analysis 

in a GIS (ESRI 1999–2005) of digital fire atlas data from 

1957–2002 and a recently developed landcover map for the 

Owyhee Plateau (Roth 2004). Historic wildfire probabilities were estimated based on the 40–60 year fire interval 

suggested by Jones and DeByle (1985a) for aspen woodland 

with increasing fire probability later in succession where 

flammable conifers are present. The fire occurrence probability for juniper woodlands at their initiation was derived 

from the 40–50 year mean fire return interval suggested by 

Burkhardt and Tisdale (1976). As western juniper woodlands mature, there is a decrease in understory productivity 

resulting in lower amounts of fine fuels and a reduced 

ability to carry fire in these older woodlands (Miller et al. 

2005, Bunting et al. 2007). For mid- and late seral juniper 

woodlands, we employed fire occurrence probabilities used 

by Bunting et al. (2007). 

During a TELSA simulation, fires start in random 

locations according to the assigned disturbance probability. A fire that starts in a landscape unit can spread into 

an adjacent landscape unit if that unit is eligible for fire 

disturbance. The size of wildfires and prescribed fires were 

randomly assigned to each fire based on the pre-defined fire 

size probability distribution. 

Six major assumptions and simplifications relating to 

aspen ecology and succession are important parts of this 

model. They are: 

1) Aspen reproduction from seed is not included in 

this model. 

2) Aspen are not allowed to spread laterally into 

other PVTs (e.g. sagebrush). 

3) Adjacency between vegetation types does not 

affect succession. 

4) Fire will convert a conifer dominated aspen stand 

to a young aspen stand regardless of the predisturbance conifer cover in the stand, i.e. no 

legacy effects are considered. 

5) Aspen stands are permanently converted to 

conifer stands 120 years after aspen suckering 

has ceased due to conifer dominance (i.e. ~230 

years after conifer initiation into the stand). 

6) Effects of insects, disease, and animal use are 

not included in this model. 

The potential effects of these assumptions and simplifications on model outcome and interpretation are addressed 

in the Discussion section. 

Model Scenarios

To determine whether the assigned model parameters were 

realistic, we tested the model by subtracting 100 years from 

the age of each landscape unit followed by a simulation 100 

years into the future using assigned successional rates, fire 

probabilities, and fire size distributions. The actual current 

landscape composition was then compared to the modeled 

composition. Future landscape compositions for the two 

study areas were evaluated at 25, 50, 100 and 200 years 

from current time. Fire management regimes included: 

Scenario 1: Current fire management i.e. suppressed 

wildfire only. 

Scenario 2: Historic wildfire probabilities. 

Scenario 3: Historic wildfire probabilities with larger 

fires. 

Scenario 4: Prescribed fire in aspen/conifer woodlands according to historic fire probabilities, 

no prescribed fire applied in other cover types. 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Scenario 5: Prescribed fire in aspen/conifer woodlands and young juniper woodlands according to 

historic fire probabilities. 

Although succession in TELSA is treated as a 

deterministic variable with a pre-determined time period 

between transitions, fire starts and fire size are stochastic 

components in the model. Because of this stochastic 

element, the model results will vary between runs even 

though the input variables and landscape maps are identical. 

Simulations were therefore run 10 times for each management scenario to quantify the variability between runs. 

Means and variances were calculated and displayed as error 

bars in the resulting graphs. 

Results

Fire Occurrence, Size, and Probabilities

Fire perimeter data from the Bureau of Land Management 

(BLM) 1957–2002 show that only 94 ha of the combined 

Table 3—The TELSA model requires estimates of the disturbance size distribution as part of the input. This table describes the percent of fires in each size 

class for the five simulation scenarios. The current wildfire size distribution was 

estimated from the Interior Columbia Basin Ecosystem Management Project 

geographic database (ICBEMP 1995) 

