Integrating Social, Economic, 

and Ecological Values Across 

Large Landscapes

General Technical Report

PNW-GTR-896

United States Department of Agriculture

Forest 

Service

Pacific Northwest

Research Station

November

2014

Vegetation

State A

Vegetation

State B

Vegetation

State C

Vegetation

State D

Vegetation

State E

Ecological

Values

Social

Values

Economic

Values

DEPAR TMENT OF AGRICULT URE
Editors

Jessica E. Halofsky is a research ecologist, University of Washington, College 

of the Environment, School of Environmental and Forest Sciences, Box 352100, 

Seattle, WA 98195-2100; Megan K. Creutzburg is a research ecologist, 

Department of Environmental Science and Management and Institute for Natural 

Resources, Portland State University, PO Box 751, Portland, OR 97207-0751; and 

Miles A. Hemstrom is a senior scientist, Institute for Natural Resources, Oregon 

State University, PO Box 751, Portland, OR 97207-0751 (formerly a research 

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

Research Station, Forestry Sciences Laboratory, 620 SW Main St., Suite 400, 

Portland, OR 97205).

Cover photographs: Miles Hemstrom.

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Integrating Social, Economic, and Ecological 

Values Across Large Landscapes

Jessica E. Halofsky, Megan K. Creutzburg, and Miles A. Hemstrom

Editors

U.S. Department of Agriculture, Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report PNW-GTR-896

October 2014
Abstract

Halofsky, Jessica E.; Creutzburg, Megan K.; Hemstrom, Miles A., eds. 2014.

Integrating social, economic, and ecological values across large landscapes. 

Gen.Tech. Rep. PNW-GTR-896. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 206 p.

The Integrated Landscape Assessment Project (ILAP) was a multiyear effort to 

produce information, maps, and models to help land managers, policymakers, and 

others conduct mid- to broad-scale (e.g., watersheds to states and larger areas) 

prioritization of land management actions, perform landscape assessments, and 

estimate cumulative effects of management actions for planning and other purposes. The ILAP provided complete cross-ownership geospatial data and maps on 

current vegetation, potential vegetation, land ownership and management allocation classes, and other landscape attributes across Arizona, New Mexico, Oregon, 

and Washington. State-and-transition models, developed to cover all major upland 

vegetation types in the four states, integrated vegetation development, management 

actions, and natural disturbances to allow users to examine the mid- and long-term 

effects of alternative management and disturbance scenarios. New model linkages 

to wildlife habitat, economics, aboveground carbon pools, biomass, and wildfire 

hazard were developed and integrated through decision-support systems. Models 

incorporating potential effects of climate change were also developed for focus 

areas in Oregon and Arizona. This report includes an overview of the structure and 

components of ILAP along with descriptions of methods and example results for 

state-and-transition modeling, fuel characterization, treatment economics, wildlife 

habitat, community economics, and climate change. This report serves as a guide 

to ILAP. Complete collections of the project’s models, maps, data, and tools will 

be archived and available online through the Western Landscapes Explorer portal 

(www.westernlandscapesexplorer.info) so that scientists and managers will be able 

to use and build upon ILAP’s products. 

Keywords: Landscapes, state-and-transition modeling, vegetation mapping, 

fuels, wildfire hazard, treatment costs, biomass, wildlife habitat, decision support, 

climate change, watersheds, community economics, all ownerships, geographic 

information systems, landscape assessment.
Contents

1 Chapter 1: Overview of the Integrated Landscape Assessment Project 

 Miles A. Hemstrom, Jessica E. Halofsky, F. Jack Triepke, R. James Barbour, 

 and Janine Salwasser

1 Introduction

5 Structure of ILAP

5 Partners and Oversight

5 Science Delivery and Knowledge Discovery

11 Access to ILAP Data and Information

11 Acknowledgments

12 Literature Cited

15 Chapter 2: Dynamic Vegetation Modeling of Forest, Woodland, 

	 	 Shrubland, and Grassland Vegetation Communities in the Pacific

 Northwest and Southwest Regions of the United States 

 Theresa K. Burcsu, Joshua S. Halofsky, Simon A. Bisrat, Treg A. 

 Christopher, Megan K. Creutzburg, Emilie B. Henderson, Miles A. 

 Hemstrom, F. Jack Triepke, and Melissa Whitman

15 Chapter Summary

16 Introduction

17 State-and-Transition Modeling Framework

22 State-and-Transition Model Collection and Development

22 Oregon and Washington Forest Models

22 Modeled Vegetation Types

26 Model Structure

28 Model Sources

30 Oregon and Washington Arid Land Models

30 Modeled Vegetation Types

30 Model Structure

30 Model Sources

32 Arizona and New Mexico Forest and Woodland Models

32 Modeled Vegetation Types

32 Model Structure

35 Model Sources

35 Arizona and New Mexico Arid Land Models

35 Modeled Vegetation Types

35 Model Structure

37 Model Sources

37 Model Parameters

37 Wildfire Probabilities

42 Forest Vegetation Simulator Calibration
42 Transition Multipliers

42 Interannual Variation Using Monte Carlo Multipliers

43 Spatial Data

44 Modeling Zones

44 Potential Vegetation

45 Current Vegetation

46 Ownership-Management

46 Watershed

46 Mask

47 Data Processing

49 Model Execution

49 Model Output

49 Output Summarization

49 Output Visualization

51 Example Results

51 Washington Dry Forest Example

54 Arid Lands Example

58 Limitations

58 Spatial Representation Constraints

59 Logistical Constraints

59 Thematic Constraints

61 Scenario Analysis

61 Data Products and Tools

62 Conclusions

63 Acknowledgments

63 Literature Cited

71 Chapter 3: Simulating Fire Hazard Across Landscapes Through Time: Integrating 

	 	 State-and-Transition Models With the Fuel Characteristic Classification System

 Jessica E. Halofsky, Stephanie K. Hart, Miles A. Hemstrom, Joshua S. Halofsky, and 

 Morris C. Johnson

71 Chapter Summary

72 Introduction

74 Methods

76 Plot Selection and Classification Into State-and-Transition Model State Classes

88 Building Fuel Beds

92 Fire Hazard Analysis

92 Summarizing Fuel Bed Information and Linking to State-and-Transition Model Output

93 Results
98 Washington East Cascades Case Study

102 Discussion

102 Management Applications

102 Washington East Cascades Case Study

104 Future Improvements and Research Needs

105 Acknowledgments

105 Literature Cited

111 Chapter 4: Overview of the Vegetation Management Treatment Economic 

 Analysis Module in the Integrated Landscape Assessment Project

 Xiaoping Zhou and Miles A. Hemstrom

111 Chapter Summary

111 Introduction

112 Study Area

112 Methods

112 State-and-Transition Models

115 Volume and Biomass Look-Up Tables

116 Vegetation Management Treatment Economic Analysis

117 Results

117 Standing Biomass and Timber Volume

119 Removals From Vegetation Management Treatments

121 Economic Analysis

125 Discussion

126 Conclusions

126 Acknowledgments

126 Literature Cited

129 Chapter 5: Application of State-and-Transition Models to Evaluate 

 Wildlife Habitat

 Anita T. Morzillo, Pamela Comeleo, Blair Csuti, and Stephanie Lee

129 Chapter Summary

129 Introduction

131 Methods

131 Phase 1: Identify Data Capabilities and Limitations

132 Phase 2: Select Focal Species

134 Phase 3: Build Wildlife Habitat Models

137 Example Results and Discussion

143 Conclusions

143 Acknowledgments

144 Literature Cited
147 Chapter 6: Incorporating Rural Community Characteristics Into Forest 

 Management Decisions

 Mindy S. Crandall, Jane L. Harrison, and Claire A. Montgomery

147 Chapter Summary

147 Introduction

148 Why We Should Care About Rural Communities When Considering Fuel Treatment 

 Location Decisions

150 Methods and Data

152 Criteria for an Origin Location

152 Criteria for a Destination Location

154 Community Characteristics That May Be of Importance to Decisionmakers

160 Estimates of Potential Biomass Supply

163 Weights and Structure of the Gravity Index

164 Resulting Datasets

166 Conclusions

169 Acknowledgments

169 Literature Cited

175 Chapter 7: Developing Climate-Informed State-and-Transition Models

 Miles A. Hemstrom, Jessica E. Halofsky, David R. Conklin, Joshua S. Halofsky, 

 Dominique Bachelet, and Becky K. Kerns

175 Chapter Summary

175 Introduction

177 Study Area

179 Methods

179 MC1 Modeling

182 State-and-Transition Models

183 Developing Climate-Informed State-and-Transition Models

188 Results

188 Historical Period Potential Vegetation and Wildfire—MC1 Simulation Results

190 Future Vegetation Conditions—MC1 Simulation Results

191 Future Vegetation Conditions—State-and-Transition Model Simulation Results

191 Discussion

191 Effects of Model Linkage

193 Limitations

195 Conclusions

195 Acknowledgments

196 Literature Cited

202 English Equivalents

203	 Appendix: Common and Scientific Names
1

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Miles A. Hemstrom, Jessica E. Halofsky, F. Jack Triepke, R. James Barbour, and 

Janine Salwasser1

Introduction

Fire suppression, vegetation management activities, wildfires, grazing, climate 

change, and other factors result in constantly changing vegetation 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 resource-dependent 

human communities is declining, and resources available for land management are 

limited. Techniques are needed to prioritize where natural resource management 

activities are likely to be most effective and result in desirable conditions. Solutions driven by single-resource concerns have proven problematic in most cases, as 

multiple ecological resources and human systems are necessarily intertwined.

To help resource managers prioritize management actions across large landscapes, the Integrated Landscape Assessment Project (ILAP) produced databases, 

reports, maps, analyses, and other information showing mid- to broad-scale (thousands to hundreds of thousands of hectares and larger areas) vegetation conditions 

and potential future trends, key wildlife habitat conditions and trends, wildfire 

hazard, 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. The ILAP 

work involved gathering and consolidating existing information, developing new 

information to fill data holes, and merging vegetation model information with fuel 

classifications, wildlife habitat models, community and economic information, and 

potential climate change effects. Information resulting from ILAP highlighted priority areas for management, considering a combination of landscape characteristics.

1 Miles A. Hemstrom is a senior scientist, Institute for Natural Resources, Oregon State 

University, PO Box 751, Portland, OR 97207-0751 (formerly a research ecologist, U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station, Forestry 

Sciences Laboratory, 620 SW Main St., Suite 400, Portland, OR 97205); Jessica E. Halofsky is a research ecologist, University of Washington, College of the Environment, School 

of Environmental and Forest Sciences, Box 352100, Seattle, WA 98195-2100; F. Jack Triepke is regional ecologist, U.S. Department of Agriculture, Forest Service, Southwestern 

Region, 333 Broadway Blvd SE, Albuquerque, NM 87102; R. James Barbour is a program 

manager, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 620 SW Main St., Suite 400, Portland, OR 97205; and 

Janine Salwasser is a project coordinator, Institute for Natural Resources, Oregon State 

University, 210 Strand Agricultural Hall, Corvallis, OR 97331-2208.

Chapter 1: Overview of the Integrated 

Landscape Assessment Project
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GENERAL TECHNICAL REPORT PNW-GTR-896

The ILAP was designed to allow resource managers, planners, analysts, and 

other potential users to answer many questions about the integrated effects of 

vegetation change, management activities, natural disturbances, and climate change 

on important natural resources across all major upland ecological systems in the 

four-state study area. Questions addressed by ILAP included, but were not limited 

to, the following:

1. What are the conditions and trends of vegetation and natural disturbances 

in forests, woodlands, shrublands, grasslands, deserts, and other ecological 

systems in Arizona, New Mexico, Oregon, and Washington?

2. What are the implications of vegetation change, management activities, 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 or scenarios?

4. How will alternative vegetation management scenarios meet land management objectives and generate economic products that might offset treatment 

costs and benefit local communities?

5. What areas and management regimes might be most likely to produce high 

combined potential to reduce critical fuels, sustain or improve key wildlife 

habitat, and generate positive economic value?

To ensure that relevant and useful information was produced, ILAP worked 

with local collaborative groups in focus areas (fig. 1.1) to forecast the potential 

effects of alternative land management scenarios on important landscape characteristics. Questions addressed in these landscapes were developed in collaboration 

with local users, in particular the Tapash Sustainable Forestry Collaborative in central Washington and the Firescape-Sky Islands group in southern Arizona. Alternative landscape management scenarios were simulated for each area. Examples of 

the questions addressed in focus areas include:

1. Central Washington landscape area—How might the Tapash Sustainable 

Forestry Collaborative partners simultaneously achieve individual landscape objectives while sustaining or improving critical wildlife habitat, 

reducing wildfire hazards, and generating economic benefits for local communities?

2. Sky Islands landscape area—How could fuel treatments be used to move 

vegetation toward desired or reference conditions in the Sky Islands 

landscape and how much would that cost? What effects might climate 

change have on the effectiveness of fuel treatment programs and associated 

wildfire hazards?

ILAP was designed 

to allow resource 

managers, planners, 

analysts, and other 

potential users 

to answer many 

questions about the 

integrated effects of 

vegetation change, 

management activities, 

natural disturbances, 

and climate change 

on important natural 

resources across 

all major upland 

ecological systems in 

the four-state study 

area.
3

Integrating Social, Economic, and Ecological Values Across Large Landscapes

In addition to working with collaborative groups at the local level, ILAP also 

worked to address issues that were important over larger regional landscapes. The 

Interagency Mapping and Assessment Project (IMAP) User Group served as the 

advisory group to ILAP. This group included regional and state land managers and 

planners from Oregon and Washington. They identified the east-side dry forests in 

Oregon and Washington as highest priority for ILAP to address, with these specific 

management questions:

1. What are likely trends in dry forest conditions in eastern Oregon and 

Washington under a fire suppression only and hypothetical resilience/forest 

health scenario?

2. Where are dry forests most at risk for loss of large, old trees?

Figure 1.1—The focus areas and climate change areas within the Integrated Landscape Assessment Project.
4

GENERAL TECHNICAL REPORT PNW-GTR-896

3. What are the land ownership and management allocation circumstances 

(ownership-management hereafter) in these areas?

4. Which landowners might need to collaborate to achieve mutual forest management objectives?

Each of these landscape-level questions was addressed by ILAP through 

vegetation mapping and modeling and integration with newly developed information, as well as subsequent discussions with each of the collaborative groups in each 

focus area. The structure of ILAP is described below and represented in figure 1.2, 

and methods used for ILAP are outlined below and described in detail throughout 

this report.

Figure 1.2—Organization of science delivery and knowledge discovery partners in the Integrated Landscape Assessment Project. 

IMAP = Interagency Mapping Assessment Project, OSU = Oregon State University, WA = Washington, USFS = U.S. Forest Service, 

GIS = geographic information system, VDDT = Vegetation Dynamics Development Tool.
5

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Structure of ILAP

Partners and Oversight

The ILAP was a collaborative effort and incorporated expertise from several 

institutions and disciplines (fig. 1.2). An oversight team, composed of representatives from the major collaborators (the Institute for Natural Resources, Oregon State 

University College of Forestry, the U.S. Department of Agriculture Forest Service 

[USDA FS], Pacific Northwest Research Station, and the USDA FS Southwest 

Region) provided overall direction at monthly meetings. Other clients and partners 

include the USDA FS Pacific Northwest Region, Washington Department of Natural Resources, Oregon Department of Forestry, University of Washington, University of New Mexico, University of Arizona, The Nature Conservancy, and others. 

Two groups of project advisors, one from Oregon and Washington and one from 

Arizona and New Mexico, connected the project goals, objectives, and products to 

state and federal agencies, nonprofit organizations, private contractors, universities, 

and the interested public by providing comment, feedback, and review throughout 

the project. The project lead scientist and project coordinator oversaw the technical 

and outreach aspects of project work. Science delivery, as a whole, was jointly led 

by scientists from the Institute for Natural Resources and the Washington Department of Natural Resources. Each science delivery module (described below) had a 

lead investigator and production team, as necessary. Knowledge discovery modules 

(described below) were led by several universities and nonprofit organizations, 

and each module had a lead scientist, and as appropriate, a production team. User 

involvement was critical throughout the project, particularly in the development of 

management scenarios and review of draft products.

Science Delivery and Knowledge Discovery

The ILAP was organized into two broad themes that roughly followed each other 

sequentially—science delivery and knowledge discovery. Science delivery teams 

generally worked with existing methodologies to develop landscape-level information (primarily related to vegetation conditions), while the knowledge discovery 

teams developed and applied new methodologies to link the science delivery vegetation outputs with associated landscape-level information on wildlife habitat, fuel 

conditions, treatment economics, community impacts, and climate change impacts.

Science delivery modules—

The science delivery portion of ILAP consisted of two main modular 

components: geographic information system (GIS) data and state-and-transition 

models (STMs). These two modules worked together to create the following across 

the four-state area:
6

GENERAL TECHNICAL REPORT PNW-GTR-896

• Maps of potential vegetation that depicted the kinds and spatial extents of 

all major upland ecosystem types in the four-state area (riparian, aquatic, 

agricultural, urban, and barren areas were not modeled for this project).

• Maps of current vegetation characterizing current vegetation cover and 

structure conditions. These maps were summarized into state classes that 

matched STMs.

• Parameterized STMs used for simulation forecasting of future vegetation 

condition.

The potential and current vegetation mapping work, STM development, vegetation classification, and simulation forecasting are described briefly below and in 

detail in chapter 2.

Geographic information system module—The GIS module processed the best 

available spatial data for use by the other ILAP modules. The GIS data sets compiled by ILAP include more than 40 statewide spatial data sets detailing current 

and potential vegetation conditions, watershed boundaries, ownership-management 

categories, and others. The team gathered data from various sources, merged and 

appended it, combined attribute data into consistent formats, and created detailed 

documentation. The GIS module delivered standardized data in raster/grid and vector formats from various hardware and software platforms. A data management 

process and protocol were put in place to facilitate the incorporation of any data updates or improvements and maintain and house original data sets over the long term.

Where existing maps were not available or contained insufficient detail, current 

and potential vegetation were mapped using imputation methods and geo-referenced plot data from various sources. Plot data for mapping came from the USDA 

FS Forest Inventory and Analysis (FIA) program (USDA FS 2012), the LANDFIRE 

plot database (www.landfire.gov; Rollins 2009), U.S. Department of the Interior 

Bureau of Land Management data, and various other sources. Current and potential 

vegetation were mapped using a combination of gradient nearest neighbor imputation (Ohmann and Gregory 2002) and random forest nearest neighbor imputation 

(Crookston and Finley 2008), 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 current vegetation and an assignment 

of potential vegetation across the four-state region.

State-and-transition modeling module—The STM module collected, integrated, 

and as necessary, built new vegetation models for forest, woodland, shrubland, 

grassland, and desert vegetation types across the four-state study area. Using a 

summarization of layers from the GIS data to establish initial (current) conditions, 

The GIS data sets 

compiled by ILAP 

include more than 

40 statewide spatial 

data sets detailing 

current and potential 

vegetation conditions, 

watershed boundaries, 

ownership-management 

categories, and others.

The STM module 

collected, integrated, 

and as necessary, built 

new vegetation models 

for forest, woodland, 

shrubland, grassland, 

and desert vegetation 

types across the fourstate study area.
7

Integrating Social, Economic, and Ecological Values Across Large Landscapes

STMs were used to project future landscape conditions according to alternative 

vegetation management scenarios.

The STM approach represents vegetation types by state classes (boxes), each 

characterizing combinations of cover type (i.e., dominant species or functional 

group composition) and structural stage (i.e., vegetation cover, size class, and 

canopy layers) within a particular biophysical environment. Boxes are linked by 

arrows (transitions) that represent natural disturbances, management actions, or 

vegetation growth and development (fig. 1.3). The ILAP team sometimes augmented existing STMs used by various organizations for land management planning, restoration planning, and ecoregional assessments in Arizona, New Mexico, 

Oregon, Washington, and elsewhere (e.g., Evers 2010, Forbis et al. 2006, Hann et al. 

1997, Hemstrom et al. 2007, Holsinger et al. 2006, Merzenich and Frid 2005, Weisz 

et al. 2009). In some cases, STMs consistent with the project framework did not 

exist and new models were constructed using similar existing models as templates. 

State-and-transition models were developed for units of potential vegetation within 

18 modeling zones (see chapter 2, fig. 2.4), resulting in 275 STMs across the fourstate study area. Transition probabilities in the STMs come from a combination 

of expert opinion, available literature, and empirical data analysis (see chapter 2). 

However, the structure of ILAP STMs allows relatively easy updating of transitions 

and probabilities from empirical data (e.g., annual insect and disease surveys) and 

Figure 1.3—Generalized state-and-transition model (STM) diagram used in the Integrated Landscape Assessment Project. Boxes represent state classes, comprised of cover type and structural 

stage. Arrows represent transitions that simulate processes that can cause an area to move from one 

state class to another. One STM is built for each potential vegetation type.
8

GENERAL TECHNICAL REPORT PNW-GTR-896

ancillary models, such as the Forest Vegetation Simulator (Crookston and Dixon 

2005, Stage 1997) and new inventory data.

Models were stratified and run on combinations of land ownership-management 

classes, potential vegetation, and watershed (fifth-code hydrologic unit [HUC5]; 

USGS and USDA NRCS 2011). Results forecast potential future amounts and distributions of important landscape characteristics, but vegetation data accuracy limit 

appropriate use to landscape scales rather than vegetation stands. The ILAP analyses were often summarized by watershed as an appropriate spatial scale. Alternative land management scenarios can easily be generated by changing assumptions 

about vegetation management treatments and rates by land ownership-management 

allocation. The resulting forecasts of vegetation conditions, management activities, 

and natural disturbances were linked to wildlife habitat characteristics, economic 

values, and other important attributes (see below and chapters 3 through 7).

Knowledge discovery modules—

The ILAP was organized into nine knowledge discovery modules, each generating new information on a particular natural resource issue. Knowledge discovery 

modules are described briefly below and selected modules are described in detail in 

chapters 3 through 7.

Fire and fuel characterization module—The fire and fuel characterization module evaluated current and potential future fuel characteristics and fire hazard for 

forests and woodlands across Arizona, New Mexico, Oregon, and Washington. The 

module team built fuel beds (descriptions of burnable biomass extending from the 

forest floor to the canopy) in the Fuel Characteristic Classification System (Ottmar 

et al. 2007) from inventory plots for each vegetation state class in the STMs. The 

resulting fuel beds (over 14,000) were analyzed for fire potential and linked to STM 

output, allow clients and users to assess current conditions and trends in fuels and 

potential fire behavior over time under different management scenarios. See chapter 

3 for further detail.

Fuel treatment economics module—The fuel treatment economics module estimated potential tree-based biomass (by diameter classes and tree species groups), 

timber volume, and aboveground, tree-based carbon pools by STM state class and 

potential vegetation type for all forests and woodlands in Oregon and Washington. 

The study used STM simulation outputs of the removed timber products from proposed treatments over the simulation period to perform cost-benefit analyses. The 

analyses considered harvesting costs associated with each treatment using the Fuel 

Reduction Cost Simulator (Fight et al. 2006), transportation cost to mill locations, 

The ILAP was 

organized into 

nine knowledge 

discovery modules, 

each generating 

new information on 

a particular natural 

resource issue.
9

Integrating Social, Economic, and Ecological Values Across Large Landscapes

products prices, and other economic factors. It provided data and methods to allow 

managers and others to assess the financial feasibility of proposed forest vegetation 

management treatments. See chapter 4 for further details.

Wildlife habitat module—The wildlife habitat module developed species-habitat 

relationships for 24 species in Oregon and Washington and 13 species in Arizona 

and New Mexico, linking habitat and nonhabitat classifications to STM state classes 

in forest, woodland, shrubland, grassland, and desert models. The team generated 

tools that estimate the amount of current and potential future habitat area for selected species across the four-state area. These species-habitat relationships can be 

used for mid- to broad-scale assessments of management and on potential wildlife 

habitat. See chapter 5 for further detail.

Community economics module—The community economics module addressed the 

question of whether large-scale forest vegetation treatment programs can stimulate economic activity and contribute to well-being in communities that have been 

negatively impacted by recent federal forest policy changes. The team produced 

indicators for each HUC5 watershed (and ownership-management allocation within 

watershed) that describe the potential for fuel treatment in those watersheds to produce benefits to communities for the forested landscapes in Arizona, New Mexico, 

Oregon, and Washington. See chapter 6 for further detail.

Climate change and vegetation module—The climate change and vegetation module used the MC1 dynamic global vegetation model (Bachelet et al. 2001) to inform 

vegetation change and wildfire trends in STMs for two focus areas: central Oregon 

and the Apache-Sitgreaves area in eastern Arizona (fig. 1.1). The result is a set of 

“climate-informed” STMs that can be used to determine likely shifts in vegetation 

structure and species composition and abundance with climate change, and can be 

used by land managers to weigh potential benefits or tradeoffs associated with alternative management approaches under a changing climate. Analyses were conducted for three climate change scenarios that bracket the range of projected climatic 

changes for the study areas. See chapter 7 for further detail.

In addition to the coupled model approach for two focus areas, the climate 

change and vegetation module team ran coarser scale (4-km grid) simulations for 

Arizona, New Mexico, Oregon and Washington. These simulation data cover the 

historical (1895–2009) and future (2010–2100) time periods for the entire four-state 

area, and an Envision software-based tool (Bolte 2007) was developed and constructed as a GIS plug-in that allows users to extract climatic, hydrologic, vegetation, and other data from these MC1 outputs.
10

GENERAL TECHNICAL REPORT PNW-GTR-896

Climate change and watersheds module—The climate change and watersheds 

module developed information on the current condition of all HUC5 watersheds 

that contain national forest lands in Oregon and Washington. The module used 

NetMap (http://www.netmaptools.org/; Benda et al. 2007) to generate estimates of 

erosion hazards, in-channel habitat conditions, and other important watershed characteristics. It also allows 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 under different future climates.

Climate change and fire probabilities module—The objective of the climate 

change and fire probabilities module was to provide refined insight into the potential variation of wildfire probabilities and vegetation dynamics that may occur with 

climate change. Wildfire probabilities and vegetation transitions are key to STM 

parameterization, but we know little about how variations in wildfire probability 

or shifts in vegetation composition may vary with changes in climate. This module 

used spatially explicit landscape simulation models to examine variation in wildfire 

probabilities and vegetation dynamics with climate change in a prototype area of 

the upper Deschutes subbasin in Oregon. The methods and results of these analyses 

will be described in a forthcoming report.

Ecosystem management decision support module—This module used the 

Ecosystem Management Decision Support system (Reynolds 1999, Reynolds and 

Hessburg 2005) to integrate information on current and potential future vegetation, 

fuels, wildlife habitat, and economic conditions into a combined, flexible assessment and prioritization tool. This tool will help managers and others explore and 

set priorities using color-coded maps, tables, and reports based on various combinations of characteristics that best reflect local values. The methods and results of this 

work will be described in a forthcoming report.

Optimized decision support module—The optimized decision support module developed methods that integrate fuels, wildlife habitat, and economic conditions into 

a spatial analytical decision support process. Weighting and prioritization are driven by direct user input or through an evaluative technique, such as the Analytical 

Hierarchy Process (e.g., Kangas 1992, Thomas 1990). Methods and results of this 

work will be described in a forthcoming report.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Access to ILAP Data and Information

The ILAP created a number of methods and outputs that were developed to help 

land managers and planners integrate and prioritize management activities. 

Through the Western Landscapes Explorer (www.westernlandscapesexplorer.info), 

the project’s publications, models, maps, data, and tools will be archived and available online so that scientists and managers will be able to use and build upon the 

project’s products. Land managers, planners, analysts, scientists, policymakers, and 

large-area landowners can use the project’s tools and information for many applications, including:

• Watershed restoration strategies

• Land management planning across all lands

• Statewide assessments and bioregional plans

The ILAP data and information are being used to support USDA FS Forest Plan 

Revisions, Collaborative Forest Restoration Program projects, and can be used to 

inform upcoming statewide forest assessments, statewide wildlife action plans, and 

ecoregional assessments. As this first phase of ILAP concludes, a strong foundation 

of landscape-level data, STMs, tools and expertise has been built for future landscape planning and assessments across a broad range of western landscapes.

Acknowledgments

The ILAP was funded by the American Recovery and Reinvestment Act, and 

the USDA FS Pacific Northwest Research Station, Pacific Northwest Region, 

and Southwestern Region. The USDA FS Washington office and Intermountain 

Research Station provided project advice and review. The U.S. Department of the 

Interior Bureau of Land Management in Arizona, New Mexico, and Oregon/Washington contributed plot data and other valuable information. Several state agencies, 

including the Arizona Department of Forestry, Arizona Department of Fish and 

Wildlife, 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. was contracted to compile plot data, 

wildlife habitat information, and other supporting data in the Southwest.

Through the Western 

Landscapes Explorer, 

the project’s 

publications, models, 

maps, data, and tools 

will be archived and 

available online so 

that scientists and 

managers will be able 

to use and build upon 

the project’s products. 
12

GENERAL TECHNICAL REPORT PNW-GTR-896

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Bachelet, D.; Lenihan, J.M.; Daly, C.; Neilson, R.P.; Ojima, D.S.; Parton, 

W.J. 2001. MC1, a dynamic vegetation model for estimating the distribution of 

vegetation and associated carbon and nutrient fluxes: technical documentation 

version 1.0. Gen. Tech. Rep. PNW-GTR-508. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Station. 95 p.

Benda, L.; Miller, D.; Andras, K.; Bigelow, P.; Reeves, G.; Michael. D. 2007. 

NetMap: a new tool in support of watershed science and resource management. 

Forest Science. 53: 206–219.

Bolte, J P. 2007. ENVISION—software for alternative futuring applications. http://

envision.bioe.orst.edu/. (21 August 2012).

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.

Crookston, N.L.; Finley, A.O. 2008. yaImpute: an R package for k-NN 

imputation. Journal of Statistical Software. 23: 1–16.

Evers, L. 2010. Modeling sage-grouse habitat using a state-and-transition model. 

Corvallis, OR: Oregon State University. 168 p. Ph.D. dissertation.

Fight, R.D.; Hartsough, B.R.; Noordijk, P. 2006. Users guide for FRCS: fuel 

reduction cost simulator software. Gen. Tech. Rep. PNW-GTR-668. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station. 23 p.

Forbis, T.A; Provencher, L.; Frid, L.; Medlyn, G. 2006. Great Basin land 

management planning using ecological modeling. Environmental Management. 

38: 62–83.

Hann, W.J.; Jones, J.L.; Karl, M.G.; Hessburg, P.F.; Keane, R.E.; Long, D.G.; 

Menakis, J.P.; McNicoll, C.H.; Leonard, S.G.; Gravenmier, R.A.; Smith, 

B.G. 1997. Landscape dynamics of the basin. In: Quigley, T.M.; Arbelbide, S.J., 

eds. An assessment of ecosystem components in the Interior Columbia Basin 

and portions of the Klamath and Great Basins. Gen. Tech. Rep. PNW-GTR-405. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station: 337–1055.
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Hemstrom, M.A.; Merzenich, J.; Reger, A.; Wales, B. 2007. Integrated analysis 

of landscape management scenarios using state-and-transition models in the 

upper Grande Ronde River Subbasin, Oregon, USA. Landscape and Urban 

Planning. 80: 198–211.

Holsinger, L.; Keane, R.E.; Steele, B.; Reeves, M.C.; Pratt, S. 2006. Using 

historical simulations of vegetation to assess departure of current vegetation 

conditions across large landscapes. In: Rollins, M.G.; Frame, C.K., eds. The 

LANDFIRE prototype project: nationally consistent and locally relevant 

geospatial data for wildland fire management. Gen. Tech. Rep. RMRS-GTR-175. 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station: 315–366.

Kangas, J. 1992. Multiple-use planning of forest resources by using the analytic 

hierarchy process. Scandinavian Journal of Forest Research. 7: 259–268.

Merzenich, J.; Frid, L. 2005. Projecting landscape conditions in southern Utah 

using VDDT. In: Bevers, M.; Barrett, T.M., eds. Systems analysis in forest 

resources: proceedings of the 2003 symposium. Proceedings RMRS-P-000. 

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

Research Station: 157–163.

Ohmann, J.L.; 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 Forest Research. 32: 725–741.

Ottmar, R.D.; Sandberg, D.V.; Riccardi, C.L.; Prichard, S.J. 2007. An overview 

of the fuel characteristic classification system—quantifying, classifying, and 

creating fuelbeds for resource planning. Canadian Journal of Forest Research. 37: 

2383–2393.

Reynolds, K.M. 1999. EMDS users guide (version 2.0): knowledge-based decision 

support for ecological assessment. Gen. Tech. Rep. PNW-GTR-470. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station. 63 p.

Reynolds, K.M.; Hessburg, P.F. 2005. Decision support for integrated landscape 

evaluation and restoration planning. Forest Ecology and Management. 207: 

263–278.

Rollins, M.G. 2009. LANDFIRE: a nationally consistent vegetation, wildland fire, 

and fuel assessment. International Journal of Wildland Fire. 18: 235–249.
14

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Stage, A.R. 1997. Using FVS to provide structural class attributes to a forest 

succession model (CRBSUM). In: Teck, R.; Moeur, M.; Adams, J., eds. 

Proceedings, forest vegetation simulator conference. Gen. Tech. Rep. INTGTR-373. Ogden, UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Research Station: 139–147.

Thomas, L.S. 1990. How to make a decision: the analytic hierarchy process. 

European Journal of Operational Research. 48: 9–26.

U.S. Department of Agriculture, Forest Service [USDA FS]. 2012. Forest 

inventory and analysis national program. http://fia.fs.fed.us. (5 April 2012).

U.S. Geological Survey; U.S. Department of Agriculture, Natural Resources 

Conservation Service [USGS and USDA NRCS]. 2011. Federal standards and 

procedures for the national watershed boundary dataset. 2nd ed. U.S. Geological 

Survey Techniques and Methods 11–A3. 62 p. http://pubs.usgs.gov/tm/tm11a3/. 

(29 August 2012).

Weisz, R.; Triepke, J.; Truman, R. 2009. Evaluating the ecological sustainability 

of a ponderosa pine ecosystem on the Kaibab Plateau in Northern Arizona. Fire 

Ecology. 5: 100–115.
15

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Theresa K. Burcsu, Joshua S. Halofsky, Simon A. Bisrat, Treg A. Christopher, 

Megan K. Creutzburg, Emilie B. Henderson, Miles A. Hemstrom, F. Jack Triepke , 

and Melissa Whitman1

Chapter Summary

Land management planning at broad scales requires integrative techniques to 

understand and synthesize the effects of different land management activities and 

address socioeconomic and conservation concerns. The Integrated Landscape 

Assessment Project was developed to support the vital but complex task of broadscale integration of information to assess ecological sustainability at multiple scales. 

The project supports ecosystem management planning at a regional scale across all 

lands of Arizona, New Mexico, Oregon, and Washington by simulating landscape 

dynamics using state-and-transition models (STMs) and linking model output to 

management planning considerations such as fuel conditions, wildlife habitat, community economics, and climate change. The stakeholders and target users for the 

Chapter 2: Dynamic Vegetation Modeling 

of Forest, Woodland, Shrubland, and 

Grassland Vegetation Communities in the 

Pacific Northwest and Southwest Regions 

of the United States

1 Theresa K. Burcsu is a research scientist, Institute for Natural Resources, Oregon State 

University, PO Box 751, Portland, OR 97207-0751; Joshua S. Halofsky is a landscape 

ecologist, Washington Department of Natural Resources, PO Box 47000, 1111 Washington 

Street SE, Olympia, WA 98504-7000; Simon A. Bisrat is a senior analyst/postdoctoral 

researcher, Biogeographic Data Branch, California Department of Fish and Game, 1807 

13th Street, Suite 202, Sacramento, CA 95811 (formerly a modeling analyst, Institute for 

Natural Resources, Oregon State University, PO Box 751, Portland, OR 97207-0751); Treg 

A. Christopher is a geographic information system analyst, Washington Department of 

Fish and Wildlife, 600 Capitol Way N, Olympia, WA 98501 (formerly a research ecologist, Institute for Natural Resources, Oregon State University, PO Box 751, Portland, OR 

97207-0751); Megan K. Creutzburg is a research ecologist, Department of Environmental 

Science and Management and Institute for Natural Resources, Portland State University, 

1719 SW 10th Ave, Portland, OR 97201 (formerly a research ecologist, Institute for Natural 

Resources, Oregon State University, PO Box 751, Portland, OR 97207-0751); Emilie B. 

Henderson is a research ecologist, Institute for Natural Resources, Oregon State University, 

PO Box 751, Portland, OR 97207-0751; Miles A. Hemstrom is a senior scientist, Institute 

for Natural Resources, Oregon State University, PO Box 751, Portland, OR 97207-0751 

(formerly a research ecologist, U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station, Forestry Sciences Laboratory, 620 SW Main St., Suite 400, 

Portland, OR 97205); F. Jack Triepke is regional ecologist, U.S. Department of Agriculture, Forest Service, Southwestern Region, 333 Broadway Blvd SE, Albuquerque, NM 

87102; and Melissa Whitman is a Ph.D. student, 348 Manter Hall, School of Biological Sciences, University of Nebraska, Lincoln, NE 68588 (formerly a research assistant, Institute 

for Natural Resources, Oregon State University, PO Box 751, Portland, OR 97207-0751).
16

GENERAL TECHNICAL REPORT PNW-GTR-896

project products include natural resource planners, decisionmakers, and modelers 

who can provide additional analyses in support of planning and policy. This chapter 

reports on the STM component of the project. The STMs were designed for all 

major potential vegetation types in the study area with a focus on watershed-level 

prioritization of land management actions. One baseline scenario, depicting vegetation dynamics with no management activity, was applied across the full study area. 

Other management scenarios were developed for focus areas within the four-state 

project area. The modeling framework was sometimes linked with other modeling 

systems (e.g., Forest Vegetation Simulator) and other data sets for validation or calibration, and incorporated expert opinion where data were lacking. The process was 

flexible and modular to allow alternative data sources for vegetation and other base 

data to be incorporated. Products resulting from this work include STMs of vegetation dynamics, tools for preparing and initializing models, data summarization 

and visualization tools, and model output data sets across the four-state study area. 

Most data, tools, and products will be available online via the Western Landscapes 

Explorer (www.westernlandscapesexplorer.info).

Introduction

Understanding shifts in ecosystem dynamics in the face of continued human 

influence is vital for sustaining ecosystems (Christensen et al. 1996). Twentiethcentury management practices, including timber harvest and fire suppression, 

have altered the ecosystem dynamics of many native communities throughout 

the Western United States. In forested communities, management has resulted in 

changes to forest structure, composition, and ecological function (Franklin et al. 

2002), as well as shifts in natural disturbance regimes such as wildfire and insect 

outbreaks, particularly in drier environments (Ottmar and Sandberg 2001). In the 

Southwestern United States, management has contributed to changes in forest and 

woodland fire regimes, as witnessed by the massive Whitewater-Baldy Fire of 2012, 

as well as community structure, composition, and function (Allen et al. 2002). In 

warm, dry shrub steppe and desert environments across the Western United States, 

invasion by exotic grasses and forbs has promoted frequent, stand-replacing fire 

(Knapp 1996, Whisenant 1990). In many cooler, upland shrub steppe sites, juniper 

expansion beyond its historical range has converted historical grassland and shrub 

steppe communities to woodlands, resulting in loss of sagebrush and other habitat 

(Burkhardt and Tisdale 1976, Miller et al. 2005).

Management plans are being revised for many of these altered landscapes, 

and there is an opportunity to provide increased understanding of coupled humannatural systems through methods that incorporate current models for landscape 

dynamics, agency management treatments, and common conventions for vegetation 
17

Integrating Social, Economic, and Ecological Values Across Large Landscapes

description. Integrating management methods and accepted concepts, socioeconomic and conservation concerns, and other needs into land management and 

planning is challenging but provides a means to weigh the influence of a wide range 

of system drivers, especially those related to vegetation dynamics at regional scales 

(Christensen et al. 1996, Dale et al. 1998, Hann and Bunnell 2001).

The Integrated Landscape Assessment Project (ILAP) was designed to support 

ecosystem management planning at a regional scale with a focus on watershed-level 

prioritization of land management actions across all forests, woodlands, and arid 

lands of Arizona, New Mexico, Oregon, and Washington. The project explored the 

dynamics of broad-scale, multiownership landscapes by integrating information 

about current and future vegetation and fuel conditions, climate change, wildlife 

habitat, fuel treatment economics, and community economics. These factors were 

chosen because they are being managed for or considered as part of the management planning process. The stakeholders and target users for the project products 

included natural resource planners, decisionmakers, and modelers who can provide 

additional analyses in support of planning and policy. At the heart of the project 

were state-and-transition models (STMs) depicting vegetation state classes and 

processes that cause vegetation change over time. The models were built to represent the range of vegetation types from forested to arid lands in all four states, and 

project changes from vegetation community development, natural disturbances, and 

management events. One baseline scenario depicting no management except for fire 

suppression (fire suppression only [FSO]) was modeled for the entire project study 

area, and several other scenarios were customized for geographic focus areas. Products from the work included vegetation models, spatial data sets describing current 

vegetation conditions, and projections of vegetation conditions. A number of tools 

were also developed to facilitate project work and applications beyond the life of the 

project, such as visualization tools and two decision support frameworks for land 

management. In this chapter, we describe the data and methods used to construct, 

parameterize, and run the STMs for ILAP.

State-and-Transition Modeling Framework

In landscape ecology and resource management, STMs have been applied to 

simulate vegetation change in rangelands, woodlands, and forests. The STMs can 

be thought of as box and arrow diagrams (fig. 2.1), where boxes represent state 

classes describing structural and functional attributes, and may include one or more 

successional phases (Bestelmeyer et al. 2009). Arrows represent the drivers causing 

state class change, such as succession, disturbance, and management (Westoby et 

al. 1989). The STMs can be designed to address both broad- and fine-scale research 
18

GENERAL TECHNICAL REPORT PNW-GTR-896

Figure 2.1—Simplified state-and-transition models (STMs), with state classes (boxes) depicting 

combinations of vegetation cover and structure, and transitions (arrows) showing process that 

causes vegetation change. Black arrows indicate growth and succession, and grey arrows represent 

disturbances and management activities. The figure includes (a) a schematic of the basic structure of 

a forest or woodland STM. Columns in a model represent the progression (left to right) from younger 

to older trees (and larger size classes). Rows typically represent the progression (top to bottom) from 

open to closed density classes, based on percentage of canopy cover. (b) An example of an arid land 

STM of sagebrush-steppe vegetation dynamics. Succession proceeds from left to right as shrubs and 

trees become dominant (woodland phases from Miller et al. 2005). Fires and other major disturbances return cells to early successional state classes. Rangeland condition is characterized 

by the composition of the herbaceous layer, ranging from good-condition native bunchgrass (top) 

to a semidegraded mix of native and exotic species (middle) to an exotic-dominated herbaceous 

layer (bottom).

Grass/

forb

Open

Small

Open

Medium

Open

Large

Open

Small

Medium

Medium

Medium

Large

Medium

Multistory

Small

Closed

Medium

Closed

Multistory

Large

Closed

Multistory

Herb

bunchgrass

Herb/shrub

bunchgrass

Woodland

Phase II

bunchgrass

Woodland

Phase III

bunchgrass

Herb

mix

Herb/shrub

mix

Woodland

Phase II

mix

Woodland

Phase III

mix

Herb

exotic

Herb/shrub

exotic

Woodland

Phase II

exotic

Woodland

Phase III

exotic

Depleted

shrub

Increasing density and complexity Increasingly degraded condition

Increasing shrub/tree cover

Increasing diameter class

(a)

(b)
19

Integrating Social, Economic, and Ecological Values Across Large Landscapes

questions. They have been used extensively in rangeland management to represent 

highly dynamic and perturbation-sensitive rangeland ecosystems (Briske et al. 

