A Comprehensive Guide to Fuel

Management Practices for Dry Mixed Conifer 

Forests in the Northwestern United States

Theresa B. Jain, Mike A. Battaglia, Han-Sup Han, 

Russell T. Graham, Christopher R. Keyes, 

Jeremy S. Fried, and Jonathan E. Sandquist 

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-292

October 2012

Dry Mixed Conifer Forest Ecology

Fuel Treatment Feasibility and Longevity

Planning and Implementation
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Jain, Theresa B.; Battaglia, Mike A.; Han, Han-Sup; Graham, Russell T.; Keyes, Christopher R.; Fried, 

Jeremy S.; Sandquist, Jonathan E. 2012. A comprehensive guide to fuel management practices for 

dry mixed conifer forests in the northwestern United States. Gen. Tech. Rep. RMRS-GTR-292. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 331 p.

Abstract

This guide describes the benefits, opportunities, and trade-offs concerning fuel treatments in the dry mixed 

conifer forests of northern California and the Klamath Mountains, Pacific Northwest Interior, northern and 

central Rocky Mountains, and Utah. Multiple interacting disturbances and diverse physical settings have 

created a forest mosaic with historically low- to mixed-severity fire regimes. Analysis of forest inventory 

data found nearly 80 percent of these forests rate hazardous by at least one measure and 20 to 30 percent 

rate hazardous by multiple measures. Modeled mechanical treatments designed to mimic what is typically 

implemented, such as thinning, are effective on less than 20 percent of the forest in single entry, but can be 

self-funding more often than not. We provide: (1) exhaustive summaries and links to supporting guides and 

literature on the mechanics of fuel treatments, including mechanical manipulation, prescribed fire, targeted 

grazing and chemical use; (2) a decision tree to help managers select the best mechanical method for any 

situation in these regions; (3) discussion on how to apply prescribed fire to achieve diverse and specific 

objectives; (4) key principles for developing an effective monitoring plan; (5) economic analysis of mechanical fuel treatments in each region; and (6) discussion on fuel treatment longevity. In the electronic version of 

the document, we have provided links to electronic copies of cited literature available in TreeSearch online 

document library (http://www.treesearch.fs.fed.us/)
The Authors

Theresa Jain is a Research Forester in the Forest and Woodland Ecosystem Program at the Rocky Mountain Research 

Station, U.S. Forest Service, Moscow, Idaho. Theresa’s 

research expertise is in silviculture and fuels. Currently her 

research focuses on developing, implementing, and evaluating 

alternative silvicultural management strategies for addressing 

integrated fuel treatment and restoration objectives in moist 

and dry mixed conifer forest She has developed and installed 

integrated fuel treatment landscape studies at Boise Basin 

and the Black Hills Experimental Forests, both of which occur 

within the dry mixed conifer forests. Her work has also included 

landscape installations of restoration and integrated fuel treatments in the moist mixed conifer forests on the Priest River and 

Deception Creek Experimental Forests. She received all three 

of her degrees from the University of Idaho. She received her 

Ph.D. in Silviculture with an emphasis on landscape ecology 

and applied statistics, Master of Science in Silviculture with 

emphasis on soil processes, and the Bachelor of Science in 

Forest Management. 

Mike A. Battaglia is a Research Forester in the Forest and 

Woodland Ecosystem program at the Rocky Mountain Research Station, U.S. Forest Service, Fort Collins, Colorado. 

Mike’s research focuses on developing and implementing innovative management strategies that address the challenges and 

issues faced by forest managers. These research interests 

include forest restoration, hazardous fuels mitigation, and forest resilience across multiple spatial scales. He has a Ph.D. in 

Silviculture and Fire Science from Colorado State University, 

Master of Science in Forestry from Virginia Tech, and a Bachelor 

of Science in Biology from the University of South Carolina. 

Han-Sup Han is currently a Professor in the Department of 

Forestry and Wildland Resources, Humboldt State University 

(HSU), Arcata, California. At HSU, he teaches undergraduate 

and graduate courses related to forest harvesting, road design 

and layout, and forest operations analysis. His primary area 

of research interest and expertise is in economic analysis 

and environmental impact assessments of forest harvesting 

operations. Current research efforts focus on the economic 

analysis of fuel reduction thinning treatments and development 

of innovative biomass feedstock logistic systems. Along with 

this effort, he is looking at the maximum utilization and value 

recovery from logs and forest residues resulting from thinning 

treatments and timber operations. He received his Ph.D. in 

Forest Engineering from Oregon State University, Corvallis. 

He received two Master of Science degrees (in Forest Ecology 

from Kangwon National University, South Korea, and in Forest Operations from the University of Maine), and received a 

Bachelor of Science degree in Forestry from Kangwon National 

University, South Korea.

Russell T. Graham has over 35 years of experience as a Research Forester with the Rocky Mountain Research Station. He 

started with the Forest Service working the summers of 1966 

through 1969 on the Black Hills National Forest in Wyoming 

and worked the summers of 1970 and 1971 on the Idaho 

Panhandle National Forests. After receiving his Bachelor of 

Science degree in Forestry from the University of Montana, he 

started his professional career as a Forester for the Bitterroot 

National Forest in western Montana. At that time, he started 

his continuing education in silviculture (CEFES) and in 1975 he 

transferred to the Intermountain Research Station in Moscow, 

Idaho (now part of the Rocky Mountain Research Station). 

He maintained his silvicultural certification through 1983 and 

completed a Master of Science in Silviculture in 1976 and a 

Ph.D. in Silviculture in 1981, both from the University of Idaho. 

Dr. Graham has published over 200 scholarly articles with his 

principle research focusing on long-term forest productivity, 

landscape processes, and wildlife habitat. 

Christopher R. Keyes is an Associate Research Professor 

of Silviculture and the Director of Applied Forest Management 

Program at the University of Montana, in Missoula, Montana. 

His expertise is in silviculture and applied forest stand dynamics. His interests are in natural regeneration ecology and 

reforestation, silvicultural restoration of disturbed forests, and 

dynamics and silvicultural management of forest fuels. He 

has a Ph.D. in Silviculture with a forest regeneration ecology 

emphasis from Oregon State University; a Master of Science 

in Silviculture and Forest Stand Dynamics from University of 

Montana; a Post-Baccalaureate Study in Forest Management 

from the University of Massachusetts; and a Bachelor of Arts 

in Political Science from the College of the Holy Cross.

Jeremy S. Fried is a Research Forester in the Resource Monitoring and Assessment Program at thePacific Northwest Research 

Station, U.S. Forest Service, Portland, Oregon. Fried has been 

with the Station’s Forest Inventory and Analysis unit since 1999, 

serving as a California FIA analyst since 2000 and as team 

leader from 1999 to 2009. His research focus is interdisciplinary application of systems analysis and geographic information 

science to contemporary natural resource management issues. 

Current and recent examples include economic feasibility of 

landscape-scale fuel treatments, holistic understanding of 

interactions between forest and climate systems, initial attack 

simulation and optimization, and building inventory-based 

models of fire effects. Before joining the PNW Research Station, he served on the forestry faculties at Michigan State and 

Helsinki universities. He has a Ph.D. in Forest Management 

and Economics and a Bachelor of Science in Forestry from 

the University of California–Berkeley and Master of Science in 

Forest Ecology and Soils from Oregon State University.

Jonathan E. Sandquist is a Forestry Technician in the Forest 

and Woodland Ecosystem Program at the Rocky Mountain 

Research Station. Jonathan has a Bachelor of Arts from 

Evergreen State College, Master of Science in Environmental 

Science from Washington State University, and GIS Certificate 

from the University of Idaho. He has worked the past 10 years 

supporting silviculture research. His skills are in data management, preparing manuscripts, and GIS and FFE-FVS analysis. 
Acknowledgments

This fuel synthesis for the dry mixed conifer forests would 

not have been possible without critical contributions from a 

great many managers, scientists, and other natural resource 

professionals. Although the listed authors did most of the writing 

and are collectively and solely responsible for accuracy and 

completeness, the content and structure of the synthesis was 

driven by the hundreds of comments, questions, and anecdotes 

shared with us by managers and scientists. We interviewed over 

50 specialists throughout the synthesis area and are indebted 

to every one of them. Thank you for your time and effort! Many 

thanks to those who helped out by providing peer-review: Andris 

Eglitis, Barry Bollenbacher, Daron Reynolds, David Powell, 

David Peterson, Deirdre Dether, Gabe Dumm, Geoff Babb, 

Louisa Evers, Jason Moghaddas, Jonathan Oppenheimer, Morris 

Johnson, Roby Darbyshire, Tim Bumgarner, Tobin Kelly, Tyre 

Holfeltz, Morgan Varner, and Nikia Hernandez. Your reviews 

were extensive and informative and we addressed every comment, and many of them led to revisions, which we believe 

improved this product. We are particularly indebted to Louisa 

Evers. Her knowledge of management history was so impressive that we invited her to help write Chapter 4, “Actions and 

Impacts of Past Management,” and she graciously agreed. Jeff 

Halbrook’s comments on mechanical fuel reduction treatments 

were helpful in developing the flow chart of equipment selection for Chapter 8. Morgan Varner, thank you for providing a 

review of decision tools at the critical time when we needed 

it. Reading proofs prior to publication takes time and focused 

concentration to catch subtle errors in the manuscript; we 

needed an individual who was not directly involved in the 

writing to conduct a final review; in addition to the authors. 

Lance Asherin set aside the time to critically review the 

manuscript; we are grateful for the errors he identified and 

appreciate his professional expertise and comments.

James Donley, Nichole Studevant, and Tess Pinkney were 

instrumental in preparing the document; they compiled the reference list and information for several tables and figures. There 

were also key people who worked with Jeremy Fried to compile 

the information we present in Chapters 5 and 11. They include 

Demetrios Gatziolis, who assisted with R script programming for 

the appendices, Glenn Christensen, who resolved FVS keyfile 

issues during the BioSum analysis, and Larry Potts, who wrote, 

and then repeatedly debugged and revised BioSum to enable 

the analysis to be completed successfully. 

Our science writer, Josh McDaniel, proved masterful at 

blending our diverse writing styles to help create a consistent 

and integrated document. His knowledge of wildland fire and 

his writing expertise made this synthesis far more accessible 

than would otherwise have been possible. The professional 

appearance and publication standards reflected in this 

document can only be attributed to Lane Eskew and the 

Publishing Services staff for the Rocky Mountain Research Station. Lindy Larson, Lane Eskew, Suzy Stephens, 

Loa Collins, and Nancy Chadwick provided their expertise in 

editing, developing figures, and layout of the document. Suzy 

was instrumental in creating the interactive reference list. We 

feel privileged to have the support of all these highly capable 

professionals.

We gratefully acknowledge funding support provided by the 

Joint Fire Science Program and the Rocky Mountain Research 

Station, through the National Fire Plan, without which this 

synthesis would not have been possible. 
Chapter 1—Preamble.........................................................................1

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

Setting ........................................................................................................................... 1

Purpose ......................................................................................................................... 3

Organization and Key Points....................................................................................... 4

Key Messages From Section I........................................................................... 5

Key Messages From Section II.......................................................................... 6

Key Messages From Section III......................................................................... 7

Information Sources..................................................................................................... 7

Relevant Literature ............................................................................................ 7

Expert Knowledge ............................................................................................. 8

Conclusion.................................................................................................................... 8

Section I: Ecology of Dry Mixed Conifer Forests .........................9

Chapter 2—Potential Vegetation and Biophysical Setting ......11

Introduction..................................................................................................................11

Biophysical Settings ...................................................................................................11

Descriptions of Biophysical Settings....................................................................... 12

Northern California and Klamath ..................................................................... 13

Pacific Northwest Interior................................................................................. 18

Northern and Central Rocky Mountains .......................................................... 23

Utah ................................................................................................................ 29

Conclusion.................................................................................................................. 32

Further Reading.......................................................................................................... 32

Chapter 3—The Role of Disturbance and Climate in 

Sustaining Dry Mixed Conifer Forests.............................. 35

Introduction................................................................................................................. 35

Weather, Insects, and Diseases ................................................................................ 37

Weather........................................................................................................... 38

Insects and Disease ........................................................................................ 40

Contents
Fire Regimes............................................................................................................... 46

Regional Variability in Fire Regimes................................................................ 48

Northern California and Klamath ......................................................... 50

Pacific Northwest Interior..................................................................... 50

Northern and Central Rocky Mountains .............................................. 52

Utah ..................................................................................................... 53

Climate Patterns ......................................................................................................... 54

Variation in Fuels, Topography, and Weather.......................................................... 56

Vegetation Autecology and Fire Tolerance .............................................................. 57

Conclusion.................................................................................................................. 60

Further Reading.......................................................................................................... 62

Chapter 4—Actions and Impacts of Past Management ........... 63

Chapter Rationale....................................................................................................... 63

Introduction................................................................................................................. 63

Human Influence on Dry Mixed Conifer Forests ..................................................... 64

Consequences of Past Management for Forests and Fuels .................................. 71

Tree Species Shifts and Insects and Disease ................................................. 71

Soil Impacts..................................................................................................... 72

Conclusion.................................................................................................................. 74

Further Reading.......................................................................................................... 75

Chapter 5—Inventory Modeling of Current 

Fire Hazard Conditions ........................................................ 77

Chapter Rationale....................................................................................................... 77

Introduction................................................................................................................. 77

Analytic Approach...................................................................................................... 77

Findings....................................................................................................................... 80

Douglas-fir, True Fir, Pine and Western Larch Types Dominate...................... 80

Steep Slopes are Common in Some Sub-Regions ......................................... 80

Hazard Dimensions are Highly Varied............................................................. 82

Hazard is Common in All Forest Type Groups ................................................ 84

Description of Current Conditions............................................................................ 84

What These Data Tell Us................................................................................. 85

A baseline for comparing fuel treatment outcomes and the 

characteristics of stands amenable to fuel treatment .......................... 87

Conclusion.................................................................................................................. 87

Further Reading.......................................................................................................... 87
Section II: Fuel Treatment Planning and 

Implementation in Dry Mixed Conifer 

Forests of the Northwestern United States..................... 89

Chapter 6—Integrating Wildlife Habitat into Fuels 

Planning and Implementation ...........................................91

Chapter Rationale....................................................................................................... 91

Introduction................................................................................................................. 91

Which Habitat(s) Will the Fuel Treatments Impact and for How Long?........... 91

Composition and structure of the habitat............................................. 92

Sensitivity of habitat............................................................................. 92

Fragmentation and edge ..................................................................... 92

Snags .................................................................................................. 95

Dead and down wood.......................................................................... 99

Connectivity ......................................................................................... 99

Which Wildlife Species Are in the Proposed Planning Area? ........................ 101

How Will the Proposed Treatment Impact the Habitat and Wildlife Species? 102

Conclusion................................................................................................................ 103

Further Reading........................................................................................................ 103

Chapter 7—Planning and Conducting Integrated Fuel 

Treatments........................................................................... 105

Chapter Rationale..................................................................................................... 105

Introduction............................................................................................................... 105

Important Concepts, Challenges, and Trade-Offs................................................. 106

Heterogeneity Matters ................................................................................... 107

Integrated Treatments ....................................................................................111

The Silvicultural Prescription ..........................................................................114

Decision Support Tools ............................................................................................117

Conclusion.................................................................................................................117

Further Reading.........................................................................................................117

Chapter 8—Mechanical, Chemical, And Biological Fuel 

Treatment Methods ............................................................119

Chapter Rationale......................................................................................................119

Introduction................................................................................................................119

Removing Biomass and Fuels ................................................................................ 123

Treating Fuels to Support Active Fire Suppression.............................................. 123

Treating Vegetation (Fuels) to Enhance Resilience to Wildfire............................ 128
Types of Treatments................................................................................................. 132

Mechanical Methods...................................................................................... 132

Retain Biomass ............................................................................................. 133

Mastication ........................................................................................ 133

Hand thinning (lop and scatter) ......................................................... 135

Remove Biomass .......................................................................................... 136

Fuels reduction thinning using ground-based systems ..................... 136

Fuels reduction thinning using skyline yarding systems.................... 138

Fuels reduction thinning using helicopter yarding ............................. 138

Equipment Selection for Mechanical Applications......................................... 141

Chemical Control........................................................................................... 145

Biological Control/Grazing............................................................................. 146

Conclusion................................................................................................................ 147

Further Reading........................................................................................................ 147

Chapter 9—Prescribed Fire.......................................................... 149

Chapter Rationale..................................................................................................... 149

Introduction............................................................................................................... 149

Developing a Burn Plan ........................................................................................... 150

Objectives, Complexity, and Prescription ...................................................... 152

Pre-Burn Considerations and Weather.......................................................... 153

Organization, Equipment, and Communications ........................................... 153

Complexity Analysis....................................................................................... 154

Common Oversights in Prescribed Fire Planning................................................. 154

Implementation of Prescribed Fire ......................................................................... 156

Atmospheric Stability and Atmospheric Dispersion ....................................... 158

Firing Techniques .......................................................................................... 159

Monitoring...................................................................................................... 161

Unique Attributes That Favor Specific Post-Fire Outcomes ................................ 161

Live and Dead Fuel Moistures....................................................................... 161

Lower duff.......................................................................................... 161

Live fuel moistures............................................................................. 162

Seasonality of Burning................................................................................... 163

Fuel consumption .............................................................................. 164

Soils................................................................................................... 165

Wildlife ............................................................................................... 165

Prescribed Fire as a Thinning Agent ............................................................. 166

Mortality thresholds ........................................................................... 168

Prescribed Burning Masticated Fuels............................................................ 168

Minimizing Mortality of Large Ponderosa Pine .............................................. 170

Dealing With Duff in Old Growth Ponderosa Pine......................................... 170

Managing Wildfire for Meeting Resource Objectives..................................... 174

Conclusion................................................................................................................ 175

Further Reading........................................................................................................ 175

Chapter 10—Monitoring............................................................... 177

Chapter Rationale..................................................................................................... 177
Introduction............................................................................................................... 177

Developing a Monitoring Plan................................................................................. 178

Two Phases of Monitoring....................................................................................... 181

Elements of a Monitoring Design............................................................................ 182

Sampling........................................................................................................ 183

Non-Statistical Sampling ............................................................................... 183

Statistical Sampling Principles for Monitoring................................................ 183

Model-Assisted Monitoring ..................................................................................... 185

Conclusion................................................................................................................ 185

Further Reading........................................................................................................ 186

Section III. Reality Check: The Economics, Feasibility, 

Longevity, and Effectiveness of Fuel Treatments........ 187

Chapter 11—Inventory and Model-Based Economic 

Analysis of Mechanical Fuel Treatments ....................... 189

Chapter Rationale..................................................................................................... 189

Introduction............................................................................................................... 189

Applying the BioSum Analysis Framework ........................................................... 190

Data Acquisition and Treatment Development...................................................... 192

FIA Plot Data ................................................................................................. 192

Silvicultural Treatments ................................................................................. 193

Overstory treatments ......................................................................... 193

Harvesting method ............................................................................ 193

Excluded plots ................................................................................... 194

Calculating Economic Feasibility................................................................... 194

Estimating treatment and transport costs .......................................... 194

Estimating product quantities and values .......................................... 195

Fire Hazard Criteria and Evaluation........................................................................ 195

Selecting Evaluation Criteria ......................................................................... 195

Findings..................................................................................................................... 197

Area Treated by Silvicultural Treatments....................................................... 198

Evaluating Treatment Success...................................................................... 198

Costs, Revenues, and Product Flows Associated With Fuel Treatments...... 200

Finding Insights Beyond the Means .............................................................. 203

Discussion ................................................................................................................ 205

Best Places to Add Processing Capacity....................................................... 207

Conclusion................................................................................................................ 208

Chapter 12—Fuel Dynamics and Treatment Longevity ......... 209

Chapter Rationale..................................................................................................... 209

Introduction............................................................................................................... 209
Longevity in Fuel Treatment Planning.................................................................... 209

Studies of Treatment Longevity.............................................................................. 210

Dead Surface Fuels and Longevity ............................................................... 212

Forest Stand Dynamics as Forest Fuel Dynamics ........................................ 213

Crown Fuels Growth...................................................................................... 213

Live Surface and Ladder Fuels Growth......................................................... 216

Ladder Fuels Recruitment ............................................................................. 219

Longevity and the Fire Environment ...................................................................... 220

Longevity and Rates of Change in Live Fuels............................................... 221

Longevity and Decision Support Tools .......................................................... 222

Conclusion................................................................................................................ 223

Literature Cited......................................................................................................... 226

Appendix A: Chapter 5 Supplement ......................................... 245

Inventory Modeling of Current Fire Hazard Conditions........................................ 245

Example Interpretation of Histograms for the Northern California and 

Klamath Subregion’s Douglas-fir and True Fir Forests.................................. 246

Forest characteristics ........................................................................ 246

Fire characteristics............................................................................. 246

Appendix B: Decision Support Tools for Managers ............... 261

Emissions ...................................................................................................... 261

Fire and Technology Transfer Portals............................................................ 261

Fire Behavior ................................................................................................. 262

Fire Effects .................................................................................................... 262

Fire Weather.................................................................................................. 263

Fuels Description........................................................................................... 263

Fuels Planning............................................................................................... 263

Databases ..................................................................................................... 266

Appendix C: Chapter 11 Supplements ...................................... 267

Evaluating Silvicultural Treatment Scenarios Using Forest 

Inventory and Analysis.................................................................................... 267

All Versus Treatable....................................................................................... 271

Hazard Score................................................................................................. 283

Stand Structure Metrics................................................................................. 289

Fire Hazard Metrics ....................................................................................... 301

Economic Implications .................................................................................. 313

Appendix D: Common and Scientific Names of Species ....... 325

Appendix E: English to Metric Unit Conversions .................... 331
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 1

Chapter 1

Introduction

Preamble

I

daho’s Warm Lake Basin sits at 5,300 ft near the origin of the South Fork of the Salmon 

River in the Payette Crest and Salmon River Mountain ranges. The area is a popular 

summer vacation destination, and summer homes, cabins, and campgrounds are scattered 

throughout the mixed conifer forests that cover the basin (Graham and others 2009).

Because of the increasing fire risk to the wildland-urban interface (WUI) around Warm 

Lake, the Forest Service began fuel treatments in the mid-1990s to reduce the amounts, 

distribution, and juxtaposition of surface and ladder fuels by homes and campgrounds. 

Seven prescribed burns totaling over 8,000 acres were used to clean the forest floor of 

litter and fine woody fuels (≤3 inches in diameter) and kill many of the small trees that 

served as ladder fuels in these dry mixed conifer forests. Mechanical fuel treatments 

were used on lodgepole pine near cabins and homes to reduce canopy density, remove 

ladder fuels, and raise canopy base heights. A total of over 9,000 acres were treated at 

a cost of about $181 per acre (Graham and others 2009).

In August of 2007, the fuel treatments were put to the test when two wildfires, the 

Monumental and North Fork Fires, burned 150,000 acres in the vicinity of Warm Lake. 

Only two structures were burned during the fires; without the treatments, many more 

would likely have burned. The fuel treatments did not stop the fires but they disrupted 

their advance and influenced burn severity. Although fire behavior changed from a crown 

to a surface fire when the flames entered the treatments, the fire moved into the treatments 200 to 400 feet before fire intensity was reduced sufficiently to leave unburned 

soils and live trees. The treatments produced suppression opportunities (creating safe 

zones and locations suitable for igniting burnouts and facilitating construction of handand machine-built fire line) that would not have otherwise been available. Post-fire 

surveys showed that the treatments helped to create a mosaic of burn patterns, forest 

structures, and species compositions that will result in enhanced wildlife habitat and 

more fire resilient forests in the future.

The way the North Fork and Monumental Fires interacted with fuel treatments, roads, 

and associated suppression efforts reinforced the notion that the treatment of surface 

fuels, ladder fuels, and crown fuels (in this order of importance), and the location and 

juxtaposition of those treatments, are major determinants of both wildfire intensity and 

burn severity. The Warm Lake experience exemplifies how fuel treatments combined 

with fire suppression can affect a wildfire outcome in dry mixed conifer forests (Graham 

and others 2009).

Setting

In the United States, dry mixed conifer forests occur from the northern and 

central Rocky Mountains to the Pacific Northwest and into the Great Basin, Utah, 

and California and throughout the Southwest (fig. 1). These forests are associated with 

complex fire regimes. Predominantly low and mixed severity wildfires historically 

burned through these forests leaving a variety of forest compositions and structures. 

Since the 1800s, insects, disease, fire exclusion, livestock grazing, timber harvesting, 
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 1.1. The focus of the synthesis is the dry mixed conifer forests in Idaho, Montana, Wyoming, South Dakota, 

Utah, Oregon, Washington, and northern California.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 3

and widespread human settlement have shaped these forests. Stand structure, species 

composition, fuel dynamics, and forest succession have all been affected by fire regimes in these forests. One consequence is a proliferation of larger and more intense 

wildfires, such as the 2002 Biscuit Fire (500,000 acres), the 2007 Cascade Complex 

(302,376 acres), and the 2006 Tripod Complex (113,011 acres) (Prichard and others 

2010; Thompson and Spies 2009).

Dry mixed conifer forests challenge ecological classification systems because of the 

diversity and complexity of the landscapes where they occur. Many contain ponderosa 

pine and a mixture of other tree species in the overstory and understory and are highly 

productive, in contrast with very dry sites in the southwestern United States that resemble 

a ponderosa pine monoculture. Dry mixed conifer forests often mix with both drier and 

wetter forests, creating a mosaic of forest types quite unlike the expansive stands of 

comparatively pure ponderosa pine found elsewhere in the West. Moreover, these forests 

occupy a variety of aspects, slopes, and topographic contexts, further contributing to the 

classification challenge. Past management and disturbance are also influential; ponderosa 

pine may not be present on every site classified as dry mixed conifer due to selective 

harvesting, fire exclusion, succession, fire, insects, disease, or other disturbances. The 

different combinations of disturbances and successional pathways can lead to a vast 

number of possible ground level and overstory vegetative compositions, structures, and 

mosaics (Jain and Graham 2005; Perry and others 2011; Quigley and others 1997a).

Moreover, the disturbances that influence the species and structural diversity of these 

forests also operate at different time frames and spatial extents. Therefore, regardless 

of the fuel treatment location, its timing, or its objective, it is important to recognize 

the dynamic nature of the forests. In other words, “one size does not fit all.” Fuel treatments should be tailored to the forest setting and the values they are designed to protect.

The design and implementation of fuel treatments, however, is not solely influenced 

by forest and fire ecology. Social issues necessarily play an important role. For example, 

approximately 9 percent of the land area in the contiguous United States is considered 

wildland–urban interface (WUI) where houses intermingle with wildland vegetation. 

However, approximately 39 percent of all houses occur within the WUI (Radeloff and 

others 2005). Dry mixed conifer forests of the American West in particular contain 

many areas that are attractive to those who prefer rural settings for living and recreation. 

Accordingly, fuel treatments in and around WUI communities focus on creating forest 

structures and compositions designed to protect homes and infrastructure and to enhance 

fire suppression effectiveness and firefighter safety. In contrast, much of the land base 

in Idaho, for example, is federally administered; consequently, treatments in the less 

populated areas may favor increasing forest resilience and may or may not be directly 

related to facilitating suppression.

Purpose

The purpose of this guide is to provide the most up-to-date information regarding 

the benefits, challenges, opportunities, and trade-offs among the different strategies and 

tools related to fuel treatment applications within dry mixed conifer forests of the 

western United States. Our geographic area includes the dry mixed conifer forests in 

northern California and Klamath, Pacific Northwest Interior, northern and central 

Rocky Mountains, and Great Basin (primarily in Utah) and covers over 37 million acres. 

This guide for managing fuels is not a “how to” or “cookbook” for fuels management, 

but rather an information resource that can be used to help plan and execute forest treatments directed at altering fire behavior and burn severity.

All live and dead vegetation is fuel in dry mixed conifer forests. Thus, regardless of 

objective, all vegetation manipulation alters fuels and fuel dynamics. This document 
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

cannot prescribe or predict all of the possible outcomes of treating fuels in dry 

mixed conifer forests, but it does describe many common region-wide patterns as well 

as some of the more unique, site-specific observations associated with fuel treatments 

in these forests.

Throughout this guide, we emphasize the importance of designing fuel treatments 

with the full range of potential fire behaviors in mind, in addition to the possible fuel 

and weather conditions. However, it is also important to address other disturbances and 

factors (for example, climate, diseases, insects, snow, and wind) that may impact treatment effectiveness and longevity. Because vegetation regenerates and develops rapidly 

in dry mixed conifer forests, ladder fuel development is also rapid, so maintenance (retreatment) may be essential for long-term effectiveness. Monitoring fuel development 

over short and long time scales and adjusting treatment schedules based on what is 

learned can ensure effectiveness in mitigating the effects of an unwanted fire.

There are many ways to remove or alter biomass, in this guide we focus primarily on 

prescribed fire and mechanical manipulation, though we also address targeted grazing 

and chemical applications. Accordingly, we present a variety of treatment tools and 

suggestions on where they are most effective in treating forest fuels. Prescribed fire 

is the preferred method in many settings but can be difficult to implement due to its 

complexity and risk. A successful prescribed fire is dependent upon factors such as the 

physical setting, short- and long-term weather, vegetation composition and structure, 

fuel moisture, and the knowledge and experience of the fire practitioner (Fernandes 

and Botelho 2003).

There are several excellent fuel treatment syntheses already available that provide 

general information concerning fuel treatments that does not require repetition in this 

document. We refer to those documents and highlight the unique aspects and alternative 

views they provide with regard to dry mixed conifer forests.

The synthesis focuses on providing knowledge associated with fuel treatment planning, implementation, and monitoring. Within a planning framework, this document 

can inform the process of defining the purpose of and need for a fuel treatment, help 

in determining where and when a particular fuel treatment needs to be conducted, and 

assist with integrating other resource objectives into fuel treatments.

Organization and Key Points

The synthesis is organized into three broad sections: ecology of dry mixed conifer 

forests (Section I), fuel treatment planning and implementation (Section II), and treatment 

feasibility and effectiveness (Section III). Where relevant, we have inserted manager 

comments, and inserts with information on related topics.

Section I (Chapters 1 through 5) describes the ecology of dry mixed conifer forests 

and emphasizes the forest elements that influence fuel treatment planning and implementation. This information can assist in discussions regarding desired forest conditions 

favoring resilience to fire and other disturbances. To describe the physical and biological 

setting, we used the LANDFIRE (2008) biophysical setting (BpS) classification system 

to categorize the various forest types of the dry mixed conifer forests within four broad 

geographic regions: northern California and Klamath, Pacific Northwest Interior, 

northern and central Rocky Mountains, and Great Basin (primarily Utah) (Chapter 

2). Chapter 3 discusses the suite of disturbances (and their frequency and intensity) 

that influences the composition and structure of these forests, with some reference to 

specific areas, such as the Blue Mountains of Oregon and northeastern Washington. 

Chapter 4 provides a short summary of the management practices (for example, past 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 5

timber harvests, fire suppression, grazing) that have impacted dry mixed conifer forest 

characteristics (for example, disturbances, vegetation, soils) and how these changes 

affect fuel treatment decisions. Chapter 5 is a summary of the current condition of the 

forests using information from the Pacific Northwest and Interior West Forest Inventory 

and Analysis (FIA) network http://www.fia.fs.fed.us/regional-offices/.

Key Messages From Section I.

	 • Dry mixed conifer forests are influenced by multiple disturbances (insects, 

disease, storms) and contain diverse topography and soils, and when combined, 

create a diverse set of vegetative compositions and structures.

	 • Understory vegetation in these forests is diverse and can include grasses, forbs, 

shrubs and/or trees. Overstory canopies contain a minimum of two tree species, 

but can have as many as six different coniferous and/or deciduous tree species. 

Thus, depending on past disturbances, these forests are spatially and temporally 

diverse and contain many different structural and successional stages.

	 • Dry mixed conifer forests experience low severity to mixed severity fire regimes.

Low severity fire regimes tend to occur in landscapes with nominal topographic 

relief. Mixed severity fire regimes tend to occur in landscapes with complex 

topography and an abundance of tree and plant species and disturbances.

	 • Historical and current use of these forests indicates that these forests are important to society.

	 • Analysis of current conditions can reveal to what extent certain areas within the 

dry mixed conifer forests need some type of treatment to address fuel hazards, 

such as: surface flame lengths (>4 ft), probability of torching (>20 percent), 

torching index (<20 mph), and mortality (>30 percent). Up to 80 percent of 

the dry mixed conifer forests contain at least one of these hazard elements and 

approximately 20 to 30 percent of the Douglas-fir, true fir, pine, and western 

larch have all four hazard elements.

Section II (Chapters 6 through 10) focuses on the tools, techniques, equipment, and 

details associated with fuel treatment planning and implementation. This is not a “how-to” 

section, but rather a description of the steps, conditions, and situations to consider when 

implementing fuel treatments. In Chapter 6, we provide basic concepts and considerations associated with wildlife habitat relationships, with an introduction to the concepts 

and questions that are important when manipulating wildlife habitat. Chapter 7 is an 

overview of the fuel treatment planning process, covering general treatment principles, 

approaches, opportunities, and challenges. This chapter also discusses how to integrate 

a variety of objectives into fuel treatment planning. Chapter 8 covers techniques used 

to implement fuel treatments and discusses mechanical methods, chemical control and 

targeted grazing. This information is presented in the form of decision-support guides 

(checklists, flow charts, opportunities) for selecting a fuel treatment technique. 

Chapter 9 focuses on prescribed fire and discusses basic but important elements of 

conducting a successful prescribed fire. We include the elements of a burn plan, common oversights in fire planning, factors to consider when implementing a prescribed 

fire, and unique dry mixed conifer forest situations that may require specific prescribed 

fire conditions to favor specific outcomes. In interviews, land managers stressed the 

value of monitoring in fuel treatment programs, but many also acknowledged that they 

lack the funding or expertise to effectively prepare and implement a monitoring plan. 

Chapter 10 presents a step-by-step process to aid in the development and implementation of a monitoring program.
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Key Messages From Section II.

• Based on our interviews with wildlife biologists, we developed three questions 

designed to improve communication between vegetation managers and wildlife 

biologists. The first question is “which habitat elements will the fuel treatments 

impact and for how long?” To address this question, we provide an expanded 

definition of plausible wildlife habitat elements, and encourage integration of 

these elements early in the planning stage. This information provides background 

to begin addressing the other two questions: “Which wildlife species could be 

impacted by fuel treatments?” “Will the fuel treatment improve, degrade, or 

have a neutral impact on the habitat and wildlife species?”

• We propose taking an integrated approach in planning as a plausible method for 

addressing fuel treatments and other objectives. Integration involves the blending of multiple resources when designing objectives, which can then be used to 

develop management strategies for treatment placement and design (Stockmann 

and others 2010). This process promotes communication and mutual learning 

among different disciplines. The success of using integrated management strategies is dependent on the relationships among the involved managers, the public, 

and the element of uncertainty associated with ecosystem management.

• Selection of a particular mechanical harvesting or surface fuel treatment depends 

on several factors including objective, current conditions, and the physical setting. The dry mixed conifer forests offer additional challenges in understory 

vegetation management. We cover the specific situations and opportunities in 

which mechanical harvesting, mastication, chemical herbicides, and targeted 

grazing may provide unique advantages.

• There are a number of steps fire practitioners take before, during, and after 

ignition of a prescribed fire including burn plan, pre-burn considerations and 

weather, organization, equipment and communications, and complexity 

analysis. In addition, there are several unique situations that may benefit from 

a different approach. When restoring old forests, extra caution and modified 

burning parameters may be needed to protect individual trees. Killing understory 

vegetation such as seedlings, saplings, and shrubs may require a particular fire 

intensity and severity.

• Fuel moisture often dictates prescribed fire outcomes in dry mixed conifer 

forests, so they are a critical parameter to consider.

• Often there is neither the time nor funds to conduct thorough monitoring of 

treatment outcomes and longevity. However, time spent on monitoring design 

is time well spent if it leads to clear objectives and a focused, results-oriented 

monitoring protocol that can be sustained over time, even as responsibilities for 

data collection, management, and analysis are transferred among individuals 

over time.

Section III is intended to be a “reality check,” focusing on the challenges and opportunities of fuel treatment implementation. It covers, at least conceptually, what can and 

cannot be achieved through removal of fuels. In Chapter 11, we provide an evaluation 

of a set of potential fuel treatments and discuss the economic feasibility and potential 

for success of each using publicly available data from the U.S. Forest Service’s Forest 

Inventory and Analysis Program, coupled with Fire and Fuels Extension of the Forest 

Vegetation Simulator (FFE-FVS) and FIA BIOSUM (Biomass Summarization System) 

computer simulation programs (Fried and others 2005; Reinhardt and Crookston 2003). 

Chapter 12 addresses the current knowledge regarding fuel treatment longevity and 

effectiveness.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 7

Key Messages From Section III.

• The economic feasibility of conducting fuel treatments can also offer challenges. 

Elements of forest and local industry infrastructure (mills, access, and bioenergy) all can dictate whether a particular area is treated. It is not possible to 

implement a fuel treatment in every place that would benefit from one, and 

there are many kinds of fuel treatments, only some of which will be effective in 

any particular stand. There are many stands where no fuel treatment is likely to 

be effective and many more where an effective treatment will be prohibitively 

costly. Therefore, it is important to understand the economic reality of treating 

fuels.

• An understanding of forest fuel treatment longevity and the processes contributing to it are central to a complete evaluation of the effectiveness of treatment 

alternatives. The changes to fuel structures are a function of pre-treatment condition, post-treatment condition, site productivity, and time. Recognizing the 

elements that contribute to treatment longevity during the planning process 

may guide the selection of treatments and treatment combinations.

We also include various appendices to supplement the information presented in some 

of the chapters. Appendix A presents current conditions using a series of histograms 

showing the distribution of current fire hazard on forest lands. Appendix B is a list of 

the variety of decision support tools available, with a short summary and listing of 

where to find the tools and supporting information on the worldwide web. Appendix 

C presents the local results from the economic feasibility analysis described in Chapter 11. Managers may want to review our results by region and forest type group, and 

this appendix will allow them to do so. Appendix D is a list of the Latin terms for the 

common names of species mentioned in the synthesis. Appendix E provides English to 

metric unit conversions.

Information Sources

Relevant Literature

For this synthesis, we combed through published materials (journals, U.S. Government 

documents, symposium proceedings, etc.) that address implementation of specific fuel 

treatments and consequences for fire behavior and intensity. Nonetheless, we could not 

review or summarize all of the available literature related to fuel treatments, soil protection, wildlife habitat management, and silviculture, to name a few of the relevant topics. 

Thus, we selected what we found to be the key literature that fit within the context of the 

planned objectives and goals of this synthesis. We used several available fuel synthesis 

documents (including other Joint Fire Science Program syntheses) and provide short 

summaries of their findings. In these cases, we only cite the synthesis document. When 

literature specific to dry mixed conifer forests was insufficient to address a particular 

subject, we also incorporated literature that is relevant to fuel treatments in other forest 

types (with qualification of the unique attributes of fuel treatments in these forests). In 

the electronic version of the document, we have provided links to electronic copies of 

cited literature available in TreeSearch online document library (http://www.treesearch.

fs.fed.us/). However, some of the publications have copyright restrictions, and, in these 

situations, we provide the location where the document can be accessed.
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Expert Knowledge

Dry mixed conifer forests cover a broad and diverse region. Therefore, to provide 

regional and site-specific context to this document, we visited federal, state, and tribal 

land entities and interviewed over 50 resource specialists in Montana, Idaho, Utah, 

Washington, Wyoming, Oregon, South Dakota, and California. A specific set of questions 

guided the discussions between our research team and resource specialists/managers. 

There were 2 to 10 specialists present during these discussions. The specialists/managers who were interviewed included fire management officers, fuels specialists, wildlife 

biologists, fuels planners, hydrologists, forest staff officers, silviculturists, and others 

with shared responsibility for the planning and implementation of fuel treatments at 

sites within the synthesis area. Comments and discussions generated via this interview 

process guided both the organization and content of this synthesis. Throughout this 

document, we provide short summaries of the key points expressed by specialists and 

managers. We emphasized openness and candor in discussions related to the challenges 

managers faced during the planning and implementation process; therefore, we have 

kept all interviewees anonymous. We appreciate the time each person set aside to participate in these discussions.

Through our interviews, managers provided anecdotal information that we considered an important contribution to the knowledge of these forest systems. Some have 

been included within the chapters and are labeled “Manager’s Comment.” It is our 

hope that these valuable insights gained through hard-earned experience may stimulate 

new ideas or techniques to address comparable problems on similar sites or that they 

help others address completely different challenges. Comments and questions and 

subjects expressed through our interviews led to the rationale for each chapter, which 

is documented at the beginning of several chapters.

Conclusion

Creating fire-resilient ecosystems must be integrated with a range of other forest management objectives and societal needs. Furthermore, a clear understanding of the steps, 

challenges, opportunities, and feasibility of implementing fuel treatments must also be 

recognized. All aspects of fuel treatments have their unique challenges and opportunities, 

but these can be addressed with planning, knowledge, and expertise. Using prescribed 

fire requires considerable planning and analysis before ignition. Implementing a useful 

monitoring plan takes time, funding, and commitment. Integrating wildlife habitat has 

to begin with the first walk through the woods and requires incorporation into a fuel 

manager’s design and planning process. Practical consideration of economic feasibility 

and available infrastructure (mills and roads) is essential. Continuous communication 

among disciplines and the public will be part of some fuel treatment planning, and at 

times there can be considerable confusion in terminology and concepts. It is our hope 

that this synthesis provides information that will be used for dialogue, mentoring, and 

understanding the challenges, opportunities, and techniques for incorporating treatments 

that promote resilient dry mixed conifer forests.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 9

Section I: Ecology of Dry Mixed Conifer Forests 
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 11

Chapter 2

Potential Vegetation and 

Biophysical Setting

Introduction

Dry mixed conifer forests can be complex in terms of weather, physical setting, potential vegetation, disturbances, and forest succession. Depending on the combination 

of these components, multiple tree, shrub, and forb species can be found within stands 

and across landscapes. These vegetation communities are continually changing in response to the extent and severity of both natural and human caused disturbances (for 

example, see Arno and others 1997). Because these forests generally occupy transition 

areas across moisture gradients, they are often adjacent to areas that are relatively drier 

or wetter. As a result, dry mixed conifer forests occur as part of a mosaic of a diverse set 

of forest types across the western United States. This chapter describes how we define 

the dry mixed conifer forests and their distribution within the synthesis area.

Biophysical Settings

The Biophysical Setting Model (BpS) is defined as the types of vegetation communities that could naturally exist based on the current biophysical settings and historic 

disturbance regimes (LANDFIRE 2008). For a given setting and species mix, forest 

and plant community development can be relatively predictable, eventually resulting 

in what is traditionally referred to as climax vegetation. This concept can be used to 

classify sites and allows for summaries of current vegetation, disturbance regimes, successional pathways, potential vegetation, and other vegetative descriptors relevant to 

forest development. Although climax vegetation refers to what species could eventually 

occupy a site, disturbances typically arrest, delay, accelerate, or reset that developmental 

process, depending on the type and intensity of disturbances. In many areas, the current 

vegetation will likely be different from the potential vegetation. In a changing climate, 

we consider these vegetation communities not as static assemblages, but rather a reflection of the environmental setting within which a particular set of species can grow.

Current vegetation can range from shade-intolerant species that occur in open areas 

during the early stages of stand development (early-seral) to more shade-tolerant species (late-seral) that thrive in closed canopies. However, whether a species is considered 

early- or late-seral depends on the site where it is growing as well as its associates. 

For example, although ponderosa pine is generally an early-seral species in dry mixed 

conifer forests, there are sites where ponderosa pine tends to be late-seral to species 

such as quaking aspen, paper birch, and pinyon pine. Douglas-fir is another example. 

Although it is a late-seral species on many ponderosa pine sites, it is frequently an earlyseral species, along with western larch and ponderosa pine, when growing on true fir 

(for example, grand fir or white fir) and western redcedar sites (Mauk and Henderson 

1984, Steele and others 1983).
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 2.1. Domains, divisions, and provinces within the fuel synthesis area. Domains and divisions are 

based on climate zones. Provinces (shown on the figure) represent a further refinement of the domains 

and divisions. The code in parenthesis is labeled on the map. Those beginning with “M” are mountain 

provinces. Provinces 242 and 342 typically do not contain mixed dry conifer forests although some small 

isolated areas can be present. See Descriptions of the Ecoregions of the United States (Bailey 1995).

Descriptions of Biophysical Settings

To define the synthesis area consistently, we found it useful to examine the LANDFIRE BpS to see which ones could be considered dry mixed conifer. These groups are 

diverse, ranging from those that occur on dry sites—where only ponderosa pine and/or 

Douglas-fir grow—to more moist sites, such as grand fir and even western redcedar 

habitat types. Many, but not all, include ponderosa pine, and some groups could be considered woodlands rather than forests. Some have shrubby and herbaceous understories, 

whereas others have sparse understories. They occur within a wide range of elevations, 

on all aspects, and on slopes ranging from flat to steep.

The descriptions that follow are summarized from the LANDFIRE BpS models. 

In addition to summaries of vegetation and stand developmental stages, information 

is given on geographic range, elevation (in numbers or a general description), and the 

provinces described and mapped by Robert G. Bailey (1994, 1995) (fig. 2.1, table 2.1). 

The latter are part of a hierarchical description of ecoregions based upon climate, with 

domain the broadest level, followed by division, and then provinces which represent a 

more refined subdivision based upon climatic differences (see Bailey [1995] for more 

details). The descriptions are organized by the following four sub-regions based on the 

LANDFIRE modeling zones in which they occur: (1) Northern California and Klamath, 

(2) Pacific Northwest Interior, (3) Northern and Central Rocky Mountains, and (4) Utah. 

For readers who do not use the BpS models; look for the vegetation description that 

best aligns with your mixed dry conifer forest.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 13

Northern California and Klamath

Many of the vegetation descriptions occur in more than one of our sub-regions; 

therefore, the descriptions we describe within a given sub-region can occur in other 

locations within our synthesis area. Figure 2.2 shows the distribution of the dry mixed 

conifer forests within the northern California and Klamath sub-region.

Klamath-Siskiyou Lower Montane Serpentine Mixed Conifer Woodland (10210)

Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland (10220)

Geographic area: northern California and southwestern Oregon

Elevation: 1200-4500 ft for BpS 10210; >4500 ft for BpS 10220

Provinces: Mostly in M261, but small pockets can occur in 242 and 263

Table 2.1. These are descriptions of general climate, topography, and geology based on Bailey’s ecoregions that contain 

mixed dry conifer forests. Descriptions from: McNab and Avers. 1994. Ecological subregions of the United States. 

WO-WSA-5. www.fs.fed.us/land/pubs/ecoregions

Province Climate Topography Geology

M242 Maritime; High mountains; foothills; plateaus; Volcanic; basalt; granitic; diorite; 

Continental glaciation andesite; sedimentary; ash; pumice;

 cinders

263 Mediterranean Valley bottoms interspersed among Sedimentary rocks

ow-elevation long mountain ranges

that run parallel in a northwestern

 direction

M261 Mediterranean; Highly dissected by river systems; Granitic; sedimentary; metamorphic;

Continental steep mountains with rounded ridges; ultramafic; volcanic

narrow canyons; block mountains

331 Continental Rolling hills; dissected shale plains; Loess, sedimentary rocks; granitic;

flat-topped buttes; rugged high- metasedimentary

elevation mountains; glaciated and

non-glaciated areas

M331 Continental Rugged mountains with rounded Volcanic; gneiss; carbonate; shale;

ridges; high mountains with sharp sedimentary; igneous; metasedimentary

crests; high elevation plateaus;

broad to narrow valleys; glaciated

M332 Maritime; Granitic plutons; high, glaciated Loess; volcanic ash; granite; 

Continental mountains with narrow valleys; metasedimentary; sedimentary

sharp-crested mountains;

moderately dissected, uplifted

 plateau

M333 Maritime; High rugged mountains; rounded Igneous; sedimentary; metamorphic;

Continental mountains; steep dissected metasedimentary; loess; volcanic ash

mountains; glaciation

M334 Continental Dissected mountains; unglaciated Granitic; limestone plateau; sedimentary

M341 Continental High plateaus; north-south Folded and faulted sedimentary;

mountains separated by broad volcanic rocks

sediment-filled valleys

341 Continental North-south trending mountains are Lower Tertiary volcanic rock with

separated by broad sediment-filled Miocene volcanic rock. Quaternary

valleys deposits in valleys.
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 2.2. Distribution of the biophysical settings (BpS) for northern California and Klamath. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 15

These types have limited distribution, occurring on ultramafic soils. A wide variety 

of tree species can be present, including Douglas-fir, incense cedar, sugar pine, western white pine, Jeffrey pine, and Port Orford cedar. Shrub species include oaks, dwarf 

silktassel, western azalea, California buckthorn, and buckbrush (table 2.2).

The early developmental stage is dominated by grasses, shrubs, and tanoak, Douglas-fir, 

incense cedar, and canyon live oak seedlings and saplings. As the stand develops, canopy 

cover increases to over 50 percent, unless fires occur frequently or if the particular site 

is less productive. As the stand ages, large trees are present, but not as many or as large 

as in other more productive mixed conifer sites. Douglas-fir and incense cedar occur in 

all stories and tanoak and canyon live oak dominate the low to mid canopy positions.

The difference between BpS 10210 and 10220 is based on elevation and the presence 

of Shasta red fir in the latter. Otherwise, the models are the same.

Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland (10270)

Geographic area: California and southern Oregon

Elevation: 2000-5900 ft in northern California; 4000-7000 ft in southern Oregon

Provinces: 263, M261, and some in M242 and 263

At lower elevations this BpS may be adjacent to woodlands and grasslands and at 

upper elevations next to mesic mixed conifer. Unlike the mesic mixed conifer type, this 

BpS does not have white fir. Douglas-fir, ponderosa pine, and incense cedar are the most 

common conifer tree species that are co-dominants in the overstory (table 2.2). Others 

that may be present include Jeffrey pine, knobcone pine, and sugar pine. In the lower 

canopy, California black oak and canyon live oak are common, and Pacific madrone is 

common in southern Oregon. Understory shrubs include poison oak, ceanothus, currant, 

barberry, ocean spray, and many other species.

Early development is dominated by grasses, shrubs, and Douglas-fir, ponderosa pine, 

and sugar pine seedlings/saplings. As the stand develops and the canopy closes, pole- to 

medium-sized ponderosa pine, Douglas-fir, incense cedar, and sugar pine dominate the 

overstory, with California black oak in the mid-story. The typical successional pathway, 

driven by frequent low-intensity fires, leads to open stands dominated by ponderosa pine, 

Douglas-fir, and various hardwoods. Longer intervals between fires (30 years) lead to 

crowded conifer stands with hardwoods in the understory. In some sites, Douglas-fir is 

able to recruit beneath the larger ponderosa pine and Douglas-fir. These kinds of stands 

are composed of ladder fuels, creating the potential for the initiation of crown fires.

Mediterranean California Mesic Mixed Conifer Forest and Woodland (10280)

Geographic area: California, southern Oregon

Elevation: 2400-3000 ft in the Sierra Nevada and 3800-6700 ft in the Klamath Mountains

Provinces: M242, M261

This BpS is similar to 10270 (the preceding BpS just described above), except that 

it has white fir. During the early stages of stand development, Douglas-fir, ponderosa 

pine, and sugar pine are in the overstory, with white fir in the overstory and mid-story 

(table 2.2). Sometimes a continuous canopy may develop from seedlings, whereas other 

times shrubby conditions can persist for long periods of time, composed of ceanothus 

species, greenleaf manzanita, ocean spray, and other species. Hardwood sprouting from 

Pacific madrone, chinquapin, tanoak, and live oak can be significant.
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 Table 2.2. Biophysical setting models from the LANDFIRE project (2008) for Northern California and Klamath. Northern California and Klamath Late seral species1 Late seral species1 Biophysical setting Dominant species1 Early seral species1 Open stands Closed stands Shrub species Klamath-Siskiyou Montane Douglas-fir, incense tanoak, Douglas-fir, tanoak, Douglas-fir, tanoak, Douglas-fir, huckleberry oak, dwarf silktassel, Serpentine Mixed Conifer cedar, Port Orford incense cedar, canyon incense cedar, canyon incense cedar, canyon deer oak, pinemat manzanita, Woodland (lower and upper) cedar, Jeffrey pine, live oak live oak live oak western azalea, Pacific poison western white pine, oak, California buckthorn, tanoak greenleaf manzanita, buckbrush Mediterranean California Dry-Mesic Douglas-fir, ponderosa Douglas-fir, ponderosa ponderosa pine, ponderosa pine, Pacific poison oak, deerbrush, Mixed Conifer Forest and Woodland pine, incense cedar, pine, sugar pine Douglas-fir, incense Douglas-fir, incense snowbrush ceanothus, sticky California black oak, cedar, California black cedar, California black whiteleaf manzanita, barberry, sugar pine oak oak creeping snowberry Mediterranean California Mesic white fir, Douglas-fir, Douglas-fir, ponderosa white fir, ponderosa white fir, Douglas-fir, hazelnut, Pacific dogwood, bush Mixed Conifer Forest and sugar pine, ponderosa pine, sugar pine, white pine, sugar pine, ponderosa pine, sugar chinquapin, greenleaf manzanita, Woodland pine fir Douglas-fir pine, ceanothus species Mediterranean California Lower California black oak, California black oak, N/A N/A whiteleaf manzanita, ceanothus Montane Black Oak-Conifer Forest canyon live oak, canyon live oak species, Pacific poison oak and Woodland ponderosa pine, Douglas-fir, tanoak, pacific madrone California Montane Jeffrey Pine Jeffrey pine, ponderosa Jeffrey pine Jeffrey pine Jeffrey pine, white fir bitterbrush, manzanita, ceanothus (-Ponderosa Pine) Woodland pine species Mediterranean California Mixed Douglas-fir, tanoak, tanoak, Pacific N/A Douglas-fir, tanoak, hazelnut, California huckleberry, Evergreen Forest Pacific madrone, madrone, canyon live Pacific madrone, Pacific rhododendron, salal, canyon live oak, oak, Douglas-fir canyon live oak Pacific poison oak sugar pine 1Tree species are in order of dominance.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 17

When trees dominate the early stages rather than shrubs, the stand can develop into 

pole- to medium-sized conifers with >50 percent canopy cover, along with the decline 

of shrubs and herbaceous species. These dense stands are susceptible to insects and 

diseases. Frequent surface fires can maintain more open conditions during the middevelopmental stages. Open conditions also favor the hardwoods present in the understory. When disturbances occur less often, white fir may begin to dominate the stand. 

In later developmental stages, patches of relatively open canopies (<50 percent) occur 

on southerly aspects and ridgetops. Open late seral stands may persist for hundreds of 

years in a matrix across eastern and northwestern aspects. In more closed conditions 

(canopy >50 percent), patches occur on northern aspects and lower slope positions, with 

an understory filled with shade-tolerant species (primarily white fir).

Mediterranean California Lower Montane Black Oak-Conifer Forest and Woodland 

(10300)

Geographic area: Coast Range, Klamath Mountains of California and Oregon, and 

lower slopes of the western Sierras

Elevation: 1200-4850 ft

Provinces: M261, 263, and some in 242

Ponderosa pine and oaks such as California black oak and canyon live oak, along 

with Douglas-fir, characterize this BpS. The oaks will form a dense canopy below the 

conifers dominated by California black oak. Common shrubs in the understory include 

whiteleaf manzanita, ceanothus species (for example, buckbrush), and Pacific poison 

oak. Grasses that occur, although not as commonly occurring as shrubs, include California and Idaho fescue (table 2.2).

Early stages of stand development following a disturbance consist of coppicing oak 

sprouts. Pacific poison oak may also be abundant, along with bunch grasses and forbs. 

Some sites will have ponderosa pine and Douglas-fir up to six inches in diameter. After 

about 25 years, this stage may succeed to a mid-developmental open stage, unless surface fires and browsing from herbivores maintain this early successional stage. Areas 

that do not experience these disturbances will develop into mid-developmental closed 

stages that may have a dense canopy of oak and conifers in the upper canopy position, 

with sod-forming grasses and shade-tolerant shrubs in the understory. Mid-seral open 

stands will be dominated by hardwoods, with a more sporadic occurrence of conifers 

compared to the closed-canopy stage; bunchgrasses and shrubs will be in the understory. 

Oak diameters can range from 8–30 inches.

California Montane Jeffrey Pine (-Ponderosa Pine) Woodland (10310)

Geographic area: northern California and southern Oregon

Elevation: 2500-3500 ft

Provinces: Mostly in M261 but some in M242 and 341

Jeffrey pine dominates this BpS, but ponderosa pine can also be a dominant tree species (table 2.2); white fir occurs as a codominant in closed developmental stages, in the 

absence of fire. A substantial shrub community is present in the understory that includes 

mountain big sagebrush, bitterbrush, greenleaf manzanita, snowbrush ceanothus, and 

in more mesic sites, snowberry.

The early developmental stage is dominated by fire-dependent shrubs, perennial 

bunch grasses, forbs, and Jeffrey pine seedlings. When fires are infrequent enough 

to thin small trees and a shrub layer does not develop, this stage will transition into a 
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

closed, mid-developmental stage. With the continued absence of fire, the closed, middevelopmental stage transitions into a closed, late-developmental stage consisting of a 

dense stand of medium- to large-diameter Jeffrey pine and white fir co-dominating the 

overstory, with white fir in the understory. This condition is characterized by withinstand mortality and surface and ladder fuel accumulation. However, in areas that do 

experience low intensity surface fires, the early developmental stage transitions into 

an open, mid-developmental stage. The open, mid-developmental stage consists of 

pole- to medium-sized Jeffrey pines along with a shrub layer. As the stand grows and 

low intensity fires are allowed to continue, the open, late-developmental stage emerges, 

consisting of large Jeffrey pines with scattered shrubs, forbs, and grasses; surface fuels 

are scarce due to frequent surface fires.

Mediterranean California Mixed Evergreen Forest (10430)

Geographic area: northern California Coast Range, southern Oregon coast, and 

Klamath-Siskiyou Mountains

Elevation: Mostly below 3500 ft but can be up to 4000 ft

Provinces: 263, M242, M261, and some in 242

This BpS occurs on all aspects, and although it is influenced by maritime climates, 

does not occur on the coast itself. Douglas-fir and sugar pine grow alongside hardwoods 

such as tanoak, Pacific madrone, canyon live oak, California black oak, and California 

laurel (table 2.2).

The early developmental stage is a mixture of thickets of sprouting hardwoods and 

sprouting shrubs such as Oregon grape, salal, rhododendron, and ceanothus, with some 

Douglas-fir. Tanoak usually will dominate. After about 25 years, the Douglas-fir seedlings begin to emerge from the dense thickets of hardwoods and shrubs and share the 

upper story with tanoak, canyon live oak, and Pacific madrone. Later developmental 

stages can have trees with diameters >30 inches; sugar pines may be present. Because 

of the epicormic sprouting of the hardwoods and shrubs, any moderate or high severity 

fire disturbance promotes the development of a hardwood-dominated stand, whereas 

low severity fires favor the dominance of Douglas-fir and other conifers.

Pacific Northwest Interior

There are four primary vegetation descriptions within the dry mixed conifer forests 

of the Pacific Northwest Interior. Figure 2.3 illustrates the distribution of these characterizations.

East Cascades Mesic Montane Mixed-Conifer Forest and Woodland (10180)

Geographic area: maritime-influenced sites in the eastern Cascades in Washington

Elevation: low to mid elevation slopes

Provinces: Mostly in M242, but some in 242 and M261

Historically, this BpS had much higher proportions of western white pine and western larch than what is present today. Currently, these stands are dominated by western 

hemlock, grand fir, and Douglas-fir. Other species present include western larch, western 

white pine, western redcedar, and Engelmann spruce (table 2.3). In the drier portions of 

this BpS, ponderosa pine is important. Understory species include vine maple, currant, 

thimbleberry, and queen cup beadlily.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 19

Figure 2.3. Distribution of the biophysical settings (BpS) for Pacific Northwest Interior.
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 Table 2.3. Biophysical setting models from the LANDFIRE project (2008) for the Pacific Northwest Interior. Interior Pacific Northwest Late seral species1 Late seral species1 Biophysical setting Dominant species1 Early seral species1 Open stands Closed stands Shrub species East Cascades Mesic Montane Douglas-fir, grand fir, Douglas-fir, western western white pine, western redcedar, vine maple, thimbleberry, Mixed-Conifer Forest and western hemlock, larch, western white western larch, western western hemlock, currant, thinleaf huckleberry Woodland western redcedar, pine redcedar, grand fir Douglas-fir, grand fir western white pine, western larch, Engelmann spruce, lodgepole pine Northern Rocky Mountain Dry- ponderosa pine, grand ponderosa pine, ponderosa pine, ponderosa pine, Early: ceanothus species, Mesic Montane Mixed Conifer fir, Douglas-fir, white Douglas-fir, western Douglas-fir, western Douglas-fir, grand fir, Scouler’s willow Forest fir (in southern Oregon) larch larch, grand fir western larch Late: snowberry, rose, mountain mahogany Northern Rocky Mountain Mesic grand fir, Douglas-fir, See shrub species Douglas-fir, ponderosa grand fir, Douglas-fir, Early: ceanothus, manzanita, Montane Mixed Conifer Forest ponderosa pine, western pine, western larch, ponderosa pine, western vine maple, ninebark larch grand fir larch Sierran-Intermontane Desert western white pine, western white pine, western white pine, white fir, ponderosa greenleaf manzanita, pinemat Western White Pine-White Fir white fir, ponderosa ponderosa pine, white white fir, ponderosa pine manzanita, bush chinquapin, Woodland pine fir pine ceanothus species, sticky currant 1Tree species are in order of dominance.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 21

Early stages of development after a fire are dominated by shrubs such as vine maple, 

thimbleberry, currant, and thinleaf huckleberry; some tree seedling and saplings are also 

present. Eventually, the trees will overtop the shrubs and canopy cover will become 

dense, with Douglas-fir, western larch, grand fir, and western pine in the overstory, and 

western redcedar in the understory. However, disturbances can create patches of open 

areas that favor western larch and western white pine. In later developmental stages, 

open canopy conditions are uncommon. In open, patchy conditions, several tree species 

will be co-dominant, including both early and late seral species. Without disturbances 

such as mixed severity fires that create these openings, multi-story dense canopy conditions will develop with a depauperate understory. Because of mortality from in-stand 

competition and diseases such as root rot, large woody debris is abundant. Replacement 

fires can occur in this BpS, as well as other weather-related disturbances, that will set 

the stand back to early developmental open conditions. Otherwise, mixed severity fires 

will maintain the stand in late developmental open or closed stages.

Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest (10450)

Geographic area: Oregon, Washington (more detail below)

Elevation: 2000-6000 ft (most of these stands occur between 3000 and 4500 ft)

Provinces: M242, M261, 331, M332, M333

Notes: Dry upland forest Potential Vegetation Group (PVG); Warm dry Plant Association 

Group (PAG); plant associations include Douglas-fir/elk sedge, Douglas-fir/pinegrass, 

Douglas-fir/snowberry, Douglas-fir/ninebark, grand fir/elk sedge, and grand fir/pinegrass 

(Powell and others 2007).

This BpS occurs on the eastern side of the Cascades in Oregon and Washington, the 

Blue Mountains of Oregon and Washington, the Ochoco Mountains of central Oregon, 

and the Wallowa-Snake province of Oregon and Washington. It occurs just above ponderosa type forests and below fir-dominated, mesic, mixed conifer forest types. Conifers 

present are ponderosa pine (often the dominant species), grand fir, Douglas-fir, and 

western larch (table 2.3); Douglas-fir dominates the dry areas in the northern portions. 

Grand fir is less frequent in the northern portion (near Wenatchee, Washington) and 

white fir occurs in southeastern Oregon.

The early developmental stage is dominated by open stands of ponderosa pine, 

Douglas-fir, and larch seedlings/saplings. These trees are often mixed with grasses, 

sedges (for example, Geyer’s sedge), and shrubs such as ceanothus and Scouler’s willow. In the absence of disturbances, dense stands develop consisting of 5 to 20 inch 

ponderosa pine in the upper story and Douglas-fir and western larch in the mid-to-upper 

story. Open stands with the same tree diameter range and species composition often 

occur, with understories consisting of snowberry, rose, mountain mahogany, arnica, and 

lupine. In the late stages of stand development, both open-canopy forests and dense 

forests will have trees with diameters exceeding 20 inches. The difference is that the 

dense forests will have a sparse understory (not as shrub dominated as the open canopy 

forests) (table 2.3). Grand fir will often be present in the mid-to-upper canopy.

Northern Rocky Mountain Mesic Montane Mixed Conifer Forest (10470)

Geographic area: Oregon and Washington

Elevation: 1000-5000 ft

Provinces: M332, M333

This BpS is found on the eastern side of the Cascades in Oregon and Washington, the 

Blue Mountains of Oregon and Washington, the Ochoco Mountains of central Oregon, 
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

and the Wallowa-Snake province of Oregon and Washington. In the Blue Mountains, 

it occurs above pine-dominated, dry mixed conifer types and below subalpine fir. In 

the Cascades, it occurs below silver fir/western hemlock or mountain hemlock types. 

A mixture of conifers such as grand fir, white fir, and Douglas-fir can occur, along with 

various amounts of other conifers such as western larch, ponderosa pine, lodgepole pine, 

and Engelmann spruce (table 2.3). In areas north of McKenzie Pass, Oregon, grand fir 

replaces white fir and no western larch is found south of Bend, Oregon.

Shrubs dominate early development. In the Cascade region, snowbrush ceanothus, 

manzanita, and Cascade barberry are important. In the Blue Mountains, Rocky Mountain maple, snowbrush ceanothus, and mallow ninebark are important. Although some 

areas may persist as shrubfields for long periods of time, usually within 30 years or so 

a mixture of shade-tolerant and shade-intolerant conifers will begin to dominate the 

closed, mid-developmental stage. Although Douglas-fir and/or grand fir often dominate, 

ponderosa pine, western larch, western white pine, and lodgepole pine can be present. 

This stage has prolific regeneration and in the absence of disturbance can transition into 

a closed, late developmental stand within 70 years. However, if insect, disease, or fire 

occurs during the closed, mid-developmental stage, the stand will transition into an open, 

mid-developmental stage dominated by shade-intolerant species such as ponderosa pine 

and western larch, with some Douglas-fir and/or grand fir present. This stage is either 

multi-storied or single-storied. Mixed severity fires govern maintenance of this stage. 

Insects and disease target the older and large trees and can cause a transition back to 

an open, mid-developmental stage. Without disturbances, this stage can persist for 50 

years before transitioning to a closed, late developmental stand with multiple canopies, 

large diameter grand fir and Douglas-fir, and some ponderosa pine and western larch. 

Fire transitions this stage back to an open, late developmental stand. Insects and disease return the stage to an open, mid- or late-developmental stand, depending on the 

disturbance factor.

Sierran-Intermontane Desert Western White Pine-White Fir Woodland (11720)

Geographic area: northern California and southwestern Oregon (northern Sierra Nevada, east into the Modoc Plateau).

Elevation: 4,500–7,000 ft

Provinces: M242, M261

This BpS occurs just above the montane ponderosa pine zone. This system is somewhat in the rainshadow of the Sierras and Cascades and thus has a continental regime. 

Western white pine is most often the dominant tree species, but white fir and ponderosa 

pine are often present. Although shrub cover is sparse, the species that are present include greenleaf and pinemat manzanita, bush chinquapin, ceanothus species, and sticky 

currant (table 2.3); herbaceous species, also sparse, include heartleaf arnica and various 

grass and sedge species.

Tree seedlings mixed with shrubs and grasses characterize early stages of stand 

development; usually, ceanothus species will be present. Replacement fires may maintain a shrub-dominated stand that can persist for decades. In the absence of fire, closed 

stands of 5 to 20 inch diameter trees will develop, but rarely will exceed 80 percent 

canopy closure. These stands have a higher probability of mixed severity fire than the 

more open stands because of a denser understory; mixed severity and surface fires will 

maintain more open conditions. As stands age, open or closed conditions depend on 

how often fires occur. Late developmental open stands transition to closed stands if fire 

does not occur for 30 years. Closed stands are more likely to have mixed severity fires, 

surface fires are less common, and other disturbances such as insects/diseases play a 

role; competition between species in dense stands is also important.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 23

Northern and Central Rocky Mountains

There were eight vegetation descriptions identified for this sub-region. Figure 2.4 

shows their distribution. They include types dominated by ponderosa pine and Douglasfir, western larch, and grand fir.