Fire size 0-1 Fire size 1-10 Fire size 10-100 Fire size 100-1000

Scenario ha ha ha ha

1 90 5 3 2

2 90 5 3 2

3 50 20 15 15

4 1 4 25 70

5 1 4 25 70

Table 4—This table described the current and historic probability of wildfire

occurrence in the major PVTs and structural stages on the Owyhee Plateau

Current Historic

wildfire wildfire

PVT Structural stage probability probability

Low sagebrush steppe Grassland 0.00064 0.002

Low sagebrush steppe 0.00064 0.005

Mtn big sagebrush steppe Grassland 0.001 0.002

Mtn big sagebrush steppe 0.001 0.02

Juniper woodlands/Low Grassland 0.00064 0.002

sagebrush steppe Low sagebrush steppe 0.00064 0.02

Stand initiation juniper 0.0008 0.01

Open young woodland 0.0008 0.001

Young multistory woodland 0.0005 0.002

Old multistory woodland 0.0004 0.006

Juniper woodlands/Mtn. Grassland 0.001 0.005

big sagebrush steppe Mtn. Big sagebrush steppe 0.001 0.02

Stand initiation juniper 0.001 0.02

Open young woodland 0.0007 0.01

Young multistory woodland 0.0002 0.002

Old multistory woodland 0.00009 0.001

Aspen woodlands/conifer Young woodlands 0.0002 0.0002

Mature woodlands 0.0002 0.005

Woodlands with conifer 0.0002 0.01

Conifer/aspen woodland 0.0002 0.0
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GENERAL TECHNICAL REPORT PNW-GTR-869

37 000 ha study region has burned in wildfires within this 

time period. Overlay analysis in GIS reveals that none of 

these fires occurred on soils that support aspen woodlands. 

Fire records prior to 1957 are not available. Prescribed fire 

in aspen stands has occurred on the Owyhee Plateau, but to 

this date not in modeled areas. 

The current wildfire size distribution was estimated 

from the Interior Columbia Basin Ecosystem Management 

Project database (ICBEMP 1995, table 3), which indicates 

that most wildfires within the region become less than 1 

hectare in size. Information about the historical wildfire 

size distribution is not available for the study area and we 

therefore simulated two historical wildfire scenarios with 

two different fire size distributions (scenarios 2 and 3, table 

3) to test the sensitivity of fire size within the model. In scenario 2 we used the same fire size distribution as scenario 1 

(90 percent of fires become < 1 ha in size) while in scenario 

3 the proportion of fires larger than 1 ha was increased 

(see table 3 for more detail). Commonly, prescribed fires 

are in the size class 10-1000 ha (scenarios 4 and 5, table 3). 

Current wildfire probabilities were estimated via overlay 

analysis between current cover types (Roth, 2004) and the 

digital fire atlas obtained from the BLM for the time period 

1957–2002. Historical wildfire probabilities were based 

on literature references (DeByle et al. 1987, Bunting et al. 

2007; see table 4). 

Management Scenarios

To evaluate the input model parameters, we tested the model 

by subtracting 100 years from the age of each landscape 

unit followed by a simulation 100 years into the future 

using assigned successional rates, fire probabilities, and size 

distributions. We compare the resultant modeled landscape 

composition to the actual current landscape composition 

in table 5. The model accurately simulated the current area 

of aspen using the inputs from 100 years back in time. The 

simulated area of juniper woodlands was larger, and the 

area in sagebrush steppe and grasslands was smaller than 

observed. These results suggest that the simulated successional rates within the juniper PVTs are slightly overestimated in the model. We attribute this to the fact that the 

juniper successional models were developed in a different 

study area on Juniper Mountain south of South Mountain. 

Future landscape composition of aspen seral stages 

was predicted under varying management scenarios for 

South Mountain and the Silver City Range (figs. 3 and 4). 

Under current wildfire regimes the early, mid, and late seral 

woodlands are predicted to decrease within the next 100 

years while the old woodlands are predicted to increase. 

Continuation of current fire management is predicted to 

result in loss of aspen woodlands within the next 100 years, 

with additional losses in the following century. 

Modeled historical fire regimes predicted an increase 

in early and mid seral woodlands while the area in late seral 

woodlands decreased and old woodlands remained at current levels. Scenarios 2 and 3, historic fire probabilities with 

smaller and larger fire size distributions, yielded similar 

results with an increase in the mean area of the early and 

mid seral aspen classes for the scenario with larger fire size 

compared to the smaller fire size. This difference, however, 

falls within the variability of the 10 runs (figs. 3 and 4). 