2005, Petersen et al. 2009, Westoby et al. 1989), examine ecosystem resilience and 

the effects of restoration (Forbis et al. 2006), and project the distribution of state 

classes on the landscape through time as part of an integrative modeling framework 

for planning (Baker 1989, Bestelmeyer et al. 2009, Hemstrom et al. 2007, Vavra et 

al. 2007, Wales et al. 2007). As a decision support tool, STMs allow synthesis of 

assumptions about vegetation growth, natural disturbance regimes, and management regimes in a single modeling environment (Bestelmeyer et al. 2003, Hemstrom et al. 2007). Models developed under this framework are relatively simple to 

parameterize and can integrate expert opinion and information derived from data 

(Provencher et al. 2009, Westoby et al. 1989).

For this project, individual STMs represented vegetation dynamics (alternate 

state classes, successional processes, disturbance, etc.) within broad units of potential vegetation. Potential vegetation provided a useful basis for building ecological 

units because common species assemblages, site productivity, and disturbance 

patterns are represented, and finer classifications (plant association groups and plant 

associations) commonly used by federal land management agencies for planning 

and project implementation (e.g., Hall 1998) could also be included. The resolution 

of potential vegetation is also compatible with the strategic and landscape-scale 

perspectives of contemporary resource managers. More importantly, potential 

vegetation units provide concise descriptions of biophysical conditions and disturbance regimes. Within our project area, potential vegetation maps provided the 

spatial extent in which each STM operated, similar to the use of biophysical setting 

by the LANDFIRE project (Rollins 2009), or the ecological site descriptions by the 

Natural Resources Conservation Service (www.nrcs.usda.gov).

Because spatial modeling would be impractical at the scale of the project area, 

we used nonspatial STMs. However, our modeling process combined spatial layers 

of potential vegetation, watersheds, and land ownership-management allocation 

types (hereafter ownership-management) to form discrete spatial units called modeling strata (see “Spatial Data”; fig. 2.2). The features in each layer worked much 

like a set of interconnected cookie cutters that divided the landscape into modeling 

strata. Each modeling stratum was linked to an STM model run, and STM results 

were summarized for each modeling stratum. This allowed STM output to be 

summarized and mapped onto meaningful landscape units to illustrate changes in 

modeled state classes through space and time and link to other characteristics of 

interest (fig. 2.3; chapters 3 through 7). We also used spatial data from the current 

vegetation layer to initialize STMs with current vegetation conditions in the first 

time step of the simulations (see “Spatial Data”).

This allowed 

STM output to 

be summarized 

and mapped onto 

meaningful landscape 

units to illustrate 

changes in modeled 

state classes through 

space and time and link 

to other characteristics 

of interest.
20

GENERAL TECHNICAL REPORT PNW-GTR-896

Figure 2.2—Modeling strata combined land ownership-management, potential vegetation type (PVT), 

and watershed and provided the basic spatial unit for state-and-transition modeling. Modeling strata 

organized modeling activities so that divisions of the landscape that share model assumptions were 

assigned parameters that were specific to the conditions found in sites with those characteristics (e.g., 

a wildfire rate specific to a PVT and a management rate specific to the ownership-management type).

Figure 2.3—Flow diagram illustrating the data inputs and outputs for the state-and-transition modeling 

process. Base geographic information system (GIS) layers were processed to comprise model inputs, 

which were brought into the Path modeling platform. Path model output was used to summarize vegetation dynamics in graphical and map form, and output was linked to other characteristics of interest by 

other project modules. ILAP = Integrated Landscape Assessment Project.
21

Integrating Social, Economic, and Ecological Values Across Large Landscapes

The individual state classes (boxes) within ILAP STMs represented cover 

types, usually the dominant species or vegetation assemblage, and structural stages, 

based on physical attributes such as vegetation height, percentage cover, and canopy 

layers. For example, in a forest STM (fig. 2.1a), a state class may represent ponderosa pine of the 25- to 38-cm diameter class in multiple structural stages, depending on whether it had open-, mid-, or closed-canopy cover. In a shrub steppe model 

(fig. 2.1b), a state class may represent shrub steppe with native bunchgrasses and 

sagebrush, where grass cover was open and shrub cover was sparse. To link these 

conceptual STM state classes to current landscape conditions, we utilized spatial 

data of current vegetation. The spatial data in the current vegetation map (cover 

type and structure) was allocated to each of the state classes within a STM, forming 

the modeling initial conditions (see “Data Processing”). These initial conditions 

provided the starting point (initial time step) from which a given STM simulated 

vegetation dynamics (fig. 2.2).

The transitions (arrows) in the STMs simulated successional processes such 

as growth and development, natural disturbances such as wildfire and insect 

outbreaks, and management actions such as prescribed fire, seeding of native 

bunchgrasses, and tree harvesting. Transitions were classified as either deterministic or probabilistic. Deterministic transitions occurred at a specific vegetation 

age, whereas probabilistic transitions were defined by an annual transition probability. Transitions could be linked to other transition types using a “time-sincedisturbance” clock, allowing the modeler to simulate interaction effects, such as a 

the delayed onset of an insect outbreak with tree size, or the undesirable effect of 

livestock grazing following a major disturbance. Some transitions moved vegetation 

from one state class to another; for instance, a stand-replacing disturbance may 

move vegetation from a dense forest to a grass/forb state class. Other transitions 

resulted in vegetation remaining in the same state class, such as surface fires, mild 

insect activity, drought, and some types of grazing.

The STMs were developed in the Vegetation Dynamics Development Tool 

(VDDT), version 6.0.25 (ESSA Technologies Ltd. 2007), and simulations were run 

using the Path landscape model (Path), version 3.0.4 (Apex Resource Management 

Solutions 2012; Daniel and Frid 2012). The software simulated vegetation dynamics 

by dividing the landscape into cells and simulating movement among state classes 

according to user-defined transition probabilities at each time step (ESSA Technologies Ltd. 2007, Kurz et al. 1999). VDDT is a nonspatial STM framework that has 

been used to support broad landscape modeling efforts such as the Interior Northwest Landscape Analysis System (Barbour et al. 2007) and the LANDFIRE project 
22

GENERAL TECHNICAL REPORT PNW-GTR-896

(www.landfire.gov; Rollins 2009). More recently, it has been folded into the Path 

landscape model interface (Apex Resource Management Solutions 2012; Daniel and 

Frid 2012), which facilitates running multiple STMs concurrently over large and 

diverse landscapes.

State-and-Transition Model Collection and Development

A collection of STMs representing the major vegetation types already existed for 

much of the study area; using and adding to this collection was part of the original 

vision for producing project results rapidly. The STMs used for this project were 

catalogued and run in three overarching groups: forests, woodlands, and arid lands 

(nonforested types), which were further divided by geographic regions. In the 

U.S. Department of Agriculture Forest Service (USDA FS) Southwestern Region 

3 (Arizona and New Mexico; AZ/NM hereafter), forest and woodland areas were 

generally defined as areas with the potential to support mature trees and a canopy 

cover >10 percent, and dominant species composition was used to separate forests 

from woodlands. In USDA FS Pacific Northwest Region 6 (Oregon and Washington; OR/WA hereafter), forested areas were generally defined using a ≥10 percent 

tree cover cutoff, with the exception of juniper communities and locations identified as burned or cut. In both regions, arid lands included areas that contained <10 

percent tree cover historically and have the potential to support shrub and herb 

vegetation. Model sources included the USDA FS Region 6, USDA FS Region 3, the 

LANDFIRE project, the USDI Bureau of Land Management (USDI BLM), and The 

Nature Conservancy (TNC). Many existing STMs did not require modifications, 

but for those that did, modifications included calibration of transitions and vegetation state classes using the Forest Vegetation Simulator (FVS; see Forest Vegetation 

Simulator Calibration), modifying fire probabilities based on the Monitoring Trends 

in Burn Severity (MTBS) data (see “Wildfire Probabilities”), reconciling multiple 

model versions using expert opinion and literature, and altering transition probabilities and model state classes based on expert review and scientific literature. 

New models were developed for many of the AZ/NM arid lands and for some OR/

WA arid land vegetation types. In all, 275 STMs were adapted or constructed for 

the project.

Oregon and Washington Forest Models

Modeled Vegetation Types

The ILAP effort compiled or constructed 53 STMs that covered all major forested 

vegetation types in OR/WA (table 2.1). The FVS was used for model calibration in 

the Washington Coast Range, Washington West Cascades, and Washington North 

Cascades areas of OR/WA (table 2.1; see Forest Vegetation Simulator Calibration). 

Using and adding to 

the collection of STMs 

for major vegetation 

types in the study area 

was part of the original 

vision for the project 

and its timeline.
23

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 2.1—Descriptions of forested vegetation types modeled in Oregon 

and Washingtona

Potential vegetation type Description

Douglas-fir-dry Dry Douglas-fir with ponderosa pine as a long-lived, 

early-seral species.

Douglas-fir-moist Moist Douglas-fir with ponderosa pine and incense cedar as 

long-lived early-seral species. Pacific madrone, big leaf 

maple, and Oregon white oak are also common.

Douglas-fir-white oak Douglas-fir on sites too dry for western hemlock and white 

fir. Oregon white oak and Pacific madrone are also common.

Douglas-fir-xeric Douglas-fir on sites too dry to support western hemlock or 

white fir but moister than Douglas-fir/white oak.

Grand fir-cool/moist Grand fir and Douglas-fir on mid- to high-elevation sites in 

eastern Oregon and Washington.

Grand fir-valley Grand fir and Douglas-fir on low-elevation, warm, dry sites in 

western Washington.

Grand fir-valley (Oregon) Grand fir and Douglas-fir on low-elevation, warm, dry sites in 

western Oregon. Big leaf maple, Oregon white oak, and 

Pacific madrone may also be common.

Grand fir-warm/dry Grand fir and Douglas-fir on low- to mid-elevation, warm, 

dry sites in eastern Oregon and Washington.

Lodgepole pine-dry Lodgepole pine found on pumice parent material but without 

the presence of ponderosa pine.

Lodgepole pine-wet Lodgepole pine found in cold-air pockets and riparian zones 

with transitions to meadows, willow, and quaking aspen.

Mixed conifer-cold/dry Mixed conifer at mid to high elevations in Oregon’s 

eastern Cascades. 

Mixed conifer-dry Mixed conifer at mid elevations in the eastern Cascades. 

Mixed conifer-dry Mixed conifer at mid elevations in Oregon’s eastern Cascades 

 (pumice soils) and defined by the pumice parent material.

Mixed conifer-moist Mixed conifer at mid to high elevations in the 

eastern Cascades. 

Mountain hemlock-cold/dry Mountain hemlock at high elevations in the eastern Cascades. 

Lodgepole pine is a frequent seral species.

Mountain hemlock-cold/dry Mountain hemlock at high elevations west of the Cascades 

 (coastal/west Cascades) or in the coastal ranges. Lodgepole pine is a frequent 

seral species.

Mountain Mountain hemlock with temperature and moisture 

 hemlock-intermediate intermediate to the cold/dry vegetation type. Alaska cedar 

and Pacific silver fir may also be present.

Mountain hemlock-wet Mountain hemlock at high elevations on wetter locations. 

Lodgepole pine is infrequent.

Oregon white oak Oregon white oak and ponderosa pine in southwestern 

Oregon. Sites are too warm and dry to support Douglas-fir.
24

GENERAL TECHNICAL REPORT PNW-GTR-896

Table 2.1—Descriptions of forested vegetation types modeled in Oregon 

and Washingtona (continued)

Potential vegetation type Description

Oregon white Oregon white oak and ponderosa pine in the eastern 

 oak-ponderosa pine** Cascades. Sites are too warm and dry to support Douglas-fir.

Pacific silver Pacific silver fir and Douglas-fir at mid to high elevations of 

 fir-intermediate** the coastal and Cascade mountain ranges.

Pacific silver fir-warm Pacific silver fir and Douglas-fir at lower elevations of the 

coastal and Cascade mountain ranges.

Pacific silver fir-wet Pacific silver fir and Douglas-fir on very moist sites at mid to 

high elevations in western Washington.

Ponderosa pine-dry** Ponderosa pine and Douglas-fir found at lower elevations 

in Washington’s east Cascades or limited areas of 

southwestern Oregon.

Ponderosa pine-dry, Ponderosa pine and Douglas-fir found at lower elevations in 

 with juniper** Oregon’s east Cascades where western juniper may encroach.

Ponderosa pine-lodgepole pine Ponderosa pine and lodgepole pine found on pumice 

parent material.

Ponderosa pine-xeric Ponderosa pine on sites that are transitional to dry woodland 

species such as curl-leaf mountain mahogany and 

western juniper.

Shasta red fir-dry Mixed conifer at mid to high elevations in the eastern 

Cascades. Shasta red fir is the dominant late-seral species.

Shasta red fir-moist Douglas-fir at moist, high-elevation locations in southwestern 

Oregon. Shasta red fir is the dominant late-seral species.

Sitka spruce Near-coastal, low-elevation forest dominated by 

Douglas-fir and with Sitka spruce and western hemlock as 

late-seral species.

Subalpine fir* Subalpine fir at high elevations in western Washington. A 

variety of species may be codominant in this type.

Subalpine fir-cold/dry Subalpine fir at high elevations in eastern Oregon and 

Washington. A variety of species may be codominant in this 

vegetation type.

Subalpine parkland** Subalpine fir at the highest elevations still capable of 

supporting trees. Tree distribution is patchy and surrounded 

by shrubby or herbaceous communities.

Subalpine woodland Subalpine forests in eastern Oregon, historically dominated 

by white bark pine or limber pine at the highest elevations 

still capable of supporting trees. Tree distribution is patchy 

and surrounded by shrubby or herbaceous communities.

Tan oak-Douglas-fir-dry Douglas-fir and tan oak on moderately dry sites in 

southwestern Oregon.

Tan oak-Douglas-fir-moist Douglas-fir and tan oak on moist sites in 

southwestern Oregon.

Tan oak-moist Tan oak on moist sites in coastal Oregon.
25

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 2.1—Descriptions of forested vegetation types modeled in Oregon 

and Washingtona (continued)

Potential vegetation type Description

Ultramafic Vegetation type defined by serpentine and peridotite soil 

parent materials. Jeffrey pine, Douglas-fir, and incense cedar 

are common species.

Western hemlock-coastal Western hemlock and Douglas-fir found in a relatively narrow 

strip along the coast in southwestern Oregon. 

Western hemlock-cold Western hemlock and Douglas-fir on very moist sites in the 

higher, colder portions of the western hemlock zone.

Western hemlock-hyperdry Western hemlock and Douglas-fir on the very driest sites 

still capable of supporting western hemlock. Often 

transitional to Douglas-fir and grand fir vegetation types.

Western hemlock-intermediate Western hemlock and Douglas-fir at lower to middle 

elevations on well-drained but not dry sites. 

Western hemlock-moist Western hemlock, Douglas-fir, and western redcedar on moist 

and productive sites west of the Cascade crest.

Western hemlock-moist Western hemlock, Douglas-fir, and western redcedar on moist 

 (coastal) and productive sites in the coastal mountain ranges.

Western hemlock-wet Western hemlock, Douglas-fir, and western redcedar on wet 

sites from poor soil drainage or concave topography.

Western redcedar/western Western hemlock, Douglas-fir, and western redcedar in 

 hemlock-moist northeastern Washington.

White fir-cool White fir and Douglas-fir in southwestern Oregon. In this 

area, white fir has replaced Pacific silver fir in mid- to highelevation forests.

White fir-intermediate White fir and Douglas-fir in southwestern Oregon with 

intermediate temperature and moisture.

White fir-moist White fir and Douglas-fir in southwestern Oregon. 

a Model source is indicated as a superscript where: * = new model built for the project, and ** = model built by the 

U.S. Department of Agriculture Forest Service (USDA FS) and Washington State Department of Natural Resources. 

Vegetation types with no superscript represent models that were developed by the USDA FS.
26

GENERAL TECHNICAL REPORT PNW-GTR-896

Modifications to these STMs included calibration of existing growth and succession 

transition rates and the omission or addition of state classes, depending on the STM 

and potential vegetation. In addition, wildfire probabilities under current climate, 

vegetation, and fire suppression conditions were computed from MTBS data for 

most models of potential vegetation on the east side of the Cascade Mountains 

(table 2.1; see “Wildfire Probabilities”).

Model Structure

The ILAP models necessarily abstracted the broad range of structural variety found 

in forests into four density classes and six diameter size classes (table 2.2). Models 

typically included growth and development transitions, natural disturbance transitions, and management transitions (fig. 2.1a, table 2.3). Growth and development 

transitions increased forest age and complexity. Natural disturbance transitions 

differed across modeling zones (fig. 2.4), but most models included disturbances 

related to wind, wildfire, insects, or fungal pathogens. Depending on disturbance 

severity, the effects of a transition ranged from simply delaying deterministic 

growth to returning vegetation to an open, seedling-sapling state class. Management transitions also varied across models and included regeneration harvest, salvage harvest following a natural disturbance, precommercial thinning, commercial 

thinning, thinning from below, and others. Transitions can be customized to better 

fit the management plans of landownership types or other conditions by altering 

rates and the removal or addition of transition types as necessary.

Table 2.2—Structural attributes modeled in forest state-and-transition 

models for Oregon and Washington

Density classes Size classes

Model Diameter

Density (percent density at breast Model size

canopy cover) classes height range classes

 Centimeters

<10 Grass/shrub Nonstocked Grass/shrub

10 to 40 Open <13 Seedling/sapling

40 to 60 >60 Medium 13 to 25 Pole

Closed 25 to 38 Small

38 to 51 Med

51 to 76 Large

 >76 Giant
27

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 2.3—Probabilistic transition types used in Oregon and Washington forest 

state-and-transition models

Transition type Transition description

Natural transitions:

 Insects and disease All insect and disease damage to trees, usually delaying growth or 

transitioning to postdisturbance state classes

 Succession All succession types, representing both the growth of trees and 

changes to tree species composition within a stand

 Wildfire All wildfire transitions

 Alternate succession Alternative successional pathways, such as when snags fall over and 

result in a change in state class

 Insects and Low-severity effects of insects and diseases (background levels) that 

 disease-low-severity delay deterministic growth from one state class to another

 Forest Vegetation Probabilistic growth transitions derived from the FVS to replace 

 Simulator (FVS) growth deterministic growth transitions

 Canopy growth Canopy growth that increases stand density after a set number of 

years without any disturbance or management

 Hemlock looper Damage related to hemlock looper insect outbreaks

 Mistletoe Damage related to dwarf mistletoe

 Mountain pine beetle Damage related to mountain pine beetle insect outbreaks

 Natural regeneration Natural conifer regeneration

 Other bark beetles Damage related to bark beetles other than the western pine beetle, 

such as Jeffrey and mountain pine beetles

 Root disease Damage related to root diseases

 Spruce budworm and Damage related to spruce budworm and tussock moth 

 tussock moth insect outbreaks

 Understory Understory development that moves stands from single to multistory 

 development state classes after a certain number of years following a fire or 

management treatment

 Mixed-severity wildfire Wildfire that kills 25 to 75 percent of the forest canopy

 Nonlethal wildfire Wildfire that kills less than 25 percent of the forest canopy

 Stand-replacing wildfire Wildfire that kills more than 75 percent of the overstory

 Windthrow Damage related to severe windthrow

 Wooly adelgid Wooly adelgid insect outbreaks causing subalpine fir mortality

 Western pine beetle Damage related to western pine beetle insect outbreaks

Management transitions:

 Timber harvest All harvest types including partial, salvage, selection, 

and regeneration

 Partial harvest All partial harvest types including selection
28

GENERAL TECHNICAL REPORT PNW-GTR-896

Model Sources

Most OR/WA forest STMs were adapted from existing models, built through the 

Interagency Mapping and Assessment Project (IMAP; http://ecoshare.info/projects/

interagency-mapping-and-assessment-project/), with state classes and transitions 

informed primarily through local, expert opinion. Forest Service ecologists worked 

with silviculturists and other specialists to develop and parameterize most of the 

models (table 2.1). Preexisting models did not exist for several modeling zones 

(Washington Coast Range, Washington West Cascades, Washington North Cascades, and Washington Northeast) and, in those cases, new models were derived 

from existing models in neighboring regions. Model adaptation involved maintaining general model state classes but altering disturbance regimes and vegetation 

growth and development. Changes to growth and development transition probabilities were informed by simulations in FVS (see “Forest Vegetation Simulator 

Calibration”), and in some cases, vegetation state classes were omitted or added. 

Disturbance regimes were derived from both expert opinion and existing literature.

Table 2.3—Probabilistic transition types used in Oregon and Washington forest 

state-and-transition models (continued)

Transition type Transition description

 Grazing and The effects of grazing on seedling/sapling development, generally 

 miscellaneous accelerating the transition from a postdisturbance state class to 

seedling/sapling and pole-sized state classes in dry forests

 Precommercial Precommercial thinning in which vegetation and trees are removed 

 thinning to increase available resources for the most vital and valuable trees

 Prescribed fire Prescribed fire transitions of all severity levels

 Partial harvest-medium Partial harvest (commercial thinning from below) in medium and 

 and closed stand closed stands

 Partial harvest- Partial harvest (light maintenance thinning) to maintain an 

 open stand open canopy

 Partial harvest- Partial harvest in pole-sized stands, representing fuel treatments 

 pole stand that do not generally produce a commercial product and are applied 

after the normal age of a precommercial thin

 Replant-postdisturbance Replanting with seedlings after harvesting or disturbance

 Regeneration harvest Regeneration harvest, including clearcut or shelterwood

 Salvage harvest Salvage harvest following stand-replacing disturbance, often 

followed by replanting

 Selection harvest Selection harvest that represents a periodic individual tree selection 

of mature stands
29

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 2.4—State-and-transition modeling zones in (a) Oregon and Washington (OR/WA), and 

(b) Arizona and New Mexico (AZ/NM). ILAP = Integrated Landscape Assessment Project.
30

GENERAL TECHNICAL REPORT PNW-GTR-896

Oregon and Washington (OR/WA) Arid Land Models

Modeled Vegetation Types

We adapted or constructed 19 STMs characterizing contemporary vegetation 

dynamics for all major nonforested systems (called arid lands) in OR/WA (table 

2.4). The model set primarily covered rangeland vegetation types, but also included 

other nonforested systems such as subalpine meadows and montane/canyon shrublands. Detailed documentation of OR/WA arid land models is available on the 

Western Landscapes Explorer website (www.westernlandscapesexplorer.info)

Model Structure

Arid land state classes for OR/WA STMs were defined by combinations of cover 

types and structural stages (fig. 2.1b). Arid land cover types generally specified 

the dominant shrub and the dominant grass functional group(s). Alternative state 

classes within STMs were distinguished primarily by dominant life form (grass/

shrub/tree) and composition of the herbaceous layer, which often indicated the level 

of degradation compared to historical plant communities. Grass functional groups 

included native grass (i.e., bunchgrass), robust grass (grass species that are fairly 

tolerant of grazing pressure, and therefore often indicate semidegraded conditions), 

and exotic grass, including cheatgrass and medusahead. Structural stages indicated 

the cover of the dominant life form, and included herbaceous stages, open, mid and 

closed shrub steppe, woodland phases I, II, and III (Miller et al. 2005) that characterized the level of site dominance by western juniper, and others (table 2.5).

Growth and succession were represented as deterministic transitions, and other 

processes such as wildfire, insects, and management activities were represented 

as probabilistic transitions (table 2.6). Wildfire probabilities were derived from the 

MTBS data for potential vegetation groups and three levels of exotic grass invasion 

(native, semidegraded, and degraded), reflecting changes in fire return interval with 

the introduction of exotic species (see “Wildfire Probabilities” and www.westernlandscapesexplorer.info). Management transitions were built into the models but 

were deactivated for the baseline ILAP FSO scenario.

Model Sources

The ILAP arid lands STMs were obtained from a variety of sources and were 

adapted for use across all OR/WA arid lands (table 2.4). Final models often incorporated information from multiple sources, including expert opinion, literature, and 

data. The STMs developed by USDA FS ecologists for the Blue Mountains of northeastern Oregon were the primary source for many ILAP arid land models, because 

the model set covered most of the major potential vegetation in eastern Washington 

and Oregon, and because the models were thoroughly documented by the original 
31

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 2.4—Descriptions of arid land vegetation types modeled in Oregon and 

Washingtona

Potential vegetation type Vegetation description 

Bitterbrush-no juniper Upland sites dominated by bitterbrush shrub steppe, often 

with some sagebrush

Bitterbrush-sand Dry, sandy deposits of the Columbia River basin with 

bitterbrush, other shrubs, and needle-and-thread

Bitterbrush-with juniper Upland sites dominated by bitterbrush shrub steppe, 

often with some sagebrush, and with potential for 

juniper encroachment

Bluebunch wheatgrass- Low-elevation grasslands dominated by bluebunch 

 Sandberg bluegrass wheatgrass and Sandberg bluegrass

Idaho fescue-prairie Mid-elevation grasslands growing on productive plateau, 

 junegrass canyon, and ridge sites, dominated by Idaho fescue

Low sagebrush-mesic, Upland sites with soils that are shallow or have a restrictive 

 no juniper* layer supporting low sagebrush steppe

Low sagebrush-mesic, Upland sites with soils that are shallow or have a restrictive 

 with juniper* layer supporting low sagebrush steppe and with potential for 

western juniper encroachment

Montane and canyon Low- to mid-elevation montane shrub community with a 

 shrubland diversity of species, including mountain snowberry, mallow 

ninebark, and many others

Mountain big sagebrush- Cool, moist sites with mountain big sagebrush that are 

 no juniper* outside of the range of juniper (most of Washington)

Mountain big sagebrush- Cool, moist sites with mountain big sagebrush steppe and 

 with juniper* high potential for juniper encroachment

Mountain mahogany Rocky sites dominated by curl-leaf mountain mahogany

Rigid sagebrush and low Shallow soil sites supporting rigid sagebrush, low sagebrush 

 sagebrush-xeric and/or buckwheat

Salt desert shrub-lowland** Lowland salt desert shrub flats, dominated by greasewood 

and salt grass and intermittently flooded, usually on 

Pleistocene lake beds

Salt desert shrub-upland** Salt desert shrub sites above playas, dominated by saltbush 

and sparse grasses

Subalpine meadows- Montane to subalpine grasslands occurring on mountain 

 green fescue slopes and ridges, dominated by green fescue

Threetip sage Highly productive three-tip sagebrush steppe sites

Western juniper woodland Sites historically and currently considered western 

juniper woodlands

Wyoming big sagebrush- Warm, dry low- to mid-elevation sites supporting Wyoming 

 no juniper* big sagebrush steppe

Wyoming big sagebrush- Warm, dry mid-elevation sites supporting Wyoming big 

 with juniper* sagebrush steppe with enough moisture for potential 

juniper encroachment

a Model source is indicated with a superscript where: * = U.S. Department of the Interior, Bureau of Land 

Management, and ** = The Nature Conservancy of Nevada. Vegetation types without a superscript were developed by 

the U.S. Department of Agriculture, Forest Service.
32

GENERAL TECHNICAL REPORT PNW-GTR-896

model creators.2 Models developed by Evers (2010) for the Malheur High Plateau 

region of southeastern Oregon were our primary source for sagebrush steppe STMs. 

Lastly, we adapted models from TNC of Nevada (Provencher and Anderson 2011) 

for salt desert shrub communities and from TNC of Idaho (Landscape Toolbox 

Project; www.landscapetoolbox.org) for three-tip sagebrush communities.

Arizona and New Mexico Forest and Woodland Models

Modeled Vegetation Types

We included five forested and seven woodland potential vegetation types in AZ/NM 

(table 2.7). The AZ/NM forest and woodland models were developed to examine 

ecosystem sustainability (Weisz et al. 2009) and for forest planning.

Model Structure

The AZ/NM forested and woodland STMs were similar in structure to the OR/WA 

models (fig. 2.1a), but contained fewer, more generalized state classes. Structural 

stages characterized canopy cover and tree size class, and sometimes included 

dominance and the number of canopy layers. Canopy cover classes included grass/

forb/shrub (0 to 10 percent tree cover), open (10 to 30 percent tree cover), and closed 

2 Swanson, D. unpublished documentation. 

Table 2.5—Major structural stages used in Oregon and Washington arid land 

state-and-transition models

Name Description

Herbland Grassland with few shrubs and trees (shrub cover less than 5 

percent, tree cover less than 2 percent)

Open shrub Open shrub steppe (shrub cover greater than 5 to 15 percent, 

tree cover less than 2 percent)

Mid shrub Mid shrub steppe (shrub cover greater than 15 to 25 percent, 

tree cover less than 2 percent)

Closed shrub Closed shrub steppe (shrub cover greater than 25 percent, tree 

cover less than 2 percent)

Depleted shrub Depleted shrubland with high shrub cover and overgrazed 

herbaceous layer (shrub cover greater than 25 percent, herb 

cover less than 5 percent, tree cover less than 2 percent)

Woodland-phase I Phase I juniper invasion, consisting of shrub steppe with 

shrubs or grasses dominant and scattered juniper (juniper 

cover greater than 2 to 10 percent)

Woodland-phase II Phase II juniper invasion, with shrubs/grasses codominant with 

juniper (juniper cover greater than 10 to 20 percent)

Woodland-phase III Phase III juniper invasion—juniper woodland with 

herbaceous understory and few shrubs (juniper cover 

greater than 20 percent) 

Source: Shrub cover cutoffs are from Karl and Sadowski (2005), and woodland phases are from Miller et al. (2005).
33

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 2.6—Probabilistic transitions used in Oregon and Washington arid land stateand-transition models

Transition type Transition description

Natural transitions:

 Alternative succession Recovery of herbaceous species (from exotics to natives) 

following rest from grazing

 Browse Browsing of palatable shrubs (e.g., bitterbrush and 

mountain mahogany)

 Drought A multiyear drought that, when combined with grazing, may 

result in degradation of rangeland condition

 Insects and disease General transition for insects or disease that can kill shrubs 

or trees

 Insect-cyclical outbreak Cyclical transition that simulates outbreaks of sagebrushdefoliating insects, occurring at an interval of 20 to 48 years

 Natural recovery Natural reseeding of native species. Species differ 

depending on the model

 Severe drought Severe drought that can kill sagebrush, occurring only once 

every 100 to 200 years

 Wildfire-mixed-severity Mosaic fire that results in a heterogeneous burn pattern

 Wildfire-nonlethal Surface fire (only modeled in phase III juniper woodlands 

with exotic grass)

 Wildfire-stand-replacing Stand-replacing wildfire that leads to early successional 

(herbaceous) state classes

Livestock grazing transitions:

 Graze-degrade Grazing-related degradation that is unrelated to disturbance 

 Postdisturbance graze-degrade Heavy grazing following major disturbance (within 2 years 

of fire or drought) that can lead to grazing-related 

degradation. The transition probability is 10-fold higher 

than graze-degrade

 Maintenance graze Maintenance grazing that does not affect vegetation 

composition or structure

 Moderate graze Moderate grazing that decreases herbaceous cover and 

alters succession but does not cause degradation. This 

can either accelerate or delay succession, depending on 

the model

 Juniper cutting Mechanical treatment (i.e., chain saw removal) of juniper

 Shrub treatment Mechanical treatment of depleted sagebrush, leading an 

open sagebrush stand

 Prescribed fire Prescribed fire treatment

 Seed-exotic Seeding of a degraded site with nonnative grass 

(i.e., crested wheatgrass)

 Seed-native Seeding of a degraded site with native species

 Agriculture Conversion of native vegetation to agricultural land
34

GENERAL TECHNICAL REPORT PNW-GTR-896

Table 2.7—Descriptions of forest and woodland vegetation types modeled in Arizona 

and New Mexicoa

Potential vegetation type Description

Forest models:

 Ponderosa pine forest* Warm, dry forests with ponderosa pine, border pinyon, juniper 

species, and evergreen oak species

 Ponderosa pine- Ponderosa pine, Apache pine, Chihuahuan pine, and evergreen

 evergreen oak* oak forests

 Mixed conifer Mixed-conifer forests occurring in small patches at a broad 

 with aspen* elevation range, with Douglas-fir, Engelmann spruce, white fir,

and big tooth maple as the dominant species, and quaking

aspen as an important seral component

 Mixed conifer dry** Dry mixed-conifer forest containing Douglas-fir and white fir

associations and with Arizona fescue in the understory

 Spruce-fir forest* Engelmann spruce, subalpine fir, and corkbark fir forests, 

containing quaking aspen as an important seral species and 

twinflower as an important understory component

Woodland models:

 Pinyon-juniper Pinyon-juniper woodlands occurring in either mild or cold 

 woodland** climates, with Arizona orange, rubber rabbitbrush, Apache 

plume, and Toumey oak as the dominant shrub species

 Pinyon-juniper Pinyon-juniper woodlands occurring as mix of trees and shrubs

 sagebrush** in cold climates, typically above the Mogollon Rim in northern 

Arizona and New Mexico, with sagebrush or blackbrush in the 

shrub layer 

 Pinyon-juniper Pinyon-juniper woodlands occurring below the Mogollon Rim

 evergreen shrubland** in southern Arizona and New Mexico with crucifixion thorn,

Sonoran scrub oak, pointleaf manzanita, alderleaf mountain

mahogany, and gray oak as the dominant evergreen species

 Pinyon-juniper Pinyon-juniper woodlands occurring in either cold or mild 

 grassland** climates, with understory typically dominated by grass species,

and less abundant shrubs and trees occurring as individuals or 

occasionally in small groups

 Juniper grassland** Woodlands comprised of one or several juniper species (e.g., 

oneseed, alligator, and Pinchot’s juniper), with an understory 

dominated by grass species

 Madrean pine- Woodland bounded by semidesert grasslands at the lowest 

 oak woodland* elevations and montane forests at higher elevations. Emory oak,

Arizona white oak, Mexican blue oak, and gray oak are the 

common oak species, and Douglas-fir, ponderosa pine, and 

Chihuahuan pine are the common conifer species

 Madrean encinal Oak woodlands occurring in isolated mountain ranges grading

 woodland** into semidesert grasslands at lower elevations and pine-oak

woodlands at higher elevations, with Emory oak, Mexican blue

oak, Arizona white oak, and gray oak as the common species

a Models described below were adapted and calibrated by the U.S. Department of Agriculture Forest Service 

Southwest Region. Original model source is indicated by a superscript where: * = The Nature Conservancy, and ** 

= LANDFIRE.
35

Integrating Social, Economic, and Ecological Values Across Large Landscapes

(>30 percent tree cover). Tree size classes included seedling/sapling (<13 cm diameter at breast height [DBH]), small (13 to 25 cm DBH), medium (25 to 50 cm DBH), 

large or very large (>50 cm DBH). Where canopy layers were included, the number 

of canopy layers was either single storied or multistoried. Transitions represented 

growth and development, natural disturbance, and management transitions (table 

2.8). The AZ/NM forest and woodland models did not contain any deterministic 

transitions; all succession transitions were calibrated using FVS runs based on 

Forest Inventory and Analysis (FIA) plot data (Weisz et al. 2010) and treated as 

probabilistic transitions. Wildfire probabilities for some models came from field 

plot data across the national forests in the region and expert opinion, and for others 

were derived using the Fire and Fuels Extension (FFE) of FVS (Reinhardt and 

Crookston 2003). Wildfire transition multipliers describing interannual variation 

in fire weather were derived from an analysis of the MTBS data (see “Interannual 

Variation Using Monte Carlo Multipliers”).

Model Sources

Individual national forests in AZ/NM developed their own versions of forest and 

woodland models based largely on LANDFIRE and TNC efforts to describe potential natural vegetation and vegetation dynamics in AZ/NM (table 2.9). The models 

used for ILAP were adapted from those model variants developed at each national 

forest in AZ/NM.

Arizona and New Mexico Arid Land Models

Modeled Vegetation Types

The Integrated Landscape Assessment Project compiled and developed models to 

simulate contemporary vegetation dynamics for all arid land (nonforested) potential 

vegetation in AZ/NM (table 2.9). The AZ/NM arid lands encompassed a broad 

range of temperate life zones, from very dry lowland desert scrub to upland chaparral and montane meadow systems. Detailed documentation of AZ/NM arid land 

models is available on the Western Landscapes Explorer website (www.westernlandscapesexplorer.info).

Model Structure

Model structure varied considerably, depending on model source (table 2.9). In 

general, cover types indicated the dominant life form(s), such as perennial grass, 

forb, sagebrush, or juniper. Structural stages defined the cover range in each of the 

three major vertical structure layers: grass/forb, low to mid shrub, and tall shrub/

tree. Cover ranges of each vertical layer were specified as absent (zero percent), 

sparse (1 to 10 percent), open (>10 to 25 percent), mid (>25 to 60 percent), and 
36

GENERAL TECHNICAL REPORT PNW-GTR-896

Table 2.8—Probabilistic transition types used in Arizona and New Mexico forest and 

woodland state-and-transition models

Transition type Transition description

Natural transitions:

 Growth and succession Forest succession and growth transition where models are calibrated 

by the Forest Vegetation Simulator

 Nonlethal fire Wildfire resulting in less than 25 percent top kill

 Mixed-severity fire Wildfire resulting in 25 to 75 percent top kill

 Stand-replacing fire Wildfire resulting in 75 to 100 percent top kill

 Wildfire—low Wildfire under low burning conditions, as modeled by the Fire and 

 conditions Fuels Extension of the Forest Vegetation Simulator (FFE-FVS)

 Wildfire—moderate Wildfire under moderate burning conditions, as modeled by the 

 conditions FFE-FVS

 Wildfire—high Wildfire under high burning conditions, as modeled by the FFE-FVS

 conditions

 Insects and disease Transition caused by forest insect and disease agents

 Wind/weather/stress Transition caused by wind, weather, and stress

 Alternative succession Transition resulting from a lack of disturbance

 Elk herbivory Elk browsing, according to summer range mapping

 Natural regeneration Natural regeneration and recruitment by species

Management transitions:

 All initiate regeneration All management activities that initiate tree regeneration

 All prescribed burning All management activities related to prescribed burning, typically

low severity

 All fuel treatments All fuel treatment management activities

 Intermediate thin— Precommercial thinning of intermediate intensity to a low basal area 

 low basal area that takes place as part of the maintenance of a stand in preparation 

for a commercial harvest

 Intermediate thin— Precommercial thinning of intermediate intensity to a moderate 

 moderate basal area basal area that takes place as part of the maintenance of a stand in 

preparation for a commercial harvest

 Intermediate thin— Precommercial thinning of intermediate intensity of trees less than 

 41 cm limit 41 cm (diameter cap) that takes place as part of the maintenance of a 

stand in preparation for a commercial harvest

 Shelterwood Harvest in which some large trees are retained to provide some shade 

and other protection to remaining trees. Trees may be removed in 

several harvests over a 10- to 15-year period to encourage 

regeneration of medium to low shade-tolerant species

 Free thin to target Free thin of all sizes to a target basal area to reduce risk and allow 

 basal area growth in the residual, by favoring the removal of suppressed, defect, 

and excess trees beyond the target basal area of each size class

 Thin from below to Thin from below to a target basal area. Trees of sizes below the target 

 target basal area size class are thinned from the stand. Typically results in a state class 

change to a more open larger size class
37

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 2.8—Probabilistic transition types used in Arizona and New Mexico forest and 

woodland state-and-transition models (continued)

Transition type Transition description

 Thin under 41 cm to Thinning trees under 41 cm in diameter to achieve a target basal 

 target basal area area. Typically results in a state class change to a more open larger 

size class, where repeat entries result in a closed, single-storied stand

 Group selection/ Group selection with a matrix thin in which groups of trees are 

 matrix thin removed from an area over a period of several entries and trees in the 

surrounding matrix are thinned to a target basal area

 Shelterwood seed cut Shelterwood harvest to achieve a target basal area in which retained 

 to target basal area trees also provide seed for regeneration

 Clearcut-legacy trees Clearcut in which some trees are retained (also called clearcut 

with reserves)

 Clearcut-coppice Clearcut to produce even-aged stand of species that regenerate by 

sprouting from the roots or stem base (coppice)

 Plant seedlings Replant with seedlings following harvest or disturbance

 Prescribed fire Application of controlled fire

 Thin under 23 cm to Thinning trees under 23 cm in diameter to achieve a target basal area

 target basal area

closed (>60 percent). Processes modeled in AZ/NM arid land models included a 

range of natural processes, livestock grazing practices, and management activities 

(table 2.10).

Model Sources

Southwestern arid land models came from a variety of sources (table 2.9). Eleven 

new models for the major vegetation types in the Chihuahuan Desert and Shortgrass Prairie modeling zones (fig. 2.4b) were constructed by Natural Heritage New 

Mexico. Models for many Colorado Plateau, Sonora-Mojave, and interior chaparral 

types were adapted from The Nature Conservancy in Nevada and Arizona. LANDFIRE reference condition models (www.landfire.gov; Rollins 2009) were the basis 

for many STMs in the Sonoran desert and other regions. In most of these cases, 

substantial additions were made to the models to include contemporary state classes 

and transitions not represented in the reference condition models, such as tree and 

exotic grass invasion (see documentation for details).

Model Parameters

Wildfire Probabilities

The STM framework provided a means to customize transitions to region-specific 

disturbance probabilities. Wildfire is an important natural disturbance in nearly all 

ILAP models and differs regionally. We developed methods to use wildfire records 

Wildfire is an important 

natural disturbance in 

nearly all ILAP models 

and differs regionally. 