Northern Rocky Mountain Dry-Mesic Montane Mixed Forest – Ponderosa Pine-Douglasfir (10451)

Geographic area: northern Idaho, western Montana, and eastern Washington, extending south into the Great Basin

Elevation: 2500 to over 4000 ft

Provinces: 331, M332, M333

Notes: Potential Vegetation Types (PVT) include Douglas-fir warm dry type 1, Douglasfir moist type 2, Douglas-fir cool dry type 3, and grand fir dry type 1 (USDA Forest 

Service, Northern Region 2004).

In this BpS, ponderosa pine dominates dry sites on southerly aspects, whereas 

Douglas-fir tends to be dominant on northerly aspects. On mesic sites and in the absence of fire, Douglas-fir often can be a co-dominant in the upper canopy. Shrubs and 

grasses are prevalent through all stages of stand development. At lower elevations and 

on southerly aspects, this BpS occurs next to dry ponderosa pine and shrub systems. 

At higher elevations and on northerly aspects, it occurs next to systems with western 

larch, grand fir, and subalpine fir.

Stand development begins with shrubs such as ninebark and ceanothus dominating, along 

with ponderosa pine, western larch, Douglas-fir, and lodgepole pine seedlings and saplings; 

sedges and pine grass may also be present. As the stand develops and the canopy closes, 

sapling and pole-sized ponderosa pine and Douglas-fir dominate; western larch abundance 

will decrease and grand fir will remain or increase because of its tolerance to shade. In open 

mid developmental stages, shrubs such as ninebark, ceanothus, and spiraea will be dominant in the understory, along with elk sedge and pinegrass. Ponderosa pine and Douglas-fir 

dominate the later developmental open and closed stages; grand fir will be present in the 

mid-story in late developmental closed stands (table 2.4).

Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest – Western Larch 

(10452)

Geographic area: northern Idaho and western Montana

Elevation: 3000-6000 ft

Provinces: M332, M333

Notes: Potential Vegetation Types (PVT) include grand fir wet type 3, subalpine fir moist 

type 2, and Douglas-fir moist type 2 (USDA Forest Service, Northern Region 2004).

This BpS occurs primarily on northerly aspects and includes Douglas-fir, lodgepole 

pine, and some ponderosa in the overstory along with western larch. In the absence of 

fire, Engelmann spruce and subalpine fir may also be present. Shrubs in the understory 

usually include huckleberry, twinflower, Sitka alder, and mallow ninebark (table 2.4).

Although western larch is usually dominant during the early stages of stand development, lodgepole pine and Douglas-fir may also occur in the upper canopy position, with 

subalpine fir beneath. As the stand develops, western larch continues to dominate, with 

other species present as co-dominants. During this stage, Douglas-fir may increase in 

the understory if there are no disturbances. In the later stages, if disturbances do 

occur, openings are created in the canopy favoring large western larch and Douglasfir while reducing the abundance of subalpine fir, grand fir, and lodgepole pine. 
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 2.4. Distribution of the biophysical settings (BpS) for the northern and central Rocky Mountains.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 25 Table 2.4. Biophysical setting models from the LANDFIRE project (2008) for the Northern and Central Rocky Mountains. North and Central Rocky Mountains Late seral species1 Late seral species1 Biophysical setting Dominant species1 Early seral species1 Open stands Closed stands Shrub species Northern Rocky Mountain Dry- ponderosa pine, ponderosa pine, ponderosa pine, ponderosa pine, ceanothus species, mallow Mesic Montane Mixed Forest – Douglas-fir, lodgepole lodgepole pine, Douglas-fir, western Douglas-fir, grand fir, ninebark, spiraea, Scouler’s Ponderosa Pine-Douglas-fir pine, grand fir, western larch, larch western larch willow, ocean spray western larch Douglas-fir Northern Rocky Mountain Dry- western larch, western larch, western larch, subalpine fir, Douglas- thinleaf huckleberry, Mesic Montane Mixed Conifer lodgepole pine, lodgepole pine, Douglas-fir, lodgepole fir, western larch, grand twinflower, Sitka alder, Forest – Western Larch Douglas-fir, subalpine Douglas-fir, pine, ponderosa pine fir ninebark fir subalpine fir Northern Rocky Mountain Dry- lodgepole pine, lodgepole pine, western larch, western larch, grand fir, mountain huckleberry, grouse Mesic Montane Mixed Conifer Douglas-fir, western Douglas-fir, Douglas-fir, Douglas-fir, lodgepole whortleberry, serviceberry, Forest – Grand Fir larch, grand fir lodgepole pine ponderosa pine pine snowberry Middle Rocky Mountain Montane Douglas-fir, lodgepole Douglas-fir, lodgepole Douglas-fir, lodgepole Douglas-fir, lodgepole Shrubs are sparse-not listed in Douglas-fir Forest and Woodland pine, limber pine pine, limber pine pine, limber pine pine, limber pine the biophysical setting model description Northwestern Great Plains ponderosa pine, white quaking aspen, paper ponderosa pine, white ponderosa pine, white bearberry, serviceberry Highland White Spruce Woodland spruce birch spruce, quaking aspen, spruce paper birch Northern Rocky Mountain ponderosa pine ponderosa pine ponderosa pine, ponderosa pine, common snowberry, antelope Ponderosa Pine Woodland Douglas-fir Douglas-fir bitterbrush, chokecherry, and Savanna mountain mahogany Northern Rocky Mountain Mesic western white pine, western white pine, western white pine, western redcedar, snowbrush ceanothus, Scouler’s Montane Mixed Conifer Forest western larch, Douglas- western larch western larch, western western hemlock, willow, thinleaf alder, Rocky fir, grand fir, western redcedar, grand fir Douglas-fir, grand fir Mountain maple, spiraea, redcedar, western thimbleberry, big huckleberry hemlock Northern Rocky Mountain Foothill Douglas-fir, ponderosa Douglas-fir, limber Douglas-fir, limber N/A mountain big sagebrush, Conifer Wooded Steppe pine, limber pine, pine pine common juniper Rocky Mountain juniper 1Tree species are in order of dominance.
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

If disturbances are absent, large western larch and Douglas-fir will still dominate the 

overstory, with subalpine fir present in the mid-story and understory. At later stages in 

stand development, lodgepole pine is no longer present in high numbers.

Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest – Grand Fir (10453)

Geographic area: Idaho, western Montana, eastern Oregon, eastern Washington.

Elevation: Above 4500 ft

Provinces: M332, M333

Notes: Potential Vegetation Types (PVT) include grand fir moist type 2; includes warm/

moderately moist grand fir habitat types such as grand fir/twinflower (ABGR/CLUN) and 

grand fir/wild ginger (ABGR/ASCA) (USDA Forest Service, Northern Region 2004).

Lodgepole pine and western larch dominate the forests in this BpS, with lesser amounts 

of grand fir, ponderosa pine, and Engelmann spruce. In the absence of disturbances, 

grand fir increases in the understory during mid to late successional stages. Understory 

species include beargrass, mountain huckleberry, serviceberry, and snowberry (table 2.4).

Forbs and shrubs dominate the early stage of stand development before transitioning to a seedling and sapling stage dominated by lodgepole pine, with Douglas-fir and 

western larch common; ponderosa pine and grand fir are less common. Between 30 and 

100 years, pole-sized lodgepole pines begin to dominate, along with Douglas-fir and 

western larch as secondary dominants in closed-canopy stands. As stands continue to 

develop and mature, species dominance will depend on the creation of canopy openings from fire, insects, or disease. Open-canopy stands are dominated by western larch 

and Douglas-fir, with some residual ponderosa pine, grand fir, and lodgepole pine also 

present. In mature forests, whether open or closed, western larch is often the dominant 

overstory species. However, other species may also be dominant or secondary dominants, 

depending on whether the canopy is open or closed. For example, grand fir can dominate 

closed canopies and Douglas-fir can dominate those that are more open.

Middle Rocky Mountain Montane Douglas-fir Forest and Woodland (11660)

Geographic area: eastern Idaho, southwestern Montana, and northwestern Wyoming

Elevation: Lower foothills, above grasslands and shrublands

Provinces: 331, M331, M332, M333

Notes: Potential Vegetation Types (PVT) include Douglas-fir moist type 2 and Douglasfir cool dry type 3; includes dry Douglas-fir habitat types such as Douglas-fir/ninebark 

(PSME/PHMA) and Douglas-fir/snowberry (PSME/SYAL) (USDA Forest Service, 

Northern Region 2004).

This BpS occurs on an ecotone with grasslands and shrublands below and dry subalpine fir above. Stands are open and dominated by large Douglas-fir, with a presence 

of limber pine and, lodgepole pine (in cooler locations). Some areas have quaking aspen and other areas can have ponderosa pine occurring as an incidental species. Shrub 

cover in the understory is often sparse, consisting of juniper species and mountain big 

sagebrush. In some areas, however, shrub cover can be denser and be composed of 

species such as snowberry and ninebark. Important graminoids include pinegrass and 

Idaho fescue (table 2.4).

Early stages of stand development consist of Douglas-fir seedlings and saplings 

(quaking aspen, limber pine, and ponderosa pine may also be present), bunchgrasses, 

and shrubs. Over time, in the absence of disturbance, the density of pole-sized Douglasfir will increase at the expense of shrubs such as sagebrush. Surface fire, however, will 

keep the stand open, allowing sagebrush and bunchgrasses to persist along with the trees. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 27

As long as these conditions continue to occur, the open canopy persists, allowing the 

development of medium-to-large diameter trees along with an understory of sagebrush 

and bunchgrass. Without surface fires, a closed-canopy, multistoried forest with a sparse 

understory will develop that is susceptible to mixed severity fires.

Northwestern Great Plains Highland White Spruce Woodland (10480)

Geographic area: northeastern Wyoming and western South Dakota (Black Hills)

Elevation: 5700-6000 ft

Provinces: M334

This BpS represents the white spruce habitat type (Hoffman and Alexander 1987). 

Ponderosa pine, white spruce, paper birch, and aspen dominate the overstory. Northern 

aspects and higher elevation areas are dominated by white spruce and paper birch, 

whereas ponderosa pine dominates the southern aspects and lower elevations. Shrub 

species include bearberry, serviceberry, and snowberry (table 2.4). Mountain grasslands 

are dispersed throughout, which influences burn patterns.

Stand development begins with a mixture of quaking aspen, paper birch, shrubs, and 

a dense herbaceous cover of forbs. As the stand develops and the canopy closes, paper 

birch becomes dominant on northern aspects and moist slopes, with quaking aspen on 

the remaining sites. With time, ponderosa pine and white spruce develop and comprise 

20 to 50 percent of the overstory, depending on time since disturbance. In the absence 

of disturbance, canopy cover eventually increases and shades out the birch and aspen. 

White spruce dominates northern aspects and higher elevations, whereas ponderosa 

pine dominates southern aspects and lower elevations; pockets of quaking aspen, paper 

birch, and shrubs occur throughout these areas.

Northern Rocky Mountain Ponderosa Pine Woodland and Savanna (10530)

Geographic area: central Idaho, Montana, northeastern Washington

Elevation: Low to mid

Provinces: M242, M332, 342

Notes: Potential Vegetation Type (PVT) is the ponderosa pine type (USDA Forest 

Service, Northern Region 2004).

This BpS represents the ponderosa pine series from Pfister and others (1977) and 

Douglas-fir-ponderosa pine from Williams and others (1995) in the Colville National 

Forest in Region 6. It occurs on hot, dry south and west-facing on gentle-to-moderately 

steep slopes adjacent to grasslands and shrublands at its lower elevation limit. Ponderosa 

pine dominates the overstory, small amounts of Douglas-fir and Rocky Mountain juniper 

may be present, and shrubs are sparse; grasses include Idaho fescue and rough fescue 

(table 2.4).

Stand development begins with a mixture of herbaceous plants and/or ponderosa pine 

seedlings and saplings less than five inches dbh. Without disturbance, stand density increases, a closed canopy develops, density dependent mortality begins, and susceptibility 

to insects and disease increases. During this time, Douglas-fir may be occasionally present 

in small amounts. In areas that have experienced recent disturbance or that are too dry to 

maintain dense stands of trees, the canopy is open and there is greater herbaceous cover. 

In cases where disturbances maintain the open canopy, the forest develops into an open, 

park-like ponderosa pine stand with a minor component of Douglas-fir. Seedling regeneration is minimal, with less than 10 percent cover and an understory dominated by grasses. 

However, in the absence of disturbance, the later stages can be characterized as high density, 

multi-storied ponderosa pine stands with Douglas-fir regeneration in some sites. A range of 
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

tree sizes, from pole-sized to large-diameter trees, are present and susceptibility to insects, 

disease, and fire hazard is extremely high.

Northern Rocky Mountain Mesic Montane Mixed Conifer Forest (10471)

Geographic area: northern Idaho, northwestern Montana, northeastern Washington

Elevation: Below 5000 ft

Provinces: M332, M333

Notes: Potential Vegetation Types (PVT) include western redcedar moist type 2; habitat 

types include THPL/CLUN and THPL/ASCA (USDA Forest Service, Northern Region 

2004).

Douglas-fir and grand fir usually dominate sites in this BpS, with several other species occurring such as western larch, western white pine, western redcedar, ponderosa 

pine (warmer, drier sites), Engelmann spruce (cooler sites), and subalpine fir (cooler 

sites) (table 2.4).

Early stages of stand development are dominated by shrubs such as ceanothus and 

Scouler’s willow and western larch seedlings and saplings; western white pine saplings 

and seedlings will also be present. As the stand develops, pole-sized western white 

pine, western larch, grand fir, and Douglas-fir will overtop the shrub layer and begin 

to dominate. As canopy cover increases and the stand becomes very dense, western 

redcedar and western hemlock will be present in the understory. Usually, the stand will 

transition to a closed-canopy stage with pole-sized thickets consisting mainly of western 

redcedar and grand fir, as well as other species such as western larch. Occasionally, 

localized disturbances such as mixed severity fires and blow-downs will create a more 

open, mid-developmental stage dominated by the same species mentioned above. This 

rare stage can remain open as the stand ages if small disturbances (including diseases) 

continue to occur, and will include the same species. The more common late developmental stage is one composed of densely stocked groves of western redcedar with a 

sparse understory due to heavy shading.

Northern Rocky Mountain Foothill Conifer Wooded Steppe (11650)

Geographic area: eastern Idaho, northern Montana, central Oregon, Wyoming, northeastern Washington (Okanogan), eastern Cascades

Elevation: 1600-5300 ft

Provinces: M261, M332, 342

This BpS is found on all slopes and aspects above grasslands and shrublands and 

below mesic coniferous forests. These areas receive more winter and spring rains than 

the drier woodlands and savannas in the central Rocky Mountains. Douglas-fir usually dominates but ponderosa pine and limber pine can also occur as incidentals. The 

understory consists of bunchgrasses (Idaho fescue) and shrubs such as mountain big 

sagebrush and common juniper (table 2.4).

Early stages of stand development are dominated by Douglas-fir and sometimes limber 

pine seedlings and saplings in the upper story, with Idaho fescue and mountain fescue 

in the understory. As the stand develops and canopy closes, limber pine and ponderosa 

pine may be present along with Douglas-fir; sagebrush has diminished. However, mixed 

severity fires will create more open conditions and Idaho fescue and sagebrush will remain in the understory. These open conditions with large Douglas-fir, along with some 

limber and ponderosa pine, are maintained into later stand development by surface fires.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 29

Utah

There are four vegetation descriptions located in Utah. Many of them fall also in southern 

Idaho within the Great Basin. Figure 2.5 shows the distribution of the vegetation types.

Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland (10610)

Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland - Low Elevation 

(10611)

Geographic area: California, Idaho, Montana, eastern Oregon, Utah, and Wyoming 

(also in other states that are outside of the study area, such as Arizona, New Mexico, 

and Colorado)

Elevation: 6000-9000 ft

Provinces: M261, M331, M332, M341, 313, 331, 341, 342

This BpS is considered a fire-adapted community, where in the absence of disturbance, conifers will replace quaking aspen. These include white fir, subalpine fir, and 

Douglas-fir, and limber pine in BpS 10610 and ponderosa pine, lodgepole pine, and 

Douglas-fir in BpS 10611 (table 2.5).

Quaking aspen suckers, along with grasses and forbs, dominate the early stage of 

stand development and after around 10 years (in the absence of disturbance), may reach 

heights over 6 ft. During this stage, conifers may invade and after 30 years continue to be 

present in the understory in a closed-canopy forest dominated by aspen. In the event of 

a fire, this area may transition into an open-canopy forest dominated by quaking aspen, 

with a conifer understory. In the absence of fire, conifers can dominate within 100 to 

150 years, although large-diameter quaking aspens may still be present.

Southern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland 

(10510)

Geographic area: southern Idaho, Utah, and western Wyoming

Elevation: 4000-8700 ft

Provinces: M331, M341, 341, 342

In this BpS, white fir and Douglas-fir are the most common conifer species; ponderosa 

pine occurs less frequently. Shrubs include bearberry (kinnikinnick), snowberry, and 

creeping barberry (table 2.5). At lower elevations this BpS is found above sagebrush 

ecosystems and adjacent to ponderosa pine woodlands. At higher elevations it is adjacent 

to spruce-fir forests.

Lodgepole and ponderosa pines seedlings, forbs, shrubs, and grasses such as pinegrass 

and elk sedge dominate the early developmental stage. If a fire occurs at this stage, it 

resets vegetative development. In the absence of fire, this stage will succeed to a closed, 

mid-developmental stage within about 35 years, where canopy cover exceeds 35 percent 

and Douglas-fir, white fir, and limber pine saplings, poles, and trees dominate the stand. 

This stage can persist for several decades and future trajectory depends on disturbances. 

If the stand remains open (<35 percent), pole and sapling Douglas-fir and ponderosa 

pine will be dominant, with grass and scattered shrubs in the understory.

In the absence of disturbances, the closed, mid-developmental stage succeeds to a 

closed, late-developmental stage, with canopy cover >35 percent, consisting of white fir, 

limber pine, ponderosa pine, and Douglas-fir. If a mixed severity fire or insect/disease 

outbreak occurs, the stand transitions into an open, mid-developmental stage dominated 

by ponderosa pine and Douglas-fir, with poles, saplings, grass, and scattered shrubs 

throughout. Late developmental open stages, with large trees and grass and scattered 

shrubs in the understory, are dominated by Douglas-fir and the occasional ponderosa pine.
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 2.5. Distribution of the biophysical settings (BpS) for Utah.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 31 Table 2.5. Biophysical setting models from the LANDFIRE project (2008) for Utah. Utah Late seral species1 Late seral species1 Biophysical setting Dominant species1 Early seral species1 Open stands Closed stands Shrub species Inter-Mountain Basins Aspen- quaking aspen, white quaking aspen quaking aspen, white subalpine fir, white fir, mountain snowberry, currant, Mixed Conifer Forest and fir, subalpine fir, limber fir, subalpine fir, quaking aspen, limber serviceberry, chokecherry, Woodland pine limber pine pine Woods’ rose, common snowberry, creeping barberry Inter-Mountain Basins Aspen- quaking aspen, quaking aspen quaking aspen, white ponderosa pine, serviceberry, chokecherry, Mixed Conifer Forest and ponderosa pine, fir, Douglas-fir lodgepole pine, common snowberry, bigtooth Woodland-Low Elevation lodgepole pine, Douglas-fir, maple, spiraea, Woods’ rose, Douglas-fir quaking aspen creeping snowberry, creeping barberry Southern Rocky Mountain Dry- white fir, limber pine, Douglas-fir, ponderosa ponderosa pine, white fir, limber pine, kinnikinnick, snowberry, Mesic Montane Mixed Conifer Douglas-fir, lodgepole pine Douglas-fir, limber ponderosa pine, creeping barberry, ceanothus Forest and Woodland pine, ponderosa pine pine, white fir Douglas-fir species Southern Rocky Mountain ponderosa pine ponderosa pine ponderosa pine ponderosa pine, white Shrubs are uncommon (not listed Ponderosa Pine Savanna fir, Douglas-fir in the BpS model description) 1Tree species are in order of dominance.
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Southern Rocky Mountain Ponderosa Pine Savanna (11170)

Geographic area: Utah

Elevation: 5500-8900 ft

Provinces: M331, M341, 331, 341

This BpS is found in the southern and eastern Uinta Mountains of northern Utah 

and on the plateaus and mountains of central and southern Utah. Areas within this BpS 

are characterized by open park-like stands of ponderosa pine with a grass understory; 

understory trees and shrubs are uncommon (table 2.5). Grasses include Arizona fescue, 

bluebunch wheatgrass, and big bluestem.

Early developmental stages are dominated by grasses and scattered thickets of ponderosa pine regeneration, with shrubs present in moist areas. As ponderosa pine reaches 

sapling to pole-sized, stand trajectory from this point depends on disturbances. In the 

absence of fire, the trees grow from sapling to sawtimber size (2 to 14 inches dbh), 

canopy cover can exceed 30 percent, and understory plant cover diminishes. This closed, 

mid-development forest succeeds to a late-development, closed forest, which has trees 

>14inches dbh, severely suppressed understory trees, and a poorly developed understory. 

If the closed, mid-development forest experiences fire, it usually results in either a stand 

replacement fire, which can change the area to an early development stand, or a mixed 

severity fire, which converts the stand to an open, mid-development stand with trees 

ranging from 2 to 14 inches dbh and canopy cover <30 percent. However, if low intensity 

and mixed severity fires maintain the stand, it remains open. Once the trees reach >14 

inches dbh the stand is considered to be late-successional. Again, disturbance dictates 

if the stand is open or closed. Surface fires maintain an open, savanna-like stand with 

a grass- and shrub-dominated understory. In the absence of fire, the stand transitions to 

a closed, late-successional forest as described above.

Conclusion

The purpose of this chapter is to described the wide range of forest compositions 

and developmental stages of the dry mixed conifer forests within the synthesis area to 

help guide fuel treatments. Knowing what species composition and forest structures to 

expect during different stages of stand development and how these stages are affected 

by disturbances can help with making management decisions. For example, identifying 

which BpS are present within a management area and their current developmental stage 

can provide a basis for evaluating fuel conditions, whether fuel treatments are necessary, and what methods to use. In addition, because there is variability among these 

BpS concerning site conditions, particularly with regards to moisture, productivity can 

vary. Therefore, information concerning expected developmental stages following fuel 

treatments and how often treatments need to be applied can be useful.

Further Reading

Many of the references below were used in the LANDFIRE BpS descriptions. The 

descriptions can be found at: http://www.landfire.gov/national_veg_models_op2.php.

Bradley, Anne F.; Noste, Nonan V.; Fischer, William C. 1992. Fire ecology of forest and woodlands in Utah. 

Gen. Tech. Rep. INT-287. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain 

Research Station. 128 p.

Cooper, Stephen V., Kenneth E. Neiman, and David W. Rev. 1991. Forest habitat types of northern Idaho: a 

second approximation. General Technical Report INT-236. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Research Station. 143p.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 33

Johnson, Charles Grier Jr.; Clausnitzer, Rodrick R. Plant associations of the Blue and Ochoco Mountains. 

1991. R6-ERW-TP-036-92. USDA Forest Service, Pacific Northwest Region. 164 p.

Pfister, Robert D., Bernard L. Kovalchik, Stephen F. Arno, and Richard C. Presby. 1977. Forest habitat types 

of Montana. USDA Forest Service General Technical Report INT-34. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Research Station. 174 p.

Powell, David C.; Johnson, Charles G. Jr.; Crowe, Elizabeth A.; Wells, Aaron; Swanson, David K. 2007. 

Potential vegetation hierarchy for the Blue Mountains section of northeastern Oregon, southeastern 

Washington, and west-central Idaho. Gen. Tech. Rep. PNW-GTR-709. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station. 87 p.

Steele, Robert; Pfister, Robert D.; Ryker, Russell A.; Kittams, Jay A. 1981. Forest habitat types of central 

Idaho. Gen. Tech. Rep. INT-114. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experimental Station. 138 p.

Williams, Clinton K.; Kelley, Brian F.; Smith, Bradley G.; Lillybridge, Terry R. 1995. Forested plant associations of the Colville National Forest. Gen. Tech. Rep. PNW-GTR-360. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station.
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 35

Chapter 3

The Role of Disturbance 

and Climate in Sustaining 

Dry Mixed Conifer Forests

Introduction

Dry mixed conifer forests are disturbance-driven, meaning that combinations 

of disturbance collectively drive forest development, structure, and composition 

(Attiwill 1994) (fig. 3.1). Natural disturbances are integral to the ecological processes 

operating in these forests, altering both stand structure and species composition. 

Over time, such disturbances create and maintain a diverse distribution of successional stages and vegetation types across the landscape. Within the synthesis area, 

several tree species have characteristics that lend resilience to forests where they 

occur. The more resilient trees, such as western larch and ponderosa pine, require 

moderate to high light intensities and are comparatively drought-tolerant. As trees 

of these species mature, they also exhibit increased resistance to fire, such that their 

largely intact post-fire canopy tends to help the species maintain site occupancy. 

Trees of other species may not survive a fire, but their presence may be maintained 

or restored via sprouting or regeneration from seed post-fire. Other disturbances, 

such as insects and diseases, also affect forest structure and, because they are often 

host-specific, the result is variation in species composition.

In this chapter, we discuss the disturbances that have been 

driving vegetation development in these landscapes, starting 

with a detailed inventory of disturbance agents and how they 

manifest themselves. This is followed by a regional tour of fire 

regimes, focusing on fire return intervals and size, along with the 

temporal and spatial variance in post-fire outcomes in each subregion. We provide a brief crash course on what is known today 

about global-scale climate cycles and their role in the remarkable 

variety of weather observed in the dry mixed conifer region. Climate and weather have 

important implications for fire periodicity, spatial and temporal grain, and the complex 

and constantly shifting assemblages of species and structures present on the landscape 

today. As changing climate trends are superimposed on all current climate cycles, 

succession processes and other drivers will influence and change the environment in 

which forests grow and develop. Disturbances are part and parcel of that trend driver, 

and fire is perhaps the dominant one—and likely to accelerate the shifts, for example, 

in the ranges of individual species. For silviculturists, fuels specialists, and fire managers charged with steering this key disturbance agent by manipulating vegetation, an 

understanding of how all these pieces fit together is crucial. We conclude the chapter 

with a discussion of how the variation in fuels, topography and weather combine and 

overlap to create mixed fire regimes, and provide a primer on vegetation autecology 

and fire tolerance to enhance understanding of vegetation impacts from fire, and the 

process of post-fire recovery.

Resilience

Resilience is the capacity of a plant community 

or ecosystem to maintain or regain normal function and development following a disturbance 

(Helms 1998).
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 3.1. The dry mixed conifer forests are disturbance driven. Where insects (a), disease (root 

disease (b), weather (ice, wind, snow) (c), mistletoe (d), and fire (e) all play a role in creating a variety 

of forest compositions and structures. Photos a, d, and e by David Powell, USDA Forest Service, b by 

Ralph Williams, USDA Forest Service, and c by Robert Denner, USDA Forest Service. Photos a, b, d, 

and e from Bugwood.org.

a

e

c

b

d
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 37

Weather, Insects, and Diseases

The epidemic-scale mountain pine beetle infestations (as of 2012) 

in some dry mixed conifer forests of the western United States are 

altering species composition by targeting host species (lodgepole 

pine and/or ponderosa pine) and allowing non-host tree species to 

expand their site occupancy (fig. 3.2). In some cases, when established trees are not present, the outcome is an open stand containing 

shrubs or other vegetation. A period that is particularly important 

in terms of fire hazard occurs when a tree has been attacked but the 

needles are green or in the red needle stage (prior to needle drop). 

Recent literature has noted that decreased moisture and change in 

foliar chemistry increase flammability (Hicke and others 2012; 

Jolly and others 2012). After infestations, substantial increases in surface fuel loads 

occur as infested trees respond by shedding needles and branches in the short-term, and 

by transitioning to snags and down wood over the long-term. More importantly, beetle 

infestations create open forests, favoring an increase in wind speed, decreased air relative humidity, and higher air temperature—all factors that influence fire behavior. This 

interaction of species composition change along with increased surface fuel loadings 

and weather variables will likely alter the potential fire behavior and fire effects in these 

forests compared to those of uninfested forests (Jenkins and others 2008; Klutsch and 

others 2009). However, the combination of beetle mortality and increased likelihood of 

fire creates opportunities for a variety of species to expand their occupancy of the site and 

Disturbance

Pickett and White (1985) defined a disturbance 

as “any relatively discrete event in time that 

disrupts ecosystem, community, or population 

structure and changes resources, substrate availability, or the physical environment.” Moreover, 

disturbances interact with each other to create 

conditions that promote and/or alter subsequent 

disturbances (Hessburg and Agee 2003).

Figure 3.2. Mountain pine beetle infestation with Mount Jefferson in the background. Taken from 

the Warm Springs Indian Reservation by Andris Eglitis, Entomologists, Deschutes National Forest.
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

may generate new habitat niches (Lehmkuhl and others 1994). Synergistic combinations 

are possible for the many kinds of disturbance common in dry mixed conifer forests, 

including insects, diseases, wind, and snow/ice damage. Understanding the interactions 

among natural disturbances and how the resulting forests create conditions that promote 

different fire behaviors, intensities, and burn severities are important for planning and 

implementing fuel treatments.

Weather

Weather can be a formidable disturbance in dry mixed conifer forests. Snow, ice, and 

wind can create canopy openings sometimes tens of acres in size (Boldt and Van Deusen 

1974; Shepperd and Battaglia 2002). Moreover, these disturbances are episodic, isolated, 

and indiscriminant in terms of location and spatial extent. Wind is a common disturbance 

that can create a variety of patch sizes by creating gaps and shattering individual trees. 

Intense isolated thunderstorms can cause considerable damage and increase surface fuel 

amounts (fig. 3.3); large windstorms can affect hundreds or thousands of acres. Although 

wind is recognized as a major disturbance, much of the focus on this disturbance has 

been centered on hurricanes, a form of disturbance extremely rare if nonexistent in our 

synthesis area, and little attention has been devoted to wind’s effect on fuel dynamics. 

However, there is a long history of wind events in the Pacific Northwest with an estimate 

of 48 notable wind storms that occurred between 1880 and 2007 (Read 2008), resulting 

in a mean return interval of 2.6 years (standard deviation of 4.5) and a range from less 

than 1 to 26 years. Winds associated with thunderstorms account for half of all severe 

weather reports in the lower 48 states and are more common than damage caused by 

tornadoes (http://www.nssl.noaa.gov/primer/wind/). Damaging winds are those that 

exceed 50 to 60 mph, a point at which wind can uproot trees and break boles. In dry 

mixed conifer forests, ponderosa pine, western larch, and Douglas-fir are more resistant 

to wind damage than other species (Minore 1979); however, even trees of these species 

can be damaged in major wind events. Silvicultural prescriptions designed to reduce 

Figure 3.3. Windstorm damage at Priest River Experimental Forests. Photo 

by Robert Denner, USDA Forest Service, Rocky Mountain Research Station.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 39

crown fire potential by reducing canopy density can make the remaining trees more 

susceptible to wind damage, and lead to an increase in surface fuels in the short-term. 

Over the long-term, the lower stand densities created by thinning promotes increases 

mechanical stability and wind resistance (Wonn and O’Hara 2001). Thoughtful silvicultural prescriptions acknowledge information on tree susceptibility to reduce wind 

damage and increase wind firmness. These factors include tree age (young trees fare 

better than old trees), topographic influences and wind direction, and species composition. Treatment design and layout can also be incorporated. For example, Smith and 

others (1997) suggest that arranging a sequence of strips or strip-shelterwood blocks 

oriented from leeward to windward can create a streamlined pattern that increases stand 

resistance to downbursts.

Ice and snow can cause significant damage in dry mixed conifer forests. Russ Graham 

(personal communication) offers anecdotal evidence of clear, windless winter days during which heavy snow dropped onto the large branches of grand fir as it melted, causing 

these trees to break and topple like dominoes. As with wind, historical records are limited for assessing the frequency and severity of snow and ice storms. Ice accumulation 

varies with topography, elevation, aspect, and the areal extent of susceptibility (Irland 

2000). In mixed conifer forests, deciduous conifers (for example, western larch) and 

fine-needled pines (for example, western white pine, sugar pine) tend to shed snow and 

ice. Others, with fuller crowns (for example, grand fir, Douglas-fir) tend to be more 

susceptible to ice and snow damage. Damage ranges from minor branch breakage, to 

major branch and crown loss and breakage of trunks within or below the crown (Jain 

and Graham 2005) (fig. 3.4), resulting in canopy gaps, decreased forest densities, and 

altered species composition (Jain and Graham 2005).

Figure 3.4. 1996 snow damage in northern Idaho. Photo by David Koob, USDA 

Forest Service.
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Insects and Disease

Within dry mixed conifer forests, fire exclusion allowed dense stand conditions to 

develop, thereby increasing the likelihood of insect and disease epidemics. These can 

dramatically alter forest composition and structure (Harvey and others 2000) (table 3.1). 

Table 3.1. Common diseases and insect pests for conifers throughout the synthesis area (compiled from Hagle, Gibson & Tunnock, 

2003). DF- Douglas-fir; GF - grand fir; ES - Engelmann spruce; JP - Jeffrey pine; LP - lodgepole pine; PP - ponderosa 

pine; SRF - Shasta red fir; SPF - subapline fir; sugar pine – SP; WH - western hemlock; WL - western larch; WRC - 

western redcedar; WWP - western white pine; WF - white fir. We did not include sudden oak death, which can infect oak 

species in Oregon and northern California.

Species DF 1 GF ES JP LP PP SRF SPF SP WH WL WRC WWP WF

Stem 

decay

Blue stain X X X X X X X

Cedar brown pocket rot X

Indian paint fungus X X X X X

Pini rot X X X X X X X X X X

Pouch fungus X X X X X X X X X X

Red belt fungus X X X X X X X X X X X X X X

Schwinitzii butt rot X X X X

Cankers

Atropellis canker X

Fir canker X X X X X

Spruce canker X X

Root 

diseases

Annosus X X X X X X X

Armillaria X X X X X X X X X

Blackstain X X

Laminated root rot X X

Tomentosus root disease X X

branch 

and 

terminal

Camandra & Stalactiform X X

Dwarf mistletoe X X X X X X X

Needle cast X X X X X X X X

Swiss needle cast X

White pine blister rust X X X X

Brown felt blight X X

Larch needle blight X

Spruce broom rust X

Western gall rust X X

Larch Casebearer X

Larch sawfly X

Western spruce budworm X X

Lodgepole terminal weevil X

Beetles

Cedar bark beetles X

Douglas-fir beetle X

Jeffrey pine beetle X

Mountain pine beetle X X X

Pine engraver beetle X X X

Western pine beetle X

Wood 

borers

Fir engraver X X X

Metallic wood borers X X X X X X X X

Roundheaded borers X X X X X X X X
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 41

Hessburg and others (1994) noted that bark beetles, defoliators, and diseases exert 

considerable influence on the composition and structure of northwestern forests. Bark 

beetles active in northwestern forests include western and mountain pine beetle, pine and 

fir engraver beetles, Douglas-fir beetle, and spruce beetle. Defoliators include western 

spruce budworm and Douglas-fir tussock moth. Diseases include laminated root rot, 

Armillaria, Annosus, Indian paint fungus, blister rust, and brown cubical butt rot. Dwarf 

mistletoe, though actually a parasitic plant, not a disease per se, is the most common 

and easily identified damage agent influencing conifer species and may increase with 

climate change (Woods and others 2010). Many of these agents can be found in dry 

mixed conifer forests, and the majority of the impacts of insect and pathogen agents 

have occurred in these forests.

Western pine beetle was a primary insect associated with historical fire regimes. 

Large, old ponderosa pine trees, preferred by the beetle, were vigorously attacked when 

stressed by drought and had little capacity to resist (Hessburg and others 1994). For 

example, the drought of 1920s and 1930s killed many trees (Miller and Keen 1960). 

In years not characterized by drought, the western pine beetle is endemic and typically 

kills trees that have been struck by lightning, that are infected by root disease, or that 

are too old to successfully resist attack. However, some management activities, such 

as reintroducing fire after decades of fire suppression 

absence can make normally resilient trees susceptible 

to attack (see Chapter 10) (Hood 2010; Wu and others 

1996) (fig. 3.5).

Mountain pine beetle readily attacks lodgepole pine 

and ponderosa pine within the Douglas-fir and grand fir 

PNVGs (fig. 3.6). Leiberg (1899) and Langille (1903) 

recorded the first insect outbreak in the northern Rocky 

Mountains more than a century ago. Although these turnof-the-century outbreaks were severe, evidence suggests 

that their extent and duration were less than contemporary 

infestations. Before the advent of fire suppression, fires 

created a heterogeneous pattern of forest developmental 

stages, and stands at a vulnerable stage of development 

tended to be spatially isolated, limiting the extent of 

infestations. Today, these same forested landscapes are 

characterized by large expanses of single developmental 

stages, and this facilitates larger and more severe infestations (Hessburg and others 1994). Historically, pine, 

Douglas-fir and fir engraver beetles tended to kill trees 

scorched by low-intensity surface fires. Where stand 

densities are high, forests are stressed by drought, or trees 

are infested by disease, mistletoe, or root rot, engraver 

beetles would attack susceptible trees in old growth 

refugia. Evidence indicates that there were periodic 

outbreaks of western spruce budworm and Douglas-fir 

tussock moth in the 1700s and 1800s (Hessburg and others 

1994; Wickman 1992). These outbreaks were limited in 

extent and duration because the host species (Douglasfir, grand fir, and white fir stands) were discontinuous 

and dispersed. Also, the host species tended to occur in 

mixed stands containing ponderosa pine, western larch, 

and other early-seral or non-host species, reducing both 

the impact of the defoliation and its rate of spread.

Figure 3.5. Western pine beetle attack in ponderosa 

pine, this photograph was taken by Andris Eglitis from 

the Deschutes National Forest.
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 3.6. Ponderosa pine killed by mountain pine beetle; couple years 

after infestation. Photo by David Powell, USDA Forest Service, and obtained from Bugwood.org

Beetles and fuels

Over the past several decades, bark beetle outbreaks have affected millions of acres of western coniferous forests (Man 2010; Raffa and others 2008). These outbreaks have altered the fuel complex of 

dry mixed conifer forests. Recent reviews have addressed how the fuel complex is affected by such 

outbreaks in lodgepole pine and Engelmann spruce-subalpine fir forests (Hicke and others 2012; 

Jenkins and others 2012), and these findings hint at how fuel complexes will change in dry mixed 

conifer forests that may be relevant. However, we would expect a different extent and magnitude of 

change in dry mixed conifer fuel complexes owing to the presence of non-susceptible species, which 

could mitigate stand-level impacts and influence the extent of the outbreak.

Forests impacted by beetle outbreaks transition through three stages. In the first, “red,” stage, needles 

on beetle-killed trees fade to a reddish color. Within 1 to 5 years, these needles separate and fall 

from the branches, increasing the litter fuel loading, and leaving behind a “gray” stage of standing, 

gray-colored snags that continue to shed twigs and branches over many years, adding to fine fuels 

on the forest floor. During both of these stages, increased light resulting from canopy opening facilitates establishment by shrubs, graminoids, forbs, and seedlings, ultimately increasing the potential 

for future problems with ladder fuels. At the same time, advanced regeneration and tree species not 

vulnerable to beetle attack respond with increased growth rates. Eventually, the snags decay and fall 

at rates that vary based on site characteristics and species, generating increased loadings of coarse, 

surface fuels. This “post-epidemic” phase can last up to several decades.

Strategies for managing fuels in bark beetle infested forests depend on the intensity and distribution 

of mortality. In some cases, managers have capitalized on the beetle induced mortality pockets and 

the stand heterogeneity it created. Managers have used resulting pockets of mortality as focal points 

in which to create holes, gaps, and meadows within the stand as wildlife habitat enhancement; to 

increase stand and landscape structural and compositional heterogeneity; and to alter potential wildfire 

behavior. Jenkins and others (2012) raise several important considerations from a fire suppression 

perspective. Removal of dead trees has been proposed to facilitate firefighter safety and effectiveness, 

for example, to prevent injury by falling snags during a fire, to provide more areas for safe zones in 

which fire shelters can be deployed, and to facilitate future fireline construction. Leaving nature to 

take its course in a stand containing an abundance of beetle-killed trees and the increasing surface 

fuel loadings they will generate effectively increases the area of fuelbeds that will be receptive to 

fire brands. Such increased spotting potential could greatly impact fire suppression effectiveness and 

compromise firefighter safety.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 43

Figure 3.7. Root disease in dry mixed conifer forests. Root disease maintains 

high surface fuel in the form of woody debris and tall shrubs for decades. 

These pockets can be several acres in size and occur throughout the dry 

mixed conifer forests. Photo by John Schwandt, USDA Forest Service.
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

The root diseases that occur in dry mixed conifer forests are laminated root rot, 

Armillaria root disease, and Annosum root rot, and they typically occur in spatially 

aggregated clusters known as root rot centers (table 3.1). Douglas-fir and grand fir are 

particularly susceptible to root diseases. Historically, these diseases provided structural 

diversity and enhanced diversity in opening size, shape, and edge of patches (Hessburg 

and others 1994; Spies and Franklin 1989). Patches created by these diseases most likely 

contributed to the mixed fire regime because the patches were more likely to support 

moderate and high severity fire. These root rot centers generate snags and woody debris 

that eventually decompose and are incorporated into the soil. However, they can produce 

a significant amount of woody fuel and perpetuate ladder fuels (Hessburg and others 

1994). Armillaria root disease is an opportunistic pathogen in that it attacks low-vigor, 

older, or weakened and injured trees. Armillaria also attacks stressed trees affected by 

drought, lightning, fire, or other root pathogens (fig. 3.1).

Root disease and fuels

Rippy and others (2005) identify critical elements meriting attention when conducting fuel treatments in areas infested by root disease. Thinning may lead to increased 

damage from some root diseases (e.g., Armillaria, laminated and Annosus root disease), particularly if root disease is already well-established and residual trees are of 

susceptible species (e.g., true firs and Douglas-fir), so it is important to select leave 

trees of disease resistant species, such as western larch, ponderosa pine, sugar pine, 

and western white pine (table 3.1).

Timing of treatments may also be important, as there is some preliminary, though not 

entirely conclusive, evidence that thinning in summer or fall leads to less root disease 

damage than in spring, perhaps because warmer temperatures favor the establishment 

of other kinds of fungi in newly created stumps, leaving no unfilled niche in which 

detrimental root disease fungi can become established. Where root disease is evident, 

it is important to prevent wounding of residual trees, whether through prescribed fire or 

mechanical injury. Favoring tree species diversity, using locally adapted seed sources, 

and promoting tree vigor are all practices that reduce vulnerability to root disease. 

Rippy and others (2005) also suggest more aggressive practices such as removing 

stumps and push-felling to extract root systems. However, it would be important to 

evaluate the effects of such practices on soil productivity, and economic feasibility 

may be difficult to establish without better information on the effect on rates of disease 

impacts and operational costs.

Dwarf mistletoe can occur in several coniferous species (Hessburg and others 1994; 

Parker and others 2006) (fig. 3.8). Mistletoe in ponderosa pine creates an abundance 

of brooms, fine fuels, resinous stems and branches, and occasionally dead trees. During surface fires, these mistletoe-infested trees tend to torch and a fire can kill severely 

infected trees and small infected patches. In Douglas-fir, mistletoe brooms (clusters of 

branches) have been observed to interfere with the self-pruning that typically occurs as 

a result of competition among trees in a stand, leaving flammable ladder fuels between 

forest floor and canopy. Abundant mistletoe in Douglas-fir most likely contributed to 

the high fire intensity observed during the Bitterroot fires of 2000. Historically, dwarf 

mistletoe in western larch was perhaps the most widespread, because western larch can 

live long periods and survive fires even with mistletoe infection, so the mistletoe also 

survives.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 45

Figure 3.8. Dwarf mistletoe on Douglas-fir (a), western larch 

(b), lodgepole pine (c), and ponderosa pine (d). Photos a 

and b by Oscar Dooling (USDA Forest Service), photo c 

by William Jacobi (Colorado State University), and photo d 

obtained from Bugwood.org. 

a

c d

b

Mistletoe and fuels

Fire does not necessarily decrease the area affected by mistletoe; however, even 

predominantly surface and low-intensity fires tend to kill the most infected trees 

because the extreme branching and “brooms” created by mistletoe infection act as 

a fuel ladder that conveys fire into the crowns of infected trees. Prescribed fire and 

mechanical fuel treatments can reduce tree densities and elevate canopy base height 

to discourage crown fire; but by selecting infected trees for removal, they can also 

reduce mistletoe seed dispersal, and via reduction in sapling densities, reduce the 

probability of mistletoe seed dispersal to susceptible saplings and lateral spread 

among the host tree species (Hessburg and others 1994; Stanton and Hadley 2010).
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Perhaps due to our perceived success in fire exclusion, fire has often been noted as 

the primary disturbance regime responsible for having maintained resilient forests. We 

suggest that a combination of disturbances other than fire also played key roles in creating the variety of compositions and structures that we see today, and which are responsible for both the fuel diversity that drives mixed-severity fire regimes and that created 

wildlife habitat. Parker and others (2006) noted that the two- and three-way interactions 

of storms, forest insects, pathogens, and fire created and maintained forests that were 

dominated by resistant species to insects, disease, and fire. It is important to recognize 

the contribution of these diverse disturbance forces when planning fuel treatments.

Fire Regimes

Fire is a fundamental ecological process that modifies forest composition, density, 

and surface fuel loads. How this modification is achieved depends on many factors, 

including the type of fire (intensity and severity), the amount and arrangement of fuels 

(aerial and surface), topography, and site productivity. In addition to these factors, local 

and broad scale climatic conditions create regional variability in fire frequency, behavior, 

and effects. In this section, we define and discuss common terms used when discussing 

fire and highlight some of the variability in fire within the synthesis area.

Fires and other disturbances create 

mosaics of forest composition and structure within and among stands and across 

landscapes. These mosaics can occur over 

a range of scales, from relatively fine 

scale, with patches smaller than an acre, 

to broad, landscape scales with patch 

sizes exceeding hundreds of acres (Perry 

and others 2011). Wildfires historically 

burned dry mixed conifer forests at a range 

of intensities and frequencies giving rise 

to a wide variety of burn severities, and 

creating diverse post-fire environments. 

Thus, contemporary vegetation compositions and structures reflect a variety 

of successional pathways, and both the 

vegetation and forest floor tend to be 

especially diverse.

A generalized description of the role 

fire plays in a forest is termed a fire regime 

and is typically articulated in terms of 

frequency, severity, and intensity of fire 

(Agee 1993). In this synthesis we will 

use fire severity as the description of a 

fire regime because it relates directly to 

disturbance and subsequent effects on vegetation development after a fire and is more 

commonly used among resource managers and stakeholders to describe fire’s role (Smith 

2000; Smith and others 2008). The three kinds of fire regime classes most useful for 

characterizing fires that occur in dry mixed forests are high severity (typically lethal 

to most trees), mixed severity fires, and low severity fires (typically non-lethal to most 

trees) (Hann and others 1997; Schmidt and others 2002).

Fire types

The fire hazard for any particular forest or landscape relates to the potential 

for the particular fuel conditions to cause specific types of fire behavior 

and effects. There are three types of fires. A ground fire spreads within the 

organic layers beneath the surface litter and typically smolders with a slowmoving rate of spread. Surface fires burn forest floor litter and debris as 

well as low understory vegetation. Partly because they tend to occur under 

more extreme weather conditions, Crown fires typically have a rapid rate 

of spread and move through the tops of trees or shrubs. Some practitioners 

further divide crown fire into more refined classes based on specific aspects 

of fire behavior. Passive crown fires occur when a tree or a group of trees is 

ignited by the passing front of the fire, but the flames do not actively move 

from tree crown to tree crown (trees experiencing passive crown fires are 

sometimes described as torching or candling). In an active crown fire, a 

continuous volume of flame develops in the tree crowns and on the surface 

as a linked and interdependent unit (NWCG 2011). A subcategory of the 

active crown fire is the independent crown fire, which occurs when the fire 

advances in the tree crowns, not requiring any energy from the surface fire 

to sustain combustion or forward progress (sometimes referred to as a running crown fire) (NWCG 2011). A conditional crown fire is when canopy 

base height is too high, thus this prevents a surface fire to transition to a 

crown fire. But the canopy bulk density can support the horizontal spread 

of a crown fire (which is possible but rare).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 47

Low severity fires (non-lethal) have frequent fire return intervals, create very small 

patch sizes, and have very little edge (Hessburg and Agee 2003) (fig. 3.9). These fires 

clean the forest floor of vegetation and accumulated woody debris, yet leave the majority 

of the high forest cover alive. Soil burn severity can 

range from non-existent to highly severe depending on 

the amount, condition, and extent of the surface fuels 

and the heat and residence time of the fire.

High severity fire regimes kill canopy layers across 

stands or even larger areas (fig. 3.9). In general, these 

fires kill all or virtually all standing vegetation through 

either crown fires or high intensity surface fires and 

are classified as severe, though some trees may exhibit evidence of moderately severe fire (for example, 

brown needles remain attached to branches, at least 

temporarily) in some areas. Within forests containing burned and blackened trees, the level of soil burn 

severity can be highly variable, ranging from light to 

highly severe depending on the state of the groundlevel vegetation, surface fuels, forest floor, and fire 

weather. These fires tend to occur in high-elevation 

forests and grow large (hundreds of acres, with few if 

any unburned patches); however, they are infrequent 

(Hessburg and Agee 2003).

Mixed severity fire is the most diverse fire regime 

because it can contain both low severity and high 

severity fires and everything in between (Arno and 

others 2000; Hessburg and Agree 2003) (fig. 3.9). 

Most importantly, the key phrase associated with 

this fire regime is “inconsistent and highly variable.” 

Smith (2000) discussed the elements that comprise a 

mixed-severity fire regime.

Figure 3.9. Examples of a high, mixed, and low 

severity fire. The high severity post-fire outcome was 

taken after the Bitterroot fires of 2000 by Jonathan 

Sandquist, USDA Forest Service. The mixed severity fire (middle) photo was taken on the Wenatchee 

National Forest by Paul Hessburg. The low severity 

post-fire outcome (bottom) photo was taken by Tim 

Baumgarner, USDA Forest Service.
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

1. Mixed severity fires contain a diversity of post-fire outcomes concerning soil 

effects and degree of tree mortality. Surface fires kill many smaller trees, but 

many larger, fire resistant species have sufficient size and thick bark to survive.

2. Mixed severity fire regimes also vary over space, resulting in a fine-grained 

pattern of forest structure and composition. This fine-grained pattern is most 

likely the result of fluctuations in weather during fires, daily changes in burning 

conditions (Perry and others 2011; Halofsky and others 2011), and variation 

in topography, surface fuels and stand structure, and composition. This type of 

fire regime tends to be favored in highly dissected terrain.

3. The type of fire and its severity varies over time and is often referred to as a 

variable fire regime. These fires may burn for days to weeks or months; but 

within that period can be highly variable.

Leiberg (1897), while conducting vegetation inventories in the northern Rocky 

Mountains, referred to fires burning for months—smoldering or torching depending 

on the conditions. Another example is the 1910 fire in the northern Rocky Mountains. 

The fires started in early June and burned most of the summer prior to the August blow 

up (Pyne 2001). Thus, this fire regime can be described as a spatially heterogeneous 

combination of fire types that kills small- to medium-sized groups of trees in some areas, 

while burning surface fuels and leaving behind live trees in other areas. The abundance 

of edges and clumps in forest structure, composition, and successional stages creates a 

mix of habitats that are often suitable for a variety of early, mid-, and late-successional 

plant species and wildlife specialists. Low and mixed severity fires are the most common kind of fire found in dry mixed conifer forests; however, even within the mixed 

severity fire regime, as the term implies, there may be areas within the dry mixed conifer 

that have consistently experienced low severity fire regimes. However, because of the 

vegetative mosaic, these areas are often smaller in extent or vary across the landscape.

A historical fire regime refers to a combination of fire frequency and severity under 

which plant communities evolved and were maintained (Schmidt and others 2002). 

Five fire regimes are currently recognized (Barrett and others 2010). These five groups 

include: I – frequent (0 to 35 years), low and mixed severity; II – frequent (0 to 35 

years), replacement severity; III – 35 to 200 years, low and mixed severity; IV– 35 to 

200 years, replacement severity; and V – 200+ years, replacement severity. The majority 

of the dry mixed conifer forests in our synthesis area is classified as fire regime group 

I (frequent 0 to 35 years, low and mixed severity) and group III (35 to 200 years, low 

and mixed severity).

Regional Variability in Fire Regimes

Many fire history studies and historical narratives on fire regimes completed throughout 

our synthesis area provide some evidence of the variability in fire history across the region 

(table 3.2). This variation in fire occurrence is a critical element sometimes overlooked, 

but it has important implications for creating diversity in forest structure and composition 

(Agee 2003). In general, fine dead fuels can accumulate quickly in dry mixed conifer 

forests because of their productivity; they are also more continuous and, therefore, create 

intermediate-sized patches (Hessburg and Agee 2003). Fire intensities in the drier sites 

with grass or kinnikinnick understories burn less severely and occur more often than the 

more productive sites that favor snowberry, ninebark, or huckleberry (Agee 1994). They 

also burned in a mosaic of small- to medium-sized patches (in tens of acres) with high amounts 

of edge between patches (Hessburg and Agee 2003). For example, this created “clumped 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 49

distributions” of ponderosa pine, sugar pine, and white fir near Crater Lake, Oregon (Agee 

1994). We suspect that clumpy species composition across the landscape is an important 

element of these forests. The clumps and openings provided a variety of wildlife habitats and 

opportunities for different vegetation to establish, grow, and develop. They also disrupted 

the abundance and the continuity of disease and insect prone species, decreasing the extent 

and severity of other disturbances.

Table 3.2. Fire history and fire regime literature on dry mixed conifer forests within the synthesis area.

Author Date Region Subject 

North and Central Rocky Mountains

Arno 1976 Bitteroot Mtn. Historical narrative of the role of fire

Arno 1980 Northern Rocky Mtn. Fire history dry and moist mixed conifer forests

Arno and others 1995 Bitteroot Mtn. Historical Range of Variability

Arno and others 2000 Northern Rocky Mtn. Historical narrative of mixed fire regimes

Brown and others 1994 Selway-Bitterroot Mtn. Historical fires and current wildfires

Brunell and Whitlock 2003 Clearwater, Idaho Sediment cores fire, vegetation, and climate history

Brunell and Whitlock 2005 North central Idaho Sediment cores fire and vegetation

Cissel and others 1999 Blue Mtn. Management strategy based on fire regimes

Heyerdahl and others 2001 Interior northwest Spatial controls of historical fire regimes

Pacific Northwest Interior and Northern California

Agee 1994 Eastern Cascades Summary of fire history studies

Agee 2003 Eastern Cascades Historical range of variability

Agee 1991 Klamath Mtn. History

Collins and Stephens 2010 Sierra Nevada Patch mosaic in mixed conifer

Colombaroli and Gavin 2010 Klamath Mtn. Charcoal in sediment cores

Cwynar 1987 North Cascades Pollen, plant macrofossils, charcoal in sediment cores

Everett and others 2000 East Cascades, WA. Fire history of ponderosa pine/Douglas-fir forests

Halofsky and others 2011 Klamath Mtn. Mixed and low severity fire regime comparison

Hessburg and Agee 2003 Inland Northwest Historical narrative of Inland Northwest, USA forests

Hessburg and others 2007 Eastern Washington Patch mosaic in dry and mixed conifer forests

Hessl and others 2004 Pacific Northwest Climate relation to fire seasons

Heyerdahl 1997 Blue Mtn. Dissertation – Climate and fire regimes

Heyerdahl and others 2012 British Columbia Fire history to describe mixed-severity fire regimes

Klenner and others 2008 British Columbia Determined mixed-fire regime dominated dry forests

Maruoka 1994 Blue Mtn. Thesis – fire history – dry mixed conifer

McNeil and Zobel 1980 Crater Lake NP Fire history – ponderosa pine and white fir

Mohr and others 2000 Klamath Mtn. Pollen and charcoal from lake sediment cores

Morrison and Swanson 1990 Cascade Mtn. Fire history and patch dynamics

Mote and others 1999 OR and WA Climate variation in fire activity 

Odion and others 2004 Klamath Mtn. Fire severity mosaic in current conditions

Olson 2000 Blue Mtn. Thesis: Fire occurrence in riparian and upslope areas

Perry and others 2011 WA, OR, and CA Ecology of mixed fire regimes

Powell 2011 Blue Mtn. White paper – historical fire regimes

Sensenig 2002 Southwestern Oregon Thesis: fire history mixed conifer

Stuart and Salazar 2000 2000 Coastal Mtn. CA. Fire history mixed conifer forests

Taylor 1993 Klamath Mtn. History on red fir forests

Taylor 2000 Lassen Volcanic NP Fire history and low and mixed severity fire regimes

Whitlock 1992 Pacific Northwest Historical narrative of vegetation and climate

Williamson 1999 Blue Mtn. Thesis: Forest structure & fire hazard in riparian areas

Utah

Battaglia and Shepperd 2007 Utah Narrative on disturbance history

Heyerdahl and others 2011 Utah Fire history – dry and mixed conifer forests

North America

Keeley and others 2009 North America Fundamental fire ecology synthesis
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Northern California and Klamath

Collins and Stephens (2010) conducted a fire history study within Yosemite National 

Park and considered this area to be influenced by the mixed fire regime. Although they 

noted that this tended to create a mosaic of small patches, there was also a large range 

of high-severity patch sizes. They also stated that topographic factors such as slope 

position might have influenced patch size, with the smallest patches occurring in upper 

slopes, lower slopes, and valleys. The larger patches tended to occur mid-slope and on 

flat topographic positions. They also noted that the patch size could be influenced by 

the underlying vegetation configuration and abundance, with shrub-dominated vegetation leading to smaller patches and fir dominated systems tending to favor larger stand 

replacing patches. The key point from Collins and Stephens (2010) was that although 

the majority of the area had a mosaic of small patch sizes and variability in topographic 

features influencing the fire regime, there were also large, stand-replacing patches 

(<15 percent of the total burned area) that were likely a part of the fire regime as well.

As we summarized the fire regimes for different areas, one constant theme appeared. 

In most dry mixed conifer forests, a mixed fire regime tends to be dominant, generating both high-severity stand replacing patches and low-severity patches. (Halofsky and 

others [2011] presented unique features, specific to the Klamath region, of the mixed 

severity fire regimes that could be applicable to other regions within our synthesis area 

[table 3.3].) There is a broad range of intermixing of diverse patches of vegetation ages 

and structures, resulting in high variability in fire regime parameters such as return interval, dominant drivers of fire, and subsequent fire effects. Because of the variability 

in topography, fuels, and the topographical influence on weather, the mixed severity 

regime tends to create a fine-scale mosaic of patches of vegetation, which burns at various levels of severity. Also, the variability in fire return interval creates a wide range 

of patch ages, structures and compositions. Accordingly, Halofsky and others (2011) 

infer that although these forests do have elements of high- and low-severity fire regimes, 

they are predominantly mixed fire regimes and are distinctly different from forests 

that experience low- or high-severity fire regimes. The tendency towards mixed fire 

regimes derives from complex topography, a broad range of soil depths, and diversity 

in vegetation with respect to successional stage, species composition, and horizontal 

and vertical structure.

Pacific Northwest Interior

Fire return intervals and intensities in the Pacific Northwest have been highly variable. 

Everett and others (2000) noted that the fire regime favored a mixed severity, which 

created a heterogeneous landscape across north central Washington consisting of patches 

that had burned at varying severities at different times. Small patches were created in 

places where surface and ladder fuels accumulated (facilitating the movement of fire 

into crowns); however, the pattern of these patches was not consistent and they did 

not appear to be a function of aspect or slope. Similarly, in southern British Columbia, 

Heyerdahl and others (2012) found evidence of extensive low-severity fires intermixed 

with small patches of high-severity fire. Again, there was no evidence supporting a 

link to topography, a finding consistent with what Klenner and others (2008) observed.

Powell (2011) summarized key characteristics of the historical fire regimes that occurred within the Blue Mountains, Oregon (table 3.4). The predominant fire regime is 

either fire regime 1 (75 percent of historical burned area) or III (15 percent of historical 

burned area), with fires either consisting of low severity to mixed severity fire regimes. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 51

Table 3.3. Comparison of fire characteristics and subsequent ecosystem response between mixed and low severity 

fires from studies conducted in the Klamath. Adapted from Halofsky and others (2011).

Mixed-severity fires Low-severity fires

Fire Characteristics

The influence of topography, weather, and fuels Fuels are the dominant driver of fire

are dominant drivers of fire behavior and effects. behavior and effects

Fuel structure plays in important role in patch 

size effects, but periods of extreme weather

can override other factors

Occurrence of high severity fire even with a Short intervals between fires are primarily

relatively short fire return interval associated with low-severity surface fires.

Spatial mosaic of a fire tends to be reinforced Fires consume surface fuels and make

through subsequent fires over the short term additional fires less likely for a period

(<30 years), with implication for long-term 

landscape forest structure

High amount of edge between seral stages due Low amount of edge-between seral stages due

to repeated and spatially heterogeneous burns to more homogeneous burns

Ecosystem Response

Abundant post-fire conifer regeneration based Moderate to high conifer regeneration under

on proximity of seed source or seed storage in intact canopies after surface fires and in smallsoil, in all but the largest high-severity patches fire created openings, with little regeneration in

on the driest sites large openings.

Juxtaposition of early and later seral vegetation, Limited intermixing of seral stages; early seral

provides habitat for a arrange of wildlife species patches typically confined to small areas within

in relatively close proximity mature forest cover.

High community resilience because of the Reported delays in regeneration after repeated

presence of species adapted to regenerate high severity fires and some state changes after

after disturbance, spatial intermixing of seral uncharacteristically severe fire in ponderosa pine.

stages, and close proximity of seed sources.

Heyerdahl and others (2001) noted regional variability in fire regimes. They found that 

the southern portion of the Blue Mountains burned more frequently than the northern 

portion. They suggested that this variability was influenced by latitudinal variations in 

climate (which affect fuel moistures), lightning frequency, summer precipitation, and 

snow melt patterns. These factors resulted in longer dry periods in the southern portion versus the northern portion of the Blue Mountains, consequently favoring more 

frequent fires. In addition to climate as a broad scale influence, the researchers also 

found fine-scale, topographic influences. For example, the patterns of historical fire 

frequency were dependent on size and juxtaposition of aspect and slopes in conjunction 

with natural fire barriers, which isolated or influenced fire size. By contrast, in areas 

without fire barriers and little variation in slopes or elevation, fire frequency did not 

vary with aspect or slope angle.
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Table 3.4. Powell (2011) developed and summarized the historical characteristics and fire regimes for the Blue Mountains, Oregon. 

Fire regime V is rare in the Blue Mountains, thus it is not shown.

Historical fire regimes1

Fire regime characteristic I II III IV

Return interval (mean; years)2 < 25 < 35 35-100+ 35-100+

FRCC: fire frequency interval (years)3 0-35 0-35 35-200 35-300

Fire severity on upper canopy layer4 Low Replacement Mixed Replacement

Upper canopy layer mortality(%)4 ≤ 25% > 75 26-75 > 75

FRCC: fire severity name3 Low to mixed Replacement Mixed to low Replacement

Fire intensity adjective5 Low Low to Moderate Moderate to high High

Fireline intensity (flame length; feet)6 < 3 < 3 3 to 10 > 10

Fuel component driving fire spread5 Surface Surface Surface and canopy Canopy

Ecosystem example5 Ponderosa pine Grassland/shrub Mixed-conifer Subalpine fir

Historical burned area (%)7 75 5 15 5

Estimated fire size (ac)8 1 to 3,000 Unknown 1 to 10,000 1 to 5,000

Measured fire size (ac)9 2,850 Unknown 900 Unknown

Fire size variability (min-max acres)10 50 to 19,960 Unknown 250 to 1,940 Unknown

Fire seasonality11 Summer and fall Spring and summer Summer and fall Summer and fall 

1 Historical fire regime is a characterization of the historical combination of fire frequency and severity under which plant communities evolved 

and were maintained (Schmidt and others 2002). Five fire regimes are currently recognized (Barrett and others 2010). These five groups include: 

I – frequent (0 to 35 years), low and mixed severity; II – frequent (0-35), replacement severity; III – 35 to 200 years, low and mixed 

severity; IV- 35 to 200 years, replacement severity; and V – 200+ years, replacement severity. The majority of the dry mixed conifer forests in 

our synthesis area are classified as fire regime group I (frequent 0 to 35 years, low and mixed severity) and group III (35 to 200 years, low and 

mixed severity) 2Fire return interval (years) is the frequency between successive fire events; values based on Hall (1976), Heyerdahl and Agee (1996), Maruoka 

(1994), and Schmidt and others (2002). 3 FRCC (fire regime condition class) is a process for evaluating whether current conditions have departed from historical reference conditions 

and, if so, the magnitude of the departure. 4 Fire severity on upper canopy layer is the effect of fire on dominant plants: no more than 25 percent of upper canopy layer plants are killed 

by low-severity fire, whereas 75 percent or more are killed by high-severity fire; moderate-severity fires have survival percentages between these 

extremes (the 25 percent and 75 percent mortality thresholds were established by FRCC; see Barrett et al. 2010, page 99). 5 Fire intensity, fuel component, and ecosystem examples were taken from Keeley and others 2009 (table 1).

6 Fireline intensity refers to the energy release rate of a fire. Since intensity is generally proportional to flame length, fireline intensity is frequently 

expressed as a flame length, in feet. Information was from Agee (1996). 7 Historical burned area is an estimate of annual burned area (percent) for the Blue Mountains area prior to Euro-American settlement (defined 

as pre-1850); information adapted from Agee (1996). 8 Estimated fire size provides an indication of average wildfire extent (in acres) for the Blue Mountains, as derived using an expert panel approach 

and involving 50 employees from the Malheur, Umatilla, and Wallowa-Whitman National Forests (Johnson 1993). 9 Measured fire size provides an indication of average wildfire extent (in acres) from a Blue Mountains fire history study (Heyerdahl and Agee 

1996; Heyerdahl 1997). 10 Fire size variability shows how historical wildfire extent varied (in acres) from a Blue Mountains fire history study (Heyerdahl and Agee 1996; 

Heyerdahl 1997). 11 Fire timing refers to the typical season of wildland fire. Information was taken from Agee (1996).

Northern and Central Rocky Mountains

Across dry mixed conifer forests in Idaho and western Montana, the average fire 

return interval (FRI) was as low as 6 years, with many sites experiencing fires every 

11 to 15 years, on average (Heyerdahl and others 2008) (table 3.5). The Lowman Research Natural Area on the Boise National Forest (southern Idaho) was unusual in that 

its average FRI was 29 years. Even more striking is the exceptionally broad range of 

FRI among sites in this forest—from one year on many sites to over 20 years between 

fires on others, with some exceeding 30 years. Arno (1976) noted that in the Bitterroot 

National Forest (western Montana), fires burned on southern aspects more frequently 

and killed ponderosa pine saplings and torched large Douglas-fir trees. Northern aspects 

burned less frequently, but more intensely and the dense Douglas-fir stands, common at 

such sites, tended to carry crown fires. However, old and large western larch, ponderosa 

pine, and/or Douglas-fir frequently survived within a matrix of crown killed forest.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 53

The Black Hills of South Dakota and Wyoming are unusual in their isolation. They 

are disconnected from other major mountain ranges and they contain forests that are 

composed predominantly of ponderosa pine at the far eastern edge of its range. 

However, patches of dry mixed conifer forests with white spruce, paper birch, and 

quaking aspen can occur, particularly in protected areas, have deep soils, and occur at 

elevations above the dry ponderosa pine forests of the Black Hill. Mean FRIs in these 

dry mixed conifer forests ranged between 20 to 35 years (Brown 2003; Brown and others 2008). The Black Hills tend to have a mixed fire regime dominated by surface and 

some passive crown fire (Brown and others 2008)

Utah

There is considerable diversity in the historical fire regimes within the dry mixed 

conifer forests in Utah. In ponderosa pine forests, low- to moderate-severity surface 

fires dominated with very few occurrences of crown fires. As productivity increases and 

dry mixed conifer forests became more common (where Douglas-fir and white fir can 

Table 3.5. Fire histories quantified by Heyerdahl and others (2008) from sites in mixed-dry conifer sites in 

Idaho and western Montana (see figure of locations) and Heyerdahl and others (2012) in Utah. 