Table 5—This table provides a comparison of the current cover type 

distribution and the 100-year simulated current cover type distribution for South Mountain

Current area Simulated current 

Cover type ha ha 

Aspen 2611 2610

Ceanothus / Mesic shrub 477 362

Curlleaf mountain-mahogany 223 117

Douglas-fir 298 284

Grasslands/Meadow 70 402

Juniper woodland 10193 11831

Sagebrush steppe 3053 1136
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Figure 3—Area of aspen woodland in different seral stages under five simulated management scenarios 

on South Mountain. The total area in aspen vegetation is currently 2610 ha.

Prescribed fire applied in aspen only (scenario 4) and in 

aspen and young juniper (scenario 5) resulted in a decrease 

in early and mid seral aspen woodlands. The area in late 

seral aspen woodlands initially decreased but reached a 

stable level, similar to the current area, approximately 100 

years into the future. The area in old aspen and the loss 
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GENERAL TECHNICAL REPORT PNW-GTR-869

Figure 4—Area of aspen woodland in different seral stages under five simulated management scenarios in 

the Silver City Range. The total area in aspen vegetation is currently 2765 ha.

of aspen is similar for the prescribed fire and historical 

fire management scenarios. Under historical fire regimes 

a larger portion of the landscape was stable in mid seral 

woodlands, while for the prescribed fire simulations a 

larger portion of the area stabilized in late seral woodlands. 

These predictions indicate that the aspen loss can largely be 
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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

Table 6—Fire rotation and decadal proportion of the landscape burned under 

modeled fire regimes

Fire Fire area

rotation per decade

Study area Scenario (years) (percent) 

South Mountain Current wildfire (1) 340 2.9

South Mountain Historic fire probabilities (2) 82 12.2

South Mountain Historic prob. large fires (3) 72 13.9

South Mountain Prescribed fire in aspen (4) 466 2.1

South Mountain Prescribed fire in aspen+young juniper (5) 192 5.2

Silver City Current wildfire (1) 449 2.2

Silver City Historic fire probabilities (2) 79 12.7

Silver City Historic prob. large fires (3) 66 15.1

Silver City Prescribed fire in aspen (4) 448 2.2

Silver City Prescribed fire in aspen+young juniper (5) 178 5.6

mitigated by implementing appropriate prescribed burning 

programs. 

Fire rotation is a measure of how many years it would 

take to burn an area equal to the study area under a given 

fire regime. Under historical fire probabilities, our simulations indicate that the fire rotation for the two study areas 

was 70–80 years, while at current fire management the 

estimated fire rotation was 340 years on South Mountain 

and 449 years in the Silver City area (table 6). Fire rotations were also computed for the prescribed fire scenarios, 

although these numbers may not be meaningful for aspen 

management because the simulated prescribed fire programs here target aspen stands. According to this model, 

the historical fire regimes—which are able to maintain 

the majority of aspen stands in early and mid seral woodlands—required that approximately 12–14 percent of the 

area burns per decade. Currently, only 2–3 percent of the 

landscape burns per decade, of which the majority of the 

burned area is sagebrush steppe rather than juniper or aspen 

woodlands. 

Aspen Expansion

Given the aspen expansion rate into sagebrush of approximately 0.5 m per year (20 m expansion in 40 years) and the 

length of the aspen/sagebrush steppe boundary within the 

South Mountain study area, the maximum area gained by 

aspen clones in 100 years would be 340 ha, corresponding to 13 percent of the current aspen cover. These results 

indicate how much assumption 2, “Aspen is not allowed to 

spread laterally in the model”, affects the interpretation of 

the model results. Although we realize that the expansion 

rate likely varies with annual precipitation, site productivity, 

and other environmental conditions, the average expansion 

rate estimated here provides a guideline for assumptions 

made regarding the importance of aspen expansion for 

landscape composition. 

Discussion

Fire Disturbance and Landscape Dynamics

Modeling results suggest that under a continuation of 

current fire regimes, aspen will continue to decline on both 

South Mountain and in the Silver City Range. Current midand late seral aspen/conifer stands will continue to age over 

the next 50–100 years and eventually become permanently 

converted to conifer woodlands in the absence of disturbance (figs. 3 and 4). Through simulations of successiondisturbance dynamics in TELSA under current and historic 

fire regimes and prescribed fire scenarios, we are able to 

address the four questions posted in the introduction. 