We developed methods 

to use wildfire records 

from the Monitoring 

Trends in Burn Severity 

program (MTBS).
38

GENERAL TECHNICAL REPORT PNW-GTR-896

Table 2.9—Descriptions of arid land vegetation types modeled in Arizona and 

New Mexico 

Potential Vegetation

vegetation type subclasses Description

Intermountain Warm, dry desert scrub environments 

 salt scrub* dominated by saltbush, greasewood, and other 

shrub species, often occurring in alkaline or 

calcareous soils above playas

Colorado Plateau/ No juniper Xeric Colorado Plateau grasslands characterized by 

 Great Basin drought-tolerant bunchgrasses such as blue grama 

 grassland* and scattered shrubs

Juniper potential Xeric Colorado Plateau grasslands characterized by 

drought-tolerant bunchgrasses and scattered 

shrubs, occurring adjacent to juniper woodlands

Sagebrush shrubland* Dry, open sagebrush shrublands with scattered 

semiarid grasses such as blue grama and Indian 

ricegrass, sometimes with rabbitbrush

Sand sheet shrubland** Sand sheets of the Colorado Plateau characterized 

by joint fir species at low cover, with sparse grasses

Interior chaparral*** Mid-elevation chaparral with a diverse community 

of shrubs, including oak, manzanita, ceanothus 

species, and others

Mountain mahogany Mid-elevation rocky outcrops dominated by 

 mixed shrubland** curlleaf mountain mahogany, sometimes with 

sagebrush, oak, manzanita, and ceanothus species

Gambel oak Mid- to upper-elevation shrublands with Gambel 

 shrubland** oak as the dominant overstory species and 

serviceberry or sagebrush forming the understory

Montane/subalpine Montane and subalpine grasslands characterized by 

 grassland ** Arizona fescue, mountain muhly, Kentucky 

bluegrass, and other grasses

Sonora-Mojave Sand Sonoran dunes and sand flats dominated by white 

 creosote-bursage bursage, big galleta, and creosote bush

 desert scrub*

Valleys Creosote bush—white bursage desert scrub found 

in valleys of the Sonoran desert

Mountains Creosote bush and white bursage desert scrub 

found on low-elevation mountain slopes in the 

Sonoran desert

Sonoran paloverde- Mountains Mountain slopes with paloverde, desert ironwood, 

 mixed cactus saguaro and other cacti

 desert scrub* **

Bajada/foothills Alluvial fans and pediments with paloverde, desert 

ironwood, saguaro and other cacti

Sonoran mid- Mixed desert scrub consisting of varying 

 elevation desert assemblages across its range, occurring at 

 scrub** elevations between paloverde-mixed cactus 

and chaparral vegetation types above the 

Sonoran desert
39

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 2.9—Descriptions of arid land vegetation types modeled in Arizona and 

New Mexico (continued)

Potential Vegetation

vegetation type subclasses Description

Sonoran mixed salt Sonoran desert valleys dominated by saltbush, 

 Desert scrub* with sagebrush, creosote bush, or white bursage 

as codominants

Mojave mid-elevation Mixed desert scrub dominated by blackbrush but 

 desert scrub* including other codominants, occurring between 

the creosote-bursage desert scrub and montane 

woodlands above the Mojave desert

Chihuahuan Chihuahuan desert scrub occupying a variety of 

 desert scrub land forms, dominated by creosote bush, tarbush, 

and acacia

Chihuahuan salt Chihuahuan desert scrub found in basin bottoms 

 desert scrub where salts and other minerals have accumulated in 

the soil, characterized by saltbush and other species

Semidesert Piedmont Semidesert grasslands on coalesced alluvial fan 

 grassland piedmonts, characterized by black grama, bush 

muhly, and fluffgrass

Foothill Semidesert grasslands on colluvial foothill slopes, 

dominated by sideoats grama, curlyleaf muhly, 

New Mexico feathergrass, and bullgrass

Lowland Semidesert grasslands found in lowland basins and 

playas, with tobosagrass, burrograss, alkali 

sacaton, big sacaton, or vine mesquite

Sandy plains Semidesert grasslands typical of sand sheets, with 

black grama, dropseed, and muhly species

Great Plains Mixed-grass Southern Great Plains grasslands dominated by 

grassland prairie warm-season grasses, including sideoats grama, 

blue grama, switchgrass, and bluestem

Shortgrass prairie No juniper Western and southern Great Plains grasslands 

characterized by blue grama, buffalograss, New 

Mexico feathergrass, James’ galleta, and other 

grama species

Juniper potential Western and southern Great Plains grasslands 

found on the edges of the prairie adjacent to 

juniper woodlands

Sandsage Scrubland vegetation typical of sandy, winddeposited soils and characterized by 

sand sagebrush

Shinnery oak Scrubland vegetation typical of sandy, winddeposited soils and dunes, with shinnery oak as the 

dominant shrub

a Model source is indicated as a superscript, based on the primary source of the model, including: *The Nature 

Conservancy (TNC) – Nevada, ** LANDFIRE , and *** TNC – Arizona. Vegetation types with no superscript 

indicate models that were built specifically for the project.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Table 2.10—Probabilistic transition types used in Arizona and New Mexico arid 

land state-and-transition models

Transition type Transition description

Mosaic fire Mosaic fire that results in a patchy burn pattern or 

partial mortality

Stand-replacing fire Wildfire that results in high mortality of shrubs or trees

Surface fire Wildfire that burns the herbaceous or shrub layer under 

tall shrubs or trees

Drought Decrease in precipitation for 1 or more years

Exotic invasion Invasion by exotic species, including cheatgrass, red 

brome, buffelgrass, and others

Freeze Freezing temperatures that kill frost-intolerant species, 

such as some cacti

Increased winter Increased winter precipitation that causes increased 

 precipitation shrub growth compared to grasses

Natural recovery Natural recovery of the herbaceous layer from exotic to 

native species

Oak expansion Expansion of shinnery oak into grasslands

Perennial establishment Invasion by and growth of perennial grass species

Prairie dog establishment Prairie dog colony establishment

Prairie dog extinction Prairie dog colony extinction

Recreation—off-highway Recreation by OHV that results in vegetation 

 vehicles (OHV) and soil disturbance

Shrub encroachment Invasion by shrubs, particularly mesquite, into grasslands

Tree encroachment Encroachment by juniper into shrublands or grasslands

Very wet year Periodic flooding of low-lying basins

Weather and stress Abiotic stress

General livestock grazing A general transition for livestock grazing

Excessive grazing Excessive livestock grazing that can degrade range condition

Managed grazing Managed livestock grazing that alters succession

Graze-mesquite invasion Invasion of grasslands or shrublands by mesquite owing 

to livestock grazing

Native grazing Grazing by native wildlife

Abandoned agriculture Abandonment of agricultural planting

Herbicide treatment Herbicide applied singly or in combination with 

mowing or seeding

Prescribed fire Application of controlled fire

Root plowing Conversion of native species to agriculture

Tree lopping Chainsaw removal of trees

Tree thinning Thinning of trees, occurring singly or in combination 

with herbicide and seeding
41

Integrating Social, Economic, and Ecological Values Across Large Landscapes

from the Monitoring Trends in Burn Severity program (MTBS) (Eidenshink et al. 

2007, www.mtbs.gov) as a basis for many wildfire probabilities. We used the MTBS 

data to calculate wildfire probability in southwestern Oregon, all eastern OR/WA 

forests, and all OR/WA arid lands. The MTBS data set included burn perimeters 

and burn severity classes for all fires over 405 ha in the Western United States over 

a 25-year timespan (1984 to 2008). To obtain wildfire probabilities, MTBS burn 

perimeter polygons were overlaid in a geographic information system (GIS) with 

our potential vegetation map. We grouped potential vegetation into groups representing similar fire regimes (e.g., dry forests, moist forests, semidesert shrub steppe, 

mesic shrub steppe) to increase our wildfire sample size in each category. We then 

calculated the number of hectares burned each year for each potential vegetation 

group and calculated annual fire probabilities by dividing the area burned in each 

potential vegetation group by the area in the group and the number of years in the 

fire record. These annual values became the wildfire probabilities in our models. 

For OR/WA arid lands, we included an additional spatial data layer containing 

exotic annual grass to account for variation in fire return interval from invasion by 

exotic species. This exotic grass layer was developed from the current vegetation 

layer (see “Spatial Data”) and was categorized into low (0 to 10 percent), moderate (10 to 25 percent), and high (>25 percent) exotic grass cover. We combined the 

exotic grass data layer with potential vegetation groups and MTBS fire perimeters 

to obtain fire probabilities for each combination of potential vegetation group and 

exotic grass level.

The MTBS data set provided a tractable improvement to reliance on expertopinion derived wildfire transition rates. However, there were limitations to the 

MTBS data and our derivatives. Fire-severity information was available as part 

of the MTBS data but was not used for this study owing to inconsistencies with 

data reporting and image classification. Fire probabilities derived from MTBS 

data likely underestimated fire activity in an area, as some fires may not have been 

reported throughout the 25-year record, and no fires under 405 ha were included in 

the data set. We did not have a sufficient sample size in western OR/WA to conduct 

similar analyses. However, we did not consider this to be critical, because fire intervals are quite long in these moist forest types. MTBS data were not used to obtain 

fire probabilities for forests and woodlands in AZ/NM because local analysts were 

already able to provide inventory plot data, local severity mapping, and FFE-FVS to 

estimate wildfire effects and probabilities. In addition, the MTBS data set for arid 

lands of AZ/NM appeared to be incomplete and therefore were not used to derive 

fire probabilities. Note that the MTBS data produced wildfire probabilities that 

assumed a continuation of fire suppression at current rates, and thus fire suppression 

was incorporated into the models inherently through the probabilities rather than 

explicitly as a separate disturbance or probability multiplier.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Forest Vegetation Simulator Calibration

We used FVS to calibrate forest succession transitions for all forest and woodland 

STMs in AZ/NM and some forest models in OR/WA. The FVS is an individual tree 

growth and yield model system that predicts distance-independent tree mortality 

and tree growth in terms of tree diameter, height, crown ratio, and crown width 

over time (Dixon 2006, Hoover and Rebain 2011, Moeur and Vandendriesche 2010). 

The FVS model system uses empirically derived life-history equations to simulate 

stand dynamics over time and has been used to support management decisionmaking at scales from the local project-level to regional assessments (Crookston and 

Dixon 2005). Forest Inventory and Analysis (USDA FS 2012) plot data provide 

long-term data collected from private and public forest land in the United States and 

served as reference data for input to FVS.

Transition Multipliers

Transition multipliers are values that increase or decrease probability values simultaneously for all transitions of one type. Transition multipliers operated at modeling 

strata and regional levels to simulate variation owing to vegetation type and land 

ownership or management. They were used to (1) deactivate a transition completely 

(multiplier equals zero), (2) decrease a transition rate (multiplier between zero and 

one), or (3) increase a transition rate (multiplier greater than 1). For the ILAP default 

FSO scenario, all management transitions were deactivated using a multiplier of 

zero, but for other scenarios, such as the resilience scenario developed for focus 

area analyses, multipliers were used to adjust the average probability of a management event, such as a thinning from below, to levels corresponding to management 

prescriptions in the scenario. Transition multipliers were linked to our modeling 

strata via the ownership-management layer, so management transitions could be 

adjusted differently for each ownership-management combination. In addition, 

transition multipliers could be used to generate trends for selected transitions.

Interannual Variation Using Monte Carlo Multipliers

The modeling framework provided a means to simulate year-to-year variability in 

STM simulations using Monte Carlo multipliers (MCMs). We incorporated interannual model variation for fire weather, insect outbreaks, and severe drought (in a 

small number of OR/WA arid land models) to simulate stochastic or cyclic variation 

in fire weather, insect outbreaks, and other disturbances by creating a stream of 

randomly generated multiplier sequences. The interannual multipliers were applied 

to individual model simulations and selected transition types, and augmented the 

software’s built-in variability. The MCM sequences were generated using the MCM 
43

Integrating Social, Economic, and Ecological Values Across Large Landscapes

builder tool in VDDT (ESSA Technologies Ltd. 2007) for the desired number of 

model simulations.

Fire weather is a major driver of fire behavior (Agee 1993) and is one of the 

determinants of total land area burned in any year, as well as fire severity. We 

assumed fire weather was a random occurrence for this project, and generated an 

annual sequence of multipliers for each Monte Carlo simulation. Our multipliers 

were based on the MTBS fire occurrence data, assuming that 80 percent of years 

were normal fire years, 15 percent of years were high fire years, and 5 percent of 

years were severe, in terms of area burned.

Insect outbreaks were also modeled using MCMs but were assumed to occur 

as semirandom occurrences in OR/WA dry forest types and sagebrush arid land 

models. The onset of an outbreak was determined randomly or within a range of 

years since the previous disturbance, but once an outbreak occurred, its behavior 

was controlled by the boundaries established in the MCM multiplier streams. The 

MCM streams generated for insects were constrained by (1) the probability of a low 

to normal, high, and severe outbreak year occurring; (2) minimum and maximum 

lengths of outbreaks; (3) minimum and maximum length of time between outbreak 

peaks; (4) time since the peak of the previous outbreak; and (5) length of time it 

takes to develop and then diminish outbreak conditions. In this way, the initiation, 

length, and severity of an outbreak were randomized but informed by the previous 

outbreak occurrence. The OR/WA forest models included mountain pine beetle, 

western spruce budworm, and Douglas-fir tussock moth outbreaks, and multipliers 

varied among modeling zones. The OR/WA arid lands modeled sagebrush defoliating insect outbreaks (such as Aroga moth) and severe drought using MCMs. Interannual variation was not considered for insect outbreaks in the AZ/NM models, 

although insect/disease transitions were included in some models.

Spatial Data

The STM modeling framework required several spatial data layers, described 

below. These spatial layers were used to form modeling strata, the spatial basis 

of our modeling, and provided a means to quantify abundance and distribution 

of current vegetation to initialize the STMs. Modeling strata were formed by 

the overlay of three spatial layers, including potential vegetation, ownershipmanagement, and watershed, and one STM was run for each. State-and-transition 

models were run for about 30,000 modeling strata across the entire project area. 

The initial vegetation conditions (vegetation composition and structure at time step 

zero) assigned to each modeling stratum came from the current vegetation layer 

attributes (see “Data Processing”).
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GENERAL TECHNICAL REPORT PNW-GTR-896

Modeling Zones

We divided the four-state study area into 18 modeling zones (fig. 2.4), representing 

manageable spatial units for mapping and modeling. In AZ/NM, our mapping zone 

boundaries corresponded to the Multi-Resolution Landcover Consortium mapzones 

(Homer et al. 2004), modified to match watershed boundaries. In OR/WA, our 

modeling zones corresponded to level III ecoregions (Omernik 1987), with the 

boundaries modified to follow watershed boundaries (see “Watershed”).

Potential Vegetation

Potential vegetation maps identified which STMs applied to each pixel in the modeled area. We used existing datasets where they were available and constructed our 

own where they were not. Our potential vegetation maps were categorical raster 

maps, and represented a vegetation classification that corresponded with the STMs. 

Within OR/WA, these vegetation concepts are called potential vegetation types, 

which represent environmental zones where vegetation tends to exhibit consistent 

dynamics through time (e.g., similar growth rates, species composition through succession, disturbance regimes). Within AZ/NM, the vegetation concepts illustrated 

in the potential vegetation map correspond with potential natural vegetation types 

refined by historic fire regimes or the equivalent of biophysical settings or ecological 

systems (Winthers et al. 2005). 

In OR/WA, we used existing maps of potential vegetation for some locations, 

including maps of plant association groups (western OR), and vegetation zones 

(WA forests). For the parts of OR/WA that lacked potential vegetation data, and 

for all of AZ/NM, we mapped vegetation plots using a modeling technique called 

Random Forest Nearest Neighbor (RFNN) (Crookston and Finley 2008). RFNN 

links mapped pixels to forest inventory plots. It is one type of imputation, which is 

a general term for the technique of filling in missing values in a dataset with known 

values in the same dataset (Eskelson et al. 2009). RFNN uses a random forest 

model that illustrates the relationship between vegetation composition and explanatory environmental variables to match the best plot to each pixel. Explanatory 

variables fall within three broad categories: soil, climate, and topography. In OR/

WA, we used Current Vegetation Survey plots from the USDA FS, LANDFIRE, 

plots from the USDI BLM, and other local supplemental datasets. In AZ/NM, we 

used all of the plots that were used for existing vegetation maps, plus supplemental 

plot data from the US Department of Defense. Each plot was assigned to a potential 

vegetation type through either a crosswalk or a keying process that assigned potential vegetation to plots based on plant community composition. Potential vegetation 

maps were drafted multiple times, based on expert review feedback. RFNN model 

accuracy was assessed through error matrices built comparing cross-validated 
45

Integrating Social, Economic, and Ecological Values Across Large Landscapes

model predictions with actual potential vegetation values for the plots. Map accuracy was further assessed by expert review. In some areas in AZ/NM, mapped 

potential vegetation data from national forests was layered on top of our modeled 

potential vegetation surface, a procedure recommended by our expert reviewers to 

improve local credibility and utility to forest managers on national forests.

Current Vegetation

Current vegetation maps provided information on state classes within each potential 

vegetation type. As with the potential vegetation maps, we used existing maps where 

available, otherwise we developed new maps. Current vegetation maps were raster 

layers, where each pixel value corresponded with a single vegetation survey plot 

identifier. Summarized variables from those plots were attached to the grid attribute 

table, yielding a multivariate map. These variables described vegetation composition 

and structure and included four variables for forests and 22 for arid lands. 

For the forests of OR/WA, maps of current vegetation (map date 2006) were 

available through the Landscape Ecology Modeling Mapping and Analysis 

(LEMMA) team within the USDA FS (http://lemma.forestry.oregonstate.edu/). 

These maps were created using the gradient nearest neighbor imputation mapping 

technique (GNN) (Ohmann and Gregory 2002). GNN differs from RFNN only in 

the statistical method it uses to link plots to pixels, relying on an ordination model 

describing vegetation-environment relationships (canonical correspondence analysis). These maps were built from the same explanatory variables used for potential 

vegetation, with the addition of LANDSAT spectral reflectance. All maps from the 

LEMMA team are extensively assessed for accuracy. Within AZ/NM, we created 

maps of current forest vegetation RFNN. These maps were functionally identical to 

the GNN maps from R6 for the purpose of initializing STMs. The environmental 

explanatory variables used for our new maps were similar to those used by the 

LEMMA team, but our forest inventory plot data set was smaller and included only 

FIA annual plots, following procedures developed through the Nationwide Forest 

Imputation Study (Grossmann et al. 2009). 

We also built current vegetation maps for the arid lands of AZ/NM and eastern 

OR/WA using the RFNN technique. The environmental explanatory data used to 

inform these models was the same as for forests, but the plot datasets differed. Within 

OR/WA, we obtained plot data primarily from the public LANDFIRE plot reference 

database, and the USDI BLM, with a few other minor sources. Within AZ/NM, we 

relied on LANDFIRE as well, supplemented by data from the USDI BLM and New 

Mexico Natural Heritage program. All new current vegetation maps were evaluated 

for statistical accuracy using cross-validated predictions for the plots and expert 

review. In AZ/NM, woodlands were mapped with arid lands instead of forests.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Ownership-Management

The ownership-management spatial layer differentiated landowners and management goals, allowing different treatment levels to be applied to different management areas within a single ownership. To derive the ownership-management 

allocation layer for OR/WA, we collected available management plans and layers 

from federal and state land management agencies. We used six ownership/stewardship categories: USDI BLM, USDA FS, other federal lands (e.g., National Park 

Service, Department of Defense, U.S. Fish and Wildlife Service), state, private, 

and tribal. Within each ownership category, management plans were matched 

to mapped management data. The management data were classified into visual 

resource classes that capture the visual management objectives for a particular 

portion of land. We defined our visual resource objective based on categories and 

definitions created by the USDI BLM, and added one category that reflected an 

additional management class (table 2.11). We intersected the geographic boundaries of the land ownership types with the boundaries of management categories to 

construct the comprehensive set of ownership and management intensity classes 

represented in the ownership-management layer. This process generated 30 combinations of ownership-management in OR/WA. The ownership-management layer 

for AZ/NM was compiled using the protected area database for the United States 

(www.protectedlands.net/padus/preview.php) stewardship codes combined into the 

same categories used in OR/WA.

Watershed

Watersheds divide the landscape into commonly used units for reporting model 

results. Watersheds were defined as 5th-field or 10-digit hydrologic unit codes 

(HUCs), and were acquired from the standardized watershed boundary data set 

(WBD) (USGS and USDA NRCS 2011). The WBD uses the federal standards for 

delineation of hydrologic unit boundaries for delineating watershed boundaries. 

Watersheds generally represent drainage areas between 20 000 and 100 000 ha. In 

OR/WA, there are about 950 watersheds with an average size of 45 023 ha. In AZ/

NM, there are about 1,020 watersheds with an average size of 60 560 ha.

Mask

A mask layer was applied to exclude lands not modeled, including riparian, barren, 

water, urban, and developed agricultural areas. The 2008 Pacific Northwest Gap 

Analysis Project (GAP) (available at www.oregon.gov/DAS/EISPD/GEO/sdlibrary.

shtml) land cover map was used to create the mask as it was available across all 

four states, had been recently updated, and used a consistent classification system. 
47

Integrating Social, Economic, and Ecological Values Across Large Landscapes

After extracting vegetation categories that were not modeled, the grid was resampled from a 30- to a 300-m cell size to remove the excessive fragmentation caused 

by linear features, such as riparian areas and highways. Small polygons <405 ha 

were removed, then resampled back to 30 m.

Data Processing

To run the STMs developed for the project area, we developed a process to handle 

large data sets and run many STMs simultaneously. The process was specifically 

designed to allow flexibility in updating any of the data layers that form the 

foundation of the modeling effort, as updated vegetation maps or new data 

become available.

Several steps were performed to integrate spatial layers, calculate initial 

vegetation conditions, and set up STM model runs in Path, a process we refer to as 

Table 2.11—Management categories used to define management objectives for the ownershipmanagement spatial data layera

Management 

category Objectives Description

Category 1 Protection/ VRC 1 protected lands (primarily national forests)—the objective 

preservation was to preserve the existing character of the landscape with no 

management or very limited management activity. Examples 

include wilderness areas and congressional reserves.

Category 3 Retention VRC 2 lands—the management objective was to retain the existing 

character of the landscape. The level of change to the landscape was 

low and management activities were minimal. Any changes repeated 

the basic elements of form, line, color, and texture found in the 

predominant natural features of the characteristic landscape. 

Examples include late-successional reserves and state natural areas.

Category 4 Partial retention VRC 3 lands—the objective was to partially retain the existing 

character of the landscape. The level of change to the characteristic 

landscape was moderate, and changes repeated the basic elements 

found in the predominant natural features of the landscape. 

Examples include state wildlife management areas and federal 

adaptive management areas. 

Category 5 Modification VRC 4 lands—allows management activities that cause major 

modifications of the existing character of the landscape. The level 

of change to the characteristic landscape can be high. Examples 

include federal matrix lands, and general ecological management 

on state lands.

Category 6 Modification private This category was outside of the scope of the USDI BLM definitions 

and was used to define management on private industrial lands. 

These lands typically are heavily managed with high volumes 

of timber harvest and other commercial activities.

a Visual Resource Class (VRC) definitions developed by the U.S. Department of the Interior, Bureau of Land Management (USDI BLM) 

are largely used as a basis to create the categories. USDI BLM categories 1 and 2 were combined to simplify the classification system.

Several steps 

were performed to 

integrate spatial 

layers, calculate initial 

vegetation conditions, 

and set up STM model 

runs in Path, a process 

we refer to as the “data 

rollup.”
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GENERAL TECHNICAL REPORT PNW-GTR-896

the “data rollup.” We developed several software tools to automate the data rollup 

process (available online at www.westernlandscapesexplorer.info). The first step 

employed a series of clipping, data format conversion (where needed), and combining operations customized in the GIS data rollup tool. This step assigned a modeling stratum, current vegetation cover, and current vegetation structure class to 

each raster cell. The second step used the Data Rollup Query Tool to transform the 

modeling strata and associated current vegetation conditions from separate spatial 

data sets into the tabular summary required by the modeling software. The tool 

used a rule set of if/else statements to assign all modeled pixels in the landscape 

to a model state class in the applicable STM based on vegetation cover, dominant 

species, and other criteria. The result was a current vegetation map classified into 

model state classes.

In OR/WA forest models, the process of assigning mapped pixels to model 

state classes involved an additional step not required for OR/WA arid land models 

or AZ/NM models. First, each pixel was assigned a cover type based on the importance value of the dominant tree species, then assigned a structural stage based on 

tree density. In many cases, not all the resulting cover and structure combinations 

occurred in the STMs. These pixels were subjected to a reclassification scheme 

until all pixels were assigned to existing STM state classes.

The intersection of watershed, ownership-management, and potential vegetation often yielded modeling strata with very little area. Because STMs do not yield 

reliable results when applied at very small spatial scales, some additional processing 

was needed to handle the smallest modeling strata. We set our minimum modeling 

stratum size to 405 ha and developed a two-tiered strategy to cope with small modeling strata. Wherever possible, we reassigned pixels from small modeling strata 

to larger, similar modeling strata within the same watershed. To reassign pixels to 

other modeling strata, we made decisions using two relative similarity indices, one 

for potential vegetation and another for ownership-management categories. The 

two indices ranked all pair-wise combinations of each category with respect to their 

relative similarity in multiple dimensions. The potential vegetation similarity index 

reflected overlap in elevation of the potential vegetation, species compositional 

similarity from the current vegetation map, and similarity in moisture regime (e.g., 

dry vs. mesic). The ownership-management similarity index was constricted in 

a similar fashion, scoring pair-wise similarity from information on management 

activities that may or may not occur within each ownership-management category. 

When there was no reasonable reassignment, we removed the small modeling strata 

from our analysis. This size constraint process is optional, and minimum size can 

be altered. All functionality necessary to create and use a similarity index for lumping modeling strata was contained within the LumpStrata script programmed using 

R statistical software (R Development Core Team 2008).
49

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Once current vegetation pixels were properly assigned to state classes, there 

were logistical issues of moving initial conditions for hundreds or thousands of 

modeling strata into the Path software. To facilitate this process, we developed the 

Get into Path (GIP) tool to automatically upload the STMs, initial conditions, and 

transition multipliers for each modeling unit into the Path software. The result was 

a set of organized parameter scenarios customized for ILAP modeling that were 

ready for simulation (all of the tools described in this section are available at www.

westernlandscapesexplorer.info).

Model Execution

All models were run using the baseline FSO scenario that represented vegetation 

dynamics under no management activities except continued fire suppression. In this 

scenario, all management transitions were deactivated. Grazing transitions were 

included in arid land and dry forest models, where applicable. In addition, some 

areas were analyzed with multiple scenarios, which included management actions 

such as timber harvesting, prescribed fire, seeding, and other treatments (see 

“Example Outputs”). Thirty Monte Carlo simulations were run for each modeling 

stratum, using a model resolution of 4 ha.

Model Output

Output Summarization

Output from the Path model runs was exported outside of the Path interface as 

comma separated variable (csv) text files. Each of these files was quite large, sometimes having one or more rows for each combination of state class/transition, time 

step (150 or 300 years for arid lands and forests, respectively), and Monte Carlo 

simulation (30). We developed the SumPath summarization package programmed 

in the R statistical software package (R Development Core Team 2008) to calculate 

summary statistics (mean, median, minimum, and maximum area) for each state 

class and transition at each time step, summarized over the Monte Carlo runs. Summaries for each modeling stratum were unified in a single state class file and single 

transition file for each modeling zone.

Output Visualization

Model output can take the form of graphs, tables, and maps. For any output format, 

data can be summarized across the entire area of interest, by potential vegetation, 

or by ownership. We used several basic visualization tools to summarize output for 

all modeling strata.

All models were run 

using the baseline 

FSO scenario that 

represented vegetation 

dynamics under no 

management activities 

except continued 

fire suppression. In 

addition, some areas 

were analyzed with 

multiple scenarios, 

which included 

management actions 

such as timber 

harvesting, prescribed 

fire, seeding, and other 

treatments.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Pivot charts—

If the data set is small enough, output from the model runs can be assessed using a 

Microsoft® Office Excel-based graphing tool developed for the project (available at 

www.westernlandscapesexplorer.info). The graphing tool allows an average Excel 

user to graph the results of the summarized text files in Excel based on modeling 

zone, potential vegetation, ownership-management, and watershed. It also allows 

the user to group state classes into fewer categories with meaningful interpretations, such as broad forest structural groups or levels of exotic grass invasion. The 

tool builds a pivot table in Excel, and a graph is automatically generated based 

on this table. This prebuilt graph can be changed to suit the user’s needs based on 

any of the information available in the pivot table. The Excel tool currently graphs 

changes in state classes but not changes in transitions over time.

Maps—

Spatial display of model output can occur at a variety of scales, including the scales 

of modeling strata and whole watersheds (fig. 2.5). However, our process generated 

STM results that were spatially aggregated into modeling strata at a minimum; 

our maps were not meant to be used on a pixel-by-pixel basis (see “Limitations”). 

One method of displaying output spatially was in the form of risk maps, depicting 

conversion of areas to undesirable state classes such as dense, dry forest types or 

exotic-dominated grasslands. Risk maps were constructed to show the accumulation of cover or structural type(s) of interest through time. Maps also displayed 

temporal information in different ways, either depicting model results for a single 

year or indicating the magnitude and direction of change from the beginning to the 

end of the simulation.

Example Results

Although STM results are available across the four-state study area, the following 

examples illustrate STM projections and interpretations from a subset of the forest 

and arid land output in OR/WA. The forest example is taken from the Washington 

East Cascades (WEC) modeling zone, an area of approximately 2 million ha. The 

arid land example is from the southeastern Oregon modeling zone and spans about 

5.3 million ha.

Washington Dry Forest Example

In this example (fig. 2.4), we ran two scenarios with 30 simulations each: (1) an 

FSO scenario where all types of management other than fire suppression did not 

occur, and (2) a resilience scenario where the intent was to actively manage drier 

forest types assuming fire suppression continued into the future. We were unable 

to obtain data on resilience-promoting activities in the area and therefore used our 
51

Integrating Social, Economic, and Ecological Values Across Large Landscapes

knowledge of the environment to generate example treatment rates. The resilience 

scenario largely utilized prescribed fire and thinning from below to try to create 

more resilient and open dry forests, although other treatments were also included. 

This scenario should not be viewed as an example of what land managers in the 

area might actually do in the area; rather, it demonstrates a small set of the kinds of 

analyses that can be done with our process.

Current forest conditions—

Model initial conditions, derived from a classification of the current vegetation map 

(see “Data Processing”), provided a means to understand current conditions across 

the landscape. Grouping the STMs and displaying the current vegetation data by 

ownership provided a unique perspective on the landscape. More than half of the 

landscape was in drier forest types like oak, ponderosa pine, or dry mixed conifer, 

and federal lands had the greatest proportion and area of these dry forest types 

(fig. 2.6).

Figure 2.5—Example output (dense forest in eastern Washington [WA]), showing two spatial resolutions of output: the 

watershed level (left) and the modeling stratum level (right).
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GENERAL TECHNICAL REPORT PNW-GTR-896

Trends in dense forests through time—

Because federal lands comprised such a large proportion of the landscape, we 

focused on these lands for the remainder of the forest example. In comparing the 

distribution of forest structures (i.e., tree diameter size classes and percentage 

canopy cover) across all federal lands, we found an increase in the average amount 

of open forest under the resilience scenario (fig. 2.7) relative to the FSO scenario. 

In contrast, forests became denser under the FSO scenario owing mostly to fire 

suppression effects. As we ran 30 simulations per scenario, we were also able to 

illustrate trends based on a range of potential future conditions and variation around 

those trends. Model results across all 30 model simulations suggested an increase 

in the amount of open forest with resilience activities, although the rate of increase 

varied among the simulations (fig. 2.8).

In this example, the FSO scenario generated declining amounts of dense forest 

in areas that are currently dense and prone to wildfire and insect outbreaks (fig. 

2.9a). However, closed forests increased through time (green colors) in areas that 

were initially open, owing to recent active management, and where halting forest 

management resulted in the forests becoming more dense. In contrast, little of the 

landscape increased in density through time under the resilience scenario, owing to 

a high rate of active management across forested lands outside reserves and wilderness (fig. 2.9b).

Figure 2.6—Hectares of dry forest across the landscape (left) and by ownership (right). Dry forests were composed of 

vegetation types such as oak, ponderosa pine, and dry mixed conifer. More mesic forests were assigned to the “other 

forest” category.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 2.7—Projected trends in forest structure through time on federal lands under two different management scenarios. Structural 

stages represent the following tree diameter ranges in centimeters: sapling/shrub >0 to 13; pole >13 to 25; small >25 to 38; medium >38 

to 51; and large/giant >51. Open and closed refer to canopy cover, where open structures have 10 to 40 percent canopy cover and closed 

structures have more than 40 percent canopy cover.

Although graphs can highlight overall trends, displaying output spatially 

provides the geographic context helpful in identifying areas likely to increase or 

decrease in open and dense forest through time (fig. 2.9). The use of potential vegetation, watershed, and ownership-management spatial layers allowed the creation 

of maps at the modeling stratum scale. Such maps should be used with some caution, however, as the information they depict may appear to have been generated by 

spatial rather than nonspatial modeling processes. Our nonspatial STM framework 

assumed that dynamics occur anywhere within the modeling strata, and the spatial 

maps illustrate results summarized to the scale of the modeling strata. Therefore, 

mapped results should be interpreted at the modeling strata scale, not at an individual pixel scale.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Arid Lands Example

We ran three scenarios to illustrate analysis using ILAP data in arid lands in southeastern Oregon: (1) a scenario with no grazing or active management (ungrazed/

unmanaged), (2) a scenario where heavy livestock grazing occurred but no active 

management was implemented (grazed/unmanaged), and (3) a scenario with heavy 

livestock grazing and active restoration management that included postfire seeding of native grasses and juniper removal treatments (grazed/managed). These 

scenarios were intended to inform management by providing starting points for 

more refined scenarios with varying treatment levels and grazing intensities. Each 

scenario was run for 30 Monte Carlo simulations to project vegetation 50 years into 

the future (year 2050).

Comparison of management scenarios—

Under the unmanaged/grazed scenario, the warm, dry Wyoming big sagebrush 

types became dominated by exotic grasses (fig. 2.10). This resulted from our 

assumption that grazing (particularly in combination with fire and drought disturbances) reduced the presence of native grasses and provided a competitive 

Figure 2.8—Projected trends in open (10 to 40 percent canopy cover) and closed (>40 percent canopy 

cover) forest under the resilience scenario on federal lands. Solid lines represent average trends; dashed 

lines indicate variation around the average. Lower dashed lines within a shade represent the minimum 

hectares for a given time step and simulation run. Upper dashed lines within a shade represent maximum 

hectares for a given time step and simulation run. 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 2.9—Trends in closed forest through time (after 50 years) in a portion of the Washington East Cascades modeling zone under the 

(a) fire suppression only scenario, and (b) resilience scenario. Colors represent those areas that either cumulatively increased or decreased 

in the amount of closed forest relative to current (2006) conditions.

Figure 2.10—Trends in native and exotic vegetation in Wyoming big sagebrush under the grazed/unmanaged scenario in the 

Oregon southeast modeling zone.
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GENERAL TECHNICAL REPORT PNW-GTR-896

advantage for exotic grasses. A comparison of initial to future conditions across 

all scenarios suggested invasion by exotic grasses is likely to continue across all 

three management options, but the rate of invasion might vary depending on the 

amount of active management and grazing (fig. 2.11). By time step 50, about half of 

the landscape was projected to be dominated by exotic grasses (shrub steppe-exotic 

and exotic grass monocultures) under the unmanaged/grazed scenario. With the 

removal of grazing (unmanaged/ungrazed), vegetation condition still declined over 

time owing to conversion of already semidegraded shrub steppe to exotic grass 

state classes. Through active seeding of exotic grass encroached state classes, the 

managed/grazed scenario resulted in relatively constant levels of exotic grass monocultures over time, with slightly more area in exotic shrub steppe and native state 

classes, but less area in semidegraded shrub steppe by 2050. The model outcomes 

suggested that removal of grazing alone will not likely improve range condition 

in these warm, dry sagebrush systems, and that active management is required to 

restore already degraded systems.

Figure 2.11—Generalized state class composition in Wyoming big sagebrush across three management scenarios in the 

Oregon southeast modeling zone. Columns show mapped landscape proportion in 2000 (left) and projected landscape 

proportion in year 2050 under three management scenarios (middle and right).
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 2.12—Exotic grass invasion risk map under the grazed/unmanaged scenario for Wyoming big sagebrush. The map identifies 

the percentage of each modeling stratum comprised of exotic grass at year 2050 in the Oregon southeast modeling zone. 

Maps of model results by modeling stratum show the variability in invasion 

risk across the landscape (fig. 2.12) and may help managers identify locations likely 

to have high levels of invasion by exotic grasses and other conservation targets. 

Projected exotic grass invasion levels at year 2050 were highly heterogeneous 

across the warm, dry Wyoming sagebrush of southeastern Oregon. These maps can 

be useful to prioritize areas for treatment across large landscapes when budgetary 

constraints limit the ability to implement restoration treatments widely.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Limitations

Although our STMs, model projections, and other products are useful tools for 

answering a variety of land management questions, they have several limitations. 

We identified three major types of constraints and limitations to our approach: 

spatial, logistical, and thematic.

Spatial Representation Constraints

The spatial resolution of our modeling outputs was limited by several interacting 

factors. First, our approach did not model interpixel or among-stand processes. 

Instead, our framework applied STM results to mapped modeling strata (intersection of potential vegetation, ownership-management, and watershed) to yield 

mapped stratum-scale output. Although maps of model projections appear to have 

a value for every modeled pixel on the landscape, our results are only applicable at 

the modeling stratum scale and larger aggregations. Thus, the spatial resolution of 

our modeling strata was constrained to a minimum modeling stratum size of 405 

ha. In addition, constraints on modeling unit size reflected limitations in the spatial 

accuracy of the base data sets (especially potential and current vegetation, and 

their input data sets) that were available, but were not inherent limitations on our 

approach and process. Fine-scale analyses could be performed by substituting data 

with higher spatial resolution and accuracy.

In contrast to the size of our modeling strata, our input data were generally 

raster data with a 30-m pixel resolution. This difference in scale between inputs 

and outputs was a consequence of base data accuracy, our selection of modeling 

zone size, and logistical considerations. In general, our potential and current 

vegetation maps were reasonably robust at intermediate to broad scales, but 

limited in accuracy when examined on a pixel-by-pixel basis. Because of these 

spatial uncertainties at fine scales, our modeling framework is best adapted to 

addressing management-related questions at mid to broad scales (e.g., across 

multiple watersheds).

In addition, our projections incorporated error from each spatial layer used to 

define our modeling strata and initialize our models, in addition to error inherent in 

the STMs themselves. Although all of these maps were distributed as rasters with 

30-m ground pixel resolution, they are all best used at broader scales. A more formal analysis on the relationship between summary scale and potential and current 

vegetation map accuracy performed by the LEMMA team showed that GNN maps 

of an old-growth structure index are quite robust when summarized over 8660-ha 

hexagons, and that accuracy increases with larger summary units (Gregory et al. 

2011). Similarly, the ILAP variables are also likely to improve with coarser scales 
59

Integrating Social, Economic, and Ecological Values Across Large Landscapes

of summary. Map error was particularly prevalent at the forest/arid land margin, 

where low tree cover values were particularly challenging to map. Note that these 

errors were carried through the modeling process and contribute to modeling uncertainty, particularly at the boundaries between features within the spatial layers. For 

broad-scale analyses, the ramifications of boundary errors were likely minimal, but 

for analyses at intermediate and fine scales, additional assessments to quantify the 

error would be useful, and data developed for finer scale use should be substituted.

Logistical Constraints

Our approach had some practical limitations inherent to working with large data 

sets. To create, analyze, and display data from our models, users need a fairly high 

level of technical proficiency with databases and GIS software. The capacity to use 

and modify scripted procedures is also helpful if users plan to build new analyses to 

compare management options. We have packaged and documented our procedures 

to make them as user-friendly as possible, but there would still be a significant 

learning curve for anyone wishing to run the models and conduct further analyses. 

Improvements to the Path software are likely, and we hope that workflow and data 

management procedures will continue to improve.

An additional challenge stems from our models not being optimization models. 

Our process facilitated a gaming approach rather than producing “best” or “optimal” answers. The ILAP decision support tools currently being developed will help 

to fill some of this need.

Thematic Constraints

The simple structure of STMs is a strength of the modeling approach. The models 

are well-suited for both communication of results to nontechnical audiences, and for 

honing or building new models in collaboration with land managers, who may have 

deep ecological understanding but limited technical skills. However, this simplicity 

comes with some inherent limitations. These limitations are primarily thematic and 

relate to the definitions of the state classes and the transitions that are incorporated 

in each model. Definitions for state classes and transitions for vegetation were not 

standardized across STMs (Stringham et al. 2003), and the models adapted and 

developed for our project have important differences among the major vegetation 

types within the project and as compared to other projects.

The STMs do not explicitly model ecosystem processes, such as nutrient 

cycling, net primary productivity (NPP), seed dispersal, and interspecific 

competition. Vegetation dynamics may affect numerous ecosystem processes, and 

vice versa (Chapin et al. 1997). Although vegetation-ecosystem process interactions 

were not explicitly defined within ILAP STMs, they were implicit within the 
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GENERAL TECHNICAL REPORT PNW-GTR-896

potential vegetation definitions, and the associated STM structures. For example, in 

the alpine STM, succession and growth-related transition rates were slow, reflecting 

low NPP common at high elevations where cold weather constrains nutrient cycling 

and the growing season. Because ecosystem processes were implicit within our 

models and not explicitly defined, they cannot be explored in and of themselves 

within the basic ILAP framework without additional efforts to link model state 

classes, transitions, and outputs to ecological processes. The data and information 

available to inform transition pathways and transition rates varies widely among 

STMs. Some transitions are based largely on empirical data (e.g., MTBS fire 

frequencies), some on models (e.g., FVS succession rates), and others almost 

entirely on expert judgment. Most STMs use a variety of information sources to 

parameterize transitions and use expert judgment to fill in the knowledge gaps. The 

STMs compiled and developed for ILAP can be easily updated and changed as new 

data and information become available.

The types of questions that can be addressed by our STMs were constrained to 

those that relate directly to the state classes and transitions contained within each 

model. For example, the vegetation units within the forested potential vegetation 

state classes for OR/WA represented classified vegetation types that were labeled 

only with a dominant tree species. Subdominants were not explicitly labeled, 

although they were implicitly associated with each cover type, and understory 

species were not considered. Because of this setup, the behavior of individual species through time could only be extracted from a limited set of our ILAP potential 

vegetation types. However, arid land vegetation cover types usually contained 

more compositional detail than the forest cover types, including functional groups 

(perennial grass, annual grass) or individual species (e.g., Wyoming big sagebrush, 

western juniper). Still, the thematic resolution of the models was generally constrained to vegetation communities or groups of species, rather than individual 

species.

An additional constraint inherent to the basic ILAP framework is the implicit 

assumption that vegetation potential does not shift over time. Each modeling stratum was linked to a single potential vegetation type, which was linked to a single 

STM. This assumption may be robust enough on a short timeframe but becomes 

problematic in the context of climate change, which will likely shift site potential, 

and reorganize species assemblages. On the other hand, given the uncertainties 

embedded in current climate modeling, information on the historical range of variability (HRV) may be our most certain available guide as to how ecosystems will 

behave in the future (Keane et al. 2009) and what management actions may prevent 

irreversible, deleterious changes. Our potential vegetation types were based on 

The types of questions 

that can be addressed 

by our STMs were 

constrained to those 

that relate directly to 

the state classes and 

transitions contained 

within each model.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

HRV, and our STMs were parameterized from HRV wherever data were available. 

This does not imply that modeling approaches that do incorporate climate change 

are not worth exploring, just that they do not yet necessarily yield a more reliable 

projection than HRV-based models. Other work as a part of ILAP explored how 

to extend the ILAP models to encompass shifts in site potential, linking multiple 

models to allow for transitions among vegetation types (chapter 7).

Another important driver of vegetation change through time is the legacy of 

past climate, disturbance, and land use (Chase 2003, Rhemtulla and Mladenoff 

2007). Historical influences on vegetation composition operate at a variety of 

spatio-temporal scales. On a millennial timeframe, vegetation shifts related to 

historical climate change have been documented (Hotchkiss et al. 2007) and vegetation species composition and abundance is affected by past climate. On a decadal 

timeframe, or even over centuries, vegetation patterns can also reflect the legacy 

of human land use (Dupouey et al. 2002, Foster et al. 1998). Because the legacy of 

past climate, disturbance regime, and land use is reflected within our initial conditions (via our current vegetation layer), and also in our transition rates (via the fire 

regime), many important dimensions of legacy effects are accounted for implicitly 

within the ILAP framework. Because of this construction limitation, as with ecosystem processes, questions relating directly to legacy effects cannot be explicitly 

addressed with the data we have developed for our particular application.

Scenario Analysis

The baseline ILAP scenario with no management activities except for fire suppression only (FSO) is available across all lands. Within the focus areas, additional 

scenarios were also run to reflect timber harvesting, restoration activities, and other 

management. However, the range of management options is limited, and more work 

is needed to develop realistic management transition rates across ecological and 

administrative boundaries. The interpretation of model results would be greatly 

improved by a larger number of management scenarios, and development of alternative management scenarios will greatly improve the utility of ILAP models.

Data Products and Tools

Most of the data, models, and tools produced for ILAP are publically available on 

the Western Landscapes Explorer website, maintained by the Institute for Natural 

Resources (www.westernlandscapesexplorer.info). The STMs were run for all lands 

in AZ/NM and OR/WA, and input data, models, and summarized output for the 

default FSO scenario are available in files called rollout packages. Rollout packages 

each contain the Vegetation Dynamics Development Tool (VDDT) and Path modeling databases, model documentation, initial GIS data needed for the rollup process, 
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GENERAL TECHNICAL REPORT PNW-GTR-896

post-rollup modeling strata and state class spatial data, and STM outputs (i.e., 

projected future landscape conditions). Rollout packages are separated by modeling 

zone, and separated between forests and arid lands in OR/WA, and forests, woodlands, and arid lands in AZ/NM.

In addition to the FSO scenario results for all lands, additional summarized 

results are available for focus areas to characterize alternative management actions 

and summarize across variables of interest (see “Example Results”). An alternative 

resilience scenario was run for the forested environments of eastern Washington 

and Oregon (called east-side forests), and two additional scenarios were run for arid 

lands of southeastern Oregon. In AZ/NM, results for various management scenarios 

are available for the Sky Islands focus area.