Size of the area where data were collected from individual trees. Trees sampled, are the number 

of trees that were cross-dated and used to calculate the average and range of fire occurrence.

Fire occurrence

Location Area Trees (Years)

Site name National Forest size sampled Mean Range

Idaho and Montana

Sheafman Creek Bitterroot 35 41 11 2 to 30

Sawmill Creek RNA Bitterroot 109 32 15 3 to 32

Wash Creek Boise 77 25 11 1 to 22

Bannock Creek Boise 69 30 8 3 to 19

Warm Springs Ridge Boise 37 27 9 3 to 20

Lowman RNA Boise 57 9 29 14 to 47

Crane Lookout Flathead 45 18 16 7 to 31

Holland Lake Road Flathead 94 19 15 3 to 30

Sheldon Flats Kootenai 89 29 6 2 to 17

McMillan Mountain Kootenai 20 32 12 4 to 25

Hunter Point Kootenai 77 25 14 2 to 27

Blue Mountain Lolo 82 36 6 1 to 17

Butler Creek Lolo 5 38 8 3 to 23

McCormick Creek Lolo 367 17 13 3 to 30

Sophie Lake Montana 32 46 6 1 to 15

Keating Ridge Nez Perce 10 22 14 3 to 26

Cove Mountain Nez Perce 30 25 18 3 to 46

Poverty Flat Payette 45 33 12 3 to 26

Corona Road Private 7 26 11 4 to 25

Flannigan Creek Private 69 25 6 2 to 24

Friedorf Gulch Salmon-Challis 116 31 12 1 to 37

Utah

Snake Range Great Basin National Park 1569 103 19 1 to 62

Boulder Mountain Dixie 1963 95 30 5 to 110

Beaver Creek Fishlake 2837 164 34 2 to 100

Wah Wah Mountains Bureau of Land Management 1593 16 20 2 to 86

Indian Creek Fishlake 624 34 28 6 to 75
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

potentially occur), mixed severity and stand replacing fires tended to occur (Battaglia 

and Shepperd 2007; Bradley and others 1992). Kilgore (1981) noted that topography, 

weather, stand structure, and fuel loading all contributed to generate a variety of different patterns of fire intensity and frequency; in other words, the result was a much more 

mixed and variable fire regime. Consequently, a mosaic of different fuel types created 

a mosaic of different fire outcomes. In some places, the fire resistant species such as 

ponderosa pine and larger Douglas-fir trees survived these fires and were dominant.

Wildfire size and climate variability were recently studied by Heyerdahl and others 

(2011) (table 3.5). They found that many of these areas where they documented fire 

history via fire scar evidence experienced range of fire severities through time and when 

analyzed in aggregate, it was possible to gain insight about fire size. Large fires occurred 

when summers were drier than average and were more likely during La Nina years, 

whereas no fires occurred in years that were wetter than average. Recently, Heyerdahl 

and others (2011) summarized fire histories on many sites in the Great Basin and found 

that sites within the dry mixed conifer forests had mean FRIs of 19 to 34 years; however, 

the FRI range was large: 2 to 86 years in the Wah Wah Mountains and Beaver Creek 

and 2 to 100 years on the Fishlake National Forest of Utah.

Climate Patterns

This section was included to promote understanding of how there may be nonrandom processes behind the weather that, when combined, generate an impression of 

randomness. These processes have left a residue of signals (warm periods, dry periods, 

wet periods, times with lots of fires, and times with few) that we may have mistakenly 

ascribed entirely to other factors (for example, the onset of aggressive fire suppression, a 

gravitation toward using wildfire to meet resource objectives, or changes in fire suppression policies). These may all indeed be factors, but knowing more about the underlying 

climate may help us place them in better context. It may also help us recognize that 

there are factors that we cannot control but deserve consideration in making informed 

decisions concerning fuel treatment planning.

Changes in ocean currents and sea surface temperatures influence the jet stream, which 

alters precipitation and temperature patterns, which in turn influence fire occurrence and 

extent. Two specific phenomena that influence moisture patterns in the western United 

States are the El Nino/Southern Oscillation (ENSO) and the Pacific Decadal Oscillation 

(PDO). Our goal in this section is to describe how climate plays a role in the temporal 

pattern in fire and other disturbances.

For ENSO, variation in the surface temperature and air surface pressure in the tropical Pacific Ocean determines whether an El Nino or a La Nina event occurs. When 

the ocean surface temperature is warm in the eastern Pacific Ocean and the air surface 

pressure is high in the western Pacific Ocean, it is known as an El Nino event. When 

the ocean surface temperature is cold in the eastern Pacific Ocean and the air surface 

pressure is low in the western Pacific Ocean, it is known as a La Nina event. El Nino 

and La Nina events occur on average every 2 to 7 years. La Nina events usually occur 

for 1 to 3 years but occur half as frequently as El Nino events. El Nino events tend to 

last 9 to 12 months. These events alter temperature and precipitation patterns across 

the globe. In the United States, the largest impact is during the winter months. ENSO 

alters the location of the jet stream, which changes precipitation and temperature patterns across the western United States. Each region within our synthesis area is impacted 

in different ways by ENSO (table 3.6). These differences highlight the importance of 

understanding regional differences in large-scale climatic patterns when discussing fire 

regimes, regeneration dynamics, and tree growth within the dry mixed conifer forests 

because these patterns relate to the potential fire season.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 55

In Northern California, fall temperatures are normal 

to cooler than normal and the precipitation is normal 

during La Nina years. Throughout the winter, temperatures are normal to warmer, but precipitation increases. In 

the spring, the region is warmer and wetter. In Washington 

and Oregon, fall temperatures are normal but it is dry. In 

early winter, it is warm and dry, but by late winter, normal 

moisture is expected. By spring, the region is warmer 

and wetter. The northern Rocky Mountains experience 

warm and dry weather from fall through the end of 

winter. By spring, the region is warmer and wetter than 

average. In fall, Utah experiences normal temperatures 

and normal precipitation in the northern portions, but increased precipitation in the 

southern areas. Early winter has normal temperatures but it is dry in the northern areas 

and receives normal precipitation in the southern areas. By late winter, it is warmer in 

both regions of Utah, but the northern portions are dry and the southern portions are 

wet. By spring, it is warmer in both regions of Utah; the northern portions experience 

normal precipitation but the southern portions are moist (table 3.6).

The Pacific Decadal Oscillation is similar to the El Nino Southern Oscillation in 

climatic variability, but instead of only lasting a few years, the PDO persists for 20 to 

30 years. For PDO, variation in the surface temperature and air surface pressure north 

of 20°N in the Pacific Ocean determines whether PDO enters the “warm/positive” or 

the “cool/negative” phase. During the warm phase, the western portion of the Pacific 

Ocean cools and the eastern portion of the Pacific Ocean (along the west coast of the 

United States) is warm. The strength of an ENSO event can depend on the PDO phase 

(Brown and Comrie 2004).

El Nino

To demonstrate how El Nino impacts a specific region 

within a state, NOAA has developed a website that provides maps of precipitation and temperature departures for 

each climate division within a state: (http://www.cpc.ncep.

noaa.gov/products/predictions/threats2/enso/elnino/index.

shtml). NOAA also provides maps of La Nina impacts on 

temperature and precipitation on specific regions: (http://

www.cpc.ncep.noaa.gov/products/analysis_monitoring/

lanina/usdivtp/writeup.shtml#usmaps).

Table 3.6. Seasonal patterns of temperature and precipitation for strong El Nino and La Nina years for different locations within our 

synthesis area. La Nina is characterized by unusually cold ocean temperatures in the eastern equatorial Pacific and El 

Nino is characterized by unusually warm ocean temperatures in the equatorial Pacific.

Temperature (departure from normal) Precipitation (departure from normal)

Climate Oct. – Dec. – Jan. – Apr. – Oct. – Dec. – Jan. – Apr. – 

Locations pattern Dec. Jan. Mar. Jun. Dec. Jan. Mar. Jun.

Northern El Nino Normal Normal Normal Above Normal Above Above Above

California to below to above to above

La Nina Normal Normal Below Normal Above Normal Normal Normal

to below

Eastern El Nino Normal Above Above Above Below Below Normal Above

Cascades La Nina Normal Normal Below Below Above Above Above Below

Northern El Nino Above Above Above Above Below Below Below Above

Rocky La Nina Normal Normal Normal Below Above Above Above Normal

Mountains to below to below

Black El Nino Normal Above Above Above Normal Below Normal Above

Hills La Nina Above Above Normal Below Normal Normal Normal Normal

Northern El Nino Normal Normal Above Above Normal Below Below Normal

Utah La Nina Above Normal Normal Normal Above Normal Normal Normal

Southern El Nino Normal Normal Above Above Above Normal Above Above

Utah La Nina Above Normal Normal Normal Below Below Below Below
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Studies located within the synthesis area identified multiple spatial and temporal 

sources of variation that influence fire frequency (table 3.6). On the broadest scale, 

the cycles of drought driven by the Pacific Decadal Oscillation (PDO) and El Nino/

Southern Oscillation (ENSO) are important contributors to variation in fire frequency. 

One study noted that large fires tend to occur more frequently during the positive phase 

of the PDO than during the negative phase in the Inland Northwest (Hessl and others 

2004). Morgan and others (2008) verified this pattern for the northern Rocky Mountains. 

They found that fires burned large areas across the geographic region during what they 

call “regional fire years.” Up to 74 percent of the total fire extent occurred during these 

regional fire years, which occurred early and late in the last century. Six regional fire 

years occurred between 1910 and 1934 and five regional fire years occurred between 

1988 and 2003. The PDO index was positive during nine of the 11 regional fire years. 

These years were characterized by warm springs followed by significantly warm, dry 

summers. The mid-20th century period, which limited regional fire years (1935–1987), 

generally had cool springs, negative PDO, and a scarcity of extremely dry summers. The 

researchers hypothesized that the PDO influences multidecadal winter moisture, subsequently accounting for long-term variation in fine fuel conditions and fuel abundance 

(they also noted that this result should be viewed cautiously due to potential errors in 

reconstruction of the PDO).

Whereas ENSO is an important driver of fire in southwestern ponderosa pine systems 

(Swetnam and Betancourt 1990, 1998), fire history studies in the Inland Northwest have 

shown more ambiguous results (Hessl and others 2004; Heyerdahl and others 2002; 

Morgan and others 2008). Some researchers have suggested that within the Pacific 

Northwest, the ENSO relationship to fire recurrence is not as strong as in the Southwest 

because fuel structures are not as responsive to climatic variability associated with ENSO 

temporal or seasonal variation (and also because the ENSO signal may be weaker and 

therefore not as strong a driver in the Northwest) (Hessl and others 2004).

Variation in Fuels, Topography, and Weather

Brown and Smith (2000) recognized that with respect to impacts on forests, mixed 

severity fire regimes have three defining characteristics: (1) many trees are killed by 

surface or smoldering fire, but also many fire-resistant, large trees survive; and (2) severity varies between understory and stand-replacement fires, which create a fine-grained 

pattern of young and older trees—a pattern most likely explained by: (i) variations in 

fire weather, such as diurnal cycling in fire intensity over a multi-day fire; (ii) variation 

in topography, fuels, and stand structure within a fire perimeter; and (iii) the severity at 

a given location varies over time with individual fires alternating between understory 

burns and stand-replacing fires (Kilgore 1981).

Arno (1980) noted that mixed severity regimes involve a variety of fire intensities. 

When fire weather is severe, for example, when strong winds are blowing, surface fires 

can convert to crown fires and cover vast areas. The largest patches on the Biscuit Fire 

occurred on days with strong prevailing winds out of the northeast (Halofsky and others 2011). Nearly all of the growth and extreme fire behavior exhibited on the Hayman 

Fire, Colorado in 2002 occurred on a day when average relative air humidity was below 

8 percent, maximum wind gusts were 84 miles per hour, and the Haines Index was 6 

(Graham 2003). Under these conditions, no fuel treatment had much affect on severity 

or direction, nor would any conceivably realistic treatment have made much difference 

(Finney and others 2003).

Arno (1980) noted another characteristic of mixed fire regimes—under moderate 

weather conditions, ground fires creep and smolder with occasional flare-ups; however, 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 57

any given fire can burn at several intensities and severities, depending on fuels (composition and structure), topography, and weather. Consequently, a single fire can produce 

a mosaic of fire effects. Several of the fire history studies we summarized describe 

mixed fire regime outcomes as a mosaic of severities and patches of different composition and structures, and some link this to complex topography. In his botanical report, 

Leiberg (1897) mentioned riding through vast areas of the northern Rocky Mountains 

where fires smoldered. Dillion and others (2011) also found that topography (elevation, 

slope, aspect, position, complexity) was consistently more important in predicting fire 

severity than climate or weather variables across the Inland Northwest and northern 

Rocky Mountains.

Mixed fire regimes are closely related to fuel moisture. Jolly (2007) concluded that 

fire behavior is highly sensitive to live fuel moisture, and noted that wind speed and 

direction, air relative humidity, solar radiation, and air temperature can indirectly change 

the fuel moisture content, as well as contribute directly to fire spread. In northern Idaho, 

topography forms a fine-grained mosaic, while vegetation spans a moisture continuum 

from mesic western redcedar in the draws to xeric ponderosa pine on the ridges. This 

topographic and vegetation diversity translates into diverse environments with differing 

soil moisture dynamics, and most likely, live and dead fuel moistures. Moreover, the 

ratio between live fuel loading and dead fuel loading can also influence fire behavior, 

and likely varies with composition and successional stage. For example, in the Bitterroot fires in western Montana, some areas with high live fuel loadings did not burn, 

while the surrounding forest experienced stand replacing fire (Jain and Graham 2007).

The key point is that mixed fire regimes are complex (Arno 2000; Halofsky and others 

2011; Perry and others 2011; Schoennagel and others 2004). Fuels and topography play 

a major role in fire behavior and effects. Accordingly, in planning for fuel treatments, a 

single prescription is rarely sufficient; a diversity of vegetation structure and compositions are important elements to incorporate. It is also important to note topography and 

the variability in vegetation. Even the best designed fuel treatments may not be effective 

under extreme weather conditions, exemplified by strong wind and low air relative humidity, which can produce fire behavior that overwhelms otherwise effective treatments.

Vegetation Autecology and Fire Tolerance

The combination of disturbances and environmental heterogeneity favors multiple tree 

species, each with different tolerances and adaptive traits. Understanding fuel dynamics 

is critical for planning, implementing, and measuring success of fuel treatments to meet 

a variety of objectives. In this synthesis we were unable to summarize every potential 

adaptation a particular species may have; however, we do provide some examples of 

how different species have evolved to take advantage of opportunities to regenerate, 

grow, and develop. This section summarizes some key autecological characteristics of 

the key tree and shrub species, with an emphasis on fuel dynamics.

Species develop a variety of adaptive traits (species autecology) that make 

them resilient to a range of disturbances over their life and help them to regenerate, grow, develop, and thrive (table 3.7). There are a few key elements that may 

be of interest to fuel management. Canopy openings have a strong influence on 

the type of species that are favored. Because early-seral species have adapted to 

regenerate after disturbances in open environments with high light availability, 

they tend to be the most sensitive to canopy opening size and are the most successful in large openings. However, it is widely assumed that early-seral species 

require very large openings when in fact many of these species are well-adapted 

to a range of post-fire environments, including those with residual snags and 

limited residual overstory biomass (Jain and others 2004; Haig and others 1941). 

Autecology

Autecology is the study of 

environmental factors, including disturbance, and how they 

affect particular plant species. 

Comparative autecology is when 

multiple species are ranked by 

their resiliency to environmental 

factors.
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Table 3.7. Species autecology and resilience to climate change tolerances (Minore 1979).

Adaptation to

Light conditions Frost Heat warm average Drought Dry

Species for establishment tolerance tolerance temperatures tolerance conditions

Quaking aspen Moderate-high — — — — —

Douglas-fir Moderate Low-moderate Moderate Moderate-high Moderate-high Moderate

Grand fir Moderate Moderate Moderate Moderate Moderate Moderate

Engelmann spruce Low-moderate High — Low Moderate —

Jeffrey pine — Moderate-high High — High —

Lodgepole pine Low High Moderate Low-moderate Moderate-high —

Oregon white oak — — — High High High

Pacific madrone — — — — — High

Ponderosa pine Moderate-high Moderate-high High High High Moderate-high

California red fir Low to moderate Moderate-high Moderate Moderate Low Low

Subalpine fir Moderate Moderate Moderate Low Moderate Low

Sugar pine High Moderate High Moderate-high Moderate High

Tanoak Moderate — — — — —

Western hemlock — Low Low Moderate Low-Moderate Low-moderate

Western larch High — Moderate-high Moderate-high Moderate Moderate

Western red cedar Low Low-moderate Low Moderate Moderate Low

Western white pine Moderate to high High Moderate Moderate Moderate Low

White fir Moderate Moderate Moderate Low Moderate Low

For example, ponderosa pine in the Black Hills can regenerate under very dense canopies, 

even up to 70 percent canopy closure; however, rate of growth and individual tree vigor 

can be quite poor in these circumstances. Western white pine, which has moderate to 

high tolerance for low light conditions, can regenerate at 75 to 55 percent canopy closure 

and species such as grand fir can regenerate in places with approximately 85 percent 

canopy closure or more (Jain and others 2004). If the objective is to conduct a thinning 

and sufficient growing space is available post-treatment, true firs can readily regenerate 

naturally and create unwanted ladder fuels, decreasing the long-term effectiveness of 

the treatment. However, acknowledging the influence of opening size on tree growth 

can be an advantage. For example, higher overstory densities slow growth. Thus, a tree 

that is growing in the understory will take a longer period of time to become a ladder 

fuel, extending the useful life of a fuel treatment.

Shrubs have similar adaptive traits (table 3.8). For example, ninebark is adapted to 

establish quickly after a disturbance and is most successful in larger openings–it sprouts 

and increases after fire, and is very intolerant of shade. Thus, if canopy density is decreased on sites that have ninebark, they will respond quickly and profusely, creating 

a surface fuels that may or may not be desired. When the overstory is manipulated, 

growing space is released in the understory, creating conditions for the establishment 

and regeneration of vegetation that influences surface fuels. Table 3.8 lists the most 

common shrub species within the synthesis area and summarizes their adaptations to 

the environment.

Soil disturbance can influence the trajectory of vegetation regeneration, growth and 

development. Depending on the type of soil substrate, mineral, organic, or blackened 

surfaces can favor the regeneration or sprouting of a particular species. Although all 

species can regenerate on all substrates, depending on location, specific substrates can 

favor one species over another. For example, grand fir and western redcedar can readily 

regenerate on organic material such as litter and duff. In contrast, western larch prefers 

mineral soil or blackened surfaces (such as those present after fire). Western white pine 

can regenerate on all substrates quite readily. Shrubs sprout or regenerate from seed 

and their adaptive traits vary depending on the location and type of substrate as well as 

the type of disturbance.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 59 Table 3.8. Understory characteristics in fire dominated forests within the Northwest dry mixed conifer (FEIS http://www.fs.fed.us/database/feis/plants/index.html. Fire High Low Recover to Shade Rooting Mature Plant name tolerance Establishment severity fire severity fire pre-fire levels tolerance Drought habit height (ft) Forbs Common beargrass High Seed Decrease Increase 9-36 years Intermediate Medium Deep 5 Twinflower Low — Killed Killed 6 years Tolerant — Shallow 0.5 Grasses Bluebunch wheatgrass Low Seed/sprout — Increase — Intolerant High Deep 3 Idaho fescue Medium Seed Decrease Decrease Up to 25 years Intermediate Low Shallow 2 Low shrubs Kinnikinnick Low Sprouting — — Slow Intermediate High Deep 0.5 Medium shrubs Black sagebrush Low Seed Killed Decrease 15-60 years Intolerant High Shallow 2 Common snowberry High Sprouting Decrease Increase 1 year Intolerant High Shallow 3 Greenleaf manzanita High Seeding Increase Increase 1 year Intermediate High Deep 6 Mallow ninebark High Sprouting Increase Increase — Intolerant High Deep 15 Mountain big sagebrush None Seeding Killed Killed 15-20 years Intolerant High Deep 4 Serviceberry High Sprouting Increase Increase 1 year Intermediate Low Deep 15 Snowbrush ceanothus High Seed/sprout Increase Increase 1 year Intolerant Low Deep 10 Spiraea Medium Sprouting Increase Neither 1 year Tolerant Low Shallow 3 Thinleaf huckleberry High Sprouting Increase Increase 3-7 years Intermediate Low Deep 4.5 Tall shrubs Gambel oak High Sprouting Increase Increase 18 years Intolerant Low Deep 40 Rocky Mountain maple High Seed/sprout Increase Increase 5 years Intermediate Medium — 30 Scouler’s willow High Seed/sprout Increase Increase 5 years Intermediate Medium Deep 50 Vine maple Medium Sprouting Decrease Decrease 2-25 years Tolerant Low Shallow 20 Invader species Cheatgrass Medium Seed Increase Increase 1 year Intolerant High Shallow 2 Canada thistle Medium Seed/sprout Increase Increase 2-9 years Intermediate Low Deep 6 Spotted knapweed High Seed/sprout Increase Increase 1 year Intolerant High Shallow 3
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

All species have evolved to take advantage of conditions that favor their regeneration abilities because the plant that captures a site first dominates that site. In addition, 

different species have adapted to the conditions that are created by the predominant 

disturbances that maintain the forest. Considering the differences among the different 

plant species and recognizing the effect fuel treatments have on the growing environment will provide a basis for predicting the type of surface fuels that will be facilitated 

by a given treatment, the likely growth rate of that fuel, and ultimately treatment effectiveness and longevity.

In addition to having different adaptations to the physical characteristics of the sites 

where they grow, many species have developed adaptive strategies to resist disease and 

insect infestations (table 3.1). This is particularly true for the early-seral tree species 

such as western white pine, western larch, sugar pine, and ponderosa pine. In contrast, 

species such as grand fir, white fir, and Douglas-fir tend to be comparatively welcoming 

hosts to numerous native insects and diseases (Hagle and others 2003; Rocky Mountain 

Region, Forest Health Protection 2010) (table 3.1). Pine beetle can generate prodigious 

quantities of dead wood (standing and down) and many species of trees are susceptible 

to this insect. However, susceptibility varies with age and tree density. For example, it 

is well recognized that ponderosa pine is less susceptible to bark beetle infestation when 

stem density is between 80 to 100 ft2 of basal area per acre (Fettig and others 2007; 

Schmid and others 2007). Thus, current conditions (age, size, and species) will dictate 

particular treatment characteristics that may favor resilience. Silvicultural knowledge that 

incorporates these nuances and acknowledges treatment combinations that favor resilient 

combinations of size and species compositions are a part of fuel treatment development.

A variety of species have adapted to different levels of fire resistance (Arno and 

Allison-Bunnell 2002; Perry and others 2011) (table 3.9). Ponderosa pine, western 

larch, Jeffrey pine, and Douglas-fir are all very tolerant of fire when they are mature 

(table 3.9). By the time trees of these species become middle-aged (~100 years), they 

tend to have thick bark, which protects the cambium from heat generated by low intensity surface fires. Also, these trees tend to be tall and self-prune, making their crowns 

resistant to ignition by surface fires. Many of the species within the dry mixed conifer 

forests—such as grand fir, white fir, sugar pine, western redcedar, and western white 

pine—have moderate tolerance. For shrubs, many of the species such as ninebark, spirea, 

and serviceberry can quickly recover after fire (table 3.9) through vigorous production 

of fast growing sprouts.

Conclusion

Dry mixed conifer forests are complex. The combination of disturbances, topography, 

weather patterns, and vegetation creates a diversity of forest structures and compositions. 

Therefore, there are not one, two, or three sets of desired future conditions, but rather 

a mosaic of many different conditions. Thus, the dry mixed conifer forests offer many 

challenges. However, recognizing the forces that influence and maintain these forests 

is an important step. With complexity comes challenges but with variability there can 

be diverse opportunities to create a variety of forest structures, compositions, and fuels 

that achieve integrated fuels management objectives.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 61 Table 3.9. Tree characteristics important to surviving a fire and an overall species resistance to fire rating (Flint 1925; Starker 1934; Minore 1979; FEIS (http://www.fs.fed.us/database/ feis/plants/index.html). Many of these characteristics are the most effective when trees are mid-aged to old when the bark begins to become thick. Basal bark Canopy Canopy Canopy Size of Needle Tree Fire Burn Lichen Root Species thickness base ht. density habit buds length Sprout size resistance characteristics receptivity habit Aspen Medium — — — — — Yes Mature Low/Med — Low Shallow Douglas-fir Thick Low Mod. Mod. Medium Medium No Pole High High Mod.-high Deep Grand fir Medium Low High Dense Medium Medium No Mature Medium Mod. Low-mod. Shallow Engelmann spruce Thin Low High Dense Medium Medium No None Low High High Shallow Jeffrey pine Thick — — — Medium Long No Pole High High — — Lodgepole pine Very thin Mod. Low Open Medium Short No Mature Medium Low-mod. Low Shallow to deep Oregon white oak Thin/Med — — — — — Yes Pole Medium — — — Ponderosa pine Thick Mod. Low Open Large Long No Pole High High Low-mod. Deep California red fir Thick Mod. High Dense — — No — Medium High — — Subalpine fir Very thin Low High Mod. Medium Medium No None Very low High Mod.-high Shallow Sugar pine Thick — — Open Medium Medium No Mature Medium Mod. Low Medium Tanoak Medium — — — — — Yes Pole Medium — — — Western hemlock Medium Low High Dense Small Short No None Low High High Shallow Western larch Very thick High Low Open Small Medium No Pole High Low Mod.-high Deep Western red cedar Thin Low High Dense Small Short No Mature Medium Low-Mod. Mod. Shallow Western white pine Medium High Mod. Mod. Medium Medium No Mature Medium Mod. Mod. Medium White fir Thin Low High Mod. — — — — — Mod. Low-mod. —
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Further Reading

The following literature provides a synthesis on disturbances we thought would be 

of interest to our readers.

Agee, James K. 1994. Fire and weather disturbances in terrestrial ecosystems of the eastern Cascades. 

Gen. Tech. Rep. PNW-GTR-320. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. 52 p.

Fettig, Christopher J.; Klepzig, Kier D.; Billings, Ronald F.; Munson, A. Steven; Nebeker, T. Evan; Negrón, 

José F.; Nowak, John T. 2007. The effectiveness of vegetation management practices for prevention and 

control of bark beetle infestations in coniferous forests of the western and southern United States. Forest 

Ecology and Management. 238(1-3): 24-53.

Hessburg, Paul F.; Mitchell, Russel G.; Filip, Gregory M. 1994. Historical and current roles of insects and 

pathogens in eastern Oregon and Washington forested landscapes. Gen. Tech. Rep. PNW-GTR-327. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 72 p.

Parker, Thomas J.; Clancy, Karen M.; Mathiasen, Robert L. 2006. Interactions among fire, insects and 

pathogens in coniferous forests of the interior western United States and Canada. Agricultural and Forest Entomology 8: 167-189.

Rippy, Raini C.; Stewart, Jane E.; Zambino, Paul J.; Klopfenstein, Ned B. Tirocke, Joanne M.; Kim, MeeSook: Theis, Walter G. 2005. Root diseases in coniferous forests of the Inland West: potential implications 

of fuels treatments. Gen. Tech. Rep. RMRS-GTR-141. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 32 p.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 63

Chapter 4

Actions and Impacts of 

Past Management

Chapter Rationale

The purpose of this chapter is to provide a history of actions and impacts that have 

influenced the forests of today. 

	 • How have human impacts altered dry mixed conifer forests?

	 • Humans are part of the dry mixed conifer forests; thus what can be learned from 

the past actions to inform decisions for the future?

Introduction

Humans have had an influence on mixed dry conifer forests for millennia. For example Native American burning, fire suppression and exclusion, animal grazing, and 

timber harvests have all contributed to the current structure and characteristics of dry 

mixed conifer forests. Many of these actions benefited the dry mixed conifer forests and 

added to their resilience. However, due to shortsightedness or a failure to recognize the 

role of disturbance in maintaining these forests, past management activities have also 

altered these forests in ways that have decreased their resilience. Plant compositions 

have shifted in some locations. There are sites where stand density has increased when 

compared to historical conditions; leading to an increase in fire hazard. Soil chemical and physical properties have changed with the buildup of excess organic matter. 

Non-native plants have been introduced that have replaced native plant species. The 

introduction of diseases such as white pine blister rust has altered the role of keystone 

species in today’s forests. Although important components of these forests for creating 

diversity, insects and diseases are occurring over larger extents; with homogeneity in 

forest species composition and structure, they are becoming a continuous disturbance 

rather than playing their historical endemic role. Obviously, these types of changes have 

also had profound implications for fire regimes and most likely these regimes will not 

return to historical frequency and severity throughout the entire forest type. 

Humans are part of the dry mixed conifer forests and, for that matter, all of the forests of the Northwest. They will continue to influence these forests into the future. Our 

objective in this chapter is to promote discussions concerning lessons learned from the 

past and how they can influence what we do in the future in forest management, including the implementation of fuel treatments. In this chapter, we briefly cover some of the 

influences of past management activities on the forests and describe the consequences 

of these actions on dry mixed conifer forests. We finish with some views of how the past 

can allow us to make more informed decisions concerning future management strategies.
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 4.1. American Indians used fire to provide habitat for a variety 

of different uses including perpetuating important plants. The piyake 

gatherer 1910: Woman in long dress and bandana stooped over collecting piyake (roots) with hand scythe, leather basket at waist, trees 

in background. Library of Congress, Prints and Photographs Division, 

Edward S. Curtis collection. [reproduction number: LC-USZ62-47003].

Human Influence on Dry Mixed Conifer Forests

Cultural burning by American Indian tribes played a large role in fire regimes throughout the region (Botkin 1990; Stewart 2002). American Indians used fire to influence 

the character of the landscape. Fire was used to promote a diversity of habitats and 

increased edge effect, which provided security and stability (Boyd 1999; Pyne 1982, 

1984; Stewart 2002; Williams 2001). Like hunter-gather societies the world over, most 

American Indian groups rarely extinguished their campfires and signal fires; they merely 

abandoned them and some eventually spread over large areas (Pyne 1982). Fire was 

used to enhance hunting opportunities by increasing forage quality and to drive game 

into impoundments, narrow chutes, rivers or lakes, or over cliffs. Burning was used 

to create and enhance conditions for growing food and medicinal plants and basketry 

materials, such as camas, berries, tarweed, beargrass, and willows (fig. 4.1). American 

Indians used fire to collect and roast crickets, grasshoppers, and Pandora moths or collect honey from bee nests as well as reduce insect pests, rodents, and poisonous snakes. 

For tribes with horses, burning enhanced pasturage. American Indians used fire to 

maintain travel paths across mountains. Fire was a tool of war, used to deprive enemies 

of hiding places in tall grass or dense thickets of dense vegetation. It was also used for 

escaping from enemies and signaling. In addition, fire was used to shape canoes, fell 

trees, and shape weapon shafts and bows. For tribes that used ceramics, firing the clay 

was an essential part of the manufacturing process. Lewis and Clark even documented 

American Indians using fire as a form of entertainment in the Rockies by torching fir 

trees (Pyne 1982). Despite all these uses, American Indian use of fire was not ubiquitous 

or evenly distributed. However, in the dry mixed conifer forests of our synthesis area, 

fire was an important tool for American Indian tribes (Stewart 2002). 

The influence of American Indians started to change with the introduction of new 

diseases such as smallpox, yellow fever, and malaria, which decimated many tribes–often 

well before Euro-American settlers appeared on the horizon (Mann 2011). Moreover, 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 65

Figure 4.2. Settling the West, altered land uses. Camp 120, Eagle Lake, 

Sierra Nevada’s 1860. Photograph obtained from Library of Congress, 

Prints and Photographs Division, Daniel A. Jenks, artist, [reproduction 

number: LC-DIG-ppmsc-04821].

with the arrival of Europeans, American Indian lives were changed by the introduction 

of a wide variety of plants and animals, advanced metallurgy, advanced weaponry, the 

fur trade, livestock grazing, and treaties—all of which had major consequence on native 

cultures and populations (Pyne 1982; Williams 2001; Mann 2011).

As settlers moved into the West during the 1800s, the forests and fire regimes of the 

region were quickly transformed by the effects European settlement had on American 

Indians and by the creation of settlements and other uses of the land (fig. 4.2). Resource 

extraction has always been a draw of people to the region. The discovery of gold and 

silver brought thousands of miners to the region, and led to the establishment of towns 

and associated hotels, restaurants, general stores, and other business ventures to support 

the miners. Mining, although concentrated in areas such as Idaho City, Boise Basin, 

and Coeur d’Alene Mountains in Idaho, Anaconda in Montana, and the Black Hills in 

South Dakota, contributed to changes in the dry mixed conifer forests (fig. 4.3). More 

importantly, mining contributed to settlement and land development, and the associated 

use of federal forest lands to support those activities.

The impacts of overgrazing by cattle, sheep, and horses, similar to other locations 

within the western United States, influenced dry mixed conifer forests, particularly 

those areas that were adjacent to preferred rangelands, riparian areas, ridge tops, 

and meadows (Covington and others 1994; Johnson and others 1994). Grazing had 

its greatest impact from overgrazing between the mid-1800s and early-1900s when 

grazing was unregulated (fig. 4.4). Several rangeland researchers have documented 

many adverse effects of excessive livestock grazing. Undesirable ecological effects 

include introduction and spread of invasive plants, compaction and displacement of 

soil, reduced water infiltration, increased erosion, and changes in plant community 

species composition and diversity (Belsky and Blumenthal 1997; Bunting and others 

2003; Cottam and Evans 1945; Daubenmire 1940; Griffiths 1902). The reduction 

in forage (grass) has diminished and woody vegetation has increased, changing fire 

regimes from high frequency, low severity to low frequency, high severity (Belsky 

and Blumenthal 1997; Bunting and others 2003; Irwin and others 1994). 
66 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 4.3. Mining was an important part of Idaho history particularly in northern Idaho 

(Silver Valley), central Idaho (Elk City), and the Boise Basin. Placer mining, Twin Springs, 

Idaho (published 1901). Photograph obtained from Library of Congress, Prints and Photographs Division, Horace C. Myers, [reproduction number: LC-DIG-ppmsca-17299].

Figure 4.4. Sheep grazing in central 

Oregon, 1936. Photograph obtained 

from Library of Congress, Prints 

and Photographs Division, Arthur 

Rothstein, artist [reproduction 

number: LC-DIG-ppmsc-04821].
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 67

Adverse grazing effects were not evenly distributed across the landscape. Livestock 

driveways and bedding areas and the area around water sources are generally more likely 

to be severely degraded even today. Because of livestock preference, some waterways, 

basin meadows, and ridge tops have been used as stock driveways and bedding grounds 

every season for decades (fig. 4.4), accelerating erosion and shifts in plant communities 

in as little as 20 years (Griffiths 1902). 

Animal numbers peaked on the National Forest System lands in 1919 at approximately 

10.8 million animal unit months (AUMs). By the 1910s, the Forest Service began to 

regulate grazing (Jardine and Anderson 1919), although the success of regulation varied (Fedkiw 1998). The primary emphasis was on selecting the appropriate species for 

the range type and topography, reseeding degraded rangelands, and reducing livestock 

numbers (Jardine and Anderson 1919). Grazing on other federal lands became fully 

regulated following passage of the Taylor Grazing Act in 1935, although the number 

of animals was already in decline. After World War II, grazing management emphasis shifted towards improving range conditions and less on reducing animal numbers 

(Fedkiw 1998). The emphasis of cattle producers also shifted from hides to meat due 

to a growing demand for beef. Since 1970, however, cattle numbers began to decline 

again due to decreased demand for beef, increasing environmental regulation as range 

conditions failed to improve, and drought. Grazing by sheep has declined more or less 

steadily since the end of World War II as demand for mutton declined and synthetic 

fabrics replaced wool (Fedkiw 1998).

The introduction and spread of invasive species is often associated with livestock 

grazing. Invasive annual grasses have proven especially problematic in semi-arid landscapes, although less so in forests. Cheatgrass was initially introduced as a contaminant 

in imported wheat seed and initially spread along railroads from contaminated straw 

and wheat (Reid and others 2008). The exact role of livestock in spreading cheatgrass 

is less certain, although the dominant grazing practices prior to the early 1900s created 

plenty of growing space (Cottam and Evans 1945; Daubenmire 1940; Griffiths 1902). 

Cheatgrass also spread rapidly from abandoned farms in the 1920s and ’30s in combination with severe drought (Piemeisel 1938). Other rarely acknowledged factors in the 

spread of cheatgrass and other invasive plants include wildlife and recreational users.

The introduction of tree diseases has also significantly altered tree species compositions and probable post-fire responses of various types of dry mixed conifer forests. 

The accidental introduction of white pine blister rust in the early 1900s provides a case 

example. This disease dramatically reduced the numbers of western white pine in the 

northern Rocky Mountains and sugar pine in the southern Cascades (Kinloch 2003) 

(fig. 4.5). Control attempts included eradication of the alternate hosts, gooseberry (Ribes

species); the other option was to create large clearcuts (hundreds of acres) and burn them, 

this was followed by pulling the gooseberry bushes and planting western white pine 

in the middle of the clearcut (Kinloch 2003). This practice led to the incorporation of 

clearcutting and burning as a dominant method for forest management. After these initial 

efforts at eradication, the disease was not eradicated; thus the philosophy that all western 

white pine would be eventually killed by blister rust. A massive salvage effort ensued, 

further decreasing the abundance of western white pine. Recently, the introduction 

of Sudden Oak Death has the potential to greatly alter species compositions in the dry 

mixed conifer forests of southern Oregon and northern California by impacting Tanoak.

Timber harvesting policies and practices on federal lands have been a primary factor 

in shaping the current condition of the dry mixed conifer forests. Fedkiw (1998) provides 

a detailed history of management and policy related to the National Forest System lands, 

much of which also applies to other federal forest lands. 

Prior to 1940, the national harvest levels were less than 1 billion board feet annually, 

largely to meet local needs. Most harvesting used selection-cutting methods, except in 
68 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

the Pacific Northwest Douglas-fir region, where clearcutting was the norm and in areas 

where railroad logging was used (fig. 4.6). Following World War II, decreased supply 

of private timber in combination with increased U.S. population, economic activity, and 

transportation networks fueled a dramatic increase in the demand for federal timber. 

Harvest levels increased from 1950 to 1970, temporarily peaking in 1969 at 12.6 billion board feet, and fluctuated around 11.0 billion board feet per year until the 1990s. 

Clearcutting and other even-aged management techniques became dominant along with 

a variety of other practices intended to protect and enhance forest production, such as 

reforestation, sometimes favoring single species, the use of herbicides to control competing vegetation, intermediate treatments, and attempts to control insects, disease and 

fire. Harvesting was concentrated on old growth stands and on the most commercially 

important species, such as ponderosa pine, western larch, and Douglas-fir. 

Beginning in the 1960s and accelerating through the 1970s, ’80s, and ’90s, controversy over these intensive forest management practices increased, resulting in the passage of numerous laws intended to increase the timber supply from federal lands and 

manage the environmental impacts of timber management practices. By 1994, timber 

harvest levels on federal lands had dropped to a level last seen in the mid-1940s 

and has remained at that level since. The decline was largely driven by concerns over 

the impacts of intensive forest management on fish and wildlife habitat with the 

passage of the Endangered Species Act.

Figure 4.5. Introduction of blister rust in the early part of last 

century led to a variety of attempts to eradicate the alternate 

host the gooseberry. The disease enters through a needle 

and then progresses to the stem where the tree is killed 

through girdling. This is a blister rust canker. Photo by Ed 

F. Wicker, U.S. Department of Agriculture, Forest Service.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 69

Fire policy was probably the other major factor 

shaping the dry mixed conifer forest. Initially, many 

settlers continued burning the landscape, using many 

of the same practices as the American Indians and often 

for the same purposes, as well as for clearing land for 

farming, grazing, or prospecting (Pyne 1982, 1984). 

Abandoned campfires remain a persistent problem 

even today. However, some of the largest and most 

lethal fires arose from primitive logging practices in 

combination with drought, lightning, sparks from railroads, and human carelessness. Fires were also started 

accidentally and as retaliation for real and perceived 

wrongs and to create work.

The perceived need for rapid suppression of all 

wildfires was reinforced by wildfires that burned in 

northern Idaho, eastern Washington, and western Montana in August of 1910. These fires burned millions of 

forested acres, towns, and caused considerable loss of 

life (Egan 2009; Pyne 2001) (fig. 4.7). Public perception of these events as destructive cemented the mission of the newly formed Forest Service to protect the 

valuable forestlands of the United States from fire and 

other damaging disturbances (Steen 1976). Systematic 

fire protection developed through the 1910s and ’20s 

as a result of the 1910 fire (fig. 4.7). The drought of 

the 1920s and early 1930s and subsequent large fires 

resulted in creation of the so-called 10 a.m. policy, 

requiring that all fires be controlled by 10 a.m. the 

following day (Pyne 1982, 1984). Smoke-jumping as a 

primary attack method began during World War II (Pyne 

1984). The availability of surplus military equipment 

following the end of World War II, in combination with 

increased road building, greatly enhanced firefighting 

capability through increased use of mechanized equipment and aerial attack tactics (Pyne 1984). The result 

was a large reduction in the number of acres burned in 

western forests, although in recent years, some have 

argued that a change in climate to cooler, wetter conditions during this same period was also a significant 

factor. By the early 1970s, however, forest managers 

were noticing an increase in the accumulation of forest 

fuels. This factor, in conjunction with passage of the 

Wilderness Act in 1964 and increasing understanding 

of forest ecology, led the National Park Service and 

Forest Service to begin experimenting with the use of lightning-caused fires in large 

back country and wilderness areas (Pyne 1982, 1984). Widespread use of prescribed 

fire to eliminate logging slash and improve wildlife habitat also began in the western 

United States (Fedkiw 1998; Pyne 1984). This period also saw the development of 

greater coordination between the federal land management agencies and states, leading 

to standardization of equipment, crew configurations, fire training and qualifications, 

and so forth (Pyne 1984).

Figure 4.6. Preferential harvest of fire resistant species 

such as western larch and ponderosa pine favored the 

growth of the remaining shade tolerant species such as 

grand fir and white fir. In addition, with the advent of fire 

suppression, trees such as grand fir, Douglas-fir, and white 

fir regenerated underneath remaining stands of western 

larch and ponderosa pine. Photograph obtained from 

Library of Congress, Prints and Photographs Division, Photographs of five Idaho loggers taken by Dorothea Lange, 

1939 [reproduction number:LC-USF346-BN-021622], 

and steam logging by T. M. Kelso, 1902 [reproduction 

number: LC-USZ62-97669].
70 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

The use of lightning-caused fires to meet land management objectives was called 

into question following a series of large and expensive wildfires in and around Yellowstone National Park in 1988. Although the basic policy was determined to be sound, 

additional implementation procedures were developed. At this time, each federal agency 

still had separate fire management policies. That changed following the deaths of 14 

firefighters in the South Canyon Fire in 1994. In 1995, a unified federal fire policy was 

created. Implementation of this policy remained irregular until the events of the 2000 

fire season, which saw large escaped fires in the Southwest and fires approaching the 

scale of 1910 in the northern Rockies.

The 2000 fire season resulted in two significant changes in federal fire policy. First, 

aspects of the federal fire policy were strengthened and Congress directed the federal 

agencies to standardize all aspects of wildfire response across the five federal agencies. 

Second, the National Fire Plan was created to respond to the growing threats of large, 

costly, and life-threatening wildfires arising from changes in forest conditions, the growing number of homes constructed where communities met wildlands (the wildland-urban 

interface (WUI)), and the changing climate.

Figure 4.7. Photograph taken September 1910, after the 1910 wildfire, the Pulaski 

Tunnel where fire fighters took refuge and five perished. The tunnel is 60 ft long, 

Near Wallace, Idaho. U.S. Department of Agriculture, Forest Service photograph 

courtesy of the Forest History Society, Durham, N.C. FHS2658.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 71

Consequences of Past Management for Forests and Fuels

Tree Species Shifts and Insects and Disease

The combination of harvesting, overgrazing, fire suppression and exclusion, and 

access through roads and railroads all contributed to the composition and structure of 

the forests we see today (Harvey and others 2000). For example, abundant fir in the 

understory creates nutrient-rich ladder fuels that facilitate crown-fire initiation, increasing the likelihood of nutrient loss (van Wagner 1977; Minore 1979; Harvey and others 

1999) (fig. 4.8). The risk of nutrient loss is greater on infertile sites because dense stands 

of late-seral species tend to contain more nutrients than the historical stands dominated 

by widely spaced, early-seral species (Minore 1979; Harvey and others 1999) (fig. 4.8). 

Historically, native insects and disease (for example, pine beetle, root disease, and 

mistletoe) infected and killed very old or stressed individuals, a process that tended to 

diversify vegetation communities (Hessburg and others 1994). However, in presentday forests, changes in vegetation coupled with subtle changes in climate cycles have 

facilitated development of unprecedented epidemic levels of insects and diseases in 

many locales. These disturbance agents have often been encouraged by weather events 

such as ice storms, windstorms, and periodic droughts. In addition, settlers moving into 

the western United States introduced exotic plant and animal species, often displacing 

native species. Non-native insects and diseases found no natural checks, enabling them 

to invade western forest ecosystems unimpeded. 

Figure 4.8. Fire suppression promoted the regeneration of shade tolerant species. Photograph on 

left taken at Boise Basin Experimental Forest, near Idaho City, Idaho. Photo by Jonathan Sandquist, 

USFS. Photo on the right is the Crane Mountain Roadless area, Oregon, by Andris Eglitis, U.S. 

Department of Agriculture, Forest Service.
72 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Today, ponderosa pine continues to be susceptible to the western pine beetle. In addition, mountain pine beetle frequently kills ponderosa pine on Douglas-fir and grand fir/

white fir LANDFIRE BpS (see chapter 2). The pine engraver beetle is more abundant 

and destructive today with some of the severest outbreaks occurring in low-elevation 

ponderosa pine LANDFIRE BpS (see chapter 2). (Hessburg and others 1994). 

Within the Inland Northwest, ponderosa pine is being succeeded by Douglas-fir and 

grand fir (Jain and Graham 2005; Gruell and others 1982; Keane and others 2002; Smith 

and Arno 1999). The amount of mid-seral (for example, Douglas-fir) vegetation has 

increased by nearly 3.2 million ha (8 million acres) and the amount of single-storied, 

mature vegetation (for example, ponderosa pine) has decreased by over 1.6 million ha 

(4million acres) (Hann and others 1997). The accumulation of fire-intolerant vegetation, 

dense forest canopies, with large areas (hundreds of acres) with continuous compositions and structures has resulted in forests favoring crown fires rather than the historical 

mixed severity fires (Halofsky and others 2011; Hessburg and Agee 2003; Perry and 

others 2011). In many areas, these changes have shortened successional timeframes. 

For example, under historical, frequent fire regimes, ponderosa pine, western larch, 

and sugar pine were normally succeeded by Douglas-fir or true firs in 300 to 400 years 

without disturbance; however, these species have succeeded ponderosa pine in less than 

50 years in many locations (Hann and others 1997; Harvey and others 1999; Smith and 

Arno 1999). In addition, stands of Douglas-fir, grand fir, and white fir are susceptible 

to both defoliators and root diseases, thus perpetuating disease and insect infestations.

Soil Impacts

In addition to noticeable changes in plant composition and structure, the soils (surface 

and mineral) in many settings have also changed considerably during the last century 

(fig. 4.9). The regeneration and growth of grand fir/white fir and Douglas-fir in the 

dry forests has subsequently led to the accumulation of both above- and below-ground 

biomass and associated nutrients close to the soil surface (Harvey and others 1986). 

Figure 4.9. Often the changes in overstory composition and structure are emphasized; however, 

soils have also changed. Photograph on left taken by 1874 Custer Expedition. Notice the rock 

outcrop. On the right side is the same location, photograph taken 128 years later; the rock is 

almost buried by organic matter and needles. Photo by Paul Horsted. Published in Grafe and 

Horsted (1992).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 73

Figure 4.10. Deep duff layers have increased around 

the bole of the tree (top photograph) in some places 

within the dry mixed conifer forests. In highly productive 

places, fine roots can migrate into the deep duff. Thus 

when fire is reintroduced, there is a risk of causing fine 

root and/or bole injury to the tree leading to potential 

delayed mortality (Hood 2010). Photo by Theresa 

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

Today, even low-intensity surface fires can consume the surface organic layers, killing 

tree cambiums and/or fine roots, volatilizing nutrients, killing trees, and increasing soil 

erosion potential (DeBano 1991; Hood 2010; Hungerford and others 1991; Ryan and 

Amman 1996; Robichaud and others 2000) (fig. 4.10).

The accumulation of organic materials on the soil surface and the frequent changes in 

their composition (for example, ponderosa pine litter to true fir litter) can also alter fine 

root and ectomycorrhizae (a root fungus that has a symbiotic relationship with plants 

that helps with nutrient and water uptake) habitat and water-holding properties (Harvey 

and others 1999; Harvey and others 2000). Fine root and ectomycorrhizal activity in 

historical fire dependent sites occurred deeper in the mineral soil of forests dominated 
74 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

by ponderosa pine; thus, they were protected from being damaged during fires (Harvey 

and others 1986). On true fir-dominated sites and where fire has not occurred for many 

decades, fine roots and ectomycorrhizae have migrated into shallow organic horizons. 

Depending on the aspect, dry mixed conifer areas of Idaho, Oregon, and Washington 

can be susceptible to root and ectomycorrhizae migration (Hood 2010) (fig. 4.10). 

In general, historical dry mixed conifer forests were long-lived and resilient. They 

were also likely well matched to soil resources, relatively resistant to detrimental fire 

effects, well adapted to wide ranges of site and short-term climate variation, and subject 

to modest (largely beneficial) insect and pathogen mortality. In contrast, forests that 

were dominated by ponderosa pine and are now dominated by Douglas-fir, grand fir or 

white fir are probably not well matched to soil resources and are also not likely resistant 

to the wide range of site and climate variation found within the dry forests. In turn, they 

are often subject to high insect and pathogen mortality and cannot be considered either 

long-lived or stable (Harvey and others 1999).

Forests in our synthesis area offer many amenities that are valued by people. Today, 

the forests provide livelihoods (logging and mining), places to live, and recreational 

activities such as berry picking, hunting, hiking, biking, fishing, and camping. Federally 

administered lands dominate states like Idaho, promoting the value of public lands for 

recreation and other values. Consequently, the attraction of these rural forested settings 

has encouraged the expansion of the WUI. As the WUI has expanded, sociocultural 

factors have become increasingly important in forest management and fuel treatment 

programs. Surveys of the general American public show support for the environment 

and the value of forests; however, there is variability in people’s attitudes, beliefs, and 

values based on where they live and their livelihood. (Attitudes are peoples’ evaluation 

of something favorable or unfavorable. Beliefs reflect what people think is true about 

something. Values reflect the things people hold dear to them) (USDA 1996, page 37). 

The Columbia River Basin Assessment (Quigley and others 1997a) conducted a comprehensive survey of residents in an area roughly corresponding with the synthesis area. 

The authors found that support for endangered species laws and regulations remains 

strong but the public is concerned with the balance between species protection and costs 

to society. This concern is particularly strong among rural residents. Disturbance events 

such as fire, insects, and disease have negative connotations to many people. Although 

people understand that these occur naturally, they consider the results to be “a waste 

of good resources.” An important value held by survey respondents is the “sense of 

place,” which is how people define ecosystems and specific locations in the landscape 

based on their experiences, meanings, and images. The assessment also noted that the 

public prefers to work with Federal agencies and others having management authority 

rather than relying on legislation or court cases to achieve mutually desirable outcomes. 

Humans have had a profound effect on natural resources and their attitudes, beliefs, and 

values will continue to play a major role in fuel treatment implementation.

Conclusion

Although many forests of the Northwest, including the dry mixed conifer forests, 

have experienced an array of repeated disturbances over time, they have shown remarkable resilience. These forests still have many elements that maintain their resilience and 

afford numerous opportunities for improved management. We have the opportunity to 

decide how we will influence them going into the future. We also have to recognize our 

limitations and incorporate those into discussions and management decisions.

It is important to recognize that humans are part of these forests, and we are going 

to continue to influence how they function. Although the public recognizes the value 

of fire, many are still apprehensive. Therefore, issues such as smoke impacts will most 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 75

likely always prevent a broad application of fire. Also, dry mixed conifer forests are 

located within or close to the rapidly expanding wildland urban interface. Fires will 

continue to be suppressed, depending on the time of the year, location, and other factors. Therefore, the full re-introduction of fire across the entire range of the dry mixed 

conifer forests will most likely not be achieved. 

The seed sources of resilient species such as ponderosa pine, western larch, sugar 

pine, and western white pine may no longer exist on some sites. If they were to burn 

in a wildfire, the species currently present will most likely recapture the site creating a 

different successional cycle than what historically occurred.

Insects, disease, and the effects from physical disturbances such as ice, snow, and 

wind will continue to occur and with the uncertain influences of a changing climate, they 

may potentially play a major role in promoting mortality and altering the pattern and 

distribution of fuels. Some areas that are disease or insect infested are not accessible, and 

there is considerable sensitivity to increasing road networks in federally administered 

forests. Other management alternatives are economically prohibitive. Thus, this future 

fuel matrix and continued tree mortality will also be a part of these forests. 

Given these realities, how do we manage the dry mixed conifer forests into the future? Humans most likely will take a major role in the shape of these forests over the 

next several centuries. Fuels management is going to continue and consideration of the 

challenges and opportunities may promote discussion about the consequences of our 

actions and decisions. 

The objective of this chapter (and Chapter 3) was to recognize that: (1) given all of 

the disturbances these forest have experienced, these forests still show resilience and 

can be managed to meet a range of ecological, economic, and social needs; (2) changes 

have occurred and management aimed at mimicking the past may not be possible; and 

(3) disturbance, not just fire, is what maintained these forests and will continue to function in these forests into the future. Accepting these realities and respecting the role 

disturbance played and continues to play in influencing how these forests function will 

allow for improved forest management moving forward. 

Further Reading

Belsky, Joy A.; Blumenthal, Dana M. 1997. Effects of livestock grazing on stand dynamics and soils in 

upland forests of the Interior West. Conservation Biology. 11(2): 315-327. (Provides a comprehensive 

review of the effects of cattle and sheep grazing on forest structure and processes, including the effects 

on fire regimes.)

Fedkiw, John. 1998. Managing multiple uses on National Forests, 1905-1995: a 90-year learning experience and it isn’t finished yet, [Online]. Available: http://www.foresthistory.org/ASPNET/Publications/

multiple_use/contents.htm [2012, May]. (Provides a detailed summary of the management history of 

the National Forests.)

Keane, Robert E.; Ryan, Kevin C.; Veblen, Tom T.; Allen, Craig D.; Logan, Jesse; Hawkes, Brad. 2002. 

Cascading effects of fire exclusion in the Rocky Mountain ecosystems: a literature review. Gen. Tech. 

Rep. RMRS-GTR-91. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 24 p. (Provides a literature review of the factors that led to changes in forest structure 

and composition; with emphasis on the effects of fire suppression and exclusion.)

Williams, Gerald W. 2001. References on the American Indian use of fire in ecosystems, [Online]. Available: http://www.wildlandfire.com/docs/biblio_indianfire.htm [2012, June]. Williams (2001). (Provides 

a short summary of the influence of American Indian burning.)
76 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 77

Chapter 5

Inventory Modeling of Current Fire 

Hazard Conditions

Chapter Rationale

The following questions motivated inclusion of this chapter:

What are the current conditions of the dry mixed conifer forests?

What is the status concerning fire hazard?

We address these questions via an analysis of forest inventory plot data using a fire 

hazard model.

Introduction

This chapter and appendix A summarize what we know about current conditions in 

dry mixed conifer forests, drawing on analysis of FIA (Forest Inventory and Analysis 

National Program) inventory data and applying the FFE-FVS (Fire and Fuels Extension 

of the Forest Vegetation Simulator) (Reinhardt and Crookston 2003) model. Relying on 

the systematic random sample of FIA data ensures a statistically robust representation 

of the entire forested landscape—not just the stands that merit or appear manageable 

through fuel treatment today. We summarize the current extent of forest fire hazard 

from multiple perspectives by sub-region and broad forest type groups to facilitate local 

application of the information in this chapter and appendix A.

Analytic Approach

A total of 5,174 FIA plots in Oregon, Washington, Idaho, Montana, Utah, and extreme 

northern California1 were selected for this analysis via overlay on a GIS grid coverage 

representing dry mixed conifer types derived from the LANDFIRE vegetation layers 

(see chapter 2 for details). These plots, installed between 2001 and 2009 and representing 

nearly 37.7 million acres of forest land, were processed using FFE-FVS to summarize 

stand attributes related to fire hazard. Each accessible forest land (Bechtold and Patterson 2005) condition2, which can be thought of as a plot or partial plot that represents 

or “stands in for” thousands of acres in the forested landscape, was processed using the 

FVS variant appropriate to its location. However, for simplicity, we report model results 

1Due to the late initiation of annual inventory in Nevada and Wyoming, there were no annual inventory, dry mixed conifer plots in the parts of those states included in our study area, so this analysis does 

not apply to those states.

2Because FIA uses a mapped plot design in which plots are subdivided into “conditions” when there 

are sharp discontinuities within the forested parts of a plot (for example, in size class, forest type, stand 

density, land owner group, and reserve status) or when an otherwise forested plot includes non-forested 

area, the basic unit of analysis is condition, not plot.
78 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

by study area sub-regions, created as aggregations of FVS variant regions: Northern 

California and Klamath, Pacific Northwest Interior, Northern and Central Rocky Mountains, and Utah, as shown in fig. 5.1 and table 5.1. Results are summarized using FIA 

plot expansion factors to represent acres and/or fractions of the forested landscape in 

each sub-region and forest type group that are deemed hazardous by various criteria. 

For this analysis, we grouped forest types into six forest type groups: Douglas-fir and 

True Fir, Pine and Western Larch, Quaking Aspen (includes birch), Cedar (western 

redcedar), Non-stocked, and a grab bag of everything else that we call Other Species3.

3Other Species includes forests belonging to one of 45 FIA forest types: Bigleaf maple, Blue oak, Blue 

spruce, California black oak, California laurel, California mixed conifer, California white oak, Canyon live 

oak, Cercocarpus woodland, Cottonwood, Cottonwood / willow, Deciduous oak woodland, Engelmann 

spruce, Engelmann spruce / subalpine fir, Foxtail pine / bristlecone pine, Giant chinkapin, Gray pine, 

Incense-cedar, Intermountain maple woodland, Jeffrey pine, Juniper woodland, Knobcone pine, Limber 

pine, Lodgepole pine, Misc. western softwoods, Mountain hemlock, Noble fir, Oregon white oak, Other 

hardwoods, Pacific madrone, Pacific silver fir, Pinyon / juniper woodland, Port-Orford-cedar, Red alder, 

Red fir, Redwood, Rocky Mountain juniper, Sitka spruce, Subalpine fir, Sugar pine, Tanoak, Western 

hemlock, Western juniper, White fir, or Whitebark pine.

Figure 5.1. Map of Forest Vegetation Simulator (FVS) variant applicability for the variants used 

in analysis of current condition and to model treatment alternatives in the dry mixed conifer 

fuel synthesis area. Two-letter variant code translations are as follows: PN—Pacific Northwest 

Coast, NC – Klamath Mountains, CA – Inland California and Southern Cascades, WC – Westside 

Cascades, EC – East Cascades, BM – Blue Mountains, SO – Southern Oregon and Northeast 

California, IE – Northern Idaho (Inland Empire), CI – Central Idaho, TT – Tetons, EM – Eastern 

Montana, UT – Utah, CR – Central Rockies (note: not used in this analysis due to lack of annual inventory data). 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 79

FFE requires a surface fuel model (see Anderson, 1982), or composite (weighted 

combination of) surface fuel models, and fuel moisture data to compute some indices. 

We relied on FFE’s variant-specific, default surface fuel model selections (as informed 

by the plot tree list data) and default values for fuel moisture under severe weather 

conditions. For example, for the Inland Empire variant, severe weather fuel moistures 

for 1-hour, 10-hour, 100-hour, 1000-hour fuels (which correspond to woody debris diameter classes of 0-1⁄4, 1⁄4-1, 1-3 and >3 inches), duff , live woody, and live herb were, 

respectively, 4, 4, 5, 10, 15, 70, 70 percent; 20 ft wind speed above the canopy was 20 

mph; and temperature was 70 °F. FFE-FVS uses the tree list, information on surface 

fuels, and the fuel moisture assumptions to compute various fire hazard, behavior and 

effects indices. We evaluated these indices for their utility in assessing current hazard 

and selected four to fully analyze, track and report. Probability of torching in severe 

weather conditions (ptorch) and torching index (TI), the wind speed at which crown 

Table 5.1. Area of all forest and of unreserved forest and number of conditions (full or partial FIA plots) contained 

in each FVS (Forest Vegetation Simulator) variant applicability area and study area subregion. All 

forests include areas such as wilderness, natural areas and parks where harvest is prohibited. 

Unreserved forest is forest land where harvest is not prohibited by law. 

All forest area Unreserved forest

FVS variant (1000 ac) Area (1000 ac) Conditions

Northern California and Klamath

Inland California and Southern Cascades (CA) 5,902 5,060 930

Klamath Mountains (NC) 1,962 1,666 284

Pacific Northwest Coast (PN) 117 117 25

Total 7,981 6,843 1,239

Pacific Northwest Interior

Blue Mountains (BM) 2,690 2,136 394

Eastern Cascades (EC) 4,404 4,063 645

South Central Oregon and Southeast California (SO) 4,293 4,058 737

Westside Cascades (WC) 2,023 1,879 350

Total 13,411 12,136 2,126

Northern and Central Rocky Mountains

Central Idaho (CI) 2,942 2,254 260

Eastern Montana (EM) 2,609 2,389 309

Inland Empire (IE) 8,221 7,158 915

Teton (TT) 620 579 64

Total 14,392 12,381 1,548

Utah

Utah (UT) 1,963 1,787 333

Total 1,963 1,787 333

Omitted

Southeast Alaska and Coastal British Columbia (AK) 5 5 3

Western Sierra Nevada (WS) 20 20 3

Central Rockies (CR) 11 1 1

Total 37 27 7

Grand total 37,784 33,173 5,253
80 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

fire initiation would be expected, both relate to torching—the transition of a surface 

fire, via ladder fuels, into the crowns of some or all of the trees, making the fire partly 

or fully stand-replacing. We rely on surface flame length (SFL) as a proxy for both 

fire intensity and fire suppression effectiveness (e.g., high flames may preclude direct 

attack). We use mortality volume, after transforming it into a percent of pre-fire, livetree volume (MortVolPct), as an indicator of economic and resource loss, as well as the 

viability of the residual stand, and because of its implications for reversals of carbon 

storage. Acres were deemed hazardous for purposes of this overview of current conditions if ptorch > 20 percent, TI < 20 mph, SFL >4 ft, or MortVolPct > 30 percent. Note 

that this conceptualization of hazard is only one of many potentially sensible choices. 

Depending, for example, on stand management objectives, whether or not fire suppression would be attempted, and whether or not timber management plays a role, a 

manager might choose other thresholds for these criteria, or perhaps a different set of 

criteria altogether. We also considered and report a composite hazard score calculated 

as the number of these four criteria by which an acre qualifies as hazardous, producing 

a hazard score between 0 and 4.

Findings

Of the 37.7 million acres of dry mixed conifer forest represented by the inventory 

database, 21, 36, 38, and 5 percent of these acres are within the Northern California and 

Klamath, Pacific Northwest Interior, Northern and Central Rocky Mountains, and Utah 

sub-regions, respectively (table 5.2). Across all forest type groups, reserved forested 

lands (in other words, in state and national parks, and statutorily designated wilderness) 

account for 8.5 to 14.3 percent of forested area, depending on sub-region. Considering 

all sub-regions together, for both reserved and unreserved forests, there are more than 

six times as many acres rated hazardous by one or more criteria as there are acres rated 

resilient by all criteria, though there is considerable variation among sub-regions—for 

example, the ratios of hazardous to resilient acres is 4.8 and 15.2 in the northern and 

central Rocky Mountains and Utah, respectively. Because this synthesis focuses on 

forested acres where fuel treatments might conceivably be contemplated, the rest of 

this chapter and the chapter addressing fuel treatment feasibility (Chapter 11) consider 

only forested conditions on unreserved lands, and exclude forest land that is not currently stocked with trees.

Douglas-fir, True Fir, Pine and Western Larch Types Dominate

Focusing on unreserved forests, we find that about half the area represented by 

conditions selected via LANDFIRE BpS overlay are attributed on the ground as forest 

types we typically think of as dry mixed conifer types, with the Douglas-fir and True 

Fir type group covering the greatest share of this, and the Pine and Western Larch type 

group covering most of the rest (table 5.3a).

Steep Slopes are Common in Some Sub-Regions

Considering that slope is an important determinant of treatment difficulty and cost, 

we summarized unreserved forest area by sub-region and slope class, and found considerable regional variation: the proportion of area in steep slopes ranges from 0.2 in 

the Pacific Northwest Interior to 0.5 in Northern California and Klamath (table 5.3b).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 81

Table 5.2. Area of forest land and percent of forest land by reserve status and hazard status, by FVS subregion 

and forest type groups, as of 2009. Percent of acres rated resilient versus hazardous by forest class. 

Those that were rated hazardous had a hazard score of > 0. Reserved forests are those that are in 

state and national parks and wilderness. Unreserved are forests that are accessible for fuel treatments.

Percent of area in forest class

Unreserved Reserved

Forest type groups Area (acres) Resilient Hazardous Resilient Hazardous

Northern California and Klamath

Douglas-fir and true fir 1,179,300 18.9 77.4 0.6 3.1

Pine and Larch 346,829 21.7 68.7 0.0 9.6

Aspen NA NA NA NA NA

Cedar 7,173 0.0 100.0 0.0 0.0

Other Species 6,298,022 10.2 73.7 2.3 13.8

Nonstocked 149,670 0.0 69.4 0.0 30.6

All forest 7,980,993 11.8 74.0 1.9 12.4

Pacific Northwest Interior

Douglas-fir and true fir 4,774,498 9.5 79.5 0.7 10.2

Pine and Larch 3,058,771 11.9 83.2 0.6 4.3

Aspen 36,972 0.0 100.0 0.0 0.0

Cedar 37,368 0.0 82.9 0.0 17.1

Other Species 5,129,783 2.7 86.6 0.8 10.0

Nonstocked 373,815 0.9 87.2 0.0 11.9

All forest 13,411,208 7.2 83.3 0.7 8.8

North and Central Rocky Mountains

Douglas-fir and true fir 6,179,017 21.8 67.0 1.7 9.5

Pine and Larch 2,058,917 26.5 66.5 1.9 5.1

Aspen 340,395 17.1 71.1 2.6 9.2

Cedar 269,370 2.0 90.8 0.0 7.2

Other Species 4,777,662 1.1 81.1 0.5 17.3

Nonstocked 766,443 15.5 49.7 1.4 33.4

All forest 14,391,805 14.8 71.2 1.3 12.7

Utah

Douglas-fir and true fir 311,145 23.9 65.1 6.3 4.8

Pine and Larch 50,493 37.2 47.5 12.2 3.1

Aspen 772,677 1.6 94.5 0.0 3.9

Cedar NA NA NA NA NA

Other Species 808,244 0.6 86.6 0.0 12.8

Nonstocked 20,204 0.0 100.0 0.0 0.0

All forest 1,962,762 5.6 85.4 1.3 7.6
82 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Table 5.3. Acres by forest type group for unreserved (land outside of State and Federal 

parks and wilderness areas). a) Unreserved forest area by forest type group; 

b) Unreserved forest area by subregion and slope class.

a)

Forest type groups Area (acres)

Douglas-fir and true fir 11,153,051

Pine and Larch 5,178,247

Aspen 1,079,628

Cedar 288,211

Other Species* 14,493,511

Nonstocked 953,187

Total 33,145,835

b)

Forest area Percent of forest in subregion

Subregion acres Steep (>40%) Gentle (<= 40%)

Northern California and Klamath 12,380,552 41 59

PNW Interior 12,135,708 20 80

North and Central Rockies 1,787,021 33 67

Utah 6,842,555 50 50

Total Study Area 33,145,835

* “Other” consists of forests belonging to one of 45 FIA forest types: bigleaf maple, blue oak, blue 

spruce, California black oak, California laurel, California mixed conifer, California white oak, canyon live 

oak, cercocarpus woodland, cottonwood, cottonwood / willow, deciduous oak woodland, engelmann 

spruce, engelmann spruce / subalpine fir, foxtail pine / bristlecone pine, giant chinkapin, gray pine, 

incense-cedar, intermountain maple woodland, jeffrey pine, juniper woodland, knobcone pine, limber 

pine, lodgepole pine, misc. western softwoods, mountain hemlock, noble fir, Oregon white oak, other 

hardwoods, Pacific madrone, Pacific silver fir, pinyon / juniper woodland, Port-Orford-cedar, red alder, 

red fir, redwood, Rocky Mountain juniper, Sitka spruce, subalpine fir, sugar pine, tanoak, western 

hemlock, western juniper, white fir, and whitebark pine.