1) Can we simulate the historical fire regime that

maintained aspen stands prior to Euro-American 

settlement? 

Results produced under the historical fire conditions 

yield a landscape where over half of the aspen area is in 

early or mid seral successional classes and the loss of aspen 

is low. The distribution between successional stages is: 
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14 percent in the early seral stage, 45 percent in mid seral 

and 35 percent in late seral (late seral and old combined, see 

figs. 3 and 4). We predict an ~ 6 percent loss of aspen (compared to the current area occupied by aspen) over the 200 

year simulated time period even under historic fire regimes, 

which is likely due to caveats in the model assumptions. 

Within the model there is no avenue for aspen recruitment via seed or expansion of aspen into previously aspen 

free habitats. Under stochastic and randomly distributed 

application of fire, by necessity, some aspen stands will by 

chance escape fire for a long enough time period to convert 

to conifer woodlands. Sexual reproduction of aspen is not 

likely to occur in the West, although such infrequent severe 

fire events enabling seedling establishment may be important for aspen regeneration long term. This model also did 

not include expansion of aspen into shrub and grasslands. 

We here estimate that the maximum estimated expansion 

rate for aspen on South Mountain (340 ha in 100 years or 13 

percent of the current aspen area) would more than counteract the predicted loss of 6 percent in our model. 

Whether this model scenario is indeed a fair representation of fire regimes prior to European settlement is difficult 

to assess, but comparisons can be made to independent 

estimates from other researchers. Our simulated historical 

fire regime resulted in a fire rotation of 70–80 years, which 

is somewhat longer than the mean fire frequency of 50 years 

suggested by Jones and DeByle (1985a). We also compared 

the area in successional classes to predictions presented 

as part of the LANDFIRE Rapid Assessment Reference 

Condition Models. For the aspen biophysical setting in mapping zone 18, which includes southern Idaho, the suggested 

distribution among successional stages is 14 percent in early 

seral, 40 percent in mid seral and 45 percent in the late seral 

class, which is very similar to our modeled results. Loss of 

aspen is avoided in the LANDFIRE reference models by 

including an insect/disease outbreak every 200 years, which 

reverts aging aspen stands to earlier successional stages. 

2) What extent and frequency of fire (burned area

per decade) is required to stabilize the current 

land cover composition within aspen woodlands? 

Under historical conditions we predict that 12–14 

percent of the landscape burned per decade and that this 

amount of fire largely maintained the aspen stands in 

early and mid seral stages. Current fire regimes, resulting 

in approximately 2 percent of the landscape burned per 

decade, is (according to model predictions) clearly not 

enough to avoid aspen loss or to maintain aspen in early and 

mid seral stages. Prescribed fire applied in aspen and young 

juniper woodland results in 5–6 percent of the landscape 

burned per decade while application of fire in aspen stands 

only results in 2 percent of the landscape burned per 

decade. By targeting only aspen/conifer stands, aspen could 

theoretically be kept on the landscape with minimal burning 

efforts. In reality this may not be a feasible management 

scenario considering that all surrounding conifer woodlands 

would be allowed to mature to late successional stages 

providing an increasing source of conifer seeds and probability for conifer establishment. Application of prescribed 

fire in both aspen and young juniper according to historic 

fire occurrence probabilities would both maintain aspen in 

a younger stage and minimize the source of conifer seeds. 

Prescribed fire applied also in mature juniper woodlands 

was not considered due to the practical difficulty of burning 

such areas. In both prescribed fire scenarios, all conifer 

woodlands that currently exist in mature successional stages 

would therefore continue to mature and remain on the 

landscape. 

3) What is the structural composition within aspen 

woodlands under historical and current fire

probabilities? What is the structural composition 

under prescribed burning scenarios? 