Conclusions

The methods, models, and data presented here provide a means to perform integrated landscape assessments in support of mid- to broad-scale planning efforts that 

span multiple watersheds and affect vegetation, habitat, economics, and other processes. Similar models have been employed over smaller spatial extents to address 

shortcomings related to integrated metrics at the landscape scale, future conditions, 

alternative strategies, and cost-benefit analyses in commonly used approaches to 

conservation planning (Low et al. 2010). The original thrust of the project was to 

generate information for land management planning that could be used to support 

prioritization of management activities at mid to broad scales, and to advance 

efforts to integrate daunting issues such as including climate change in the planning 

process in a tractable manner. It has become exceptionally clear over the course 

of this project that when modeling at multistate scales, a wide range of complex 

questions can be studied using ILAP data and models. Examples in this chapter 

represent a small glimpse of the potential for graphs, maps, and other reporting that 

can be built from the VDDT/Path output. Linking STM output to other characteristics of interest can provide information about vegetation change integrated with 

fuels, wildlife, economic, and other data (chapters 3 through 7). Graphs, maps, and 

charts of economic, ecological, and social variables can be linked to the raw output, 

and summarized to further increase the breadth of questions that can be addressed 

across a landscape, watershed, ownership, or potential vegetation type. The 

framework can also be linked with other modeling systems (e.g., FVS) for validation or calibration. Although there are constraints to using STMs related to scale, 

and logistics, the ILAP framework is a demonstrably robust and flexible tool for 

assessing management alternatives and prioritizing management actions at regional 

scales, for a diversity of ecological and social contexts. 

The original thrust 

of the project was to 

generate information 

for land management 

planning that could 

be used to support 

prioritization of 

management activities 

at mid to broad scales, 

and to advance 

efforts to integrate 

daunting issues such 

as including climate 

change in the planning 

process in a tractable 

manner.
63

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Acknowledgments

The Integrated Landscape Assessment Project was funded by the American 

Recovery and Reinvestment Act (ARRA), and the U.S. Department of Agriculture 

Forest Service Pacific Northwest Research Station, Pacific Northwest Region, and 

Southwest Region. The U.S. Department of the Interior Bureau of Land Management in Arizona, New Mexico, and Oregon/Washington contributed plot data 

and other valuable information. Without the expertise, patience, and guidance of 

James Merzenich, Reuben Weisz, Esteban Muldavan, Paul Arbetan, Jimmy Kagan, 

Robin Lescher, Jan Henderson, Mike Simpson, Louisa Evers, Jim Malusa, Kathryn 

Thomas, Bruce Higgins, and Wayne Robbie, the models used to complete this work 

would not exist nor be integrated and calibrated as completely. We would also like 

to thank GIS team members Joe Bernert, Jenny Dimiceli, Mike Polly, Matt Noone, 

and Myrica McCune for their hard work and patience in preparing the required GIS 

data, and Lindsey Wise for database assistance. Thanks also to the LEMMA team, 

including Janet Ohmann, Heather Roberts, and Matthew Gregory, for assistance 

with vegetation mapping. Jamie Barbour was instrumental in obtaining and coordinating ARRA funding, and in project oversight. Ecosystem Management, Inc. 

provided assistance in collecting data, developing the ownership-management layer, 

and building wildlife habitat models for Arizona and New Mexico, along with STM 

construction. We would lastly like to thank Louis Provencher and Tom DeMeo for 

their insightful reviews and suggestions to improve the manuscript, as well as Rich 

Gwozdz, a former member of our team, for his development of the GIS data rollup 

tool, the data rollup query tool, and the GIP tool.

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Chapter 3: Simulating Fire Hazard 

Across Landscapes Through Time: 

Integrating State-and-Transition 

Models With the Fuel Characteristic 

Classification System

Jessica E. Halofsky, Stephanie K. Hart, Miles A. Hemstrom, Joshua S. Halofsky, 

and Morris C. Johnson1

Chapter Summary

Information on the effects of management activities such as fuel reduction treatments and of processes such as vegetation growth and disturbance on fire hazard 

can help land managers prioritize treatments across a landscape to best meet 

management goals. State-and-transition models (STMs) allow landscape-scale 

simulations that incorporate effects of succession, management, and disturbance on 

vegetation composition and structure. State-and-transition models have been used 

for many different types of landscape-scale assessments. However, STMs do not 

currently assess fuels and fire hazard for different vegetation state classes. 

We integrated STMs with a software application called the Fuel Characteristic 

Classification System (FCCS) to enable assessment of fuel properties and fire 

hazard with succession, disturbance, and management across landscapes over time. 

We created FCCS fuel beds from inventory plots for each vegetation state class 

in STMs covering forests and woodlands in Arizona, New Mexico, Oregon, and 

Washington. We used FCCS to analyze each fuel bed for its potential fire behavior, 

and we linked results to STM simulation output to assess potential changes in fire 

hazard with management and natural disturbance regimes over time.

1 Jessica E. Halofsky is a research ecologist, University of Washington, College of the 

Environment, School of Environmental and Forest Sciences, Box 352100, Seattle, WA 

98195-2100; Stephanie K. Hart is an ecologist, University of Washington, School of Environmental and Forest Sciences, Box 352100, Seattle, WA 98195-2100; Miles A. Hemstrom

is a senior scientist, Institute for Natural Resources, Oregon State University, PO Box 751, 

Portland, OR 97207-0751 (formerly a research ecologist, U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 620 SW 

Main St., Suite 400, Portland, OR 97205); Joshua S. Halofsky is a landscape ecologist, 

Washington Department of Natural Resources, PO Box 47000, 1111 Washington Street 

SE, Olympia, WA 98504-7000; and Morris C. Johnson is an ecologist, U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station, Pacific Wildland Fire 

Sciences Laboratory, 400 N 34th Street, Suite 201, Seattle, WA 98103.
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GENERAL TECHNICAL REPORT PNW-GTR-896

The analysis across the four-state study area resulted in thousands of fuel 

beds that cover a broad range of fuel conditions, and the links between these fuel 

beds and STMs can be used to help develop successful fuel treatment regimes 

in fire-prone forests. We present a Washington East Cascades (WEC) case study 

that illustrates potential application of this work. We analyzed potential future fire 

hazard under fire-suppression-only and resilience scenarios for the WEC region. 

We found that crown fire potential was reduced under the resilience scenario; area 

of high crown fire potential was reduced by 13 percent by 2056. However, patterns 

in surface fire potential were obscured by variation in surface fuel characteristics 

within a vegetation state class. The fuels analysis in the WEC gives land managers information they need to prioritize areas for fuel treatments and help them to 

determine what types of activities will result in the greatest reduction in crown fire 

potential.

Introduction

Twentieth-century fire suppression policies have led to fuel accumulations and 

greater risk of high-severity fire in many dry forest types of western North America 

that were historically characterized by relatively high frequency and low- to moderate-severity fire regimes (Allen et al. 2002, Brown et al. 2004, Covington 2003, 

Hessburg et al. 2005). Fire area burned has increased in the Western United States 

over the past few decades (Westerling et al. 2006), and this trend is expected to 

continue with warmer and drier conditions associated with climate change (Littell 

et al. 2010, McKenzie et al. 2004). Climate change may also lead to fires becoming more difficult to control because of more frequent extreme burning conditions 

(Fried et al. 2004). To reduce stem densities and fire intensity and support suppression efforts, vegetation management treatments are often implemented in areas 

characterized by historically low- to moderate-severity fire regimes (Graham et al. 

2004, Peterson et al. 2005). However, the effectiveness of these treatments varies 

by treatment type and treatment intensity within managed forest stands and by 

treatment type, intensity, and arrangement across landscapes (Finney et al. 2007, 

Johnson et al. 2011, Prichard et al. 2010a, Schmidt et al. 2008), making it difficult 

for managers to choose what type of treatments to conduct and where to prioritize 

treatments on a landscape.

Fire hazard, or the potential fire behavior for a fuel type (Hardy 2003), concerns fire and land managers because it gives an indication of the potential fireline intensity, flame lengths, crown fire activity, resistance to control, and potential 

physical and biological effects of fire in a given area of vegetation. Fire hazard also 

reflects the only element of fire behavior that can be affected by management—

fuels. Information on the effects of management activities and forest succession and 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

disturbances on fire hazard can help land managers prioritize vegetation management treatments on a landscape to meet management objectives. 

State-and-transition models (STMs), which subsume vegetation dynamics into 

state classes (boxes) and transitions (arrows), are tools that provide landscape-scale 

information on the effects of management, forest growth and development, and 

natural disturbance on vegetation composition and structure (chapter 2). Thus, 

STMs can provide information that is useful to managers in prioritizing treatments 

across a landscape. The STMs have been used for many types of landscape-scale 

assessments that incorporate potential effects of management on vegetation composition and structure over time (e.g., Arbaugh et al. 2000; Forbis et al. 2006; Hemstrom et al. 2001, 2007; Merzenich et al. 2003; Merzenich and Frid 2005; Ryan et al. 

2006; Weisz et al. 2009). However, STMs do not currently allow direct assessment 

of fuels and fire hazard for different vegetation state classes.

The Fuel Characteristic Classification System (FCCS) (McKenzie et al. 2007; 

Ottmar et al. 2007; Riccardi et al. 2007a, 2007b; Sandberg et al. 2007a, 2007b; 

Schaaf et al. 2007) is a software application that allows users to analyze fuel 

properties and fire potential of wildland and managed vegetation. The FCCS 

analysis involves development of fuel beds (detailed descriptions of all burnable 

biomass, from the litter layer to the canopy), and the software evaluates those fuel 

beds for fire behavior potential (the intrinsic physical capacity of a fuel bed to 

support fire) (Sandberg et al. 2007a, 2007b). The FCCS is a flexible tool that allows 

fuel bed development and analysis for any relatively homogeneous unit. Providing 

an alternative to the categorization of fuel characteristics into standard fuel models 

(e.g., Scott and Burgan 2005), FCCS allows development of detailed fuel beds 

and analysis for any chosen unit of land. The flexibility and detailed analysis that 

characterize FCCS allowed us to integrate FCCS with STMs to enable assessment 

of fuel properties and fire hazard with succession, disturbance, and management 

across landscapes over time.

To integrate FCCS with STMs, we used inventory plot data to construct FCCS 

fuel beds that represent each vegetation state class in STMs covering forested and 

woodland ecosystems in Arizona, New Mexico, Oregon, and Washington. We then 

analyzed the potential fire behavior for each fuel bed and linked the results to STM 

simulation output to assess potential changes in fire hazard with management and 

natural disturbance regimes over time.

This project was conducted as a part of the Integrated Landscape Assessment 

Project (ILAP), which involved the examination of current and potential future 

dynamics of broad-scale, multiownership landscapes by integrating and evaluating 

information about current and future vegetation and related resources (see chapter 1

The Fuel Characteristic 

Classification System 

(FCCS) is a software 

application that allows 

users to analyze 

fuel properties and 

fire potential of 

wildland and managed 

vegetation.
74

GENERAL TECHNICAL REPORT PNW-GTR-896

for further detail on the ILAP project). Linking our fuel bed analysis with output 

from the ILAP STM modeling effort (chapter 2) allowed us to address a number of 

research and management questions, including, (1) How do different forest management scenarios affect fuel conditions and fire hazard across a given landscape? and 

(2) To what extent can fuel treatment programs reduce fire hazard?

This chapter describes methods used to integrate STMs with FCCS in the 

four-state study areas and illustrates results of our process with a case study in the 

Washington East Cascades (WEC) modeling zone. We chose to use a case study to 

illustrate results because results are more clearly displayed and conceptualized at 

the scale of a region than at the scale of the four-state study area. We chose eastern 

Washington as a case study area for two main reasons: (1) the fire and fuels management questions on which ILAP was focused (see chapter 1) are highly relevant 

in this region; and (2) ILAP researchers worked with a land management collaborative in the WEC to get user input on models, output, and management scenarios. 

Thus, the vegetation models and management scenarios for WEC are likely to be 

used by land managers to answer management questions.

Methods

We integrated fuels information into STMs covering forest and woodland 

vegetation types in the states of Arizona, New Mexico, Oregon, and Washington 

(fig. 3.1). The integration of fuels information in STMs for the study area involved 

five main steps:

• Select field-measured inventory plots from existing data sets to represent 

each state class, or vegetation structure and cover combination, in STMs. 

Inventory plots were selected from the U.S. Department of Agriculture 

Forest Service (USDA FS) Forest Inventory and Analysis (FIA) program 

(USDA FS 2012) and Current Vegetation Survey (CVS) program data sets.

• Construct FCCS fuel beds (descriptions of burnable biomass extending 

from the forest floor to the canopy) for each plot.

• Use FCCS to analyze fuel beds for fire hazard (e.g., crown fire potential).

• Summarize fire potentials for all fuel beds representing each state class in 

STMs.

• Link summarized fuel beds and associated fire hazard to results of STM 

simulations.

Each of these steps is described in further detail below.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 3.1—Area covered by forest and woodland vegetation types in the four-state study area.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Plot Selection and Classification Into State-and-Transition Model 

State Classes

State-and-transition models—

Inventory plots were selected to represent combinations of vegetation cover and 

structure within each STM. These combinations of vegetation cover and structure, 

called STM state classes, represent a subset of vegetative conditions found within 

the broader landscape. The STMs are represented by boxes (vegetation state classes) 

and arrows (transitions between state classes). Transitions between state classes are 

either deterministic (occurring with time, e.g., succession) or probabilistic (with a 

given probability of occurring at each time step, e.g., disturbance or management). 

The STM runs incorporate Monte Carlo simulations and track both the state of the 

landscape over time and the occurrence of transitions. The STMs were developed in 

the Vegetation Dynamics Development Tool (VDDT) framework (ESSA Technologies Ltd. 2007) and run using the Path Landscape Model platform (Apex Resource 

Management Solutions 2012; Daniel and Frid 2012). The VDDT and Path simulate 

vegetation dynamics by dividing the landscape into state classes, assigning probabilities to transitions between state classes, and simulating the state of the landscape 

over time using Monte Carlo methods (see chapter 2 for more detailed information 

on STMs).

The ILAP STM modeling effort (chapter 2) encompassed all lands in Arizona, 

New Mexico, Oregon, and Washington. For modeling purposes, Oregon and Washington (OR/WA hereafter) were divided into 12 modeling zones, and Arizona and 

New Mexico (AZ/NM hereafter) were divided into six zones (see maps in chapter 

2). These modeling zones represent Omernik ecoregions (Omernik 1987), with 

boundaries modified to coincide with hydrologic unit code 5 watershed boundaries 

(USGS and USDA NRCS 2011). One STM was built for every potential vegetation 

type (e.g., fig. 3.2) resulting in 7 to 22 models in each modeling zone. Potential 

vegetation type maps were downloaded from Ecoshare (http://ecoshare.info/). Each 

STM was characterized by 5 to 60 state classes.

Inventory plot data—

We used inventory plot data from the FIA and CVS programs to develop FCCS fuel 

beds for forested and woodland STM state classes. We limited our analysis to forests and woodlands (canopy cover >10 percent) because insufficient inventory plot 

data were available to characterize arid lands with canopy cover <10 percent. We 

used the most recent or comprehensive inventory plot data sets available for each 

state (the comprehensive CVS data set for OR/WA, and the most recent FIA data 

sets for AZ (annual) and NM (periodic)). Only the forested portions of CVS inventory plots were used in our analysis in OR/WA because our goal was to characterize 

Inventory plots were 

selected to represent 

combinations of 

vegetation cover and 

structure within each 

STM.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 3.2—Potential vegetation types in the Washington East Cascades modeling zone. One state-and-transition model was 

built for each potential vegetation type. 
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GENERAL TECHNICAL REPORT PNW-GTR-896

fuel conditions in forested ecosystems and information was available at that scale 

for OR/WA. Owing to the unavailability of data at the forested condition level in 

AZ/NM, we used the entire FIA plot database in our analysis for AZ/NM.

Inventory plot classification—

The process used to select inventory plots to represent each state class in the STMs 

differed somewhat between AZ/NM and OR/WA. For AZ/NM, plots were first 

classified to one potential vegetation type, corresponding to one STM, by experts 

using plant association information associated with each plot. Once a plot was classified to a potential vegetation type, it was assigned a specific STM vegetation state 

class based on size class of the dominant cohort (defined by basal area), percentage 

canopy cover, number of canopy layers (1 or >1), and in some cases (i.e., aspen 

cover types) forest type importance value (Horn 1975). The rule-set for classification was vegetation type-specific. Owing to the relatively low number of FIA inventory plots for AZ/NM, inventory plots were used to represent state classes without 

regard for the geographic location of the plot. For example, an inventory plot from 

southwestern Arizona, classified into the dry pine vegetation type, could be used to 

represent a state class in the dry pine vegetation type in northeastern New Mexico. 

We classified a total of 1,734 inventory plots into 62 forest state classes and 1,870 

inventory plots into 49 woodland state classes (state classes were consistent across 

modeling zones in AZ/NM). 

Owing to a greater sample size, we further geographically constrained which 

inventory plots could be used to represent a given potential vegetation type in OR/

WA. For each modeling zone in OR/WA, we considered all inventory plots within 

ECOMAP sections (Cleland et al. 2007) that fell within the modeling zone. For 

example, if four ECOMAP sections fell within a modeling zone boundary, we 

would consider all plots within those four ECOMAP sections, and not just the plots 

within the modeling zone boundary (see fig. 3.3 for illustration). We used plant 

association information (ecoclass codes from Hall 1998) for each plot to determine 

which plots to include in the analysis for each modeling zone. If plant association 

for a plot was determined to not occur in a modeling zone, the plot was dropped 

from the analysis for that region. Each plot was used only once (to represent a single 

state class within an STM) within a modeling zone, but plots could be used in more 

than one modeling zone because ECOMAP sections typically overlapped with multiple zones. Because one to five subplots were aggregated to represent a plot, it was 

possible for individual subplots to represent different potential vegetation types. 

When this occurred, the majority type (covering >60 percent of analysis area) was 

assigned to the plot.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 3.3—Ecomap sections in the Washington East Cascades (WEC) modeling zone. We used 

Ecomap sections to determine which inventory plots to use to represent vegetation state classes in 

state-and-transition models. For each modeling zone in Oregon and Washington, we considered all 

plots within Ecomap sections that were intersected by the modeling zone boundary. In this example, 

the analysis for the WEC modeling zone included all inventory plots that fell within the Columbia 

Basin, Eastern Cascades, Northern Cascades, and Western Cascades Ecomap sections.
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GENERAL TECHNICAL REPORT PNW-GTR-896

In OR/WA, plots determined to be representative of each potential vegetation 

type were put into STM-specific cover categories based on forest type importance 

values as calculated by the Landscape Ecology, Modeling, Mapping, and Analysis 

(LEMMA) team (www.fsl.orst.edu/lemma/splash.php). Plots were then classified into structure categories based on calculated quadratic mean diameter (0 cm 

diameter at breast height (DBH) = grass/forb; <13 cm DBH = seedling/sapling; 13 

to 25 cm DBH = pole; 25 to 38 cm DBH = small; 38 to 51 cm DBH = medium; 51 to 

76 cm DBH = large; >76 cm DBH = giant), percentage canopy cover (<10 percent = 

grass/forb; 10 to 40 percent = low; 40 to 60 percent = medium; >60 percent = high), 

and number of canopy layers (1 = single or >1 = multiple) (these variables were also 

calculated by the LEMMA team). However, because a broader suite of conditions 

exist on a landscape than are modeled, many plots were reclassified to fit into one of 

the STM boxes. Our reclassification rules allowed plots with the same species cover, 

canopy cover, and canopy layers to either shift up or down one diameter size class. 

For example, a plot in the 25- to 38-cm DBH category could be reclassified into a 

state class in the 13- to 25-cm or 38- to 51-cm DBH category. Despite this potential 

reclassification by diameter, some plots still did not fit into any of the state classes 

included in the model, and these plots were dropped from the analysis. We classified a total of 10,581 inventory plots into 3,716 state classes in OR/WA (see table 3.1 

for a modeling zone-specific list of number of state classes and number of inventory 

plots classified into state classes; see table 3.2 for a list of specific state classes and 

number of plot classified into each state class for the WEC modeling zone).

Once our classification of inventory plots into STM potential vegetation types 

and state classes was complete, we found some STM state classes had no representative plots (e.g., see table 3.2). In those cases, we chose plots with characteristics 

similar to the missing state class to represent the state class. We first selected plots 

from the size class above or below the missing state class (within potential vegetation and cover type). If there were no plots in the size classes above or below that 

of the missing state class, we selected plots with the same structural attributes in 

a similar potential vegetation type or species cover. If representative plots were 

still not found, we considered plots with a different number of layers but otherwise 

identical attributes to that of the missing state class. For example, in multiple zones, 

many medium canopy closure, single-storied state classes had no representative plots in the large and medium size classes. In such cases, we used plots with 

medium canopy closure and multiple layers within the same vegetation type, cover 

We classified a total 

of 10,581 inventory 

plots into 3,716 state 

classes in Oregon and 

Washington.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

type and size class to represent the missing state class. Best judgment was used in 

the remaining cases. If no suitable plots were found to represent a state class, we did 

not include that state class in our analysis.

Although it is likely that some of the inventory plots used in our analysis were 

actively managed or experienced natural disturbance not long before the measurements were taken, we were not able to account for the management and disturbance 

history of the inventory plots in our analysis. Thus, one type of STM state class 

that was not covered by inventory data was postdisturbance state classes, which 

were included in all OR/WA STMs (but generally not in AZ/NM STMs). For the 

postdisturbance state classes in OR/WA, we used a set of expert-developed postwildfire fuel beds from similar vegetation types (R. Ottmar, Central Oregon and 

Okanogan-Wenatchee Fuel Succession Pathways, http://www.fs.fed.us/pnw/fera/

fccs/applications/oakwen.shtml) to represent the postdisturbance state classes (see 

table 3.3 for an example list of postdisturbance state classes and representative fuel 

beds for the WEC).

Table 3.1—Number of state classes (combinations of vegetation cover and 

structure; excluding development state classes) that characterized state-andtransition models for each modeling zone in Oregon and Washington, and 

number of inventory plots classified into state classes in each zonea

Number of Number of inventory plots 

Modeling zone	 state classes	 classified into state classes

Oregon Blue Mountains 330 2,301

Oregon Coast Range 368 824

Oregon East Cascades 491 1,675

Oregon Southeast 134 321

Oregon Southwest 342 854

Oregon West Cascades 421 1,075

Washington Columbia Basin 477 1,117

Washington Coast Range 229 821

Washington East Cascades 207 3,000

Washington North Cascades 228 875

Washington Northeast 253 519

Washington West Cascades 236 1,008

a Total number of inventory plots classified into state classes was 10,581. Plots were used only once within a 

modeling zone but could be used in more than one modeling zone.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Table 3.2—State classes (combinations of vegetation cover and structure) included in state-andtransition models for the Washington East Cascades modeling zone (excluding postdisturbance state 

classes; see table 3.4), and number of inventory plots classified into and corresponding fuel beds built 

for each state class

Potential Canopy Canopy Number of

vegetation type Cover typea Size classb density layers fuel beds

Dry mixed conifer Douglas-fir/ Grass/forb NA NA 0

 grand fir

Seedling/ Low NA 1

 sapling

Pole Low Single 7

 Medium Single 4

Small Low Single 6

 Multiple 5

 Medium Single 0

 Multiple 23

 High Single 19

 Multiple 34

Medium Low Single 10

 Multiple 7

 Medium Single 0

 Multiple 25

 High Single 0

 Multiple 46

Large Low Single 13

 Multiple 9

 Medium Single 0

 Multiple 61

 High Single 0

 Multiple 94

Giant Low Single 6

 Multiple 6

Ponderosa Grass/forb NA NA 50

 pine

Seedling/ Low NA 8

 sapling

Pole Low Single 9

 Medium Single 1

Small Low Single 10

 Multiple 4

 Medium Single 2

 Multiple 7

Medium Low Single 9

 Multiple 4

 Medium Single 0

 Multiple 17

Large Low Single 12

 Multiple 7

 Medium Single 0

 Multiple 7

Giant Low Single 2

 Multiple 5
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 3.2—State classes (combinations of vegetation cover and structure) included in state-andtransition models for the Washington East Cascades modeling zone (excluding postdisturbance state 

classes; see table 3.4), and number of inventory plots classified into and corresponding fuel beds built 

for each state class (continued)

Potential Canopy Canopy Number of

vegetation type Cover typea Size classb density layers fuel beds

Dry pine Ponderosa Grass/forb NA NA 72

 pine

Seedling/ Low NA 6

 sapling

Pole Low Single 33

 Medium Single 25

Small Low Single 39

 Multiple 41

 Medium Single 3

 Multiple 61

Medium Low Single 58

 Multiple 58

 Medium Single 6

 Multiple 46

Large Low Single 75

 Multiple 60

 Medium Single 0

 Multiple 40

Giant Low Single 21

 Multiple 8

Moist mixed conifer Grand fir Grass/forb NA NA 0

Seedling/ Low NA 10

 sapling

Pole Low Single 10

 Medium Single 14

 High Single 27

Small Low Single 11

 Multiple 0

 Medium Single 0

 Multiple 14

 High Single 0

 Multiple 66

Medium Low Single 8

 Multiple 3

 Medium Single 1

 Multiple 27

 High Single 0

 Multiple 108

Large Low Single 2

 Multiple 8

 Medium Single 0

 Multiple 45

 High Single 1

 Multiple 173

Giant Low Single 1

 Multiple 1
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GENERAL TECHNICAL REPORT PNW-GTR-896

Table 3.2—State classes (combinations of vegetation cover and structure) included in state-and-transition 

models for the Washington East Cascades modeling zone (excluding postdisturbance state classes; see table 

3.4), and number of inventory plots classified into and corresponding fuel beds built for each state class 

(continued)

Potential Canopy Canopy Number of

vegetation type Cover typea Size classb density layers fuel beds

 Medium Single 0

 Multiple 11

 High Single 0

 Multiple 81

Ponderosa Grass/forb NA NA 23

 pine

Seedling/ Low NA 9

 sapling

Pole Low Single 19

 Medium Single 7

 High Single 2

Small Low Single 5

 Multiple 3

 Medium Single 2

 Multiple 11

 High Single 0

 Multiple 9

Medium Low Single 5

 Multiple 4

 Medium Single 0

 Multiple 7

 High Single 0

 Multiple 10

Large Low Single 6

 Multiple 2

 Medium Single 0

 Multiple 14

 High Single 0

 Multiple 9

Giant Low Single 2

 Multiple 0

 Medium Single 0

 Multiple 1

 High Single 0

 Multiple 3

Mountain hemlock Lodgepole Grass/forb NA NA 0

 pine

Seedling/ Low NA 0

 sapling

Pole Low Single 11

 Medium Single 20

 High Single 28

Small Low Single 5

 Medium Single 0

 High Single 0

Mountain Grass/forb NA NA 23

 hemlock
85

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 3.2—State classes (combinations of vegetation cover and structure) included in state-and-transition 

models for the Washington East Cascades modeling zone (excluding postdisturbance state classes; see table 

3.4), and number of inventory plots classified into and corresponding fuel beds built for each state class 

(continued)

Potential Canopy Canopy Number of

vegetation type Cover typea Size classb density layers fuel beds

Seedling/ Low NA 9

 sapling

Pole Low Single 16

 Medium Single 3

Small Low Single 5

 Medium Single 1

 Multiple 10

Medium Low Single 5

 Medium Single 0

 Multiple 22

 High Multiple 378

Large Low Single 4

 Medium Single 1

 Multiple 14

Oak/pine Grass/shrub Open shrub Low NA 1

Oregon white Pole Low Single 2

 oak/ponderosa 

 pine

 Medium Single 0

Small Low Single 4

 Medium Multiple 0

Medium Low Single 0

 Medium Multiple 0

Pacific silver fir Pacific silver Grass/forb NA NA 11

 fir mix

Seedling/ Low NA 22

 sapling

Pole High Single 28

Small Medium Single 18

 High Single 0

Medium Medium Single 0

 High Single 1

 Multiple 113

Large Medium Single 0

 High Multiple 158

Giant Medium Single 0

 High Multiple 117

Subalpine parkland Subalpine fir Grass/forb NA NA 5

Seedling/ Low NA 2

 sapling

Pole Medium Single 5

Small Medium Single 1

Medium Medium Single 0

NA = not applicable.

a Cover type was determined for each plot using calculated forest type importance value.

b Size class was determined using quadratic mean diameter (0 = grass/forb; <13 cm DBH = seedling/sapling; 13 to 25 cm DBH = pole; 25 to 38 cm DBH 

= small; 38 to 51 cm DBH = medium; 51 to 76 cm DBH = large; >76 cm DBH = giant). Plots with canopy density of <10 percent were classified as grass/

forb, while plots with canopy density of 10 to 40 percent were classified as low, 40 to 60 percent medium, and >60 percent high. 
86

GENERAL TECHNICAL REPORT PNW-GTR-896

Table 3.3—Postdisturbance state classes in state-and-transition models for the Washington East 

Cascades modeling zone, and brief descriptions of expert-based postdisturbance fuel beds used to 

represent the postdisturbance state classesa

Potential vegetation type Cover type Tree size class Representative fuel bed description

Dry mixed conifer Douglas-fir/grand fir Grass/forb Douglas-fir, ponderosa pine, grand fir 

 (dry site) (post-wildfire)

Seedling/sapling Douglas-fir, ponderosa pine, grand fir 

 (dry site) (post-wildfire)

Pole Douglas-fir, ponderosa pine, grand fir 

 (dry site) (post-wildfire)

Small Douglas-fir, ponderosa pine, grand fir 

 (dry site) (post-wildfire)

Medium Douglas-fir, ponderosa pine, grand fir 

 (dry site) (post-wildfire)

Large Douglas-fir, ponderosa pine, grand fir 

 (dry site) (post-wildfire)

Ponderosa pine Grass/forb Ponderosa pine (post-wildfire)

Seedling/sapling Ponderosa pine (post-wildfire)

Pole Ponderosa pine (post-wildfire)

Small Ponderosa pine (post-wildfire)

Medium Ponderosa pine (post-wildfire)

Large Ponderosa pine (post-wildfire)

Giant Ponderosa pine (post-wildfire)

Dry pine Ponderosa pine Grass/forb Ponderosa pine (post-wildfire)

Seedling/sapling Ponderosa pine (post-wildfire)

Pole Ponderosa pine (post-wildfire)

Small Ponderosa pine (post-wildfire)

Medium Ponderosa pine (post-wildfire)

Large Ponderosa pine (post-wildfire)

Giant Ponderosa pine (post-wildfire)

Moist mixed conifer Grand fir Grass/forb Douglas-fir, grand fir (post-wildfire)

Seedling/sapling Douglas-fir, grand fir (post-wildfire)

Pole Douglas-fir, grand fir (post-wildfire)

Small Douglas-fir, grand fir (post-wildfire)

Medium Douglas-fir, grand fir (post-wildfire)

Large Douglas-fir, grand fir (post-wildfire)
87

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 3.3—Postdisturbance state classes in state-and-transition models for the Washington East 

Cascades modeling zone, and brief descriptions of expert-based postdisturbance fuel beds used to 

represent the postdisturbance state classesa (continued)

Potential vegetation type Cover type Tree size class Representative fuel bed description

Ponderosa pine Grass/forb Ponderosa pine, Douglas-fir, western 

 larch (post-wildfire)

Seedling/sapling Ponderosa pine, Douglas-fir, western 

 larch (post-wildfire)

Pole Ponderosa pine, Douglas-fir, western 

 larch (post-wildfire)

Small Ponderosa pine, Douglas-fir, western 

 larch (post-wildfire)

Medium Ponderosa pine, Douglas-fir, western 

 larch (post-wildfire)

Large Ponderosa pine, Douglas-fir, western 

 larch (post-wildfire)

Giant Ponderosa pine, Douglas-fir, western 

 larch (post-wildfire)

Mountain hemlock Lodgepole pine Grass/forb Lodgepole pine (post-wildfire)

Seedling/sapling Lodgepole pine (post-wildfire)

Mountain hemlock Grass/forb Mountain hemlock

Seedling/sapling Mountain hemlock

Pacific silver fir Pacific silver fir mix Grass/forb Western hemlock, Pacific silver fir, 

 mountain hemlock (post-wildfire)

Seedling/sapling Western hemlock, Pacific silver fir, 

 mountain hemlock (post-wildfire)

Small Western hemlock, Pacific silver fir, 

 mountain hemlock (post-wildfire)

Medium Western hemlock, Pacific silver fir, 

 mountain hemlock (post-wildfire)

Large Western hemlock, Pacific silver fir, 

 mountain hemlock (post-wildfire)

Giant Western hemlock, Pacific silver fir, 

 mountain hemlock (post-wildfire)

a All representative fuel beds for the Washington East Cascades were from the Okanogan-Wenatchee Fuel Succession Pathways project 

(http://www.fs.fed.us/pnw/fera/fccs/applications/oakwen.shtml). 
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GENERAL TECHNICAL REPORT PNW-GTR-896

Building Fuel Beds

The FCCS defines a fuel bed as the inherent physical characteristics of fuels that 

contribute to fire behavior and effects (Riccardi et al. 2007a). The FCCS fuel beds 

are stratified into six strata that represent every fuel element that has the potential 

to combust, including canopy, shrubs, nonwoody fuels, woody fuels, litter, lichen, 

moss, and ground fuels (fig. 3.4). We used inventory plot data to calculate the 

variables to build one or more FCCS fuel beds for each STM state class (fuel bed 

variables listed in table 3.4). We chose to construct one fuel bed for each inventory 

plot and used one to many plots to represent each state class, rather than statistically 

summarizing data from multiple inventory plots to construct a single composite 

fuel bed for each state class, because we wanted to use real world fuels assemblages 

to represent the state classes instead of creating a composite fuel bed that may not 

exist under natural conditions.

Figure 3.4—Strata and categories of vegetation information included in Fuel Characteristic Classification System (FCCS) 

fuel beds. We summarized FCCS fuel bed attributes for each state class (vegetation cover and structure combinations) in 

state-and-transition models of vegetation growth and dynamics. (Figure from Ottmar et al. 2007.)

We used inventory 

plot data to calculate 

the variables to build 

one or more FCCS fuel 

beds for each STM 

state class.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 3.4—Fuel Characteristic Classification System fuel bed variables used in the calculation of 

physical characteristics and properties of wildland fuelsa

Fuel strata Category Subcategory Variable

Canopy Total canopy Percentage cover

Trees Overstory Percentage cover

Midstory Height (m)

Understory Height to live crown (m)

Density (number of stems/ha)

Diameter at breast height (cm)

Species and relative cover (%)

Snags Class 1 with foliage Density (number of stems/ha)

Class 1 without foliage Diameter (cm)

Class 2 Height (m)

Class 3 Species and relative cover (%)

Ladder fuels Arboreal lichens Minimum height (m)

` and moss

Climbing ferns and Maximum height (m)

 other epiphytes Is there vertical continuity

Dead branches sufficient to carry fire

Leaning snags between the canopy and

Stringy or fuzzy bark lower fuel strata?

Tree regeneration (yes/no)

 Vines–liana

Shrub Primary layer Percentage cover

Secondary layer Height (m)

Percentage live

Species and relative cover (%)

Needle drape Is needle drape on shrubs 

 sufficient to affect fire behavior?

 (yes/no)

Nonwoody fuels Primary layer Percentage cover

Secondary layer Height (m)

Percentage live

Loading (tons/ha)

Species and relative cover (%)

Woody fuels All woody Total percentage cover

Depth (m)

Sound wood All sound wood For >7.6 cm sound wood—

 Size classes— Species and relative cover (%)

 <0.6 cm, >0.6 to For size classes—

 2.5 cm, >2.5 to Loading (Mg/ha)

 7.6 cm, >7.6 to

 22.9 cm, >22.9 to 

 50.8 cm, >50.8 cm

Rotten wood All rotten wood For all rotten wood—

 Size classes— Species and relative cover (%)

 >7.6 to 22.9 cm, For size classes—

 >22.9 to 50.8 cm, Loading (Mg/ha)

 >50.8 cm
90

GENERAL TECHNICAL REPORT PNW-GTR-896

Table 3.4—Fuel Characteristic Classification System fuel bed variables used in the calculation of physical 

characteristics and properties of wildland fuelsa (continued)

Fuel strata Category Subcategory Variable

Stumps Sound Density (number of stumps/ha)

Rotten Diameter (cm)

Lightered-pitchy Height (m)

Species and relative cover (%)

Woody fuel Piles Width (m)

 accumulation Jackpots Length (m)

Windrows Height (m)

Density (number of 

 accumulations/ha)

Litter-lichen-moss Litter Arrangement— For overall litter—

 Fluffy, normal, Depth (cm)

 perched

Type— Percentage cover

 Short needle pine, For each litter type—

 long needle pine, Relative cover (%)

 other conifer, 

 broadleaf deciduous, 

 broadleaf evergreen, 

 palm frond, grass

Lichen None Depth (cm)

Moss Type— Percentage cover

 Spaghnum, other moss Depth (cm)

Ground fuels Duff Percentage rotten wood For percentage rotten wood—

Upper layer— Percentage cover

 Partially decomposed For duff layers—

 dead moss and litter, Depth (cm)

 partially decomposed Percentage cover

 sphagnum moss 

 and sedge

Lower layer—

 Fully decomposed dead 

 moss and litter, fully 

 decomposed sphagnum 

 moss and sedge

Squirrel middens None Depth (cm)

Radius (m)

Density (number of middens/ha)

Basal accumulations Type— Depth (cm)

 Bark slough, branches, Radius (m)

 broadleaf deciduous, Percentage of trees affected

 broadleaf evergreen, 

 grass, needle litter, 

 palm fronds

a Adapted from Prichard et al. 2010b. 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Inventory data did not include all of the potential inputs for FCCS, including 

information on ladder fuels, needle drape, fine (0 to 7.6 cm) woody fuel depth, 

woody fuel accumulations (piles, jackpots, windthrows), stumps, litter, lichens, 

moss, and ground fuels (duff, squirrel middens, and basal accumulations). With the 

exception of fine woody fuel depth, missing variables were not required by FCCS 

to calculate fire potentials for a fuel bed and were omitted. Fine woody fuel depth 

was estimated for each fuel bed as described below. Owing to a lack of inventory 

plot information that would indicate otherwise, we also assumed 100 percent of the 

shrub and nonwoody cover was live and assumed that only a primary shrub and 

nonwoody layer was present.

Fine woody fuel depth was estimated from formulas developed for the Forest 

Vegetation Simulator Fire and Fuels Extension (Reinhardt and Crookston 2003; see 

addendum). Formulas used fuel loading variables for which we had information 

(e.g., 10-hour fuels (diameter 0.64 to 2.54 cm), and 100-hour fuels (diameter 2.5 

to 7.6 cm). The exception was 1-hour (diameter <0.64 cm) fuel loading information, which was estimated by matching inventory plots to timber-understory and 

timber-litter fire behavior fuel models in Scott and Burgan (2005) based on 10-hour, 

100-hour, live herb, and live woody fuel loads, and using the 1-hour fuel load value 

from the matching fire behavior fuel model.

In some cases, categories for variables in FCCS did not match those of our 

information sources, so we had to reclassify accordingly. For example, FCCS 

requires information on snag decay class and uses a four-category system, including 

class 1 with foliage, class 1 without foliage, class 2, and class 3. The FIA (annual) 

and CVS programs use a five-class system and do not collect information on snag 

foliage. Therefore, based on the descriptions for both classification systems (Cline et 

al. 1980 for FIA/CVS classification, Prichard et al. 2010b for FCCS classification), 

we cross-walked the categories between the two systems (FIA/CVS class 1 with 

FCCS class 1 without foliage, FIA/CVS class 2 with FCCS class 2, and FIA/CVS 

class 3–5 with FCCS class 3). Similarly, FCCS differentiates between two categories 

of down wood (sound and rotten). The FIA protocol similarly differentiates between 

sound and rotten down wood. However, with CVS protocols, down wood was put 

into one of three decay classes. Therefore, based on the description of the CVS classification system in the metadata for the database, we classified decay classes 1 and 

2 in the FCCS sound category and decay class 3 in the FCCS rotten category. 

Inventory data were used to calculate many of the variables needed for FCCS. 

For example, percentage cover of fine woody fuels was calculated from inventory 

plot data using equations described in Woodall and Monleon (2009). Also, total 

canopy cover and average height of overstory, midstory, and understory canopy 
92

GENERAL TECHNICAL REPORT PNW-GTR-896

layers were calculated using equations from the Forest Vegetation Simulator 

(Crookston and Stage 1999).

Calculated variables from inventory plot data were used to build 10,581 fuel 

beds in OR/WA and 3,604 fuel beds for AZ/NM. These fuel beds were analyzed in 

FCCS as described below.

Fire Hazard Analysis

We used FCCS to analyze fire hazard of fuel beds constructed from inventory 

plot data. Based on fuel characteristics, FCCS calculates fire potentials, including 

surface fire behavior potential (Sandberg et al. 2007a, 2007b), crown fire potential 

(Schaaf et al. 2007), and available fuel potential (Sandberg et al. 2007a), which rate 

the intrinsic physical capacity of a fuel bed to support surface fire, crown fire, and 

consume and smolder fuels, respectively (Ottmar et al. 2007). The FCCS fire potentials are indexed values, scaled between 0 and 9, and are based on default environmental conditions (6.4 km/h for midflame windspeed, and 0, 30, and 60 percent 

moisture content for the dead, herbaceous, and live moisture contents, respectively) 

(Sandberg et al. 2007a, 2007b). With user inputs of fuel moisture and windspeed 

values, FCCS also calculates surface fire behavior outputs including reaction intensity (kJ/m2

), rate of spread (m/s), and flame length (m) (Sandberg 2007b).

To calculate surface fire behavior outputs from FCCS fuel beds, we used fuel 

moisture and windspeed values that are typical during extreme fire weather in dry 

forest types (Agee and Lolley 2006, Ager et al. 2010; fuel moistures of 3 percent 

for 1-hour fuels, 4 percent for 10-hour fuels, 6 percent for 100-hour fuels, 7 percent 

for 1000-hour fuels (diameter 7.6 to 20.3 cm), 31 percent for nonwoody fuels, 90 

percent for shrub and crown fuels, and 20 percent for duff, and a midflame windspeed value of 36 km/h). We did not account for topography within inventory plots 

(slope was set at zero for FCCS analysis) because we were using inventory plots 

to represent a general condition (an STM state class) with no defined topography. 

Increased slope leads to increased fireline intensity and surface fire rate of spread 

(Rothermel 1983), and thus setting slope at zero for FCCS analysis resulted in lower 

estimates of surface fire behavior potentials than those that would have resulted if 

slope were increased. 

Summarizing Fuel Bed Information and Linking to 

State-and-Transition Model Output

We associated calculated FCCS fire behavior and fire potential variables for fuel 

beds with the appropriate STM state class. Because each STM state class could 

have multiple fuel beds to represent it, we calculated a mean and standard error for 

all fire behavior and potential variables for each state class. Once we had calculated 

We used FCCS to 

analyze fire hazard of 

fuel beds constructed 

from inventory plot 

data.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

means for fuel bed variables for each STM state class, we linked the mean fuel bed 

information to STM simulation output for each modeling zone to look at trends in 

fuels and fire potential over time. For spatial displays, we took the area-weighted 

average of each variable for each modeling stratum (a combination of watershed, 

potential vegetation type, and ownership-management; see chapter 2). Geodatabases used in this analysis are available at www.WesternLandscapeExplorer.com.

The STMs were run under a fire-suppression-only (FSO) scenario in all modeling zones in both OR/WA and AZ/NM. The FSO scenario was characterized by 

current levels of fire suppression (i.e., current fire frequency based on a 25-year 

Monitoring Trends in Burn Severity record for the study area; Eidenshink et al. 

2007; see chapter 2 for further detail) but no other land management actions. For the 

WECs, Oregon East Cascades, Oregon Blue Mountains, and Washington Northeast 

modeling zones in OR/WA, STMs were also run under a resilience scenario, developed by ILAP to reflect management activities that could be undertaken to increase 

resilience of dry forests on the east side of the Cascades in Oregon and Washington. 