Hazard Dimensions are Highly Varied

Our four indicators of hazard are only weakly correlated. For example, in every 

sub-region, most of the acres fall into a middle ground between hazardous by every 

indicator and resilient (not hazardous) by every indicator (table 5.4). In every sub-region, 

the most common hazard status is hazardous by all four indicators (“yes” to all four 

indicators), and there are a considerable number of areas rated resilient by the torching 

index criterion but hazardous by one or more of the other criteria. The amount of area 

rated resilient on all counts amounts to as little as 6 percent of forested acres in Utah, to 

a maximum of 17 percent in the northern and central Rocky Mountains. These statistics 

would certainly shift with different choices of hazard thresholds, but a key message is 

that hazard rating does depend on the criterion considered.

Combining the criteria into a hazard score (the count of criteria by which an acre 

qualifies as hazardous) is one approach to considering relative hazard in a multi-criteria 

context. Conceivably, an acre that is hazardous with respect to crown fire likelihood, 

predicted mortality losses, and likely effectiveness of fire suppression (as indicated by 

a hazard score of 3 or 4) would be a higher priority for fuels management than one with 

hazard in only one dimension. The distribution of acres by hazard score appears to differ 

among sub-regions, with Utah having the fewest resilient acres as a percent of the total, 

and the northern and central Rocky Mountains the most (fig. 5.2).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 83

Table 5.4. Area and percent of unreserved forested land by subregion, rated hazardous by different combinations of hazard concepts: Ptorch >20%, TI <20, 

SFL >4 ft and VolMortPCT >30. 

Ptorch TI SFH VolMortPct Area (ac) Percent

Northern California and Klamath

Yes Yes Yes Yes 1,972,023 28.8

Yes Yes Yes No 13,575 0.2

Yes Yes No Yes 107,339 1.6

Yes Yes No No 13,162 0.2

Yes No Yes Yes 1,122,019 16.4

Yes No Yes No 400,759 5.9

Yes No No Yes 497,425 7.3

Yes No No No 413,563 6.0

No Yes Yes Yes 63,645 0.9

No Yes No Yes 8,669 0.1

No No Yes Yes 148,415 2.2

No No Yes No 397,125 5.8

No No No Yes 746,644 10.9

No No No No 938,193 13.7

 6,842,555 100.0

Pacific Northwest

Yes Yes Yes Yes 4,282,413 35.3

Yes Yes Yes No 39,844 0.3

Yes Yes No Yes 499,963 4.1

Yes Yes No No 26,032 0.2

Yes No Yes Yes 1,488,994 12.3

Yes No No Yes 980,810 8.1

Yes No Yes No 649,996 5.4

Yes No No No 575,332 4.7

No Yes Yes Yes 299,469 2.5

No Yes Yes No 44,524 0.4

No Yes No Yes 42,997 0.4

No No Yes Yes 429,786 3.5

No No No Yes 823,979 6.8

No No Yes No 991,111 8.2

No No No No 960,459 7.9

 12,135,708 100.0

Northern and Central Rocky Mountains

Yes Yes Yes Yes 2,913,820 23.5

Yes Yes Yes No 19,476 0.2

Yes Yes No No 11,535 0.1

Yes Yes No Yes 344,432 2.8

Yes No Yes Yes 1,160,783 9.4

Yes No Yes No 187,433 1.5

Yes No No No 332,713 2.7

Yes No No Yes 1,618,850 13.1

No Yes Yes Yes 120,978 1.0

No Yes No Yes 10,588 0.1

No No Yes Yes 326,987 2.6

No No No Yes 2,719,559 22.0

No No Yes No 486,656 3.9

No No No No 2,126,743 17.2

 12,380,552 100.0

Utah

Yes Yes Yes Yes 640,066 35.8

Yes Yes No Yes 65,853 3.7

Yes No Yes Yes 395,750 22.1

Yes No No Yes 250,713 14.0

Yes No Yes No 20,620 1.2

Yes No No No 78,186 4.4

No Yes Yes Yes 5,626 0.3

No No Yes Yes 45,061 2.5

No No Yes No 26,568 1.5

No No No Yes 148,189 8.3

No No No No 110,388 6.2

 1,787,021 100.0
84 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Hazard is Common in All Forest Type Groups

Although wildfire is possible in almost any forest type, we intended that our emphasis 

in this synthesis be primarily on the Douglas-fir, True Fir, Pine and Western Larch type 

groups where fuels management is most commonly practiced, on western redcedar in 

the north and central Rocky Mountains where it occurs in non-negligible abundance, 

and on aspen/birch in the north and central Rocky Mountains and Utah sub-regions. 

However, hazard summaries based on the FIA data show that hazard, by our criteria, is 

high in all forest type groups (fig. 5.3), including Other, though high hazard alone does 

not necessarily make other forest types a priority for fuels management.

Description of Current Conditions

Histograms4 showing the distribution of current fire hazard on unreserved forestland in terms of three of the attributes discussed above (TI, MortVolPct, and SFL) and 

crowning index, canopy cover, trees per acre, basal area, and quadratic mean diameter 

are published in Appendix A. There is one page of eight histograms (one per attribute) 

for each combination of sub-region and forest type group, and the pages are grouped 

by sub-region. In each histogram, the Y-axis represents the proportion of forest area in 

a sub-region/forest type group. These can be easily translated into acres, if desired, by 

multiplying the proportions by the area (in acres) printed in the histogram. The mean 

Figure 5.2. The percent of area within each subregion by hazard score (number of 

ways rated hazardous). There are four hazards that were used to develop the hazard 

score. The hazard score is based on number of hazards that existed in a given location. The four hazards that were rated are: 1) probability of torching, 2) torching index, 

3) surface flame length, and 4) mortality volume as a percent of pre-fire volume. A 

hazard score of 4 indicates that an acre is subject to all four hazards.

4 Histograms are only plotted for cases where there are at least 10 conditions for a sub-region/forest 

type group combination. Meaningful histograms can rarely be constructed from less than 10 observations, 

and in any case, it is inadvisable to attempt inferences from such a small sample.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 85

and a roughly calculated5 sampling error are also printed in the histogram. Sampling 

errors tend to be smaller as area represented (and the associated sample) increases. 

The true (population) mean has a 95 percent chance of being contained by the interval 

constructed as the sample mean ± 2 times the sampling error. In some cases, the X-axis 

of the histogram is truncated and the last histogram bar on the X-axis also represents 

all values larger than the x value at that bar; in these cases, the last bar is labeled to also 

show the maximum value.

What These Data Tell Us

Comparisons among mean values per sub-region/forest type group combination 

can provide some useful insights (table 5.5). For example, mean ptorch (probability of 

torching) is greater in Pine and Larch than in Douglas-fir and True Fir and mean ptorch 

is greater in the Pacific Northwest Interior than for northern California and Klamath or 

north and central Rocky Mountains and these differences appear to be significant. Mean 

surface flame length (SFL) is substantially greater in the Pacific Northwest Interior and 

Northern California and Klamath than in north and central Rocky Mountains for both 

these type groups, again with indications that the differences are significant. Yet, in 

neither case do the frequency distributions look like the classical, well-behaved normal 

distributions—for both these variables, they are either relatively flat, multi-modal (more 

than one peak), or skewed, and in no case is there a strong, central tendency required 

for arithmetic means, medians (central value) and modes (most common values) to 

Figure 5.3. Percent of area within each forest type by hazard score (number of ways 

rated hazardous). There are four hazards that were used to develop the hazard score. 

The hazard score is based on number of hazards that existed in a given location. The 

four hazards that were rated are: 1) probability of torching, 2) torching index, 3) surface 

flame length, and 4) mortality volume as a percent of pre-fire volume. A hazard score 

of 4 indicates that an acre is subject to all four hazards.

5 These standard errors do not account for the different landscape weights (acre expansion factors) associated with each forested condition.
86 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

roughly coincide. So in addition to this table of average values, many of which are very 

representative of their respective distributions, we report via histograms that show the 

full range, relative frequency at each attribute level, and selected statistics like mean 

and sampling error (Appendix A).

One striking pattern is similarity among forest type groups; for example, percent 

mortality volume (MortVolPct) for Douglas-fir and True Fir and Pine and Larch is 

remarkably similar. Basal area, trees per acre, and canopy cover were all substantially 

greater in Douglas-fir and True Fir than in Pine and Larch in every sub-region, and 

canopy cover was much greater in Northern California and Klamath than the other two 

sub-regions. Such patterns are consistent with expectations, but the sizable FIA sample 

provides strong statistical support for such conclusions.

Perhaps more importantly, we hope these histograms provide a useful context for 

comparison against any particular stand contemplated for hazard reduction. If these attributes are available for such a stand, it is easy to consult the distribution representing 

its type and sub-region to see where it lands—is it at the high hazard end of the spectrum, 

or somewhere in the middle? If post-treatment hazard attributes can be computed or 

estimated, how far through the distribution will the stand be moved? Does treatment 

put it at the low hazard end of the histogram, or leave it somewhere in the middle? The 

answers may differ among hazard descriptors, and it is up to the manager to prioritize 

among these.

These histograms also provide the user with the option of considering different 

hazard thresholds than were assumed for this analysis. By summing up the proportions 

for histogram bars, it is easy to see how much of a landscape rates as hazardous for 

nearly any threshold. For example, in Pacific Northwest Interior Pine and Larch, about 

half of stands would rate as hazardous with respect to mortality volume percent using a 

threshold of 50 percent versus the approximately 63 percent that rate hazardous using 

our 30 percent threshold.

Table 5.5. Mean and standard errors of selected fire hazard and forest structure attributes for each forest type group, by sub-region.

Probability Mortality Quadratic

Surface of torching volume Crown mean Trees per Canopy

flame length (percent) (percent) index diameter acre Basal area cover

Forest type group Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE

Northern California & Klamath

Douglas-fir and true fir 5.0 0.2 39 3 60 3 37 3 9.7 0.5 430 25 155 7 57 2

Pine/larch 6.6 0.5 49 5 61 5 59 6 10.5 0.8 287 38 104 10 39 3

Other Species 5.3 0.1 53 1 64 1 47 2 9.1 0.2 466 14 142 3 56 1

Pacific Northwest Interior

Douglas-fir and true fir 5.9 0.1 48 1 65 2 36 1 10.2 0.2 334 12 133 4 50 1

Pine/larch 6.2 0.1 50 2 55 2 61 2 10.4 0.3 215 10 83 2 34 1

Other Species 5.5 0.1 58 1 77 1 46 2 8.1 0.2 441 17 100 3 38 1

Northern and Central Rocky Mountains

Douglas-fir and true fir 3.3 0.1 30 1 64 1 35 1 8.8 0.2 340 14 100 2 45 1

Pine/larch 4.6 2.0 34 2 49 2 56 3 10.0 0.3 232 16 79 3 33 1

Aspen 5.3 0.6 43 6 59 6 91 12 5.1 0.5 356 5 51 7 34 4

Cedar 8.7 0.4 73 6 86 5 33 3 10.6 1.0 452 73 175 20 62 4

Other Species 4.9 0.1 60 2 81 1 34 1 6.9 0.2 580 24 110 3 43 1

Utah

Douglas-fir and true fir 2.1 0.3 29 5 83 5 28 3 7.5 0.4 407 42 100 8 44 3

Aspen 5.5 0.2 69 3 78 2 70 7 6.4 0.2 555 36 96 5 54 2

Other Species 5.4 0.3 76 3 82 3 34 5 6.9 0.3 651 53 115 6 39 2
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 87

A baseline for comparing fuel treatment outcomes and the 

characteristics of stands amenable to fuel treatment

These current conditions data provide a foundation for the fuel treatment effectiveness and feasibility analysis reported on in Chapter 11. It is useful to compare these 

histograms representing ALL forests with the histograms in Appendix C associated 

with Chapter 11; the set of stands for which effective treatment is possible often has 

pre- and post-treatment distribution of hazard descriptors that are different from these 

all-forest histograms.

Conclusion

The objective of this chapter was to provide context on the scope of the task of 

implementing fuel treatments across the synthesis area. Most of the synthesis area would 

benefit from some level of treatment to either reduce fire hazard or to realign forests to 

put them within the historical range of mixed fire regime conditions. As noted in chapter 

4 there are several factors that have led to these changes over a century of management.

Further Reading

Barrett, S.; Havlina, D.; Jones, J.; Hann, W.; Frame, C.; Hamilton, D.; Schon, K.; Demeo, T.; Hutter, L.; 

Menakis, J. 2010. Interagency fire regime condition class guidebook. Version 3.0 [Homepage of the 

Interagency Fire Regime Condition Class website], [Online]. Available: www.frcc.gov [2012, June]. 

(Provides the methods and protocol for determining the fire regime condition class.)

Hann, Wendel J.; Bunnell, David L. 2001. Fire and land management planning and implementation across 

multiple scales. International Journal of Wildland Fire. 10(4): 289-403. (Provides a summary of options 

for planning and implementation at different spatial scales.)
88 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 89

Section II: Fuel Treatment Planning and Implementation in Dry 

Mixed Conifer Forests of the Northwestern United States
90 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 91

Chapter 6

Integrating Wildlife Habitat into 

Fuels Planning and Implementation

Chapter Rationale

During our interviews, wildlife was often described as a challenge to implementing 

fuel treatments:

	 •	 Land allocation for threatened and endangered or sensitive species limits the 

ability to implement fuel treatments in the best location or in locations with the 

greatest fuel hazards.

	 •	 Wildlife habitat needs conflict with hazardous fuels reduction goals.

	 •	 Fuels reduction treatments are limited in time or space to avoid impacts to important wildlife habitat elements (for example, dens) or seasons (for example, 

nesting season).

	 •	 Improved communication among resource specialists who specialize in vegetation and fire management and those who work with wildlife is needed for fuel 

treatment planning and implementation.

Introduction

Wildlife habitat is a critical element in many fuel treatment decisions, and treatments 

may need to be designed and implemented in concert with habitat needs of the different 

wildlife species of conservation concern within and near a treated area. We do not attempt 

to summarize habitat needs by species or by region because the lists vary depending on 

the agency and they also change from year to year and place to place.

In our interviews with wildlife biologists and managers, a recurring issue was the 

need to improve communication among resource specialists that specialize in vegetation manipulation (for example, silviculturists and fuels specialists) and those that work 

with wildlife. To better understand their perspectives, we asked wildlife biologists to 

identify the concerns and questions that proposals for fuel treatments typically produce. 

We organized this chapter around those questions with the aim of enhancing communication and integration of wildlife elements in fuels planning and implementation. The 

objective is not to provide the exact answers to these general questions but to identify 

the types of information (with reference to a literature synthesis) that can be collected 

as a starting point for planning and discussion among specialists.

Which Habitat(s) Will the Fuel Treatments Impact and for 

How Long?

For some wildlife species, knowledge of their exact habitat requirements may be 

incomplete; however, key habitat elements for specific wildlife species have usually 

been identified. Wildlife species occur in all plant communities and stand conditions. 

An individual species and its use of a given habitat will vary depending on how specific environmental conditions fulfill habitat requirements; therefore, any type of fuel 
92 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

treatment will also influence wildlife habitat management as it alters these conditions. 

As forests regenerate and develop, wildlife habitats change, creating variability in the 

species that use the different stages of forest development (Johnson and O’Neil 2001; 

Morrison and others 2006; Reynolds and others 1992). Disturbance, succession, productivity, and species composition drive changes in stand conditions (Morrison and 

others 2006; Pilliod and others 2006). These four drivers are particularly important in 

dry mixed conifer forests because in combination they provide a diversity of vegetative 

compositions and structures over time and space. Moreover, because dry mixed conifer 

forests are intermixed with both dryer and wetter forest types (which add complexity 

to the landscape and provide a level of heterogeneity), the forest type characteristics 

may favor integration of fuel treatments and wildlife habitat objectives. In terms of fuel 

treatment planning needs, important habitat elements to consider include: sensitivity 

of habitat, tree or cover type, density, diameter of the trees, canopy cover, patch size, 

fragmentation and edge effects, snags, down wood, and connectivity. Understanding 

these wildlife habitat elements and their importance may help improve fuel treatment 

planning and provide an avenue for effective communication between disciplines. Understanding these elements in space and through time provides the basis for treatment 

proposals that can integrate forest composition and structure (in other words, desired 

conditions) at multiple scales to meet multiple resource management objectives.

Composition and structure of the habitat

Each wildlife species has habitat requirements related to dominant tree species, a 

range of tree diameters, densities, crown cover, and size of habitat patches. These elements can be readily mapped at various spatial scales to quantify potential habitat both 

spatially and temporally (Ecosystem Research Group 2010; Reynolds and others 1992). 

Additionally, inventory information, such as Forest Inventory and Analysis (FIA) data, 

can be used to incorporate other associated habitat elements (for example, snags and 

down wood) that can also be used at broad spatial scales. Finer-scale information from 

stand inventories at the project scale can include these and other habitat elements to 

evaluate conditions within project areas. It is important to assess treatment effects on 

the type and amount of habitat at multiple scales to judge threshold amounts and the 

distribution of habitat modified by site-specific proposals. This type of information can 

help answer the common questions of why here and why now?

Sensitivity of habitat

How much value does the landscape have for a particular species? If habitat is 

abundant, there may be more flexibility in implementing fuel treatments. However, if 

the landscape contains critical habitat and in limited amounts, then more attention may 

need to be given to setting up the fuel treatments for the habitat needs of that species. 

For example, the Yaak River drainage in northwest Montana provides important habitat 

for Canada lynx, a threatened species; therefore, fuel treatments conducted in this drainage require careful consideration of lynx habitat needs for breeding, feeding (snowshoe 

hare), resting, and other requirements.

Fragmentation and edge

Fragmentation is the level of heterogeneity that negatively affects the ability of a 

particular species to disperse for breeding or obtain critical resources (Saunders and 

others 1991). However, not all species are equally affected by the spatial structure of 

the landscape, and a high degree of fragmentation may be needed to begin affecting a 

particular species. Also, definitions of heterogeneity and fragmentation vary in different 

landscapes. Tews and others (2004) explained this concept in the context of a heavily 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 93

used cultural landscape such as central Europe. In central Europe, an increase in forest 

patchiness likely increases species diversity as more potential habitat is added. However, 

disruption of formerly closed-canopied, tropical forests likely decreases diversity as a 

result of habitat fragmentation. Normally, heterogeneity can enhance wildlife habitat 

for some species. Thus, species of interest and their habitat needs may dictate when 

a landscape can be considered fragmented or heterogeneous. In addition to affecting 

longevity, the current composition, structure of landscapes, and fuel treatments also 

support heterogeneous landscapes because they can alter fire behavior and the post-fire 

environment, which, in turn, can favor heterogeneous forest structures and compositions favorable to wildlife species. Therefore, creating heterogeneity can be mutually 

beneficial to both wildlife habitat needs and fuel treatment objectives. The challenge is 

in balancing heterogeneity and fragmentation. This is best accomplished by considering 

wildlife needs prior to planning treatment locations so that integration occurs early in 

the planning process.

An edge is the location where plant communities meet or where successional stages 

or vegetation conditions within a plant community intersect (Thomas and others 1979b). 

Edges host the mingling of different vegetation complexes; thus, they tend to be more 

biologically diverse. Edges create ecotones, which tend to have higher species diversity 

than the adjoining plant communities. The contribution of an edge to habitat richness 

is a function of the types of forest stands that come together at an edge and the specific 

type of habitats and habitat elements that are created.

Two types of edges are inherent and induced edges (fig. 6.1). An inherent edge is one 

that is produced by abrupt changes in soil type (forest versus meadow), topographic 

differences (north versus south facing ridge tops), or geomorphic differences or changes 

Figure 6.1. Examples and causes of inherent and induced edges (adapted from 

Thomas and others 1979b).
94 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

in microclimate. Induced edges occur from disturbance such as wildfire, disease, insects 

harvesting and prescribed fire. Many dry mixed conifer forests have inherent edges 

between different forest types. In these cases, geomorphic differences often drive the 

variability. For example, on south facing aspects in northern Idaho, inherent edges occur 

between western redcedar (located in draws) and grand fir on hillsides and Douglas-fir 

sites occurring on ridges (fig. 6.2). Induced edges are relatively short lived compared 

to inherent edges that occur between north and south aspects. The value of an edge at 

providing habitat for wildlife species is dictated by the amount of edge (length, width, 

Figure 6.2. Induced edge created by prescribed fire in northern Idaho. The 

top photograph is forest type change (western redcedar on left/Douglas-fir 

on right). The bottom photograph shows two forest developmental stages 

induced by harvest. The foreground is a young forest and background is 

a mature forest.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 95

and configuration) and the level of contrast between the two communities coming 

together. A measure of contrast can be illustrated in transition zones between different 

fire types. In the Bitterroot fires in western Montana, many regenerated sites did not 

burn at the same level of severity as the surrounding forest (fig. 6.3), creating a dramatic contrast at the edge. However, a transition between a surface fire and torching 

fire may show a less dramatic contrast between the two post-fire outcomes. Thus, the 

boundaries and variability within and outside the treatments can be important for both 

wildlife habitat and altering fire behavior and providing an avenue for integration of 

wildlife habitat features.

Snags

Figure 6.3. Induced edge caused by wildfire in the Bitterroots where regenerated 

sites did not burn as severely as surrounding forests.

Snags provide habitat for fungi, mosses, lichens, invertebrates, birds, and mammals 

(fig. 6.4). Maintenance of a certain abundance of snags is a common wildlife management principle (Morrison and others 2006; Pilliod and others 2006; Reynolds and others 

1992; Thomas and others 1979a). Wildlife use of snags depends on the successional 

stage of the surrounding community (fig. 6.5) and the internal and external characteristics of the snag (fig. 6.6). Snags progress through successional changes from death of 

the tree to final decomposition (fig. 6.7). Depending on the species and the fire history 

of a particular area, a snag may exist for years to decades (Smith 1999). Each stage in 

the decay process provides a habitat element for a different suite of species. There are 

hard snags and soft snags, with soft snags developing from hard snags through decomposition. A western larch will take much longer to produce a soft snag in comparison 

to a grand fir. Also, soft snags are relatively rare because their longevity is shorter than 

hard snags—they simply fall down. Due to changes in forest composition over the past 
96 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 6.4. Snags provide habitat for various life forms (adapted from Thomas and 

others 1979a).

Figure 6.5. Wildlife use of snags depends on the structural stage of the surrounding 

community (adapted from Thomas and others 1979a).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 97

Figure 6.6. Examples of cavities in live and dead trees 

(adapted from Thomas and others 1979a).

Figure 6.7. Snags progress through successional changes from live trees to death to 

final decomposition (adapted from Thomas and others 1979a).
98 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

century, many shade-tolerant trees such as grand fir, hemlock, and Douglas-fir are more 

available today, providing short-term soft snags. By contrast, long-lived seral species 

such as ponderosa pine and western larch that provide persistent snags are now less 

common on the landscape, making them a more important habitat attribute.

A number of snag characteristics are important for wildlife (Bull and others 1997; 

Thomas and others 1979a). For example, loose bark (stage 4) is important for bats 

(fig. 6.7), and excavating woodpeckers such as the pileated woodpecker prefer largediameter (20+ inch dbh) stage 6 trees (fig. 6.7). Similarly, pileated woodpeckers will 

nest in a snag that is surrounded by trees but tend to avoid nesting in snags in freshly 

harvested sites. The size and height of snags also influence their habitat value. 

Figure 6.8 shows the number of cavity-nesting species that use two snags of different 

heights and diameters. Snags that are 10 ft or less tend to have a larger suite of cavity 

nesters than taller snags irrelative of diameter. However, a range of different species 

use snags based on their diameter, and the preferred size varies by species and location. 

Within the dry mixed conifer forest range, some locations grow larger trees than others, and large-diameter trees tend to stand longer than small-diameter trees. Therefore, 

managers may need to acknowledge that the size of snags to target for habitat enhancement may be location specific.

Understanding the preferred snag size for targeted wildlife species provides direction for maintaining snags. Taller snags are more hazardous, and if wildlife in a certain 

area utilize snags 10 feet or shorter, it may not make sense to maintain the taller snags. 

Furthermore, identifying tree species that are utilized by specific wildlife species is an 

important consideration. Field guides such as the one developed for the Interior 

Columbia River Basin (Parks and others 1997) can be helpful. Another source of information on the relative abundance of snags of various sizes is obtained from assessFigure 6.8. The diameter and height of a snag influences habitat value (adapted 

from Thomas and others 1979a). Values above bars refer to the number of cavity 

nesting species of birds that use snags of different diameter and height.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 99

ments using FIA inventory data. It has been noted that large-diameter snag abundance is 

relatively rare across the landscapes that contain roads compared to those areas without 

roads. Similarly, areas under historical fire regimes contain more snags than areas in fire 

excluded areas. This type of information can help inform snag maintenance by diameter 

groups to satisfy habitat needs of wildlife species that depend on this forest element 

(Bollenbacher and others 2008, 2009a, 2009b)

Dead and down wood

Dead and down wood (often 1000 hour fuels or greater) also provide habitat for many 

wildlife species (Bull and others 1997; Maser and others 1979a) (fig. 6.9). For wildlife 

purposes, logs are used for hiding cover, feeding (both consumption and food storage), 

and reproduction (for example, denning and mating rituals) (fig. 6.10). For example, 

stage 1 snags such as down logs can offer hiding and thermal cover for snowshoe hares, 

lynx, fisher, pine martens, and porcupines, particularly if there are support points that 

offer access to portions of the log off of the ground. These logs can also be used as 

lookout posts for chipmunks and, when elevated, can be bridges for a variety of species and drumming sites for grouse. Also, these logs provide feeding sites for lizards, 

chickadees, and red squirrels. As the log progresses in decomposition, it sits closer to 

the ground and provides cover for gopher snakes, shrews, chipmunks, or voles. As the 

log decays, the bark becomes a location where tree frogs and western skinks can catch 

invertebrates. By the time a log reaches stage 4, the interior may be soft enough for burrowing and can be used by shrews, deer mice, and voles. Even when logs reach stage 5, 

small animals use them for reproduction, feeding and caching, and protection. Bull and 

others (1997) found that the larger the log the better for wildlife habitat; however, the 

minimum size identified for habitat purposes tended to be 12 to 15 inches dbh for species such as western larch, Douglas-fir, ponderosa pine, and lodgepole pine.

Connectivity

Connectivity generally refers to movement among animal populations in vegetation 

corridors, with more precise terms such as emigration and immigration used to describe 

the nature of the movement. Connectivity helps keep populations stable, decreases the 

likelihood of extinction, and maintains genetic variation (Mills and others 2003).

Corridors provide biodiversity protection to rare and endangered species as well as 

a wide range of other species (Forman 1995). Connectivity is a structural attribute that 

affects corridor function. Habitat is functionally enhanced by higher connectivity. Wildlife survival may depend on access and use of corridors as they provide connectivity 

between different areas. Corridors can also play a key role in providing dispersal routes 

for recolonization following disturbance, which may create local extinctions. Wildlife 

species tend to not live in corridors but primarily use them as an avenue for movement. 

Corridors are dynamic and can be created or removed over time and space, a change 

that is often dependent on wildlife needs. In some cases, such as in riparian areas, which 

have many unique attributes that favor multiple species, corridors may be present and 

protected for long periods of time.

Corridors can be integrated into fuels management since they can contribute to creating 

heterogeneity in fuels, altering fire behavior and effects. It is important to understand 

the historic role fire played in shaping vegetation corridors to help determine current 

desired conditions (Quigley and others 1997a). Definitions of connectivity in terms 

of the seral stages of vegetation and what constitutes a corridor for a specific wildlife 

species are a scale-dependent issue.
100 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 6.9. Two logs at different decomposition stages show the diversity of structural features important for 

wildlife (adapted from Maser and others 1979).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 101

Figure 6.10. Log succession from a sound, intact log to a nearly decomposed log (adapted 

from Maser and others 1979).

Which Wildlife Species Are in the Proposed Planning Area?

A variety of methods may be used to understand and identify desired conditions for 

wildlife habitat and to determine which wildlife species will be impacted (positively 

or negatively) by a fuel treatment. The simplest and most direct method is to discuss 

how a particular fuel treatment might affect habitat elements for a species with wildlife 

biologists who have direct knowledge of specific wildlife habitat elements. Developing 

this partnership early in the planning process, prior to the reconnaissance period, can 

inform the silvicultural diagnosis and prescription process by helping identify desired 

stand conditions to be evaluated against current stand conditions, thereby providing 

insight into treatment alternatives (Bollenbacher 1995; Sturtevant and others 2005). 

These discussions are also the time when unit locations can be proposed, treatment size 

can vary, residual canopy or protected areas can be identified, and other habitat elements 

can be integrated into the treatment planning.

Manager comment: The job of the fuels specialist is to work with a wildlife biologist and silviculturist to quantify the risk of habitat loss from severe wildfire with and 

without treatment and weigh those risks against the impacts from a fuel treatment. In 

this way, the decision maker can make informed decisions and communicate about 

relative risk to the habitat.

In addition to site-specific habitat attributes, wildlife population dynamics, insect 

outbreaks, and wildland fires function at larger landscape scales; therefore, adding 

wildlife habitat elements to a landscape evaluation may be beneficial. In the OkanaganWenatchee National Forest Restoration Strategy (2010), a landscape evaluation technique 
102 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

was developed to allow managers to analyze and prepare restoration plans that integrated 

vegetation, fire, wildlife habitat, aquatics, and road networks. The Ecosystem Management Decision Support Framework (EMDS 3.0.3, Reynolds and Hessburg 2005) was 

used to combine the specific information needed for the evaluation. The land managers 

used a seven-step process, including a step (step 4) dedicated to identifying key wildlife 

habitats and restoration opportunities. First, managers identified the key species of interest. Then, for each of the identified species in the particular landscape evaluation area, 

they (1) determined the location and amount of habitat within the landscape evaluation 

area (they also compared the current amount and configuration of habitats to historical 

and future reference conditions); and (2) identified habitat restoration opportunities and 

priorities that could be integrated with the other resource priorities (and determined 

which priorities would be carried into project level planning). This approach allowed 

for evaluation of wildlife needs at a much broader landscape scale and integration into 

other resources issues. It also helped identify specific elements or priorities that could 

be brought down to smaller spatial scales for individual projects. This strategy may be 

helpful for integrating a particular wildlife species and its habitat into fuels planning.

The Beaverhead-Deerlodge National Forest in southwestern Montana demonstrated 

another example of landscape planning at the scale of an entire National Forest. This 

area utilized landscape simulation modeling to quantify habitat for multiple species in 

space and through time, incorporating disturbances from fuel treatments, bark beetles, 

and wildfire to gain insight into the relative persistence of wildlife habitat (Ecosystem 

Research Group 2010).

How Will the Proposed Treatment Impact the Habitat and 

Wildlife Species?

This answer to this question is a function of the habitat elements being affected in the 

proposed action. There have been several documents that have addressed the impacts 

of wildfire and fuel reduction treatments (mechanical and prescribed fire) on wildlife 

and wildlife habitat in recent years (Kennedy and Fontaine 2009; Lehmkuhl and others 2007; Pilliod and others 2006; Saab and others 2007). For example, Pilliod and 

others (2006) provided a synthesis to help fuels planners, fire managers, and National 

Environmental Policy Act specialists evaluate the potential effects of fuels reduction 

treatments on terrestrial vertebrate and invertebrate species in dry coniferous forests 

of the western United States. In that document, Pilliod and others (2006) described the 

general impacts of thinning and prescribed fire on structural habitat features and the 

implications for different wildlife species, including forest carnivores, ungulates, small 

mammals, bats, raptors, cavity-nesting birds, general birds, reptiles, amphibians, and 

invertebrates. Based on this information, the authors developed the web-based Wildlife 

Habitat Response Model. This model provides information about specific species habitat associations, life history requirements, potential predators, and hazards, and how a 

fuels reduction treatment might influence those factors. The information is based on 

published literature about species/habitat relationships and provides information about 

how different fuel treatment activities might alter the habitat.

Kennedy and Fontaine (2009) provided a synthesis of wildlife response in dry coniferous forests within the Fire and Fire Surrogate Network (FFS). The FFS consisted 

of operational-scale experiments that tested silvicultural and prescribed fire restoration 

treatments. These research sites were found throughout the western United States and 

some were located within the geographic scope of this synthesis, including: northeastern 

Cascades (ponderosa pine/Douglas-fir/grand fir), southern Cascades (ponderosa pine and 

white and red fir), Blue Mountains (ponderosa pine/Douglas-fir), and northern Rocky 

Mountains (ponderosa pine/Douglas-fir). Other sites outside of the geographic area 

covered in this synthesis that could still be useful include the central Sierra Nevadas 

(Sierran mixed conifer), southern Sierra Nevadas (Sierran mixed conifer), and the 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 103

southwestern plateau (ponderosa pine). The goal of this document was to provide land 

managers with a resource that allowed them to easily access species-level data related 

to prescribed fire and mechanical fuels reduction treatments. The ultimate goal was for 

managers to use the data presented in that document to answer the following questions 

(from Kennedy and Fontaine 2009):

1. Does information exist on the response of the species in the project area to the 

proposed treatment(s)?

2. Is the response of the species to the treatment consistent (in other words positive, negative, no response)?

3. Is the response short-term or is there evidence of long-term responses?

See “Further Reading” for more information from Kennedy and Fontaine (2009).

Conclusion

This chapter was designed to:

	 •	 present key wildlife habitat elements (although not all elements will be important 

for a particular wildlife species);

	 •	 provide important concepts and ideas to encourage discussion between vegetation managers and wildlife and range biologists; and

	 •	 provide vegetation managers with key questions that may help identify wildlife 

needs when planning fuel treatments.

Wildlife biologists identified these questions as important considerations when planning integrated fuel treatments.

Further Reading

The following documents provide more details and information to interested readers. 

We found these books to contain extensive and detailed discussions of wildlife-habitat 

relationships.

Johnson, David H.; O’Neil, Thomas A. 2001. Wildlife-Habitat Relationships in Oregon and Washington. 

Oregon State University Press, Corvallis OR. 736 p. (Presents the state of the science for amphibians, birds, 

mammals, and reptiles and their terrestrial, freshwater, and marine habitats in Oregon and Washington. 

The information was designed for local, watershed, state, and regional applications. Where appropriate, 

some of the information and overall framework may be applicable in surrounding states and provinces. 

This is a large, detailed reference book that may be appreciated by wildlife biologists, conservation 

biologists, or others with a keen interest in wildlife-habitat relationships.)

Morrison, Michael; Marcot, Bruce G.; Mannan, R. William. 2006. Wildlife-habitat relationships: concepts 

and applications. 3rd ed. Washington DC: Island Press. 493 p. (Detailed description and discussion of 

wildlife-habitat relationships that may be of interest to those who wish to learn the nuances of wildlife 

habitat.)

Pilliod, David S.; Bull, Evelyn L.; Hayes, Jane L.; Wales, Barbara C. 2006. Wildlife and invertebrate response 

to fuel reduction treatments in dry coniferous forests of the western United States: a synthesis. Gen. Tech. 

Rep. RMRS-GTR-173. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 34 p. (This summarizes fuel treatments influences on different wildlife species.)

Kennedy, Patricia L.; Fontaine, Joseph B. 2009. Synthesis of knowledge on the effects of fire and fire 

surrogates on wildlife in U.S. dry forests. Corvallis, OR: Oregon State University, Agricultural Experiment Station. 132 p. (Summary and details of the literature cited can be found in Appendix 1 through 

3. Appendix 1 provides information on the different wildlife response studies, time since disturbance, 

type of disturbance, and different wildlife types [amphibian, raptors, birds, bat, small mammal, and 

large mammal]. Appendix 3 summarizes information from a quantitative analysis of wildlife response 

to the FFS treatments.)
104 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 105

Chapter 7

Planning and Conducting 

Integrated Fuel Treatments

Chapter Rationale

In our interviews, many fuel managers emphasized that the primary challenge in conducting fuel treatments is planning. Consequently, this chapter is focused on integrating 

fuel treatments with other objectives. The following questions guided organization of 

this chapter:

	 •	 How does the ecology of the dry mixed conifer forests influence fuel treatment 

planning and implementation?

	 •	 When addressing a variety of objectives, how do we increase consideration of 

fuels in planning?

	 •	 What decision support tools are available for fuel treatment planning?

Introduction

Planning and implementing fuel treatments primarily involves working through a 

number of important challenges. When fuel treatments are combined with multipleobjective management, achieving fuel treatment goals sometimes becomes (or at least 

appears) more limiting. Fuel treatments are a subset or type of vegetative treatment in 

which the specific purpose of the manipulation is to alter fire behavior and/or effects. 

Therefore, it is important to understand what fuel treatments cannot do (see Reinhardt 

and others 2008 in Further Reading):

	 •	 Forests cannot be fireproofed; all live and dead vegetation are fuel and can burn 

in undesirable ways given the proper conditions.

	 •	 A fuel treatment is not static; vegetation grows and develops over time and thus 

repetition of treatments may be necessary.

	 •	 Unless properly positioned, treatments may not have the intended effect. Even 

large and extensive landscape fuel treatments may not reduce the size of area 

burned. However, they can alter post-fire outcomes, protect areas of concern, 

and provide suppression opportunities.

	 •	 Depending on the residual forest structure and composition, fuel treatments may 

not improve forest health.

	 •	 A fuel treatment may not slow down rate of fire spread and could potentially 

increase how fast a fire moves through a treated area.

	 •	 Some vegetation manipulation treatments such as mastication may increase 

fuel hazard in the short term by increasing fine surface fuel loadings, which 

take time to decompose; however, in the long-term, they may decrease the fuel 

hazard by treating ladder and surface fuels.

Defining the short- and long-term objectives of the fuel treatment early in the planning process is the most important and critical step. Included in the objectives is the 

recognition of what fuel treatments cannot do. For example, short-term objectives such 
106 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

as protecting values at risk and providing suppression opportunities or firefighter safety 

(which will be tested during a wildfire event and will only be useful for one or two 

days) drive the rationale behind a fuel treatment. However, because forests continue 

to grow and develop, including fuel treatment maintenance and the periodicity of this 

maintenance may need to be incorporated into the objectives. So when a fire does occur, 

the treatments perform as expected or planned. Also, if one of the objectives for a fuel 

treatment is to support active fire suppression and firefighter safety, the placement and 

juxtaposition of fuel treatments could potentially influence fire management decisions 

such as where to place burn outs, fireline building, retardant drops, and structure protection. Thus treatments can be more successful in aiding wildfire response when there is 

time to initiate these activities prior to the arrival of a wildfire, including time to identify 

safety zones and escape routes (for example, Graham and others 2009). The probability 

of a wildfire burning through any particular fuel treatment is low, and the period that a 

fuel treatment performs its intended function may only last one day. However, if never 

exposed to wildfire, the treatment will be present on the site for decades to centuries; 

therefore, long-term benefits warrant consideration in terms of post-fire outcomes or 

fuel treatment objectives over the long haul. Forest type, objectives, spatial extent of 

the treatment area, current forest conditions, and surrounding forests influence which 

combination of treatments will create the most effective post-fire outcome or response 

opportunities. Also, it is important to acknowledge vegetation dynamics; understanding growing space and the potential response of vegetation to treatments will influence 

treatment planning. The bottom line is that the exact set of treatments will vary for each 

site, and one type or combination of fuel treatments will not fit all situations (Graham 

and others 2007).

Social, economic, or political aspects will also influence the kind, location, and 

intensity of treatments. For instance, prescribed fire is often restricted near communities because of smoke limitations and the inherent risk for its escape. On public lands, 

achieving the desired forest conditions and implementing management actions may often 

meet multiple and sometimes conflicting objectives. For example, on the east side of 

the Cascade Mountains in Washington, several forests modified by timber harvest and 

fire exclusion are providing habitat (late-seral grand fir) for the endangered northern 

spotted owl (Everett and others 1994; Hessburg and others 1994). These conditions are 

not resilient to fire and warrant treatment, but fuel treatments cannot be applied because 

of potential impacts to owl habitat. In addition, many of the areas with northern spotted owl habitat also contain homes, lodges, and other infrastructure where use of prescribed fire and vegetation removal are often limited. Accordingly, the most applicable 

fuel treatment for modifying burn severity and/or fire behavior often is diminished in 

extent and degree of manipulation because of the regulatory, administrative, or social 

acceptance of treating forests.

This chapter focuses on concepts to consider when planning fuel treatments as the 

primary objective or as one of many objectives. There is no specific recipe or perfect 

treatment combination; there are always benefits and trade-offs among a seemingly 

infinite number of treatment options that can be applied over time and space. Thus, the 

best suite of fuel treatment combinations incorporates science, experience, values, and 

common sense to create innovative solutions when treating fuels within the context of 

multiple-objective projects.

Important Concepts, Challenges, and Trade-Offs

In dry mixed conifer forests, fuel treatment planning and implementation are often 

limited by the forest conditions that result from past management actions (see chapter 4). The resulting dense forests containing late-seral species (for example, grand fir 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 107

and white fir) can lack large, fire-resistant trees, which limits the type and intensity of 

treatments that can be used. Because of the productivity of dry mixed conifer forests, 

the longevity of a fuel treatment will be reduced. Therefore, plans should include some 

consideration of continued surface and crown fuels maintenance. In addition, a mosaic 

of fuel treatments may need to be dispersed over time and space to allow the landscape 

to grow and develop diverse successional stages and plant community structures and 

compositions that reflect vegetative patterns found in either a low severity or mixed 

severity fire regime (Halolfsky and others 2011; Perry and others 2011). In places where 

low severity fires maintained historical composition in the dry mixed conifer forests, 

vegetation pattern and structure could reflect similar attributes in dry ponderosa pine 

forests. However, these sites will need continued disturbance over time to maintain the 

preferred forest structures and compositions. In the mixed severity fire regime, treatment 

location is related to the diversity of topography, soils, and other biophysical settings 

(refer to Chapter 2).

Dry mixed conifer forests are fire dependent. Thus, even when fuel treatments are not 

the primary objective, we recommend that some discussion concerning fuels and fire 

occur during objective development. For example, timber production requires a longterm investment, and in fire-dominated forests, wildfire always provides an element of 

uncertainty. It may be beneficial to integrate fuels planning into timber management 

plans, thus accepting and addressing the potential for wildfires and recognizing potential 

post-fire outcomes. Within a wildlife management context, if there is a place particularly 

important for a wildlife species a question such as: if a wildfire occurred what would 

be the result? Discussing this question may lead to identifying opportunities to protect 

or minimize damage by implementing fuel treatment in strategic locations. Including 

fuels planning with other objectives makes articulating the management objectives for 

a particular area one of the most difficult tasks in forest management, but it is a critical 

element that must be done with care and thought. By fully understanding the treatment 

objectives, desired forest conditions can be described over time and space and a series 

of treatments can be designed to meet these other objectives.

Manager comment: The planning stage of a fuel treatment involves many players 

that represent a variety of resources. Each resource specialist has a stake in the outcome of treatments and is tasked with the responsibility of maintaining the integrity 

of their specific resource. Often, impacts of the proposed treatment negatively affect 

one resource while benefiting another. These impacts require compromises by some 

resource specialists to reach a mutually agreeable management action.

Heterogeneity Matters

Manager comment: The need for effective fuel treatment patterns on the landscape 

is not limited to extreme fire weather. In fact, fuel treatments can be least relevant 

in periods of extreme fire behavior, which often create conditions beyond limitations 

of a reasonable treatment prescription. Large fires under less extreme fire weather 

conditions may be managed for cost containment, reducing firefighter exposure, or 

resource benefits, and the spatial patterns of treatments should be designed for these 

potential objectives as well.

Manager comment: For mixed conifer forests, where multiple objectives such as 

maintenance or improvement of ecological and/or wildlife values are desired, it is often 

not possible to reduce surface and ladder fuels to a point that adequately diminishes 

potential for transitions to crown fire. Often, the best way to harmonize competing 

objectives is through a mix of treatments of the fuels profile (to an acceptable degree), combining the removal of canopy fuels with the creation of small openings 
108 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

and preservation of other patches with tighter crown spacing. In this way, a crown 

fire may initiate, but it won’t gather momentum as it is constantly disrupted by areas 

of wider spacing and gaps and will drop back to the ground. If the fire weather and 

fuels conditions realign for the fire to climb back into the canopy somewhere else, 

if designed properly, the fire will again hit an opening and drop back to the ground 

(it is very difficult to simulate this design in current computer models, by the way).

Again, this prescription may or may not be acceptable given the objectives of the 

fuel treatment. Treating for particular types of fire behavior may only be acceptable 

in areas far from the wildland urban interface. Any treatment, however, will result 

in fire behavior (and effects) that is an improvement over the untreated alternative; 

however, this may not be a surface fire that can be safely attacked by ground forces. In 

the right location, more severe fire behavior may be desirable (and would contribute 

to resource benefits). A more hazard-centric and homogeneous prescription is likely 

to fit better in treatments designed to create defensible space. Or perhaps in the same 

project area as the canopy fire treatment described above, but near a road that fire 

mangers determined is a good location for a fuel break. There are some areas where 

fire management control, firefighter safety, and hazard reduction are always favored 

over other objectives.

Often, creating heterogeneity within treatments and across landscapes is the most 

efficient way to meet multiple objectives. This idea has been suggested by a number 

of researchers. Reinhardt and others (2008) suggested that a more realistic approach 

to fuel treatments is to “focus on creating conditions in which fire can occur without 

devastating consequences.” Larson and Churchill (2012) suggested that restoration efforts in ponderosa pine and mixed conifer forests introduce or retain spatial variation 

in structure and composition within treated areas. Modeling approaches have indicated 

that the effect of many patches containing slow-burning fuels dispersed throughout the 

landscape in a herringbone pattern can disrupt the forward progress of a fire and create 

variability in the intensity of the fire as it moves across the landscape (Brackebusch 

1973; Finney 2001). Although an ideal strategic distribution of fuel treatments may not 

be possible and may be difficult to implement, the key point of the herringbone pattern 

concept suggests that heterogeneity matters when integrating fuel treatment planning. 

Jain and others (2008) advocated heterogeneity and diversity across landscapes and 

presented different ways to implement heterogeneous treatments. Graham and Jain 

(2005a) developed the free selection silviculture system to allow for the introduction of 

heterogeneity. Perry and others (2011) stated that a “large amount of edge and clumpiness 

in forest structure, composition, and seral status within and among patches provides a 

rich intermingling of habitats for early, mid-, and late-successional specialists as well 

as a variety of individual species.” The authors also stated that forests that evolved with 

mixed-severity fire regimes “exhibit temporal as well as spatial variability.” These suggestions all advocate some form of heterogeneity to be introduced into management 

scenarios designed to address a variety of integrated objectives.

Manager comment: While the concept of Finney’s herringbone design was described as intriguing, placement of the herringbone design often is not practical due 

to values at risk, land ownership patterns, and many other factors. In addition, other 

disturbances do not work as specifically as fire; beetle attacks do not necessarily follow the herringbone pattern.

There is evidence that a range of forest structures and compositions can be resilient, 

including closed canopy conditions (Jain and others 2007). For example, self-pruning 

increases canopy base heights and shading from the overstory canopy reduced growth 

rate of understory fuels, thus retarding ladder fuels development (fig. 7.1). Areas that 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 109

Figure 7.1. Canopy base height in the photograph (way above individual’s head) was created through self-pruning. When trees grow 

close together, the lower branches die, raising 

the canopy base heights. Photo by Theresa B. 

Jain, U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station.

have very little amounts of dead fuels and that contain succulent plant species and an 

abundance of live fuels can be also very resilient (fig. 7.2). Plantations treated with 

prescribed fire to remove surface fuels and promote herbaceous and young tree 

compositions tend to be less susceptible to crown fire. Dry forests with large, tall, fireresistant trees that have high canopy base heights dispersed throughout the landscape 

also are very resilient (Agee and Skinner 2005). The biophysical setting is likewise an 

important element to integrate into fuels planning. Topographic diversity drives the 

mixed fire regime and favors a variety of species compositions and structures. For example, high-density, multi-story stands that contain western redcedar in Idaho are often 

located in stringers adjacent to dry mixed conifer (Daubenmire 1980) (fig. 7.3). These 

locations have deeper soils, higher relative humidity, more soil moisture, or lower air 

temperatures than the neighboring slopes or ridges.

Integrating heterogeneity, biophysical setting, and a variety of structures on the 

landscape is essential for achieving desired conditions for the dry mixed conifer forests. 

The challenge in developing landscape-scale treatments is getting from the conceptual 

stage through planning to implementation. The following section discusses this process 

and provides examples of where others have been successful at developing a planning 

process that favors integration over mitigation.
110 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 7.3. Illustration of the topographic relationships to vegetation within dry mixed conifer forests (adapted 

from Daubenmire 1980).

Figure 7.2. Different successional stages and past treatments affected 

fuel moisture and subsequent resilience in wildfires. The combination of past treatments, lack of dead fuels, and roads contributed to 

preventing this plantation from burning. Photo by Theresa Jain, U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research 

Station after the 2000 Bitterroot wildfires. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 111

Manager comment: Prioritizing the location of a fuel treatment is often dictated 

by many factors besides the most strategic location. Often, fuel treatments are located 

where National Environmental Policy Act (NEPA) (Environmental Assessment [EA] 

or Environmental Impact Statement [EIS] has been conducted and signed) has already been performed, such as in timber harvest units or wildlife enhancement areas. 

Although these areas are not always the best location for strategic placement, many 

managers cited that these integrated projects allow more efficient use of monetary and 

personnel resources. On some forests, fuel treatments are limited to the WUI (wildland 

urban interface), while others have the ability to treat outside the WUI. In many areas, 

County Wildfire Protection Plans (CWPP) designate where the treatments are placed. 

When managers were given the opportunity to be strategic with their fuel treatment 

placement, they considered factors such as wind flow patterns, topographic features, 

roads, fire history, and past management activities. In addition, higher prioritization 

of areas with mid- to late-seral species can provide opportunities to promote early 

seral species like ponderosa pine, western larch, and Quaking aspen.

Integrated Treatments

Manager comment: The key to any management activity is to have a clear understanding of the objectives and goals you are working toward. The objectives identified 

by the resource specialists were similar across the region: modify fire behavior and 

severity, create resilient forests, protect values at risk, and provide for firefighter safety. 

How these objectives were achieved often differed greatly depending on several factors, including: location (WUI versus non-WUI), funding, risk of litigation, market 

availability, leadership, smoke limitations, and wildlife concerns.

Manager comment: One of the most important elements in planning is to clearly 

define the desired future condition of the treatment that meets the objectives and goals. 

Too often, the focus of fuel treatments is on the immediate short-term benefit of the 

reduction in fire hazard. We know that forests are dynamic and maintenance of low 

fire hazard is required. However, funding and short-term planning windows prevent 

the successful and guaranteed planning and implementation of future maintenance 

treatments.

Manager comment: A blend of approaches can be melded in a single planning effort, 

with greater emphasis on hazard mitigation near structures or other areas of concern 

(for example, municipal watersheds) and on meeting other objectives in other areas.

There are unique differences between planning processes that emphasize mitigation 

and those that attempt integration. Mitigation planning first develops a proposed action 

with only one or a few objectives in mind. The action is then evaluated in terms of its 

potential impact on other values such as wildlife species and soil productivity. Based 

on results from this analysis, treatments are mitigated to avoid direct impacts on these 

other values. Integration is the blending of multiple resources that lead to the integrated 

design of objectives which are then used to develop management strategies for treatment 

placement and design (Stockmann and others 2010). This favors communication and 

mutual learning among different disciplines. The success of using integrated management 

strategies is dependent on the relationships among the involved managers, the public, 

and the element of uncertainty associated with alternative development of treatments.

To address many of the challenges in planning, we highlight an integrated planning process outlined in a publication by Stockmann and others (2010) that focuses 

on integrated fuel treatment projects and a multi-objective process called Integrated 

Assessment Modeling. This recent case study, completed within a dry mixed conifer 
112 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

forest in our synthesis area, illustrates many of the challenges expressed in our discussions with managers.

An integrated planning process focuses on multiple-objective planning rather than 

single-objective planning from the beginning of the project. It favors a transparent and 

interactive process that offers opportunities for understanding ecosystem complexity, 

stakeholder positions, and clear articulation of decision trade-offs and benefits. Stockmann and others (2010) stated it as: “application of the approach... especially within 

a collaborative working environment... may increase support for more balanced land 

management decisions. This has the potential to improve implementation success rates 

and reduce time and resources spent in litigation.”

Stockmann and others (2010) provided a conceptual framework for integrating fuel 

treatments into multiple resource objective project planning. It is intended for use with 

projects attempting to develop and place strategic fuel treatments. The authors’ objective 

was to use various reporting metrics in project-level NEPA analysis to summarize how 

well alternatives meet the project objectives and to identify costs, benefits, and tradeoffs. Monitoring can confirm our expectations or force us to modify those expectations 

in future projects. If these measures are used in multiple projects and with adequate 

monitoring, it may be possible to make statements eventually about how well agency 

programs are meeting the descriptions of alternatives in Forest Plans. So, for the most 

part, the approach is focused on the NEPA environmental analysis more than proposal 

development (Keith Stockmann, personal communication). The iterative process Stockmann and others (2010) described uses five steps.

Inclusion of stakeholder preferences and concerns begins early in the process at 

Step 1. Having stakeholders involved in early planning stages creates a collaborative 

decision framework that can be useful throughout the planning process. When alternatives are constructed and refined in Step 5, the process provides another opportunity 

for stakeholder input that ties back to Step 1.

Step 1. “Convert resource assessments and observations into a preliminary set 

of management issues, leading to a ‘purpose and need’ statement with clearly stated 

management objectives.”

The goal is to identify relevant resource issues and use them to create management 

objectives. There is no limit to the number of objectives. Moreover, the list may actually contain objectives that address fire and fuels, vegetation, watershed, fisheries, 

soils, wildlife, aesthetics, economics, social contributions, air quality, and threatened/

endangered/sensitive plant species. Objective statements should be short and concise. 

An example statement to address fire and fuels is: change forest structure so that crown 

fire is less likely to occur under more extreme fire conditions. These conditions could 

be further specified based on identified thresholds, such as the 80th percentile in fire 

weather conditions. An objective statement for wildlife may be to ensure habitat attributes for Federally listed, threatened, or sensitive species are identified and incorporated 

into the fuel treatment design. This step is also used to evaluate whether the identified 

objectives complement or conflict with each other, and can assist teams attempting to 

identify whether each objective should be meshed into the analysis and the degree to 

which it should be emphasized.

Manager Comment: It is a challenge to integrate wildlife habitat preferences that 

require dense, multi-storied, and open conditions.

Step 2. Develop an integrated assessment modeling framework by translating management objectives, including the no action alternative, into quantitative and qualitative 

metrics (measureable characteristics that gauge some quantifiable component) that 

evaluate the diversity of alternatives can meet the identified objectives.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 113

The purpose is to develop ways to measure, model, and evaluate whether the proposed 

action will achieve the objective or objectives. A variety of techniques and decisionmaking tools can be used to quantify whether a treatment has achieved an objective 

(in the absence of an actual test from a wildfire). For example, if the objective is to 

reduce the extent of crown fire, then FARSITE, FlamMap, FFE-FVS, and other tools 

can indicate potential changes in the fire type from a crown fire to a surface fire (refer 

to Appendix B for list of possible tools). An objective related to fisheries may involve 

the removal or improvement of man-made barriers from streams to allow upstream or 

downstream passage of aquatic species. Another objective may be to improve the resiliency of vegetation to insects and disease by favoring resistant species compositions. 

This could be evaluated by performing growth predictions for resistant species such as 

western larch, sugar pine, and ponderosa pine using FFE-FVS. Although these resource 

objectives may not be the primary reason for a fuel treatment, they could be considered 

as secondary benefits and identified as options during the analysis process.

In addition, those who implement the decision, including contract officers, sale 

administrators, burn bosses, harvest and road engineers, should early and periodically 

throughout the planning phase be included in the discussions to ensure that the plans 

are economically and physically feasible.

Manager comment: Models and decision-making tools are used to validate expert 

knowledge and address uncertainties.

Step 3. Develop a format to display the benefits and trade-offs among the metrics 

that were developed in Step 2 for each alternative.

Two components are in Step 3: (1) decide on the technique or method that will be 

used in the comparisons among alternatives, and (2) weight the relative importance of 

the various objectives identified in Step 1. Ultimately, the goal is to produce a performance (benefit) and trade-off report. This step provides a quantitative mechanism for 

evaluating and communicating the trade-offs among planning team members, partners, 

stakeholders, and decision-makers.

Step 4. Assign metric values for the no-action alternative.

The no-action alternative reflects the current conditions, which provide a baseline 

for comparison between the treatment alternatives, and can help evaluate the effects of 

future changes from the different treatment options. The goal is to predict how these 

conditions are expected to change with treatment.

Step 5. Develop the various silvicultural treatments and/or activities suitable for 

meeting objectives.

Once different management scenarios are developed, an analysis is conducted to evaluate resource trade-offs across the management alternatives, thus refining the preferred 

alternative. Ideally, treatments or activities can fulfill multiple objectives. For example, 

road upgrades on haul routes can reduce sediment delivery to fish-bearing streams and 

allow passage of aquatic species. Another example is removing ladder fuels that compete 

with large trees for water and nutrients to increase residual large tree growth.

A number of challenges consistently occur in the planning process. Accounting for 

uncertainty when modeling alternatives is always a challenge because of the inherent 

assumptions and simplifications in the modeling process; however, model results are 

useful in showing different results as a function of different scenarios. Therefore, it is 

important to document selection criteria, assumptions, limitations, and rationales used.

A good example of documenting the process is the use of a silvicultural prescription, 

which details the timing and treatment specifics throughout the life of a forest (silvicultural system). Although the prescription may be modified over time, it incorporates 

long time frames and addresses treatment longevity.
114 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

The Silvicultural Prescription

Manager comment: Although there are places for targeted treatment planning 

with single objectives, integrated fuel treatments help to develop an awareness and 

understanding of complimentary and contradictory resource objectives and the need 

to develop prescriptions that best harmonize them.

Silviculture is founded on the basic science and life history of forests. The practice 

is designed to integrate forest ecology and silvics (scientific study of trees and their 

environment) in order to develop treatment pathways over time and space (a silvicultural 

system) that influence vegetation growth and dynamics for achieving forest management 

objectives. Developing a silvicultural prescription requires integrating knowledge from 

multiple disciplines (fig. 7.4). Within a fuel treatment context, this involves working 

with fuels specialists and fire practitioners. If the project is driven by multiple objectives, integration will require a close working relationship among many disciplines. A 

silviculturist is trained to ask the right questions and think through a process in order to 

develop a series of fuel treatments that address single or multiple objectives.

We suggest five concepts that support a silvicultural system that may be useful in 

developing a fuel treatment strategy (Step 5 from the integrated management process) 

(Nyland 2002). These concepts should be integrated to subsequently determine what 

treatments can be applied in which locations to meet the desired future conditions over 

the short and long term.

1. The interaction of vegetation composition and structures with manmade, physical (fire), biological (insects and disease), and environmental (wind and ice) 

disturbances. All vegetation is a fuel for fire, so it is important to understand 

vegetation (fuel) dynamics.

2. Plant vigor in relation to biomass produced, growth rate, and disturbance 

resistance. This knowledge can be used to understand treatment longevity.

3. How current conditions coupled with changes in canopy opening and soil substrate influence the subsequent growth and development of vegetation. This can 

be related to the potential effect of management actions on fuel dynamics.

4. The combination of treatment types and timing of implementation best suited for 

the site and the landscape to meet future desired conditions over time and space.

In terms of fire resilience, this requires working with experts in fire behavior to 

understand large fire movement over a landscape and determine the best location 

for fuel treatments. In addition, it involves developing an understanding of the 

diversity of vegetation that will create heterogeneity in post-fire outcomes.

5. Identify the forest structure, composition, and landscape pattern that will meet 

multiple objectives given the resources available for implementation. In this 

sense, implementation resources include elements such as road access, slope 

limitations, and economic feasibility. Therefore, to develop the silvicultural prescription, specific characteristics of the critical infrastructure need to be identified.

A suite of treatments can be developed that incorporates all of these concepts, 

including:

	 •	 Harvests: can include regeneration cuts, improvement cuts, and other large 

tree manipulation; often not considered a “fuel treatment.” Harvests introduce 

younger age classes and different forest compositions.

	 •	 Surface treatments: prescribed fire, grapple piling, or mastication, determined 

by the residual tree species and goals for treating surface fuels.

	 •	 Regeneration potential: covers both natural and planting regeneration and can 

include all plant species (not only trees), recognizing which species may respond 

to the treatment and their potential growth rate and potential fire resistance.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 115

	 •	 Tending: can include thinning, cleanings, weedings, and pruning to address 

ladder fuels.

	 •	 Placement, patch size, and spatial variation within patches: the concept of 

landscape silviculture (applying treatments with an eye on the landscape effects) is evolving. Specific prescription parameters such as the placement of 

treatments, juxtaposition of patches, and favoring irregularity in composition 

and structure within patches are being developed and tested (for example, Abry 

and others 1999; Graham and Jain 2005; Graham and others 2007; Jain and 

others 2008; Larson and Churchill 2012).

It is important to note that a silvicultural prescription is not developed in isolation, 

but rather through strong leadership from decision-makers and partnerships across all 

disciplines involved in planning the project. The silviculturist has the education and 

expertise to write the prescription but depends on the planning team to provide key elements, desired future conditions, and implementation skills (such as preparing a burn 

plan). A significant “artistic” component is required to bring all of the elements together 

to: (1) write a prescription; (2) implement the treatments given the infrastructure available (contracting, funding, accessibility, and personnel); and (3) monitor outcomes and 

apply adaptive management. While these steps can guide the process, they are not a 

strict recipe that can be applied to every situation. Fuel treatment planning requires a 

great deal of experiential learning.

Figure 7.4. In fuels treatments, silviculture is the art and science of integration. 

A silviculturist cannot work alone, but rather depends on a variety of expertise to 

describe a set of desired future conditions. Designing fuel treatments alone from the 

site to the landscape takes expertise in fuels and silviculture. However, integrating 

other objectives into fuel treatment planning takes the interaction among several 

disciplines to blend and develop a fuel treatment (adapted from Nyland 2002). 
116 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Communication in Planning

Interdisciplinary teams, science teams, and a variety of multiple discipline teams can be defined as the “pooling of tangible 

resources, such as information, money, labor, etc., by two or more [individuals] to solve a set of problems which neither can 

solve individually” (Gray 1985: page 912). When working with multiple agencies and stakeholders in resource management, 

the term “collaboration” is often defined as “building understanding by fostering exchange of information and ideas among 

agencies, organizations, and the public, and providing a mechanism for resolving uncertainty” and “effective decision making 

though processes that focus on common problems and build support for decisions” Wondolleck and Yaffe (2000: page 8).

Although Sturtevant and Jakes (2008) emphasized these elements in collaboration, many of the same elements can be used 

when working within interdisciplinary teams (fig. 7.5). Fuel treatment planning begins by assessing the risk (Why here?), 

describing the goals (Why now?), and evaluating the alternatives. This evaluation often involves sharing information, building relationships, pooling resources, and conducting outreach among disciplines. Strong leadership coupled with a team 

that incorporates many of these elements can lead to shared responsibility, implementation of an integrated fuel treatment, 

mutual learning, and building of relationships and networks.

Many of these elements also apply to successful integrated science teams. In some cases, these teams are more ecologically 

driven and may be focused on basic science. However, interdisciplinary applied science also considers the social, economic, 

and ecological aspects of research questions. The success of integrated science teams depends on establishment of common 

goals (research objectives) and collaboration among scientists who may have different views. Thus, trust, collaboration, and 

relationship building are essential on scientific teams. Scientists pool resources (people, funding, and instruments) and learn 

from each other through considerable information sharing. Outreach is necessary when building teams and obtaining the 

needed science expertise to address a research problem. The entire process is built on mutual learning, and scientists judge 

research effectiveness through social acceptance of the relevance, impact, and scope of the research results. 

Figure 7.5. Sturtevant and Jakes (2008) described the elements of teamwork and collaboration that lead to successful outcomes in fuel treatment planning. There are three main categories: context, team or collaborative process, and 

outcomes. Teams can use these basic concepts in fuel treatment planning. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 117

Decision Support Tools

Manager comment: There are many decision support tools and all must have a use, 

otherwise they would not have been developed. However, some are too complicated or 

cumbersome and there is insufficient time to learn them or use them effectively. The 

tools that are commonly used tend to have regional or national support.

Integrated management sometimes requires use of decision support tools, and many 

are available for fuels management; we identified more than 20 fuels planning tools. 

Some tools such as BehavePlus, Fire Area Spread Simulator (FARSITE), and FlamMap 

address fire behavior. Others such as FOFEM, BlueSky, and Consume focus on fire effects and smoke emissions. The Fuel Characteristic Classification System and Natural 

Fuels Photo Series characterize fuels. Internet portals such as FIREHouse and FRAMES 

are available to access tools. Some portals offer training and downloadable software for 

sets of tools as well as a variety of fire specific databases that provide information on 

fire history, plant responses after fire, and Forest Inventory and Analysis. There are also 

decision support user interface platforms for obtaining and using results from multiple 

models (for example, the Interagency Fuels Treatment Decision Support System).

With the expanding availability of tools, managers are challenged to determine which 

best fits their needs. It can also be difficult to find the time, data, and expertise to use 

a tool effectively. We have identified some published guides that are designed to help 

managers choose among the variety of decision support tools available and use them 

effectively (see Appendix B for short summaries and links to appropriate web sites).

Conclusion

A key aspect of integrating fuel treatments is to fully define the conditions under 

which they are designed to modify wildfire behavior and burn severity. This can be 

done through description of the targeted forest conditions over time and space. It is 

also important to integrate multiple objectives and develop a fuel manipulation design 

that weaves together a variety of forest structures and compositions. Compromises are 

always made in integrated planning, but if the integration begins early in the planning 

process, a thorough evaluation of the benefits and trade-offs can be clearly articulated. 

Successfully creating resilient forests may require more than a focus on the number of 

acres treated; treatments must also be evaluated in terms of the effectiveness at achieving fuel treatment objectives and other important elements in dry mixed conifer forests. 

Strong partnerships and working relationships with silviculturists, wildlife biologists, 

hydrologists (including those who implement the decision), and stakeholders are critical 

to develop integrated management strategies.

Further Reading

Peterson, David L.; Evers, Louisa; Gravenmier, Rebecca A.; Eberhardt, Ellen. 2007. A consumer guide: 

tools to manage vegetation and fuels. Gen. Tech. Rep. PNW-GTR-690. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station. 151 p. (Provides the most comprehensive summary of available tools as of 2007. We list those tools and others that have been developed 

since 2007 in Appendix A. In addition to providing a list of tools, the authors also described how to 

select a particular model, provided examples of how to use the decision support tools at various spatial 

scales, and suggested steps to take in the planning process.)
118 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Stratton, Richard D. 2006. Guidance on spatial wildland fire analysis: models, tools, and techniques. Gen. 

Tech. Rep. RMRS-GTR-183. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 15 p. (Summarizes and guides spatial wildland fire analysis, including models, tools, and techniques. This report guides fire managers, planners, and specialists in analyses using 

FARSITE, FlamMap, REPAT-Term, KCFAST, and FireFamily Plus. The authors also provide detailed 

instructions on conducting a spatial fire analysis and explicitly list the geographic information systems 

data requirements and considerations of conducting an analysis, including calibrating the models.)