Landscape composition at user selected times is 

reported by TELSA at defined disturbance regimes and 

initial landscape composition. The initial landscape composition is only important to gain understanding about a 

certain study area over a relatively short period. As the 

model is allowed to run for a sufficiently long time period 

the landscape composition at the equilibrium state is 

independent of the initial landscape composition. Under 

historic fire regimes approximately 60 percent of the aspen 

woodlands exist in an early or mid successional stage, while 

this proportion is ~10 percent for current fire regimes and 

~30 percent for the prescribed burning scenarios. Under 
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prescribed burning scenarios ~45 percent of the aspen 

develop into late 406 seral woodlands, of which the majority is the self-regenerating pure aspen stands where 407 

prescribed fire was not applied. The amount of aspen in the 

old successional class and lost aspen woodlands is similar in 

the historic and prescribed burning scenarios (figs. 3 and 4). 

4) What is the effect of fire size on the long-term

maintenance of aspen woodlands? 

Historical fire regimes (scenarios 2 and 3) were 

simulated with two fire size distributions (table 3). Although 

the scenario with larger fires (scenario 3) results in a larger 

area in early and mid seral woodlands, the difference is 

within the error bar generated for multiple runs. Based on 

these results we conclude that there is marginal effect of 

fire size on the structural composition of aspen woodlands 

and the long-term maintenance of aspen woodlands. It is 

important to note that these results in the “model world” do 

not necessarily apply to the “real world”. A closer evaluation of the model assumptions leads us to believe that this 

model is not well suited to answer question 4. One could 

speculate that larger fires would benefit the fire dependent 

aspen woodlands in several ways. Larger fires would reduce 

the conifer seed source and probability of conifer establishment within newly established aspen stands. Modeling of 

this phenomenon would require the spatial model to account 

for seed dispersal to adjacent stands such that aspen stands 

that are closer to conifer woodlands would be more likely to 

experience conifer establishment and eventually dominance. 

Larger fires would also clear larger areas, into which aspen 

could expand. Aspen clones surrounded by closed conifer 

woodlands have no means of extending their area. The ability for aspen to expand into adjacent grass and shrub lands 

was not incorporated in this model. An improved model 

where the distance to seed source and expansion of existing 

aspen stands were included would likely show different 

results with regards to the importance of fire size. 

Model Assumptions and Their Potential Effects on 

Model Outcomes

The full complexity of ecosystem interactions is neither 

feasible nor necessary to capture in a model to improve the 

understanding for how the system functions. The model 

presented here is a form of deductive reasoning where the 

model results are a product of the input data and model 

assumptions. In the following section, we discuss the major 

assumptions and their potential effect on model outcomes. 

1) Aspen reproduction from seed is not included. 

Although aspen in the western mountains reproduce 

primarily via vegetative suckering (Baker 1925, 

Barnes 1975, Mitton and Grant 1996, Romme et al. 

2005), recruitment via sexual reproduction has 

occurred after severe fires such as the 1988 fires in 

Yellowstone National Park (Romme et al. 2005). We 

did not include the occurrence of such infrequent and 

severe fires because the occurrence probability and 

the probability of aspen establishment are unknown. 

Also, such a fire is unlikely to occur within the 

modeled time period due to the stochastic nature of 

these events combined with fire suppression. Such 

large infrequent fire events represent non-equilibrium 

conditions (Turner and Romme 1994) over the spatial 

and temporal extents addressed in this model. Including infrequent severe fires leading to aspen regeneration by seed would require modeling over a much 

longer time period and extent. 

2) Aspen cannot spread into other potential vegetation 

types. Expansion of aspen into adjacent shrub- or 

grasslands has been observed (Manier and Laven 

2001). We calculated that aspen on South Mountain 

could expand as much as 340 ha in 100 years (13 

percent of the current aspen cover) in the absence of 

fire if all aspen along aspen/sagebrush boundaries 

were expanding. This expansion would to some 

extent counteract the small aspen loss predicted 

under historical fire regime scenarios. 

3) Adjacency between vegetation types does not affect 

succession. In our model, the presence of a conifer 

seed source near an aspen stand does not affect the 

rate of succession. Incorporation of such effects 

would result in variability in successional rates 

between stands far away and close to conifers. 

Considering adjacent conifer seed sources would 

increase successional rates in scenarios where only 
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GENERAL TECHNICAL REPORT PNW-GTR-869

aspen stands are burned while conifer stands are left 

to mature and become a neighboring seed source. 