Thus, under the resilience scenario, management treatments were focused in the dry 

forest types, including oak-pine, dry pine, and dry mixed-conifer vegetation types. 

Prescribed fire was conducted on USDA FS and U.S. Department of the Interior 

Bureau of Land Management (USDI BLM) lands, excluding federally protected 

lands such as wilderness, with 1 to 4 percent of the available landscape treated with 

prescribed fire annually. Thinning from below was also conducted in dry forest 

types on USDA FS and USDI BLM lands, with annual area treated ranging from 

0.005 to 5 percent of high- and medium-density stands and 0.25 to 1 percent of low 

density stands. On state and tribal lands, thinning from below was conducted on 

1.25 to 5 percent of medium and dense forests, while on private industrial lands, 10 

percent of available land was treated annually. Planting was conducted across ownerships and management allocations on 2.5 to 20 percent of available lands. Salvage 

logging was conducted on 5 to 20 percent of available federal lands and 12.5 to 50 

percent of available state and tribal lands. See chapter 2 and www.WesternLandscapeExplorer.com for more detail on how scenarios were run.

Results

We built over 14,000 fuel beds characterizing approximately 3,800 vegetation conditions (state classes) in forests and woodlands of Arizona, New Mexico, Oregon, 

and Washington. A database with complete lists of inventory plots classified into 

each STM state class, and fuel bed input and output data is located at www.WesternLandscapeExplorer.com. Example fuel bed output for state classes in the WEC 

is shown in table 3.5, and results for the WEC are discussed further below.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Table 3.5—Mean flame length and crown fire potential (unit-less index) for state classes in the Washington 

East Cascades state-and-transition modelsa

Potential Canopy	 Canopy	 Flame	 Crown fire

vegetation type Cover type Size class density layers length potential

 Meters

Dry mixed conifer Douglas-fir/ Grass/forb NA NA 6.7 2.1

 grand fir

Postdisturbance Low NA 8.2 1.8

Seedling/sapling Low NA 1.3 1.9

Pole Low Single 0.1 1.5

 Medium Single 0.4 2.4

Small Low Single 0.5 1.3

 Multiple 0.1 1.1

 Medium Single 0.4 2.4

 Multiple 0.4 2.2

 High Single 0.5 3.2

 Multiple 0.4 2.8

Medium Low Single 0.4 1.4

 Multiple 0.4 1.3

 Medium Single 0.4 2.3

 Multiple 0.4 2.3

 High Single 0.5 3.2

 Multiple 0.6 3.1

Large Low Single 0.7 1.7

 Multiple 0.5 1.6

 Medium Single 0.6 2.6

 Multiple 0.6 2.6

 High Single 0.9 3.5

 Multiple 0.4 3.2

Giant Low Single 0.5 1.1

 Multiple 0.5 1.4

Ponderosa Grass/forb NA NA 0.7 0.4

 pine

Postdisturbance Low Single 3.4 1.6

Seedling/sapling Low NA 1.1 3.2

Pole Low Single 0.7 1.8

 Medium Single 1.3 4.0

Small Low Single 0.7 1.8

 Multiple 0.4 1.1

 Medium Single 0.7 2.8

 Multiple 0.4 2.0

Medium Low Single 0.5 1.5

 Multiple 0.7 1.9

 Medium Single 0.5 2.1

 Multiple 0.5 2.1

Large Low Single 0.6 1.2

 Multiple 0.4 1.2

 Medium Single 0.8 2.7

 Multiple 0.8 2.7

Giant Low Single 0.2 0.5

 Multiple 0.5 1.3
95

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 3.5—Mean flame length and crown fire potential (unit-less index) for state classes in the Washington 

East Cascades state-and-transition modelsa (continued)

Potential Canopy	 Canopy	 Flame	 Crown fire

vegetation type Cover type Size class density layers length potential

Dry pine Ponderosa Grass/forb NA NA 0.6 0.9

 pine

Postdisturbance Low Single 3.4 1.6

Seedling/sapling Low NA 0.4 1.8

Pole Low Single 0.4 1.8

 Medium Single 0.4 2.2

Small Low Single 0.5 1.6

 Multiple 0.4 1.4

 Medium Single 0.3 1.6

 Multiple 0.5 2.3

Medium Low Single 0.4 1.3

 Multiple 0.4 1.5

 Medium Single 0.2 1.3

 Multiple 0.3 2.1

Large Low Single 0.5 1.4

 Multiple 0.3 1.4

 Medium Single 0.2 1.3

 Multiple 0.4 2.5

Giant Low Single 0.4 1.4

 Multiple 0.4 1.6

Moist mixed conifer Grand fir Grass/forb NA NA 2.0 2.1

Postdisturbance Low Single 2.4 2.3

Seedling/sapling Low NA 0.4 2.5

Pole Low Single 0.6 2.3

 Medium Single 0.9 3.3

 High Single 0.7 3.5

Small Low Single 1.0 2.5

 Multiple 0.2 2.0

 Medium Single 0.7 2.1

 Multiple 0.4 2.4

 High Single 0.7 3.5

 Multiple 0.6 3.8

Medium Low Single 0.3 1.4

 Multiple 0.2 2.0

 Medium Single 0.7 2.1

 Multiple 0.5 2.8

 High Single 0.9 3.5

 Multiple 0.5 3.5

Large Low Single 1.1 1.4

 Multiple 0.6 1.4

 Medium Single 0.6 2.8

 Multiple 0.6 2.8

 High Single 0.9 3.5

 Multiple 0.5 3.7

Giant Low Single 1.0 2.6

 Multiple 0.8 2.6
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GENERAL TECHNICAL REPORT PNW-GTR-896

Table 3.5—Mean flame length and crown fire potential (unit-less index) for state classes in the Washington 

East Cascades state-and-transition modelsa (continued)

Potential Canopy	 Canopy	 Flame	 Crown fire

vegetation type Cover type Size class density layers length potential

 Medium Single 0.7 2.7

 Multiple 0.7 2.7

 High Single 0.7 3.5

 Multiple 0.7 3.5

Ponderosa pine Grass/forb NA NA 0.7 0.6

Postdisturbance NA NA 0.8 1.9

Seedling/sapling Low NA 1.1 2.4

Pole Low Single 0.8 2.5

 Medium Single 0.5 2.7

 High Single 0.1 3.7

Small Low Single 1.0 2.4

 Multiple 1.2 2.1

 Medium Single 1.0 2.7

 Multiple 0.7 2.4

 High Single 0.2 2.9

 Multiple 0.2 2.9

Medium Low Single 0.5 1.7

 Multiple 0.7 2.3

 Medium Single 1.0 2.7

 Multiple 0.1 2.6

 High Single 0.2 2.9

 Multiple 0.2 3.3

Large Low Single 0.7 1.6

 Multiple 0.5 2.2

 Medium Single 1.2 3.3

 Multiple 1.2 3.3

 High Single 0.9 3.5

 Multiple 0.6 3.6

Giant Low Single 0.5 0.9

 Multiple 0.5 2.2

 Medium Single 1.2 3.3

 Multiple 0.5 1.8

 High Single 0.9 3.5

 Multiple 0.5 2.4

Mountain hemlock Lodgepole Grass/forb NA NA 1.8 4.0

 pine

Postdisturbance NA NA 1.8 4.0

Seedling/sapling Low NA 0.1 1.0

Pole Low Single 0.1 1.0

 Medium Single 0.3 2.4

 High Single 0.4 3.1

Small Low Single 0.4 1.4

 Medium Single 0.3 2.4

 High Single 0.4 3.1
97

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 3.5—Mean flame length and crown fire potential (unit-less index) for state classes in the Washington 

East Cascades state-and-transition modelsa (continued)

Potential Canopy	 Canopy	 Flame	 Crown fire

vegetation type Cover type Size class density layers length potential

Mountain Grass/forb NA NA 0.5 0.6

 hemlock

Postdisturbance NA NA 10.6 3.9

Seedling/sapling Low NA 0.4 2.2

Pole Low Single 0.7 2.0

 Medium Single 0.8 3.4

Small Low Single 0.4 1.7

 Medium Single 0.8 4.1

 Multiple 0.4 2.5

Medium Low Single 0.4 1.2

 Medium Single 0.5 2.8

 Multiple 0.5 2.8

 High Multiple 0.4 4.2

Large Low Single 0.4 1.6

 Medium Single 1.4 3.7

 Multiple 0.5 3.1

Oak/pine Grass shrub Open shrub Low NA 0.9 1.5

Oregon white Pole Low Single 0.9 3.1

 oak/

 ponderosa 

 pine

 Medium Single 0.9 3.1

Small Low Single 0.2 0.7

 Medium Multiple 0.2 0.7

Medium Low Single 0.2 0.7

 Medium Multiple 0.2 0.7

Pacific silver fir Pacific silver Grass/forb NA NA 0.8 1.1

 fir mix

Postdisturbance NA NA 5.8 3.5

Seedling/sapling Low NA 0.7 2.7

Pole High Single 0.4 3.7

Small Medium Single 0.6 3.1

 High Single 0.4 3.7

Medium Medium Single 0.6 3.1

 High Single 0.6 2.7

 Multiple 0.3 3.7

Large Medium Single 0.6 2.6

 High Multiple 0.4 3.8

Giant Medium Single 0.6 2.6

 High Multiple 0.6 4.0

Subalpine parkland Subalpine fir Seedling/sapling Low NA 0.4 2.5

Pole Medium Single 0.5 2.9

Small Medium Single 0.1 3.4

Medium Medium Single 0.1 3.4

Grass/forb NA NA 0.1 0.8

NA = not applicable. a Inventory plots were classified into each state class, fuel beds were built from inventory plot data, and fuel beds were analyzed for potential fire 

behavior in the Fuel Characteristic Classification System. Each state class is represented by at least one inventory plot, and thus values represent the 

means for fuel beds classified into each state class. 
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GENERAL TECHNICAL REPORT PNW-GTR-896

Washington East Cascades Case Study

For the WEC modeling zone (figs. 3.2 and 3.3), we focused on two indicators of fire 

hazard: crown fire potential and flame length. Crown fire potential gives an indication of the potential for fire to spread into, and propagate through, the canopy of 

forests and woodlands. The FCCS crown fire potential index is based on whether 

the energy supplied by a surface fuel bed layer is sufficient to ignite and sustain fire 

spread in the canopy. Flame length, or the distance from the ground at the leading 

edge of the flame to the tip of the flame, is an indicator of surface fuels and potential surface fire behavior and can be interpreted to determine wildfire suppression 

strategies (Andrews and Rothermel 1982).

Crown fire potential—

Although the crown fire potential index was >7 (on a scale of 0 to 9) for some individual fuel beds in the WEC, the mean crown fire potential index for any state class 

in the WEC did not exceed 4.2 (table 3.5). To examine crown fire potential results in 

the region on a relative scale, we classified the state classes into low, moderate, and 

high crown fire potential categories by ordering the data based on crown fire potential and separating it into thirds. Although we cannot associate these categories 

with specific information on potential crown fire intensity and resulting mortality, 

we can assume that the sites in the high crown fire potential category would have a 

high likelihood of experiencing stand-replacing fire, while those in the low category 

would have a relatively low likelihood of experiencing stand-replacement fire.

For both the entire landscape and in the dry forest types (dry mixed conifer and 

dry ponderosa pine) crown fire potential increased over time under the FSO scenario but decreased over time under the resilience scenario (figs. 3.5 and 3.6). For 

the entire landscape under the FSO scenario, area in the high crown fire potential 

category increased by 61 163 ha (from 977 549 ha in 2007 to 1 038 711 ha in 2056), 

but under the resilience scenario, area in the high crown fire potential category 

decreased by 110 341 ha (from 966 336 ha in 2007 to 855 995 ha in 2056; fig. 3.5). 

In addition, in 2056, area in the low crown fire potential category was lower under 

the FSO scenario compared to the resilience scenario (141 936 ha versus 279 843; 

fig. 3.5).

In the dry mixed conifer and dry ponderosa pine types, which are the vegetation types in which fuel treatments such as thinning and prescribed fire are focused 

in the WEC, crown fire potential increased over time under the FSO scenario, 

with increases in area in the high crown fire potential category (from 211 087 ha in 

2007 to 281 094 ha in 2056) and moderate crown fire potential category (from 132 

484 ha in 2007 to 173 130 ha in 2056) and a decrease in area in the low crown fire 

potential category (from 228 247 ha in 2007 to 117 586 ha in 2056; fig. 3.6). Crown 

For both the entire 

landscape and in the 

dry forest types (dry 

mixed conifer and dry 

ponderosa pine) crown 

fire potential increased 

over time under the 

FSO scenario but 

decreased over time 

under the resilience 

scenario.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

(a)

(b)

Figure 3.5—Area of the Washington East Cascades landscape in low, moderate, and high crown fire potential categories under (a) fire suppression only, and (b) resilience scenarios, as simulated by state-and-transition models. Dashed 

lines represent minimum and maximum area in each category across Monte Carlo simulations. Crown fire potential 

was assessed for model state classes by using inventory plots to represent each state class, building fuel beds with the 

inventory plot data, and analyzing the fuel beds in the Fuel Characteristic Classification System.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Figure 3.6—Area of the dry mixed conifer and dry pine potential vegetation types in low, moderate, and high crown fire 

potential categories under (a) fire suppression only, and (b) resilience scenarios, as simulated by state-and-transition models 

for the Washington East Cascades landscape. Dashed lines represent minimum and maximum area in each category across 

Monte Carlo simulations. 

(a)

(b)
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 3.7—Crown fire potential for the Washington East Cascades region for current conditions (2007; left panel), simulated 2056 conditions under a fire-suppression-only (FSO) scenario (center panel), and simulated 2056 conditions under 

a resilience scenario (right panel). Crown fire potential was assessed for state-and-transition model state classes by using 

inventory plots to represent each state class, building fuel beds with the inventory plot data, and analyzing the fuel beds 

in the Fuel Characteristic Classification System. The FSO scenario was characterized by current levels of fire suppression 

(i.e., current fire frequency based on a 25-year Monitoring Trends in Burn Severity record for the study area) but no other 

land management actions. The resilience scenario was characterized by light to moderate levels of thinning and some 

prescribed fire in dry forest types. The area-weighted average for crown fire potential index was calculated for each modeling stratum (potential vegetation type, ownership, and land allocation within a watershed), and each modeling stratum was 

categorized into the low, moderate, and high crown fire potential category for the spatial display.

fire potential also increased over time under the resilience scenario, but not to the 

same degree as under the FSO scenario. Under the resilience scenario, area in the 

high crown fire potential category remained relatively constant (206 309 ha in 2007 

versus 208 377 ha in 2056), while area in the moderate category increased (from 

134 950 ha in 2007 to 175 384 ha in 2056), and area in the low category decreased 

(from 230 552 ha in 2007 to 188 055 ha in 2056; fig. 3.6).

These patterns were also apparent when results were examined spatially (fig. 

3.7). Although crown fire potential was not substantially reduced compared to current conditions in the resilience scenario, crown fire potential was lower under the 

resilience scenario than under the FSO scenario by 2056, particularly in the lower 

elevation dry forest types where fuel treatments are focused (generally the southeastern and eastern portions of the study area; see dry pine, dry mixed-conifer, and 

moist mixed-conifer potential vegetation types in fig. 3.2). 
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GENERAL TECHNICAL REPORT PNW-GTR-896

Flame length—

Patterns in potential flame length did not differ substantially between the FSO and 

resilience scenarios in the WEC. Under both scenarios, there was an increase in 

potential flame length over time, with a decrease in area in the low flame length 

category (<1.2 m), and slight increases in the area in the higher flame length categories (fig. 3.8). These patterns were similar between all lands (fig. 3.8) and the dry 

forest types (not shown). 

Discussion

Management Applications

Our integrated FCCS-STM approach can give land managers information they need 

to determine the types, extent, and locations of management activities that will 

result in the greatest reduction in crown fire potential. Acquiring this information is 

a critical step in the development of successful fuel treatment regimes in fire-prone 

forests. Fire hazard information can be used alone or in conjunction with other 

types of information, such as wildlife habitat and economic potential, to prioritize 

areas for fuel treatments or other types of management activities. Analyses integrating fuels data with data on wildlife habitat, economic potential, and community 

economics are forthcoming products of ILAP.

The thousands of fuel beds developed for this work cover a broad range of fuel 

conditions in both OR/WA and AZ/NM. The link between these fuel beds and 

specific forest and woodland compositional and structural conditions has potential 

for other types of applications other than fire hazard analysis. For example, fuel 

beds generated from this work are being used in USDA FS Region 3 (AZ/NM) to 

assess potential emissions from wildfire using a software program that is complementary to FCCS, called CONSUME (Prichard et al. 2005). In addition, inventory 

data summaries could be used to assess wildlife habitat suitability in different STM 

state classes. 

Washington East Cascades Case Study

Results of the integrated FCCS-STM simulations in the WEC region indicated 

that a management regime characterized by targeted fuel treatments in dry forest 

types can lead to lower crown fire potential than a FSO management regime. High 

crown fire potential is associated with stand-replacing fire events. Avoiding standreplacement fire events and maintaining substantial live basal area is often a goal 

of vegetation management treatments (Agee and Skinner 2005), and our results 

suggest that the resilience scenario tested in this study would be successful in 

reducing the likelihood of stand-replacement fire events in dry forests compared 

to an FSO scenario.

Results of the 

integrated FCCS-STM 

simulations in the 

WEC region indicated 

that a management 

regime characterized 

by targeted fuel 

treatments in dry 

forest types can lead 

to lower crown fire 

potential than a FSO 

management regime. 
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(a)

(b)

Figure 3.8—Area of the Washington East Cascades landscape in four flame length categories under (A) fire suppression only, and (B) resilience scenarios, as simulated by state-and-transition models. Each flame length category reflects 

thresholds in ease of fire suppression (Andrews and Rothermel 1982).
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GENERAL TECHNICAL REPORT PNW-GTR-896

Although crown fire potential was reduced under the resilience scenario 

compared to the FSO scenario in the WEC, resilience activities did not decrease 

potential flame length in the study area. These results illustrate that forest thinning 

to reduce tree density, which was the focus of the resilience scenario evaluated 

in this study, does not necessarily reduce surface fuels and surface fire hazard, 

which is reflected in flame length (Agee and Skinner 2005). Instead of reducing 

surface fire hazard, thinning activities can increase surface fuel levels and surface 

fire intensity (Johnson et al. 2007, Raymond and Peterson 2005), and thus surface 

fuel treatments are essential to reduce surface fire hazard after thinning treatments 

(Agee and Skinner 2005).

The flame length results also illustrate a limitation to our approach. Although 

flame length potential did vary substantially among individual inventory plots used 

to represent the STM state classes (with flame lengths up to 10 m), mean flame 

lengths were much less variable (with most below 1.2 m). The state classes in STMs 

represent a general condition and are based primarily on forest and woodland overstory structure, whereas flame lengths are based largely on surface fuels. In addition, we could not account for the management and disturbance history of inventory 

plots used to develop fuel beds. Thus, representative plots for each state class had 

variable surface fuel levels, and this variation obscured patterns when surface fuel 

variables such as potential flame length were averaged by state class.

Future Improvements and Research Needs

There are several ways this analysis could be improved for future applications. 

First, although we used the most comprehensive inventory plot data set available in 

OR/WA (CVS), this data set only covered national forests in the two states. Thus, 

expanding the analysis to use more recent FIA annual data would help us to cover a 

broader range of vegetation/fuels conditions than what exists on national forests. We 

also discovered that inventory plot data for arid lands with sufficient detail for fuel 

bed characterization are lacking, particularly in Oregon and Washington. Further 

field-based data will be needed to integrate fuels information with arid lands STMs 

in the four states. We were also lacking information on postdisturbance fuel conditions, and thus more field-based data on postdisturbance conditions for different 

vegetation types and structural conditions would improve the estimates of fire hazard for postdisturbance state classes. Finally, to more accurately capture effects of 

management and disturbance on surface fuels and potential surface fire hazard, our 

approach would need to be modified. One way to do this would be to differentiate 

state classes based on surface fuel properties (e.g., include a ponderosa pine, large 

size class, open canopy, single layer, low surface fuels, and a ponderosa pine, large 

size class, open canopy, single layer, high surface fuels state class), and incorporate 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

surface fuel characteristics in the plot classification process. Another way would 

be to do a post-hoc analysis of effects of specific treatments on surface fuel properties using the Forest Vegetation Simulator (Dixon 2003), and use estimates of area 

affected by those treatments to determine effects on surface fire hazard.

Acknowledgments

We thank Paige Eagle for her critical work in creating files and running fuel beds 

through the FCCS batch processor. Jim Menlove graciously conducted analysis 

on understory and down wood for FIA plots in AZ/NM. Rich Gwozdz created 

an invaluable tool to help us classify inventory plots into state classes in Oregon 

and Washington, and Emilie Henderson, Joe Bernert, and Jack Triepke helped us 

with plot classification in AZ/NM. Joe Bernert created maps of output. Andrew 

Gray, Matt Gregory, Heather Roberts, and Karen Waddell provided inventory plot 

analysis advice. Roger Ottmar, Susan Prichard, and Anne Andreu provided advice 

on fuel bed development and analysis. Ryan Haugo and Susan Prichard provided 

helpful reviews of an earlier version of the manuscript. The Integrated Landscape 

Assessment Project was funded by the American Recovery and Reinvestment Act, 

and the U.S. Department of Agriculture Forest Service Pacific Northwest Research 

Station, Pacific Northwest Region, and Southwest Region.

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Chapter 4: Overview of the Vegetation 

Management Treatment Economic 

Analysis Module in the Integrated 

Landscape Assessment Project

Xiaoping Zhou and Miles A. Hemstrom1

Chapter Summary

Forest land provides various ecosystem services, including timber, biomass, and 

carbon sequestration. Estimating trends in these ecosystem services is essential for 

assessing potential outcomes of landscape management scenarios. However, the 

state-and-transition models used in the Integrated Landscape Assessment Project 

for simulating landscape changes over time do not directly compute the variables 

necessary for volume and biomass estimation. This study illustrates a methodology 

for modeling timber production and biomass supply potential over time in central 

Washington, and gives an example on cost-benefit analysis of fuel treatment under 

different management scenarios to provide information on economic viability of 

potential vegetation management activities.

Introduction

The Integrated Landscape Assessment Project (ILAP) explores the dynamics of 

broad-scale, multiownership landscapes and develops information that can be 

used to prioritize management activities over time by evaluating and integrating 

information on fuel conditions, wildlife and aquatic habitats, economic values, 

and projected climate change in Arizona, New Mexico, Oregon, and Washington. 

Products from ILAP will help land managers, planners, and policymakers evaluate management strategies that reduce fire risk, improve habitat, and benefit rural 

communities.

This chapter documents the fuel treatment economic analysis completed as 

part of ILAP. This work involved analyzing economic feasibilities of proposed fuel 

treatments and addressed the following questions under alternative management 

scenarios: (1) Where might fuel treatments create biomass and timber products, 

and how much are they likely to create? (2) How much might it cost to harvest the 

1 Xiaoping Zhou is a research economist, U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station, Forestry Sciences Laboratory, 620 SW Main St., 

Suite 400, Portland, OR 97205; and Miles A. Hemstrom is a senior scientist, Institute 

for Natural Resources, Oregon State University, PO Box 751, Portland, OR 97207-0751 

(formerly a research ecologist, U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station, Forestry Sciences Laboratory, 620 SW Main St., Suite 400, 

Portland, OR 97205).
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GENERAL TECHNICAL REPORT PNW-GTR-896

timber and transport biomass from specific areas? How does transportation distance 

affect the financial outcome of fuel treatments? and (3) How might government 

subsidies and other related policies affect the financial viability of fuel treatments?

The major objectives of this chapter are to (1) document methodology that 

can be used to estimate the potential timber volume and biomass for alternative 

landscape management scenarios, (2) describe procedures for economic assessment of vegetation management treatments related to alternative land management 

scenarios, and (3) present some preliminary results of the fuel treatment economic 

analysis in a central Washington study area (tools developed and results from this 

work can be found at www.westernlandscapesexplorer.info).

Study Area

The study area is approximately 1 million ha of mostly forested lands in central 

Washington (fig. 4.1). It includes 25 hydrologic unit code (HUC) 5 watersheds 

(USGS and USDA NRCS 2011) and 16 combinations of ownership and land 

management allocations (see chapter 2). Forested vegetation ranges from very 

dry environments that support ponderosa pine stands to upper elevation forests of 

subalpine fir and mountain hemlock. Much of the forest consists of mixed-conifer 

stands dominated by Douglas-fir, grand fir, and ponderosa pine.

Methods

State-and-Transition Models

We used state-and-transition models (STMs) developed by ILAP (chapter 2) to 

simulate the effects of natural disturbances and management treatments on forested 

vegetation. The STM approach treats vegetation as combinations of cover type 

and structural stages (boxes) within different biophysical environments. Boxes are 

linked by arrows (transitions) that represent natural disturbances, management 

actions, or vegetation growth and development. For example, grass/forb herblands 

might become dominated by small trees and shrubs after a period of time or might 

remain as grass/forb communities following wildfire. This approach builds on 

transition matrix methods that represent vegetation development as a set of transition probabilities among various vegetative state classes (e.g., Hann et al.1997, Hemstrom et al. 2007, Keane et al.1996, Laycock 1991, Westoby et al.1989). The STMs 

were developed in the Vegetation Development Dynamics Tool framework, version 

6.0.25, (VDDT; ESSA Technologies Ltd. 2007) and run using the Path modeling 

platform, version 3.0.4 (Apex Resource Management Solutions 2012, Daniel and 

Frid 2012).
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Figure 4.1—Central Washington study area.
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Forest structural stages in the STMs were based on tree diameter, overstory 

canopy cover, and canopy layering (chapter 2). Forest diameter classes were based 

on the quadratic mean diameter of the dominant and codominant trees: (1) seedling/

sapling (>0 to 13 cm diameter at breast height [DBH]), (2) young (>13 to 25 cm 

DBH), (3) small (>25 to 38 cm DBH), (4) medium (>38 to 51 cm DBH), (5) large 

(>51 to 75 cmDBH, and (6) very large (>75 cm DBH). Canopy cover classes were 

open (0 to 40 percent), medium (41 to 60 percent), and closed (>60 percent). Canopy 

layering was either single or multiple. A grass-forb condition represented early-seral 

state classes before establishment of a significant tree canopy. Postdisturbance state 

classes were also included, representing conditions resulting from a stand-replacing 

disturbance (insect outbreak or wildfire) prior to any salvage logging and consequently containing abundant large dead wood.

A current vegetation map was developed for the study area (chapter 2). The vegetation was mapped using nearest neighbor imputation techniques based on random 

forest nearest neighbor (Crookston and Finley 2008) and gradient nearest neighbor 

(Ohmann and Gregory 2002) methods. A rule set was developed for classifying the 

current vegetation to STM state classes within each vegetation type. These data 

became the initial conditions for STM simulations. Burcsu et al. (chapter 2) used 

these initial conditions to project future vegetation conditions, management activities, and natural disturbances for alternative management scenarios. The resulting 

vegetation, disturbance, and management activity projections were summarized by 

time step, potential vegetation, and state class.

Two forest management scenarios were applied in this study. The fire-suppression-only (FSO) scenario assumed that no management treatments were practiced 

except continued current levels of wildfire suppression. The current management 

(CM) scenario was based on estimated forest management treatment rates currently 

in place by land ownership and land management allocation (ownership-management; see chapter 2). Management differed widely among ownerships and management allocations from no treatments (except wildfire suppression) in wilderness 

and similar reserved areas, to commercial timber harvest on private timberlands. 

The treatment rates by ownership-management were gathered as part of an ongoing 

local collaboration. Treatments in the CM scenario included regeneration harvests, 

commercial thinnings, precommercial thinning, planting, prescribed fire, and 

mechanical fuel treatments. Importantly, timber harvests from federal lands did 

not include trees over 53 cm in diameter. Wildfire probabilities were computed by 

potential vegetation groups (dry, moist, and cold forest) using data from the Monitoring Trends in Burn Severity project (Eidenshink et al. 2007; www.mtbs.gov), and 

reflect wildfire occurrence in the Washington East Cascades ecological region for 

the 1984–2008 time period (chapter 2).
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Volume and Biomass Look-Up Tables

The STM simulation output does not carry detailed vegetation attributes over time 

such as tree species, diameter, and height, which are key variables for estimating 

timber volume and tree biomass. The challenge for users interested in information 

about timber products and biomass is how to quantify timber production and biomass supply from vegetation management treatments and other disturbances with 

STM simulation output. The U.S. Department of Agriculture Forest Service (USDA 

FS) Forest Inventory and Analysis (FIA; USDA FS 2012) plot and tree information 

were used to build look-up tables with volume and biomass attributes linked to 

each state class for each ILAP modeling zone (see chapter 2; fig. 2.4). The process 

involved the following major steps:

1. Gather detailed tree information from the FIA periodic and annual plots 

that occurred in the study area and in the same potential vegetation in adjacent ILAP modeling zones.

2. Assign each plot to a state class within the study area (chapter 2).

3. Calculate volume and biomass for each plot (Zhou and Hemstrom 2010).

4. Summarize volume and biomass attributes, as well as other desired variables, for each plot by product group or by total.

5. Compute attribute averages for each STM state class and build a look-up 

table with the computed attributes associated with each state class.

6. Fill in holes for those state classes with no plot samples by using a rule set 

that substituted the most similar state class with plot samples.

Look-up tables were built in step five for each modeling zone. Volume attributes included in look-up tables were total volume (DBH ≥2.5 cm), merchantable 

tree volume (DBH ≥12.7 cm), and sawtimber volume (DBH ≥22.9 cm for softwood 

and DBH ≥27.9 cm 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. Five products were defined in this study based on 

tree diameters: (1) small tree with DBH <12.7 cm; (2) chip tree with DBH ≥12.7 

cm, and <17.8 cm; (3) pole tree with DBH ≥17.8 cm and <22.9 cm for softwood and 

27.9 cm for hardwood; (4) small sawtimber, a sawtimber tree with DBH ≥22.9 cm 

for softwood and 27.9 cm for hardwood and <50.8 cm; and (5) large sawtimber, a 

sawtimber tree with DBH ≥50.8 cm.

The attributes included in the look-up table were then linked to STM output, 

and the desired attributes were summarized for each modeling stratum by year.
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Vegetation Management Treatment Economic Analysis

Timber products and biomass were estimated annually to examine potential economic outcomes from the two alternative land management scenarios. The process 

included the estimation of removed materials, harvesting costs, the transportation 

costs, and the market values of the removed timber, as well as the values of the 

woody biomass delivered to known mill locations.

Estimation of removals—

Various management activities, such as harvesting and prescribed fires, were 

applied to achieve ecological objectives and reduce fuel accumulations. The STM 

simulations forecasted annual area treated with different management activities for 

each year by modeling stratum and state class. In addition, model output included 

the transition pathway (“from” state class, and “to” state class) for every management transition that occurred in the simulations. Using this information, removed 

volume and biomass were calculated based on the difference of volume and biomass 

in the “from” state class and the “to” state class. Treatment was assumed to have 

occurred in the middle of the year, and growth was not considered during the treatment year. Merchantable timber volume was calculated for trees with DBH ≥12.7 

cm, including pulpwood size trees (chip or pole trees) and sawtimber size trees. The 

sawtimber volume is the tree volume with DBH greater than sawtimber size (22.9 

cm and above for softwood and 27.9 cm and above for hardwood). Aboveground 

biomass was calculated including stem, bark, branch, top, and leaves of all trees.

Harvesting cost—

The Fuel Reduction Cost Simulator (FRCS; Fight et al. 2006) software was modified and used to estimate harvesting costs, including removal of trees of mixed 

sizes in the form of whole trees, logs, or chips. In FRCS, equipment production 

rates were developed from existing studies, and cost assumptions for labor and 

equipment were modified with the most current available information. The FRCS 

includes four ground-based systems, four cable systems, and two helicopter systems. The helicopter systems were not included owing to high costs. Harvesting 

estimates generated by FRCS were in U.S. dollars per 100 ft3

, or per 2.83 m3. The 

harvesting costs included five major components: felling (and bunching), skidding, 

processing, loading, chipping (if applicable), and machine move-in cost.

Stumpage and product prices—

Stumpage price differs among regions and ownership. The average stumpage price 

for eastern Washington on public timberland was about $147 per thousand board 

feet (MBF) from 2003 to 2010, with an average of $218/MBF for the Washington 

state-owned timberland and $76/MBF for the Forest Service-owned timberland 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

(Warren 2011). The average log export price from port districts in Washington and 

Oregon to major Asian countries (China and South Korea) was used as a ceiling 

log price. The averaged saw-log price to these two Asian countries from 2006 

to 2010 was about $560/MBF from Seattle and Columbia-Snake districts, $619/

MBF to China, and $500/MBF to South Korea (Warren 2011). The pulpwood size 

timber was valued as average chip export prices from Seattle and Columbia-Snake 

districts, with an average of $75/tonne (t) from 2006 to 2010, $56/t from Seattle and 

$93/t from the ports of Columbia-Snake districts. The potential woody biomass 

supply included the removed trees with diameter at breast height <12.7 cm, plus the 

tops and branches from the larger removed trees. We assumed $33 per bone dry 

tonne of delivered biomass.

Transportation cost—

The transportation cost of biomass substantially affected the economic feasibility of 

the biomass supply. The distance from each watershed to all the biomass facilities 

was estimated, and only the facilities with transportation distance <402 km were 

considered for biomass supply. The total transportation cost depended on hauling 

distance, the truck load, labor, and fuel costs. Travel time for transporting biomass 

from watershed source areas to various mills was calculated based on posted road 

speed limit. Railroad transportation was not considered for this study. We assumed 

22.7 t per truck load and $4.40 per hour and per tonne truck for transportation costs.

Results

Standing Biomass and Timber Volume

Total aboveground live tree biomass peaked at year 44 for both scenarios, at 214 

billion dry tonnes for the FSO scenario and at 204 billion dry tonnes under the CM 

scenario; it declined after that time and then stabilized or slightly increased near 

the end of the simulation period (fig. 4.2a). Total standing merchantable tree inventory followed the same trajectory as biomass; it increased rapidly for both the CM 

and FSO scenarios during the first three decades, stabilized for 10 to 20 years, and 

then decreased. The timber inventory under the CM scenario peaked at 0.31 billion 

m3

 around year 33, about 13.5 percent higher than the initial inventory, and then 

declined. The inventory under the FSO scenario reached its maximum of 0.33 billion m3

 at year 40 and remained steady for about a decade, then declined (fig. 4.2b). 

In both scenarios, timber inventories at the end of simulation period were above 

their initial level. The merchantable tree inventory under the FSO scenario ended 

about 10 percent higher than under the CM scenario.
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(a)

(b)

Simulation year

With management Without Management

Simulation year

With management Without Management

Figure 4.2—(a) Total aboveground live tree biomass in the central Washington landscape, and (b) total merchantable volume in 

the central Washington landscape.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Removals From Vegetation Management Treatments

We examined the first 50 years of simulation results for the economic analysis. The 

removals of merchantable tree volume from USDA FS and Yakama Nation tribal 

lands increased gradually for the first two to three decades and then stabilized, 

while the removals from private lands were relatively low during the entire simulation period owing to the small area of privately owned forest land in the study area 

(fig. 4.3a). Removals of merchantable tree volume from the Washington state-owned 

forest lands were about 68 percent of the total removals at the beginning of the simulation period, decreased dramatically to less than 40 percent of the total after three 

decades, then stabilized at 20 to 25 percent of the total at the end of the simulation 

(fig. 4.3a). These patterns reflect differences in management treatments and initial 

forest conditions on different ownership-management strata. Management treatments were more likely, given appropriate stand conditions, on Washington state 

and tribal lands compared to those on lands managed by the USDA FS. Conversely, 

much of the forested land in the Yakama reservation had been treated following 

extensive, recent insect damage, while state lands had a mixed history and many 

had not been recently managed. Lands managed by the USDA FS were generally not 

recently managed, and there was considerable area in reserves that are not subject 

to much active management. As a result, removals rates reflected (1) relatively 

stable levels on tribal lands, (2) initially high then declining levels on state lands, as 

most of the available area became managed, and (3) stable and relatively low levels 

on USDA FS lands constrained by lower management probabilities and relatively 

extensive reserves. The same general patterns emerged for biomass removals.

The woody biomass supply from USDA FS and Yakama tribal lands increased 

gradually over the first two to three decades and then stabilized, while the removals 

from private land remained at relatively low levels during the simulation period 

(fig. 4.3b). Total biomass removals from USDA FS lands averaged 12 percent of 

the total biomass removals initially, and reached 25 percent of the total at the end 

of 50 years. Biomass removals from tribal lands during the same period increased 

from 19 to 35 percent. The biomass supply potentials from Washington state-owned 

forest were about 65 percent of the total initially, and decreased dramatically for the 

first half of the century to 35 percent of the total potential biomass supply. The contribution of woody biomass from private forest land in the study area was relatively 

small, but increased from 3.8 to 5.4 percent of the total over 50 simulation years.

The quantity of potential biomass supply varied substantially by watershed and 

ownership-management (fig. 4.3c). Four major watersheds produced woody biomass 

over 181,000 dry tonnes each over the 50-year simulation period, while other watersheds produced much less. Most of the Yakama Tribe contributions were from two 

watersheds. The biomass removals in four watersheds were all from Yakama Tribal 

lands. Washington state lands produced most of the biomass from three watersheds.
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(a)

(b)

(c)

Figure 4.3—(a) Merchantable volume removals (trees with diameter at breast height [DBH] >12.7 

cm) from fuel treatments by ownership under the current management scenario, (b) potential biomass 

(trees with DBH <12.7 cm and residues) supply from fuel treatments by ownership under the current 

management scenario, and (c) total biomass (DBH <12.7 cm plus branch and top) of removals over 50 

years by watershed and ownership.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Economic Analysis

The economic outcome of vegetation management treatments was determined by 

the total cost of the treatment and the possible revenues generated from the removed 

products, including commercial timber and woody biomass. For this study, woody 

biomass supply included all removed trees <12.7 cm DBH, plus the branches and 

tops from the larger removed trees, estimated in bone-dry tonnes. Trees above 12.7 

cm in diameter were considered as timber products. The biomass value was also 

affected by the distance from the supply point to the processing facility.

The analyses in this section addressed two major questions under the CM scenario: (1) For given timber prices, would selling only commercial timber products 

be profitable? (2) Would adding woody biomass as a commercial product for fuel 

energy improve the economic outcome?

We estimated the total stumpage value from potential treatments under the CM 

scenario to be over $15 million annually. To simplify the calculation process, and 

for the purposes of this paper, we assumed an average stumpage price of $218/MBF 

for all ownerships (fig. 4.4a), likely an overestimate for stumpage on USDA FS land. 

The average harvesting cost for all ownership-management ranges from $159/MBF 

to $170/MBF, with USDA FS averaging from $169 to $191/MBF (fig. 4.4b).

Potential profit varied considerably among owners. Assuming an average 

stumpage price of $218/MBF, figure 4.5a shows the potential treated hectares with 

positive profits at different log prices of $360, $380, $400, $420, and $450. When 

log prices averaged $360/MBF, about 10 percent of the area treated generated 

profits, mostly Yakama and private forest land. Almost all treated area on USDA 

FS land lost money at this log price. However, almost all treated hectares generated 

profit when log prices were above $450/MBF, regardless of ownership. Figure 4.5b 

maps the profitability for the first 30 years for each watershed at the saw-log price 

of $400/MBF and pulpwood log price of $75 per dry tonne, with timber as the 

only product for different ownerships. In this case, profits occurred for USDA FS 

managed lands in 4 out of 19 watersheds, Washington state owned lands in 2 of 13 

watersheds, private lands in 8 of 21 watersheds, and Yakama tribal lands in five of 

seven watersheds.

Including woody biomass as biofuel made a considerable difference to profits 

(fig. 4.6a). For example, at a log price of $380/MBF, selling the woody biomass 

added 1 to 7 percent to the area that could be treated at a profit. However, the 

marginal benefit of selling woody biomass decreased as the log price increased. 

When log price was $420/MBF and more, the marginal benefit of selling the woody 

biomass was insignificant (fig. 4.6a).

Potential profit 

varied considerably 

among owners 

including woody 

biomass as biofuel 

made a considerable 

difference to profits. 
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GENERAL TECHNICAL REPORT PNW-GTR-896

The quantity of potential biomass supply from watersheds and the distance 

or transportation time considerably changed the economic outcome of using woody 

biomass as fuel. Depending on where the biomass facilities were located, the 

contribution of selling woody biomass as products increased or decreased the total 

revenue. At a saw-log price of $400/MBF, pulpwood log price of $75 per dry tonne, 

a biomass price of $33 per dry tonne, and delivery to the processing facility with 

cheapest transportation costs, all the ownerships in all watersheds benefitted 

Figure 4.4—(a) Stumpage value by owner (at a price of $218 per thousand board feet [MBF]), and (b) average 

harvesting cost by owner.

(a)

(b)
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

from selling the woody biomass. In addition, compared to selling timber only, all 

owner groups increased the number of watersheds with positive economic returns 

when biomass sales were included (fig. 4.6b). In this case, profits came from 

USDA FS lands in 5 of 19 watersheds, Washington state owned lands in 3 of 13 

watersheds, private lands in 13 of 21 watersheds, and Yakama tribal lands in six 

of seven watersheds.

Figure 4.5—(a) The potential treated hectares with positive profits at different log prices of $360, $380, 

$400, $420, and $450 (including timber volume only), and (b) the profitability of timber only by owner and 

watershed. Profits are 30-year totals in dollars.

(a)

(b)
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GENERAL TECHNICAL REPORT PNW-GTR-896

Figure 4.6—(a) The potential treated hectares with positive profits at different log prices of $360, $380, 

$400, $420, and $450 (timber and timber plus biomass (dotted lines), and (b) profitability of both timber 

and biomass by owner and watershed. Profits are 30-year totals in dollars.

(a)

(b)
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Discussion

Management to achieve forest health and restoration objectives can be expensive. 

Many management treatments could produce a variety of potentially valuable forest 

products, but economic costs and benefits differ widely depending on the kind of 

product, transportation distances, product prices, and other factors. Although the 

ecological value of treatments is often debated, the fact is, treatments cost money. 

The realities of treatment economics play a critical role in the selection of a set of 

treatments to achieve objectives. In a setting where ecological restoration and forest 

health are primary considerations, the value of forest products generated by management treatments may be relatively low. This suggests that several land managers 

and organizations in a large landscape may need to coordinate treatments to generate enough product value to make overall forest objectives economically viable. In 

many cases in the Western United States, federal lands make up a large portion of 

the area that might generate forest products in the course of restoration, but declining budgets, transportation costs, product values, treatment costs, and available 

infrastructure can severely limit the ability of federal land managers to implement 

restoration treatments.

Our study illustrates procedures that can be used for economic analysis of 

STM simulations across large landscapes. Vegetation management treatment may 

effectively treat dense stands to create a desirable mix of future stand conditions 

to meet forest health and habitat objectives and to reduce fire hazard. However, the 

cost of harvesting and the limited use of small trees removed from treatment can 

directly affect the economics of management practices. The study presented in this 

paper for the central Washington landscape reveals that the economic outcome of 

fuel treatments differs significantly among owners and watersheds, depending on 

the quality of the timber and the market value of logs and woody biomass. Although 

we focused on vegetation management treatments, any combination of treatments to 

achieve a wide variety of forest conditions could be addressed with our methods.

We found that adding the woody biomass as biofuel could add value to the 

treatment if the cost of processing small trees and branches onsite is low and the 

facility is relatively nearby. In general, we found that when the log prices were low, 

the marginal benefit of adding biomass as a commercial product was higher, but 

that marginal benefit diminished as log price increased. In our CM scenario, the 

area that could be profitably treated annually increased by 5 to 10 percent when 

log prices were $400/MBF or less (fig. 4.6a). Economic values associated with 

biomass production were primarily limited by transportation costs. Even though 

this scenario did not harvest trees over 53 cm diameter on federal lands, the production of timber products drove most of the economic value derived from treatments. 
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GENERAL TECHNICAL REPORT PNW-GTR-896

This does not necessarily imply that biomass harvest could not provide benefits to 

local communities. It does imply that, in this area and under this scenario, biomass 

generated by the proposed treatments is of relatively small marginal value.