Reinhardt, Elizabeth D.; Keane, Robert E.; Calkin, David E.; Cohen, Jack D. 2008. Objectives and considerations for wildland fuel treatment in forested ecosystems of the interior western United States. Forest 

Ecology and Management 256(12): 1997-2006. (Describes the reality of what fuel treatments can and 

cannot do. This publication provides a good discussion of the benefits and trade-offs associated with 

fuel treatments.)

Stockmann, Keith D.; Hyde, Kevin D.; Jones, J. Greg; [and others]. 2010. Integrating fuel treatment into 

ecosystem management: a proposed project planning process. International Journal of Wildland Fire 

19(6): 725-736. (Describes a process that was applied on the Bitterroot National Forest.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 119

Chapter 8

Mechanical, Chemical, and 

Biological Fuel Treatment Methods

Chapter Rationale

Depending on location, timing, and objectives there are a variety of methods that can 

be used alone or in combination to treat different fuels. In chapters 8 and 9, we describe 

the methods available for treating fuels. Chapter 8 focuses on mechanical treatments and 

herbicide and biological techniques. Chapter 9 is focused on fire as a tool. We discuss 

when and where these methods are best suited and present the basics of planning and 

implementing particular methods. The motivating questions that guided this chapter are:

	 •	 What fuels should be treated?

	 •	 What is the best mechanical method to treat fuels given the management, physical

setting, and economic objectives?

	 •	 What are the fuel strata that require treatments?

	 •	 What alternative methods are available for treating fuels?

Introduction

There are a variety of techniques to remove or alter plant biomass (live, dead, or decomposed). This chapter provides an overview of three techniques and reference sources 

that may be useful to pursue for more detailed information. The techniques we discuss in 

the chapter are mechanical removal or alteration of biomass, which is the most common 

method used to address fuels. Chemical techniques can be very effective at controlling 

understory vegetation and there is considerable information on implementation. 

Targeted grazing by goats, sheep, cattle, and horses is also another option and we highlight a guide that has a focus on treating vegetation using these biological techniques.

Fuel Strata

There are three broad fuel bed types: ground, surface, and crown (aerial fuels). These three broad categories can be further developed into six layers: (1) canopy, (2) shrubs and small trees, (3) low, nonwoody 

vegetation, (4) woody fuels, (5) moss and lichens, and (6) ground fuels (Riccardi and others 2007; Sandberg and others 2001). Modification of any of these fuel layers has implications for fire behavior, fire 

suppression, and fire severity.
120 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Canopy Fuels

Canopy fuels are those suspended above the ground in trees, snags, and ladder fuels. These 

fuels consist mostly of live and fine material less than 0.25 inches. Depending on the forest, its setting, and inherent disturbances, these canopy layers may be simple and uniform 

such as those that occur in young (30 to 50 years) to mid-aged (80 to 120 years), early seral 

species such as lodgepole pine and western larch. In contrast, dense and highly complex 

upper canopy layers often occur in late-seral, dry mixed conifer forests (see chapter 2).

Ladder fuels provide access for flames from a surface fire to climb into the higher fuel 

layers and tree crowns. Ladder fuels can include lichens and moss, climbing ferns or other 

epiphytes that live on the trees. Some trees such as western larch are receptive to lichens 

growing on their stem or on branches due to bark properties. Other ladder fuels include 

dead branches, vines (such as honeysuckle), leaning snags, and stringy or fuzzy bark such 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 121

as that of western redcedar. Understory trees that reach the lower crown of the dominant 

trees can provide a ladder to the upper crown during a wildfire. These essentially bridge 

the vertical gap between surface and canopy layers. The size of this gap is critical to ignition of a crown fire from a surface fire below. Aerial fuels separated from surface fuels by 

large gaps are more difficult to ignite because of the distance above the surface fire, thus 

requiring higher intensity surface fires, surface fires of longer duration that dry the canopy 

before ignition, or mass ignition from spotting (Byram 1966). Once ignited, high-density 

canopy fuels are more likely to result in a spreading crown fire (active crown fire) than 

low-density canopies.

Surface Fuels

Surface fuels consist of grasses, shrubs, litter, and woody material lying on, or in contact 

with the ground surface (Sandberg and others 2001). Surface fuel bulk densities (weight 

within a given volume), and size class distribution (for example, number of pieces in 0-0.25 

inch, 0.25-1.0 inches, 1.0-3.0 inches, and greater than 3 inches size classes) are critical to 

frontal surface fire behavior (spread rate and intensity). Other characteristics of surface 

fuels that determine surface fire behavior are fuel depth, continuity, and chemistry. High 

surface fire intensity usually increases the likelihood for igniting overstory canopy fuels, 

but surface fuel types with longer residence times can contribute to drying aerial fuels 

above a forest canopy, which also leads to torching (when tree foliage ignites and flares 

up) (Graham and others 2004).

Low nonwoody vegetation includes grasses and low shrubs. Snowberry, spirea, and russet 

buffaloberry are frequent mid-level shrubs. In the dry mixed conifer forests, the ground 

level vegetation or surface fuels are often some of the most important components for 

treating vegetation to influence fire behavior and burn severity. Snowberry, buck brush, 

willow, and greenleaf manzanita, along with juniper are frequent low to mid-sized shrub 

species. Depending on the setting, juniper can have both shrub and tree forms. Mosses and 

lichens grow on rocks and forest floor litter and are sometimes overlooked as a fuel source 

(Riccardi and others 2007). When dry, they can act as a fuel that may favor smoldering 

or long-term heating.
122 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

The influence of woody debris as a fuel is dependent on the bulk density of surface fuels 

and size class distribution of fine fuels (which are more critical to frontal surface fire behavior, spread rate, and intensity) than fuel loading alone. The more productive dry forests 

tend to accumulate relatively high amounts of coarse woody debris (CWD), especially in 

the absence of fire. For example, on grand fir and Douglas-fir forest types in Montana and 

Idaho, CWD ranges from 5-20 tons/acre. In contrast, on ponderosa pine forest types in 

Utah, CWD can range from 5-10 tons/acre, but can quickly accumulate in forests in which 

white fir are the late-seral species (5-15 tons/acre) (Graham and others 1994).

Ground Fuels

Ground fuels consist of humus, the fermentation layer, surface and partially buried rotted 

wood. Needles and bark often accumulate at the base of trees and eventually create deep 

organic layers in which fine roots and ectomycorrhizae of trees and ground level vegetation 

may accumulate (Graham and others 2000). Ground fuels typically burn by smoldering 

and may burn for many hours, days, or even weeks if initial moisture contents are high 

(Frandsen 1991; Hungerford and others 1991). This long duration smoldering can often 

lead to soil damage, tree mortality (high severity), and smoke (Ryan and Noste 1983; Ryan 

and Reinhardt 1988; Wells and others 1979). Rotten material on the ground surface is particularly ignitable by firebrands (small twig segments or bark flakes supporting glowing 

combustion) falling ahead of an advancing fire front (spotting).
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Removing Biomass and Fuels

Fuel treatments are designed to meet short-term and long-term fire management 

objectives. This may include altering fire behavior or influencing post-fire outcomes. 

If the objective is to create a more resilient fire-dependent dry mixed conifer forest, 

consequently live and dead vegetation are fuel and thus have the potential to burn. 

Even the most benign surface fuels will burn during wildfires if they are dry, and fire 

behavior and weather prevent quick suppression (Reinhardt and others 2008). Thus, 

all treatments, even if not designated as fuel treatments, manipulate vegetation and 

subsequently alter fuels.

Determining the specific objectives early in the planning process is critical when 

designing fuel treatments. Depending on the setting, forest management activities may 

need to be viewed within the larger landscape when evaluating fuel dynamics and fuel 

diversity. This is particularly important in the dry mixed conifer forests since a mosaic 

of different fuel structures and compositions are an important component of many of 

the landscapes in which they occur.

To aid in this evaluation, there are basic questions to address. Is altering fire behavior 

the primary concern? Is a treatment designed to support active fire suppression within 

the fuel treatment area during a wildfire? Is treating fuels the primary objective or is 

there flexibility in integrating other resource values? Are the treatments designed to 

create fire resilience in the stand without directly creating space for direct fire suppression activities, while providing options in fire management? What are the long-term 

implications of the treatment? What are the treatment effects on fuels in the short- and 

long-term? This section discusses options and elements to consider when addressing 

these questions.

Treating Fuels to Support Active Fire Suppression

When the treatment objective is to change fire behavior and create protected space 

for active fire suppression, then biomass removal to aid fire suppression efforts tends 

to follow the suggestions of Agee and Skinner (2005): (1) reduce surface fuels, (2) 

increase height to live crown, (3) decrease crown density, and (4) keep large trees of 

fire resistant species. Moreover, the size of the treated sites may need to be effective at 

reducing the energy of the expected fire (especially in the case of a running headfire) 

in order to implement fire suppression tactics. This normally involves creating fuel 

conditions that will support direct suppression activities or act as control points. Within 

the fire suppression zone, safe zones and escape routes could be identified during the 

design phase as well as locations for suppression tactics such as fireline placement, 

burnouts, and other techniques.

A good example of a successful fuel treatment plan was demonstrated in Warm Lake, 

Idaho, in 2007 (Graham and others 2009). Warm Lake is located in north-central Idaho 

on the Cascade Ranger District in Boise National Forest and is a high valued recreation 

area with vacation cabins and resorts. The District, led by Fire Management Officer 

Mark Loseke, designed a series of fuel treatments surrounding the Warm Lake Wildland 

Urban Interface. They identified the values at risk (fig. 8.1), identified the appropriate treatments for the given biophysical setting (fig. 8.2), and implemented treatments 

between 1996 and 2005.
124 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 8.1. Values at risk identified in the Warm Lake Fuels 

project (Graham and others 2009).

Figure 8.2. Series of fuel treatments that were implemented to 

address values of risk (Graham and others 2009).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 125

Table 8.2. The mechanical treatments were designed to remove surface and ladder fuels around the Warm Lake 

values of interest (see figure 8.1) (Graham and others 2009).

Project Year Size (acres) Treatment combination

Warm Lake South 2000 221 Commercial harvest, hand slash, hand prune, hand pile & burn

Warm Lake North 2003 71 Commercial harvest, hand slash, hand prune, hand pile & burn

Warm Lake Highway 2004 182 Mastication

Church Camp 2004 110 Hand slash, hand prune, hand pile & burn

Paradise Valley 2005 124 Hand slash, hand prune, hand pile & burn

Warm Lake East 2005 59 Hand slash, hand prune, hand pile & burn

Table 8.1. Prescribed fire was used in and near the Warm Lake Basin to alter the 

surface and ladder fuels. Treatments began in spring of 1996 and progressed through 2006 (see figure 8.2) (Graham and others 2009).

Project Year burned Season burned Size (acres)

Warm Lake Creek 1996 Spring 480

Reeves Creek 1998 Spring 1,636

Chipmunk Creek 1997 Fall 1,958

Trail Creek 1999 Spring 612

Six-bit Creek 2004 Spring 2,342

Kline Mountain 2006 Fall 438

Total acres 6,348

Large prescribed fires (ranging in size from 438 to 2300 acres) were applied in 1996 

through 1999, 2004, and 2006. Mechanical treatments were completed in 2000 through 

2005 (tables 8.1 and 8.2). Within areas identified for mechanical treatments, canopy 

base heights were raised, overstory crowns were dispersed, and surface fuels in the form 

of slash were removed, either through piling and burning or chipping. On the steeper 

slopes, prescribed fire was used to increase canopy heights and diminish the amount of 

surface fuels (figs. 8.3, 8.4, and 8.5).

These treatments were tested in August of 2007. The treatments provided fire suppression opportunities because it allowed fire crews to burn surface fuels prior to the 

wildfire reaching the treated areas. Spot fires were also easily extinguished in the treated 

areas. The fuel treatments were placed in proximity to the lake, which during the fire 

serendipitously provided a safe zone for firefighters. In this example, the fuel treatments 

were placed within the context of the landscape and incorporated both fine scale and 

broad scale treatment placement. They were large enough in size that fire fighter safety 

was ensured within the mechanically treated areas and three of the four principles outlined by Agee and Skinner (2005) were met, particularly in the mechanical treatments. 

As a result, all identified values at risk were maintained. However, the fuel treatments 

did not stop the wildfires and most likely did not alter the size of the fires. However, 

the treatments facilitated protection of the values at risk, resulting in minimal damage 

to infrastructure while reducing safety risks to the firefighters on the scene, which made 

them successful given the objectives they were originally designed to achieve.
126 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 8.3. The Warm Lake South treatment of ladder and surface fuels. A combination of hand slashing, 

piling, and burning the piles were used to accomplish objectives. The figure shows (a) before treatment, 

(b) hand slashed piles, (c) pile burning, and (d) after treatment (Graham and others 2009). 

Figure 8.4. Before (a) and after (b) treatment of the Church Camp project. Notice red cabin in background (Graham 

and others 2009).

a

a

b

b

c d
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 127

Figure 8.5. Before treatment (a), mulching of surface and ladder fuels (b), and 

after treatment (c) on the Warm Lake Highway Project (table 8.2) (Graham and 

others 2009).

a

b

c
128 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Treating Vegetation (Fuels) to Enhance 

Resilience to Wildfire

In dry mixed conifer forests, resilience to wildfire refers to its ability to recover after a 

wildfire and to have desired post-fire outcomes such as live trees and intact forest soils. 

Even if a treatment is not specifically designated as a “fuel treatment,” the resulting 

vegetation will still influence fuel dynamics. Accordingly, a variety of thinning methods 

can be used to manipulate crown fuels (Graham and others 1999; Peterson and others 

2005). These include low thinning or thinning from below, crown thinning or thinning 

from above, selection thinning or diameter-limit thinning, free thinning, and mechanical 

thinning. In addition, other types of cuttings include non-commercial and pre-commercial 

thinning (in other words, cleanings, weedings, liberation, and improvement cuttings), 

which, depending on what is being removed, may or may not produce forest products. 

Another set of treatments that alter crown fuels, but are rarely considered as fuel treatments are even-aged regeneration cutting methods such as shelterwoods and clearcuts. 

Alternative treatments such as shelterwood with reserves or uneven-aged management 

through individual or group selections are quite effective at altering the spatial arrangement of fuels and overall fuel dynamics over time. Additionally, post-harvest treatments 

designed for site preparation, fuels reduction, and other such treatments affect surface 

fuels in a variety of ways.

Manager comment: Spatial context is crucial when designing fuel treatments to 

reduce the likelihood of crown fire. Nature is messy and not all treatments need spacing between all trees on a given site. Spacing between clumps of trees will effectively 

minimize sustained crown fires and sometimes the clumpy nature of these stands 

provides habitat elements as well.

Dry mixed conifer forests, because of their productivity, individual tree species, 

microsite differences, and a variety of disturbance and fire suppression histories, will 

tend to favor multiple crown classes—dominant, codominant, intermediate, and suppressed. Dominant crown classes are trees with crowns that extend above the general 

canopy and receive full light from above. The codominant crown class includes trees 

with crowns that form the general cover and receive full light from above but little light 

from the sides. Intermediate crown classes contain trees shorter than the codominants 

but with crowns that can be ladder fuels to the dominants and codominants. Suppressed 

trees have crowns entirely below the general level of cover and receive little light from 

above or the sides and are often overtopped.

Each type of thinning focuses on removing or reducing one or more of these crown 

classes. Thinning is also used to influence species composition since many species tend 

to dominate specific canopy layers. Crown thinning removes crowding within the canopy 

cover. Low thinning removes the lower canopy, leaving large trees on the site. Selection thinning removes dominant trees in favor of smaller trees; therefore, trees above a 

particular diameter are removed. Free thinning is very flexible and can increase stand 

diversity by releasing selected trees. It can be used in any of the crown classes for releasing specific tree species such as ponderosa pine (Graham and others 1999) (fig. 8.6).

Regeneration methods, such as shelterwood cutting, seed tree methods, and clearcuts, 

can remove sufficient overstory biomass to diminish crown fire potential for a period 

of time (fig. 8.7 and 8.8) (Graham and others 1999). In addition to removal of trees, a 

variety of surface treatments can be implemented to alter surface fuels, including: grapple 

piling, prescribed fire, and mastication. Therefore, when the objective is to increase forest resilience in fire-dependent forests, all treatments can be considered fuel treatments.

Often the most successful treatments use a combination of techniques that address 

crown, ladder, and surface fuels (Stephens and others 2012). Clear objectives are critical 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 129

Figure 8.6. A 120-year old conifer stand, with dominant, codominant, intermediate, 

and suppressed trees receiving a crown, selection, and free thinning (Graham and 

others 1999).
130 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 8.7. Shelterwood regeneration method the preparatory cut is to improve seed production and 

wind firmness and is typically used before a seed cut. This type of method tends to leave a more 

homogeneous forest structure with trees often regularly spaced. Another type of method also can 

be used is a group shelterwood, which tends to create a heterogeneous structure that may address 

some wildlife habitat attributes (from Graham and others 1999).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 131

Figure 8.8. An individual tree selection system on a 30-year cutting cycle (Graham and others 1999).
132 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

in the implementation of any treatment. Ideally, treatment objectives will include a good 

description of the desired conditions in the short- and long-term, and an understanding 

of the time needed to implement the treatments, which in most cases covers years (as 

was case with the Warm Lake fuel treatments, which took 5 to 10 years to implement; 

some treatments were incomplete when the wildfires occurred).

Types Of Treatments

Mechanical Methods

Treatments to reduce hazardous fuel accumulations in the forests of the western United 

States often involve mechanical thinning since these treatments effectively address high 

levels of fuel continuity (fuels that can support the spread of fire) in high density stands 

and reduce the complexity of prescribed burning. Mechanical fuel treatments use power 

tools or heavy equipment to mechanically remove or rearrange trees and shrubs to reduce 

fire hazard. These treatments can cover numerous steps including: felling, cutting and 

spreading, skidding (or yarding), and processing (for example, grinding and mastication). Mechanical fuel treatments can alter surface fuels, increase the height to the base 

of live crowns, and open the canopy by removing trees (Agee and others 2000). There 

are a number of studies indicating that mechanical fuel treatments that remove small 

diameter trees can be beneficial for reduction of wildfire hazard including crown fires 

(Brose and Wade 2002; Graham and others 1999; Pollet and Omi 2002).

Compared to prescribed fire, mechanical fuel treatments allow a higher degree of 

control for managers and can be used to accomplish objectives related to tree spacing 

and species composition in residual stands. Undesirable species and tree sizes can be 

effectively removed through a mechanical felling approach, followed by skidding the 

felled trees to the roadsides or landings. Smoke management is another factor favoring mechanical fuel treatment. Excessive fuels may be consumed by burning when 

weather conditions and the biomass moisture content are within specified prescriptions, 

but burning is often impractical because of the potential negative effects (for example, 

air quality, risk of escape, damage to soils and residual trees) and time constraints (for 

example, limited operating periods, short burn windows).

Mechanical fuels reduction treatments can produce forest products such as sawlogs 

and energy fuels (fig. 8.9). An integrated approach to harvesting both roundwood and 

biomass, referred to as an integrated harvesting system, is becoming common in fuels 

reduction thinning treatments. This system uses a set of equipment to produce roundwood products while also removing all sub-merchantable size trees and logging slash 

for biomass energy (Han and others 2004). The stewardship contracts utilized by the 

U.S. Department of Agriculture, Forest Service include mechanical fuels reduction 

thinning units that produce both sawlogs and biomass. Revenues generated from selling 

these products may partially or fully cover the expenses of other fuel treatment units 

that produce no sawlogs. Other mechanical fuel treatments include pre-commercial 

thinnings (do not produce sawlogs), hand thinning (lop and scatter), and mastication.

There are various equipment options and systems for mechanical fuel treatments, and 

it is important to understand the capability and limitations for each piece of equipment 

and system in accomplishing fuels management objectives. Timber harvesting equipment and systems are commonly used in fuels reduction thinning projects because they 

were originally designed to handle felling, extraction, and processing of trees. Machine 

types and thinning methods used in mechanical fuel treatment directly affect the fuel 

conditions (surface fuels, ladder fuels, and canopy bulk density) resulting from treatment and the vegetation responses after treatment. Therefore, fuels managers need to 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 133

specify equipment type, size and how it should function in its operation. Selecting an 

optimal set of equipment or system that meets treatment needs at low costs and minimal 

environmental impacts requires a high level of knowledge on equipment options for 

mechanical fuel treatments.

The biomass generated from mechanical fuel treatments can be left on site, removed 

to landings for further treatments (pile or pile and burn), or processed as sawlogs and 

energy fuels. The decision of whether biomass will be left on the site or removed will 

directly affect the equipment selection decision, which determines the outcome of the 

overall fuel treatment. In this guide, we include five major options that are used to treat 

hazardous fuels. Two of these options include leaving biomass on site: mastication and 

hand thinning. The other three options involve biomass removal and include traditional 

forest harvesting systems: ground-based, cable, and helicopter. Each system will have 

different treatment costs and result in different outcomes, effectiveness, and impacts 

on other ecological values. This section details the five major options for mechanical 

fuel treatment.

Retain Biomass

Mastication

Mastication affects fire behavior by altering the orientation, depth, distribution, and 

size of fuels through grinding and shredding shrubs, standing small trees, and downed 

woody material, and leaving a mat of shredded wood on the soil surface (Battaglia and 

others 2010; Glitzenstein and others 2006; Kane and others 2009) (fig. 8.10 and 8.11). 

Figure 8.9. Skidder extracting whole trees to a landing where trees are processed into 

sawlogs or biomass for energy production.
134 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

 Figure 8.10. Horizontal drum masticator effectively masticating a blanket of shrubs.

Figure 8.11. Rotary head masticator (boom-mounted) effectively shredding single trees.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 135

Mastication treatments decrease fire hazard by raising canopy base height of standing trees 

and maintaining the masticated materials close to the ground (Battaglia and others 2010; 

Kreye and others 2011). Nutrients remain on site and soil compaction can be minimal 

if the equipment travels on the masticated materials with minimal to no repeated traffic 

on the same path. Sedimentation or soil erosion following rain or snowmelt from areas 

treated by mastication is of less concern because of the thick layer of mulched wood 

left on site. Mastication is often considered when thick shrubs or dense small diameter 

trees are a prevalent fuel type in a high fire hazard area or fuels have accumulated from 

thinning. Mastication treatment costs can be high, but it is a viable, effective alternative for fire hazard reduction when the treatment area cannot be burned, removal of 

excessive fuels is not economical, or impacts on soil and sedimentation are of concern.

The objective for using mastication is to favor rapid decomposition, and, as mentioned, it is an alternative treatment when no other options will meet the objectives. 

The following elements deserve consideration prior to using mastication. Factors that 

influence decomposition include temperature, moisture, oxygen availability, substrate 

quality, size, and decomposing organisms (Harmon and others 1986). Wood decaying 

fungi have an optimum temperature of 55-85 °F (15 to 30 °C) and optimal moisture will 

vary from 30 to 240 percent moisture content depending on the organisms. The species 

and state of decay targeted for decomposition, along with the wood substrate, are also 

important. The proportion of bark, sapwood, and heartwood will influence decay rate; 

heartwood takes more time to decay than sapwood.

The type of plant also influences the decay rate; shrubs tend to decay before conifers 

(Enríquez and others 1993). True firs decompose quicker than Douglas-fir, western larch, 

and western redcedar, which are more decay resistant. Piece size is also important; if 

the material is ground too fine it may insulate the ground and the pieces may dry too 

quickly, thus disfavoring decomposition. Also, when burned, small pieces tend to be 

dispersed creating fire brands that may ignite outside of the masticated area. Depending 

on the material being masticated, it may be preferable to create chunks or larger shredded material rather than small pieces (aim for leaving material >3” when opportunities 

exist). This accomplishes a couple objectives: it disfavors the increase in fine fuels, and 

larger material will contain higher moisture than small material and longer logs. In addition, larger pieces may address wildlife habitat elements. Treatments should avoid a 

homogeneous mastication bed, leaving areas with no masticated material and isolating 

masticated material with other site preparation or slash treatment methods. Not every 

square foot of the area needs to be treated, so it is important to leave a diversity of 

soil substrates. Mastication increases fine fuels and potentially creates a deep organic 

bed favoring high soil temperatures and long-term smoldering during wildfires; thus, 

evaluation of the short-term versus the long-term benefits, as well as the do nothing 

alternative, deserves consideration (Busse and others 2005; Reiner and others 2009). 

Prescribed burning of masticated material will require different burning parameters than 

typically apply and may be difficult to implement because of the risk of smoldering. 

Other options such as hand slashing and grapple piling may need some consideration. 

For further discussion concerning burning masticated material refer to unique situations 

in chapter 9, prescribed fire.

Hand thinning (lop and scatter)

Hand thinning, often referred as lop and scatter, is used to reduce the connectivity 

of vertical and horizontal fuels by felling trees, cutting them into lengths, and spreading severed woody materials over the ground (flowchart 8.2, fig. 8.12). This method is 

often considered when it is impractical to remove excessive fuels (in other words, trees 

and shrubs) from the site due to lack of wood markets and road access. Mastication 
136 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 8.12. Trees were cut to lengths and left on site for natural decomposition.

may be used for this situation, but steep slopes (>30 percent) make it difficult to use 

masticators. A lop and scatter approach may also be appropriate when there is a small 

amount of non-merchantable, small-diameter trees that need to be treated to accomplish 

fire hazard reduction objectives. This method is also used as a pretreatment for pile and 

burn or prescribed under-burning. Nutrients from cut trees remain on site, which may 

be an important factor for some nutrient-lacking sites or vegetation types demanding a 

high level of nutrient recycling. Large woody debris and limb/tops also create favorable 

environments for wildlife and microorganisms. To enhance decomposition, severed 

materials need to be in contact with the ground, the location where the most active 

decomposition occurs. Nevertheless, natural decomposition of fuels left on the soil 

surface may take several years (Carlton and Pickford 1982) and untreated residue may 

result in undesirable fire behavior (Stephens 1998; van Wagtendonk 1996). To address 

this issue, pile and burn is often combined with a hand thinning method. Chainsaws are 

commonly used to fell shrubs and trees and cut them into small pieces to be scattered 

throughout the treated stand.

Remove Biomass

Fuels reduction thinning using ground-based systems

Treatments to reduce hazardous fuel accumulations in forests frequently involve mechanical thinnings that retain larger trees and thin dense stands, effectively reducing fuels and 

allowing prescribed burning (Fiedler and others 2003; Graham and others 1999; Pollet 

and Omi 2002) (flowchart 3). In the context of fuels reduction, a whole tree harvesting (WT) method is often favored by managers since the method effectively removes limbs 

and branches as well as boles at the time of thinning, leading to a minimal increase in 

surface fuel loading (fig. 8.13). Another typical harvesting option for fuel treatments is a 

cut-to-length (CTL) method that processes trees to log length at the stump (fig. 8.14). 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 137

Figure 8.13. Feller-buncher felling and creating bunches of whole trees for skidding.

Figure 8.14. Harvester felling and processing trees at the stump, leaving limbs and 

tops on site. 
138 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

 The processed logs are extracted using a forwarder, leaving all branches and treetops 

on site. This approach reduces problems related to nutrient retention, but short-term 

surface fuel concerns remain because of limbs and tops that are often concentrated on the 

operating trail to minimize soil disturbance from log extraction activities. Roundwood 

products (for example, sawlogs and pulp wood) and hog fuel for energy production may 

be harvested from a WT thinning, but it is common to produce roundwood products only 

in a CTL thinning. While production rates and costs for these two systems are comparable, 

impacts to on-site productivity and residual trees can differ significantly (Adebayo and 

others 2007; Han and others 2009; Han Kellogg 2000; Lanford and Stokes 1995, 1996).

Woody biomass generated from thinning may be piled at landings for decomposition 

or burning. More commonly, forest products such as sawlogs and pulpwood are recovered 

from the trees removed from thinning treatments and sold to local markets to generate 

revenues. Integrated harvesting strategies that combine mechanical thinning and biomass 

harvesting are often preferred over harvesting and burning treatments because of smoke, 

risks of fire escape, and residual tree damage. An integrated harvesting system uses a set 

of equipment to produce roundwood products and remove all sub-merchantable sized 

trees and logging slash. Compared to typical harvesting systems that only remove merchantable materials, an integrated harvesting system often requires a chipper or grinder 

that converts sub-merchantable size trees and logging slash into materials of uniform size 

that can be handled, transported, and/or stored efficiently (Lambert and Howard 1990).

Fuels reduction thinning using skyline yarding systems

Because of the effects of pre-heating, fires spread rapidly on steep slopes (Rothermel 

1983) and greater concerns over high levels of fuel loading exist on steep slopes than 

on gentle slopes. However, fuels reduction thinning on steep ground is more difficult 

and costly to implement than doing similar work on flat terrain. Cable yarding systems 

can be used to reduce excessive fuels on steep slopes (>35 percent) not operable using 

ground-based skidding machines. In cable yarding, logs or whole trees are partially or 

fully suspended to the skyline and pulled to the landing or roadside areas (fig. 8.15). 

Fuels reduction thinning using skyline yarding systems utilizes the same methods as 

cable logging: a yarder is set up at a landing or road to yard whole trees or logs from 

the stand. For the purpose of reducing fuels in the forest, sub-merchantable size trees 

as well as large, merchantable logs/trees may be removed from the stand.

A new approach for skyline operations is the use of yoaders (yarding equipment with 

no guylines) to remove small-diameter trees for short yarding distances on steep slopes 

and unstable soils. Yoaders have been shown to be highly productive, comparable with 

conventional skyline yarders due to reduced skyline road/landing changing times (up 

to 44 percent reduction with no guylines) and short yarding distances (<600 ft) (Largo 

and Han 2004). Yoaders may be effectively used in steep and remote areas since this 

yarder can move into a harvesting unit without roads or skid trails. The use of yoaders 

in fuels reduction thinning is a relatively new concept, but has been considered in many 

places (fig. 8.16).

Fuels reduction thinning using helicopter yarding

Helicopter yarding is often considered when environmental concerns are high, 

road access is limited, and work needs to be done quickly (flowchart 8.3, fig. 8.17). 

Helicopter yarding distance is typically longer than ground-based and skyline yarding 

systems, ranging from 2500 to 5000 ft (Studier and Binkley 1974). This long yarding 

distance requires fewer roads to be built or maintained to implement fuels reduction 

thinning. It should be noted that roads are still needed to access landing locations. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 139

Figure 8.15. Yarder extracting whole trees or processed logs on steep ground (>35 

percent slope).

Figure 8.16. An excavator-based yoader performing a fuel reduction thinning in 

northern Idaho.
140 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Yarding productivity using helicopters is high. For example, consider a small-diameter 

tree thinning operation in Oregon: the net production estimate for helicopter yarding 

using a Sikorsky S58-T was 4.71 MBF per hour compared to only 1.65 MBF per hour 

for the cable yarding system (Born 1995). The major drawbacks with helicopter yarding 

are the high fixed costs (for example, machine prices) and variable costs (for example, 

fuel and labor). For this reason, helicopter yarding is not a favorable option for small 

treatment areas (<500 acres): helicopter yarding may be financially justified only for a 

large contract to minimize fixed costs.

Figure 8.17. Vertol helicopter (10,000 lbs maximum external load) 

yarding in a fuel reduction thinning.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 141

Equipment Selection for 

Mechanical Applications

A systematic approach using a flow chart 

is helpful to identify the right equipment that 

addresses requirements and constraints for 

a given fuel treatment task. The flow chart 

approach requires ranking the important factors (in other words, treatment requirements 

and operational constraints) that influence 

equipment selection decisions. Highly 

ranked variables have a greater influence on 

equipment selection than those listed as low 

priority items. However, in some cases, an 

overriding issue may specify that a certain 

type or size of machine be used for the job, 

or eliminate most equipment options that do 

not meet certain specific needs.

Forest managers need to decide whether hazardous fuels are to be removed or treated 

and left on site. A decision to leave biomass on site eliminates any equipment options that 

are used to harvest biomass. This decision also determines potential forest products that 

may be recovered from mechanical fuel treatments. Once a decision to leave or remove 

biomass is made, terrain becomes the next consideration as equipment operability is 

limited by ground slope. By applying decisions on biomass removal or retention and 

ground slopes, there are five major categories of mechanical fuel treatment systems. 

An overview of this general approach is summarized in figure 8.18.

Once the decision has been narrowed down to one of these five options (mastication, 

hand thinning, ground-based system, cable system, or helicopter system), the next step 

is to work through the steps for each option to specify an equipment option (fig. 8.19). 

The previous sections of this document provided general descriptions of those five options in regards to applicability to meet treatment needs and operational advantages and 

limitations of each option. It is important to note that an initial decision to select one 

of those five options may change to another option because of important constraints or 

requirements. For example, a ground-based system may be selected because hazardous fuels are to be removed from the site and ground slopes are gentle (<30 percent). 

However, it should be noted that soil impacts from ground-skidding activities are a 

major concern. In this case, either a skyline or helicopter system may be selected to 

minimize soil impacts. This case is presented using arrows in the flow chart (fig. 8.20). 

Alternatively, ground skidding may be delayed until the dry season when soil moisture 

content is low (<15 percent; Han and others 2006).

Within the set of equipment options for leaving biomass on site, mastication equipment can be used only on gentle terrain: running a masticator on steep ground may 

create operator safety issues, excessive soil impacts, and increase costs (fig. 8.21). A 

thick blanket of shrubs that cause high fire hazard favors a mastication treatment over 

hand thinning. Mastication also leaves small particles of woody materials, which may 

be favored when leaving untreated activity fuels on site is not allowed. Masticators need 

to be further specified to effectively reflect work conditions and treatment needs. Hand 

thinning (lop and scatter) effectively targets single or multiple stems of trees and shrubs, 

leaving processed materials (cut into several pieces) on the ground. These materials are 

retained on site for decomposition or burned if needed. As an alternative to these two 

options, trees may be felled, chipped, or blown back into the stand.

Selection of Equipment for Mechanical Fuel Treatment: 

A Flowchart Approach

Selecting the appropriate equipment for a mechanical fuel treatment is a 

complicated task because a final decision on equipment selection should 

effectively address: (1) requirements or objectives of mechanical fuel 

treatments, (2) work conditions under which treatment operations are 

implemented, and (3) efficient use of a limited budget. It is important to 

recognize critical variables that override other constraints and requirements. For example, certain sites that are identified for a mechanical 

treatment require that biomass be left on site because of nutrient retention 

concerns. This constraint eliminates any equipment options commonly 

used to extract forest biomass. Further, within the set of equipment options 

that leave biomass on site, there are a wide range of equipment options 

that may work only for certain terrain and stand conditions. This requires 

additional steps to select the best equipment option in terms of type and 

size for a given mechanical fuel treatment task.
142 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Sawlogs Pulpwood, Energy wood

Mechanical Fuels Treatment

Leave Biomass Remove Biomass

Ground slope < 35% Ground slope >= 35% Ground slope < 35% Ground slope >= 35%

Ground-based system Cable system Helicopter system

No Forest Products

Mastication Hand Thinning

YES NO

Decking wood on

road required?

Large

yarder

Medium

yarder Yoader

Logs Chipping or

grinding at

energy plant

Small-scale

CTL system Track size < 10 acres?

Horizontal

drum masticator

Rotary

head masticator

Mechanical Fuels Treatment

Ground slope < 35%

<

Ground slope ≥ 35%

Leave Biomass

Ground slope < 35% Ground slope ≥ 35%

Remove Biomass

NO

NO

YES

YES

Retention Removal

YES

NO

YES

NO

NO

YES

Any one of

these is

YES

YES

NO NO

< 500 ft.

< 300 ft.

500-

1000 ft.

1000-

2500 ft.

> 2500 ft.

YES NO

Any one of

these is

YES

Any one of

these is

YES

NO

NO

NO

YES

Tracked

carrier Wheeledcarrier

• Soil prone to compaction?

• Is ground slope < 20%?

• Are the majority of

 fuels standing trees

 < 8” dbh?

• Is soil nutrient

 conservation a

 high priority?

Ground slope broken or constant?

Constant

Front-end attached

masticator

Broken

Boom- mounted

masticator

Mastication Hand Thinning Ground-based system Cable system Helicopter system

• Track size

 < 500 acres?

• Good road

 access

 available?

Large landings (> 1 acre)

available?

Soil disturbance

concerned

Fuel consumption

method

Natural

decomposition Burning

Thinning,

pruning, piling

method

• Desired outcome

 prohibit fuels < 3 in.

 diameter?

• Is the project area

 adjacent to a road?

Lop and Scatter

method

Hand thin,

chip and

blow chips

into woods

Large

masticator Small masticator

Chunks Mulch

• Nutrient recycling

 required?

• Log extraction

 distance?

Market for short CTL system logs preferred?

WT system

Track size < 10 acres? Small-scale

WT system

Mechanized

WT system Mechanized

CTL system

Product market availability

Chipper Grinder Bundler Debarker- Processor Chipper

Clean chips

Hog fuel

Average external

yarding distance

Small

yarder

Desired outcome: leaves broadcast

slash for nutrient recycling?

Swing Tower

WT system CTL system

Is there more than 10 acres to treat? NO YES

Is the desired outcome chunks or mulch?

Figure 8.18. An overview of selecting a mechanical fuels treatment option and potential forest byproducts using two 

criteria: biomass treatment and ground slopes.

Figure 8.19. This flow chart illustrates the complexity associated with identifying the appropriate mechanical treatment.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 143

Equipment options and methods for retaining woody biomass on the ground 

YES NO

Horizontal

drum

masticator

Rotary

head

masticator

Ground slope < 35% Ground slope ≥ 35%

Leave Biomass

Retention

Any one of

these is

YES

Any one of

these is

YES

NO

NO

NO

YES

Tracked

carrier

Wheeled

carrier

• Soil prone to compaction?

• Is ground slope < 20%?

• Are the majority of

 fuels standing trees

 < 8” dbh?

• Is soil nutrient

 conservation a

 high priority?

Ground slope broken or constant?

Constant

Front-end

attached

masticator

Broken

Boom- mounted

masticator

Mastication Hand Thinning Ground-based system

Fuel consumption

method

Natural

decomposition Burning

Thinning,

pruning, piling

method

• Desired outcome

 prohibit fuels < 3 in.

 diameter?

• Is the project area

 adjacent to a road?

Lop and

Scatter

method

Hand thin,

chip and

blow chips

into woods

Large

masticator

Small

masticator

Chunks Mulch

Is there more than 10 acres to treat?

Is the desired outcome

chunks or mulch?

Mechanical Fuels Treatment

Figure 8.20. A flow chart to select a mechanical fuel treatment option when leaving biomass on the site.
144 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 8.21. A flow chart to select a mechanical fuel treatment option when removing biomass.

Equipment options and methods for removing woody biomass and product recovery

Decking wood on

road required?

Large

yarder Medium yarder Yoader

Logs Chipping or

grinding at

energy plant

Small-scale

CTL system

Track size < 10 acres?

Mechanical Fuels Treatment

<

Ground slope < 35% Ground slope ≥ 35%

Remove Biomass

NO

NO

YES

YES

Removal

YES

NO

YES

NO

NO

YES

Any one of

these is

YES

YES

NO NO

< 500 ft.

< 300 ft.

500-

1000 ft.

1000-

2500 ft.

> 2500 ft.

YES NO

Ground-based system Cable system Helicopter system

Large landings

(> 1 acre)

available?

Soil disturbance

concerned

• Nutrient recycling

 required?

• Log extraction

 distance?

Market for short CTL system logs preferred?

WT system

Track size < 10 acres? Small-scale

WT system

Mechanized

WT system Mechanized

CTL system

Product market availability

Chipper Grinder Bundler Debarker- Processor Chipper

Clean chips

Hog fuel

Average external

yarding distance

Small

yarder

Desired outcome: leaves broadcast

slash for nutrient recycling?

Swing Tower

WT system CTL system

NO YES

• Track size

 < 500 acres?

• Good road

 access

 available?

Treatment or utilization of biomass collected at a landing or roadside needs to be 

carefully evaluated when biomass removal is required. This directly affects equipment 

selection decisions. While terrain has a major impact on equipment selection for biomass 

removal, decisions on removing whole trees or bole-wood (in other words, main stem 

wood) only determines tree processing location (in other words, stump or landing) and 

the equipment to be used. Whole tree removal may require a chipper or a grinder at the 

landing to process sub-merchantable trees and forest residues left from sawlog processing. Removing only bole-wood generally does not require a grinder or a chipper at the 

landing because limbs and tops are retained. Surface fuel treatments such as prescribed 

burning or mastication may be necessary to address the short-term increase in fire hazard.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 145

Chemical Control

Marshall and others (2008) provide an excellent discussion of herbicide use for fuels 

management in loblolly pine forests in the southern United States. Rather than repeating 

much of what they suggest, we summarize key points to consider when using a chemical agent to treat fuels, and we point to additional considerations for dry mixed conifer 

forests. There are a series of factors to consider when determining whether herbicides 

can be effective at treating fuels.

Factor 1: Current condition. Herbicides become a practical option when one or more 

of the following conditions occur: (1) overstory trees are able to respond to, release, 

and fill canopy openings after targeted vegetation is killed using herbicides, (2) fast 

growing or sprouting vegetation must be treated at regular intervals to increase the 

longevity of a fuel treatment, and (3) herbicides are the only effective way to remove 

invasive plant species.

Factor 2: Terrain and soils. Similar to mechanical treatments, herbicides have to be 

applied using some type of equipment. Therefore, slopes should be accessible mechanically. Also, if steep slopes surround a relatively gentle sloped treatment area, then the 

treated areas may be of insufficient size or juxtaposition to allow effective or viable fuel 

treatments. Soils are also critical: if they are too sandy, they drain too quickly, limiting 

the effectiveness of the herbicide in killing the roots. In contrast, clayey and loamy soils 

can quickly immobilize soil-active herbicide, making it an impractical option.

Factor 3: Size and type of vegetation. Herbicides become more impractical with 

large and tall vegetation. If the vegetation is low to the ground then the next option is 

to determine if there is a good match between the herbicide and the vegetation targeted 

for removal. Herbicides used for shrub control include 2,4-D, Glyphosate, imazapyr, 

picloram and triclopyr. To control grasses and forbs, atrizine, 2,4-D, sulfometuron, and 

hexazinone are suggested (Peachey 2011).

Factor 4: Effects on fuel. Herbicides do not kill vegetation immediately. Depending 

on the target vegetation, it may take a few months to kill leaves and needles, and up 

to 1–2 years for branches, depending on their size. In addition, herbicides change the 

fuel matrix from live to dead fuels, which may actually increase susceptibility to fire. A 

considerable amount of dead fuel is contributed to the forest floor; yet much is still aerial 

and is not in contact with the forest floor, meaning the dead fuel may not decompose 

or break down quickly. Therefore, using herbicides alone may not be a viable option 

and most likely should be used in combination with prescribed fire or another form of 

biomass removal (Gagnon and Jack 2004).

Factor 5: Cost. The cost of herbicide applications depends on the acreage being 

treated, the mode of application, and the type and amount of herbicide. Although an 

exact estimate depends on many factors, in general, cost per acre increases for manual 

application and decreases for aerial applications. However, this relation is dictated by 

size of the treated area: small areas may be more cost effective using manual application versus aerial application.

In the dry mixed conifer forests this may be a viable option in highly dense shrub 

areas that are too large for a prescribed fire to kill or the burning windows are such that 

prescribed fire is so limited that it is difficult to implement. Herbicide applications may 

also be the best option in areas where there is insufficient value to justify a mechanical 

treatment. Using a two treatment approach—herbicide followed by prescribed fire—may 

be a viable option in treating more difficult fuel beds that are common within these forest 

types. A good source of additional information concerning herbicide use is the Pacific 

Northwest Weed Management Handbook (Peachey 2011).
146 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Biological Control/Grazing

Biological control of surface fuels through targeted grazing is another alternative 

that may be effective under the right conditions. The feasibility of biological control 

depends on whether the appropriate livestock are available (goats, sheep, cattle) for the 

particular species of fuel that needs to be controlled. Also personnel have to be available 

with the proper expertise to herd and manage the livestock. Finally, the livestock have 

to be on site when the plant’s phenological stage is the most conducive to control. When 

these conditions are met, biological control can remove biomass, diminish subsequent 

growth, and reduce seed input (Diamond and others 2009).

Options: Successful implementation is a function of a clear understanding of the target vegetation (fuel) and post-treatment outcome and the appropriate type of livestock 

and species for meeting the particular target (Launchbaugh and Walker 2006). The 

most effective way to combat invasive species is to select the species that consumes 

the targeted plant for control (Burritt and Frost 2006). It is not necessary to select only 

one species; multi-species grazing has some advantages. Multiple species with different 

dietary preferences can provide more even utilization of all forage species, avoiding an 

advantage to one plant species or class of species. Burritt and Frost (2006) illustrated 

a system in which sheep were combined with cattle grazing to control leafy spurge. 

The sheep are used initially to consume the flower heads (when spurge is in the yellow 

bract stage), and this removed the seed production. Cattle were than released during the 

normal grazing season. In addition, there are a variety of other factors to consider such 

as animal age, body condition, and sex of the animal, which can affect diet selection 

and foraging behavior. For example, goats with worn teeth tend to avoid grasses and 

choose tender-leaved shrubs versus goats with unworn incisors (Burritt and Frost 2006) 

(please refer to Targeted Grazing Handbook for further information).

There are four primary species that can be used in targeted grazing: cattle, horses, 

sheep, and goats. In general, cattle and horses are grazers and primarily consume grass. 

Cattle, because of size and mouth design, are better adapted to grazing than browsing 

(Burritt and Frost 2006). Also, the muzzles of cattle prevent them from selecting among 

plants or specific plant parts. However, cattle also have a digestive system that allows 

consumption of lower quality roughage. This gives them an advantage over sheep or 

goats, because they consume fibrous and abundant herbaceous vegetation like dormant 

grasses. Sheep are intermediate feeders with their narrow muzzle and large rumen relative to body mass, which allows them to graze selectively, but also tolerate substantial 

fiber content. Sheep have small mouths, which allow them to graze close to the ground 

and select specific plant parts such as small leaves or buds (Burritt and Frost 2006). 

This attribute gives them an advantage over cattle because they can strip leaves or flowers from stems. Sheep generally favor forb consumption; however, they also consume 

grass when they are succulent and other forage is unavailable. Sheep are sure-footed 

and well suited for rough topography and will graze steeper terrain than cattle; they 

tend to avoid marshy areas. Goats are browsers and can eat many types of unwanted 

vegetation. They also produce marketable products (goat meat and milk) (Hart 2001). 

Goats are resistant to many plant toxins and non-nutritive factors. Goats prefer browse 

(73 percent) to grasses (23 percent) and some forbs (4 percent) (McMahan 1964). Goats 

can restore cycling of plant nutrients, which are sequestered by woody plants. Goats 

prefer to consume seeding stems, reducing the spread and perpetuation of weeds by 

seed. Targeted grazing with goats may increase treatment longevity after mechanical 

or prescribed fire by maintaining low levels of brush.

Challenges: As with any fuel treatment method, a successful target grazing prescription requires substantial preparation and a level of experience and knowledge. Targeted 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 147

grazing involves pairing the specific species and breed of livestock during a particular 

season, for a predetermined duration, and at a sufficient intensity to accomplish the defined 

vegetation or landscape management goals. Substantial knowledge of the ecology and 

life cycle of plants, a thorough knowledge of breeds and species available, and animal 

management skills are required. An established monitoring plan is recommended so that 

the grazing prescription is adapted based on short- and long-term results (Sharrow and 

Seefeldt 2006). Also, patience and commitment is required because it may be three years 

of implementation before noticeable differences are detected. If interested in pursuing 

this method for fuels management we recommend visiting the web site “Targeted grazing: a natural approach to vegetation and management handbook (2006).” The internet 

link and full citation is provided under the Further Reading section at the end of this 

chapter. Direct contact with the authors may also provide up to date information since 

the publication of this resource.

Conclusion

There are a variety of ways to treat forest fuels through mechanical means, prescribed fire, grazing, and chemicals. In many cases, mechanical treatments followed 

by prescribed fire or other mechanical techniques such as mastication, grapple piling, 

or other combinations of treatments have proven to be the most effective. The main 

point is not to limit what treatment techniques are used but to select the best treatment 

or combination that meets the objective.

Further Reading

Launchbaugh, K. L.; Walker, J. W.; Daines, R. L., eds. 2006. Targeted grazing: a natural approach to vegetation management and landscape enhancement. Centennial, CO: American Sheep Industry Association, 

A. Peischel and D. D. Henry Jr. 199 p. (Provides a wealth of knowledge concerning the use of targeted 

grazing to meet vegetation management goals. There are five sections in this synthesis. Principles and 

overview provide a primer on targeted grazing, animal behavior principles and practices, understanding plant response to grazing and other basic and valuable information. Section 2 focuses on objective 

specific information and how targeted grazing can be applied. Section 3 provides grazing and browsing 

guidelines for invasive plants. Section 4 discussing how to develop contracts for grazing and browsing 

and other administrative aspects in applying targeted grazing. The fifth section provides a variety of 

other resources for targeted grazing.)

Peachey, Ed, ed. 2011. Pacific Northwest weed management handbook. Corvallis, OR: Pacific Northwest 

Extension. 464 p. (Provides detailed information concerning herbicide use.)
148 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 149

Chapter 9

Prescribed Fire

Chapter Rationale

Fire is an important process that has played a major role in shaping the evolution of 

dry mixed conifer forests. This has led many to believe that fire is also the best option 

for treating fuels in these systems. However, the use of fire is never straightforward 

and always contains elements of uncertainty and complexity. Therefore, the following 

questions were used to organize the information provided in this chapter.

	 •	 Prescribed fire has an element of uncertainty and risk; thus there are several 

steps that are required prior to ignition. Is there a way we can communicate 

these steps so that other disciplines and stakeholders can better understand this 

uncertainty and risk?

	 •	 Mentoring is an important aspect of passing prescribed fire skills and knowledge 

from one generation of fire practitioners to another. Is there information that 

can be provided to aid in mentoring?

	 •	 Are there unique situations where unique fire parameters need some consideration?

Introduction

Prescribed fire can be used to achieve a variety of objectives. It can be used to reduce hazardous fuels, dispose of logging debris, prepare sites for natural or artificial 

regeneration, improve wildlife habitat, manage competing vegetation, control insects 

and disease, and improve forage. Prescribed fire can also be used to enhance aesthetics, 

improve access, and restore or maintain forest structures, species, and function within 

fire-dependent forests. Good fire practitioners combine science, decision support tools (for 

example, fire behavior models), and monitoring with their own experience and instinctive knowledge (art) when applying fire. Because the critical elements vary considerably 

based on the location and conditions, fire practitioners we interviewed suggested not to 

provide specific parameters for the use of prescribed fire. Each site is unique in fuels, 

on-site conditions, and predicted fire behavior; therefore, what works in one place may 

not work in another location. However, we also want to illustrate the complexity of 

prescribed fire and the steps each manager takes to promote success. Consequently, we 

have included the key elements of a burn plan and an associated complexity analysis. 

Even with the maximum amount of care and time invested in planning a prescribed 

fire, there is always an element of uncertainty. Within this context, we also summarize 

analysis of lessons learned from previous escaped fires.

The use and effectiveness of prescribed fire depends on objectives, current conditions, 

and desired post-fire outcomes. Prescribed fire alone tends to work best when used for 

maintaining resilient forest conditions such as open park-like stands (where appropriate), which have had either mechanical treatments or treated with multiple prescribed 

fires (Busse and others 2009). However, when current forest conditions contain ladder 

fuels, non-resilient species compositions, and other factors that complicate prescribed 

fire, a combination of mechanical treatments and fire may be the most desirable option 
150 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

(Busse and others 2009; Schwilk and others 2009). In harvested sites with complex 

surface fuels and fire resistant species, prescribed fire is often the preferred treatment. 

However, multiple prescribed fires may be needed to produce desired outcomes (Busse 

and others 2009). Therefore, combining prescribed fire with other tools such as commercial harvesting and mastication may be more successful at meeting desired outcomes 

(Agee and Lehmkuhl 2009; Jain and others 2008; Schwilk and others 2009; Stephens 

and others 2012).

Prescribed fire can be an effective and, in some cases, an economically viable option; 

nevertheless, implementing a prescribed fire is complicated. There are several steps fire 

managers execute before conducting a prescribed fire. The process is far more complex 

than using other treatment techniques, because of unavoidable risks and the ever present 

potential for an escape. Experienced fire practitioners use considerable finesse when 

planning and implementing a prescribed fire. Fires require a commitment to monitoring 

and adaptation of prescriptions based on experience.

Manager comment: Prescribed fires can exceed mechanical treatments in implementation costs and at times are not practical due to vegetative conditions, proximity 

to values, and smoke impacts.

Manager comment: Historical data, especially relating to live fuel moistures, is 

critical. The national live fuel moisture database and the trend data collected on site 

can be used to plan and implement a burn.

Manager comment: Prescribed fire reflects many of the natural ecological processes 

that these systems need, even at the smallest scale. The processes of combustion, fuel 

consumption, and nutrient cycling are beneficial to fire dependent forests. For fire 

dependent ecosystems, these processes are critical even if the actual fire effects on the 

vegetation (in other words, desired conditions) are not necessarily leading to natural 

patterns or compositions (in other words, artificial fuel breaks).

Developing a Burn Plan

The Interagency Prescribed Fire Planning and Implementation Procedures Guide 

(Prescribed Fire Guide 2008) outlines 21 elements for developing and implementing a 

burn plan (table 9.1). Unlike mechanical treatments, fire contains more uncertainty in 

implementation. Weather can change, creating conditions that may lead to an escape, 

produce smoke, and pose risks to human and resource safety. The agency administrator, 

line officer, or decision-maker in charge of a fire is aware of all elements associated 

with a particular prescribed fire prior to making a decision. Similarly, part of preparing 

for any prescribed fire is to ensure that hazards and risks are identified and mitigation 

for such events is planned. Accordingly, a series of steps have been identified to ensure 

that these aspects are carefully considered (table 9.1). Although each step is critical 

when developing and implementing a burn plan, many are self-explanatory, such as the 

description of the signature page, prescription of the area, and scheduling. Therefore, 

we focus our discussion only on the steps that managers referred to during interviews.

Manager comment: When developing a burn plan, it needs to be specific enough 

to successfully implement the burn but allow enough flexibility that the project is 

not pushed into a corner, losing sight of the scope and purpose of the burn. It’s is a 

matter of finding the right balance.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 151

Table 9.1. There are 21 elements that need to be considered and documented in preparing and implementing a prescribed fire (Rx) plan 

(Prescribe Fire Guide 2008).

Element Name of element Included documentation Description of documentation

1 Signature page Signatures of responsible officials Project name, signatures, and complexity

 rating, and signed amendments.

2 Go/NO-GO checklist Administrative checklist Should be completed prior to

 implementing prescribed fire.

 Prescribed fire checklist Prior to ignition operations, the burn

 boss will complete and sign and will be

 revisited each day prior to ignition.

3 Complexity analysis Process used to identify risks and Includes evaluation on risk, potential

 uncertainties related to Rx activities consequences, and technical difficulty.

4 Description of area Physical description Location, size topography, project

 boundary.

 Vegetation and fuels description Includes within project boundary but also

 adjacent to the boundary for potential

 escaped fire threat.

 Identify and describe unique Identify special features, hazards,

 features and resources regulations, issues, and constraints.

 Vicinity map and project maps

 with title, preparer, date, north arrow,

 scale and legend.

5 Objectives Resources and fire objectives Clear, concise, and measureable and

 quantifiable prescription elements

6 Funding Funding source and cost The cost may be broad or itemized by

 phase.

7 Prescription Measurable criteria that define Low and high limits for environment

 range of conditions during ignition and fire behavior to meet objectives

 and be held as a prescribed fire concerning: resources, smoke, and control.

8 Scheduling What is the time frame in which Include general ignition time frame (time

 the prescribed fire will occur of day, duration of ignition), season, and no

 burn days.

9 Pre-burn considerations Document all on and off-site Include information on: clearances,

and weather tasks to be addressed prior to mitigation actions, line building, holding

 implementation points, etc; fuel sampling and weather data

 needed and frequency during burning for

 weather measurements; and organizations

 and landowners to notify.

10 Briefing All personal are briefed at beginning To ensure safety, objectives, and operations

 of each operational period. are clearly understood including contingency

 Checklists are provided to ensure plans and assignments.

 all subjects are covered

11 Organization and Determined by complexity of the fire Includes implementation organization,

equipment to safely achieve Rx objectives Iorganizational chart and responsibilities

 and equipment needed.

12 Communication Communication plan Identify and assign command, tactical,

 and air operations if needed and contact

 information.

13 Public & personnel Describe safety provisions for public This includes protective clothing, safety

safety, medical and personnel conducting the hazards, measures take to reduce

 prescribed fire safety hazards, safety plan, evacuation

 methods and emergency facilities and any

 associated job hazard analysis specific to

 the Rx.

14 Test fire Verifies prescribed fire behavior Test must be ignited in a representative

 characteristic and whether it will location and an area that can easily be

 meet objectives and predicted controlled.

 smoke dispersion (con.)
152 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Manager comment: In terms of burn plan development, the overriding goal for 

the burn plan is to provide a wide range of conditions designed to achieve two things: 

(1) desired fire effects on the vegetation, and (2) reduction in the size and/or severity 

of a future wildfire. Prescription parameters should be done in such a way that does 

not limit the decision space, making it too difficult to achieve the “perfect window.” 

Also, it is important not to fall into the trap of thinking and using model outputs as 

actual truth. A range of numbers should be used to help develop environmental parameters to achieve post-fire outcomes.

Objectives, Complexity, and Prescription

The development of clear and concise objectives that are measureable and quantifiable is an important element in any burn plan. Objectives are used to design the burn 

prescription to produce a post-burn outcome that meets the overall land management 

goals. For example, for a management strategy focused on natural regeneration, one 

of the major objectives of a prescribed burn will likely relate to the amount and degree 

of exposure of mineral soil. Accomplishing this objective requires knowledge of the 

relationship between lower duff moisture and consumption. In burns designed to kill 

advanced regeneration, knowing the flame lengths required to injure and subsequently 

kill small trees without killing large overstory trees will be key. Therefore, a particular 

15 Ignition plan Describes the planned ignition Includes fire methods, devices, techniques,

 operations sequences, patterns, and ignition staffing

 for single or multiple unit operations.

16 Holding plan Describes general procedures to Identifies critical holding points with map

 be used for operations to maintain and minimum capabilities needed for all

 the fire within the project area and phases of implementation.

 meet objectives until the fire is

 declared out

17 Contingency plant If the objectives are not being met Determines the initial actions and additional

 the contingency plan is implemented resources needed if Rx fire is not meeting,

 exceeds, or threatens to exceed project

 boundary, objectives, prescription parameters,

 smoke management, insufficient personnel,

 or other prescribed fire plan elements.

18 Wildfire conversion The authority who will declare Description will include who or whom

 wildfire and parameters in which declares a wildfire, Incident commander

 the Rx becomes a wildfire assignment, and who to notify. (Once

 declared a wildfire cannot return to a

 prescribed fire designation.)

19 Smoke management Includes how project complies with Identify smoke sensitive areas that the

and air quality air quality regulations at multiple prescribed fire may affect and describe

 levels (community, county, state, mitigation or management techniques

 tribal, and federal) for reducing or redistribution emissions.

20 Monitoring Describe the collection and analysis Includes detailed measurements that

 for monitoring are used to evaluate the effectiveness

 of prescribed fire and at a minimum

 describe the weather, fire behavior, fuels

 and smoke dispersal during the prescribed

 fire.

21 Post-burn activities Includes all activities after the burn This may include post-burn report, safety

 is accomplished mitigation measures, rehabilitation needs.

Table 9.1. (Continued).

Element Name of element Included documentation Description of documentation
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 153

flame length, ignition pattern, and fire weather will be required to achieve this objective. 

The fire practitioner uses his/her knowledge of fire behavior, fuel amounts, fuel moisture, 

wind, and relative air humidity to develop the burn plan parameters.

On complex prescribed fires, clear objectives guide the burn. The fire practitioner is 

often aware when burning conditions may be too risky because of spotting potential, 

low relative air humidity, potential holding problems and other risk thresholds. Without 

clear and concise objectives, this type of evaluation cannot be achieved.

It is also important to note that on more complex burns the costs can increase; therefore, funding is strongly dictated by the ability to successfully fulfill the objectives.

Manager comment: We have prescribed fires I call “1 in 5” year burns, meaning 

that the window for implementation comes along once every 5 years. Trying to budget 

for those in any given fiscal year is difficult.

The prescription details the quantitative parameters such as range of the air relative humidity, temperature, fuel moistures, and wind direction and speed that work in 

combination to produce acceptable fire behavior. These same factors are also important 

for smoke management. Experience and professional judgment are used to identify or 

calibrate prescriptions. Simulation modeling is a tool that is often used, but professional judgment, empirical evidence, or verified actual fire behavior can override model 

estimates. As the complexity of the site increases, so do the prescriptions. If there are 

multiple fuel models or different types of ignitions, this may require multiple prescriptions and complexity analysis to identify multiple levels of management, organization, 

and pre-burn considerations.

Holding and contingency plans are developed for fire occurring outside of the project 

boundary to account for potential escape (Prescribed Fire Guide 2008). Model predictions 

and expert knowledge can be used to analyze previous fires that have burned during the 

most severe prescription limits, such as the hottest, driest, and windiest conditions. The 

prescription may include a short discussion on expected fire behavior and how the fire 

will achieve objectives. All evidence, either from modeling, monitoring data, or expert 

knowledge, for the prescription and contingency plans can be included in an appendix. 

Important elements are the time of day, duration of ignition, and season of the burn. The 

plan also may identify the dates or conditions when limited resources are available, and 

when burning will not occur.

Pre-Burn Considerations and Weather

The pre-burn considerations of the burn plan provides the list of on-site actions, considerations, and safeguards that are required prior to implementation. This may include 

line building, snag removal, preparation of critical holding points, and special features 

that need protection. This section also details the fuel sampling and weather measurements (time, source, and frequency) to be taken prior, during, and after the burn and 

who (person or position) will conduct the measurements. In addition, off-site actions 

and considerations also are discussed at this point, along with issues such as approving 

and distributing burn plans to dispatch and other cooperators.

Organization, Equipment, and Communications

The complexity of the prescribed fire will dictate the organization and equipment 

required to safely achieve the objectives and control the prescribed fire. At a minimum, 

a prescribed fire burn boss will be assigned to every prescribed fire. However, some 

locations, particularly within the WUI or on large landscape burns, may require a minimum organization level equivalent to a type III fire management team in place for all 

prescribed fires. Accordingly, if an escape occurs, the proper organization is present 
154 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

on site. An organizational chart displays individuals and associated tasks, including 

changes in the organization. Communication among all individuals involved is critical 

for maintaining safety and tactical resource needs.

Manager comment: The purpose of a contingency plan is to identify the organization needed if an escape were to occur and to plan for those contingency resources 

to arrive on site in a timely manner. In a perfect world, contingency resources would 

be on site but this is not always the case, nor is it required by policy.

Complexity Analysis

In the past century, prescribed fire was predominantly used for removing residual 

fuels after harvest or for preparing a site for regeneration. Today, sites that are selected 

for burn treatments are more complex and may contain ladder fuels, fire resistant and 

non-fire resistant plant species, variable topography, large spatial areas requiring multiple burning days, and increased concerns about smoke dispersion. A major component 

of any burn plan is to conduct a complexity analysis (fig. 9.1). The 14 elements of the 

complexity analysis discussed in the Prescribed Fire Complexity Rating System Guide 

(2004) include:

	 •	 Potential for escape (managers say smaller sites have greater propensity for 

escape than large landscapes)

	 •	 Number of different prescribed fire actions and dependence among them; the 

individual activities are dependent (similar to the links in a chain), and the 

success of one factor is directly connected to the success of another. The more 

dependence among the different actions, the more complex the prescribed fire.

	 •	 On-site value

	 •	 Off-site values

	 •	 Fire behavior

	 •	 Management organization

	 •	 Public and political interest

	 •	 Treatment objectives

	 •	 Constraints

	 •	 Safety

	 •	 Ignition procedures and methods

	 •	 Interagency coordination

	 •	 Logistics

	 •	 Smoke management

For each of these elements, a rating of low, moderate or high are given for each of three 

risk factors (fig. 9.1): (1) risk, or the probability or likelihood that an adverse event or 

situation may occur, (2) potential consequences if an adverse event or situation occurs, 

and (3) technical difficulty, which indicates the skills needed to implement the project 

and address unexpected events. For detailed descriptions of how these factors are used 

rate the elements please refer to Prescribed Fire Complexity Rating System Guide (2004).

Common Oversights in Prescribed Fire Planning

Within the planning and preparation phase, areas consistently cited by managers as 

problematic were the lack of smoke management, simplifying potential challenges in 

burning heavy fuel loads or in timing of the burn, weakness in contingency planning for 

unexpected events, and not thoroughly identifying management action points. The lack 

of smoke management can negatively influence public perception; thus, it is important 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 155

to appropriately model and consider smoke impacts to avoid negative impacts to public 

health and safety. In addition, during the planning stages, the influence of heavy fuel 

loads on fire behavior is often simplified, resulting in unexpected outcomes after ignition. 

Timing of burns is also critical and there are potential risks if the burns are conducted 

prior to the normal fire season.

Manager comment: A related issue is unfamiliarity with local fuel types. Many 

practitioners do not realize, for example, that old western redcedar needles (gray, but 

still attached to branches) often burn as readily as the red needles of other species. 

They may not recognize a particular species of shrub as flammable or recognize that 

drought-stress has made some shrub species more flammable than typical. The failure 

to recognize what drought stress had done to shrub flammability can compromise 

prescribed fire outcomes.

Figure 9.1. A complexity analysis has 14 elements, with each 

rated on three factors. The combination of this analysis results 

in a complexity determination.
156 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Basal rot can also play a role in igniting otherwise live trees. In some cases, these 

rot pockets may be hidden by the duff layer. One fire manager described a burn in 

Arizona in which several large ponderosa pines were lost due to fire getting into the 

tree through hidden basal rot pockets.

Lastly, our ability to keep snags and downed wood from burning is much more 

limited than many recognize. Someone may spend a lot of time and energy during 

the day to protect snags and downed wood, only to lose them overnight. It is not very 

encouraging to come back to a pile of white ash in the place of a big log in which you 

had spent so much time and energy protecting the day before.

In all of these cases, adequate modeling, monitoring, attention to fuel moisture trends, 

and use of other sources of information are beneficial to fully understand the potential 

for spotting outside the unit, as well as for evaluating the best time to ignite given the 

potential for extended burning or smoldering. No matter the complexity of the prescribed 

fire, the ability to manage the unexpected depends on having comprehensive contingency 

plans. If contingency plans encompass a full range of potential problems, a prepared 

team can recognize subtle changes and take the appropriate action quickly. Developing a 

thorough contingency plan can also help in identifying the required qualifications when 

assembling the burn team. Contingency plans identify management actions trigger points 

or thresholds (for example, weather, fire behavior) and these locations are discussed 

and briefed prior to implementation. In addition, the briefing details the availability 

and deployment period of contingency resources. On the day of the burn, contingency 

resource availability often is validated and coordinated prior to ignition and during each 

phase of the burn until it is declared out.

Manager comment: Escaped burns often occur days if not weeks after ignition 

because of a lack of long-term commitment to monitoring/patrolling the burn and 

assigning contingency resources.

Implementation of Prescribed Fire

There are many intuitive ways to influence fire behavior and fire severity. The more 

experience a fire practitioner has, the more they are able to use a variety of techniques. 

Some of these include: fuel manipulation, firing techniques, live and dead fuel moistures, 

and timing between ignitions. In addition, the number of people involved in ignition, 

and the fire weather (relative humidity, air temperature, wind speed, etc.) during ignition all influence fire intensity. Conditions also vary throughout the day and across the 

site (slope aspect or relief) and subsequently affect fire behavior; therefore, continuous 

vigilance and organization are needed to avoid unplanned consequences.