4) Fire will convert a conifer dominated aspen stand to 

a young aspen stand regardless of the pre-disturbance 

conifer cover in the stand, i.e. no legacy effects are 

considered. It can be expected that an aspen stand 

with high cover of seed producing conifers is more 

likely to experience more rapid succession after a fire 

than a stand with only a few conifer seedlings pre-fire. 

Western juniper seeds, for example, are persistent in 

the seed bank (Chambers et al., 1999) and may 

survive a low severity fire and hence become an 

immediate source of juniper seedlings after a fire. 

5) Aspen stands are permanently converted to conifer

stands 120 years after aspen suckering has ceased 

due to conifer dominance, i.e. ~230 years after conifer 

initiation into the stand. Reduced vegetative reproduction in aspen stands that are becoming dominated 

by conifers has been observed by several researchers 

(Bartos and Campbell 1998, Kaye et al. 2005, Strand 

et al. 2009). It is however not known how long an 

aspen clone can remain dormant in a non-reproductive state and still return to an aspen woodland after 

a fire, hereafter referred to as the persistence time. 

The actual time an aspen clone can remain 

under conifer dominance could be significantly different from 120 years. The 120-year time period was 

selected because this can be considered the life expectancy of existing mature aspen ramets in the coniferdominated stand. When all mature ramets are gone 

and the stand is no longer regenerating, permanent 

loss of the stand is assumed to occur resulting in a 

change from an aspen/conifer PVT to a conifer PVT. 

Strand et al. (2009) show that the length of the persistence time only affects the starting point of rapid 

aspen decline (see figs. 3 and 4). The length of the 

persistence time is also extremely important when 

considering the possibility that one avenue for aspen 

rejuvenation is infrequent intense wildfires creating a 

substrate suitable for aspen seedling establishment. In 

a scenario of effective fire suppression where large 

intense fires (ones not possible to suppress) occur at 

an interval longer than the persistence time for all 

aspen clones in the area, local extinction of aspen will 

occur in aspen/confer PVTs.

6) Effects of insects, disease, and animal use on aspen 

and conifers are not included in this model. Fire is 

the only disturbance included in this model, although 

previous work has demonstrated that insects, disease, 

animal browsing, and wind felling are examples of 

other disturbances affecting aspen and conifer 

succession (Jones and DeByle 1985b, Jones et al. 

1985, Kay and Bartos 2000, Kaye et al. 2005). We 

deliberately omitted these disturbance agents in the 

model to gain a clearer understanding of the effects 

of fire disturbance alone on the ecosystem. The 

LANDFIRE rapid assessment program (http://www.

Landfire.gov) has produced a series of reference 

condition (RC) models, which provide an estimate 

of the expected distribution of successional classes 

under pre-European settlement conditions. The 

LANDFIRE RC model for aspen in the northern 

Great Basin incorporates an insect/disease disturbance in aging aspen/conifer stands every 200 years 

which restores aspen to an earlier successional state 

and maintains aspen on the landscape. Regardless 

of whether the infrequent catastrophic event is a large 

severe fire promoting sexual reproduction of aspen, 

an infrequent disease outbreak, or a land- slide, it is 

questionable whether managers of aspen resources 

can rely on such infrequent stochastic events for 

ecosystem maintenance. Kulakowski et al. (2006, p. 

1397) state that “human perceptions of ecosystems 

are often on time scales that are shorter than the 

cycles of natural variation within ecosystems”. With 

the help of field observations, mapping, and modeling we can begin to comprehend aspen ecosystem 

succession and disturbance dynamics at multiple 

spatial and temporal scales. The question is, 

can we manage aspen and other resources at such 

broad temporal scales? 
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Management Implications