Conclusions

The estimation of the materials or products removed from treatments, product 

market projections, and bioenergy policies all affected the economic outcome of 

the CM scenario. When considered across all lands, the CM scenario generated net 

positive revenue if the price of log was above the sum of harvesting cost and stumpage value, and the cost of transporting woody biomass was lower than that value. 

But revenues differed widely by ownership, treatment type, and general landscape 

location. In general, Yakama tribal and privately owned lands with lower harvesting 

cost generated more net positive revenues, while public lands with higher harvesting costs generated net negative revenues. By working together in a collaborative 

fashion, the local land managers might generate additional positive benefits. Our 

process, as part of ILAP, allows land managers, policymakers, and others to evaluate potential economic benefits in conjunction with the effects that alternative 

scenarios might have on wildlife habitat conditions, wildfire and fuel hazards, and 

other important resource values.

Acknowledgments

The Integrated Landscape Assessment Project was funded by the American 

Recovery and Reinvestment Act, and the U.S. Department of Agriculture Forest 

Service Pacific Northwest Research Station, Pacific Northwest Region, and Southwest Region.

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Chapter 5: Application of State-andTransition Models to Evaluate Wildlife 

Habitat

Anita T. Morzillo, Pamela Comeleo, Blair Csuti, and Stephanie Lee1

Summary

Wildlife habitat analysis often is a central focus of natural resources management 

and policy. State-and-transition models (STMs) allow for simulation of landscapelevel ecological processes, and for managers to test “what if” scenarios of how 

those processes may affect wildlife habitat. This chapter describes the methods 

used to link STM output to wildlife habitat to determine how estimated habitat 

varies across the landscape, how habitat is affected by different land management 

scenarios, and how management might enhance estimated habitat for particular species. Using the Washington East Cascades as an example, we provide sample output 

of habitat analysis for the American marten and western bluebird under two management scenarios. Wildlife habitat assessments based on the methods illustrated 

here will differ greatly based on habitat characteristics important to individual 

species, and the ability to interpret wildlife information accurately.

Introduction

Wildlife habitat analysis and conservation often are a central focus of natural 

resources management for both ecological and social objectives. Ecologically, 

wildlife is an indicator of ecosystem conditions. Each species depends on a range 

of ecosystem features for life activities such as foraging, roosting, nesting, denning, and hiding from predators (Bolen and Robinson 1999). Therefore, presence or 

absence of a species in a location assumed to contain habitat characteristics linked 

to that species may provide clues about both habitat condition and integrity of ecological processes (Grimm 1995). Wildlife also has social value (Decker et al. 2001). 

This value is illustrated by hunting and fishing fees paid, wildlife viewing (e.g., 

birdwatching), and the wealth of existing nonprofit groups that focus on wildlife 

(e.g., World Wildlife Fund, National Audubon Society, Boone and Crockett Club). 

Ultimately, wildlife plays an important role in both ecological and social systems.

1 Anita T. Morzillo is an assistant professor, Department of Natural Resources and the Environment, University of Connecticut, 1376 Storrs Rd., Storrs, CT 06269 (formerly a landscape 

ecologist, Oregon State University, Department of Forest Ecosystems and Society, 321 Richardson Hall, Corvallis, OR 97331); Pamela Comeleo is a research assistant, Oregon State University, Department of Forest Ecosystems and Society, 321 Richardson Hall, Corvallis, OR 97331; 

Blair Csuti is a research associate, Oregon State University, Department of Forest Ecosystems 

and Society, 321 Richardson Hall, Corvallis, OR 97331; and Stephanie Lee is a biologist, 

Ecosystem Management, Inc., 3737 Princeton Drive NE, Suite 150, Albuquerque, NM 87107.

Wildlife habitat is 

relevant to both 

ecological and social 

natural resource 

objectives.
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GENERAL TECHNICAL REPORT PNW-GTR-896

The importance of wildlife within both ecological and social contexts has 

resulted in many wildlife-related policies. Among the most notable federal policies 

is the Endangered Species Act (ESA) which was established “to protect and recover 

imperiled species and the ecosystems upon which they depend” (USFWS 2011). 

Regional and state-level policies, such as the U.S. Department of Agriculture Forest 

Service (USDA FS) Northwest Forest Plan (USDA FS 1997) and state land harvest 

regulations, respectively, may complement or supplement federal guidelines for 

wildlife conservation, or focus on regional priorities. However, mismatches between 

geopolitical boundaries, land use, and geographic ranges of wildlife species often 

result in inconsistent management needs and conflict related to management strategies for species (Morzillo et al. 2012). Such conflict can become even more prolific 

when wildlife management is placed within a context of broader natural resource 

policy goals. A need exists to provide a useful interpretation of available knowledge 

at a defined scale of analysis for decisionmaking about wildlife in the context of 

other management objectives.

State-and-transition models allow for landscape-level evaluation of simulated 

ecological processes. The STMs can be used to project changes in future vegetation 

condition and allow managers to test “what if?” scenarios about how landscape 

change may affect natural resources. The STMs divide the landscape into state 

classes that characterize the cover type (dominant species or functional group) 

and structural stage (percentage cover, canopy layers, etc.). The STM state classes 

do not measure wildlife habitat directly, but rather particular vegetation features 

that may be related to species-specific habitat variables. Transitions between state 

classes simulate dynamic processes such as succession, disturbance, and management activities. The STMs have been used in many coarse-scale analyses of vegetation dynamics, as well as those that include examples of management questions 

focused on wildlife (Barbour et al. 2005, Evers et al. 2011, Wisdom et al. 2002, 

Wondzell et al. 2007). In this study, we linked STM state classes to habitat relationships for selected wildlife species.

This research was part of the Integrated Landscape Assessment Project (ILAP), 

the objective of which was to prioritize land management actions based on fuels 

conditions, wildlife habitat, economic values, and climate change across Arizona, 

New Mexico, Oregon, and Washington. This chapter describes the methods used to 

link STM output with wildlife habitat information. Specific research and management questions pursued with that linkage include:
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

• How does estimated habitat for focal species differ across the 

study landscapes?

• How do different land management scenarios affect estimated habitat?

• What management efforts are necessary to enhance estimated habitat for 

focal species?

As follows, we describe the use of STMs to answer these questions. Using the 

Washington East Cascades modeling zone (chapter 2; fig. 2.4) as an example, we 

provide example output of habitat analysis from STM modeling results for two 

species and two management scenarios.

Methods

We integrated species-habitat relationships with STM output for two regional analyses: the USDA FS Pacific Northwest Region 6 (Oregon and Washington; OR/WA 

hereafter), and the USDA FS Southwestern Region 3 (Arizona and New Mexico; 

AZ/NM hereafter). See chapter 2 for details on STMs. Our analysis included all 

forested and arid land areas, and our observational unit of analysis was habitat. 

Construction of the wildlife habitat module consisted of three phases:

• Phase 1: Identify data capabilities and limitations

• Phase 2: Select focal species

• Phase 3: Build wildlife habitat models

Phase 1: Identify Data Capabilities and Limitations

There are many approaches to modeling estimated wildlife habitat, which we 

classify here into two categories: “bottom-up” and “top-down” models. Bottom-up 

models are constructed using ecological variables known to be important to a species. For example, a habitat suitability index (HSI) is a mathematical index based 

on known habitat characteristics that represents the estimated ability for a location 

to support a particular species (US EPA 2008). Many agencies, including the U.S. 

Geological Survey, U.S. Environmental Protection Agency, U.S. Fish and Wildlife 

Service, and U.S. Army Corps of Engineers use HSIs as tools for landscape evaluation. In contrast, top-down models are constructed using information derived from 

models used for observation of ecological processes (e.g., vegetation community 

succession or response to disturbance), but were not built primarily for evaluation of 

wildlife habitat dynamics. The species-habitat relationships described here are topdown models, such that they are constructed based on output variables from STMs.
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GENERAL TECHNICAL REPORT PNW-GTR-896

We used STM state classes, comprised of vegetation cover type and structural 

stage, as a basis for evaluating potential wildlife habitat (see chapter 2 for a detailed 

description about the construction and application of STMs). For forest STMs, 

cover types were based on forest type importance value (Horn 1975), and structural 

stages included tree size (quadratic mean diameter), canopy cover (percentage closure), and canopy layers. Because we used a top-down modeling approach, wildlife 

habitat models were constructed solely from species-habitat relationships that could 

be derived from STM state classes. As a result, STM state classes do not align perfectly with key wildlife habitat features that might be identified as important when 

using a bottom-up habitat modeling approach. Therefore, as also suggested by other 

researchers (e.g., Shifley et al. 2008), we recommend that the utility and precision of 

our wildlife habitat models be perceived as limited to mid- to coarse-scale evaluation, the state classes defined by STMs, and the available scientific information that 

aligns habitat features with those state classes.

Phase 2: Select Focal Species

Our objective was to construct wildlife habitat models for approximately two-dozen 

focal species across both study regions. Identifying focal species consisted of a 

three-step process.

Step 1: Focus on terrestrial vertebrate species—

We included only terrestrial vertebrates in this analysis. Terrestrial vertebrates were 

more likely than other species to be related to habitat characteristics that could be 

evaluated using ILAP STM state classes. During our investigation, we determined 

that wildlife habitat models for many amphibians and small mammals required 

consideration of fine-scale habitat features, such as water, soil moisture, and ground 

nest or burrow sites that are not associated with ILAP STM state classes (but could 

be associated with STM state classes if models were designed differently). Although 

we initially sought to include a wide variety of terrestrial vertebrate species, or even 

invertebrates, this step resulted in a general focus on mammals and birds because 

the habitat features important to those species are best represented in the coarsescale ILAP STMs.

Step 2: Identify species of management concern—

Species of management concern include endangered and threatened species under 

the ESA, those species proposed to be listed under ESA, state-level listed species, 

and management indicator species (i.e., species identified by agencies to be representative of particular land cover characteristics such as forest types). We compiled 

information about species of management concern for several organizations, including federal and state agencies and nongovernment organizations. We sought further 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

guidance for OR/WA from sources such as the USDA FS Region 6 focal species 

list2

 and Washington Department of Fish and Wildlife Comprehensive Wildlife 

Conservation Strategy (WDFW 2005). For AZ/NM, we used USDA FS Region 3 

focal species list,3

 U.S. Department of the Interior Bureau of Land Management 

(USDI BLM) sensitive species lists, and New Mexico State Game and Fish BISONM database.

Use of listed and sensitive species was advantageous for several reasons. First, 

those species already are recognized by federal agencies or other land management 

organizations. For example, the northern and Mexican subspecies of the spotted owl 

are listed as federally threatened in northern and southern portions of their range, 

and therefore, were selected as candidate focal species in both study areas. Second, 

lists of species of management concern already exist, and mechanisms are in place 

for consideration of them in project and land use planning. Third, there already 

exists a statutory, regulatory, and policy framework for designation and consideration of those species. Fourth, there already is substantial “buy-in” for those species 

within agencies and nongovernment organizations. Thus, species of management 

concern already are part of the planning process.

Step 3: Match species-habitat relationships with STM output—

After identifying species of management concern, we attempted to match habitat 

requirements for those species to STM state classes (see chapter 2 for a detailed 

description of state classes). This step greatly reduced the potential number of 

focal species, as many critical habitat associations were beyond the scope of STM 

models. For example, the mule deer is a species of economic interest in both study 

regions. Important habitat characteristics of mule deer include thermal cover (i.e., 

canopy cover), hiding cover (i.e., shrub and stem density), and forage quality (i.e., 

forbs and shrubs; Feldhamer et al. 2003). Assessment of thermal cover characteristics was possible from canopy information derived from STM state classes. 

However, we were unable to associate hiding cover and forage quality to STM state 

classes with acceptable precision, and as a result, we were unable to include mule 

deer as a focal species. Therefore, focal species were limited to those with habitat 

characteristics that could be matched to the thematic detail of vegetation data 

provided by STM models. Our final lists included 24 focal species for OR/WA and 

13 focal species for AZ/NM (table 5.1). 

2 Terrestrial species assessments: Region 6 forest plan revisions. Unpublished document. on 

file with: U.S. Department of Agriculture, Forest Service, Portland, OR. 

3 Federally listed threatened and endangered (including critical habitat) found within 

national forests in the USDA Forest Service Southwestern Region. Albuquerque, NM: U.S. 

Department of Agriculture, Forest Service. Unpublished document.

Focal species were 

limited to those with 

habitat characteristics 

that could be matched 

to the thematic detail 

of vegetation data 

provided by STM 

models.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Phase 3: Build Wildlife Habitat Models

We used simple tools and decision rules to build wildlife habitat models, which 

allowed for transparency, transferability, and consistency in methods and between 

regions. The model-building process consisted of three parts:

Part 1: Build species-habitat relationships—

We developed data sheets in Microsoft Excel to construct habitat models for focal 

species identified in Phase 2 above. For each focal species, we constructed two 

spreadsheets. One spreadsheet was used to develop a matrix linking STM state 

classes to species-habitat relationships. The other spreadsheet was used to summarize the data matrix (i.e., first spreadsheet) into lists of state classes that were identified as “habitat” for each species. All remaining state classes not identified as habitat 

for an individual species were considered “nonhabitat.” This summary of habitat 

relationship information was used as a foundation for step two of this section.

To create a matrix linking STM state classes to species habitat information, we 

reviewed available scientific literature for each focal species. Literature reviewed 

included peer-reviewed publications, general technical reports, reference books, 

Table 5.1—Focal species for the two study regions

Oregon and Washington Arizona and New Mexico

American marten Desert bighorn sheep

Ash-throated flycatcher Giant spotted whiptail

Black-backed woodpecker Gray vireo

Cassin’s finch Grey checkered whiptail

Fisher Lesser prairie chicken

Flammulated owl Mountain plover

Gray wolf Northern goshawk

Greater sage-grouse Northern sagebrush lizard

Lark sparrow Mexican spotted owl

Lewis’s woodpecker White Sands woodrat

Loggerhead shrike White-sided jackrabbit

Northern goshawk Yellow-nosed cotton rat

Northern harrier Zone-tailed hawk

Northern spotted owl

Olive-sided flycatcher

Pileated woodpecker

Pygmy rabbit

Red tree vole

Sharp-shinned hawk

Snowshoe hare

Swainson’s hawk

Western bluebird

Western gray squirrel

White-headed woodpecker
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

theses and dissertations, gray literature from dependable sources (e.g., habitat 

management plans), and Internet references from reliable sources (e.g., “The Birds 

of North America,” distributed by the Cornell Laboratory of Ornithology and the 

American Ornithologist’s Union; http://bna.birds.cornell.edu/bna/). We extracted 

any information that would allow for matching STM state classes to important features of a species’ habitat, and recorded that information on our spreadsheet matrix. 

Our focus was primarily on quantitative data (e.g., certain size class categories of 

trees), but we used best judgment to interpret qualitative data (e.g., “large trees”) 

into STM categories, as appropriate.

During this matching process, we developed decision rules for useful yet 

incongruent information related to particular situations in order to maintain consistency among selections of cover types and structural stages for all species. In 

some cases, a number of variations were nested within a dominant cover type in the 

STMs. For example, lodgepole pine and lodgepole pine/mixed conifer represented 

variations of cover types nested within lodgepole pine-associated cover types. 

Thus, we developed a decision rule to select all cover types that contained variations of the cover type of interest. In this situation, focal species habitat that was 

matched to lodgepole pine would also be matched to lodgepole pine/mixed conifer. 

Because our observational unit of habitat (and projected potential habitat) was based 

on habitat characteristics rather than current species occurrence, similar decision 

rules were adapted for actual versus potential geographic range of each species. In 

other words, cover type and structural stage combinations within a modeling zone 

were classified as habitat if that cover type and structural stage combination would 

provide suitable habitat for the species, even if the species does not currently occur 

in that modeling zone. For example, the gray wolf is a habitat generalist for which 

forest cover types and structural stages in western Oregon and Washington provide 

potential habitat because of potential presence of prey species (e.g., deer and elk). 

Although gray wolves currently do not occupy western Oregon and Washington, 

suitable habitat exists and was identified in those locations. Conversely, the blackbacked woodpecker is a habitat specialist that occupies more specific dry-forest 

cover types and structural stages particularly in eastern Oregon and Washington, 

and identified habitat closely mirrors current distribution of the species.

Decision rules remained intact with very few exceptions, but we recorded 

those exceptions on the habitat summary Excel spreadsheet. Exceptions included 

completely illogical matches, and additions or deletions recommended by external 

reviewers during the quality assurance process (see “Step Three” of this section). 

For example, Douglas-fir is one cover type associated with spotted owl habitat. 

Following our decision rule, all cover types containing Douglas-fir would be 
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GENERAL TECHNICAL REPORT PNW-GTR-896

considered spotted owl habitat. However, one reviewer suggested that Douglas-fir/

oak should not be considered habitat because of increased probability of contact 

with the more aggressive barred owl. Therefore, even though the Douglas-fir/oak 

cover type contains Douglas-fir, it was not considered a suitable cover type for the 

spotted owl. Similarly, cover types that included mountain big sagebrush initially 

were included as lark sparrow habitat based on the described decision rules and 

information from the literature. However, the mountain big sagebrush cover type 

(and combinations thereof) was later removed because an external reviewer noted 

that lark sparrows only are observed among it during migration.

Part 2: Build wildlife habitat database—

We constructed a database in Microsoft Access to link information from the wildlife habitat models to STM simulation output. This database served as an external 

module that can “dock” to the STMs. This module approach allows for flexibility 

for additional focal species, linkages with STM output from future study areas, and 

addition of new wildlife habitat research and information that becomes available. 

Because of limited ability to derive species-habitat relationships from STM state 

classes, we used a conservative binomial numbering method to transfer information 

from the Excel worksheets to Access. In the Access query for each focal species, we 

entered a “1” to indicate state classes that were identified as habitat, whereas those 

state classes that were not identified as habitat were left blank.

Part 3: Quality assurance—

Our quality assurance process consisted of an external review of wildlife habitat 

models and intragroup proofreading of Access data. We solicited expert reviews 

of habitat models to identify errors created during model-building, and clarify 

information from the scientific literature that did not match regional observations. 

At least one external review was completed for each species. Reviewers were sent 

the Excel datasheets and directions for interpreting habitat information. Although 

information provided by reviewers was extremely helpful for fine-tuning the habitat 

models, several reviewers commented that STM state classes were not well-suited 

for capturing key habitat features related to particular focal species (see “Identify 

Data Capabilities and Limitations”).

We completed an intragroup proofreading process to error check transformations of data from wildlife habitat models to the Access database. For each species, 

this process was led by a different individual than the one who constructed the 

habitat model for the same species. The Excel worksheet containing summarized 

STM state class information was used as the basis for proofreading efforts. Any 

discrepancies between the Excel worksheet and Access database were recorded by 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

the proofreader, and the worksheet or database were then modified by the lead for 

that species as appropriate. A second proofreading was completed, if needed.

Example Results and Discussion

The objective of this example is to evaluate estimated changes in area of wildlife 

habitat in the Washington East Cascades (WEC) modeling zone (see fig. 2.4 in 

chapter 2) during a 50-year period (2006 to 2056). Our focal species of interest are 

the American marten and western bluebird. The American marten is a small- to 

medium-sized carnivore, typically associated with a variety of forests that contain 

large trees and closed canopy (Feldhamer et al. 2003). This species is a USDA FS 

management indicator species for late-successional conditions for all forests in 

the Pacific Northwest. The western bluebird is a medium-sized songbird typically 

associated with a variety of open forests (Guinan et al. 2008). It is a focal species 

for open forest communities across all forest types (USDA FS 2006). We used 

the process described in preceding sections to construct habitat models for both 

species (tables 5.2 and 5.3). Then, we applied the habitat models to STM output 

for two management scenarios within forested areas of the WEC. For the STM 

runs, 30 Monte Carlo simulations were completed for each management scenario, 

and thus results are reported as average values across those 30 simulations. Results 

of habitat analysis were visualized at the 5th field HUC scale (USGS and USDA 

NRCS 2011), as both hectares per watershed and percentage of area modeled within 

each watershed.

The first scenario was a fire-suppression-only (FSO) scenario, in which no 

management prescriptions, such as logging or prescribed burning, are implemented. 

Under this scenario, it is assumed that fire suppression continues at the same rates 

as it has under the management regimes of the last few decades. With STM simulations for this scenario, change was minimal in moist and cold forest types where 

there was a long fire-return interval compared to dry forests. However, under the 

FSO scenario, dry forests became denser and more susceptible over time to standreplacing fires.

In the FSO scenario, about 1.83 million ha of estimated marten habitat existed 

initially (currently), as well as 415 700 ha of estimated bluebird habitat (fig. 5.1). 

During the course of the simulations, estimated marten habitat decreased gradually to 1.68 million ha in 2056 (net loss = 8 percent). Estimated bluebird habitat 

remained relatively constant during the first 25 years of simulations, but then 

increased to approximately 516 900 ha in 2056 (net gain = 24 percent; fig. 5.1). Loss 

of marten habitat appears to be concentrated in the northern portions of the study 

area, as well as at relatively lower elevations (fig. 5.2). Gains in bluebird habitat 

are well-distributed across the study area (fig. 5.3). Thus, the small net decrease in 

The marten is a 

carnivore typically 

associated with large 

trees and closed 

canopy. The western 

bluebird is a songbird 

typically associated 

with open forests.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Table 5.2—Habitat model for the American marten, as derived from state-andtransition model state classes for Oregon and Washington

Suitable size classes: Size description:

 Grass/forb 0 or nonstocked

 Small tree 25.4 to 38.1 cm quadratic mean diameter (10 to 15 inches)

 Medium tree 38.1 to 50.8 cm quadratic mean diameter (15 to 20 inches)

 Large tree 50.8 to 76.2 cm quadratic mean diameter (20 to 30 inches)

 Giant tree >76.2 cm quadratic mean diameter (>30 inches)

Suitable canopy closure:

 Medium 40 to 60 percent

 Closed >60 percent

Suitable canopy layers:

 Multiple layers

Suitable cover types:

 Alaska cedar

 Barren

 Black spruce

 Cedar swamp 

 Douglas-fir

 Engelmann spruce

 Grand fir

 Grass/forb

 Jeffrey pine

 Lodgepole pine

 Montane chaparral

 Montane hardwoods

 Montane riparian

 Mountain hemlock

 Pacific silver fir

 Ponderosa pine

 Red fir

 Riparian lodgepole pine

 Sitka spruce

 Spruce

 Subalpine fir

 Western hemlock

 Western larch

 White fir

 White bark pine

 White spruce
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Table 5.3—Habitat model for the western bluebird, as derived from state-andtransition model state classes for Oregon and Washington

Suitable size classes: Size description:

 Grass/forb 0 or nonstocked

 Open shrub NA

 Seedling/sapling 12.7 to 25.4 cm quadratic mean diameter (5 to 10 inches)

 Pole tree <12.7 cm quadratic mean diameter (<5 inches)

 Shrub NA

 Small tree 25.4 to 38.1 cm quadratic mean diameter (10 to 15 inches)

 Medium tree 38.1 to 50.8 cm quadratic mean diameter (15 to 20 inches)

 Large tree 50.8 to 76.2 cm quadratic mean diameter (20 to 30 inches)

Suitable canopy closure:

 Open 10 to 40 percent

 Postdisturbance

Suitable canopy layers:

 Single

Suitable cover types:

 Douglas-fir

 Grand fir

 Noble fir

 Ponderosa pine

 Ponderosa pine/Gambel oak

 Pacific silver fir

 Western hemlock

 Western hemlock/Douglas-fir

Figure 5.1—Estimated American marten (solid line) and western bluebird (dashed line) habitat for the fire suppression 

only scenario.

2006 2016 2026 2036 2046 2056

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

Hectares of habitat (millions)

Year
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GENERAL TECHNICAL REPORT PNW-GTR-896

estimated area of marten habitat likely corresponds to an initial increased amount 

of closed forest, yet is concurrent with losses of big trees and closed forest as a 

result of an increase in high-severity and stand-replacing fires. Conversely, the 

gradual then more-pronounced increase in bluebird habitat likely is influenced by 

an initial closing of the forest followed by forest opening as a result of projected 

stand-replacing fire.

The second scenario is a resilience scenario, the objective of which is to create 

more fire-resilient dry forest while also maintaining or even increasing the number 

of large trees in the dry forest landscape. Treatments included prescribed fire and 

thinning from below on USDA FS and USDI BLM lands, excluding wilderness 

areas. We assumed that more area was treated annually on private and tribal versus 

public land. We also assumed that some prescribed fire took place, particularly on 

public lands. With STM simulations for this scenario, there was an increase in open 

forest and reduction in closed forest over time, even for the large tree category.

Year 1 Year 50 — Suppression only Year 50 —Resilience

Hectares

< 8000

8000 – 16000

16000 – 24000

24000 – 32000

> 32000

0 1530 60 90

Kilometers

Percentage

< 15

15 – 30

30 – 45

45 – 60

> 60

Figure 5.2—Projected habitat for the American marten at the watershed scale under fire-suppression-only and 

resilience scenarios. The top row illustrates area (hectares) of habitat within each watershed. The bottom row 

illustrates percentage of area of each watershed that is classified as habitat. 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

In the resilience scenario, approximately 1.83 million ha of estimated marten 

habitat existed initially (fig. 5.4), as well as 415 700 ha of estimated bluebird habitat 

(i.e., same initial conditions as the FSO scenario; fig. 5.4). During the simulation, 

projected estimated marten habitat decreased to 1.26 million ha in 2056 (net loss 

= 31 percent). Estimated area of bluebird habitat doubled during the first 25 years 

of simulations; this rate then slowed, but estimated habitat continued to increase to 

865 600 ha in 2056 (net gain = 108 percent). Losses of marten habitat appear to be 

concentrated in the northern portions of the WEC study area, as well as at relatively 

low elevations (fig. 5.2). Although similar spatially to the FSO scenario, marten 

habitat losses are greater in magnitude in the resilience scenario. The greatest gains 

in bluebird habitat are projected to take place among the central and southern portions of the area (fig. 5.3). Thus, although efforts to maintain or increase large trees 

initially may seem beneficial for promoting marten habitat, thinning and burning to 

Figure 5.3— Projected habitat for the western bluebird at the watershed scale under fire-suppression-only and 

resilience scenarios. The top row illustrates area (in hectares) of habitat within each watershed. The bottom row 

illustrates percentage of area of each watershed that is classified as habitat. 

Year 1 Year 50 — Suppression only Year 50 — Resilience

Hectares

< 3000

3000 – 6000

6000 – 9000

9000 – 12000

> 12000

Percentage

< 15

15 – 30

30 – 45

45 – 60

> 60

0 1530 60 90

Kilometers
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GENERAL TECHNICAL REPORT PNW-GTR-896

open the forest may outweigh benefits of maintaining large trees. However, opening 

of the forest is expected to be beneficial for bluebird habitat across all size classes 

of trees. Simulations using additional management scenarios will allow users 

to further evaluate estimated changes as a result of management treatments and 

disturbance.

The methods and habitat models described here are not intended to be used for 

site-level management, but rather to provide managers with a vision for broader 

land-use planning. General trends in wildlife habitat across the study area (figs. 5.1 

and 5.4) provide information about how decisions at the regional level may affect 

net habitat across the landscape within the context of other management objectives. 

Further assessment at the watershed scale (figs. 5.2 and 5.3) provides spatial information about where within the study area changes in habitat are projected to occur. 

Hypothetically, the resilience scenario may not be a viable option if the primary 

management goal is to increase the area of marten habitat in the eastern portions 

of the study area. However, management actions often have positive effects on 

Figure 5.4—Estimated American marten (solid line) and western bluebird (dashed line) habitat for the resilience scenario.

2006 2016 2026 2036 2046 2056

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

Hectares of habitat (millions)

Year
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

some species and negative effects on others. In this case, management actions had 

a positive effect on bluebird habitat. Ultimately, coarse-scale analysis can provide a 

broad illustration of potential impacts on wildlife habitat as a result of management 

actions, but further site-specific evaluation will enhance this approach by providing 

more detailed assessment of habitat characteristics.

Conclusions

Wildlife habitat is an important component of land management and related policy. 

Using habitat as the unit of observation, the approach described and illustrated here 

can aid managers with evaluating potential impacts of management activities on 

estimated wildlife habitat across landscapes at mid to coarse scales. A benefit of 

such analysis is the ability to assess both spatial and temporal effects of land use 

decisions, and where and when those effects might be most beneficial or harmful to estimated wildlife habitat in the context of overall management objectives. 

Caveats of using this approach to address wildlife habitat management objectives, 

particularly the use of STM models, are that information reported at mid to coarse 

scales does not account for site level distribution of wildlife, and the ability to build 

wildlife models is limited to those variables used to evaluate vegetation dynamics. 

For example, this assessment at the watershed scale cannot account for management impacts on habitat distribution, and related life history traits such as dispersal. Therefore, confidence in wildlife habitat assessments based on the methods 

illustrated here will vary greatly based on ability to address habitat characteristics 

important to individual species, and ability to interpret wildlife information created 

by these models accurately.

Acknowledgments

The authors thank J. Bernert, J. DiMiceli, J. Halofsky, B. Kreakie, S. Walters, and 

the technical editors of this report. This research is part of the Integrated Landscape 

Assessment Project, funded by the American Recovery and Reinvestment Act, and 

the U.S. Department of Agriculture Forest Service Pacific Northwest Research Station, Pacific Northwest Region, and Southwest Region. This work was completed 

under Joint Venture Agreement 10-JV-11260489-003 between the USDA Forest 

Service Pacific Northwest Research Station and Oregon State University.
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U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station and Pacific Southwest Research Station. www.fs.fed.us/pnw/pfp.pdf.

(2 February 2012).

U.S. Environmental Protection Agency [US EPA]. 2008. Habitat suitability index 

(HSI). www.epa.gov/aed/html/research/scallop/hsi.html. (23 February 2012).

U.S. Fish and Wildlife Service [USFWS]. 2011. ESA basics: more than 30 

years of conserving endangered species. Arlington, VA: U.S. Fish and Wildlife 

Service. www.fws.gov/endangered/esa-library/pdf/ESA_basics.pdf. (30 August 

2012)

U.S. Geological Survey; U.S. Department of Agriculture, Natural Resources 

Conservation Service [USGS and USDA NRCS]. 2011. Federal standards and 

procedures for the national watershed boundary dataset. 2nd ed. U.S. Geological 

Survey Techniques and Methods 11–A3. 62 p. http://pubs.usgs.gov/tm/tm11a3/. 

(29 August 2012).

Westoby, M.; Walker, B.; Noy-Meir, I. 1989. Opportunistic management for 

rangelands not at equilibrium. Journal of Range Management. 42: 266-274.

Washington Department of Fish and Wildlife [WDFW]. 2005. Comprehensive 

wildlife conservation strategy. http://wdfw.wa.gov/conservation/cwcs/ (23 

February 2012)

Wisdom, M.J.; Rowland, M.M.; Wales, B.C.; Hemstrom, M.A.; Hann, W.J.; 

Raphael, M.G.; Holthausen, R.S.; Gravenmier, R.A.: Rich, T.D. 2002.

Modeled effects of sagebrush-steppe restoration on Greater Sage-Grouse in the 

Interior Columbia Basin, U.S.A. Conservation Biology. 16: 1223–1231.

Wondzell, S.M.; Hemstrom, M.A.; Bisson, P.A. 2007. Simulating riparian 

vegetation and aquatic habitat dynamics in response to natural and anthropogenic 

disturbance regimes in the Upper Grande Ronde River, Oregon, USA. Landscape 

and Urban Planning. 80: 249–267.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Chapter 6: Incorporating Rural 

Community Characteristics Into Forest 

Management Decisions

Mindy S. Crandall, Jane L. Harrison, and Claire A. Montgomery1

Chapter Summary

As part of the Integrated Landscape Assessment Project, we developed a methodology for managers to include potential community benefits when considering forest 

management treatments. To do this, we created a watershed impact score that scores 

each watershed (potential source of wood material) with respect to the communities 

that are likely to benefit from increased wood supply. The communities we consider 

are census county subdivisions that correspond to all rural places in four states 

(Arizona, New Mexico, Oregon, and Washington). Incorporated into the watershed 

impact score are indices of community characteristics that may be of concern (indicators of socioeconomic well-being, business capacity, and effects of forest policies) 

and potential biomass supply. A gravity index combined the watershed-level biomass supply with the community-level information and weighted it by the distance 

between watershed and community, producing an impact score that indicated the 

potential of each watershed to supply material to communities. Our process allows 

managers to consider the potential for community benefits when choosing where on 

the landscape to act.

Introduction

United States federal forest fire policy during the last century has been to 

aggressively suppress wildfires as rapidly as possible. Consequently, forest 

conditions are well outside the natural range of variation in many of the forest types 

of the Western United States (Agee 1993, Franklin and Agee 2003). According to 

the U.S. Department of Agriculture Forest Service (USDA FS) and the Department 

of the Interior, about 263 million ha are susceptible to wildland fire (U.S. General 

Accounting Office 2003). Massive accumulation of forest fuels and changes in the 

species composition and structure of forests that historically experienced frequent, 

1 Mindy S. Crandall and Jane L. Harrison are Ph.D. students and Claire A. Montgomery

is a professor, Department of Forest Engineering, Resources, and Management, 280 Peavy 

Hall, Oregon State University, Corvallis, OR 97331.
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GENERAL TECHNICAL REPORT PNW-GTR-896

low-intensity fires create conditions in which wildfire, when it does occur, is far 

more likely than in the past to be catastrophic and expensive, and if not impossible, 

to contain. Average annual USDA FS expenditures on fire suppression in the 2000s 

was three times what it was in the previous three decades (Abt et al. 2009). Forest 

ecologists have become increasingly aware in recent decades, however, that 

wildfire plays an important role in maintaining healthy forests in fire-adapted 

forest ecosystems

Recognition of the effects of fire suppression on future fire risk led to the passage of the Healthy Forest Restoration Act (HFRA) in 2003, which was designed to 

expedite hazardous fuel reduction and forest restoration projects on federal lands. 

Mechanical fuel removal and restoration thinning have been proposed as potential remedies to this forest health crisis. But because these treatments are costly, 

resource constraints prevent managers from treating as many hectares as need 

treating. For example, even after the passage of the HFRA, only 8 million of the 

263 million ha at risk were planned for treatment to reduce hazardous fuels between 

2000 and 2007 (Rey 2006). Prioritizing the placement of fuel treatments in order to 

use limited resources efficiently is critically important.

The Integrated Landscape Assessment Project (ILAP) was a watershed-level 

prioritization of land management actions based on vegetation and fuel conditions, 

wildlife and aquatic habitats, and economic values across all lands in Arizona, 

New Mexico, Oregon, and Washington. One of the goals of ILAP was to provide 

land managers, policy analysts, and others with tools to enable them to determine 

how to place fuel treatments across the landscape in a way that maximizes diverse 

benefits—such as wildfire risk reduction and wildlife habitat improvements—while 

still ensuring the treatments are as financially feasible as possible. Limited funding 

means that managers must pick and choose where across the landscape to place 

treatments. The human benefits of these treatments—the possible increases in 

mill processing activity, jobs, and other economic activities that may arise from an 

increase in wood material harvested—will also be located in specific places across 

the landscape. That means that managers and others may also have a very real 

opportunity to consider which communities may benefit from harvest action.

Why Should We Care About Rural Communities When 

Considering Fuel Treatment Location Decisions?

Throughout the United States, timber harvests from federal lands have declined 

dramatically since the 1980s. Harvest declines on federal lands, as well as greater 

domestic and international competition and technological advances, have contributed to fewer employment opportunities in the timber industry. Mechanization, 

Limited funding means 

that managers must 

pick and choose where 

across the landscape 

to place treatments. 

The human benefits of 

these treatments—the 

possible increases 

in mill processing 

activity, jobs, and other 

economic activities 

that may arise from 

an increase in wood 

material harvested—

will also be located in 

specific places across 

the landscape. 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

consolidation, and closures of mills continue to concern rural forest communities. 

Harvest decline was exacerbated in Oregon and Washington with injunctions 

prohibiting logging on USDA FS and U.S. Department of the Interior Bureau of 

Land Management (USDI BLM) lands within the range of the northern spotted owl, 

which was listed as threatened under the Endangered Species Act in 1990. By the 

late 2000s, national forest harvests in Oregon and Washington declined to about 

10 percent of what they were in the late 1980s (USDA FS 2010). The listing of the 

Mexican spotted owl in 1993, and the subsequent temporary injunction against 

logging on national forests in 1995, had a similar effect in the southwest. By the 

late 1990s, national forest harvests had declined to about 19 percent and 13 percent 

of late 1980s levels in Arizona and New Mexico, respectively (Keegan et al. 2001a, 

2001b). These harvest figures include fuelwood harvest, still a significant proportion 

of use in the southwest, so the decline in useable material for mills was even more 

pronounced; in 1998, less than half of the cut from national forests in Arizona was 

available for processing in local community mills.

Federal timber harvests have also contributed directly to county revenues in 

all four states, through a revenue sharing program from timber sales designed to 

compensate counties for the loss of tax revenue associated with federal ownership 

status (Gorte 2010). Beginning in the early 20th century, the USDA FS paid 25 

percent of gross timber receipts to the states where the timber was harvested to 

provide and maintain roads and schools in those counties where national forests 

are located, while the USDI BLM shared 50 percent of revenues from harvests on 

the Oregon and California revested grantlands. These lands cover 970 000 ha of 

forest land, originally granted to railroads, in 18 western Oregon counties (Corn 

and Alexander 2012). In counties with considerable public lands, these receipts 

contributed substantially to county budgets after World War II, when harvest activity on public lands accelerated substantially (Corn and Alexander 2012). Although 

the initial goal of the revenue-sharing was compensation for foregone tax revenue, 

concern over effects on county budgets of declines in timber harvest resulting from 

the spotted owl listing led to the appropriation of payments to Oregon, Washington, 

and California in the early 1990s, and to an adjustment of the revenue-sharing 

program through the Secure Rural Schools and Community Self-Determination 

Act of 2000 (USDA FS 2011). Still, adjusted for inflation, Oregon county payments 

declined by 32 percent on average from the late 1980s to the late 2000s, while 

Washington county payments declined by 23 percent (USDA FS 2011). Payments 

from the Secure Rural Schools ended in 2011, which created further uncertainty for 

rural communities.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Changing public forest values mean that communities near public forest lands 

can no longer depend on federal agencies to promote rural economic growth and 

provide sustained-yield employment through timber harvest. Kennedy et al. (2001) 

suggested that federal agencies have shifted their rural development roles to help 

facilitate effective rural community adaptation to broader socioeconomic change. 

The USDA FS has maintained the goal to, “help states and communities to wisely 

use the forests to promote rural economic development and a quality rural environment” (USDA FS 1994). The USDI BLM maintains that the “most effective, realistic 

role for public forest resource agencies is to assist rural economies and communities 

in adapting to changing regional and global forces” (USDI BLM 1994).

Vegetation management (which can include the removal of small trees, excessive brush, and larger trees on overstocked sites to reduce fuels) has the potential 

to meet the diverse goals of these federal agencies. Treatments can mitigate future 

wildfire damage, restore forest ecosystem health, and provide many benefits to rural 

communities, including local employment opportunities, wood supply for mills, and 

biomass supply for emerging energy markets.

For this study, we have compiled information to be included in a decision support system that allows managers who are considering fire-hazard reducing treatments to include potential community benefits in their considerations. The decision 

support system consists of layers provided by ILAP teams that address future 

effects on wildlife, wildfire hazard, and economic feasibility of fuel treatments at 

the watershed level in the four states. The support system uses Ecosystem Management Decision Support (EMDS; Reynolds 1999, Reynolds and Hessburg 2005), an 

application framework that runs within a geographic information system, allowing 

for spatially explicit information on multiple topics to be analyzed simultaneously 

to aid decisionmaking. The goal of the community economics team was to provide 

information that enables land managers to consider the current and historical state 

of communities that may be affected by increased harvest activity. We did this by 

creating a watershed impact score (WIS) that rates each watershed (potential source 

of wood material) with respect to the communities that are likely to benefit from an 

increase in locally delivered wood supply resulting from forest health treatments.

Methods and Data

To give land managers the ability to consider potential effects on communities when 

choosing where to place fuel treatments, we sought a method to combine disparate 

information into a single, easy-to-interpret index. Input data included indicators of 

community conditions, estimates of potential biomass that each watershed could 

produce, and the distance between sources of material and potential places that can 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

utilize material. The WIS is based on the idea of a gravity index: that the transfer or 

flow of products is related to both the characteristics of the origin (the push factors) 

as well as the destination (the pull factors). Although distance is clearly important 

in determining where material goes, it is not the only consideration. To calculate the 

WIS, we needed to establish the following:

• Criteria for an origin location

• Criteria for a destination location

• Community characteristics that may be of importance to decisionmakers

• Estimates of potential biomass supply

• Weights and structure of the gravity index

Gravity index models are a widely used spatial interaction model; they have 

applications in market analysis (e.g., placing and optimizing shopping center size), 

predicting migration patterns, or even analyzing university enrollment (Haynes 

and Fotheringham 1984). For example, shoppers from a rural area faced with two 

cities to shop in will take many things into consideration when deciding which city 

to go to, not just the distance involved. Certain mixes of shops or amenities may be 

more attractive to consumers. Destinations that offer more or have more weight can 

attract input from a wider radius than destinations that have less weight. Gravity 

indices have also been used in landscape-level assessments to model the urbanization potential of undeveloped land as a function of population and proximity (Kline 

et al. 2001). The WIS in the ILAP project built directly on this use of a gravity 

index in service of both ecological and social issues, but represented, to our knowledge, the first use of a gravity index to include community concerns in planning 

land management activities.

Following Haynes and Fotheringham (1984), we considered a single sending 

center gravity index:

WIS = ∑i

(viwj)

, dij

where

WISj

 = watershed impact score assigned to jth watershed,

vi = drawing attribute of ith community (the “pull” factors, e.g., mill presence or 

poverty, of each destination),

wj = sending attribute of jth watershed (the “push” factors, e.g., biomass potential, of each origin), and

dij = travel time between ith community and jth watershed.

The WIS is based on 

the idea of a gravity 

index: that the transfer 

or flow of products 

is related to both 

the characteristics 

of the origin (the 

push factors) as well 

as the destination 

(the pull factors). 

Although distance is 

clearly important in 

determining where 

material goes, it is not 

the only consideration.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Criteria for an Origin Location

The base layer for origins was all watersheds (5th field hydrologic units; USGS and 

USDA NRCS 2011) in USDA FS Pacific Northwest Region 6 (Oregon and Washington; OR/WA hereafter) and USDA FS Southwestern Region 3 (Arizona and 

New Mexico; AZ/NM hereafter). Watersheds average about 44 000 to 56 000 ha. 

We created a centroid point for each watershed—a geographically-weighted center 

latitude and longitude point for each unique watershed in the four states, excluding 

masked urban areas (masked areas represented nonmodeled areas for the purposes 

of determining future potential vegetation, wildlife habitat potential, fire hazard, 

and economic feasibility of fuel treatments). The result was 945 unique potential 

origin points in OR/WA and 1,015 in AZ/NM. In the end, many of these watersheds 

had no biomass that could potentially be supplied to local communities and were 

not given scores (the process for determining biomass potential is discussed below).

Criteria for a Destination Location

Potential destinations for material removed in fuel treatments are likely to be existing mills but may also include future processing development in places currently 

without facilities. Thus, we included both those places that are currently processing 

wood products and those that may potentially be a site for future capital investment 

as destination locations. Capital investment (both past and future) is likely to be 

concentrated in existing communities and places with infrastructure and population. In addition, any discrete place also draws workers in from a surrounding area 

that may be much larger than one physical town or city. Both of these levels of 

geography are accounted for in our destinations.