Weather and fuel considerations and their effect on fire behavior are critical components in planning and executing a prescribed fire. Large scale (synoptic) weather 

patterns can be used to estimate favorable burning weather conditions or when burning 

weather conditions will deteriorate. Important weather elements are wind speed, relative 

humidity, air temperature, rainfall, and air mass stability. These elements influence fuel 

moisture, and short- and long-term fire behavior, which all determine the success of a 

burn. Steady and persistent wind speed and direction and homogeneous slopes provide 

the most consistent prescribed fire behavior. Some wind is usually desired to give the 

fire direction and prevent heat from rising directly into tree crowns. However, high wind 

speeds can cause fires to spread too quickly and increase spotting distances and become 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 157

too intense. Air relative humidity reflects the moisture in the air—if too low, prescribed 

burning can be hazardous and it may become difficult to hold the fire. If air relative 

humidity is too high, the fire may not burn to the intensity required to meet objectives. 

Temperature affects moisture changes of the live and dead fuels. High temperatures 

dry fuels quickly. If the fuels are in the sun, they can become much warmer than the 

surrounding air even when the relative humidity of the air is high. Low temperatures 

can retard fire intensity. Also, increases in moisture, either in the air or in the fuel, can 

reduce fire behavior and movement.

Escaped Prescribed Fire and Near Misses

There is always the potential for a prescribed fire to escape and 

result in a negative outcome. Every year, up to 5,000 prescribed 

fires are implemented and approximately 99 percent of those 

fires are successful in that there was not an escape or adverse 

effects (near misses). However, approximately 1 percent of these 

fires (up to 50 fires per year) result in damage or undesired outcomes (Dether 2005). A near miss is when a potential accident did 

not occur, either through prevention, education, hazard reduction, 

or luck (NWCG Glossary of Wildland Fire Terminology 2011) 

http://www.nwcg.gov/pms/pubs/glossary/n.htm. Close calls and 

near misses are regarded as a type of failure that reveals potential 

and ongoing danger, rather than as evidence of the organization’s 

success and ability to avoid danger (Keller 2004).

There are two interconnected factors that, if not properly managed, 

can lead to an increased probability of a potential escape or near 

miss: lack of mindful communication and the snowball effect. 

Mindful communication is essential for successful prescribed 

fires because it enforces continuous, open, and comprehensive 

communication between agency administrators, planners, 

cooperators, dispatch centers, and implementation personnel. 

When gaps or weaknesses in coordination and communication 

occur, there is an increased probability of an unsuccessful burn. 

The snowball effect refers to the observation that unexpected 

weather events or small problems are encountered throughout 

the prescribed fire which have a cumulative effect that can 

significantly reduce the probability of a successful burn. Often, 

the accumulation of multiple small problems can be mitigated 

through comprehensive mindful communication.

During prescribed fire implementation, onsite leadership is 

critical. Fire behavior, weather predictions, and staffing levels 

fluctuate on long-term burns; therefore, as the complexity of 

the burn increases the onsite leadership must also increase 

accordingly. Often, typical problems on short-duration burns 

(one operational period) are not apparent until after ignition; 

therefore, it is important that risks, benefits, resource needs, and 

comprehensive contingency plans are identified before any burn.
158 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Atmospheric Stability and Atmospheric Dispersion

Mixing height and the Haines Index are two descriptors that aid in determining atmospheric stability. The mixing height is the height at which the lower atmosphere will 

undergo mechanical or turbulent mixing, producing a nearly homogeneous air mass. 

This determines the altitude at which smoke will be dispersed from a site. The Haines 

Index is a measurement that helps determine the potential for fire growth. This index is 

based on a combination of atmospheric stability and dryness of the air. The atmospheric 

stability is calculated by calculating the difference in temperature of two atmospheric 

layers. The dryness of the air is calculated based on differences in temperature and 

moisture content. The Haines Index ranges between 2 and 6, with a value of 2 indicating 

a moist, stable, lower atmosphere with very low potential for fire growth. A value of 6 

indicates a dry, unstable, lower atmosphere and high potential for fire growth.

During a prescribed burn, the stability of the atmosphere impacts air quality, visibility, 

and fire behavior. Air rises when it is warmer than the surrounding air. The rate and 

height that heat rises into the atmosphere is determined by the stability of the atmosphere. If the atmosphere is unstable, heat can rise rapidly due to the large differences 

in air temperature (air temperature typically decreases with altitude). High instability 

allows more convection, which can increase fire intensity. An unstable atmosphere 

also carries smoke into the upper atmosphere, helping to disperse it. Sometimes, the 

atmosphere is stable and resists the upper movement of heated air. This often occurs 

when air temperature decreases slowly with increasing altitude, creating an inversion. 

High Reliability Organizing

Nasiatka and others (2008) identified consistent elements that tend to be found 

in evaluations of escaped fires. They summarized these elements and placed 

them within the context of the High Reliability Organizing (HRO) concept of 

mindfulness. A HRO is an organization that operates in a high-risk environment 

but maintains a relatively low accident and error rate. Mindfulness is managing for the unexpected and paying close attention to small, at first appearance, 

inconsequential problems and being wary of oversimplification and sensitive to 

operations and possible glitches so they can be corrected or addressed quickly 

(Welck and Sutcliffe 2007).

Anticipating needs becomes particularly important for long-duration burns 

(multiple ignition periods). With this added element, future conditions and 

resources may need to be anticipated as far in advance as possible to account 

for potential changes in conditions over multiple operational periods. Simultaneously conducting multiple burns can have both advantages and disadvantages. The primary disadvantage is that unexpected problems can increase 

exponentially as problems with communication, response time, the splitting 

of available resources, and coordination of efforts and resources among the 

burns becomes problematic.

The most interesting factors that may lead to an escaped prescribed fire or a 

near miss are the pressures and expectations of the burn boss and team. Often 

these are self-imposed pressures—for example, a feeling that one has to treat 

acres. Consequently, productivity can be an added pressure. In addition, there 

are professional and personal rewards in increased productivity and the desire 

for these incentives can lead to further self-imposed pressure.

Manager comment: In some cases, there are substantial pressures to complete 

burns in a manner that reduces the cost per acre.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 159

Burning when the atmosphere is stable keeps the smoke closer to the ground, reducing 

visibility, and limiting its dispersal. This often occurs at night when air temperatures 

cool near the ground creating a stable air layer. Indicators of an unstable atmosphere 

include observations of dust devils, gusty winds, and clouds that show vertical growth. 

Indicators of a stable atmosphere include steady winds, clouds that form in layers, and 

poor visibility due to haze and smoke.

Incorporating all of these factors to ensure a successful burn varies depending on 

variety of factors such as location, topography, and previous weather patterns, to name 

a few; therefore, the fire practitioner has to use their knowledge and skills in order to 

determine the appropriate time to begin, hold, or stop burning—all of which depend 

on the intuitive knowledge of the burn boss and the objectives of the prescribed fire, 

weighted within the risk of potential escape or other complications.

Firing Techniques

There are various firing patterns that can be used to ignited prescribed fires (Wade 

and others 1989; Wade and Lundsford 1990; Weir 2009). The selected technique is 

dependent on the fuels, topography, and weather needed to meet goals but not damage 

forest resources. Regardless of the technique or combination of techniques that are 

used, firing begins at an established anchor-point and proceeds carefully with attuned 

situational awareness concerning changes in wind direction and speed, as well as topographic features (ravines, V-shaped canyons, hollows, steep slopes, rolling material, 

spotting), which may result in unexpected fire behavior. Each firing technique has its 

unique attributes, advantages, and disadvantages. In this section, we introduce each 

technique and provide short definitions. These are general descriptions and the exact 

implementation will vary for different situations. Please refer to Weir (2009) and Wade 

and others (1989; full reference in the Further Reading section) to obtain more detailed 

descriptions of the different firing techniques. Learning from experienced individuals 

is a necessary element when learning to use prescribed fire, and one of the best ways 

to learn the advantages and limitations of different firing techniques is to work with 

experienced practitioners.

The most common firing techniques are backing, strip, spot firing or point source 

(sometimes referred to as dot firing), flanking, and chevron. Head fires are not commonly 

used since they tend to have a fast spread rate, wide flaming zone, and long flames. 

Once it is lit, there is little to no control.

Backing fires are started along a baseline (road, plough line, stream or other fire 

barrier) and allowed to back into the wind or downhill. Backing fires are effective in 

many fuel situations and tend to result in less heat transferred to the overstory canopy. 

However, backing fires take time, and there is a chance that more heat is produced in 

the lower duff, which can damage roots if duff moisture is insufficient. Backing fires 

work best with continuous fuels and are effective in heavy fuels—they consume fuels 

better than other ignition patterns, but results depend on the size of woody fuels due to 

variability in fuel moistures.

Manager comment: Backing fires also have an increased potential for developing fingers (based on heterogeneity of fuels and slope), which in turn could create 

opportunities for these fingers to turn into head fires and make short runs that burn 

out the areas between fingers.

Strip-head firing involves a series of lines of fire set progressively upwind of a firebreak. In this method, no individual line of fire can develop a high energy level before 

it reaches either a firebreak or another line of fire. The number of lines and strip width 

is based on desired flame length. This tends to be the most commonly used technique 
160 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

and is faster than a backing fire. Strip fires can consume large areas in a shorter period 

of time than backing fires.

Manager comment: Fundamentally, strip fires are actually “strip head fires”... 

that are true head fires that are kept under control by maintaining short strip widths 

in which the fire can grow.

Spot firing involves a grid of spot ignitions that can produce a fire with intensity 

greater than a backing fire, but less than a heading fire. It is often used in conjunction 

with strip firing. It conserves the amount of fuel used for ignition; however, it can potentially lead to fires that develop hot spots if the spots are incorrectly spaced. Therefore, 

timing and spacing of individual ignition spots are critical to successful application of 

this method. Point source or spot firing is good for removing pockets of heavy fuels 

when fuel moistures are high.

Center ring firing is useful in cut-over areas where a hot fire is needed to reduce fuels. 

The firing technique encircles the perimeter of an area. However, it can trap wildlife 

because they have no escape route. This technique is useful in burning isolated areas 

that have good escape routes and safety zones. It works best when winds are light and 

variable. The firing method can be used in any season, given the right conditions. This 

type of fire tends to develop a strong convection column and can cause spotting.

Chevron firing is best suited for unique terrain features. This technique is used to 

burn down slopes on ridge points. This technique uses a series of igniters starting at the 

same point at the top of ridge. A line of fire is set in a V-shaped pattern to burn ridges, 

points, or ends and the burn progression must be downhill.

Manager comment: Chevron firing can also pull heat and smoke off the line. When 

used correctly the tip of the V starts to pull together.

The Value of Patience

Some prescribed fires are becoming more complex—involving understory burning or the reintroduction of fire in old-growth stands 

or other locales where the prescribed fire outcome is of critical 

importance (from Kilgore and Curtis 1987). In these situations, and 

in most prescribed burning contexts, patience can be an important 

element. Patience comes into play in many facets of the burn, such 

as waiting until the prescription parameters are met; or in strip firing, 

it may require letting one strip die down before starting another (and 

not allowing strips to get too wide, which can happen when ignition 

crews get in a hurry to finish). Thus, patience may mean not moving 

too fast to take an action but also not moving too slowly as well. 

Patience means demonstrating “torch finesse”—occasionally raising 

the drip torch, watching the last strip, and observing fire behavior. 

The flame length can be a good indicator of fire intensity and it varies 

greatly based on strip width and rate of ignition. If the flame length 

is too great, then narrow the strips; if it is too little, then widen the 

strips slightly. Small ignition crews of two to eight persons may be 

better than large crews. Adding more crew members can speed up 

burning but can sacrifice overall crew patience. Good communication 

is essential between holding, ignition, and burn bosses. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 161

Monitoring

For prescribed fire, monitoring includes the collection and analysis of repeated 

observations or measurements to evaluate changes in conditions and progress toward 

meeting the management objectives. Monitoring includes identifying the elements that 

can directly address quantitative objectives needed for an evaluation of prescribed fire 

success. During the burn, measurements in relation to the weather, fire behavior, and 

live and dead fuel moistures and smoke dispersal are often recorded with individual 

assignments to each set of tasks (see Chapter 10 for further discussion on monitoring).

Unique Attributes That Favor Specific Post-Fire Outcomes

A successful burn is one that meets objectives without substantial unexpected events. 

Fuel managers explicitly expressed that expert knowledge is invaluable in determining 

when to ignite a prescribed fire. However, there are a variety of general prescription 

parameters that can lead to preferred post-fire outcomes in meeting the objectives of 

the burn. Prescribed fires tend to be predominantly surface fires, ignited with the primary objective of removing fine woody fuels. They can also be used for other resource 

management objectives such as exposing a portion of the soil for planting and natural 

regeneration of trees, or promoting sprouting for browse. Therefore, the type, amount, 

and moisture of the surface organic materials can greatly influence a post-fire outcome. 

Prescribed fire can also be used in restoration projects in old growth stands or as a thinning agent to reduce seedling density. To successfully determine if a prescribed fire can 

achieve these outcomes, there are some parameters that may be useful in the planning and 

implementation of the prescribed fire. Accordingly, we provide some suggestions below.

Live and Dead Fuel Moistures

Lower duff

Prolonged consumption of accumulated duff around the base of old trees (duff 

mounds) is a primary contributor to mortality in North American pine species (for example, Hood 2010). Duff is defined as decomposing organic matter above the mineral 

soil and below freshly fallen litter (Miyanishi 2001; Miyanishi and Johnson 2002). Duff 

is generally composed of two distinct layers: an upper fermentation layer of organic 

material, including branchwood, cones, and bark in the early stages of decomposition 

and a lower humus layer of mostly indistinguishable organic matter (Harrington 1987). 

The humus layer, or lower duff, is in an advanced state of decomposition and is dark 

brown to black in color (Potts and others 1986).

Lower duff is primarily consumed by smoldering combustion (Frandsen 1987; 

Miyanishi and Johnson 2002). Several characteristics of duff—moisture content, mineral 

content, depth, and bulk density—have been identified as the primary determinants of 

rate and amount of duff consumption and resulting damage in pine species (Hood 2010). 

Among these, moisture content is most important, with the probability of sustained 

smoldering combustion inversely related to duff moisture content (Hungerford and others 

1991; Valette and others 1994). Adequate soil and duff moisture can protect tree roots 

and micro-organisms and minimize volatilization of nutrients (Hungerford and others 

1991). Studies have shown that lower duff burns independently of surface fuels below 

90 percent moisture content (Brown and others 1985; Norum 1977; Sandberg 1980). 

Conversely, duff rarely burns above a moisture content of 120 percent. In controlled 

laboratory burning of lower duff from a ponderosa pine dry mixed conifer stand in the 

southern Klamath Mountains, smoldering combustion was halted at moisture contents 
162 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

exceeding 102 percent moisture content, and was variable below that threshold 

(Garlough and Keyes 2011; fig. 9.2).

Mineral content is secondarily important through its interaction with moisture 

content; higher levels of mineral content in the duff require drier duff in order for consumption to occur (Garlough and Keyes 2011; fig. 9.3).

Live fuel moistures

Manager comment: I use live fuels moistures (on site and from the national database) to target species I want to remove as well as ones I want to keep. I also use 

them to track fuels I have identified that I want to use as a natural fuel break, mostly 

sage but other brush too.

Moisture content of shrubs and herbaceous vegetation will influence whether this 

vegetation is either a heat source or heat sink in contributing to surface fire behavior 

(Agee and others 2002). The importance of live fuel moisture in influencing fire behavior is not new and has been used extensively in estimating fire danger (in fact, it is an 

important aspect of the Fire Danger Rating system) (Weise and others 1998b). In the 

dry mixed conifer forests, where shrubs and herbaceous vegetation are abundant, trends 

in diurnal and seasonal trends in live fuel moisture can dictate whether a prescribed fire 

is successful. Interestingly enough, we found limited information concerning the use of 

live fuel moistures in burn prescriptions. Moreover, Jolly (2007) indicates that despite 

decades of research in fuel moisture dynamics, there is a lack of information on the 

contribution of live fuel moisture to fire behavior. However, as stated above, live fuel 

moistures can be an invaluable tool when applying prescribed fire either in identifying 

fuel breaks or to focus treatments on particular species for maintenance or removal.

Figure 9.2. Data from a laboratory combustion study of ponderosa 

pine duff from California’s southern Klamath Mountains (Garlough 

and Keyes 2011) illustrates the association of lower duff moisture 

content to percent consumption. The dashed line denotes the upper boundary of moisture contents (102 percent) beyond which duff 

failed to sustain smoldering combustion. Below the threshold, percent 

consumption proved highly variable.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 163

Seasonality of Burning

The majority of lightning fires historically burned during or at the end of the growing 

season; however, prescribed fire is often implemented during the spring or fall. This 

difference in timing of burns is due to the operational and liability constraints associated with the heavier fuel loads present in these fire-excluded forests, safety concerns, 

smoke management, and biological management (Knapp and others 2009). From an 

ecological point of view, there is concern that burning outside of the historic range of 

variation will result in a disruption of ecological processes and, in some cases, have 

negative impacts on plant and wildlife species. In this section, we briefly discuss the 

expected fire behavior and effects that may occur for prescribed burns implemented 

outside of the historical fire season.

Manager comment: Most deliberate human-caused ignitions prior to 1850 were in 

late winter/early spring and in fall, before and after the primary growing seasons. Accidental human caused fires could occur at any time of the year (escaped campfires). 

Deliberate human ignitions during the growing season were most often acts of war, 

intended to drive enemies or deprive them of cover or food. We need to stop pretending or denying that human-caused fires did not occur over the last 15,000+ years.

Figure 9.3. Data from a laboratory combustion study of ponderosa pine 

duff from California’s southern Klamath Mountains (Garlough and Keyes 

2011) illustrates how lower duff’s moisture content and its mineral content 

(percent inorganics) interact to determine its ability to ignite and burn. The 

dashed line roughly demarcates Burn and No-burn outcomes (circles 

and triangles, respectively). Moisture content is the most important factor; as moisture content drops, the probability of smoldering combustion 

declines accordingly. But the duff’s mineral content suppresses its ability 

to combust at any moisture content. In this example, duff combustion was 

a common outcome at higher moisture contents (y-axis), when mineral 

contents (x-axis) were low. But combustion outcomes declined at higher 

mineral contents, and combustion failed at mineral contents above 60 

percent, even when that duff was very dry.
164 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Historically, the majority of fires in dry mixed conifer forests within our synthesis area 

occurred in late August to October (in other words, late in the growing season) (Knapp 

and others 2009). This historic fire season corresponded to a time when air temperatures 

were high, live and dead fuel moistures were low, and soils were dry. Burning under 

these conditions would have allowed a substantial amount of fuel to be consumed and 

produced heat that impacted stand structure and species composition. Fires during this 

period would also have favored fire-adapted vegetation, such as the thick bark which 

protected ponderosa pine and western larch. However, there are times of the year when 

even resistant species are susceptible.

Today, we burn when there is less risk of escape such as in the spring or late fall. 

However, the intensity generated during the prescribed burns may be similar to or exceed 

historic fires due to the heavy amounts of fuels that have accumulated through the many 

years of fire exclusion. In dry mixed conifer forests, this similarity in fuel consumption 

and fire intensity between the historic fire season and the current prescribed burn season 

is thought to override the importance of organism phenology (Knapp and others 2009). 

This is especially relevant when comparing the differences of not burning to burning in 

different seasons. However, this is not to state that phenology does not matter. In fact, 

understanding changes in plant susceptibility throughout the different seasons can help 

in developing plans that meet specific objectives, especially for reentry burns when 

fuel loads are lower.

Manager comment: These are the same conditions under which much burning by 

Native Americans occurred and for much the same reason—maintaining control of 

the fire. We differ from Native Americans in that our early season burning is rarely 

before the period of most active growth. Instead, it is during the period of most active 

growth.

Fuel consumption

In general, moisture conditions differ for spring and fall prescribed burns. Typically, 

spring fuel (prior to green-up) and soil moistures are higher, due to spring precipitation 

patterns and snowmelt. In contrast, fall fuel and soil moistures are low, as a result of 

summer drying. In terms of fire intensity and fuel consumption, spring burns often result 

in less fuel consumption, are less intense, and result in patchier surface fuel consumption. Examples of individual studies include Kauffman and Martin (1989); Knapp and 

others (2005; Sierra Nevada); Monsanto and Agee (2008; Cascade); Reinhardt and others 

(1994; northern Idaho); and Hungerford and others (1991; northern Rocky Mountains). 

However, consumption and intensity are more a function of moisture conditions rather 

than season. Therefore, if an area has experienced a dry winter and spring, a spring 

prescribed fire could result in similar fire intensity and fuel consumption as one that 

takes place in the fall (Swezy and Agee 1991). For that reason, when evaluating and 

reporting prescribed burning results, it is important to provide information on weather 

conditions, drought status, fuel moistures at the time of the burn, fire behavior (in other 

words, fire intensity, fire type), and time of year (Knapp and others 2009).

The influence of the burning season and plant phenological state on mortality is 

mixed. Biologically, the actively growing tissue may be more susceptible to fire damage 

than dormant tissue. Burning early in the growing season reduces the ability of a plant 

to photosynthesize and store carbohydrates. Instead, the plant must use stored carbohydrates to regrow the photosynthetic tissue and heal other fire injuries. However, the 

majority of research on season of burn and plant mortality suggests that fire intensity 

from fuel consumption, which is a function of fuel moisture and fuel availability, can 

override phenology. It is the damage induced by the fire that explains mortality, rather 

than the seasonal susceptibility of trees (Harrington 1993; McHugh and Kolb 2003; 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 165

Perrakis and Agee 2006; Schwilk and others 2006; Thies and others 2005; Thies and 

others 2008) and herbaceous plants (Kerns and others 2006; Knapp and others 2007; 

Knapp and others 2009), at least in initial burn entries.

Soils

The impact of burn season on soils is related to soil moisture, fuel consumption, the 

heat produced, and burn duration. Each of these factors is influenced by moisture availability, which is typically higher in the spring than the summer or fall. Fuel consumption 

and duration of heat is influenced by fuel moisture. The higher the fuel moisture, the 

more energy it takes to evaporate the water, bring the wood particles to ignition temperature, and burn. Higher fuel moistures reduce the potential fire intensity that would 

heat the soil. Soil moisture also creates a buffer to soil heating. As with wood, moist 

soils take substantial energy to heat up. Therefore, the depth that heat penetrates the 

soil is largely dependent on soil moisture (Campbell and others 1995); dry soils heat 

up quicker and the heat goes deeper (Busse and others 2005; Frandsen and Ryan 1986; 

Monsanto and Agee 2008). Heat penetration impacts roots (Smith and others 2004), soil 

nitrogen (Hamman and others 2008; Hatten and others 2008), and soil microbial activity 

(Hamman and others 2008). Based on these factors, managers can tailor their burning 

based on fuel moisture and soil moisture conditions in order to meet their objectives. 

For instance, if fuel consumption with limited damage to soils or roots is the objective, 

then burning when fuel moisture is low, but soil moisture is high (for example, above 

100 percent moisture content) could be a trigger for a burn window (figs. 9.2 and 9.3.

Wildlife

Wildlife that occupies fire-adapted forests presumably have behavioral adaptations 

that promote their ability to escape from fires. However, wildlife may have coevolved 

with a fire season that peaked after their young had been reared and were able to escape 

fire. There is concern that our current practice of burning during the spring months might 

negatively impact wildlife populations because they are still giving birth and rearing their 

young. At this stage in development, many of the wildlife species are unable to escape 

the fire, although some species do have the ability to produce young again in the same 

year. However, much of the evidence for seasonality of burning and impacts to wildlife 

populations is anecdotal and further study is needed (Pilliod and others 2006; Knapp and 

others 2009). Nevertheless, many of the managers we interviewed stated that burning 

during the spring is often limited to protect wildlife populations rearing their young.

While direct mortality of wildlife from a prescribed fire is a concern, the change in 

the habitat due to a fire is likely the larger impact to wildlife (Pilliod and others 2006). 

For example, many wildlife species use snags and coarse woody debris for forage and 

habitat. The loss of these important habitat features could negatively impact some wildlife. 

In some cases, protection of snags during burning and ensuring that some coarse woody 

debris (Brown and others 2003) is still present can help alleviate the loss of important 

habitat features. While the loss of old snags and coarse woody debris is a concern, 

prescribed fire often creates new snags, especially in areas that have been impacted by 

full fire suppression efforts. Burning can also alter the vertical and horizontal continuity 

of vegetation, impacting those wildlife species that prefer closed-canopied conditions, 

while favoring those that prefer open-canopied conditions. Prescribed burning can aid in 

creating heterogeneity across the landscape and allow for areas with different densities 

and species composition to serve as population source and sink areas to help maintain 

population viability. As mentioned above, fuel moisture conditions when burning impact 

fire intensity and the amount of fuel consumption. Therefore, burning in the fall versus 

the spring often results in different fire intensity and fuel consumption.
166 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

The impact of prescribed burning and the season of burn on wildlife populations is 

complex (Knapp and others 2009; Pilliod and others 2006). Determining impacts is 

hindered by natural annual variability in populations, difficulty in implementing valid 

study design, and variability among sites (Pilliod and others 2006). Until more studies 

are performed, resource specialists are limited to their knowledge and experience to aid 

in making informed decisions. In a synthesis of fuels reduction treatments and impacts 

on wildlife species, Pilliod and others (2006) suggest several factors to consider: species 

distribution and abundance, migratory and dispersal characteristics, habitat associations, 

and potential responses to habitat alterations. We refer the reader to that synthesis to find 

more detailed information about specific wildlife species (see chapter 6).

Evaluation of the impact of prescribed fire and other fuels reduction treatments on 

wildlife habitat is currently being explored. The Wildlife Habitat Response Model is a 

web-based model that provides information about specific species habitat associations, 

life history requirements, potential predators and hazards, and how a fuels reduction 

treatment might influence these factors. The information is based on published literature 

about species/habitat relationships and provides information about how different fuel 

treatment activities might alter the habitat. The Wildlife Habitat Response Model can 

be found at: http://forest.moscowfsl.wsu.edu/fuels/whrm/whrm.html.

Prescribed Fire as a Thinning Agent

Successful modification of forest structure with prescribed fire has been demonstrated across a range of forest types and stand development stages (Battaglia and others 2010; Peterson and others 2007; Youngblood 2010). The level of success of using 

prescribed fire is dependent upon the target tree species and size, the availability of fuel 

to contribute to fire intensity, the weather conditions, and the live/dead fuel moisture 

in which the burn is implemented. As discussed in Chapter 3, tree species have various 

levels of fire resistance. A manager can tailor a burn prescription to exploit or protect 

these weaknesses in order to meet objectives. This is especially helpful in mixed species stands where the survival of one species is desired over another. In this section, we 

briefly discuss types of fire injury, thresholds for post-fire injury and related mortality, 

and possible techniques to create the conditions to induce injury.

Fire induced mortality in trees requires damage to the crown, the cambium, the 

roots, or a combination of all three. Crown injury through scorch and/or consumption 

of the foliage and buds reduces the photosynthetic capacity of a tree, which reduces the 

amount of sugars produced to keep the tree alive. Cambial damage occurs by heating 

the cambial tissue to temperatures that kill the tissue, girdling the tree and reducing 

the transport of nutrients from the needles to the roots. This kills the roots and reduces 

the water intake of the tree. Root tissue can also be killed via soil heating, resulting 

in a reduction in nutrient intake. The rate of death is often variable, depending on the 

damage to each of these factors. Often, fire induced tree mortality can take up to three 

years if the damage is moderate.

Crown injury is often classified as crown scorch and crown consumption. Crown 

scorch occurs when foliage tissue is heated to a lethal temperature, but the foliage does 

not ignite. When the heat generated from the fire is hot enough to ignite the foliage, 

crown consumption occurs, which removes the foliage and often kills the buds. Without buds, the tree cannot produce new foliage (flush) and death is imminent. For some 

fire-adapted tree species, such as ponderosa pine, thick, large buds and long needles 

protect the buds, and the buds often remain alive even if crown scorch occurs. However, 

mortality is still possible, depending on the amount of crown scorch and other damage 

to the cambium and roots. The probability of crown injury is a function of proximity of 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 167

the crown to the ground, the intensity and duration of a fire beneath the crown, and the 

susceptibility of a specific tree’s foliage and buds to heat.

Cambial injury occurs when cambial tissue is heated to a lethal temperature. Tree 

diameter and bark thickness influence the amount of heat that reaches the cambium. In 

general, tree diameter and bark thickness are positively related, but this relationship is 

very species-dependent. For example, even though a mature Douglas-fir, grand fir, and 

white fir may have the same diameter, the bark thickness of the white fir and grand fir 

is substantially thinner and more susceptible to cambial damage. Tree bark acts as an 

insulator to the cambium; therefore, bark thickness and thermal resistance combined 

with fire intensity and duration determine the extent of cambium damage (Ryan and 

Frandsen 1991; van Mantgem and Schwartz 2003).

Mortality thresholds

The ability to predict post-fire mortality based on damage to crowns, cambium, and 

roots is essential for planning prescribed burns. Over the past several decades, numerous 

studies have identified thresholds based on easily measured crown and cambial damage variables to develop post-fire mortality predictions using logistic regressions for a 

number of coniferous species (Hood and others 2007a; Sieg and others 2006; Woolley 

and others 2012). In a recent review, Woolley and others (2012) summarize the various 

tree mortality logistic regressions and the explanatory variables (damage) used and 

discuss how the models were developed, validated, and interpreted. They report on over 

33 studies that have developed post-fire mortality prediction equations for 19 coniferous 

tree species and three hardwood species. These include white fir, grand fir, subalpine 

fir, red fir, incense-cedar, western larch, tanoak, whitebark pine, lodgepole pine, coulter 

pine, Engelmann spruce, Jeffery pine, sugar pine, ponderosa pine, California foothill 

pine, Douglas-fir, Oregon white oak, canyon live oak, coast redwood, giant sequoia, 

western redcedar, and western hemlock. Hood and others (2007a) developed a guide to 

interpret tree mortality using logistic regression outputs to use for post-fire management 

and prescribed burn plans. In addition, they developed a supplemental field guide that 

includes photographs of the ranges of fire-related injuries and descriptions on how to 

measure each in the field (Hood and others 2007a).

The logistic regression models are intended for planning purposes to facilitate management activities. Although there are over 100 logistic regressions published in the 

literature, only one model (Ryan and Amman 1994) is being used in the simulation 

models used by federal agencies (Woolley and others 2012). These models require 

diameter at breast height (a surrogate measure for bark thickness) and crown scorch as 

input variables. For prescribed burning purposes, Hood and others (2007b) reported that 

these models correctly predicted overall stand-level mortality within + 20 percent of the 

observed mortality for lodgepole pine, whitebark pine, Jeffery pine, Douglas-fir, and 

sugar pine. However, red fir, incense cedar, and western larch mortality was overpredicted 

and western hemlock was underpredicted. When attempting to predict individual tree 

mortality, the accuracy of these models decreases. Better accuracy might be obtained 

by including a root injury, cambial injury variable, and/or utilizing local bark thickness, which can vary by site and geographic region and is being used in the simulation 

models used by federal agencies (Ryan and Amman 1994; Woolley and others 2012). 

Alternatively, using locally derived equations in conjunction with the simulation model 

predictions could aid in predicting tree mortality. Woolley and others (2012) provide a 

list of geographically derived logistic regressions that predict mortality based on tree 

characteristics and damage variables.

The differences in mortality thresholds for different tree species and tree sizes 

highlight their susceptibility to different damage pathways and give managers several 
168 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

options when designing burn prescriptions. For example, some tree species are highly 

susceptible to crown damage due to low crown base heights, highly flammable foliage, 

and/or heat sensitive buds. In these cases, mortality can be achieved with fast-moving, 

low-intensity surface fires that cause crown scorch and consumption, but with light 

damage to the cambium and roots. Burning when ambient air temperature is higher can 

aid in additional scorch because less additional heat is needed to raise foliar temperature to lethal levels (Albini 1976). Wind can also be used to facilitate foliage scorch 

or consumption. At high wind speeds, flame lengths increase and can ignite foliage. 

Wind also influences heat dissipation, so at low wind speeds, heat moving toward the 

crowns can increase the amount of crown scorch, whereas with high wind speeds, the 

heat is dissipated decreasing crown scorch (van Wagner 1973). In circumstances where 

a prescription limits flame lengths, a slow-moving fire with slow windspeeds that results 

in moderate levels of crown scorch and moderate to high damage to the cambium can 

achieve mortality. If tree crowns are too high to ignite or scorch, then heat damage to 

the cambial and root tissue from flaming and smoldering combustion will be needed 

to cause mortality. This is possible if managers burn under drier conditions to allow 

larger-diameter (>3 inches) fuels to contribute to fire intensity.

Prescribed Burning Masticated Fuels

Recently, within the last decade, the use of masticators to treat non-commercial 

vegetation have been used to treat small diameter material, ladder fuels, shrubs, and 

regeneration (Battaglia and others 2010; Glitzenstein and others 2006; Kane and others 

2009). The application of mastication is discussed in Chapter 8; thus in this discussion, 

we will focus on information concerning fuels and what information that is available 

on fire behavior in prescribed or laboratory burning. Masticated materials are different 

from natural or logging slash fuel beds (Battaglia and others 2010; Kane and others 

2009). They differ in 1 and 10 hour fuels distribution, bulk density, and particle shape 

(shredded or has angular edges) (fig. 9.4). The fuel beds created are influenced by the 

material that is masticated (shrub species and or tree size), the distribution of the material across the site (heterogeneous or homogeneous distribution), the type of machine 

used, the intensity and duration of the mastication (which can influence piece size), and 

the contract guidelines. Mastication is different than using a chipper; with mastication 

there are more options for controlling piece size and distribution that can influence the 

fire behavior. Chips are typically small and compact versus masticated material, which 

are shredded or in chunks and usually vary in fuel size across the site. Therefore there 

is considerable variability from site to site. Moreover, current fuel models do not reflect 

the characteristics of masticated material, so fire behavior models may not provide satisfactory predictions (Busse and others 2010; Knapp and others 2011; Kreye and others 

2011). Also the woody debris planer intersect method (Brown 1974) to measure woody 

fuels tends to underestimate the amount of 1 hour and 10-hour fuels compared to a plot 

method (50 x 50 cm frame) that collects and weighs the fuels in order to develop unique 

regression estimates of fuel loadings as a function of depth. In contrast, for 100- and 

1000-hour fuel loads, the planer transect method estimates fuel loads more effectively 

than the plot method (Kane and others 2009; Kreye and others 2011).

There are a limited number of studies that have either quantified fire behavior in 

laboratory studies or under prescribed fire conditions. Results indicate:

1. Masticated fuelbeds burn with shorter flame lengths and slower rates of spread 

than natural or slash fuel beds (Knapp and others 2011) (fig. 9.4).

2. Duration of heating tends to be longer (Busse and others 2010).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 169

Figure 9.4. Burning of masticated material on Boise 

Basin Experimental Forest, Idaho. Burning occurred in 

early spring with lower duff moisture greater than 100 

percent. Flame lengths were less than 18 inches (a and 

b). Masticated material makes a unique fuel bed (c) that 

burns differently than logging slash. There currently is 

no fuel model that represents this material.

a

b

c
170 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

3. Soil moisture influences soil heating. Busse and others (2010) suggest that soil 

damage from prescribed burning will be nominal if soil moisture contents are 

20 percent or greater (under laboratory conditions).

4. Scorch height on residual trees was substantially greater (Knapp and others 

2011). They suggest that crown scorch can be mitigated by adjusting burning 

prescriptions such as burning in cool temperatures, or when wind speeds are 

sufficient to disperse heat horizontally. Altering firing techniques may also 

aid in mitigating the amount of upward heat that is produced. In summary the 

burning of both chipped and masticated material is highly variable, so caution is 

suggested in burning this material until more research and experience is gained 

as to the methods, parameters, and resulting outcomes.

Minimizing Mortality of Large Ponderosa Pine

Many of the dry mixed conifer forests have not burned in over 100 years. As a consequence, high litter and duff accumulations have increased in the areas surrounding 

large old ponderosa pine and western larch (Hood 2010). In restoration efforts of old 

growth forests, large-diameter tree mortality has been documented from basal injury 

caused by long-term smoldering of duff mounds. One study found that soil temperatures 

in smoldering duff mounds exceeded 799 °F (400 °C) with temperatures above 212 °F 

(100 °C) for over 16 hours (Hartford and Frandsen 1992). The first introduction of fire 

into a long unburned forest has two potential factors that may lead to delayed largediameter tree mortality (three to five years after the prescribed fire). Smoldering duff 

mounds can girdle the cambium. In addition, if there is sufficient damage to fine roots, 

which have migrated into the duff layer, the damage can lead to a weakening of the tree 

and opportunities for disease or insects to ultimately kill the tree. For example, Thomas 

and Agee (1986) reported in a study at Crater Lake that tree mortality (for trees larger 

than 7.9 inches dbh) in unburned sites was 10 percent for sugar pine versus the burned 

sites where there was 36 percent mortality of the sugar pine.

Dealing With Duff in Old Growth Ponderosa Pine

There are a variety of factors to consider in choosing appropriate management 

techniques for minimizing large tree mortality in old growth ponderosa pine sites 

when deep duff is located at the base of the tree (fig. 9.5). Foremost, the sensitivity 

of the site (in terms of environmental concerns, recreational user conflicts, etc.) can 

influence the best approach to use when conducting treatments in places reflecting old 

forest conditions. If a site such as a campground is highly visible to the public, or the 

goal is to minimize tree mortality, it may be advisable to take a conservative approach 

in restoration efforts. In many cases, it is the initial entry into an old ponderosa pine 

stand that requires additional consideration and care. Once fire is introduced and the 

frequency reflects a more historical range, these extra steps may no longer be needed. 

If this is recognized as being sensitive and mortality is a concern, addressing a few key 

elements can aid in determining which set of treatments are most appropriate given 

the particular circumstance. Following is a decision-tree to aid in identifying the most 

likely set of treatments (fig. 9.6).

Do the bases of the large trees have substantial duff consisting of bark slough 

(>5 inches) (fig. 9.6)? If roots are not present, then raking the duff away from the base 

is needed to avoid smoldering and potential killing of the cambium. Remove duff at least 

9 inches away from the tree base by light raking (lawn rakes are wide and remove most 

material quickly) or if the site is dry, a leaf blower may be a good tool (Hood 2010).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 171

Figure 9.5. Deep duff from bark slough in ponderosa pine.

Figure 9.6. A decision process to determine if other alternatives should be implemented in minimizing 

mortality in old ponderosa pine (figure modified from Hood 2010).
172 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 9.7. Fine woody roots in deep 

duff that is greater than 5 inches.

Are there fine woody roots in the duff, close to the mineral soil and throughout the 

lower duff? If roots are present (particularly just above the mineral soil) treatments will 

be needed to address two factors: diminishing smoldering of the duff that may kill the 

cambium and conducting treatments when the roots are the least active. Roots are most 

active before bud burst and through the growing season (Pregitzer 2003).

The first objective is to alter the environment so as to force new roots to grow in the 

mineral soil rather than in the duff. Secondly, it has to occur when the roots are not active 

and the tree is dormant (fig. 9.7). All treatments should occur when soil temperatures 

range between 38 and 45 °F (early spring or late fall) when the tree is dormant and roots 

are the least active. In recent studies we noted that it takes a minimum of two years to 

notice changes in root location where roots are primarily in the mineral soil and not 

in the duff. Treatments can be incorporated into a broadcast burn of the site as long as 

temperatures are cool; it does not require single tree treatments.

If the area is large and conducting treatments around individual trees is impractical, 

yet minimizing tree mortality is an objective, then the introduction of fire may need to 

be completed in multiple entries. The initial entry may be used to blacken the surface 

to enhance decomposition and to only remove a portion of the duff. High levels of 

moisture in the duff will minimize smoldering potential. Smoldering is minimal when 

the lower duff holds greater than 100 percent moisture content (up to 130 percent) in 

protected areas. The deep duff layers may have as low as 90 percent moisture content 

on more exposed locations. It may take up to three burns (three consecutive years) to 

prepare the site for a fall or late-summer prescribed fire.

Mechanical removal: Raking the material away from the tree bole works well when 

smoke issues are of concern such as smoke impact zones or when the burn window is 

such that it prevents prescribed burning (see Chapter 8). The objective is to remove 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 173

Figure 9.8. Mechanical treatment of duff. Two options are possible: (a) remove most of the duff with 

a rake or leaf blower, or (b) mix the duff to enhance 

decomposition with a fire rake. 

a

b

most of the needle and duff mat, but not all the way to the mineral soil (thus it may be 

effective to leave approximately one inch of duff above the soil surface). Treatment 

still requires installation when soil temperatures are low, but the lower duff moisture 

is not a required criterion. This provides opportunities for fall and/or spring treatment 

implementation. Preferred tools will vary depending on situation, however; tools of 

choice include leaf blowers, garden rakes, fire rake, or McClouds (fig. 9.8).

Prescribed fire: When using prescribed fire, an additional criterion is lower duff 

moisture content. Lower duff moistures ideally would average 85 to 130 percent moisture 

content when fine roots are present. Experience has indicated that on southerly facing 

aspects lower duff moisture content can vary from as low as 50 percent (on exposed 

areas on the downhill side of the tree on steep slopes) and as high as 150 percent moisture content in shaded and protected areas (uphill side of tree) with average moisture 

content being about 88 percent. If the consumption is insufficient to remove some of the 

duff, it may need to be repeated within two years (needle layers begin to build and the 

effectiveness of first treatment is diminished). Otherwise, if burn windows do not allow 

for a second implementation, a rake treatment can follow the burn treatment (fig. 9.9a).
174 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Treatment combinations: Sometimes the upper litter layer is too wet to ignite; however, the lower duff is dry enough to ignite using a propane torch. This “top and torch” 

treatment involves raking the upper layer and igniting the lower layer to consume the 

duff when soil temperatures are cool (fig. 9.9b). Leaf blowing may or may not work 

and the duff may be too wet to readily be blown away, but it may be an option in certain 

conditions. In this situation, we have seen lower duff moistures as low as 40 percent 

moisture content (Theresa Jain personal communication). Another combination is to 

rake the fluffy material with a garden rake and remove the deep duff with a combi-tool 

or McCloud. A leaf blower is another option, but this only works when the upper needle 

and duff layers are dry (Hood 2010).

Managing Wildfire for Meeting Resource Objectives

The use of wildfire as a management tool is increasingly becoming a technique that 

allows large acreages to be treated. In 2009, the “Guidance for Implementation of Federal 

a

b

Figure 9.9. Prescribed fire techniques. In ponderosa 

pine where surface litter is dry, a drip torch was used. 

In areas where litter is too wet to ignite, but lower duff is 

<40% moisture content. The top litter layer was removed 

with a rake and then propane torch used to ignite duff 

was used. Crews called this the “Top-N-Torch.”
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 175

Wildland Fire Management Policy” was enacted (USDA and USDI 2009). This updated 

policy gave the authority for a wildfire to be managed for multiple objectives. Basically, 

a section of a wildfire can be allowed to burn to meet resource objectives while another 

section of the wildfire could be suppressed. This allows naturally (in other words lightning) ignited wildfires to burn in landscapes that once had fire as a major ecosystem 

disturbance, as long as the impacts were within limits determined beforehand. In order 

to allow a wildfire to burn as a management tool, the land agency unit must go through 

a planning and analysis process, identifying areas where this practice is permitted, and 

determining acceptable fire behavior and effects. This analysis is currently performed 

in the Wildfire Decision Support System (WFDSS). This policy allows fire managers to 

use wildfire as another cost-effective management tool to aid in the restoration of fire 

on the landscape during favorable burning conditions. There are a tremendous amount 

of acres that need treatment across the West. The use of wildfire is another tool that 

can move us toward the treatment of large acreages. Implementing these types of fires 

requires planning and acceptance from line officers, resource specialists, and the public.

Manager comment: The Land and Resource Management Plan along with the 

Fire Management Plan establish the resource objectives and goals in which to address multiple resource objectives for a given incident. Not only can some portions 

of a given fire have different objectives but these objectives can change as the fire 

burns across the landscape. Long duration, multiple objective fires are truly the best 

approach for managing federal lands as the fire process will influence vegetation 

structure and composition.

Conclusion

The objective of this chapter was to introduce many of the elements associated with 

the use of prescribed fire. Some may be unfamiliar to those who recognize the value 

of prescribed fire but do not necessarily understand the complexity associated with 

implementation. Other sections focus on unique situations that may require a different 

approach. The managers’ comments interspersed throughout the chapter demonstrate 

that use of prescribed fire requires experience and knowledge, and the greatest lessons 

come from working alongside experienced practitioners.

Further Reading

The following suggested readings are guides, syntheses, and information that provide 

details concerning prescribed fire or unique situations.

Hood, Sharon M. 2010. Mitigating old tree mortality in long-unburned, fire-dependent forests: a synthesis. 

Gen. Tech. Rep. RMRS-GTR-238. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 71 p.

Kilgore, Bruce M.; Curtis, George A. 1987. Guide to understory burning in ponderosa pine-larch-fir forests 

in the Intermountain West. Gen. Tech. Rep. INT-233. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station. 39 p.

Thomas, Terri L.; Agee, James K. 1986. Prescribed fire effects on mixed conifer forest structure at Crater 

Lake, Oregon. Canadian Journal of Forest Research. 16(5): 1082-1087. 

Wade, Dale D.; Lunsford, James D.; Dixon, Merlin J.; Mobley, Hugh E. 1989. A guide for prescribed fire 

in southern forests. Tech. Publ. R8-TP 11. Atlanta, GA: U.S. Department of Agriculture, Forest Service, 

Southern Region. 56 p.

Weir, John R. 2009. Conducting prescribed fires: a comprehensive manual. College Station, TX: Texas 

A&M University Press. 194 p.
176 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 177

Chapter 10

Monitoring

Chapter Rationale

Monitoring was noted as a very important component in our interviews. However, 

time, people, and expertise were always limiting monitoring efforts. The motivating 

questions that guided this chapter are:

	 •	 What are the elements to consider when developing a monitoring plan?

	 •	 What methods are preferred when developing a monitoring plan?

	 •	 Do methods have to be quantitative or can qualitative methods be used?

Introduction

Monitoring is an oft-neglected yet critically important component of the fuel management cycle. Skillfully implemented, it can facilitate manager and stakeholder review of 

and modifications to both entire fuel treatment programs and the formulation of particular 

prescriptions (Derr and others 2005). It can also be an effective way to communicate 

to stakeholders and resource staff about the desired forest conditions that result from 

thoughtfully designed management. Data from monitoring can be used to characterize 

a baseline, or frame of reference, against which treated stands can be compared when 

forest conditions are assessed both before and after applying treatments and, when the 

opportunity arises, after a treated stand is encountered by a wildfire.

One theme that emerged from manager interviews was a strong connection between 

commitment to monitoring and presence of a vigorous fuel treatment program. This is 

perhaps either because commitment leads to formal feedback and, ultimately, a more 

effective fuel treatment program, or because where there is an active commitment to 

fuel treatment, sufficient resources are dedicated and monitoring becomes a compelling 

component to ensure that the investment in fuel treatments pays off.

Another emergent theme was that local, long-term (>5 years) knowledge of the area 

and length of fuels management experience were key determinants of fuel treatment 

success. Understanding the site-specific nuances of fire weather, the type of fire behavior 

to expect in a prescribed fire, and the dependence of post-fire outcomes on prescription 

parameters is most commonly obtained by experiential learning and is often specific to 

a local context. Connecting this local, experience-derived understanding with information gained from monitoring offers an opportunity to reach a deeper and more nuanced 

understanding of subtle variations in treatment outcomes. For example, such connection 

could answer the question of how a fire prescription influences fuel consumption as a 

function of duff moisture content and wind speed in a particular kind of forest structure 

and topographic setting.

Formal monitoring, and the knowledge it can inform, is especially important given 

that mentoring opportunities are increasingly scarce as tight budgets and high workloads 

decrease the frequency with which new managers work side-by-side with seasoned 

practitioners. Unbiased, local monitoring information, correctly interpreted, offers another path to better fit fuels management practices to achieve the objectives for an area 

of interest. Preferably, the anecdotal knowledge of local, experienced managers can be 
178 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

tapped for these interpretations to provide even better, context-specific predictions of 

potential treatment effects. Monitoring information processed in this way represents a 

critical resource for current and future decision-makers responsible for designing and 

carrying out landscape-scale fuels management programs.

Developing a Monitoring Plan

Several standardized but elaborate monitoring protocols have been developed (for 

example, FFI: software tool for ecological monitoring [Lutes and others 2009] and 

FireMon: Fire effects fire monitoring system [Lutes and others 2006]); however, these 

may not be ideally suited as templates for generating the timely monitoring data needed 

to support the previously outlined objectives. Derr and others (2006) offered a questiondriven recipe for formulating a customized, targeted, relevant, and concise monitoring 

plan. Focusing on addressing a few key questions ensures that the right information is 

collected to efficiently address specific objectives.

Question 1: What are the project objectives and goals?

The Purpose and Need section of the typical National Environmental Protection Act 

document is a useful point of departure for managers in Federal agencies. Managers 

in state agencies and other organizations seeking guidance may look to institutional 

documents that detail corporate fuels management objectives and consider the local 

ecological, geographic and social context.

Question 2: How will the monitoring information be used to adapt future prescriptions so that they are more successful? How will monitoring information obtained after 

a disturbance such as a wildfire be used to guide future management activities?

Considering these questions is essential; answering them can provide a basis for 

choosing what to monitor and ensuring that monitoring insights will drive adaptations 

to management and future monitoring. In the process of answering the questions, the 

best methods may emerge in terms of: (1) addressing the objectives and goals associated with implementation (did the project do what it said it would do?); (2) ensuring 

effectiveness (did the project achieve the goals?); and (3) validation (were initial assumptions justified, considering the outcomes?).

Question 3: What will be monitored?

Thorough efforts to address Questions 1 and 2 will typically define the list of what 

should be monitored. However, continuous attention is often needed to ensure focus—it 

is easy to think of additional information that, if collected, could be interesting or useful, 

but past experience suggests that adding unnecessary items into a monitoring plan can 

lead to a daunting data collection protocol or sampling design. Monitoring efforts can 

then fail due to complexity and cost, and the information required to address Question 

2 is never captured.

Question 4: How much will monitoring cost, and who will pay?

Often, this question is considered at the end of the monitoring design process—that 

is too late. Budgets dictate what kind of monitoring is possible, and as design efforts 

invariably entail choices among alternative approaches, budget considerations play 

a crucial role in making choices. Thus, availability of expertise, time, and financial 

resources must be considered up-front. It is also reasonable to anticipate some adaptation occurring as monitoring prospects are explored. In addition, the information needs 

of multiple stakeholders may present opportunities to garner additional monitoring 

resources when efforts are coordinated.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 179

Question 5: How will monitoring be accomplished?

This question covers numerous technical aspects of the effort, including decisions 

about when data will be collected (for example, interval and time of year), where data 

will be collected (for example, sampling density and plot size), and how many repeated 

measures will be needed to address Question 2. This is where sampling theory and 

statistical considerations become crucial.

There are four ways to collect data: census, mapping, non-statistical (for example, 

convenience) sampling, and statistical sampling. A census assesses every individual in 

a population, just as the U.S. census attempts to count 100 percent of U.S. residents. 

Mapping, typically implemented as the compilation of data using geographic information systems or remote sensing, produces results that appear similar to those of a census; 

however, the mapping methods are founded on classification, modeling, or both, and 

these are inherently scale dependent. Consequently, the result is not a true census and 

any particular indicator of interest may or may not be well represented. Non-statistical 

sampling, sometimes termed convenience sampling, occurs when subjective choices 

affect the when, where, or what of sampling and where different choices by different 

individuals would yield different results. Statistical sampling occurs when data are collected from a representative sample (i.e., every unit in the population has an equal probability of being selected). However, the detail and extent of sampling is often dictated 

by time and money. Moreover, some of these methods can be combined—for example, 

generating maps from a convenience sample and validating map classifications using 

another ground based sample. (See the Monitoring Design section for further details.)

Question 6: Who will do the monitoring?

Answers to this question have implications for quality assurance and data consistency, 

data management, and training. For example, it is often helpful to have one person on 

a data collection crew act as the crew leader. Also for consistency, it is ideal to have 

the same group of individuals involved in data collection, particularly when estimates 

involve judgments that may be subjective (for example, ocular estimates of percent 

cover, exposed mineral soil, or vegetation). The value of solid training tools and protocol guides in enhancing data quality and reducing variation among observers cannot 

be overestimated, and some such tools have already been developed.

Question 7: When and where does the monitoring need to occur?

The answer to this question depends on the goals and objectives of the monitoring 

plan. Typically, two types of monitoring are of interest: (1) evaluation of treatment 

implementation and (2) assessment of treatment effectiveness.

Evaluating implementation: for prescribed fire, treatment implementation occurs 

if, and only if, a burn would be within prescription (for example, with respect to fuel 

moisture, humidity, wind speed, and direction). Waiting for the right burn window can 

take months to years, so obtaining pre-treatment monitoring data can be more challenging than is generally understood. When a prescribed fire is a “go,” there is rarely 

time to get a monitoring plot installed. However, if a monitoring plot is installed and 

it takes years to get the right burn window, the monitoring plot data may no longer be 

representative of conditions (due to vegetative growth) when the treatment takes place. 

One technique to address this challenge is to compare treatment outcomes to baseline 

information. Reference conditions are sometimes used as baseline information, and 

photograph points with data can be considered a reference condition.

Treatment effectiveness: the best, truest test of treatment effectiveness is to have a 

wildfire burn through the treatment. Ideally, treatment effectiveness would be determined 

by comparing change in plots established before a fire on treated and untreated land 

with re-measurements of those plots following fire. Because the time and place of a 
180 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

fire disturbance is rarely known in advance, conducting pre-disturbance measurements 

is problematic and most instances in which pre-treatment data are available depend 

on the serendipity of existing, systematic, permanent forest inventory plots. However, 

it is expensive to have a pre-existing grid of plots distributed across the landscape at 

sufficient density to capture the results of a particular event, and the Forest Inventory 

and Analysis (FIA) grid density is so low that only very large fires are likely to have a 

sufficient plot sample. Unless fuel treatments become much more widely implemented 

than they are today, the number of inventory plots located in fuel treatments will be 

miniscule. However, there are examples of fuel treatment effectiveness evaluations 

in dry mixed conifer forests in the western United States that either quantify post-fire 

outcomes as a function of pre-wildfire forest structure or that use information from a 

reference condition (untreated sites) to compare against the prescribed fire and wildfire 

outcomes (Fulé and others 2012; Graham and others 2009; Jain and others 2007; LyonsTinsley and Peterson 2012; Moghaddas and Craggs 2007; Prichard and others 2010; 

Ritchie and others 2007; Safford and others 2009, 2012; Wimberly and others 2009 ).

Question 8: Where will monitoring data be stored, archived, and documented?

The logistics of maintaining data are very important to consider prior to conducting 

any monitoring, especially given the sometimes long intervals between data collection 

and the need to conduct analysis (for example, not until a fire occurs). It is not uncommon for monitoring of one plot (depending on location, protocol detail, etc.) to cost in 

excess of $3500; therefore, taking time to ensure data integrity, security, and accessibility 

over time is critical. The data have to be: (1) accessible to managers and researchers; (2) 

archived in stable formats on stable media (for example, at Missouri Breaks, data collected in the field are archived in an ASCII format), which can be imported into almost 

any current and, hopefully, future software environment; (3) resistant to corruption and 

accidental destruction; (4) accompanied by detailed metadata containing sufficient descriptive information about the data and their collection that others can use to interpret 

the data, possibly for purposes beyond what was initially envisioned; and (5) stored and 

archived as corporate data backed by a long-term information management commitment.

Question 9: How, when, and by whom will monitoring data be analyzed?

Analysis should be considered at the same time as Question 2. Ideally, when monitoring is an interdisciplinary enterprise with multiple constituencies, there will be multiple 

analysts representing a range of disciplinary expertise with a stake in analyzing the data 

to address different questions. If data are publicly available, there can be entire networks 

of university and agency research analysts that may be interested in exploring the data. 

However, fuels managers must plan and budget for analysis of monitoring data that leads 

to better fuels management in the future, and assign sufficient priority to ensuring that 

it happens routinely if the investment in monitoring is to produce the promised payoff. 

Some of this may be accomplished via collaboration with research networks, but building at least some analysis capacity in-house will likely lead to more timely results and 

provide the flexibility to employ exploratory analysis that ultimately addresses a broad 

range of questions about treatment effectiveness and efficiency.

Question 10: Is the monitoring plan well designed?

Any monitoring plan can benefit from review and refinement, and if people familiar 

with monitoring protocols (such as a monitoring coordinator or an academic or consultant expert) are encouraged to provide critical feedback that will be used to improve 

the plan, the chances for successful monitoring are increased. The monitoring plan may 

also be more widely applicable and can be picked up to support monitoring work at 

other locations. Remember, the more important the project, the more important it is to 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 181

devote sufficient time and money to design an effective monitoring plan, even before 

the first treatment is implemented.

Two Phases of Monitoring

It is critical to remember that there are essentially two phases of fuels management 

outcomes being addressed: one is short term and one will likely take much longer.

The short-term questions at the local or stand scale are centered on how the fuel 

treatments performed:

	 •	 Were surface fuels reduced by as much as expected?

	 •	 Was canopy base height elevated to the level called for in the prescription?

	 •	 When predictive models such as FFE-FVS are run with pre- and post-treatment 

plot data, have key hazard criteria such as probability of crown fire, surface 

flame height, and mortality volume generally moved in the desired directions?

Medium-term questions at this scale center on fuel treatment longevity and require 

information about tree establishment and growth rates, growth response of advanced 

regeneration, and rates of surface fuel accumulation. At the landscape scale, short- to 

medium-term questions might address not only the sum of the stand-level results but 

may also consider whether the spatial pattern of reduced hazard is likely to produce 

significant changes in fire regimes:

	 •	 Will large fires be less common?

	 •	 Will the WUI be less threatened?

Landscape fire modeling software such as FSPro, FARSITE, and FlamMap may be 

helpful in evaluating landscape benefits of treatments as they accumulate over time as 

more land undergoes treatment and as the effectiveness of older treatments diminishes 

(Appendix B).

How treatments perform when a fire encounters them (and how untreated acres respond to the same fire) is, by nature, learning that can only occur over a longer period 

of time as more and more treatments are eventually visited by wildfire. Managers have 

no control over when this will occur, so patience and consistency in monitoring implementation are crucial. While the full benefits of this kind of monitoring information may 

be some years away, delaying the start of this kind of monitoring is truly short-sighted 

and assures that the benefits are even further delayed.

Short-term, stand-level monitoring data (pre- versus post-treatment) can be of substantial value to managers to provide near-immediate feedback that can be incorporated 

into next season’s fuels management activities. For example, if surface fuel reductions 

are not being realized as expected, the reasons can be investigated and remedies or different prescriptions can be explored. The immediate utility of this monitoring data calls 

for building capacity within the management organization to not only collect but also to 

analyze monitoring data covering this phase. The short- and medium-term, landscapescale evaluation of how treatments are working is the only way to know if the hundreds 

of activities on the ground are adding up to the more resilient landscapes that are the goal 

as well as increasing safety for people and property. While the field organization is best 

situated to collect the monitoring data, there may be value in partnering with research 

organizations or enterprise teams as well as other agencies and landowner groups to 

carry out analysis collaboratively at this scale.

There are also more long-term questions:

	 •	 How well do treatments work when burned?

	 •	 Which treatments best promote resilient forests?

	 •	 How do the treatments affect carbon storage?
182 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

These types of questions do not lend themselves to immediate, definitive answers. The 

variability in our forests (structure and topography) and in the fire weather that controls 

fire behavior means that large samples are required to develop statistically significant 

findings. Over even the 5- to 10-year career of a fuels manager, it would not be typical 

for more than a handful of treatments to be encountered by fire, and the sample would 

probably not be large enough to support conclusive interpretation. So, while the fuels 

manager can learn something opportunistically from such anecdotal evidence, the full 

power of monitoring data is only realized when a sizable store of statistically representative monitoring data has accumulated and can be analyzed while controlling for 

such sources of variation as stand structure, type of treatment, and fire weather. Thus, 

addressing this class of questions becomes a shared mission to which everyone in an 

organization contributes, and capacity to address such questions increases over time. 

For monitoring data to effectively inform such inquiries, it is essential that protocols 

remain consistent over time; monitoring plots are revisited to periodically update data 

as vegetation regrows or new disturbances occur; and good records are kept on what 

treatments were implemented, at what locations, and at what times.

Elements of a Monitoring Design

Monitoring to evaluate the implementation: did the project do what it said it 

would do?

Evaluation is completed either with measurements of pre- versus post-treatment conditions or by using a reference condition as a baseline compared to the post-treatment 

conditions. Using the reference condition can be an acceptable alternative when it is 

not feasible to complete a pre-treatment measurement. Reference conditions can be the 

current condition or desired future condition—the key is that the reference condition 

provides a basis against which the treated condition can be compared. Features selected 

for measurement should relate to the elements identified as fundamental to the desired 

future condition.

Monitoring to evaluate effectiveness of treatment: did the project achieve what we 

wanted to accomplish (meeting objectives)?

Some parts of monitoring focus on indicators that are long-term and generally more 

ambiguous. The effectiveness of a treatment can only be truly evaluated when there is a 

response to observe. But in the case of response to a wildfire, measuring the effectiveness of the treatment after a wildfire burns through it usually entails years to decades. 

Typically, effectiveness monitoring will yield short-term results for only a tiny subset 

of the locations where it is attempted (for example, when fire burns through a treated 

stand that contains an FIA or other monumented, or physically marked, inventory plot).

Validation monitoring: were the assumptions correct? Are the objectives being met?

Validation typically requires a combination of implementation and effectiveness 

monitoring. It is difficult to validate the assumptions without these two elements. For 

example, if a fuel treatment is never burned in a wildfire, it is difficult to validate whether 

the treatment truly altered fire behavior. Often, people use simulation models to offer 

some level of face validation; however, simulation models have their own assumptions, 

which may or may not be valid for a given scenario.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 183

Sampling

Even the best-intentioned monitoring effort will not meet the intended objectives 

if not preceded by adoption of a statistically sound sampling design and a long-term 

commitment from management to a sustained monitoring effort. If monitoring will 

be location based, where should plots or photo-points be established? When will data 

be collected and what attributes should be included? These key questions relate to the 

representativeness of monitoring findings, the precision with which effects can be assessed, and the spatial and temporal resolution of insights obtained from monitoring.

Non-Statistical Sampling

When there is no budget or sustained commitment to support monitoring, managers 

interested in assessing whether objectives were met have been known to fall back on 

what is sometimes described as a “convenience sample.” Visual inspection on a walkthrough of a treatment area, “drive-by” observation or “windshield surveys” that may 

serendipitously fit into other scheduled work, and after-action review reports derived 

from such efforts are examples of non-statistical sampling that can cheaply provide 

limited information on treatment outcomes. However, benefits are quite restricted relative to what is possible with a genuine, statistically based process.

Interpretation of treatment effectiveness will vary among observers, and there may 

be no reliable way to effectively and systematically communicate what is learned to 

others. Information quality and detail are typically insufficient to inform adaptive 

management, and the information is inherently biased (“sample” is concentrated along 

the road or in the areas easily accessible in a walk through). There is no known precision (or confidence) that can be associated with the information, and the reliability of 

the assessment is sacrificed. There is also no assurance of long-term continuity that is 

critical for assessing treatment effectiveness in the event of a fire—with no formally 

established points on the ground, how can fire effects be related to post-treatment 

conditions? Hopefully, these limitations underscore the importance of undertaking a 

statistically supported monitoring framework.

Manager comment: While it’s recognized that this type of monitoring isn’t statistically 

rigorous, the return to a site with the interdisciplinary team and other implementers 

through time has helped with self-learning and adaptive management within the team. 

It helps demonstrate and educate resource specialists not directly involved with fuels 

about the various impacts of fuel treatments.

Statistical Sampling Principles for Monitoring

We discuss two types of statistical sampling: point-based, which is primarily tied to 

repeated observations of a location using photographs; and plot-based, which is based 

on measured attributes on field plots. Three monitoring concepts are fundamental to 

both types of sampling: experimental unit, replication, and independence.

The experimental unit in the case of monitoring is the treatment unit, which is the 

smallest area for which a treatment is applied—it may be a single stand or a collection 

of similar stands receiving the same treatment, and it need not be spatially contiguous. 

Ultimately, measurements from all samples in that unit can be averaged together to 

generate a composite value for that experimental unit representing a single sample from 

the larger population of forested land.

To ensure the statistical replication that is required in order to assess precision (provide 

confidence intervals around estimates), multiple samples (treatment units) are needed 

for each treatment type. Ideally, the more replication of a treatment, the better. However, 
184 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

time and money limit the number of treatment units that can be assessed; therefore, a 

minimum of three replicates of the treatment unit is recommended, as three is the fewest treatment units that could be sampled and still provide the information needed to 

generate confidence levels around the estimates.

Treatment units included in the sample for a treatment type 

need to be independent, meaning that what happens on any one 

treatment unit will have no influence on any other treatment 

unit. In the simple case of equal-sized units, each should have 

an equal probability of being selected as part of the sample. 

Making a list of all units where treatment X is applied, numbering them, and rolling a dice or using a random number 

generator to choose units in which to install monitoring sites 

ensures independence. Where treatment units are of different 

sizes, random selection can still be made using area weighting.

Point-based sampling relies on photographs taken at specific 

points within a fuel treatment, at different times, to detect changes in vegetation and 

soils. Repeat photography requires that camera position and orientation with respect to 

the photo-point are constant throughout the monitoring period. One way to fulfill this 

requirement is to set up permanent markers for the photo-point and camera location. 

Hall (2001a, 2001b) provided an excellent handbook, based on 45 years of experience, 

that covers the principles of repeat photography and photo-point sampling. It includes 

data forms and information about techniques, equipment, and analysis. Hall suggested 

that before points are set up, the monitor should consider five basic questions: why, 

where, what, when, and how.

	 •	 Why are you monitoring?

	 •	 Where is the best location to set up the photo-points that will answer your 

monitoring questions?

	 •	 What are the variables you want to monitor?

	 •	 When and how often should you take pictures?

	 •	 How detailed do the data need to be, and must they be quantitative or will 

qualitative data be sufficient?

Plot-based sampling is familiar to most managers, many of whom may have had 

experience assessing timber cruise plots such as those used during timber sale preparation or stand exams. Plots can be and frequently are used to characterize and describe 

vegetation characteristics relevant to fire and fuels such as tree size and density, canopy 

base height, composition and abundance of forest understory, soil cover characteristics, 

and tree height. Transect-based sampling is a kind of sampling that is often used on field 

plots to characterize loadings of down woody fuels (for example, Brown 1974), though 

it is potentially suitable for sampling any kind of linear features (for example, at the 

right scale, roads, and trails). However, for predicting fire behavior and effects, photoseries and expert characterization of the surface fuel model (via Albini 1976; Riccardi 

and others 2007b; Scott and Reinhardt 2001) have greater power. Borrowing inventory 

design elements from other field-tested inventory and monitoring systems such as FIA, 

Forest Health Monitoring or the various fire monitoring protocols as a starting point is a 

time-honored strategy with the potential to leverage previous investments in statistical 

design, field guide language, data recorder software development, and database design 

(including quality assurance checks and calculated variables). The prudent manager 

will be selective about which attributes are included to ensure that the data collected 

will align with monitoring objectives and that the implementation can be accomplished 

with available budget, field personnel, and computing resources.

Terminology

A confidence interval is the upper and lower 

boundary of an interval that contains a population 

parameter at a pre-specified level of probability 

(confidence level)—note that given a level of 

confidence, for example 95 percent, a larger data 

sample will typically reduce the error estimate 

and narrow the confidence interval.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 185

Due to sampling variability, which in typically heterogeneous, dry mixed conifer 

types is usually much greater than for forest plantations, monitoring data will have the 

greatest power under a system of permanent plot locations that are remeasured at appropriate intervals (for example, immediately pre- and post-treatment, after significant 

regrowth has occurred, and immediately post-fire [FIA grid is an example]).

Model-Assisted Monitoring

Collecting plot data that can be modeled in FFE-FVS offers some significant advantages. This model can predict many fire and fuels-relevant attributes. While accuracy of 

model predictions may vary depending on the scenario, such models are typically more 

robust when used to assess differences—for example, among treatments or between preand post-treatment conditions. Moreover, this model can accept a remarkably diverse 

array of plot data types, so one is not locked in to fixed or variable radius plots of any 

particular size, and plot design can be tailored to available resources.