Over long time periods (i.e. centennial) aspen will most 

likely remain a part of the western landscape unless the 

climate changes drastically such that it is unfavorable 

for the species. Quaking aspen are apparently tolerant 

to a variety of fire frequencies and severities; vegetative 

reproduction occurs when fires are less severe and more 

frequent. Reproduction via seed can occur after extensive 

severe fire events if the soil moisture and weather conditions are within the ‘window of opportunity’ for aspen 

regeneration (Romme et al. 2005). Therefore, even if aspen 

that is seral to conifers are eliminated from the landscape 

due to fire suppression, eventually a large-scale disturbance 

event will likely occur and pure aspen stands, riparian 

aspen, and aspen occurring on microsites may provide seed 

for aspen recruitment and establishment. This optimistic 

outlook does not offer a solution to the immediate concern 

over the current aspen declines across the West. Human 

activity and needs, and current fire policy makes it unlikely 

that aspen woodlands within the West will return to historic 

fire regimes and active management has been proposed in 

locations where maintenance of aspen is a priority. Before 

engaging in management activities it is naturally important 

to make appropriate ecological field assessments to evaluate the current state of the aspen stands, their successional 

trajectories in a landscape context, and the presence of 

possible stressors. 

In this analysis we show via modeling that the historical fire frequency suggested by Jones and DeByle (1985a) 

maintains aspen on the landscape. In many areas it is not 

feasible or desirable to return to historic fire regimes, and 

prescribed burning may be an alternative. Model predictions suggest that in theory prescribed burning programs 

can mitigate aspen loss and maintain aspen woodlands in 

younger seral stages. Restoration of aspen woodlands has 

been suggested (Bartos et al. 1991, Brown and DeByle 

1989, Miller et al. 2005) and such restoration projects 

(e.g., Bates and Miller 2004, Bates et al. 2004, Brown and 

DeByle 1989) have been carried out by managers. Ecological factors that must be considered prior to burning are 

the fuels composition and structure, current understory 

composition, presence of weeds, and the successional stage 

of aspen woodland development (Miller et al. 2005). Other 

concerns are post-fire wildlife and animal use (Bartos and 

Campbell 1998, Kay and Bartos 2001, Kaye et al. 2005), 

which can jeopardize aspen suckers and prevent the aspen 

clone recovery. Post-treatment monitoring is recommended 

to better understanding the browsing pressure on the treated 

aspen clone. 

Where fire is undesirable for restoration, Shepperd 

(2001) has suggested a series of alternative management 

activities including commercial harvest, mechanical root 

stimulation, removal of competing vegetation, protection 

of regeneration from herbivory and regeneration from seed. 

Cutting of conifers followed by prescribed fire has also been 

applied (Bates and Miller 2004). The felled conifers provide 

a fuel ladder that help carry the fire in aspen stands which 

are commonly difficult to burn. 

Ecosystem management requires assessment of interactions among succession, natural disturbance regimes and 

management activities. Landscape dynamics models such 

as TELSA provide an avenue for managers, scientists, and 

stakeholders to evaluate the long-term effect of changing 

natural disturbance regimes and management activities 

on landscape vegetation composition. All models have 

limitations. It is important to clearly understand the model 

assumptions during interpretation of model results and 

during the decision making process. The ultimate test of a 

model is not how accurate or truthful it is, but only whether 

one is likely to make a better decision with it than without it 

(Starfield 1997). 

The modeling results presented here indicate that active 

management is necessary in areas where aspen are seral 

to conifers and aspen maintenance is a management goal 

unless we rely on infrequent severe disturbance events to 

maintain these aspen resources. Reliance on severe disturbance will likely lead to continued decline of aspen in our 

study region and across the western U.S. 

Acknowledgments

We thank the USDA Bureau of Land Management, Boise 

District in Idaho for partial funding of this project. Field 

assistance was provided by B. Smith and J. Young, College 

of Natural Resources, University of Idaho. We also thank 
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GENERAL TECHNICAL REPORT PNW-GTR-869

Dr. K.L. Launchbaugh, Dr. P.E. Gessler, and anonymous 

reviewers for their thoughtful comments on earlier versions 

of this manuscript. 

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Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, June 14–16, 2011

 English Equivalents

When you know: Multiply by: To find:

Millimeters (mm) 0.039 Inches

Centimeters (cm) .394 Inches

Meters (m) 3.28 Feet

Kilometers (km) .621 Miles

Hectares (ha) 2.47 Acres

Square meters (m2) 10.76 Square feet (ft3)

Square kilometers (km2) .386 Square miles

Cubic meters per second (m3/sec) 35.3 Cubic feet per second (cfs)

Degrees Celsius 1.8 °C + 32 Degrees Fahrenheit
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