For places with infrastructure and population (potential destinations for material), we used the census designated place (CDP) geography from 2000. Places 

included designated places, consolidated cities, and incorporated places. Designated 

places are the census-defined counterparts of incorporated places. They are delineated to provide data for settled concentrations of population that are identifiable 

by name but are not legally incorporated. Boundaries usually coincide with visible 

features of the boundary of an adjacent incorporated place or other legal entity 

boundary and are defined in cooperation with local or tribal officials. To focus on 

rural places, we omitted urban places (those with populations over 50,000) and 

urban-linked places (those with populations from 10,000 to 50,000 and that are 

within 32 km of an urban place). Recognizing that both material and people may 

cross state borders for work, we included those rural places within a 96-straightline-km distance of the border in each two-state region.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

These rural places, along with census county divisions (CCDs), defined community in our study. Census county divisions are delineated by the U.S. Census 

Bureau to follow visible features, have a community orientation, and coincide with 

census tracts where applicable. Defining communities as subcounty units helps to 

mitigate concerns over the use of larger geographies, which may not reflect local 

peoples’ sense of community boundaries. Production, marketing, consumption, and 

local institutions factored into the delineation of CCDs by the census. The locality 

on which a CCD is centered usually is an incorporated place or an unincorporated 

community, which might be identified as a census designated place. In some cases, 

the CCD may center on a major area of significantly different topography, land use, 

or ownership, such as a large military installation or American Indian reservation. 

The name of each CCD is based on a place, county, or well-known local name that 

identifies its location (USDC Bureau of the Census 1998). We considered CCDs that 

contain a rural place, as defined above, to be rural communities.

To our knowledge, this is the first use of combined census CDP and CCD geographies to delineate communities. More often, single geographic layers are used 

that are either smaller or larger, such as census tracts or block groups for the former, 

and counties for the latter. Although geographies smaller than CCDs may better 

represent a person’s sense of place, the use of census tracts or block groups is problematic. Both census tracts and block groups are based on population and neighborhoods, but do not necessarily correspond to recognizable communities. In addition, 

both block groups and tracts change boundaries between censuses. In more densely 

populated areas, aggregation is necessary to represent communities using these 

geographies. This type of aggregation is a time-consuming process, which requires 

key informants from all regions to determine the appropriate aggregation (Donoghue 2003). Counties are frequently used for ease of data gathering from multiple 

sources, availability of historical data, and ease of recognition. However, counties 

are large, heterogeneous areas with multiple recognizable communities contained 

within. We defined communities in terms of the CCDs that contain rural CDPs 

in order to maximize the strengths and minimize the individual weaknesses of 

these geographical definitions as units of analysis, recognizing that this limited the 

community-level data available to that produced by the U.S. Census Bureau.

The distance between each watershed centroid point and place centroid point 

was calculated by creating an origin-destination matrix in ArcGIS. This matrix 

contained distance and travel time estimates for all community-watershed pairs. We 

added 5 minutes to each distance to account for startup time.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Community Characteristics That May Be of Importance to 

Decisionmakers

To calculate vi, the drawing attributes of the ith community, we considered indicators of (1) community distress, (2) business capacity, and (3) USDA FS policy 

impacts (table 6.1). Our gravity index can, thus, prioritize communities experiencing distress, those with business capacity ready to utilize new supply, those that 

have been adversely affected by USDA FS policy, or those that share combinations 

of characteristics. All drawing attributes and their data sources are defined in table 

6.1. The rationale for inclusion of each drawing attribute follows.

Indicators of communities in distress (SES)—

The fates of rural communities highly dependent on natural resources has been a 

concern of land managers for decades. The Sustained Yield Forest Management Act 

of 1944, for example, had as part of its stated purpose to promote the stability of 

communities (Hibbard 1999). It is possible that managers may want to target areas 

of high distress as potential recipients of forest material. Inputs of supply may be 

able to help stimulate positive economic activity within the community. Although 

Table 6.1—Drawing attributes for communities

Indicators Description Data source

Communities in distress:

 Poverty Percentage of population at 185 Census 2000

 percent poverty level or below

 Income inequality Gini index: measures inequality of Census 2000

 household income distribution

 Unemployment Percentage of labor force unemployed Census 2000

 Unattached youth Percentage of youth (16 to 19 years old) Census 2000

 not in school and unemployed

 Population decline Percentage of decline in population Census 2000

 from 1990 to 2000

Business capacity: 

 Education Percentage of population 25 years and Census 2000

 over with associates degree or higher

 Labor force Percentage of population in labor force Census 2000

 Mill capacity Presence of a mill in a census county Various sources

 division since 2000 (see text)

 Specialization Percentage of employment in forestry, Bureau of Land

 wood products, or paper Management 2000

Federal forest policy impact:

 Forest Service impacts Percentage change in U.S. Department USDA FS 2010

 of Agriculture, Forest Service county 

 payments from 1986–90 to 2003–07 average
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

increasing levels of community distress act as an increasing draw within the WIS, 

it is worth noting that higher values of these indicators correspond to lower socioeconomic status, and overall, more negative conditions within the communities. 

Community distress indicators used in the WIS include poverty, income inequality, 

unemployment, unattached youth, and population decline. A measure of overall 

economic diversity was considered, but rejected owing to lack of variability in the 

calculated data.

Poverty—Poverty is a ubiquitous measure that indicates community distress 

(Charnley et al. 2008, Donoghue and Haynes 2002, Isserman et al. 2009, Kusel and 

Fortmann 1991, Overdevest and Green 1995, Reeder and Brown 2005, Stedman et 

al. 2004, 2005). The most widely used definition of poverty is the U.S. Census definition, in which a family is considered poor if its annual pretax money income is 

less than the poverty threshold for a family of that composition and size. Although 

the official definition of poverty generally considers only those below the threshold 

as poor, agencies that provide housing subsidies, food assistance, and low-income 

health insurance often use income thresholds of 185 percent of poverty or greater. 

These families may be working enough to keep them above the threshold, but are 

at great risk of slipping into poverty should their job be lost or work hours reduced. 

Additionally, studies exploring alternative measures of poverty have generally concluded that a threshold of 150 to 200 percent of the current poverty threshold would 

be a better measure to adequately capture the modern costs of living (Boushey et al. 

2001). For poverty, we used the percentage of households at 185 percent of the poverty level or below, a measure reported in the Decennial Census.

Income inequality—Timberlands have rapidly changed hands in the last decade 

with millions of hectares of forested land being acquired by real estate investment 

companies and timber investment management organizations. Many of these parcels of land have been developed into high-end residential properties, which have 

attracted wealthy retirees and newcomers to forest communities (Bliss et al. 2008). 

Inmigration of wealthy suburbanites searching for recreation and retirement has 

greatly altered the socioeconomic structure of amenity-rich communities in the 

west. While the new residents bring skills, experience, and extra-local connections 

to these communities, they tend to support low-paying menial jobs instead of employment opportunities with large multiplier effects like wood processing. Nelson 

(1997) and Brown (1995) found socioeconomic differences between long-time 

(less wealthy) and newer (more wealthy) residents in rural counties in the Pacific 

Northwest. Wilkinson (1991) found that income inequality is often a source of social tension.

Community distress 

indicators used in the 

WIS include poverty, 

income inequality, 

unemployment, 

unattached youth, and 

population decline.
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GENERAL TECHNICAL REPORT PNW-GTR-896

We measured income inequality using the Gini index, a standard measure of 

inequality (Allison 1978). To calculate the Gini index for inequality of household 

income, households are ranked by income, and the aggregate share of income held 

by each group of households is divided by the total aggregate income. For example, 

the bottom 10 percent of households may hold 1 percent of the total household 

wealth, and the next 10 percent of households may hold 5 percent of the total household wealth, and so on until all households are included. The Gini index compares 

this distribution to a perfectly proportional distribution and incorporates all of this 

information into a single measure with a value of [0, 1] (DeNavas-Walt et al. 2009). 

A zero Gini index expresses total equality, where all households receive the same 

income, while a value of 1 expresses complete inequality (one household receives 

all income, the others receive none). The Gini index was calculated using household 

income data from the U.S. Decennial Census.

Unemployment—Unemployment is a common metric in calculating socioeconomic 

well-being (Ashton and Pickens 1995, Charnley et al. 2008, Feser and Sweeney 

2003, Isserman et al. 2009, Stedman et al. 2004, 2005). At an individual level, access to work is a basic economic need and is typically a necessary, though not sufficient, condition for generating a livable income. At a regional level, a high level of 

unemployment constitutes wasted economic potential, a drain on public resources, 

and a limitation on public service capacity. The U.S. Census Bureau defines civilians as unemployed if they (1) were neither “at work” nor “with a job but not at 

work” during the reference week, (2) were looking for work during the last 4 weeks, 

and (3) were available to start a job. The percentage of the population unemployed 

was taken directly from this Decennial Census information.

Unattached youth—young adults not working or in school represent both the lack 

of opportunity in an area, as well as the potential for greater social disruption. This 

cohort tends to contribute to community distress, as youth who move away from the 

school and work trajectory are associated with lower lifetime earnings, increased 

poverty, homelessness, and criminal activity (Montalvo and O’Hara 2008). Such 

youth are often found in black and Hispanic populations, among teen mothers, 

in rural areas, and in the youth criminal justice system (Snyder and McLaughlin 

2008). To calculate the percentage of unattached youth in a community, we used the 

population of people ages 16 to 19 not in school, not in the armed forces, nor currently employed from the U.S. Decennial Census data.

Population decline—Dramatic changes in population demographics likely decrease 

the level of social capital in a community and contribute to community distress, at 

least in the short term. Measuring population turnover is problematic, though, in that 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

a stable population does not necessarily imply a stable community. Inmigration and 

outmigration occurring in tandem may result in a stable population, but a very different demographic.

We used percentage of decline in population from the 1990 to 2000 Decennial 

Censuses as one indicator of community distress. Communities that remained 

stable or increased in population were given a value of zero. Although increases in 

population may indicate both positive and negative impacts on community wellbeing, population decline is less ambiguous. Population adjustment in areas of high 

unemployment or low income could be considered a positive shift in labor resources 

regionally, but most certainly indicate negative conditions in the community 

experiencing this loss (Feser and Sweeney 2003). At a minimum, an absolute loss 

of population can result in a shrinking work force, lower tax revenues, and smaller 

school enrollments. When we consider that migration tends to be a highly selective 

process, with younger, better educated, and higher skilled workers moving first in 

response to economic decline, the negative effects of population change become 

more pronounced (Bronars and Trejo 1992, Kwok and Leland 1982, Sjaastad 1962).

Indicators of business capacity (BC)—

Harvest activities may also be focused with a goal of helping communities that are 

positioned for success, but lacking only in timber supply. In contrast to the idea of 

assisting communities that are struggling, managers under this scenario would want 

to target fuels reduction treatments in watersheds that may benefit communities 

with high business capacity, where small increases in supply may have the potential 

to secure mill longevity and wage income for the immediate future. In all cases, 

higher values of each indicator used represent positive factors of the community 

that contribute to higher business capacity. Business capacity indicators for the WIS 

include education, the size of the labor force, presence of a mill, and employment 

specialization in forestry, wood products, or paper.

Educational attainment—Education is a common proxy for human capital, another 

variable that contributes to socioeconomic well-being and community resilience 

(Donoghue and Haynes 2002, Reeder and Brown 2005, Stedman et al. 2004). In 

the past, family-wage jobs in forest communities traditionally did not require much 

education past high school. However, the decline of the forest products industry 

requires new skills on the part of community residents if they are to adapt successfully to this change. We calculated the percentage of population with an associate’s 

degree or higher to represent educational attainment, one indicator of business capacity. These data are available through the U.S. Decennial Census.

Business capacity 

indicators for the WIS 

include education, 

the size of the labor 

force, presence of a 

mill, and employment 

specialization in 

forestry, wood 

products, or paper.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Labor force size—The percentage of the population in the labor force, also calculated from Decennial Census data, indicates the relative number of people available 

for employment. Communities with large proportions of youth or older residents 

are unlikely to take advantage of new business and employment opportunities. 

Retirement income and transfer payments account for one quarter or more of economic activity in some rural areas (Rasker 1994). Although some retirees may possess financial capital to invest in new enterprises, a robust labor force is needed to 

attract new investment and take advantage of new jobs.

Recent mill presence—The presence of a mill indicates whether the physical infrastructure is available to process new supplies of wood. A community with a mill 

currently operating has the most capacity to easily utilize raw materials supplied 

as a result of forest treatments, but mills that have recently closed may also be reopened with capital investment. We drew on several sources to obtain data on current mill locations in the four states. The methodology and specific sources used are 

described below.

Random Lengths publishes a buyers’ and sellers’ directory of the forest 

products industry annually (Random Lengths 2010, 2000). This publication, titled 

“The Big Book,” is compiled from information voluntarily provided by mills and 

secondary processers across the country. Mills listed in the 2000 or 2010 editions of 

The Big Book were considered as open in those years. The USDA FS periodically 

publishes profiles of softwood sawmills by state, along with recent capacity data 

(Spelter and Alderman 2005, Spelter et al. 2009); these reports were used to add to 

or verify open mills listed in the Big Book. In addition, data from industry consultants of open mills in OR/WA in 2011 (Ehinger 2011) were used to further verify 

currently open mills.

A community with evidence of a mill open in 2011 or 2010 was given a mill 

capacity value of 1. If a mill was present prior to or in 2005, the mill capacity was 

0.5; if there was a mill present prior to or in 2000, the mill capacity was 0.25. In 

all cases, all reported mills were counted, regardless of processing capacity or size 

of logs used in the mill. Communities with no evidence of a mill since 2000 were 

given a capacity of zero.

Specialization—We used the percentage of the labor force currently employed in 

forestry, wood products, or the paper industry to indicate whether the labor force 

in a given community has the appropriate skills and experience to take advantage 

of employment and business opportunities from fuel treatments. Percentage of 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

specialization in relevant industries was calculated as the number of employees in 

three industries (three-digit North American Industry Classification System Codes): 

forestry and logging (113), wood product manufacturing (321), and paper manufacturing (322), as a proportion of all employees. County-level percentage of specialization was calculated in this way from U.S. Bureau of Labor Statistics employment 

data and applied to all CCDs in that county.

Indicators of federal forest policy impact (FP)—

In addition to focusing on communities with high distress or those with high 

business capacity to target through fuels reduction treatments, an additional motivation for public land managers may be to assist communities that have experienced 

changes in fortune as a result of shifting federal forest management policies. If federal land management policies have affected some communities more than others, 

these communities may have more weight for managers hoping to impact specific 

places through harvest activities. Forest Service policy impacts are represented by 

the percentage of decline in county payments from the late-1980s to the mid-2000s.

Decline in county payments—It is difficult to quantify the effect of changes in 

public forest management on rural communities. One very real effect, however, 

of declines in public timber harvest has been the decline in contributions to county budgets related to the revenue-sharing agreements that date back to the early 

1900s. Although initially payments were small (along with public timber harvest), 

payments to counties were still linked to harvest receipts in the late-1980s and 

peaked at that time in terms of the amount of money distributed to rural counties. 

Legislation that replaced the timber harvest county payments in the 1990s altered 

the relative payment amounts to each county and increased some counties’ payments, while decreasing others. The large declines in public timber harvest in the 

late 1990s led to alternative payments designed to ease the strain on county budgets. USDA FS Policy impacts of the USDA FS were indicated by the percentage of 

decline in county payments from the late-1980s to the mid-2000s. These two time 

points represent recent overall highs and lows, respectively, in payments to counties from all sources, and were used to proxy negative effects of changes in land 

management policy. Communities in counties that experienced a decline in county 

payments were represented by their percentage of decline, whereas communities in 

counties that experienced no change or an increase in county payments were given 

a value of 0.

Forest Service 

policy impacts are 

represented by the 

percentage of decline 

in county payments 

from the late-1980s to 

the mid-2000s.
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GENERAL TECHNICAL REPORT PNW-GTR-896

We originally included harvest decline as a USDA FS policy impact variable, 

but discarded it because of lack of variation between communities. Because all 

national forests in OR/WA witnessed a comparable decline in harvest rates, inclusion of this variable did not add to our analysis. Although the cut and sold reports 

were not as easily available at the level needed to check similar trends for AZ/

NM, evidence of the decline in overall harvest in AZ/NM indicated that the same 

was likely true for each national forest there (Keegan et al. 2001a, 2001b). Another 

indicator considered but not included owing to data gathering difficulties was the 

change in USDA FS employment.

Estimates of Potential Biomass Supply

Biomass supply is a key component of the possible benefits of fuel treatments for 

rural communities. Through the use of secondary data, primarily from the U.S. 

Census Bureau, the WIS captures the need (the Socioeconomic Status, Business 

Capacity, and Forest Policy indices) at the community level, but also must incorporate the potential for each watershed to actually supply material to nearby places. To 

quantify the potential biomass, we relied on two different methods: one for OR/WA 

and one for AZ/NM (where a lack of data precluded utilization of the method used 

for OR/WA). Both methods are detailed below.

Potential biomass in the Pacific Northwest (OR/WA)—

In OR/WA, potential biomass estimates for each watershed began with maps of current vegetation and vegetation models created for ILAP. Current vegetation conditions were imputed using a nearest neighbor modeling method to match USDA FS 

Forest Inventory and Analysis plot-level data to remotely sensed imagery (chapter 

2). Vegetation state classes were defined using state-and-transition models (STMs). 

The STMs are used to represent alternative vegetation state classes (combinations of 

vegetation cover and structure) within potential vegetation types (chapter 2). Transitions link state classes and represent processes such as succession, disturbance, and 

management activities. The current vegetation condition map was classified into 

STM state classes, which were used to calculate potential biomass (this analysis is 

described in detail in chapter 4).

For the eastern forests in OR/WA and the southwestern forests in Oregon,4

ecologists from the USDA FS identified the desired ending state classes for each 

current state class and the transition required to get there given a goal of forest 

restoration. For example, within the dry mixed-conifer potential vegetation type 

4 West-side forests in OR/WA have a very infrequent fire regime and forest conditions 

are generally not out of historical range (Agee 1993), so these regions are unlikely to be 

targeted for large-scale vegetation management or restoration treatments and were omitted 

from the analysis.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

in the Oregon East Cascades modeling zone, there is a state class characterized by 

Douglas-fir/white fir trees of medium size and medium canopy cover. A restoration 

target state class in this vegetation type and region is Douglas-fir/white fir trees of 

medium size with open canopy. To move from the former state class to the latter, a 

partial harvest of small- to medium-sized trees at a medium density was selected as 

the transition.

An estimate of standing biomass was assigned to each state class using methodology documented in Zhou and Hemstrom (2010) and chapter 4. To obtain an 

estimate of potential biomass removal for each state class that was a candidate for 

treatment, we subtracted the standing biomass estimate for the ideal ending state 

class from the standing biomass estimate for the current state class. Using the 

example above, the total nonleaf biomass for the more dense state class is estimated 

at 73.9 oven-dry metric tons (odt) per hectare, while the total non-leaf biomass of 

the more open state class is an estimated 42.9 oven-dry metric tons per hectare. The 

difference between these two values (73.9 – 42.9 = 31 odt of material removed per 

hectare of land in that initial state class) represents the maximum amount of biomass material that could be removed under a restoration scenario, where all areas 

that are currently in a more dense condition are harvested to create more open, fireresistant conditions. To calculate total potential removals per watershed, we simply 

multiplied the per-hectare amount of potential biomass removed under resilience 

goals by the amount of that state class currently in each watershed.

Because we calculated biomass based on the potential removed from each and 

every hectare of land, this estimate represented an upper bound on the quantity of 

biomass that might be removed and supplied to communities for processing from 

each watershed as a result of resilience activities. These estimates included removal 

of all available biomass, whether stems, branches, or leaves, without leaving any 

material behind for wildlife or nutrient concerns. Byproducts of current management (logging slash or mill residues) are not included. In addition, different types of 

biomass destined for specific forest products (e.g., sawtimber, hog fuel chips) were 

not identified. This estimate was intended only to indicate maximum potential supply relative to other watersheds and was not meant to represent a likely management 

scenario. It also did not assess financial feasibility.

Given the total biomass potential in each watershed, all watersheds in OR/

WA were then ranked by their contribution to total potential biomass supply in the 

region. The biomass supply variable, as incorporated into the WIS, weighted each 

watershed relative to all others in the region in terms of its potential biomass supply 

on a scale of zero to one.

Because we calculated 

biomass based on 

the potential removed 

from each and every 

hectare of land, this 

estimate represented 

an upper bound 

on the quantity of 

biomass that might be 

removed and supplied 

to communities for 

processing from each 

watershed as a result 

of resilience activities.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Potential biomass in the Southwest—

Biomass estimates in AZ/NM, where the more detailed resilience strategies and 

biomass amounts by state class information was not available, were calculated 

differently from OR/WA. For AZ/NM, biomass estimates were based on the Forest 

Biomass Resources Supply Study (Cook and O’Laughlin 2011a, 2011c). Researchers 

at the University of Idaho provided county-level estimates of biomass that might 

be available at different roadside prices (from $10 to $40 an odt) for all Western 

States for the Western Governors’ Association, following a method established 

in an earlier, state-level analysis of biomass supply potential. These biomass estimates incorporated several potential sources: forest thinning to reduce fuel loads 

or improve forest health, logging slash, and mill residues (Cook and O’Laughlin 

2011b). Most of this material is, by definition, unmerchantable poles, small and 

low-value sawtimber, and chip material. As such, it represents opportunities for 

either use in energy production, or in sawmills with small-diameter stem processing 

capability.

To portion out the county-level biomass estimates to the watershed level 

necessary for the WIS, we used the current vegetation map (chapter 2) to identify 

areas of each watershed with at least 20 percent forest canopy cover. County and 

watershed layers were combined to create modified counties that coincided with 

watershed boundaries. County-level biomass total estimates were portioned out to 

each watershed, with forest canopy cover based on the watershed’s contribution to 

the total county area. For example, if a county had a potential biomass of 25,000 

odts per year, and a watershed with complete forest cover comprised 20 percent of 

the modified county’s forested area, then the watershed would be allocated a proportional amount of biomass (20 percent of 25,000 = 5,000 odt/yr). At the modified 

county level, the total estimates of the biomass for all watersheds in a given county 

equal the totals given in the Forest Biomass Supply Analysis at a roadside price of 

$40 per dry ton.

Similar to the process used following the calculations of actual biomass estimates in OR/WA, watersheds were ranked by their contribution to total regional 

biomass that might be provided. The variable used in the WIS represented the 

potential contribution of each watershed relative to other watersheds in the region 

that have forest cover. However, the different methodology used in the two regions, 

along with the use of a regional baseline in ranking the watersheds, means that the 

resulting WIS indices are not directly comparable between OR/WA and AZ/NM. 

For example, a watershed with a high ranking of biomass potential in OR/WA is not 

the same as a watershed with a high ranking in AZ/NM. Each considered different 

pools of material as potential biomass, was developed with different motivations, 

and each was ranked against a different regional total potential supply.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Weights and Structure of the Gravity Index

The component data for each of the community-level indices—the socioeconomic 

status variables, the business capacity variables, and forest policy variable in OR/

WA—were all scaled to lie in the interval [0, 1], inverted when necessary, and 

truncated in cases when only some of the data values were of interest (e.g., we were 

only interested in communities with population decline). Adding the component 

data together resulted in a range of values for the two regions and three indices 

(table 6.2). Higher values of the SES and FP indices indicate greater distress, a 

negative measure, while higher values of the BC index indicate greater capacity, 

a positive measure. High values for all three increase the priority ranking of the 

watershed.

The indices were inversely weighted by travel time between each watershedcommunity pair. We constructed the WIS for each watershed by summing the 

weighted indices for the five nearest communities. This resulted in a score for each 

watershed consisting of the attributes of the communities most likely to receive 

biomass supply from that watershed. By limiting the analysis to include only the 

nearest five communities, we avoided creating a WIS that is dominated by population; it is more responsive to the attributes of the communities. For inclusion in the 

final calculations, the biomass rankings in tenths based on each watershed’s contribution to the total regional biomass potential were scaled to lie in the interval [0, 1].

The resulting comprehensive WIS for watershed j was calculated as:

WISj = ∑i=1

n BSj

 (SESi+BCi+FPi)

, dij

where

WISj = watershed impact score for the jth watershed,

BSj

 = relative biomass supply for the jth watershed (a push factor),

SESi = socioeconomic status index for the ith community (a pull factor),

BCi = business capacity index for the ith community (a pull factor),

FPi = forest policy impact index for the ith community (a pull factor), and

dij = travel time between the jth watershed and the ith community.

Table 6.2—Ranges of nonstandardized community indices for Oregon and 

Washington (OR/WA), and Arizona and New Mexico (AZ/NM)

Range of values Range of values Range of values

of socioeconomic of business of forest policy

Region status index capacity index impacts Index

OR/WA 0 to 2.82 0.30 to 2.58 0 to 1

AZ/NM 0 to 3.00 0.04 to 2.54 NA

NA = not applicable.
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GENERAL TECHNICAL REPORT PNW-GTR-896

The implicit weights on the indices are 5, 4, and 1 for SES, BC, and FP, respectively, based on the number of variables they each contain. These weights can be 

adjusted to reflect the values of the policymaker.

Resulting Datasets

We produced nine gravity indices. Each gravity index represented a different 

combination of data on the numerator; all summed up the values at the watershed 

level for the nearest five communities and inversely weighted the total by the travel 

time between watershed and community. Our nine gravity indices included:

• Three simple WIS that incorporated only the standardized community condition variables (SES, BC, and FP indices).

• A WIS that summed these three variables together.

• A WIS that comprised only the relative biomass supply ranking.

• Three WIS that combined each of the community characteristics with the 

biomass supply ranking (e.g., SES weighted by biomass ranking).

• A comprehensive WIS that combined all information.

We created maps to illustrate how current community conditions translate into 

WIS, shown in figures 6.1, 6.2, and 6.3. While the WIS is designed for use in a 

geographic information system-based decision support system for full flexibility, 

static maps allow the resulting data sets to be displayed in multiple media. In addition, maps allow any stakeholder to visually identify areas where treatments may be 

able to have the largest positive impact on communities. A full set of maps will be 

presented in subsequent reports that will describe and interpret our results for both 

regions.

Current conditions at the community level (the nonstandardized SES, BC, and 

FP index values) are shown for OR/WA (fig. 6.1). Values are grouped into quintiles, 

where each quintile represents one-fifth of the observations. In all cases, the first 

quintile (shown in yellow) are the communities making up the lowest fifth of all the 

possible values. The dark blues (4th and 5th quintiles) are communities with characteristics that may be targeted by land managers as places to benefit from increases 

in supply.

We also mapped the relative potential biomass supply for OR/WA (fig. 6.2). The 

figure shows the volume removed for each watershed per treated acre, relative to the 

mean amount removed, and the contribution of each watershed to the total potential 

biomass (the value used in the WIS). The map displays the relative rankings in 

fifths, so the lowest group (watersheds in yellow) contributes the lowest amounts 

per acre or the last 20 percent of the total potential biomass supplied in the region. 

The watersheds in blue contribute either a high amount of biomass per treated acre 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

or a high amount of the total regional biomass potential. These watersheds have the 

potential to supply a large amount of material relative to other watersheds in the 

region.

Because the biomass calculation methodology was quite different between the 

OR/WA and AZ/NM, and the rankings were of each watershed relative to others in 

the region, maps and values are not comparable between regions in the same way as 

community conditions. In OR/WA, there was wide variation in potential biomass 

supply estimated between the heavily stocked east side of the Cascades in both 

states to the minimally forested Columbia Basin and southeastern Oregon. In AZ/

NM, conditions are much less variable across the landscape, but OR/WA has the 

potential to produce far more overall biomass.

One possible configuration of the gravity index for OR/WA is shown in figure 

6.3. In this case, the scores for each community index and the biomass ranking are 

added together and weighted by the distance between the watershed and the nearest 

five communities, as given in the formula above. The data are, however, flexible 

enough that a manager could compute a different gravity index by combining all 

community characteristics together or incorporating more nearby communities in 

the score.

Figure 6.1—Community characteristics in the Pacific Northwest (Oregon and Washington). Data are grouped by quintile. 

Yellow indicates the lowest values of the indices. Communities in shades of blue are areas where managers may want to 

prioritize action, either because they have a high business capacity to utilize material, have low socioeconomic status and 

may benefit from stimulus, or because they have been impacted by changes in federal forest policy.
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GENERAL TECHNICAL REPORT PNW-GTR-896

Conclusions

Our goal was to provide a spatial data layer for inclusion in an EMDS system, 

similar to those provided by other teams of ILAP (see chapter 1). This knowledgebased system operates within ArcGIS and allows land managers or policy analysts 

to incorporate disparate information across landscapes and prioritize activities to 

meet management goals easily and flexibly. For example, layers showing the future 

condition of the forest in terms of wildlife habitat, fire hazard, and financial feasibility of treatments can allow managers to highlight watersheds for treatment that 

can meet all goals of reducing hazard, improving habitat, and having treatments pay 

for themselves.

Figure 6.2—Biomass potential in the Pacific Northwest (Oregon and Washington). The map on the left 

shows the volume removed for each watershed per treated acre, relative to the mean amount removed. 

Watersheds in blues have a high amount of biomass potential per treated acre. The map on the right 

shows the contribution of each watershed to the total potential biomass. The watersheds in blue, again, 

contribute a high amount of the total regional biomass potential, and these watersheds have the potential 

to supply a large amount of material relative to other watersheds in the region.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

The community economics information produced in this study allows managers 

or analysts to add an additional criterion that reflects potential effects of treatments 

on rural communities in the two regions. The data layer we provided for the EMDS 

system included nine gravity indices that can be mapped at the watershed level, 

along with other watershed-level information. Gravity indices are a useful tool for 

weighting conflicting factors that are spatially related. Distance matters to managers when considering where to place fuel treatments, as they must weigh the costs 

of removing the material against the benefits created by removing it. The material 

removed has an opportunity to benefit nearby communities, and the gravity index 

allows us to incorporate possible community weighing factors into the decisionmaking process. The compilation of already-existing data in this way allows 

managers to consider communities when choosing where to place fuel treatments 

on the landscape.

Figure 6.3—Watershed Impact Scores (WIS) for the Pacific Northwest (Oregon and Washington). These maps combine 

biomass potential with business capacity (left), socioeconomic status (center), and forest policy effects (right). Watersheds 

in yellow offer the lowest values for each combination, while watersheds in blues offer the highest potential in terms of both 

biomass and potential to contribute to communities with each of the characteristics. In all cases, the east side of the Cascades 

in both states along with southwestern Oregon consistently combine high biomass potential in watersheds near to communities 

who are in socioeconomic distress, have high business capacity, and have been affected by U.S. Forest Service policy. Areas of 

central Oregon (Grant and Union Counties) and northeastern Washington (Stevens, Pend Oreille, and Spokane Counties) show 

mixed potential, depending on which community characteristic is of importance.

Distance matters 

to managers when 

considering where to 

place fuel treatments, 

as they must weigh 

the costs of removing 

the material against 

the benefits created by 

removing it. 
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GENERAL TECHNICAL REPORT PNW-GTR-896

However, there are limitations to this work. Because we want this tool to be 

replicated and updated in the future, we chose to use census data in many cases. 

This also allowed the use of a smaller level of geographic aggregation than counties. However, the use of census data resulted in a tradeoff between timeliness of 

the information for more geographically specific and readily available information. 

Most of the data were obtained from the 2000 Census, and yet we can expect that 

the fortunes of many communities, particularly rural, timber-reliant ones, have 

changed since then, as the United States entered a prolonged recession, and the 

reduction in demand for wood products was particularly felt in rural OR/WA. In 

addition, our WIS provides only a snapshot view of community conditions. With 

the release of the 2010 census data at the subcounty level (assuming no major 

changes in CCD boundaries occur), both updating and incorporating trend information into the community characteristic indices would increase the relevance of the 

WIS.

Although we tried to balance considerations of simplicity and accuracy when 

determining what to use as a community in this study, our boundaries and definitions are clearly somewhat subjective. Reliance on preexisting data limits the true 

accuracy with which researchers can define communities.

For this study, we made every effort to keep the methodology simple enough 

to be easily updated and expanded (updated when the new census information 

is released or expanded to other states). Nonetheless, there were more steps and 

complicating factors than we had anticipated, particularly in incorporating biomass 

estimates for watersheds. Still, it is our hope that versions of this work can be 

adapted to other locations with modifications as needed.

Across the dry forest regions of the west, forest health has declined and the risk 

for catastrophic wildfire has increased. At the same time, rural communities in the 

region have struggled in the face of declining harvests from public lands and losses 

in employment in traditional industries. Fuel treatments have been widely promoted 

as a way to improve forest health while providing jobs for rural residents skilled in 

forestry, but the fact that budget limitations will force managers to choose priority forest areas to treat is sometimes neglected. The ILAP project was developed 

to provide land managers with a way to account for multiple goals by creating a 

decision support system capable of combining disparate information about wildlife 

habitat, wildfire hazard reduction, and financial feasibility of treatments in order to 

inform their on-the-ground prioritization. The potential increase in supply material will not be dispersed evenly across the landscape, but will by necessity have 

the potential to benefit specific locations and not others, with concurrent benefits 

for particular rural communities. We developed the watershed impact scores as 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

a way of incorporating information about communities into the prioritization of 

watersheds to treat. Our primary goal is that this analysis will help managers to 

understand the implications of actions in specific locations with respect to local 

communities, and to make intentional decisions when choosing where to act on the 

landscape.

Acknowledgments

The authors thank Josh Halofsky, Joe Bernert, Miles Hemstrom, and Xiaoping 

Zhou for providing invaluable assistance with data compilation and processing, 

and Myrica McCune for assisting with data collection. The Integrated Landscape 

Assessment Project was funded by the American Recovery and Reinvestment Act 

and the U.S. Department of Agriculture Forest Service Pacific Northwest Research 

Station, Pacific Northwest Region, and Southwest Region. 

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Chapter 7: Developing Climate-Informed 

State-and-Transition Models

Miles A. Hemstrom, Jessica E. Halofsky, David R. Conklin, Joshua S. Halofsky, 

Dominique Bachelet, and Becky K. Kerns1

Chapter Summary

Land managers and others need ways to understand the potential effects of climate 

change on local vegetation types and how management activities might be impacted 

by climate change. To date, climate change impact models have not included localized vegetation communities or the integrated effects of vegetation development 

dynamics, natural disturbances, and management activities. We developed methods 

to link a dynamic global vegetation model to local state-and-transition models. Our 

methods allow examination of how climate change effects might play out in localized areas given a combination of vegetation dynamics, natural disturbances, and 

management actions. Initial results for the eastern Cascades of central Oregon suggest that ponderosa pine forests are likely to remain the dominant forest vegetation 

type while moist and subalpine forests decline. Sagebrush shrublands will likely 

also remain the dominant arid land vegetation type, but could undergo substantial 

fluctuations and begin to give way to shrublands dominated by warm-season species.

Introduction

Resource planners and managers are often inundated with information on climate 

change and potential corresponding trends in wildfire, drought, insect outbreaks, 

and silvicultural treatment effects. These factors are frequently discussed independently of each other without considering how one may affect the other. Incorporating and utilizing various pieces of information can be difficult, because information 

on these factors is often generated at different spatial and temporal scales with both 

known and unknown uncertainties.

1 Miles A. Hemstrom is a senior scientist, Institute for Natural Resources, Oregon State 

University, PO Box 751, Portland, OR 97207-0751 (formerly a research ecologist, U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station, Forestry 

Sciences Laboratory, 620 SW Main St., Suite 400, Portland, OR 97205); Jessica E. Halofsky is a research ecologist, University of Washington, College of the Environment, School 

of Environmental and Forest Sciences, Box 352100, Seattle, WA 98195-2100; David R. 

Conklin is a climate change scientist, Conservation Biology Institute, 136 SW Washington 

Avenue, Suite 202, Corvallis, OR 97333; Joshua S. Halofsky is a landscape ecologist, 

Washington Department of Natural Resources, PO Box 47000, 1111 Washington Street 

SE, Olympia, WA 98504-7000; Dominique Bachelet is a senior climate change scientist, 

Conservation Biology Institute, 136 SW Washington Avenue, Suite 202, Corvallis, OR 

97333; and Becky K. Kerns is a research ecologist, U.S. Department of Agriculture, Forest 

Service, Forestry Sciences Laboratory, 3200 SW Jefferson Way, Corvallis, OR 97331.

Our methods allow 

examination of how 

climate change effects 

might play out in 

localized areas given 

a combination of 

vegetation dynamics, 

natural disturbances, 

and management 

actions.
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GENERAL TECHNICAL REPORT PNW-GTR-896

One source of confusion is the interpretation of projections from general 

circulation models (also referred to as global climate models [GCMs]). The GCMs 

reflect the current state of knowledge and understanding of how the planet’s climate system works. However, our climate system is very complex, and different 

GCMs incorporate different assumptions about the physical drivers of the Earth’s 

climate, resulting in varied projections of future temperature and precipitation. It 

is difficult for managers and planners to translate these various climate projections 

into plausible scenarios of future ecosystem change and to develop robust adaptation strategies, because there are many models and assumptions about the current 

understanding of local feedbacks.

Downscaling climate projections from different GCMs to drive projections of 

vegetation growth, and corresponding changes in drought and wildfire, is a necessary step to begin to make GCM results useful to natural resource managers and 

planners. Dynamic global vegetation models (DGVMs) such as MC1 (Bachelet et 

al. 2001, Daly et al. 2000) have been developed to integrate climate information and 

grow vegetation through time by simulating fundamental ecological processes like 

carbon and water uptake and losses, and competition for light, water, and nutrients. 

The required climate information to run the DGVMs, including minimum and 

maximum temperatures, precipitation, and vapor pressure deficit, is provided by 

interpolated climate observations for both the historical period and climate scenarios for the future. However, because DGVMs simulate changes in broad functional vegetation types, the output is not readily usable by managers and planners 

and is often not appropriately scaled to address many management and planning 

questions. Moreover, many DGVMs omit important drivers of vegetation dynamics, 

such as species-specific fire tolerance and human-driven landscape change, and 

generalize processes such as plant competition and succession.

State-and-transition models (STMs) incorporate effects of succession, management, and disturbance on relatively fine-scale vegetation composition (i.e., 

communities) and structure. The STMs have been used for many different types of 

landscape-scale assessments (e.g., Arbaugh et al. 2000; Forbis et al. 2006; Hemstrom et al. 2001, 2007; Merzenich et al. 2003; Merzenich and Frid 2005; Weisz et 

al. 2009). However, most STMs do not currently incorporate potential effects of 

climate change. Because STMs do not consider a changing climate, site potential in 

these models currently does not change through time, resulting in static potential 

vegetation for any modeled location. Fire frequency and severity is also currently 

unchanged through time in STMs. Integrating vegetation models such as MC1 

with STMs would help make the information provided by MC1 about climate 

change effects on vegetation and disturbance regimes more useful for natural 

resource managers.
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The Integrated Landscape Assessment Project (ILAP) was a $6 million investment funded by the American Recovery and Reinvestment Act to inform watershed-level planning, assessment, and prioritization of land management actions 

across wildlands in Arizona, New Mexico, Oregon, and Washington. The ILAP 

has partnered with many organizations across the Western United States, including state and federal agencies, universities, nonprofit organizations, and tribes. As 

part of ILAP, we used the MC1 DGVM to inform changes in vegetation potential 

and changing trends in fire probabilities in a suite of local STMs. This process was 

used in two focus areas (central Oregon and eastern Arizona). The outcome of our 

process was a collection of climate-informed STMs, which are connected to each 

other, allowing climate-driven shifts in potential vegetation to occur. These climateinformed STMs can be used to weigh potential benefits or tradeoffs associated with 

alternative land management approaches in mid- to broad-scale landscapes. In this 

chapter, we describe the methods we used to develop climate-informed STMs in 

central Oregon. Although the approach was nearly identical in eastern Arizona, we 

use central Oregon here to illustrate the process and results that can be produced.

Study Area

The study area is a landscape of forested and arid vegetation that covers approximately 1 023 808 ha in eastern central Oregon (fig. 7.1). The study area lies along a 

sharp rain shadow generated by the north-south trending Cascade Mountain range. 

Elevations vary from about 1200 m to above 2400 m. The climate is transitional 

between moist, maritime conditions west of the Cascade crest and continental 

conditions to the east. Annual precipitation varies from over 200 cm along the 

Cascade crest to less than 35 cm along lower timberline, and 25 cm at the lowest 

elevations in shrub-steppe environments. Most of the precipitation falls as rain 

and snow during the winter months, with snowpacks of more than 2 m common in 

upper elevations. Summers are warm and dry, often with several weeks of very low 

or no precipitation and warmest temperatures at lower elevations exceeding 30  ̊C. 

Thunder storms and lightning commonly occur from April through September.

A variety of mostly coniferous tree species dominate the forested lands. We 

defined seven potential vegetation classes (PVCs) by aggregating plant associations 

described by Simpson (2007), Johnson and Simon (1987), Johnson and Clausnitzer 

(1992), and Johnson and Swanson (2005), and added an additional eighth type that 

the DGVM indicates might be common in the future:

1. Temperate needle-leaved forests. Ponderosa pine is the dominant early 

early-seral tree species at the lowest forested elevations. Dry forests containing a mix of ponderosa pine, Douglas-fir, and grand fir are abundant at 

middle elevations.

Climate-informed STMs 

can be used to weigh 

potential benefits or 

tradeoffs associated 

with alternative 

land management 

approaches in 

mid- to broad-scale 

landscapes.
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GENERAL TECHNICAL REPORT PNW-GTR-896

2. Cool needle-leaved forests. Upper montane forests are a variable mixture 

of conifer species, including Douglas-fir, white fir, Shasta red fir, ponderosa 

pine, and other species. In very moist environments, several conifers more 

common to the western Cascades may be abundant, including Pacific silver 

fir, western hemlock, and western redcedar.

3. Subalpine forests. Mountain hemlock dominates upper elevation cold forests in moist environments, while whitebark pine, Engelmann spruce, and 

subalpine fir dominate in drier cold forests. Lodgepole pine is common in 

disturbed, especially burned, areas at upper elevations.

4. Temperate needle-leaved woodland. Western juniper and mountain big 

sagebrush, with several other shrubs, grasses, and forbs, occupy mid- to 

upper elevation woodlands and shrublands that do not currently support 

forests.

5. Temperate shrubland. Much of the lower elevation, warm, dry temperate 

shrubland is dominated by Wyoming big sagebrush communities that have 

little or no potential for invasion by western juniper. Cheatgrass is a prevalent invasive annual grass in many Wyoming big sagebrush types.

6. Xeromorphic shrubland. Salt desert shrub communities with greasewood, 

saltbush, and Wyoming big sagebrush are of somewhat limited distribution 

in low-elevation areas, particularly those with saline soils. Cheatgrass is 

often invasive in these shrublands.

Figure 7.1—Study area and ecological (Omernik level III) regions in central Oregon.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

7. Temperate grassland. Various bunchgrass-dominated grasslands, often 

including Idaho fescue and bluebunch wheatgrass, form scattered grasslands dominated by cool-season (i.e., C3) species. Cheatgrass can be invasive in these grasslands.

8. Warm-season grassland. Warm-season grasslands do not occur to any significant extent in the study area at present, but do occur in warmer areas to 

the south. We included a warm-season grassland potential vegetation type 

because MC1 model projections indicated they might occur in our area in 

the future.

Methods

MC1 Modeling

MC1 (Bachelet et al. 2001) is a DGVM that reads soil and monthly climate data, 

and calls interacting modules that simulate biogeography, biogeochemistry, and fire 

disturbance. The biogeography module simulates life form or plant functional type 

mixtures, classifying them into PVCs; the biogeochemistry module simulates fluxes 

and pools of carbon, nitrogen, and water through ecosystems; and the fire module 

simulates natural fire disturbance. Each module is described in the following 

paragraphs. MC1 routinely generates century-long, regional-scale simulations (e.g., 

Bachelet et al. 2000, 2003, 2005; Lenihan et al. 2008).