As we learned in Chapter 5, modeling fuel treatment effectiveness is easy to do in 

Forest Vegetation Simulator (FVS)—as long as the modeler and manager have a clear 

vision of: (1) what constitutes a hazardous condition, (2) what constitutes hazard reduction, (3) which kind of hazard reduction is most important, and (4) model assumptions. 

Also, if the goal is to use the model to determine fuel treatment longevity, then calibrating 

the growth rate is suggested. Plot data can then be converted to frequency distributions 

of the kind found in Appendices A and C, where one can look at a characteristic (for 

example, mortality volume under severe fire weather or indices of crown fire potential) 

over the entire landscape, or for any subset, such as the places under consideration for 

fuel treatment in the coming year (if such pre-treatment monitoring data are readily 

available). Then, if treatments are modeled in FVS, their outcomes, in terms of mortality volume or any other fire- or fuels-related trait, can be predicted along with other 

useful information such as net treatment cost and post treatment stand attributes. When 

combined with post-treatment monitoring and, ultimately, post-disturbance monitoring, 

there is the advantage of a reality-check on the modeled outcomes. But even absent 

post-disturbance modeling, this kind of monitoring/modeling database is a powerful 

tool for exploring assumptions, alternatives, and likely outcomes and can help identify 

opportunities to accomplish the greatest fuel treatment impact for the lowest cost.

Conclusion

A commitment to monitoring reinforces a commitment to active fuels management as 

it can lead to a more effective and efficient fuels management program. Just as a clear 

and shared understanding of fuels management objectives is critical to the success of a 

fuel treatment, clear and focused objectives for monitoring are essential to ensure that 

investments in monitoring produce results in terms of useful and timely information 

that improves the effectiveness and efficiency of the fuels management effort. Time 

spent on monitoring design is well spent if it leads to clear objectives and a focused, 

results-oriented monitoring protocol that can be sustained over time, even as responsibilities for data collection, management, and analysis are transferred among individuals. 

Informal, non-statistical, “windshield surveys” are no substitute for thoughtful, carefully planned, consistently implemented, and adequately funded (over the long-term) 

monitoring. Although managers often have insufficient time to become proficient with 

models such as FFE-FVS, models have the potential to supplement monitoring data with 

effectiveness predictions and provide key insights that could improve fuel treatments. 

Analytic capacity can be brought to bear via partnerships, collaborative agreements, and 
186 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

enterprise teams. Cooperative arrangements may also be useful to periodically and opportunistically evaluate treatment effectiveness when wildfires encounter treated stands.

Further Reading

A variety of monitoring protocols are available that focus on fire effects, soils, 

wildlife, and other elements. However, new information and protocols are continually 

being developed; therefore, we provide just a view examples of what is available. (See 

Appendix B: Decision support tools for managers.)

FEAT/FIREMON (FFI) (A monitoring software tool designed to assist managers with collection, storage, 

and analysis of ecological information.)

Forest Health Monitoring Program, U.S. Department of Agriculture, Forest Service. (A national program 

designed to determine the status, changes, and trends in indicators of forest condition on an annual basis. 

The program uses a variety of approaches to address forest health issues that affect the sustainability 

of forest ecosystems.)

Forest Inventory and Analysis National Program (FIA). (Reports on status and trends in United States Forests.)

Hall, Frederick C. 2001a. Photo point monitoring handbook: part A–field procedures. Gen. Tech. Rep. 

PNW-GTR-526. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station: 1-48.

Hall, Frederick C. 2001b. Photo point monitoring handbook: part B–concepts and analysis. Gen. Tech. 

Rep. PNW-GTR-526. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station: 49-134.

Lutes, Duncan C.; Benson, Nathan C.; Keifer, MaryBeth; Caratti, John F.; Streetman, S. Austin 2009. FFI: 

a software tool for ecological monitoring. International Journal of Wildland Fire 18: 310-314.

Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland, Steve; 

Gangi, Larry J. 2006. FIREMON: fire effects monitoring and inventory system. Gen. Tech. Rep. RMRSGTR-164-CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 1 CD.

Soil Quality Monitoring for Long Term Ecosystem Sustainability on Northern Region National Forests 

(SOLO). (Focused on monitoring soil disturbance and includes documents on how to conduct a rapid assessment of soil disturbances (Volume 1 and Volume 2). Soil-disturbance field guide and associated forms.)

Vesely, D.; McComb, B.C.; Vojta, C.D.; Suring, L.H.; Halaj, J.; Holthausen, R.S.; Zuckerberg, B.; Manley, 

P.M. 2006. Development of protocols to inventory or monitor wildlife, fish, or rare plants. Gen. Tech. 

Rep. WO-72. Washington, DC: U.S. Department of Agriculture, Forest Service. 100 p.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 187

Section III. Reality Check: The Economics, Feasibility, 

Longevity, and Effectiveness of Fuel Treatments 
188 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 189

Chapter 11

Inventory and Model-Based Economic 

Analysis of Mechanical Fuel Treatments

Chapter Rationale

Several key questions guided our analysis, including:

	 •	 What is the scope of the fuel management challenge in the dry mixed conifer 

types of the northern West? How many acres should be classified as high hazard 

based on the likelihood of crown fire, high surface flame height and/or high 

rate of tree mortality?

	 •	 How do these high hazard characteristics compromise the health of post-fire 

forests and/or negate ecosystem functions such as long-term carbon storage 

benefits?

	 •	 Can silvicultural manipulation reduce fire hazard while retaining viable, resilient 

forests? If so, how many acres would benefit from treatment?

	 •	 What fuel treatment approaches are likely to be both effective and economically 

feasible (e.g., be self-supporting) in reducing fire hazard?

	 •	 How do the answers to the above questions vary among forest types and subregions in our study area?

Introduction

In this chapter, we evaluate the feasibility of conducting fuel treatments within our 

synthesis area. We cannot assume it is possible to implement a fuel treatment in every 

place that would benefit from one, and there are many kinds of fuel treatments, only 

some of which will be effective in any particular stand. There are many stands where 

no fuel treatment is likely to be effective and many more where an effective treatment 

will be more costly than it is worth. We use the Forest Inventory and Analysis (FIA) 

BioSum framework to assess the effectiveness, costs, and potential returns from generic, 

multi-purpose mechanical fuel treatment approaches commonly implemented in this 

region. For effective treatments, we describe treatment costs, yields, and gross revenues 

associated with merchantable and energy wood; costs of transporting wood to mills and 

bioenergy facilities; and net revenues (or costs) of treatment operations. Results in this chapter and in Appendix 

C are presented by the same sub-regions and broad forest 

type groups introduced in Chapter 5 to facilitate localized 

application of the information in this synthesis.

Silvicultural simulation using the FIA BioSum framework is ideally suited to answering these types of questions. 

The model can assess a large, statistically representative 

sample of the entire forested landscape in the dry mixed 

conifer region, including all forest types, forest structures, 

ownership classes, and degrees of accessibility (for example, 

slopes, distance from roads, and distances to sawmills and 

FIA BioSum

Forest Inventory and Analysis Biomass Summarization System (FIA BioSum) is an analysis framework 

that combines forest inventory data representing 

an analysis region, a treatment cost model, a fuel 

treatment effectiveness model, and a raw material 

hauling cost model to explore alternative landscapescale treatment scenarios that achieve a variety of 

management objectives.
190 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

bioenergy facilities). FIA BioSum produces statistically reliable summaries of the proportions of the forested landscape in the dry mixed conifer region that would respond 

positively to treatment. The system can also estimate the costs, revenues, and product 

flows that could be associated with a comprehensive fuel treatment program.

Model output includes reports of the area for which each kind of treatment would 

make the most effective choice. Trade-offs between area effectively treated and net 

treatment cost can also be summarized in order to understand the effects of policies 

such as requiring sales of derived products to cover treatment costs.

The analysis we describe is not designed to endorse any particular fuel treatment 

or fuels management strategy for any given stand. Rather, it conveys a sense of the 

opportunities and outcomes, and range of costs and revenues over a broad, dry mixed 

conifer landscape, and how these vary by forest type and sub-region. Necessarily, these 

are inextricably linked to assumptions about treatment options, hazard concept, and effectiveness determination as well as unit costs, product prices, and existing processing 

infrastructure. The information presented here and in Appendix C is a useful point of 

reference against which to compare stands and contemplated prescriptions. Managers 

seeking a solution for a particular stand may find it worthwhile to run models such as 

FFE-FVS (Reinhardt and Crookston 2003) and My Fuel Treatment Planner (http://www.

fs.fed.us/pnw/data/myftp/myftp.shtml) on sample data collected from that stand using 

their own assumptions to understand the potential impacts and costs of alternative prescriptions in terms that are relevant to the challenges managers face. Managers seeking 

to analyze entire landscapes in support of a fuel treatment management program should 

consider learning and using the BioSum tool that is scheduled for general release in early 

2013 (software, data, tutorials, and user guide will be available at www.BioSum.info).

Although some readers may be eager to jump straight to the results of this economic 

analysis, results should be interpreted in the context of how the analysis was conducted, 

as both results and interpretations ultimately depend on the bevy of assumptions and 

analytical choices described in the next few pages.

Applying the BioSum Analysis Framework

The BioSum analysis framework (fig. 11.1) was developed to combine a number 

of data sources and models to explore alternative landscape-scale treatment scenarios 

that achieve a variety of management objectives (Fried and others 2005). Data sources 

and models include:

	 •	 systematic forest inventory data representing an analysis region

	 •	 silvicultural treatment implementation model (FVS; Dixon 2002)

	 •	 fuel treatment effectiveness model (based on outputs from FFE-FVS; Rebain 

2010; Reinhardt and Crookston 2003)

	 •	 fuel treatment cost model

	 •	 raw material haul-cost model

	 •	 wood product allocation model

A variety of treatments, developed in consultation with silviculturists and fuels management experts, are simulated to assess which stands could benefit from treatment, 

treatment effectiveness, and net and gross treatment costs (Fried and Christensen 2004; 

Fried and others 2003). A series of steps are used to conduct this analysis:

1. raw materials (logs, biomass, and pulp) generated by a variety of mechanical 

treatments designed to reduce canopy fuels are estimated using data derived 

from forest inventory plots and processed via FVS (Dixon 2002);
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 191

2. treatment costs are estimated via the Fuel Reduction Cost Simulator (FRCS; 

Fight and others 2006);

3. gross product economic values are calculated for each product species group 

and size class of harvested material based on local product prices; and

4. the wood produced by fuel treatments can be summarized by species group and 

size class (Barbour and others 2008).

Moreover, candidate sites for building processing facilities can be simulated and 

evaluated with respect to economic feasibility (Fried and others 2005), or the process 

can use optimization to select both the best treatment for each acre and the best places 

to add bioenergy capacity (Daugherty and Fried 2007).

Figure 11.1. Process flow chart for evaluating the feasibility evaluation using the FIA BioSum model.
192 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Data Acquisition and Treatment Development

FIA Plot Data

We used the forested, unreserved, condition-level data1 from a set of 5174 FIA plots 

in Oregon, Washington, Idaho, Montana, Utah, and extreme northern California collected between 2000 and 2009 (described in Chapter 5). These plots were selected for 

this analysis via overlay of FIA plot coordinates on a grid coverage representing dry 

mixed conifer types derived from the LANDFIRE BpS (see Chapter 2 for details). As 

explained in Chapter 5, each forested condition on the selected plots was assigned a 

forest type group: Douglas-fir and True Fir, Pine and Western Larch, Aspen (includes 

birch), Western Redcedar, Non-stocked2, or a grab bag of everything else that we call 

Other3) based on its FIA forest type4 and a Forest Vegetation Simulator (FVS) variant 

based on its location (for reporting analysis results, we group FVS variants into four 

sub-regions; see fig. 5.1).

Model results are summarized using FIA plot expansion factors to represent acres 

and/or fractions of the forested landscape in each sub-region and forest type group that 

can be successfully treated. (An FIA expansion factor is a statistically derived estimate 

to interpret the number of forested acres represented by a plot.) We eliminated plots 

from consideration that could not be mechanically thinned without building new roads 

(>5000 ft from the nearest road if slope on plot was less than 40 percent; >2000 ft 

from nearest road if slope on plot ≥40 percent, to account for the limiting distance of 

cable logging). Plot data were projected in FFE-FVS to a common date of 2009 before 

analyzing stand attributes such as fire hazard and treatment effects. Tree list data from 

fixed-radius samples on these conditions include field measurements and estimates of 

diameter, height, crown ratio, and species, and model estimates of stem volume and 

merchantable and non-merchantable biomass (see Zhou and Hemstrom 2010 for details 

on models used).

1 Because FIA uses a mapped plot design in which plots are subdivided into “conditions” when there 

are sharp discontinuities within the forested parts of a plot (for example, in size class, forest type, stand 

density, land owner group, and reserve status) or when an otherwise forested plot includes non-forest area, 

the basic unit of analysis is condition, not plot.

2 We do not apply treatments to non-stocked conditions, so these are not addressed in this chapter.

3 Other includes forests belonging to one of 45 FIA forest types: Bigleaf maple, Blue oak, Blue spruce, 

California black oak, California laurel, California mixed conifer, California white oak, Canyon live oak, 

Cercocarpus woodland, Cottonwood, Cottonwood/willow, Deciduous oak woodland, Engelmann spruce, 

Engelmann spruce/subalpine fir, Foxtail pine/bristlecone pine, Giant chinkapin, Gray pine, Incense-cedar, 

Intermountain maple woodland, Jeffrey pine, Juniper woodland, Knobcone pine, Limber pine, Lodgepole 

pine, Miscellaneous western softwoods, Mountain hemlock, Noble fir, Oregon white oak, Other hardwoods, Pacific madrone, Pacific silver fir, Pinyon/juniper woodland, Port-Orford-cedar, Red alder, Red 

fir, Redwood, Rocky Mountain juniper, Sitka spruce, Subalpine fir, Sugar pine, Tanoak, Western hemlock, 

Western juniper, White fir, or Whitebark pine.

4 Usually derived as the species with the plurality of stocking in the condition.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 193

Silvicultural Treatments

Overstory treatments

Designing treatments for dry mixed conifer forests is challenging due to diverse topography, species composition, and tree sizes, and in some cases, the need to integrate 

multiple objectives. In developing generic (in the sense that they are not dependent on 

or tuned to initial stand conditions) treatments to model for this economic analysis, we 

sought to represent elements of the types of treatments that are typically considered 

when developing fuel treatments, meeting restoration objectives, or managing for timber 

production (though we did not include regeneration harvest). We developed three silvicultural treatments—two thinning treatments, a crown thinning, and a thin from below 

(Graham and others 1999)—and a restoration treatment (table 11.1). Crown thinning, 

which focused on separating overstory crowns and reducing canopy density, reduced 

canopy cover to 50 percent. The thin from below treatment was designed to leave all 

trees that were >21 inches diameter breast height (dbh) and harvest everything smaller. 

The objective was to eliminate ladder fuels. In the restoration treatment, we removed 

understory trees such as grand fir and Douglas-fir and retained ponderosa pine, western larch, and other disturbance-resilient species. At times, this resulted in an outcome 

indistinguishable from a regeneration harvest when disturbance resilient species were 

absent or present in very low densities.

Table 11.1. The six fuel treatment prescriptions modeled in BioSum can be thought of as three core, generic prescriptions 

with two alternative harvest system implementations for each.

Treatment name Description

Crown thinning (WT) Thin to 50 percent canopy cover, “good” (early seral) species cut last (assigned super 

low probability of removal), but Douglas-fir has equal chance of removal as white fir, 

grand fir, etc.; whole tree yarding, no slash disposal needed.

Crown thinning (CTL) Thin to 50 percent canopy cover, “good” species cut last (assigned super low probability 

of removal), but Douglas-fir has equal chance of removal as white fir, grand fir, etc.; cutto-length, slash disposal via pile and burn at $350/acre.

Thin from below (WT) Leave only big (>21 inches DBH) trees, no species preferences, whole tree yarding, no 

slash disposal needed.

Thin from below (CTL) Leave only big (>21inches DBH) trees, no species preferences, cut-to-length, slash 

disposal via pile and burn at $350/acre.

Restoration (WT) Leave early seral species, cut everything else (including Douglas-fir), whole tree yarding, 

no slash disposal needed.

Restoration (CTL) Leave early seral species, cut everything else (including Douglas-fir), slash disposal via 

pile and burn at $350/acre.

Harvesting method

For each of these three treatments, we modeled two automated harvesting systems to 

account for the surface fuels produced by harvest activity: whole tree harvesting (WT), 

which effectively moves tops and limbs, as well as boles, to the landing where they can 

be recovered for utilization (producing a minimal increase in surface fuel loading); and 

cut-to-length (CTL), which severs and discards tops and limbs as it processes trees to 

log length at the stump. For WT, we assumed that 10 percent of harvest slash (mainly 
194 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

limbs and tops) remained on site, and we did not apply a slash treatment in our FFE-FVS 

analysis. With CTL, processed logs were extracted using a forwarder, leaving all limbs 

and tops as slash on the site. While this reduced problems related to nutrient retention, 

short-term surface fuel concerns remain, particularly when a “bed” of slash is created 

on the trail along which equipment is operated in order to minimize soil disturbance. To 

account for this increased fire hazard, this treatment was followed by modeling a pile 

and burn treatment. While 28 percent of the slash remained uncombusted after treatment, all fine fuels smaller than 1 inch diameter were consumed according to the model.

Excluded plots

Because our treatments were designed to treat three very different kinds of forest: 

high overstory density, >50 percent canopy cover; stands containing remnant trees >21 

inches dbh; and stands containing an abundance of fire- and disturbance-resilient species, 

not every treatment is applicable on every forested condition, as evidenced in modeled 

treatment outcomes. For example, in stands with a canopy cover of 35 percent, it is 

impossible to devise a crown thinning treatment that will leave a residual canopy cover 

of 50 percent. A condition with no trees less than 21 inches dbh cannot be treated using 

a thin-from-below with a 21-inch dbh maximum diameter for removed trees, and when 

no resilient species are present, a restoration treatment morphs into a regeneration cut. 

To avoid such outcomes in our modeling, we dropped treatments that left a residual stand 

that would not be viable without regeneration (<20 percent canopy cover). Choosing 

a lower limit of 10 percent canopy cover instead would increase the treatable area by 

40 percent with little difference in average hazard improvement, treatment cost, and 

net revenue. Occasionally, a modeled treatment removed so few trees or so many large 

trees that assumptions of the harvest cost model were violated and the model could not 

be applied, forcing us to drop these condition-treatment combinations. As noted earlier, 

conditions on plots that are too far removed from roads for any treatment to be viable 

were dropped from the analysis.

Calculating Economic Feasibility

Estimating treatment and transport costs

Plot attributes such as slope, distance to a road, and position on the road network 

relative to processing facilities (for both merchantable and energy wood) were used to 

calculate both on-site treatment costs and haul costs. We used a custom, geographic 

information system-based, travel-time analysis engine parameterized with current truck 

and driver rental rates ($75/hr) and comprehensive, segment-specific road speed data 

drawn from several sources to calculate transportation costs. We found the least cost 

path for moving harvested material to each of up to 259 existing processing sites for 

merchantable wood and/or biomass energy, producing a haul cost knowledgebase containing over one million entries (round trip travel times >24 hours were filtered out to 

reduce database size and processing time). On-site treatment costs were estimated for 

each condition-treatment combination via a version of the Fuel Reduction Cost Simulator (FRCS) that had been updated for this study to reflect circa 2010 costs for various 

types of logging equipment (Han-Sup Han, personal communication).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 195

Estimating product quantities and values

The “cut lists” generated by FFE-FVS (one list per condition/treatment combination) 

were linked to the FIA tree tables containing the weight and volume of energy wood 

derived from tops and limbs from merchantable (trees ≥7 inch dbh) and nonmerchantable wood (the entirety of trees <7 inch dbh and non-commercial 

species of all sizes) and the weight and volume of sawlogs from merchantable trees. Trees less than 5 inches dbh on steep (>40 percent) slopes and 

less than 3 inches dbh on gentle slope were assumed to be felled, cut in half, 

and left on site or piled and burned, depending on the treatment.

Delivered product values for merchantable wood were calculated as the 

product of the harvest quantities and prices by diameter class (for 5-9, 9-16, 16-21, and 

>21 inch classes) and product species group. The product species groups we used were 

cedar, Douglas-fir/larch, hardwoods, pine/spruce, true fir/hemlock, and other conifer. 

Prices were obtained as the three-year average (mid-2007 through mid-2010) of quarterly average delivered prices per thousand board feet in Montana converted to price 

per cubic foot based on diameter class and product species group calculated from trees 

in the FIA database. Delivered price of energy wood was assumed to be $36/green ton 

for all species.

Fire Hazard Criteria and Evaluation

Selecting Evaluation Criteria

The end product of all these analytic steps is a genuinely massive knowledgebase of 

treatment outcomes, product yields, costs, and revenues that provides unparalleled opportunities for describing “best” alternatives under a wide range of assumptions, objectives, 

and goals. There are countless ways that treatment effectiveness could be assessed. The 

best hazard descriptor to focus on will depend on management objectives and, in many 

cases, more than one hazard descriptor may be relevant. For example, treatments that 

lead to improvements in the crowning index often produce greater hazard as indicated 

by torching index, at least as represented in FFE-FVS. Even with a single descriptor, 

one must decide on the threshold beyond which initial hazard is unacceptable and warrants some kind of treatment, assuming that some kind of treatment can be effective.

The challenge in evaluating treatment effectiveness is to choose a logical basis for 

evaluating a change in hazard. One can select a particular evaluation criterion—for 

example, first seeking to achieve success with one descriptor before considering effects 

on other descriptors, or rely on some weighted combination of change in descriptors. 

Recognizing this complexity, and that there is no one “right” way to evaluate hazard or 

treatment effectiveness, we selected thresholds for each of four hazard descriptors (as 

discussed in Chapter 5): probability of torching >20 percent (ptorch), Torching Index 

<20 mph (TI), Surface Flame Length >4 ft (SFL), and Mortality Volume Percent >30 

percent (MortVolPct). If a descriptor exceeded a hazard threshold, its descriptor score 

was assigned a 1; if it didn’t, a score of 0 was assigned. By adding the four descriptor 

scores, we obtained a composite hazard score with implicitly equal weights for each 

descriptor; however, because torching is addressed by two descriptors, that aspect of 

hazard is implicitly double-weighted. The composite hazard score has a maximum value 

of 4 (a score of 1 for each descriptor score) and a minimum of 0 (table 11.2).
196 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Pre-treatment and post-treatment composite hazard scores were computed for each 

silvicultural treatment. We calculated a hazard score reduction between pre- and posttreatment (pre-treatment score - post-treatment score = hazard score reduction) for each 

treatment on each condition. We devised five, rule-based scenarios to explore four alternative perspectives for evaluating treatment effectiveness using the hazard score reduction and selecting a “best” treatment for each forested condition (table 11.3). Two broad 

types of scenarios were identified: the “Any” scenarios can be thought of as supporting 

any kind of hazard reduction while the “All” scenarios can be thought of as requiring 

a post-treatment status in which all hazards have been mitigated (a composite hazard 

score of 0). Conditions with an initial composite hazard score of 0 were not considered 

in any scenario because they are already low hazard by definition.

For a treatment to be effective under an “Any” scenario, it had to produce the lowest 

post-treatment composite hazard score of any treatment. We considered two variations 

of “Any” scenarios to reflect different assumptions about which treatment would be 

considered “best” when more than one treatment was capable of achieving the same 

composite hazard score. Under Any:ptorch, if more than one silvicultural treatment had 

the same hazard reduction value (for example, two treatments have a post-treatment 

Table 11.2. Example of hazard score computation for three hypothetical conditions: A, B, and C. Score is incremented by one for each hazard 

descriptor where the calculated value for a condition exceeds the 

corresponding threshold. 

Condition

Descriptor Threshold A B C

Probability of torching (Ptorch) >20 25 35 15

Torching Index (TI) <20 18 25 10

Surface flame length (SFL) >4 5 6 4

Mortality volume (%) (MortVolPct) >30 25 35 10

Hazard Score 2 4 0

Table 11.3. Five scenarios, representing different perspectives and priorities, were developed to evaluate 

treatment success and select a “best” treatment for all conditions with initial composite hazard 

scores greater than 0. “NA” indicates not applicable. The “Any” scenarios represent any kind of 

hazard reduction. The “All” requires a post-treatment status where all hazards have been mitigated 

(defined under success defined). In case of ties in the Any:ptorch, if more than one silvicultural 

treatment had the same post-treatment hazard score, a tie-breaker was selected. The primary 

tie breaker was dictated on the probability of torching (ptorch). If the observation still resulted in 

a tie then maximum net revenue was deemed best. 

Optimize Primary Secondary

Scenario Success defined (maximize) tie-breaker tie-breaker

Any:ptorch Score Reduction >0 Score reduction ptorch net revenue

Any:NetRev Score Reduction >0 Score reduction net revenue NA

All:ptorch Score = 0 NA ptorch net revenue

All:NetRev Score = 0 NA net revenue NA

Any: NR+ Score Reduction >0 Net Revenue NA NA
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 197

composite hazard score of 2), then the treatment with the lowest post-treatment ptorch 

is deemed “best.” If treatment A leaves ptorch at 10 percent probability while treatment B leaves it at 6 percent probability, treatment B would be selected as “best” if 

their resultant hazard scores are identical. If, even after this “tie-breaker,” a condition 

has multiple treatments that have the same post-treatment, hazard score, and ptorch, 

then the treatment with the maximum net revenue is deemed best. If ties remain, then 

an arbitrary selection is made from among the tied treatments. In essence, we identify 

which treatments are most effective as a function of fire hazard descriptors. If more than 

one of these has the same lowest probability of torching, then the one that maximizes 

net revenue is applied.

Any:NetRev is similar to Any:ptorch except that to break the first tie (two treatments 

have the same post-treatment composite hazard score), we use maximum net revenue to 

determine the best treatment without 

first excluding treatments that do not 

have the lowest ptorch.

The “All” scenarios define success 

as moving every hazard descriptor 

to the “safe” side of its respective 

threshold so that the post-treatment 

composite hazard score equals 0 and, 

like the “Any” scenarios, has two 

versions that differ only in terms of 

tie breaking rules: All:NetRev picks a 

treatment that maximizes net revenue 

from among those that achieve a 0 hazard score, while All:ptorch excludes 

treatments that do not have the lowest 

probability of torching before applying 

the net revenue criterion.

We developed a fifth scenario, dubbed Any:NR+, to identify the “pay their own way” 

acres where receipts from products (merchantable and energy wood) cover the costs of 

treatment without requiring subsidy. In this scenario, we only consider treatments that 

generate positive net revenue, and these are sometimes different than the “best” treatments selected in the other “Any” scenarios.

Findings

What we report here is one possible slice of the knowledgebase based on the conceptualization of hazard discussed in Chapter 5 (based on four hazard descriptors 

and associated thresholds) and two simple hazard reduction accomplishment criteria: 

(1) treatment is successful if any reduction in composite hazard score is achieved, or 

(2) treatment is successful only if composite hazard score is reduced to 0. When more 

than one treatment will satisfy these effectiveness criteria, heuristics (rules) are applied 

to choose the best alternative. This section summarizes three kinds of findings from our 

analysis: (1) scenario-based assessments of treatable area, (2) treatment popularity (or 

frequency with which treatments are rated “best”), and (3) costs, revenues, and product 

flows. Results can also be used to compute average potential product quantities, costs, 

and revenues that could be associated with a fully implemented program of landscapescale fuel treatment.

Note that mean on-site costs vary considerably by sub-region, forest type group, and 

scenario, but generally range between $1000 

and $2000/acre. Though sometimes useful to 

people familiar with logging cost ranges, cost 

expressed as dollars per 100 ft3 of merchantable wood are extremely variable, as there are 

acres where costs are high but merchantable 

yield is very low, and other acres where costs 

are moderate and yields are very high. It does 

not take many high cost/low yield acres to 

exert high influence on the mean statistic—in 

some cases, the median cost per 100 ft3 is only 

a fourth of the mean value.
198 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Area Treated by Silvicultural Treatments

Nearly 31 million of the 33 million acres analyzed have a hazard score greater than 

0. We assumed that to be eligible for fuel treatment, a stand must initially have a canopy 

cover of at least 20 percent, and over 24 million acres (73 percent) did. On 18.8 million 

(61 percent) of the 30.8 million acres with a non-0 hazard score, one or more treatment 

options were effective in reducing the hazard score (though rarely so effective that they 

reduced it to 0). However, we also assumed that treatments would only be considered if 

they resulted in residual stands with at least 20 percent canopy cover. This assumption 

proved highly limiting in that only 5.4 million acres met this criterion (or 18 percent of 

the acres at risk). The vast majority of acres excluded from treatment by this assumption 

had a residual canopy cover under 5 percent and were all but indistinguishable from 

a regeneration harvest. While regeneration harvests are a legitimate management approach in some cases, we focused in this synthesis on mechanical treatments that leave 

a viable residual stand (as evidenced by a residual canopy cover of at least 20 percent). 

However, the condition-treatment combinations that remain are a very large (nearly 1000 

cases), representative sample of the potential hazard reduction accomplishments, costs, 

and revenues associated with mechanical fuel treatments on high hazard stands in this 

landscape. The statistical representativeness of the sample, based as it is on a systematic 

forest inventory sample, lends great power in making inferences and generalizations 

from this economic analysis.

Evaluating Treatment Success

Depending on current conditions, the different treatment scenarios and their effectiveness varied by sub-region and forest type. For example, under the Any:ptorch scenario, 

in the Pacific Northwest Interior and northern California and Klamath, thin-from-below 

was chosen most frequently as the best option, and restoration ranked second (table 11.4). 

Restoration was most frequently selected by the model in the northern and central Rocky 

Mountains (table 11.4). When combined with crown thinning, a total of 72 percent of 

the area was targeted by these two treatments. The restoration treatment was selected at 

least as frequently under the All:NetRev scenario; however, thin-from-below was rarely, 

if ever, rated “best” in any region. This difference is likely driven both by effectiveness 

Table 11.4. For the Any:ptorch and All:NetRev scenarios, total acres treated and percent of area for which each treatment best 

achieves the scenario objectives by sub-region. The overstory treatment types were crown thinning, thin-from-below, 

and restoration. Two harvesting methods were used to treat the slash: whole tree (WT) and cut-to-length followed by 

pile and burn (CTL).

Crown thinning Thin from below Restoration

(% of area) (% of area) (% of area)

Sub-region Acres Total WT CTL Total WT CTL Total WT CTL

Any:ptorch

Northern California & Klamath 1,798,299 13 4 8 58 13 45 29 9 19

Pacific Northwest Interior 2,064,415 13 3 10 57 16 41 35 13 17

Northern & Central Rocky Mountains 1,436,080 32 12 20 28 12 16 40 17 24

Utah 112,803 42 30 12 30 12 17 28 16 12

All:NetRev

Northern California & Klamath 521,526 4 20 22 10 3 7 49 26 22

Pacific Northwest Interior 417,880 44 16 28 5 4 2 51 36 15

Northern & Central Rocky Mountains 528,457 54 30 24 0 0 0 46 28 18

Utah 6,716 0 0 0 0 0 0 100 0 100
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 199

Table 11.5. For the Any:ptorch and the All:NetRev scenarios, total acres treated and percent of area for which each 

treatment best achieves the scenario objectives by forest type. The overstory treatment types were crown 

thinning, thin-from-below, and restoration. Two harvesting methods were used to treat the slash: whole tree 

(WT) and cut-to-length followed by pile and burn (CTL).

Crown thinning Thin from below Restoration

Sub-region Acres Total WT CTL Total WT CTL Total WT CTL

Any:ptorch

Western redcedar 108,366 0 0 0 61 0 61 39 0 39

Douglas-fir & true fir 2,177,012 25 8 17 48 10 38 27 9 18

Pine and larch 456,263 15 1 14 40 28 12 45 32 13

Aspen/birch 112,400 49 30 19 12 6 6 39 33 6

Other species 2,557,556 14 6 8 43 15 38 34 12 22

All:NetRev

Douglas-fir & true fir 781,117 57 30 27 1 0 1 42 26 16

Pine and larch 155,084 44 26 18 12 8 4 44 30 14

Aspen/birch 23,065 33 0 33 0 0 0 67 38 29

Other species 515,313 33 11 22 9 3 6 58 34 24

considerations and by the fact that trees harvested under thin-from-below are smaller 

and generate less revenue than with restoration and crown thinning. In Utah, the crown 

thinning prescription tended to fit much of the area with about 42 percent of the 113,000 

thousand acres being treated under this scenario. Across sub-regions, crown thinning 

appears to have been more frequently selected for the aspen/birch forest types than for 

other forest types (table 11.5). The restoration treatment was selected 45 percent of the 

time for pine and western larch forests; by contrast, crown thinning rated best on only 15 

percent of these forests. Within Douglas-fir and True Fir forest types, thin-from-below 

was the most effective given that it was selected to treat 48 percent of the area, but it 

also had the fewest acres that produced positive net revenue.

Table 11.6 summarizes the average change in composite hazard score and five FFEFVS computed fire hazard metrics for the Any:ptorch scenario for forest type-sub-region 

combinations with at least 100,000 acres in the study area (i.e., a sample size sufficient 

to support inferences). The table demonstrates the differences in effectiveness of these 

treatments among sub-regions and forest type groups and among hazard metrics. 

Hazard score for this population (which consists of stands where any improvement 

in hazard can be accomplished by one of the modeled treatments) dropped by 1-2 on 

average, but the drop was consistently less in the north and central Rocky Mountains 

sub-region compared to the Pacific Northwest Interior and northern California and 

Klamath. Signs of changes in hazard metrics were generally, but not always, as expected. For example, torching index dropped, on average, in the pine and larch forests 

of the north and central Rocky Mountains, indicating an average increase in torching 

hazard. Ptorch also dropped (suggesting reduced hazard), but by less than any other 

sub-region-forest type group combination. Average change in surface flame length was 

sleight, highly variable, and more frequently positive than negative, suggesting that 

this hazard is comparatively resistant to amelioration via the treatments we modeled. 

By contrast, MortVolPct was substantially reduced, on average, by 50-60 percentage 

points in Douglas-fir and true fir, and by 30-40 percentage points in pine and western 

larch forests. There was considerable variability in mean treatment accomplishment in 

the Other forest types among sub-regions—perhaps because the composition of Other 

forests varied so much among sub-regions.
200 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Table 11.6. Average change in composite hazard score, torching index (TI), crowning index (CI), probability of torching (ptorch), 

surface flame length (SFL), and mortality as a percent of pre-fire live tree volume (MortVolPct), resulting from implementing the best treatments under the Any:ptorch scenario for sub-region/forest type group combinations with at least 

100,000 acres in the study area.

Mean Mean Mean Mean Mean

hazard TI CI Mean SFL MortVolPct

score change change ptorch change change

Subregion Acres change (mph) (mph) change (ft) (%)

Douglas-fir and true fir

Northern California & Klamath 368,072 -1.7 89 13 -41 -0.2 -50

PNW Interior 1,028,007 -1.7 59 15 -37 1.0 -57

North and central Rocky Mountains 768,022 -1.2 70 12 -19 0.7 -55

Pine and larch

PNW Interior 313,491 -1.8 57 18 -30 0.1 -39

North and central Rocky Mountains 104,468 -1.1 -13 21 -17 1.0 -31

Other

Northern California & Klamath 1,378,830 -1.9 195 28 -47 0.0 -55

PNW Interior 697,394 -1.8 98 29 -37 0.3 -61

North and central Rocky Mountains 443,875 -1.2 56 13 -24 -0.2 -26

Costs, Revenues, and Product Flows 

Associated With Fuel Treatments

We summarize per acre production quantities and values of merchantable wood, energy 

wood (“dirty chips” suitable for stoking a biomass facility), and associated costs for all 

four scenarios plus the modified Any hazard reduction scenario (Any:NR+)in which 

acres are treated only if they “pay their own way” (in other words, net revenue >0). 

Results are summarized by sub-region (table 11.7) and by forest type group (table 11.8).

Complete data summaries are reported for all sub-regions and forest type groups. 

However, estimates from sparse samples of forest inventory data are not robust. For 

example, the statistics for Utah in the All scenarios, representing 7000 acres, is based 

on a single inventory plot, so a confidence interval can't even be computed. Even the 

113,000 acres for Utah in the Any hazard reduction case would be associated with 

large standard error values (and wide confidence intervals) so the associated estimates 

should be assumed to be imprecise. In the forest types table, the reliability of estimates 

for aspen/birch and western redcedar are similarly suspect due to small sample size.

Those cautions aside, these tables provide a wealth of information on quantities and 

costs of fuel treatment. In general, mean merchantable value in the Net Revenue scenarios (Any:NetRev and All:NetRev) was greater (and/or on-site costs were less) than in 

the Hazard Reduction scenarios (Any:ptorch and All:ptorch) due to the selection of the 

treatment with the most net revenue when ties need to be resolved in the Net Revenue 

scenarios. Wood removals tended to be less in the All scenarios than in the Any scenarios. 

These scenarios address different populations of acres because the All scenarios require a 

0 hazard score post-treatment—a goal that may be impossible to achieve on most acres. 

As evidenced in Appendix C, summary type 2, the mean pre-treatment hazard score in 

the All scenarios was less than in the Any scenarios—the frequency with which three 

or four hazards can be reduced to 0 is likely far smaller than the frequency with which 

an initial hazard score of risk of 1 or 2 can be reduced to 0.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 201

Table 11.7. Production, economic, hazard, and area statistics, by subregion and scenario. The sub-regions are the Northern California and Klamath 

(N. California) Pacific Northwest Interior (PNW Interior), northern and central Rocky Mountains (N & C RM), and Utah. Each table entry 

is a mean value for an attribute for all acres in a sub-region and scenario, or for the entire study area, for a scenario, ordered as follows: 

merchantable wood produced (ft3/acre), energy wood produced (green tons/acre), percent of product value generated from energy 

wood (chips), percent of the total costs of the operation (on site and haul) represented by the cost of hauling chips, on-site costs per 

100 ft3 of merchantable wood recovered, on-site treatment costs ($/acre), value of energy wood ($/acre), value of merchantable wood 

($/acre), net revenue ($/acre), and area treated (1000 acres).

 Chip Chip

value as haul On-site

Chips % of all costs as cost On-site Chip Merchantable Net Area

Merchantable green values % of all $/100 cost value value revenue 1000

Sub-region ft3/acre tons/acre % costs ft3 $/acre $/acre $/acre $/acre acres

Any:ptorch

N. California 2,147 30 40 9 289 2,197 1,081 2,379 641 1,798

PNW Interior 1,752 21 23 8 275 1,707 746 3,146 1,746 2,064

N. & C. RM 1,458 9 21 7 477 2,123 307 2,504 330 1,436

Utah 1,336 8 49 31 179 1,161 286 1,137 -506 113

Study Area 1,823 21 29 9 319 1,954 765 2,699 1,015 5,412

Any:NetRev

N. California 2,259 32 38 11 257 2,012 1,137 2,947 1,425 1,798

PNW Interior 1,960 23 22 9 197 1,586 841 3,644 2,406 2,064

N. & C. RM 1,476 9 20 8 1,248 1,993 307 2,587 541 1,436

Utah 840 5 46 29 186 798 172 822 -261 113

Study Area 1,943 23 28 10 434 1,803 822 3,119 1,618 5,412

All:ptorch

N. California 1,181 17 40 10 582 1,157 597 1,954 1,080 522

PNW Interior 1,009 12 24 8 259 955 430 1,866 1,075 418

N. & C. RM 706 4 23 6 471 2,025 159 1,208 -821 528

Utah 639 5 14 11 473 3,024 182 1,157 -2,101 7

Study Area 986 12 30 8 447 1,355 415 1,705 509 1,475

All:NetRev

N. California 1,475 20 36 11 498 1,128 707 2,694 1,898 522

PNW Interior 1,055 12 24 9 247 908 449 2,002 1,260 418

N. & C. RM 687 4 22 7 437 1,914 154 1,175 -745 528

Utah 639 5 14 11 473 3,024 182 1,157 -2,101 7

Study Area 1,109 13 28 9 401 1,297 462 2,026 907 1,475

Any:NR+

N. California 2,188 29 25 11 89 1,623 1,061 3,357 2,229 1,003

PNW Interior 1,797 21 20 9 85 1,324 769 3,264 2,254 1,622

N. & C. RM 1,213 7 12 8 93 1,094 242 2,254 1,122 779

Utah 923 5 9 36 77 448 169 2,112 1,369 32

Study Area 2,112 24 20 10 85 1,530 857 3,655 2,456 3,625
202 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Table 11.8. Production, economic, hazard, and area statistics, by forest type group and scenario. Each table entry is a mean value for an attribute for all acres in a forest type group and scenario, or for the entire study area, for a scenario, ordered as follows: merchantable 

wood produced (ft3/acre), energy wood produced (green tons/acre), percent of product value generated from energy wood (chips), 

percent of the total costs of the operation (on-site and haul) represented by the cost of hauling chips, on-site costs per 100 ft3 of 

merchantable wood recovered, on-site treatment costs ($/acre), value of energy wood ($/acre), value of merchantable wood ($/acre), 

net revenue ($/acre), area treated (1000 acres).

 Chip Chip

value as haul On-site

Chips % of all costs as cost On-site Chip Merchantable Net Area

Merchantable green values % of all $/100 cost value value revenue 1000

Sub-region ft3/acre tons/acre % costs ft3 $/acre $/acre $/acre $/acre acres

Any:ptorch

Douglas/true fir 1,757 16 22 6 410 2,217 582 3,050 1,016 2,177

Pine and larch 1,266 13 23 7 146 1,021 468 2,143 1,340 456

Aspen 994 6 63 25 275 857 215 405 -791 112

Cedar 2,010 17 28 6 543 1,574 607 3,655 2,294 108

Other species 2,003 27 34 10 274 1,974 989 2,587 988 2,558

Study Area 1,823 21 29 9 319 1,954 765 2,699 1,015 5,412

Any:NetRev

Douglas/true fir 1,903 17 21 7 368 2,078 630 3,403 1,534 2,177

Pine and larch 1,260 13 23 8 136 945 474 2,206 1,486 456

Aspen 701 4 60 24 277 719 159 364 -605 112

Cedar 2,317 21 21 8 288 1,369 749 4,492 3,453 108

Other species 2,135 30 33 12 549 1,802 1,065 3,128 1,738 2,558

Study Area 1,943 23 28 10 434 1,803 822 3,119 1,618 5,412

All:ptorch

Douglas/true fir 911 8 26 6 627 1,781 295 1,641 -33 781

Pine and larch 913 9 29 7 271 950 307 1,676 825 155

Aspen 220 2 44 13 320 502 64 306 -256 23

Cedar 0

Other species 1,124 17 33 11 291 1,000 601 1,840 1,086 515

Study Area 986 12 30 8 447 1,355 415 1,705 509 1,475

All:Net Revenue

Douglas/true fir 1,032 9 24 7 557 1,750 335 1,910 283 781

Pine and larch 898 8 29 7 258 844 303 1,658 921 155

Aspen 220 2 44 13 320 502 64 306 -256 23

Cedar 0

Other species 1,294 19 32 13 261 922 673 2,333 1,685 515

Study Area 1,109 13 28 9 401 1,297 462 2,026 907 1,475

Any:Net Revenue +

Douglas/true fir 2,236 20 16 7 88 1,673 729 4,005 2,570 1,527

Pine and larch 1,548 16 19 8 73 1,107 567 2,651 1,807 352

Aspen 547 4 14 31 92 497 153 942 173 22

Cedar 2,522 21 15 8 90 1,750 769 4,630 3,152 83

Other species 2,130 29 24 12 85 1,501 1,041 3,565 2,498 1,641

Study Area 2,112 24 20 10 85 1,530 857 3,655 2,456 3,625
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 203

Chips (energy wood) nearly always generated less than half the overall value, and 

less than a quarter in the case of acres that “pay their own way” (those included in the 

Any:NR+ scenario). Haul costs for energy wood as a percent of total operational costs 

may be a useful indicator for determining the extent to which limited energy wood 

markets will influence treatment choice. When costs for hauling forest residues are 

high, it may be economically preferable to burn forest residues at the landing (e.g., in 

an air curtain destructor) rather than pay for their transport to a distant bioenergy facility. However, this choice forfeits the potential to capture carbon benefits via bioenergy, 

and as environmental services become an increasingly important component of forest 

management, this may figure more prominently in such decisions, especially if avoided 

carbon emissions values increase dramatically (e.g., 5-10 fold) from their very low value 

today ($2-3/ton) and markets for capturing such values are operating.

Under the All scenarios, mean net revenue is negative in the northern and central 

Rocky Mountains and under All:ptorch in the Douglas-fir forest type group. While this 

does not mean that every acre requires subsidy, just as a positive mean net revenue does 

not assure that no acres would lose money, it suggests that external subsidies (i.e., to 

supplement the revenue provided from the sale of wood) will often or usually be required 

to support fuel treatment in those sub-regions or in those forest types.

Every treatment was “best” for some part of the forested landscape, although the relative popularity of treatments, as judged by the rule-based selection, varied by scenario. 

It appears that the thin-from-below treatments are most frequently chosen for the Any 

scenarios (under which 18 percent of hazardous acres are treated), while the restoration 

treatments are most frequent for the All scenarios (under which 5 percent of hazardous acres are treated). Moreover, the harvest method (whole tree or cut-to-length) also 

presented trade-offs between fire hazard reduction and net revenue. When whole tree 

harvesting was used, there was higher net revenue because more of the biomass was 

extracted processed and sold as energy wood. Net revenue increased by an average of 

$424/acre when ties among treatments were first resolved based on net revenue (i.e., 

under the Any:NetRev scenario as compared to the Any:ptorch scenario) though much 

of this net revenue difference was also probably the result of selecting prescriptions 

that removed more large trees. When cut-to-length was used, slash was left on site 

(thus sacrificing some revenue and incurring the additional costs of surface fuel treatment). However, by piling and burning the slash, the hazard decreased by removing the 

fine fuels and decreasing surface flame length. The whole tree harvest method did not 

contain a pile-and-burn component or have any other kind of post-harvest treatments, 

so slash that remained was sufficient to increase surface flame length, causing stands 

to fall short of our hazard reduction criteria in some cases. Similar dynamics can be 

seen in All:ptorch and All:NetRev, where switching 167,000 acres from crown thinning 

(CTL) to whole tree harvesting (WT) resulted in an average increase in net revenue 

from -$391/acre to -$43/acre, along with increases in ptorch (1.6 to 3.2), SFL (2.9 to 

3.3 ft), and Mortality (13.8 percent to 15.5 percent of pre-treatment live tree volume). 

These changes in mean values indicate the trade-offs required to achieve incrementally 

better hazard reduction (or more feasible treatment), though they are specific to the 

treatments assumed in this study.

Finding Insights Beyond the Means

One should be cautious about relying excessively on average values, as they do not 

take into account the considerable variation in fuel treatment outcomes. The effectiveness, costs, revenues, and product flows associated with a fuel treatment can be more 

fully understood via histograms (frequency distributions) as these depict ranges and 

dispersion (variability), not just central tendency (such as means). Histograms have the 
204 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

added advantage of clearly depicting when distributions are skewed and the dynamics 

of treatment effects. For example, comparing untreated and treated plots with respect to 

a hazard variable such as Mortality Volume Percent in Douglas-fir and true fir stands in 

the Pacific Northwest Interior—not only does the expected value (mean) drop dramatically (from 82 percent to 25 percent), but a histogram comparison reveals an even more 

dramatic shift in the modal (most frequent value) tree mortality from 90-100 percent of 

pre-treatment volume to 0-10 percent after treatment (fig. 11.2).

A compelling reason to invest effort in preparing and understanding histograms is that 

they provide a clear picture of the range of potential outcomes. For example, fig. 11.3 

shows net revenue per acre for conditions in the Pine and western larch forests in the 

north and central Rocky Mountains under the Any:ptorch scenario. Considering only 

the mean ($665/acre) and attempting to draw inferences from that, one could reach illfounded conclusions (for example, we could generally expect to earn this return from 

treating a few pine and western larch stands in this sub-region). The histogram shows 

that the mean is not very representative or relevant because there are as many areas 

below this value as there are above. Also, the range is from -$800/acre to $2100+/acre, 

reflecting a wide range of potential net revenue outcomes. In addition, a large standard 

error of $301/acre (nearly half the mean) indicates there is substantial variation in the 

outcomes. We conclude that the mean is not a very useful representation of this distribution in terms of how many acres can be treated with a particular budget—some will 

cost a lot and others will pay their own way. The histogram also provides a basis for 

policy analysis. For example, it is easy to tally the proportion of the forest that could 

be treated if a subsidy of $200/acre was available: the sum of all bars starting at and to 

the right of net revenue = -$200.

In this chapter, we report overall findings, often averages, across large areas; however, 

Appendix C presents sub-region and forest type group-specific histograms of many 

hazard and economic attributes for the entire study area to help readers obtain locally 

relevant findings.

Figure 11.2. Predicted tree mortality shifts dramatically by applying the best treatment under the assumptions of the Any:ptorch scenario in the Pacific Northwest Interior sub-region’s Douglas-fir and True 

Fir forests. Pre-treatment, most stands experienced 90 to 100 percent mortality, and post-treatment, 

the distribution changed so that 10 and 20 percent mortality rates were far more common.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 205

Figure 11.3. Distribution of net revenue 

per acre for conditions in the Douglasfir and True Fir forest type group in the 

northern and central Rocky Mountains 

under Scenario 1. Y-axis values are 

the proportion of forested area at each 

value range of net revenue.

Dollars/acre

0.0 0.1 0.2 0.3 0.4 0.5 0.6 −2000 −1200 −400 400 1200 2000 2000−

2829

Net Revenue

 Mean = 665

 SE = 302

 Acres=104,000

Discussion

The evaluations of treatments and characterization of treatable area, levels of products 

produced, and likely costs of a fuel treatment program that follow are dependent on 

whether the hazard criteria we devised (for example, attributes and their thresholds) are 

valid and whether the hazard descriptors are equally important (given how we combined 

them into a composite hazard score or required that all hazards be decreased in order to 

label a treatment “successful” under some scenarios). Moreover, there is no guarantee 

that in the “Any” scenarios, we do not end up with an improvement in two hazard descriptors but a worsening of another. For example, Mortality might decrease to less than 

20 percent and SFL might decrease to less than 4 ft, but ptorch might increase to more 

than 20 percent. It is also possible for some hazard descriptors to trend toward greater 

hazard following a “successful” treatment, even if they do not exceed our somewhat 

subjectively chosen thresholds. However, the intent of this analysis was to evaluate fuel 

treatment economics, so some characterization of treatment effectiveness is unavoidable 

if we are to produce meaningful conclusions about treatment feasibility for treatments 

and places that actually make sense.

This analysis reveals that, at least under our definitions of hazards and treatment 

effectiveness, most dry mixed conifer forests currently have high fire hazard, and only 

a fraction of those forests can be effectively treated (at least by the fuel treatments that 

we modeled that were intended to represent the style of thinning treatments typically 

undertaken in the dry mixed conifer region). While this might be viewed as discouraging 

in that prospects for effective treatment are limited to a relatively small subset of the 

forest, it does make the fuels management problem more workable in that it gives license 

to tackle a more limited area of forest. It also suggests that on many acres, choices are 

limited: hazard reduction is not an achievable goal, or a major reset in expectations is 

required—almost twice as much area could be effectively treated if the constraint that 

residual stands must support a canopy cover of 20 percent is removed. Conversely, initial 
206 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

exploration using BioSum with other kinds of thinning treatments focused exclusively 

on fire hazards that leave more overstory and perhaps reduce effective surface wind 

speeds as computed in FFE-FVS indicates that treatable area could as much as double 

given the same assumptions and scenarios but different treatment options, though the 

costs and feasibility would be different.

When material removed by a fuel treatment is utilized for wood products and bioenergy, revenues contribute a great deal to off-setting treatment costs and, on most acres, 

can more than offset the costs of treatment activities and transportion of harvested 

material, though this varies by sub-region and effectiveness scenario. For example, 

in Utah, mean net revenues were negative under every scenario except (by definition)

Any:NR+ given that acres are treated only if net revenue is ≥0, and were negative in the 

northern and central Rocky Mountains under the All:ptorch and All:NetRev scenarios. 

Aspen had negative mean net revenues because of the low commercial values of harvested material (most is utilized for energy wood in our scenarios). In most scenarios, 

net revenue was greater for pine and western larch than for Douglas-fir and true fir, 

and scenario assumptions are extremely influential. For example, comparing All:ptorch 

with All:NetRev, both of which require a residual composite hazard score of 0, we see 

that when ties among treatments that achieve this goal are resolved by choosing the 

treatment with lowest residual stand ptorch value, the result is a mean net revenue per 

acre for pine and fir of $825 and -$33, respectively. When ties are instead resolved by 

choosing the treatment with the greatest net revenue, mean net revenues are $921/acre 

and $283/acre, respectively. This highlights the importance of hazard and effectiveness 

definitions, as well as how to determine the “best” treatment when there is more than 

one way to achieve a given hazard score.

The Any scenarios, which attempt the best that can be done to reduce hazard score, 

even when a 0 hazard score post-treatment is not achievable, can be implemented on 

more than three times as many acres as the All scenarios, and on average, achieve a 

big drop (from 2.8 to 1.1) in hazard score. The All scenarios achieve about the same 

numerical reduction in hazard score (by 1.7) on the far smaller selection of acres they 

treat and leave these acres with a 0 hazard score, but this would also be achieved on these 

same acres under the Any scenarios because they also pick the treatment that maximizes 

hazard score reduction on these acres. Net revenue under the Any scenarios is higher 

than for the All scenarios because the Any scenarios add acres of fuel treatment that 

generate more revenue relative to costs, even if treatment effectiveness on those acres 

is less impressive than on the acres that meet the All requirements.

The huge gap in both treatment area and net revenue between the “Any” and “All” 

scenarios highlights the importance of first focusing on the treatment goals to reduce 

fire hazard, regardless of economic implications. Selecting acres for treatment that are 

not genuinely hazardous, or that fall short in terms of what can be demonstrated analytically for the sake of economic viability of a fuel treatment program, presents risks 

to gaining and maintaining public trust in fuels management. Models such as BioSum 

and FFE-FVS provide the decision support that can be used to demonstrate treatment 

accomplishment (effectiveness and economics) objectively and provide a science basis 

for choosing a given treatment. For example, the models could be used to show that other, 

more financially rewarding treatments were not selected because they were predicted to 

be ineffective by the model. And in most cases, they can show that a treatment that puts 

all trees over a certain size (say 10 or 14 inches dbh) off-limits to harvest is ineffective 

under almost any effectiveness criterion.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 207

Best Places to Add Processing Capacity

One interesting finding is that, except in Utah where bioenergy facilities are largely 

absent and the benefits of adding some bioenergy capacity are fairly compelling, a lack of 

transportation infrastructure and energy wood markets does not appear to be the most limiting factor in fuel treatment feasibility. Most of the wood produced by the treatments we 

modeled is of merchantable size, and by far, most of the revenue that could be used to 

support fuel treatment activities comes from sales of merchantable wood. On average, 

the mass of merchantable wood flowing from these fuel treatments in the pine and fir 

type classes under these scenarios is nearly three times the mass of energy wood, yet 

energy wood contributes to the bottom line when there are nearby markets. The great 

majority of the treatment costs are on-site costs, and prices for delivered wood are apparently high enough to support treatment activity even when material must be delivered 

relatively long distances to be processed. A key determinant of financial feasibility, 

then, is removal of at least some merchantable trees, and this is likely a more important 

consideration than is the cost for transporting the low-valued energy wood derived from 

small trees. This requires a willingness by managers to design treatments that include 

removing merchantable trees and a granting of social license to do so by parties with a 

stake who are interested in the implementation details of fuel management.

Note that this analysis has some aspects in common with earlier work that led to 

Johnson and others (2007a, 2007b) and more recent work testing the principles of a 

fire-safe forest (Johnson and others 2011). Like the analysis presented here, Johnson and 

others relied on FFE-FVS to apply alternative prescriptions for canopy and surface fuels 

to inventory plots and relies on FVS default assignments of surface fuel models. Unlike 

this BioSum analysis, which modeled the entire dry mixed conifer forest via thousands 

of statistically representative inventory plots, the analysis that led to Johnson and others 

(2007a) modeled just a handful of analyst-selected stands per region, matched up data 

from local weather stations to get the best representation of severe weather with which 

to parameterize the FFE-FVS model for those stands, and projected stand attributes 

forward for five decades with and without treatment, producing pages of detailed model 

reports for each stand (Johnson and others 2007a). The analysis that tested the principles 

of a fire-safe forest (Johnson and others 2011) extracted from the FSVEG corporate 

database tens of thousands of National Forest inventory plots (not based on a statistical 

sample) and selected only those with greater than 700 trees per acre to model using four 

levels of residual trees per acre and several surface fuel treatments, limiting the capacity to make inferences about the whole forest. By contrast, this BioSum analysis was 

designed to produce summary information that reflected effectiveness and economics 

of treatment opportunities over the entire forest, and to explore the kinds of treatment 

options that are actually being practiced, which are typically less straightforward than 

a residual trees per acre target.

A few caveats must be noted. This analysis depends for its characterization of hazard 

and effectiveness on both a set of logically structured, but inherently subjective assumptions (about hazard attributes and thresholds) and the accuracy of the FFE-FVS model. 

We relied on each FFE variant’s default characterization of surface fuels and severe 

weather. More accurate representations of these parameters may be possible using local 

data. We also assume currently available technology, equipment, and practices for conducting fuel treatments. It is always possible that lower-cost techniques for mechanical 

removal will be developed that could reduce fuel treatment costs. It is also possible, 

and desirable, that product values for both merchantable and energy wood will increase 

above the values used for this analysis. As the carbon benefits of wood use (instead of 

steel, concrete, and plastic) become more widely appreciated and translate to building 
208 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

codes, incentives, and other policies that favor wood use, product prices could rise. 

Along the same lines, renewable portfolio standards that give credit for bioenergy may 

already be elevating the price for energy wood above where it would otherwise be; any 

movement toward carbon taxes, or cap and trade systems—even at the state level as has 

been occurring in California— could further elevate the price of energy wood, pushing 

additional acres into the range of feasibility.

Conclusion

In addition to exploring other kinds of treatments in the dry mixed conifer types using 

the BioSum system, we see potential to develop models from this dataset that can be 

used to predict costs and effectiveness from key stand attributes, for example, descriptors 

of stand structure, slope, distance from the nearest road, and distance from processing 

facilities. This dataset is already being analyzed to assess carbon implications of fuel 

treatments, accounting for full life cycle analysis of treatment generated products and 

energy wood and the model-based mortality predictions with and without fuel treatment.

To be truly effective in mitigating fuel hazard, it is important to pursue all options that 

promote self-funding fuel treatments. Given the current and likely future Federal budget 

climate, it is hard to imagine successful implementation of anything but treatments that 

pay their own way for the foreseeable future. The future of active forest management on 

public lands may well depend on reconciling ourselves as foresters and natural resource 

professionals to that hard reality.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 209

Chapter 12

Fuel Dynamics and 

Treatment Longevity

Chapter Rationale

The dry mixed conifer forests are quite productive compared to the dry ponderosa pine 

forests in the Southwest. Therefore, treatment longevity will in some places be relatively 

short. An important question arose in our interviews: How often should I conduct fuel 

treatment maintenance? In this chapter, we address questions such as:

	 •	 What is the current information on fuel treatment longevity?

	 •	 How do treatments affect fire behavior as forest stands grow and develop after 

a treatment is implemented?

	 •	 Are there options during initial implementation to increase the longevity of a 

fuel treatment?

Introduction

Forest fuel treatments are commonly evaluated by managers for their immediate effects on potential fire behavior, but since forests are dynamic systems, post-treatment 

conditions will change over time and treatment alternatives will vary in the duration of 

their effectiveness, or treatment longevity. An understanding of forest fuel treatment 

longevity and the processes contributing to it is central to a complete evaluation of the 

effectiveness of treatment alternatives.

In this section, we provide fuel managers with a summary of the information on 

treatment longevity. We review current knowledge and describe the elements of forest 

dynamics that collectively determine fuel treatment longevity. Longevity is a general 

concept, but the processes contributing to it are specific and many are founded on 

well-developed relationships that underpin silvicultural practices. We summarize them 

and propose a conceptual model that links those often dissimilar processes. We also 

evaluate the potential and limitations of available decision tools that aid fuel managers 

in comparing the longevity of treatment alternatives, and identify opportunities where 

improvements to the understanding of treatment longevity can be made.

Longevity In Fuel Treatment Planning

Longevity is an important, oft-overlooked consideration in fuel treatment planning. 

As Reinhardt and others (2008) noted, “A common misconception is that fuel treatments 

are durable and will last for a long time. In reality, fuel treatments have a somewhat 

limited lifespan.” Fuel loads, as well as their availability, change tremendously following treatment. This is true in both the short- and long-term. Although it is important, 

fuel treatment longevity is often overlooked or oversimplified. This is even apparent 

in modeling: when projecting fuel conditions, model users often maintain static fuel 

values (Varner and Keyes 2009).
210 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Longevity is an important consideration for different fuels management objectives. 

Fuel is not inventoried regularly, so understanding longevity is important for anticipating changes that have occurred to the fuel load of a site and its potential fire behavior 

since the previous fuel treatment. The degree to which longevity is accurately estimated 

will affect suppression opportunities and firefighter safety, the determination of whether 

points or areas can be successfully protected, and whether areas of special concern possess the resilience to survive a wildfire.

In prescribing fuel treatments, managers can select alternatives that are not only effective today but also possess attributes that enable their effectiveness to persist into 

the future. Incorporating longevity into prescriptions reduces the annualized costs of 

the treatment and enables more area or new areas to be treated rather than re-treating 

the same areas.Shortcomings in treatment longevity can be mitigated only by frequent 

re-treatment. Reinhardt and others (2008) concluded, “Fuel treatment benefits are 

transient...we must think of fuel treatment regimes rather than single fuel treatment 

projects.”Approaching fuels planning in this way encourages managers to consider 

elements of longevity and the effects of treatment alternatives on longevity. Treatments 

with low longevity will require greater frequency in re-treatment and shorter periods 

between those re-treatments.

Managers have noted a concern that current fuel treatments can (if not adequately 

planned) create an unmanageable future workload for fuel maintenance that cannot be met. Initial fuel treatments can generate merchantable products that offset 

treatment costs (in the case of treatments that involve commercial thinning). But often 

those merchantable elements are not available during subsequent entries, which will 

necessarily focus on new growth and ladder fuels. Adopting a regime-based approach 

to fuels management that considers treatment longevity can help alleviate this concern.

Studies of Treatment Longevity

Empirical data on treatment longevity are scarce. Few studies have quantified changes 

in fuels following treatment. The existing post-treatment analyses are limited to just a 

few years after treatment. For example, Martinson and Omi (2003) evaluated fuel treatment effectiveness in stands that were subjected to wildfire less than five years following treatment. As a result, the majority of fuel modeling studies have ignored treatment 

longevity altogether and focus instead on the immediate effect of treatment alternatives 

on fuels and potential fire behavior (for example, Stephens 1998).

In the West, longevity studies are mainly limited to locations where continuous inventory of fuel loads in prescribed fire monitoring programs has allowed for the analysis 

of post-burn fuel load recovery. At Yosemite National Park, for example, fuel loads 

recovered to 55 percent of pre-treatment levels within six years (van Wagtendonk and 

Sydoriak 1987). Within a decade of prescribed fire treatments at Sequoia and Kings 

Canyon National Parks, ponderosa pine forest fuel loads recovered to 84 percent of 

pre-treatment levels, and white fir/mixed-conifer forests recovered to 83 percent of 

pre-treatment levels (Keifer and others 2006). In that study, ponderosa pine forest fuel 

loads (fine and sound woody fuels) at Yosemite National Park exceeded pre-treatment 

levels within 31 years.

Knowledge of fuel accumulation and decomposition rates can provide insight 

into dead surface fuel loads, which can help managers anticipate treatment longevity 

(Fernandes and Botelho 2003). At higher elevations, snow pack can also reduce the depth 

of treatment-generated slash. In the study by Keifer and others (2006), white fir and 

giant sequoia/mixed-conifer forests left untreated for approximately 40 to 90 years 

exhibited slow rates of surface fuel accumulation, leading the authors to speculate that 

decay and accumulation rates had achieved a steady state. Nevertheless, for the most part, 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 211

A severe wildfire season in Florida in 1998 inspired one of the few direct comparisons of the longevity 

of common fuel treatments on fuel loads and potential fire behavior. Researchers evaluated different 

fuel treatments (prescribed fire, thinning, and herbicides) applied to vegetation in pine flatwood forests, 

measuring fuel loads in stands from one to five years following each treatment (Brose and Wade 2002). 

From those data, they created custom fuel models, simulated potential fire behavior using BEHAVE, and 

compared the results among treatments and a no-treatment alternative.

Thinning and prescribed fire treatments were far more effective than herbicide treatments on immediate 

post-treatment surface fire flame lengths and spread rates. But that effectiveness steadily waned in the 

following years as the understory vegetation rebounded in growth, such that within five years, simulated 

flame lengths approached the no-treatment levels. In contrast, herbicide treatments had no effect on immediate post-treatment flame lengths and actually increased spread rates. But thereafter, as the dead surface 

fuels decayed, fire behavior dropped sharply and remained low through year five. The forest contexts and 

fuel treatments will differ in other places, and the five-year period is probably insufficient for adequately 

comparing the longevity of fuel treatment alternatives. Nonetheless, the study demonstrates the importance 

of evaluating fuel treatments on the basis of longevity and shows how that approach can reveal important 

differences among treatment alternatives that can only become apparent over time.

Simulated wildfire flame lengths (top) and spread rates (bottom) for fuel 

treatment alternatives from untreated (“Unt”) through year five for drought 

and normal weather scenarios (redrafted from Brose and Wade 2002). The 

treatments differed dramatically in both their immediate post-treatment effects and in trends over time. Thinning and prescribed fire treatments had 

the greatest but shortest-lived effect on fire behavior; herbicide treatments 

showed a slower response but greater longevity.
212 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

fuel accumulation and decomposition models are still in development, and with few 

exceptions, such as Keane’s (2008) study of northern Rocky Mountain forests, those 

rates are poorly understood for many ecosystems. Among Keane’s findings were the 

following: (1) litterfall rates are highest on the most productive sites with highest stand 

densities, (2) litterfall rates differ by cover types and site qualities but the distribution 

by woody fuel type is about the same, and (3) decomposition rates are greatest on the 

most productive sites.

Work by Vaillant and others (in preparation) should shed light on treatment longevity 

in California forests. A previous study that evaluated the effects of mechanical treatments and prescribed fire on 14 California National Forests (Vaillant and others 2009) 

revealed widely different effects on post-treatment fuel structures between these two 

approaches. That study’s permanent plot network, initiated in 2000 immediately prior 

to operational fuel treatments, now serves as a key tool for monitoring changes over 

time to fuel loads and potential fire behaviors associated with different treatment types.

Empirical knowledge regarding treatment longevity still lags, but fuel treatment studies 

and reviews have increasingly acknowledged the subject of longevity and longevityrelated responses. For example, in the southern Rocky Mountains and on the Colorado 

plateau, mastication (mulching) treatments have shown to be suitable for replacing or 

complementing traditional thinning treatments. In their examination of near term treatment 

effects, Battaglia and others (2010) noted and discussed the potential longevity-related 

effects of mastication treatments, including potentially different fuel moisture dynamics, 

fuel decomposition rates, vegetation recruitment patterns (including invasive species), 

nitrogen cycles and site productivity, and rates of tree seedling establishment and growth. 

Each of these elements operates as a driver of longevity, but the cumulative effect is 

unclear. For example, ladder fuel development might be accelerated by enhancement 

of site productivity as the mulch layers decompose, or the mulch layer might serve as a 

physical obstruction that delays tree seedling recruitment and enhances longevity. The 

authors concluded that the number of factors and their interactions requires the study of 

long-term responses in order to quantify the longevity of this treatment. We are aware 

of some cases in which managers have opportunistically performed their own local, 

informal comparisons of fuel treatment alternatives (for example, mastication versus 

hand thinning and piling), but are unaware of any attempt by researchers or others to 

formally document the extent of such case studies and their collective inferential value.

Longevity is a complicated and dynamic phenomenon, but the main elements can be 

distinguished and treated separately in three distinct categories, each of which influences 

treatment longevity: (1) fuel decay, (2) fuel growth, and (3) fuel recruitment. The first 

focuses on dead surface fuels; the second and third focus on live surface and canopy 

fuels. Interrelated disturbances constitute a fourth category that can strongly influence 

longevity. Examples include fuel treatment interactions with bark beetle damage. However, for simplification in this review, we set those extraneous agents of disturbance 

aside and focus on the direct effects of fuel treatments on subsequent changes to fuels.

Dead Surface Fuels and Longevity

Decomposition rates of dead surface fuels are primarily affected by site quality and 

climate. Whereas higher site qualities promote ladder and crown fuel growth, they also 

promote a more rapid degradation of dead activity fuels generated from the fuel treatment. Decomposition rates are most strongly determined by those same attributes that 

drive site quality: temperature and precipitation regimes. Decomposition is facilitated 

by contact with the ground and other particles, but compaction can slow decomposition. Consequently, higher-quality sites see more rapid deterioration of dead surface 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 213

fuels that are generated by treatment activity. However, the type of fuel treatment can 

determine slash quantity and height, or bulk density. The amount of residual overstory 

and its spatial arrangement can have strong effects on surface fuel loads and availability 

(Carlton and Pickford 1982).

In many cases, maintaining soil productivity or wildlife habitat often requires retaining a certain proportion of woody debris after harvest. The retention of activity fuels to 

serve wildlife habitat requirements may not necessarily pose a fire hazard (Reynolds 

and others 1992). If a fuel hazard is identified, surrounding it with less hazardous fuels 

can often isolate it. It is widely assumed that fuel treatments involving commercial 

thinning will inflate surface fuel loads via the pulse of slash created during the cutting 

process. Those concerns can usually be addressed by matching harvest techniques to 

the silvicultural prescription in combinations that meet slash and woody debris targets. 

Selection of harvesting equipment and practices are based in part on site and stand 

conditions and on the silvicultural prescription. Integrating these aspects early in the 

planning process can result in the selection of systems that avoid fuel jackpots (heavy 

fuel concentrations) or that reduce activity fuels via underburning or jackpot burning. 

The development of biomass markets in some locations may yield an additional instrument for managing activity fuel accumulations. Alternatively, contract specifications 

can serve to relegate the excessive biomass in closer contact with the ground to enhance 

decomposition or require post-harvest activities to redistribute heavy pockets of fuel.

Forest Stand Dynamics as Forest Fuel Dynamics

The elements that drive vegetation responses to fuel treatments are reasonably well 

understood. Lacking adequate model support, an understanding of principles of vegetation dynamics that are affected by treatments is vital. The models may be limited, but 

forest fuels consist simply of dead and live vegetation that grow, decay, and regenerate and do so in ways that are reasonably well understood from past studies in silvics, 

silviculture, and ecology. If these dynamics cannot be quantified, they can at least be 

understood in ways that let managers better simulate treatment effects and better choose 

between treatment alternatives on the basis of longevity.

Post-treatment conditions differ in their capacity for vegetative change. Vegetation 

that remains following treatment is positioned for accelerated growth. Like all forms of 

natural or human disturbance, fuel treatments create vacancies in available growing space 

that will be exploited by the growth of the remaining vegetation. Among that vegetation, 

the patterns of development following a disturbance (such as a fuel treatment) will be 

similar across a range of sites, but the rates of change will differ.

Crown Fuels Growth

Familiarity with stand development patterns allows for better understanding of crown 

fuel dynamics that affect potential fire behavior and the effect of thinning treatments 

on those patterns. Forest stand density in particular is a strong determinant of the morphological development of trees, including crown fuel loads and crown base heights. 

Spacing strongly affects the process of crown recession, which occurs as the lower 

branches are shed in response to declining light conditions. In general, early successional species readily self-prune, but late successional species tend to retain branches 

even in closed conditions. Figure 12.1 illustrates the typical relationship of inter-tree 

spacing to crown development that is observed in even-aged pure stands or cohorts. 

Collectively, the crown recession patterns among individual trees determine the lifting 

of canopy base height in such closed-canopy stands.
214 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 12.1. Generalized relationship of spacing to crown and canopy 

development in even-aged pure stands or cohorts, here through three 

phases following establishment (based on figure from Oliver and Larson 

1996). The figure demonstrates how inter-tree spacings within a stand affect 

crown development of individual trees over time. For fuels managers, the 

concept is directly transferable to understanding thinning effects on crown 

fuel dynamics, and canopy base height in particular. Of special note here 

is that crown recession occurs earlier and more rapidly for trees at dense 

spacings (A), and occurs later and more slowly for trees at wider spacings 

(B). At first stage (bottom), crown recession has commenced among those 

trees at close spacings. By the third stage (top), the initial variability in spacing has been manifested in an uneven canopy base height.

Although thinning reduces near-term crown fire behavior by reducing canopy bulk 

densities, it interrupts the natural recession of crown bases. There are many good reasons for thinning and maintaining low stand densities in addition to fuel management 

objectives; however, the effect of thinning intensity on temporal patterns of canopy base 

height development (and therefore crown fire initiation potential and torching index) 

is important to recognize and account for in prescription development, if longevity is 

a consideration.

Figure 12.2 provides an example of this situation. An even-aged ponderosa pine 

stand in central Oregon was subjected to a fuel reduction-based crown thinning to 

reduce canopy bulk density. Previous stand development had served to steadily lift 

the canopy base height to its current height at the time of treatment. The thinning successfully reduced canopy bulk density but it also stalled crown recession; during the 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 215

Figure 12.2. Thinning intensity in canopy fuel treatment often involves a trade-off 

between immediate treatment impact and treatment longevity. In this example from 

central Oregon, heavy thinning in a ponderosa pine /mixed-conifer stand successfully 

reduced canopy bulk density. However, it stalled crown recession and promoted the 

development of ladder fuels, thereby reducing the capacity of the stand to sustain a 

reduced torching potential over time.
216 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

15 years following treatment, the canopy base height remained constant while ladder 

fuels became established and grew rapidly. A lighter thinning would have resulted in 

a lesser reduction in canopy bulk density but could have enabled crown recession to 

continue while also suppressing the recruitment of ladder fuels, thereby sustaining a 

lower torching potential in the stand over a longer period.

Trees grown at low densities have lower canopy base heights (if always grown in that 

condition) than if they are grown in dense stands and then thinned, in which case they 

often have a higher crown base height. This is particularly true of shade-intolerant early 

successional species such as ponderosa pine, and less so of shade-tolerant late successional species in which lower branches are retained at similar density and understory 

light levels. A great deal of crown modeling work has been performed by researchers 

beyond the fire science community. Such crown research offers much potential to inform 

further development of canopy fuel models. However, information about the time lag 

size classes (1-hour, 10-hour, etc.) typically required for application in fire behavior 

models is usually not distinguished in research performed in other fields (Affleck and 

others 2012).

Fuel treatment effects on crown fuel characteristics have been simulated but not 

observed. In one study of temporal changes to crown fuel characteristics, Scott and 

Reinhardt (2007) utilized FFE-FVS to simulate the effect of various treatments on 

crown fuels. Yet it appears that no long-term study of actual fuel treatment effects 

on observed crown fuel characteristics has been conducted to date. It seems that the 

prevalence of several silvicultural trials—thinning studies and levels-of-growing-stock 

(LOGS) studies—could provide information on post-treatment crown dynamics and the 

accuracy with which the crowns are characterized by the present generation of fuels 

models (Affleck and others 2012).

Live Surface and Ladder Fuels Growth

Latent ladder fuels affect longevity and are affected by fuel treatments. In many 

forests, latent ladder fuels exist in the form of advance regeneration—seedlings and 

saplings occurring in the understory that are capable of accelerated growth upon disturbance to (or cutting of) the overstory. In contrast to dry ponderosa pine forests, dry 

mixed conifer forests often include species with high or moderate shade tolerance, and 

advance regeneration of those species beneath a ponderosa pine dominated canopy is 

common. The rate of accelerated growth upon treatment is proportional to site quality 

and thinning intensity.

Ladder fuel growth is governed by the number and sizes of overstory trees. The relation of overstory trees to understory growth has been a subject of concern in two-age 

and uneven-aged silvicultural systems. Some studies have regarded overstory trees as 

inhibitors of seedling growth, but they can help fuel managers identify cutting levels that 

meet fuels management goals yet suppress seedling (ladder fuel) growth. One concise 

illustration of the relationship in dry mixed conifer forests was provided by McDonald 

(1976) who studied regeneration growth beneath ponderosa pine seed tree harvests at 

the Challenge Experimental Forest in northern California (fig. 12.3). Some of the cutting levels in that study exceed what fuels managers would consider reasonable fuel 

treatments, but the study demonstrates how ladder fuel development is directly related 

to the distance from overstory trees and negatively related to the amount of overstory 

density retained.

Another illustration of this phenomenon is provided by a LOGS thinning installation 

at Pringle Falls Experimental Forest in central Oregon (fig. 12.4). Seedling regeneration was not a focus of the study, nor did it occur at any thinning levels, except at the 

two heaviest thinnings that retained overstories of 60 ft2/acre and 30 ft2/acre basal area. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 217

At the 60 ft2/acre cutting level, seedling recruitment was present but was scant and its 

height growth was minimal. At the 30 ft2/acre cutting level, regeneration was prolific 

and growing rapidly into the subcanopy. As a fuel treatment, the latter prescription would 

require maintenance relatively soon, whereas re-treatment in the former prescription 

will remain unnecessary for many years.

In addition to the number of trees left following treatment, the sizes of those trees 

is an important influence on ladder fuel growth. Based on data from a ponderosa pine 

variable-retention silvicultural study in central Oregon, the model in fig. 12.5 provides 

a quantitative illustration of how the two work together to suppress understory height 

growth. In this example, the lines signify the reduction in a site’s capacity for ladder 

fuel height growth (average annual height growth) occurring beneath overstories that 

vary in the number and sizes of canopy trees retained following treatment. Plotting the 

number of retained overstory trees (X-axis) and their average size (Y-axis), the user 

identifies the diagonal line closest to the x-y pair; its associated number indicates the 

effective reduction in ladder fuel height growth (meters) over a 100-year period, versus 

seedlings growing at the same site in an open condition. The scope of inference is limited 

to central Oregon, from which that modeling effort drew stand and regeneration data, 

but it concisely demonstrates how retaining more and larger trees serves to suppress 

ladder fuel development.

Figure 12.3—Figures from McDonald’s (1976) study of the effect of overstory tree 

distance (0 to 40 ft) to regeneration (ladder fuel) height development at 5, 9, and 14 

years following treatment. At left, the cutting treatment retained 12 trees per acre; at 

right, the treatment retained 8 trees per acre. Most fuel treatments will retain more 

overstory trees, but this example demonstrates how ladder fuel development is directly 

related to the distance from overstory trees and negatively related to the amount of 

overstory density retained.
218 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 12.4. Untreated ladder fuels development beneath two thinning levels at a LOGS 

study installation, Pringle Falls Experimental Forest, central Oregon. Top, overstory 

thinned to 30 ft2/ac basal area; Bottom, overstory thinned to 60 ft2/acre basal area.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 219

Ladder Fuels Recruitment

Recruitment of new ladder fuels quickly reduces longevity. The recruitment of ladder fuels is highly complex and dependent upon the co-occurrence of multiple factors 

(regeneration dynamics), including: seed production, seed dispersal, seed predation, 

germination, establishment, and early survival. Although these have been addressed in 

separate or several stages at a time, comprehensive models are just now being developed 

and are highly complicated. Therefore, an understanding of the general principles of tree 

(ladder fuel) recruitment is vital for fuels managers to adequately anticipate the effect 

of fuel treatments on this aspect of longevity.

Figure 12.5. Fuels treatments that resemble thinnings or shelterwood harvests will vary 

in their suppression of ladder fuel development over time, depending on the number 

and sizes of trees retained following treatment. Data from a variable-retention study in 

central Oregon dry mixed conifer forests dominated by ponderosa pine are used here 

to model that relationship. The overstory tree sizes (QMD; or quadratic mean diameter, 

Y-axis) and numbers (Trees per hectare; X-axis) together determine the suppression 

of regeneration height growth rates. The diagonal lines denote the projected reduction 

in ladder fuel height growth (meters) over 100 years, relative to open-grown trees at 

the same site.
220 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Studies of regeneration ecology, tree silvics, and the influence of silvicultural treatments on forest dynamics can provide good information for fuels managers. Ground 

disturbance is a form of site preparation that has long been used by silviculturists to 

naturally regenerate seedlings (Smith and others 1997; Nyland 2002). The same information that underpins silvicultural regeneration practices can be utilized by fuels 

managers inversely: selecting treatments that minimize the recruitment of seedlings (or 

ladder fuels). The silvics of western tree species, and their responses to disturbance, 

have been documented in many places, including several compilations (for example, 

Barrett 1995; Burns 1983; Burns and Honkala 1990a, 1990b).

Fuel treatments directly affect the recruitment of ladder fuels by the extent to which 

bare mineral soil is exposed. Treatments that burn or scarify the forest floor will generally result in the accelerated recruitmentof new ladder fuels. Commercial thinning, for 

example, scarifies the forest floor and provides a mineral soil seedbed that enhances 

ponderosa pine seedling germination and survival (Oliver and Ryker 1990), increases 

light to the forest floor that facilitates litter decomposition, and enhances the growth 

and fecundity of residual trees that can result in greater seed production.

The types, intensities, and spatial arrangements of fuel treatments will determine 

whether seedling recruitment occurs either in a pulse or is protracted in stages. They 

also determine whether seedling recruitment occurs in patches or at high densities with 

spatial continuity. As a rule, fuel treatments that result in rapid pulse recruitment also 

result in high seedling densities and spatial continuity. Hence, mechanical and prescribed fire treatments that result in greater ground disturbance will typically result in 

less longevity than treatments that minimize forest floor scarification. For newer and 

less conventional treatments, the effects on these factors are unclear. It is still uncertain, 

for example, whether mastication enhances or delays ladder fuel recruitment (Battaglia 

and others 2010).

Fire models tend to ignore the effects of the specific treatment implementation technique and contract specifications. Aside from the prescription, the specific techniques 

that are used to implement treatments have their own direct impacts on fuel dynamics, particularly fuels recruitment, which are not represented in models. In the case of 

mastication, for example, contract specifications vary by project, as do operator skill 

and mastication equipment type, influencing both the chip size and distribution and the 

level of post-treatment soil exposure. FFE-FVS can be customized to reflect anticipated 

changes in stand development associated with these site-specific project outcomes, 

but such customizations require user understanding of vegetation dynamics and some 

tweaking of model assumptions.

Longevity and the Fire Environment

Like fuels, the fire environment is dynamic. It is affected by stand structure and 

changes over time in sync with the changes to stand structure that occur during the 

course of stand development. Treatments that alter fuel structures also change the fire 

environment and change the trajectory of the fire environment as stand structure develops 

following treatment. Subcanopy wind and solar penetration directly affect elements of 

the fire environment that affect treatment longevity, including fuel moisture contents, 

air turbulence, and midflame windspeeds.

The effect of moisture content on the availability of dead surface fuels is well understood by fire managers (as is the concept of “moisture of extinction” or the point at 

which a fire will not spread). The effect of moisture content in live fuels is perhaps less 

understood, but those moisture contents determine live fuel availability and potential 

fire behavior. In a modeling study of the sensitivity of surface fire to live fuel moisture, 

Jolly (2007) demonstrated that fire behavior characteristics are highly sensitive to live 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 221

fuel moisture. In the aerial fuel complex, the foliar moisture content (FMC) of tree 

crowns is a known determinant of crown fire behavior (van Wagner 1977). Studies 

of North American conifers have demonstrated that FMC differs among species, varies seasonally, and sometimes differs between old and new foliage (Agee and others 

2002; Keyes 2006). But it appears that the effects of different fuel treatments on those 

moisture content trends have not been investigated. Studies of the effect of different 

fuel treatments on moisture contents are needed in order to account for this factor when 

modeling treatment effects on potential fire behavior.

Longevity and Rates of Change in Live Fuels

Treatment longevity is fundamentally controlled by site quality, which drives rates 

of change that occur among fuel structures. As a rule, treatment longevity is greatest 

at less productive sites where changes to forest vegetation occur slowly, and is least at 

highly productive sites (Weatherspoon and Skinner 1996). In general, growth processes 

occur more rapidly in dry mixed conifer forests than in dry ponderosa pine forests, and 

fuel treatment longevity can be expected to be shorter than that of similar treatments applied to drier ponderosa pine forests. The rate at which vegetation grows following fuel 

treatments is fairly predictable, having been the subject of growth and yield studies for 

decades. That rate is directly proportional to the quality of the site. For fuels managers, 

several familiar proxies representing the cumulative factors contributing to site quality 

exist in the traditional measures of site and can be useful as longevity indicators, i.e., 

(1) site index and (2) habitat types. Site index and habitat types (or plant associations) 

provide a useful coarse method of categorizing anticipated treatment longevity among 

sites.

Site index is a measure of site quality as expressed by the height growth of trees over 

time. Because height growth is relatively independent of stand density, the quality of a 

site is revealed primarily in the observed rate of height growth. Site index curves have 

been developed across the range of dry mixed conifer forest types. Examples include 

curves by Barrett (1978) for the interior Pacific Northwest, by Krumland and Eng (2005) 

for northern California, and by Milner (1992) for western Montana (see Chapter 2). In 

most regions within the dry mixed conifer range, fuels managers will use site index when 

using Forest Vegetation Simulator (FVS), as that metric is the driver of stand growth and 

the changes to canopy fuels in the Fire and Fuels Extension (FFE). But fuels managers 

can apply site index (using local site curves and basic stand data) to map site quality of 

stands within landscapes, and thereby classify rates of fuel longevity over large areas.

Throughout the range of dry mixed conifer forests, but perhaps most notably in the 

northern Rocky Mountains, habitat types are used more commonly than site index to 

communicate both potential vegetation and site quality. Habitat types are not forest cover 

types, which describe the current canopy species composition of a stand. Rather, they 

are classes that indicate sites of similar potential, (or climax vegetation communities 

(Daubenmire 1966).

The terminology may vary from place to place. For example, at central Oregon’s 

Warm Springs Reservation, “plant associations” rather than habitat types are available 

(Marsh and others 1987). But insofar as they describe potential vegetation, they allow 

for comparisons of site quality within regions. Examples include the habitat types by 

Pfister and others (1977) for Montana, Williams and others (1995) for northeastern Washington, and Cooper and others (1991) for northern Idaho. Since potential vegetation is 

tied to the same environmental gradients that determine vegetation growth, habitat types 

are useful indicators of site quality as well. In the northern Rocky Mountains, habitat 

types are such a dominant descriptor of site quality that they are utilized as the drivers 

of growth within the region’s FVS variants, rather than site index. For fuels managers, 
222 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

mapping dry mixed conifer forests by habitat type offers another way of categorizing 

post-treatment fuels growth and treatment longevity. To identify locally relevant systems 

of habitat types, fuels managers can consult the useful listing of forest habitat types 

prepared for Rocky Mountain forests by Alexander (1985).

Longevity and Decision Support Tools

Longevity is poorly represented in fire simulation tools. Some models can examine fuel 

longevity, but many sub-models must be synthesized into one comprehensive measure of 

fuel projection changes over time in order to scale up to address questions of treatment 

longevity at stand scales. As Fernandes (2009) noted, “fire simulation...rests more on 

theory than on sound field data.” The model most appropriate for the task is FFE-FVS 

(Reinhardt and Crookston 2003), which links forest stand growth and fire behavior 

models. However, many important components of change that affect longevity are not 

adequately represented in the model by stand-level treatments, including decomposition 

models, regeneration recruitment and growth models, and crown expansion models. All 

require calibration with local values.

Existing decision support models are poor predictors of ladder fuel recruitment. 

Although FFE-FVS projects tree and stand growth reasonably well, depending on the 

variant, the model poorly predicts natural regeneration patterns that result in ladder fuel 

recruitment. Without calibration and variant specific regeneration modules, it will exaggerate ladder fuel development and underestimate treatment longevity. Hence, when 

using FFE-FVS it is vital to understand the model, both its features and limitations, and 

apply it properly. Training modules are available to understand and use fire behavior 

models, including FFE-FVS, as are staff and support at the Forest Service Management 

Center (http://www.fs.fed.us/fmsc/index.shtml).

Some useful models of ladder growth development exist, and others are being developed. In California, growth models of seedlings and saplings in dry mixed conifer 

stands have been in development for more than a decade (SYSTUM-1 by Ritchie and 

Powers 1993). The motivation stemmed from an interest in quantifying the effects of site 

preparation practices on regeneration growth rates, with the goal of enabling managers 

to better select from treatment alternatives. This topic is the primary focus of modeling 

efforts by the Inland Empire Growth and Yield Cooperative (J. Goodburn, personal 

communication) and should soon be implemented in the regional variants of FVS, which 

would result in better fire behavior projections over time in FFE. Whether the same 

progress is being made in other regions where dry mixed conifer forests exist is unclear.

Opportunities exist to improve empirical treatment longevity models for western 

forests. The appropriateness of linking multiple submodels for fire simulation already 

remains somewhat in question (Cruz and Alexander 2010), and adding models of 

temporal change to fuel and fire prediction models magnifies that concern. Yet, further 

refinement of those submodels and input parameters is possible. At the Fire and Fire 

Surrogates study installations, for example, only the immediate post-treatment effects 

of thinning, burning, and thinning-burning treatments have yet been reported (Stephens 

and others 2009). But that replicated, multi-site study provides an excellent foundation 

for testing the longevity of those common fuel treatments.

Silvicultural trials provide untapped opportunities for improving longevity models. 

Long-term silvicultural thinning trials with long data records exist throughout major 

forest types of the western United States, including ponderosa pine, for a range of stand 

ages, compositions, and densities (varying by location). Since thinning (commercial and 

pre-commercial) is a common fuel treatment, these can serve as excellent laboratories 

for understanding the effect of thinning on fuel attributes over time. For example, the 

original purpose of the West-wide LOGS studies was to investigate the effect of thinning 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 223

intensity and post-treatment tree spacing on stand growth (Oliver 2005). The LOGS 

plots have been much studied, but they have not been utilized for the study of fuels 

dynamics. They can be readily exploited to relate thinning intensity to temporal changes 

in crown fuel loads, ladder fuels, and surface fuels, as well as subcanopy microclimates 

that determine midflame windspeeds and fuel moisture contents. Fuels-specific experiments involving common western fuel treatments and treatment combinations provide 

more opportunities for generating empirical longevity data.

Conclusion

Fuel treatments can appear static, but they create fuel structures that are highly dynamic. That dynamism ensures that treatment alternatives will differ not only in their 

immediate effect on potential fire behavior but in their longevity or persistence as well. 

In the days and years following treatment, dead fuels degrade and aggrade; existing 

live fuels grow and new live fuels are recruited; some live fuels convert to dead fuels; 

and the horizontal and vertical arrangements of all fuel layers change. The changes to 

fuel structures are a function of pre-treatment condition, post-treatment condition, site 

productivity, and time. Recognizing those elements that contribute to treatment longevity 

during the planning process will help result in the selection of treatments and treatment 

combinations that have enduring effects.

How is it possible to integrate all the elements of treatment longevity in decisionmaking? One way is in a visual ranking format that synthesizes elements of longevity 

in that rank. Quantifying years of longevity remains challenging to current modeling 

capabilities, but it is feasible to rank a project’s treatment alternatives by the posttreatment environments they create. Figure 12.6 presents an example of one possible 

ordinal ranking system, with cutting intensity and spatial arrangement serving as the 

primary longevity criteria. In this example, treatments affect torching potential (initiation 

of crown fire from surface fire) and crowning potential (spread of crown fire following 

canopy ignition) differently, and with longevity tradeoffs between them; hence, scores 

(1 to 9) are assigned for each of those elements. The photos in fig. 12.7 accompany the 

fig. 12.6 ranking system and portray examples of possible fuel treatments resembling 

thinning, shelterwood or seed tree cuts (dispersed retention cuttings), and group selection or patch cuts (aggregated retention cuttings). In this framework, high ranks indicate 

greater treatment longevity (or persistence of treatment effect), with low ranks representing lesser treatment longevity. For example, cuttings that remove more trees and 

create larger gaps between trees result in greater crowning potential longevity, as it will 

take longer for the crowns of residual trees to close the gaps between them. In contrast, 

those same treatments have the least torching longevity since the large gaps facilitate 

the recruitment and growth of ladder fuels while also halting crown recession of residual 

trees. The rankings in this schematic are subjective, but this type of ranking system is 

proposed as a template that fuels managers and researchers can consider and develop 

further, with adaptations that fit local species, treatment alternatives, and knowledge.

In many cases, the best treatment alternative is a compromise between achieving 

immediate impacts and achieving results with longevity. As Reinhardt and others (2008) 

stated, “we must think of fuel treatment regimes rather than single fuel treatment projects.” 

A regime approach to fuels planning will help ensure that fuel dynamics and treatment 

longevity are addressed in the planning of individual treatments.
224 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Figure 12.6. Proposed system for ranking different canopy fuels treatment prescriptions on the basis of treatment longevity. Longevity ranks range 1 to 9, with highest values signifying longer persistence of treatment 

effect. Treatment prescriptions affect trends in crowning potential and torching potential differently; hence, 

scores are assigned for each of those elements separately (Crowning Potential, left; Torching Potential, 

right). First decision tier refers to spatial patterns of trees following treatment, from dispersed retention 

(trees uniformly or randomly spaced) to aggregated retention (canopy gaps and untreated clumps). Second 

decision tier is the best approximation of the visual appearance of the treatment following implementation. 

Third decision tier is the relative amount of reduced stand density, represented as percent basal area cut 

(dispersed retention) or gap size (aggregated retention). Fourth tier (aggregated retention only) is the proportional project area that is represented by clearings (relative to untreated matrix forest).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 225

Figure 12.7. Classifying the cutting intensity and spatial pattern/scale 

in fuels treatments (to accompany fig. 12.6). First row treatments (A, B) 

are best characterized as thinnings; second and third row treatments are 

heavier dispersed-retention cuttings that resemble shelterwood cuts (C, D) 

or seed tree cuts (E, F), respectively. Fourth and fifth row treatments (G-J) 

are aggregated retention cuts resembling group selection or patch cuts.

a b

c d

e f

g h

i j
226 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Special Feature: An interactive references database is available at: 

http://www.fs.fed.us/rm/pubs/rmrs_gtr292.html and on the CD accompanying this 

publication.

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Appendix A: Chapter 5 Supplement

Inventory Modeling of Current Fire Hazard Conditions

In this appendix, we provide histograms of FFE-FVS model-summarized, 2000-2009 

Forest Inventory and Analysis data that depict forest structure and fire hazard metrics 

for the dry mixed conifer forests of Washington, Oregon, Idaho, Montana, Utah, and 

extreme northern California. The information is intended to supplement Chapter 5 (Inventory Modeling of Current Fire Hazard Conditions) and to provide useful context for 

planning fuels management throughout the synthesis area. Ordered by region (Northern 

California and Klamath, Pacific Northwest Interior, Northern and Central Rockies, and 

Utah), and within each region by forest type group (Douglas-fir and True Fir, Pine and 

western larch, Aspen/birch, Western Redcedar, and Other), each page summarizes eight 

attributes for one combination of region and forest type group. 

These information-rich histograms provide a statistically unbiased basis for evaluating current conditions, and much can be learned by comparison among regions and 

forest type groups. We present one set of interpretations for the Douglas-fir and true 

fir forests in the Northern California and Klamath region as an example of the kinds of 

interpretation that are possible using this information resource.

In the left side of each page, the stand structure attributes QMD (quadratic mean 

diameter, in inches), TPA (trees per acre), BA (basal area in ft2), and Canopy Cover 

(percent canopy cover, as calculated via FVS) are summarized as frequency histograms. 

FFE-FVS-estimated fire hazard attributes are summarized on the right side of each page: 

surface flame length (predicted height of flames from combustion of surface fuels under 

extreme conditions), torching index (wind speed, in miles per hour, at which torching 

behavior would be expected), mortality volume percent (percent of the timber volume 

predicted to convert from live to dead given an extreme fire), and crowning index (wind 

speed, in miles per hour, at which active crown fire behavior would be expected). Each 

histogram describes the relative frequency of each attribute level for the region and forest type group represented on that page. The Y-axis represents the proportion of forest 

area in one subregion/forest type group combination. These can be easily translated into 

acres, if desired, by multiplying the proportions by the area (in acres) printed in the text 

block included in each histogram. 

Each subfigure containing a histogram also contains a printed mean and a rough 

approximation1 of the sampling error (SE) for the attribute. Sampling errors tend to be 

smaller as area represented (and the associated sample size) increases. The true (population) mean has a 95 percent chance of being contained by the interval constructed as the 

sample mean +/- 2 times the sampling error. In some cases, the X-axis of the histogram 

is truncated and the last histogram bar on the X-axis represents a larger range than the 

other histogram bars, and the last X-axis label is modified accordingly.

1 These sampling errors do not account for the different landscape weights (acre expansion factors) 

associated with each forested condition; they also do not reflect any error or uncertainty associated with 

models used to generate estimates of, for example, volume or surface flame height.
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Note that histograms are only plotted for cases where there are at least 10 conditions 

for a subregion/forest type group combination. It is inadvisable to attempt inferences 

from fewer than 10 conditions, and some analysts are more comfortable with a sample 

size minimum of 30. 

Example Interpretation of Histograms for the Northern 

California and Klamath Subregion’s Douglas-fir and True Fir 

Forests

There are approximately 1.1 million acres of Douglas-fir and true fir forests in this 

subregion. Four structure attributes and four fire hazard attributes provide key insights 

into current hazard and opportunities for promoting resilience and reducing hazard.

Forest characteristics

Mean QMD of these forests is relatively small (under 10 inches dbh), and nearly 50 

percent have a QMD of <8 inches. However, in a small proportion of the area (< 2 percent), stands are dominated by large trees such that QMD exceeds 40 inches dbh. About 

half of the area has less than 400 TPA, but 8 percent has densities over 1000 TPA. Most 

stands have a basal area (BA) between 50 and 250 (mean is 155) ft2 acre-1, though there 

are a few instances of BA exceeding 400 ft2 acre-1. Canopy cover tends to be high, with 

a mean at 67 and a mode between 60 and 80. 

Fire characteristics

Surface flame lengths vary widely with the great majority of the conditions expected 

to have a flame length under 6 ft. Torching index is low in a large proportion (40 percent) of the area (i.e., less than 30 mph). However, another 40 percent of the area has 

torching indices in excess of 100 mph, such that even relatively high intensity surface 

fires could be expected to incur minimal torching. Due to species composition in these 

stands (for example, tolerant vs. intolerant), mortality from a wildfire, while averaging 60 percent, has a bimodal distribution (most values are well below or above 

the mean). Approximately 50 percent would have >90 percent mortality and another 

50 percent would have less than <30 percent. Consistent with the prevalence of high 

canopy cover, the majority (>50 percent) of the forests have crowning indices under 

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Appendix B: Decision Support Tools 

for Managers

Manager comment: There is an abundance of decision support tools; the challenge 

is finding the time to investigate, learn, and use all that is available.

There is an abundance of decision support models designed to provide scientific and 

technical support to inform decision-making (summarized by Peterson and others 2007; 

Stratton 2006). Although managers recognize the value of decision support tools, they 

often do not have the time to become proficient using the tools or do not know which 

is the most appropriate for a given need. In our interviews, we found that a specific set 

of models are recognized and used, typically these more popular decision support tools 

are supported nationally and have technical support to maintain the model’s integrity.

We encourage reviewing both Peterson and others (2007) and Stratton (2006) because 

the authors provide a wealth of information that may address questions and challenges. 

Each tool described has its own assumptions, trade-offs, and benefits. Peterson and 

others (2007) summarize several tools that address application, spatial scale, analysis 

requirement, and data requirements, and link to other tools that we found to be useful. 

The authors also provide examples of site specific, small watershed, and large watershed 

projects in which they discuss appropriate tools for addressing particular decision needs.

We provide a short summary and web site for each of the models Peterson and others (2007) and Stratton (2006) identified, in addition to other models that have recently 

been developed. Tools are arranged based on their primary use, primary objective, and 

most current web site. These tools were identified in our manager interviews, synthesis 

documents, Joint Fire Sciences Program, and team knowledge on available tools. We 

identified every tool we could find, but this is not an exhaustive list. If we were unable 

to find a web site or access the tool, we did not list it. The list is organized in order in the 

following categories: Emissions, Fire and Technology Transfer Portals, Fire Behavior, 

Fire Effects, Fire Weather, Fuels Description, Fuels Planning, and Databases.

Emissions

BlueSky Smoke Forecast System (BlueSky): Modeling framework that links a variety 

of independent models of fire information, fuel loading, fire consumption, fire emissions, and smoke dispersion. http://www.airfire.org/bluesky

Fire Emission Production Simulator (FEPS): Simulates fuel consumption, emission 

production, and plume buoyancy for prescribed burns and wildland fires under various 

meteorological conditions. http://www.fs.fed.us/pnw/fera/feps/

Consume 3.0: Predicts fuel consumption and pollutant emissions from wildland fires 

in all fuelbed types based on fuel loadings, weather conditions, site environmental data, 

and fuel moisture. Resource managers can determine when and where to conduct a 

prescribed burn to achieve desired objectives. http://www.fs.fed.us/pnw/fera/research/

smoke/consume/index.shtml

Fire and Technology Transfer Portals

FIREHouse: Web-based project providing information about fire science and technology relevant to Washington, Oregon, Idaho, and Alaska. The portal provides one 

location for resource managers, decision-makers, scientists, students, and communities 
262 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

who want access to the results of efforts to understand and manage fire and fuels on 

public lands in these regions. http://depts.washington.edu/nwfire/

FRAMES: Web-based portal providing a systematic method of exchanging information and transferring technology among wildland fire researchers, managers, and other 

stakeholders in order to make wildland fire documents, data, tools, and other information 

resources easy to find, access, distribute, compare, and use. http://www.frames.gov/

Wildland Fire Air Quality Tools Portal: Provides a “one-stop shopping” portal for air 

quality tools. Designed to access the Wildland Fire Decision Support Systems. http://

www.firesmoke.us/wfdss/

National Interagency Fuels, Fire, and Vegetation Technology (NIFTT): Provides land 

managers with science-based analysis tools and training focused on the assessment of 

fire behavior, fire effects, fire regimes, and vegetation dynamics. http://www.frames.

gov/portal/server.pt/community/niftt/382/home/1626

Fire Behavior

BehavePlus: Predicts surface fire flame length, rate of spread, tree mortality, crown 

scorch height, spotting distance, and fire containment. Individual stand scale to 6th

field HUCs. Not a data intensive tool but requires familiarity with fire behavior fuel 

models. BehavePlus equations are basic underlying equations used in FOFEM, FMA 

Plus, FFE-FVS, NEXUS, FARSITE, and FlamMap Supported by U.S. Forest Service 

Fire and Aviation Management. http://www.firemodels.org/index.php/national-systems/

behaveplus

Fire Area Spread Simulator (FARSITE 4.0.4): Tests the potential effects of different 

fuel treatments on landscapes subjected to a fire or group of fires burning under given 

weather conditions. This is a fire growth simulation model that uses spatial information on topography and fuels along with weather and wind fields. Supported by U.S. 

Forest Service Fire and Aviation Management. http://www.firemodels.org/index.php/

national-systems/farsite

FlamMap: Spatial fire behavior mapping and analysis program that requires a GIS 

layer of vegetation characteristics and fuel moisture and weather data. Unlike FARSITE, 

FlamMap makes independent fire behavior calculations (for example, fireline intensity 

and flame length) for each location of the raster landscape (cell), independent of one 

another. Supported by U.S. Forest Service Fire and Aviation Management. http://www.

firemodels.org/index.php/national-systems/flammap

Fire Behavior Assessment Tool (FBAT): Design and prioritize hazardous fuel treatments and evaluate the effectiveness of proposed treatments in altering potential fire 

behavior. FBAT runs FlamMap in the background from an ArcMap platform while 

producing many of the same fire behavior characteristics that are output by FlamMap 

(such as flame length, fireline intensity, rate of spread, and fire type). The tool maps fire 

behavior metrics and maps each threshold, including only those polygons exceeding 

the threshold. Stand level tool. http://www.fire.org/index.php?option=com_content&t

ask=view&id=143&Itemid=299

Fire Effects

Armillaria Response Tool (ART): Evaluates potential impacts of fuel treatments on 

Armillaria root disease. http://forest.moscowfsl.wsu.edu/fuels/art/
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 263

First Order Fire Effects Model (FOFEM): Predicts fuel consumption, soil heating, 

smoke production, and tree mortality caused by prescribed fire or wildfire. http://www.

firelab.org/science-applications/fire-fuel/111-fofem

Wildlife Habitat Response Model (WHRM): Web-based tool to help fuel treatment 

planners evaluate the effects of fuel treatment alternatives on wildlife habitat elements. 

Developed by David Pilliod and Elena Velasquez. http://forest.moscowfsl.wsu.edu/

fuels/whrm/whrm.html.

Combined Habitat Assessment Protocols (CHAP): Accounting tool and can generate 

an appraised habitat value for fish and wildlife when applied to a site or area. http://

nwhi.org/inc/data/gisdata/hab/CHAP%20General%20Approach%20Version%2009-

14-2011.pdf

Fire Weather

FireFamily Plus: Windows-based program that analyzes and summarizes an integrated database of fire weather and fire occurrence. Supported by U.S. Forest Service 

Fire and Aviation Management. http://www.firemodels.org/index.php/national-systems/

firefamilyplus

WindNinja: Computer program that computes spatially varying wind fields for wildland 

fire application. http://www.firelab.org/research-projects/physical-fire/145-windninja

Fuels Description

Natural Fuels Photo Series: Helps land managers appraise fuel and vegetation 

conditions in natural settings. Includes 11 volumes representing various regions of the 

United States. There are 1 to 4 series in each volume, each having 4 to 17 sites. Sites 

include standard, wide-angle, and stereo-pair photographs. http://www.fs.fed.us/pnw/

fera/research/fuels/photo_series/index.shtml

Fuel Characteristic Classification System (FCCS): Assigns fuel properties and fire 

potentials to landscapes at all scales across the United States. Consists of a large database 

of physical parameters that describe the abundance, physical character, and arrangement 

of wildland fuelbeds. http://www.fs.fed.us/pnw/fera/fccs/index.shtml

Fuels Planning

ArcFuels: Integrates a number of fire behavior models and corporate spatial data 

within a GIS framework. http://www.arcfuels.org/

Fire Regime Condition Class (FRCC): Interagency, standardized tool for determining the degree of departure from reference condition vegetation, fuels, and disturbance 

regimes. http://frames.nbii.gov/portal/server.pt/community/frcc/309/home/1397

Comparative Risk Assessment in Fire and Fuels Planning (CRAFT): Calculates relative risks and trade-offs associated with alternative fire and fuel management scenarios. 

Designed to lead natural resource managers through an integrated assessment of risks, 

uncertainties, and trade-offs that surround fire and fuel management. http://www.fs.fed.

us/psw/topics/fire_science/craft/craft/

Most recently, Norman and others (2010) used a case study to illustrate the CRAFT 

planning process, which includes four stages: (1) objective setting and problem conceptualization, (2) alternative design, (3) probabilistic modeling of effects, and (4) synthesis. 

http://www.fs.fed.us/pnw/pubs/gtr802/Vol2/pnw_gtr802vol2_norman.pdf
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Ecosystem Management Decision Support System (EMDS): Application framework 

for knowledge-based decision support of ecological assessments at any geographic 

scale. Integrates GIS with knowledge-based reasoning and decision support modeling 

technologies to provide decision support for a substantial portion of the adaptive management process. http://www.institute.redlands.edu/emds/manuscripts/docs/EMDS%20

User%20Guide.pdf

Fire Effects Planning Framework (FEPF): Creates map libraries useful for identifying where and under what burning conditions fire may be beneficial for achieving fuel 

treatments with respect to fire behavior and effect upon other management objectives. 

http://leopold.wilderness.net/research/fprojects/F005.htm

Fire Effects Tradeoff Model (FETM): Simulates the effects of alternative land management practices on future landscape conditions over long periods and under diverse 

environmental conditions, natural fire regimes, and fuel and fire management strategies. 

http://www.fs.fed.us/r6/aq/fetm/index.htm

Rare Event Risk Assessment Process (RERAP): Estimates the risk that a fire will reach 

a particular point of concern before a fire-ending event occurs. Web site not located.

Simulating Patterns and Processes at Landscape Scales (SIMPPLLE): Watershedlevel disturbance model designed as a management tool to improve understanding of 

how disturbance processes and vegetation interact to affect landscape change. http://

www.fs.fed.us/rm/missoula/4151/SIMPPLLE/

Forest Inventory and Analysis Biomass Summarization System (FIA BioSum): 

Analysis framework that combines forest inventory data representing an analysis region, 

a treatment cost model, a fuel treatment effectiveness model, and a raw material hauling cost model to explore alternative landscape-scale treatment scenarios that achieve 

a variety of management objectives. http://www.fs.fed.us/pnw/fia/biosum/

Integrated Forest Resource Management System (INFORMS): Facilitates planning 

activities in the USDA Forest Service. Designed specifically to help support projectscale (NEPA) and landscape-scale planning. However, some users may find it useful 

for other types of planning exercises. http://www.fs.fed.us/informs/index.php

My Fuel Treatment Planner (MyFTP): Allows planners working at the level of a 

National Forest District, or similarly sized unit, to estimate costs, revenues, economic 

impacts, and surface fuels resulting from operations designed to reduce fuel loads in 

fire-prone forests. Software is limited in scope to the dry forests of the western United 

States. http://www.fs.fed.us/pnw/data/myftp/myftp.shtml

Fuels Management Analyst Plus (FMA Plus): Determines dead and down woody fuel 

loading by using either Brown’s inventory methods or photo guides to assess crown 

fire risk and predict slash resulting from thinning and logging operations. Consists of 

three modules: (1) DDWoodyPC for estimating dead, downed woody fuel; (2) Photo 

Series Explorer to view scanned images of older photo guides to fuel loadings; and (3) 

CrownMass to predict crown fire risks and estimate slash loadings and Fuel Model 

Manager to create custom fuel models for use in CrownMass. http://www.fireps.com/

fmanalyst3/index.htm
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Vegetation Dynamics Development Tool (VDDT): Models the short- and long-term 

interactions of vegetation, management, and disturbance. Most analysts use it to depict 

vegetation as structure (e.g., grass/shrub, seedling, sapling, and pole) and cover (e.g., 

dominant species group) classes connected by growth, succession, management activities, and natural disturbances. Can be used for any system that connects state classes 

through probabilities of different kinds of changes. Is not spatial; produces a variety of 

database and graphical outputs, but not maps.

LANDIS and LANDIS-II: Models forest succession, disturbance (including fire, wind, 

harvesting, fuel treatments, insects, and climate change), and seed dispersal across large 

landscapes. LANDIS tracks age and spatial distribution of individual species and has a 

flexible spatial resolution. LANDIS-II also tracks living and dead biomass of species 

cohorts (using PnET-II).

Fire and Fuels Extension-Forest Vegetation Simulator (FFE-FVS): Stand-level model 

provides forest managers with a method for assessing the effects of treatment alternatives on fuel dynamics and fire potential into the future.

Fireshed Assessment: Integrated approach to landscape planning. “Firesheds” are 

large (thousands of acres) landscapes delineated based on fire regime, condition class, 

fire history, fire hazard and risk, and potential wildland fire behavior. Fireshed assessment is an interdisciplinary and collaborative process for designing and scheduling 

site-specific projects consistent with the goals of the Healthy Forests Restoration Act, 

National Fire Plan, and National Forest land and resource management plans (Ager 

2006). http://www.treesearch.fs.fed.us/pubs/25943

Optimizing Fuel Solutions and Ecological Values in Landscapes (FUELSOLVE): 

Planning fuel treatment and vegetation management with fuel treatment projects and 

forest planning efforts (e.g., scenario planning, prioritization, describing desired future 

conditions, and fire effects modeling).

Smoke Impact Spreadsheet (SIS): Planning model for calculating particulate matter emissions and concentrations downwind of wildland fires. Conservatively predicts 

downwind particulate matter concentrations for comparison with appropriate Federal or 

state air quality standards. http://www.airsci.com/SISmodel/SIS_Users_Manual-6.17.03.

pdf131

Tool for Exploratory Landscape Scenario Analysis (TELSA): Spatially explicit state 

and transition model that can be used to model the short- and long-term interactions 

of vegetation, management, and disturbance. Most analysts use it to depict vegetation 

as structure (e.g., grass/shrub, seedling, sapling, and pole) and cover (e.g., dominant 

species group) classes connected by growth, succession, management activities, and 

natural disturbances.

The Interagency Fuels Treatment Decision Support System (IFT-DSS): Provides a 

user interface for the applications of a combination of models that are currently under 

development (JFSP 2009).

Quick-Silver: Economic analysis tool for long-term and on-the-ground resource 

management projects. Provides a cost/benefit framework to evaluate different management scenarios.
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Databases

National Fuel Moisture Database (NFMD): Web-based system that enables users to 

view sampled and measured live- and dead-fuel moisture information. Routinely updated 

by fuels specialists who monitor, sample, and calculate fuel moisture data. Provides a 

repository for sampled live- and dead-fuel moisture and a resource for anyone interested 

in obtaining fuel moisture data. Designed to be used in land management. Extent and 

details of available fuel moisture data varies by state, site, and measurement frequency; 

thus, there is considerable inconsistency. http://72.32.186.224/nfmd/public/index.php

Fire Effects Information System (FEIS): Provides background information on the 

potential effects of fire on flora and fauna. Summarizes the flora and fauna biology, 

ecology, and relationships to fire. http://www.fs.fed.us/database/feis/

Glossary of Wildland Fire Terminology (2011): http://www.nwcg.gov/pms/pubs/

glossary/m.htm

Wildland Fire Lessons Learned Center: Provides cutting-edge knowledge to enhance 

and sustain safe and effective work practices in the wildland fire community using past 

and present lessons. http://www.wildfirelessons.net/

NOAA Paleoclimatology Fire History Data Sets: International Multiproxy Paleofire 

Database (IMPD) is an archive that contains fire history data derived from tree scars and 

charcoal in sediment records. Designed to be a permanent repository for high-quality 

paleofire records from around the world. Search engine functions by the contributor of 

the data or by state. For Idaho, Oregon, Washington, Utah, and Montana there were 122 

different data sets. http://www.ncdc.noaa.gov/paleo/impd/paleofire.html

Forest Inventory and Analysis (FIA): Nationwide database from permanent plots 

on a grid. Allows users to generate tables and maps of forest statistics through a web 

browser without having to understand the underlying data structures. Also enables users to run any of the standard reports for a specific area of interest and survey year or 

create completely customized reports based on user-specified criteria. Saves work to 

a hard drive to load/re-run custom retrievals. (Data from FIA were used in Chapters 5 

and 11.) http://apps.fs.fed.us/fido/ 
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Appendix C: Chapter 11 Supplements

Evaluating Silvicultural Treatment Scenarios Using Forest 

Inventory and Analysis

This appendix summarizes the pre- and post-treatment forest and fire characteristics 

represented as frequency histograms, and compares characteristics of treatable forests 

with all forests. Up to six treatments were evaluated for each condition (stand), including 

crown thinning, thinning from below, and restoration treatments. A rule-based scenario, 

Any:ptorch, was used to select the best treatment for each condition based on the amount 

by which hazard score could be reduced. When there were ties among treatments, reduction in the probability of torching and net revenue were used to select the best one. 

Conditions with an initial hazard score of zero (no hazard with respect to probability 

of torching, torching index, surface flame height, and mortality volume percent) were 

not included in the set of treated conditions because they were already low hazard by 

definition. Those with no reduction in hazard score under any treatment were omitted 

from the set of treated conditions because no modeled treatment was deemed effective. 

The histogram summaries are organized by region: Northern California and Klamath, 

Pacific Northwest Interior, Northern and Central Rocky Mountains, and Utah. For each 

region, we present five kinds of summaries of treatment results under the Any: Ptorch 

scenario for up to three forest type groups with sufficient numbers of treatable conditions (at least 10) in the region to generate a viable histogram output (Douglas-fir and 

True Fir, Pine and Western Larch, and Other). 

The five summaries are:

1. All versus Treatable—a comparison of all conditions versus the conditions 

that were treatable. The forest characteristics show a comparison of quadratic 

mean diameter, trees per acre, canopy cover, and hazard score for all forests 

in that species group versus the forests for which one or more of our modeled 

silvicultural treatments were estimated to be effective.

2. Composite Hazard Score—a comparison of hazard score before and after 

treatment using the most effective treatment, only for forests where effective 

treatment was possible.

3. Stand structure metrics—a comparison of the stand metrics quadratic mean diameter, trees per acre, basal area, and FFE-FVS-estimated canopy cover before 

and after treatment using the most effective treatment, only for forests where 

effective treatment was possible.

4. Fire hazard metrics—a comparison of the hazard metrics Ptorch, surface flame 

length, torching index, and mortality volume percent, before and after treatment 

using the most effective treatment, only for forests where effective treatment 

was possible

5. Economic implications—on-site treatment cost, net revenue, value at the mill-gate 

and volume of merchantable wood extracted, value at the mill-gate and weight 

of energy wood extracted, percent of all costs represented by the transportation 

of energy wood, and percent of total revenues accounted for by sales of energy 

wood.
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These histograms, organized in most cases as one region-forest type group combination 

and multiple attributes per page, describe the unreserved forest landscape and treated 

acres under the Any:Ptorch scenario by reporting the proportion of forested area (on 

the Y-axis) at each attribute level (on the X-axis). The areal proportions can be easily 

translated into acres, if desired, by multiplying the proportions by the area, in acres, 

printed in the text block included in each histogram. 

As a point of reference, each histogram also contains the mean and a roughly calculated sampling error1 (SE) for one attribute in one region/forest type group combination. 

Sampling errors tend to be smaller as area represented (and the associated sample size) 

increases. The true (population) mean has a 95 percent chance of being contained by 

the interval constructed as the sample mean +/- two times the SE. In some cases, the Xaxis of the histogram is truncated and the last histogram bar on the X-axis represents a 

larger range than the other histogram bars; the last X-axis label is modified accordingly. 

Note that histograms are only plotted for cases where there are at least 10 conditions 

for a region/forest type group combination. It is inadvisable to attempt inferences from 

fewer than 10 conditions, and some analysts are more comfortable with a sample size 

minimum of 30.

To stimulate interest in and understanding of this extensive statistical data resource, 

we offer interpretation of each histogram for the Douglas-fir and True Fir forest type 

group in the Northern California and Klamath region. Interpretations for other regions 

and forest type groups will differ, but we hope that the highlights provided for this region 

and forest type group will inspire curiosity and serve as examples of how to extract 

useful information from the data summaries. 

All Versus Treatable

From a total of 1.1 million acres of Douglas/grand fir types in the Northern California 

and Klamath region, only 32 percent of the area (368,000 acres) could be effectively 

treated by one or more of the six modeled silvicultural treatments. By definition, a 

condition considered for treatment must be initially hazardous; thus those conditions 

that didn’t rate as hazardous (as shown in the 0 bar of the Hazard Score histogram at 

the bottom of the page) were untreatable, as were those where no treatment achieved 

a reduction in hazard score—the number of ways that a condition would rate as hazardous (with respect to probability of torching >20 percent, torching index <20 mph, 

surface flame height >4 ft, and mortality volume >30 percent of total volume). Hazard 

score is calculated as the number of hazard thresholds exceeded on a condition. The 

treatable acres were approximately evenly distributed among the four hazard scores. 

The conditions that were deemed treatable roughly reflect the all stands distribution of 

quadratic mean diameter and trees per acre, but treatable conditions had significantly 

greater canopy cover, typically 40 to 100 percent. 

Composite Hazard Score

By definition, a condition considered for treatment must be initially hazardous (no 

pre-treatment condition has a hazard score of zero), and since treatment was considered effective only if hazard score was reduced by at least one point, no post-treatment 

1 These sampling errors do not account for the different landscape weights (acre expansion factors) 

associated with each forested condition; given that conditions that are smaller than a full plot tend to have 

greater variability and that their contribution to SE is not scaled down accordingly, calculated SE somewhat 

overstates true SE. It is important to note that SE does not reflect any error or uncertainty associated with 

models used to generate estimates of, for example, volume or surface flame height.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 269

condition has a hazard score >3. In these forests, mean hazard score dropped from 2.5 

to 0.8, and the acres rated as hazardous by three or four criteria dropped from about 40 

percent of the treatable acres to less than 7 percent. Impressively, compared to other 

forest types and regions, hazard score was reduced to zero on over 40 percent of the 

effectively treatable area and was reduced to 1 on another 40 percent. 

Stand Structure Metrics

The best treatment for each condition achieved an average 50 percent reduction in 

tree density and a net increase in QMD by virtue of removing more small than large 

trees. For this reason, and the fact that most of the basal area could be found in larger 

trees, reduction in basal area was not as dramatic as reduction in tree density but was 

significant nevertheless, as evidenced by a 58 ft2 drop in the mean with a SE on the order 

of 10. Canopy cover was also reduced significantly (by 20 percent cover on average). 

This highlights the importance of looking at both the histogram and the mean and SE 

statistics, as shifts in frequency distributions that maintain an approximately constant 

shape can be hard to detect visually.

Fire Hazard Metrics

Fuel treatment changed the potential fire characteristics from the pre-treatment 

conditions, illustrating that these generic prescriptions would influence the fire characteristics in these forests. The probability of torching dropped from a mean value of 

53 to 12; many sites (more than half) had a pre-treatment probability of greater than 

50 percent, but following treatment, nearly 80 percent of sites had less than 10 percent 

probability of torching. Incidence of low torching index (high torching hazard) was 

significantly reduced. Treatment had little impact on mean surface flame length, but 

there was a notable uptick in the frequency with which it was below our target of 4 ft. 

The impact on mortality volume was perhaps most impressive. Not only did the mean 

value drop from 76 to 26 percent, in 20 percent of the cases, post-treatment mortality 

was predicted at >30 percent, whereas close to 80 percent of pre-treatment acres had 

>30 percent mortality volume. 

Economic Implications 

On-site treatment cost per acre is highly variable, depending, for example on tree size, 

number of trees harvested, slope, stand density, distance to road, and is skewed with a 

long tail because some acres are very expensive to treat. The high mean ($1616/acre) 

reflects this skew, while the median is $1225/acre and the mode is between $500 and $750/

acre. However, sales of products and energy associated with the prescriptions modeled 

for these conditions make net revenue a more relevant attribute to consider. This varies 

from a net cost of more than $2000 (negative net revenue) to a positive net revenue of 

over $9400/acre. In this case, the skew generates a long tail toward high net revenues as 

the conditions that cost the most to treat (have the most material to remove) also have 

the most material to sell. Whether or not high volume conditions would realistically be 

available for treatment is an open question, but it is encouraging that the median net 

revenue is positive ($244/acre), suggesting that in most cases, treatment revenues can 

cover treatment costs. The value of merchantable wood extracted in these treatments is 

skewed with a long tail and varies from $0 to nearly $10,000 per acre, with a quarter 

of the area capable of generating in excess of $4000/acre of merchantable wood and 

another quarter capable of generating less than one-tenth that value. The distribution of 

merchantable volume is similarly bi-modal, with 30 percent of the treatable area generating less than 250 ft3/acre and another 30 percent generating in excess of 2500 ft3/acre. 
270 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Similar patterns persist for energy wood, though the dollar amounts per acre are far 

less than for merchantable wood, and nearly half of the area generates less than 10 green 

tons/acre of energy wood. When considering the total costs of treatments (include costs 

for on site and transportation associated with what is extracted), the transportation costs 

for delivering energy wood to bioenergy facilities is comparatively small, averaging 4 

percent of total costs; however, considering how much of the harvest value is in energy 

wood and that on-site costs are generally the greatest share of costs, these transportation costs are not insignificant and, in some scenarios, may preclude recovery of energy 

wood from treated conditions. On average, 37 percent of the value of materials sold in 

association with these treatments is generated by sales of energy wood, though a portion 

of the merchantable wood that is transported to and processed at lumber and plywood 

mills (i.e., mill residues not utilized for manufactured wood or paper products) will also 

be used for energy. The average percent value is skewed by the nearly 20 percent of the 

area where no merchantable wood is recovered due to either tree size or an abundance of 

non-commercial species such that total sales are much less than for areas where energy 

wood is a smaller proportion of the mix. In any case, treatment costs would rarely be 

covered without the revenue obtained from sales of merchantable wood.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 271

All Versus Treatable
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Hazard Score
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Stand Structure Metrics
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Fire Hazard Metrics
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Economic Implications
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Table 1. List of tree species and associated scientific name and symbol.

Plants

Common name Scientific name symbol

Bigleaf maple Acer macrophyllum Pursh ACMA3

Blue oak Quercus douglasii Hook. & Arn. QUDO

Blue spruce Picea pungens Engelm. PIPU

Bristlecone pine Pinus aristata Engelm. PIAR

California black oak Quercus kelloggii Newberry QUKE

California foothill pine/ gray pine Pinus sabiniana Douglas ex Douglas PISA2

California laurel Umbellularia californica (Hook. & Arn.) Nutt. UMCA

California white oak Quercus lobata Née QULO

Canyon Live oak Quercus chrysolepis Liebm. QUCH2

Chinquapin Chrysolepis Hjelmquist CHRYS15

Cottonwood Populus L. POPUL

Coulter pine Pinus coulteri D. Don PICO3

Douglas-fir Pseudotsuga menziesii (Mirb.) Franco PSME

Engelmann spruce Picea engelmannii Parry ex Engelm. PIEN

Foxtail pine Pinus balfouriana Balf. PIBA

Gambel oak Quercus gambelii Nutt. QUGA

Giant chinquapin Chrysolepis chrysophylla (Douglas ex Hook.) Hjelmqvist CHCH7

Giant sequoia Sequoiadendron giganteum (Lindl.) J. Buchholz SEGI2

Grand fir Abies grandis (Douglas ex D. Don) Lindl. ABGR

Incense-cedar Calocedrus decurrens (Torr.) Florin CADE27

Jeffrey pine Pinus jeffreyi Balf. PIJE

Juniper Juniperus L. JUNIP

Knobcone pine Pinus attenuata Lemmon PIAT

Limber pine Pinus flexilis James PIFL2

Live oak Quercus agrifolia Née QUAG

Loblolly pine Pinus taeda L. PITA

Lodgepole pine Pinus contorta Douglas ex Loudon PICO

Mountain hemlock Tsuga mertensiana (Bong.) Carrière TSME

Noble fir Abies procera Rehder ABPR

Oregon white oak Quercus garryana Douglas ex Hook. QUGA4

Pacific madrone Arbutus menziesii Pursh ARME

Pacific silver fir Abies amabilis (Douglas ex Louden) Douglas ex Forbes ABAM

Paper birch Betula papyrifera Marsh. BEPA

Pinyon pine Pinus edulis Engelm. PIED

Ponderosa pine Pinus ponderosa Lawson and C. Lawson PIPO

Port Orford cedar Chamaecyparis lawsoniana (A. Murray bis) Parl. CHLA

Quaking aspen Populus tremuloides Michx. POTR5

Red alder Alnus rubra Bong. ALRU2

Red fir Abies magnifica A. Murray bis ABMA

Redwood Sequoia sempervirens (Lamb. ex D. Don) Endl. SESE3

Appendix D: Common and 

Scientific Names of Species

These tables provide the Latin and authorities for the species that were referred to 

in the synthesis. We also provide the plant symbols. The tables are organized by tree 

species; understory plant species beginning with grasses, sedges, forbs, and shrubs; 

diseases; and wildlife species and insects.

Tree Species

(continued)
326 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012

Rocky Mountain juniper Juniperus scopulorum Sarg. JUSC2

Shasta red fir Abies magnifica A. Murray bis var. shastensis Lemmon ABSH

Silver fir Abies alba Mill. ABAL3

Sitka spruce Picea sitchensis (Bong.) Carrière PISI

Subalpine fir Abies lasiocarpa (Hook.) Nutt. ABLA

Sugar pine Pinus lambertiana Douglas PILA

Tanoak Lithocarpus densiflorus (Hook. & Arn.) Rehder LIDE3

Western hemlock Tsuga heterophylla (Raf.) Sarg. TSHE

Western juniper Juniperus occidentalis Hook. JUOC

Western larch Larix occidentalis Nutt. LAOC

Western redcedar Thuja plicata Donn ex D. Don THPL

Western white pine Pinus monticola Douglas ex D. Don PIMO3

White fir Abies concolor (Gord. & Glend.) Lindl. ex Hildebr. ABCO

White spruce Picea glauca (Moench) Voss PIGL

Whitebark pine Pinus albicaulis Engelm. PIAL

Table 1. Continued).

Plants

Common name Scientific name symbol

Table 2. Common and scientific name with plant symbol for grasses and shrubs.

Plants

Common name Scientific name symbol

Arizona fescue Festuca arizonica Vasey FEAR2

Beargrass Xerophyllum tenax (Pursh) Nutt. XETE

Big bluestem Andropogon gerardii Vitman ANGE

bluebunch wheat grass Pseudoroegneria spicata (Pursh) Á. Löve PSSP6

California fescue Festuca californica Vasey FECA

Cheatgrass Bromus tectorum L. BRTE

Elk sedge Carex garberi Fernald CAGA3

Geyer’s sedge Carex geyeri Boott CAGE2

Idaho fescue Festuca idahoensis Elmer FEID

Pinegrass Calamagrostis rubescens Buckley CARU

Rough fescue Festuca campestris Rydb. FECA4

Sedges Carex L. CAREX

Grasses
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Table 3. Shrub and forb common name, scientific name, and plant symbol.

 Plants

Common name Scientific name symbol

Arnica Arnica L. ARNIC

Barberry Berberis L. BERBE 

Bearberry Arctostaphylos alpina (L.) Spreng. ARAL2

Bitterbrush Purshia DC. Ex Poir. PURSH

Black sagebrush Artemisia nova A. Nelson ARNO4

Buckbrush Ceanothus cuneatus (Hook.) Nutt. CECU

Bush chinquapin Chrysolepis sempervirens (Kellogg) Hjelmqvist CHSE11

California buckthorn Frangula californica (Eschsch.) A. Gray FRCA12

Canada thistle Cirsium arvense (L.) Scop. CIAR4

Cascade barberry Mahonia nervosa (Pursh) Nutt. MANE2

Ceanothus Ceanothus L. CEANO

Common juniper Juniperus communis L. JUCO6

Creeping barberry Mahonia repens (Lindl.) G. Don MARE11

Currant/ gooseberry Ribes L. RIBES

Dwarf silktassel Garrya buxifolia A. Gray GABU2

Greenleaf manzanita Arctostaphylos patula Greene ARPA6

Heartleaf arnica Arnica cordifolia Hook. ARCO9

Kinnikinnick Arctostaphylos uva-ursi (L.) Spreng. RUV

Leafy spurge Euphorbia esula L. EUES

Lupine Lupinus L. LUPIN

Mallow ninebark Physocarpus malvaceus (Greene) Kuntze PHMA5

Manzanita Arctostaphylos Adans. ARCTO3

Mountain big sagebrush Artemisia tridentata Nutt. ssp. vaseyana (Rydb.) ARTRV

Mountain mahogany Cercocarpus Kunth CERCO

Oceanspray Holodiscus discolor (Pursh) Maxim. HODI

Oregon grape Mahonia aquifolium (Pursh) Nutt. MAAQ2

Pacific poison oak Toxicodendron diversilobum (Torr. & A. Gray) Greene TODI

Pinemat manzanita Arctostaphylos nevadensis A. Gray ARNE

Rhododendron Rhododendron macrophyllum D. Don ex G. Don RHMA3

Rocky mountain maple Acer glabrum Torr. ACGL

Rose Rosa L. ROSA5

Salal Gaultheria shallon Pursh GASH

Scouler’s willow Salix scouleriana Barratt ex Hook. SASC

Serviceberry Amelanchier alnifolia (Nutt.) Nutt. ex M. Roem. AMAL2

Sitka alder Alnus viridis (Chaix) DC. ssp. sinuata (Regel) Á. Löve & D. Löve ALVIS 

Snowberry Symphoricarpos albus (L.) S.F. Blake SYAL

Snowbrush ceanothus Ceanothus velutinus Douglas ex Hook. CEVE

Spirea Spiraea L. SPIRA

Spiraea Spiraea betulifolia var. lucida (Doublas ex Greene) C.L. Hitchc. SPBEL

Sticky currant Ribes viscosissimum Pursh RIVI3

Thimbleberry Rubus parviflorus Nutt. RUPA

Thinleaf huckleberry Vaccinium membranaceum Douglas ex Torr. VAME

Twinflower Linnaea borealis L. LIBO3

Vine maple Acer circinatum Pursh ACCI

Western azalea Rhododendron occidentale (Torr. & A. Gray) A. Gray RHOC

Whiteleaf manzanita Arctostaphylos manzanita Parry ARMA

Wild ginger Asarum L. ASARU

Queen cup beadlily Clintonia uniflora (Menzies ex Schult. & Schult. f.) Kunth CLUN2

Shrubs and Forbs
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Diseases

Table 4. A variety of diseases occur in the dry mixed conifer forests. 

Common name Scientific name

Stem decay

Blue stain Ophiostoma spp./Ceratocystis spp.

Indian paint fungus Echinodontium tinctorium

Pini rot Porodaedalea (Phellinus) pini

Pouch fungus Polyporus volvatus

Red belt fungus Fomitopsis pinicola 

Schwinitzii butt rot Phaeolus schweinitzii

Cankers

Atropellis canker Atropellis piniphila

Fir canker Cytospora kunzei

Spruce canker Cytospora kunzei

Root disease

Annosus Heterobasidion annosum/ Heterobasidion parvporum

Armillaria Armillaria spp.

Blackstain Leptographium wageneri var. wageneri

Laminated root rot Phellinus weirii

Tomentosus root disease Inonotus tomentosus

Rusts

White pine bliser rust Cronartium ribicola

Camandra & Stalactiform Cronartium comandrae/ Cronartium coleosporioides

Branch and terminal

Mistletoe Aceuthobium spp.

Needle cast Lophodermium spp.

Swiss needle cast Phaeocryptopus gaeumannii

Foliage diseases

Brown felt blight Neopeckia coulteri/ Herpotrichia juniperi

Larch needle blight Hypodermella laricis

Spruce broom rust Melampsorella caryophyllacearum

Sudden oak death Phytophthora ramorum

Western gall rust Peridermium (Endocronarium) harkessii
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-292. 2012 329

Wildlife and Insects

Table 5. List of the various species we referred to in the 

synthesis with their common and scientific name.

Common name Scientific name

Mammal carnivores

American marten Martes americana

Fisher Martes pennanti

Canada lynx Lynx canadensis

Mammal herbivores

Deer mice Peromyscus

Porcupine Erethizon dorsatum

Red squirrel Tamiasciurus hudsonicus

Snowshoe hare Lepus americanus

Birds

Northern spotted owl Strix occidentalis caurina

Pileated woodpecker Dryocopus pileatus 

Reptiles 

Gopher snakes Pituophis catenifer 

Western skink Plestiodon skiltonianus

Insects

Cedarbark beetles Phloeosinus spp.

Defoliating weevil Pissodes spp.

Douglas-fir beetle Dendroctonus pseudotsugae

Douglas-fir tussock moth Orgyia pseudotsugata 

Fir engraver Scolytus ventralis

Larch Casebearer Coleophora larcella

Larch sawfly Pristophora erichsonii

Lodgepole terminal weevil Pissodes terminalis

Metallic wood borers Buprestidae family

Mountain pine beetle Dendroctonus ponderosae

Pine engraver beetle Ips spp.

Roundhead borers Cerambydicae family

Spruce beetle Dendroctonus rufipennis

Western pine beetle Dendroctonus brevicomis

Western spruce budworm Choristoneura occidentalis
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Appendix E. English to 

Metric Unit Conversions

We present values and numbers in English units because many FFE-FVS and some 

of the Fire Behavior models use English units. However, because this synthesis is not 

limited to the United States, we include the following English to metric conversion table. 

Table 1. Weights and measures conversion used in the United States and 

United Kingdom and their metric equivalent.

Weights and measures Metric equivalent

Acre 4047 m2 or 0.404686 hectares

Foot 30.48 cm, 0.305 m

Inch 2.54 cm

Mile 1.609 km

Basal area ft2 per acre 0.205921151 m2 per hectare

2.47105 acres 1 hectare


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