The MC1 biogeography module simulates mixtures of needle-leaved or broadleaved evergreen and deciduous trees, as well as temperate (C3) and warm-season 

(C4) grasses. The tree life form mixture is determined at each annual time-step as 

a function of annual minimum temperature and growing season precipitation. The 

temperate/warm-season grass mixture is determined by reference to their relative 

potential productivity during the three warmest consecutive months. Tree and 

grass life form mixtures, their biomass levels as simulated by the biogeochemistry 

module, and growing degree-day sums are used to determine which of several 

dozen possible PVCs occurs in each simulated grid cell each year (grid cell size was 

800 m in this study). Shrubs are simulated as short-stature trees.

The MC1 biogeochemistry module is a modified version of the CENTURY 

model (Parton et al.1993), which simulates plant growth, organic matter decomposition, and the movement of water and nutrients through the ecosystem. Plant growth 

is determined by empirical functions of temperature, moisture, and nutrient availability. In this model, plant growth was assumed not to be limited by nutrient availability. The direct effect of an increase in atmospheric carbon dioxide is simulated 

using a beta factor (Friedlingstein et al.1995) that increases maximum potential 

productivity and reduces the moisture constraint on productivity. Grasses compete 

with woody plants for soil moisture and nutrients in the upper soil layers, where 
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GENERAL TECHNICAL REPORT PNW-GTR-896

both are rooted, while the deeper rooted woody plants have sole access to resources 

in deeper layers. The growth of grasses may be limited by reduced light levels in 

the shade cast by woody plants. The values of model parameters that control woody 

plant and grass growth are adjusted with shifts in the life form mixture determined 

annually by the biogeography module.

The MC1 fire module simulates the occurrence, behavior, and effects of fire. 

The module simulates the behavior of a fire event in terms of the potential rate of 

fire spread, fireline intensity, and the transition from surface to crown fire (Cohen 

and Deeming 1985, Rothermel 1972, van Wagner 1993). Several measurements of 

the fuel bed are required for simulating fire behavior, and they are estimated by the 

fire module using information provided by the other two MC1 modules. The current 

life form mixture is used to select factors that allocate live and dead biomass into 

different classes of live and dead fuels. The moisture content of the two live fuel 

classes (grasses and leaves/twigs of woody plants) is derived from soil moisture 

provided by the biogeochemical module. Dead fuel moisture content is estimated 

from climatic inputs to MC1 using different functions for each of four dead fuel 

size-classes (Cohen and Deeming 1985).

Fire events are triggered in the model when the fine fuel moisture code (FFMC) 

and the buildup index (BUI) simultaneously exceed prescribed thresholds. The 

FFMC is a numerical rating of the relative ease of ignition and flammability, used in 

the Canadian Forest Fire Danger Rating System. The BUI is a number that reflects 

the combined cumulative effects of daily drying and precipitation in fuels, part of 

the 1964 National Fire Danger Rating System in the U.S. Sources of ignition (e.g., 

lightning or anthropogenic) are assumed to be always available. Area burned is not 

simulated explicitly as fire spread within a given cell. Instead, the fraction of a cell 

burned by a fire event is estimated as a function of set minimum and maximum fire 

return intervals for the dynamically simulated PVC and the number of years since 

the last simulated fire event.

The fire effects simulated by the model include the mortality or consumption of 

vegetation carbon, which is removed from (or transferred to) the appropriate carbon 

pools in the biogeochemistry module. Live carbon mortality and consumption are 

simulated as a function of fireline intensity and tree canopy structure (Peterson 

and Ryan 1986). Dead biomass consumption is simulated using functions of fuel 

moisture that are fuel class specific (Prichard et al. 2005).

Climate Data—

Climate data sets required for running the MC1 model consisted of monthly values 

of four variables: precipitation, the monthly means of diurnal extreme temperatures 

(minimum and maximum), and a measure of atmospheric water content. We used 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

the Parameter-elevation Relationships on Independent Slopes Model (PRISM) 

climate data set at 30 arc-second spatial grain for the historical period (Daly et al. 

2008). This data set contains monthly data for the years 1895 to 2008, for the conterminous United States on a 30 arc-second grid (about 800 m grain). PRISM estimates spatial means of temperature and precipitation for grid cells based on records 

from nearby weather stations, assigning weights to the station data according to the 

similarity of the station location to the grid cell location. PRISM, although not the 

only source for gridded historical climate data for the United States, is well established and has been widely used (Daly et al.1994, Guentchev et al. 2010).

Future climate projections from GCMs included in the Intergovernmental 

Panel on Climate Change (IPCC) fourth assessment report (Solomon et al. 2007) 

were used to drive MC1 projections for the 21st century. In this respect, our study 

is similar to a number of other studies using MC1 at various scales on a variety 

of landscapes (Bachelet et al. 2008, Conklin 2009, Gonzalez et al. 2010, Lenihan 

et al. 2008, Rogers 2011). We obtained complete time series of the four necessary 

climate variables from the data repository at Lawrence Livermore Laboratories 

produced by the Hadley CM3 (Gordon et al. 2000), MIROC 3.2 medres (Hasumi 

and Emori 2004), and CSIRO Mk3 (Gordon et al. 2002) GCMs (hereafter Hadley, 

MIROC, and CSIRO, respectively) for the IPCC Special Report on Emissions 

Scenarios (SRES) A2 emissions scenario (Nakićenović and Swart 2000). For the 

future 2070-2099 period, CSIRO projects a relatively cool and wet U.S. Pacific 

Northwest (+2.6 °C and +176 mm mean annual precipitation), MIROC projects a 

relatively hot and wet U.S. Pacific Northwest (+4.2 °C and +82 mm mean annual 

precipitation), and Hadley projects a relatively hot and dry U.S. Pacific Northwest 

(+4.2 °C and −78 mm mean annual precipitation) (Rogers et al. 2011). For all three 

GCMs, temperature increases are generally greater in summer than in winter, while 

precipitation increases in winter and decreases for summer (Rogers et al. 2011). 

Thus, the seasonality in temperature and precipitation that characterized the Pacific 

Northwest historically is increased (i.e., there is a greater difference in temperature 

and precipitation between seasons) in future projections by all three GCMs.

The spatial grids used by the Hadley, MIROC, and CSIRO GCMs are coarse 

(1.9 x 1.9 degrees for CSIRO, 2.8 x 2.8 degrees for MIROC, and 2.5 x 3.75 degrees 

for Hadley). We down-scaled the coarse grid GCM data to a 30 arc-second grid 

using a “delta” or “anomaly” method (Fowler et al. 2007). We first calculated a historical climate baseline (mean values for each month) for 1971-2000, our reference 

period, using PRISM data. We also calculated a coarse-scale historical baseline for 

the same reference period using the GCM projections. Secondly, for each month of 

each year of the future period, we calculated the difference (for temperature and 
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GENERAL TECHNICAL REPORT PNW-GTR-896

vapor pressure) between or the ratio (for precipitation), of the future value and the 

reference historical baseline climate (the “deltas”). We then estimated the values 

of the deltas on the fine (30 arc-second) grid using bilinear interpolation between 

deltas from the coarse GCM grid. Finally, we calculated the fine grid climate values 

for each month of each year of the future period using the PRISM historical baseline modified by the fine-scale deltas. Difference deltas were added to the baseline, 

while ratio deltas were multiplied with the baseline.

State-and-Transition Models

We used a set of STMs that included both previously developed models that were 

modified and standardized in ILAP, and some new models built for ILAP (chapter 

2). The STMs divide vegetation into state classes (boxes), representing combinations of cover type and structure stage within different biophysical environments 

(potential vegetation types). Boxes are linked by transitions (arrows) that represent 

natural disturbances, management actions, or vegetation growth and development. 

This approach builds on methods that represent vegetation development as a set 

of transition probabilities among various vegetative state classes (within, but not 

between biophysical environments; e.g., Hann et al.1997, Hemstrom et al. 2007, 

Keane et al.1996, Laycock 1991, Westoby et al.1989). For example, grass/forb lands 

might become dominated by small trees and shrubs after a period of time or might 

remain as grass/forb communities following wildfire. Our STMs include major 

natural disturbances, such as wildfire (low-severity [<25 percent canopy mortality], mixed-severity [25 to 75 percent canopy mortality], and high-severity [>75 

percent canopy mortality]), insect outbreaks, wind disturbance, drought mortality, 

and others as appropriate to individual ecological systems. Wildfire probabilities in 

base STMs came from an analysis of empirical wildfire data for the 1984-2008 time 

period (Monitoring Trends in Burn Severity [MTBS]; Eidenshink et al. 2007; www.

mtbs.gov) in the Oregon East Cascades modeling zone (chapter 2). The STMs were 

developed in the Vegetation Development Dynamics Tool (VDDT) framework, version 6.0.25, (ESSA Technologies Ltd. 2007) and run in the Path modeling platform, 

version 3.0.4 (Apex Resource Management Solutions 2012, Daniel and Frid 2012).

With the STM approach, a given area can transition from one state class to 

another within a potential vegetation type with growth, succession, natural disturbance, or management. However, the environmental conditions, and thus site 

potential, of an area do not change with time. For example, an area classified as 

having ponderosa pine potential cannot become a juniper woodland over time with 

drought. However, by adding climate information from the MC1 DGVM, we are 

able to simulate changes in the distribution of vegetation types over time, allowing 

us to create more realistic projections of future conditions under changing climate.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Vegetation maps—

We used a set of potential and current vegetation maps as spatial inputs for ILAP 

(chapter 2). Previously developed current and potential vegetation maps were used 

for portions of the four-state study area, including most of the forests in Oregon and 

Washington (with the exception of southeastern Oregon). These maps were developed by the U.S. Department of Agriculture, Forest Service Landscape Ecology 

Monitoring Mapping and Analysis team (http://www.fsl.orst.edu/lemma/splash.

php). However, in most other regions of the study area, existing maps contained 

insufficient detail or were not sufficiently accurate to inform the STMs for ILAP. 

Where existing maps were insufficient to initialize the STMs (arid lands in Oregon 

and Washington and all lands in Arizona and New Mexico), ILAP built new maps 

using imputation modeling techniques (Crookston and Finley 2008, Ohmann and 

Gregory 2002). For potential vegetation maps, pixels were essentially assigned the 

plant association identified in inventory plots as a statistical function of grids of 

environmental variables, such as topography, slope, and climate. A rule set was 

developed that classified the plant association identified on each imputed pixel into 

one of the potential vegetation types. Current vegetation was also mapped using 

nearest neighbor imputation, but uses plot data (cover by species) instead of plant 

association and incorporates LANDSAT-TM imagery in addition to environmental 

variables to characterize current vegetation at the site. A rule-set was used to classify the current vegetation from each mapped pixel to one of our STM state classes. 

The resulting maps of potential and current vegetation were used to determine 

which STM to run in each geographic location and the initial starting conditions of 

the STMs (chapter 2).

Developing Climate-Informed State-and-Transition Models

We made several basic assumptions in connecting MC1 output with the STMs. 

First, we assumed that the general vegetation dynamics of major potential vegetation types near the study area adequately represented the general dynamics of 

similar vegetation in the future. We did not assume that the species mix in future 

vegetation types is static, only that the kinds of disturbances, successional patterns, 

and reactions to management in current plant communities sufficiently represent 

future communities well enough to be useful for examining vegetation and disturbance trends with climate change.

Second, we assumed that possible changes in vegetation potential from future 

climate change will be actuated by disturbances that return vegetation to early-seral 

conditions. Our supposition was that vegetation existing on a site uses resources 

and impedes development of altered plant communities unless a disturbance makes 

site resources available. We assumed that mixed- and high-severity wildfire, some 
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GENERAL TECHNICAL REPORT PNW-GTR-896

management activities, some kinds of insect outbreaks, direct drought-related 

mortality in arid lands, and a few other major disturbances were of a sufficient 

magnitude to facilitate vegetation type change. We accounted for potential changes 

in fire regimes with climate change in our STMs through integration with MC1, but 

potential changes in insect disturbances with climate change are not incorporated in 

MC1 and thus were not incorporated in our STMs.

Third, we assumed that we could use a common, overlapping time period 

(1984–2008) to compare current vegetation and MC1 modeled vegetation to develop 

scaling factors that might allow us to attach trends and variation from MC1 projections to the vegetation potential and dynamics in the study area.

Step 1: Match local vegetation types to MC1 potential vegetation classes—

The connection between MC1 and STMs required cross-walking recent historical 

vegetation as mapped by MC1 to maps of ILAP potential vegetation types. MC1 

produced an output grid for every simulation year showing the expected PVC for 

each grid cell (800 m). We compared the modeled MC1 PVC grid for the recent historical period (1984-2008) to the potential vegetation grid for the study area using a 

geographic information system (GIS) (fig. 7.2). The resulting GIS data were used to 

compute the area of ILAP potential vegetation types within each of the MC1 PVCs 

and for visual comparisons of PVC with potential vegetation locations for the recent 

historical period. The ILAP included more than two dozen potential vegetation 

types, substantially more spatial detail than that contained in the MC1 PVC maps. 

Consequently, we selected one ILAP potential vegetation type to represent each of 

the PVCs that occurred in MC1 simulations for either the recent historical or future 

time periods (table 7.1). We generally chose the most common potential vegetation to represent MC1 PVCs. For simplicity, we dropped any MC1 PVC that never 

exceeded 1 percent of the study area in either time period. This process resulted in 

seven potential vegetation types that we related to seven MC1 PVCs. In addition, 

MC1 simulated more than 1 percent of the study area in warm-season (i.e., C4) 

grasslands under some climate scenarios in the future. The map of potential vegetation from ILAP did not include any warm-season grasslands, so we added an eighth 

type that only occurred in future simulations. Our warm-season grassland STM was 

modified from a similar potential vegetation type that occurs in adjacent Nevada.

Step 2: Tune MC1 potential vegetation classes—

MC1 used a set of rules that assign combinations of biomass, carbon, climate factors, and other data to a PVC. We examined the rules and PVCs simulated for the 

historical period (1971–2000) to better understand how they related to other maps 

of historical vegetation (Kuchler 1964). We changed the interpretation of two PVCs 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

(a)

(b)

20 10 0 20 kilometers N

Antelope bitterbrush shrubland

Grassland

Western juniper woodland

Lodgepole pine forest

Dry mixed–conifer forest

Moist mixed–conifer forest

Other (lava, rock, etc.)

Ponderosa pine forest

Wyoming big sagebrush shrubland

Salt desert shrubland

Engelmann spruce–subalpine fir forest

Mountain hemlock forest

Other (wetlands)

20 10 0 20 kilometers N

Subalpine forest

Cool needle–leaved forest

Temperate needle–leaved forest

Temperate needle–leaved woodland

Temperate shrubland

Temperate grassland

Xeromorphic shrubland

Not vegetated

Figure 7.2—Potential vegetation types mapped from (a) imputation of inventory plots and (b) recent historical (1971–2000) 

vegetation simulated by MC1.
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GENERAL TECHNICAL REPORT PNW-GTR-896

to better match maps of historical vegetation. MC1 simulated substantial areas of 

maritime needle-leaved forests in middle- and upper-elevation forested areas in the 

study area, where the historical vegetation map indicated cool white fir and moist 

mixed-conifer conditions. MC1 suggested that the diurnal temperature fluctuation 

in these areas was less than that in most inland forests. Rather than label these as 

maritime forests, we labeled them as cool and moist needle-leaved forests. Likewise, MC1 simulated some areas in very warm, dry environments as coniferous 

xeromorphic woodland. Comparisons to the historical vegetation map suggested 

they should be labeled xeromorphic shrubland instead.

Step 3: Run MC1 and summarize for future climate scenarios—

We ran MC1 for the three future climate scenarios using down-scaled (800-m 

grid) climate input data for the 2010 to 2100 time period. We analyzed the annual 

output from each scenario and developed a set of tables that depict the yearly total 

proportion of the landscape in each PVC, the yearly directional change in area 

among PVCs, and the yearly proportion of the landscape burned. Yearly directional 

change among PVCs was the pair-wise proportion of area moving from one PVC to 

another over the 1-year time step. The resulting tables, when combined with output 

from the historical run of MC1, allowed us to examine the historical and possible 

future PVC trends and wildfire trends for all the selected PVCs during the 1895 to 

2100 time period.

Table 7.1—MC1 potential vegetation classes and their corresponding potential vegetation types in 

the study areaa

MC1 potential State-and-transition model Mapped MC1 current

vegetation classes potential vegetation type current extent extent

- - - - - - - - Percent - - - - - - - -

Subalpine forest Mountain hemlock forests 2 2

Cool needle-leaved Moist mixed conifer forests 11 4

 forest

Temperate needle- Ponderosa pine–lodgepole pine forests 59 67

 leaved forest

Temperate needle- Mountain big sagebrush–with juniper 14 6

 leaved woodland

Temperate shrubland Wyoming big sagebrush 12 18

Xeromorphic shrubland Salt desert shrub 2 <1

Temperate grassland Bluebunch wheatgrass–Sandberg <1 3

 bluegrass grassland

Warm-season grassland Warm-season grassland 0 0

a The “Mapped current extent” of forested potential vegetation types was developed from the U.S. Department of Agriculture Forest 

Service’s Landscape Ecology Monitoring Mapping and Analysis team (http://www.fsl.orst.edu/lemma/splash.php), and the maps of arid 

land potential vegetation types were developed as a part of the Integrated Landscape Assessment Project. 
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Step 4: Construct a state-and-transition megamodel—

We developed an STM “mega-model” by combining all eight individual STMs representing the selected potential vegetation into one large, interconnected STM. MC1 

output determined which climate change transitions among potential vegetation 

types to include in the STM megamodel, but we also had to determine when those 

changes were going to occur. Our assumption that climate change effects could be 

expressed only from early-seral vegetation conditions meant that climate change 

transitions connected early-seral state classes in STMs losing area to parallel earlyseral state classes in STMs receiving area.

Step 5: Compute climate change and wildfire trends—

We identified PVC changes simulated by MC1 between 2010 and 2100 for three 

climate change scenarios (MIROC-A2, CSIRO-A2, and Hadley-A2). We then 

calculated the average annual probability for each of those changes by dividing 

the area in which the PVC change occurred each year by the total area of the study 

region, and averaging it across all years. Average annual probabilities for PVC 

changes were incorporated in STMs as transitions that led from postdisturbance 

and early-seral state classes of a source STM toward a different STM state class.

We assumed that these new climate change transitions would occur either following stand-replacing disturbances of any kind that affected any state class in the 

STMs or when vegetation was in an open condition. Essentially, change in vegetation potential could happen when a shift in vegetation potential was simulated by 

MC1 and when vegetation was in an open condition or when disturbance removed 

most of the current vegetation. We then computed the annual departure of PVC 

change from the overall mean change for each climate change transition. For each 

year simulated by MC1, a scalar indicated how much the PVC trend was above or 

below the long-term average trend. This method produced a set of trend multipliers 

for our STM that tracked the year-to-year changes in PVCs simulated by MC1. These 

trend multipliers were applied as annual multipliers to scale the proportional annual 

movement of area among the relevant state classes within and between the STMs.

In addition to vegetation type trends, MC1 projects wildfire dynamics into 

the future. We analyzed the wildfire trends from MC1 and applied them in similar 

fashion as trend multipliers to the wildfire transitions in our STM. Since our MC1 

runs assumed no fire suppression, but the MTBS data were from a period with fire 

suppression (1984–2008), we could not use the MC1-derived probabilities directly. 

Instead, we used the overlap in wildfire data from the MTBS sampling period and 

the MC1 outputs for the 1984 to 2008 time period to develop a base relationship 

between MC1 proportion of cells burned to the average wildfire probabilities for 

the study area from MTBS data. We then scaled future wildfire probabilities in our 

STM according to trends from MC1.
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The MTBS does not capture small fires (<405 ha) and may underestimate fire 

frequency. Thus, using the MTBS record could lead to relatively low estimates of 

fire area burned for the future. However, MC1 assumes that ignitions are not limiting. In other words, fire always occurs in a cell when fuel and weather conditions 

are in a certain range. Thus, MC1 may overestimate future fire area burned.

Step 6: Run and check state-and-transition megamodel—

The process of summarizing and computing climate change transitions from MC1 

data was performed as a set of database and linked spreadsheet operations. Three 

individuals checked the databases and spreadsheets for errors. Once the STM megamodel was developed, we ran at least 10 simulation tests to check for logic errors 

and model sensitivity. Test simulations were carefully reviewed and the STM output 

compared to raw MC1 projections. We ran multiples of climate change trends, each 

ramping up climate change trends by two, four, and eight times. Similarly, we ran 

multiples of wildfire trends. We looked at figures of proportion of the landscape in 

different PVCs over time (e.g., fig. 7.3) to qualitatively assess model sensitivity. We 

found that the STM megamodel was highly sensitive to changes in climate change 

trends, but not as sensitive to changes in wildfire, especially in forested types. 

Increased fire frequency in several forest types merely reinforced development of 

open forests with large, fire-resistant trees (e.g., ponderosa pine). On the other hand, 

increased wildfire rates had larger effects in arid lands, where frequent wildfire 

favors increases in exotic annual grasses.

Results

Although we ran MC1 and our climate-informed STM for three future climate scenarios, we limit our results here to the MIROC-A2 scenario. We chose this scenario 

for illustration purposes because it is moderate in terms of temperature and precipitation change for the Pacific Northwest. In addition, although spatial results are 

possible with our coupled model process, we did not examine them for the purposes 

of this report, but plan to include them in forthcoming reports.

Historical Period Potential Vegetation and Wildfire—MC1 

Simulation Results

Except for a short period in the early 1900s, temperate needle-leaved forest (TNF) 

occupied over half the study area for the entire historical period, and at present, 

TNF potential existed across 50 to 70 percent of the study area (fig. 7.3). This 

agreed reasonably well with our potential vegetation maps for the study area, in 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

which the corresponding ponderosa pine/lodgepole pine forest type occupied about 

59 percent of the landscape area. In addition to temperate needle-leaved forests, 

MC1 projected significant but variable amounts of cool needle-leaved forests and 

small amounts of subalpine forests. These also compared well with our estimates of 

current amounts. Temperate shrubland dominated arid lands in simulated historical 

conditions and generally occupied 10 to 15 percent of the landscape area. Temperate 

needle-leaved woodlands were also prominent, and there were minor amounts of 

temperate grasslands, as was the case in our current vegetation map. Our historical 

simulations did not show significant areas of xeromorphic shrublands or warmseason grasslands.

Overall, wildfire simulations from MC1 for the historical period (1895–2008) 

varied considerably from year to year, especially in arid lands (fig. 7.4). There were 

high fire years in the early 1930s and in the 1994 to 2008 period. Relatively less arid 

land burned in other years, and forested lands generally experienced little wildfire. 

The hot and wet MIROC-A2 scenario produced highly variable wildfire in arid 

lands, with spikes to over 80 percent of cells burned in 2041, 2049, 2069, 2087, and 

Figure 7.3—MC1 projections of annual extent of selected vegetation types for historical (1895–2008) and 

future (2009–2100) periods (using MIROC general circulation model climate projections under the A2 

emissions scenario).
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GENERAL TECHNICAL REPORT PNW-GTR-896

2099. Wildfire also increased in forested lands under MIROC-A2, but amounts 

were much lower (<40 percent burned) compared to arid lands.

Future Vegetation Conditions—MC1 Simulation Results

Under the MC1 DGVM simulation results alone (without the STMs), temperate needle-leaved forests rose to >70 percent of the study area by 2039, and then 

dropped to 40 percent to 55 percent of the area until 2100 (fig. 7.3). Cool needleleaved forests occupied between zero and 20 percent of the landscape, then area of 

climatically suitable habitat was nearly zero by 2075. Climatically suitable habitat 

for subalpine forests declined to nearly zero by 2020. Temperate shrublands were 

variable, initially declining somewhat from current conditions, then rising sharply 

to more than 25 percent of the area, and ultimately declining to <10 percent of the 

landscape by 2010. Area of temperate needle-leaved woodlands rose slowly from 

Figure 7.4—Simulated historical (1895–2008) and future (2009–2100) annual area burned as a proportion 

of the study area simulated by the MC1 model. Future projections used MIROC general circulation model 

climate information under the A2 emissions scenario. 
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

about 5 percent to >25 percent from 2010 to 2100, but variation in simulated area 

also increased, especially in the last decade. Climatically suitable habitat for xeromorphic shrublands was absent until 2045, then rose slowly to nearly 20 percent of 

the landscape by 2100. Climatically suitable habitat for temperate grasslands varied 

from nearly zero to >10 percent over the 2010-2080 period, and then declined to 

almost zero. Climatically suitable habitat for warm-season grasslands was absent 

until 2080, and then never increased to more than 1 percent of the study area.

Future Vegetation Conditions—State-and-Transition Model 

Simulation Results

Our combined DGVM-STM indicated continued temperate needle-leaf forest 

dominance, though with increasing temporal variation in area (fig. 7.5). Area of 

cool needle-leaf forests was relatively constant, at slightly over 10 percent of the 

landscape until 2085, and then dropped to <5 percent. Subalpine forests remained 

at about 2 percent of the study area for most of the simulation period, then declined. 

Area of temperate needle-leaf woodlands was generally steady at about 10 to 15 

percent of the landscape, but temporal variation in simulated area increased, especially in the last 20 years. Area of temperate shrublands declined slowly to about 5 

percent by 2040, then rose to vary around 15 percent of the landscape. Climatically 

suitable habitat for xeromorphic shrublands was absent until the last three decades, 

then increased to about 20 percent of the area. Climatically suitable habitat for temperate grasslands was nearly absent until 2040, rose sharply to vary between 1 and 

6 percent of the landscape, and then declined to near zero by 2080. Warm-season 

grasslands were never present in any significant amount.

Discussion

Effects of Model Linkage

Future vegetation conditions projected by our climate-informed STM differed from 

those generated by MC1 alone. MC1 included basic ecosystem processes such as 

carbon sequestration, wildfire disturbance, and a biogeography rule-base defining 

PVCs based on climate and biomass thresholds. However, the combined DGVMSTM model also incorporated vegetation successional trends, lag times owing to 

fire-resistant species, and other local vegetation dynamics. The coupled MC1-STM 

model showed, for example, significantly less variation in the area of temperate 

needle-leaved forest, which corresponds to ponderosa pine forest in the study area, 

over the next century compared to MC1 alone. We suggest that including species 

resistance to disturbance and ecological constraints on climate-driven vegetation 

change dampens episodic shifts in vegetation conditions and results in greater 

vegetation resilience than a DGVM alone might indicate.

Our combined 

DGVM-STM indicated 

continued temperate 

needle-leaf forest 

dominance, though 

with increasing 

temporal variation in 

area.
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GENERAL TECHNICAL REPORT PNW-GTR-896

The biogeography rule-base in MC1 estimates how changes in the carbon cycle 

can affect vegetation distribution given these future climate projections. For example, if the climate is generally cool and temperatures do not fluctuate widely seasonally, a given amount of carbon in a simulation pixel might be classified as cool 

needle-leaved forest. The same amount of carbon in a different climate zone that 

experiences warmer, drier summers might be called temperate needle-leaved forest. 

Although there is some lag built into MC1, PVCs can and do fluctuate sharply over 

relatively short timeframes as climate conditions change. The vegetation dynamics 

Figure 7.5—State-and-transition model simulation of potential future potential vegetation type trends using vegetation 

type trends from MC1 for MIROC general circulation model climate projections under the A2 emissions scenario.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

simulated by the model do not reflect a lag between changes in environmental conditions and the replacement of the current vegetation, especially when plant species 

are resistant to disturbance and long lived. Consequently, DGVM results should be 

used as a guide to what might happen if large-scale disturbances such as extensive 

insect outbreak, a stand-replacing fire, an extensive windthrow or heatwave mortality, wipe the landscape slate clean and facilitate quick replacement of plant communities. Even more importantly, DGVM results alone should not be associated with 

specific years, as natural climate variability is poorly represented by climate models 

in the future and decreases the temporal accuracy of climate trends in the future.

On the other hand, STMs simulate detailed vegetation dynamics, including 

growth, disturbance resistance, species competition and succession, natural disturbance and management impacts, and others, but have not traditionally allowed for 

changes in climate. Our results show that simulating detailed vegetation dynamics 

and including factors such as specific growth rates and resistances to disturbance, 

can produce decade- to century-long lags to climate change and associated disturbance impacts.

Woodlands, shrublands, and grasslands did not show the same level of resistance as dry forests to climate change in our coupled model simulations, largely 

because the species that comprise these communities in our study area are generally not as long lived or fire resistant as many of the early-seral forest tree species. 

Rather, the tree, shrub, and grass species that characterize arid-land communities 

are generally killed or top killed by fire, and then recolonize via sprouting, stored 

propagules, or dispersed propagules. Thus, when fire occurs in these communities, 

they often move to early-successional or open condition state classes, at which 

point available resources and lack of competition facilitate potential vegetation 

shifts. While the ponderosa pine forests in our area resisted change and dampened 

climate-induced fluctuations because of the fire resistance of mature ponderosa 

pine, the woodlands, shrublands, and grasslands all showed climate-mediated fluctuations over relatively short timeframes. Thus, we might expect similar landscapes 

dominated now and in the future by woodlands, shrublands, and grasslands to be 

less stable than dry forests in the face of climate variation.

Limitations

Our approach has several assumptions and limitations:

• We assume that wildfire suppression remains as effective in the future 

as it is now. However, extreme fire weather may become more frequent 

(Westerling et al. 2006), and thus fire frequency and fire size may increase 

more than our coupled model reflects.

Woodlands, 

shrublands, and 

grasslands did not 

show the same level 

of resistance as dry 

forests to climate 

change in our coupled 

model simulations, 

largely because the 

species that comprise 

these communities 

in our study area are 

generally not as long 

lived or fire resistant as 

many of the early-seral 

forest tree species. 
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GENERAL TECHNICAL REPORT PNW-GTR-896

• MC1 does not incorporate insect outbreaks. The STMs do incorporate 

insect outbreaks, but our approach assumes current roles and probabilities 

of insect outbreaks will be similar in the future. This may well not be the 

case. The severity and distribution of some recent insect outbreaks differ 

from what can be inferred from historical records, and higher temperatures associated with climate change are believed to be a significant factor in these differences (Aukema et al. 2008, Carroll et al. 2004, Logan 

and Powell 2001). For example, the large mountain pine beetle outbreak in 

British Columbia has expanded into northern areas and into areas east of 

the Rocky Mountains in Alberta, where mountain pine beetles were not 

successful in the past because of cold winter temperatures (Carroll et al. 

2004). Our process could accommodate changes in insect or other disturbances if we included changing trends in future frequency and severity of 

insect outbreaks in our STMs.

• We did not build in lag times for species migration in the STMs. 

Paleoecological records indicate that migration rates for tree species are 

approximately 200 to 300 m/yr (Fischlin et al. 2007), but there is great 

uncertainty in that estimate, lags in response may vary considerably, and 

landscape fragmentation will also affect migration. All the PVCs projected 

to occur in the three climate scenarios examined in this study exist in or 

near to our study area at present, making incorporation of migration lags in 

our models less critical.

• We assume that the general dynamics of current vegetation and potential 

vegetation types adequately reflect similar vegetation conditions in the 

future under different climatic conditions, and that the types we chose to 

represent broader PVCs are indeed representative. Growth rates, succession, and other factors and interactions could change in the future, even in 

generally similar vegetation conditions. Past species response to changing 

climate observed in the paleoecological record shows that the abundance 

and distribution of plant species shift individualistically in response to 

climate fluctuations (Davis and Shaw 2001, Delcourt and Delcourt 1991, 

Whitlock 1992), leading to new species assemblages and communities. 

Increasing temperatures associated with climate change, and corresponding increases in summer drought stress and fire frequency in the Pacific 

Northwest, will probably lead to changing species distribution in the region, 

resulting in vegetation types different from those we see today (Zolbrod 

and Peterson 1999).
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

• Our MC1 simulations did not account for complex topography, owing to the 

scale of available climate projections, which may be important in mountainous regions such as that of our study area.

• Our coupled model results should not be associated with specific years, as 

natural climate variability is poorly represented by climate models in the 

future. Rather, our projections should be considered as potential trends 

across many years to decades.

Despite these limitations, the results of our coupled model exercise can help in 

understanding potential interactions between coarse-scale vegetation processes and 

finer scale vegetation dynamics in a changing climate.

Conclusions

Our modeling process links a DGVM to STMs to localize potential climate change 

effects on vegetation. With this linkage, we used information from climate-vegetation-disturbance interactions in DGVM simulations and used it to inform vegetation change and fire parameters in local STMs, which model finer scale vegetation 

dynamics. The addition of finer scale vegetation dynamics resulted in lags in 

climate effects and increased vegetation resilience to climate change than what was 

indicated by DGVM output alone.

Managers and others can use our process to better understand potential climateinduced shifts in local vegetation, and through linkage with other ILAP information, fuel conditions, potential wildlife habitats, biomass, silvicultural treatment 

economics, and other important vegetation-related trends and values. Managers 

wishing to retain moist forests with large, old trees, for example, could examine the 

various kinds of stand management activities (thinnings from below, fuel treatments, prescribed fire, no vegetation treatment, and others) included in our STMs 

and determine what set of treatments results in the conservation of moist forests 

across all three climate scenarios. Expanding this work to other areas could help 

natural resource planners and others more easily incorporate climate change into 

their decisionmaking process.

Acknowledgments

Funding for this project was provided by the Western Wildlands Environmental 

Threat Assessment Center, the U.S. Department of Agriculture Pacific Northwest 

Research Station, and the American Recovery and Reinvestment Act.

Managers and others 

can use our process 

to better understand 

potential climateinduced shifts in 

local vegetation, and 

through linkage with 

other ILAP information, 

fuel conditions, 

potential wildlife 

habitats, biomass, 

silvicultural treatment 

economics, and other 

important vegetationrelated trends and 

values. 
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English Equivalents

When you know: 	 Multiply by: 	 To find:

Millimeters (mm) 0.03934 Inches

Centimeters (cm) 0.394 Inches

Meters (m) 3.28 Feet

Meters per second (m/s) 2.24 Miles per hour

Kilometers (km) 0.621 Miles

Hectares (ha) 2.47 Acres

Cubic meters (m3) 35.3 Cubic feet

Tonnes (t) 1.102 Tons

Kilojoules per square 0.08806 British thermal units 

 meter (kJ/m2) per square foot

Degrees Celsius 1.8 °C + 32 Degrees Fahrenheit
203

Integrating Social, Economic, and Ecological Values Across Large Landscapes

Appendix: Common and Scientific Names

Common name	 Scientific name

Acacia Acacia spp.

Alaska cedar Callitropsis nootkatensis (D. Don) Oerst. ex D.P. Little 

Alderleaf mountain mahogany Cercocarpus montanus Raf.

Alkali sacaton Sporobolus airoides Torr.

Alligator juniper Juniperus deppeana Steud.

American marten Martes americana Turton

Apache pine Pinus engelmannii Carrière 

Apache plume Fallugia paradoxa (D. Don) Endl. ex Torr. 

Arizona fescue Festuca arizonica Vasey

Arizona orange Choisya dumosa (Torr.) A. Gray var. arizonica (Standl.) L.D. 

 Benson

Arizona white oak Quercus arizonica Sarg. 

Aroga moth Aroga websteri Clarke

Ash-throated flycatcher Myiarchus cinerascens

Barred owl Strix varia Barton

Big galleta Pleuraphis rigida Thurb. 

Big leaf maple Acer macrophyllum Pursh

Big sacaton Sporobolus wrightii Munro ex Scribn. 

Bitterbrush Purshia tridentata (Pursh) DC.

Black grama Bouteloua eriopoda (Torr.) Torr. 

Black spruce Picea mariana (Mill.) Britton, Sterns & Poggenb.

Black-backed woodpecker Picoides arcticus

Blackbrush Coleogyne ramosissima Torr.

Blue grama Bouteloua gracilis (Willd. ex Kunth) Lag. ex Griffiths 

Bluebunch wheatgrass Pseudoregeneria spicata (Pursh) A. Löve

Bluestem Andropogon spp.

Bobcat Lynx rufus

Border pinyon Pinus discolor D.K. Bailey & Hawksw. 

Buckwheat Eriogonum spp.

Buffalograss Bouteloua dactyloides (Nutt.) J.T. Columbus 

Buffelgrass Pennisetum ciliare (L.) Link

Bullgrass Muhlenbergia emersleyi Vasey 

Burrograss Scleropogon brevifolius Phil. 

Bush muhly Muhlenbergia porteri Scribn. ex Beal 

Cassin’s finch Carpodacus cassinii

Ceanothus Ceanothus spp.

Cheatgrass Bromus tectorum L.

Chihuahuan pine Pinus leiophylla Schiede & Deppe

Corkbark fir Abies lasiocarpa (Hook.) Nutt. var. arizonica (Merriam) 

 Lemmon

Creosote bush Larrea tridentata (DC.) Coville

Crucufixion thorn Canotia holacantha Torr. 

Curl-leaf mountain mahogany Cercocarpus ledifolius Nutt.

Curlyleaf muhly Muhlenbergia setifolia Vasey 

Desert bighorn sheep Ovis canadensis nelsoni

Desert ironwood Olneya tesota A. Gray
204

GENERAL TECHNICAL REPORT PNW-GTR-896

Appendix: Common and Scientific Names (continued)

Common name	 Scientific name

Douglas-fir Pseudotsuga menziesii (Mirb.) Franco

Douglas-fir tussock moth Orgyia pseudotsugata McDunnough

Dropseed Sporobolus spp.

Emory oak Quercus emoryi Torr.

Englemann spruce Picea engelmanii Parry ex Engelm.

Fisher Martes pennanti

Flammulated owl Otus flammeolus

Fluffgrass Dasyochloa pulchella (Kunth) Willd. ex Rydb. 

Gambel oak Quercus gambelii Nutt.

Giant spotted whiptail Aspidoscelis burti stictogrammus

Grama Bouteloua spp.

Grand fir Abies grandis (Douglas ex D. Don) Lindl.

Gray oak Quercus grisea Liebm.

Gray vireo Vireo vicinior

Gray wolf Canis lupus L.

Greasewood Sarcobatus vermiculatus (Hook.) Torr.

Greater sage-grouse Centrocercus urophasianus

Green fescue Festuca viridula Vasey

Grey checkered whiptail Cnemidophorus dixoni

Idaho fescue Festuca idahoensis Elmer

Incense cedar Calocedrus decurrens (Torr.) Florin 

Indian ricegrass Achnatherum hymenoides (Roem. & Schult.) Barkworth

James’ galleta Pleuraphis jamesii Torr.

Jeffrey pine Pinus jeffreyi Balf.

Jointfir Ephedra spp.

Juniper Juniperus spp.

Kentucky bluegrass Poa pratensis L.

Lark sparrow Chondestes grammacus

Lesser prairie chicken Tympanuchus pallidicinctus

Lewis’s woodpecker Melanerpes lewis

Limber pine Pinus flexilis James

Lodgepole pine Pinus contorta Dougl. ex Laud.

Loggerhead shrike Lanius ludovicianus

Low sagebrush Artemisia arbuscula Nutt.

Mallow ninebark Physocarpus malvaceu (Greene) Kuntze

Manzanita Arctostaphylos spp.

Medusahead Taeniatherum caput-medusae (L.) Nevski

Mesquite Prosopis spp.

Mexican blue oak Quercus oblongifolia Torr. 

Mexican spotted owl Strix occidentalis lucida

Mountain big sagebrush Artemisia tridentata ssp. Vaseyana (Rydb.) B.

Mountain hemlock Tsuga mertensiana (Bong) Carr.

Mountain muhly Muhlenbergia montana (Nutt.) Hitchc. 

Mountain pine beetle Dendroctonus ponderosae Hopkins

Mountain plover Charadrius montanus

Mountain snowberry Symphoricarpos albus (L.) S.F. Blake

Muhly Muhlenbergia spp.
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Integrating Social, Economic, and Ecological Values Across Large Landscapes

Appendix: Common and Scientific Names (continued)

Common name	 Scientific name

Needle-and-thread Hesperostipa comata (Trin. & Rupr.) Barkworth 

New Mexico feathergrass Hesperostipa neomexicana (Thurb. ex J.M. 

 Coult.) Barkworth

Noble fir Abies procera Rehder 

North American black bear Ursus americanus Pallas

Northern goshawk Accipiter gentilis

Northern harrier Circus cyaneus

Northern sagebrush lizard Sceloporus graciosus graciosus

Northern spotted owl Strix occidentalis caurina Merriam

Northwestern salamander Ambystoma gracile Baird

Oak Quercus spp.

Olive-sided flycatcher Contopus cooperi

Oneseed juniper Juniperus monosperma (Engelm.) Sarg.

Oregon white oak Quercus garryana Douglas ex Hook.

Pacific madrone Arbutus menziesii Pursh

Pacific silver fir Abies amabilis (Douglas ex Louden) Douglas ex Forbes

Paloverde Parkinsonia spp.

Pileated woodpecker Dryocopus pileatu L.

Pinchot’s juniper Juniperus pinchotii Sudw.

Pointleaf manzanita Arctostaphylos pungens Kunth

Ponderosa pine Pinus ponderosa Dougl. ex C. Laws.

Prairie dog Cynomys spp.

Prairie junegrass Koeleria macrantha (Ledeb.) Schult.

Pygmy rabbit Brachylagus idahoensis

Quaking aspen Populus tremuloides Michx.

Rabbitbrush Ericameria or Chrysothamnus spp.

Red brome Bromus rubens L.

Red tree vole Arborimus longicaudus

Rigid sagebrush Artemisia rigida (Nutt.) A. Gray 

Rubber rabbitbrush Ericameria nauseosa (Pall. ex Pursh) G.L. 

 Nesom & Baird

Sagebrush Artemisia spp.

Saguaro Carnegiea gigantea (Engelm.) Britton & Rose 

Sahara mustard Brassica tournefortii Gouan

Salt grass Distichlis spicata (L.) Greene

Saltbush Atriplex spp. 

Sand sagebrush Artemisia filifolia Torr. 

Sandberg bluegrass Poa secunda J. Presl

Serviceberry Amelanchier spp.

Sharp-shinned hawk Accipiter striatus

Shasta red fir Abies magnifica A. Murray bis var. shastensis Lemmon

Shinnery oak Quercus havardii Rydb.

Sideoats grama Bouteloua curtipendula (Michx.) Torr.

Sitka spruce Picea sitchensis (Bong.) Carrière 

Snowshoe hare Lepus americanus

Sonoran scrub oak Quercus turbinella Greene

Subalpine fir Abies lasiocarpa (Hook.) Nutt.
206

GENERAL TECHNICAL REPORT PNW-GTR-896

Appendix: Common and Scientific Names (continued)

Common name	 Scientific name

Swainson’s hawk Buteo swainsoni

Switchgrass Panicum virgatum L.

Tan oak Lithocarpus densiflorus (Hook. & Arn.) Rehder

Tarbush Flourensia cernua DC.

Three-tip sagebrush Artemisia tripartita Rydb. ssp. tripartita

Tobosagrass Pleuraphis mutica Buckley 

Tourney oak Quercus toumeyi Sarg.

Twinflower Linnaea borealis L.

Vine mesquite Panicum obtusum Kunth 

Western bluebird Sialia mexicana

Western gray squirrel Sciurus griseus

Western hemlock Tsuga heterophylla (Raf.) Sarg.

Western juniper Juniperus occidentalis Hook.

Western larch Larix occidentalis Nutt.

Western redcedar Thuja plicata Donn ex D. Don.

Western spruce budworm Choristoneura occidentalis Freeman

Western white pine Pinus monticola Douglas ex D. Don

White bursage Ambrosia dumosa (A. Gray) Payne

White fir Abies concolor (Gord. & Glend.) Lindl. ex Hildebr.

White Sands woodrat Neotoma micropus leucophaea

White spruce Picea glauca (Moench) Voss 

Whitebark pine Pinus albicauli Engelm.

White-headed woodpecker Picoides albolarvatus

White-sided jackrabbit Lepus callotis gaillardi Mearns

Willow Salix spp.

Wyoming big sagebrush Artemisia tridentata Nutt. ssp. wyomingensis 

 Beetle & Young

Yellow-nosed cotton rat Sigmodon ochrognathus V. Bailey

Zone-tailed Hawk Buteo albonotatus Kaup
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